From 468dbc355794dec156c02804abd593fd56b9442f Mon Sep 17 00:00:00 2001
From: GinFung <yujianfeng5@huawei.com>
Date: Mon, 30 Mar 2020 09:54:05 +0800
Subject: [PATCH 001/367] Add matmul biasadd fusion pass

---
 .../ascend/ascend_backend_optimization.cc     |  2 +
 .../ascend/ir_fusion/matmul_biasadd_fusion.cc | 51 +++++++++++++++++
 .../ascend/ir_fusion/matmul_biasadd_fusion.h  | 34 +++++++++++
 mindspore/ccsrc/pre_activate/common/helper.h  |  1 +
 mindspore/ccsrc/utils/utils.h                 |  2 +
 .../ir_fusion/matmul_biasadd_fusion_test.cc   | 56 +++++++++++++++++++
 .../matmul_biasadd_fusion_test.py             | 46 +++++++++++++++
 7 files changed, 192 insertions(+)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h
 create mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 9883443910..b9b324e5dd 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -43,6 +43,7 @@
 #include "pre_activate/ascend/ir_fusion/momentum_lossscale_fusion.h"
 #include "pre_activate/ascend/ir_fusion/mul_add_fusion.h"
 #include "pre_activate/ascend/ir_fusion/mul_addn_fusion.h"
+#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h"
 #include "pre_activate/ascend/format_type/insert_trans_op.h"
 #include "pre_activate/pass/getitem_tuple.h"
 #include "pre_activate/pass/optimize_dependence.h"
@@ -173,6 +174,7 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
     ir_fusion_pm->AddPass(std::make_shared<MomentumLossscaleFusion>());
     ir_fusion_pm->AddPass(std::make_shared<MulAddFusion>());
     ir_fusion_pm->AddPass(std::make_shared<MulAddNFusion>());
+    ir_fusion_pm->AddPass(std::make_shared<MatmulBiasaddFusion>());
     ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
     ir_fusion_pm->AddPass(std::make_shared<TransposeTransDataFusion>());
   }
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc
new file mode 100644
index 0000000000..e81c804b71
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc
@@ -0,0 +1,51 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h"
+#include <memory>
+#include "pre_activate/common/helper.h"
+#include "session/anf_runtime_algorithm.h"
+#include "utils/utils.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+constexpr size_t kMatMulInputIndex = 1;
+constexpr size_t kBiasInputIndex = 2;
+}  // namespace
+
+const BaseRef MatmulBiasaddFusion::DefinePattern() const {
+  VarPtr X0 = std::make_shared<Var>();
+  VarPtr X1 = std::make_shared<Var>();
+  VarPtr X2 = std::make_shared<Var>();
+  const auto prim_bias_add = std::make_shared<Primitive>(kBiasAddOpName);
+  return VectorRef({prim_bias_add, VectorRef({prim::kPrimMatMul, X0, X1}), X2});
+}
+
+const AnfNodePtr MatmulBiasaddFusion::Process(const FuncGraphPtr &, const AnfNodePtr &node, const EquivPtr &) const {
+  MS_EXCEPTION_IF_NULL(node);
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  CheckCNodeInputSize(cnode, kBiasAddInputNum);
+  AnfNodePtr matmul = cnode->input(kMatMulInputIndex);
+  MS_EXCEPTION_IF_NULL(matmul);
+  auto matmul_cnode = matmul->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(matmul_cnode);
+  matmul_cnode->add_input(cnode->input(kBiasInputIndex));
+  AnfAlgo::SetNodeAttr(kAttrHasBias, MakeValue(true), matmul);
+  return matmul;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h
new file mode 100644
index 0000000000..56675243de
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h
@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_
+
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class MatmulBiasaddFusion : public PatternProcessPass {
+ public:
+  explicit MatmulBiasaddFusion(bool multigraph = true) : PatternProcessPass("matmul_biasadd_fusion", multigraph) {}
+
+  ~MatmulBiasaddFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_
diff --git a/mindspore/ccsrc/pre_activate/common/helper.h b/mindspore/ccsrc/pre_activate/common/helper.h
index ba8db8bd29..4f30a935af 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.h
+++ b/mindspore/ccsrc/pre_activate/common/helper.h
@@ -84,6 +84,7 @@ constexpr size_t kLayerNormGradInputNum = 6;
 constexpr size_t kAdamApplyOneOutputNum = 3;
 constexpr size_t kBackendTransDataInputNum = 2;
 constexpr size_t kApplyMomentumInputNum = 6;
+constexpr size_t kBiasAddInputNum = 3;
 
 enum FusedBatchNormInput {
   kX = 1,
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 3328480d00..229c0547cc 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -110,6 +110,7 @@ constexpr auto kResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
 constexpr auto kFusedMulAddOpName = "FusedMulAdd";
 constexpr auto kFusedMulAddNOpName = "FusedMulAddN";
 constexpr auto kFusedMulApplyMomentumOpName = "FusedMulApplyMomentum";
+constexpr auto kBiasAddOpName = "BiasAdd";
 
 // attr key name
 constexpr auto kAttrInputNames = "input_names";
@@ -140,6 +141,7 @@ constexpr auto kAttrDynInput = "dynamic";
 constexpr auto kAttrDynInputSizes = "dyn_input_sizes";
 constexpr auto kAttrSrcFormat = "src_format";
 constexpr auto kAttrOutputUsedNum = "output_used_num";
+constexpr auto kAttrHasBias = "has_bias";
 
 // attr value
 constexpr auto kValueTargetSwitch = "target_switch";
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc
new file mode 100644
index 0000000000..c8f97be290
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc
@@ -0,0 +1,56 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h"
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+
+namespace mindspore {
+namespace opt {
+class TestHWMatmulBiasaddFusion : public BackendCommon {
+ public:
+  TestHWMatmulBiasaddFusion() : get_py_fun_("gtest_input.pre_activate.matmul_biasadd_fusion_test", true) {}
+  ~TestHWMatmulBiasaddFusion() override = default;
+
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWMatmulBiasaddFusion, test_matmul_biasadd_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_matmul_biasadd_fusion", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shpx{1, 3};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shpx);
+  std::vector<int> shpy{3, 4};
+  auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shpy);
+  std::vector<int> shp_bias{4};
+  auto bias_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_bias);
+  AbstractBasePtrList args_spec_list;
+  args_spec_list.push_back(x_abstract);
+  args_spec_list.push_back(y_abstract);
+  args_spec_list.push_back(bias_abstract);
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::MatmulBiasaddFusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_matmul_biasadd_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+}  // namespace opt
+}  // namespace mindspore
\ No newline at end of file
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py
new file mode 100644
index 0000000000..5d55e854f7
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py
@@ -0,0 +1,46 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+
+MatMul = P.MatMul()
+BiasAdd = P.BiasAdd()
+make_tuple = Primitive('make_tuple')
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_matmul_biasadd_fusion(tag):
+    fns = FnDict()
+
+    @fns
+    def before(input0, input1, input2):
+        matmul = MatMul(input0, input1)
+        biasadd = BiasAdd(matmul, input2)
+        return biasadd
+
+    @fns
+    def after(input0, input1, input2):
+        return make_tuple(MatMul(input0, input1, input2))
+
+    return fns[tag]

From 7b7a6a45a043f5e32d0daa4b82ef214ff70592f6 Mon Sep 17 00:00:00 2001
From: seatea <t.cai@huawei.com>
Date: Mon, 30 Mar 2020 12:10:35 +0800
Subject: [PATCH 002/367] Check if the shape of the input of NMSWithMask is (N,
 5).

---
 mindspore/ops/operations/math_ops.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 213e04e351..ad928d792f 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1745,11 +1745,14 @@ class NMSWithMask(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['bboxes'], outputs=['selected_boxes', 'selected_idx', 'selected_mask'])
 
     def infer_shape(self, bboxes_shape):
+        validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ)
+        validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT)
+        validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ)
         num = bboxes_shape[0]
-        validator.check_integer("bboxes_shape[0]", num, 0, Rel.GT)
         return (bboxes_shape, (num,), (num,))
 
     def infer_dtype(self, bboxes_dtype):
+        validator.check_subclass("bboxes_dtype", bboxes_dtype, mstype.tensor)
         validator.check_typename("bboxes_dtype", bboxes_dtype, [mstype.float16, mstype.float32])
         return (bboxes_dtype, mstype.int32, mstype.bool_)
 

From c6d261b27787bf605d473b2d41466950ee5b3c69 Mon Sep 17 00:00:00 2001
From: yoonlee666 <qiuyunlei@huawei.com>
Date: Mon, 30 Mar 2020 16:21:57 +0800
Subject: [PATCH 003/367] add bert script to master

---
 example/Bert_NEZHA/config.py                  | 55 ----------------
 example/Bert_NEZHA_cnwiki/config.py           | 57 ++++++++++++++++
 .../main.py => Bert_NEZHA_cnwiki/train.py}    | 66 +++++++------------
 3 files changed, 82 insertions(+), 96 deletions(-)
 delete mode 100644 example/Bert_NEZHA/config.py
 create mode 100644 example/Bert_NEZHA_cnwiki/config.py
 rename example/{Bert_NEZHA/main.py => Bert_NEZHA_cnwiki/train.py} (57%)

diff --git a/example/Bert_NEZHA/config.py b/example/Bert_NEZHA/config.py
deleted file mode 100644
index 2f3b22fe50..0000000000
--- a/example/Bert_NEZHA/config.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-"""
-network config setting, will be used in main.py
-"""
-
-from easydict import EasyDict as edict
-import mindspore.common.dtype as mstype
-from mindspore.model_zoo.Bert_NEZHA import BertConfig
-bert_cfg = edict({
-    'epoch_size': 10,
-    'num_warmup_steps': 0,
-    'start_learning_rate': 1e-4,
-    'end_learning_rate': 1,
-    'decay_steps': 1000,
-    'power': 10.0,
-    'save_checkpoint_steps': 2000,
-    'keep_checkpoint_max': 10,
-    'checkpoint_prefix': "checkpoint_bert",
-    'DATA_DIR' = "/your/path/examples.tfrecord"
-    'SCHEMA_DIR' = "/your/path/datasetSchema.json"
-    'bert_config': BertConfig(
-                       batch_size=16,
-                       seq_length=128,
-                       vocab_size=21136,
-                       hidden_size=1024,
-                       num_hidden_layers=24,
-                       num_attention_heads=16,
-                       intermediate_size=4096,
-                       hidden_act="gelu",
-                       hidden_dropout_prob=0.0,
-                       attention_probs_dropout_prob=0.0,
-                       max_position_embeddings=512,
-                       type_vocab_size=2,
-                       initializer_range=0.02,
-                       use_relative_positions=True,
-                       input_mask_from_dataset=True,
-                       token_type_ids_from_dataset=True,
-                       dtype=mstype.float32,
-                       compute_type=mstype.float16, 
-                   )
-})
diff --git a/example/Bert_NEZHA_cnwiki/config.py b/example/Bert_NEZHA_cnwiki/config.py
new file mode 100644
index 0000000000..a704d9a264
--- /dev/null
+++ b/example/Bert_NEZHA_cnwiki/config.py
@@ -0,0 +1,57 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""
+network config setting, will be used in train.py
+"""
+
+from easydict import EasyDict as edict
+import mindspore.common.dtype as mstype
+from mindspore.model_zoo.Bert_NEZHA import BertConfig
+bert_train_cfg = edict({
+    'epoch_size': 10,
+    'num_warmup_steps': 0,
+    'start_learning_rate': 1e-4,
+    'end_learning_rate': 0.0,
+    'decay_steps': 1000,
+    'power': 10.0,
+    'save_checkpoint_steps': 2000,
+    'keep_checkpoint_max': 10,
+    'checkpoint_prefix': "checkpoint_bert",
+    # please add your own dataset path
+    'DATA_DIR': "/your/path/examples.tfrecord",
+    # please add your own dataset schema path
+    'SCHEMA_DIR': "/your/path/datasetSchema.json"
+})
+bert_net_cfg = BertConfig(
+    batch_size=16,
+    seq_length=128,
+    vocab_size=21136,
+    hidden_size=1024,
+    num_hidden_layers=24,
+    num_attention_heads=16,
+    intermediate_size=4096,
+    hidden_act="gelu",
+    hidden_dropout_prob=0.0,
+    attention_probs_dropout_prob=0.0,
+    max_position_embeddings=512,
+    type_vocab_size=2,
+    initializer_range=0.02,
+    use_relative_positions=True,
+    input_mask_from_dataset=True,
+    token_type_ids_from_dataset=True,
+    dtype=mstype.float32,
+    compute_type=mstype.float16,
+)
diff --git a/example/Bert_NEZHA/main.py b/example/Bert_NEZHA_cnwiki/train.py
similarity index 57%
rename from example/Bert_NEZHA/main.py
rename to example/Bert_NEZHA_cnwiki/train.py
index a5500f25a9..87f425e21c 100644
--- a/example/Bert_NEZHA/main.py
+++ b/example/Bert_NEZHA_cnwiki/train.py
@@ -14,7 +14,8 @@
 # ============================================================================
 
 """
-NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) is the Chinese pretrained language model currently based on BERT developed by Huawei.
+NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) is the Chinese pretrained language
+model currently based on BERT developed by Huawei.
 1. Prepare data
 Following the data preparation as in BERT, run command as below to get dataset for training:
     python ./create_pretraining_data.py \
@@ -28,35 +29,29 @@ Following the data preparation as in BERT, run command as below to get dataset f
       --random_seed=12345 \
       --dupe_factor=5
 2. Pretrain
-First, prepare the distributed training environment, then adjust configurations in config.py, finally run main.py.
+First, prepare the distributed training environment, then adjust configurations in config.py, finally run train.py.
 """
 
 import os
-import pytest
 import numpy as np
-from numpy import allclose
-from config import bert_cfg as cfg
-import mindspore.common.dtype as mstype
+from config import bert_train_cfg, bert_net_cfg
 import mindspore.dataset.engine.datasets as de
 import mindspore._c_dataengine as deMap
 from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
-from mindspore.train.callback import Callback
-from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.model_zoo.Bert_NEZHA import BertNetworkWithLoss, BertTrainOneStepCell
 from mindspore.nn.optim import Lamb
-from mindspore import log as logger
 _current_dir = os.path.dirname(os.path.realpath(__file__))
-DATA_DIR = [cfg.DATA_DIR]
-SCHEMA_DIR = cfg.SCHEMA_DIR
 
-def me_de_train_dataset(batch_size):
-    """test me de train dataset"""
+def create_train_dataset(batch_size):
+    """create train dataset"""
     # apply repeat operations
-    repeat_count = cfg.epoch_size
-    ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
-                                                               "next_sentence_labels", "masked_lm_positions",
-                                                               "masked_lm_ids", "masked_lm_weights"])
+    repeat_count = bert_train_cfg.epoch_size
+    ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR,
+                           columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
+                                         "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"])
     type_cast_op = deMap.TypeCastOp("int32")
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
@@ -69,43 +64,32 @@ def me_de_train_dataset(batch_size):
     ds = ds.repeat(repeat_count)
     return ds
 
-
 def weight_variable(shape):
     """weight variable"""
     np.random.seed(1)
     ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
     return Tensor(ones)
 
-
-class ModelCallback(Callback):
-    def __init__(self):
-        super(ModelCallback, self).__init__()
-        self.loss_list = []
-
-    def step_end(self, run_context):
-        cb_params = run_context.original_args()
-        self.loss_list.append(cb_params.net_outputs.asnumpy()[0])
-        logger.info("epoch: {}, outputs are {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
-
-def test_bert_tdt():
-    """test bert tdt"""
+def train_bert():
+    """train bert"""
     context.set_context(mode=context.GRAPH_MODE)
     context.set_context(device_target="Ascend")
     context.set_context(enable_task_sink=True)
     context.set_context(enable_loop_sink=True)
     context.set_context(enable_mem_reuse=True)
-    parallel_callback = ModelCallback()
-    ds = me_de_train_dataset(cfg.bert_config.batch_size)
-    config = cfg.bert_config
-    netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=cfg.decay_steps, start_learning_rate=cfg.start_learning_rate,
-                     end_learning_rate=cfg.end_learning_rate, power=cfg.power, warmup_steps=cfg.num_warmup_steps, decay_filter=lambda x: False)
+    ds = create_train_dataset(bert_net_cfg.batch_size)
+    netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
+    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=bert_train_cfg.decay_steps,
+                     start_learning_rate=bert_train_cfg.start_learning_rate,
+                     end_learning_rate=bert_train_cfg.end_learning_rate, power=bert_train_cfg.power,
+                     warmup_steps=bert_train_cfg.num_warmup_steps, decay_filter=lambda x: False)
     netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
     netwithgrads.set_train(True)
     model = Model(netwithgrads)
-    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max)
-    ckpoint_cb = ModelCheckpoint(prefix=cfg.checkpoint_prefix, config=config_ck)
-    model.train(ds.get_repeat_count(), ds, callbacks=[parallel_callback, ckpoint_cb], dataset_sink_mode=False)
+    config_ck = CheckpointConfig(save_checkpoint_steps=bert_train_cfg.save_checkpoint_steps,
+                                 keep_checkpoint_max=bert_train_cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix=bert_train_cfg.checkpoint_prefix, config=config_ck)
+    model.train(ds.get_repeat_count(), ds, callbacks=[LossMonitor(), ckpoint_cb], dataset_sink_mode=False)
 
 if __name__ == '__main__':
-    test_bert_tdt()
+    train_bert()

From 6c03542eec0bf591b48a9993e530e0ce1e67fc67 Mon Sep 17 00:00:00 2001
From: seatea <t.cai@huawei.com>
Date: Mon, 30 Mar 2020 11:46:53 +0800
Subject: [PATCH 004/367] Fix dtype bug for loss_scale and weight_decay.
 1.Change dtype of scale to dtype of grad in loss_scale.py; 2.Change dtype of
 weight_decay to dtype of weight in optimizer.py.

---
 mindspore/nn/optim/optimizer.py | 2 +-
 mindspore/nn/wrap/loss_scale.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index e2b0cddb71..cd0ed93a10 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -84,7 +84,7 @@ apply_decay = C.MultitypeFuncGraph("apply_decay")
 def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
     """Get grad with weight_decay."""
     if if_apply:
-        return op_add((gradient, weight * F.scalar_to_array(weight_decay)))
+        return op_add((gradient, weight * weight_decay))
     return gradient
 
 
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index f7c686f535..a11c753eda 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -32,7 +32,7 @@ reciprocal = P.Reciprocal()
 
 @_grad_scale.register("Tensor", "Tensor")
 def tensor_grad_scale(scale, grad):
-    return grad * reciprocal(scale)
+    return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
 
 class DynamicLossScaleUpdateCell(Cell):

From 34e42bd6f924407608cda54eeffcfbbab1675b4c Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Mon, 30 Mar 2020 17:23:44 +0800
Subject: [PATCH 005/367] 1. add more log info for dataset & mindrecord, 2. add
 two new testcase for MindDataset

---
 mindspore/dataset/engine/iterators.py       | 20 ++++-----
 mindspore/mindrecord/filewriter.py          |  7 ++++
 tests/ut/python/dataset/test_minddataset.py | 46 +++++++++++++++++++++
 3 files changed, 63 insertions(+), 10 deletions(-)

diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index 806df64fdc..268a66c0cf 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -66,11 +66,10 @@ def _alter_node(node):
 
 class Iterator:
     """
-        General Iterator over a dataset.
-
-        Attributes:
-            dataset: Dataset to be iterated over
+    General Iterator over a dataset.
 
+    Attributes:
+        dataset: Dataset to be iterated over
     """
 
     def __init__(self, dataset):
@@ -86,6 +85,7 @@ class Iterator:
         root = self.__convert_node_postorder(self.dataset)
         self.depipeline.AssignRootNode(root)
         self.depipeline.LaunchTreeExec()
+        self._index = 0
 
     def __is_tree_node(self, node):
         """Check if a node is tree node."""
@@ -185,10 +185,7 @@ class Iterator:
             Iterator.__print_local(input_op, level + 1)
 
     def print(self):
-        """
-            Print the dataset tree
-
-        """
+        """Print the dataset tree"""
         self.__print_local(self.dataset, 0)
 
     def release(self):
@@ -202,7 +199,10 @@ class Iterator:
     def __next__(self):
         data = self.get_next()
         if not data:
+            if self._index == 0:
+                logger.warning("No records available.")
             raise StopIteration
+        self._index += 1
         return data
 
     def get_output_shapes(self):
@@ -234,7 +234,7 @@ class DictIterator(Iterator):
 
     def get_next(self):
         """
-            Returns the next record in the dataset as dictionary
+        Returns the next record in the dataset as dictionary
 
         Returns:
             Dict, the next record in the dataset.
@@ -260,7 +260,7 @@ class TupleIterator(Iterator):
 
     def get_next(self):
         """
-            Returns the next record in the dataset as a list
+        Returns the next record in the dataset as a list
 
         Returns:
             List, the next record in the dataset.
diff --git a/mindspore/mindrecord/filewriter.py b/mindspore/mindrecord/filewriter.py
index 0e44b61857..d1471f47cb 100644
--- a/mindspore/mindrecord/filewriter.py
+++ b/mindspore/mindrecord/filewriter.py
@@ -328,13 +328,20 @@ class FileWriter:
             self._generator.build()
             self._generator.write_to_db()
 
+        mindrecord_files = []
+        index_files = []
         # change the file mode to 600
         for item in self._paths:
             if os.path.exists(item):
                 os.chmod(item, stat.S_IRUSR | stat.S_IWUSR)
+                mindrecord_files.append(item)
             index_file = item + ".db"
             if os.path.exists(index_file):
                 os.chmod(index_file, stat.S_IRUSR | stat.S_IWUSR)
+                index_files.append(index_file)
+
+        logger.info("The list of mindrecord files created are: {}, and the list of index files are: {}".format(
+            mindrecord_files, index_files))
 
         return ret
 
diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py
index 99b31b64ec..8b8cbc807a 100644
--- a/tests/ut/python/dataset/test_minddataset.py
+++ b/tests/ut/python/dataset/test_minddataset.py
@@ -25,6 +25,7 @@ import mindspore.dataset.transforms.vision.c_transforms as vision
 import numpy as np
 import pytest
 from mindspore._c_dataengine import InterpolationMode
+from mindspore.dataset.transforms.vision import Inter
 from mindspore import log as logger
 
 import mindspore.dataset as ds
@@ -151,6 +152,51 @@ def test_cv_minddataset_dataset_size(add_and_remove_cv_file):
     assert data_set.get_dataset_size() == 3
 
 
+def test_cv_minddataset_repeat_reshuffle(add_and_remove_cv_file):
+    """tutorial for cv minddataset."""
+    columns_list = ["data", "label"]
+    num_readers = 4
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
+    decode_op = vision.Decode()
+    data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2)
+    resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR)
+    data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2)
+    data_set = data_set.batch(2)
+    data_set = data_set.repeat(2)
+    num_iter = 0
+    labels = []
+    for item in data_set.create_dict_iterator():
+        logger.info("-------------- get dataset size {} -----------------".format(num_iter))
+        logger.info("-------------- item[label]: {} ---------------------".format(item["label"]))
+        logger.info("-------------- item[data]: {} ----------------------".format(item["data"]))
+        num_iter += 1
+        labels.append(item["label"])
+    assert num_iter == 10
+    logger.info("repeat shuffle: {}".format(labels))
+    assert len(labels) == 10
+    assert labels[0:5] == labels[0:5]
+    assert labels[0:5] != labels[5:5]
+
+
+def test_cv_minddataset_batch_size_larger_than_records(add_and_remove_cv_file):
+    """tutorial for cv minddataset."""
+    columns_list = ["data", "label"]
+    num_readers = 4
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
+    decode_op = vision.Decode()
+    data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2)
+    resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR)
+    data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2)
+    data_set = data_set.batch(32, drop_remainder=True)
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info("-------------- get dataset size {} -----------------".format(num_iter))
+        logger.info("-------------- item[label]: {} ---------------------".format(item["label"]))
+        logger.info("-------------- item[data]: {} ----------------------".format(item["data"]))
+        num_iter += 1
+    assert num_iter == 0
+
+
 def test_cv_minddataset_issue_888(add_and_remove_cv_file):
     """issue 888 test."""
     columns_list = ["data", "label"]

From 468e257a146dca74251ad7470e5515c624ffc614 Mon Sep 17 00:00:00 2001
From: shibeiji <shibeiji@huawei.com>
Date: Mon, 30 Mar 2020 17:55:28 +0800
Subject: [PATCH 006/367] add log for kernel runtime in order to trace
 performance

---
 mindspore/ccsrc/device/kernel_runtime.cc | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index f320aa9cf6..99f5d491ac 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -50,12 +50,19 @@ bool KernelRuntime::Run(session::KernelGraph *graph) {
   bool ret = false;
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
+  struct timeval start_time, end_time;
+  (void)gettimeofday(&start_time, nullptr);
   bool is_task_sink = context_ptr->enable_task_sink();
   if (is_task_sink) {
     ret = RunTask(graph);
   } else {
     ret = LaunchKernel(graph);
   }
+  (void)gettimeofday(&end_time, nullptr);
+  const uint64_t kUSecondInSecond = 1000000;
+  uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
+  cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
+  MS_LOG(INFO) << "Call MS Run Success in " << cost << " us";
   return ret;
 }
 

From 9862dea3cffea33119987882471db9cf35dda458 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Mon, 30 Mar 2020 09:34:34 +0800
Subject: [PATCH 007/367] adapt relu6grad and graphengine modified

---
 graphengine                             | 2 +-
 mindspore/ccsrc/transform/op_declare.cc | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphengine b/graphengine
index 5f763679fa..49351fb73a 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 5f763679fa33de1608d07f7651c6f16012b953ea
+Subproject commit 49351fb73ac7786b9ed9e807568a1a8e06183693
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 78b949c525..82dcd62572 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -506,9 +506,9 @@ ATTR_MAP(Relu6) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}};
 
 // Relu6Grad
-INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(dy)}, {2, INPUT_DESC(y)}};
+INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}};
 ATTR_MAP(Relu6Grad) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(z)}};
+OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(backprops)}};
 
 // ResizeBilinearGrad
 INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}};

From 4cbcd8e90736158f54e0fceb87ec9262a0f85acc Mon Sep 17 00:00:00 2001
From: Ziyan <gongziyan1@huawei.com>
Date: Mon, 30 Mar 2020 15:17:05 +0800
Subject: [PATCH 008/367] enable use float type learning rate in lars optimizer

---
 mindspore/nn/optim/lars.py            |  9 ++++++---
 tests/ut/python/nn/optim/test_lars.py | 19 ++++++++++++++++++-
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py
index cdfe45de62..a69057215d 100755
--- a/mindspore/nn/optim/lars.py
+++ b/mindspore/nn/optim/lars.py
@@ -13,12 +13,14 @@
 # limitations under the License.
 # ============================================================================
 """lars optimizer"""
+from typing import Iterable
 from mindspore.common import dtype as mstype
+from mindspore.common import Tensor
 from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
-from mindspore.common.parameter import Parameter
 from mindspore.nn.cell import Cell
 from .optimizer import grad_scale
 
@@ -111,7 +113,8 @@ class LARS(Cell):
         self.gather = None
         self.global_step = None
         self.axis = None
-        if not isinstance(self.learning_rate, float):
+        if isinstance(self.learning_rate.default_input, Iterable) or \
+                (isinstance(self.learning_rate.default_input, Tensor) and self.learning_rate.default_input.dim() == 1):
             self.dynamic_lr = True
             self.assignadd = P.AssignAdd()
             self.gather = P.GatherV2()
@@ -124,7 +127,7 @@ class LARS(Cell):
             lr = self.gather(self.learning_rate, self.global_step, self.axis)
             F.control_depend(lr, self.assignadd(self.global_step, 1))
         else:
-            lr = F.scalar_to_array(self.learning_rate)
+            lr = self.learning_rate
         if self.reciprocal_scale != 1.0:
             gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
 
diff --git a/tests/ut/python/nn/optim/test_lars.py b/tests/ut/python/nn/optim/test_lars.py
index 92d218a32b..17bbe69fe6 100644
--- a/tests/ut/python/nn/optim/test_lars.py
+++ b/tests/ut/python/nn/optim/test_lars.py
@@ -46,7 +46,7 @@ class Net(nn.Cell):
         return x
 
 
-def test_lars():
+def test_lars_multi_step_lr():
     inputs = Tensor(np.ones([1, 64]).astype(np.float32))
     label = Tensor(np.zeros([1, 10]).astype(np.float32))
     net = Net()
@@ -61,3 +61,20 @@ def test_lars():
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
     _executor.compile(train_network, inputs, label)
+
+
+def test_lars_float_lr():
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+
+    lr = 0.1
+    SGD = Momentum(net.trainable_params(), lr, 0.9)
+    optimizer = LARS(SGD, epsilon=1e-08, hyperpara=0.02, decay_filter=lambda x: 'bn' not in x.name,
+                     lars_filter=lambda x: 'bn' not in x.name)
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
\ No newline at end of file

From 0da0bdcf40efaa45e8cac01b1730e8b8ec9b934e Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Wed, 25 Mar 2020 20:04:12 +0800
Subject: [PATCH 009/367] Fix bug structure output when there is depend whose
 first input is constant in outputs

---
 mindspore/ccsrc/pipeline/pipeline.cc        | 81 +++++++++++++++------
 tests/ut/python/nn/test_structure_output.py | 32 ++++++--
 2 files changed, 84 insertions(+), 29 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 35336e975b..0c2edfc9c3 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -725,23 +725,15 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
   return BaseRefToPyData(value);
 }
 
-py::object StructureOutput(const AbstractBasePtr& output, const py::tuple& data, size_t* count) {
-  MS_EXCEPTION_IF_NULL(output);
+py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) {
+  MS_EXCEPTION_IF_NULL(cnode_data);
+  if (*count >= data.size()) {
+    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
+                      << " less than the number of elements required. ";
+  }
 
-  if (!output->isa<AbstractTuple>()) {
-    ValuePtr value = output->BuildValue();
-    if (value != kAnyValue) {
-      return ValuePtrToPyData(value);
-    }
-    if (!output->isa<AbstractTensor>()) {
-      MS_LOG(EXCEPTION) << "Output can only be tensor except for constants, but got "
-                        << output->BuildValue()->ToString() << ".";
-    }
-    if (*count >= data.size()) {
-      MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
-                        << " less than the number of elements required. ";
-    }
-    auto shape = output->BuildShape();
+  if (cnode_data->isa<AbstractTensor>()) {
+    BaseShapePtr shape = cnode_data->BuildShape();
     auto shape_act = shape->cast<abstract::ShapePtr>()->shape();
     Tensor tensor_exp = py::cast<Tensor>(data[*count]);
     if (shape_act != tensor_exp.shape()) {
@@ -751,16 +743,58 @@ py::object StructureOutput(const AbstractBasePtr& output, const py::tuple& data,
     return data[(*count)++];
   }
 
-  auto tuple_output = output->cast<AbstractTuplePtr>();
-  AbstractBasePtrList elements = tuple_output->elements();
-  size_t size = elements.size();
+  if (!cnode_data->isa<AbstractTuple>()) {
+    MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could "
+                      << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString()
+                      << ".";
+  }
+  auto data_tp = cnode_data->cast<AbstractTuplePtr>();
+  auto elements = data_tp->elements();
+  size_t size = data_tp->size();
   py::tuple tp = py::tuple(size);
   for (size_t i = 0; i < size; i++) {
-    tp[i] = StructureOutput(elements[i], data, count);
+    tp[i] = ExtractGeneralCnodeRet(elements[i], data, count);
   }
   return std::move(tp);
 }
 
+py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) {
+  MS_EXCEPTION_IF_NULL(output_node);
+
+  if (output_node->isa<ValueNode>()) {
+    return ValuePtrToPyData(GetValueNode(output_node));
+  }
+
+  if (*count >= data.size()) {
+    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
+                      << " less than the number of elements required. ";
+  }
+  if (output_node->isa<Parameter>()) {
+    return data[(*count)++];
+  }
+
+  auto output_c = output_node->cast<CNodePtr>();
+  if (output_c == nullptr) {
+    MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got "
+                      << output_node->ToString();
+  }
+
+  if (output_c->IsApply(prim::kPrimMakeTuple)) {
+    auto input_list = output_c->inputs();
+    size_t size = input_list.size();
+    py::tuple tp = py::tuple(size - 1);
+    for (size_t i = 1; i < size; i++) {
+      tp[i - 1] = StructureOutput(input_list[i], data, count);
+    }
+    return std::move(tp);
+  }
+  if (output_c->IsApply(prim::kPrimDepend)) {
+    return StructureOutput(output_c->input(1), data, count);
+  }
+
+  return ExtractGeneralCnodeRet(output_c->abstract(), data, count);
+}
+
 std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::vector<MeTensorPtr>& inputs,
                                         const std::string& phase) {
   std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
@@ -806,11 +840,10 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
   std::shared_ptr<py::object> ret = nullptr;
 
 #ifdef ENABLE_GE
-  AnfNodePtr root = graph->get_return();
-  MS_EXCEPTION_IF_NULL(root);
-  AbstractBasePtr output = root->abstract();
+  AnfNodePtr output_node = graph->get_return()->input(1);
+  MS_EXCEPTION_IF_NULL(output_node);
   size_t count = 0;
-  py::object oj = StructureOutput(output, outputs, &count);
+  py::object oj = StructureOutput(output_node, outputs, &count);
   ret = std::make_shared<py::object>(oj);
 #else
   if (outputs.size() == 1) {
diff --git a/tests/ut/python/nn/test_structure_output.py b/tests/ut/python/nn/test_structure_output.py
index eb2722878a..f5f6d77a67 100644
--- a/tests/ut/python/nn/test_structure_output.py
+++ b/tests/ut/python/nn/test_structure_output.py
@@ -236,7 +236,7 @@ def test_soft():
         def __init__(self):
             super(SoftmaxCrossEntropyWithLogitsNet, self).__init__()
             self.soft = P.SoftmaxCrossEntropyWithLogits()
-            self.value = (Tensor(np.zeros((2,)).astype(np.float32)), Tensor(np.ones((2,)).astype(np.float32)))
+            self.value = (Tensor(np.zeros((2, 2)).astype(np.float32)), Tensor(np.ones((2, 2)).astype(np.float32)))
 
         def construct(self, x, y, z):
             xx = x + y
@@ -246,8 +246,30 @@ def test_soft():
             ret = (ret, self.value)
             return ret
 
-    input1 = Tensor(np.zeros((2,)).astype(np.float32))
-    input2 = Tensor(np.ones((2,)).astype(np.float32))
-    input3 = Tensor((np.ones((2,)) + np.ones((2,))).astype(np.float32))
+    input1 = Tensor(np.zeros((2, 2)).astype(np.float32))
+    input2 = Tensor(np.ones((2, 2)).astype(np.float32))
+    input3 = Tensor((np.ones((2, 2)) + np.ones((2, 2))).astype(np.float32))
     net = SoftmaxCrossEntropyWithLogitsNet()
-    print(net(input1, input2, input3))
+    net(input1, input2, input3)
+
+
+def test_const_depend():
+    class ConstDepend(Cell):
+        def __init__(self):
+            super(ConstDepend, self).__init__()
+            self.value = (Tensor(np.zeros((2, 3)).astype(np.float32)), Tensor(np.ones((2, 3)).astype(np.float32)))
+            self.soft = P.SoftmaxCrossEntropyWithLogits()
+            self.depend = depend
+
+        def construct(self, x, y, z):
+            ret = x + y
+            ret = ret * z
+            ret = self.depend(self.value, ret)
+            ret = (ret, self.soft(x, y))
+            return ret
+
+    input1 = Tensor(np.zeros((2, 2)).astype(np.float32))
+    input2 = Tensor(np.ones((2, 2)).astype(np.float32))
+    input3 = Tensor((np.ones((2, 2)) + np.ones((2, 2))).astype(np.float32))
+    net = ConstDepend()
+    net(input1, input2, input3)

From cab5503280c594b48475c0f9d8bd3d7216933295 Mon Sep 17 00:00:00 2001
From: chenhaozhe <chenhaozhe1@huawei.com>
Date: Mon, 30 Mar 2020 20:10:56 +0800
Subject: [PATCH 010/367] use find instead of equal to distinguish training
 graph

---
 mindspore/ccsrc/pipeline/pipeline.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 35336e975b..70ef9a5407 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -1071,7 +1071,7 @@ bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phas
   }
   std::string init_graph = "init_subgraph." + net_id;
   std::string checkpoint_name = "save." + net_id;
-  if (phase == "train") {
+  if (phase.find("train") != std::string::npos) {
     (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}});
   } else {
     (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());

From 66714da29f7beb91c725a58afe44a2afc7eb089e Mon Sep 17 00:00:00 2001
From: helloway <simple_hlw@163.com>
Date: Mon, 30 Mar 2020 20:17:13 +0800
Subject: [PATCH 011/367] Add issue and PR templates for gitee and github

---
 .gitee/PULL_REQUEST_TEMPLATE.md         | 26 +++++++++++++++
 .github/ISSUE_TEMPLATE/RFC.md           | 19 +++++++++++
 .github/ISSUE_TEMPLATE/bug-report.md    | 43 +++++++++++++++++++++++++
 .github/ISSUE_TEMPLATE/task-tracking.md | 19 +++++++++++
 .github/PULL_REQUEST_TEMPLATE.md        | 24 ++++++++++++++
 5 files changed, 131 insertions(+)
 create mode 100644 .gitee/PULL_REQUEST_TEMPLATE.md
 create mode 100644 .github/ISSUE_TEMPLATE/RFC.md
 create mode 100644 .github/ISSUE_TEMPLATE/bug-report.md
 create mode 100644 .github/ISSUE_TEMPLATE/task-tracking.md
 create mode 100644 .github/PULL_REQUEST_TEMPLATE.md

diff --git a/.gitee/PULL_REQUEST_TEMPLATE.md b/.gitee/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000000..b58f1abbf3
--- /dev/null
+++ b/.gitee/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,26 @@
+<!--  Thanks for sending a pull request!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+**What type of PR is this?**
+> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> /kind bug
+> /kind task
+> /kind feature
+
+
+**What does this PR do / why do we need it**:
+
+
+**Which issue(s) this PR fixes**:
+<!-- 
+*Automatically closes linked issue when PR is merged.
+Usage: `Fixes #<issue number>`, or `Fixes (paste link of issue)`.
+-->
+Fixes #
+
+**Special notes for your reviewers**:
+
+
diff --git a/.github/ISSUE_TEMPLATE/RFC.md b/.github/ISSUE_TEMPLATE/RFC.md
new file mode 100644
index 0000000000..7dd17f56b6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/RFC.md
@@ -0,0 +1,19 @@
+---
+name: RFC
+about: Use this template for the new feature or enhancement
+labels: kind/feature or kind/enhancement
+
+---
+
+## Background
+- Describe the status of the problem you wish to solve
+- Attach the relevant issue if have
+
+## Introduction
+- Describe the general solution, design and/or pseudo-code
+
+## Trail
+| No. | Task Description | Related Issue(URL) |
+| --- | ---------------- | ------------------ |
+| 1   |                  |                    |
+| 2   |                  |                    |
diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md
new file mode 100644
index 0000000000..2c0260ae2b
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug-report.md
@@ -0,0 +1,43 @@
+---
+name: Bug Report
+about: Use this template for reporting a bug
+labels: kind/bug
+
+---
+
+<!--  Thanks for sending an issue!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://github.com/mindspore-ai/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+## Environment
+### Hardware Environment(`Ascend`/`GPU`/`CPU`): 
+> Uncomment only one ` /device <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> `/device ascend`</br>
+> `/device gpu`</br>
+> `/device cpu`</br>
+
+### Software Environment:
+- **MindSpore version (source or binary)**:
+- **Python version (e.g., Python 3.7.5)**:
+- **OS platform and distribution (e.g., Linux Ubuntu 16.04)**:
+- **GCC/Compiler version (if compiled from source)**: 
+
+## Describe the current behavior
+
+
+## Describe the expected behavior
+
+
+## Steps to reproduce the issue
+1. 
+2. 
+3.
+
+## Related log / screenshot
+
+
+## Special notes for this issue
+
+
diff --git a/.github/ISSUE_TEMPLATE/task-tracking.md b/.github/ISSUE_TEMPLATE/task-tracking.md
new file mode 100644
index 0000000000..f2d3d23ae7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/task-tracking.md
@@ -0,0 +1,19 @@
+---
+name: Task
+about: Use this template for task tracking
+labels: kind/task
+
+---
+
+## Task Description
+
+
+## Task Goal
+
+
+## Sub Task
+| No. | Task Description | Issue ID |
+| --- | ---------------- | -------- |
+| 1   |                  |          |
+| 2   |                  |          |
+
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000000..96800b55f7
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,24 @@
+<!--  Thanks for sending a pull request!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://github.com/mindspore-ai/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+**What type of PR is this?**
+> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> `/kind bug`</br>
+> `/kind task`</br>
+> `/kind feature`</br>
+
+**What does this PR do / why do we need it**:
+
+
+**Which issue(s) this PR fixes**:
+<!-- 
+*Automatically closes linked issue when PR is merged.
+Usage: `Fixes #<issue number>`, or `Fixes (paste link of issue)`.
+-->
+Fixes #
+
+**Special notes for your reviewers**:
+

From b4d34973bc172df475c199aa6d30e3b42aaacfdd Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Mon, 30 Mar 2020 11:31:45 +0800
Subject: [PATCH 012/367] fix_cast_bug

---
 .../ccsrc/parallel/step_auto_parallel.cc      |  6 ++--
 mindspore/ccsrc/parallel/step_parallel.cc     | 21 ++++++++++----
 .../parallel/test_element_wise_function.py    | 29 +++++++++++++++++++
 3 files changed, 49 insertions(+), 7 deletions(-)

diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index c3e3f5893e..aee4247755 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -346,6 +346,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) {
 }
 
 OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(prim);
+  MS_EXCEPTION_IF_NULL(cnode);
   auto attrs = prim->attrs();
   std::vector<Shapes> shape_list = ExtractShape(cnode);
   if (shape_list.empty()) {
@@ -381,8 +383,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
   operator_info->set_outputs_dtype(cnode->Type());
   operator_info->set_cnode(cnode);
   // If no strategy has been configured for this operator, then candidate strategies are generated for
-  // auto-strategy searching
-  if (!StrategyFound(attrs)) {
+  // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy
+  if (!StrategyFound(attrs) || prim->name() == CAST) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
     operator_info->ComputeBatchSplitFlagList();
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 927acea705..2d948679d7 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -370,15 +370,12 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
   if (prim == nullptr) {
     return false;
   }
-  auto attrs = prim->attrs();
   if (IsInBlackList(prim)) {
     MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
     return false;
   }
-  if ((prim->name() == CAST)) {
-    if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
-      return false;
-    }
+  if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) {
+    return false;
   }
 
   return cnode->in_forward_flag();
@@ -648,6 +645,13 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) {
   MS_EXCEPTION_IF_NULL(pre_node);
 
   LossNodeInfo node_info;
+  // return -> cast
+  auto pre_cnode = pre_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(pre_cnode);
+  auto pre_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
+  if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
+    pre_node = pre_cnode->input(1);
+  }
 
   // return -> loss
   if (pre_node == loss_node) {
@@ -1943,6 +1947,13 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
   MS_EXCEPTION_IF_NULL(current_value);
   PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(current_prim);
+  // return -> cast
+  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
+    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(pre_cnode);
+    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
+  }
+
   // notice: the GetNext op has not input
   if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(INFO) << "The loss is: " << current_prim->name();
diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py
index dfcebdc5ab..a917dce9b6 100644
--- a/tests/ut/python/parallel/test_element_wise_function.py
+++ b/tests/ut/python/parallel/test_element_wise_function.py
@@ -272,3 +272,32 @@ def test_cast_before_mirror3():
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
+
+
+def test_mul_two_cast():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2, strategy3):
+            super().__init__()
+            self.mul = P.Mul().set_strategy(strategy1)
+            self.mul2 = P.Mul().set_strategy(strategy2)
+            self.cast = P.Cast().set_strategy(strategy3)
+            self.cast2 = P.Cast().set_strategy(strategy3)
+
+        def construct(self, x, y, b):
+            out = self.mul(x, y)
+            out = self.mul2(out, b)
+            out = self.cast(out, ms.int32)
+            out = self.cast2(out, ms.bool_)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((8, 1), (8, 1))
+    strategy3 = ((8, 1), )
+    net = GradWrap(Net(strategy1, strategy2, strategy3))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    b = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)

From 840280e7843ecaf4014bda8cbe4fdf76aa75b614 Mon Sep 17 00:00:00 2001
From: seatea <t.cai@huawei.com>
Date: Mon, 30 Mar 2020 12:18:02 +0800
Subject: [PATCH 013/367] Correct the comments for `RandomChoiceWithMask` op.

---
 mindspore/ops/operations/random_ops.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index c8f59e898d..9ef5b301f9 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -25,20 +25,23 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
     """
     Generates a random samply as index tensor with a mask tensor from a given tensor.
 
-    The input must be a tensor of rank >= 2, the first dimension specify the number of sample.
-    The index tensor and the mask tensor have the same and fixed shape. The index tensor denotes the index
-    of the nonzero sample, while the mask tensor denotes which element in the index tensor are valid.
+    The input must be a tensor of rank >= 1. If its rank >= 2, the first dimension specify the number of sample.
+    The index tensor and the mask tensor have the fixed shapes. The index tensor denotes the index of the nonzero
+    sample, while the mask tensor denotes which elements in the index tensor are valid.
 
     Args:
-        count (int): Number of items expected to get. Default: 256.
-        seed (int): Random seed.
-        seed2 (int): Random seed2.
+        count (int): Number of items expected to get and the number should be greater than 0. Default: 256.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor.
+        - **input_x** (Tensor[bool]) - The input tensor.
 
     Outputs:
-        Tuple, two tensors, the first one is the index tensor and the other one is the mask tensor.
+        Two tensors, the first one is the index tensor and the other one is the mask tensor.
+
+        - **index** (Tensor) - The output has shape between 2-D and 5-D.
+        - **mask** (Tensor) - The output has shape 1-D.
 
     Examples:
         >>> rnd_choice_mask = RandomChoiceWithMask()

From 080bcda0214c7120d6f06b34b9421587c4e52276 Mon Sep 17 00:00:00 2001
From: Jonathan Yan <jonathan.yan1@huawei.com>
Date: Mon, 30 Mar 2020 11:02:08 -0400
Subject: [PATCH 014/367] Replace std::cout with MS_LOG in dataset unit test

Signed-off-by: Jonathan Yan <jonathan.yan1@huawei.com>
---
 tests/ut/cpp/dataset/arena_test.cc            |  2 +-
 tests/ut/cpp/dataset/btree_test.cc            |  2 +-
 tests/ut/cpp/dataset/cifar_op_test.cc         | 12 ++++--
 tests/ut/cpp/dataset/circular_pool_test.cc    |  6 +--
 tests/ut/cpp/dataset/image_folder_op_test.cc  | 33 ++++++++++-----
 tests/ut/cpp/dataset/manifest_op_test.cc      | 12 ++++--
 tests/ut/cpp/dataset/map_op_test.cc           |  4 +-
 tests/ut/cpp/dataset/memory_pool_test.cc      |  4 +-
 tests/ut/cpp/dataset/mind_record_op_test.cc   | 12 +++---
 tests/ut/cpp/dataset/mnist_op_test.cc         |  6 ++-
 tests/ut/cpp/dataset/normalize_op_test.cc     |  2 +-
 tests/ut/cpp/dataset/one_hot_op_test.cc       |  4 +-
 tests/ut/cpp/dataset/path_test.cc             |  4 +-
 tests/ut/cpp/dataset/queue_test.cc            |  2 +-
 .../random_crop_decode_resizeOp_test.cc       |  4 +-
 .../cpp/dataset/stand_alone_samplers_test.cc  |  2 +-
 tests/ut/cpp/dataset/status_test.cc           |  8 ++--
 tests/ut/cpp/dataset/task_manager_test.cc     |  2 +-
 tests/ut/cpp/dataset/tensor_test.cc           |  2 +-
 tests/ut/cpp/dataset/type_cast_op_test.cc     |  2 +-
 .../cpp/mindrecord/ut_shard_operator_test.cc  | 42 ++++++++++++-------
 tests/ut/cpp/runtest.sh                       |  1 +
 22 files changed, 102 insertions(+), 66 deletions(-)

diff --git a/tests/ut/cpp/dataset/arena_test.cc b/tests/ut/cpp/dataset/arena_test.cc
index 4b5f50c47f..0809c40ee3 100644
--- a/tests/ut/cpp/dataset/arena_test.cc
+++ b/tests/ut/cpp/dataset/arena_test.cc
@@ -45,5 +45,5 @@ TEST_F(MindDataTestArena, TestALLFunction) {
   for (int i = 0; i < 1000; i++) {
     mp->Deallocate(v.at(i));
   }
-  std::cout << *mp;
+  MS_LOG(DEBUG) << *mp;
 }
diff --git a/tests/ut/cpp/dataset/btree_test.cc b/tests/ut/cpp/dataset/btree_test.cc
index 993f8f465f..3e0a867fba 100644
--- a/tests/ut/cpp/dataset/btree_test.cc
+++ b/tests/ut/cpp/dataset/btree_test.cc
@@ -196,6 +196,6 @@ TEST_F(MindDataTestBPlusTree, Test3) {
   EXPECT_EQ(it.value(), "b");
   MS_LOG(INFO) << "Dump all the values using [] operator.";
   for (uint64_t i = min; i <= max; i++) {
-    std::cout << ai[i] << std::endl;
+    MS_LOG(DEBUG) << ai[i] << std::endl;
   }
 }
diff --git a/tests/ut/cpp/dataset/cifar_op_test.cc b/tests/ut/cpp/dataset/cifar_op_test.cc
index 48c332bc13..0cd1db65b5 100644
--- a/tests/ut/cpp/dataset/cifar_op_test.cc
+++ b/tests/ut/cpp/dataset/cifar_op_test.cc
@@ -81,7 +81,8 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar10) {
     uint32_t label = 0;
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 100);
@@ -108,7 +109,8 @@ TEST_F(MindDataTestCifarOp, TestRandomSamplerCifar10) {
     uint32_t label = 0;
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 12);
@@ -133,7 +135,8 @@ TEST_F(MindDataTestCifarOp, TestCifar10NumSample) {
     uint32_t label = 0;
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 100);
@@ -159,8 +162,9 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar100) {
     while (tensor_map.size() != 0) {
       tensor_map["coarse_label"]->GetItemAt<uint32_t>(&coarse, {});
       tensor_map["fine_label"]->GetItemAt<uint32_t>(&fine, {});
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << " coarse:"
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << " coarse:"
                 << coarse << " fine:" << fine << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 100);
diff --git a/tests/ut/cpp/dataset/circular_pool_test.cc b/tests/ut/cpp/dataset/circular_pool_test.cc
index 47ceae5930..c42b08ddcd 100644
--- a/tests/ut/cpp/dataset/circular_pool_test.cc
+++ b/tests/ut/cpp/dataset/circular_pool_test.cc
@@ -52,7 +52,7 @@ Status TestMem(MindDataTestCircularPool *tp, int32_t num_iterations) {
     uint64_t new_sz = dist(gen);
     std::string str = "Allocate " + std::to_string(old_sz) +
                       " bytes of memory and then resize to " + std::to_string(new_sz);
-    std::cout << str << std::endl;
+    MS_LOG(DEBUG) << str << std::endl;
     std::string id = Services::GetUniqueID();
     void *p;
     RETURN_IF_NOT_OK(tp->mp_->Allocate(old_sz, &p));
@@ -76,9 +76,9 @@ TEST_F(MindDataTestCircularPool, TestALLFunction) {
     vg_.CreateAsyncTask("TestMem", f);
   }
   vg_.join_all();
-  std::cout << vg_.GetTaskErrorIfAny() << std::endl;
+  MS_LOG(DEBUG) << vg_.GetTaskErrorIfAny() << std::endl;
   ASSERT_TRUE(vg_.GetTaskErrorIfAny().IsOk());
   CircularPool *cp = dynamic_cast<CircularPool *>(mp_.get());
-  std::cout << *cp << std::endl;
+  MS_LOG(DEBUG) << *cp << std::endl;
 }
 
diff --git a/tests/ut/cpp/dataset/image_folder_op_test.cc b/tests/ut/cpp/dataset/image_folder_op_test.cc
index 82513a29a5..5b118a629a 100644
--- a/tests/ut/cpp/dataset/image_folder_op_test.cc
+++ b/tests/ut/cpp/dataset/image_folder_op_test.cc
@@ -102,7 +102,8 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeat) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_TRUE(res[(i % 44) / 11] == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 88);
@@ -126,7 +127,8 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomImageFolder) {
     int32_t label = 0;
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 44);
@@ -155,7 +157,8 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomSamplerImageFolder) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_TRUE(res[i] == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 12);
@@ -185,8 +188,9 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeatBatch)
       std::shared_ptr<Tensor> label;
       Create1DTensor(&label, 11, reinterpret_cast<unsigned char *>(res[i % 4]), DataType::DE_INT32);
       EXPECT_TRUE((*label) == (*tensor_map["label"]));
-      std::cout << "row: " << i++ << " " << tensor_map["image"]->shape() << " (*label):" << (*label)
+      MS_LOG(DEBUG) << "row: " << i << " " << tensor_map["image"]->shape() << " (*label):" << (*label)
                 << " *tensor_map[label]: " << *tensor_map["label"] << std::endl;
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 8);
@@ -282,7 +286,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderClassIndex) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_TRUE(label == res[i / 11]);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 22);
@@ -308,7 +313,8 @@ TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_EQ(i % 4, label);
-      std::cout << "row:" << i++ << "\tlabel:" << label << "\n";
+      MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 16);
@@ -335,7 +341,8 @@ TEST_F(MindDataTestImageFolderSampler, TestPKSamplerImageFolder) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_TRUE(res[i] == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 12);
@@ -360,7 +367,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderNumSamples) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_TRUE(0 == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 22);
@@ -392,7 +400,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderDecode) {
       EXPECT_TRUE(label == res[i / 11]);
       EXPECT_TRUE(
         tensor_map["image"]->shape() == TensorShape({2268, 4032, 3}));  // verify shapes are correct after decode
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 20);
@@ -442,7 +451,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_EQ(labels[i], label);
-      std::cout << "row:" << i++ << "\tlabel:" << label << "\n";
+      MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 5);
@@ -470,7 +480,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding2) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<int32_t>(&label, {});
       EXPECT_EQ(labels[i], label);
-      std::cout << "row:" << i++ << "\tlabel:" << label << "\n";
+      MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 11);
diff --git a/tests/ut/cpp/dataset/manifest_op_test.cc b/tests/ut/cpp/dataset/manifest_op_test.cc
index e062335fa6..9e36f8c747 100644
--- a/tests/ut/cpp/dataset/manifest_op_test.cc
+++ b/tests/ut/cpp/dataset/manifest_op_test.cc
@@ -76,7 +76,8 @@ TEST_F(MindDataTestManifest, TestSequentialManifestWithRepeat) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
       EXPECT_TRUE(res[i] == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 4);
@@ -134,7 +135,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestClassIndex) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
       EXPECT_TRUE(label == res[i]);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 2);
@@ -159,7 +161,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestNumSamples) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
       EXPECT_TRUE(0 == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 4);
@@ -184,7 +187,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestEval) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
       EXPECT_TRUE(0 == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 1);
diff --git a/tests/ut/cpp/dataset/map_op_test.cc b/tests/ut/cpp/dataset/map_op_test.cc
index 746c1e8d5f..271cbbe190 100644
--- a/tests/ut/cpp/dataset/map_op_test.cc
+++ b/tests/ut/cpp/dataset/map_op_test.cc
@@ -697,7 +697,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) {
   std::string result;
   while (tensor_map.size() != 0) {
     tensor_map["label"]->GetItemAt<int32_t>(&label, {});
-    std::cout << "row:" << i << "\tlabel:" << label << "\n";
+    MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
     EXPECT_TRUE(img_class[(i % 44) / 11] == label);
     // Dump all the image into string, to be used as a comparison later.
     result.append((char *) tensor_map["image"]->StartAddr(), (int64_t) tensor_map["image"]->Size());
@@ -743,7 +743,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) {
   std::string result2;
   while (tensor_map.size() != 0) {
     tensor_map["label"]->GetItemAt<int32_t>(&label, {});
-    std::cout << "row:" << i << "\tlabel:" << label << "\n";
+    MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
     EXPECT_TRUE(img_class[(i % 44) / 11] == label);
     result2.append((char *) tensor_map["image"]->StartAddr(), (int64_t) tensor_map["image"]->Size());
     di2.GetNextAsMap(&tensor_map);
diff --git a/tests/ut/cpp/dataset/memory_pool_test.cc b/tests/ut/cpp/dataset/memory_pool_test.cc
index c988a09e89..136f3fe1b8 100644
--- a/tests/ut/cpp/dataset/memory_pool_test.cc
+++ b/tests/ut/cpp/dataset/memory_pool_test.cc
@@ -35,7 +35,7 @@ class MindDataTestMemoryPool : public UT::Common {
 };
 
 TEST_F(MindDataTestMemoryPool, DumpPoolInfo) {
-  std::cout << *(std::dynamic_pointer_cast<CircularPool>(mp_)) << std::endl;
+  MS_LOG(DEBUG) << *(std::dynamic_pointer_cast<CircularPool>(mp_)) << std::endl;
 }
 
 TEST_F(MindDataTestMemoryPool, TestOperator1) {
@@ -72,5 +72,5 @@ TEST_F(MindDataTestMemoryPool, TestAllocator) {
   std::shared_ptr<A> obj_a = std::allocate_shared<A>(alloc, 3);
   int v = obj_a->val_a();
   ASSERT_EQ(v, 3);
-  std::cout << *(std::dynamic_pointer_cast<CircularPool>(mp_)) << std::endl;
+  MS_LOG(DEBUG) << *(std::dynamic_pointer_cast<CircularPool>(mp_)) << std::endl;
 }
diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc
index 9664b66ecb..abe7faef14 100644
--- a/tests/ut/cpp/dataset/mind_record_op_test.cc
+++ b/tests/ut/cpp/dataset/mind_record_op_test.cc
@@ -69,7 +69,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBasic) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   my_tree->AssociateNode(my_mindrecord_op);
 
@@ -140,7 +140,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordSample) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   my_tree->AssociateNode(my_mindrecord_op);
 
@@ -211,7 +211,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordShuffle) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   my_tree->AssociateNode(my_mindrecord_op);
 
@@ -285,7 +285,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordCategory) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   my_tree->AssociateNode(my_mindrecord_op);
 
@@ -352,7 +352,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordRepeat) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   rc = my_tree->AssociateNode(my_mindrecord_op);
   EXPECT_TRUE(rc.IsOk());
@@ -434,7 +434,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) {
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());
 
-  std::cout << (*my_mindrecord_op);
+  MS_LOG(DEBUG) << (*my_mindrecord_op);
 
   rc = my_tree->AssociateNode(my_mindrecord_op);
   EXPECT_TRUE(rc.IsOk());
diff --git a/tests/ut/cpp/dataset/mnist_op_test.cc b/tests/ut/cpp/dataset/mnist_op_test.cc
index 3d5c4b05ca..2733597b35 100644
--- a/tests/ut/cpp/dataset/mnist_op_test.cc
+++ b/tests/ut/cpp/dataset/mnist_op_test.cc
@@ -91,7 +91,8 @@ TEST_F(MindDataTestMnistSampler, TestSequentialMnistWithRepeat) {
     while (tensor_map.size() != 0) {
       tensor_map["label"]->GetItemAt<uint32_t>(&label, {});
       EXPECT_TRUE(res[i % 10] == label);
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 20);
@@ -120,7 +121,8 @@ TEST_F(MindDataTestMnistSampler, TestSequentialImageFolderWithRepeatBatch) {
       std::shared_ptr<Tensor> label;
       Create1DTensor(&label, 5, reinterpret_cast<unsigned char *>(res[i % 4]));
       EXPECT_TRUE((*label) == (*tensor_map["label"]));
-      std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << *tensor_map["label"] << "\n";
+      MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << *tensor_map["label"] << "\n";
+      i++;
       di.GetNextAsMap(&tensor_map);
     }
     EXPECT_TRUE(i == 4);
diff --git a/tests/ut/cpp/dataset/normalize_op_test.cc b/tests/ut/cpp/dataset/normalize_op_test.cc
index 373669a473..05ac3f6289 100644
--- a/tests/ut/cpp/dataset/normalize_op_test.cc
+++ b/tests/ut/cpp/dataset/normalize_op_test.cc
@@ -51,7 +51,7 @@ TEST_F(MindDataTestNormalizeOP, TestOp) {
   cv::Mat cv_output_image;
   cv_output_image = p->mat();
 
-  std::cout << "Storing output file to : " << output_filename << std::endl;
+  MS_LOG(DEBUG) << "Storing output file to : " << output_filename << std::endl;
   cv::FileStorage file(output_filename, cv::FileStorage::WRITE);
   file << "imageData" << cv_output_image;
 }
diff --git a/tests/ut/cpp/dataset/one_hot_op_test.cc b/tests/ut/cpp/dataset/one_hot_op_test.cc
index 3928aeffd4..4b8bbc1bdd 100644
--- a/tests/ut/cpp/dataset/one_hot_op_test.cc
+++ b/tests/ut/cpp/dataset/one_hot_op_test.cc
@@ -47,8 +47,8 @@ TEST_F(MindDataTestOneHotOp, TestOp) {
   EXPECT_TRUE(s.IsOk());
   ASSERT_TRUE(output->shape() == expected->shape());
   ASSERT_TRUE(output->type() == expected->type());
-  std::cout << *output << std::endl;
-  std::cout << *expected << std::endl;
+  MS_LOG(DEBUG) << *output << std::endl;
+  MS_LOG(DEBUG) << *expected << std::endl;
 
   ASSERT_TRUE(*output == *expected);
   MS_LOG(INFO) << "MindDataTestOneHotOp end.";
diff --git a/tests/ut/cpp/dataset/path_test.cc b/tests/ut/cpp/dataset/path_test.cc
index 7bd2abe7e5..3839d659aa 100644
--- a/tests/ut/cpp/dataset/path_test.cc
+++ b/tests/ut/cpp/dataset/path_test.cc
@@ -38,7 +38,7 @@ TEST_F(MindDataTestPath, Test1) {
   int i = 0;
   while (dir_it->hasNext()) {
     Path v = dir_it->next();
-    std::cout << v.toString() << "\n";
+    MS_LOG(DEBUG) << v.toString() << "\n";
     i++;
     if (i == 10) {
       break;
@@ -46,7 +46,7 @@ TEST_F(MindDataTestPath, Test1) {
   }
   // Test extension.
   Path g("file.jpeg");
-  std::cout << g.Extension() << "\n";
+  MS_LOG(DEBUG) << g.Extension() << "\n";
   ASSERT_EQ(g.Extension(), ".jpeg");
 }
 
diff --git a/tests/ut/cpp/dataset/queue_test.cc b/tests/ut/cpp/dataset/queue_test.cc
index 37560e5dfb..00366fcafd 100644
--- a/tests/ut/cpp/dataset/queue_test.cc
+++ b/tests/ut/cpp/dataset/queue_test.cc
@@ -44,7 +44,7 @@ class RefCount {
     explicit RefCount(int x) : v_(std::make_shared<int>(x)) {}
     explicit RefCount(const RefCount &o) : v_(o.v_) {}
     ~RefCount() {
-      std::cout << "Destructor of RefCount called" << std::endl;
+      MS_LOG(DEBUG) << "Destructor of RefCount called" << std::endl;
       gRefCountDestructorCalled++;
     }
     RefCount& operator=(const RefCount &o) {
diff --git a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc b/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc
index a4c4fbfdff..facd35c4f7 100644
--- a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc
+++ b/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc
@@ -78,7 +78,7 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
     } else {
       mse = mse_sum;
     }
-    std::cout << "mse: " << mse << std::endl;
+    MS_LOG(DEBUG) << "mse: " << mse << std::endl;
   }
   MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
 }
@@ -150,7 +150,7 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
     }
 
     mse = (count == 0) ? mse_sum : static_cast<float>(mse_sum) / count;
-    std::cout << "mse: " << mse << std::endl;
+    MS_LOG(DEBUG) << "mse: " << mse << std::endl;
   }
   MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
 }
diff --git a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
index 2626add22e..c686a9486b 100644
--- a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
+++ b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
@@ -78,7 +78,7 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) {
     sampler->Init(&mock);
     sampler->GetNextBuffer(&db);
     db->GetTensor(&tensor, 0, 0);
-    std::cout << (*tensor);
+    MS_LOG(DEBUG) << (*tensor);
     if(i < 3) {  // This is added due to std::shuffle()
       EXPECT_TRUE((*tensor) == (*row[i]));
     }
diff --git a/tests/ut/cpp/dataset/status_test.cc b/tests/ut/cpp/dataset/status_test.cc
index 835e6303b0..30bbd1e909 100644
--- a/tests/ut/cpp/dataset/status_test.cc
+++ b/tests/ut/cpp/dataset/status_test.cc
@@ -43,19 +43,19 @@ TEST_F(MindDataTestStatus, Test1) {
   Status rc;
   ASSERT_TRUE(rc.IsOk());
   Status err1(StatusCode::kOutOfMemory, __LINE__, __FILE__);
-  std::cout << err1;
+  MS_LOG(DEBUG) << err1;
   ASSERT_TRUE(err1.IsOutofMemory());
   ASSERT_TRUE(err1.IsError());
   Status err2(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Oops");
-  std::cout << err2;
+  MS_LOG(DEBUG) << err2;
 }
 
 TEST_F(MindDataTestStatus, Test2) {
   Status rc = f1();
-  std::cout << rc;
+  MS_LOG(DEBUG) << rc;
 }
 
 TEST_F(MindDataTestStatus, Test3) {
   Status rc = f3();
-  std::cout << rc;
+  MS_LOG(DEBUG) << rc;
 }
diff --git a/tests/ut/cpp/dataset/task_manager_test.cc b/tests/ut/cpp/dataset/task_manager_test.cc
index 182eee1882..c6fccd51cb 100644
--- a/tests/ut/cpp/dataset/task_manager_test.cc
+++ b/tests/ut/cpp/dataset/task_manager_test.cc
@@ -36,7 +36,7 @@ Status f(TaskGroup &vg){
     RETURN_IF_NOT_OK(vg.CreateAsyncTask("Infinity", [&]() -> Status {
         TaskManager::FindMe()->Post();
         int a = v.fetch_add(1);
-        std::cout << a << std::endl;
+        MS_LOG(DEBUG) << a << std::endl;
         return f(vg);
     }));
   }
diff --git a/tests/ut/cpp/dataset/tensor_test.cc b/tests/ut/cpp/dataset/tensor_test.cc
index 6686be15be..7437b3d942 100644
--- a/tests/ut/cpp/dataset/tensor_test.cc
+++ b/tests/ut/cpp/dataset/tensor_test.cc
@@ -311,7 +311,7 @@ TEST_F(MindDataTestTensorDE, CVTensorAs) {
   m = 2 * m;
   ASSERT_EQ(ctv->StartAddr(), addr);
   ASSERT_TRUE(*t2 == *ctv);
-  std::cout << *t2 << std::endl << *ctv;
+  MS_LOG(DEBUG) << *t2 << std::endl << *ctv;
 }
 
 TEST_F(MindDataTestTensorDE, CVTensorMatSlice) {
diff --git a/tests/ut/cpp/dataset/type_cast_op_test.cc b/tests/ut/cpp/dataset/type_cast_op_test.cc
index 0bd1e90eae..f5fa035579 100644
--- a/tests/ut/cpp/dataset/type_cast_op_test.cc
+++ b/tests/ut/cpp/dataset/type_cast_op_test.cc
@@ -54,7 +54,7 @@ void testCast(std::vector<FROM> values, const DataType &from, const DataType &to
   EXPECT_TRUE(op->Compute(t, &output));
   ASSERT_TRUE(t->shape() == output->shape());
   ASSERT_TRUE(DataType(to)==output->type());
-  std::cout << *output << std::endl;
+  MS_LOG(DEBUG) << *output << std::endl;
   auto out = output->begin<TO>();
   auto v = values.begin();
   for (; out != output->end<TO>(); out++, v++) {
diff --git a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
index acea1cefae..46ea1712b2 100644
--- a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
+++ b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
@@ -58,7 +58,8 @@ TEST_F(TestShardOperator, TestShardSampleBasic) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    i++;
   }
   dataset.Finish();
   ASSERT_TRUE(i <= kSampleCount);
@@ -83,7 +84,8 @@ TEST_F(TestShardOperator, TestShardSampleWrongNumber) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    i++;
   }
   dataset.Finish();
   ASSERT_TRUE(i <= 5);
@@ -108,7 +110,8 @@ TEST_F(TestShardOperator, TestShardSampleRatio) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    i++;
   }
   dataset.Finish();
   ASSERT_TRUE(i <= 10);
@@ -137,7 +140,8 @@ TEST_F(TestShardOperator, TestShardSamplePartition) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    i++;
   }
   dataset.Finish();
   ASSERT_TRUE(i <= 10);
@@ -166,8 +170,9 @@ TEST_F(TestShardOperator, TestShardCategory) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
 
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
 
@@ -194,8 +199,9 @@ TEST_F(TestShardOperator, TestShardShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
   }
   dataset.Finish();
 }
@@ -218,8 +224,9 @@ TEST_F(TestShardOperator, TestShardSampleShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
   }
   dataset.Finish();
   ASSERT_LE(i, 35);
@@ -244,8 +251,9 @@ TEST_F(TestShardOperator, TestShardShuffleSample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
   }
   dataset.Finish();
   ASSERT_TRUE(i <= kSampleSize);
@@ -270,8 +278,9 @@ TEST_F(TestShardOperator, TestShardSampleShuffleSample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
   }
   dataset.Finish();
   ASSERT_LE(i, 35);
@@ -298,8 +307,9 @@ TEST_F(TestShardOperator, TestShardShuffleCompare) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
 
     auto y = compare_dataset.GetNext();
     if ((std::get<1>(x[0]))["file_name"] != (std::get<1>(y[0]))["file_name"]) different = true;
@@ -332,8 +342,9 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle1) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
     category_no++;
@@ -365,8 +376,9 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle2) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
     category_no++;
     category_no %= static_cast<int>(categories.size());
@@ -398,8 +410,9 @@ TEST_F(TestShardOperator, TestShardCategorySample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
     category_no++;
@@ -435,8 +448,9 @@ TEST_F(TestShardOperator, TestShardCategorySampleShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
         ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
     category_no++;
diff --git a/tests/ut/cpp/runtest.sh b/tests/ut/cpp/runtest.sh
index a50e1d4933..283d6bc639 100755
--- a/tests/ut/cpp/runtest.sh
+++ b/tests/ut/cpp/runtest.sh
@@ -28,6 +28,7 @@ cd ${BUILD_PATH}/mindspore/tests/ut/cpp
 
 export LD_LIBRARY_PATH=${BUILD_PATH}/mindspore/googletest/googlemock/gtest:${PROJECT_PATH}/mindspore:${PROJECT_PATH}/mindspore/lib:$LD_LIBRARY_PATH
 export PYTHONPATH=${PROJECT_PATH}/tests/ut/cpp/python_input:$PYTHONPATH:${PROJECT_PATH}
+export GLOG_v=2
 
 ## prepare data for dataset & mindrecord
 cp -fr $PROJECT_PATH/tests/ut/data ${PROJECT_PATH}/build/mindspore/tests/ut/cpp/

From 9b2dabccfe11d43496c9760fbc0ea32382518160 Mon Sep 17 00:00:00 2001
From: cathwong <cathy.wong@huawei.com>
Date: Tue, 31 Mar 2020 04:37:59 +0800
Subject: [PATCH 015/367] update CONTRIBUTING.md. typo fix.

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 591beaef82..85fee704c2 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -78,7 +78,7 @@ Please follow this style to make MindSpore easy to review, maintain and develop.
 
 * Pull a request to MindSpore repository
 
-    In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jekins CI will be automatically set up for building test.
+    In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jenkins CI will be automatically set up for building test.
 
 ### Report issues
 

From 8c88b39da1328fe9054209891003ea7916adc6bc Mon Sep 17 00:00:00 2001
From: qianlong <qianlong3@huawei.com>
Date: Mon, 30 Mar 2020 20:16:45 +0800
Subject: [PATCH 016/367] Optimize the execution time of test case
 test_rgb_hsv.py

---
 tests/ut/python/dataset/test_rgb_hsv.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/ut/python/dataset/test_rgb_hsv.py b/tests/ut/python/dataset/test_rgb_hsv.py
index e887acb01d..14ab9ddc2d 100644
--- a/tests/ut/python/dataset/test_rgb_hsv.py
+++ b/tests/ut/python/dataset/test_rgb_hsv.py
@@ -133,6 +133,7 @@ def test_rgb_hsv_pipeline():
     # First dataset
     transforms1 = [
         vision.Decode(),
+        vision.Resize([64, 64]),
         vision.ToTensor()
     ]
     transforms1 = vision.ComposeOp(transforms1)
@@ -142,6 +143,7 @@ def test_rgb_hsv_pipeline():
     # Second dataset
     transforms2 = [
         vision.Decode(),
+        vision.Resize([64, 64]),
         vision.ToTensor(),
         vision.RgbToHsv(),
         vision.HsvToRgb()

From b12e6ff780385759258c070674251ea0f0b99867 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Tue, 31 Mar 2020 09:34:09 +0800
Subject: [PATCH 017/367] add operator diag and diag_part

---
 mindspore/ccsrc/transform/convert.cc    |  6 +-
 mindspore/ccsrc/transform/op_declare.cc | 10 +++
 mindspore/ccsrc/transform/op_declare.h  |  4 ++
 mindspore/ops/_grad/grad_array_ops.py   | 22 +++++++
 mindspore/ops/operations/__init__.py    |  3 +-
 mindspore/ops/operations/array_ops.py   | 85 +++++++++++++++++++++----
 tests/ut/python/ops/test_ops.py         | 10 +++
 7 files changed, 125 insertions(+), 15 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 74b0695cff..c8c6abea4c 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -178,6 +178,8 @@ const char kNameLARSUpdate[] = "LARSUpdate";
 const char kNameRound[] = "Round";
 const char kNamePrint[] = "Print";
 const char kNameApplyFtrl[] = "ApplyFtrl";
+const char kNameDiag[] = "Diag";
+const char kNameDiagPart[] = "DiagPart";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -357,7 +359,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)},
     {string(kNameSign), ADPT_DESC(Sign)},
     {string(kNameRound), ADPT_DESC(Round)},
-    {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)}};
+    {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)},
+    {string(kNameDiag), ADPT_DESC(Diag)},
+    {string(kNameDiagPart), ADPT_DESC(DiagPart)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 82dcd62572..af0ce29f85 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -1173,6 +1173,16 @@ INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)},  {2, INPUT_DESC(accum)},   {3, INP
 ATTR_MAP(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}};
 
+// Diag
+INPUT_MAP(Diag) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(Diag) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(Diag) = {{0, OUTPUT_DESC(y)}};
+
+// DiagPart
+INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}};
+
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 03463b978f..59014b8b7d 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -435,6 +435,10 @@ DECLARE_OP_ADAPTER(Round)
 DECLARE_OP_USE_OUTPUT(Round)
 DECLARE_OP_ADAPTER(ApplyFtrl)
 DECLARE_OP_USE_OUTPUT(ApplyFtrl)
+DECLARE_OP_ADAPTER(Diag)
+DECLARE_OP_USE_OUTPUT(Diag)
+DECLARE_OP_ADAPTER(DiagPart)
+DECLARE_OP_USE_OUTPUT(DiagPart)
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index cf6247023e..79841cf27a 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -408,3 +408,25 @@ def get_bprop_depth_to_space(self):
         return (op(dout),)
 
     return bprop
+
+
+@bprop_getters.register(P.Diag)
+def get_bprop_diag(self):
+    """Generate bprop for Diag"""
+    op = P.DiagPart()
+
+    def bprop(x, out, dout):
+        return (op(dout),)
+
+    return bprop
+
+
+@bprop_getters.register(P.DiagPart)
+def get_bprop_diag_part(self):
+    """Generate bprop for DiagPart"""
+    op = P.Diag()
+
+    def bprop(x, out, dout):
+        return (op(dout),)
+
+    return bprop
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index a75b078df8..295e2edaf8 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -20,7 +20,7 @@ A collection of operators to build nerual networks or computing functions.
 """
 
 from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat,
-                        Diag, DType, ExpandDims, Eye,
+                        Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, GatherNd, GatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
@@ -208,6 +208,7 @@ __all__ = [
     "Cos",
     "ACos",
     "Diag",
+    "DiagPart",
     'Eye',
     'Assign',
     'AssignAdd',
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index d0d3d5006c..6740f172b4 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1615,37 +1615,96 @@ class StridedSlice(PrimitiveWithInfer):
 class Diag(PrimitiveWithInfer):
     r"""
 
-    Extract or construct a diagonal array.
+    Construct a diagonal tensor with a given diagonal values.
 
-    If input is a 2-D tensor, returns the diagonal of the input with the given offset. If
-    input is a 1-D tensor, returns the array of diagonals. If you use this function
-    to extract the diagonal and want to write to the result array, see the more
-    detailed documentation for "numpy.diagonal", whether you return a copy or a
-    view depends on the version of numpy you are using.
+    Assume `input_x` has dimensions :math:`[D_1,... D_k]`, the output is a tensor of
+    rank 2k with dimensions :math:`[D_1,..., D_k, D_1,..., D_k]` where:
+    :math:`output[i_1,..., i_k, i_1,..., i_k] = input_x[i_1,..., i_k]` and 0 everywhere else.
 
     Inputs:
-        - **input_x** (Tensor) - 1-D tensor or 2-D tensor.
+        - **input_x** (Tensor) - The input tensor.
 
     Outputs:
         Tensor.
 
     Examples:
+        >>> input_x = Tensor([1, 2, 3, 4])
         >>> diag = P.Diag()
         >>> diag(x)
+        [[1, 0, 0, 0],
+         [0, 2, 0, 0],
+         [0, 0, 3, 0],
+         [0, 0, 0, 4]]
     """
 
     @prim_attr_register
     def __init__(self):
         """init Diag"""
 
-    def infer_type(self, x):
-        args = {"x_dtype": x}
-        validator.check_subclass('input_x', x, mstype.tensor)
-        validator.check_type_same(args, mstype.number_type)
-        return x
+    def infer_dtype(self, x_type):
+        validator.check_subclass('input_x', x_type, mstype.tensor)
+        return x_type
+
+    def infer_shape(self, x_shape):
+        validator.check("x rank", len(x_shape), "", 1, Rel.GE)
+        ret_shape = copy.deepcopy(x_shape)
+        ret_shape = ret_shape + ret_shape
+        return ret_shape
+
+    def infer_value(self, x):
+        if x is None:
+            return None
+        validator.check("input x rank", len(x.shape()), "", 1)
+        ret = np.diag(x.asnumpy())
+        return Tensor(ret)
+
+
+class DiagPart(PrimitiveWithInfer):
+    r"""
+
+    Extract the diagonal part from given tensor.
+
+    Assume input has dimensions :math:`[D_1,..., D_k, D_1,..., D_k]`, the output is a tensor
+    of rank k with dimensions :math:`[D_1,..., D_k]` where:
+    :math:`output[i_1,..., i_k] = input[i_1,..., i_k, i_1,..., i_k]`.
+
+    Inputs:
+        - **input_x** (Tensor) - The input Tensor.
+
+    Outputs:
+        Tensor.
+
+    Examples
+        >>> input_x = Tensor([[1, 0, 0, 0],
+        >>>                   [0, 2, 0, 0],
+        >>>                   [0, 0, 3, 0],
+        >>>                   [0, 0, 0, 4]])
+        >>> diag_part = P.DiagPart()
+        >>> diag_part(x)
+        [1, 2, 3, 4]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init DiagPart"""
+
+    def infer_dtype(self, x_type):
+        validator.check_subclass('input_x', x_type, mstype.tensor)
+        return x_type
+
+    def infer_shape(self, x_shape):
+        if len(x_shape)%2 != 0 or \
+                not x_shape:
+            raise ValueError(f"DiagPart input rank must be non-zero and even, but got rank {len(x_shape)}, "
+                             f"with shapes {x_shape}")
+        length = len(x_shape) // 2
+        ret_shape = x_shape[0:length]
+        return ret_shape
 
     def infer_value(self, x):
-        validator.check("shape_length", len(x.shape()), "length", [1, 2], Rel.IN)
+        if x is None:
+            return None
+        validator.check("x rank", len(x.shape()), "", 2)
         ret = np.diag(x.asnumpy())
         return Tensor(ret)
 
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index bfe8075972..e917c12748 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -942,6 +942,16 @@ test_case_array_ops = [
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
         'desc_bprop': [[3,]]}),
+    ('Diag', {
+        'block': P.Diag(),
+        'desc_inputs': [[4]],
+        'desc_bprop': [[4, 4]],
+    }),
+    ('DiagPart', {
+        'block': P.DiagPart(),
+        'desc_inputs': [[4, 4]],
+        'desc_bprop': [[4]],
+    }),
 ]
 
 test_case_other_ops = [

From 9d5890d9b99da399b618a6f78556673c3fcc26c9 Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Tue, 31 Mar 2020 09:35:44 +0800
Subject: [PATCH 018/367] fix bug of got a error transdata's dest format

---
 mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
index 6fff8ccd2a..7f11c8f2c7 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
@@ -187,10 +187,10 @@ AnfNodePtr InsertTransOpForSingleOutput(const FuncGraphPtr &func_graph, const An
   return node;
 }
 
-void GetTransDataInputFormat(const AnfNodePtr &node, std::string *input_format) {
+void GetTransDataInputFormat(const AnfNodePtr &node, size_t idx, std::string *input_format) {
   MS_EXCEPTION_IF_NULL(input_format);
   if (AnfAlgo::IsRealKernel(node)) {
-    *input_format = AnfAlgo::GetOutputFormat(node, 0);
+    *input_format = AnfAlgo::GetOutputFormat(node, idx);
   } else {
     *input_format = AnfAlgo::GetPrevNodeOutputFormat(node, 0);
   }
@@ -206,7 +206,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
     bool padding_flag = false;
 
     std::string output_format;
-    GetTransDataInputFormat(node, &output_format);
+    GetTransDataInputFormat(node, output_idx, &output_format);
     if (output_format == kOpFormat_NC1KHKWHWC0) {
       MS_LOG(EXCEPTION) << "got the hw format" << output_format << " when insert the transdata node "
                         << node->DebugString();

From 976af212e94ba25ad2c339c3923c76c240aad8f5 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Tue, 31 Mar 2020 10:27:40 +0800
Subject: [PATCH 019/367] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?=
 =?UTF-8?q?!17=20:=20[AutoParallel]Fix=20bug=20in=20the=20case=20of=20two?=
 =?UTF-8?q?=20cast'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../ccsrc/parallel/step_auto_parallel.cc      |  6 ++--
 mindspore/ccsrc/parallel/step_parallel.cc     | 21 ++++----------
 .../parallel/test_element_wise_function.py    | 29 -------------------
 3 files changed, 7 insertions(+), 49 deletions(-)

diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index aee4247755..c3e3f5893e 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -346,8 +346,6 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) {
 }
 
 OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) {
-  MS_EXCEPTION_IF_NULL(prim);
-  MS_EXCEPTION_IF_NULL(cnode);
   auto attrs = prim->attrs();
   std::vector<Shapes> shape_list = ExtractShape(cnode);
   if (shape_list.empty()) {
@@ -383,8 +381,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
   operator_info->set_outputs_dtype(cnode->Type());
   operator_info->set_cnode(cnode);
   // If no strategy has been configured for this operator, then candidate strategies are generated for
-  // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy
-  if (!StrategyFound(attrs) || prim->name() == CAST) {
+  // auto-strategy searching
+  if (!StrategyFound(attrs)) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
     operator_info->ComputeBatchSplitFlagList();
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 2d948679d7..927acea705 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -370,12 +370,15 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
   if (prim == nullptr) {
     return false;
   }
+  auto attrs = prim->attrs();
   if (IsInBlackList(prim)) {
     MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
     return false;
   }
-  if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) {
-    return false;
+  if ((prim->name() == CAST)) {
+    if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
+      return false;
+    }
   }
 
   return cnode->in_forward_flag();
@@ -645,13 +648,6 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) {
   MS_EXCEPTION_IF_NULL(pre_node);
 
   LossNodeInfo node_info;
-  // return -> cast
-  auto pre_cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(pre_cnode);
-  auto pre_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
-    pre_node = pre_cnode->input(1);
-  }
 
   // return -> loss
   if (pre_node == loss_node) {
@@ -1947,13 +1943,6 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
   MS_EXCEPTION_IF_NULL(current_value);
   PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(current_prim);
-  // return -> cast
-  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
-    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
-    MS_EXCEPTION_IF_NULL(pre_cnode);
-    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  }
-
   // notice: the GetNext op has not input
   if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(INFO) << "The loss is: " << current_prim->name();
diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py
index a917dce9b6..dfcebdc5ab 100644
--- a/tests/ut/python/parallel/test_element_wise_function.py
+++ b/tests/ut/python/parallel/test_element_wise_function.py
@@ -272,32 +272,3 @@ def test_cast_before_mirror3():
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
-
-
-def test_mul_two_cast():
-    class Net(nn.Cell):
-        def __init__(self, strategy1, strategy2, strategy3):
-            super().__init__()
-            self.mul = P.Mul().set_strategy(strategy1)
-            self.mul2 = P.Mul().set_strategy(strategy2)
-            self.cast = P.Cast().set_strategy(strategy3)
-            self.cast2 = P.Cast().set_strategy(strategy3)
-
-        def construct(self, x, y, b):
-            out = self.mul(x, y)
-            out = self.mul2(out, b)
-            out = self.cast(out, ms.int32)
-            out = self.cast2(out, ms.bool_)
-            return out
-
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
-    strategy1 = ((2, 2), (2, 2))
-    strategy2 = ((8, 1), (8, 1))
-    strategy3 = ((8, 1), )
-    net = GradWrap(Net(strategy1, strategy2, strategy3))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
-
-    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    b = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    _executor.compile(net, x, y, b)

From b3a306489d35982fc1d2781a0a0aac7f6b7ce3dc Mon Sep 17 00:00:00 2001
From: lvliang <lvliang18@huawei.com>
Date: Mon, 30 Mar 2020 22:01:24 +0800
Subject: [PATCH 020/367] auto enbale dynamic mem pool

---
 .../device/ascend/ascend_kernel_runtime.cc    | 44 ++++++-------------
 .../device/ascend/ascend_memory_allocator.cc  |  2 +-
 .../device/ascend/ascend_memory_allocator.h   |  2 +-
 mindspore/ccsrc/session/session_basic.cc      |  2 +-
 4 files changed, 17 insertions(+), 33 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 7be480e5e3..a7dfc96b2f 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -239,22 +239,11 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size
   return std::make_shared<AscendDeviceAddress>(device_ptr, device_size, format, type_id);
 }
 
-void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) {
-  MS_EXCEPTION_IF_NULL(MsContext::GetInstance());
-  if (MsContext::GetInstance()->enable_dynamic_mem_pool()) {
-    auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size);
-    MS_EXCEPTION_IF_NULL(device_ptr);
-    address->ptr_ = device_ptr;
-    address->mem_dynamic_alloc_ = true;
-    return;
-  }
-  if (flag == kStaticMem) {
-    address->ptr_ = MallocStaticMem(size, false);
-  } else if (flag == kDynamicMem) {
-    address->ptr_ = MallocDynamicMem(size, false);
-  } else {
-    MS_LOG(EXCEPTION) << "Unknown memory type!";
-  }
+void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) {
+  auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size);
+  MS_EXCEPTION_IF_NULL(device_ptr);
+  address->ptr_ = device_ptr;
+  address->mem_dynamic_alloc_ = true;
 }
 
 bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
@@ -488,23 +477,18 @@ bool AscendKernelRuntime::DestroyHccl() {
 
 bool AscendKernelRuntime::MallocDeviceMemory() {
   device_mem_size_ = ASCEND_MEM_SIZE_BYTE;
-  MS_EXCEPTION_IF_NULL(MsContext::GetInstance());
-  if (MsContext::GetInstance()->enable_dynamic_mem_pool()) {
-    static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_DAVINCI_MEM_RATIO);
-    device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_DAVINCI_MEM_RATIO));
-    auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM);
-    if (ret != RT_ERROR_NONE) {
-      MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
-    }
-    AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
-    AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
-  } else {
-    static_mem_offset_ = device_mem_size_;
+  static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO);
+  auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM);
+  if (ret != RT_ERROR_NONE) {
+    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]";
   }
-  auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), device_mem_size_, RT_MEMORY_HBM);
+  device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
+  ret = rtMalloc(reinterpret_cast<void **>(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM);
   if (ret != RT_ERROR_NONE) {
-    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]";
+    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
   }
+  AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
+  AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
   return true;
 }
 
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc
index 06b921f509..08a30a28b7 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc
@@ -26,7 +26,7 @@ const uint64_t MEM_SIZE_BYTE = (MEM_SIZE << 30);
 
 AscendMemoryAllocator::AscendMemoryAllocator() {
   hasMalloc_ = false;
-  free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_DAVINCI_MEM_RATIO));
+  free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
   total_mem_size_ = free_mem_size_;
 }
 
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h
index 74cc50834f..8b0f89a9b8 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h
@@ -24,7 +24,7 @@ namespace mindspore {
 namespace device {
 namespace ascend {
 // The fraction of total ascend memory used to compute the graph.
-static const float GRAPH_INIT_DAVINCI_MEM_RATIO = 0.8;
+static const float GRAPH_INIT_ASCEND_MEM_RATIO = 0.8;
 
 class AscendMemoryAllocator : public DynamicMemPoolBestFit {
  public:
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index 1d786708a2..be03f54f3c 100644
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -497,7 +497,7 @@ void SessionBasic::LoadInputData(const std::shared_ptr<KernelGraph> &kernel_grap
       auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0);
       bool need_sync = false;
       if (ms_context->enable_pynative_infer()) {
-        if (tensor->device_address().get() == nullptr) {
+        if (tensor->device_address().get() == nullptr || tensor->device_address() != device_address) {
           need_sync = true;
         }
       } else {

From 30231eab2f5ae6d7892d5806f5dbd61bcbcbac85 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Mon, 30 Mar 2020 21:41:59 +0800
Subject: [PATCH 021/367] fix auto parallel st

---
 .../st/auto_parallel/onehot_model_parallel.py |  9 +----
 .../run_auto_parallel_loss_expand.sh          |  3 +-
 .../run_auto_parallel_resnet50_expand_loss.sh |  2 +-
 .../run_onehot_model_parallel.sh              |  2 +-
 .../soft_entropy_loss_expand_parallel.py      | 40 +++++++------------
 tests/st/auto_parallel/test_expand_loss.py    |  4 +-
 .../test_model_parallel_onehot.py             |  3 --
 7 files changed, 22 insertions(+), 41 deletions(-)

diff --git a/tests/st/auto_parallel/onehot_model_parallel.py b/tests/st/auto_parallel/onehot_model_parallel.py
index b0e6f1eb91..14b351c0ee 100644
--- a/tests/st/auto_parallel/onehot_model_parallel.py
+++ b/tests/st/auto_parallel/onehot_model_parallel.py
@@ -130,9 +130,7 @@ class OneHotFactory:
         context.reset_auto_parallel_context()
         assert np.allclose(out_mindspore_single, out_mindspore_parallel, 0.0001, 0.0001)
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
+
 def test_reid_onehot_forward_int32_128_depth1024_model_parallel():
     fact = OneHotFactory(batch_size=128,
                          classes=1024,
@@ -142,9 +140,7 @@ def test_reid_onehot_forward_int32_128_depth1024_model_parallel():
                          strategy=((1,device_num),(),()))
     fact.forward_cmp()
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
+
 def test_reid_onehot_forward_int32_1024_depth128_model_parallel():
     fact = OneHotFactory(batch_size=1024,
                          classes=128,
@@ -153,4 +149,3 @@ def test_reid_onehot_forward_int32_1024_depth128_model_parallel():
                          axis=-1,
                          strategy=((1,device_num),(),()))
     fact.forward_cmp()
-
diff --git a/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh b/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh
index 83acc6ad54..71dee820cb 100644
--- a/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh
+++ b/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh
@@ -18,7 +18,6 @@ BASE_PATH=$(cd "$(dirname $0)"; pwd)
 CONFIG_PATH=/home/workspace/mindspore_config
 export DEVICE_NUM=8
 export RANK_SIZE=$DEVICE_NUM
-ulimit -n 65535
 source ${BASE_PATH}/env.sh
 unset SLOG_PRINT_TO_STDOUT
 export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_table_${DEVICE_NUM}p.json
@@ -27,7 +26,7 @@ process_pid=()
 for((i=0; i<$DEVICE_NUM; i++)); do
     rm -rf ${BASE_PATH}/loss_expand${i}
     mkdir ${BASE_PATH}/loss_expand${i}
-    cp -r soft_entropy_loss_expand_parallel.py  ${BASE_PATH}/loss_expand${i}/
+    cp -r ${BASE_PATH}/soft_entropy_loss_expand_parallel.py  ${BASE_PATH}/loss_expand${i}/
     cd ${BASE_PATH}/loss_expand${i}
     export RANK_ID=${i}
     export DEVICE_ID=${i}
diff --git a/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh b/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh
index efc1e0c0de..094668ba5c 100644
--- a/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh
+++ b/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh
@@ -27,7 +27,7 @@ process_pid=()
 for((i=0; i<$DEVICE_NUM; i++)); do
     rm -rf ${BASE_PATH}/resnet50_expand_loss${i}
     mkdir ${BASE_PATH}/resnet50_expand_loss${i}
-    cp -r resnet50_expand_loss.py  ${BASE_PATH}/resnet50_expand_loss${i}/
+    cp -r ${BASE_PATH}/resnet50_expand_loss.py  ${BASE_PATH}/resnet50_expand_loss${i}/
     cd ${BASE_PATH}/resnet50_expand_loss${i}
     export RANK_ID=${i}
     export DEVICE_ID=${i}
diff --git a/tests/st/auto_parallel/run_onehot_model_parallel.sh b/tests/st/auto_parallel/run_onehot_model_parallel.sh
index 93229aab4e..8e931c2cdd 100644
--- a/tests/st/auto_parallel/run_onehot_model_parallel.sh
+++ b/tests/st/auto_parallel/run_onehot_model_parallel.sh
@@ -27,7 +27,7 @@ process_pid=()
 for((i=0; i<$DEVICE_NUM; i++)); do
     rm -rf ${BASE_PATH}/onehot_model_parallel${i}
     mkdir ${BASE_PATH}/onehot_model_parallel${i}
-    cp -r onehot_model_parallel.py  ${BASE_PATH}/onehot_model_parallel${i}/
+    cp -r ${BASE_PATH}/onehot_model_parallel.py  ${BASE_PATH}/onehot_model_parallel${i}/
     cd ${BASE_PATH}/onehot_model_parallel${i}
     export RANK_ID=${i}
     export DEVICE_ID=${i}
diff --git a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py
index 081c6bd647..17dbe8f304 100644
--- a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py
+++ b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py
@@ -118,6 +118,9 @@ class Dataset():
     def get_dataset_size(self):
         return self.length
 
+    def get_repeat_count(self):
+        return self.length
+
 class ModelCallback(Callback):
     def __init__(self):
         super(ModelCallback, self).__init__()
@@ -177,7 +180,6 @@ class LossFactory():
         dataGen = DataGenerator()
         self.input_full, self.input_part = dataGen.input_data((batch_size, embed))
         self.label_full, self.label_part = dataGen.label_data((batch_size,),embed)
-        self.expect_out = np.array([0.9205861 , 0.9205861 , 0.9205861 , 0.9201946 , 0.91951686, 0.919343])
 
     def single_matmul_trains(self):
         single_callback = ModelCallback()
@@ -187,7 +189,8 @@ class LossFactory():
         epoch_size = 6
         dataset = Dataset(self.input_full, self.label_full)
         model.train(epoch_size, dataset, callbacks=single_callback, dataset_sink_mode=False)
-        print("---loss---",single_callback.loss_list)
+        loss_value = np.array(single_callback.loss_list)
+        return loss_value
 
     def data_parallel_matmul_trains(self):
         parallel_callback = ModelCallback()
@@ -199,7 +202,7 @@ class LossFactory():
         dataset = Dataset(self.input_part, self.label_part)
         model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
         loss_value = np.array(parallel_callback.loss_list)
-        assert allclose(loss_value, self.expect_out, 0.00001, 0.00001)
+        return loss_value
     
     def model_parallel_matmul_trains(self):
         parallel_callback = ModelCallback()
@@ -224,7 +227,7 @@ class LossFactory():
         dataset = Dataset(self.input_part, self.label_part)
         model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
         loss_value = np.array(parallel_callback.loss_list)
-        assert allclose(loss_value, self.expect_out, 0.00001, 0.00001)
+        return loss_value
 
     def mix_parallel_matmul_trains(self):
         parallel_callback = ModelCallback()
@@ -249,28 +252,13 @@ class LossFactory():
         dataset = Dataset(self.input_part, self.label_part)
         model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
         loss_value = np.array(parallel_callback.loss_list)
-        assert allclose(loss_value, self.expect_out, 0.00001, 0.00001)
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
-def test_matmul_loss_data_parallel_trains():
-    loss_factory = LossFactory()
-    context.reset_auto_parallel_context()
-    loss_factory.data_parallel_matmul_trains()
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
-def test_matmul_loss_model_parallel_trains():
-    loss_factory = LossFactory()
-    context.reset_auto_parallel_context()
-    loss_factory.model_parallel_matmul_trains()
+        return loss_value
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single 
-def test_matmul_loss_mix_parallel_trains():
+def test_all_trains():
     loss_factory = LossFactory()
     context.reset_auto_parallel_context()
-    loss_factory.mix_parallel_matmul_trains()
+    single_loss = loss_factory.single_matmul_trains()
+    model_parallel_loss = loss_factory.model_parallel_matmul_trains()
+    mix_parallel_loss = loss_factory.mix_parallel_matmul_trains()
+    assert allclose(single_loss, model_parallel_loss)
+    assert allclose(single_loss, mix_parallel_loss)
diff --git a/tests/st/auto_parallel/test_expand_loss.py b/tests/st/auto_parallel/test_expand_loss.py
index c3ab64395a..786cbff980 100644
--- a/tests/st/auto_parallel/test_expand_loss.py
+++ b/tests/st/auto_parallel/test_expand_loss.py
@@ -18,7 +18,9 @@ import pytest
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_single
 def test_expand_loss():
-    ret = os.system("sh run_auto_parallel_loss_expand.sh")
+    sh_path = os.path.split(os.path.realpath(__file__))[0]
+    ret = os.system(f"sh {sh_path}/run_auto_parallel_loss_expand.sh")
     assert(ret==0)
diff --git a/tests/st/auto_parallel/test_model_parallel_onehot.py b/tests/st/auto_parallel/test_model_parallel_onehot.py
index aeebf1b78b..1df7ad1e99 100644
--- a/tests/st/auto_parallel/test_model_parallel_onehot.py
+++ b/tests/st/auto_parallel/test_model_parallel_onehot.py
@@ -16,9 +16,6 @@
 import os
 import pytest
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
 def test_expand_loss():
     ret = os.system("sh run_onehot_model_parallel.sh")
     assert(ret==0)

From cb29f60bf18ad3ce9c714749c534baa16e64cbcf Mon Sep 17 00:00:00 2001
From: Ting Wang <kathy.wangting@huawei.com>
Date: Tue, 31 Mar 2020 12:01:58 +0800
Subject: [PATCH 022/367] fix typo

Signed-off-by: Ting Wang <kathy.wangting@huawei.com>
---
 docs/Automatic-parallel.png | Bin 124010 -> 36199 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/Automatic-parallel.png b/docs/Automatic-parallel.png
index abf572d568d35bbb61690b30d5c1d1c66be76f31..f52d8b757d11a07ce99aebf5458d10059ea6dd3b 100644
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From ddbf6f574b0bb1464856fe494c69cad2dad32191 Mon Sep 17 00:00:00 2001
From: Ting Wang <kathy.wangting@huawei.com>
Date: Tue, 31 Mar 2020 14:28:46 +0800
Subject: [PATCH 023/367] update the supported version of EulerOS

Signed-off-by: Ting Wang <kathy.wangting@huawei.com>
---
 RELEASE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RELEASE.md b/RELEASE.md
index 76d86c8bff..8920095bb5 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -3,7 +3,7 @@
 ## Main Features
 
 ### Ascend 910 Training and Inference Framework
-* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.0
+* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
 * Python version: 3.7.5
 * Preset models
     * ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.

From 3bb48ffee17d3d2c5bcbfc545ae0a97ba425a3f6 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Tue, 31 Mar 2020 12:29:14 +0800
Subject: [PATCH 024/367] use std::vector instead of std::list to promote
 performance for parallel module

---
 mindspore/ccsrc/parallel/context.cc           |  1 -
 mindspore/ccsrc/parallel/context.h            |  1 -
 mindspore/ccsrc/parallel/device.h             |  1 -
 mindspore/ccsrc/parallel/device_manager.cc    | 19 ++++++++--------
 mindspore/ccsrc/parallel/device_manager.h     | 20 ++++++++---------
 mindspore/ccsrc/parallel/device_matrix.cc     |  6 ++---
 mindspore/ccsrc/parallel/device_matrix.h      |  9 ++++----
 .../parallel/graph_util/generate_graph.cc     |  1 -
 .../parallel/graph_util/generate_graph.h      |  1 -
 mindspore/ccsrc/parallel/group_manager.cc     |  6 ++---
 mindspore/ccsrc/parallel/group_manager.h      | 10 ++++-----
 .../ccsrc/parallel/ops_info/activation_info.h |  1 -
 .../ccsrc/parallel/ops_info/arithmetic_info.h |  1 -
 .../parallel/ops_info/batch_parallel_info.h   |  1 -
 .../ccsrc/parallel/ops_info/bias_add_info.h   |  2 +-
 .../ops_info/comparison_function_info.h       |  1 -
 .../parallel/ops_info/dropout_do_mask_info.h  |  1 -
 .../ops_info/elementary_function_info.h       |  1 -
 .../ccsrc/parallel/ops_info/generator_info.h  |  1 -
 .../ccsrc/parallel/ops_info/get_next_info.h   |  1 -
 .../parallel/ops_info/l2_normalize_info.h     |  1 -
 mindspore/ccsrc/parallel/ops_info/loss_info.h |  2 +-
 .../ccsrc/parallel/ops_info/matmul_info.cc    |  2 +-
 .../ccsrc/parallel/ops_info/matmul_info.h     |  2 +-
 .../ccsrc/parallel/ops_info/onehot_info.h     |  2 +-
 .../ccsrc/parallel/ops_info/operator_info.h   |  1 -
 .../ccsrc/parallel/ops_info/prelu_info.h      |  2 +-
 .../parallel/ops_info/reduce_method_info.cc   |  2 +-
 .../parallel/ops_info/reduce_method_info.h    |  1 -
 .../ccsrc/parallel/ops_info/reshape_info.h    |  1 -
 .../parallel/ops_info/tmp_identity_info.h     |  1 +
 .../ccsrc/parallel/ops_info/transpose_info.h  |  2 +-
 .../parallel/ops_info/virtual_dataset_info.h  |  2 +-
 mindspore/ccsrc/parallel/status.h             |  1 -
 mindspore/ccsrc/parallel/step_parallel.cc     |  2 +-
 mindspore/ccsrc/parallel/step_parallel.h      |  2 +-
 mindspore/ccsrc/parallel/strategy.h           |  1 -
 .../redistribution_operator_infer.h           |  1 -
 .../parallel/auto_parallel/dp_algo_test.cc    |  4 ++--
 .../auto_parallel/edge_costmodel_test.cc      |  4 ++--
 .../auto_parallel/graph_costmodel_test.cc     |  4 ++--
 .../auto_parallel/operator_costmodel_test.cc  | 12 +++++-----
 tests/ut/cpp/parallel/device_manager_test.cc  | 16 +++++++-------
 tests/ut/cpp/parallel/device_matrix_test.cc   | 16 +++++++-------
 tests/ut/cpp/parallel/group_manager_test.cc   | 22 +++++++++----------
 .../parallel/ops_info/activation_info_test.cc |  4 ++--
 .../cpp/parallel/ops_info/activation_test.cc  |  4 ++--
 .../ops_info/dropout_do_mask_info_test.cc     |  4 ++--
 .../cpp/parallel/ops_info/gelu_info_test.cc   |  4 ++--
 .../ops_info/generate_strategy_test.cc        |  4 ++--
 .../parallel/ops_info/generator_info_test.cc  |  4 ++--
 .../parallel/ops_info/get_next_info_test.cc   |  4 ++--
 .../ops_info/l2_normalize_info_test.cc        |  4 ++--
 .../ops_info/log_softmax_info_test.cc         |  4 ++--
 .../cpp/parallel/ops_info/matmul_info_test.cc |  4 ++--
 .../cpp/parallel/ops_info/onehot_info_test.cc |  4 ++--
 .../ops_info/onehot_info_test_axis_0.cc       |  4 ++--
 .../ut/cpp/parallel/ops_info/pow_info_test.cc |  4 ++--
 tests/ut/cpp/parallel/ops_info/prelu_test.cc  |  4 ++--
 .../parallel/ops_info/reduce_method_test.cc   |  4 ++--
 .../ut/cpp/parallel/ops_info/reshape_test.cc  |  4 ++--
 .../softmax_entropy_loss_info_test.cc         |  4 ++--
 .../parallel/ops_info/softmax_info_test.cc    |  4 ++--
 .../cpp/parallel/ops_info/tanh_info_test.cc   |  4 ++--
 .../parallel/ops_info/tensor_add_info_test.cc |  4 ++--
 .../cpp/parallel/ops_info/tmpidentity_test.cc |  4 ++--
 .../cpp/parallel/ops_info/transpose_test.cc   |  4 ++--
 .../cpp/parallel/step_auto_parallel_test.cc   |  4 ++--
 tests/ut/cpp/parallel/step_parallel_test.cc   |  4 ++--
 .../tensor_layout/construct_operator_test.cc  |  4 ++--
 .../redistribution_operator_infer_test.cc     |  4 ++--
 .../tensor_redistribution_test.cc             |  2 +-
 tests/ut/cpp/parallel/virtual_dataset_test.cc |  4 ++--
 73 files changed, 141 insertions(+), 160 deletions(-)

diff --git a/mindspore/ccsrc/parallel/context.cc b/mindspore/ccsrc/parallel/context.cc
index 74ec948c49..64cecf1669 100644
--- a/mindspore/ccsrc/parallel/context.cc
+++ b/mindspore/ccsrc/parallel/context.cc
@@ -21,7 +21,6 @@
 #include <utility>
 #include <numeric>
 #include <functional>
-#include <list>
 #include <memory>
 
 #include "parallel/device_manager.h"
diff --git a/mindspore/ccsrc/parallel/context.h b/mindspore/ccsrc/parallel/context.h
index 22a5f89e38..866609fbfe 100644
--- a/mindspore/ccsrc/parallel/context.h
+++ b/mindspore/ccsrc/parallel/context.h
@@ -20,7 +20,6 @@
 #include <cstdint>
 #include <string>
 #include <vector>
-#include <list>
 #include <memory>
 
 #include "parallel/status.h"
diff --git a/mindspore/ccsrc/parallel/device.h b/mindspore/ccsrc/parallel/device.h
index 0373617ad5..8c3174ae55 100644
--- a/mindspore/ccsrc/parallel/device.h
+++ b/mindspore/ccsrc/parallel/device.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_DEVICE_H_
 
 #include <cstdint>
-#include <list>
 #include <string>
 #include <utility>
 
diff --git a/mindspore/ccsrc/parallel/device_manager.cc b/mindspore/ccsrc/parallel/device_manager.cc
index 999d6b4432..3a553e08ec 100644
--- a/mindspore/ccsrc/parallel/device_manager.cc
+++ b/mindspore/ccsrc/parallel/device_manager.cc
@@ -30,7 +30,7 @@ namespace mindspore {
 namespace parallel {
 DeviceManagerPtr g_device_manager = nullptr;
 
-Stage::Stage(const std::list<mindspore::parallel::Device>& devices, int num, int rank)
+Stage::Stage(const std::vector<mindspore::parallel::Device>& devices, int num, int rank)
     : devices_(devices), number_(num), rank_(rank) {
   gm_ = GroupManager();
 }
@@ -104,7 +104,7 @@ int32_t GetListMemberByIndex(size_t index, const RankList& devices) {
   return result;
 }
 
-std::shared_ptr<Device> GetListMemberByIndex(size_t index, const std::list<std::shared_ptr<Device>>& device_list) {
+std::shared_ptr<Device> GetListMemberByIndex(size_t index, const std::vector<std::shared_ptr<Device>>& device_list) {
   size_t i = 0;
   std::shared_ptr<Device> result;
   if ((device_list.empty()) || (index >= device_list.size())) {
@@ -178,7 +178,7 @@ Status DeviceManager::Init(const RankList& devices, int32_t global_device_rank,
       MS_LOG(ERROR) << "The number of 'devices' in a stage must be positive";
       return Status::FAILED;
     }
-    std::list<Device> curr_dev_list;
+    std::vector<Device> curr_dev_list;
     for (int i = 0; i < num_device; ++i) {
       curr_dev_list.push_back(*GetListMemberByIndex(global_index, devices_));
       global_index++;
@@ -278,8 +278,8 @@ RankList DeviceManager::global_device_list(int32_t stage_id, int32_t rank, int32
 
 Device DeviceManager::CreateNewDeviceByRank(int32_t rank) const { return Device(rank); }
 
-std::list<Device> DeviceManager::CreateDeviceListByRankList(RankList ranks) {
-  std::list<Device> dev_list;
+std::vector<Device> DeviceManager::CreateDeviceListByRankList(RankList ranks) {
+  std::vector<Device> dev_list;
   for (auto& rank : ranks) {
     Device one = CreateNewDeviceByRank(rank);
     dev_list.push_back(one);
@@ -312,8 +312,8 @@ std::string HashName(const std::string& origin_name) { return std::to_string(std
 // is '0-1-3-5-7'.
 std::string DeviceManager::GenerateGroupNameByRanks(RankList ranks) {
   std::string rank_list_name;
-  std::list<int32_t>::iterator it;
-  ranks.sort();  // sorted in increasing order
+  std::vector<int32_t>::iterator it;
+  std::sort(ranks.begin(), ranks.end());  // sorted in increasing order
   for (it = ranks.begin(); it != ranks.end(); ++it) {
     if (it == ranks.begin()) {
       rank_list_name = std::to_string(*it);
@@ -343,7 +343,8 @@ std::string DeviceManager::GenerateGroupNameByRanks(RankList ranks) {
 // Create the group with the given devices and the given name. The GroupManager
 // gm_ will create a new group only if there does not exit a group with the same
 // name. Otherwise, let the pointer g point to that group.
-Group DeviceManager::CreateGroup(const std::string& group_name, const std::list<mindspore::parallel::Device>& devices) {
+Group DeviceManager::CreateGroup(const std::string& group_name,
+                                 const std::vector<mindspore::parallel::Device>& devices) {
   if ((world_group() == NCCL_WORLD_GROUP) && (devices.size() != devices_.size())) {
     MS_LOG(EXCEPTION) << "Do not support sub group for nccl";
   }
@@ -360,7 +361,7 @@ Group DeviceManager::CreateGroup(const RankList& dev_ranks) {
   }
 
   std::string group_name = GenerateGroupNameByRanks(dev_ranks);
-  std::list<Device> dev_list = CreateDeviceListByRankList(dev_ranks);
+  auto dev_list = CreateDeviceListByRankList(dev_ranks);
   return CreateGroup(group_name, dev_list);
 }
 
diff --git a/mindspore/ccsrc/parallel/device_manager.h b/mindspore/ccsrc/parallel/device_manager.h
index 80beb15f86..798d99383d 100644
--- a/mindspore/ccsrc/parallel/device_manager.h
+++ b/mindspore/ccsrc/parallel/device_manager.h
@@ -19,7 +19,7 @@
 
 #include <cstdint>
 #include <cstring>
-#include <list>
+#include <vector>
 #include <map>
 #include <memory>
 #include <string>
@@ -50,19 +50,19 @@ class Stage {
   // This class is used in pipeline-parallelization. Available devices are partitioned into multiple stages.
   // Currently, the function of pipeline-parallelization and this class are NOT implemented.
  public:
-  explicit Stage(std::list<Device> devices) : devices_(std::move(devices)), number_(0), rank_(0) {
+  explicit Stage(std::vector<Device> devices) : devices_(std::move(devices)), number_(0), rank_(0) {
     gm_ = GroupManager();
   }
-  Stage(const std::list<mindspore::parallel::Device>& devices, int num, int rank);
+  Stage(const std::vector<mindspore::parallel::Device>& devices, int num, int rank);
   ~Stage() = default;
 
   int GetStageNum() const { return number_; }
   size_t GetDevicesNum() const { return devices_.size(); }
-  std::list<Device> GetDevicesList() { return devices_; }
+  std::vector<Device> GetDevicesList() { return devices_; }
   int global_rank(Group* g) const;
 
  private:
-  std::list<Device> devices_;
+  std::vector<Device> devices_;
   int number_;
   int32_t rank_;
   GroupManager gm_;
@@ -89,10 +89,10 @@ class DeviceManager {
   RankList global_device_list(int32_t stage_id, int32_t rank, int32_t split_num) const;
 
   Device CreateNewDeviceByRank(int32_t rank) const;
-  std::list<Device> CreateDeviceListByRankList(RankList ranks);
+  std::vector<Device> CreateDeviceListByRankList(RankList ranks);
 
   std::string GenerateGroupNameByRanks(RankList dev_ranks);
-  Group CreateGroup(const std::string& group_name, const std::list<Device>& devices);
+  Group CreateGroup(const std::string& group_name, const std::vector<Device>& devices);
   Group CreateGroup(const RankList& dev_ranks);
   std::shared_ptr<Stage> GetStageById(int32_t stage_id);
 
@@ -108,11 +108,11 @@ class DeviceManager {
   std::string FindRankListNameByHashName(const std::string& hash_name);
 
  private:
-  std::list<std::shared_ptr<Device>> devices_;
+  std::vector<std::shared_ptr<Device>> devices_;
   // each stage has a list of devices
-  std::list<std::list<int32_t>> stage_devices_;
+  std::vector<std::vector<int32_t>> stage_devices_;
   std::shared_ptr<Device> device_;
-  std::list<std::shared_ptr<Stage>> stages_;
+  std::vector<std::shared_ptr<Stage>> stages_;
   GroupManager gm_;
   std::string backend_;
 
diff --git a/mindspore/ccsrc/parallel/device_matrix.cc b/mindspore/ccsrc/parallel/device_matrix.cc
index 5a7aa36c8e..f9f314d5a3 100644
--- a/mindspore/ccsrc/parallel/device_matrix.cc
+++ b/mindspore/ccsrc/parallel/device_matrix.cc
@@ -21,7 +21,7 @@
 #include <utility>
 #include <numeric>
 #include <functional>
-#include <list>
+#include <vector>
 
 #include "parallel/status.h"
 #include "parallel/ops_info/operator_info.h"
@@ -64,7 +64,7 @@ Status DeviceMatrix::GetDevicesAlongDim(const uint32_t& dim, RankList* devices)
   }
 
   RankList group;
-  std::list<RankList> local_group_list;
+  std::vector<RankList> local_group_list;
 
   // lower than dim
   int32_t step = 1;
@@ -160,7 +160,7 @@ std::string ShapeToString(const Shape& shape) {
   return str + "]";
 }
 
-std::string ListToString(const std::list<int32_t>& list) {
+std::string ListToString(const std::vector<int32_t>& list) {
   std::string str = "[";
   for (auto& element : list) {
     str += std::to_string(element) + ", ";
diff --git a/mindspore/ccsrc/parallel/device_matrix.h b/mindspore/ccsrc/parallel/device_matrix.h
index d3b0fdcb49..a912000604 100644
--- a/mindspore/ccsrc/parallel/device_matrix.h
+++ b/mindspore/ccsrc/parallel/device_matrix.h
@@ -20,7 +20,6 @@
 #include <cstdint>
 #include <string>
 #include <vector>
-#include <list>
 
 #include "parallel/status.h"
 #include "utils/convert_utils.h"
@@ -28,7 +27,7 @@
 namespace mindspore {
 namespace parallel {
 
-using RankList = std::list<int32_t>;
+using RankList = std::vector<int32_t>;
 using Shape = std::vector<int32_t>;
 
 class DeviceMatrix {
@@ -36,7 +35,7 @@ class DeviceMatrix {
   DeviceMatrix(int32_t rank, RankList devices, Shape dev_shape);
   DeviceMatrix() = default;
   ~DeviceMatrix() = default;
-  std::list<RankList> group_list() const { return group_list_; }
+  std::vector<RankList> group_list() const { return group_list_; }
   Status CreateGroupList();
   Status GetDevicesByTensorMap(const Shape& tensor_map, RankList* rank_list);
   Status GetDevicesAlongDim(const uint32_t& dim, RankList* devices);
@@ -46,11 +45,11 @@ class DeviceMatrix {
   RankList dev_list_;
   // From low dim to high dim. eg: [D0 D1 D2 D3]
   Shape dev_shape_;
-  std::list<RankList> group_list_;
+  std::vector<RankList> group_list_;
 };
 
 std::string ShapeToString(const Shape& shape);
-std::string ListToString(const std::list<int32_t>& list);
+std::string ListToString(const std::vector<int32_t>& list);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.cc b/mindspore/ccsrc/parallel/graph_util/generate_graph.cc
index d0548ec647..43df9fe802 100644
--- a/mindspore/ccsrc/parallel/graph_util/generate_graph.cc
+++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.cc
@@ -17,7 +17,6 @@
 #include "parallel/graph_util/generate_graph.h"
 
 #include <algorithm>
-#include <list>
 #include <memory>
 #include <string>
 #include <utility>
diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.h b/mindspore/ccsrc/parallel/graph_util/generate_graph.h
index 713c13dfb5..bb1f811f2f 100644
--- a/mindspore/ccsrc/parallel/graph_util/generate_graph.h
+++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_
 
 #include <vector>
-#include <list>
 #include <memory>
 #include <unordered_map>
 #include <map>
diff --git a/mindspore/ccsrc/parallel/group_manager.cc b/mindspore/ccsrc/parallel/group_manager.cc
index 35e81a7d83..cff5877f0a 100644
--- a/mindspore/ccsrc/parallel/group_manager.cc
+++ b/mindspore/ccsrc/parallel/group_manager.cc
@@ -30,13 +30,13 @@ Group::Group() {
   devices_.clear();
 }
 
-Status Group::Init(const std::string &name, const std::list<Device> &devices) {
+Status Group::Init(const std::string &name, const std::vector<Device> &devices) {
   this->name_ = name;
   this->devices_ = devices;
   return Status::SUCCESS;
 }
 
-std::list<Device> Group::GetDevicesList() const { return devices_; }
+std::vector<Device> Group::GetDevicesList() const { return devices_; }
 
 bool Group::IsInThisGroup(int32_t device_rank) {
   for (auto &device : devices_) {
@@ -66,7 +66,7 @@ Status Group::GetIndex(size_t *index) {
 
 GroupManager::GroupManager() { groups_.clear(); }
 
-Status GroupManager::CreateGroup(const std::string &group_name, const std::list<Device> &devices,
+Status GroupManager::CreateGroup(const std::string &group_name, const std::vector<Device> &devices,
                                  mindspore::parallel::Group *const group) {
   // it is simple to use size to determine whether it is a world group
   uint32_t world_size = 0;
diff --git a/mindspore/ccsrc/parallel/group_manager.h b/mindspore/ccsrc/parallel/group_manager.h
index c3b95b99e0..9e23569b15 100644
--- a/mindspore/ccsrc/parallel/group_manager.h
+++ b/mindspore/ccsrc/parallel/group_manager.h
@@ -18,7 +18,7 @@
 #define MINDSPORE_CCSRC_PARALLEL_GROUP_MANAGER_H_
 
 #include <cstdint>
-#include <list>
+#include <vector>
 #include <map>
 #include <string>
 
@@ -37,8 +37,8 @@ class Group {
  public:
   Group();
   ~Group() = default;
-  Status Init(const std::string& name, const std::list<Device>& devices);
-  std::list<Device> GetDevicesList() const;
+  Status Init(const std::string& name, const std::vector<Device>& devices);
+  std::vector<Device> GetDevicesList() const;
   std::string name() const { return name_; }
   bool IsInThisGroup(int32_t device_rank);
   Status GetIndex(size_t* index);
@@ -46,7 +46,7 @@ class Group {
 
  private:
   std::string name_;
-  std::list<Device> devices_;
+  std::vector<Device> devices_;
 };
 
 class GroupManager {
@@ -54,7 +54,7 @@ class GroupManager {
   GroupManager();
   ~GroupManager() = default;
 
-  Status CreateGroup(const std::string& name, const std::list<Device>& devices, Group* group);
+  Status CreateGroup(const std::string& name, const std::vector<Device>& devices, Group* group);
   Status DestroyGroup(Group* group);
   Status DestroyAllGroups();
   Status GetRankID(const std::string& name, unsigned int* rank_id);
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index e988ccf54c..d8de19b328 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -19,7 +19,6 @@
 
 #include <ir/value.h>
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index 1b641152aa..734368a533 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
index fb08fbe9fc..0ffdea97f3 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
@@ -17,7 +17,6 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_
 
-#include <list>
 #include <string>
 #include <unordered_map>
 #include <vector>
diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
index ac263ec91f..e5001fc0d3 100644
--- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
@@ -18,7 +18,7 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BIAS_ADD_INFO_H_
 
 #include <string>
-#include <list>
+
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
index 2762f94eb9..8760ce5666 100644
--- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_COMPARISON_FUNCTION_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include "ir/value.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
index 2fc18b521d..45d4c28d8e 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
index cf85d28fb0..33ad04023b 100644
--- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ELEMENTARY_FUNCTION_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include "ir/value.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h
index 0f1d7209c8..1473fead67 100644
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
index 1280d3b191..3dd639ba57 100644
--- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
index 10bf46d8ff..1a67073065 100644
--- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h
index 70de84fda3..585545302f 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h
@@ -18,10 +18,10 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/ops_info/activation_info.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index 2cd8e0df8f..6103087a1d 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -397,7 +397,7 @@ Status MatMulBase::GenerateStrategies(int32_t stage_id) {
     return FAILED;
   }
   CheckGlobalDeviceManager();
-  std::list<int32_t> dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
+  std::vector<int32_t> dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
   size_t dev_num = dev_list.size();
   Shape input0_shape = inputs_shape_[0], input1_shape = inputs_shape_[1];
   if (transpose_a_) {
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
index daf422e6d6..c9feae55b6 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
@@ -18,10 +18,10 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
index fd5d6be8f6..62c66695fa 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
@@ -18,10 +18,10 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index 9902c69ce7..89fd73564f 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_OPERATOR_INFO_H_
 
 #include <cstdint>
-#include <list>
 #include <map>
 #include <memory>
 #include <string>
diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
index d7dac46291..2e74118f80 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
@@ -17,11 +17,11 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_
 
-#include <list>
 #include <string>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
index 216357d5ab..5f9a67c22d 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
@@ -198,7 +198,7 @@ ForwardOp CreatReduceMeanForwardOp(const std::vector<Group> &forward_group, cons
 
   // Creat RealDiv op
   OperatorName operator1_name = REAL_DIV;
-  std::list<Device> device_list = forward_group[0].GetDevicesList();
+  std::vector<Device> device_list = forward_group[0].GetDevicesList();
   auto divisor = static_cast<float>(device_list.size());
   py::tuple tuple = py::make_tuple(divisor);
   mindspore::tensor::TensorPtr tensor_ptr = std::make_shared<mindspore::tensor::Tensor>(tuple, dtype);
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
index e9f03bcdee..6f26b99ffb 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_
 
 #include <string>
-#include <list>
 #include <unordered_map>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
index a8a9323540..982894a8e0 100644
--- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
@@ -19,7 +19,6 @@
 
 #include <ir/value.h>
 
-#include <list>
 #include <string>
 #include <unordered_map>
 #include <vector>
diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
index 1ef7341f3b..304e336adf 100644
--- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
@@ -20,6 +20,7 @@
 #include <vector>
 #include <memory>
 #include <string>
+
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
index 6c5e98e3b9..c7c1c96675 100644
--- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
@@ -17,11 +17,11 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_
 
-#include <list>
 #include <string>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
index d22565d305..c4fdfcef04 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
@@ -17,11 +17,11 @@
 #ifndef PARALLEL_OPS_INFO_DATASET_INFO_H_
 #define PARALLEL_OPS_INFO_DATASET_INFO_H_
 
-#include <list>
 #include <string>
 #include <unordered_map>
 #include <vector>
 #include <memory>
+
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/status.h b/mindspore/ccsrc/parallel/status.h
index 4c018c7c17..9d773f0d9b 100644
--- a/mindspore/ccsrc/parallel/status.h
+++ b/mindspore/ccsrc/parallel/status.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_STATUS_H_
 
 #include <cstdint>
-#include <list>
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 927acea705..379a8a473e 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -19,7 +19,7 @@
 #include <inttypes.h>
 #include <sys/time.h>
 #include <algorithm>
-#include <list>
+
 #include <map>
 #include <memory>
 #include <string>
diff --git a/mindspore/ccsrc/parallel/step_parallel.h b/mindspore/ccsrc/parallel/step_parallel.h
index ac7376a09d..2d1982dc9c 100644
--- a/mindspore/ccsrc/parallel/step_parallel.h
+++ b/mindspore/ccsrc/parallel/step_parallel.h
@@ -18,7 +18,7 @@
 #define MINDSPORE_CCSRC_PARALLEL_STEP_PARALLEL_H_
 
 #include <vector>
-#include <list>
+
 #include <memory>
 #include <unordered_map>
 #include <map>
diff --git a/mindspore/ccsrc/parallel/strategy.h b/mindspore/ccsrc/parallel/strategy.h
index 24879c12c1..68ba4962d7 100644
--- a/mindspore/ccsrc/parallel/strategy.h
+++ b/mindspore/ccsrc/parallel/strategy.h
@@ -18,7 +18,6 @@
 #define MINDSPORE_CCSRC_PARALLEL_STRATEGY_H_
 
 #include <cstdint>
-#include <list>
 #include <string>
 #include <vector>
 #include <memory>
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
index 77955b5d1a..3515c7383a 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
@@ -22,7 +22,6 @@
 #include <string>
 #include <vector>
 #include <utility>
-#include <list>
 
 #include "parallel/tensor_layout/redistribution_layout_transfer.h"
 #include "parallel/tensor_layout/construct_operator.h"
diff --git a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
index 73eff99304..d0243d5327 100644
--- a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
@@ -154,13 +154,13 @@ class TestDPAlgo : public UT::Common {
 void TestDPAlgo::SetUp() {
   cost_graph = std::make_shared<CostGraph>();
   cost_graph->SetDeviceMemoryAndCostParameter();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
index adf06d8fcc..467f4976e8 100644
--- a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
@@ -42,13 +42,13 @@ class TestEdgeCostModel : public UT::Common {
 };
 
 void TestEdgeCostModel::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
index 1428193481..83a9eceacc 100644
--- a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
@@ -53,13 +53,13 @@ class TestCostGraph : public UT::Common {
 
 void TestCostGraph::SetUp() {
   cost_graph.SetDeviceMemoryAndCostParameter();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
index 3b71c80c4b..3bd65c049c 100644
--- a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
@@ -33,13 +33,13 @@ class TestMatMulCost : public UT::Common {
 
 void TestMatMulCost::SetUp() {
   mmcost_ = MatMulCost();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -90,13 +90,13 @@ class TestActivationCost : public UT::Common {
 
 void TestActivationCost::SetUp() {
   ac_cost_ = ActivationCost();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -142,13 +142,13 @@ class TestPReLUCost : public UT::Common {
 
 void TestPReLUCost::SetUp() {
   prelu_cost_ = PReLUCost();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/device_manager_test.cc b/tests/ut/cpp/parallel/device_manager_test.cc
index 0814ca64d0..056896f514 100644
--- a/tests/ut/cpp/parallel/device_manager_test.cc
+++ b/tests/ut/cpp/parallel/device_manager_test.cc
@@ -69,8 +69,8 @@ void TestDeviceManager::TearDown() {
 }
 
 TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) {
-  std::list<int32_t> dev_list;
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> dev_list;
+  std::vector<int32_t> stage_map;
   int32_t local_dev = 0;
 
   dev_list.push_back(5);
@@ -85,12 +85,12 @@ TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) {
   ASSERT_EQ(dm_.DeviceNum(), 4);
   ASSERT_EQ(dm_.GetStageNum(), (int32_t)(2));
 
-  std::list<int32_t> dev_list_0 = dm_.GetDeviceListByStageId(0);
-  std::list<int32_t> dev_list_1 = dm_.GetDeviceListByStageId(1);
+  std::vector<int32_t> dev_list_0 = dm_.GetDeviceListByStageId(0);
+  std::vector<int32_t> dev_list_1 = dm_.GetDeviceListByStageId(1);
   ASSERT_EQ(dev_list_0.size(), 2);
   ASSERT_EQ(dev_list_1.size(), 2);
 
-  std::list<int32_t>::iterator it = dev_list_0.begin();
+  std::vector<int32_t>::iterator it = dev_list_0.begin();
   ASSERT_EQ((*it), int32_t(5));
   it++;
   ASSERT_EQ((*it), int32_t(3));
@@ -111,13 +111,13 @@ TEST_F(TestDeviceManager, test_CreateNewDeviceByRank) {
 }
 
 TEST_F(TestDeviceManager, test_CreateDeviceListByRankList) {
-  std::list<Device> dev_list;
-  std::list<int32_t> rlist;
+  std::vector<Device> dev_list;
+  std::vector<int32_t> rlist;
   rlist.push_back(int32_t(2));
   rlist.push_back(int32_t(1));
   dev_list = dm_.CreateDeviceListByRankList(rlist);
 
-  std::list<Device>::iterator it = dev_list.begin();
+  std::vector<Device>::iterator it = dev_list.begin();
   ASSERT_EQ(it->rank(), int32_t(2));
   it++;
   ASSERT_EQ(it->rank(), int32_t(1));
diff --git a/tests/ut/cpp/parallel/device_matrix_test.cc b/tests/ut/cpp/parallel/device_matrix_test.cc
index 9c3fa9a861..877a211df8 100644
--- a/tests/ut/cpp/parallel/device_matrix_test.cc
+++ b/tests/ut/cpp/parallel/device_matrix_test.cc
@@ -35,9 +35,9 @@ TEST_F(TestDeviceMatrix, Test2Dgroup_list) {
   Shape shape = {2, 3};
 
   DeviceMatrix arr(0, dev_list, shape);
-  std::list<RankList> group_list;
+  std::vector<RankList> group_list;
   if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list();
-  std::list<RankList> group_list_expect = {{0, 3}, {0, 1, 2}};
+  std::vector<RankList> group_list_expect = {{0, 3}, {0, 1, 2}};
   ASSERT_EQ(group_list, group_list_expect);
 }
 
@@ -46,9 +46,9 @@ TEST_F(TestDeviceMatrix, Test3Dgroup_list) {
   Shape shape = {2, 2, 3};
 
   DeviceMatrix arr(5, dev_list, shape);
-  std::list<RankList> group_list;
+  std::vector<RankList> group_list;
   if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list();
-  std::list<RankList> group_list_expect = {{5, 11}, {2, 5}, {3, 4, 5}};
+  std::vector<RankList> group_list_expect = {{5, 11}, {2, 5}, {3, 4, 5}};
   ASSERT_EQ(group_list, group_list_expect);
 }
 
@@ -57,9 +57,9 @@ TEST_F(TestDeviceMatrix, Test4DGetAlongDim) {
   Shape shape = {2, 1, 4, 2};
 
   DeviceMatrix arr(5, dev_list, shape);
-  std::list<RankList> group_list;
+  std::vector<RankList> group_list;
   if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list();
-  std::list<RankList> group_list_expect = {{5, 13}, {5}, {1, 3, 5, 7}, {4, 5}};
+  std::vector<RankList> group_list_expect = {{5, 13}, {5}, {1, 3, 5, 7}, {4, 5}};
   ASSERT_EQ(group_list, group_list_expect);
 }
 
@@ -69,9 +69,9 @@ TEST_F(TestDeviceMatrix, Test5DGetAlongDim) {
   Shape shape = {3, 4, 2, 3, 2};
 
   DeviceMatrix arr(5, dev_list, shape);
-  std::list<RankList> group_list;
+  std::vector<RankList> group_list;
   if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list();
-  std::list<RankList> group_list_expect = {{5, 53, 101}, {5, 17, 29, 41}, {5, 11}, {1, 3, 5}, {4, 5}};
+  std::vector<RankList> group_list_expect = {{5, 53, 101}, {5, 17, 29, 41}, {5, 11}, {1, 3, 5}, {4, 5}};
   ASSERT_EQ(group_list, group_list_expect);
 }
 
diff --git a/tests/ut/cpp/parallel/group_manager_test.cc b/tests/ut/cpp/parallel/group_manager_test.cc
index 1e9ec9c060..e3d2b3a364 100644
--- a/tests/ut/cpp/parallel/group_manager_test.cc
+++ b/tests/ut/cpp/parallel/group_manager_test.cc
@@ -42,7 +42,7 @@ void TestGroup::TearDown() {
 
 Status TestGroup::Init() {
   std::string gname = "1-2";
-  std::list<Device> dev_list;
+  std::vector<Device> dev_list;
   Device one = Device(int32_t(1));
   dev_list.push_back(one);
   Device two = Device(int32_t(2));
@@ -55,8 +55,8 @@ TEST_F(TestGroup, test_Init) { ASSERT_EQ(Init(), Status::SUCCESS); }
 
 TEST_F(TestGroup, test_GetDevicesList) {
   Init();
-  std::list<Device> res_dev_list = gp.GetDevicesList();
-  std::list<Device>::iterator it = res_dev_list.begin();
+  std::vector<Device> res_dev_list = gp.GetDevicesList();
+  std::vector<Device>::iterator it = res_dev_list.begin();
   ASSERT_EQ(it->rank(), int32_t(1));
   it++;
   ASSERT_EQ(it->rank(), int32_t(2));
@@ -88,7 +88,7 @@ void TestGroupManager::TearDown() {
 
 Status TestGroupManager::Init(Group** gp_ptr) {
   std::string gname = "1-2";
-  std::list<Device> dev_list;
+  std::vector<Device> dev_list;
   Device one = Device(int32_t(1));
   dev_list.push_back(one);
   Device two = Device(int32_t(2));
@@ -102,15 +102,15 @@ TEST_F(TestGroupManager, test_CreateGroup) {
   Group* gp_ptr = new Group();
   ASSERT_EQ(Init(&gp_ptr), Status::SUCCESS);
 
-  std::list<Device> res_dev_list = gp_ptr->GetDevicesList();
-  std::list<Device>::iterator it = res_dev_list.begin();
+  std::vector<Device> res_dev_list = gp_ptr->GetDevicesList();
+  std::vector<Device>::iterator it = res_dev_list.begin();
   ASSERT_EQ(it->rank(), int32_t(1));
   it++;
   ASSERT_EQ(it->rank(), int32_t(2));
   delete gp_ptr;
 
   // testing for creating a group with an existing group name
-  std::list<Device> dev_list2;
+  std::vector<Device> dev_list2;
   Device three = Device(int32_t(3));
   dev_list2.push_back(three);
   Device four = Device(int32_t(4));
@@ -119,8 +119,8 @@ TEST_F(TestGroupManager, test_CreateGroup) {
   ASSERT_EQ(gm.CreateGroup("1-2", dev_list2, gp_ptr), Status::SUCCESS);
 
   ASSERT_STREQ(gp_ptr->name().data(), "1-2");
-  std::list<Device> res_dev_list2 = gp_ptr->GetDevicesList();
-  std::list<Device>::iterator it2 = res_dev_list2.begin();
+  std::vector<Device> res_dev_list2 = gp_ptr->GetDevicesList();
+  std::vector<Device>::iterator it2 = res_dev_list2.begin();
   ASSERT_EQ(it2->rank(), int32_t(1));
   it2++;
   ASSERT_EQ(it2->rank(), int32_t(2));
@@ -136,8 +136,8 @@ TEST_F(TestGroupManager, test_FindGroup) {
 
   ASSERT_EQ(gm.FindGroup(gname, &gp_ptr2), Status::SUCCESS);
 
-  std::list<Device> res_dev_list = gp_ptr2->GetDevicesList();
-  std::list<Device>::iterator it = res_dev_list.begin();
+  std::vector<Device> res_dev_list = gp_ptr2->GetDevicesList();
+  std::vector<Device>::iterator it = res_dev_list.begin();
   ASSERT_EQ(it->rank(), int32_t(1));
   it++;
   ASSERT_EQ(it->rank(), int32_t(2));
diff --git a/tests/ut/cpp/parallel/ops_info/activation_info_test.cc b/tests/ut/cpp/parallel/ops_info/activation_info_test.cc
index a725cb0bf7..a9fe9b4c48 100644
--- a/tests/ut/cpp/parallel/ops_info/activation_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/activation_info_test.cc
@@ -38,13 +38,13 @@ class TestActivationInfo : public UT::Common {
 };
 
 void TestActivationInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc
index 200b96eb2e..149aa9d5af 100644
--- a/tests/ut/cpp/parallel/ops_info/activation_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc
@@ -40,13 +40,13 @@ class TestActivation : public UT::Common {
 };
 
 void TestActivation::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc b/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
index 5ae79e2851..2f17fb4450 100644
--- a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
@@ -38,13 +38,13 @@ class TestDropoutDoMaskInfo : public UT::Common {
 };
 
 void TestDropoutDoMaskInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc b/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc
index d07794f63d..e54d1f2423 100644
--- a/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc
@@ -38,13 +38,13 @@ class TestGeluInfo : public UT::Common {
 };
 
 void TestGeluInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc b/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc
index 81b1d3a1ec..947ad60cca 100644
--- a/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc
@@ -34,13 +34,13 @@ class TestGenerateStrategy : public UT::Common {
 };
 
 void TestGenerateStrategy::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc b/tests/ut/cpp/parallel/ops_info/generator_info_test.cc
index bfd14ced30..eb463066a6 100644
--- a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/generator_info_test.cc
@@ -38,13 +38,13 @@ class TestDropoutGenMaskInfo : public UT::Common {
 };
 
 void TestDropoutGenMaskInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc b/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc
index 1276a30225..503edf2eda 100644
--- a/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc
@@ -38,13 +38,13 @@ class TestGetNextInfo : public UT::Common {
 };
 
 void TestGetNextInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 8; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   int32_t local_dev = 0;
   // create a new g_device_manager
diff --git a/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc b/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc
index 07a160ec99..b59481e1f6 100644
--- a/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc
@@ -38,13 +38,13 @@ class TestL2NormalizeInfo : public UT::Common {
 };
 
 void TestL2NormalizeInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc b/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc
index 1c31463c88..cf5a4239a2 100644
--- a/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc
@@ -38,13 +38,13 @@ class TestLogSoftmaxInfo : public UT::Common {
 };
 
 void TestLogSoftmaxInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
index 02f4488744..978b792a0c 100644
--- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
@@ -42,13 +42,13 @@ class TestMatmulInfo : public UT::Common {
 };
 
 void TestMatmulInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc b/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc
index df67175ae6..07d150a294 100644
--- a/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc
@@ -38,13 +38,13 @@ class TestOneHotInfo : public UT::Common {
 };
 
 void TestOneHotInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc b/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc
index 17fe365176..c89bf97fb3 100644
--- a/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc
+++ b/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc
@@ -38,13 +38,13 @@ class TestOneHotInfo2 : public UT::Common {
 };
 
 void TestOneHotInfo2::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
index 86d62190a7..f6ea2c3d3c 100644
--- a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
@@ -38,13 +38,13 @@ class TestPowInfo : public UT::Common {
 };
 
 void TestPowInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 66; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(64);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/prelu_test.cc b/tests/ut/cpp/parallel/ops_info/prelu_test.cc
index bb815921e0..5ff261234f 100644
--- a/tests/ut/cpp/parallel/ops_info/prelu_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/prelu_test.cc
@@ -39,13 +39,13 @@ class TestPReLUInfo : public UT::Common {
 };
 
 void TestPReLUInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
   int32_t local_dev = 0;
diff --git a/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc b/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc
index 0ce81b0cd0..a1fe46ca33 100644
--- a/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc
@@ -39,13 +39,13 @@ class TestReduceSumInfo : public UT::Common {
 
 void TestReduceSumInfo::SetUp() {
   UT::InitPythonPath();
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/reshape_test.cc b/tests/ut/cpp/parallel/ops_info/reshape_test.cc
index 73a0d68be5..7ff94e9af5 100644
--- a/tests/ut/cpp/parallel/ops_info/reshape_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/reshape_test.cc
@@ -38,13 +38,13 @@ class TestReshapeInfo : public UT::Common {
 };
 
 void TestReshapeInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc b/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc
index cba9b7ecd6..03634b9a6f 100644
--- a/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc
@@ -38,13 +38,13 @@ class TestSoftmaxLoss : public UT::Common {
 };
 
 void TestSoftmaxLoss::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 65; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(64);
   stage_map.push_back(1);
 
diff --git a/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc b/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc
index 65fd46fb9c..bba6e89626 100644
--- a/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc
@@ -39,13 +39,13 @@ class TestSoftmaxInfo : public UT::Common {
 };
 
 void TestSoftmaxInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc b/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc
index 3ee99d093f..a892c5c84a 100644
--- a/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc
@@ -38,13 +38,13 @@ class TestTanhInfo : public UT::Common {
 };
 
 void TestTanhInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
index 612b344514..e7736a4b3e 100644
--- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
@@ -38,13 +38,13 @@ class TestTensorAddInfo : public UT::Common {
 };
 
 void TestTensorAddInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
index 7edeb5a553..ce1238baeb 100644
--- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
@@ -38,13 +38,13 @@ class TestTmpIdentityInfo : public UT::Common {
 };
 
 void TestTmpIdentityInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/ops_info/transpose_test.cc b/tests/ut/cpp/parallel/ops_info/transpose_test.cc
index d54258915d..991ec47820 100644
--- a/tests/ut/cpp/parallel/ops_info/transpose_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/transpose_test.cc
@@ -38,13 +38,13 @@ class TestTransposeInfo : public UT::Common {
 };
 
 void TestTransposeInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
diff --git a/tests/ut/cpp/parallel/step_auto_parallel_test.cc b/tests/ut/cpp/parallel/step_auto_parallel_test.cc
index 5503377ee2..a1474ca244 100644
--- a/tests/ut/cpp/parallel/step_auto_parallel_test.cc
+++ b/tests/ut/cpp/parallel/step_auto_parallel_test.cc
@@ -32,13 +32,13 @@ class TestStepAutoParallel : public UT::Common {
 };
 
 void TestStepAutoParallel::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 20; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(4);
 
diff --git a/tests/ut/cpp/parallel/step_parallel_test.cc b/tests/ut/cpp/parallel/step_parallel_test.cc
index 23debf11ac..afc898907b 100644
--- a/tests/ut/cpp/parallel/step_parallel_test.cc
+++ b/tests/ut/cpp/parallel/step_parallel_test.cc
@@ -34,13 +34,13 @@ class TestStepParallel : public UT::Common {
 void TestStepParallel::SetUp() { UT::InitPythonPath(); }
 
 void Init_Device_Manager() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 20; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(4);
 
diff --git a/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc b/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc
index bebe55250b..2ba8cc9dfc 100644
--- a/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc
@@ -39,12 +39,12 @@ class TestConstructOperator : public UT::Common {
 };
 
 void TestConstructOperator::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc b/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc
index 5ffd9b22ce..1b1dd4af04 100644
--- a/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc
@@ -28,13 +28,13 @@ class TestRedistributionOperatorInfer : public UT::Common {
   TestRedistributionOperatorInfer() {}
 
   void SetUp() {
-    std::list<int32_t> dev_list;
+    std::vector<int32_t> dev_list;
 
     for (int32_t i = 0; i < 1050; i++) {
       dev_list.push_back(i);
     }
 
-    std::list<int32_t> stage_map;
+    std::vector<int32_t> stage_map;
     stage_map.push_back(1024);
     stage_map.push_back(26);
 
diff --git a/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc b/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc
index ad20793417..572763faa3 100644
--- a/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc
@@ -33,7 +33,7 @@ class TestTensorRedistribution : public UT::Common {
       dev_list.push_back(i);
     }
 
-    std::list<int32_t> stage_map;
+    std::vector<int32_t> stage_map;
     stage_map.push_back(16);
     stage_map.push_back(4);
 
diff --git a/tests/ut/cpp/parallel/virtual_dataset_test.cc b/tests/ut/cpp/parallel/virtual_dataset_test.cc
index 602a7370f1..1d3ff081c7 100644
--- a/tests/ut/cpp/parallel/virtual_dataset_test.cc
+++ b/tests/ut/cpp/parallel/virtual_dataset_test.cc
@@ -37,13 +37,13 @@ class TestVirtualDatasetInfo : public UT::Common {
 };
 
 void TestVirtualDatasetInfo::SetUp() {
-  std::list<int32_t> dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  std::list<int32_t> stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(114);
 

From 5a00d8cb585a8b8e26fa131d4262d9a612e27d5b Mon Sep 17 00:00:00 2001
From: wenchunjiang <wenchunjiang@huawei.com>
Date: Mon, 30 Mar 2020 17:35:19 +0800
Subject: [PATCH 025/367] This fixes an issue about mindspore process cannot
 exit when calling python api op_select_format failed in select kernel steps.
 Previously function op_select_format and check_supported raise an exception
 directly on the tbe_process python side, but we don't deal with the
 exception, and raise an exeception on c++ side to frontend ME, that will
 cause some conflict when recycle resource on ME and tbe_process python
 interpreter. This changes adding try...catch in function op_select_format and
 check_supported on the python side, and return the Exception string to c++
 side, so that we can raise an exception to frontend ME and ME will deal with
 resouce clearning and exit.

---
 .../parallel_compile/tbe_compiler/helper.py   | 114 ++++++++++++++++++
 .../tbe_compiler/tbe_process.py               | 108 +++++------------
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     |   2 +-
 .../ccsrc/kernel/tbe/tbe_kernel_select.cc     |   6 +-
 .../ccsrc/kernel/tbe/tbe_python_funcs.cc      |  18 ++-
 5 files changed, 167 insertions(+), 81 deletions(-)
 create mode 100644 mindspore/_extends/parallel_compile/tbe_compiler/helper.py

diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/helper.py b/mindspore/_extends/parallel_compile/tbe_compiler/helper.py
new file mode 100644
index 0000000000..b5f7750187
--- /dev/null
+++ b/mindspore/_extends/parallel_compile/tbe_compiler/helper.py
@@ -0,0 +1,114 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""tbe process"""
+import sys
+import os
+from .common import get_args, get_build_in_impl_path, TBEException
+
+build_in_impl_path = get_build_in_impl_path()
+
+
+def _op_select_format(kernel_info):
+    """
+    call op's op_select_format to get op supported format
+
+    Args:
+        kernel_info (dict): kernel info load by json string
+
+    Returns:
+        op supported format
+    """
+    try:
+        # import module
+        op_name = kernel_info['op_info']['name']
+        impl_path = build_in_impl_path
+        custom_flag = False
+        if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None:
+            op_impl_path = os.path.realpath(kernel_info['impl_path'])
+            if os.path.isfile(op_impl_path):
+                path, file_name = os.path.split(op_impl_path)
+                op_name, _ = os.path.splitext(file_name)
+                impl_path = path
+                custom_flag = True
+        if impl_path not in sys.path:
+            sys.path.insert(0, impl_path)
+
+        if custom_flag:
+            op_module = __import__(op_name)
+        else:
+            op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0)
+        # get function
+        if not hasattr(op_module, "op_select_format"):
+            return ""
+        op_func = getattr(op_module, "op_select_format", None)
+
+        # call function
+        inputs_args = get_args(kernel_info['op_info'], 'inputs')
+        outputs_args = get_args(kernel_info['op_info'], 'outputs')
+        attrs_args = get_args(kernel_info['op_info'], 'attrs')
+        kernel_name = kernel_info['op_info']['kernel_name']
+        ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name)
+
+    except Exception as e:
+        raise TBEException(str(e))
+
+    return ret
+
+
+def _check_supported(kernel_info):
+    """
+    call op's check_supported to check supported or not
+
+    Args:
+        kernel_info (dict): kernel info load by json string
+
+    Returns:
+        bool: check result, true or false
+    """
+    try:
+        # import module
+        op_name = kernel_info['op_info']['name']
+        impl_path = build_in_impl_path
+        custom_flag = False
+        if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None:
+            op_impl_path = os.path.realpath(kernel_info['impl_path'])
+            if os.path.isfile(op_impl_path):
+                path, file_name = os.path.split(op_impl_path)
+                op_name, _ = os.path.splitext(file_name)
+                impl_path = path
+                custom_flag = True
+        if impl_path not in sys.path:
+            sys.path.insert(0, impl_path)
+
+        if custom_flag:
+            op_module = __import__(op_name)
+        else:
+            op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0)
+        # get function
+        if not hasattr(op_module, "check_supported"):
+            return ""
+        op_func = getattr(op_module, "check_supported", None)
+
+        # call function
+        inputs_args = get_args(kernel_info['op_info'], 'inputs')
+        outputs_args = get_args(kernel_info['op_info'], 'outputs')
+        attrs_args = get_args(kernel_info['op_info'], 'attrs')
+        kernel_name = kernel_info['op_info']['kernel_name']
+        ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name)
+
+    except Exception as e:
+        raise TBEException(str(e))
+
+    return ret
diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py b/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py
index 9b970b7242..2f73ced061 100644
--- a/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py
+++ b/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py
@@ -19,10 +19,8 @@ import subprocess
 import sys
 import os
 import json
-from .common import check_kernel_info, get_args, get_build_in_impl_path
-
-build_in_impl_path = get_build_in_impl_path()
-
+from .common import check_kernel_info, TBEException
+from .helper import _op_select_format, _check_supported
 
 def create_tbe_parallel_compiler():
     """
@@ -41,40 +39,17 @@ def op_select_format(op_json: str):
         op_json (str): json string of the op
 
     Returns:
-        op supported format
+        op supported format or exception message
     """
     ret = ""
-    kernel_info = json.loads(op_json)
-    check_kernel_info(kernel_info)
-
-    # import module
-    op_name = kernel_info['op_info']['name']
-    impl_path = build_in_impl_path
-    custom_flag = False
-    if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None:
-        op_impl_path = os.path.realpath(kernel_info['impl_path'])
-        if os.path.isfile(op_impl_path):
-            path, file_name = os.path.split(op_impl_path)
-            op_name, _ = os.path.splitext(file_name)
-            impl_path = path
-            custom_flag = True
-    sys.path.insert(0, impl_path)
-
-    if custom_flag:
-        op_module = __import__(op_name)
-    else:
-        op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0)
-    # get function
-    if not hasattr(op_module, "op_select_format"):
-        return ""
-    op_func = getattr(op_module, "op_select_format", None)
-
-    # call function
-    inputs_args = get_args(kernel_info['op_info'], 'inputs')
-    outputs_args = get_args(kernel_info['op_info'], 'outputs')
-    attrs_args = get_args(kernel_info['op_info'], 'attrs')
-    kernel_name = kernel_info['op_info']['kernel_name']
-    ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name)
+    try:
+        kernel_info = json.loads(op_json)
+        check_kernel_info(kernel_info)
+        ret = _op_select_format(kernel_info)
+
+    except TBEException as e:
+        return "TBEException: " + str(e)
+
     return ret
 
 
@@ -86,40 +61,18 @@ def check_supported(op_json: str):
         op_json (str): json string of the op
 
     Returns:
-        true or false
+        bool: check result, true or false
+        str: exception message when catch an Exception
     """
     ret = ""
-    kernel_info = json.loads(op_json)
-    check_kernel_info(kernel_info)
-
-    # import module
-    op_name = kernel_info['op_info']['name']
-    impl_path = build_in_impl_path
-    custom_flag = False
-    if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None:
-        op_impl_path = os.path.realpath(kernel_info['impl_path'])
-        if os.path.isfile(op_impl_path):
-            path, file_name = os.path.split(op_impl_path)
-            op_name, _ = os.path.splitext(file_name)
-            impl_path = path
-            custom_flag = True
-    sys.path.insert(0, impl_path)
-
-    if custom_flag:
-        op_module = __import__(op_name)
-    else:
-        op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0)
-    # get function
-    if not hasattr(op_module, "check_supported"):
-        return ""
-    op_func = getattr(op_module, "check_supported", None)
-
-    # call function
-    inputs_args = get_args(kernel_info['op_info'], 'inputs')
-    outputs_args = get_args(kernel_info['op_info'], 'outputs')
-    attrs_args = get_args(kernel_info['op_info'], 'attrs')
-    kernel_name = kernel_info['op_info']['kernel_name']
-    ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name)
+    try:
+        kernel_info = json.loads(op_json)
+        check_kernel_info(kernel_info)
+        ret = _check_supported(kernel_info)
+
+    except TBEException as e:
+        return "TBEException: " + str(e)
+
     return ret
 
 
@@ -149,12 +102,12 @@ class CompilerPool:
     """compiler pool"""
 
     def __init__(self):
-        processes = multiprocessing.cpu_count()
+        self.__processe_num = multiprocessing.cpu_count()
         # max_processes_num: Set the maximum number of concurrent processes for compiler
         max_processes_num = 16
-        if processes > max_processes_num:
-            processes = max_processes_num
-        self.__pool = multiprocessing.Pool(processes=processes)
+        if self.__processe_num > max_processes_num:
+            self.__processe_num = max_processes_num
+        self.__pool = None
         self.__next_task_id = 1
         self.__running_tasks = []
 
@@ -165,11 +118,10 @@ class CompilerPool:
             del self.__pool
 
     def exit(self):
-        return
-        # self.__pool.terminate()
-        # self.__pool.join()
-        # if self.__pool is not None:
-        #     del self.__pool
+        if self.__pool is not None:
+            self.__pool.terminate()
+            self.__pool.join()
+            del self.__pool
 
     def start_compile_op(self, op_json):
         """
@@ -183,6 +135,8 @@ class CompilerPool:
         """
         task_id = self.__next_task_id
         self.__next_task_id = self.__next_task_id + 1
+        if self.__pool is None:
+            self.__pool = multiprocessing.Pool(processes=self.__processe_num)
         task_future = self.__pool.apply_async(func=run_compiler, args=(op_json,))
         self.__running_tasks.append((task_id, task_future))
         return task_id
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index ddb78a08df..50fed77a9a 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -98,7 +98,7 @@ void TbeAdapter::NormalizeFuncName(std::string *func_name) {
   *func_name = name_tmp;
   auto iter = tbe_func_adapter_map.find(*func_name);
   if (iter != tbe_func_adapter_map.end()) {
-    MS_LOG(INFO) << "map actual op fron me " << func_name << "to tbe op" << iter->second;
+    MS_LOG(INFO) << "map actual op from me " << func_name << "to tbe op" << iter->second;
     *func_name = iter->second;
   }
 }
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
index 25495d1e68..1953fd0c72 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
@@ -35,6 +35,8 @@ namespace kernel {
 constexpr auto kName = "name";
 constexpr auto kDtype = "dtype";
 constexpr auto kFormat = "format";
+constexpr auto kPrefixInput = "input";
+constexpr auto kPrefixOutput = "output";
 const std::map<std::string, std::string> DYNAMIC_FORMAT_MAP = {{"NCHW", "DefaultFormat"},
                                                                {"NHWC", "DefaultFormat"},
                                                                {"ND", "DefaultFormat"},
@@ -146,13 +148,13 @@ bool ParseDynamicFormatJson(const std::string &jsonStr, std::vector<std::shared_
     if (!CheckJsonItemValidity(json_obj, key_name, keys)) {
       return false;
     }
-    if (key_name.find("input", 0) != std::string::npos) {
+    if (key_name.compare(0, strlen(kPrefixInput), kPrefixInput) == 0) {
       std::shared_ptr<OpIOInfo> input = std::make_shared<OpIOInfo>();
       MS_EXCEPTION_IF_NULL(input);
       input->set_name(json_obj[key_name].at(kName));
       ConvertFormatDtype(json_obj[key_name].at(kFormat), json_obj[key_name].at(kDtype), input);
       inputs->emplace_back(input);
-    } else if (key_name.find("output", 0) != std::string::npos) {
+    } else if (key_name.compare(0, strlen(kPrefixOutput), kPrefixOutput) == 0) {
       std::shared_ptr<OpIOInfo> output = std::make_shared<OpIOInfo>();
       MS_EXCEPTION_IF_NULL(output);
       output->set_name(json_obj[key_name].at(kName));
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc b/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc
index 1e60742fc4..7204fb7f96 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc
@@ -26,6 +26,7 @@ constexpr auto kTbeProcessModule = "mindspore._extends.parallel_compile.tbe_comp
 constexpr auto kCreateTbeParallelCompilerFunc = "create_tbe_parallel_compiler";
 constexpr auto kOpSelectFormatFunc = "op_select_format";
 constexpr auto kCheckSupportedFunc = "check_supported";
+constexpr auto kTBEException = "TBEException";
 
 PyObject *TbePythonFuncs::pCreateTbeParallelCompilerFunc_ = nullptr;
 PyObject *TbePythonFuncs::pTbeCompiler_ = nullptr;
@@ -133,6 +134,10 @@ std::string TbePythonFuncs::OpSelectFormat(const nlohmann::json &kernel_json) {
   char *pstr = nullptr;
   (void)PyArg_Parse(pRet, "s", &pstr);
   res_json_str = pstr;
+  if (res_json_str.compare(0, strlen(kTBEException), kTBEException) == 0) {
+    MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kOpSelectFormatFunc << "], " << res_json_str
+                                << " ,function args:" << PyObjectToStr(pArg);
+  }
   return res_json_str;
 }
 
@@ -167,7 +172,18 @@ bool TbePythonFuncs::CheckSupported(const nlohmann::json &kernel_json) {
     MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kCheckSupportedFunc
                                 << "], function args: " << PyObjectToStr(pArg);
   }
-  ret = PyObject_IsTrue(pRes) != 0;
+  if (PyBool_Check(pRes)) {
+    ret = PyObject_IsTrue(pRes) != 0;
+  } else {
+    char *pstr = nullptr;
+    (void)PyArg_Parse(pRes, "s", &pstr);
+    std::string res_str = pstr;
+    if (res_str.compare(0, strlen(kTBEException), kTBEException) == 0) {
+      MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kCheckSupportedFunc << "], " << res_str
+                                  << ", function args: " << PyObjectToStr(pArg);
+    }
+  }
+
   return ret;
 }
 

From 31b165a57efb33fe36006905a29b48be1fc172bf Mon Sep 17 00:00:00 2001
From: wangjun260 <wangjun260@huawei.com>
Date: Tue, 31 Mar 2020 10:43:24 +0800
Subject: [PATCH 026/367] add vgg scripts

---
 example/vgg16_cifar10/config.py  | 31 +++++++++++++
 example/vgg16_cifar10/dataset.py | 65 ++++++++++++++++++++++++++
 example/vgg16_cifar10/eval.py    | 53 ++++++++++++++++++++++
 example/vgg16_cifar10/train.py   | 78 ++++++++++++++++++++++++++++++++
 mindspore/model_zoo/vgg.py       | 30 +++++++-----
 5 files changed, 246 insertions(+), 11 deletions(-)
 create mode 100644 example/vgg16_cifar10/config.py
 create mode 100644 example/vgg16_cifar10/dataset.py
 create mode 100644 example/vgg16_cifar10/eval.py
 create mode 100644 example/vgg16_cifar10/train.py

diff --git a/example/vgg16_cifar10/config.py b/example/vgg16_cifar10/config.py
new file mode 100644
index 0000000000..8c6ffee98b
--- /dev/null
+++ b/example/vgg16_cifar10/config.py
@@ -0,0 +1,31 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in main.py
+"""
+from easydict import EasyDict as edict
+
+cifar_cfg = edict({
+    'num_classes': 10,
+    'lr_init': 0.05,
+    'batch_size': 64,
+    'epoch_size': 70,
+    'momentum': 0.9,
+    'weight_decay': 5e-4,
+    'buffer_size': 10,
+    'image_height': 224,
+    'image_width': 224,
+    'keep_checkpoint_max': 10
+})
diff --git a/example/vgg16_cifar10/dataset.py b/example/vgg16_cifar10/dataset.py
new file mode 100644
index 0000000000..4e82beb2e3
--- /dev/null
+++ b/example/vgg16_cifar10/dataset.py
@@ -0,0 +1,65 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Data operations, will be used in train.py and eval.py
+"""
+import os
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as vision
+import mindspore.common.dtype as mstype
+from config import cifar_cfg as cfg
+
+def create_dataset(data_home, repeat_num=1, training=True):
+    """Data operations."""
+    ds.config.set_seed(1)
+    data_dir = os.path.join(data_home, "cifar-10-batches-bin")
+    if not training:
+        data_dir = os.path.join(data_home, "cifar-10-verify-bin")
+    data_set = ds.Cifar10Dataset(data_dir)
+    resize_height = cfg.image_height
+    resize_width = cfg.image_width
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    # define map operations
+    random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
+    random_horizontal_op = vision.RandomHorizontalFlip()
+    resize_op = vision.Resize((resize_height, resize_width))  # interpolation default BILINEAR
+    rescale_op = vision.Rescale(rescale, shift)
+    normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
+    changeswap_op = vision.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    c_trans = []
+    if training:
+        c_trans = [random_crop_op, random_horizontal_op]
+    c_trans += [resize_op, rescale_op, normalize_op,
+                changeswap_op]
+
+    # apply map operations on images
+    data_set = data_set.map(input_columns="label", operations=type_cast_op)
+    data_set = data_set.map(input_columns="image", operations=c_trans)
+
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
+    # apply shuffle operations
+    data_set = data_set.shuffle(buffer_size=10)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
+
+    return data_set
diff --git a/example/vgg16_cifar10/eval.py b/example/vgg16_cifar10/eval.py
new file mode 100644
index 0000000000..b034183373
--- /dev/null
+++ b/example/vgg16_cifar10/eval.py
@@ -0,0 +1,53 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+##############test vgg16 example on cifar10#################
+python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
+"""
+import argparse
+import mindspore.nn as nn
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.model_zoo.vgg import vgg16
+from config import cifar_cfg as cfg
+import dataset
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Cifar10 classification')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
+    parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
+    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+    context.set_context(device_id=args_opt.device_id)
+    context.set_context(enable_mem_reuse=True, enable_hccl=False)
+
+    net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
+                   weight_decay=cfg.weight_decay)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+    dataset = dataset.create_dataset(args_opt.data_path, 1, False)
+    res = model.eval(dataset)
+    print("result: ", res)
diff --git a/example/vgg16_cifar10/train.py b/example/vgg16_cifar10/train.py
new file mode 100644
index 0000000000..32cd344d50
--- /dev/null
+++ b/example/vgg16_cifar10/train.py
@@ -0,0 +1,78 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+#################train vgg16 example on cifar10########################
+python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
+"""
+import argparse
+import random
+import numpy as np
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore import context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.model_zoo.vgg import vgg16
+import dataset
+from config import cifar_cfg as cfg
+random.seed(1)
+np.random.seed(1)
+
+def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
+    """Set learning rate."""
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
+    for i in range(total_steps):
+        if i < decay_epoch_index[0]:
+            lr_each_step.append(lr_max)
+        elif i < decay_epoch_index[1]:
+            lr_each_step.append(lr_max * 0.1)
+        elif i < decay_epoch_index[2]:
+            lr_each_step.append(lr_max * 0.01)
+        else:
+            lr_each_step.append(lr_max * 0.001)
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Cifar10 classification')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
+    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+    context.set_context(device_id=args_opt.device_id)
+    context.set_context(enable_mem_reuse=True, enable_hccl=False)
+
+    net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
+    lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=50000 // cfg.batch_size)
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+    dataset = dataset.create_dataset(args_opt.data_path, cfg.epoch_size)
+    batch_num = dataset.get_dataset_size()
+    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
+    loss_cb = LossMonitor()
+    model.train(cfg.epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
diff --git a/mindspore/model_zoo/vgg.py b/mindspore/model_zoo/vgg.py
index 76f265c326..6fcd075cc8 100644
--- a/mindspore/model_zoo/vgg.py
+++ b/mindspore/model_zoo/vgg.py
@@ -15,7 +15,8 @@
 """VGG."""
 import mindspore.nn as nn
 from mindspore.ops import operations as P
-
+from mindspore.common.initializer import initializer
+import mindspore.common.dtype as mstype
 
 def _make_layer(base, batch_norm):
     """Make stage network of VGG."""
@@ -25,11 +26,14 @@ def _make_layer(base, batch_norm):
         if v == 'M':
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
         else:
+            weight_shape = (v, in_channels, 3, 3)
+            weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
             conv2d = nn.Conv2d(in_channels=in_channels,
                                out_channels=v,
                                kernel_size=3,
-                               padding=1,
-                               pad_mode='pad')
+                               padding=0,
+                               pad_mode='same',
+                               weight_init=weight)
             if batch_norm:
                 layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
             else:
@@ -52,13 +56,13 @@ class Vgg(nn.Cell):
         Tensor, infer output tensor.
 
     Examples:
-        >>> VGG([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+        >>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
         >>>     num_classes=1000, batch_norm=False, batch_size=1)
     """
+
     def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
         super(Vgg, self).__init__()
         self.layers = _make_layer(base, batch_norm=batch_norm)
-        self.avgpool = nn.AvgPool2d(7)
         self.reshape = P.Reshape()
         self.shp = (batch_size, -1)
         self.classifier = nn.SequentialCell([
@@ -70,7 +74,6 @@ class Vgg(nn.Cell):
 
     def construct(self, x):
         x = self.layers(x)
-        x = self.avgpool(x)
         x = self.reshape(x, self.shp)
         x = self.classifier(x)
         return x
@@ -84,15 +87,20 @@ cfg = {
 }
 
 
-def vgg16():
+def vgg16(batch_size=1, num_classes=1000):
     """
-    Get VGG16 neural network.
+    Get Vgg16 neural network with batch normalization.
+
+    Args:
+        batch_size (int): Batch size. Default: 1.
+        num_classes (int): Class numbers. Default: 1000.
 
     Returns:
-        Cell, cell instance of VGG16 neural network.
+        Cell, cell instance of Vgg16 neural network with batch normalization.
 
     Examples:
-        >>> vgg16()
+        >>> vgg16(batch_size=1, num_classes=1000)
     """
-    net = Vgg(cfg['16'], num_classes=1000, batch_norm=False, batch_size=1)
+
+    net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True, batch_size=batch_size)
     return net

From f946aea10d1832c39ed0696937b470c3c0b3d5e0 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Tue, 31 Mar 2020 11:14:28 +0800
Subject: [PATCH 027/367] fix grpah mode loop sink bug in auto parallel

---
 .../ccsrc/parallel/auto_parallel/graph_costmodel.h   |  4 ++--
 mindspore/ccsrc/parallel/step_parallel.cc            |  4 ++++
 mindspore/train/dataset_helper.py                    |  2 +-
 .../parallel/test_auto_parallel_parameter_cast.py    | 12 ++++++------
 .../python/parallel/test_auto_parallel_two_matmul.py |  7 ++++---
 5 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index 4efb77055f..fde9514540 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -34,8 +34,8 @@ namespace parallel {
 #define OPERATOR_TO_OPERATOR_CONNECTOR "-"
 #define DEFAULT_DEVICE_MEMORY_CAPACITY (1024.0 * 1024.0 * 1024.0 * 16.0)
 #define DEFAULT_COST_MODEL_ALPHA 1.0
-#define DEFAULT_COST_MODEL_BETA 65.0
-#define DEFAULT_COST_MODEL_GAMMA 0.02
+#define DEFAULT_COST_MODEL_BETA 260.0
+#define DEFAULT_COST_MODEL_GAMMA 0.001
 #define DEFAULT_COST_MODEL_SIMPLIFY_CALCULATION true
 #define DEFAULT_COST_MODEL_COMMUNI_THRESHOLD 2048.0
 #define DEFAULT_COST_MODEL_COMMUNI_CONST 3072.0
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 927acea705..21da11ec21 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -375,6 +375,10 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
     MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
     return false;
   }
+  // get_next is not in the forward graph, we need mark the get_next as the forward node
+  if (prim->name() == GET_NEXT) {
+    return true;
+  }
   if ((prim->name() == CAST)) {
     if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
       return false;
diff --git a/mindspore/train/dataset_helper.py b/mindspore/train/dataset_helper.py
index de26d72108..6252116efe 100644
--- a/mindspore/train/dataset_helper.py
+++ b/mindspore/train/dataset_helper.py
@@ -88,7 +88,7 @@ class _DatasetIter:
         # times the batch dimension of tensors for run
         if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
             device_num = _get_device_num()
-            dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
+            self.dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
 
     def __iter__(self):
         self.ind = 0
diff --git a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
index 67b8f98faf..b7a3255f7c 100644
--- a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
+++ b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
@@ -80,9 +80,9 @@ def test_common_parameter():
 
     _executor.compile(net, x, y, z, w, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/MatMul-op8': [[1, 1], [1, 8]],
-                           'Default/network-Net/MatMul-op9': [[1, 1], [1, 8]],
-                           'Default/network-Net/Cast-op10': [[1, 8]],
-                           'Default/network-Net/MatMul-op0': [[1, 1], [1, 8]],
-                           'Default/network-Net/Cast-op11': [[1, 8]]}
-    assert strategies == expected_strategies
\ No newline at end of file
+    expected_strategies = {'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]],
+                           'Default/network-Net/Cast-op7': [[1, 1]],
+                           'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]],
+                           'Default/network-Net/Cast-op9': [[1, 1]]}
+    assert strategies == expected_strategies
diff --git a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
index 5155db41f6..e7beed384e 100644
--- a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
+++ b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
@@ -86,9 +86,9 @@ def test_two_matmul():
     costmodel_alpha = cost_model_context.get_cost_model_context("costmodel_alpha")
     assert costmodel_alpha == 1.0
     costmodel_beta = cost_model_context.get_cost_model_context("costmodel_beta")
-    assert costmodel_beta == 65.0
+    assert costmodel_beta == 260.0
     costmodel_gamma = cost_model_context.get_cost_model_context("costmodel_gamma")
-    assert costmodel_gamma == 0.02
+    assert costmodel_gamma == 0.001
     costmodel_communi_threshold = cost_model_context.get_cost_model_context("costmodel_communi_threshold")
     assert costmodel_communi_threshold == 2048.0
     costmodel_communi_const = cost_model_context.get_cost_model_context("costmodel_communi_const")
@@ -137,4 +137,5 @@ def test_two_matmul():
     strategies = _executor._get_strategy(net)
     expected_strategies = {'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
                      'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
-    assert strategies == expected_strategies
\ No newline at end of file
+    assert strategies == expected_strategies
+

From eaf7146d4689168c23f2601c1f47c8675848fccc Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 31 Mar 2020 16:18:04 +0800
Subject: [PATCH 028/367] modify longtime python ut

---
 mindspore/nn/layer/conv.py                                | 2 +-
 tests/ut/python/parallel/test_auto_parallel_reshape.py    | 6 +++---
 tests/ut/python/parallel/test_one_hot_net.py              | 8 ++++----
 tests/ut/python/parallel/test_reshape.py                  | 8 ++++----
 tests/ut/python/train/summary/test_summary_performance.py | 6 +++---
 5 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index 6c78e1a715..666be93451 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -159,7 +159,7 @@ class Conv2d(_Conv):
         >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
         >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
         >>> net(input).shape()
-        (1, 240, 1024, 637)
+        (1, 240, 1024, 640)
     """
     @cell_attr_register
     def __init__(self,
diff --git a/tests/ut/python/parallel/test_auto_parallel_reshape.py b/tests/ut/python/parallel/test_auto_parallel_reshape.py
index 26e7e95a9f..ed9f24cc0b 100644
--- a/tests/ut/python/parallel/test_auto_parallel_reshape.py
+++ b/tests/ut/python/parallel/test_auto_parallel_reshape.py
@@ -49,16 +49,16 @@ def test_reshape_matmul():
             super().__init__()
             self.reshape = P.Reshape()
             self.matmul = P.MatMul()
-            self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
+            self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
 
         def construct(self, x):
-            out = self.reshape(x, (256, 25088))
+            out = self.reshape(x, (64, 28))
             out = self.matmul(out, self.matmul_weight)
             return out
 
     size = 8
     context.set_auto_parallel_context(device_num=size, global_rank=0)
-    x = Tensor(np.ones([32*size, 512, 7, 7]), dtype=ms.float32)
+    x = Tensor(np.ones([8*size, 28, 1, 1]), dtype=ms.float32)
 
     net = GradWrap(NetWithLoss(Net()))
     context.set_auto_parallel_context(parallel_mode="auto_parallel")
diff --git a/tests/ut/python/parallel/test_one_hot_net.py b/tests/ut/python/parallel/test_one_hot_net.py
index bf2677056e..87b4acfe37 100644
--- a/tests/ut/python/parallel/test_one_hot_net.py
+++ b/tests/ut/python/parallel/test_one_hot_net.py
@@ -247,15 +247,15 @@ def fc_with_initialize(input_channels, out_channels):
 class BNReshapeDenseBNNet(nn.Cell):
     def __init__(self):
         super(BNReshapeDenseBNNet, self).__init__()
-        self.batch_norm = bn_with_initialize(512)
+        self.batch_norm = bn_with_initialize(2)
         self.reshape = P.Reshape()
         self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
-        self.fc = fc_with_initialize(512 * 32 * 32, 512)
+        self.fc = fc_with_initialize(2 * 32 * 32, 512)
         self.loss = SemiAutoOneHotNet(args=Args(), strategy=StrategyBatch())
 
     def construct(self, x, label):
         x = self.batch_norm(x)
-        x = self.reshape(x, (16, 512*32*32))
+        x = self.reshape(x, (16, 2*32*32))
         x = self.fc(x)
         x = self.batch_norm2(x)
         loss = self.loss(x, label)
@@ -266,7 +266,7 @@ def test_bn_reshape_dense_bn_train_loss():
     batch_size = 16
     device_num = 16
     context.set_auto_parallel_context(device_num=device_num, global_rank=0)
-    input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01)
+    input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([batch_size]), dtype=ms.int32)
 
     net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))
diff --git a/tests/ut/python/parallel/test_reshape.py b/tests/ut/python/parallel/test_reshape.py
index 11ca435e5b..43906aec23 100644
--- a/tests/ut/python/parallel/test_reshape.py
+++ b/tests/ut/python/parallel/test_reshape.py
@@ -490,15 +490,15 @@ def fc_with_initialize(input_channels, out_channels):
 class BNReshapeDenseBNNet(nn.Cell):
     def __init__(self):
         super(BNReshapeDenseBNNet, self).__init__()
-        self.batch_norm = bn_with_initialize(512)
+        self.batch_norm = bn_with_initialize(2)
         self.reshape = P.Reshape()
         self.cast = P.Cast()
         self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
-        self.fc = fc_with_initialize(512 * 32 * 32, 512)
+        self.fc = fc_with_initialize(2 * 32 * 32, 512)
 
     def construct(self, x):
         x = self.batch_norm(x)
-        x = self.reshape(x, (16, 512*32*32))
+        x = self.reshape(x, (16, 2*32*32))
         x = self.fc(x)
         x = self.batch_norm2(x)
         return x
@@ -508,7 +508,7 @@ def test_bn_reshape_dense_bn_train():
     batch_size = 16
     device_num = 16
     context.set_auto_parallel_context(device_num=device_num, global_rank=0)
-    input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01)
+    input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01)
 
     net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py
index f004d4e584..9ee9725d13 100644
--- a/tests/ut/python/train/summary/test_summary_performance.py
+++ b/tests/ut/python/train/summary/test_summary_performance.py
@@ -43,9 +43,9 @@ def get_test_data(step):
     tag1 = "xt1[:Tensor]"
     tag2 = "xt2[:Tensor]"
     tag3 = "xt3[:Tensor]"
-    np1 = np.random.random((50, 40, 30, 50))
-    np2 = np.random.random((50, 50, 30, 50))
-    np3 = np.random.random((40, 55, 30, 50))
+    np1 = np.random.random((5, 4, 3, 5))
+    np2 = np.random.random((5, 5, 3, 5))
+    np3 = np.random.random((4, 5, 3, 5))
 
     dict1 = {}
     dict1["name"] = tag1

From e6c15b82c8f7555ab1bb91685e7392507874f49a Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Tue, 31 Mar 2020 17:02:25 +0800
Subject: [PATCH 029/367] remove cce dependency

---
 mindspore/ccsrc/CMakeLists.txt                          | 3 ---
 mindspore/ccsrc/device/ascend/tasksink/task_generator.h | 3 ---
 mindspore/ccsrc/kernel/CMakeLists.txt                   | 3 +--
 3 files changed, 1 insertion(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 72ad4d7b09..1c684b6736 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -185,7 +185,6 @@ if(ENABLE_D)
             "device/ascend/*.cc"
             "device/ascend/profiling/*.cc"
             "device/ascend/tasksink/*.cc"
-            "kernel/akg/cce/*.cc"
             "device/kernel_adjust.cc"
             "kernel/kernel_fusion.cc"
             "kernel/tbe/*.cc"
@@ -361,7 +360,6 @@ if(ENABLE_GPU)
         ${TVM_DIR}/src/node/*.cc
         ${TVM_DIR}/src/schedule/*.cc
         ${TVM_DIR}/src/runtime/*.cc
-        ${TVM_DIR}/src/runtime/cce/*.cc
         ${TVM_DIR}/src/runtime/vm/*.cc
         ${TVM_DIR}/src/runtime/vm/profiler/*.cc
         ${TVM_DIR}/src/codegen/stackvm/*.cc)
@@ -379,7 +377,6 @@ if(ENABLE_GPU)
 
     file(GLOB RUNTIME_SRCS
         ${TVM_DIR}/src/runtime/*.cc
-        ${TVM_DIR}/src/runtime/cce/*.cc
         ${TVM_DIR}/src/runtime/vm/*.cc
         ${TVM_DIR}/src/runtime/stub/*.cc
         ${TVM_DIR}/src/runtime/stackvm/*.cc)
diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
index 4d1fba9db4..0c56fcc744 100644
--- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
+++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
@@ -22,9 +22,6 @@
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include "cce/aicpu_engine_struct.h"
-#include "cce/taskdown_api.h"
-#include "cce/fwk_adpt_struct.h"
 #include "device/kernel_runtime.h"
 #include "ir/anf.h"
 #include "kernel/kernel.h"
diff --git a/mindspore/ccsrc/kernel/CMakeLists.txt b/mindspore/ccsrc/kernel/CMakeLists.txt
index 4cd60b591c..9c5e2c1890 100644
--- a/mindspore/ccsrc/kernel/CMakeLists.txt
+++ b/mindspore/ccsrc/kernel/CMakeLists.txt
@@ -25,11 +25,10 @@ endif()
 
 if(ENABLE_D)
     file(GLOB_RECURSE _D_SRC_LIST  RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
-						"akg/cce/*.cc"
 						"tbe/*.cc"
 						"aicpu/*.cc"
 						"mng/*.cc"
 						"hccl/*.cc"
             )
     target_sources(_mindspore_kernel_obj  PRIVATE ${_D_SRC_LIST})
-endif()
\ No newline at end of file
+endif()

From 0173c401240b3158d2cbf7fc9c38ecc63c1b978f Mon Sep 17 00:00:00 2001
From: wukesong <wukesong1@huawei.com>
Date: Sat, 28 Mar 2020 18:26:56 +0800
Subject: [PATCH 030/367] add lenet & alexnet in master branch

---
 example/alexnet_cifar10/config.py        |  32 ++++++
 example/alexnet_cifar10/dataset.py       |  54 ++++++++++
 example/alexnet_cifar10/eval.py          |  58 +++++++++++
 example/alexnet_cifar10/train.py         |  58 +++++++++++
 example/lenet/main.py                    | 125 -----------------------
 example/{lenet => lenet_mnist}/config.py |   4 +-
 example/lenet_mnist/dataset.py           |  60 +++++++++++
 example/lenet_mnist/eval.py              |  64 ++++++++++++
 example/lenet_mnist/train.py             |  58 +++++++++++
 mindspore/model_zoo/alexnet.py           |  73 +++++++++++++
 mindspore/model_zoo/lenet.py             |   5 +-
 11 files changed, 461 insertions(+), 130 deletions(-)
 create mode 100644 example/alexnet_cifar10/config.py
 create mode 100644 example/alexnet_cifar10/dataset.py
 create mode 100644 example/alexnet_cifar10/eval.py
 create mode 100644 example/alexnet_cifar10/train.py
 delete mode 100644 example/lenet/main.py
 rename example/{lenet => lenet_mnist}/config.py (93%)
 create mode 100644 example/lenet_mnist/dataset.py
 create mode 100644 example/lenet_mnist/eval.py
 create mode 100644 example/lenet_mnist/train.py
 create mode 100644 mindspore/model_zoo/alexnet.py

diff --git a/example/alexnet_cifar10/config.py b/example/alexnet_cifar10/config.py
new file mode 100644
index 0000000000..9edfec2b60
--- /dev/null
+++ b/example/alexnet_cifar10/config.py
@@ -0,0 +1,32 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in train.py
+"""
+
+from easydict import EasyDict as edict
+
+alexnet_cfg = edict({
+    'num_classes': 10,
+    'learning_rate': 0.002,
+    'momentum': 0.9,
+    'epoch_size': 1,
+    'batch_size': 32,
+    'buffer_size': 1000,
+    'image_height': 227,
+    'image_width': 227,
+    'save_checkpoint_steps': 1562,
+    'keep_checkpoint_max': 10,
+})
diff --git a/example/alexnet_cifar10/dataset.py b/example/alexnet_cifar10/dataset.py
new file mode 100644
index 0000000000..d62ed2852d
--- /dev/null
+++ b/example/alexnet_cifar10/dataset.py
@@ -0,0 +1,54 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+from config import alexnet_cfg as cfg
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as CV
+from mindspore.common import dtype as mstype
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, status="train"):
+    """
+    create dataset for train or test
+    """
+    cifar_ds = ds.Cifar10Dataset(data_path)
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    resize_op = CV.Resize((cfg.image_height, cfg.image_width))
+    rescale_op = CV.Rescale(rescale, shift)
+    normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    if status == "train":
+        random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
+        random_horizontal_op = CV.RandomHorizontalFlip()
+    channel_swap_op = CV.HWC2CHW()
+    typecast_op = C.TypeCast(mstype.int32)
+    cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
+    if status == "train":
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
+
+    cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size)
+    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
+    cifar_ds = cifar_ds.repeat(repeat_size)
+    return cifar_ds
diff --git a/example/alexnet_cifar10/eval.py b/example/alexnet_cifar10/eval.py
new file mode 100644
index 0000000000..be71e33995
--- /dev/null
+++ b/example/alexnet_cifar10/eval.py
@@ -0,0 +1,58 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## eval alexnet example ########################
+eval alexnet according to model file:
+python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
+"""
+
+import argparse
+from config import alexnet_cfg as cfg
+from dataset import create_dataset
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.model_zoo.alexnet import AlexNet
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
+    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
+                        path where the trained ckpt file')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+
+    network = AlexNet(cfg.num_classes)
+    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    repeat_size = cfg.epoch_size
+    opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
+    model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})  # test
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    ds_eval = create_dataset(args.data_path,
+                             cfg.batch_size,
+                             1,
+                             "test")
+    acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== Accuracy:{} ==============".format(acc))
diff --git a/example/alexnet_cifar10/train.py b/example/alexnet_cifar10/train.py
new file mode 100644
index 0000000000..b97843902d
--- /dev/null
+++ b/example/alexnet_cifar10/train.py
@@ -0,0 +1,58 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## train alexnet example ########################
+train alexnet and get network model files(.ckpt) :
+python train.py --data_path /YourDataPath
+"""
+
+import argparse
+from config import alexnet_cfg as cfg
+from dataset import create_dataset
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore.model_zoo.alexnet import AlexNet
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
+    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
+                        path where the trained ckpt file')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+
+    network = AlexNet(cfg.num_classes)
+    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
+    model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})  # test
+
+    print("============== Starting Training ==============")
+    ds_train = create_dataset(args.data_path,
+                              cfg.batch_size,
+                              cfg.epoch_size,
+                              "train")
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet", directory=args.ckpt_path, config=config_ck)
+    model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()],
+                dataset_sink_mode=args.dataset_sink_mode)
diff --git a/example/lenet/main.py b/example/lenet/main.py
deleted file mode 100644
index fe20264d95..0000000000
--- a/example/lenet/main.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-######################## train and test lenet example ########################
-1. train lenet and get network model files(.ckpt) :
-python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
-
-2. test lenet according to model file:
-python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
-               --mode test --ckpt_path checkpoint_lenet_1-1_1875.ckpt
-"""
-import os
-import argparse
-from config import mnist_cfg as cfg
-
-import mindspore.dataengine as de
-import mindspore.nn as nn
-from mindspore.model_zoo.lenet import LeNet5
-from mindspore import context, Tensor
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
-from mindspore.train import Model
-import mindspore.ops.operations as P
-import mindspore.transforms.c_transforms as C
-from mindspore.transforms import Inter
-from mindspore.nn.metrics import Accuracy
-from mindspore.ops import functional as F
-from mindspore.common import dtype as mstype
-
-
-class CrossEntropyLoss(nn.Cell):
-    """
-    Define loss for network
-    """
-    def __init__(self):
-        super(CrossEntropyLoss, self).__init__()
-        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
-        self.mean = P.ReduceMean()
-        self.one_hot = P.OneHot()
-        self.on_value = Tensor(1.0, mstype.float32)
-        self.off_value = Tensor(0.0, mstype.float32)
-
-    def construct(self, logits, label):
-        label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
-        loss = self.cross_entropy(logits, label)[0]
-        loss = self.mean(loss, (-1,))
-        return loss
-
-def create_dataset(data_path, batch_size=32, repeat_size=1,
-                   num_parallel_workers=1):
-    """
-    create dataset for train or test
-    """
-    # define dataset
-    ds1 = de.MnistDataset(data_path)
-
-    # apply map operations on images
-    ds1 = ds1.map(input_columns="label", operations=C.TypeCast(mstype.int32))
-    ds1 = ds1.map(input_columns="image", operations=C.Resize((cfg.image_height, cfg.image_width),
-                                                             interpolation=Inter.LINEAR),
-                  num_parallel_workers=num_parallel_workers)
-    ds1 = ds1.map(input_columns="image", operations=C.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081),
-                  num_parallel_workers=num_parallel_workers)
-    ds1 = ds1.map(input_columns="image", operations=C.Rescale(1.0 / 255.0, 0.0),
-                  num_parallel_workers=num_parallel_workers)
-    ds1 = ds1.map(input_columns="image", operations=C.HWC2CHW(), num_parallel_workers=num_parallel_workers)
-
-    # apply DatasetOps
-    ds1 = ds1.shuffle(buffer_size=cfg.buffer_size)  # 10000 as in LeNet train script
-    ds1 = ds1.batch(batch_size, drop_remainder=True)
-    ds1 = ds1.repeat(repeat_size)
-
-    return ds1
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
-    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
-                        help='device where the code will be implemented (default: Ascend)')
-    parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
-                        help='implement phase, set to train or test')
-    parser.add_argument('--data_path', type=str, default="./MNIST_Data",
-                        help='path where the dataset is saved')
-    parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\
-                        path where the trained ckpt file')
-
-    args = parser.parse_args()
-
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-
-    network = LeNet5(cfg.num_classes)
-    network.set_train()
-    # net_loss = nn.SoftmaxCrossEntropyWithLogits()  # support this loss soon
-    net_loss = CrossEntropyLoss()
-    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
-    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
-                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
-    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
-    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
-
-    if args.mode == 'train':  # train
-        ds = create_dataset(os.path.join(args.data_path, args.mode), batch_size=cfg.batch_size,
-                            repeat_size=cfg.epoch_size)
-        print("============== Starting Training ==============")
-        model.train(cfg['epoch_size'], ds, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)
-    elif args.mode == 'test':  # test
-        print("============== Starting Testing ==============")
-        param_dict = load_checkpoint(args.ckpt_path)
-        load_param_into_net(network, param_dict)
-        ds_eval = create_dataset(os.path.join(args.data_path, "test"), 32, 1)
-        acc = model.eval(ds_eval, dataset_sink_mode=False)
-        print("============== Accuracy:{} ==============".format(acc))
-    else:
-        raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
diff --git a/example/lenet/config.py b/example/lenet_mnist/config.py
similarity index 93%
rename from example/lenet/config.py
rename to example/lenet_mnist/config.py
index 3ad78f3bde..9a13ae535f 100644
--- a/example/lenet/config.py
+++ b/example/lenet_mnist/config.py
@@ -13,8 +13,9 @@
 # limitations under the License.
 # ============================================================================
 """
-network config setting, will be used in main.py
+network config setting, will be used in train.py
 """
+
 from easydict import EasyDict as edict
 
 mnist_cfg = edict({
@@ -23,7 +24,6 @@ mnist_cfg = edict({
     'momentum': 0.9,
     'epoch_size': 1,
     'batch_size': 32,
-    'repeat_size': 1,
     'buffer_size': 1000,
     'image_height': 32,
     'image_width': 32,
diff --git a/example/lenet_mnist/dataset.py b/example/lenet_mnist/dataset.py
new file mode 100644
index 0000000000..cef6973483
--- /dev/null
+++ b/example/lenet_mnist/dataset.py
@@ -0,0 +1,60 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,
+                   num_parallel_workers=1):
+    """
+    create dataset for train or test
+    """
+    # define dataset
+    mnist_ds = ds.MnistDataset(data_path)
+
+    resize_height, resize_width = 32, 32
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+    rescale_op = CV.Rescale(rescale, shift)
+    hwc2chw_op = CV.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds
diff --git a/example/lenet_mnist/eval.py b/example/lenet_mnist/eval.py
new file mode 100644
index 0000000000..3473a99532
--- /dev/null
+++ b/example/lenet_mnist/eval.py
@@ -0,0 +1,64 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## eval lenet example ########################
+eval lenet according to model file:
+python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
+"""
+
+import os
+import argparse
+from dataset import create_dataset
+from config import mnist_cfg as cfg
+import mindspore.nn as nn
+from mindspore.model_zoo.lenet import LeNet5
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./MNIST_Data",
+                        help='path where the dataset is saved')
+    parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\
+                        path where the trained ckpt file')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    repeat_size = cfg.epoch_size
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    ds_eval = create_dataset(os.path.join(args.data_path, "test"),
+                             cfg.batch_size,
+                             1)
+    acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== Accuracy:{} ==============".format(acc))
diff --git a/example/lenet_mnist/train.py b/example/lenet_mnist/train.py
new file mode 100644
index 0000000000..2fa8d3c27f
--- /dev/null
+++ b/example/lenet_mnist/train.py
@@ -0,0 +1,58 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## train lenet example ########################
+train lenet and get network model files(.ckpt) :
+python train.py --data_path /YourDataPath
+"""
+
+import os
+import argparse
+from config import mnist_cfg as cfg
+from dataset import create_dataset
+import mindspore.nn as nn
+from mindspore.model_zoo.lenet import LeNet5
+from mindspore import context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./MNIST_Data",
+                        help='path where the dataset is saved')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    ds_train = create_dataset(os.path.join(args.data_path, "train"),
+                              cfg.batch_size,
+                              cfg.epoch_size)
+    print("============== Starting Training ==============")
+    model.train(cfg['epoch_size'], ds_train, callbacks=[ckpoint_cb, LossMonitor()],
+                dataset_sink_mode=args.dataset_sink_mode)
diff --git a/mindspore/model_zoo/alexnet.py b/mindspore/model_zoo/alexnet.py
new file mode 100644
index 0000000000..8cd316229c
--- /dev/null
+++ b/mindspore/model_zoo/alexnet.py
@@ -0,0 +1,73 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Alexnet."""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"):
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode=pad_mode)
+
+def fc_with_initialize(input_channels, out_channels):
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+def weight_variable():
+    return TruncatedNormal(0.02)  # 0.02
+
+
+class AlexNet(nn.Cell):
+    """
+    Alexnet
+    """
+    def __init__(self, num_classes=10):
+        super(AlexNet, self).__init__()
+        self.batch_size = 32
+        self.conv1 = conv(3, 96, 11, stride=4)
+        self.conv2 = conv(96, 256, 5, pad_mode="same")
+        self.conv3 = conv(256, 384, 3, pad_mode="same")
+        self.conv4 = conv(384, 384, 3, pad_mode="same")
+        self.conv5 = conv(384, 256, 3, pad_mode="same")
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2)
+        self.flatten = nn.Flatten()
+        self.fc1 = fc_with_initialize(6*6*256, 4096)
+        self.fc2 = fc_with_initialize(4096, 4096)
+        self.fc3 = fc_with_initialize(4096, num_classes)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv3(x)
+        x = self.relu(x)
+        x = self.conv4(x)
+        x = self.relu(x)
+        x = self.conv5(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
diff --git a/mindspore/model_zoo/lenet.py b/mindspore/model_zoo/lenet.py
index a22eef1a96..6e39c439bf 100644
--- a/mindspore/model_zoo/lenet.py
+++ b/mindspore/model_zoo/lenet.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """LeNet."""
-import mindspore.ops.operations as P
 import mindspore.nn as nn
 from mindspore.common.initializer import TruncatedNormal
 
@@ -62,7 +61,7 @@ class LeNet5(nn.Cell):
         self.fc3 = fc_with_initialize(84, self.num_class)
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.reshape = P.Reshape()
+        self.flatten = nn.Flatten()
 
     def construct(self, x):
         x = self.conv1(x)
@@ -71,7 +70,7 @@ class LeNet5(nn.Cell):
         x = self.conv2(x)
         x = self.relu(x)
         x = self.max_pool2d(x)
-        x = self.reshape(x, (self.batch_size, -1))
+        x = self.flatten(x)
         x = self.fc1(x)
         x = self.relu(x)
         x = self.fc2(x)

From 2da38ad4016f69799dfdc16b4be873962e6a2ce9 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Tue, 31 Mar 2020 18:43:42 +0800
Subject: [PATCH 031/367] fix two cast bug in auto parallel

---
 .../ccsrc/parallel/step_auto_parallel.cc      |  6 ++--
 mindspore/ccsrc/parallel/step_parallel.cc     | 23 ++++++++++---
 .../parallel/test_element_wise_function.py    | 33 ++++++++++++++++---
 3 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index c3e3f5893e..400e985f18 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -346,6 +346,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) {
 }
 
 OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(prim);
+  MS_EXCEPTION_IF_NULL(cnode);
   auto attrs = prim->attrs();
   std::vector<Shapes> shape_list = ExtractShape(cnode);
   if (shape_list.empty()) {
@@ -381,8 +383,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
   operator_info->set_outputs_dtype(cnode->Type());
   operator_info->set_cnode(cnode);
   // If no strategy has been configured for this operator, then candidate strategies are generated for
-  // auto-strategy searching
-  if (!StrategyFound(attrs)) {
+  // auto-strategy searchingm if this primitive is Cast, we ignore the user-specified strategy
+  if (!StrategyFound(attrs) || prim->name() == CAST) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
     operator_info->ComputeBatchSplitFlagList();
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 21da11ec21..54ad438430 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -370,7 +370,6 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
   if (prim == nullptr) {
     return false;
   }
-  auto attrs = prim->attrs();
   if (IsInBlackList(prim)) {
     MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
     return false;
@@ -379,10 +378,8 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
   if (prim->name() == GET_NEXT) {
     return true;
   }
-  if ((prim->name() == CAST)) {
-    if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
-      return false;
-    }
+  if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) {
+    return false;
   }
 
   return cnode->in_forward_flag();
@@ -653,6 +650,14 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) {
 
   LossNodeInfo node_info;
 
+  // return -> cast
+  auto pre_cnode = pre_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(pre_cnode);
+  auto pre_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
+  if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
+    pre_node = pre_cnode->input(1);
+  }
+
   // return -> loss
   if (pre_node == loss_node) {
     node_info.has_tuple_getitem = false;
@@ -1947,6 +1952,14 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
   MS_EXCEPTION_IF_NULL(current_value);
   PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(current_prim);
+
+  // return -> cast
+  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
+    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(pre_cnode);
+    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
+  }
+
   // notice: the GetNext op has not input
   if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(INFO) << "The loss is: " << current_prim->name();
diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py
index dfcebdc5ab..2eb3a22ed2 100644
--- a/tests/ut/python/parallel/test_element_wise_function.py
+++ b/tests/ut/python/parallel/test_element_wise_function.py
@@ -192,7 +192,6 @@ def test_cast_before_mirror():
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
     
-
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
     b = Tensor(np.ones([64, 64]), dtype=ms.float16)
@@ -217,7 +216,6 @@ def test_cast_before_mirror1():
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
     
-
     x = Tensor(np.ones([128, 32]), dtype=ms.float16)
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
@@ -242,7 +240,6 @@ def test_cast_before_mirror2():
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
     
-
     x = Tensor(np.ones([128, 32]), dtype=ms.float16)
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
@@ -267,8 +264,36 @@ def test_cast_before_mirror3():
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
     
-
     x = Tensor(np.ones([128, 32]), dtype=ms.float16)
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
+
+
+def test_mul_two_cast():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2, strategy3):
+            super().__init__()
+            self.mul = P.Mul().set_strategy(strategy1)
+            self.mul2 = P.Mul().set_strategy(strategy2)
+            self.cast = P.Cast().set_strategy(strategy3)
+            self.cast2 = P.Cast().set_strategy(strategy3)
+
+        def construct(self, x, y, b):
+            out = self.mul(x, y)
+            out = self.mul2(out, b)
+            out = self.cast(out, ms.int32)
+            out = self.cast2(out, ms.bool_)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((8, 1), (8, 1))
+    strategy3 = ((8, 1), )
+    net = GradWrap(Net(strategy1, strategy2, strategy3))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    b = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)

From 7b5640da4e6d4c8d27f34e1289de65f5b96d0737 Mon Sep 17 00:00:00 2001
From: ms_yan <yanpanhui@huawei.com>
Date: Tue, 31 Mar 2020 18:46:59 +0800
Subject: [PATCH 032/367] Repair parameter check problem in TFRecordDataset

---
 mindspore/dataset/engine/validators.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 4dc1867808..adfe54a02e 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -398,6 +398,7 @@ def check_tfrecorddataset(method):
 
         nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
         nreq_param_list = ['columns_list']
+        nreq_param_bool = ['shard_equal_rows']
 
         # check dataset_files; required argument
         dataset_files = param_dict.get('dataset_files')
@@ -410,6 +411,10 @@ def check_tfrecorddataset(method):
 
         check_param_type(nreq_param_list, param_dict, list)
 
+        check_param_type(nreq_param_bool, param_dict, bool)
+
+        check_sampler_shuffle_shard_options(param_dict)
+
         return method(*args, **kwargs)
 
     return new_method

From feb1c36811ce59a41f8cabf359468f76ad21fbe2 Mon Sep 17 00:00:00 2001
From: panyifeng <panyifeng@huawei.com>
Date: Tue, 31 Mar 2020 14:55:29 +0800
Subject: [PATCH 033/367] fix parallel related valuenode merging error

---
 mindspore/ccsrc/parallel/step_parallel.cc |  1 +
 mindspore/ccsrc/pipeline/action.cc        | 28 +++++++++++++++++++++++
 2 files changed, 29 insertions(+)

diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 927acea705..65e5cb976a 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -112,6 +112,7 @@ void InsertNode(const Operator& op, const CNodePtr& node, size_t index, const An
   MS_EXCEPTION_IF_NULL(new_node_value);
   PrimitivePtr new_node_prim = new_node_value->value()->cast<PrimitivePtr>();
   new_node_prim->set_instance_name(instance_name);
+  new_node_prim->set_attr("keep_value_node_input", MakeValue(true));
   new_node->set_scope(scope);
   node_input[0]->set_scope(scope);
   manager->SetEdge(node, SizeToInt(index), new_node);
diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc
index f3742ab654..392602f419 100644
--- a/mindspore/ccsrc/pipeline/action.cc
+++ b/mindspore/ccsrc/pipeline/action.cc
@@ -276,6 +276,31 @@ bool ExecuteAction(const ResourcePtr& res) {
   return true;
 }
 
+// The parallel primitive related valuenode might be partitioned so that its value changes by device,
+// that will result in a syncronization error due to different executing order.
+// Here we temporarily avoid the problem by skipping valuenode merging used by parallel related primitive,
+// the final solution will be proposed later as a parallel feature.
+bool KeepValueNodeDuplication(const AnfNodePtr& value_node, const ResourcePtr& res) {
+  auto& node_users = res->manager()->node_users();
+  auto& users = node_users[value_node];
+  auto used_by_keep_value_prim =
+    std::any_of(users.begin(), users.end(), [](const std::pair<AnfNodePtr, int>& user) -> bool {
+      MS_EXCEPTION_IF_NULL(user.first);
+      auto cnode = user.first->cast<CNodePtr>();
+      if (cnode == nullptr) {
+        return false;
+      }
+      auto prim_node = cnode->input(0);
+      if (IsValueNode<Primitive>(prim_node)) {
+        auto prim = GetValue<PrimitivePtr>(prim_node->cast<ValueNodePtr>()->value());
+        // value_node is referenced by some parallel primitive
+        return prim->HasAttr("keep_value_node_input");
+      }
+      return false;
+    });
+  return used_by_keep_value_prim;
+}
+
 bool RemoveValueNodeDuplicationsAction(const ResourcePtr& res) {
   if (res->func_graph() == nullptr) {
     MS_LOG(EXCEPTION) << "Remove value node duplications error.";
@@ -287,6 +312,9 @@ bool RemoveValueNodeDuplicationsAction(const ResourcePtr& res) {
   HashCache hash_cache;
   HashValue hashes;
   for (const auto& value_pair : value_nodes) {
+    if (KeepValueNodeDuplication(value_pair.first, res)) {
+      continue;
+    }
     TryToDoReplace(manager.get(), value_pair.first, &hash_cache, &hashes);
   }
   return true;

From 624ab97de6f28444b7bff27f743deddcc0a80a46 Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Tue, 31 Mar 2020 20:13:49 +0800
Subject: [PATCH 034/367] repair parameter check problem in random_resize_crop
 and random_crop

---
 mindspore/dataset/transforms/vision/validators.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py
index 17f50e068d..443232d780 100644
--- a/mindspore/dataset/transforms/vision/validators.py
+++ b/mindspore/dataset/transforms/vision/validators.py
@@ -41,7 +41,7 @@ def check_crop_size(size):
     else:
         raise TypeError("Size should be a single integer or a list/tuple (h, w) of length 2.")
     for value in size:
-        check_value(value, (1, INT32_MAX))
+        check_pos_int32(value)
     return size
 
 
@@ -239,6 +239,7 @@ def check_random_resize_crop(method):
             kwargs["scale"] = scale
         if ratio is not None:
             check_range(ratio, [0, FLOAT_MAX_INTEGER])
+            check_positive(ratio[0])
             kwargs["ratio"] = ratio
         if interpolation is not None:
             check_inter_mode(interpolation)

From cf40305bf05bd5a1e4a37857e325b25b77730940 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Tue, 31 Mar 2020 15:15:08 +0800
Subject: [PATCH 035/367] add operator SpaceToBatch and BatchToSpace for ge

---
 mindspore/ccsrc/transform/convert.cc    |   6 +-
 mindspore/ccsrc/transform/op_adapter.h  |  22 ++++
 mindspore/ccsrc/transform/op_declare.cc |  13 +++
 mindspore/ccsrc/transform/op_declare.h  |   4 +
 mindspore/ops/_grad/grad_array_ops.py   |  20 ++++
 mindspore/ops/operations/__init__.py    |   4 +-
 mindspore/ops/operations/array_ops.py   | 141 ++++++++++++++++++++++++
 tests/ut/python/ops/test_ops.py         |  20 ++++
 8 files changed, 228 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c8c6abea4c..c975b18812 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -180,6 +180,8 @@ const char kNamePrint[] = "Print";
 const char kNameApplyFtrl[] = "ApplyFtrl";
 const char kNameDiag[] = "Diag";
 const char kNameDiagPart[] = "DiagPart";
+const char kNameSpaceToBatch[] = "SpaceToBatch";
+const char kNameBatchToSpace[] = "BatchToSpace";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -361,7 +363,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameRound), ADPT_DESC(Round)},
     {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)},
     {string(kNameDiag), ADPT_DESC(Diag)},
-    {string(kNameDiagPart), ADPT_DESC(DiagPart)}};
+    {string(kNameDiagPart), ADPT_DESC(DiagPart)},
+    {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
+    {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif
diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h
index aa466adbb8..3dd299f83d 100644
--- a/mindspore/ccsrc/transform/op_adapter.h
+++ b/mindspore/ccsrc/transform/op_adapter.h
@@ -744,6 +744,28 @@ class OpAdapter : public BaseOpAdapter {
     return list;
   }
 
+  static std::vector<int64_t> ConvertAny(const ValuePtr& value, const AnyTraits<std::vector<std::vector<int64_t>>>,
+                                         const AnyTraits<std::vector<int64_t>>) {
+    MS_EXCEPTION_IF_NULL(value);
+    MS_LOG(DEBUG) << "Value: " << value->type_name();
+    if (!value->isa<ValueList>()) {
+      MS_LOG(EXCEPTION) << "Value should be ValueList, but got " << value->type_name();
+    }
+    auto vec = value->cast<ValueListPtr>();
+    std::vector<int64_t> list;
+    for (auto& it : vec->value()) {
+      MS_EXCEPTION_IF_NULL(it);
+      if (!it->isa<ValueList>()) {
+        MS_LOG(EXCEPTION) << "It should be ValueList, but got " << it->type_name();
+      }
+      auto sub_vector = it->cast<ValueListPtr>();
+      for (auto& item : sub_vector->value()) {
+        list.push_back(static_cast<int64_t>(GetValue<int>(item)));
+      }
+    }
+    return list;
+  }
+
   static std::vector<int64_t> ConvertAny(const ValuePtr& value, const AnyTraits<std::vector<int64_t>>,
                                          const AnyTraits<std::vector<int64_t>>) {
     MS_EXCEPTION_IF_NULL(value);
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index af0ce29f85..cc63bacd8a 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -1183,6 +1183,19 @@ INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}};
 
+// SpaceToBatchD
+INPUT_MAP(SpaceToBatchD) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(SpaceToBatchD) = {
+  {"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())},
+  {"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::vector<int64_t>>>(), AnyTraits<std::vector<int64_t>>())}};
+OUTPUT_MAP(SpaceToBatchD) = {{0, OUTPUT_DESC(y)}};
+
+// BatchToSpaceD
+INPUT_MAP(BatchToSpaceD) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(BatchToSpaceD) = {
+  {"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())},
+  {"crops", ATTR_DESC(crops, AnyTraits<std::vector<std::vector<int64_t>>>(), AnyTraits<std::vector<int64_t>>())}};
+OUTPUT_MAP(BatchToSpaceD) = {{0, OUTPUT_DESC(y)}};
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 59014b8b7d..978828e16d 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -439,6 +439,10 @@ DECLARE_OP_ADAPTER(Diag)
 DECLARE_OP_USE_OUTPUT(Diag)
 DECLARE_OP_ADAPTER(DiagPart)
 DECLARE_OP_USE_OUTPUT(DiagPart)
+DECLARE_OP_ADAPTER(SpaceToBatchD)
+DECLARE_OP_USE_OUTPUT(SpaceToBatchD)
+DECLARE_OP_ADAPTER(BatchToSpaceD)
+DECLARE_OP_USE_OUTPUT(BatchToSpaceD)
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index 79841cf27a..81d38a1e1e 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -430,3 +430,23 @@ def get_bprop_diag_part(self):
         return (op(dout),)
 
     return bprop
+
+
+@bprop_getters.register(P.SpaceToBatch)
+def get_bprop_space_to_batch(self):
+    """Generate bprop for SpaceToBatch"""
+    space_to_batch_grad = P.BatchToSpace(self.block_size, self.paddings)
+    def bprop(x, out, dout):
+        dx = space_to_batch_grad(dout)
+        return (dx,)
+    return bprop
+
+
+@bprop_getters.register(P.BatchToSpace)
+def get_bprop_batch_to_space(self):
+    """Generate bprop for BatchToSpace"""
+    batch_to_space_grad = P.SpaceToBatch(self.block_size, self.crops)
+    def bprop(x, out, dout):
+        dx = batch_to_space_grad(dout)
+        return (dx,)
+    return bprop
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 295e2edaf8..899b2f8d0f 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -29,7 +29,7 @@ from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat,
                         Shape, Size, Slice, Split,
                         Squeeze, StridedSlice, Tile,
                         Transpose, TruncatedNormal, TupleToArray,
-                        UnsortedSegmentSum, SpaceToDepth, DepthToSpace)
+                        UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace)
 from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast,
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice)
@@ -225,6 +225,8 @@ __all__ = [
     "LARSUpdate",
     "Round",
     "ApplyFtrl",
+    "SpaceToBatch",
+    "BatchToSpace"
 ]
 
 __all__.sort()
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 6740f172b4..8585f873e9 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -20,6 +20,7 @@
 
 import copy
 import functools
+import itertools
 import numbers
 import numpy as np
 
@@ -2020,3 +2021,143 @@ class DepthToSpace(PrimitiveWithInfer):
     def infer_dtype(self, x_dtype):
         validator.check_subclass("x_dtype", x_dtype, mstype.tensor)
         return x_dtype
+
+
+class SpaceToBatch(PrimitiveWithInfer):
+    r"""
+    Divide spatial dimensions into blocks and combine the block size with the original batch.
+
+    This operation will divide spatial dimensions (H, W) into blocks with block_size, the output tensor's H and W
+    dimension is the corresponding number of blocks after division. The output tensor's batch dimension is the
+    product of the original batch and the square of block_size. Prior to division into blocks, the spatial dimensions
+    of the input are zero padded according to paddings if necessary.
+
+    Args:
+        block_size (int): The block size of dividing block with value >= 1.
+        paddings (list): The padding value for H and W dimension, containing 2 sub list, each containing 2 int value.
+            All values must be >= 0. paddings[i] specifies the paddings for spatial dimension i, which corresponds to
+            input dimension i+2. It is required that input_shape[i+2]+paddings[i][0]+paddings[i][1] is divisible
+            by block_size.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, the output tensor with the same type as input. Assume input shape is :math:`(n, c, h, w)` with
+        :math:`block\_size` and :math:`padddings`. The output tensor shape will be :math:`(n', c', h', w')`, where
+
+            :math:`n' = n*(block\_size*block\_size)`
+
+            :math:`c' = c`
+
+            :math:`h' = (h+paddings[0][0]+paddings[0][1])//block\_size`
+
+            :math:`w' = (w+paddings[1][0]+paddings[1][1])//block\_size`
+
+    Examples:
+        >>> block_size = 2
+        >>> paddings = [[0, 0], [0, 0]]
+        >>> space_to_batch = P.SpaceToBatch(block_size, paddings)
+        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32)
+        >>> space_to_batch(x)
+        [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
+
+    """
+    @prim_attr_register
+    def __init__(self, block_size, paddings):
+        """Init SpaceToBatch"""
+        validator.check_type('block_size', block_size, [int])
+        validator.check('block_size', block_size, '', 1, Rel.GT)
+        self.block_size = block_size
+        validator.check('paddings shape', np.array(paddings).shape, '', (2, 2))
+        for elem in itertools.chain(*paddings):
+            validator.check_type('paddings element', elem, [int])
+        self.paddings = paddings
+
+    def infer_dtype(self, x_dtype):
+        validator.check_subclass("input_x", x_dtype, mstype.tensor)
+        validator.check_typename('input_x', x_dtype, mstype.number_type)
+        return x_dtype
+
+    def infer_shape(self, x_shape):
+        validator.check('rank of input_x', len(x_shape), '', 4)
+        out_shape = copy.deepcopy(x_shape)
+        for i in range(2):
+            padded = out_shape[i+2] + self.paddings[i][0] + \
+                     self.paddings[i][1]
+            if padded % self.block_size != 0:
+                raise ValueError(f'padded[{i}] {padded} should be divisible by '
+                                 f'block_size {self.block_size}')
+            out_shape[i+2] = padded // self.block_size
+        out_shape[0] *= self.block_size * self.block_size
+        return out_shape
+
+
+class BatchToSpace(PrimitiveWithInfer):
+    r"""
+    Divide batch dimension with blocks and interleaves these blocks back into spatial dimensions.
+
+    This operation will divide batch dimension N into blocks with block_size, the output tensor's N dimension
+    is the corresponding number of blocks after division. The output tensor's H, W dimension is product of original H, W
+    dimension and block_size with given amount to crop from dimension, respectively.
+
+    Args:
+        block_size (int): The block size of dividing block with value >= 1.
+        crops (list): The crop value for H and W dimension, containing 2 sub list, each containing 2 int value.
+            All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to
+            input dimension i+2. It is required that input_shape[i+2]*block_size >= crops[i][0]+crops[i][1].
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, the output tensor with the same type as input. Assume input shape is (n, c, h, w) with block_size
+        and crops. The output shape will be (n', c', h', w'), where
+
+                :math:`n' = n//(block\_size*block\_size)`
+
+                :math:`c' = c`
+
+                :math:`h' = h*block\_size-crops[0][0]-crops[0][1]`
+
+                :math:`w' = w*block\_size-crops[1][0]-crops[1][1]`
+
+    Examples:
+        >>> block_size = 2
+        >>> crops = [[0, 0], [0, 0]]
+        >>> op = P.BatchToSpace(block_size, crops)
+        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32)
+        >>> output = op(x)
+        [[[[1., 2.], [3., 4.]]]]
+
+    """
+    @prim_attr_register
+    def __init__(self, block_size, crops):
+        """Init BatchToSpace"""
+        validator.check_type('block_size', block_size, [int])
+        validator.check('block_size', block_size, '', 1, Rel.GT)
+        self.block_size = block_size
+        validator.check('crops shape', np.array(crops).shape, '', (2, 2))
+        for elem in itertools.chain(*crops):
+            validator.check_type('crops element', elem, [int])
+        self.crops = crops
+
+    def infer_dtype(self, x_dtype):
+        validator.check_subclass("input_x", x_dtype, mstype.tensor)
+        validator.check_typename('input_x', x_dtype, mstype.number_type)
+        return x_dtype
+
+    def infer_shape(self, x_shape):
+        validator.check('rank of input_x', len(x_shape), '', 4)
+        out_shape = copy.deepcopy(x_shape)
+        for i in range(2):
+            x_block_prod = out_shape[i+2] * self.block_size
+            crops_sum = self.crops[i][0] + self.crops[i][1]
+            validator.check("x block shape prod", x_block_prod, 'crops sum', crops_sum, Rel.GT)
+            out_shape[i+2] = x_block_prod - crops_sum
+        block_size_prod = self.block_size * self.block_size
+        if out_shape[0] % block_size_prod != 0:
+            raise ValueError(f'input_x dimension 0 {out_shape[0]}  should be divisible by '
+                             f'block_size_prod {block_size_prod}')
+        out_shape[0] = out_shape[0] // block_size_prod
+        return out_shape
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index e917c12748..f1d365e9cf 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -952,6 +952,26 @@ test_case_array_ops = [
         'desc_inputs': [[4, 4]],
         'desc_bprop': [[4]],
     }),
+    ('SpaceToBatch_1', {
+        'block': P.SpaceToBatch(2, [[0, 0], [0, 0]]),
+        'desc_inputs': [[1, 3, 2, 2]],
+        'desc_bprop': [[4, 3, 1, 1]],
+    }),
+    ('SpaceToBatch_2', {
+        'block': P.SpaceToBatch(2, [[1, 1], [0, 4]]),
+        'desc_inputs': [[1, 3, 2, 2]],
+        'desc_bprop': [[4, 3, 2, 4]],
+    }),
+    ('BatchToSpace_1', {
+        'block': P.BatchToSpace(2, [[0, 0], [0, 0]]),
+        'desc_inputs': [[4, 3, 1, 1]],
+        'desc_bprop': [[1, 3, 2, 2]],
+    }),
+    ('BatchToSpace_2', {
+        'block': P.BatchToSpace(2, [[0, 0], [0, 1]]),
+        'desc_inputs': [[4, 3, 1, 1]],
+        'desc_bprop': [[1, 3, 2, 1]],
+    }),
 ]
 
 test_case_other_ops = [

From 2f031e154033f3dc82c03fbf084c1ba3ce7bc127 Mon Sep 17 00:00:00 2001
From: mxm <83028974@qq.com>
Date: Tue, 31 Mar 2020 12:08:02 +0800
Subject: [PATCH 036/367] fixed: PrimitiveToInferImplMap map is global, and key
 of the map PrimitivePtr also a global variable. If key is initialized later
 than the map initialized during compilation, will cause the primitive map
 initialize failed. Variable initialization order is not guaranteed during
 compilation.

---
 .../ccsrc/pipeline/static_analysis/prim.cc    | 248 +++++++++---------
 .../static_analysis/static_analysis.cc        |  49 ++--
 2 files changed, 148 insertions(+), 149 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 98d82de5d5..e06d58466d 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -42,92 +42,95 @@
 
 namespace mindspore {
 namespace abstract {
-PrimitiveEvalImplMap PrimitiveToInferImplMap = {
-  // Statements
-  {prim::kPrimReturn, {InferImplReturn, true}},
-  {prim::kPrimTypeOf, {InferImplTypeof, false}},
-  {prim::kPrimHasType, {InferImplHasType, false}},
-  {prim::kPrimDot, {InferImplDot, true}},
-  {prim::kPrimSwitch, {InferImplSwitch, true}},
-  {prim::kPrimIs_, {InferImplIs_, true}},
-  {prim::kPrimIsNot, {InferImplIsNot, true}},
-  // Maths
-  {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}},
-  {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}},
-  // Array
-  {prim::kPrimScalarToArray, {InferImplScalarToArray, true}},
-  {prim::kPrimArrayToScalar, {InferImplArrayToScalar, true}},
-  {prim::kPrimBroadcastShape, {InferImplBroadCastShape, true}},
-  {prim::kPrimShape, {InferImplShape, true}},
-  {prim::kPrimPack, {InferImplPack, true}},
-  // Structure
-  {prim::kPrimMakeTuple, {InferImplMakeTuple, true}},
-  {prim::kPrimMakeList, {InferImplMakeList, true}},
-  {prim::kPrimMakeDict, {InferImplMakeDict, true}},
-  {prim::kPrimMakeSlice, {InferImplMakeSlice, true}},
-  {prim::kPrimMakeKeywordArg, {InferImplMakeKwarg, true}},
-  {prim::kPrimExtractKeywordArg, {InferImplExtractKwarg, true}},
-  {prim::kPrimMakeRecord, {InferImplMakeRecord, false}},
-  {prim::kPrimTupleGetItem, {InferImplTupleGetItem, true}},
-  {prim::kPrimListGetItem, {InferImplListGetItem, true}},
-  {prim::kPrimTupleSetItem, {InferImplTupleSetItem, true}},
-  {prim::kPrimListSetItem, {InferImplListSetItem, true}},
-  {prim::kPrimDictGetItem, {InferImplDictGetItem, true}},
-  {prim::kPrimDictSetItem, {InferImplDictSetItem, true}},
-  {prim::kPrimListAppend, {InferImplListAppend, true}},
-  {prim::kPrimTupleLen, {InferImplTupleLen, true}},
-  {prim::kPrimListLen, {InferImplListLen, true}},
-  {prim::kPrimArrayLen, {InferImplArrayLen, true}},
-  {prim::kPrimListMap, {InferImplListMap, false}},
-  {prim::kPrimListReduce, {InferImplListReduce, false}},
-  {prim::kPrimTupleReversed, {InferImplTupleReversed, false}},
-  {prim::kPrimReducedShape, {InferImplReduceShape, false}},
-  {prim::kPrimTupleDiv, {InferImplTupleDiv, false}},
-  {prim::kPrimTupleToArray, {InferImplTuple2Array, false}},
-  {prim::kPrimShapeMul, {InferImplShapeMul, false}},
-  {prim::kPrimTupleEqual, {InferImplTupleEqual, false}},
-  {prim::kPrimListEqual, {InferImplListEqual, false}},
-  {prim::kPrimMakeRange, {InferImplMakeRange, false}},
-  {prim::kPrimStopGradient, {InferImplStopGradient, false}},
-  {prim::kPrimStringEqual, {InferImplStringEqual, false}},
-  {prim::kPrimDictLen, {InferImplDictLen, false}},
-  // NN
-  {prim::kPrimPooling, {InferImplPooling, true}},
-  {prim::kPrimPoolingGrad, {InferImplPoolingGrad, true}},
-  {prim::kPrimFusedBatchNorm, {InferImplFusedBatchNorm, true}},
-  {prim::kPrimFusedBatchNormGrad, {InferImplFusedBatchNormGrad, true}},
-  {prim::kPrimReluGrad, {InferImplReluGrad, true}},
-  {prim::kPrimConv2DBackpropInput, {InferImplConv2DBackpropInput, true}},
-  {prim::kPrimConv2DBackpropFilter, {InferImplConv2DBackpropFilter, true}},
-  {prim::kPrimBiasAddGrad, {InferImplBiasAddGrad, true}},
-  {prim::kPrimRelu, {InferImplRelu, true}},
-  {prim::kPrimZerosLikeTensor, {InferImplZerosLikeTensor, true}},
-  {prim::kPrimFakeBprop, {InferImplFakeBprop, false}},
-  {prim::kPrimLayerNorm, {InferImplLayerNorm, true}},
-  {prim::kPrimLayerNormGrad, {InferImplLayerNormGrad, true}},
-  {prim::kPrimDropoutGenMask, {InferImplDropoutGenMask, true}},
-  // Others
-  {prim::kPrimIdentity, {InferImplIdentity, true}},
-  // Set impl to null as it will use PartialEvaluator;
-  {prim::kPrimPartial, {nullptr, true}},
-  {prim::kPrimJ, {InferImplJ, false}},
-  {prim::kPrimEnvGetItem, {InferImplEnvGetItem, true}},
-  {prim::kPrimEnvSetItem, {InferImplEnvSetItem, true}},
-  {prim::kPrimEnvAdd, {InferImplEnvAdd, true}},
-  {prim::kPrimMakeRefKey, {InferImplMakeRefKey, true}},
-  {prim::kPrimMakeRef, {InferImplMakeRef, true}},
-  {prim::kPrimGetRefKey, {InferImplGetRefKey, true}},
-  {prim::kPrimGetRefValue, {InferImplGetRefValue, true}},
-  {prim::kPrimGetRefOrigin, {InferImplGetRefOrigin, true}},
-  {prim::kPrimStateSetItem, {InferImplStateSetItem, true}},
-  {prim::kPrimDepend, {InferImplDepend, true}},
-  {prim::kPrimBroadcastGradientArgs, {InferImplBroadcastGradientArgs, false}},
-  {prim::kPrimControlDepend, {InferImplControlDepend, true}},
-  // Debug
-  {prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
-  {prim::kPrimImageSummary, {InferImplTensorSummary, true}},
-  {prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
-};
+PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
+  static PrimitiveEvalImplMap prim_eval_implement_map = {
+    // Statements
+    {prim::kPrimReturn, {InferImplReturn, true}},
+    {prim::kPrimTypeOf, {InferImplTypeof, false}},
+    {prim::kPrimHasType, {InferImplHasType, false}},
+    {prim::kPrimDot, {InferImplDot, true}},
+    {prim::kPrimSwitch, {InferImplSwitch, true}},
+    {prim::kPrimIs_, {InferImplIs_, true}},
+    {prim::kPrimIsNot, {InferImplIsNot, true}},
+    // Maths
+    {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}},
+    {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}},
+    // Array
+    {prim::kPrimScalarToArray, {InferImplScalarToArray, true}},
+    {prim::kPrimArrayToScalar, {InferImplArrayToScalar, true}},
+    {prim::kPrimBroadcastShape, {InferImplBroadCastShape, true}},
+    {prim::kPrimShape, {InferImplShape, true}},
+    {prim::kPrimPack, {InferImplPack, true}},
+    // Structure
+    {prim::kPrimMakeTuple, {InferImplMakeTuple, true}},
+    {prim::kPrimMakeList, {InferImplMakeList, true}},
+    {prim::kPrimMakeDict, {InferImplMakeDict, true}},
+    {prim::kPrimMakeSlice, {InferImplMakeSlice, true}},
+    {prim::kPrimMakeKeywordArg, {InferImplMakeKwarg, true}},
+    {prim::kPrimExtractKeywordArg, {InferImplExtractKwarg, true}},
+    {prim::kPrimMakeRecord, {InferImplMakeRecord, false}},
+    {prim::kPrimTupleGetItem, {InferImplTupleGetItem, true}},
+    {prim::kPrimListGetItem, {InferImplListGetItem, true}},
+    {prim::kPrimTupleSetItem, {InferImplTupleSetItem, true}},
+    {prim::kPrimListSetItem, {InferImplListSetItem, true}},
+    {prim::kPrimDictGetItem, {InferImplDictGetItem, true}},
+    {prim::kPrimDictSetItem, {InferImplDictSetItem, true}},
+    {prim::kPrimListAppend, {InferImplListAppend, true}},
+    {prim::kPrimTupleLen, {InferImplTupleLen, true}},
+    {prim::kPrimListLen, {InferImplListLen, true}},
+    {prim::kPrimArrayLen, {InferImplArrayLen, true}},
+    {prim::kPrimListMap, {InferImplListMap, false}},
+    {prim::kPrimListReduce, {InferImplListReduce, false}},
+    {prim::kPrimTupleReversed, {InferImplTupleReversed, false}},
+    {prim::kPrimReducedShape, {InferImplReduceShape, false}},
+    {prim::kPrimTupleDiv, {InferImplTupleDiv, false}},
+    {prim::kPrimTupleToArray, {InferImplTuple2Array, false}},
+    {prim::kPrimShapeMul, {InferImplShapeMul, false}},
+    {prim::kPrimTupleEqual, {InferImplTupleEqual, false}},
+    {prim::kPrimListEqual, {InferImplListEqual, false}},
+    {prim::kPrimMakeRange, {InferImplMakeRange, false}},
+    {prim::kPrimStopGradient, {InferImplStopGradient, false}},
+    {prim::kPrimStringEqual, {InferImplStringEqual, false}},
+    {prim::kPrimDictLen, {InferImplDictLen, false}},
+    // NN
+    {prim::kPrimPooling, {InferImplPooling, true}},
+    {prim::kPrimPoolingGrad, {InferImplPoolingGrad, true}},
+    {prim::kPrimFusedBatchNorm, {InferImplFusedBatchNorm, true}},
+    {prim::kPrimFusedBatchNormGrad, {InferImplFusedBatchNormGrad, true}},
+    {prim::kPrimReluGrad, {InferImplReluGrad, true}},
+    {prim::kPrimConv2DBackpropInput, {InferImplConv2DBackpropInput, true}},
+    {prim::kPrimConv2DBackpropFilter, {InferImplConv2DBackpropFilter, true}},
+    {prim::kPrimBiasAddGrad, {InferImplBiasAddGrad, true}},
+    {prim::kPrimRelu, {InferImplRelu, true}},
+    {prim::kPrimZerosLikeTensor, {InferImplZerosLikeTensor, true}},
+    {prim::kPrimFakeBprop, {InferImplFakeBprop, false}},
+    {prim::kPrimLayerNorm, {InferImplLayerNorm, true}},
+    {prim::kPrimLayerNormGrad, {InferImplLayerNormGrad, true}},
+    {prim::kPrimDropoutGenMask, {InferImplDropoutGenMask, true}},
+    // Others
+    {prim::kPrimIdentity, {InferImplIdentity, true}},
+    // Set impl to null as it will use PartialEvaluator;
+    {prim::kPrimPartial, {nullptr, true}},
+    {prim::kPrimJ, {InferImplJ, false}},
+    {prim::kPrimEnvGetItem, {InferImplEnvGetItem, true}},
+    {prim::kPrimEnvSetItem, {InferImplEnvSetItem, true}},
+    {prim::kPrimEnvAdd, {InferImplEnvAdd, true}},
+    {prim::kPrimMakeRefKey, {InferImplMakeRefKey, true}},
+    {prim::kPrimMakeRef, {InferImplMakeRef, true}},
+    {prim::kPrimGetRefKey, {InferImplGetRefKey, true}},
+    {prim::kPrimGetRefValue, {InferImplGetRefValue, true}},
+    {prim::kPrimGetRefOrigin, {InferImplGetRefOrigin, true}},
+    {prim::kPrimStateSetItem, {InferImplStateSetItem, true}},
+    {prim::kPrimDepend, {InferImplDepend, true}},
+    {prim::kPrimBroadcastGradientArgs, {InferImplBroadcastGradientArgs, false}},
+    {prim::kPrimControlDepend, {InferImplControlDepend, true}},
+    // Debug
+    {prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
+    {prim::kPrimImageSummary, {InferImplTensorSummary, true}},
+    {prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
+  };
+  return prim_eval_implement_map;
+}
 
 using mindspore::parse::PyObjectWrapper;
 
@@ -907,10 +910,7 @@ class PartialEvaluator : public Evaluator {
     new_nodes_inputs[1] = NewValueNode(new_signature_value);
     FuncGraphPtr func_graph = cnode->func_graph();
 
-    ScopePtr scope = kDefaultScope;
-    if (out_conf != nullptr) {
-      scope = out_conf->node()->scope();
-    }
+    ScopePtr scope = out_conf->node()->scope();
     ScopeGuard scope_guard(scope);
 
     CNodePtr new_cnode = func_graph->NewCNode(new_nodes_inputs);
@@ -927,39 +927,41 @@ struct PrimitiveImplInferValue {
 };
 
 using PrimitiveToImplMap = std::unordered_map<PrimitivePtr, PrimitiveImplInferValue, PrimitiveHasher, PrimitiveEqual>;
-
-PrimitiveToImplMap UniformPrimitiveToImplMapValue = {
-  {prim::kPrimScalarAdd, {prim::ScalarAdd, true, nullptr, true}},
-  {prim::kPrimScalarSub, {prim::ScalarSub, true, nullptr, true}},
-  {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}},
-  {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}},
-  {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}},
-  {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}},
-  {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}},
-  {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}},
-  {prim::kPrimScalarEq, {prim::ScalarEq, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimScalarLt, {prim::ScalarLt, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimScalarGt, {prim::ScalarGt, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimScalarNe, {prim::ScalarNe, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimScalarLe, {prim::ScalarLe, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimScalarGe, {prim::ScalarGe, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimBoolNot, {prim::BoolNot, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimBoolAnd, {prim::BoolAnd, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimBoolEq, {prim::BoolEq, true, std::make_shared<Bool>(), true}},
-  {prim::kPrimBoolOr, {prim::BoolOr, true, std::make_shared<Bool>(), true}},
-};
+PrimitiveToImplMap &GetUniformPrimitiveToImplMap() {
+  static PrimitiveToImplMap uniform_prim_implement_map = {
+    {prim::kPrimScalarAdd, {prim::ScalarAdd, true, nullptr, true}},
+    {prim::kPrimScalarSub, {prim::ScalarSub, true, nullptr, true}},
+    {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}},
+    {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}},
+    {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}},
+    {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}},
+    {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}},
+    {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}},
+    {prim::kPrimScalarEq, {prim::ScalarEq, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimScalarLt, {prim::ScalarLt, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimScalarGt, {prim::ScalarGt, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimScalarNe, {prim::ScalarNe, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimScalarLe, {prim::ScalarLe, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimScalarGe, {prim::ScalarGe, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimBoolNot, {prim::BoolNot, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimBoolAnd, {prim::BoolAnd, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimBoolEq, {prim::BoolEq, true, std::make_shared<Bool>(), true}},
+    {prim::kPrimBoolOr, {prim::BoolOr, true, std::make_shared<Bool>(), true}},
+  };
+  return uniform_prim_implement_map;
+}
 
 PrimEvaluatorMap PrimEvaluatorConstructors = PrimEvaluatorMap();
 std::mutex PrimEvaluatorConstructorMutex;
 
-void InitPrimEvaluatorConstructors(const PrimitiveEvalImplMap &prim_eval_impl_map) {
+void InitPrimEvaluatorConstructors() {
   PrimEvaluatorMap &constructor = PrimEvaluatorConstructors;
 
-  for (const auto &iter : prim_eval_impl_map) {
+  for (const auto &iter : GetPrimitiveToEvalImplMap()) {
     constructor[iter.first] = InitStandardPrimEvaluator(iter.first, iter.second.impl_);
   }
 
-  for (const auto &iter : UniformPrimitiveToImplMapValue) {
+  for (const auto &iter : GetUniformPrimitiveToImplMap()) {
     constructor[iter.first] =
       InitUniformPrimEvaluator(iter.first, iter.second.impl_, iter.second.eval_value_, iter.second.specify_out_type_);
   }
@@ -974,20 +976,20 @@ void InitPrimEvaluatorConstructors(const PrimitiveEvalImplMap &prim_eval_impl_ma
 
 void ClearPrimEvaluatorMap() {
   PrimEvaluatorConstructors.clear();
-  PrimitiveToInferImplMap.clear();
-  UniformPrimitiveToImplMapValue.clear();
+  GetPrimitiveToEvalImplMap().clear();
+  GetUniformPrimitiveToImplMap().clear();
 }
 
 bool IsInWhiteList(const PrimitivePtr primitive) {
   MS_EXCEPTION_IF_NULL(primitive);
 
-  auto iter = PrimitiveToInferImplMap.find(primitive);
-  if (iter != PrimitiveToInferImplMap.end()) {
+  auto iter = GetPrimitiveToEvalImplMap().find(primitive);
+  if (iter != GetPrimitiveToEvalImplMap().end()) {
     return iter->second.in_white_list_;
   }
 
-  auto uni_iter = UniformPrimitiveToImplMapValue.find(primitive);
-  if (uni_iter != UniformPrimitiveToImplMapValue.end()) {
+  auto uni_iter = GetUniformPrimitiveToImplMap().find(primitive);
+  if (uni_iter != GetUniformPrimitiveToImplMap().end()) {
     return uni_iter->second.in_white_list_;
   }
 
@@ -996,8 +998,8 @@ bool IsInWhiteList(const PrimitivePtr primitive) {
 
 StandardPrimitiveEvalImpl GetPrimitiveInferImpl(const PrimitivePtr &primitive) {
   MS_EXCEPTION_IF_NULL(primitive);
-  auto iter = PrimitiveToInferImplMap.find(primitive);
-  if (iter == PrimitiveToInferImplMap.end()) {
+  auto iter = GetPrimitiveToEvalImplMap().find(primitive);
+  if (iter == GetPrimitiveToEvalImplMap().end()) {
     return nullptr;
   }
   return iter->second.impl_;
@@ -1010,7 +1012,7 @@ PrimEvaluatorMap &GetPrimEvaluatorConstructors() {
   }
   std::lock_guard<std::mutex> initLock(PrimEvaluatorConstructorMutex);
   if (constructor.empty()) {
-    InitPrimEvaluatorConstructors(PrimitiveToInferImplMap);
+    InitPrimEvaluatorConstructors();
   }
 
   return constructor;
diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
index 0bfba265db..1ac43abdd5 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
@@ -292,38 +292,35 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
   if (prim->HasPyEvaluator()) {
     auto prim_py = dyn_cast<PrimitivePy>(prim);
     if (prim_py != nullptr) {
-      evaluator = std::make_shared<PythonPrimEvaluator>(prim_py);
-    } else {
-      MS_LOG(EXCEPTION) << "The primitive with python evaluator should be a python primitive.";
+      return std::make_shared<PythonPrimEvaluator>(prim_py);
+    }
+    MS_LOG(EXCEPTION) << "The primitive with python evaluator should be a python primitive.";
+  }
+
+  if (prim->isa<PrimitivePy>() || prim->HasAttr()) {
+    if (engine == nullptr) {
+      (void)GetPrimEvaluatorConstructors();
     }
-  } else if (prim->isa<PrimitivePy>() || prim->HasAttr()) {
     // If a primitive may have attr, try to create a new evaluator.
     StandardPrimitiveEvalImpl eval_impl = GetPrimitiveInferImpl(prim);
     if (eval_impl != nullptr) {
-      std::shared_ptr<StandardPrimEvaluator> standard_evaluator =
-        std::make_shared<StandardPrimEvaluator>(prim, eval_impl);
-      evaluator = standard_evaluator;
+      return std::make_shared<StandardPrimEvaluator>(prim, eval_impl);
     }
   }
-  if (evaluator == nullptr) {
-    if (engine == nullptr) {
-      // If engine is nullptr, get constructor from default.
-      const PrimEvaluatorMap &prim_evaluator_map = GetPrimEvaluatorConstructors();
-      auto iter = prim_evaluator_map.find(prim);
-      if (iter == prim_evaluator_map.end()) {
-        evaluator = nullptr;
-      } else {
-        evaluator = iter->second;
-      }
-    } else {
-      // If engine is given, get constructor from engine resource.
-      const PrimEvaluatorMap &prim_evaluator_map = engine->PrimConstructors();
-      auto iter = prim_evaluator_map.find(prim);
-      if (iter == prim_evaluator_map.end()) {
-        evaluator = nullptr;
-      } else {
-        evaluator = iter->second;
-      }
+
+  if (engine == nullptr) {
+    // If engine is nullptr, get constructor from default.
+    const PrimEvaluatorMap &prim_evaluator_map = GetPrimEvaluatorConstructors();
+    auto iter = prim_evaluator_map.find(prim);
+    if (iter != prim_evaluator_map.end()) {
+      evaluator = iter->second;
+    }
+  } else {
+    // If engine is given, get constructor from engine resource.
+    const PrimEvaluatorMap &prim_evaluator_map = engine->PrimConstructors();
+    auto iter = prim_evaluator_map.find(prim);
+    if (iter != prim_evaluator_map.end()) {
+      evaluator = iter->second;
     }
   }
   if (evaluator == nullptr) {

From 816b60491d6e03062cf8e41986e29ee15eb2b6e3 Mon Sep 17 00:00:00 2001
From: fary86 <fary.fanrui@huawei.com>
Date: Sat, 28 Mar 2020 17:53:22 +0800
Subject: [PATCH 037/367] Dump graph with type info when static analysis failed

---
 mindspore/ccsrc/debug/anf_ir_utils.cc   |  25 +++---
 mindspore/ccsrc/debug/anf_ir_utils.h    |  14 +--
 mindspore/ccsrc/debug/trace.cc          | 108 +++++++++++++++++++++---
 mindspore/ccsrc/ir/primitive.cc         |  21 +++++
 mindspore/ccsrc/ir/primitive.h          |   1 +
 tests/ut/cpp/operator/composite_test.cc |   2 +
 6 files changed, 139 insertions(+), 32 deletions(-)

diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc
index 0891e211a0..98cd2f4b2f 100644
--- a/mindspore/ccsrc/debug/anf_ir_utils.cc
+++ b/mindspore/ccsrc/debug/anf_ir_utils.cc
@@ -34,6 +34,7 @@
 #include "utils/utils.h"
 #include "debug/trace.h"
 #include "utils/context/ms_context.h"
+#include "operator/ops.h"
 
 namespace mindspore {
 // max number of elements in sequence
@@ -69,7 +70,7 @@ py::object load_obj(const std::string& path) {
 
 // ============================================= MindSpore IR Exporter =============================================
 
-std::string GetNodeType(const AnfNodePtr& nd) {
+std::string AnfExporter::GetNodeType(const AnfNodePtr& nd) {
   abstract::ShapePtr shape = nd->Shape() == nullptr ? nullptr : dyn_cast<abstract::Shape>(nd->Shape());
   TypePtr type = dyn_cast<Type>(nd->Type());
   std::ostringstream oss;
@@ -102,7 +103,7 @@ int AnfExporter::GetParamIndex(const FuncGraphPtr& func_graph, const AnfNodePtr&
   FuncGraphPtr fg = func_graph;
   while (fg != nullptr) {
     if (exported.find(fg) == exported.end()) {
-      if (!export_used_) {
+      if (!check_integrity_) {
         break;
       }
       MS_LOG(EXCEPTION) << "Can not find func graph '" << fg->DumpText() << "." << fg->debug_info()->get_id() << "'";
@@ -255,15 +256,15 @@ std::string AnfExporter::GetPrimitiveText(const PrimitivePtr& prim) {
   }
 
   // output primitive attributes
-  auto attrs = prim->attrs();
-  if (attrs.size() > 0) {
-    oss << "[";
-    int i = 0;
-    for (auto& attr : attrs) {
-      oss << (i > 0 ? ", " : "") << attr.first << "=" << attr.second->DumpText();
-      i++;
+  oss << prim->GetAttrsText();
+
+  if (prim->isa<prim::DoSignaturePrimitive>()) {
+    auto do_signature = dyn_cast<prim::DoSignaturePrimitive>(prim);
+    auto& func = do_signature->function();
+    if (func->isa<Primitive>()) {
+      auto sig_prim = dyn_cast<Primitive>(func);
+      oss << sig_prim->GetAttrsText();
     }
-    oss << "]";
   }
 
   return oss.str();
@@ -351,7 +352,7 @@ std::string AnfExporter::GetDictText(const FuncGraphPtr& func_graph, const Value
 std::string AnfExporter::GetOtherValueText(const FuncGraphPtr&, const ValuePtr& value) {
   std::ostringstream oss;
 
-  if (export_used_) {
+  if (check_integrity_) {
     MS_LOG(EXCEPTION) << "Need to process type: " << value->type_name() << ", dump text: " << value->DumpText();
   }
   oss << value->type_name() << "[" << value->DumpText() << "]";
@@ -420,7 +421,7 @@ std::string AnfExporter::GetAnfNodeText(const FuncGraphPtr& func_graph, const An
     }
     oss << "%" << iter->second;
   } else if (node->isa<Parameter>()) {
-    oss << "%para" << GetParamIndex(func_graph, node, export_used_);
+    oss << "%para" << GetParamIndex(func_graph, node, check_integrity_);
   } else if (IsValueNode<FuncGraph>(node)) {
     FuncGraphPtr fg = GetValueNode<FuncGraphPtr>(node);
     oss << fg->type_name() << "::fg_" << fg->debug_info()->get_id();
diff --git a/mindspore/ccsrc/debug/anf_ir_utils.h b/mindspore/ccsrc/debug/anf_ir_utils.h
index 5b9ac9d2f0..5342c1ab96 100644
--- a/mindspore/ccsrc/debug/anf_ir_utils.h
+++ b/mindspore/ccsrc/debug/anf_ir_utils.h
@@ -64,17 +64,18 @@ struct ParamPtrHasher {
 
 class AnfExporter {
  public:
-  explicit AnfExporter(const std::string& id, bool export_used = true)
-      : param_index(-1), id_(id), export_used_(export_used) {
+  explicit AnfExporter(const std::string& id, bool export_used = true, bool check_integrity = false)
+      : param_index(-1), id_(id), export_used_(export_used), check_integrity_(check_integrity) {
     func_graph_set.clear();
     exported.clear();
   }
-  ~AnfExporter() {}
+  virtual ~AnfExporter() {}
 
   void ExportFuncGraph(const std::string& filename, const FuncGraphPtr& func_graph);
   void ExportFuncGraph(const std::string& filename, const std::vector<TaggedGraph>& graphs);
 
- private:
+ protected:
+  virtual std::string GetNodeType(const AnfNodePtr& nd);
   int GetParamIndex(const FuncGraphPtr& func_graph, const AnfNodePtr& param, bool throw_excp = true);
   int GetParamIndexFromExported(const AnfNodePtr& param);
   std::string DumpObject(const py::object& obj, const std::string& category) const;
@@ -101,8 +102,10 @@ class AnfExporter {
   OrderedSet<FuncGraphPtr> func_graph_set{};
   OrderedMap<FuncGraphPtr, OrderedMap<AnfNodePtr, int, ParamPtrHasher, ParamPtrEqual>> exported;
   std::string id_;
-  bool export_used_ = true;  // whether export function graphs used in current exporting function graph
+  bool export_used_ = true;       // whether export function graphs used in current exporting function graph
+  bool check_integrity_ = false;  // whether check integrity or not, when dumping ir for loading, must set it to true
   TaggedNodeMap tagged_cnodes_;
+  abstract::AnfNodeConfigPtr node_cfg_ = nullptr;
 };
 
 void ExportIR(const std::string& filename, const std::string& id, const FuncGraphPtr& func_graph);
@@ -115,7 +118,6 @@ std::string GetFuncGraphProtoString(const FuncGraphPtr& func_graph);
 void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix);
 
 std::string GetOnnxProtoString(const FuncGraphPtr& func_graph);
-std::string GetNodeType(const AnfNodePtr& nd);
 }  // namespace mindspore
 
 #endif  // MINDSPORE_CCSRC_DEBUG_ANF_IR_UTILS_H_
diff --git a/mindspore/ccsrc/debug/trace.cc b/mindspore/ccsrc/debug/trace.cc
index 4b0f4c4fb3..7ce13052c5 100644
--- a/mindspore/ccsrc/debug/trace.cc
+++ b/mindspore/ccsrc/debug/trace.cc
@@ -17,6 +17,7 @@
 #include "debug/trace.h"
 
 #include <iostream>
+#include <fstream>
 #include <map>
 #include <unordered_map>
 #include <vector>
@@ -194,37 +195,116 @@ void TraceGraphInfer() {
   MS_LOG(INFO) << "\n*************************************************************************************";
 }
 
-void OutputAnalysisGraphInfo() {
-  MS_LOG(INFO) << "Output analysis graph begin";
-  std::unordered_map<FuncGraphPtr, size_t> index_map;
-  std::vector<TaggedGraph> tagged_graphs;
+class AnalyzedFuncGraphExporter : public AnfExporter {
+ public:
+  AnalyzedFuncGraphExporter() : AnfExporter("", true, false) {}
+  ~AnalyzedFuncGraphExporter() override = default;
 
+  void ExportFuncGraph(const std::string& filename, const std::vector<abstract::AnfNodeConfigPtr>& node_cfgs);
+
+ private:
+  std::string GetNodeType(const AnfNodePtr& nd) override;
+};
+
+std::unordered_map<FuncGraphPtr, TaggedNodeMap> CalcTaggedFuncGraphs() {
+  std::unordered_map<FuncGraphPtr, TaggedNodeMap> tagged_func_graphs;
   auto& list = GetCNodeDebugStack();
   for (size_t i = 0; i < list.size(); ++i) {
-    auto& node_cfg = list[i];
+    auto node_cfg = list[i];
     auto fg = node_cfg->context()->func_graph();
     auto node = node_cfg->node();
-    auto idx = tagged_graphs.size();
-    std::pair<FuncGraphPtr, size_t> item(fg, idx);
-    if (index_map.insert(item).second) {
-      tagged_graphs.emplace_back(TaggedGraph(fg, TaggedNodeMap()));
+    tagged_func_graphs[fg][node] = i;
+  }
+  return tagged_func_graphs;
+}
+
+void OutputAnalyzedGraphWithType() {
+  AnalyzedFuncGraphExporter exporter;
+  exporter.ExportFuncGraph("analyze_fail.dat", GetCNodeDebugStack());
+}
+
+std::string AnalyzedFuncGraphExporter::GetNodeType(const AnfNodePtr& node) {
+  if (node_cfg_ == nullptr) {
+    return AnfExporter::GetNodeType(node);
+  }
+  auto ctx = node_cfg_->context();
+  auto engine = node_cfg_->engine();
+  auto cfg = engine->MakeConfig(node, ctx);
+  auto abs = engine->cache().GetValue(cfg);
+
+  if (abs == nullptr) {
+    return "Undefined";
+  }
+  auto dtype = abs->BuildType();
+  auto shape = abs->BuildShape();
+  std::ostringstream oss;
+  if (dtype != nullptr && abs->isa<abstract::AbstractTensor>() && shape != nullptr) {
+    oss << dtype->DumpText() << shape->DumpText();
+  } else if (dtype != nullptr) {
+    oss << dtype->DumpText();
+  } else {
+    oss << "Undefined";
+  }
+  return oss.str();
+}
+
+void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename,
+                                                const std::vector<abstract::AnfNodeConfigPtr>& node_cfgs) {
+  if (node_cfgs.empty()) {
+    MS_LOG(DEBUG) << "Node configs is empty";
+    return;
+  }
+
+  std::ofstream ofs(filename);
+  if (!ofs.is_open()) {
+    MS_LOG(ERROR) << "Open file '" << filename << "' failed!";
+    return;
+  }
+
+  param_index = 1;
+  auto tagged_func_graphs = CalcTaggedFuncGraphs();
+
+  // first output grapn on the analysis stack
+  for (const auto& node_cfg : node_cfgs) {
+    auto fg = node_cfg->context()->func_graph();
+    // the graph is already output, skip it
+    if (exported.find(fg) != exported.end()) {
+      continue;
     }
-    tagged_graphs[index_map[fg]].second[node] = i;
+    // set node_cfg info for getting type
+    node_cfg_ = node_cfg;
+    tagged_cnodes_ = tagged_func_graphs[fg];
+    ExportOneFuncGraph(ofs, fg);
+    ofs << "\n\n";
+  }
+
+  node_cfg_ = nullptr;
+  tagged_cnodes_.clear();
+
+  // print seperator between function graphs on analyzed graph call stack and others
+  ofs << "#===============================================================================\n\n\n";
+
+  // second output other graphs
+  while (!func_graph_set.empty()) {
+    FuncGraphPtr fg = *func_graph_set.begin();
+    ExportOneFuncGraph(ofs, fg);
+    ofs << "\n\n";
+    (void)func_graph_set.erase(fg);
   }
+  ofs << "# num of total funcgraphs: " << exported.size();
 
-  ExportIR("analyze_fail.dat", tagged_graphs);
-  MS_LOG(INFO) << "Output analysis graph *end*";
+  ofs.close();
 }
 
 void GetInferStackInfo(std::ostringstream& oss) {
   MS_LOG(INFO) << "Get graph analysis information begin";
-  auto& stack = GetCNodeDebugStack();
+  auto stack = GetCNodeDebugStack();
   if (stack.empty()) {
     MS_LOG(INFO) << "Length of analysis information stack is empty.";
     return;
   }
 
-  OutputAnalysisGraphInfo();
+  OutputAnalyzedGraphWithType();
   oss << "\nThe function call stack:\n";
 
   int index = 0;
diff --git a/mindspore/ccsrc/ir/primitive.cc b/mindspore/ccsrc/ir/primitive.cc
index d62553ef60..a576c1e76b 100644
--- a/mindspore/ccsrc/ir/primitive.cc
+++ b/mindspore/ccsrc/ir/primitive.cc
@@ -106,6 +106,27 @@ void Primitive::set_signatures(
   }
 }
 
+std::string Primitive::GetAttrsText() const {
+  if (attrs_.empty()) {
+    return "";
+  }
+
+  std::ostringstream oss;
+  oss << "[";
+  bool is_first = true;
+  for (auto& attr : attrs_) {
+    if (is_first) {
+      is_first = false;
+    } else {
+      oss << ", ";
+    }
+    oss << attr.first << "=" << attr.second->DumpText();
+  }
+  oss << "]";
+
+  return oss.str();
+}
+
 py::function PrimitivePy::GetBpropFunction() {
   static const char* const get_bprop_func_name = "get_bprop";
   if (py::hasattr(python_obj_, get_bprop_func_name)) {
diff --git a/mindspore/ccsrc/ir/primitive.h b/mindspore/ccsrc/ir/primitive.h
index 8a60412e44..7dd37eb15f 100644
--- a/mindspore/ccsrc/ir/primitive.h
+++ b/mindspore/ccsrc/ir/primitive.h
@@ -102,6 +102,7 @@ class Primitive : public Named {
 
   PrimType prim_type() const { return prim_type_; }
   std::string instance_name() const { return instance_name_; }
+  std::string GetAttrsText() const;
   bool operator==(const Value& other) const override;
   bool operator==(const Primitive& other) const;
   ~Primitive() override = default;
diff --git a/tests/ut/cpp/operator/composite_test.cc b/tests/ut/cpp/operator/composite_test.cc
index 984f24b326..d9dd9e5e99 100644
--- a/tests/ut/cpp/operator/composite_test.cc
+++ b/tests/ut/cpp/operator/composite_test.cc
@@ -22,6 +22,7 @@
 #include "operator/ops.h"
 #include "pipeline/static_analysis/prim.h"
 #include "pipeline/static_analysis/abstract_function.h"
+#include "debug/trace.h"
 
 namespace mindspore {
 using Shape = abstract::Shape;
@@ -124,6 +125,7 @@ TEST_F(TestComposite, test_TupleSlice_arg_one_number) {
   AbstractBasePtrList args_spec_list = {tuple_tensor, start_index};
 
   try {
+    trace::ClearTraceStack();
     engine_->Run(tupleSliceGraphPtr, args_spec_list);
     FAIL() << "Excepted exception :Args type is wrong";
   } catch (std::runtime_error const &err) {

From 0727c2e76aa7a7663f3e96efc7913bf3b4591718 Mon Sep 17 00:00:00 2001
From: chenhaozhe <chenhaozhe1@huawei.com>
Date: Tue, 31 Mar 2020 19:37:57 +0800
Subject: [PATCH 038/367] modify log level in DfGraphManager

---
 mindspore/ccsrc/pipeline/pipeline.cc          | 7 ++++---
 mindspore/ccsrc/transform/convert.cc          | 4 ++--
 mindspore/ccsrc/transform/df_graph_manager.cc | 2 +-
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 7b3cf06b5e..3c3478e89d 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -1110,10 +1110,11 @@ bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phas
     (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
   }
   (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
-  (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
   (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
-
-  DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
+  Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
+  if (ret == Status::SUCCESS) {
+    DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
+  }
 
   return true;
 }
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c8c6abea4c..6ef1025513 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -1100,12 +1100,12 @@ void DfGraphConvertor::UpdateDataOpDesc(const AnfNodePtr &it, const OperatorPtr
   auto normal_shape_ptr = dyn_cast<abstract::Shape>(node->Shape());
   vector<int> shape;
   if (normal_shape_ptr == nullptr) {
-    MS_LOG(ERROR) << "Update data op descriptor failed! Invalid shape.";
+    MS_LOG(INFO) << "Invalid shape to update data op descriptor.";
     return;
   }
   shape = normal_shape_ptr->shape();
   if (node->Type() == nullptr) {
-    MS_LOG(ERROR) << "Update data op descriptor failed! Invalid type.";
+    MS_LOG(INFO) << "Invalid type to update data op descriptor.";
     return;
   }
   TypeId me_type = node->Type()->type_id();
diff --git a/mindspore/ccsrc/transform/df_graph_manager.cc b/mindspore/ccsrc/transform/df_graph_manager.cc
index 3339f43145..bfe4d9f5d2 100644
--- a/mindspore/ccsrc/transform/df_graph_manager.cc
+++ b/mindspore/ccsrc/transform/df_graph_manager.cc
@@ -112,7 +112,7 @@ DfGraphWrapperPtr DfGraphManager::GetGraphByName(const std::string& name) {
 
   auto it = graphs_.find(name);
   if (it == graphs_.end()) {
-    MS_LOG(ERROR) << "Can't found graph name: " << name;
+    MS_LOG(INFO) << "Can't found graph name: " << name;
     return nullptr;
   }
   MS_LOG(INFO) << "Return graph: " << name;

From 2dc9f632c17992535fa7e775d9e60f2051dea6f8 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Tue, 31 Mar 2020 21:25:48 +0800
Subject: [PATCH 039/367] add cpu st lenet

---
 ...{test_cpu_network.py => test_cpu_lenet.py} | 67 ++++++++++---------
 1 file changed, 34 insertions(+), 33 deletions(-)
 rename tests/st/networks/{test_cpu_network.py => test_cpu_lenet.py} (59%)

diff --git a/tests/st/networks/test_cpu_network.py b/tests/st/networks/test_cpu_lenet.py
similarity index 59%
rename from tests/st/networks/test_cpu_network.py
rename to tests/st/networks/test_cpu_lenet.py
index 6745a7626b..a3105721d3 100644
--- a/tests/st/networks/test_cpu_network.py
+++ b/tests/st/networks/test_cpu_lenet.py
@@ -12,25 +12,44 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""
-Function: 
-    test network
-Usage: 
-    python test_network_main.py --net lenet --target Davinci
-"""
-import os
-import time
 import pytest
-import numpy as np
-import argparse
-import mindspore.nn as nn
-from mindspore.common.tensor import Tensor
 from mindspore.nn import TrainOneStepCell, WithLossCell
 import mindspore.context as context
 from mindspore.nn.optim import Momentum
-from models.lenet import LeNet
-from models.resnetv1_5 import resnet50
-from models.alexnet import AlexNet
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore import Tensor
+
+class LeNet(nn.Cell):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.relu = P.ReLU()
+        self.batch_size = 32
+
+        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.reshape = P.Reshape()
+        self.fc1 = nn.Dense(400, 120)
+        self.fc2 = nn.Dense(120, 84)
+        self.fc3 = nn.Dense(84, 10)
+
+    def construct(self, input_x):
+        output = self.conv1(input_x)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.conv2(output)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.reshape(output, (self.batch_size, -1))
+        output = self.fc1(output)
+        output = self.relu(output)
+        output = self.fc2(output)
+        output = self.relu(output)
+        output = self.fc3(output)
+        return output
+
 context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
 
 def train(net, data, label):
@@ -48,15 +67,6 @@ def train(net, data, label):
     print("+++++++++++++++++++++++++++")
     assert res
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_resnet50():
-    data = Tensor(np.ones([32, 3 ,224, 224]).astype(np.float32) * 0.01)
-    label = Tensor(np.ones([32]).astype(np.int32))
-    net = resnet50(32, 10)
-    train(net, data, label)
-
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
@@ -65,12 +75,3 @@ def test_lenet():
     label = Tensor(np.ones([32]).astype(np.int32))
     net = LeNet()
     train(net, data, label)
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_alexnet():
-    data = Tensor(np.ones([32, 3 ,227, 227]).astype(np.float32) * 0.01)
-    label = Tensor(np.ones([32]).astype(np.int32))
-    net = AlexNet()
-    train(net, data, label)

From 0ae77bb0db6708cd9b5ea19bca8edac6348ddc0c Mon Sep 17 00:00:00 2001
From: Peilin Wang <peilin.wang@huawei.com>
Date: Mon, 30 Mar 2020 18:25:14 -0400
Subject: [PATCH 040/367] TFReaderOp fix, threads will exit after reading
 necessary amount of rows

changes from yanpanhui 524009: added set_dataset_size and changed get_dataest_size according to ME requirements

CI fixes
---
 .../engine/datasetops/source/tf_reader_op.cc  | 41 ++++++++++++++++---
 .../engine/datasetops/source/tf_reader_op.h   |  1 +
 mindspore/dataset/engine/datasets.py          | 20 ++++++---
 3 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
index 16a7700012..c872c02015 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
@@ -105,6 +105,7 @@ TFReaderOp::TFReaderOp(int32_t num_workers, int32_t worker_connector_size, int64
       data_schema_(std::move(data_schema)),
       filename_index_(make_unique<StringIndex>()),
       load_io_block_queue_(true),
+      load_jagged_connector_(true),
       num_rows_(0),
       num_rows_per_shard_(0),
       equal_rows_per_shard_(equal_rows_per_shard) {
@@ -203,6 +204,25 @@ Status TFReaderOp::operator()() {
         buffer_id++;
         RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(fetched_buffer)));
       } else {
+        // user specified number of rows they want, and we read enough rows
+        //
+        // IOBlockQueue thread needs to:
+        // -stop pushing stuff to IOBlockQueue
+        // -call PostEndOfEpoch (will send EOE)
+        // -wait for reset
+        //
+        // Worker threads need to:
+        // -stop reading the file they are currently reading and throw it away
+        // -keep pulling, but dont read other files (eventually skips all IOBlocks and will get EOE)
+        //
+        // Master thread needs to:
+        // -tell IOBlockQueue thread to stop pushing
+        // -tell worker threads to stop reading the file tey are currently reading
+        // -keep pulling until EOE
+
+        // don't think we need a lock for now
+        load_jagged_connector_ = false;
+
         std::unique_lock<std::mutex> lock(load_io_block_queue_mutex_);
         load_io_block_queue_ = false;
       }
@@ -245,12 +265,14 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) {
 
   while (!io_block->eof()) {
     if (!io_block->eoe()) {
-      std::string filename;
-      RETURN_IF_NOT_OK(io_block->GetFilename(&filename, *filename_index_));
-      int64_t start_offset = io_block->GetStartOffset();
-      int64_t end_offset = io_block->GetEndOffset();
-      RETURN_IF_NOT_OK(LoadFile(filename, start_offset, end_offset, worker_id));
-      MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << common::SafeCStr(filename) << ".";
+      if (load_jagged_connector_) {
+        std::string filename;
+        RETURN_IF_NOT_OK(io_block->GetFilename(&filename, *filename_index_));
+        int64_t start_offset = io_block->GetStartOffset();
+        int64_t end_offset = io_block->GetEndOffset();
+        RETURN_IF_NOT_OK(LoadFile(filename, start_offset, end_offset, worker_id));
+        MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << filename << ".";
+      }
     } else {
       std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(1, DataBuffer::kDeBFlagEOE);
       RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(eoe_buffer)));
@@ -478,6 +500,10 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off
   std::unique_ptr<TensorQTable> new_tensor_table = make_unique<TensorQTable>();
 
   while (reader.peek() != EOF) {
+    if (!load_jagged_connector_) {
+      break;
+    }
+
     // read length
     int64_t record_length = 0;
     (void)reader.read(reinterpret_cast<char *>(&record_length), static_cast<std::streamsize>(sizeof(int64_t)));
@@ -599,6 +625,9 @@ Status TFReaderOp::LoadFeature(const std::unique_ptr<TensorQTable> *tensor_table
 // Overrides base class reset method. Cleans up any state info from it's previous execution and
 // reinitializes itself so that it can be executed again, as if it was just created.
 Status TFReaderOp::Reset() {
+  // start workers first, otherwise IOBlokcs will fall through if workers see it before this is set to true
+  load_jagged_connector_ = true;
+
   {
     std::unique_lock<std::mutex> lock(load_io_block_queue_mutex_);
     load_io_block_queue_ = true;
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h
index 69de068f9b..560cff114f 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h
@@ -369,6 +369,7 @@ class TFReaderOp : public ParallelOp {
   std::unique_ptr<DataSchema> data_schema_;
   std::unique_ptr<StringIndex> filename_index_;
   bool load_io_block_queue_;
+  bool load_jagged_connector_;
 
   std::unique_ptr<JaggedConnector> jagged_buffer_connector_;
   QueueList<std::unique_ptr<FilenameBlock>> io_block_queues_;
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 8aaa6212b6..ad3e7d8255 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1906,11 +1906,21 @@ class TFRecordDataset(SourceDataset):
         Return:
             Number, number of batches.
         """
-        num_rows = TFReaderOp.get_num_rows(self.dataset_files, 8, estimate)
-        num_rows = get_num_rows(num_rows, self.num_shards)
-        if self.num_samples is None:
-            return num_rows
-        return min(self.num_samples, num_rows)
+        if self._dataset_size is None:
+            num_rows = TFReaderOp.get_num_rows(self.dataset_files, 8, estimate)
+            num_rows = get_num_rows(num_rows, self.num_shards)
+            if self.num_samples is None:
+                return num_rows
+            return min(self.num_samples, num_rows)
+        return self._dataset_size
+
+    # manually set dataset_size as a tempoary solution.
+    def set_dataset_size(self, value):
+        logger.warning("WARN_DEPRECATED: This method is deprecated. Please use get_dataset_size directly.")
+        if value >= 0:
+            self._dataset_size = value
+        else:
+            raise ValueError('set dataset_size with negative value {}'.format(value))
 
 
 class ManifestDataset(SourceDataset):

From 2e881276ab2c5266140598a9f2024845814e8b5c Mon Sep 17 00:00:00 2001
From: Cathy Wong <cathy.wong@huawei.com>
Date: Tue, 31 Mar 2020 16:46:44 -0400
Subject: [PATCH 041/367] Enable skipped dataset zip python unit tests

Signed-off-by: Cathy Wong <cathy.wong@huawei.com>
---
 .../ut/data/dataset/golden/zip_02_result.npz  | Bin 428510 -> 1065228 bytes
 .../ut/data/dataset/golden/zip_03_result.npz  | Bin 428553 -> 1065271 bytes
 .../ut/data/dataset/golden/zip_04_result.npz  | Bin 430298 -> 1067016 bytes
 tests/ut/python/dataset/test_zip.py           |   6 +++---
 4 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/ut/data/dataset/golden/zip_02_result.npz b/tests/ut/data/dataset/golden/zip_02_result.npz
index 1e9c7a985b10c5ecfae012e1efa04ca9e78a8f81..00a59e585dcf86e6a3a1304555a8fbcf7663196b 100644
GIT binary patch
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zm!YQyWYgL)H>GMMeiANdjJ69Tw8k+LUB83R>Ou~!D*Fe_^df^~(|B6aDhg>H<-H7B
z6ru7CY-k5%Mq$hH=+}XYytP5zRUo(kf2s&&K{)zrFm@x6_7ZmDUH0NP6l+c~i)SLW
zX3d(ZTk$OBfwImi+=kY)X1WatVzG878u7uJh3Kdo37^92DJFdib$tVC5}H~QsC9m|
zv{tdN@+hT>t2*(%8<uD{_a@42r*_4BtAMMYll~m`-AWEGD6c{bk}HuilBv;8TE*O4
z1;#p?;Rm$&CPt+pO4ONFlju`2;qqnB*4%`jzM+*okC1*oqsuk)f9gARKI|oHC5rs4
zC*WG~vXXJI>bf57HIaL^P3>Teq<fY#_PY~EG7tZB6n<SMuV#whLXvLq*O}S!#dGLk
z#gu3GR&jj~fK5EMub2=!xT@0&qxtND<(bG`Ksy3lZzTU+r1#mem}Yp?-U_U&$?8S<
zJE-9gj50L4f12;KkG+gh&0k>8LAWdVC&3AWE}H1k^~C*NQf!2`9lFMqApA%|MvdHV
z1hHq)Ovo~S0~%7DW%RmxkWsm(Q1&-%&L?edeg7=z{|T1sM5T)s`4A;&SM6oEsOW&2
zS33x*ucC?Na9c<`hc&f>)^6BcNnYJcZU==V#$X}zAssG*C)(v1L|>O|E<dc+n)-<#
ze%@lzg;z;rtS3!hLQaj}TabBxy9BKezCz7|GC1I{#?qT%<!YPiMZC=vG%MdUh}9Tu
zPiIm~m)l^*Y>?bSc>%5~9#tpWd&A<Q=<o|bU9+o4EjDAY9?79O4CPnk#NXk~D`AdU
zSiv>T$8R8?o@Q^Qo~^V}<B6Zc_RZ9+zjBfC<8Zhz*5Pne*4C|VEUqkTh}>8dN%j{-
zQv6s{7gkxGUv8qk=6$Ded5oXgE^{S{mvL^hk&rH?IcDg}RJU0XYeGJfa-Brw=7Q54
zaGRjpct(R%IVpWE(~_<-hjI;!HQ6rnX`Ja%h`Vy*Ni^;RQ<!3YnP}=grp{*$IZbAq
zdCO<!#+&QC=BQJJaJ?{v<i}%F!+}`S>ovX6x;&WnQLk}&Rm>Kbd7fnsgWvF;<1xFO
z=AhH$jWx+*RoZ!<*_fnk$!_z{D6`0I&P*@?7iqn=ev0K^*BCQ%l-chzM@E??s(ERO
zNpzdmc$KxmXLe3BelH^O4L}@rno^Hx9!=p~aF5ru$uWF9=P@V7gPgg4eExmmqR1q{
zTn^0ZAliM9=9>Ejgj7H<b0(NppV{Cx#j(;GpBKfMoLDg9<BR%;Qp~A|re>5$_L?TI
zD1l-~`+(~UN=$T_=?Ug+oGBSkK9qG*`FqmL46CWzy@J|!#LqJJ;hIn4Ouq}Ij|}87
zRS%|GT~ZpZj-(Yu(lOJ?`B5cBktz8uUZ>_eOTvYTB&iLrX)JCC*EWW0?+q8*3=NTt
zqKKay%<qvlXkTyQHCz}^j+&B&`@?0$)iuQrhAYb}8XF=v7e&5G@@f9ux#vj0&j|Q1
zD5%2$slN6l!G|vA{@;H<@abO?{6B;Kf6+?F>80Mmenf1Fgo+}!K%g1wO0Vf(beefJ
z2-lpG&zD8cyc&ez=EMj96(hM0BWVbi*WjG(aO>+ukz28nEdLih|8S!+V#<Rcv#thV
z1SoX4hiiav+YkuD)BJze|L~OhUo9mZ4mU<-mrh%hALUGRX*{E!RL9=qd)_H{BHE$<
kx@Q^sX^zsTlKT4M>C<ZK)^b>Bv@S&Pdn@moN$zm`3vNg@od5s;

diff --git a/tests/ut/python/dataset/test_zip.py b/tests/ut/python/dataset/test_zip.py
index 353b5a2471..16b61079db 100644
--- a/tests/ut/python/dataset/test_zip.py
+++ b/tests/ut/python/dataset/test_zip.py
@@ -48,7 +48,7 @@ def test_zip_01():
     save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def skip_test_zip_02():
+def test_zip_02():
     """
     Test zip: zip 2 datasets, #rows-data1 < #rows-data2, #cols-data1 == #cols-data2
     """
@@ -63,7 +63,7 @@ def skip_test_zip_02():
     save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def skip_test_zip_03():
+def test_zip_03():
     """
     Test zip: zip 2 datasets, #rows-data1 > #rows-data2, #cols-data1 > #cols-data2
     """
@@ -78,7 +78,7 @@ def skip_test_zip_03():
     save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def skip_test_zip_04():
+def test_zip_04():
     """
     Test zip: zip >2 datasets
     """

From c080ec78746ad9db64a532b19388071ed02896fe Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Tue, 31 Mar 2020 14:55:31 +0800
Subject: [PATCH 042/367] change star elimination: remove some redundant and
 checking works

---
 .../ccsrc/parallel/auto_parallel/graph_costmodel.cc | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index d30522c2fe..292cc4f5f0 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -1210,19 +1210,16 @@ void CostGraph::CreateStarEliminationCostList(std::vector<std::shared_ptr<Edge>>
         MS_EXCEPTION_IF_NULL(succ_node_cost);
         for (auto& succ_edge_cost : succ_edge_clist) {
           MS_EXCEPTION_IF_NULL(succ_edge_cost);
-          if ((succ_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) &&
-              (succ_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) {
-            succ_nodes_stras[k] = succ_node_stra;
-            succ_edges_costs[k] = succ_edge_cost;
-            succ_nodes_costs[k] = succ_node_cost;
-            recursive(k + 1);
-          }
+          succ_nodes_stras[k] = succ_node_stra;
+          succ_edges_costs[k] = succ_edge_cost;
+          succ_nodes_costs[k] = succ_node_cost;
+          recursive(k + 1);
         }
       }
     }
   };
 
-  recursive(0);
+  recursive(1);
 }
 
 std::vector<std::shared_ptr<Edge>> CostGraph::EliminationStar(const OperatorInfoPtr& merged_op) {

From 110640e2ad7dd862a5f2d9ad96f4607a481bc639 Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Tue, 31 Mar 2020 15:40:43 +0800
Subject: [PATCH 043/367] add parallel ops for neg and batchmatmul

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |  2 +
 .../ccsrc/parallel/ops_info/activation_info.h |  7 ++
 .../ccsrc/parallel/ops_info/matmul_info.h     |  8 ++
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |  2 +
 .../ccsrc/parallel/step_auto_parallel.cc      |  2 +
 tests/ut/python/parallel/test_batch_matmul.py | 93 +++++++++++++++++++
 tests/ut/python/parallel/test_neg.py          | 84 +++++++++++++++++
 7 files changed, 198 insertions(+)
 create mode 100644 tests/ut/python/parallel/test_batch_matmul.py
 create mode 100644 tests/ut/python/parallel/test_neg.py

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 59b8722435..e6e1b41d76 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -123,6 +123,8 @@ REGISTER(ReLUInfo);
 REGISTER(GatherV2Info);
 REGISTER(SqrtInfo);
 REGISTER(GetNextInfo);
+REGISTER(NegInfo);
+REGISTER(BatchMatMulInfo);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index d8de19b328..d05f8743b0 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -167,6 +167,13 @@ class SqrtInfo : public ActivationOther {
       : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
   ~SqrtInfo() override = default;
 };
+
+class NegInfo : public ActivationOther {
+ public:
+  NegInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~NegInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
index c9feae55b6..b434e4522d 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
@@ -87,6 +87,14 @@ class MatMulInfo : public MatMul {
       : MatMul(name, inputs_shape, outputs_shape, attrs) {}
   ~MatMulInfo() override = default;
 };
+
+class BatchMatMulInfo : public MatMul {
+ public:
+  BatchMatMulInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                  const PrimitiveAttrs& attrs)
+      : MatMul(name, inputs_shape, outputs_shape, attrs) {}
+  ~BatchMatMulInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index a25200c3c1..2b8fc0ee3f 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -188,6 +188,8 @@ constexpr char SQRT[] = "Sqrt";
 constexpr char ASSIGN[] = "Assign";
 constexpr char GET_NEXT[] = "GetNext";
 constexpr char SQUEEZE[] = "Squeeze";
+constexpr char Neg[] = "Neg";
+constexpr char BATCH_MATMUL[] = "BatchMatMul";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index c3e3f5893e..cf388bea40 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -101,6 +101,8 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SQRT,
                                            GET_NEXT,
                                            CAST,
+                                           Neg,
+                                           BATCH_MATMUL,
                                            SQUEEZE};
 
 std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,
diff --git a/tests/ut/python/parallel/test_batch_matmul.py b/tests/ut/python/parallel/test_batch_matmul.py
new file mode 100644
index 0000000000..88ba818c91
--- /dev/null
+++ b/tests/ut/python/parallel/test_batch_matmul.py
@@ -0,0 +1,93 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, batch_matmul_weight, transpose_b=False, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.batch_matmul = P.BatchMatMul(transpose_b=transpose_b).set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+        self.batch_matmul_weight = Parameter(batch_matmul_weight, "w2")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.batch_matmul(out, self.batch_matmul_weight)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w2 = Tensor(np.ones([128, 32, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 16]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_batch_matmul_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), (16, 1, 1))
+    net = Net(_w1, _w2, False, strategy1, strategy2)
+    compile(net)
+
+
+def test_batch_matmul_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 1), (1, 1, 1))
+    strategy2 = ((1, 1, 1), (1, 1, 16))
+    net = Net(_w1, _w2, False, strategy1, strategy2)
+    compile(net)
+
+
+def test_batch_matmul_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((2, 2, 2), (2, 2, 2))
+    net = Net(_w1, _w2, False, strategy1, strategy2)
+    compile(net)
+
+
+def test_batch_matmul_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1, _w2, False)
+    compile(net)
+
+
+def test_batch_matmul_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), (1, 2, 2))
+    net = Net(_w1, _w2, False, strategy1, strategy2)
+    compile(net)
+
+
+def test_batch_matmul_transpose_b():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), (1, 2, 2))
+    net = Net(_w1, _w2, True, strategy1, strategy2)
+    compile(net)
diff --git a/tests/ut/python/parallel/test_neg.py b/tests/ut/python/parallel/test_neg.py
new file mode 100644
index 0000000000..0e08e8c096
--- /dev/null
+++ b/tests/ut/python/parallel/test_neg.py
@@ -0,0 +1,84 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.neg = P.Neg().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.neg(out)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_neg_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_neg_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_neg_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_neg_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_neg_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+

From 60f7a95b1c21d659fb6bfc13d1055a1097cbac63 Mon Sep 17 00:00:00 2001
From: WeibiaoYu <weibiao.yu@huawei.com>
Date: Tue, 31 Mar 2020 09:25:11 -0400
Subject: [PATCH 044/367] the size of tensor may be bigger than 2GB, should use
 memcpy instead of memcpy_s

---
 mindspore/ccsrc/transform/util.cc | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/transform/util.cc b/mindspore/ccsrc/transform/util.cc
index a106a20ad8..0a18763d12 100644
--- a/mindspore/ccsrc/transform/util.cc
+++ b/mindspore/ccsrc/transform/util.cc
@@ -361,12 +361,11 @@ MeTensorPtr TransformUtil::GenerateMeTensor(const GeTensorPtr& ge_tensor, const
     MS_LOG(ERROR) << "GE tensor data size is zero!";
     return nullptr;
   }
-  errno_t ret = memcpy_s(me_data_ptr, me_data_size, ge_tensor->GetData(), ge_tensor->GetSize());
-  if (ret != EOK) {
-    MS_LOG(INFO) << "GE tensor data size is " << ge_tensor->GetSize() << " bytes";
-    MS_LOG(ERROR) << "Copy GE tensor data to me tensor failed";
-    return nullptr;
-  }
+
+  // Use memcpy here, not memcpy_s, just because the size of ge_tensor may be bigger than 2GB
+  // which is the size limit of memcpy_s
+  memcpy(me_data_ptr, ge_tensor->GetData(), ge_tensor->GetSize());
+
   return make_shared<MeTensor>(me_tensor);
 }
 

From 849b84abb157c8bc66387312cf8823898338e1f0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Wed, 1 Apr 2020 11:14:21 +0800
Subject: [PATCH 045/367] fix typo in formula

---
 mindspore/nn/layer/basic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index e5ed147b85..0cf4cd5e99 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -156,10 +156,10 @@ class Dense(Cell):
         ValueError: If weight_init or bias_init shape is incorrect.
 
     Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, in_channels)`.
+        - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
 
     Outputs:
-        Tensor of shape :math:`(N, out_channels)`.
+        Tensor of shape :math:`(N, out\_channels)`.
 
     Examples:
         >>> net = nn.Dense(3, 4)

From fd96ebe3ea08238a359122b9d0e640d5eafd4130 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Wed, 1 Apr 2020 11:42:24 +0800
Subject: [PATCH 046/367] fix typo in formula

---
 mindspore/nn/layer/normalization.py   | 2 +-
 mindspore/ops/operations/array_ops.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index fdfa25e183..d43c1c8ab4 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -249,7 +249,7 @@ class LayerNorm(Cell):
             'he_uniform', etc. Default: 'zeros'.
 
     Inputs:
-        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = `(x_1, x_2, ..., x_R)`,
+        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`,
           and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
 
     Outputs:
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 8585f873e9..f6d563321c 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1790,7 +1790,7 @@ class ScatterNd(PrimitiveWithInfer):
 
 
 class ResizeNearestNeighbor(PrimitiveWithInfer):
-    """
+    r"""
     Resize the input tensor by using nearest neighbor algorithm.
 
     Resize input tensor to given size by using nearest neighbor algorithm. The nearest
@@ -1806,7 +1806,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
          - **input_x** (Tensor) - The input tensor. The shape of the tensor is :math:`(N, C, H, W)`.
 
     Outputs:
-        Tensor, the shape of the output tensor is :math:`(N, NEW_C, NEW_H, W)`.
+        Tensor, the shape of the output tensor is :math:`(N, NEW\_C, NEW\_H, W)`.
 
     Examples:
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)

From aea6b0c97478cf8268f1edd8a06b2f65c246aec0 Mon Sep 17 00:00:00 2001
From: VectorSL <shiliang10@huawei.com>
Date: Wed, 1 Apr 2020 11:49:37 +0800
Subject: [PATCH 047/367] update tests/st/ops/gpu/test_tensoradd.py. fix
 pytest.mark for testcase

---
 tests/st/ops/gpu/test_tensoradd.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/tests/st/ops/gpu/test_tensoradd.py b/tests/st/ops/gpu/test_tensoradd.py
index a60f5dcf30..23c23f12d2 100644
--- a/tests/st/ops/gpu/test_tensoradd.py
+++ b/tests/st/ops/gpu/test_tensoradd.py
@@ -25,10 +25,6 @@ from mindspore.common.parameter import Parameter
 
 context.set_context(device_target='GPU')
 
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
 class TensroAdd(nn.Cell):
     def __init__(self):
         super(TensroAdd, self).__init__()
@@ -61,7 +57,9 @@ class TensroAdd(nn.Cell):
             self.add(self.x, self.y), self.add(self.x1, self.y1), self.add(self.x2, self.y2),
             self.add(self.x3, self.y3))
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
 def test_TensroAdd():
     add = TensroAdd()
     output = add()

From 1de7011bc81d4b42cad56a08c8aeabf40cb1107b Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Wed, 1 Apr 2020 11:53:35 +0800
Subject: [PATCH 048/367] delete longtime python ut

---
 .../train/summary/test_summary_performance.py | 97 -------------------
 tests/ut/python/train/test_amp.py             |  9 +-
 2 files changed, 1 insertion(+), 105 deletions(-)
 delete mode 100644 tests/ut/python/train/summary/test_summary_performance.py

diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py
deleted file mode 100644
index 9ee9725d13..0000000000
--- a/tests/ut/python/train/summary/test_summary_performance.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-@File  : test_summary.py
-@Author:
-@Date  : 2019-07-4
-@Desc  : test summary function
-"""
-import os
-import logging
-import time
-import numpy as np
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-from mindspore.common.tensor import Tensor
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-def get_now_time_ns():
-    """get the time of second"""
-    time_second = int(time.time_ns())
-    return time_second
-
-def get_test_data(step):
-    """ get_test_data """
-    # pylint: disable=unused-argument
-    test_data_list = []
-    tag1 = "xt1[:Tensor]"
-    tag2 = "xt2[:Tensor]"
-    tag3 = "xt3[:Tensor]"
-    np1 = np.random.random((5, 4, 3, 5))
-    np2 = np.random.random((5, 5, 3, 5))
-    np3 = np.random.random((4, 5, 3, 5))
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-
-    dict2 = {}
-    dict2["name"] = tag2
-    dict2["data"] = Tensor(np2)
-
-    dict3 = {}
-    dict3["name"] = tag3
-    dict3["data"] = Tensor(np3)
-
-    test_data_list.append(dict1)
-    test_data_list.append(dict2)
-
-    return test_data_list
-
-
-# Test 1: summary sample of scalar
-def test_summary_performance():
-    """ test_summary_performance """
-    log.debug("begin test_scalar_summary_sample")
-    current_time = time.time()
-    print("time = ", current_time)
-    # step 0: create the thread
-    test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120)
-
-    # step 1: create the test data for summary
-    old_time = get_now_time_ns()
-    # step 2: create the Event
-    for i in range(1, 10):
-        test_data = get_test_data(i)
-        _cache_summary_tensor_data(test_data)
-        test_writer.record(i)
-        now_time = get_now_time_ns()
-        consume_time = (now_time - old_time)/1000/1000
-        old_time = now_time
-        print("step test_summary_performance conusmer time is:", consume_time)
-
-
-    # step 3: send the event to mq
-
-    # step 4: accept the event and write the file
-    test_writer.flush()
-    test_writer.close()
-    current_time = time.time() - current_time
-    print("consume time = ", current_time)
-    log.debug("finished test_scalar_summary_sample")
diff --git a/tests/ut/python/train/test_amp.py b/tests/ut/python/train/test_amp.py
index eebd188e60..1a26c21775 100644
--- a/tests/ut/python/train/test_amp.py
+++ b/tests/ut/python/train/test_amp.py
@@ -66,6 +66,7 @@ def test_amp_o2():
     train_network = amp.build_train_network(net, optimizer, level="O2")
     output = train_network(inputs, label)
 
+
 def test_amp_o2_loss():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -75,14 +76,6 @@ def test_amp_o2_loss():
     train_network = amp.build_train_network(net, optimizer, loss, level="O2")
     output = train_network(inputs, label)
 
-def test_amp_resnet50_loss():
-    inputs = Tensor(np.ones([2, 3, 224, 224]).astype(np.float32))
-    label = Tensor(np.zeros([2, 10]).astype(np.float32))
-    net = resnet50()
-    loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
-    optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
-    train_network = amp.build_train_network(net, optimizer, loss, level="O2")
-    train_network(inputs, label)
 
 def test_amp_o0_loss():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))

From 6a1b865c916871d4190f28f3619805f8bb0bc0f4 Mon Sep 17 00:00:00 2001
From: anzhengqi <anzhengqi.huawei.com>
Date: Tue, 31 Mar 2020 20:18:40 +0800
Subject: [PATCH 049/367] check num_samples

---
 mindspore/dataset/engine/validators.py        |  8 ++++++++
 .../python/dataset/test_datasets_sharding.py  |  6 +++---
 tests/ut/python/dataset/test_exceptions.py    | 19 +++++++++++++++++++
 3 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index adfe54a02e..b4d22a4a01 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -243,6 +243,8 @@ def check_param_type(param_list, param_dict, param_type):
         if param_dict.get(param_name) is not None:
             if param_name == 'num_parallel_workers':
                 check_num_parallel_workers(param_dict.get(param_name))
+            if param_name == 'num_samples':
+                check_num_samples(param_dict.get(param_name))
             else:
                 check_type(param_dict.get(param_name), param_name, param_type)
 
@@ -262,6 +264,12 @@ def check_num_parallel_workers(value):
         raise ValueError("num_parallel_workers exceeds the boundary between 0 and {}!".format(cpu_count()))
 
 
+def check_num_samples(value):
+    check_type(value, 'num_samples', int)
+    if value <= 0:
+        raise ValueError("num_samples must be greater than 0!")
+
+
 def check_dataset_dir(dataset_dir):
     if not os.path.isdir(dataset_dir) or not os.access(dataset_dir, os.R_OK):
         raise ValueError("The folder {} does not exist or permission denied!".format(dataset_dir))
diff --git a/tests/ut/python/dataset/test_datasets_sharding.py b/tests/ut/python/dataset/test_datasets_sharding.py
index b178298e33..b398391fb7 100644
--- a/tests/ut/python/dataset/test_datasets_sharding.py
+++ b/tests/ut/python/dataset/test_datasets_sharding.py
@@ -33,14 +33,14 @@ def test_imagefolder_shardings(print_res=False):
     # total 44 rows in dataset
     assert (sharding_config(4, 0, 5, False, dict()) == [0, 0, 0, 1, 1])  # 5 rows
     assert (sharding_config(4, 0, 12, False, dict()) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3])  # 11 rows
-    assert (sharding_config(4, 3, 0, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])  # 11 rows
+    assert (sharding_config(4, 3, None, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])  # 11 rows
     # total 22 in dataset rows because of class indexing which takes only 2 folders
-    assert (len(sharding_config(4, 0, 0, True, {"class1": 111, "class2": 999})) == 6)
+    assert (len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6)
     assert (len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3)
     # test with repeat
     assert (sharding_config(4, 0, 12, False, dict(), 3) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3] * 3)
     assert (sharding_config(4, 0, 5, False, dict(), 5) == [0, 0, 0, 1, 1] * 5)
-    assert (len(sharding_config(5, 1, 0, True, {"class1": 111, "class2": 999}, 4)) == 20)
+    assert (len(sharding_config(5, 1, None, True, {"class1": 111, "class2": 999}, 4)) == 20)
 
 
 def test_manifest_shardings(print_res=False):
diff --git a/tests/ut/python/dataset/test_exceptions.py b/tests/ut/python/dataset/test_exceptions.py
index 7668eeb2a8..631f2ddcbc 100644
--- a/tests/ut/python/dataset/test_exceptions.py
+++ b/tests/ut/python/dataset/test_exceptions.py
@@ -18,6 +18,7 @@ import pytest
 import mindspore.dataset as ds
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 
 def skip_test_exception():
@@ -29,5 +30,23 @@ def skip_test_exception():
     assert "The shape size 1 of input tensor is invalid" in str(info.value)
 
 
+def test_sample_exception():
+    num_samples = 0
+    with pytest.raises(ValueError) as info:
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    assert "num_samples must be greater than 0" in str(info.value)
+    num_samples = -1
+    with pytest.raises(ValueError) as info:
+        data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    assert "num_samples must be greater than 0" in str(info.value)
+    num_samples = 1
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples)
+    data = data.map(input_columns=["image"], operations=vision.Decode())
+    data = data.map(input_columns=["image"], operations=vision.Resize((100, 100)))
+    num_iters = 0
+    for item in data.create_dict_iterator():
+        num_iters += 1
+    assert num_iters == 1
+
 if __name__ == '__main__':
     test_exception()

From fb6eed23aeefbec5a8885498fb626e2d972158ca Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Wed, 1 Apr 2020 14:16:52 +0800
Subject: [PATCH 050/367] refining strategy-checking for resnet50

---
 .../ut/python/parallel/test_auto_parallel_resnet.py  | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/ut/python/parallel/test_auto_parallel_resnet.py b/tests/ut/python/parallel/test_auto_parallel_resnet.py
index 667e3873a6..1e0e3570b9 100644
--- a/tests/ut/python/parallel/test_auto_parallel_resnet.py
+++ b/tests/ut/python/parallel/test_auto_parallel_resnet.py
@@ -295,11 +295,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
     model.train(5, dataset, dataset_sink_mode=False)
     strategies = _executor._get_strategy(model._train_network)
     for (k, v) in strategies.items():
-        if re.match(k, 'Conv2D-op') is not None:
+        if re.search('Conv2D-op', k) is not None:
             assert v[0][0] == dev_num
-        elif re.match(k, 'MatMul-op') is not None:
+        elif re.search('MatMul-op', k) is not None:
             assert v == [[dev_num, 1], [1, 1]]
-        elif re.match(k, 'ReduceSum-op') is not None:
+        elif re.search('ReduceSum-op', k) is not None:
             assert v == [[dev_num, 1]]
 
     allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network)
@@ -490,9 +490,9 @@ def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): #1048576
     model.train(5, dataset, dataset_sink_mode=False)
     strategies = _executor._get_strategy(model._train_network)
     for (k, v) in strategies.items():
-        if re.match(k, 'Conv2D-op') is not None:
+        if re.search('Conv2D-op', k ) is not None:
             assert v[0][0] == dev_num
-        elif re.match(k, 'MatMul-op') is not None:
+        elif re.search('MatMul-op', k) is not None:
             assert v == [[1, 1], [dev_num, 1]]
-        elif re.match(k, 'ReduceSum-op') is not None:
+        elif re.search('ReduceSum-op', k) is not None:
             assert v == [[1, dev_num]]

From bffa031dbc4e99f480818aee44c689dc62db7198 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Tue, 31 Mar 2020 10:57:38 +0800
Subject: [PATCH 051/367] change MS LIB CACHE setting

---
 cmake/utils.cmake | 28 +++++++++++++++++++++++-----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index 4efaad61b9..060e400820 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -16,16 +16,34 @@ function(mindspore_add_submodule_obj des_submodule_objs sub_dir submodule_name_o
 
 endfunction()
 
-get_filename_component(_MS_LIB_CACHE ~/.mslib REALPATH)
+if (DEFINED ENV{MSLIBS_CACHE_PATH})
+    set(_MS_LIB_CACHE  $ENV{MSLIBS_CACHE_PATH})
+else()
+    set(_MS_LIB_CACHE ${CMAKE_BINARY_DIR}/.mslib)
+endif ()
+message("MS LIBS CACHE PATH:  ${_MS_LIB_CACHE}")
+
 if (NOT EXISTS ${_MS_LIB_CACHE})
     file(MAKE_DIRECTORY ${_MS_LIB_CACHE})
 endif ()
-# set(FETCHCONTENT_BASE_DIR ${_MS_LIB_CACHE})
-# set(CMAKE_PREFIX_PATH  ${_MS_LIB_CACHE})
+
 if (DEFINED ENV{MSLIBS_SERVER})
     set(LOCAL_LIBS_SERVER  $ENV{MSLIBS_SERVER})
     message("LOCAL_LIBS_SERVER:  ${LOCAL_LIBS_SERVER}")
 endif ()
+
+include(ProcessorCount)
+ProcessorCount(N)
+if (JOBS)
+    set(THNUM ${JOBS})
+else()
+    set(JOBS 8)
+    if (${JOBS} GREATER ${N})
+        set(THNUM ${N})
+    endif()
+endif ()
+message("set make thread num: ${THNUM}")
+
 if(LOCAL_LIBS_SERVER)
     if (NOT ENV{no_proxy})
         set(ENV{no_proxy} "${LOCAL_LIBS_SERVER}")
@@ -287,7 +305,7 @@ function(mindspore_add_pkg pkg_name )
                     -DCMAKE_INSTALL_PREFIX=${${pkg_name}_BASE_DIR} ..
                     WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
 
-            __exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j8
+            __exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j${THNUM}
                     WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
 
         else()
@@ -318,7 +336,7 @@ function(mindspore_add_pkg pkg_name )
                         ${${pkg_name}_MAKE_CFLAGS} ${${pkg_name}_MAKE_CXXFLAGS} ${${pkg_name}_MAKE_LDFLAGS})
             endif ()
             # build
-            __exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} ${${pkg_name}_BUILD_OPTION} -j8
+            __exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} ${${pkg_name}_BUILD_OPTION} -j${THNUM}
                     WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR})
 
             if (PKG_INSTALL_INCS OR PKG_INSTALL_LIBS)

From bb421b70db21eca7376f11a9d7bd37e53be2954e Mon Sep 17 00:00:00 2001
From: laiyongqiang <laiyongqiang1@huawei.com>
Date: Wed, 1 Apr 2020 14:51:43 +0800
Subject: [PATCH 052/367] fix bug when dump op with trans_flag true

---
 config/e2e_dump_config.json                        | 12 ++++++------
 config/e2e_dump_config_0.json                      | 14 +++++++-------
 config/e2e_dump_config_1.json                      | 14 +++++++-------
 mindspore/ccsrc/common/trans.cc                    |  7 ++++++-
 .../ccsrc/device/ascend/ascend_device_address.cc   |  6 +++---
 .../ccsrc/device/ascend/ascend_kernel_runtime.cc   | 12 ++++++------
 6 files changed, 35 insertions(+), 30 deletions(-)

diff --git a/config/e2e_dump_config.json b/config/e2e_dump_config.json
index ad75c2f27f..fdba941f97 100644
--- a/config/e2e_dump_config.json
+++ b/config/e2e_dump_config.json
@@ -6,17 +6,17 @@
     "net_name": "ResNet50",
     "mode": 0,
     "iteration": 0,
-    "kernels": ["TensorAdd"]
+    "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
   },
 
   "DumpSettingsSpec": {
-    "enable": "true: dump enable false: dump disable",
-    "trans_flag": "true: trans to host format,false: not trans format",
+    "enable": "true: dump enable, false: dump disable",
+    "trans_flag": "true: trans to host format, false: not trans format",
     "path": "the dump file folder",
     "net_name": "net name eg:ResNet50",
-    "mode": "0: dump all kernels 1: dump kernels in kernels list",
-    "iteration": "0: all iteration  others: specified iteration ",
-    "kernels": "kernel name list need to be dump"
+    "mode": "0: dump all kernels, 1: dump kernels in kernels list",
+    "iteration": "0: all iteration, others: specified iteration ",
+    "kernels": "op's full scope name which need to be dump"
   },
   "other": {}
 }
\ No newline at end of file
diff --git a/config/e2e_dump_config_0.json b/config/e2e_dump_config_0.json
index a67a4daba0..64b18b8b55 100644
--- a/config/e2e_dump_config_0.json
+++ b/config/e2e_dump_config_0.json
@@ -6,17 +6,17 @@
     "net_name": "ResNet50",
     "mode": 0,
     "iteration": 0,
-    "kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
+    "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
   },
 
   "DumpSettingsSpec": {
-    "enable": "true: dump enable false: dump disable",
-    "trans_flag": "true: trans to host format,false: not trans format",
+    "enable": "true: dump enable, false: dump disable",
+    "trans_flag": "true: trans to host format, false: not trans format",
     "path": "the dump file folder",
     "net_name": "net name eg:ResNet50",
-    "mode": "0: dump all kernels 1: dump kernels in kernels list",
-    "iteration": "0: all iteration  others: specified iteration ",
-    "kernels": "kernel name list need to be dump"
+    "mode": "0: dump all kernels, 1: dump kernels in kernels list",
+    "iteration": "0: all iteration, others: specified iteration ",
+    "kernels": "op's full scope name which need to be dump"
   },
   "other": {}
-}
+}
\ No newline at end of file
diff --git a/config/e2e_dump_config_1.json b/config/e2e_dump_config_1.json
index 226b91ae09..1486487799 100644
--- a/config/e2e_dump_config_1.json
+++ b/config/e2e_dump_config_1.json
@@ -6,17 +6,17 @@
     "net_name": "ResNet50",
     "mode": 0,
     "iteration": 0,
-    "kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
+    "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
   },
 
   "DumpSettingsSpec": {
-    "enable": "true: dump enable false: dump disable",
-    "trans_flag": "true: trans to host format,false: not trans format",
+    "enable": "true: dump enable, false: dump disable",
+    "trans_flag": "true: trans to host format, false: not trans format",
     "path": "the dump file folder",
     "net_name": "net name eg:ResNet50",
-    "mode": "0: dump all kernels 1: dump kernels in kernels list",
-    "iteration": "0: all iteration  others: specified iteration ",
-    "kernels": "kernel name list need to be dump"
+    "mode": "0: dump all kernels, 1: dump kernels in kernels list",
+    "iteration": "0: all iteration, others: specified iteration ",
+    "kernels": "op's full scope name which need to be dump"
   },
   "other": {}
-}
+}
\ No newline at end of file
diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index ea84537c1a..4748d59286 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -53,6 +53,7 @@ enum DataTypeTransMode {
   FROM_INT8_TO_FLOAT,
   FROM_INT8_TO_INT32,
   FROM_INT64_TO_INT32,
+  FROM_UINT16_TO_INT32,
 };
 
 const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
@@ -68,7 +69,8 @@ const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
   {std::pair<TypeId, TypeId>(kNumberTypeUInt8, kNumberTypeInt32), FROM_UINT8_TO_INT32},
   {std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeFloat32), FROM_INT8_TO_FLOAT},
   {std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeInt32), FROM_INT8_TO_INT32},
-  {std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32}};
+  {std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32},
+  {std::pair<TypeId, TypeId>(kNumberTypeUInt16, kNumberTypeInt32), FROM_UINT16_TO_INT32}};
 
 template <typename SrcT, typename DstT>
 void TransDataSrc2Dst(const TypeIdArgs &args, void *dst, const size_t data_size) {
@@ -116,6 +118,9 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const
     case FROM_INT64_TO_INT32:
       TransDataSrc2Dst<int64_t, int32_t>(args, dst, data_size);
       break;
+    case FROM_UINT16_TO_INT32:
+      TransDataSrc2Dst<uint16_t, int32_t>(args, dst, data_size);
+      break;
     default:
       MS_LOG(ERROR) << "unsupported datatype trans";
       return false;
diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
index a521f1516f..b8b7f452e3 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
@@ -106,13 +106,13 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector<int> &shape, size_t
     } else {
       auto shape_size = trans::ShapeSize(host_shape);
       auto host = std::vector<uint8_t>(size_);
-      const trans::TypeIdArgs type_args{ptr_, shape_size, type_id_, type};
-      sync_ok = trans::TransDataType(type_args, host.data());
+      SyncMemory(host.data(), ptr_, size_, RT_MEMCPY_DEVICE_TO_HOST);
+      const trans::TypeIdArgs type_args{host.data(), shape_size, type_id_, type};
+      sync_ok = trans::TransDataType(type_args, host_ptr);
       if (!sync_ok) {
         MS_LOG(ERROR) << "trans data type failed.";
         return false;
       }
-      SyncMemory(host_ptr, host.data(), size, RT_MEMCPY_DEVICE_TO_HOST);
     }
   } else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) {
     sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr);
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index a7dfc96b2f..dc7eb5449b 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -150,9 +150,9 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path,
     auto output_size = AnfAlgo::GetOutputTensorNum(node);
     for (size_t j = 0; j < output_size; ++j) {
       auto addr = AnfAlgo::GetOutputAddr(node, j);
-      auto shape = AnfAlgo::GetOutputDeviceShape(node, j);
-      auto type = AnfAlgo::GetOutputDeviceDataType(node, j);
-      auto format = AnfAlgo::GetOutputFormat(node, j);
+      auto shape = AnfAlgo::GetOutputInferShape(node, j);
+      auto type = AnfAlgo::GetOutputInferDataType(node, j);
+      auto format = kOpFormat_DEFAULT;
       string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
       auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
       std::vector<int> int_shapes;
@@ -181,9 +181,9 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p
       continue;
     }
     auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX);
-    auto shape = AnfAlgo::GetOutputDeviceShape(item, PRAMATER_OUTPUT_INDEX);
-    auto type = AnfAlgo::GetOutputDeviceDataType(item, PRAMATER_OUTPUT_INDEX);
-    auto format = AnfAlgo::GetOutputFormat(item, PRAMATER_OUTPUT_INDEX);
+    auto shape = AnfAlgo::GetOutputInferShape(item, PRAMATER_OUTPUT_INDEX);
+    auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX);
+    auto format = kOpFormat_DEFAULT;
     string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
     auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
     std::vector<int> int_shapes;

From bed5997a141a568a3b0802428bacee9b387f83b9 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Sat, 28 Mar 2020 12:02:46 +0800
Subject: [PATCH 053/367] add atan2 operator

---
 mindspore/ccsrc/transform/convert.cc    |  4 +++-
 mindspore/ccsrc/transform/op_declare.cc |  6 ++++++
 mindspore/ccsrc/transform/op_declare.h  |  2 ++
 mindspore/ops/_grad/grad_math_ops.py    | 13 +++++++++++++
 mindspore/ops/operations/__init__.py    |  5 +++--
 mindspore/ops/operations/math_ops.py    | 23 +++++++++++++++++++++++
 tests/ut/python/ops/test_ops.py         |  7 ++++++-
 7 files changed, 56 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c975b18812..251c855dbf 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -182,6 +182,7 @@ const char kNameDiag[] = "Diag";
 const char kNameDiagPart[] = "DiagPart";
 const char kNameSpaceToBatch[] = "SpaceToBatch";
 const char kNameBatchToSpace[] = "BatchToSpace";
+const char kNameAtan2[] = "Atan2";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -365,7 +366,8 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameDiag), ADPT_DESC(Diag)},
     {string(kNameDiagPart), ADPT_DESC(DiagPart)},
     {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
-    {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}};
+    {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)},
+    {string(kNameAtan2), ADPT_DESC(Atan2)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index cc63bacd8a..f7fdcfbe56 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -1196,6 +1196,12 @@ ATTR_MAP(BatchToSpaceD) = {
   {"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())},
   {"crops", ATTR_DESC(crops, AnyTraits<std::vector<std::vector<int64_t>>>(), AnyTraits<std::vector<int64_t>>())}};
 OUTPUT_MAP(BatchToSpaceD) = {{0, OUTPUT_DESC(y)}};
+
+// Atan2
+INPUT_MAP(Atan2) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
+ATTR_MAP(Atan2) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(Atan2) = {{0, OUTPUT_DESC(y)}};
+
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 978828e16d..1924d2719b 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -443,6 +443,8 @@ DECLARE_OP_ADAPTER(SpaceToBatchD)
 DECLARE_OP_USE_OUTPUT(SpaceToBatchD)
 DECLARE_OP_ADAPTER(BatchToSpaceD)
 DECLARE_OP_USE_OUTPUT(BatchToSpaceD)
+DECLARE_OP_ADAPTER(Atan2)
+DECLARE_OP_USE_OUTPUT(Atan2)
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)
diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py
index eb6ee9401d..9e90c5660c 100755
--- a/mindspore/ops/_grad/grad_math_ops.py
+++ b/mindspore/ops/_grad/grad_math_ops.py
@@ -738,3 +738,16 @@ def get_bprop_round(self):
     def bprop(x, out, dout):
         return (zeros_like(x),)
     return bprop
+
+
+@bprop_getters.register(P.Atan2)
+def get_bprop_atan2(self):
+    """Generate bprop for Atan2"""
+
+    square = P.Square()
+    def bprop(x, y, out, dout):
+        tmp = dout / (square(x) + square(y))
+        dx = tmp * y
+        dy = tmp * (-x)
+        return (dx, dy)
+    return bprop
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 899b2f8d0f..5c98568b8a 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -37,7 +37,7 @@ from .debug_ops import (ImageSummary, InsertGradientOf, ScalarSummary,
                         TensorSummary, Print)
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
-from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, BatchMatMul,
+from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
                        Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv,
                        Greater, GreaterEqual, Less, LessEqual, Log, LogicalAnd,
@@ -226,7 +226,8 @@ __all__ = [
     "Round",
     "ApplyFtrl",
     "SpaceToBatch",
-    "BatchToSpace"
+    "BatchToSpace",
+    "Atan2",
 ]
 
 __all__.sort()
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index ad928d792f..398a7e6f1a 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1858,3 +1858,26 @@ class Round(PrimitiveWithInfer):
         validator.check_subclass("x_dtype", x_type, mstype.tensor)
         validator.check_typename('x_dtype', x_type, mstype.number_type)
         return x_type
+
+
+class Atan2(_MathBinaryOp):
+    r"""
+    Returns arctangent of input_x/input_y element-wise.
+
+    It returns :math:`\theta\ \in\ (-\frac{\pi}{2}, \frac{\pi}{2})`
+    such that :math:`x = r*\sin(\theta), y = r*\cos(\theta)`, where :math:`r = \sqrt{x^2 + y^2}`.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+        - **input_y** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+
+    Examples:
+         >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32)
+         >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32)
+         >>> atan2 = Atan2()
+         >>> atan2(input_x, input_y)
+         [[0. 0.7853982]]
+    """
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index f1d365e9cf..117036c37e 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -481,7 +481,12 @@ test_case_math_ops = [
     ('Round', {
         'block': P.Round(),
         'desc_inputs': [[3]],
-        'desc_bprop': [[3]]})
+        'desc_bprop': [[3]]}),
+    ('Atan2', {
+        'block': P.Atan2(),
+        'desc_inputs': [Tensor(np.array([0, 1]).astype(np.float32)),
+                        Tensor(np.array([1, 1]).astype(np.float32))],
+        'desc_bprop': [[2]]})
 ]
 
 test_case_nn_ops = [

From 2fdf692c2e5973da1db0e3737832ed6f223d269c Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Wed, 1 Apr 2020 15:23:40 +0800
Subject: [PATCH 054/367] fix bug of auto control depend for bert pre training

add comment
---
 mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
index 22a212a489..bc51ba5d48 100644
--- a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
+++ b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
@@ -403,9 +403,6 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
                   sens=None):
         """Defines the computation performed."""
         weights = self.weights
-        # alloc status
-        init = self.alloc_status()
-        self.clear_before_grad(init)
         loss = self.network(input_ids,
                             input_mask,
                             token_type_id,
@@ -417,6 +414,9 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
             scaling_sens = self.loss_scale
         else:
             scaling_sens = sens
+        # alloc status and clear should be right before gradoperation
+        init = self.alloc_status()
+        self.clear_before_grad(init)
         grads = self.grad(self.network, weights)(input_ids,
                                                  input_mask,
                                                  token_type_id,

From 3d35792877b469d00c4c106eb4cd0c3c30144e7e Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Wed, 1 Apr 2020 19:24:18 +0800
Subject: [PATCH 055/367] change_star_elimination: make the non-identity
 triangle_eliminatin exact

---
 .../parallel/auto_parallel/graph_costmodel.cc | 97 +++----------------
 .../parallel/auto_parallel/graph_costmodel.h  | 11 +--
 2 files changed, 16 insertions(+), 92 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index 292cc4f5f0..f99b271894 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -948,10 +948,12 @@ OperatorInfoPtr CostGraph::EliminationContract(const OperatorInfoPtr& op) {
   return target_op;
 }
 
-void CostGraph::CreateTriangleEliminationSubCostListForIdentity(
-  StrategyPtr elimi_op_stra, StrategyPtr left_op_stra, StrategyPtr right_op_stra, const CostPtr& right_op_cost,
-  const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost,
-  const CostPtrList& left_node_clist_origin, CostPtrList* left_node_clist_new) {
+void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra, StrategyPtr left_op_stra,
+                                                     StrategyPtr right_op_stra, const CostPtr& right_op_cost,
+                                                     const CostPtrList& elimi_op_clist,
+                                                     const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost,
+                                                     const CostPtrList& left_node_clist_origin,
+                                                     CostPtrList* left_node_clist_new) {
   MS_EXCEPTION_IF_NULL(right_edge_cost);
   MS_EXCEPTION_IF_NULL(right_op_cost);
   MS_EXCEPTION_IF_NULL(left_node_clist_new);
@@ -985,93 +987,20 @@ void CostGraph::CreateTriangleEliminationSubCostListForIdentity(
   }
 }
 
-void CostGraph::CreateTriangleEliminationSubCostListForOthers(
-  StrategyPtr elimi_op_stra, StrategyPtr left_node_stra, StrategyPtr right_node_stra, const CostPtr& right_op_cost,
-  const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost,
-  const CostPtrList& left_node_clist_origin, CostPtrList* left_node_clist_new) {
-  CostPtr elimi_op_determined = nullptr, left_edge_determined = nullptr, init_ele = nullptr;
-  std::function<CostPtr(CostPtr, const CostPtr&)> LocalCompare = [&](CostPtr init, const CostPtr& cost_x) {
-    MS_EXCEPTION_IF_NULL(cost_x);
-    if ((init == nullptr) || (cost_x->memory_cost_ < DEVICE_MEMORY_CAPACITY)) {
-      init = cost_x;
-    }
-    return init;
-  };
-
-  // Find a feasible elimi_op_clist
-  elimi_op_determined = std::accumulate(elimi_op_clist.begin(), elimi_op_clist.end(), init_ele, LocalCompare);
-  init_ele = nullptr;
-  // Find a feasible left_edge_cost
-  left_edge_determined = std::accumulate(left_edge_clist.begin(), left_edge_clist.end(), init_ele, LocalCompare);
-  if ((elimi_op_determined == nullptr) || (left_edge_determined == nullptr)) {
-    return;
-  }
-  if ((elimi_op_determined->memory_cost_ >= DEVICE_MEMORY_CAPACITY) ||
-      (left_edge_determined->memory_cost_ >= DEVICE_MEMORY_CAPACITY)) {
-    return;
-  }
-
-  for (auto& left_node_cost : left_node_clist_origin) {
-    MS_EXCEPTION_IF_NULL(left_node_cost);
-    MS_EXCEPTION_IF_NULL(right_op_cost);
-    double new_memory_cost = left_node_cost->memory_cost_ + elimi_op_determined->memory_cost_ +
-                             left_edge_determined->memory_cost_ + right_edge_cost->memory_cost_ +
-                             right_op_cost->memory_cost_;
-    double commu_cost = left_node_cost->communication_cost_ + elimi_op_determined->communication_cost_ +
-                        left_edge_determined->communication_cost_ + right_edge_cost->communication_cost_ +
-                        right_op_cost->communication_cost_;
-    double commu_without =
-      left_node_cost->communication_without_parameter_ + elimi_op_determined->communication_without_parameter_ +
-      left_edge_determined->communication_without_parameter_ + right_edge_cost->communication_without_parameter_ +
-      right_op_cost->communication_without_parameter_;
-    auto decision = std::make_shared<TriangleEliminationDecision>(elimi_op_stra, elimi_op_determined,
-                                                                  left_edge_determined, right_edge_cost, left_node_stra,
-                                                                  left_node_cost, right_node_stra, right_op_cost);
-
-    auto new_cost = std::make_shared<Cost>(new_memory_cost, commu_cost, decision);
-    new_cost->communication_without_parameter_ = commu_without;
-    new_cost->communication_with_partial_para_ = commu_without + COST_MODEL_GAMMA * (commu_cost - commu_without);
-    left_node_clist_new->emplace_back(std::move(new_cost));
-  }
-}
-
 void CostGraph::CreateTriangleEliminationCostList(const OperatorInfoPtr& elimi_op, const CostPtrList& right_node_clist,
                                                   const CostPtrList& right_edge_clist, const StrategyPtr& elimi_op_stra,
                                                   const StrategyPtr& left_node_stra, const StrategyPtr& right_node_stra,
                                                   const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist,
                                                   const CostPtrList& left_node_clist_origin,
                                                   CostPtrList* left_node_clist_new) {
-  // The reason for separately dealing with when the 'elimi_op' is 'TMPIDENTITY_INFO' or others is that
-  // when 'elimi_op' is TMPIDENTITY_INFO, the computation is limited, while 'elimi_op' is others, the computation
-  // may be huge
   MS_EXCEPTION_IF_NULL(elimi_op);
-  if (elimi_op->name().find(TMPIDENTITY_INFO_NAME) != std::string::npos) {
-    for (auto& right_node_cost : right_node_clist) {
-      MS_EXCEPTION_IF_NULL(right_node_cost);
-      for (auto& right_edge_cost : right_edge_clist) {
-        MS_EXCEPTION_IF_NULL(right_edge_cost);
-        if ((right_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) &&
-            (right_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) {
-          // Exact computation for TMPIDENTITY_INFO_NAME case
-          CreateTriangleEliminationSubCostListForIdentity(elimi_op_stra, left_node_stra, right_node_stra,
-                                                          right_node_cost, elimi_op_clist, left_edge_clist,
-                                                          right_edge_cost, left_node_clist_origin, left_node_clist_new);
-        }
-      }
-    }
-  } else {
-    for (auto& right_node_cost : right_node_clist) {
-      MS_EXCEPTION_IF_NULL(right_node_cost);
-      for (auto& right_edge_cost : right_edge_clist) {
-        MS_EXCEPTION_IF_NULL(right_edge_cost);
-        if ((right_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) &&
-            (right_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) {
-          // Approximate computation for other case
-          CreateTriangleEliminationSubCostListForOthers(elimi_op_stra, left_node_stra, right_node_stra, right_node_cost,
-                                                        elimi_op_clist, left_edge_clist, right_edge_cost,
-                                                        left_node_clist_origin, left_node_clist_new);
-        }
-      }
+  for (auto& right_node_cost : right_node_clist) {
+    MS_EXCEPTION_IF_NULL(right_node_cost);
+    for (auto& right_edge_cost : right_edge_clist) {
+      MS_EXCEPTION_IF_NULL(right_edge_cost);
+      CreateTriangleEliminationSubCostList(elimi_op_stra, left_node_stra, right_node_stra, right_node_cost,
+                                           elimi_op_clist, left_edge_clist, right_edge_cost, left_node_clist_origin,
+                                           left_node_clist_new);
     }
   }
 }
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index fde9514540..3b04703a47 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -163,14 +163,9 @@ class CostGraph {
   void CreateTriangleEliminationCostList(const OperatorInfoPtr&, const CostPtrList&, const CostPtrList&,
                                          const StrategyPtr&, const StrategyPtr&, const StrategyPtr&, const CostPtrList&,
                                          const CostPtrList&, const CostPtrList&, CostPtrList*);
-  // Given the relevant costlist, create the TriangleElimination cost for eliminating TmpIdentityInfo
-  void CreateTriangleEliminationSubCostListForIdentity(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&,
-                                                       const CostPtrList&, const CostPtrList&, const CostPtr&,
-                                                       const CostPtrList&, CostPtrList*);
-  // Given the relevant costlist, create the TriangleElimination cost for eliminating other operators
-  void CreateTriangleEliminationSubCostListForOthers(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&,
-                                                     const CostPtrList&, const CostPtrList&, const CostPtr&,
-                                                     const CostPtrList&, CostPtrList*);
+  // Given the relevant costlist, create the TriangleElimination cost
+  void CreateTriangleEliminationSubCostList(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&, const CostPtrList&,
+                                            const CostPtrList&, const CostPtr&, const CostPtrList&, CostPtrList*);
 
   // Applying the Star Elimination in DP algorithm. Return the successive edges of this merged_op
   // NOTE: this elimination MUST be performed only when the above 5 operation cannot be applied.

From b4b1c55ed20144eb99e549dd51d9d01cd4ec7edb Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Wed, 1 Apr 2020 20:14:46 +0800
Subject: [PATCH 056/367] repair unsuitable warning in repeat count and epoch
 size check

---
 mindspore/train/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index fe655433fa..4174632cec 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -372,7 +372,7 @@ class Model:
             >>> model.train(2, dataset)
         """
         repeat_count = train_dataset.get_repeat_count()
-        if epoch != repeat_count:
+        if epoch != repeat_count and dataset_sink_mode is True:
             logger.warning(f"The epoch_size {epoch} is not the same with dataset repeat_count {repeat_count}")
         check_bool(dataset_sink_mode)
         _device_number_check(self._parallel_mode, self._device_number)

From d3f733fa25023f56695cb95cf337be8a140dddcb Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Sat, 28 Mar 2020 10:33:10 +0000
Subject: [PATCH 057/367] support grad on graph with variable arguments

use unpack graph primitive instead

add testcases for all grad interface

remove debug log

format code

remove dumpfuncgraph

resolve clang-format

resolve reviews

resolve cpplint

fix review
---
 .../ccsrc/operator/composite/composite.cc     |  50 ------
 .../ccsrc/operator/composite/composite.h      |  14 +-
 .../ccsrc/operator/composite/unpack_call.cc   |  94 +++++++++++
 .../ccsrc/operator/composite/unpack_call.h    |  54 +++++++
 mindspore/ccsrc/operator/ops.h                |  15 ++
 mindspore/ccsrc/optimizer/irpass.cc           |   6 +
 mindspore/ccsrc/optimizer/irpass.h            |   8 +
 .../optimizer/irpass/grad_var_prepare.cc      | 144 +++++++++++++++++
 .../ccsrc/optimizer/irpass/grad_var_prepare.h |  55 +++++++
 mindspore/ccsrc/pipeline/action.cc            |  16 +-
 mindspore/ccsrc/pipeline/pass.cc              |  17 ++
 mindspore/ccsrc/pipeline/pass.h               |   2 +-
 .../static_analysis/abstract_function.h       |   1 +
 .../static_analysis/analysis_context.cc       |   2 +-
 .../ccsrc/pipeline/static_analysis/prim.cc    |  79 +++++++++
 .../ccsrc/pipeline/static_analysis/prim.h     |  15 ++
 .../static_analysis/static_analysis.cc        |   4 +
 .../python/parameter_feature/test_var_grad.py | 151 ++++++++++++++++++
 18 files changed, 660 insertions(+), 67 deletions(-)
 create mode 100644 mindspore/ccsrc/operator/composite/unpack_call.cc
 create mode 100644 mindspore/ccsrc/operator/composite/unpack_call.h
 create mode 100644 mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc
 create mode 100644 mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h

diff --git a/mindspore/ccsrc/operator/composite/composite.cc b/mindspore/ccsrc/operator/composite/composite.cc
index 0c55b9480c..347641829d 100644
--- a/mindspore/ccsrc/operator/composite/composite.cc
+++ b/mindspore/ccsrc/operator/composite/composite.cc
@@ -1199,51 +1199,6 @@ FuncGraphPtr TensorSlice::GenerateFuncGraph(const AbstractBasePtrList& args_spec
   return ret_graph;
 }
 
-FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) {
-  // slice a tensor
-  // args: tensor, slice or slice tuple
-  const std::string op_name = std::string("UnpackCall");
-  size_t arg_length = args_spec_list.size();
-  if (arg_length < 2) {
-    MS_LOG(EXCEPTION) << "" << op_name << " requires at least two args, but got " << arg_length << ".";
-  }
-
-  (void)abstract::CheckArg<AbstractFunction>(op_name, args_spec_list, 0);
-  FuncGraphPtr ret_graph = std::make_shared<FuncGraph>();
-  ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true);
-
-  AnfNodePtr fnNode = ret_graph->add_parameter();
-  std::vector<AnfNodePtr> elems;
-  elems.push_back(fnNode);
-  for (size_t index = 1; index < arg_length; index++) {
-    MS_EXCEPTION_IF_NULL(args_spec_list[index]);
-    if (args_spec_list[index]->isa<AbstractTuple>()) {
-      AbstractTuplePtr arg_tuple = dyn_cast<AbstractTuple>(args_spec_list[index]);
-      AnfNodePtr para_tuple = ret_graph->add_parameter();
-      for (size_t i = 0; i < arg_tuple->size(); ++i) {
-        elems.push_back(
-          ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))}));
-      }
-    } else if (args_spec_list[index]->isa<AbstractDictionary>()) {
-      AbstractDictionaryPtr arg_dict = dyn_cast<AbstractDictionary>(args_spec_list[index]);
-      AnfNodePtr para_dict = ret_graph->add_parameter();
-      auto dict_elems = arg_dict->elements();
-      (void)std::transform(
-        dict_elems.begin(), dict_elems.end(), std::back_inserter(elems),
-        [ret_graph, para_dict](const AbstractAttribute& item) {
-          return ret_graph->NewCNode(
-            {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first),
-             ret_graph->NewCNode({NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)})});
-        });
-    } else {
-      MS_LOG(EXCEPTION) << "" << op_name << " require args should be tuple or dict, but got "
-                        << args_spec_list[index]->ToString();
-    }
-  }
-  ret_graph->set_output(ret_graph->NewCNode(elems));
-  return ret_graph;
-}
-
 REGISTER_PYBIND_DEFINE(
   TupleAdd_, ([](const py::module* m) {
     (void)py::class_<TupleAdd, MetaFuncGraph, std::shared_ptr<TupleAdd>>(*m, "TupleAdd_").def(py::init<std::string&>());
@@ -1258,10 +1213,5 @@ REGISTER_PYBIND_DEFINE(TensorSlice_, ([](const py::module* m) {
                          (void)py::class_<TensorSlice, MetaFuncGraph, std::shared_ptr<TensorSlice>>(*m, "TensorSlice_")
                            .def(py::init<std::string&>());
                        }));
-
-REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) {
-                         (void)py::class_<UnpackCall, MetaFuncGraph, std::shared_ptr<UnpackCall>>(*m, "UnpackCall_")
-                           .def(py::init<std::string&>());
-                       }));
 }  // namespace prim
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/operator/composite/composite.h b/mindspore/ccsrc/operator/composite/composite.h
index 176efb0425..dc8627ba61 100644
--- a/mindspore/ccsrc/operator/composite/composite.h
+++ b/mindspore/ccsrc/operator/composite/composite.h
@@ -29,6 +29,7 @@
 #include "operator/composite/zip_operation.h"
 #include "operator/composite/list_append_operation.h"
 #include "operator/composite/do_signature.h"
+#include "operator/composite/unpack_call.h"
 #include "pipeline/static_analysis/static_analysis.h"
 #include "utils/misc.h"
 #include "utils/any.h"
@@ -154,7 +155,7 @@ class GradOperation : public MetaFuncGraph {
   FuncGraphPtr GetGrad(AnfNodePtr ptrNode, const AnfNodePtr& weights, const std::vector<AnfNodePtr>& ptrParams,
                        bool applyJ = false);
   FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
-
+  bool sens_param() const { return sens_param_; }
   bool get_all_;
   bool get_by_list_;
   bool sens_param_;
@@ -208,17 +209,6 @@ class TensorSlice : public MetaFuncGraph {
 };
 using TensorSlicePtr = std::shared_ptr<TensorSlice>;
 
-// Expand the tuple and dict parameters generated when parsing the function call,
-// and generate positional parameters and key-value pairs for function.
-class UnpackCall : public MetaFuncGraph {
- public:
-  explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {}
-  ~UnpackCall() override = default;
-  MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph)
-  FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
-  friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; }
-};
-using UnpackCallPtr = std::shared_ptr<UnpackCall>;
 }  // namespace prim
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/operator/composite/unpack_call.cc b/mindspore/ccsrc/operator/composite/unpack_call.cc
new file mode 100644
index 0000000000..64d6b3433b
--- /dev/null
+++ b/mindspore/ccsrc/operator/composite/unpack_call.cc
@@ -0,0 +1,94 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "operator/composite/unpack_call.h"
+#include <algorithm>
+#include <utility>
+
+#include "./common.h"
+#include "pipeline/static_analysis/abstract_value.h"
+#include "pipeline/static_analysis/dshape.h"
+#include "pipeline/static_analysis/param_validator.h"
+#include "operator/cc_implementations.h"
+#include "ir/anf.h"
+#include "optimizer/opt.h"
+#include "utils/symbolic.h"
+#include "pybind_api/api_register.h"
+
+namespace mindspore {
+// namespace to support composite operators definition
+namespace prim {
+using mindspore::abstract::AbstractAttribute;
+using mindspore::abstract::AbstractBase;
+using mindspore::abstract::AbstractDictionary;
+using mindspore::abstract::AbstractDictionaryPtr;
+using mindspore::abstract::AbstractFunction;
+using mindspore::abstract::AbstractKeywordArg;
+using mindspore::abstract::AbstractTuple;
+using mindspore::abstract::AbstractTuplePtr;
+
+FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) {
+  // slice a tensor
+  // args: tensor, slice or slice tuple
+  const std::string op_name = std::string("UnpackCall");
+  size_t arg_length = args_spec_list.size();
+  if (arg_length < 2) {
+    MS_LOG(EXCEPTION) << op_name << " requires at least two args, but got " << arg_length << ".";
+  }
+
+  (void)abstract::CheckArg<AbstractFunction>(op_name, args_spec_list, 0);
+  auto ret_graph = std::make_shared<FuncGraph>();
+  ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true);
+
+  AnfNodePtr fnNode = ret_graph->add_parameter();
+  std::vector<AnfNodePtr> elems;
+  elems.push_back(fnNode);
+  for (size_t index = 1; index < arg_length; index++) {
+    MS_EXCEPTION_IF_NULL(args_spec_list[index]);
+    if (args_spec_list[index]->isa<AbstractTuple>()) {
+      auto arg_tuple = args_spec_list[index]->cast<AbstractTuplePtr>();
+      AnfNodePtr para_tuple = ret_graph->add_parameter();
+      for (size_t i = 0; i < arg_tuple->size(); ++i) {
+        elems.push_back(
+          ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))}));
+      }
+    } else if (args_spec_list[index]->isa<AbstractDictionary>()) {
+      AbstractDictionaryPtr arg_dict = args_spec_list[index]->cast<AbstractDictionaryPtr>();
+      AnfNodePtr para_dict = ret_graph->add_parameter();
+      auto dict_elems = arg_dict->elements();
+      (void)std::transform(dict_elems.begin(), dict_elems.end(), std::back_inserter(elems),
+                           [ret_graph, para_dict](const AbstractAttribute& item) {
+                             auto dict_get_item = ret_graph->NewCNode(
+                               {NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)});
+                             return ret_graph->NewCNode(
+                               {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first), dict_get_item});
+                           });
+    } else {
+      MS_LOG(EXCEPTION) << op_name << " require args should be tuple or dict, but got "
+                        << args_spec_list[index]->ToString();
+    }
+  }
+  ret_graph->set_output(ret_graph->NewCNode(elems));
+  return ret_graph;
+}
+
+REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) {
+                         (void)py::class_<UnpackCall, MetaFuncGraph, std::shared_ptr<UnpackCall>>(*m, "UnpackCall_")
+                           .def(py::init<std::string&>());
+                       }));
+
+}  // namespace prim
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/operator/composite/unpack_call.h b/mindspore/ccsrc/operator/composite/unpack_call.h
new file mode 100644
index 0000000000..7ec5f9ad33
--- /dev/null
+++ b/mindspore/ccsrc/operator/composite/unpack_call.h
@@ -0,0 +1,54 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_
+#define MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_
+
+#include <vector>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <map>
+#include <set>
+#include <memory>
+
+#include "pipeline/static_analysis/static_analysis.h"
+#include "utils/misc.h"
+#include "utils/any.h"
+#include "ir/dtype.h"
+#include "ir/meta_func_graph.h"
+#include "common/utils.h"
+
+namespace mindspore {
+// namespace to support composite operators definition
+namespace prim {
+
+// Expand the tuple and dict parameters generated when parsing the function call,
+// and generate positional parameters and key-value pairs for function.
+class UnpackCall : public MetaFuncGraph {
+ public:
+  explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {}
+  ~UnpackCall() override = default;
+  MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph)
+  FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
+  friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; }
+};
+using UnpackCallPtr = std::shared_ptr<UnpackCall>;
+
+}  // namespace prim
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_
diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
index f3f5dad5f1..727d66dfb3 100644
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -246,6 +246,21 @@ class DoSignaturePrimitive : public Primitive {
   ValuePtr function_;
 };
 using DoSignaturePrimitivePtr = std::shared_ptr<DoSignaturePrimitive>;
+
+class UnpackGraphPrimitive : public Primitive {
+ public:
+  explicit UnpackGraphPrimitive(const std::string& name, const bool& with_sens, const bool& need_unpack_args)
+      : Primitive("UnpackGraph"), with_sens_in_args_(with_sens), need_unpack_args_(need_unpack_args) {}
+  ~UnpackGraphPrimitive() override = default;
+  MS_DECLARE_PARENT(UnpackGraphPrimitive, Primitive)
+  bool with_sens_in_args() const { return with_sens_in_args_; }
+  bool need_unpack_args() const { return need_unpack_args_; }
+
+ private:
+  bool with_sens_in_args_;
+  bool need_unpack_args_;
+};
+using UnpackGraphPrimitivePtr = std::shared_ptr<UnpackGraphPrimitive>;
 }  // namespace prim
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc
index ba78696b38..cdc960792f 100644
--- a/mindspore/ccsrc/optimizer/irpass.cc
+++ b/mindspore/ccsrc/optimizer/irpass.cc
@@ -39,6 +39,7 @@
 #include "optimizer/irpass/specialize_transform.h"
 #include "optimizer/irpass/incorporate_getitem.h"
 #include "optimizer/irpass/incorporate_call.h"
+#include "optimizer/irpass/grad_var_prepare.h"
 
 namespace mindspore {
 namespace opt {
@@ -123,6 +124,11 @@ ResolveIRPassLib::ResolveIRPassLib() {
   resolver_resolve_ = MakeSubstitution(ResolverResolve(), "resolver_resolve", prim::kPrimResolve);
   resolver_getattr_ = MakeSubstitution(ResolverGetattr(), "resolver_getattr", prim::kPrimGetAttr);
 }
+
+InferenceOptPrepareLib::InferenceOptPrepareLib() {
+  grad_var_prepare_ = MakeSubstitution(GradVarPrepare(), "grad_var_prepare", IsCNode);
+}
+
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/optimizer/irpass.h b/mindspore/ccsrc/optimizer/irpass.h
index c2af344e32..bdaf42b3ed 100644
--- a/mindspore/ccsrc/optimizer/irpass.h
+++ b/mindspore/ccsrc/optimizer/irpass.h
@@ -102,6 +102,13 @@ class ResolveIRPassLib {
   SubstitutionPtr resolver_getattr_;
 };
 
+class InferenceOptPrepareLib {
+ public:
+  InferenceOptPrepareLib();
+  ~InferenceOptPrepareLib() = default;
+  SubstitutionPtr grad_var_prepare_;
+};
+
 // predicate functions
 inline bool IsNode(const AnfNodePtr &) { return true; }
 
@@ -151,6 +158,7 @@ inline bool IsCNodeDup(const AnfNodePtr &node) {
   }
   return false;
 }
+
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc
new file mode 100644
index 0000000000..5daeced3a5
--- /dev/null
+++ b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc
@@ -0,0 +1,144 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "optimizer/irpass/grad_var_prepare.h"
+#include <vector>
+#include <algorithm>
+#include <unordered_map>
+#include <memory>
+
+#include "operator/composite/composite.h"
+#include "operator/ops.h"
+#include "optimizer/irpass.h"
+#include "optimizer/optimizer.h"
+#include "ir/visitor.h"
+#include "ir/func_graph.h"
+#include "ir/func_graph_cloner.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+
+static AnfNodePtr GenerateUnpackGraphNode(std::vector<AnfNodePtr> inputs_y, FuncGraphPtr func_graph,
+                                          AnfNodePtr func_node, bool is_unpack, bool sens_param) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(func_node);
+  std::vector<AnfNodePtr> nodes;
+  AnfNodePtr unpack_graph_node = nullptr;
+  if (is_unpack) {
+    auto unpack_graph = std::make_shared<prim::UnpackGraphPrimitive>("unpack_graph", sens_param, true);
+    nodes.push_back(NewValueNode(unpack_graph));
+    nodes.push_back(func_node);
+    // {unpackcall, {GradOperation, ...}, args...}
+    std::transform(inputs_y.begin() + 2, inputs_y.end(), std::back_inserter(nodes),
+                   [](const AnfNodePtr& node) { return node; });
+    unpack_graph_node = func_graph->NewCNode(nodes);
+  } else {
+    auto unpack_graph = std::make_shared<prim::UnpackGraphPrimitive>("unpack_graph", sens_param, false);
+    nodes.push_back(NewValueNode(unpack_graph));
+    nodes.push_back(func_node);
+    // {{GradOperation, ...}, args...}
+    std::transform(inputs_y.begin() + 1, inputs_y.end(), std::back_inserter(nodes),
+                   [](const AnfNodePtr& node) { return node; });
+    unpack_graph_node = func_graph->NewCNode(nodes);
+  }
+  return unpack_graph_node;
+}
+
+// get metagraph of value node
+MetaFuncGraphPtr GetMetaFuncGraphOfValueNode(const AnfNodePtr& node) {
+  ValuePtr value;
+  if (IsValueNode<prim::DoSignaturePrimitive>(node)) {
+    value = GetValueNode(node)->cast<prim::DoSignaturePrimitivePtr>()->function();
+  } else {
+    value = GetValueNode(node);
+  }
+  if (value == nullptr) {
+    return nullptr;
+  }
+  return value->cast<MetaFuncGraphPtr>();
+}
+
+// check if node is a specific metafuncgraph op
+bool IsMetaFuncGraph(const AnfNodePtr& node, const MetaFuncGraphPtr meta_func_graph) {
+  if (node != nullptr) {
+    auto meta_func_graph_ptr = GetMetaFuncGraphOfValueNode(node);
+    if (meta_func_graph_ptr == nullptr) {
+      return false;
+    }
+
+    if (meta_func_graph_ptr->type_name() == meta_func_graph->type_name()) {
+      return true;
+    }
+  }
+  return false;
+}
+
+// {{GradOperation, g, w}, Ys}
+// {UnPackCall, {GradOperation, g, w}, Ys}
+AnfNodePtr GradVarPrepare::operator()(const OptimizerPtr&, const AnfNodePtr& node) {
+  if (!node->isa<CNode>() || node->func_graph() == nullptr) {
+    return nullptr;
+  }
+
+  // {{...}, Ys}
+  auto inputs_y = node->cast<CNodePtr>()->inputs();
+  std::vector<AnfNodePtr> inputs_x;
+  if (IsCNode(inputs_y[0])) {
+    inputs_x = inputs_y[0]->cast<CNodePtr>()->inputs();
+  } else if (IsMetaFuncGraph(inputs_y[0], unpack_op_) && IsCNode(inputs_y[1])) {
+    inputs_x = inputs_y[1]->cast<CNodePtr>()->inputs();
+  } else {
+    return nullptr;
+  }
+
+  // {{...}, Xs}
+  if (inputs_x.size() < 2) {
+    return nullptr;
+  }
+
+  // {GradOperation, g, w} or {GradOperation, g}
+  if (!IsMetaFuncGraph(inputs_x[0], grad_op_)) {
+    return nullptr;
+  }
+
+  auto meta_func = GetMetaFuncGraphOfValueNode(inputs_x[0]);
+  if (meta_func == nullptr) {
+    return nullptr;
+  }
+  auto grad_op_ptr = meta_func->cast<prim::GradOperationPtr>();
+  auto func_node = inputs_x[1];
+  if (!IsValueNode<FuncGraph>(func_node)) {
+    return nullptr;
+  }
+
+  AnfNodePtr unpack_graph_node =
+    GenerateUnpackGraphNode(inputs_y, node->cast<CNodePtr>()->func_graph(), func_node,
+                            IsMetaFuncGraph(inputs_y[0], unpack_op_), grad_op_ptr->sens_param());
+  // constuct new grad_opration
+  inputs_x[1] = unpack_graph_node;
+  auto grad_op_cnode = node->func_graph()->NewCNode(inputs_x);
+  if (IsMetaFuncGraph(inputs_y[0], unpack_op_)) {
+    inputs_y[1] = grad_op_cnode;
+  } else {
+    inputs_y[0] = grad_op_cnode;
+  }
+  auto cnode = node->func_graph()->NewCNode(inputs_y);
+  return cnode;
+}
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h
new file mode 100644
index 0000000000..599d1dca17
--- /dev/null
+++ b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h
@@ -0,0 +1,55 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_
+#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_
+
+#include <vector>
+#include <algorithm>
+#include <unordered_map>
+#include <memory>
+
+#include "operator/composite/composite.h"
+#include "operator/ops.h"
+#include "optimizer/irpass.h"
+#include "optimizer/optimizer.h"
+#include "ir/visitor.h"
+#include "ir/func_graph.h"
+#include "ir/func_graph_cloner.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+
+// {{GradOperation, g, w}, Ys}
+// {UnPackCall, {GradOperation, g, w}, Ys}
+class GradVarPrepare : public AnfVisitor {
+ public:
+  GradVarPrepare()
+      : grad_op_(std::make_shared<prim::GradOperation>("grad")),
+        unpack_op_(std::make_shared<prim::UnpackCall>("unpack_call")) {}
+  ~GradVarPrepare() override = default;
+
+  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override;
+
+ private:
+  MetaFuncGraphPtr grad_op_;
+  MetaFuncGraphPtr unpack_op_;
+};
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_
diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc
index f3742ab654..126992cb8f 100644
--- a/mindspore/ccsrc/pipeline/action.cc
+++ b/mindspore/ccsrc/pipeline/action.cc
@@ -175,10 +175,10 @@ bool CombineLikeGraphs(const ResourcePtr&) {
 
 bool SymbolResolveAction(const ResourcePtr& res) {
   if (res->manager() == nullptr) {
-    MS_LOG(EXCEPTION) << "Resolve error.";
+    MS_LOG(EXCEPTION) << "SymbolResolve error, manager is null";
   }
   if (res->func_graph() == nullptr) {
-    MS_LOG(EXCEPTION) << "Resolve error";
+    MS_LOG(EXCEPTION) << "SymbolResolve error, graph is null";
   }
   FuncGraphPtr func_graph = res->func_graph();
   auto succ = parse::ResolveFuncGraph(func_graph, res);
@@ -194,6 +194,16 @@ bool SymbolResolveAction(const ResourcePtr& res) {
   return succ;
 }
 
+bool InferenceOptPrepareAction(const ResourcePtr& res) {
+  if (res->manager() == nullptr) {
+    MS_LOG(EXCEPTION) << "InferenceOptPrepare error, manager is null.";
+  }
+  if (res->func_graph() == nullptr) {
+    MS_LOG(EXCEPTION) << "InferenceOptPrepare error, graph is null.";
+  }
+  return InferenceOptPreparePass(res);
+}
+
 bool AbstractSpecializeAction(const ResourcePtr& res) {
   if (res->func_graph() == nullptr) {
     MS_LOG(EXCEPTION) << "AbstractSpecialize error";
@@ -303,7 +313,7 @@ static std::vector<ActionItem> CommonPipeline() {
   // Resolve the python func
   actions.emplace_back(std::make_pair("symbol_resolve", SymbolResolveAction));
   actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs));
-
+  actions.emplace_back(std::make_pair("inference_opt_prepare", InferenceOptPrepareAction));
   // Evaluate type and shape, and specialize
   actions.emplace_back(std::make_pair("abstract_specialize", AbstractSpecializeAction));
 
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index 02e8c5277b..e2626d5314 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -160,6 +160,13 @@ OptPassGroupMap GetControlPhases(const opt::irpass::OptimizeIRPassLib& irpass) {
   return map;
 }
 
+OptPassGroupMap GetInferenceOptPreparePhases() {
+  opt::irpass::InferenceOptPrepareLib irpass;
+  auto grad_var_prepare = opt::OptPassConfig({irpass.grad_var_prepare_});
+  opt::OptPassGroupMap prepare_map({{"inference_opt_prep", grad_var_prepare}});
+  return prepare_map;
+}
+
 OptPassGroupMap GetPreparePhases(const opt::irpass::OptimizeIRPassLib& irpass) {
   opt::OptPassConfig prepare_group = opt::OptPassConfig({irpass.print_tuple_wrapper_});
   OptPassGroupMap map({{"prepare_group", prepare_group}});
@@ -239,6 +246,16 @@ bool ValidatePass(const ResourcePtr& res) {
   return true;
 }
 
+bool InferenceOptPreparePass(const ResourcePtr& res) {
+  FuncGraphPtr func_graph = res->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  abstract::AbstractBasePtrList args_spec = res->args_spec();
+  auto prepare_map = GetInferenceOptPreparePhases();
+  auto infer_opt_prepare = opt::Optimizer::MakeOptimizer("inference_prepare", res, prepare_map);
+  (void)infer_opt_prepare->step(func_graph, args_spec, false);
+  return true;
+}
+
 std::vector<PassItem> kVmPasses = {{"simplify_data_structures", SimplifyDataStructuresPass},
                                    {"opt_a", OptPassAGroup},
                                    {"opt_b", OptPassBGroup},
diff --git a/mindspore/ccsrc/pipeline/pass.h b/mindspore/ccsrc/pipeline/pass.h
index 03ed8eb370..3731d7e524 100644
--- a/mindspore/ccsrc/pipeline/pass.h
+++ b/mindspore/ccsrc/pipeline/pass.h
@@ -34,7 +34,7 @@ bool CconvPass(const ResourcePtr& res);
 bool ValidatePass(const ResourcePtr& res);
 bool ConvertPrepareAdapt(const ResourcePtr& res);
 bool AddControlDependPass(const ResourcePtr& res);
-
+bool InferenceOptPreparePass(const ResourcePtr& res);
 void ReclaimOptimizer();
 }  // namespace pipeline
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h b/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h
index 3acb22d829..133d5e99a9 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h
+++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h
@@ -133,6 +133,7 @@ class FuncGraphAbstractClosure : public AbstractFuncAtom {
   FuncGraphPtr func_graph_;
   AnalysisContextPtr context_;
 };
+using FuncGraphAbstractClosurePtr = std::shared_ptr<FuncGraphAbstractClosure>;
 
 class MetaFuncGraphAbstractClosure : public AbstractFuncAtom {
  public:
diff --git a/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc b/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc
index 9326ded2d5..aeaa6b17f8 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc
@@ -41,7 +41,7 @@ AnalysisContextPtr AnalysisContext::NewFuncGraphContext(const FuncGraphPtr &func
     } else {
       oss << "nullptr";
     }
-    MS_LOG(EXCEPTION) << "" << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info());
+    MS_LOG(EXCEPTION) << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info());
   }
   return NewContext(parent_context, func_graph, args_spec_list);
 }
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 98d82de5d5..4110f25811 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -180,6 +180,85 @@ AbstractBasePtr DoSignatureEvaluator::Run(AnalysisEnginePtr engine, const Config
   return engine->ForwardConfig(out_conf, fn_conf);
 }
 
+static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_spec_list, bool need_unpack) {
+  // arg[0] is the func graph to unpack, ignore it
+  AbstractBasePtrList sepcialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end());
+  AbstractBasePtrList graph_sepcialize_args;
+  if (need_unpack) {
+    for (size_t index = 0; index < sepcialize_args_before_unpack.size(); index++) {
+      MS_EXCEPTION_IF_NULL(sepcialize_args_before_unpack[index]);
+      if (sepcialize_args_before_unpack[index]->isa<AbstractTuple>()) {
+        AbstractTuplePtr arg_tuple = sepcialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
+        std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(),
+                       std::back_inserter(graph_sepcialize_args), [](AbstractBasePtr abs) { return abs; });
+      } else if (sepcialize_args_before_unpack[index]->isa<AbstractDictionary>()) {
+        AbstractDictionaryPtr arg_dict = sepcialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
+        auto dict_elems = arg_dict->elements();
+        (void)std::transform(
+          dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_sepcialize_args),
+          [](const AbstractAttribute &item) { return std::make_shared<AbstractKeywordArg>(item.first, item.second); });
+      } else {
+        MS_LOG(EXCEPTION) << "UnpackGraph require args should be tuple or dict, but got "
+                          << sepcialize_args_before_unpack[index]->ToString();
+      }
+    }
+  } else {
+    graph_sepcialize_args = sepcialize_args_before_unpack;
+  }
+  return graph_sepcialize_args;
+}
+
+AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list,
+                                          AnfNodeConfigPtr out_conf) {
+  if (out_conf->node() == nullptr || !out_conf->node()->isa<CNode>()) {
+    MS_LOG(EXCEPTION) << "Node of out_conf should be CNode";
+  }
+  if (!prim_->isa<prim::UnpackGraphPrimitive>()) {
+    MS_LOG(EXCEPTION) << "Primitive should be UnpackGraphPrimitive, but got " << prim_->ToString();
+  }
+
+  auto unpack_graph = prim_->cast<prim::UnpackGraphPrimitivePtr>();
+  auto out_node = out_conf->node()->cast<CNodePtr>();
+  const auto &out_node_inputs = out_node->inputs();
+  if (out_node->inputs().size() == 0 || (out_node_inputs.size() - 1) != args_conf_list.size()) {
+    MS_LOG(EXCEPTION) << "UnpackGraphPrimitive"
+                      << " args size should equal to inputs size minus 1, but args size " << args_conf_list.size()
+                      << ", inputs size " << out_node_inputs.size();
+  }
+  AnfNodePtrList args_inputs{out_node_inputs.begin() + 1, out_node_inputs.end()};
+  AbstractBasePtrList args_spec_list;
+  (void)std::transform(args_conf_list.begin(), args_conf_list.end(), std::back_inserter(args_spec_list),
+                       [](const ConfigPtr &ref) -> AbstractBasePtr { return ref->GetEvaluatedValue(); });
+  // get the forward graph
+  MS_EXCEPTION_IF_NULL(args_spec_list[0]);
+  AbstractFunctionPtr fn = args_spec_list[0]->cast<AbstractFunctionPtr>();
+  if (fn == nullptr) {
+    MS_LOG(EXCEPTION) << "UnpackGraphPrimitive arg0 must be AbstractFunction, but " << args_spec_list[0]->ToString();
+  }
+  auto real_fn = fn->cast<FuncGraphAbstractClosurePtr>();
+  MS_EXCEPTION_IF_NULL(real_fn);
+  FuncGraphPtr forward_graph = real_fn->func_graph();
+  MS_EXCEPTION_IF_NULL(forward_graph);
+  AbstractBasePtrList graph_sepcialize_args =
+    GetUnpackGraphSpecArgsList(args_spec_list, unpack_graph->need_unpack_args());
+
+  AbstractBasePtrList graph_sepcialize_args_without_sens;
+  (void)std::transform(graph_sepcialize_args.begin(),
+                       graph_sepcialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0),
+                       std::back_inserter(graph_sepcialize_args_without_sens), [](AbstractBasePtr abs) { return abs; });
+  auto new_graph = forward_graph->GenerateGraph(graph_sepcialize_args_without_sens);
+  engine->func_graph_manager()->AddFuncGraph(new_graph);
+  ScopePtr scope = kDefaultScope;
+  if (out_conf != nullptr) {
+    scope = out_conf->node()->scope();
+  }
+  ScopeGuard scope_guard(scope);
+  AnfNodePtr new_vnode = NewValueNode(new_graph);
+  AnfNodeConfigPtr fn_conf = engine->MakeConfig(new_vnode, out_conf->context());
+
+  return engine->ForwardConfig(out_conf, fn_conf);
+}
+
 namespace {
 py::object BuildValue(const ValuePtr &value_ptr) {
   if (value_ptr == nullptr) {
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.h b/mindspore/ccsrc/pipeline/static_analysis/prim.h
index 9dae576a4c..e154473dbb 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.h
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.h
@@ -87,6 +87,21 @@ class DoSignatureEvaluator : public Evaluator {
   PrimitivePtr prim_;
 };
 
+class UnpackGraphEvaluator : public Evaluator {
+ public:
+  explicit UnpackGraphEvaluator(const PrimitivePtr primitive) : Evaluator("UnpackGraphEvaluator"), prim_(primitive) {}
+  ~UnpackGraphEvaluator() override = default;
+  AbstractBasePtr Run(AnalysisEnginePtr engine, const ConfigPtrList &argrefs,
+                      AnfNodeConfigPtr out_config = nullptr) override;
+
+  AbstractBasePtr Infer(AnalysisEnginePtr, const AbstractBasePtrList &) override {
+    MS_LOG(EXCEPTION) << "Infer() should not be called, Run() method should be called";
+  }
+
+ private:
+  PrimitivePtr prim_;
+};
+
 bool IsInWhiteList(PrimitivePtr primitive);
 StandardPrimitiveEvalImpl GetPrimitiveInferImpl(const PrimitivePtr &primitive);
 
diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
index 0bfba265db..49182e8d09 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
@@ -289,6 +289,10 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
     evaluator = std::make_shared<DoSignatureEvaluator>(prim);
     return evaluator;
   }
+  if (prim->isa<prim::UnpackGraphPrimitive>()) {
+    evaluator = std::make_shared<UnpackGraphEvaluator>(prim);
+    return evaluator;
+  }
   if (prim->HasPyEvaluator()) {
     auto prim_py = dyn_cast<PrimitivePy>(prim);
     if (prim_py != nullptr) {
diff --git a/tests/ut/python/parameter_feature/test_var_grad.py b/tests/ut/python/parameter_feature/test_var_grad.py
index d51b78ed9d..12c05d0594 100644
--- a/tests/ut/python/parameter_feature/test_var_grad.py
+++ b/tests/ut/python/parameter_feature/test_var_grad.py
@@ -19,6 +19,8 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 import mindspore.ops.composite as C
 from mindspore.common.api import _executor
+from mindspore.common.parameter import ParameterTuple
+from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -34,3 +36,152 @@ def test_net_vargs_expand():
     sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
     net = AddNet()
     out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
+
+class VarNet(Cell):
+    def __init__(self, net):
+        super(VarNet, self).__init__()
+        self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+        self.w = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
+        self.net = net
+    def construct(self, *args):
+        return self.net(*args)*self.w + self.b
+    
+class SecondNet(Cell):
+    def __init__(self):
+        super(SecondNet, self).__init__()
+        self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+    def construct(self, *args):
+        res = args[0] + args[1]
+        return res + self.b2
+def test_all_var_args_grad_with_sens():
+    """"test grad_by_list_with_sens with all var args input"""
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_grad_list_var_args():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_by_list(self.net, self.weights)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)
+
+def test_grad_all_var_args():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_all(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)
+
+def test_grad_all_var_args_with_sens():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_all_with_sens(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_grad_var_args_with_sens():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_with_sens(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_var_args_grad():
+    class VarNet(Cell):
+        def __init__(self, net):
+            super(VarNet, self).__init__()
+            self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+            self.net = net
+        def construct(self, *args):
+            return self.net(*args) + self.b
+        
+    class SecondNet(Cell):
+        def __init__(self):
+            super(SecondNet, self).__init__()
+            self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+        def construct(self, *args):
+            res = args[0] + args[1]
+            return res + self.b2
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+            self.weights = ParameterTuple(net.trainable_params())
+        def construct(self, x, y, sens):
+            return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+
+def test_var_args_positional():
+    """"test grad_all with var args in inner graph"""
+    class VarNet(Cell):
+        def __init__(self, net):
+            super(VarNet, self).__init__()
+            self.net = net
+        def construct(self, x, y):
+            return self.net(x, y)*x
+
+    class SecondNet(Cell):
+        def __init__(self):
+            super(SecondNet, self).__init__()
+        def construct(self, *args):
+            return args[0] + args[1]
+
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+            self.weights = ParameterTuple(net.trainable_params())
+        def construct(self, x, y):
+            return C.grad_all(self.net)(x, y)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)

From 04be6a37f0b73dfa2c4d86be99539fbf14522210 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Wed, 1 Apr 2020 21:37:02 +0800
Subject: [PATCH 058/367] add getptr for memreuse

---
 mindspore/ccsrc/device/kernel_runtime.cc      | 33 ++++---------------
 mindspore/ccsrc/device/kernel_runtime.h       |  3 --
 .../ccsrc/pre_activate/mem_reuse/mem_reuse.cc | 30 +++++++++++++++++
 .../ccsrc/pre_activate/mem_reuse/mem_reuse.h  |  4 +++
 4 files changed, 40 insertions(+), 30 deletions(-)

diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index 99f5d491ac..878fe4a7f8 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -42,7 +42,6 @@ KernelRuntime::~KernelRuntime() {
 #ifdef ENABLE_DUMP_E2E
   dump_conf_ptr_ = nullptr;
 #endif
-  reuse_mem_base_ = nullptr;
   mem_reuse_util_ptr_ = nullptr;
 }
 
@@ -476,9 +475,9 @@ void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) {
   bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get());
   size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize();
   MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]";
-  auto base_ptr = MallocDynamicMem(total_allocated_size, false);
-  reuse_mem_base_ = base_ptr;
   mem_reuse_util_ptr_ = mem_reuse_util_ptr;
+  auto base_ptr = MallocDynamicMem(total_allocated_size, false);
+  mem_reuse_util_ptr_->set_mem_base(base_ptr);
   auto &kernels = graph->execution_order();
   for (auto &kernel : kernels) {
     AssignNodeOutputMem(kReuseDynamicMem, kernel, kGetAllOuts);
@@ -488,22 +487,13 @@ void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) {
 
 void KernelRuntime::AssignReuseWorkSpaceMem(const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
-  auto key = node.get();
   auto kernel_mod = AnfAlgo::GetKernelMod(node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
   size_t index = 0;
-  auto iter = mem_reuse_util_ptr_->kernel_workspace_refs_.find(key);
   for (auto &size : kernel_mod->GetWorkspaceSizeList()) {
-    if (iter != mem_reuse_util_ptr_->kernel_workspace_refs_.end()) {
-      if (index >= iter->second.size()) {
-        MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size()
-                          << "]";
-      }
-      auto wk_ref = iter->second[index];
-      auto wk_ptr = reuse_mem_base_ + wk_ref->offset_;
-      AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get());
-      index++;
-    }
+    auto wk_ptr = mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index);
+    AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get());
+    index++;
   }
 }
 
@@ -554,18 +544,7 @@ uint8_t *KernelRuntime::CalDeviceMem(const AnfNodePtr &node, size_t size, int fl
   } else if (flag == kDynamicMem) {
     ptr = MallocDynamicMem(size, false);
   } else if (flag == kReuseDynamicMem) {
-    auto key = node.get();
-    auto iter = mem_reuse_util_ptr_->kernel_output_refs_.find(key);
-    if (iter != mem_reuse_util_ptr_->kernel_output_refs_.end()) {
-      // private member form KernelRuntime
-      memreuse::KernelRefCountPtr kernel_ref_count_ptr = mem_reuse_util_ptr_->kernel_output_refs_[key][index];
-      if (kernel_ref_count_ptr == nullptr) {
-        return ptr;
-      }
-      ptr = reuse_mem_base_ + kernel_ref_count_ptr->offset_;
-    } else {
-      MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs";
-    }
+    ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index);
   }
   return ptr;
 }
diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h
index afdb45a698..ac9a56ed4d 100644
--- a/mindspore/ccsrc/device/kernel_runtime.h
+++ b/mindspore/ccsrc/device/kernel_runtime.h
@@ -128,9 +128,6 @@ class KernelRuntime {
   size_t total_static_size_ = 0;
   size_t total_dynamic_size_ = 0;
   MemReuseUtilPtr mem_reuse_util_ptr_{nullptr};
-
- private:
-  uint8_t *reuse_mem_base_{nullptr};
 };
 using KernelRuntimePtr = std::shared_ptr<KernelRuntime>;
 }  // namespace device
diff --git a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
index 0db3c35196..2113fec653 100644
--- a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
+++ b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
@@ -316,5 +316,35 @@ void MemReuseUtil::SetAllInfo(KernelGraph *graph) {
   MemReuseChecker::GetInstance().CheckMemReuseIR(total_refs_list_, kernel_def_ptr_list_, graph);
 #endif
 }
+
+uint8_t *MemReuseUtil::GetNodeOutputPtr(const AnfNodePtr &node, size_t index) const {
+  auto key = node.get();
+  auto iter = kernel_output_refs_.find(key);
+  uint8_t *ptr = nullptr;
+  if (iter != kernel_output_refs_.end()) {
+    if (index >= iter->second.size()) {
+      MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size() << "]";
+    }
+    auto output_ref = iter->second[index];
+    ptr = mem_base_ + output_ref->offset_;
+  } else {
+    MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs";
+  }
+  return ptr;
+}
+
+uint8_t *MemReuseUtil::GetNodeWorkSpacePtr(const AnfNodePtr &node, size_t index) const {
+  auto key = node.get();
+  auto iter = kernel_workspace_refs_.find(key);
+  uint8_t *ptr = nullptr;
+  if (iter != kernel_workspace_refs_.end()) {
+    if (index >= iter->second.size()) {
+      MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size() << "]";
+    }
+    auto wk_ref = iter->second[index];
+    ptr = mem_base_ + wk_ref->offset_;
+  }
+  return ptr;
+}
 }  // namespace memreuse
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h
index 6ecd222688..cae0e4565f 100644
--- a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h
+++ b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h
@@ -76,6 +76,9 @@ class MemReuseUtil {
   void set_kernel_def_ptr_list(const KernelDefPtrMaps &kernel_def_ptr_list) {
     kernel_def_ptr_list_ = kernel_def_ptr_list;
   }
+  void set_mem_base(uint8_t *mem_base) { mem_base_ = mem_base; }
+  uint8_t *GetNodeOutputPtr(const AnfNodePtr &node, size_t index) const;
+  uint8_t *GetNodeWorkSpacePtr(const AnfNodePtr &node, size_t index) const;
 
  private:
   int util_index_;
@@ -88,6 +91,7 @@ class MemReuseUtil {
   size_t total_dy_size_ = 0;
   size_t total_workspace_size_ = 0;
   size_t total_reuseworkspace_size_ = 0;
+  uint8_t *mem_base_{nullptr};
 };
 using MemReuseUtilPtr = std::shared_ptr<MemReuseUtil>;
 }  // namespace memreuse

From b8c16f97f73c77dbe4d7e64045a256aedc336056 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Wed, 1 Apr 2020 22:03:01 +0800
Subject: [PATCH 059/367] fix examples of P.Fill

---
 mindspore/ops/operations/array_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index f6d563321c..7b49cbf84c 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -713,7 +713,7 @@ class Fill(PrimitiveWithInfer):
 
     Examples:
         >>> fill = P.Fill()
-        >>> fill(P.DType()(x), (2, 2), 1)
+        >>> fill(mindspore.float32, (2, 2), 1)
     """
 
     @prim_attr_register

From 4feba6332de942a8e4f86b77a265cc58a773cec2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Wed, 1 Apr 2020 22:16:29 +0800
Subject: [PATCH 060/367] fix the examples of P.ReLU6

---
 mindspore/ops/operations/nn_ops.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index afa4c7dfe3..ed20286bd8 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -190,7 +190,6 @@ class ReLU6(PrimitiveWithInfer):
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
         >>> relu6 = ReLU6()
         >>> result = relu6(input_x)
-        >>> assert result.asnumpy() == Tensor(np.array([[0, 4.0, 0.0], [2.0, 0.0, 6.0]], np.float32)).asnumpy()
     """
 
     @prim_attr_register

From ed3c2d7229c1d3ca34e3ce95cb0a7acf2104564c Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Tue, 31 Mar 2020 09:14:08 +0800
Subject: [PATCH 061/367] add RMSProp optimizer

---
 mindspore/ccsrc/transform/convert.cc          |   6 +-
 mindspore/ccsrc/transform/op_declare.cc       |  16 ++
 mindspore/ccsrc/transform/op_declare.h        |   6 +
 mindspore/nn/optim/__init__.py                |   3 +-
 mindspore/nn/optim/rmsprop.py                 | 187 ++++++++++++++++++
 mindspore/ops/_grad/grad_math_ops.py          |   4 +-
 mindspore/ops/operations/__init__.py          |   5 +-
 mindspore/ops/operations/nn_ops.py            | 152 ++++++++++++++
 .../utils/block_util.py                       |   4 +
 tests/ut/python/ops/test_ops.py               |  12 ++
 10 files changed, 390 insertions(+), 5 deletions(-)
 create mode 100644 mindspore/nn/optim/rmsprop.py

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 48056c38da..fdacff7ba8 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -183,6 +183,8 @@ const char kNameDiagPart[] = "DiagPart";
 const char kNameSpaceToBatch[] = "SpaceToBatch";
 const char kNameBatchToSpace[] = "BatchToSpace";
 const char kNameAtan2[] = "Atan2";
+const char kNameApplyRMSProp[] = "ApplyRMSProp";
+const char kNameApplyCenteredRMSProp[] = "ApplyCenteredRMSProp";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -367,7 +369,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameDiagPart), ADPT_DESC(DiagPart)},
     {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
     {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)},
-    {string(kNameAtan2), ADPT_DESC(Atan2)}};
+    {string(kNameAtan2), ADPT_DESC(Atan2)},
+    {string(kNameApplyRMSProp), ADPT_DESC(ApplyRMSPropD)},
+    {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSProp)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index f7fdcfbe56..9258eb08db 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -1202,6 +1202,22 @@ INPUT_MAP(Atan2) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(Atan2) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Atan2) = {{0, OUTPUT_DESC(y)}};
 
+// ApplyRMSPropD
+INPUT_MAP(ApplyRMSPropD) = {
+  {1, INPUT_DESC(var)}, {2, INPUT_DESC(ms)}, {3, INPUT_DESC(mom)}, {4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)}};
+INPUT_ATTR_MAP(ApplyRMSPropD) = {{6, ATTR_DESC(rho, AnyTraits<float>())},
+                                 {7, ATTR_DESC(momentum, AnyTraits<float>())},
+                                 {8, ATTR_DESC(epsilon, AnyTraits<float>())}};
+ATTR_MAP(ApplyRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+OUTPUT_MAP(ApplyRMSPropD) = {{0, OUTPUT_DESC(var)}};
+
+// ApplyCenteredRMSProp
+INPUT_MAP(ApplyCenteredRMSProp) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
+                                   {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
+                                   {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
+ATTR_MAP(ApplyCenteredRMSProp) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+OUTPUT_MAP(ApplyCenteredRMSProp) = {{0, OUTPUT_DESC(var)}};
+
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 1924d2719b..031ce80865 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -445,6 +445,12 @@ DECLARE_OP_ADAPTER(BatchToSpaceD)
 DECLARE_OP_USE_OUTPUT(BatchToSpaceD)
 DECLARE_OP_ADAPTER(Atan2)
 DECLARE_OP_USE_OUTPUT(Atan2)
+DECLARE_OP_ADAPTER(ApplyRMSPropD)
+DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD)
+DECLARE_OP_USE_OUTPUT(ApplyRMSPropD)
+DECLARE_OP_ADAPTER(ApplyCenteredRMSProp)
+DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp)
+
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)
diff --git a/mindspore/nn/optim/__init__.py b/mindspore/nn/optim/__init__.py
index 6f7f6fbd46..8f21179893 100644
--- a/mindspore/nn/optim/__init__.py
+++ b/mindspore/nn/optim/__init__.py
@@ -25,6 +25,7 @@ from .lamb import Lamb
 from .sgd import SGD
 from .lars import LARS
 from .ftrl import FTRL
+from .rmsprop import RMSProp
 
 __all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'AdamWeightDecay',
-           'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL']
+           'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL', 'RMSProp']
diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py
new file mode 100644
index 0000000000..3000fdeeee
--- /dev/null
+++ b/mindspore/nn/optim/rmsprop.py
@@ -0,0 +1,187 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""rmsprop"""
+from mindspore.ops import functional as F, composite as C, operations as P
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+from mindspore._checkparam import ParamValidator as validator
+import mindspore.common.dtype as mstype
+from .optimizer import Optimizer, grad_scale
+
+rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
+centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
+
+
+@rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
+def _rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
+    """Apply rmsprop optimizer to the weight parameter."""
+    success = True
+    success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
+def _rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
+    """Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
+    success = True
+    success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
+                               "Tensor", "Tensor")
+def _centered_rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
+    """Apply centered rmsprop optimizer to the weight parameter."""
+    success = True
+    success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@centered_rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
+                               "Tensor", "Tensor")
+def _centered_rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
+    """Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate."""
+    success = True
+    success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+class RMSProp(Optimizer):
+    """
+    Implements Root Mean Squared Propagation (RMSProp) algorithm.
+
+    Note:
+        Update `params` according to the RMSProp algorithm.
+
+        The equation is as follows:
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        The first equation calculates moving average of the squared gradient for
+        each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`.
+
+        if centered is True:
+
+        ..  math::
+            g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `params`, which will be updated.
+        :math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`.
+        :math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`,
+        :math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse,
+        represents `gradients`.
+
+    Args:
+        params (list[Parameter]): A list of parameter, which will be updated. The element in `parameters`
+                                  should be class mindspore.Parameter.
+        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+                                                        use dynamic learning rate, then the i-th step will
+                                                        take the i-th value as the learning rate.
+                                                        When the learning_rate is float or learning_rate is a Tensor
+                                                        but the dims of the Tensor is 0, use fixed learning rate.
+                                                        Other cases are not supported.
+        decay (float): Decay rate.
+        momentum (float): Hyperparameter of type float, means momentum for the moving average.
+        epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than 0.
+        use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
+        centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False
+        loss_scale (float): A floating point value for the loss scale. Default: 1.0.
+
+    Inputs:
+        - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
+
+    Outputs:
+        Tensor[bool], the value is True.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
+        >>> model = Model(net, loss, opt)
+    """
+    def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
+                 use_locking=False, centered=False, loss_scale=1.0):
+        super(RMSProp, self).__init__(learning_rate, params)
+
+        if isinstance(momentum, float) and momentum < 0.0:
+            raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
+
+        if decay < 0.0:
+            raise ValueError("decay should be at least 0.0, but got dampening {}".format(decay))
+        self.decay = decay
+        self.epsilon = epsilon
+
+        validator.check_type("use_locking", use_locking, [bool])
+        validator.check_type("centered", centered, [bool])
+        self.centered = centered
+        if centered:
+            self.opt = P.ApplyCenteredRMSProp(use_locking)
+            self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
+        else:
+            self.opt = P.ApplyRMSProp(use_locking)
+
+        self.dynamic_lr = False
+        if not isinstance(learning_rate, float):
+            self.dynamic_lr = True
+            self.gather = P.GatherV2()
+            self.assignadd = P.AssignAdd()
+            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
+            self.axis = 0
+
+        self.momentum = momentum
+
+        self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
+        self.moment = self.parameters.clone(prefix="moment", init='zeros')
+        self.hyper_map = C.HyperMap()
+
+        self.decay = decay
+        self.reciprocal_scale = 1.0 / loss_scale
+
+    def construct(self, gradients):
+        params = self.parameters
+        if self.reciprocal_scale != 1.0:
+            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
+        if self.dynamic_lr:
+            lr = self.gather(self.learning_rate, self.global_step, self.axis)
+            F.control_depend(lr, self.assignadd(self.global_step, self.one))
+        else:
+            lr = self.learning_rate
+        if self.centered:
+            success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
+                                               self.momentum), params, self.mg, self.ms, self.moment, gradients)
+        else:
+            success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
+                                               self.momentum), params, self.ms, self.moment, gradients)
+        return success
diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py
index 9e90c5660c..1675855c88 100755
--- a/mindspore/ops/_grad/grad_math_ops.py
+++ b/mindspore/ops/_grad/grad_math_ops.py
@@ -394,8 +394,8 @@ def _split_shape_index(input_shape, axis):
         axis = tuple([axis])
     reduction_indices = tuple([(i + rank) % rank for i in axis])
     other_indices = tuple(set(range(rank)) - set(reduction_indices))
-    reduced_num = reduce(lambda x, y: x * y, [input_shape[i] for i in reduction_indices])
-    other_num = reduce(lambda x, y: x * y, [input_shape[i] for i in other_indices])
+    reduced_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in reduction_indices])
+    other_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in other_indices])
     perm = reduction_indices + other_indices
     return tuple([reduced_num, other_num]), perm
 
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 5c98568b8a..727ddaf88f 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -65,7 +65,8 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      SmoothL1Loss, Softmax,
                      SoftmaxCrossEntropyWithLogits, ROIAlign,
                      SparseSoftmaxCrossEntropyWithLogits, Tanh,
-                     TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl)
+                     TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl,
+                     ApplyRMSProp, ApplyCenteredRMSProp)
 from .other_ops import Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, CheckValid, MakeRefKey
 
 
@@ -228,6 +229,8 @@ __all__ = [
     "SpaceToBatch",
     "BatchToSpace",
     "Atan2",
+    "ApplyRMSProp",
+    "ApplyCenteredRMSProp"
 ]
 
 __all__.sort()
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index afa4c7dfe3..b9ab7e8dc9 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1359,6 +1359,158 @@ class SGD(PrimitiveWithInfer):
         validator.check_typename("stat_dtype", stat_dtype, [mstype.float16, mstype.float32])
         return parameters_dtype
 
+class ApplyRMSProp(PrimitiveWithInfer):
+    """
+    Optimizer that implements the Root Mean Square prop(RMSProp) algorithm.
+
+    Note:
+        Update `var` according to the RMSProp algorithm.
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `var`, which will be updated.
+        :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`,
+        :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`.
+
+    Args:
+        use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False.
+
+    Inputs:
+        - **var** (Tensor) - Weights to be update.
+        - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`.
+        - **moment** (Tensor) - Delta of `var`, must have the same type as `var`.
+        - **grad** (Tensor) - Gradients, must have the same type as `var`.
+        - **learning_rate** (Union[Number, Tensor]) - Learning rate.
+        - **decay** (float) - Decay rate.
+        - **momentum** (float) - Momentum.
+        - **epsilon** (float) - Ridge term.
+
+    Outputs:
+        Tensor, parameters to be update.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate)
+        >>> model = Model(net, loss, opt)
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        self.use_locking = validator.check_type("use_locking", use_locking, [bool])
+
+    def infer_shape(self, var_shape, mean_square_shape, moment_shape, grad_shape, learning_rate_shape, decay_shape,
+                    momentum_shape, epsilon_shape):
+        validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape)
+        validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape)
+        validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape)
+        return var_shape
+
+    def infer_dtype(self, var_dtype, mean_square_dtype, moment_dtype, grad_dtype, learning_rate_dtype, decay_dtype,
+                    momentum_dtype, epsilon_dtype):
+        validator.check_subclass("var_dtype", var_dtype, mstype.tensor)
+        validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor)
+        validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor)
+        validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor)
+        args = {"var_dtype": var_dtype, "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype,
+                "grad_dtype": grad_dtype}
+        validator.check_type_same(args, mstype.number_type)
+
+        args = {"learning_rate_dtype": learning_rate_dtype, "decay_dtype": decay_dtype,
+                'momentum_dtype': momentum_dtype, "epsilon_dtype": epsilon_dtype}
+        validator.check_type_same(args, [mstype.float16, mstype.float32])
+        return var_dtype
+
+
+class ApplyCenteredRMSProp(PrimitiveWithInfer):
+    """
+    Optimizer that implements the centered RMSProp algorithm.
+
+    Note:
+        Update `var` according to the centered RMSProp algorithm.
+
+        ..  math::
+            g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `var`, which will be updated.
+        :math:`g_{t}` represents `mean_gradient`, :math:`g_{t-1}` is the last momentent of :math:`g_{t}`.
+        :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`,
+        :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`.
+
+    Args:
+        use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False.
+
+    Inputs:
+        - **var** (Tensor) - Weights to be update.
+        - **mean_gradient** (Tensor) - Mean gradients, must have the same type as `var`.
+        - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`.
+        - **moment** (Tensor) - Delta of `var`, must have the same type as `var`.
+        - **grad** (Tensor) - Gradients, must have the same type as `var`.
+        - **learning_rate** (Union[Number, Tensor]) - Learning rate.
+        - **decay** (float) - Decay rate.
+        - **momentum** (float) - Momentum.
+        - **epsilon** (float) - Ridge term.
+
+    Outputs:
+        Tensor, parameters to be update.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True)
+        >>> model = Model(net, loss, opt)
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        self.use_locking = validator.check_type("use_locking", use_locking, [bool])
+
+    def infer_shape(self, var_shape, mean_gradient_shape, mean_square_shape, moment_shape, grad_shape,
+                    learning_rate_shape, decay_shape, momentum_shape, epsilon_shape):
+        validator.check_param_equal("var_shape", var_shape, "mean_gradient_shape", mean_gradient_shape)
+        validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape)
+        validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape)
+        validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape)
+        return var_shape
+
+    def infer_dtype(self, var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype, grad_dtype,
+                    learning_rate_dtype, rho_dtype, momentum_dtype, epsilon_dtype):
+        validator.check_subclass("var_dtype", var_dtype, mstype.tensor)
+        validator.check_subclass("mean_gradient_dtype", mean_gradient_dtype, mstype.tensor)
+        validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor)
+        validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor)
+        validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor)
+        args = {"var_dtype": var_dtype, "mean_gradient_dtype": mean_gradient_dtype,
+                "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype, "grad_dtype": grad_dtype}
+        validator.check_type_same(args, mstype.number_type)
+
+        args = {"learning_rate_dtype": learning_rate_dtype, "rho_dtype": rho_dtype, 'momentum_dtype': momentum_dtype,
+                "epsilon_dtype": epsilon_dtype}
+        validator.check_type_same(args, [mstype.float16, mstype.float32])
+        return var_dtype
+
 
 class LayerNorm(Primitive):
     r"""
diff --git a/tests/mindspore_test_framework/utils/block_util.py b/tests/mindspore_test_framework/utils/block_util.py
index 9d75ae0888..b4a926c15d 100644
--- a/tests/mindspore_test_framework/utils/block_util.py
+++ b/tests/mindspore_test_framework/utils/block_util.py
@@ -223,6 +223,10 @@ class InputOpNet(nn.Cell):
         x = self.op(x1, x2, x3, x4, x5, self.c1)
         return x
 
+    def construct5_c4(self, x1, x2, x3, x4, x5):
+        x = self.op(x1, x2, x3, x4, x5, self.c1, self.c2, self.c3, self.c4)
+        return x
+
 def gen_net(op, input_num, training=True, desc_const=(), const_first=False, add_fake_input=False):
     if isinstance(op, nn.Cell):
         return op
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 117036c37e..453ef9a652 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -805,6 +805,18 @@ test_case_nn_ops = [
         'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]],
         'desc_bprop': [3, 3],
         'skip': ['backward']}),
+    ('ApplyRMSProp', {
+        'block': P.ApplyRMSProp(),
+        'desc_const': [0.9, 0.0, 1e-10, 0.001],
+        'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]],
+        'desc_bprop': [3, 3],
+        'skip': ['backward']}),
+    ('ApplyCenteredRMSProp', {
+        'block': P.ApplyCenteredRMSProp(),
+        'desc_const': [0.9, 0.0, 1e-10, 0.001],
+        'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]],
+        'desc_bprop': [3, 3],
+        'skip': ['backward']}),
 ]
 
 test_case_array_ops = [

From ff4f935e400336fae2ec42b44ebd73c9397a982b Mon Sep 17 00:00:00 2001
From: lvliang <lvliang18@huawei.com>
Date: Wed, 1 Apr 2020 14:08:26 +0800
Subject: [PATCH 062/367] pynative-add-lenet

---
 tests/st/pynative/test_ascend_lenet.py | 157 +++++++++++++++++++++++++
 1 file changed, 157 insertions(+)
 create mode 100644 tests/st/pynative/test_ascend_lenet.py

diff --git a/tests/st/pynative/test_ascend_lenet.py b/tests/st/pynative/test_ascend_lenet.py
new file mode 100644
index 0000000000..4681454489
--- /dev/null
+++ b/tests/st/pynative/test_ascend_lenet.py
@@ -0,0 +1,157 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import pytest
+import numpy as np
+import time, math
+import mindspore.nn as nn
+from mindspore import context, Tensor, ParameterTuple
+from mindspore.ops import operations as P
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.common import dtype as mstype
+
+from mindspore.nn.wrap.cell_wrapper import WithLossCell
+from mindspore.nn.optim import Momentum
+
+np.random.seed(1)
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+class LeNet(nn.Cell):
+    """
+    Lenet network
+    Args:
+        num_class (int): Num classes, Default: 10.
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
+    """
+    def __init__(self, num_class=10):
+        super(LeNet, self).__init__()
+        self.num_class = num_class
+        self.batch_size = 32
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.reshape = P.Reshape()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.reshape(x, (self.batch_size, -1))
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
+
+
+class CrossEntropyLoss(nn.Cell):
+    """
+    Define loss for network
+    """
+    def __init__(self):
+        super(CrossEntropyLoss, self).__init__()
+        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean()
+        self.one_hot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.num = Tensor(32.0, mstype.float32)
+
+    def construct(self, logits, label):
+        label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
+        loss = self.cross_entropy(logits, label)[0]
+        loss = P.RealDiv()(P.ReduceSum()(loss, -1), self.num)
+        return loss
+
+
+class GradWrap(nn.Cell):
+    """
+    GradWrap definition
+    """
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+        self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
+
+    def construct(self, x, label):
+        weights = self.weights
+        return C.grad_by_list(self.network, weights)(x, label)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_ascend_pynative_lenet():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+    epoch_size = 20
+    batch_size = 32
+    inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32))
+    labels = Tensor(np.ones([batch_size]).astype(np.int32))
+
+    net = LeNet()
+    criterion = CrossEntropyLoss()
+    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
+
+    net_with_criterion = WithLossCell(net, criterion)
+    train_network = GradWrap(net_with_criterion)
+    train_network.set_train()
+    total_time = 0
+
+    for epoch in range(0, epoch_size):
+        start_time = time.time()
+        fw_output = net(inputs)
+        loss_output = criterion(fw_output, labels)
+        grads = train_network(inputs, labels)
+        success = optimizer(grads)
+        end_time = time.time()
+        cost_time = end_time - start_time
+        total_time = total_time + cost_time
+
+        print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
+    assert(total_time < 20.0)
+    assert(loss_output.asnumpy() < 0.01)
+    
\ No newline at end of file

From 7f53bb062d090dea2c6bef4b92e45aae3b1f5549 Mon Sep 17 00:00:00 2001
From: YuJianfeng <yujianfeng5@huawei.com>
Date: Wed, 1 Apr 2020 19:42:13 +0800
Subject: [PATCH 063/367] Optimize depend edge with make tuple input

---
 .../pre_activate/pass/optimize_dependence.cc  | 93 +++++++++++++++----
 .../pass/optimize_dependence_test.cc          | 20 ++++
 .../pre_activate/optimize_dependence_test.py  | 22 +++++
 3 files changed, 116 insertions(+), 19 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc b/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc
index 6ae3f3be36..db32354abf 100644
--- a/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc
+++ b/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc
@@ -27,6 +27,69 @@
 namespace mindspore {
 namespace opt {
 constexpr auto kSingleInputIndex = 1;
+namespace {
+AnfNodePtr GetReplaceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(node);
+  if (!node->isa<CNode>()) {
+    return nullptr;
+  }
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  string op_name = AnfAlgo::GetCNodeName(cnode);
+  // Currently we only eliminate transdata or cast nodes.
+  if (op_name != kTransDataOpName && op_name != prim::kPrimCast->name()) {
+    return nullptr;
+  }
+  auto manager = func_graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+  // Check whether the node has only one output node.
+  if (manager->node_users().find(cnode) == manager->node_users().end()) {
+    MS_LOG(EXCEPTION) << "The node should be used by at least another node's input";
+  }
+  if (manager->node_users()[cnode].size() > 1) {
+    return nullptr;
+  }
+  CheckCNodeInputSize(cnode, kSingleInputIndex + 1);
+  return cnode->input(kSingleInputIndex);
+}
+
+bool ReplaceMakeTuple(const FuncGraphPtr &func_graph, const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimMakeTuple->name()) {
+    return false;
+  }
+  std::vector<AnfNodePtr> new_make_tuple_inputs;
+  bool need_update = false;
+  for (const auto &input : cnode->inputs()) {
+    AnfNodePtr replace_input = GetReplaceNode(func_graph, input);
+    // If replace input is not null, it will be the input of the TransData or Cast.
+    if (replace_input == nullptr) {
+      new_make_tuple_inputs.push_back(input);
+      continue;
+    }
+    new_make_tuple_inputs.push_back(replace_input);
+    need_update = true;
+  }
+  if (need_update) {
+    auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>();
+    CNodePtr new_make_tuple = nullptr;
+    if (kernel_graph == nullptr) {
+      new_make_tuple = func_graph->NewCNode(new_make_tuple_inputs);
+    } else {
+      new_make_tuple = kernel_graph->NewCNode(cnode);
+    }
+    MS_EXCEPTION_IF_NULL(new_make_tuple);
+    new_make_tuple->set_inputs(new_make_tuple_inputs);
+    auto manager = func_graph->manager();
+    MS_EXCEPTION_IF_NULL(manager);
+    manager->Replace(cnode, new_make_tuple);
+  }
+  return true;
+}
+}  // namespace
+
 const BaseRef OptimizeDependence::DefinePattern() const {
   VarPtr X = std::make_shared<Var>("X");
   MS_EXCEPTION_IF_NULL(X);
@@ -43,9 +106,8 @@ const AnfNodePtr OptimizeDependence::Process(const FuncGraphPtr &func_graph, con
     return nullptr;
   }
   auto depend_cnode = node->cast<CNodePtr>();
-  if (depend_cnode->inputs().size() < kDependInputNum) {
-    return nullptr;
-  }
+  MS_EXCEPTION_IF_NULL(depend_cnode);
+  CheckCNodeInputSize(depend_cnode, kDependInputNum);
   auto replacing_node = depend_cnode->input(kDependInputNum - 1);
   MS_EXCEPTION_IF_NULL(replacing_node);
   if (!replacing_node->isa<CNode>()) {
@@ -53,36 +115,29 @@ const AnfNodePtr OptimizeDependence::Process(const FuncGraphPtr &func_graph, con
   }
   auto replacing_cnode = replacing_node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(replacing_cnode);
-  // Currently we only optimize transdata or cast nodes.
-  string replacing_cnode_op_name = AnfAlgo::GetCNodeName(replacing_cnode);
-  if (replacing_cnode_op_name != kTransDataOpName && replacing_cnode_op_name != prim::kPrimCast->name()) {
+  // Deal with the make_tuple with TransData or Cast inputs.
+  if (ReplaceMakeTuple(func_graph, replacing_cnode)) {
     return nullptr;
   }
-  auto manager = func_graph->manager();
-  MS_EXCEPTION_IF_NULL(manager);
-  // Check whether the replacing node has only one input and one output.
-  if (replacing_cnode->inputs().size() != kSingleInputIndex + 1) {
-    return nullptr;
-  }
-  if (manager->node_users().find(replacing_node) == manager->node_users().end()) {
-    MS_LOG(EXCEPTION) << "The node should be used by at least another node input";
-  }
-  if (manager->node_users()[replacing_node].size() > 1) {
+  AnfNodePtr replace_node = GetReplaceNode(func_graph, replacing_cnode);
+  if (replace_node == nullptr) {
+    MS_LOG(DEBUG) << "Can not find the TransData or Cast with single output node. Depend node: " << node->DebugString();
     return nullptr;
   }
   std::vector<AnfNodePtr> new_depend_inputs = {depend_cnode->input(kAnfPrimitiveIndex),
-                                               depend_cnode->input(kRealInputIndexInDepend),
-                                               replacing_cnode->input(kSingleInputIndex)};
+                                               depend_cnode->input(kRealInputIndexInDepend), replace_node};
   auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>();
   CNodePtr new_depend;
   if (kernel_graph == nullptr) {
     new_depend = func_graph->NewCNode(new_depend_inputs);
+    MS_EXCEPTION_IF_NULL(new_depend);
+    new_depend->set_abstract(node->abstract());
+    new_depend->set_scope(node->scope());
   } else {
     new_depend = kernel_graph->NewCNode(depend_cnode);
     MS_EXCEPTION_IF_NULL(new_depend);
     new_depend->set_inputs(new_depend_inputs);
   }
-  new_depend->set_abstract(node->abstract());
   return new_depend;
 }
 }  // namespace opt
diff --git a/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc b/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc
index 3f59b6159a..e95d63e93e 100644
--- a/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc
+++ b/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc
@@ -48,5 +48,25 @@ TEST_F(TestHWOptimizeDependence, test_optimize_dependence) {
   FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_optimize_dependence", "after");
   EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
 }
+
+TEST_F(TestHWOptimizeDependence, test_optimize_dependence_with_make_tuple) {
+  /*
+   * def before(x, y, a, b):
+   *    z = make_tuple(TransData(a), TransData(b))
+   *    depend_intput = depend(y, z)
+   *    sum = add(x, depend_intput)
+   *    return sum
+   */
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_optimize_dependence_with_make_tuple", "before");
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::OptimizeDependence>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(g);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_optimize_dependence_with_make_tuple", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py
index 45c419d25d..05eb057327 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py
@@ -18,6 +18,8 @@ from mindspore.ops import Primitive
 depend = Primitive('depend')
 TransData = Primitive('TransData')
 add = P.TensorAdd()
+make_tuple = Primitive('make_tuple')
+
 
 class FnDict:
     def __init__(self):
@@ -47,3 +49,23 @@ def test_optimize_dependence(tag):
         return sum
 
     return fns[tag]
+
+
+def test_optimize_dependence_with_make_tuple(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x, y, a, b):
+        z = make_tuple(TransData(a), TransData(b))
+        depend_intput = depend(y, z)
+        sum = add(x, depend_intput)
+        return sum
+
+    @fns
+    def after(x, y, a, b):
+        z = make_tuple(a, b)
+        depend_intput = depend(y, z)
+        sum = add(x, depend_intput)
+        return sum
+
+    return fns[tag]

From 15d061f85281a60ef0e2e558702ebd6cf454c7ba Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Thu, 2 Apr 2020 12:05:28 +0800
Subject: [PATCH 064/367] pass str to ge.exec.EnableDump

---
 mindspore/ccsrc/utils/context/ms_context.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index bf05af9858..5de28eac98 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -274,7 +274,7 @@ void MsContext::SetHcclOptions(std::map<std::string, std::string>* ge_options) c
 void MsContext::GetGeOptions(std::map<std::string, std::string>* ge_options) const {
 #ifdef ENABLE_GE
   (*ge_options)["device_id"] = "0";
-  (*ge_options)["ge.exec.enableDump"] = enable_dump_;
+  (*ge_options)["ge.exec.enableDump"] = std::to_string(enable_dump_);
   (*ge_options)["ge.exec.dumpPath"] = save_dump_path_;
   // only not supported in ge
   auto tbe_plugin_path = common::GetEnv("ME_TBE_PLUGIN_PATH");

From c42e53ae5b1b4d5355469859de84d6dc57df3b51 Mon Sep 17 00:00:00 2001
From: geekun <youjiangkun@huawei.com>
Date: Mon, 30 Mar 2020 19:20:26 +0800
Subject: [PATCH 065/367] fix custom op bug and add custom op check

---
 mindspore/ccsrc/transform/op_adapter.h       | 25 ++++++++++++++++++++
 mindspore/ccsrc/transform/op_adapter_util.cc |  8 ++++++-
 2 files changed, 32 insertions(+), 1 deletion(-)
 mode change 100755 => 100644 mindspore/ccsrc/transform/op_adapter_util.cc

diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h
index aa466adbb8..929117101b 100644
--- a/mindspore/ccsrc/transform/op_adapter.h
+++ b/mindspore/ccsrc/transform/op_adapter.h
@@ -279,6 +279,31 @@ class OpAdapter : public BaseOpAdapter {
   }
 
   OutHandler getOutput(const OperatorPtr& op, int index) override {
+    MS_EXCEPTION_IF_NULL(op);
+    if (IsCustomOp(op)) {
+      return getCustomOutput(op, index);
+    }
+    return getNormalOutput(op, index);
+  }
+
+  OutHandler getCustomOutput(const OperatorPtr& op, int index) {
+    MS_EXCEPTION_IF_NULL(op);
+    auto it = cus_output_map_.find(op->GetOpType());
+    if (it == cus_output_map_.end()) {
+      MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has both OUTPUT is not supported!";
+      return OutHandler();
+    }
+
+    std::unordered_map<int, std::string>& output_map = it->second;
+
+    if ((output_map.find(index) != output_map.end())) {
+      return OutHandler(op, output_map[index]);
+    }
+    MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has no OUTPUT index(" << index << ")!";
+    return OutHandler();
+  }
+
+  OutHandler getNormalOutput(const OperatorPtr& op, int index) {
     MS_EXCEPTION_IF_NULL(op);
     if (!dyn_output_map_.empty() && !output_map_.empty()) {
       MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has both OUTPUT and DYN_OUTPUT is not supported!";
diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc
old mode 100755
new mode 100644
index 49b8714837..d52699fa8f
--- a/mindspore/ccsrc/transform/op_adapter_util.cc
+++ b/mindspore/ccsrc/transform/op_adapter_util.cc
@@ -223,7 +223,13 @@ bool IsCustomPrim(const PrimitivePtr& prim) {
     return false;
   }
 
-  return GetValue<bool>(flag);
+  bool is_custom_op = GetValue<bool>(flag);
+  if (!is_custom_op && prim->GetAttr("_custom_op_impl_config_path") != nullptr) {
+    MS_LOG(EXCEPTION) << "The custom op flag is false, but the op information config path is not null, non-custom op "
+                         "can not assign the op information config path.";
+  }
+
+  return is_custom_op;
 }
 
 bool IsCustomCNode(const AnfNodePtr& anf) {

From 60b68a1470f43304d0b734a5e7b433a90834bee7 Mon Sep 17 00:00:00 2001
From: Su Teng <suteng@huawei.com>
Date: Thu, 2 Apr 2020 15:14:47 +0800
Subject: [PATCH 066/367] sort include file in parallel dir

---
 .../allreduce_fusion/allreduce_fusion.cc      | 12 ++++-----
 .../allreduce_fusion/allreduce_fusion.h       |  2 +-
 .../allreduce_fusion/allreduce_graph.cc       |  2 +-
 .../allreduce_fusion/allreduce_graph.h        |  6 ++---
 .../allreduce_fusion/allreduce_node.h         |  4 +--
 .../allreduce_fusion/step_allreduce_fusion.cc |  6 ++---
 .../ccsrc/parallel/auto_parallel/costmodel.cc |  4 +--
 .../ccsrc/parallel/auto_parallel/costmodel.h  |  4 +--
 .../auto_parallel/dp_algo_costmodel.cc        |  2 +-
 .../auto_parallel/dp_algo_costmodel.h         |  2 +-
 .../parallel/auto_parallel/edge_costmodel.cc  |  4 +--
 .../parallel/auto_parallel/edge_costmodel.h   | 12 ++++-----
 .../parallel/auto_parallel/graph_costmodel.cc |  4 +--
 .../parallel/auto_parallel/graph_costmodel.h  | 10 +++----
 .../auto_parallel/operator_costmodel.h        |  4 +--
 .../auto_parallel/rec_core/rec_cost.cc        |  8 +++---
 .../auto_parallel/rec_core/rec_cost.h         |  6 ++---
 .../rec_core/rec_generate_strategy.cc         |  6 ++---
 .../rec_core/rec_generate_strategy.h          |  4 +--
 .../auto_parallel/rec_core/rec_graph.h        |  4 +--
 .../auto_parallel/rec_core/rec_parse_graph.cc |  8 +++---
 .../auto_parallel/rec_core/rec_parse_graph.h  |  6 ++---
 .../auto_parallel/rec_core/rec_partition.cc   |  8 +++---
 .../auto_parallel/rec_core/rec_partition.h    | 10 +++----
 mindspore/ccsrc/parallel/context.cc           |  8 +++---
 mindspore/ccsrc/parallel/context.h            |  4 +--
 mindspore/ccsrc/parallel/costmodel_context.cc |  2 +-
 mindspore/ccsrc/parallel/costmodel_context.h  |  2 +-
 mindspore/ccsrc/parallel/device_manager.h     |  8 +++---
 mindspore/ccsrc/parallel/device_matrix.cc     |  8 +++---
 .../parallel/graph_util/generate_graph.h      |  6 ++---
 .../parallel/graph_util/get_parallel_info.cc  |  8 +++---
 .../ccsrc/parallel/graph_util/graph_info.cc   |  8 +++---
 mindspore/ccsrc/parallel/group_manager.cc     |  2 +-
 mindspore/ccsrc/parallel/group_manager.h      |  2 +-
 mindspore/ccsrc/parallel/node_check.cc        |  2 +-
 .../parallel/ops_info/activation_info.cc      |  4 +--
 .../ccsrc/parallel/ops_info/activation_info.h |  4 +--
 .../parallel/ops_info/arithmetic_info.cc      |  4 +--
 .../ccsrc/parallel/ops_info/arithmetic_info.h |  4 +--
 .../parallel/ops_info/batch_parallel_info.h   |  2 +-
 .../ccsrc/parallel/ops_info/bias_add_info.cc  |  4 +--
 .../ccsrc/parallel/ops_info/bias_add_info.h   |  4 +--
 .../ops_info/comparison_function_info.h       |  2 +-
 .../parallel/ops_info/dropout_do_mask_info.cc |  6 ++---
 .../parallel/ops_info/dropout_do_mask_info.h  |  4 +--
 .../ops_info/elementary_function_info.h       |  2 +-
 .../ccsrc/parallel/ops_info/generator_info.cc |  4 +--
 .../ccsrc/parallel/ops_info/generator_info.h  |  4 +--
 .../ccsrc/parallel/ops_info/get_next_info.cc  |  4 +--
 .../ccsrc/parallel/ops_info/get_next_info.h   |  4 +--
 .../parallel/ops_info/l2_normalize_info.cc    |  4 +--
 .../parallel/ops_info/l2_normalize_info.h     |  4 +--
 .../ccsrc/parallel/ops_info/loss_info.cc      |  4 +--
 mindspore/ccsrc/parallel/ops_info/loss_info.h |  4 +--
 .../ccsrc/parallel/ops_info/matmul_info.cc    |  8 +++---
 .../ccsrc/parallel/ops_info/matmul_info.h     |  6 ++---
 .../ccsrc/parallel/ops_info/onehot_info.cc    |  8 +++---
 .../ccsrc/parallel/ops_info/onehot_info.h     |  4 +--
 .../ccsrc/parallel/ops_info/operator_info.cc  | 14 +++++-----
 .../ccsrc/parallel/ops_info/operator_info.h   | 10 +++----
 .../parallel/ops_info/ops_info_head_files.h   | 22 ++++++++--------
 .../ccsrc/parallel/ops_info/prelu_info.h      |  2 +-
 .../parallel/ops_info/reduce_method_info.cc   |  6 ++---
 .../parallel/ops_info/reduce_method_info.h    |  6 ++---
 .../ccsrc/parallel/ops_info/reshape_info.h    |  2 +-
 .../parallel/ops_info/tmp_identity_info.cc    |  2 --
 .../parallel/ops_info/tmp_identity_info.h     |  4 +--
 .../ccsrc/parallel/ops_info/transpose_info.h  |  2 +-
 .../parallel/ops_info/virtual_dataset_info.h  |  2 +-
 .../ccsrc/parallel/step_auto_parallel.cc      | 26 +++++++++----------
 mindspore/ccsrc/parallel/step_auto_parallel.h |  6 ++---
 mindspore/ccsrc/parallel/step_parallel.cc     | 18 ++++++-------
 mindspore/ccsrc/parallel/step_parallel.h      |  4 +--
 mindspore/ccsrc/parallel/strategy.h           |  4 +--
 .../parallel_strategy_checkpoint.cc           |  4 +--
 .../parallel_strategy_checkpoint.h            |  2 +-
 .../parallel/tensor_layout/arrangement.cc     |  8 +++---
 .../parallel/tensor_layout/arrangement.h      |  2 +-
 .../ccsrc/parallel/tensor_layout/array.cc     |  2 +-
 .../tensor_layout/construct_operator.h        |  2 +-
 .../parallel/tensor_layout/layout_transfer.cc |  2 +-
 mindspore/ccsrc/parallel/tensor_layout/map.cc |  8 +++---
 mindspore/ccsrc/parallel/tensor_layout/map.h  |  2 +-
 .../redistribution_layout_transfer.cc         |  2 +-
 .../redistribution_operator_infer.h           |  6 ++---
 .../ccsrc/parallel/tensor_layout/shape_util.h |  4 +--
 .../parallel/tensor_layout/tensor_info.h      |  4 +--
 .../parallel/tensor_layout/tensor_layout.cc   | 12 ++++-----
 .../parallel/tensor_layout/tensor_layout.h    |  2 +-
 .../tensor_layout/tensor_redistribution.cc    |  2 +-
 .../tensor_layout/tensor_redistribution.h     |  8 +++---
 92 files changed, 248 insertions(+), 250 deletions(-)

diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
index 37b6eb42ed..e4f54056a8 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
@@ -15,16 +15,16 @@
  */
 
 #include "parallel/allreduce_fusion/allreduce_fusion.h"
+#include <memory>
 #include <queue>
-#include <unordered_set>
 #include <string>
-#include <memory>
-#include "utils/log_adapter.h"
-#include "parallel/status.h"
+#include <unordered_set>
 #include "ir/func_graph.h"
-#include "parallel/step_parallel.h"
-#include "parallel/graph_util/node_info.h"
 #include "parallel/costmodel_context.h"
+#include "parallel/graph_util/node_info.h"
+#include "parallel/status.h"
+#include "parallel/step_parallel.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
index de2844fa51..a2fea45187 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
@@ -20,8 +20,8 @@
 #include <unordered_map>
 #include <vector>
 #include "ir/anf.h"
-#include "parallel/status.h"
 #include "parallel/allreduce_fusion/allreduce_graph.h"
+#include "parallel/status.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
index 5c97eda8d8..559ce02349 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
@@ -17,9 +17,9 @@
 #include "parallel/allreduce_fusion/allreduce_graph.h"
 #include <algorithm>
 #include <functional>
-#include "utils/log_adapter.h"
 #include "ir/anf.h"
 #include "parallel/allreduce_fusion/allreduce_node.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h
index da4cbf8800..f0db78a130 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h
@@ -18,14 +18,14 @@
 #define MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_GRAPH_H_
 
 #include <memory>
-#include <vector>
+#include <set>
 #include <unordered_map>
 #include <unordered_set>
-#include <set>
 #include <utility>
+#include <vector>
 #include "ir/anf.h"
-#include "parallel/status.h"
 #include "parallel/allreduce_fusion/allreduce_node.h"
+#include "parallel/status.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
index f3eeb53ec7..dc6fbed4ba 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
@@ -17,9 +17,9 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_NODE_H_
 #define MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_NODE_H_
 
-#include <unordered_set>
-#include <unordered_map>
 #include <memory>
+#include <unordered_map>
+#include <unordered_set>
 #include <vector>
 #include "ir/anf.h"
 #include "parallel/status.h"
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
index 4db7007448..9dbd3a0246 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
@@ -17,12 +17,12 @@
 #include "parallel/allreduce_fusion/step_allreduce_fusion.h"
 #include <ctime>
 #include <string>
-#include "utils/log_adapter.h"
-#include "parallel/status.h"
-#include "parallel/context.h"
 #include "optimizer/optimizer.h"
 #include "parallel/allreduce_fusion/allreduce_fusion.h"
+#include "parallel/context.h"
 #include "parallel/graph_util/graph_info.h"
+#include "parallel/status.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
index 618e505eba..f5cf5069be 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
@@ -15,9 +15,9 @@
  */
 
 #include "parallel/auto_parallel/costmodel.h"
-#include <utility>
-#include <numeric>
 #include <cmath>
+#include <numeric>
+#include <utility>
 #include "parallel/auto_parallel/graph_costmodel.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
index 97155fb9c3..361c19573f 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
@@ -17,11 +17,11 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_COSTMODEL_H_
 #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_COSTMODEL_H_
 
-#include <memory>
 #include <algorithm>
-#include <vector>
+#include <memory>
 #include <string>
 #include <utility>
+#include <vector>
 #include "parallel/strategy.h"
 #include "parallel/tensor_layout/tensor_info.h"
 
diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
index 8e042eadab..060caa4cca 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
@@ -17,8 +17,8 @@
 #include "parallel/auto_parallel/dp_algo_costmodel.h"
 
 #include <memory>
-#include <vector>
 #include <utility>
+#include <vector>
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
index de8ebc6fca..c9b6a07317 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
@@ -18,8 +18,8 @@
 #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_DP_ALGO_COSTMODEL_H_
 
 #include <memory>
-#include <vector>
 #include <utility>
+#include <vector>
 #include "ir/value.h"
 #include "parallel/auto_parallel/edge_costmodel.h"
 #include "parallel/auto_parallel/graph_costmodel.h"
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
index 30fa90457e..6381049f17 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
@@ -18,11 +18,11 @@
 
 #include <algorithm>
 #include <functional>
-#include <utility>
 #include <iterator>
+#include <utility>
 #include "parallel/auto_parallel/costmodel.h"
-#include "parallel/tensor_layout/tensor_redistribution.h"
 #include "parallel/auto_parallel/graph_costmodel.h"
+#include "parallel/tensor_layout/tensor_redistribution.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
index d2bad60215..1fa49029fa 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
@@ -17,16 +17,16 @@
 #ifndef PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
 #define PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
 
-#include <memory>
-#include <vector>
-#include <utility>
 #include <map>
+#include <memory>
 #include <string>
+#include <utility>
+#include <vector>
 #include "common/utils.h"
-#include "parallel/tensor_layout/tensor_layout.h"
-#include "parallel/tensor_layout/tensor_info.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/costmodel.h"
+#include "parallel/ops_info/operator_info.h"
+#include "parallel/tensor_layout/tensor_info.h"
+#include "parallel/tensor_layout/tensor_layout.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index f99b271894..59b9d9e992 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -17,11 +17,11 @@
 #include "parallel/auto_parallel/graph_costmodel.h"
 
 #include <algorithm>
+#include <cstdlib>
+#include <iterator>
 #include <numeric>
 #include <string>
 #include <utility>
-#include <iterator>
-#include <cstdlib>
 #include <vector>
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index 3b04703a47..e4cbdffb61 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -17,17 +17,17 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_GRAPH_COSTMODEL_H_
 #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_GRAPH_COSTMODEL_H_
 
-#include <memory>
-#include <vector>
 #include <map>
-#include <utility>
+#include <memory>
 #include <string>
+#include <utility>
+#include <vector>
 #include "../../common.h"
 #include "common/utils.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/edge_costmodel.h"
-#include "parallel/ops_info/tmp_identity_info.h"
 #include "parallel/costmodel_context.h"
+#include "parallel/ops_info/operator_info.h"
+#include "parallel/ops_info/tmp_identity_info.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index ad856fc910..9fb86d467e 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -17,10 +17,10 @@
 #ifndef PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_
 #define PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_
 
-#include <vector>
 #include <memory>
-#include "parallel/tensor_layout/tensor_info.h"
+#include <vector>
 #include "parallel/device_manager.h"
+#include "parallel/tensor_layout/tensor_info.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc
index 45407a89e6..4591753efe 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc
@@ -16,13 +16,13 @@
 
 #include "parallel/auto_parallel/rec_core/rec_cost.h"
 
-#include <iostream>
-#include <vector>
-#include <string>
-#include <utility>
 #include <algorithm>
+#include <iostream>
 #include <limits>
 #include <memory>
+#include <string>
+#include <utility>
+#include <vector>
 
 #include "ir/anf.h"
 
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h
index 7e07ff9be3..af37b9178e 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h
@@ -18,13 +18,13 @@
 #define PARALLEL_AUTO_PARALLEL_REC_COST_H_
 
 #include <iostream>
-#include <vector>
+#include <memory>
 #include <string>
 #include <utility>
-#include <memory>
+#include <vector>
 
-#include "parallel/auto_parallel/rec_core/rec_strategy.h"
 #include "parallel/auto_parallel/rec_core/rec_graph.h"
+#include "parallel/auto_parallel/rec_core/rec_strategy.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
index 06b7bf544a..60f3003a42 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
@@ -16,14 +16,14 @@
 
 #include "parallel/auto_parallel/rec_core/rec_generate_strategy.h"
 
-#include <vector>
 #include <algorithm>
 #include <memory>
+#include <vector>
 
-#include "parallel/ops_info/operator_info.h"
+#include "ir/value.h"
 #include "parallel/auto_parallel/rec_core/rec_partition.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
-#include "ir/value.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h
index 7445d3940e..4abef843a8 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h
@@ -17,10 +17,10 @@
 #ifndef PARALLEL_AUTO_PARALLEL_REC_GENERATE_STRATEGY_H_
 #define PARALLEL_AUTO_PARALLEL_REC_GENERATE_STRATEGY_H_
 
-#include <vector>
+#include <memory>
 #include <string>
 #include <utility>
-#include <memory>
+#include <vector>
 
 #include "parallel/auto_parallel/rec_core/rec_graph.h"
 #include "parallel/ops_info/operator_info.h"
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h
index 209ce6b13e..6ab2782cb2 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h
@@ -18,11 +18,11 @@
 #define PARALLEL_AUTO_PARALLEL_REC_GRAPH_H_
 
 #include <iostream>
-#include <vector>
 #include <string>
+#include <vector>
 
-#include "parallel/auto_parallel/rec_core/rec_tensor.h"
 #include "parallel/auto_parallel/rec_core/rec_strategy.h"
+#include "parallel/auto_parallel/rec_core/rec_tensor.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc
index 153ce2ea17..3ff3473298 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc
@@ -17,14 +17,14 @@
 #include "parallel/auto_parallel/rec_core/rec_parse_graph.h"
 
 #include <algorithm>
+#include <memory>
 #include <string>
 #include <vector>
-#include <memory>
 
-#include "parallel/ops_info/operator_info.h"
-#include "parallel/auto_parallel/rec_core/rec_tensor.h"
-#include "parallel/auto_parallel/rec_core/rec_graph.h"
 #include "ir/value.h"
+#include "parallel/auto_parallel/rec_core/rec_graph.h"
+#include "parallel/auto_parallel/rec_core/rec_tensor.h"
+#include "parallel/ops_info/operator_info.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h
index 7904d260c0..7dfca86a21 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h
@@ -17,11 +17,11 @@
 #ifndef PARALLEL_AUTO_PARALLEL_REC_PARSE_GRAPH_H_
 #define PARALLEL_AUTO_PARALLEL_REC_PARSE_GRAPH_H_
 
-#include <vector>
+#include <map>
+#include <memory>
 #include <string>
 #include <utility>
-#include <memory>
-#include <map>
+#include <vector>
 
 #include "parallel/auto_parallel/rec_core/rec_graph.h"
 #include "parallel/ops_info/operator_info.h"
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc
index a33b861f1a..24ad8ac203 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc
@@ -16,13 +16,13 @@
 
 #include "parallel/auto_parallel/rec_core/rec_partition.h"
 
+#include <algorithm>
+#include <cmath>
 #include <iostream>
-#include <vector>
+#include <memory>
 #include <string>
 #include <utility>
-#include <memory>
-#include <cmath>
-#include <algorithm>
+#include <vector>
 
 #include "ir/anf.h"
 #include "parallel/status.h"
diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h
index 4e49305156..4f831f4f9a 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h
@@ -17,17 +17,17 @@
 #ifndef PARALLEL_AUTO_PARALLEL_REC_PARTITION_H_
 #define PARALLEL_AUTO_PARALLEL_REC_PARTITION_H_
 
+#include <cmath>
 #include <iostream>
-#include <vector>
-#include <string>
 #include <map>
-#include <utility>
-#include <cmath>
 #include <memory>
+#include <string>
+#include <utility>
+#include <vector>
 
+#include "parallel/auto_parallel/rec_core/rec_cost.h"
 #include "parallel/auto_parallel/rec_core/rec_graph.h"
 #include "parallel/auto_parallel/rec_core/rec_strategy.h"
-#include "parallel/auto_parallel/rec_core/rec_cost.h"
 #include "parallel/status.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/context.cc b/mindspore/ccsrc/parallel/context.cc
index 64cecf1669..ab216cb22c 100644
--- a/mindspore/ccsrc/parallel/context.cc
+++ b/mindspore/ccsrc/parallel/context.cc
@@ -16,15 +16,15 @@
 
 #include "parallel/context.h"
 
-#include <cstdint>
 #include <algorithm>
-#include <utility>
-#include <numeric>
+#include <cstdint>
 #include <functional>
 #include <memory>
+#include <numeric>
+#include <utility>
 
-#include "parallel/device_manager.h"
 #include "common/utils.h"
+#include "parallel/device_manager.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/context.h b/mindspore/ccsrc/parallel/context.h
index 866609fbfe..265f5bac71 100644
--- a/mindspore/ccsrc/parallel/context.h
+++ b/mindspore/ccsrc/parallel/context.h
@@ -18,12 +18,12 @@
 #define MINDSPORE_CCSRC_PARALLEL_CONTEXT_H_
 
 #include <cstdint>
+#include <memory>
 #include <string>
 #include <vector>
-#include <memory>
 
-#include "parallel/status.h"
 #include "parallel/ops_info/ops_utils.h"
+#include "parallel/status.h"
 #include "utils/convert_utils.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/costmodel_context.cc b/mindspore/ccsrc/parallel/costmodel_context.cc
index 45b158bde5..0ebbd2c626 100644
--- a/mindspore/ccsrc/parallel/costmodel_context.cc
+++ b/mindspore/ccsrc/parallel/costmodel_context.cc
@@ -18,8 +18,8 @@
 
 #include <memory>
 
-#include "parallel/auto_parallel/graph_costmodel.h"
 #include "parallel/allreduce_fusion/allreduce_fusion.h"
+#include "parallel/auto_parallel/graph_costmodel.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/costmodel_context.h b/mindspore/ccsrc/parallel/costmodel_context.h
index a808fc556f..04782fa366 100644
--- a/mindspore/ccsrc/parallel/costmodel_context.h
+++ b/mindspore/ccsrc/parallel/costmodel_context.h
@@ -17,9 +17,9 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_COSTMODEL_CONTEXT_H_
 #define MINDSPORE_CCSRC_PARALLEL_COSTMODEL_CONTEXT_H_
 
+#include <memory>
 #include <string>
 #include <vector>
-#include <memory>
 
 #include "utils/log_adapter.h"
 
diff --git a/mindspore/ccsrc/parallel/device_manager.h b/mindspore/ccsrc/parallel/device_manager.h
index 798d99383d..e87c1d740f 100644
--- a/mindspore/ccsrc/parallel/device_manager.h
+++ b/mindspore/ccsrc/parallel/device_manager.h
@@ -19,19 +19,19 @@
 
 #include <cstdint>
 #include <cstring>
-#include <vector>
 #include <map>
 #include <memory>
 #include <string>
 #include <utility>
+#include <vector>
 
-#include "utils/convert_utils.h"
 #include "common/utils.h"
 #include "parallel/device.h"
-#include "parallel/status.h"
+#include "parallel/device_matrix.h"
 #include "parallel/group_manager.h"
+#include "parallel/status.h"
 #include "parallel/strategy.h"
-#include "parallel/device_matrix.h"
+#include "utils/convert_utils.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/device_matrix.cc b/mindspore/ccsrc/parallel/device_matrix.cc
index f9f314d5a3..a581dbf275 100644
--- a/mindspore/ccsrc/parallel/device_matrix.cc
+++ b/mindspore/ccsrc/parallel/device_matrix.cc
@@ -16,15 +16,15 @@
 
 #include "parallel/device_matrix.h"
 
-#include <cstdint>
 #include <algorithm>
-#include <utility>
-#include <numeric>
+#include <cstdint>
 #include <functional>
+#include <numeric>
+#include <utility>
 #include <vector>
 
-#include "parallel/status.h"
 #include "parallel/ops_info/operator_info.h"
+#include "parallel/status.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.h b/mindspore/ccsrc/parallel/graph_util/generate_graph.h
index bb1f811f2f..c829e67b6a 100644
--- a/mindspore/ccsrc/parallel/graph_util/generate_graph.h
+++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.h
@@ -17,12 +17,12 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_
 #define MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_
 
-#include <vector>
-#include <memory>
-#include <unordered_map>
 #include <map>
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <utility>
+#include <vector>
 
 #include "./common.h"
 #include "optimizer/opt.h"
diff --git a/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc b/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc
index 6619f2cc9c..3006cb7680 100644
--- a/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc
+++ b/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc
@@ -16,16 +16,16 @@
 
 #include "parallel/graph_util/get_parallel_info.h"
 
-#include <string>
 #include <memory>
+#include <string>
 #include <utility>
 #include <vector>
 
-#include "parallel/tensor_layout/tensor_layout.h"
-#include "parallel/strategy.h"
-#include "ir/func_graph.h"
 #include "common/utils.h"
+#include "ir/func_graph.h"
 #include "parallel/graph_util/graph_info.h"
+#include "parallel/strategy.h"
+#include "parallel/tensor_layout/tensor_layout.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/graph_util/graph_info.cc b/mindspore/ccsrc/parallel/graph_util/graph_info.cc
index be73683c57..46c9a37960 100644
--- a/mindspore/ccsrc/parallel/graph_util/graph_info.cc
+++ b/mindspore/ccsrc/parallel/graph_util/graph_info.cc
@@ -15,12 +15,12 @@
  */
 
 #include "parallel/graph_util/graph_info.h"
-#include "ir/func_graph.h"
-#include "utils/graph_utils.h"
-#include "utils/context/ms_context.h"
-#include "debug/draw.h"
 #include "debug/anf_ir_dump.h"
 #include "debug/anf_ir_utils.h"
+#include "debug/draw.h"
+#include "ir/func_graph.h"
+#include "utils/context/ms_context.h"
+#include "utils/graph_utils.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/group_manager.cc b/mindspore/ccsrc/parallel/group_manager.cc
index cff5877f0a..1562cbc140 100644
--- a/mindspore/ccsrc/parallel/group_manager.cc
+++ b/mindspore/ccsrc/parallel/group_manager.cc
@@ -16,8 +16,8 @@
 
 #include "parallel/group_manager.h"
 
-#include <vector>
 #include <algorithm>
+#include <vector>
 
 #include "parallel/device_manager.h"
 #include "parallel/ops_info/ops_utils.h"
diff --git a/mindspore/ccsrc/parallel/group_manager.h b/mindspore/ccsrc/parallel/group_manager.h
index 9e23569b15..430d2f64ed 100644
--- a/mindspore/ccsrc/parallel/group_manager.h
+++ b/mindspore/ccsrc/parallel/group_manager.h
@@ -18,9 +18,9 @@
 #define MINDSPORE_CCSRC_PARALLEL_GROUP_MANAGER_H_
 
 #include <cstdint>
-#include <vector>
 #include <map>
 #include <string>
+#include <vector>
 
 #include "parallel/device.h"
 #include "parallel/status.h"
diff --git a/mindspore/ccsrc/parallel/node_check.cc b/mindspore/ccsrc/parallel/node_check.cc
index db53bbc59f..fc6115c3c5 100644
--- a/mindspore/ccsrc/parallel/node_check.cc
+++ b/mindspore/ccsrc/parallel/node_check.cc
@@ -16,8 +16,8 @@
 
 #include "parallel/node_check.h"
 
-#include <string>
 #include <set>
+#include <string>
 
 #include "parallel/ops_info/ops_utils.h"
 
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
index 6e581d6280..13155ee4f1 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
@@ -17,13 +17,13 @@
 #include "parallel/ops_info/activation_info.h"
 
 #include <algorithm>
-#include <vector>
 #include <memory>
+#include <vector>
 
 #include "ir/value.h"
+#include "parallel/auto_parallel/costmodel.h"
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
-#include "parallel/auto_parallel/costmodel.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index d05f8743b0..3cadad6b80 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -18,13 +18,13 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ACTIVATION_INFO_H_
 
 #include <ir/value.h>
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc
index 48741690ba..02c26ea965 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/arithmetic_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index 734368a533..7cd0d66b1b 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -17,14 +17,14 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
index 0ffdea97f3..57711b5298 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
@@ -17,10 +17,10 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc b/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc
index f07606e4d3..005edaf7c7 100644
--- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/bias_add_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
index e5001fc0d3..07f0bc00ff 100644
--- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
@@ -19,13 +19,13 @@
 
 #include <string>
 
+#include <memory>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
index 8760ce5666..110a9a6c38 100644
--- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
@@ -21,8 +21,8 @@
 #include <unordered_map>
 #include <vector>
 #include "ir/value.h"
-#include "parallel/ops_info/arithmetic_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/arithmetic_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
index 1b77b913da..c6cd94b7be 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
@@ -17,15 +17,15 @@
 #include "parallel/ops_info/dropout_do_mask_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
+#include "parallel/auto_parallel/costmodel.h"
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
 #include "parallel/tensor_layout/tensor_redistribution.h"
-#include "parallel/auto_parallel/costmodel.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
index 45d4c28d8e..e43601355a 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
@@ -17,14 +17,14 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
index 33ad04023b..57b4650f26 100644
--- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
@@ -22,8 +22,8 @@
 #include <vector>
 #include "ir/value.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
-#include "parallel/strategy.h"
 #include "parallel/ops_info/activation_info.h"
+#include "parallel/strategy.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.cc b/mindspore/ccsrc/parallel/ops_info/generator_info.cc
index b0fd0eaa9a..a39f9faab9 100644
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/generator_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/generator_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
 #include "parallel/device_matrix.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h
index 1473fead67..a280fac28e 100644
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h
@@ -17,13 +17,13 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.cc b/mindspore/ccsrc/parallel/ops_info/get_next_info.cc
index f38baa1e4e..ac9acff41b 100644
--- a/mindspore/ccsrc/parallel/ops_info/get_next_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/get_next_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
 #include "parallel/device_matrix.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
index 3dd639ba57..32adce1165 100644
--- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
@@ -17,13 +17,13 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc
index 183c5dec45..2955f76506 100644
--- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/l2_normalize_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
index 1a67073065..c0af9dbcb9 100644
--- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
@@ -17,15 +17,15 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
 #include "parallel/ops_info/activation_info.h"
 #include "parallel/strategy.h"
-#include "parallel/auto_parallel/operator_costmodel.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.cc b/mindspore/ccsrc/parallel/ops_info/loss_info.cc
index 5ca383ebb5..31f80e338b 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.cc
@@ -17,9 +17,9 @@
 #include "parallel/ops_info/loss_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
 #include "parallel/device_matrix.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h
index 585545302f..6a9697a447 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h
@@ -17,14 +17,14 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/ops_info/activation_info.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index 6103087a1d..ad6409be0a 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -18,16 +18,16 @@
 
 #include <algorithm>
 #include <functional>
-#include <vector>
-#include <utility>
 #include <memory>
 #include <string>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
+#include "parallel/auto_parallel/graph_costmodel.h"
+#include "parallel/device_manager.h"
 #include "parallel/device_matrix.h"
 #include "parallel/tensor_layout/tensor_redistribution.h"
-#include "parallel/device_manager.h"
-#include "parallel/auto_parallel/graph_costmodel.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
index b434e4522d..7ced12b14a 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
@@ -17,16 +17,16 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
+#include "common/utils.h"
 #include "ir/value.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
-#include "parallel/auto_parallel/operator_costmodel.h"
-#include "common/utils.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.cc b/mindspore/ccsrc/parallel/ops_info/onehot_info.cc
index 9b8e9071b0..e07609d3c4 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.cc
@@ -16,15 +16,15 @@
 
 #include "parallel/ops_info/onehot_info.h"
 
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
-#include "parallel/device_matrix.h"
-#include "parallel/strategy.h"
 #include "parallel/auto_parallel/costmodel.h"
+#include "parallel/device_matrix.h"
 #include "parallel/graph_util/generate_graph.h"
+#include "parallel/strategy.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
index 62c66695fa..4697e201a4 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
@@ -17,14 +17,14 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index f187d38673..8b96425bf7 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -17,21 +17,21 @@
 #include "parallel/ops_info/operator_info.h"
 
 #include <algorithm>
+#include <cmath>
 #include <functional>
-#include <vector>
-#include <utility>
 #include <memory>
-#include <cmath>
 #include <string>
+#include <utility>
+#include <vector>
 
-#include "ir/value.h"
 #include "ir/dtype.h"
 #include "ir/meta_tensor.h"
-#include "utils/context/ms_context.h"
-#include "utils/log_adapter.h"
-#include "parallel/context.h"
+#include "ir/value.h"
 #include "parallel/auto_parallel/edge_costmodel.h"
 #include "parallel/auto_parallel/graph_costmodel.h"
+#include "parallel/context.h"
+#include "utils/context/ms_context.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index 89fd73564f..cc70f1b870 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -25,17 +25,17 @@
 #include <utility>
 #include <vector>
 
-#include "utils/log_adapter.h"
-#include "ir/base.h"
 #include "common/utils.h"
+#include "ir/base.h"
+#include "parallel/auto_parallel/costmodel.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
 #include "parallel/device_manager.h"
 #include "parallel/device_matrix.h"
 #include "parallel/group_manager.h"
+#include "parallel/ops_info/ops_utils.h"
 #include "parallel/strategy.h"
 #include "parallel/tensor_layout/tensor_info.h"
-#include "parallel/auto_parallel/costmodel.h"
-#include "parallel/auto_parallel/operator_costmodel.h"
-#include "parallel/ops_info/ops_utils.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
index 7eb24bd30a..cc13512b54 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
@@ -18,22 +18,22 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_OPS_INFO_HEAD_FILES_H_
 
 #include "parallel/ops_info/activation_info.h"
+#include "parallel/ops_info/arithmetic_info.h"
 #include "parallel/ops_info/batch_parallel_info.h"
+#include "parallel/ops_info/bias_add_info.h"
+#include "parallel/ops_info/comparison_function_info.h"
+#include "parallel/ops_info/dropout_do_mask_info.h"
+#include "parallel/ops_info/elementary_function_info.h"
+#include "parallel/ops_info/generator_info.h"
+#include "parallel/ops_info/get_next_info.h"
+#include "parallel/ops_info/l2_normalize_info.h"
 #include "parallel/ops_info/loss_info.h"
 #include "parallel/ops_info/matmul_info.h"
 #include "parallel/ops_info/onehot_info.h"
-#include "parallel/ops_info/virtual_dataset_info.h"
-#include "parallel/ops_info/arithmetic_info.h"
-#include "parallel/ops_info/elementary_function_info.h"
-#include "parallel/ops_info/comparison_function_info.h"
-#include "parallel/ops_info/l2_normalize_info.h"
-#include "parallel/ops_info/reduce_method_info.h"
-#include "parallel/ops_info/transpose_info.h"
 #include "parallel/ops_info/prelu_info.h"
+#include "parallel/ops_info/reduce_method_info.h"
 #include "parallel/ops_info/reshape_info.h"
-#include "parallel/ops_info/generator_info.h"
-#include "parallel/ops_info/dropout_do_mask_info.h"
-#include "parallel/ops_info/get_next_info.h"
-#include "parallel/ops_info/bias_add_info.h"
+#include "parallel/ops_info/transpose_info.h"
+#include "parallel/ops_info/virtual_dataset_info.h"
 
 #endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_HEAD_FILES_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
index 2e74118f80..d491ecb331 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
@@ -17,10 +17,10 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
index 5f9a67c22d..5b07f8d0a9 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
@@ -17,14 +17,14 @@
 #include "parallel/ops_info/reduce_method_info.h"
 
 #include <algorithm>
-#include <vector>
-#include <utility>
 #include <memory>
+#include <utility>
+#include <vector>
 
 #include "ir/value.h"
+#include "parallel/device_manager.h"
 #include "parallel/device_matrix.h"
 #include "parallel/tensor_layout/tensor_redistribution.h"
-#include "parallel/device_manager.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
index 6f26b99ffb..8e2e17af99 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
@@ -17,16 +17,16 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
+#include "ir/meta_tensor.h"
 #include "ir/value.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
 #include "parallel/ops_info/activation_info.h"
 #include "parallel/strategy.h"
-#include "parallel/auto_parallel/operator_costmodel.h"
-#include "ir/meta_tensor.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
index 982894a8e0..1d6a14b1f6 100644
--- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
@@ -19,10 +19,10 @@
 
 #include <ir/value.h>
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc
index 73b18ad473..772a4f83f6 100644
--- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc
@@ -1,6 +1,4 @@
 /**
-#include "utils/log_adapter.h"
-#include "utils/log_adapter.h"
 #include "utils/log_adapter.h"
  * Copyright 2019 Huawei Technologies Co., Ltd
  *
diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
index 304e336adf..6df5856e0c 100644
--- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
@@ -17,12 +17,12 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TMP_IDENTITY_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TMP_IDENTITY_INFO_H_
 
-#include <vector>
 #include <memory>
 #include <string>
+#include <vector>
 
-#include "parallel/ops_info/operator_info.h"
 #include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
 #include "parallel/strategy.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
index c7c1c96675..4f6f6bb695 100644
--- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
@@ -17,10 +17,10 @@
 #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
index c4fdfcef04..d0278f27d9 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
@@ -17,10 +17,10 @@
 #ifndef PARALLEL_OPS_INFO_DATASET_INFO_H_
 #define PARALLEL_OPS_INFO_DATASET_INFO_H_
 
+#include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
-#include <memory>
 
 #include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index 883e0d9f15..b06ec383fa 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -18,30 +18,30 @@
 
 #include <inttypes.h>
 #include <sys/time.h>
-#include <vector>
+#include <algorithm>
+#include <map>
 #include <memory>
 #include <set>
-#include <map>
-#include <utility>
 #include <string>
-#include <algorithm>
 #include <unordered_map>
+#include <utility>
+#include <vector>
 
 #include "ir/anf.h"
+#include "ir/meta_tensor.h"
 #include "optimizer/opt.h"
 #include "optimizer/optimizer.h"
-#include "pipeline/pipeline.h"
-#include "pipeline/parse/python_adapter.h"
+#include "parallel/auto_parallel/dp_algo_costmodel.h"
 #include "parallel/auto_parallel/edge_costmodel.h"
 #include "parallel/auto_parallel/graph_costmodel.h"
-#include "parallel/step_parallel.h"
-#include "parallel/auto_parallel/dp_algo_costmodel.h"
-#include "parallel/ops_info/tmp_identity_info.h"
-#include "parallel/context.h"
-#include "parallel/auto_parallel/rec_core/rec_partition.h"
-#include "parallel/auto_parallel/rec_core/rec_parse_graph.h"
 #include "parallel/auto_parallel/rec_core/rec_generate_strategy.h"
-#include "ir/meta_tensor.h"
+#include "parallel/auto_parallel/rec_core/rec_parse_graph.h"
+#include "parallel/auto_parallel/rec_core/rec_partition.h"
+#include "parallel/context.h"
+#include "parallel/ops_info/tmp_identity_info.h"
+#include "parallel/step_parallel.h"
+#include "pipeline/parse/python_adapter.h"
+#include "pipeline/pipeline.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.h b/mindspore/ccsrc/parallel/step_auto_parallel.h
index d0d603a4f4..5ee75ca162 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.h
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.h
@@ -17,14 +17,14 @@
 #ifndef PARALLEL_STEP_AUTO_PARALLEL_H_
 #define PARALLEL_STEP_AUTO_PARALLEL_H_
 
-#include <vector>
+#include <map>
 #include <memory>
 #include <string>
-#include <map>
+#include <vector>
 #include "ir/anf.h"
 #include "optimizer/opt.h"
-#include "pipeline/pipeline.h"
 #include "parallel/status.h"
+#include "pipeline/pipeline.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index e2b4d55aad..886a0aebf4 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -22,26 +22,26 @@
 
 #include <map>
 #include <memory>
+#include <set>
 #include <string>
 #include <unordered_map>
 #include <utility>
-#include <set>
 
-#include "parallel/graph_util/graph_info.h"
 #include "ir/meta_tensor.h"
-#include "optimizer/optimizer.h"
-#include "parallel/dynamic_creator.h"
-#include "parallel/ops_info/matmul_info.h"
-#include "utils/symbolic.h"
 #include "operator/ops.h"
+#include "optimizer/optimizer.h"
 #include "parallel/auto_parallel/graph_costmodel.h"
+#include "parallel/context.h"
 #include "parallel/device_manager.h"
-#include "parallel/strategy_checkpoint/parallel_strategy_checkpoint.h"
+#include "parallel/dynamic_creator.h"
 #include "parallel/graph_util/generate_graph.h"
-#include "parallel/context.h"
+#include "parallel/graph_util/graph_info.h"
+#include "parallel/graph_util/node_info.h"
 #include "parallel/node_check.h"
+#include "parallel/ops_info/matmul_info.h"
+#include "parallel/strategy_checkpoint/parallel_strategy_checkpoint.h"
 #include "utils/comm_manager.h"
-#include "parallel/graph_util/node_info.h"
+#include "utils/symbolic.h"
 
 using mindspore::tensor::Tensor;
 
diff --git a/mindspore/ccsrc/parallel/step_parallel.h b/mindspore/ccsrc/parallel/step_parallel.h
index 2d1982dc9c..fd47a59bf5 100644
--- a/mindspore/ccsrc/parallel/step_parallel.h
+++ b/mindspore/ccsrc/parallel/step_parallel.h
@@ -19,10 +19,10 @@
 
 #include <vector>
 
-#include <memory>
-#include <unordered_map>
 #include <map>
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <utility>
 
 #include "./common.h"
diff --git a/mindspore/ccsrc/parallel/strategy.h b/mindspore/ccsrc/parallel/strategy.h
index 68ba4962d7..acc6ca928f 100644
--- a/mindspore/ccsrc/parallel/strategy.h
+++ b/mindspore/ccsrc/parallel/strategy.h
@@ -18,10 +18,10 @@
 #define MINDSPORE_CCSRC_PARALLEL_STRATEGY_H_
 
 #include <cstdint>
-#include <string>
-#include <vector>
 #include <memory>
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "parallel/status.h"
 
diff --git a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc
index 4e008feee1..9e3573eee2 100644
--- a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc
+++ b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc
@@ -20,10 +20,10 @@
 #include <memory>
 #include <vector>
 
+#include "common/utils.h"
+#include "utils/convert_utils.h"
 #include "utils/log_adapter.h"
 #include "utils/node_strategy.pb.h"
-#include "utils/convert_utils.h"
-#include "common/utils.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h
index 3cbb116b42..b5d3626f53 100644
--- a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h
+++ b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h
@@ -19,8 +19,8 @@
 
 #include <string>
 #include <unordered_map>
-#include "parallel/strategy.h"
 #include "parallel/ops_info/ops_utils.h"
+#include "parallel/strategy.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
index fea7e4ba65..68acae87f3 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
@@ -15,14 +15,14 @@
  */
 
 #include "parallel/tensor_layout/arrangement.h"
-#include <utility>
-#include <iostream>
 #include <algorithm>
+#include <iostream>
+#include <utility>
+#include "common/utils.h"
 #include "parallel/status.h"
-#include "utils/log_adapter.h"
 #include "parallel/tensor_layout/shape_util.h"
 #include "utils/convert_utils.h"
-#include "common/utils.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
index 582beeaff2..6d64e07f03 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
@@ -21,8 +21,8 @@
 #include <map>
 #include <memory>
 #include <string>
-#include <vector>
 #include <utility>
+#include <vector>
 #include "parallel/status.h"
 #include "parallel/tensor_layout/array.h"
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/array.cc b/mindspore/ccsrc/parallel/tensor_layout/array.cc
index e073c3905c..ce1b9b8ecf 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/array.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/array.cc
@@ -16,8 +16,8 @@
 
 #include "parallel/tensor_layout/array.h"
 #include <utility>
-#include "utils/log_adapter.h"
 #include "parallel/status.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
index 641b0975dd..91f5236037 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
@@ -21,9 +21,9 @@
 #include <utility>
 #include <vector>
 
+#include "ir/value.h"
 #include "parallel/ops_info/operator_info.h"
 #include "parallel/status.h"
-#include "ir/value.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
index 3321d751bb..b2ee51b40b 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
@@ -15,8 +15,8 @@
  */
 
 #include "parallel/tensor_layout/layout_transfer.h"
-#include "parallel/status.h"
 #include "common/utils.h"
+#include "parallel/status.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.cc b/mindspore/ccsrc/parallel/tensor_layout/map.cc
index 46c066b250..4f3f2369c7 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/map.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/map.cc
@@ -15,14 +15,14 @@
  */
 
 #include "parallel/tensor_layout/map.h"
-#include <utility>
-#include <iostream>
 #include <algorithm>
+#include <iostream>
+#include <utility>
+#include "common/utils.h"
 #include "parallel/status.h"
-#include "utils/log_adapter.h"
 #include "parallel/tensor_layout/shape_util.h"
 #include "utils/convert_utils.h"
-#include "common/utils.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.h b/mindspore/ccsrc/parallel/tensor_layout/map.h
index 55ed3e5577..f7bc061aa1 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/map.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/map.h
@@ -23,8 +23,8 @@
 #include <string>
 #include <vector>
 #include "parallel/status.h"
-#include "parallel/tensor_layout/array.h"
 #include "parallel/tensor_layout/arrangement.h"
+#include "parallel/tensor_layout/array.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
index dea44ee60e..2ee682fad8 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
@@ -16,8 +16,8 @@
 
 #include "parallel/tensor_layout/redistribution_layout_transfer.h"
 #include "parallel/status.h"
-#include "parallel/tensor_layout/shape_util.h"
 #include "parallel/tensor_layout/reshape_layout_transfer.h"
+#include "parallel/tensor_layout/shape_util.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
index 3515c7383a..13f9e7af24 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
@@ -18,13 +18,13 @@
 #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_REDISTRIBUTION_OPERATOR_INFER_H_
 
 #include <algorithm>
-#include <unordered_map>
 #include <string>
-#include <vector>
+#include <unordered_map>
 #include <utility>
+#include <vector>
 
-#include "parallel/tensor_layout/redistribution_layout_transfer.h"
 #include "parallel/tensor_layout/construct_operator.h"
+#include "parallel/tensor_layout/redistribution_layout_transfer.h"
 #include "utils/convert_utils.h"
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
index 5451af063e..85ca70969b 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
@@ -18,10 +18,10 @@
 #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_SHAPE_UTIL_H_
 
 #include <cstdint>
-#include <string>
-#include <vector>
 #include <map>
 #include <memory>
+#include <string>
+#include <vector>
 
 #include "parallel/status.h"
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
index b24df6bbf2..9fc6a229e2 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
@@ -19,12 +19,12 @@
 
 #include <cstdint>
 #include <string>
-#include <vector>
 #include <utility>
+#include <vector>
 
+#include "parallel/device_matrix.h"
 #include "parallel/status.h"
 #include "parallel/tensor_layout/tensor_layout.h"
-#include "parallel/device_matrix.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
index 9463b99ce6..f49b967abc 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
@@ -15,15 +15,15 @@
  */
 
 #include "parallel/tensor_layout/tensor_layout.h"
-#include <utility>
 #include <iostream>
-#include "parallel/status.h"
-#include "utils/log_adapter.h"
-#include "parallel/tensor_layout/shape_util.h"
-#include "parallel/tensor_layout/array.h"
+#include <utility>
+#include "common/utils.h"
 #include "ir/value.h"
 #include "parallel/device_matrix.h"
-#include "common/utils.h"
+#include "parallel/status.h"
+#include "parallel/tensor_layout/array.h"
+#include "parallel/tensor_layout/shape_util.h"
+#include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
index 0db356b8b1..238c9373d9 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
@@ -22,11 +22,11 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include "parallel/device_manager.h"
 #include "parallel/status.h"
 #include "parallel/tensor_layout/arrangement.h"
 #include "parallel/tensor_layout/map.h"
 #include "utils/convert_utils.h"
-#include "parallel/device_manager.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
index 365a2be1cb..93bda5da81 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
@@ -18,9 +18,9 @@
 #include <cfloat>
 #include <functional>
 #include <numeric>
+#include "common/utils.h"
 #include "parallel/status.h"
 #include "parallel/tensor_layout/shape_util.h"
-#include "common/utils.h"
 
 namespace mindspore {
 namespace parallel {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
index daf474b3d7..38fb5959ad 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
@@ -18,18 +18,18 @@
 #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_TENSOR_REDISTRIBUTION_H_
 
 #include <cstdint>
-#include <string>
-#include <vector>
 #include <map>
 #include <memory>
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "ir/value.h"
-#include "parallel/status.h"
-#include "parallel/tensor_layout/tensor_layout.h"
 #include "parallel/ops_info/operator_info.h"
+#include "parallel/status.h"
 #include "parallel/tensor_layout/construct_operator.h"
 #include "parallel/tensor_layout/redistribution_operator_infer.h"
+#include "parallel/tensor_layout/tensor_layout.h"
 
 namespace mindspore {
 namespace parallel {

From 5240b1f6039e739c1b861f9eca5535ee359ffc09 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Thu, 2 Apr 2020 11:14:45 +0800
Subject: [PATCH 067/367] fix refkey bug for auto parallel

---
 mindspore/ccsrc/parallel/step_parallel.cc   | 21 ++++++++++--
 tests/ut/python/parallel/test_arithmetic.py | 36 ++++++++++++++++++---
 2 files changed, 50 insertions(+), 7 deletions(-)

diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index e2b4d55aad..28cddd6111 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -49,6 +49,9 @@ namespace mindspore {
 namespace parallel {
 const std::set<std::string> COMMUNICATION_OPS = {ALL_REDUCE, ALL_GATHER, ALL_TO_ALL, REDUCE_SCATTER};
 const std::set<std::string> INVALID_LOSS_OPS = {GET_NEXT, VIRTUALLOSS};
+// g_RefMap, for CNode B input i is a RefKey[Parameter C],
+// it will be one item in map with key: C, and value: (B, i)
+static std::map<AnfNodePtr, std::pair<AnfNodePtr, int>> g_RefMap;
 
 void SetCommunicationOpGroupLabel(std::vector<AnfNodePtr> new_node_input) {
   if (new_node_input.empty()) {
@@ -1085,11 +1088,19 @@ std::vector<Shapes> ExtractShape(const CNodePtr& node) {
   std::vector<AnfNodePtr> all_inputs = node->inputs();
   std::vector<AnfNodePtr> node_inputs{all_inputs.begin() + 1, all_inputs.end()};
 
-  for (auto& input : node_inputs) {
+  size_t inputs_size = all_inputs.size();
+  for (size_t i = 1; i < inputs_size; ++i) {
     Shapes input_shapes;
+    AnfNodePtr input = all_inputs[i];
     if (IsValueNode<RefKey>(input)) {
       auto func_graph = node->func_graph();
       MS_EXCEPTION_IF_NULL(func_graph);
+      std::vector<AnfNodePtr> parameters = FindParameterByRefKeyNode(input, func_graph);
+      if (parameters.size() != 1) {
+        MS_LOG(EXCEPTION) << "Find parameter by ref key node failed";
+      }
+      std::pair<AnfNodePtr, int> node_pair = std::make_pair(node, SizeToInt(i));
+      g_RefMap[parameters[0]] = node_pair;
       input_shapes = GetRefKeyNodeShape(input, func_graph);
     } else if (IsValueNode<Tensor>(input) || input->isa<CNode>() || input->isa<Parameter>()) {
       input_shapes = GetNodeShape(input);
@@ -1205,14 +1216,20 @@ void CoverSliceShape(const FuncGraphPtr& root) {
   auto parameters = root->parameters();
   for (auto& parameter : parameters) {
     MS_EXCEPTION_IF_NULL(parameter->Shape());
+    auto iter = g_RefMap.find(parameter);
+    if (iter != g_RefMap.end()) {
+      SetParallelShape(parameter, g_RefMap[parameter]);
+      continue;
+    }
     std::pair<AnfNodePtr, int> res = FindSubGraph(root, parameter);
     if (res.first == nullptr) {
       MS_LOG(INFO) << "Parameter " << parameter->ToString() << " don't need to set parallel shape";
     } else {
       SetParallelShape(parameter, res);
-      MS_LOG(DEBUG) << "parameter " << parameter->ToString() << "  shape " << parameter->Shape()->ToString();
+      MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << "  shape " << parameter->Shape()->ToString();
     }
   }
+  g_RefMap.clear();
 }
 
 bool ParameterIsCloned(const FuncGraphPtr& root, const AnfNodePtr& parameter_node) {
diff --git a/tests/ut/python/parallel/test_arithmetic.py b/tests/ut/python/parallel/test_arithmetic.py
index 2c7eabc8f2..4c34c0371e 100644
--- a/tests/ut/python/parallel/test_arithmetic.py
+++ b/tests/ut/python/parallel/test_arithmetic.py
@@ -13,14 +13,13 @@
 # limitations under the License.
 
 import numpy as np
-from mindspore import context
+import mindspore as ms
+from mindspore import Parameter, Tensor, context
 import mindspore.nn as nn
 from mindspore.ops import operations as P
-from mindspore import Tensor
-from tests.ut.python.ops.test_math_ops import VirtualLoss
-import mindspore as ms
-from mindspore.common.api import _executor
 from mindspore.ops import composite as C
+from mindspore.common.api import _executor
+from tests.ut.python.ops.test_math_ops import VirtualLoss
 
 
 class NetWithLoss(nn.Cell):
@@ -470,3 +469,30 @@ def test_matmul_floordiv_broadcast2():
     y = Tensor(np.ones([32, 1]), dtype=ms.float32)
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
+
+
+def test_assign_sub():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.assign_sub = P.AssignSub()
+            self.mul = P.Mul()
+            self.mul_weight = Parameter(Tensor(np.full([128, 32],
+                                        0.5, dtype=np.float32)),
+                                        name="mul_weight")
+            self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
+                                              1.1, dtype=np.float32)),
+                                              name="assignsub_weight")
+
+        def construct(self, x, y, z):
+            out = self.mul(x, self.mul_weight)
+            out = self.assign_sub(self.assignsub_weight, out)
+            return out
+
+    context.set_auto_parallel_context(device_num=64, global_rank=15)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    net = GradWrap(NetWithLoss(Net()))
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    z = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    _executor.compile(net, x, y, z)

From 49a41bb2cc4a700573f262bdb9df518e51b4895a Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Thu, 2 Apr 2020 16:37:39 +0800
Subject: [PATCH 068/367] fix random crop parameter check

---
 mindspore/dataset/transforms/vision/validators.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py
index 443232d780..caab120af4 100644
--- a/mindspore/dataset/transforms/vision/validators.py
+++ b/mindspore/dataset/transforms/vision/validators.py
@@ -325,7 +325,7 @@ def check_random_crop(method):
 
     @wraps(method)
     def new_method(self, *args, **kwargs):
-        args = (list(args) + 4 * [None])[:5]
+        args = (list(args) + 5 * [None])[:5]
         size, padding, pad_if_needed, fill_value, padding_mode = args
 
         if "size" in kwargs:

From c3d210cc3160fcee250e9e25b06f9d615ea48a6e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Thu, 2 Apr 2020 11:13:16 +0800
Subject: [PATCH 069/367] fix issue I1DBRX

delete duplicated words in comments

fix issue I1CJAP

fix issue I1D3WS

fix issue I1DBA8

fix issue I1CJ77

fix issue I1CJG3

fix review suggestion
---
 mindspore/nn/layer/normalization.py     |   2 +-
 mindspore/ops/operations/array_ops.py   |   3 +-
 mindspore/ops/operations/control_ops.py |   4 +-
 mindspore/ops/operations/math_ops.py    | 122 ++++++++++++------------
 mindspore/ops/operations/nn_ops.py      |   7 +-
 5 files changed, 69 insertions(+), 69 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index d43c1c8ab4..4aded20ab3 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -249,7 +249,7 @@ class LayerNorm(Cell):
             'he_uniform', etc. Default: 'zeros'.
 
     Inputs:
-        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`,
+        - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`,
           and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
 
     Outputs:
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index f6d563321c..e9ea30b4cd 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -443,7 +443,6 @@ class Transpose(PrimitiveWithInfer):
     Examples:
         >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
         >>> perm = (0, 2, 1)
-        >>> expect = np.array([[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]])
         >>> transpose = Transpose()
         >>> output = transpose(input_tensor, perm)
     """
@@ -1631,7 +1630,7 @@ class Diag(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor([1, 2, 3, 4])
         >>> diag = P.Diag()
-        >>> diag(x)
+        >>> diag(input_x)
         [[1, 0, 0, 0],
          [0, 2, 0, 0],
          [0, 0, 3, 0],
diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index 242a3b155d..a7d9c3bb8a 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -107,8 +107,8 @@ class GeSwitch(PrimitiveWithInfer):
         >>>         ret = self.merge((add_ret, sq_ret))
         >>>         return ret[0]
         >>>
-        >>> x = Tensor(x_init, dtype=mindspore.float32)
-        >>> y = Tensor(y_init, dtype=mindspore.float32)
+        >>> x = Tensor(10.0, dtype=mindspore.float32)
+        >>> y = Tensor(5.0, dtype=mindspore.float32)
         >>> net = Net()
         >>> output = net(x, y)
     """
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 398a7e6f1a..d9e03a54d0 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -140,6 +140,7 @@ class AssignAdd(PrimitiveWithInfer):
     Examples:
         >>> class Net(Cell):
         >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
         >>>         self.AssignAdd = P.AssignAdd()
         >>>         self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
@@ -272,7 +273,7 @@ class ReduceMean(_Reduce):
 
     Examples:
         >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
-        >>> op = ReduceMean(keep_dims=True)
+        >>> op = P.ReduceMean(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -304,7 +305,7 @@ class ReduceSum(_Reduce):
 
     Examples:
         >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
-        >>> op = ReduceSum(keep_dims=True)
+        >>> op = P.ReduceSum(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -337,7 +338,7 @@ class ReduceAll(_Reduce):
 
     Examples:
         >>> data = Tensor(np.array([[True, False], [True, True]]))
-        >>> op = ReduceAll(keep_dims=True)
+        >>> op = P.ReduceAll(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -373,7 +374,7 @@ class ReduceMax(_Reduce):
 
     Examples:
         >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
-        >>> op = ReduceMax(keep_dims=True)
+        >>> op = P.ReduceMax(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -406,7 +407,7 @@ class ReduceMin(_Reduce):
 
     Examples:
         >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
-        >>> op = ReduceMin(keep_dims=True)
+        >>> op = P.ReduceMin(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -438,7 +439,7 @@ class ReduceProd(_Reduce):
 
     Examples:
         >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
-        >>> op = ReduceProd(keep_dims=True)
+        >>> op = P.ReduceProd(keep_dims=True)
         >>> output = op(data, 1)
     """
 
@@ -460,13 +461,13 @@ class CumProd(PrimitiveWithInfer):
 
     Examples:
         >>> data = Tensor(np.array([a, b, c]).astype(np.float32))
-        >>> op0 = CumProd()
+        >>> op0 = P.CumProd()
         >>> output = op0(data, 0) # output=[a, a * b, a * b * c]
-        >>> op1 = CumProd(exclusive=True)
+        >>> op1 = P.CumProd(exclusive=True)
         >>> output = op1(data, 0) # output=[1, a, a * b]
-        >>> op2 = CumProd(reverse=True)
+        >>> op2 = P.CumProd(reverse=True)
         >>> output = op2(data, 0) # output=[a * b * c, b * c, c]
-        >>> op3 = CumProd(exclusive=True, reverse=True)
+        >>> op3 = P.CumProd(exclusive=True, reverse=True)
         >>> output = op3(data, 0) # output=[b * c, c, 1]
     """
     @prim_attr_register
@@ -506,7 +507,7 @@ class MatMul(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32)
         >>> input_y = Tensor(np.ones(shape=[3, 4]), mindspore.float32)
-        >>> matmul = MatMul()
+        >>> matmul = P.MatMul()
         >>> output = matmul(input_x, input_y)
     """
 
@@ -582,12 +583,12 @@ class BatchMatMul(MatMul):
     Examples:
         >>> input_x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32)
         >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
-        >>> batmatmul = BatchMatMul()
+        >>> batmatmul = P.BatchMatMul()
         >>> output = batmatmul(input_x, input_y)
         >>>
         >>> input_x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32)
         >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
-        >>> batmatmul = BatchMatMul(transpose_a=True)
+        >>> batmatmul = P.BatchMatMul(transpose_a=True)
         >>> output = batmatmul(input_x, input_y)
     """
 
@@ -621,7 +622,7 @@ class CumSum(PrimitiveWithInfer):
 
     Examples:
         >>> input = Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float32))
-        >>> cumsum = CumSum()
+        >>> cumsum = P.CumSum()
         >>> output = cumsum(input, 1)
         [[ 3.  7. 13. 23.]
          [ 1.  7. 14. 23.]
@@ -666,7 +667,7 @@ class AddN(PrimitiveWithInfer):
         >>> class NetAddN(nn.Cell):
         >>>     def __init__(self):
         >>>         super(NetAddN, self).__init__()
-        >>>         self.addN = AddN()
+        >>>         self.addN = P.AddN()
         >>>
         >>>     def construct(self, *z):
         >>>         return self.addN(z)
@@ -748,7 +749,7 @@ class Sub(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32)
-        >>> sub = Sub()
+        >>> sub = P.Sub()
         >>> sub(input_x, input_y)
         [-3, -3, -3]
     """
@@ -775,7 +776,7 @@ class Mul(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32)
-        >>> mul = Mul()
+        >>> mul = P.Mul()
         >>> mul(input_x, input_y)
         [4, 10, 18]
     """
@@ -793,7 +794,7 @@ class Square(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
-        >>> square = Square()
+        >>> square = P.Square()
         >>> square(input_x)
         [1.0, 4.0, 9.0]
     """
@@ -823,7 +824,7 @@ class Rsqrt(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor([[4, 4], [9, 9]], mindspore.float32)
-        >>> rsqrt = Rsqrt()
+        >>> rsqrt = P.Rsqrt()
         >>> rsqrt(input_tensor)
         [[0.5, 0.5], [0.333333, 0.333333]]
     """
@@ -853,7 +854,7 @@ class Sqrt(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 4.0, 9.0]), mindspore.float32)
-        >>> sqrt = Sqrt()
+        >>> sqrt = P.Sqrt()
         >>> sqrt(input_x)
         [1.0, 2.0, 3.0]
     """
@@ -883,7 +884,7 @@ class Reciprocal(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
-        >>> reciprocal = Reciprocal()
+        >>> reciprocal = P.Reciprocal()
         >>> reciprocal(input_x)
         [1.0, 0.5, 0.25]
     """
@@ -916,13 +917,13 @@ class Pow(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
         >>> input_y = 3.0
-        >>> pow = Pow()
+        >>> pow = P.Pow()
         >>> pow(input_x, input_y)
         [1.0, 8.0, 64.0]
         >>>
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
         >>> input_y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
-        >>> pow = Pow()
+        >>> pow = P.Pow()
         >>> pow(input_x, input_y)
         [1.0, 16.0, 64.0]
     """
@@ -952,7 +953,7 @@ class Exp(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
-        >>> exp = Exp()
+        >>> exp = P.Exp()
         >>> exp(input_x)
         [ 2.71828183,  7.3890561 , 54.59815003]
     """
@@ -982,7 +983,7 @@ class Log(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
-        >>> log = Log()
+        >>> log = P.Log()
         >>> log(input_x)
         [0.0, 0.69314718, 1.38629436]
     """
@@ -1020,7 +1021,7 @@ class Minimum(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
         >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
-        >>> minimum = Minimum()
+        >>> minimum = P.Minimum()
         >>> minimum(input_x, input_y)
         [1.0, 2.0, 3.0]
     """
@@ -1047,7 +1048,7 @@ class Maximum(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
         >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
-        >>> maximum = Maximum()
+        >>> maximum = P.Maximum()
         >>> maximum(input_x, input_y)
         [4.0, 5.0, 6.0]
     """
@@ -1074,7 +1075,7 @@ class RealDiv(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
         >>> input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
-        >>> realdiv = RealDiv()
+        >>> realdiv = P.RealDiv()
         >>> realdiv(input_x, input_y)
         [0.25, 0.4, 0.5]
     """
@@ -1113,9 +1114,8 @@ class Div(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32)
         >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32)
-        >>> div = Div()
+        >>> div = P.Div()
         >>> div(input_x, input_y)
-        [-2.0, 2.0, 2.0]
     """
 
     def infer_value(self, x, y):
@@ -1147,7 +1147,7 @@ class FloorDiv(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)
         >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32)
-        >>> floor_div = FloorDiv()
+        >>> floor_div = P.FloorDiv()
         >>> floor_div(input_x, input_y)
         [0, 1, -1]
     """
@@ -1165,7 +1165,7 @@ class Floor(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([1.1, 2.5, -1.5]), mindspore.float32)
-        >>> floor = Floor()
+        >>> floor = P.Floor()
         >>> floor(input_x)
         [1.0, 2.0, -2.0]
     """
@@ -1221,13 +1221,13 @@ class Equal(_LogicBinaryOp):
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
-        >>> equal = Equal()
+        >>> equal = P.Equal()
         >>> equal(input_x, 2.0)
         [False, True, False]
         >>>
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32)
-        >>> equal = Equal()
+        >>> equal = P.Equal()
         >>> equal(input_x, input_y)
         [True, True, False]
     """
@@ -1252,7 +1252,7 @@ class EqualCount(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32)
-        >>> equal_count = EqualCount()
+        >>> equal_count = P.EqualCount()
         >>> equal_count(input_x, input_y)
         [2]
     """
@@ -1292,13 +1292,13 @@ class NotEqual(_LogicBinaryOp):
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
-        >>> not_equal = NotEqual()
+        >>> not_equal = P.NotEqual()
         >>> not_equal(input_x, 2.0)
         [True, False, True]
         >>>
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32)
-        >>> not_equal = NotEqual()
+        >>> not_equal = P.NotEqual()
         >>> not_equal(input_x, input_y)
         [False, False, True]
     """
@@ -1328,7 +1328,7 @@ class Greater(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32)
-        >>> greater = Greater()
+        >>> greater = P.Greater()
         >>> greater(input_x, input_y)
         [False, True, False]
     """
@@ -1355,7 +1355,7 @@ class GreaterEqual(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32)
-        >>> greater_equal = GreaterEqual()
+        >>> greater_equal = P.GreaterEqual()
         >>> greater_equal(input_x, input_y)
         [True, True, False]
     """
@@ -1382,7 +1382,7 @@ class Less(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32)
-        >>> less = Less()
+        >>> less = P.Less()
         >>> less(input_x, input_y)
         [False, False, True]
     """
@@ -1409,7 +1409,7 @@ class LessEqual(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32)
-        >>> less_equal = LessEqual()
+        >>> less_equal = P.LessEqual()
         >>> less_equal(input_x, input_y)
         [True, False, True]
     """
@@ -1427,7 +1427,7 @@ class LogicalNot(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_)
-        >>> logical_not = LogicalNot()
+        >>> logical_not = P.LogicalNot()
         >>> logical_not(input_x)
         [False, True, False]
     """
@@ -1465,7 +1465,7 @@ class LogicalAnd(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_)
         >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_)
-        >>> logical_and = LogicalAnd()
+        >>> logical_and = P.LogicalAnd()
         >>> logical_and(input_x, input_y)
         [True, False, False]
     """
@@ -1494,7 +1494,7 @@ class LogicalOr(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_)
         >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_)
-        >>> logical_or = LogicalOr()
+        >>> logical_or = P.LogicalOr()
         >>> logical_or(input_x, input_y)
         [True, True, True]
     """
@@ -1516,7 +1516,7 @@ class NPUAllocFloatStatus(PrimitiveWithInfer):
         Tensor, has the shape of `(8,)`.
 
     Examples:
-        >>> alloc_status = NPUAllocFloatStatus()
+        >>> alloc_status = P.NPUAllocFloatStatus()
         >>> init = alloc_status()
         Tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=(8,), dtype=mindspore.float32)
     """
@@ -1548,8 +1548,8 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`. All the elements in the tensor will be zero.
 
     Examples:
-        >>> alloc_status = NPUAllocFloatStatus()
-        >>> get_status = NPUGetFloatStatus()
+        >>> alloc_status = P.NPUAllocFloatStatus()
+        >>> get_status = P.NPUGetFloatStatus()
         >>> init = alloc_status()
         >>> flag = get_status(init)
         Tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=(8,), dtype=mindspore.float32)
@@ -1588,9 +1588,9 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`. All the elements in the tensor will be zero.
 
     Examples:
-        >>> alloc_status = NPUAllocFloatStatus()
-        >>> get_status = NPUGetFloatStatus()
-        >>> clear_status = NPUClearFloatStatus()
+        >>> alloc_status = P.NPUAllocFloatStatus()
+        >>> get_status = P.NPUGetFloatStatus()
+        >>> clear_status = P.NPUClearFloatStatus()
         >>> init = alloc_status()
         >>> flag = get_status(init)
         >>> clear = clear_status(init)
@@ -1624,7 +1624,7 @@ class Cos(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`.
 
     Examples:
-        >>> cos = Cos()
+        >>> cos = P.Cos()
         >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), ms.float32)
         >>> output = cos(X)
     """
@@ -1653,8 +1653,8 @@ class ACos(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`.
 
     Examples:
-        >>> acos = ACos()
-        >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), ms.float32)
+        >>> acos = P.ACos()
+        >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
         >>> output = acos(X)
     """
 
@@ -1682,9 +1682,9 @@ class Sin(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`.
 
     Examples:
-        >>> sin = Sin()
-        >>> X = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32)
-        >>> output = sin(X)
+        >>> sin = P.Sin()
+        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32)
+        >>> output = sin(input_x)
     """
 
     @prim_attr_register
@@ -1734,7 +1734,7 @@ class NMSWithMask(PrimitiveWithInfer):
         >>> bbox[:, 2] += bbox[:, 0]
         >>> bbox[:, 3] += bbox[:, 1]
         >>> inputs = Tensor(bbox)
-        >>> nms = NMSWithMask(0.5)
+        >>> nms = P.NMSWithMask(0.5)
         >>> output_boxes, indices, mask = nms(inputs)
     """
 
@@ -1769,7 +1769,7 @@ class Abs(PrimitiveWithInfer):
 
     Examples:
          >>> input_x = Tensor(np.array([-1.0, 1.0, 0.0]), mindspore.float32)
-         >>> abs = Abs()
+         >>> abs = P.Abs()
          >>> abs(input_x)
          [1.0, 1.0, 0.0]
     """
@@ -1812,7 +1812,7 @@ class Sign(PrimitiveWithInfer):
 
     Examples:
          >>> input_x = Tensor(np.array([[2.0, 0.0, -1.0]]), mindspore.float32)
-         >>> sign = Sign()
+         >>> sign = P.Sign()
          >>> output = sign(input_x)
          [[1.0, 0.0, -1.0]]
     """
@@ -1842,7 +1842,7 @@ class Round(PrimitiveWithInfer):
 
     Examples:
          >>> input_x = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]), mindspore.float32)
-         >>> round = Round()
+         >>> round = P.Round()
          >>> round(input_x)
          [1.0, 2.0, 2.0, 2.0, -4.0]
     """
@@ -1877,7 +1877,7 @@ class Atan2(_MathBinaryOp):
     Examples:
          >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32)
          >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32)
-         >>> atan2 = Atan2()
+         >>> atan2 = P.Atan2()
          >>> atan2(input_x, input_y)
          [[0. 0.7853982]]
     """
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index afa4c7dfe3..283d35725f 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1090,9 +1090,10 @@ class TopK(PrimitiveWithInfer):
         - **indices** (Tensor) - The indices of values within the last dimension of input.
 
     Examples:
-        >>> topk = TopK(sorted=True)
-        >>> x = Tensor(np.array([1, 2, 3, 4, 5]).astype(np.float16))
-        >>> values, indices = topk(x)
+        >>> topk = P.TopK(sorted=True)
+        >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16))
+        >>> k = 3
+        >>> values, indices = topk(input_x, k)
         >>> assert values == Tensor(np.array([5, 4, 3]))
         >>> assert indices == Tensor(np.array([4, 3, 2]))
     """

From fdb47860ccfe385dbb4f756b4a39ac8075bea8fb Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Thu, 2 Apr 2020 16:29:14 +0800
Subject: [PATCH 070/367] Fix some typo errors in session and device module

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 .../ccsrc/device/cpu/cpu_kernel_runtime.cc    |  10 +-
 .../ccsrc/device/cpu/cpu_resource_manager.cc  |   4 +-
 mindspore/ccsrc/device/cpu/cpu_session.cc     |  14 +-
 mindspore/ccsrc/device/kernel_adjust.cc       |  12 +-
 mindspore/ccsrc/device/kernel_runtime.cc      |  12 +-
 .../ccsrc/device/kernel_runtime_manager.cc    |   2 +-
 .../ccsrc/session/anf_runtime_algorithm.cc    |  36 ++--
 .../ccsrc/session/anf_runtime_algorithm.h     |   4 +-
 mindspore/ccsrc/session/ascend_session.cc     | 177 +++++++++---------
 mindspore/ccsrc/session/ascend_session.h      |  13 +-
 mindspore/ccsrc/session/gpu_session.cc        |   4 +-
 mindspore/ccsrc/session/kernel_graph.cc       |  42 ++---
 mindspore/ccsrc/session/kernel_graph.h        |   4 +-
 mindspore/ccsrc/session/session_basic.cc      |  26 +--
 mindspore/ccsrc/session/session_basic.h       |   4 +-
 mindspore/ccsrc/utils/utils.h                 |   2 +-
 16 files changed, 184 insertions(+), 182 deletions(-)

diff --git a/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc b/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc
index 5bf9689f0b..1dcddfa994 100644
--- a/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc
@@ -66,7 +66,7 @@ void CPUKernelRuntime::AssignValueNodeAddress(session::KernelGraph *kernel_graph
         address->ptr_ = resource_manager_.MemMalloc(tensor_size);
         if (!address->SyncHostToDevice(data_shape, LongToSize(tensor->data().nbytes()), tensor->data_type(),
                                        tensor->data_c(false))) {
-          MS_LOG(EXCEPTION) << "value node sync host to device failed!";
+          MS_LOG(EXCEPTION) << "Value node sync host to device failed!";
         }
       }
       address->ref_count_ = INIT_NODE_REF;
@@ -141,7 +141,7 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(const AnfNodePtr &input_node, siz
     MS_EXCEPTION_IF_NULL(node);
     size_t output_size = AnfAlgo::GetOutputTensorNum(node);
     if (index >= output_size) {
-      MS_LOG(EXCEPTION) << "invalid input index " << index;
+      MS_LOG(EXCEPTION) << "Invalid input index " << index;
     }
     auto address = AnfAlgo::GetMutableOutputAddr(node, index);
     MS_EXCEPTION_IF_NULL(address);
@@ -157,7 +157,7 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(const AnfNodePtr &input_node, siz
       type_id = kNumberTypeFloat32;
     }
     if (type_id != kNumberTypeInt32 && type_id != kNumberTypeFloat32) {
-      MS_LOG(EXCEPTION) << "check output type failed.";
+      MS_LOG(EXCEPTION) << "Check output type failed.";
     }
     tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
     MS_EXCEPTION_IF_NULL(tensor);
@@ -181,7 +181,7 @@ void CPUKernelRuntime::BindInputOutput(const session::KernelGraph *kernel_graph,
   // bind input ptr
   auto &input_nodes = kernel_graph->inputs();
   if (input_nodes.size() != inputs.size()) {
-    MS_LOG(EXCEPTION) << "input size not equal to input node size!";
+    MS_LOG(EXCEPTION) << "Input size not equal to input node size!";
   }
 
   std::unordered_map<AnfNode *, tensor::TensorPtr> input_map;
@@ -203,7 +203,7 @@ void CPUKernelRuntime::BindInputOutput(const session::KernelGraph *kernel_graph,
         address->ptr_ = resource_manager_.MemMalloc(tensor_size);
         if (!address->SyncHostToDevice(data_shape, LongToSize(tensor->data().nbytes()), tensor->data_type(),
                                        tensor->data_c(false))) {
-          MS_LOG(EXCEPTION) << "parameter node sync host to device failed!";
+          MS_LOG(EXCEPTION) << "Parameter node sync host to device failed!";
         }
         tensor->set_dirty(true);
       }
diff --git a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
index 5b2b7a0750..97df7d4487 100644
--- a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
+++ b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
@@ -44,7 +44,7 @@ void CPUResourceManager::MemPlan(const session::KernelGraph *graph) {
       mem_size_ = graph_mem_size;
       dynamic_malloc_ = false;
     } else {
-      MS_LOG(INFO) << "switch to dynamic malloc";
+      MS_LOG(INFO) << "Switch to dynamic malloc";
       dynamic_malloc_ = true;
     }
   }
@@ -63,7 +63,7 @@ void *CPUResourceManager::MemMalloc(size_t mem_size) {
     dynamic_mem_[ptr] = mem_size;
     return ptr;
   } else {
-    MS_LOG(EXCEPTION) << "malloc memory failed: size " << mem_size;
+    MS_LOG(EXCEPTION) << "Malloc memory failed: size " << mem_size;
   }
 }
 
diff --git a/mindspore/ccsrc/device/cpu/cpu_session.cc b/mindspore/ccsrc/device/cpu/cpu_session.cc
index 59d0318874..1613f9f91e 100644
--- a/mindspore/ccsrc/device/cpu/cpu_session.cc
+++ b/mindspore/ccsrc/device/cpu/cpu_session.cc
@@ -31,12 +31,12 @@ GraphId CPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
   auto graph_id = graph_sum_;
   auto graph = ConstructKernelGraph(lst, outputs);
   MS_EXCEPTION_IF_NULL(graph);
-  MS_LOG(INFO) << "set kernel info";
+  MS_LOG(INFO) << "Set kernel info";
   SetKernelInfo(graph.get());
   predictmodel::StepConvertGraph(graph);
-  MS_LOG(INFO) << "build kernel";
+  MS_LOG(INFO) << "Build kernel";
   BuildKernel(graph.get());
-  MS_LOG(INFO) << "assign kernel address";
+  MS_LOG(INFO) << "Assign kernel address";
   runtime_.AssignKernelAddress(graph.get());
   return graph_id;
 }
@@ -44,18 +44,18 @@ GraphId CPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
 void CPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) {
   auto &kernel_graph = graphs_[graph_id];
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  MS_LOG(INFO) << "bind input output address";
+  MS_LOG(INFO) << "Bind input output address";
   runtime_.BindInputOutput(kernel_graph.get(), inputs, outputs);
-  MS_LOG(INFO) << "run graph start";
+  MS_LOG(INFO) << "Run graph start";
   predictmodel::StepConvertWeight(inputs);
   auto execution_order = kernel_graph->execution_order();
   Reorder(&execution_order);
   kernel_graph->set_execution_order(execution_order);
   bool ret = runtime_.Run(kernel_graph.get());
   if (!ret) {
-    MS_LOG(EXCEPTION) << "run graph failed";
+    MS_LOG(EXCEPTION) << "Run graph failed";
   }
-  MS_LOG(INFO) << "run graph end";
+  MS_LOG(INFO) << "Run graph end";
 }
 
 void CPUSession::SetKernelInfo(const KernelGraph *kernel_graph) {
diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc
index a4d316d601..089813b098 100644
--- a/mindspore/ccsrc/device/kernel_adjust.cc
+++ b/mindspore/ccsrc/device/kernel_adjust.cc
@@ -49,7 +49,7 @@ void KernelAdjust::Reorder(const std::shared_ptr<session::KernelGraph> &kernel_g
   std::vector<CNodePtr> momentum_list;
   std::vector<CNodePtr> other_list;
   for (const auto &cnode : origin_cnode_list) {
-    if (kOptOpeatorSet.find(AnfAlgo::GetCNodeName(cnode)) != kOptOpeatorSet.end()) {
+    if (kOptOperatorSet.find(AnfAlgo::GetCNodeName(cnode)) != kOptOperatorSet.end()) {
       momentum_list.emplace_back(cnode);
     } else {
       other_list.emplace_back(cnode);
@@ -118,7 +118,7 @@ void KernelAdjust::CreateSwitchOpParameters(const std::shared_ptr<session::Kerne
   MS_EXCEPTION_IF_NULL(tensor_ptr);
   mindspore::abstract::AbstractBasePtr paremeter_abstract_ptr = tensor_ptr->ToAbstract();
   if (paremeter_abstract_ptr == nullptr) {
-    MS_LOG(EXCEPTION) << "create abstract brfore insert switch op failed!";
+    MS_LOG(EXCEPTION) << "create abstract before insert switch op failed!";
   }
 
   ParameterPtr loop_count = std::make_shared<Parameter>(kernel_graph_ptr);
@@ -371,7 +371,7 @@ bool KernelAdjust::StepLoadCtrlInputs(const std::shared_ptr<session::Context> &c
     auto tensor = inputs[i];
     size_t deal_index = input_nodes.size() - input_ctrl_size + i;
     if (deal_index >= input_nodes.size()) {
-      MS_LOG(EXCEPTION) << "deak_index[" << deal_index << "] outof range";
+      MS_LOG(EXCEPTION) << "deal_index[" << deal_index << "] out of range";
     }
     auto input_node = input_nodes[deal_index];
     bool need_sync = false;
@@ -439,7 +439,7 @@ void KernelAdjust::LoadSwitchInputs(std::vector<tensor::TensorPtr> *inputs) {
 
 void KernelAdjust::Profiling(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr) {
   if (!ascend::ProfilingManager::GetInstance().IsProfiling()) {
-    MS_LOG(INFO) << "no need to profiling";
+    MS_LOG(INFO) << "No need to profiling";
     return;
   }
   ProfilingTraceInfo profiling_trace_info;
@@ -452,10 +452,10 @@ void KernelAdjust::Profiling(const std::shared_ptr<session::KernelGraph> &kernel
 
 void KernelAdjust::InsertProfilingKernel(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr,
                                          const ProfilingTraceInfo &profiling_trace_info) {
-  MS_LOG(INFO) << "[profiling] insert profiling kernel start";
+  MS_LOG(INFO) << "[profiling] Insert profiling kernel start";
   MS_EXCEPTION_IF_NULL(kernel_graph_ptr);
   if (!profiling_trace_info.IsValid()) {
-    MS_LOG(WARNING) << "profiling trace point not found";
+    MS_LOG(WARNING) << "Profiling trace point not found";
     return;
   }
   std::vector<CNodePtr> new_cnode_list;
diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index 878fe4a7f8..0a9be35fb5 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -241,7 +241,7 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) {
     auto output_size = AnfAlgo::GetOutputTensorNum(item);
     for (size_t index = 0; index < output_size; index++) {
       TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index);
-      // if graph output is a weight and doesn't link to any cnode,it's data type will be unkonwn
+      // if graph output is a weight and doesn't link to any cnode, it's data type will be unknown
       if (output_type_id == kTypeUnknown) {
         MS_LOG(WARNING) << "It is not suggested to use a lonely weight parameter as the output of graph";
         output_type_id = AnfAlgo::GetOutputInferDataType(item, index);
@@ -372,7 +372,7 @@ void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int in
       continue;
     }
     if (AnfAlgo::OutputAddrExist(node, i)) {
-      MS_LOG(INFO) << "already malloc index:" << i;
+      MS_LOG(INFO) << "Already malloc index:" << i;
       continue;
     }
     auto ptr = CalDeviceMem(node, output_sizes[i], flag, i);
@@ -392,7 +392,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
   MS_EXCEPTION_IF_NULL(node_value);
   auto tensor = node_value->cast<TensorPtr>();
   if (tensor == nullptr) {
-    MS_LOG(WARNING) << "tensor is null";
+    MS_LOG(WARNING) << "Tensor is null";
     return;
   }
   size_t tensor_size = tensor->data().nbytes();
@@ -595,7 +595,7 @@ void KernelRuntime::GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod
 
 void KernelRuntime::GenAddrCleanLaunchArgs(const CNodePtr &cnode, AddressPtrList *kernel_inputs) {
   if (cnode->inputs().size() != 2) {
-    MS_LOG(EXCEPTION) << "atomic Addr clean Node Input nodes not equal 2.";
+    MS_LOG(EXCEPTION) << "Atomic Addr clean Node Input nodes not equal 2.";
   }
   auto pre_node = cnode->inputs()[1];
   // set clean output address
@@ -721,11 +721,11 @@ uint8_t *KernelRuntime::MallocDynamicMem(size_t size, bool communication_mem) {
 bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   if (!LaunchKernelMod(*graph)) {
-    MS_LOG(ERROR) << "LaunchKernelMod failed.";
+    MS_LOG(ERROR) << "LaunchKernelMod failed!";
     return false;
   }
   if (!SyncStream()) {
-    MS_LOG(ERROR) << "SyncStream failed.";
+    MS_LOG(ERROR) << "SyncStream failed!";
     return false;
   }
   return true;
diff --git a/mindspore/ccsrc/device/kernel_runtime_manager.cc b/mindspore/ccsrc/device/kernel_runtime_manager.cc
index 6303bc88a8..5a70d75cf9 100644
--- a/mindspore/ccsrc/device/kernel_runtime_manager.cc
+++ b/mindspore/ccsrc/device/kernel_runtime_manager.cc
@@ -67,7 +67,7 @@ KernelRuntime *KernelRuntimeManager::GetKernelRuntime(const std::string &device_
     MS_EXCEPTION_IF_NULL(kernel_runtime);
     runtime_map_[runtime_key] = kernel_runtime;
   } else {
-    MS_LOG(EXCEPTION) << "no kernel runtime creator for " << device_name << " with device id " << device_id;
+    MS_LOG(EXCEPTION) << "No kernel runtime creator for " << device_name << " with device id " << device_id;
   }
 
   return kernel_runtime.get();
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index c0dca4522a..cc23dbbdd2 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -65,7 +65,7 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernel(const AnfNodePtr &anf_node, siz
       return VisitKernel(node, 0);
     } else if (IsPrimitive(input0, prim::kPrimTupleGetItem)) {
       if (cnode->inputs().size() != kTupleGetItemInputSize) {
-        MS_LOG(EXCEPTION) << "the node tuple_get_item must have 2 inputs!";
+        MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!";
       }
       auto input2 = cnode->input(kInputNodeOutputIndexInTupleGetItem);
       MS_EXCEPTION_IF_NULL(input2);
@@ -102,7 +102,7 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr
     MS_EXCEPTION_IF_NULL(input0);
     if (IsPrimitive(input0, prim::kPrimTupleGetItem)) {
       if (cnode->inputs().size() != kTupleGetItemInputSize) {
-        MS_LOG(EXCEPTION) << "the node tuple_get_item must have 2 inputs!";
+        MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!";
       }
       auto input2 = cnode->input(kInputNodeOutputIndexInTupleGetItem);
       MS_EXCEPTION_IF_NULL(input2);
@@ -188,7 +188,7 @@ std::string AnfRuntimeAlgorithm::GetNodeDebugString(const AnfNodePtr &node) {
 void AnfRuntimeAlgorithm::SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node->DebugString();
+    MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString();
   }
   auto primitive = AnfAlgo::GetCNodePrimitive(node);
   MS_EXCEPTION_IF_NULL(primitive);
@@ -204,7 +204,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttr(const std::string &old_key, const std::st
   MS_EXCEPTION_IF_NULL(from);
   MS_EXCEPTION_IF_NULL(to);
   if (!from->isa<CNode>() || !to->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "only cnode has attr,but this from_anf is " << from->DebugString() << " ,to_node is "
+    MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << " ,to_node is "
                       << to->DebugString();
   }
   auto from_primitive = AnfAlgo::GetCNodePrimitive(from);
@@ -218,7 +218,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr
   MS_EXCEPTION_IF_NULL(from);
   MS_EXCEPTION_IF_NULL(to);
   if (!from->isa<CNode>() || !to->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "only cnode has attr,but this from_anf is " << from->DebugString() << ",to_node is "
+    MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << ",to_node is "
                       << from->DebugString();
   }
   auto from_primitive = AnfAlgo::GetCNodePrimitive(from);
@@ -231,7 +231,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr
 void AnfRuntimeAlgorithm::EraseNodeAttr(const std::string &key, const AnfNodePtr node) {
   MS_EXCEPTION_IF_NULL(node);
   if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node->DebugString();
+    MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString();
   }
   auto primitive = AnfAlgo::GetCNodePrimitive(node);
   MS_EXCEPTION_IF_NULL(primitive);
@@ -241,7 +241,7 @@ void AnfRuntimeAlgorithm::EraseNodeAttr(const std::string &key, const AnfNodePtr
 bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   if (!node->isa<CNode>()) {
-    MS_LOG(WARNING) << "only cnode has attr,but this anf is " << node->DebugString();
+    MS_LOG(WARNING) << "Only cnode has attr, but this anf is " << node->DebugString();
     return false;
   }
   auto primitive = AnfAlgo::GetCNodePrimitive(node);
@@ -252,7 +252,7 @@ bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const AnfNodePtr &
 size_t AnfRuntimeAlgorithm::GetInputTensorNum(const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "only cnode has real input,but this anf is " << node->DebugString();
+    MS_LOG(EXCEPTION) << "Only cnode has real input, but this anf is " << node->DebugString();
   }
   auto cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
@@ -404,7 +404,7 @@ std::vector<kernel::Axis> AnfRuntimeAlgorithm::GetInputReshapeType(const AnfNode
   MS_EXCEPTION_IF_NULL(build_info);
   std::vector<kernel::Axis> result;
   if (!build_info->GetInputReshapeType(input_idx, &result)) {
-    MS_LOG(EXCEPTION) << "filed to ge the node's[ " << node->DebugString() << "] reshape type !";
+    MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !";
   }
   return result;
 }
@@ -417,7 +417,7 @@ std::vector<kernel::Axis> AnfRuntimeAlgorithm::GetOutputReshapeType(const AnfNod
   MS_EXCEPTION_IF_NULL(build_info);
   std::vector<kernel::Axis> result;
   if (!build_info->GetOutputReshapeType(output_idx, &result)) {
-    MS_LOG(EXCEPTION) << "filed to ge the node's[ " << node->DebugString() << "] reshape type !";
+    MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !";
   }
   return result;
 }
@@ -593,7 +593,7 @@ void AnfRuntimeAlgorithm::SetOutputAddr(const DeviceAddressPtr &addr, size_t out
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   if (!kernel_info->SetOutputAddr(addr, output_idx)) {
-    MS_LOG(EXCEPTION) << "node " << node->DebugString() << "set adr" << output_idx << " fail";
+    MS_LOG(EXCEPTION) << "Node " << node->DebugString() << "set adr" << output_idx << " fail";
   }
 }
 
@@ -603,7 +603,7 @@ void AnfRuntimeAlgorithm::SetWorkspaceAddr(const DeviceAddressPtr &addr, size_t
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   if (!kernel_info->SetWorkspaceAddr(addr, output_idx)) {
-    MS_LOG(EXCEPTION) << "node " << node->DebugString() << "set adr" << output_idx << " fail";
+    MS_LOG(EXCEPTION) << "Node " << node->DebugString() << "set adr" << output_idx << " fail";
   }
 }
 
@@ -614,7 +614,7 @@ DeviceAddress *AnfRuntimeAlgorithm::GetWorkspaceAddr(const AnfNodePtr &node, siz
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto addr = kernel_info->GetWorkspaceAddr(output_idx);
   if (addr == nullptr) {
-    MS_LOG(EXCEPTION) << "output_idx " << output_idx << " of node " << node->DebugString()
+    MS_LOG(EXCEPTION) << "Output_idx " << output_idx << " of node " << node->DebugString()
                       << "] workspace addr is not exist";
   }
   return addr;
@@ -625,7 +625,7 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector<TypeId> &
                                                      const std::vector<std::vector<size_t>> &shapes, AnfNode *node) {
   MS_EXCEPTION_IF_NULL(node);
   if (types.size() != shapes.size()) {
-    MS_LOG(EXCEPTION) << "types size " << types.size() << "should be same with shapes size " << shapes.size();
+    MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size();
   }
   if (shapes.empty()) {
     MS_LOG(EXCEPTION) << "Illegal empty output_types_shapes";
@@ -636,7 +636,7 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector<TypeId> &
     auto abstract = std::make_shared<AbstractTensor>(TypeIdToType(types[0]), shape_int);
     node->set_abstract(abstract);
   } else {
-    // mutiple output handle
+    // multiple output handle
     std::vector<AbstractBasePtr> abstract_list;
     for (size_t i = 0; i < types.size(); ++i) {
       std::vector<int> shape_int;
@@ -647,12 +647,12 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector<TypeId> &
     node->set_abstract(abstract_tuple);
   }
 }
-// copy a abstract of a node to another node
+// copy an abstract of a node to another node
 void AnfRuntimeAlgorithm::CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node) {
   to_node->set_abstract(from_node->abstract());
 }
 
-// get KernelBuildType of node ,such as ATT,RT,FWK and so on
+// get KernelBuildType of node, such as ATT,RT,FWK and so on
 KernelType AnfRuntimeAlgorithm::GetKernelType(const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   auto kernel_info = node->kernel_info();
@@ -846,7 +846,7 @@ size_t AnfRuntimeAlgorithm::GetRealInputIndex(const mindspore::AnfNodePtr &anf_n
     auto find = spec_node_list.find(node_name);
     if (find != spec_node_list.end()) {
       ret = find->second[cur_index];
-      MS_LOG(INFO) << "real input index change to" << ret << ", node name:" << node_name;
+      MS_LOG(INFO) << "Real input index change to" << ret << ", node name:" << node_name;
     }
   }
   return ret;
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 60d373d5ad..2de68f0098 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -61,7 +61,7 @@ class AnfRuntimeAlgorithm {
     MS_EXCEPTION_IF_NULL(node);
     if (!node->isa<CNode>()) {
       std::string node_debug_log = node->DebugString();
-      MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node_debug_log.c_str();
+      MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node_debug_log.c_str();
     }
     auto primitive = GetCNodePrimitive(node);
     MS_EXCEPTION_IF_NULL(primitive);
@@ -105,7 +105,7 @@ class AnfRuntimeAlgorithm {
   static TypeId GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx);
   // get output original data type from prev node,input_index is the input index of current node related to prev node
   static TypeId GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx);
-  // get output select data typpe of anf node
+  // get output select data type of anf node
   static TypeId GetOutputDeviceDataType(const AnfNodePtr &node, size_t output_idx);
   // get input select data type of anf node
   static TypeId GetInputDeviceDataType(const AnfNodePtr &node, size_t input_idx);
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 1a29450313..e0027e8fe5 100644
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -55,7 +55,7 @@ void DumpGraphExeOrder(const std::vector<CNodePtr> &execution_order) {
 }
 
 void DumpGraphInputArgs(const VectorRef &args) {
-  MS_LOG(INFO) << "args size[%lu]" << args.size();
+  MS_LOG(INFO) << "Args size[%lu]" << args.size();
   for (size_t i = 0; i < args.size(); i++) {
     if (utils::isa<AnfNodePtr>(args[i])) {
       auto anf = utils::cast<AnfNodePtr>(args[i]);
@@ -66,7 +66,7 @@ void DumpGraphInputArgs(const VectorRef &args) {
       MS_EXCEPTION_IF_NULL(value);
       MS_LOG(INFO) << "Tensor arg" << i << " = " << value->ToString();
     } else {
-      MS_LOG(INFO) << "Unknonwn arg" << i << " = " << args[i].ToString();
+      MS_LOG(INFO) << "Unknown arg" << i << " = " << args[i].ToString();
     }
   }
 }
@@ -95,7 +95,7 @@ GraphId GetDistinctionLabel(const KernelGraphPtr &graph) {
 GraphId AscendSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
   MS_LOG(INFO) << "start";
   auto graph_id = graph_sum_;
-  // construct graph,if construct successs,graph_sum_ + 1
+  // construct graph, if successfully, graph_sum_ + 1
   auto graph = ConstructKernelGraph(lst, outputs);
   MS_EXCEPTION_IF_NULL(graph);
   opt::AscendBackendIRFusionOptimization(graph);
@@ -127,7 +127,7 @@ void AscendSession::BuildGraph(GraphId graph_id) {
     // merge child graph
     MergeGraphExecOrder();
   } else {
-    // set the distinciton label of single graph
+    // set the distinction label of single graph
     SetStreamDistinctionLabel(GetGraph(graph_id), graph_id, false);
   }
   // adjust execution order because  merge child graph and other special operations
@@ -143,7 +143,7 @@ void AscendSession::BuildGraph(GraphId graph_id) {
   if (ms_context->precompile_only()) {
     MS_LOG(INFO) << "Precompile only, stop in build kernel step";
   } else {
-    // alloc memeory,include static memory and dynamic memory
+    // alloc memory, including static memory and dynamic memory
     MemoryAlloc(graph.get());
     // generate task info for task sink mode
     GenerateTaskInfo(graph);
@@ -158,9 +158,9 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
   MS_LOG(INFO) << "start";
   auto kernel_graph = GetGraph(graph_id);
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  // if no child graph exist and no anf output exist
+  // if none of child graph and no anf output exists
   if (!kernel_graph->executable()) {
-    MS_LOG(INFO) << "no child graph but has anf output";
+    MS_LOG(INFO) << "No child graph has anf output";
     UpdateOutputs(kernel_graph, outputs, inputs);
     return;
   }
@@ -183,16 +183,16 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
 }
 
 void AscendSession::RunOpHardwareOptimize(const std::shared_ptr<session::KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start";
   // data layout optimization
   opt::AscendDataLayout(kernel_graph);
   // mixed precision optimization
   opt::AscendMixPrecision(kernel_graph);
-  MS_LOG(INFO) << "Finish!";
+  MS_LOG(INFO) << "Finish";
 }
 
 void AscendSession::RunOpExecTask(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   bool ret_ok = runtime_instance->LaunchKernel(kernel_graph.get());
@@ -251,26 +251,26 @@ py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &gr
 
 // compile graph steps
 void AscendSession::SelectKernel(const KernelGraph &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   for (const auto &cnode : kernel_graph.execution_order()) {
     device::ascend::SelectKernelInfo(cnode);
-    MS_LOG(INFO) << "select ApplyKernel: " << cnode->DebugString();
+    MS_LOG(INFO) << "Select ApplyKernel: " << cnode->DebugString();
   }
   MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::InitRuntimeResource() {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   if (!runtime_instance->Init()) {
-    MS_LOG(EXCEPTION) << "kernel runtime init error.";
+    MS_LOG(EXCEPTION) << "Kernel runtime init error.";
   }
   MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::HardwareOptimize(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "HardwareOptimize start !";
+  MS_LOG(INFO) << "HardwareOptimize start!";
   opt::AscendBackendOptimization(kernel_graph);
   MS_EXCEPTION_IF_NULL(kernel_graph);
   kernel_graph->SetExecOrderByDefault();
@@ -278,7 +278,7 @@ void AscendSession::HardwareOptimize(const std::shared_ptr<KernelGraph> &kernel_
 }
 
 void AscendSession::AdjustKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   device::KernelAdjust::GetInstance().Reorder(kernel_graph);
   opt::HideNopNode(kernel_graph.get());
   // Insert CLearZero op
@@ -301,7 +301,7 @@ void AscendSession::AdjustKernel(const std::shared_ptr<KernelGraph> &kernel_grap
 }
 
 void AscendSession::RunOpAdjustKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   opt::HideNopNode(kernel_graph.get());
   // Insert CLearZero op
   // prepare for next step from json get atomic info
@@ -311,18 +311,18 @@ void AscendSession::RunOpAdjustKernel(const std::shared_ptr<KernelGraph> &kernel
 }
 
 void AscendSession::AssignStream(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   device::ascend::AscendStreamAssign::GetInstance().AssignStreamNew(kernel_graph);
   MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   struct timeval start_time, end_time;
   (void)gettimeofday(&start_time, nullptr);
   auto ret = device::ascend::KernelBuild(kernel_graph.get());
   if (!ret) {
-    MS_LOG(EXCEPTION) << "kernel build error.";
+    MS_LOG(EXCEPTION) << "Kernel build error.";
   }
   (void)gettimeofday(&end_time, nullptr);
   const uint64_t kUSecondInSecond = 1000000;
@@ -333,7 +333,7 @@ void AscendSession::BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph
 }
 
 void AscendSession::MemoryAlloc(KernelGraph *kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   MS_EXCEPTION_IF_NULL(kernel_graph);
   opt::RemoveNopNode(kernel_graph);
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
@@ -344,7 +344,7 @@ void AscendSession::MemoryAlloc(KernelGraph *kernel_graph) const {
 
 void AscendSession::RunOpMemoryAlloc(const std::vector<tensor::TensorPtr> &input_tensors,
                                      KernelGraph *kernel_graph) const {
-  MS_LOG(INFO) << "start memory alloc!";
+  MS_LOG(INFO) << "Start memory alloc!";
   MS_EXCEPTION_IF_NULL(kernel_graph);
   opt::RemoveNopNode(kernel_graph);
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
@@ -354,30 +354,30 @@ void AscendSession::RunOpMemoryAlloc(const std::vector<tensor::TensorPtr> &input
 }
 
 void AscendSession::GenerateTaskInfo(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   (void)device::KernelAdjust::GetInstance().StepLoadCtrlInputs(context_, kernel_graph);
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   bool ret_ok = runtime_instance->GenTask(kernel_graph.get());
   if (!ret_ok) {
-    MS_LOG(EXCEPTION) << "generate task error!";
+    MS_LOG(EXCEPTION) << "Generate task error!";
   }
   MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::LoadTask(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   bool ret_ok = runtime_instance->LoadTask(kernel_graph.get());
   if (!ret_ok) {
-    MS_LOG(EXCEPTION) << "load task error!";
+    MS_LOG(EXCEPTION) << "Load task error!";
   }
   MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::ExecTask(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   bool ret_ok = runtime_instance->Run(kernel_graph.get());
@@ -388,7 +388,7 @@ void AscendSession::ExecTask(const std::shared_ptr<KernelGraph> &kernel_graph) c
 }
 
 void AscendSession::Dump(const std::shared_ptr<KernelGraph> &kernel_graph) const {
-  MS_LOG(INFO) << "start !";
+  MS_LOG(INFO) << "Start!";
   MS_EXCEPTION_IF_NULL(kernel_graph);
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
@@ -397,7 +397,7 @@ void AscendSession::Dump(const std::shared_ptr<KernelGraph> &kernel_graph) const
 }
 
 GraphId AscendSession::SetFinalGraphInput(const std::vector<AnfNodePtr> &args) {
-  MS_LOG(INFO) << "start!args size " << args.size();
+  MS_LOG(INFO) << "Start! Args size " << args.size();
   auto final_graph = std::make_shared<KernelGraph>();
   final_graph_id_ = graph_sum_++;
   graphs_[final_graph_id_] = final_graph;
@@ -417,12 +417,12 @@ GraphId AscendSession::SetFinalGraphInput(const std::vector<AnfNodePtr> &args) {
     if (parameter_belong_graph_id == kInvalidGraphId) {
       parameter_backend = final_graph->NewParameter(parameter->cast<ParameterPtr>());
       final_graph->FrontBackendlMapAdd(parameter, parameter_backend);
-      MS_LOG(INFO) << "new parameter" << parameter->DebugString() << "in final_graph";
+      MS_LOG(INFO) << "New parameter" << parameter->DebugString() << "in final_graph";
     } else {
       // parametr is a parameter of child graph
       auto graph = GetGraph(parameter_belong_graph_id);
       MS_EXCEPTION_IF_NULL(graph);
-      MS_LOG(INFO) << "reuse parameter [" << parameter->DebugString() << "] of child graph ["
+      MS_LOG(INFO) << "Reuse parameter [" << parameter->DebugString() << "] of child graph ["
                    << parameter_belong_graph_id << "]";
       parameter_backend = graph->GetBackendAnfByFrontAnf(parameter);
     }
@@ -434,7 +434,7 @@ GraphId AscendSession::SetFinalGraphInput(const std::vector<AnfNodePtr> &args) {
     MS_EXCEPTION_IF_NULL(final_graph_inputs);
     final_graph_inputs->push_back(parameter_backend);
   }
-  MS_LOG(INFO) << "end final_graph_id " << final_graph_id_;
+  MS_LOG(INFO) << "End final_graph_id " << final_graph_id_;
   return final_graph_id_;
 }
 
@@ -453,7 +453,7 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) {
     value_node->set_abstract(abstract::FromValue(value_ptr));
     final_graph->set_output(final_graph->NewCNode({NewValueNode(prim::kPrimMakeTuple), value_node}));
     final_graph->set_executable(false);
-    MS_LOG(INFO) << "not anf output[" << output.ToString() << "]";
+    MS_LOG(INFO) << "Not anf output[" << output.ToString() << "]";
     return;
   }
   // get the backend anf node related to the output node of front
@@ -461,12 +461,12 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) {
   auto output_from_graph_id = GetGraphIdByNode(output_anf_node);
   auto output_from_graph = GetGraph(output_from_graph_id);
   MS_EXCEPTION_IF_NULL(output_anf_node);
-  MS_LOG(INFO) << "set the output[" << output_anf_node->DebugString() << "] of graph[" << output_from_graph_id
+  MS_LOG(INFO) << "Set the output[" << output_anf_node->DebugString() << "] of graph[" << output_from_graph_id
                << "] to final graph";
   MS_EXCEPTION_IF_NULL(output_from_graph);
   // if output is from final graph,it remarks no child graph exist
   if (final_graph_id_ == output_from_graph_id) {
-    MS_LOG(INFO) << "no child graph,output is " << output_anf_node->DebugString();
+    MS_LOG(INFO) << "No child graph,output is " << output_anf_node->DebugString();
     final_graph->set_output(ConstructOutput({output_anf_node}, final_graph));
     final_graph->set_executable(false);
     return;
@@ -477,15 +477,15 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) {
 KernelGraphPtr AscendSession::GetGraph(mindspore::GraphId graph_id) {
   auto it = graphs_.find(graph_id);
   if (it == graphs_.end()) {
-    MS_LOG(WARNING) << "can't find graph " << graph_id;
+    MS_LOG(WARNING) << "Can't find graph " << graph_id;
     return nullptr;
   }
   return it->second;
 }
 
 void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true_graph_id) {
-  MS_LOG(INFO) << "start";
-  MS_LOG(INFO) << "condition graph id[" << condition_graph_id << "],true graph id[" << true_graph_id << "]";
+  MS_LOG(INFO) << "Start!";
+  MS_LOG(INFO) << "Condition graph id[" << condition_graph_id << "],true graph id[" << true_graph_id << "]";
   auto condition_graph = GetGraph(condition_graph_id);
   MS_EXCEPTION_IF_NULL(condition_graph);
   tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(kNumberTypeInt32, std::vector<int>{1});
@@ -507,7 +507,7 @@ void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true
   kernel_build_info_builder->SetKernelType(KernelType::RT_KERNEL);
   // condition graph's output must be single output
   if (condition_graph->outputs().size() != 1) {
-    MS_LOG(EXCEPTION) << "condition_graph output num " << condition_graph_id << " should be 1";
+    MS_LOG(EXCEPTION) << "Condition_graph output num " << condition_graph_id << " should be 1";
   }
   AnfNodePtr cond_output_kernel = condition_graph->outputs()[0];
   std::vector<AnfNodePtr> inputs = {NewValueNode(switch_primitive), cond_output_kernel, counter_const};
@@ -527,7 +527,7 @@ void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true
   std::vector<CNodePtr> exec_order = condition_graph->execution_order();
   exec_order.push_back(switch_node);
   condition_graph->set_execution_order(exec_order);
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::CopyOutputOfIf(GraphId false_graph_id) {
@@ -540,11 +540,11 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) {
   for (int i = SizeToInt(false_index) - 1; i >= 0; i--) {
     size_t graph_index = IntToSize(i);
     if (graph_index >= graph_execute_order.size()) {
-      MS_LOG(EXCEPTION) << "graph index[" << graph_index << "] out of range[" << graph_execute_order.size() << "]";
+      MS_LOG(EXCEPTION) << "Graph index[" << graph_index << "] out of range[" << graph_execute_order.size() << "]";
     }
     if (graph_order_type[graph_index] == COMMON_GRAPH) {
       auto true_last_id = graph_execute_order[graph_index];
-      MS_LOG(INFO) << "the last graph of if true branch is " << true_last_id;
+      MS_LOG(INFO) << "The last graph of if true branch is " << true_last_id;
       auto true_last = GetGraph(true_last_id);
       auto final_graph = GetGraph(final_graph_id_);
       MS_EXCEPTION_IF_NULL(final_graph);
@@ -552,10 +552,10 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) {
       auto false_last = GetGraph(false_last_id);
       MS_EXCEPTION_IF_NULL(true_last);
       MS_EXCEPTION_IF_NULL(false_last);
-      MS_LOG(INFO) << "the last graph of false branch is " << false_last_id;
+      MS_LOG(INFO) << "The last graph of false branch is " << false_last_id;
       // now only consider the single output
       InsertMultipleAssignToGraph(true_last_id, true_last->output(), false_last->output());
-      // insert stream acitve for loop sink
+      // insert stream active for loop sink
       auto context_ptr = MsContext::GetInstance();
       MS_EXCEPTION_IF_NULL(context_ptr);
       if (context_ptr->enable_task_sink() && context_ptr->loop_sink_flag() &&
@@ -569,32 +569,32 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) {
 }
 
 void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id, GraphId false_graph_id) {
-  if (switchs_.find(cond_graph_id) != switchs_.end()) {
-    MS_LOG(WARNING) << "condition graph" << cond_graph_id << " has been set before ";
+  if (switches_.find(cond_graph_id) != switches_.end()) {
+    MS_LOG(WARNING) << "Condition graph" << cond_graph_id << " has been set before ";
     return;
   }
-  switchs_[cond_graph_id] = std::pair<GraphId, GraphId>(true_graph_id, false_graph_id);
-  MS_LOG(INFO) << "new switch compile " << cond_graph_id << " " << true_graph_id << " " << false_graph_id;
-  // set the type of condtion graph
+  switches_[cond_graph_id] = std::pair<GraphId, GraphId>(true_graph_id, false_graph_id);
+  MS_LOG(INFO) << "New switch compile " << cond_graph_id << " " << true_graph_id << " " << false_graph_id;
+  // set the type of condition graph
   auto cond_graph_index = ExecOrderOfChildGraph(final_graph_id_, cond_graph_id);
   auto &graph_order_type = GetGraphOrderType(final_graph_id_);
   if (cond_graph_index >= graph_order_type.size()) {
     MS_LOG(EXCEPTION) << "cond_graph_index " << cond_graph_index << " out of range " << graph_order_types_.size();
   }
   graph_order_type[cond_graph_index] = CONDITION_GRAPH;
-  // update disinction label of false graph,update before merge to sure the distinction
+  // update distinction label of false graph,update before merge to sure the distinction
   if (false_graph_id != kInvalidGraphId) {
     // false graph and condition in graph same stream
-    auto conditon_graph = GetGraph(cond_graph_id);
-    SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(conditon_graph), true);
+    auto condition_graph = GetGraph(cond_graph_id);
+    SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(condition_graph), true);
     // if false graph is a condition graph and has been switch compiled before,it's false should be updated again
-    auto cond_it = switchs_.find(false_graph_id);
-    while (cond_it != switchs_.end() && cond_it->second.second != kInvalidGraphId) {
+    auto cond_it = switches_.find(false_graph_id);
+    while (cond_it != switches_.end() && cond_it->second.second != kInvalidGraphId) {
       cond_graph_id = cond_it->first;
       false_graph_id = cond_it->second.second;
-      conditon_graph = GetGraph(cond_graph_id);
-      SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(conditon_graph), true);
-      cond_it = switchs_.find(false_graph_id);
+      condition_graph = GetGraph(cond_graph_id);
+      SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(condition_graph), true);
+      cond_it = switches_.find(false_graph_id);
     }
   }
 }  // namespace session
@@ -602,11 +602,11 @@ void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id,
 void AscendSession::MergeSwitchCompile() {
   auto graph_execute_order = GetGraphOrder(final_graph_id_);
   auto &graph_order_type = GetGraphOrderType(final_graph_id_);
-  for (auto switch_compile : switchs_) {
+  for (auto switch_compile : switches_) {
     auto cond_graph_id = switch_compile.first;
     auto true_graph_id = switch_compile.second.first;
     auto false_graph_id = switch_compile.second.second;
-    MS_LOG(INFO) << "switch compile: " << cond_graph_id << " " << true_graph_id << " " << false_graph_id;
+    MS_LOG(INFO) << "Switch compile: " << cond_graph_id << " " << true_graph_id << " " << false_graph_id;
     auto condition_graph = GetGraph(cond_graph_id);
     auto final_graph = GetGraph(final_graph_id_);
     MS_EXCEPTION_IF_NULL(condition_graph);
@@ -630,8 +630,8 @@ void AscendSession::MergeSwitchCompile() {
       InsertStreamActiveToGraph(prev_graph_id, GetDistinctionLabel(condition_graph));
     }
     // if this is a 'if' condition
-    auto it = while_condtion_graphs_.find(cond_graph_id);
-    if (it == while_condtion_graphs_.end()) {
+    auto it = while_condition_graphs_.find(cond_graph_id);
+    if (it == while_condition_graphs_.end()) {
       CopyOutputOfIf(false_graph_id);
     } else {
       // if it is a while,insert a stream active to true graph
@@ -639,17 +639,17 @@ void AscendSession::MergeSwitchCompile() {
       InsertStreamActiveToGraph(from_graph, GetDistinctionLabel(condition_graph));
     }
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 // insert active to graph
 void AscendSession::SetActive(GraphId from, GraphId to) {
-  if (while_condtion_graphs_.find(to) != while_condtion_graphs_.end()) {
+  if (while_condition_graphs_.find(to) != while_condition_graphs_.end()) {
     MS_LOG(WARNING) << " to " << to << " has been exits in map,from " << from << ",exist from "
-                    << while_condtion_graphs_[to];
+                    << while_condition_graphs_[to];
     return;
   }
-  MS_LOG(INFO) << "from " << from << " to " << to;
+  MS_LOG(INFO) << "From " << from << " to " << to;
   auto &graph_order = GetGraphOrder(final_graph_id_);
   auto &graph_type = GetGraphOrderType(final_graph_id_);
   std::vector<GraphId> graph_order_new;
@@ -668,21 +668,21 @@ void AscendSession::SetActive(GraphId from, GraphId to) {
   // set the graph type of condition graph
   graph_type[ExecOrderOfChildGraph(final_graph_id_, to)] = CONDITION_GRAPH;
   // record the condition graph into while condition set
-  while_condtion_graphs_[to] = from;
+  while_condition_graphs_[to] = from;
 }
 
 void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const AnfNodePtr &backend_parameter) {
-  MS_LOG(INFO) << "start";
+  MS_LOG(INFO) << "Start!";
   MS_EXCEPTION_IF_NULL(backend_parameter);
   MS_EXCEPTION_IF_NULL(front_anf);
   if (!backend_parameter->isa<Parameter>()) {
-    MS_LOG(EXCEPTION) << "backend parameter's type is not a parameter,but is " << backend_parameter->ToString();
+    MS_LOG(EXCEPTION) << "Backend parameter's type is not a parameter,but is " << backend_parameter->ToString();
   }
   auto from_graph_id = GetGraphIdByNode(front_anf);
   auto from_graph = GetGraph(from_graph_id);
   MS_EXCEPTION_IF_NULL(from_graph);
 
-  MS_LOG(INFO) << "set node[" << front_anf->DebugString() << "] of graph[" << from_graph_id << "]to node["
+  MS_LOG(INFO) << "Set node[" << front_anf->DebugString() << "] of graph[" << from_graph_id << "]to node["
                << backend_parameter->DebugString() << "] of graph[" << AnfAlgo::GetGraphId(backend_parameter.get())
                << "]";
   // a node should not assign to itself
@@ -696,26 +696,27 @@ void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const An
     if (!AnfAlgo::OutputAddrExist(backend_arg, 0)) {
       // set parameter's addr in child graph to parameter in final graph
       AnfAlgo::SetOutputAddr(AnfAlgo::GetMutableOutputAddr(backend_parameter, 0), 0, backend_arg.get());
-      MS_LOG(INFO) << "assign mem of node" << backend_parameter->DebugString() << " of graph "
+      MS_LOG(INFO) << "Assign mem of node" << backend_parameter->DebugString() << " of graph "
                    << AnfAlgo::GetGraphId(backend_parameter.get()) << " to node" << backend_arg->DebugString()
                    << "of graph " << AnfAlgo::GetGraphId(backend_arg.get());
       return;
     }
   }
   InsertMultipleAssignToGraph(from_graph_id, backend_arg, backend_parameter);
-  // if front anf is a parameter,we can assign the value back,because backend_parameter won't be change in it's graph
-  // unless it's a weigth.If backend_parameter is a weight,we do should assign the value back
+  // if front anf is a parameter, we can assign the value back, because backend_parameter
+  // won't be changed in it's graph unless it's a weight. If backend_parameter is a weight,
+  // we do should assign the value back.
   auto to_graph_id = AnfAlgo::GetGraphId(backend_parameter.get());
   auto to_graph = GetGraph(to_graph_id);
   MS_EXCEPTION_IF_NULL(to_graph);
   if (backend_arg->isa<Parameter>() && !to_graph->execution_order().empty()) {
     InsertMultipleAssignToGraph(to_graph_id, backend_parameter, backend_arg);
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::SetChildGraphParameter(const tensor::TensorPtr &front_tensor, const AnfNodePtr &backend_parameter) {
-  MS_LOG(INFO) << "start";
+  MS_LOG(INFO) << "Start!";
   // sync data from host to device
   MS_EXCEPTION_IF_NULL(front_tensor);
   size_t tensor_size = front_tensor->data().nbytes();
@@ -723,9 +724,9 @@ void AscendSession::SetChildGraphParameter(const tensor::TensorPtr &front_tensor
   MS_EXCEPTION_IF_NULL(addr);
   if (!addr->SyncHostToDevice(front_tensor->shape(), tensor_size, front_tensor->data_type(),
                               front_tensor->data_c(false))) {
-    MS_LOG(EXCEPTION) << "tensor SyncHostToDevice fail!";
+    MS_LOG(EXCEPTION) << "Tensor SyncHostToDevice fail!";
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 void AscendSession::UpdateGraphOrder(GraphId to_graph_id) {
@@ -742,7 +743,7 @@ void AscendSession::UpdateGraphOrder(GraphId to_graph_id) {
   graph_order.push_back(to_graph_id);
   graph_type.push_back(COMMON_GRAPH);
   for (size_t i = 0; i < graph_order.size(); i++) {
-    MS_LOG(INFO) << "index " << i << ",graph_id " << graph_order[i] << ",graph_type" << graph_type[i];
+    MS_LOG(INFO) << "Index " << i << ",graph_id " << graph_order[i] << ",graph_type" << graph_type[i];
   }
 }
 
@@ -774,10 +775,10 @@ void AscendSession::SetChildGraphInput(GraphId g, const VectorRef &args) {
       SetChildGraphParameter(value->cast<TensorPtr>(), graph_inputs[input_index]);
       input_index++;
     } else {
-      MS_LOG(EXCEPTION) << "Unxpected arg type " << args[i].ToString();
+      MS_LOG(EXCEPTION) << "Unexpected arg type " << args[i].ToString();
     }
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 GraphId AscendSession::GetGraphIdByNode(const AnfNodePtr &front_anf) const {
@@ -795,7 +796,7 @@ GraphId AscendSession::GetGraphIdByNode(const AnfNodePtr &front_anf) const {
 }
 
 void AscendSession::MergeGraphExecOrder() {
-  MS_LOG(INFO) << "start";
+  MS_LOG(INFO) << "Start!";
   // insert switch to graph
   MergeSwitchCompile();
   // merge graph order
@@ -804,7 +805,7 @@ void AscendSession::MergeGraphExecOrder() {
   auto final_graph = GetGraph(final_graph_id_);
   MS_EXCEPTION_IF_NULL(final_graph);
   if (graph_order.empty()) {
-    MS_LOG(WARNING) << "graph output is a lonely variable not linked to any op!";
+    MS_LOG(WARNING) << "Graph output is a lonely variable not linked to any op!";
     return;
   }
   // if first graph is common,the final graph has no label,then set the stream of final graph same with the first graph
@@ -820,7 +821,7 @@ void AscendSession::MergeGraphExecOrder() {
     last_graph = child_graph;
     MS_EXCEPTION_IF_NULL(child_graph);
     auto exec_order = child_graph->execution_order();
-    MS_LOG(INFO) << "merge graph,graph_id " << graph_id;
+    MS_LOG(INFO) << "Merge graph,graph_id " << graph_id;
     (void)std::copy(exec_order.begin(), exec_order.end(), std::back_inserter(final_exec_order));
     // add all value nodes of child graphs to final graph
     for (auto &value_node : child_graph->graph_value_nodes()) {
@@ -874,11 +875,11 @@ void AscendSession::InsertAssignToGraph(GraphId graph_id, const AnfNodePtr &from
 void AscendSession::InsertMultipleAssignToGraph(GraphId graph_id, const AnfNodePtr &from, const AnfNodePtr &to) {
   std::vector<AnfNodePtr> from_outputs = AnfAlgo::GetAllOutput(from, {prim::kPrimTupleGetItem});
   std::vector<AnfNodePtr> to_outputs = AnfAlgo::GetAllOutput(to, {prim::kPrimTupleGetItem});
-  MS_LOG(INFO) << "insert assigns from [" << AnfAlgo::GetGraphId(from.get()) << "] to ["
+  MS_LOG(INFO) << "Insert assigns from [" << AnfAlgo::GetGraphId(from.get()) << "] to ["
                << AnfAlgo::GetGraphId(to.get()) << "]";
   if (from_outputs.size() != to_outputs.size()) {
-    MS_LOG(INFO) << "from[" << from->DebugString(5) << "] to[" << to->DebugString(5) << "]";
-    MS_LOG(EXCEPTION) << "from outputs size[" << from_outputs.size() << "] is not equal to to outputs size["
+    MS_LOG(INFO) << "From[" << from->DebugString(5) << "] to[" << to->DebugString(5) << "]";
+    MS_LOG(EXCEPTION) << "From outputs size[" << from_outputs.size() << "] is not equal to to outputs size["
                       << to_outputs.size() << "]";
   }
   for (size_t i = 0; i < from_outputs.size(); i++) {
@@ -897,7 +898,7 @@ void AscendSession::InsertStreamActiveToGraph(GraphId graph_id, uint32_t actived
   auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
   kernel_build_info_builder->SetKernelType(KernelType::RT_KERNEL);
   AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), active_node.get());
-  // set the actived stream id into the attr of active node
+  // set the active stream id into the attr of active node
   std::vector<uint32_t> active_index_value = {};
   active_index_value.push_back(actived_stream);
   AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue<std::vector<uint32_t>>(active_index_value), active_node);
@@ -921,7 +922,7 @@ size_t AscendSession::ExecOrderOfChildGraph(GraphId final_graph, GraphId child_g
 std::vector<GraphId> &AscendSession::GetGraphOrder(GraphId final_graph_id) {
   auto graph_order_iter = graph_execute_orders_.find(final_graph_id);
   if (graph_order_iter == graph_execute_orders_.end()) {
-    MS_LOG(EXCEPTION) << "final graph" << final_graph_id << "has no child graph";
+    MS_LOG(EXCEPTION) << "Final graph" << final_graph_id << "has no child graph";
   }
   return graph_order_iter->second;
 }
@@ -930,7 +931,7 @@ std::vector<GraphId> &AscendSession::GetGraphOrder(GraphId final_graph_id) {
 std::vector<GraphType> &AscendSession::GetGraphOrderType(GraphId final_graph_id) {
   auto graph_type_iter = graph_order_types_.find(final_graph_id);
   if (graph_type_iter == graph_order_types_.end()) {
-    MS_LOG(EXCEPTION) << "final graph" << final_graph_id << "has no graph_order_types_";
+    MS_LOG(EXCEPTION) << "Final graph" << final_graph_id << "has no graph_order_types_";
   }
   return graph_type_iter->second;
 }
diff --git a/mindspore/ccsrc/session/ascend_session.h b/mindspore/ccsrc/session/ascend_session.h
index e5cfb52f2a..caec4b35f7 100644
--- a/mindspore/ccsrc/session/ascend_session.h
+++ b/mindspore/ccsrc/session/ascend_session.h
@@ -48,13 +48,14 @@ class AscendSession : public SessionBasic {
   GraphId SetFinalGraphInput(const std::vector<AnfNodePtr> &args) override;
   // set output of final graph
   void SetFinalGraphOutput(const BaseRef &output) override;
-  // insert switch and set the relative acitve ops
+  // insert switch and set the relative active ops
   void SwitchCompile(GraphId cond_g, GraphId true_g, GraphId false_g) override;
-  // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter
+  // set args of child graph. the arg maybe come from a output of other child graphs,
+  // or from final graph's parameter
   void SetChildGraphInput(GraphId g, const VectorRef &args) override;
   // get graph id in child graphs by ME front anf node pointer
   GraphId GetGraphIdByNode(const AnfNodePtr &front_anf) const override;
-  // get grpah id of final graph
+  // get graph id of final graph
   GraphId GetFinalRunGraph() const override { return final_graph_id_; }
   // insert active to graph
   void SetActive(GraphId, GraphId) override;
@@ -112,9 +113,9 @@ class AscendSession : public SessionBasic {
   // key is final_graph_id,value is the graph types of child graphs
   std::unordered_map<GraphId, std::vector<GraphType>> graph_order_types_;
   // record condition graph of while
-  std::unordered_map<GraphId, GraphId> while_condtion_graphs_;
-  // record all conditons
-  std::unordered_map<GraphId, std::pair<GraphId, GraphId>> switchs_;
+  std::unordered_map<GraphId, GraphId> while_condition_graphs_;
+  // record all conditions
+  std::unordered_map<GraphId, std::pair<GraphId, GraphId>> switches_;
   // final_graph_id is used in every root graph has it's own session situation
   GraphId final_graph_id_;
 };
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index 196a2f300f..503c98e52a 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -83,7 +83,7 @@ void GPUSession::Execute(const std::shared_ptr<KernelGraph> &kernel_graph) const
 }
 
 GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
-  // Construct graph, if construct successs, graph_sum_ + 1
+  // Construct graph, if successfully, graph_sum_ + 1
   auto graph_id = graph_sum_;
   auto graph = ConstructKernelGraph(lst, outputs);
   // Select kernel build info
@@ -100,7 +100,7 @@ GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
   auto execution_order = graph->execution_order();
   Reorder(&execution_order);
   graph->set_execution_order(execution_order);
-  // Alloc memeory, include static memory and dynamic memory
+  // Alloc memory, including static memory and dynamic memory
   AllocateMemory(graph.get());
   // Reset memory resource
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc
index b07840aa98..84ff6b81a2 100644
--- a/mindspore/ccsrc/session/kernel_graph.cc
+++ b/mindspore/ccsrc/session/kernel_graph.cc
@@ -34,7 +34,7 @@ void PushNoVisitedNode(const AnfNodePtr &node, std::queue<AnfNodePtr> *que,
   if (visited_nodes->find(node) == visited_nodes->end()) {
     que->push(node);
     (void)visited_nodes->insert(node);
-    MS_LOG(DEBUG) << "push que:" << node->DebugString();
+    MS_LOG(DEBUG) << "Push que:" << node->DebugString();
   }
 }
 }  // namespace
@@ -58,7 +58,7 @@ void KernelGraph::SetExecOrderByDefault() {
   auto clear_output = [&zero_output_nodes, &allreduce_nodes, &visited_nodes, this](const AnfNodePtr &input) -> void {
     if (node_output_num_[input] == 0 && visited_nodes.find(input) == visited_nodes.end()) {
       MS_EXCEPTION_IF_NULL(input);
-      MS_LOG(DEBUG) << "clear output num:" << input->DebugString();
+      MS_LOG(DEBUG) << "Clear output num:" << input->DebugString();
       (void)visited_nodes.insert(input);
       if (input->isa<CNode>() && AnfAlgo::GetCNodeName(input) == kAllReduceOpName) {
         allreduce_nodes.push(input);
@@ -85,21 +85,21 @@ void KernelGraph::SetExecOrderByDefault() {
     if (it == node_input_edges_.end()) {
       // value node and parameter has no input,no need to print log
       if (node->isa<CNode>()) {
-        MS_LOG(DEBUG) << "can not find node [" << node->DebugString() << "]";
+        MS_LOG(DEBUG) << "Can not find node [" << node->DebugString() << "]";
       }
       continue;
     }
     for (const auto &input_edge : it->second) {
       if (node_output_num_.find(input_edge.first) == node_output_num_.end()) {
         MS_EXCEPTION_IF_NULL(input_edge.first);
-        MS_LOG(EXCEPTION) << "can't find node[" << input_edge.first->DebugString() << "]";
+        MS_LOG(EXCEPTION) << "Can't find node[" << input_edge.first->DebugString() << "]";
       }
       MS_EXCEPTION_IF_NULL(input_edge.first);
-      MS_LOG(DEBUG) << "decrese input:" << input_edge.first->DebugString() << ",node:" << node->DebugString()
+      MS_LOG(DEBUG) << "Decrease input:" << input_edge.first->DebugString() << ",node:" << node->DebugString()
                     << ",num: " << node_output_num_[input_edge.first] << ",decrease num:" << input_edge.second;
       if (node_output_num_[input_edge.first] < input_edge.second) {
-        MS_LOG(EXCEPTION) << "input node:" << input_edge.first->DebugString() << ",node_output_num"
-                          << node_output_num_[input_edge.first] << "depend edege:" << input_edge.second;
+        MS_LOG(EXCEPTION) << "Input node:" << input_edge.first->DebugString() << ",node_output_num"
+                          << node_output_num_[input_edge.first] << "depend edge:" << input_edge.second;
       }
       node_output_num_[input_edge.first] = node_output_num_[input_edge.first] - input_edge.second;
       clear_output(input_edge.first);
@@ -120,20 +120,20 @@ void KernelGraph::CheckLoop() {
     string str;
     auto node_output_it = node_output_edges_.find(it.first);
     if (node_output_it == node_output_edges_.end()) {
-      MS_LOG(EXCEPTION) << "can't find node [" << it.first->DebugString() << "]";
+      MS_LOG(EXCEPTION) << "Can't find node [" << it.first->DebugString() << "]";
     }
     for (const auto &output_edge : node_output_edges_[it.first]) {
       MS_EXCEPTION_IF_NULL(output_edge.first);
       str = str.append(output_edge.first->DebugString()).append("|");
     }
     if (it.second != 0) {
-      MS_LOG(WARNING) << "node:" << it.first->DebugString() << ",outputs:" << str << ",output num:" << it.second;
+      MS_LOG(WARNING) << "Node:" << it.first->DebugString() << ",outputs:" << str << ",output num:" << it.second;
       none_zero_output[it.first] = it.second;
     }
   }
   // if don't consider control depend and loop exit,a exception will be throw
   if (!none_zero_output.empty()) {
-    MS_LOG(EXCEPTION) << "nodes have loop,left node num:" << none_zero_output.size();
+    MS_LOG(EXCEPTION) << "Nodes have loop, left node num:" << none_zero_output.size();
   }
 }
 
@@ -152,7 +152,7 @@ CNodePtr KernelGraph::NewCNode(const CNodePtr &cnode) {
   MS_EXCEPTION_IF_NULL(cnode);
   auto new_cnode = std::make_shared<CNode>(*cnode);
   // if a cnode is created not from front,this cnode won't be in map,so when replace it,we shouldn't update map
-  if (BakcendNodeExistInFrontBackendMap(cnode)) {
+  if (BackendNodeExistInFrontBackendMap(cnode)) {
     FrontBackendlMapUpdate(cnode, new_cnode);
   }
   AnfAlgo::SetGraphId(graph_id_, cnode.get());
@@ -299,7 +299,7 @@ AnfNodePtr KernelGraph::GetBackendAnfByFrontAnf(const AnfNodePtr &front_anf) {
   return front_backend_anf_map_[front_anf];
 }
 
-bool KernelGraph::BakcendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf) {
+bool KernelGraph::BackendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf) {
   return backend_front_anf_map_.find(backend_anf) != backend_front_anf_map_.end();
 }
 
@@ -317,9 +317,9 @@ void KernelGraph::TensorValueNodeMapAdd(const tensor::TensorPtr &tensor, const V
 }
 
 void KernelGraph::AddDependEdge(const AnfNodePtr &node, const AnfNodePtr &input, size_t depend_edge_num) {
-  MS_LOG(DEBUG) << "input:" << input->DebugString() << ",  node:" << node->DebugString() << ",num:" << depend_edge_num;
+  MS_LOG(DEBUG) << "Input:" << input->DebugString() << ",  node:" << node->DebugString() << ",num:" << depend_edge_num;
   auto output_depend_edge = std::pair<AnfNodePtr, size_t>(node, depend_edge_num);
-  // add output depend eddge of input
+  // add output depend edge of input
   auto output_it = node_output_edges_.find(input);
   if (output_it == node_output_edges_.end()) {
     node_output_edges_[input] = std::vector<std::pair<AnfNodePtr, size_t>>{output_depend_edge};
@@ -346,7 +346,7 @@ std::vector<AnfNodePtr> KernelGraph::GetOutputNodes(const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   auto it = node_output_edges_.find(node);
   if (it == node_output_edges_.end()) {
-    MS_LOG(EXCEPTION) << "can'f find node[" << node->DebugString() << "]";
+    MS_LOG(EXCEPTION) << "Can't find node[" << node->DebugString() << "]";
   }
   std::vector<AnfNodePtr> output_nodes;
   auto trans = [](const std::pair<AnfNodePtr, size_t> &pair) -> AnfNodePtr { return pair.first; };
@@ -372,7 +372,7 @@ void KernelGraph::UpdateControlDependRelations(const std::vector<AnfNodePtr> &de
     MS_EXCEPTION_IF_NULL(depend_node);
     std::vector<AnfNodePtr> prior_nodes = {prior_node};
     std::vector<AnfNodePtr> depend_nodes = {depend_node};
-    MS_LOG(INFO) << "prior node[" << prior_node->DebugString() << "],depend node[" << depend_node->DebugString()
+    MS_LOG(INFO) << "Prior node[" << prior_node->DebugString() << "],depend node[" << depend_node->DebugString()
                  << "],depend_mode=[" << AnfAlgo::GetNodeAttr<int>(cnode, "depend_mode") << "]";
     if (prior_node->isa<Parameter>()) {
       prior_nodes = GetOutputNodes(prior_node);
@@ -384,7 +384,7 @@ void KernelGraph::UpdateControlDependRelations(const std::vector<AnfNodePtr> &de
       for (auto &second_node : depend_nodes) {
         MS_EXCEPTION_IF_NULL(first_node);
         MS_EXCEPTION_IF_NULL(second_node);
-        MS_LOG(INFO) << "add first node:" << first_node->DebugString() << ",second node:" << second_node->DebugString();
+        MS_LOG(INFO) << "Add first node:" << first_node->DebugString() << ",second node:" << second_node->DebugString();
         AddDependEdge(second_node, first_node, 1);
       }
     }
@@ -437,18 +437,18 @@ void KernelGraph::BfsToUpdateNodeOutput() {
     MS_EXCEPTION_IF_NULL(cnode);
     // handle data links
     for (const auto &input : cnode->inputs()) {
-      size_t dpend_edge_num = 1;
+      size_t depend_edge_num = 1;
       // handle control depend,all inputs of control depend has no depend edge
       if (HandleControlDependNode(input, &que, &visited_nodes)) {
         control_depends.push_back(input);
-        dpend_edge_num = 0;
+        depend_edge_num = 0;
       }
       // the 2rd input of depend is no depend edge
       if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) && input == cnode->input(kDependAttachNodeIndex)) {
-        dpend_edge_num = 0;
+        depend_edge_num = 0;
       }
       PushNoVisitedNode(input, &que, &visited_nodes);
-      AddDependEdge(node, input, dpend_edge_num);
+      AddDependEdge(node, input, depend_edge_num);
     }
   }
   UpdateControlDependRelations(control_depends);
diff --git a/mindspore/ccsrc/session/kernel_graph.h b/mindspore/ccsrc/session/kernel_graph.h
index d94638aa99..e11f6807f5 100644
--- a/mindspore/ccsrc/session/kernel_graph.h
+++ b/mindspore/ccsrc/session/kernel_graph.h
@@ -62,8 +62,8 @@ class KernelGraph : public FuncGraph {
   void FrontBackendlMapUpdate(const AnfNodePtr &old_backend_anf, const AnfNodePtr &new_backend_anf);
   // get backend anf by front anf
   AnfNodePtr GetBackendAnfByFrontAnf(const AnfNodePtr &front_anf);
-  // check backend node wheteher exist in map
-  bool BakcendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf);
+  // check backend node whether exist in map
+  bool BackendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf);
   // get value node by tensor
   ValueNodePtr GetValueNodeByTensor(const tensor::TensorPtr &tensor);
   // add value node tensor relation map
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index be03f54f3c..ede3ae7419 100644
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -281,7 +281,7 @@ std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, KernelG
       }
       continue;
     }
-    // creata single parameter if is a abstract real kernel
+    // create single parameter if is a abstract real kernel
     create_parameter(out_node->abstract());
   }
   return parameters;
@@ -413,7 +413,7 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph)
       cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(anf));
       continue;
     } else if (anf->isa<ValueNode>() && !IsValueNode<FuncGraph>(anf)) {
-      // if input is a value ndoe,
+      // if input is a value node,
       auto new_value_node = CreateNewValueNode(anf, graph);
       if (new_value_node != nullptr) {
         cnode_inputs.emplace_back(new_value_node);
@@ -549,7 +549,7 @@ void SessionBasic::Reorder(std::vector<CNodePtr> *node_list) {
 
   for (const auto &node : *node_list) {
     MS_EXCEPTION_IF_NULL(node);
-    if (kOptOpeatorSet.find(AnfAlgo::GetCNodeName(node)) != kOptOpeatorSet.end()) {
+    if (kOptOperatorSet.find(AnfAlgo::GetCNodeName(node)) != kOptOperatorSet.end()) {
       all_opt_list.emplace_back(node);
     } else {
       non_opt_list.emplace_back(node);
@@ -599,7 +599,7 @@ void SessionBasic::ToTensorPtr(const OpRunInfo &op_run_info, std::vector<tensor:
   MS_EXCEPTION_IF_NULL(inputs);
   MS_EXCEPTION_IF_NULL(tensor_mask);
   if (op_run_info.op_inputs.size() != op_run_info.inputs_mask.size()) {
-    MS_LOG(EXCEPTION) << "op input size " << op_run_info.op_inputs.size() << " should be equal to op input mask size "
+    MS_LOG(EXCEPTION) << "Op input size " << op_run_info.op_inputs.size() << " should be equal to op input mask size "
                       << op_run_info.inputs_mask.size();
   }
   size_t input_num = op_run_info.op_inputs.size();
@@ -636,7 +636,7 @@ CNodePtr SessionBasic::ConstructOutput(const AnfNodePtrList &outputs, const std:
     if (backend_anf != nullptr) {
       return backend_anf;
     }
-    MS_LOG(EXCEPTION) << "did not find the node in the equiv map!";
+    MS_LOG(EXCEPTION) << "Can not find the node in the equiv map!";
   };
   output_args.push_back(NewValueNode(prim::kPrimMakeTuple));
   (void)std::transform(outputs.begin(), outputs.end(), std::back_inserter(output_args),
@@ -645,7 +645,7 @@ CNodePtr SessionBasic::ConstructOutput(const AnfNodePtrList &outputs, const std:
 }
 
 void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr<KernelGraph> &graph) {
-  MS_LOG(INFO) << "start";
+  MS_LOG(INFO) << "Start!";
   std::vector<AnfNodePtr> make_tuple_inputs;
   make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
   if (AnfRuntimeAlgorithm::GetOutputTensorNum(cnode) > 1) {
@@ -667,14 +667,14 @@ void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr
   // create output
   auto g_output = graph->NewCNode(make_tuple_inputs);
   graph->set_output(g_output);
-  // set graph manager,which now is only used to get valuendoes and hardware optimizing
+  // set graph manager,which now is only used to get valuenodes and hardware optimizing
   MS_EXCEPTION_IF_NULL(context_);
   FuncGraphManagerPtr manager = context_->manager();
   if (manager != nullptr) {
     manager->AddFuncGraph(graph);
     graph->set_manager(manager);
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "Finish!";
 }
 
 std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info) {
@@ -694,9 +694,9 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInf
   std::vector<tensor::TensorPtr> input_tensors;
   std::vector<bool> tensors_mask;
   ToTensorPtr(op_run_info, &input_tensors, &tensors_mask);
-  MS_LOG(INFO) << "input tensor size" << input_tensors.size();
+  MS_LOG(INFO) << "Input tensor size" << input_tensors.size();
   if (input_tensors.size() != tensors_mask.size()) {
-    MS_LOG(EXCEPTION) << "input tensors size " << input_tensors.size() << " should be equal to tensors mask size "
+    MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors.size() << " should be equal to tensors mask size "
                       << tensors_mask.size();
   }
   for (size_t i = 0; i < input_tensors.size(); ++i) {
@@ -711,7 +711,7 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInf
   cnode->set_abstract(op_run_info.abstract);
   // set const input to attr if value is not a tensor,such as scalar or tuple
   RunOpConvertConstInputToAttr(op_run_info, cnode);
-  // set exectuion order
+  // set execution order
   std::vector<CNodePtr> exe_order = {cnode};
   graph->set_execution_order(exe_order);
   // set output
@@ -734,14 +734,14 @@ BaseRef SessionBasic::TransformBaseRefListToTuple(const BaseRef &base_ref) {
         py::tuple tensor_tuple = py::cast<py::tuple>(obj);
         output_tensors[i] = tensor_tuple;
       } else {
-        MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor !";
+        MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
       }
     }
     return output_tensors;  // turn tuple to py::object and store in PyObjectRef
   } else if (utils::isa<tensor::TensorPtr>(base_ref)) {
     return base_ref;
   } else {
-    MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor !";
+    MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
   }
 }
 }  // namespace session
diff --git a/mindspore/ccsrc/session/session_basic.h b/mindspore/ccsrc/session/session_basic.h
index f80e69bd9f..9aadb78cb2 100644
--- a/mindspore/ccsrc/session/session_basic.h
+++ b/mindspore/ccsrc/session/session_basic.h
@@ -56,7 +56,7 @@ class SessionBasic {
   virtual ~SessionBasic() { summary_callback_ = nullptr; }
 
   virtual GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) = 0;
-  // build graph ,used to handle mupltiple child graphs
+  // build graph, used to handle multiple child graphs
   virtual void BuildGraph(GraphId) {}
 
   virtual void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) = 0;
@@ -75,7 +75,7 @@ class SessionBasic {
   virtual GraphId SetFinalGraphInput(const std::vector<AnfNodePtr> &) { return kInvalidGraphId; }
   // set output of final graph
   virtual void SetFinalGraphOutput(const BaseRef &) {}
-  // insert switch and set the relative acitve ops
+  // insert switch and set the relative active ops
   virtual void SwitchCompile(GraphId, GraphId, GraphId) {}
   // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter
   virtual void SetChildGraphInput(GraphId, const VectorRef &) {}
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 229c0547cc..ee1eeaddfc 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -186,7 +186,7 @@ const std::vector<std::set<std::string>> kShapeSupportFormatMap = {k1DSupportFor
                                                                    k4DSupportFormat};
 const std::set<std::string> kDefaultCompatibleFormat = {kOpFormat_ND, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_HWCN};
 
-const std::set<std::string> kOptOpeatorSet = {
+const std::set<std::string> kOptOperatorSet = {
   kMomentumOpName,       kApplyMomentumOpName,        kApplyAdadeltaOpName,
   kApplyAdagradOpName,   kApplyAdagradDAName,         kApplyAdamOpName,
   kApplyAdaMaxOpName,    kApplyAddSignOpName,         kApplyCenteredRMSPOpName,

From c2fed0b7398bc4156f1455b93292de266d179ce5 Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Thu, 2 Apr 2020 16:07:17 +0800
Subject: [PATCH 071/367] remove (feed mode) in comment

---
 mindspore/dataset/engine/datasets.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index ad3e7d8255..db2b5169d2 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -395,7 +395,7 @@ class Dataset:
         Note:
             The order of using repeat and batch reflects the number of batches. Recommend that
             repeat operation should be used after batch operation.
-            If dataset_sink_mode is False (feed mode), here repeat operation is invalid.
+            If dataset_sink_mode is False, here repeat operation is invalid.
 
         Args:
             count (int): Number of times the dataset should be repeated (default=None).

From ab917a734db694d9e727b8b502ff7e77fc265dd3 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Wed, 1 Apr 2020 20:47:46 +0800
Subject: [PATCH 072/367] fix bug for allreduce fusion and add resnet unit test

---
 .../allreduce_fusion/allreduce_fusion.cc      |   2 +-
 .../allreduce_fusion/allreduce_fusion.h       |   1 -
 .../allreduce_fusion/allreduce_graph.cc       |  22 +-
 .../allreduce_fusion/allreduce_node.cc        |  24 +-
 .../allreduce_fusion/allreduce_node.h         |   7 +-
 .../python/parallel/test_allreduce_fusion.py  |   4 +-
 .../parallel/test_auto_parallel_resnet.py     | 223 ++++++++++++++++--
 7 files changed, 246 insertions(+), 37 deletions(-)

diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
index 37b6eb42ed..38a4eee9a2 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc
@@ -359,7 +359,7 @@ Status AllreduceFusion::SetFusionByBackwardCompAndAllreduceTime() {
     return FAILED;
   }
   double para_size = (tail_time_ - allreduce_inherent_time_) / allreduce_bandwidth_;
-  double to_cost = allreduce_graph_.max() + FUSION_COST_EPS;
+  double to_cost = allreduce_graph_.max();
   int32_t fusion = 1;
   while (to_cost != 0) {
     MS_LOG(INFO) << "to_cost: " << to_cost << " para_size: " << para_size;
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
index de2844fa51..9c722ee2f3 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h
@@ -38,7 +38,6 @@ constexpr double DEFAULT_COST_MODEL_ALLREDUCE_FUSION_COMPUTATION_TIME_PARAMETER
 constexpr char FUSION[] = "fusion";
 constexpr char PARAMETER[] = "parameter";
 const uint32_t MAX_RECURSIVE_CALL_TIMES = 100;
-const double FUSION_COST_EPS = 1e-7;
 class AllreduceFusion {
  public:
   AllreduceFusion()
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
index 5c97eda8d8..6721df6a45 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc
@@ -24,7 +24,19 @@
 namespace mindspore {
 namespace parallel {
 Status AllreduceGraph::AddNode(const CNodePtr& node, const AnfNodePtr& para) {
-  auto arnode = std::make_shared<AllreduceNode>(AllreduceNode());
+  AllreduceNodePtr arnode;
+  auto cnode_emplace_return = cnode_set_.emplace(node);
+  if (!cnode_emplace_return.second) {
+    MS_LOG(INFO) << "node: " << node->DebugString() << " has already been added!";
+    auto cnode_arnode_pair = cnode_arnode_map_.find(node);
+    if (cnode_arnode_pair == cnode_arnode_map_.end()) {
+      MS_LOG(EXCEPTION) << "node is not in cnode_arnode_map_!";
+    }
+    arnode = cnode_arnode_pair->second;
+  } else {
+    arnode = std::make_shared<AllreduceNode>(AllreduceNode());
+  }
+
   if (arnode->Init(node) != SUCCESS) {
     MS_LOG(ERROR) << "AllreduceNode Init failed";
     return FAILED;
@@ -39,10 +51,6 @@ Status AllreduceGraph::AddNode(const CNodePtr& node, const AnfNodePtr& para) {
   if (!arnode_emplace_return.second) {
     MS_LOG(INFO) << "node: " << node->DebugString() << "'s arnode has already been added!";
   }
-  auto cnode_emplace_return = cnode_set_.emplace(node);
-  if (!cnode_emplace_return.second) {
-    MS_LOG(INFO) << "node: " << node->DebugString() << " has already been added!";
-  }
   cnode_emplace_return = para_cnodeset_map_[para].emplace(node);
   if (!cnode_emplace_return.second) {
     MS_LOG(INFO) << "node: " << node->DebugString() << " already in para: " << para->fullname_with_scope()
@@ -75,7 +83,7 @@ Status AllreduceGraph::AddEdge(const CNodePtr& from, const CNodePtr& to, double
     MS_LOG(ERROR) << "from_arnode AddNext failed";
     return FAILED;
   }
-  if (to_arnode->AddPrev(from_arnode, dist) != SUCCESS) {
+  if (to_arnode->AddPrev(from_arnode, dist, &max_) != SUCCESS) {
     MS_LOG(ERROR) << "to_arnode AddPrev failed";
     return FAILED;
   }
@@ -110,7 +118,7 @@ std::pair<std::vector<AnfNodePtr>, double> AllreduceGraph::GetParaByParaSize(dou
   double cur_para_size = 0;
   double from = to;
   for (auto& arnode : arnode_vec_) {
-    if (arnode.depend_feat_size() >= to) {
+    if (arnode.depend_feat_size() != max_ && arnode.depend_feat_size() >= to) {
       continue;
     }
     if (para_size > 0 && cur_para_size >= para_size && arnode.depend_feat_size() < from) {
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc
index 8bed29f2f2..6be588928a 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc
@@ -15,6 +15,7 @@
  */
 
 #include "parallel/allreduce_fusion/allreduce_node.h"
+#include <queue>
 #include "parallel/tensor_layout/tensor_layout.h"
 #include "utils/log_adapter.h"
 
@@ -29,7 +30,7 @@ Status AllreduceNode::AddNext(const AllreduceNodePtr& next_node) {
   return SUCCESS;
 }
 
-Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist) {
+Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist, double* max) {
   if (prev_node == nullptr) {
     MS_LOG(ERROR) << "next_node is nullptr!";
     return FAILED;
@@ -39,7 +40,26 @@ Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist) {
     return FAILED;
   }
   prev_.emplace_back(prev_node);
-  depend_feat_size_ += prev_node->depend_feat_size() + dist;
+  double add_dist = prev_node->depend_feat_size() + dist;
+  depend_feat_size_ += add_dist;
+  if (depend_feat_size_ > *max) {
+    *max = depend_feat_size_;
+  }
+  std::queue<AllreduceNodePtr> next_queue;
+  for (auto& next : next_) {
+    next_queue.push(next);
+  }
+  while (!next_queue.empty()) {
+    auto ele = next_queue.front();
+    ele->AddDependFeatSize(add_dist);
+    if (ele->depend_feat_size() > *max) {
+      *max = ele->depend_feat_size();
+    }
+    for (auto& next : ele->next()) {
+      next_queue.push(next);
+    }
+    next_queue.pop();
+  }
   return SUCCESS;
 }
 
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
index f3eeb53ec7..2a765dbfcd 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h
@@ -39,9 +39,14 @@ class AllreduceNode {
   const std::unordered_set<AnfNodePtr>& paras() const { return paras_; }
   double curr_para_size() const { return curr_para_size_; }
   virtual ~AllreduceNode() = default;
-  Status AddPrev(const AllreduceNodePtr& prev_node, double dist);
+  // Add previous node
+  // prev_node is the previous to be added
+  // max is the current max depend_feat_size of the AllreduceGraph
+  Status AddPrev(const AllreduceNodePtr& prev_node, double dist, double* max);
   Status AddNext(const AllreduceNodePtr& next_node);
   double depend_feat_size() const { return depend_feat_size_; }
+  void AddDependFeatSize(double add_dist) { depend_feat_size_ += add_dist; }
+  const std::vector<AllreduceNodePtr>& next() const { return next_; }
   void ToString() const;
   bool operator<(const AllreduceNode& node) const { return depend_feat_size_ < node.depend_feat_size(); }
   bool operator>(const AllreduceNode& node) const { return depend_feat_size_ > node.depend_feat_size(); }
diff --git a/tests/ut/python/parallel/test_allreduce_fusion.py b/tests/ut/python/parallel/test_allreduce_fusion.py
index e59ce38051..fcbee10587 100644
--- a/tests/ut/python/parallel/test_allreduce_fusion.py
+++ b/tests/ut/python/parallel/test_allreduce_fusion.py
@@ -275,7 +275,7 @@ def test_allreduce_fusion5():
     expect_dict = {'backbone2.fc8.weight': 3,
                    'backbone2.fc7.weight': 3,
                    'backbone2.fc6.weight': 3,
-                   'backbone2.fc5.weight': 2,
+                   'backbone2.fc5.weight': 3,
                    'backbone2.fc4.weight': 2,
                    'backbone2.fc3.weight': 2,
                    'backbone2.fc2.weight': 1,
@@ -283,7 +283,7 @@ def test_allreduce_fusion5():
                    'backbone1.fc8.weight': 3,
                    'backbone1.fc7.weight': 3,
                    'backbone1.fc6.weight': 3,
-                   'backbone1.fc5.weight': 2,
+                   'backbone1.fc5.weight': 3,
                    'backbone1.fc4.weight': 2,
                    'backbone1.fc3.weight': 2,
                    'backbone1.fc2.weight': 1,
diff --git a/tests/ut/python/parallel/test_auto_parallel_resnet.py b/tests/ut/python/parallel/test_auto_parallel_resnet.py
index 1e0e3570b9..9b4e1fda23 100644
--- a/tests/ut/python/parallel/test_auto_parallel_resnet.py
+++ b/tests/ut/python/parallel/test_auto_parallel_resnet.py
@@ -273,13 +273,9 @@ class DatasetLenet():
         return 1
 
 
-def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192
+def train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768):
     dev_num = 8
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=dev_num)
-    cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=260.0)
-    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1)
-    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2)
-    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.5)
     set_algo_parameters(elementwise_op_strategy_follow=True)
     resset_op_id()
     np.random.seed(6)
@@ -303,8 +299,16 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
             assert v == [[dev_num, 1]]
 
     allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network)
-
     print(allreduce_fusion_dict)
+    return allreduce_fusion_dict
+
+
+def test_train_32k_8p_fusion1(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192
+    cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=260.0)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.5)
+    allreduce_fusion_dict = train_32k_8p(epoch_size, batch_size, num_classes)
     expect_dict = {'end_point.bias': 2,
                    'end_point.weight': 2,
                    'layer4.2.bn3.beta': 2,
@@ -382,11 +386,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer3.1.bn1.beta': 2,
                    'layer3.1.bn1.gamma': 2,
                    'layer3.1.conv1.weight': 2,
-                   'layer3.0.bn_down_sample.beta': 1,
-                   'layer3.0.bn_down_sample.gamma': 1,
+                   'layer3.0.bn_down_sample.beta': 2,
+                   'layer3.0.bn_down_sample.gamma': 2,
                    'layer3.0.conv_down_sample.weight': 2,
-                   'layer3.0.bn3.beta': 1,
-                   'layer3.0.bn3.gamma': 1,
+                   'layer3.0.bn3.beta': 2,
+                   'layer3.0.bn3.gamma': 2,
                    'layer3.0.conv3.weight': 2,
                    'layer3.0.bn2.beta': 2,
                    'layer3.0.bn2.gamma': 2,
@@ -412,8 +416,8 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer2.2.bn1.beta': 2,
                    'layer2.2.bn1.gamma': 2,
                    'layer2.2.conv1.weight': 2,
-                   'layer2.1.bn3.beta': 1,
-                   'layer2.1.bn3.gamma': 1,
+                   'layer2.1.bn3.beta': 2,
+                   'layer2.1.bn3.gamma': 2,
                    'layer2.1.conv3.weight': 2,
                    'layer2.1.bn2.beta': 2,
                    'layer2.1.bn2.gamma': 2,
@@ -421,11 +425,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer2.1.bn1.beta': 2,
                    'layer2.1.bn1.gamma': 2,
                    'layer2.1.conv1.weight': 2,
-                   'layer2.0.bn_down_sample.beta': 1,
-                   'layer2.0.bn_down_sample.gamma': 1,
+                   'layer2.0.bn_down_sample.beta': 2,
+                   'layer2.0.bn_down_sample.gamma': 2,
                    'layer2.0.conv_down_sample.weight': 2,
-                   'layer2.0.bn3.beta': 1,
-                   'layer2.0.bn3.gamma': 1,
+                   'layer2.0.bn3.beta': 2,
+                   'layer2.0.bn3.gamma': 2,
                    'layer2.0.conv3.weight': 2,
                    'layer2.0.bn2.beta': 2,
                    'layer2.0.bn2.gamma': 2,
@@ -442,8 +446,8 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer1.2.bn1.beta': 2,
                    'layer1.2.bn1.gamma': 2,
                    'layer1.2.conv1.weight': 2,
-                   'layer1.1.bn3.beta': 1,
-                   'layer1.1.bn3.gamma': 1,
+                   'layer1.1.bn3.beta': 2,
+                   'layer1.1.bn3.gamma': 2,
                    'layer1.1.conv3.weight': 2,
                    'layer1.1.bn2.beta': 2,
                    'layer1.1.bn2.gamma': 2,
@@ -451,11 +455,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer1.1.bn1.beta': 2,
                    'layer1.1.bn1.gamma': 2,
                    'layer1.1.conv1.weight': 2,
-                   'layer1.0.bn_down_sample.beta': 1,
-                   'layer1.0.bn_down_sample.gamma': 1,
+                   'layer1.0.bn_down_sample.beta': 2,
+                   'layer1.0.bn_down_sample.gamma': 2,
                    'layer1.0.conv_down_sample.weight': 2,
-                   'layer1.0.bn3.beta': 1,
-                   'layer1.0.bn3.gamma': 1,
+                   'layer1.0.bn3.beta': 2,
+                   'layer1.0.bn3.gamma': 2,
                    'layer1.0.conv3.weight': 2,
                    'layer1.0.bn2.beta': 2,
                    'layer1.0.bn2.gamma': 2,
@@ -465,7 +469,180 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576
                    'layer1.0.conv1.weight': 2,
                    'bn1.beta': 1,
                    'bn1.gamma': 1,
-                   'conv1.weight': 2}
+                   'conv1.weight': 1}
+
+    assert (allreduce_fusion_dict == expect_dict)
+    cost_model_context.reset_cost_model_context()
+
+
+def test_train_32k_8p_fusion2(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=2)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_time=0.1)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_inherent_time=0.05)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_bandwidth=0.000001)
+    cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_computation_time_parameter=0.0000015)
+    allreduce_fusion_dict = train_32k_8p(epoch_size, batch_size, num_classes)
+    expect_dict = {'end_point.bias': 2,
+                   'end_point.weight': 2,
+                   'layer4.2.bn3.beta': 2,
+                   'layer4.2.bn3.gamma': 2,
+                   'layer4.2.conv3.weight': 2,
+                   'layer4.2.bn2.beta': 2,
+                   'layer4.2.bn2.gamma': 2,
+                   'layer4.2.conv2.weight': 2,
+                   'layer4.2.bn1.beta': 2,
+                   'layer4.2.bn1.gamma': 2,
+                   'layer4.2.conv1.weight': 2,
+                   'layer4.1.bn3.beta': 2,
+                   'layer4.1.bn3.gamma': 2,
+                   'layer4.1.conv3.weight': 2,
+                   'layer4.1.bn2.beta': 2,
+                   'layer4.1.bn2.gamma': 2,
+                   'layer4.1.conv2.weight': 2,
+                   'layer4.1.bn1.beta': 2,
+                   'layer4.1.bn1.gamma': 2,
+                   'layer4.1.conv1.weight': 2,
+                   'layer4.0.bn_down_sample.beta': 2,
+                   'layer4.0.bn_down_sample.gamma': 2,
+                   'layer4.0.conv_down_sample.weight': 2,
+                   'layer4.0.bn3.beta': 2,
+                   'layer4.0.bn3.gamma': 2,
+                   'layer4.0.conv3.weight': 2,
+                   'layer4.0.bn2.beta': 2,
+                   'layer4.0.bn2.gamma': 2,
+                   'layer4.0.conv2.weight': 2,
+                   'layer4.0.bn1.beta': 2,
+                   'layer4.0.bn1.gamma': 2,
+                   'layer4.0.conv1.weight': 2,
+                   'layer3.5.bn3.beta': 2,
+                   'layer3.5.bn3.gamma': 2,
+                   'layer3.5.conv3.weight': 2,
+                   'layer3.5.bn2.beta': 2,
+                   'layer3.5.bn2.gamma': 2,
+                   'layer3.5.conv2.weight': 2,
+                   'layer3.5.bn1.beta': 2,
+                   'layer3.5.bn1.gamma': 2,
+                   'layer3.5.conv1.weight': 2,
+                   'layer3.4.bn3.beta': 2,
+                   'layer3.4.bn3.gamma': 2,
+                   'layer3.4.conv3.weight': 2,
+                   'layer3.4.bn2.beta': 2,
+                   'layer3.4.bn2.gamma': 2,
+                   'layer3.4.conv2.weight': 2,
+                   'layer3.4.bn1.beta': 2,
+                   'layer3.4.bn1.gamma': 2,
+                   'layer3.4.conv1.weight': 2,
+                   'layer3.3.bn3.beta': 2,
+                   'layer3.3.bn3.gamma': 2,
+                   'layer3.3.conv3.weight': 2,
+                   'layer3.3.bn2.beta': 2,
+                   'layer3.3.bn2.gamma': 2,
+                   'layer3.3.conv2.weight': 2,
+                   'layer3.3.bn1.beta': 2,
+                   'layer3.3.bn1.gamma': 2,
+                   'layer3.3.conv1.weight': 2,
+                   'layer3.2.bn3.beta': 2,
+                   'layer3.2.bn3.gamma': 2,
+                   'layer3.2.conv3.weight': 2,
+                   'layer3.2.bn2.beta': 2,
+                   'layer3.2.bn2.gamma': 2,
+                   'layer3.2.conv2.weight': 2,
+                   'layer3.2.bn1.beta': 2,
+                   'layer3.2.bn1.gamma': 2,
+                   'layer3.2.conv1.weight': 2,
+                   'layer3.1.bn3.beta': 2,
+                   'layer3.1.bn3.gamma': 2,
+                   'layer3.1.conv3.weight': 2,
+                   'layer3.1.bn2.beta': 2,
+                   'layer3.1.bn2.gamma': 2,
+                   'layer3.1.conv2.weight': 2,
+                   'layer3.1.bn1.beta': 2,
+                   'layer3.1.bn1.gamma': 2,
+                   'layer3.1.conv1.weight': 2,
+                   'layer3.0.bn_down_sample.beta': 2,
+                   'layer3.0.bn_down_sample.gamma': 2,
+                   'layer3.0.conv_down_sample.weight': 2,
+                   'layer3.0.bn3.beta': 2,
+                   'layer3.0.bn3.gamma': 2,
+                   'layer3.0.conv3.weight': 2,
+                   'layer3.0.bn2.beta': 2,
+                   'layer3.0.bn2.gamma': 2,
+                   'layer3.0.conv2.weight': 2,
+                   'layer3.0.bn1.beta': 2,
+                   'layer3.0.bn1.gamma': 2,
+                   'layer3.0.conv1.weight': 2,
+                   'layer2.3.bn3.beta': 2,
+                   'layer2.3.bn3.gamma': 2,
+                   'layer2.3.conv3.weight': 2,
+                   'layer2.3.bn2.beta': 2,
+                   'layer2.3.bn2.gamma': 2,
+                   'layer2.3.conv2.weight': 2,
+                   'layer2.3.bn1.beta': 2,
+                   'layer2.3.bn1.gamma': 2,
+                   'layer2.3.conv1.weight': 2,
+                   'layer2.2.bn3.beta': 2,
+                   'layer2.2.bn3.gamma': 2,
+                   'layer2.2.conv3.weight': 2,
+                   'layer2.2.bn2.beta': 2,
+                   'layer2.2.bn2.gamma': 2,
+                   'layer2.2.conv2.weight': 2,
+                   'layer2.2.bn1.beta': 2,
+                   'layer2.2.bn1.gamma': 2,
+                   'layer2.2.conv1.weight': 2,
+                   'layer2.1.bn3.beta': 2,
+                   'layer2.1.bn3.gamma': 2,
+                   'layer2.1.conv3.weight': 2,
+                   'layer2.1.bn2.beta': 2,
+                   'layer2.1.bn2.gamma': 2,
+                   'layer2.1.conv2.weight': 2,
+                   'layer2.1.bn1.beta': 2,
+                   'layer2.1.bn1.gamma': 2,
+                   'layer2.1.conv1.weight': 2,
+                   'layer2.0.bn_down_sample.beta': 2,
+                   'layer2.0.bn_down_sample.gamma': 2,
+                   'layer2.0.conv_down_sample.weight': 2,
+                   'layer2.0.bn3.beta': 2,
+                   'layer2.0.bn3.gamma': 2,
+                   'layer2.0.conv3.weight': 2,
+                   'layer2.0.bn2.beta': 2,
+                   'layer2.0.bn2.gamma': 2,
+                   'layer2.0.conv2.weight': 2,
+                   'layer2.0.bn1.beta': 2,
+                   'layer2.0.bn1.gamma': 2,
+                   'layer2.0.conv1.weight': 2,
+                   'layer1.2.bn3.beta': 2,
+                   'layer1.2.bn3.gamma': 2,
+                   'layer1.2.conv3.weight': 2,
+                   'layer1.2.bn2.beta': 2,
+                   'layer1.2.bn2.gamma': 2,
+                   'layer1.2.conv2.weight': 2,
+                   'layer1.2.bn1.beta': 2,
+                   'layer1.2.bn1.gamma': 2,
+                   'layer1.2.conv1.weight': 2,
+                   'layer1.1.bn3.beta': 2,
+                   'layer1.1.bn3.gamma': 2,
+                   'layer1.1.conv3.weight': 2,
+                   'layer1.1.bn2.beta': 2,
+                   'layer1.1.bn2.gamma': 2,
+                   'layer1.1.conv2.weight': 2,
+                   'layer1.1.bn1.beta': 2,
+                   'layer1.1.bn1.gamma': 2,
+                   'layer1.1.conv1.weight': 2,
+                   'layer1.0.bn_down_sample.beta': 2,
+                   'layer1.0.bn_down_sample.gamma': 2,
+                   'layer1.0.conv_down_sample.weight': 2,
+                   'layer1.0.bn3.beta': 2,
+                   'layer1.0.bn3.gamma': 2,
+                   'layer1.0.conv3.weight': 2,
+                   'layer1.0.bn2.beta': 2,
+                   'layer1.0.bn2.gamma': 2,
+                   'layer1.0.conv2.weight': 1,
+                   'layer1.0.bn1.beta': 1,
+                   'layer1.0.bn1.gamma': 1,
+                   'layer1.0.conv1.weight': 1,
+                   'bn1.beta': 1,
+                   'bn1.gamma': 1,
+                   'conv1.weight': 1}
 
     assert (allreduce_fusion_dict == expect_dict)
     cost_model_context.reset_cost_model_context()

From 5a4f17bfb676982035b0e9e206ee890f0834b1a9 Mon Sep 17 00:00:00 2001
From: yanzhenxiang2020 <yanzhenxiang@huawei.com>
Date: Wed, 1 Apr 2020 17:48:30 +0800
Subject: [PATCH 073/367] fix mindrecord ut long time

---
 mindspore/mindrecord/tools/mnist_to_mr.py     |  18 +++++++++---------
 .../testMnistData/t10k-images-idx3-ubyte.gz   | Bin 61261 -> 407 bytes
 .../testMnistData/t10k-labels-idx1-ubyte.gz   | Bin 92 -> 74 bytes
 .../testMnistData/train-images-idx3-ubyte.gz  | Bin 340460 -> 474 bytes
 .../testMnistData/train-labels-idx1-ubyte.gz  | Bin 140 -> 82 bytes
 .../python/mindrecord/test_mindrecord_base.py |  10 +++++-----
 .../ut/python/mindrecord/test_mnist_to_mr.py  |  12 ++++++------
 7 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/mindspore/mindrecord/tools/mnist_to_mr.py b/mindspore/mindrecord/tools/mnist_to_mr.py
index 462ab7fb53..046788535d 100644
--- a/mindspore/mindrecord/tools/mnist_to_mr.py
+++ b/mindspore/mindrecord/tools/mnist_to_mr.py
@@ -77,20 +77,20 @@ class MnistToMR:
 
         self.mnist_schema_json = {"label": {"type": "int64"}, "data": {"type": "bytes"}}
 
-    def _extract_images(self, filename, num_images):
+    def _extract_images(self, filename):
         """Extract the images into a 4D tensor [image index, y, x, channels]."""
         with gzip.open(filename) as bytestream:
             bytestream.read(16)
-            buf = bytestream.read(self.image_size * self.image_size * num_images * self.num_channels)
+            buf = bytestream.read()
             data = np.frombuffer(buf, dtype=np.uint8)
-            data = data.reshape(num_images, self.image_size, self.image_size, self.num_channels)
+            data = data.reshape(-1, self.image_size, self.image_size, self.num_channels)
             return data
 
-    def _extract_labels(self, filename, num_images):
+    def _extract_labels(self, filename):
         """Extract the labels into a vector of int64 label IDs."""
         with gzip.open(filename) as bytestream:
             bytestream.read(8)
-            buf = bytestream.read(1 * num_images)
+            buf = bytestream.read()
             labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
             return labels
 
@@ -101,8 +101,8 @@ class MnistToMR:
         Yields:
             data (dict of list): mnist data list which contains dict.
         """
-        train_data = self._extract_images(self.train_data_filename_, 60000)
-        train_labels = self._extract_labels(self.train_labels_filename_, 60000)
+        train_data = self._extract_images(self.train_data_filename_)
+        train_labels = self._extract_labels(self.train_labels_filename_)
         for data, label in zip(train_data, train_labels):
             _, img = cv2.imencode(".jpeg", data)
             yield {"label": int(label), "data": img.tobytes()}
@@ -114,8 +114,8 @@ class MnistToMR:
         Yields:
             data (dict of list): mnist data list which contains dict.
         """
-        test_data = self._extract_images(self.test_data_filename_, 10000)
-        test_labels = self._extract_labels(self.test_labels_filename_, 10000)
+        test_data = self._extract_images(self.test_data_filename_)
+        test_labels = self._extract_labels(self.test_labels_filename_)
         for data, label in zip(test_data, test_labels):
             _, img = cv2.imencode(".jpeg", data)
             yield {"label": int(label), "data": img.tobytes()}
diff --git a/tests/ut/data/mindrecord/testMnistData/t10k-images-idx3-ubyte.gz b/tests/ut/data/mindrecord/testMnistData/t10k-images-idx3-ubyte.gz
index 9fdddeebe95e6e0a9790fdaf19532d46e83647c8..d7a2ea5de2afb0a2e2af780ea309418c83b786cf 100644
GIT binary patch
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diff --git a/tests/ut/data/mindrecord/testMnistData/t10k-labels-idx1-ubyte.gz b/tests/ut/data/mindrecord/testMnistData/t10k-labels-idx1-ubyte.gz
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GIT binary patch
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diff --git a/tests/ut/data/mindrecord/testMnistData/train-images-idx3-ubyte.gz b/tests/ut/data/mindrecord/testMnistData/train-images-idx3-ubyte.gz
index 4f27a302031116543f7a3dd95617f39eafe97c0e..80c13bf9a37aaebcefd7a2af05ae2d7d17761142 100644
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diff --git a/tests/ut/data/mindrecord/testMnistData/train-labels-idx1-ubyte.gz b/tests/ut/data/mindrecord/testMnistData/train-labels-idx1-ubyte.gz
index abc30a7c68fca1fa28409fd4e808ef39632a98bd..61cc616763c591391896d47918dd6e02af3cfba8 100644
GIT binary patch
literal 82
zcmV-Y0ImNYiwFovVuW4*19Wm>X>KiSVPa)$b1i9Pcrh(?VtI6B09}S#4geqw!iulW
ofA4=*jWLTFpaopOkVq@!UCJ3XK)Z-N!avjFOw2g?lwtq?0KJ$Wy8r+H

literal 140
zcmb2|=HO7xFOFkiE-6aP%+t+DOiIlu*3C?*Fw`wgsw_!mcze*1u|a`>WrK6bhNVA!
z6FYwAd4KcRXU+fu4!K~8VJkD3Uf~O-7L+l8>8;!l(Ys*EA(s&%$_u8h$bqQ^-<VzN
LKD^0z#lQdn>vto=

diff --git a/tests/ut/python/mindrecord/test_mindrecord_base.py b/tests/ut/python/mindrecord/test_mindrecord_base.py
index 576063295a..7fdf1f0f94 100644
--- a/tests/ut/python/mindrecord/test_mindrecord_base.py
+++ b/tests/ut/python/mindrecord/test_mindrecord_base.py
@@ -203,9 +203,9 @@ def test_nlp_page_reader_tutorial():
         os.remove("{}".format(x))
         os.remove("{}.db".format(x))
 
-def test_cv_file_writer_shard_num_1000():
-    """test file writer when shard num equals 1000."""
-    writer = FileWriter(CV_FILE_NAME, 1000)
+def test_cv_file_writer_shard_num_10():
+    """test file writer when shard num equals 10."""
+    writer = FileWriter(CV_FILE_NAME, 10)
     data = get_data("../data/mindrecord/testImageNetData/")
     cv_schema_json = {"file_name": {"type": "string"},
                       "label": {"type": "int64"}, "data": {"type": "bytes"}}
@@ -214,8 +214,8 @@ def test_cv_file_writer_shard_num_1000():
     writer.write_raw_data(data)
     writer.commit()
 
-    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(3, '0'))
-             for x in range(1000)]
+    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
+            for x in range(10)]
     for x in paths:
         os.remove("{}".format(x))
         os.remove("{}.db".format(x))
diff --git a/tests/ut/python/mindrecord/test_mnist_to_mr.py b/tests/ut/python/mindrecord/test_mnist_to_mr.py
index c299a1f719..505b0d6b43 100644
--- a/tests/ut/python/mindrecord/test_mnist_to_mr.py
+++ b/tests/ut/python/mindrecord/test_mnist_to_mr.py
@@ -37,7 +37,7 @@ def read(train_name, test_name):
         count = count + 1
         if count == 1:
             logger.info("data: {}".format(x))
-    assert count == 60000
+    assert count == 20
     reader.close()
 
     count = 0
@@ -47,7 +47,7 @@ def read(train_name, test_name):
         count = count + 1
         if count == 1:
             logger.info("data: {}".format(x))
-    assert count == 10000
+    assert count == 10
     reader.close()
 
 
@@ -102,10 +102,10 @@ def test_mnist_to_mindrecord_compare_data():
                                        't10k-images-idx3-ubyte.gz')
     test_labels_filename_ = os.path.join(MNIST_DIR,
                                          't10k-labels-idx1-ubyte.gz')
-    train_data = _extract_images(train_data_filename_, 60000)
-    train_labels = _extract_labels(train_labels_filename_, 60000)
-    test_data = _extract_images(test_data_filename_, 10000)
-    test_labels = _extract_labels(test_labels_filename_, 10000)
+    train_data = _extract_images(train_data_filename_, 20)
+    train_labels = _extract_labels(train_labels_filename_, 20)
+    test_data = _extract_images(test_data_filename_, 10)
+    test_labels = _extract_labels(test_labels_filename_, 10)
 
     reader = FileReader(train_name)
     for x, data, label in zip(reader.get_next(), train_data, train_labels):

From ff808021c741e28eec2dd2161d2a4ff13e2047b6 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Thu, 2 Apr 2020 15:42:00 +0800
Subject: [PATCH 074/367] register not equal distributed op

---
 mindspore/ccsrc/parallel/ops_info/ops_utils.h  | 1 +
 mindspore/ccsrc/parallel/step_auto_parallel.cc | 1 +
 mindspore/ccsrc/parallel/step_parallel.cc      | 2 +-
 3 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 2b8fc0ee3f..4062847d73 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -180,6 +180,7 @@ constexpr char SIGMOID[] = "Sigmoid";
 constexpr char POW[] = "Pow";
 constexpr char MAXIMUM[] = "Maximum";
 constexpr char EQUAL[] = "Equal";
+constexpr char NOT_EQUAL[] = "NotEqual";
 constexpr char LOGICALNOT[] = "LogicalNot";
 constexpr char GATHERV2[] = "GatherV2";
 constexpr char STRIDEDSLICE[] = "StridedSlice";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index b06ec383fa..a359905494 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -95,6 +95,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            POW,
                                            MAXIMUM,
                                            EQUAL,
+                                           NOT_EQUAL,
                                            LOGICALNOT,
                                            GATHERV2,
                                            STRIDEDSLICE,
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index b79198ffca..78bec00bcf 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -1226,7 +1226,7 @@ void CoverSliceShape(const FuncGraphPtr& root) {
       MS_LOG(INFO) << "Parameter " << parameter->ToString() << " don't need to set parallel shape";
     } else {
       SetParallelShape(parameter, res);
-      MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << "  shape " << parameter->Shape()->ToString();
+      MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << " shape " << parameter->Shape()->ToString();
     }
   }
   g_RefMap.clear();

From 605d980305c1631be449c659c0f0d89621d0ab55 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Mon, 23 Mar 2020 15:33:01 +0800
Subject: [PATCH 075/367] 1. add Note refer to nn.SGD for detail 2. delete
 default value of stat 3. delete examples 4. some comments error from wangting
 review 5. modify comments from jinyaohui 6. modify examples from wanghao 7.
 modify Select operation examples

---
 mindspore/nn/layer/activation.py        |  4 ++--
 mindspore/nn/layer/basic.py             |  4 ++--
 mindspore/nn/layer/conv.py              |  2 +-
 mindspore/nn/metrics/fbeta.py           |  6 +++---
 mindspore/ops/operations/array_ops.py   |  7 +++++--
 mindspore/ops/operations/comm_ops.py    | 12 ++++++++----
 mindspore/ops/operations/control_ops.py |  1 -
 mindspore/ops/operations/math_ops.py    |  1 -
 mindspore/ops/operations/nn_ops.py      | 13 +++++--------
 mindspore/train/amp.py                  |  3 +--
 10 files changed, 27 insertions(+), 26 deletions(-)

diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py
index 00f2afe703..ad63dde8bc 100644
--- a/mindspore/nn/layer/activation.py
+++ b/mindspore/nn/layer/activation.py
@@ -40,7 +40,7 @@ class Softmax(Cell):
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
     Args:
-        axis (Union[int, tuple[int]]): The axis to apply Softmax operation. Default: -1, means the last dimension.
+        axis (Union[int, tuple[int]]): The axis to apply Softmax operation, -1 means the last dimension. Default: -1.
 
     Inputs:
         - **x** (Tensor) - The input of Softmax.
@@ -70,7 +70,7 @@ class LogSoftmax(Cell):
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
     Args:
-        axis (int): The axis to apply LogSoftmax operation. Default: -1, means the last dimension.
+        axis (int): The axis to apply LogSoftmax operation, -1 means the last dimension. Default: -1.
 
     Inputs:
         - **x** (Tensor) - The input of LogSoftmax.
diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 0cf4cd5e99..9dc0d5e623 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -32,13 +32,13 @@ class Dropout(Cell):
     r"""
     Dropout layer for the input.
 
-    Randomly set some elements of the input tensor to zero with probability :math:`1 - keep_prob` during training
+    Randomly set some elements of the input tensor to zero with probability :math:`1 - keep\_prob` during training
     using samples from a Bernoulli distribution.
 
     Note:
         Each channel will be zeroed out independently on every construct call.
 
-        The outputs are scaled by a factor of :math:`\frac{1}{keep_prob}` during training so
+        The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so
         that the output layer remains at a similar scale. During inference, this
         layer returns the same tensor as the input.
 
diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index 666be93451..dfbf96e150 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -241,7 +241,7 @@ class Conv2dTranspose(_Conv):
         in_channels (int): The number of channels in the input space.
         out_channels (int): The number of channels in the output space.
         kernel_size (Union[int, tuple]): int or tuple with 2 integers, which specifies the  height
-            and width of the 2D convolution window.Single int means the value if for both height and width of
+            and width of the 2D convolution window. Single int means the value is for both height and width of
             the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
             width of the kernel.
         stride (int): Specifies the same value for all spatial dimensions. Default: 1.
diff --git a/mindspore/nn/metrics/fbeta.py b/mindspore/nn/metrics/fbeta.py
index f38febf3b1..6771b6ba36 100755
--- a/mindspore/nn/metrics/fbeta.py
+++ b/mindspore/nn/metrics/fbeta.py
@@ -26,8 +26,8 @@ class Fbeta(Metric):
     Fbeta score is a weighted mean of precison and recall.
 
     .. math::
-        F_\beta=\frac{(1+\beta^2) \cdot true positive}
-                {(1+\beta^2) \cdot true positive +\beta^2 \cdot false negative + false positive}
+        F_\beta=\frac{(1+\beta^2) \cdot true\_positive}
+                {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive}
 
     Args:
         beta (float): The weight of precision.
@@ -123,7 +123,7 @@ class F1(Fbeta):
     Refer to class `Fbeta` for more details.
 
     .. math::
-        F_\beta=\frac{2\cdot true positive}{2\cdot true positive + false negative + false positive}
+        F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
 
     Examples:
         >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index f6d563321c..36f49c00c4 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -881,7 +881,7 @@ class ScalarToTensor(PrimitiveWithInfer):
     Inputs:
         - **input_x** (Union[int, float]) - The input is a scalar. Only constant value is allowed.
         - **dtype** (mindspore.dtype) - The target data type. Default: mindspore.float32. Only
-                    constant value is allowed.
+          constant value is allowed.
 
     Outputs:
         Tensor. 0-D Tensor and the content is the input.
@@ -1458,7 +1458,10 @@ class Select(PrimitiveWithInfer):
 
     Examples:
         >>> select = Select()
-        >>> select([True, False],[2,3],[1,2])
+        >>> input_x = Tensor([True, False])
+        >>> input_y = Tensor([2,3], mindspore.float32)
+        >>> input_z = Tensor([1,2], mindspore.float32)
+        >>> select(input_x, input_y, input_z)
     """
 
     @prim_attr_register
diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py
index 53a3686367..1644c5800a 100644
--- a/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/ops/operations/comm_ops.py
@@ -66,11 +66,12 @@ class AllReduce(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.allreduce_sum = AllReduce(ReduceOp.SUM, group="nccl_world_group")
+        >>>         self.allreduce_sum = P.AllReduce(ReduceOp.SUM, group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.allreduce_sum(x)
@@ -130,11 +131,12 @@ class AllGather(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.allgather = AllGather(group="nccl_world_group")
+        >>>         self.allgather = P.AllGather(group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.allgather(x)
@@ -184,11 +186,12 @@ class ReduceScatter(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.reducescatter = ReduceScatter(ReduceOp.SUM, group="nccl_world_group")
+        >>>         self.reducescatter = P.ReduceScatter(ReduceOp.SUM, group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.reducescatter(x)
@@ -246,11 +249,12 @@ class Broadcast(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.broadcast = Broadcast(1)
+        >>>         self.broadcast = P.Broadcast(1)
         >>>
         >>>     def construct(self, x):
         >>>         return self.broadcast((x,))
diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index 242a3b155d..1bffc09c04 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -150,7 +150,6 @@ class Merge(PrimitiveWithInfer):
         raise NotImplementedError
 
     def infer_shape(self, inputs):
-        """merge select one input as its output"""
         return (inputs[0], [1])
 
     def infer_dtype(self, inputs):
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 398a7e6f1a..ba5e596027 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1263,7 +1263,6 @@ class EqualCount(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
     def infer_shape(self, x_shape, w_shape):
-        """Infer shape."""
         output_shape = (1,)
         return output_shape
 
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index afa4c7dfe3..195d78c6c7 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1310,6 +1310,9 @@ class SGD(PrimitiveWithInfer):
     Nesterov momentum is based on the formula from On the importance of
     initialization and momentum in deep learning.
 
+    Note:
+        For details, please refer to `nn.SGD` source code.
+
     Args:
         dampening (float): The dampening for momentum. Default: 0.0.
         weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
@@ -1321,16 +1324,10 @@ class SGD(PrimitiveWithInfer):
         - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
         - **accum** (Tensor) - Accum(velocity) to be update.
         - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
-        - **stat** (Tensor) - States to be updated with the same shape as gradient. Default: 1.0.
+        - **stat** (Tensor) - States to be updated with the same shape as gradient.
 
     Outputs:
         Tensor, parameters to be update.
-
-    Examples:
-        >>> net = ResNet50()
-        >>> loss = SoftmaxCrossEntropyWithLogits()
-        >>> opt = SGD(params=net.trainable_params(), learning_rate=lr, momentum=0.9)
-        >>> model = Model(net, loss, opt)
     """
 
     @prim_attr_register
@@ -1768,7 +1765,7 @@ class LSTM(PrimitiveWithInfer):
     """
     Performs the long short term memory(LSTM) on the input.
 
-    Detailed information, please refer to `nn.layer.LSTM`.
+    Detailed information, please refer to `nn.LSTM`.
     """
 
     @prim_attr_register
diff --git a/mindspore/train/amp.py b/mindspore/train/amp.py
index 5a70a86fdd..e909b44e40 100644
--- a/mindspore/train/amp.py
+++ b/mindspore/train/amp.py
@@ -91,13 +91,12 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
         loss_fn (Union[None, Cell]): Definition of the loss_fn. If None, the `network` should have the loss inside.
             Default: None.
         optimizer (Optimizer): Optimizer to update the Parameter.
-        level (str): Supports [O0, O2].
+        level (str): Supports [O0, O2]. Default: "O0".
 
             - O0: Do not change.
             - O2: Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32,
               using dynamic loss scale.
 
-            Default: "O0"
         cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`.
             If set to `mstype.float16`, use `float16` mode to train. If set, overwrite the level setting.
         keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting.

From 73ba39936423629a875924842becbb74b4fca840 Mon Sep 17 00:00:00 2001
From: Wei Luning <invisiblewei@gmail.com>
Date: Mon, 23 Mar 2020 17:33:56 +0800
Subject: [PATCH 076/367] remove ge depend in cpu

---
 CMakeLists.txt                                |  12 +-
 cmake/mind_expression.cmake                   |   2 +-
 mindspore/ccsrc/CMakeLists.txt                |  26 +-
 mindspore/ccsrc/debug/e2e_dump.cc             |   2 +-
 .../device/ascend/kernel_build_ascend.cc      |   2 +-
 .../ascend/profiling/profiling_manager.cc     |   1 +
 .../device/ascend/tasksink/task_generator.cc  |   4 +-
 .../device/ascend/tasksink/task_generator.h   |   2 +-
 .../ccsrc/device/gpu/gpu_kernel_build.cc      |   1 -
 .../ccsrc/device/gpu/kernel_info_setter.cc    |   2 +-
 mindspore/ccsrc/device/kernel_adjust.cc       |   1 +
 mindspore/ccsrc/ir/anf.cc                     |  17 +
 mindspore/ccsrc/ir/anf.h                      |   2 +
 mindspore/ccsrc/ir/meta_tensor.cc             |   1 -
 .../ccsrc/kernel/aicpu/aicpu_kernel_mod.h     |   4 +-
 mindspore/ccsrc/kernel/akg/akgkernelbuild.cc  |   5 +-
 mindspore/ccsrc/kernel/ascend_kernel_mod.h    |  36 +
 mindspore/ccsrc/kernel/common_utils.cc        |   3 +-
 .../ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h     |   4 +-
 .../kernel/gpu/nn/lstm_grad_data_gpu_kernel.h |   6 +-
 .../gpu/nn/lstm_grad_weight_gpu_kernel.h      |   4 +-
 mindspore/ccsrc/kernel/hccl/hccl_kernel.h     |   4 +-
 mindspore/ccsrc/kernel/kernel.h               |   6 -
 mindspore/ccsrc/kernel/mng/rt_kernel.h        |   4 +-
 mindspore/ccsrc/kernel/oplib/oplib.cc         |   4 +-
 mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h |  33 +-
 mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h   |   4 +-
 .../kernel/tbe/tbe_kernel_parallel_build.h    |   5 +-
 .../ccsrc/kernel/tbe/tbe_kernel_select.cc     |   9 +-
 mindspore/ccsrc/kernel/tbe/tbe_utils.cc       |   1 +
 mindspore/ccsrc/pipeline/base.h               |  64 ++
 mindspore/ccsrc/pipeline/init.cc              |  10 +-
 .../ccsrc/pipeline/parse/python_adapter.cc    |   1 +
 .../ccsrc/pipeline/parse/python_adapter.h     |   1 +
 mindspore/ccsrc/pipeline/pipeline.cc          | 692 +++---------------
 mindspore/ccsrc/pipeline/pipeline.h           |  35 +-
 mindspore/ccsrc/pipeline/pipeline_ge.cc       | 545 ++++++++++++++
 mindspore/ccsrc/pipeline/pipeline_ge.h        |  57 ++
 mindspore/ccsrc/pipeline/resource.cc          |  29 -
 mindspore/ccsrc/pipeline/resource.h           |   7 +-
 .../ascend/ascend_backend_optimization.cc     |   2 +-
 .../ascend/buffer_fusion/buffer_fusion.cc     |   4 +-
 .../ir_fusion/allreduce_fusion.cc             |   2 +-
 .../ir_fusion/allreduce_fusion.h              |   6 +-
 .../ccsrc/predict/converter/kernel2ms.cc      |   6 +-
 mindspore/ccsrc/pynative/base.h               |  67 ++
 mindspore/ccsrc/pynative/pynative_execute.cc  | 285 +-------
 mindspore/ccsrc/pynative/pynative_execute.h   |  43 +-
 .../ccsrc/pynative/pynative_execute_ge.cc     | 311 ++++++++
 .../ccsrc/pynative/pynative_execute_ge.h      |  46 ++
 mindspore/ccsrc/session/ascend_session.cc     |   1 +
 mindspore/ccsrc/session/gpu_session.cc        |   2 +-
 mindspore/ccsrc/transform/convert.cc          |  18 -
 mindspore/ccsrc/transform/convert.h           |   1 -
 mindspore/ccsrc/utils/callbacks.cc            |  13 +-
 mindspore/ccsrc/utils/callbacks.h             |   9 +
 mindspore/ccsrc/utils/context/ms_context.cc   |   7 +-
 mindspore/ccsrc/utils/context/ms_context.h    |   1 -
 mindspore/ccsrc/utils/convert_utils.cc        |  41 ++
 mindspore/ccsrc/utils/convert_utils.h         |   4 +
 mindspore/ccsrc/vm/segment_runner.cc          |   4 +-
 mindspore/ccsrc/vm/transform.cc               |  16 +-
 mindspore/ccsrc/vm/transform.h                |   2 -
 mindspore/ccsrc/vm/vmimpl.cc                  |  35 -
 mindspore/ccsrc/vm/vmimpl.h                   |   6 -
 mindspore/common/api.py                       |   3 +-
 mindspore/common/parameter.py                 |  12 +
 mindspore/common/tensor.py                    |  10 +
 mindspore/ops/functional.py                   |   2 +-
 mindspore/train/model.py                      |   3 +
 .../cpp/device/ascend_kernel_select_test.cc   |   2 +-
 tests/ut/cpp/device/ascend_profiling_test.cc  |   2 +-
 .../ir_fusion/allreduce_fusion_test.cc        |   2 +-
 ...onvert_const_input_to_tensor_input_test.cc |   2 +-
 tests/ut/python/ir/test_tensor.py             |  18 +
 tests/ut/python/ops/test_array_ops.py         |  14 -
 tests/ut/python/parallel/__init__.py          |   5 +-
 tests/ut/python/parallel/test_alltoall.py     |  11 +-
 .../parallel/test_auto_parallel_arithmetic.py |  18 +-
 ...t_auto_parallel_assign_sub_with_ref_key.py |  10 +-
 .../parallel/test_auto_parallel_cast.py       |  12 +-
 .../test_auto_parallel_matmul_prelu.py        |   8 +-
 .../test_auto_parallel_parameter_cast.py      |   8 +-
 .../parallel/test_auto_parallel_transpose.py  |  10 +-
 .../parallel/test_auto_parallel_two_matmul.py |   5 +-
 .../python/parallel/test_dataset_interface.py |   2 +-
 tests/ut/python/parallel/test_one_dev.py      |  20 +-
 .../python/pipeline/parse/test_create_obj.py  |   3 +
 tests/ut/python/pipeline/parse/test_dtype.py  |   3 +-
 .../ut/python/pynative_mode/ops/test_grad.py  |  16 +
 .../test_summary_ops_params_valid_check.py    |  21 +-
 tests/ut/python/utils/test_serialize.py       |   3 +-
 92 files changed, 1571 insertions(+), 1234 deletions(-)
 create mode 100644 mindspore/ccsrc/kernel/ascend_kernel_mod.h
 create mode 100644 mindspore/ccsrc/pipeline/base.h
 create mode 100644 mindspore/ccsrc/pipeline/pipeline_ge.cc
 create mode 100644 mindspore/ccsrc/pipeline/pipeline_ge.h
 rename mindspore/ccsrc/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion.cc (97%)
 rename mindspore/ccsrc/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion.h (87%)
 create mode 100644 mindspore/ccsrc/pynative/base.h
 create mode 100644 mindspore/ccsrc/pynative/pynative_execute_ge.cc
 create mode 100644 mindspore/ccsrc/pynative/pynative_execute_ge.h
 rename tests/ut/cpp/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion_test.cc (99%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9cb73935ee..bdac2da46e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,11 +42,13 @@ else()
     include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake)
 endif()
 
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain)
+if (ENABLE_GE OR ENABLE_D OR ENABLE_TESTCASES)
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc)
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external)
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework)
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc)
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain)
+endif()
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden")
 add_subdirectory(mindspore/ccsrc)
diff --git a/cmake/mind_expression.cmake b/cmake/mind_expression.cmake
index 103775a4c2..345fd4675e 100644
--- a/cmake/mind_expression.cmake
+++ b/cmake/mind_expression.cmake
@@ -40,7 +40,7 @@ if (ENABLE_GE)
     include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include)
     include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external)
     include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external/graph)
-else()
+elseif(ENABLE_D OR ENABLE_TESTCASES)
     include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc)
     include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/ops)
     include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/external)
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 1c684b6736..0b4bb0d1df 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -34,6 +34,8 @@ if(ENABLE_GPU)
             "device/gpu/*.cu"
             "kernel/gpu/*.cu"
             "kernel/akg/gpu/*.cc"
+            "kernel/akg/akgkernelbuild.cc"
+            "kernel/akg/akg_kernel_attrs_process.cc"
             )
     file(GLOB_RECURSE GPU_KERNEL_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
             "kernel/gpu/*.cc"
@@ -100,14 +102,14 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "debug/*.cc"
         "onnx/onnx_exporter.cc"
         "operator/*.cc"
-        "transform/*.cc"
         "session/kernel_graph.cc"
         "utils/node_utils.cc"
         "session/session_basic.cc"
         "session/session_factory.cc"
         "session/anf_runtime_algorithm.cc"
         "vm/*.cc"
-        "pynative/*.cc"
+        "pynative/base.cc"
+        "pynative/pynative_execute.cc"
         "pybind_api/*.cc"
         "device/common/*.cc"
         "kernel/kernel_query.cc"
@@ -117,7 +119,6 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "device/kernel_runtime.cc"
         "device/kernel_runtime_manager.cc"
         "device/convert_tensor_utils.cc"
-        "pre_activate/ascend/*.cc"
         "pre_activate/common/*.cc"
         "pre_activate/pass/*.cc"
         "pre_activate/gpu/*.cc"
@@ -168,6 +169,15 @@ if(ENABLE_DUMP_PROTO)
     add_compile_definitions(ENABLE_DUMP_PROTO)
 endif()
 
+if(ENABLE_GE)
+    file(GLOB_RECURSE GE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
+            "transform/*.cc"
+            "pynative/pynative_execute_ge.cc"
+            "pipeline/pipeline_ge.cc"
+            )
+    list(APPEND MINDSPORE_SRC_LIST ${GE_SRC_LIST})
+endif()
+
 if(ENABLE_D)
     include_directories("${CMAKE_BINARY_DIR}/kernel/aicpu")
     file(GLOB_RECURSE PROTO_IN RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
@@ -188,6 +198,9 @@ if(ENABLE_D)
             "device/kernel_adjust.cc"
             "kernel/kernel_fusion.cc"
             "kernel/tbe/*.cc"
+            "pre_activate/ascend/*.cc"
+            "transform/*.cc"
+            "pipeline/pipeline_ge.cc"
             )
     list(APPEND MINDSPORE_SRC_LIST ${D_SRC_LIST})
     list(APPEND MINDSPORE_PROTO_AICPU_LIST ${PROTOSRCS})
@@ -246,9 +259,11 @@ if (ENABLE_GE)
         target_link_libraries(mindspore graph ge_client)
     endif()
     target_link_libraries(mindspore tsdclient)
-else()
+elseif(ENABLE_D)
     add_compile_definitions(NO_GE_CLIENT)
     target_link_libraries(mindspore graph)
+else()
+    add_compile_definitions(NO_GE_CLIENT)
 endif()
 
 if(ENABLE_D)
@@ -288,8 +303,6 @@ endif()
 set(PYTHON_MODULE_SOURCE
         pipeline/init.cc
         kernel/oplib/oplib.cc
-        kernel/akg/akgkernelbuild.cc
-        kernel/akg/akg_kernel_attrs_process.cc
     ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
     ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
 
@@ -350,6 +363,7 @@ if(ENABLE_GPU)
     assign_source_group("Include" ${GROUP_INCLUDE})
 
     file(GLOB COMPILER_SRCS
+        "pre_activate/gpu/*.cc"
         ${TVM_DIR}/src/api/*.cc
         ${TVM_DIR}/src/arithmetic/*.cc
         ${TVM_DIR}/src/autotvm/*.cc
diff --git a/mindspore/ccsrc/debug/e2e_dump.cc b/mindspore/ccsrc/debug/e2e_dump.cc
index 3006bb66da..ba11eafa5f 100644
--- a/mindspore/ccsrc/debug/e2e_dump.cc
+++ b/mindspore/ccsrc/debug/e2e_dump.cc
@@ -49,7 +49,7 @@ bool Dump::IsKernelNeedDump(const std::string& kernel_name) {
   return false;
 }
 
-bool Dump::ParseDumpConfig(const string& dump_config_file) {
+bool Dump::ParseDumpConfig(const std::string& dump_config_file) {
   std::ifstream jsonFile(dump_config_file);
   if (!jsonFile.is_open()) {
     MS_LOG(ERROR) << dump_config_file << " open failed.";
diff --git a/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc
index 66ce697ffc..e7c1449360 100644
--- a/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc
@@ -94,7 +94,7 @@ static bool KernelBuildParallelCompile(const mindspore::session::KernelGraph *ke
   return ret;
 }
 
-static vector<int> CalCleanZerosSize(const CNodePtr &pre_node) {
+static std::vector<int> CalCleanZerosSize(const CNodePtr &pre_node) {
   MS_EXCEPTION_IF_NULL(pre_node);
   std::vector<int> clean_size_list;
   // clean output
diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc
index 4bc68e647a..29193e5cfa 100644
--- a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc
+++ b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc
@@ -27,6 +27,7 @@
 #include "utils/log_adapter.h"
 #include "utils/context/ms_context.h"
 #include "common/utils.h"
+#include "utils/convert_utils.h"
 
 using std::vector;
 using Json = nlohmann::json;
diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc b/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc
index 88d00bc6c2..62cf809c21 100644
--- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc
+++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc
@@ -121,8 +121,8 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i
     LaunchAddrCleanKernel(anf_node_ptr, &kernel_inputs);
   }
 
-  std::vector<TaskInfoPtr> task_info_ptrs =
-    kernel_mod->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id);
+  std::vector<TaskInfoPtr> task_info_ptrs = dynamic_cast<kernel::AscendKernelMod *>(kernel_mod)
+                                              ->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id);
   task_info_list->insert(task_info_list->end(), task_info_ptrs.begin(), task_info_ptrs.end());
   return true;
 }
diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
index 0c56fcc744..ffedcd7930 100644
--- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
+++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h
@@ -24,7 +24,7 @@
 #include <vector>
 #include "device/kernel_runtime.h"
 #include "ir/anf.h"
-#include "kernel/kernel.h"
+#include "kernel/ascend_kernel_mod.h"
 #include "framework/ge_runtime/task_info.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc
index 0467b59e06..2a2a2be065 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc
@@ -21,7 +21,6 @@
 #include "kernel/gpu/gpu_kernel_factory.h"
 #include "operator/ops.h"
 #include "pybind11/stl.h"
-#include "transform/convert.h"
 #include "session/anf_runtime_algorithm.h"
 namespace mindspore {
 namespace device {
diff --git a/mindspore/ccsrc/device/gpu/kernel_info_setter.cc b/mindspore/ccsrc/device/gpu/kernel_info_setter.cc
index 3faf7d01c8..05ecf380d1 100644
--- a/mindspore/ccsrc/device/gpu/kernel_info_setter.cc
+++ b/mindspore/ccsrc/device/gpu/kernel_info_setter.cc
@@ -91,7 +91,7 @@ std::string SupportedTypeList(const CNodePtr& kernel_node) {
   return supported_type_lists;
 }
 
-bool SelectAkgKernel(const CNodePtr& kernel_node, const shared_ptr<KernelBuildInfo>& selected_kernel_info) {
+bool SelectAkgKernel(const CNodePtr& kernel_node, const std::shared_ptr<KernelBuildInfo>& selected_kernel_info) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   MS_EXCEPTION_IF_NULL(selected_kernel_info);
   std::vector<std::shared_ptr<KernelBuildInfo>> kernel_info_list;
diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc
index a4d316d601..4d3ebf9490 100644
--- a/mindspore/ccsrc/device/kernel_adjust.cc
+++ b/mindspore/ccsrc/device/kernel_adjust.cc
@@ -32,6 +32,7 @@
 #include "device/ascend/profiling/profiling_manager.h"
 #include "device/ascend/kernel_select_ascend.h"
 #include "device/kernel_info.h"
+#include "runtime/base.h"
 
 constexpr auto kLoopCountParamName = "loop_count";
 constexpr auto kIterLoopParamName = "iter_loop";
diff --git a/mindspore/ccsrc/ir/anf.cc b/mindspore/ccsrc/ir/anf.cc
index c1348bf7d7..924453a7a6 100644
--- a/mindspore/ccsrc/ir/anf.cc
+++ b/mindspore/ccsrc/ir/anf.cc
@@ -197,6 +197,23 @@ PrimitivePtr GetCNodePrimitive(const AnfNodePtr& node) {
   return nullptr;
 }
 
+std::string GetCNodeFuncName(const CNodePtr cnode) {
+  if (cnode->inputs().empty()) {
+    return "";
+  }
+
+  AnfNodePtr valuenode = cnode->input(0);
+  if (valuenode->isa<ValueNode>()) {
+    auto value = GetValueNode(valuenode);
+    // check whether the valuenode is primitive
+    if (value->isa<Primitive>()) {
+      return value->cast<PrimitivePtr>()->name();
+    }
+    return value->ToString();
+  }
+  return "";
+}
+
 bool IsPrimitive(const AnfNodePtr& node, const PrimitivePtr& value) {
   if (IsValueNode<Primitive>(node)) {
     PrimitivePtr fn_value = GetValueNode<PrimitivePtr>(node);
diff --git a/mindspore/ccsrc/ir/anf.h b/mindspore/ccsrc/ir/anf.h
index 9050a4ed16..e64b1329e9 100644
--- a/mindspore/ccsrc/ir/anf.h
+++ b/mindspore/ccsrc/ir/anf.h
@@ -384,6 +384,8 @@ static S GetValue(const ValuePtr &value) {
   return v;
 }
 
+std::string GetCNodeFuncName(CNodePtr cnode);
+
 // used to check whether an AnfNode is a cnode with a kind of Primitive as first input
 bool IsPrimitiveCNode(const AnfNodePtr &node, const PrimitivePtr &value);
 
diff --git a/mindspore/ccsrc/ir/meta_tensor.cc b/mindspore/ccsrc/ir/meta_tensor.cc
index d33bc10c27..e9221039a7 100644
--- a/mindspore/ccsrc/ir/meta_tensor.cc
+++ b/mindspore/ccsrc/ir/meta_tensor.cc
@@ -25,7 +25,6 @@
 #include "device/device_address.h"
 #include "pybind_api/api_register.h"
 #include "pybind_api/export_flags.h"
-#include "pynative/pynative_execute.h"
 #include "pipeline/static_analysis/abstract_value.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h
index d1e6f69b23..dde2afe34a 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h
@@ -18,11 +18,11 @@
 #include <vector>
 #include <memory>
 #include <string>
-#include "kernel/kernel.h"
+#include "kernel/ascend_kernel_mod.h"
 #include "kernel/aicpu/aicpu_util.h"
 namespace mindspore {
 namespace kernel {
-class AicpuOpKernelMod : public KernelMod {
+class AicpuOpKernelMod : public AscendKernelMod {
  public:
   AicpuOpKernelMod();
   ~AicpuOpKernelMod() override;
diff --git a/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc b/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc
index 8413208c4d..c0759172a5 100644
--- a/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc
+++ b/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc
@@ -35,7 +35,6 @@
 #include "utils/convert_utils.h"
 #include "utils/any.h"
 #include "utils/utils.h"
-#include "transform/convert.h"
 #include "session/anf_runtime_algorithm.h"
 #include "kernel/akg/akg_kernel_attrs_process.h"
 
@@ -240,8 +239,8 @@ bool AkgKernelBuild::CreateOutputDescJson(const AnfNodePtr &anf_node, nlohmann::
   return true;
 }
 
-void GetJson(const AnfNodePtr &anf_node, const vector<int> &dyn_input_sizes, const shared_ptr<OpAttr> &op_attr,
-             nlohmann::json *const attr_json, const ValuePtr &attr_value) {
+void GetJson(const AnfNodePtr &anf_node, const std::vector<int> &dyn_input_sizes,
+             const std::shared_ptr<OpAttr> &op_attr, nlohmann::json *const attr_json, const ValuePtr &attr_value) {
   MS_EXCEPTION_IF_NULL(anf_node);
   MS_EXCEPTION_IF_NULL(op_attr);
   MS_EXCEPTION_IF_NULL(attr_json);
diff --git a/mindspore/ccsrc/kernel/ascend_kernel_mod.h b/mindspore/ccsrc/kernel/ascend_kernel_mod.h
new file mode 100644
index 0000000000..ff8595c1a2
--- /dev/null
+++ b/mindspore/ccsrc/kernel/ascend_kernel_mod.h
@@ -0,0 +1,36 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_
+#define MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_
+
+#include <vector>
+#include <memory>
+#include "framework/ge_runtime/task_info.h"
+#include "kernel/kernel.h"
+
+using TaskInfoPtr = std::shared_ptr<ge::model_runner::TaskInfo>;
+namespace mindspore {
+namespace kernel {
+class AscendKernelMod : public KernelMod {
+ public:
+  virtual std::vector<TaskInfoPtr> GenTask(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
+                                           const std::vector<AddressPtr> &, uint32_t) = 0;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_
diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc
index d610ea736d..c2f2638753 100644
--- a/mindspore/ccsrc/kernel/common_utils.cc
+++ b/mindspore/ccsrc/kernel/common_utils.cc
@@ -19,7 +19,6 @@
 #include <map>
 #include <iostream>
 #include <fstream>
-#include "runtime/rt.h"
 #include "nlohmann/json.hpp"
 #include "session/anf_runtime_algorithm.h"
 #include "common/utils.h"
@@ -490,7 +489,7 @@ void SaveJsonInfo(const std::string &json_name, const std::string &info) {
   if (!filewrite.is_open()) {
     return;
   }
-  filewrite << info << endl;
+  filewrite << info << std::endl;
   filewrite.close();
   if (nullptr == realpath(path.c_str(), real_path)) {
     MS_LOG(DEBUG) << "dir " << path << " does not exit.";
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
index 2e284f72e8..51a2da8574 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
@@ -226,12 +226,12 @@ class LstmGpuKernel : public GpuKernel {
   size_t reserved_size_;
 
   // input desc
-  unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
   cudnnTensorDescriptor_t hx_desc_;
   cudnnTensorDescriptor_t cx_desc_;
   cudnnFilterDescriptor_t w_desc_;
   cudnnDropoutDescriptor_t dropout_desc_;
-  unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
   cudnnTensorDescriptor_t hy_desc_;
   cudnnTensorDescriptor_t cy_desc_;
   cudnnRNNDescriptor_t rnn_desc_;
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
index 2fadccb8ea..a60ab78f7d 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
@@ -258,8 +258,8 @@ class LstmGradDataGpuKernel : public GpuKernel {
   cudnnRNNDescriptor_t rnn_desc_;
 
   // input desc
-  unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
-  unique_ptr<cudnnTensorDescriptor_t[]> dy_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> dy_desc_;
   cudnnTensorDescriptor_t dhy_desc_;
   cudnnTensorDescriptor_t dcy_desc_;
   cudnnFilterDescriptor_t w_desc_;
@@ -269,7 +269,7 @@ class LstmGradDataGpuKernel : public GpuKernel {
   cudnnDropoutDescriptor_t dropout_desc_;
 
   // output desc
-  unique_ptr<cudnnTensorDescriptor_t[]> dx_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> dx_desc_;
   cudnnTensorDescriptor_t dhx_desc_;
   cudnnTensorDescriptor_t dcx_desc_;
 
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
index 6cf512f14a..b28736cc96 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
@@ -214,9 +214,9 @@ class LstmGradWeightGpuKernel : public GpuKernel {
   cudnnDropoutDescriptor_t dropout_desc_;
 
   // input desc
-  unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> x_desc_;
   cudnnTensorDescriptor_t hx_desc_;
-  unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
+  std::unique_ptr<cudnnTensorDescriptor_t[]> y_desc_;
 
   // output desc
   cudnnFilterDescriptor_t dw_desc_;
diff --git a/mindspore/ccsrc/kernel/hccl/hccl_kernel.h b/mindspore/ccsrc/kernel/hccl/hccl_kernel.h
index 71d9e5ba6a..24e1feec0a 100644
--- a/mindspore/ccsrc/kernel/hccl/hccl_kernel.h
+++ b/mindspore/ccsrc/kernel/hccl/hccl_kernel.h
@@ -23,14 +23,14 @@
 #include <vector>
 #include <algorithm>
 #include <utility>
-#include "kernel/kernel.h"
+#include "kernel/ascend_kernel_mod.h"
 #include "kernel/hccl/hcom_util.h"
 #include "hccl/hcom.h"
 #include "common/utils.h"
 
 namespace mindspore {
 namespace kernel {
-class HcclKernel : public KernelMod {
+class HcclKernel : public AscendKernelMod {
  public:
   HcclKernel();
   ~HcclKernel() override;
diff --git a/mindspore/ccsrc/kernel/kernel.h b/mindspore/ccsrc/kernel/kernel.h
index aecc51794c..80d831269c 100644
--- a/mindspore/ccsrc/kernel/kernel.h
+++ b/mindspore/ccsrc/kernel/kernel.h
@@ -25,7 +25,6 @@
 #include "ir/meta_tensor.h"
 #include "pipeline/static_analysis/dshape.h"
 #include "utils/log_adapter.h"
-#include "framework/ge_runtime/task_info.h"
 
 namespace mindspore {
 enum KernelType : int { UNKNOWN_KERNEL_TYPE = 0, AUTO_DIFF_KERNEL, AICPU_KERNEL, RT_KERNEL, HCCL_KERNEL, TBE_KERNEL };
@@ -111,7 +110,6 @@ struct Address {
   size_t size;
 };
 using AddressPtr = std::shared_ptr<Address>;
-using TaskInfoPtr = std::shared_ptr<ge::model_runner::TaskInfo>;
 
 class KernelMod {
  public:
@@ -120,10 +118,6 @@ class KernelMod {
   virtual const std::vector<size_t> &GetWorkspaceSizeList() const = 0;
   virtual bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
                       const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) = 0;
-  virtual std::vector<TaskInfoPtr> GenTask(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
-                                           const std::vector<AddressPtr> &, uint32_t) {
-    return {};
-  }
   virtual std::vector<size_t> GenParameters() { return {}; }
 
   virtual ~KernelMod() = default;
diff --git a/mindspore/ccsrc/kernel/mng/rt_kernel.h b/mindspore/ccsrc/kernel/mng/rt_kernel.h
index f86a86ef5d..54823f73cc 100644
--- a/mindspore/ccsrc/kernel/mng/rt_kernel.h
+++ b/mindspore/ccsrc/kernel/mng/rt_kernel.h
@@ -22,12 +22,12 @@
 #include <memory>
 #include <map>
 #include <string>
-#include "kernel/kernel.h"
+#include "kernel/ascend_kernel_mod.h"
 #include "kernel/task_stream.h"
 
 namespace mindspore {
 namespace kernel {
-class RtKernel : public KernelMod {
+class RtKernel : public AscendKernelMod {
  public:
   RtKernel();
   ~RtKernel() override;
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc
index 23e7014104..b20bd741f1 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.cc
+++ b/mindspore/ccsrc/kernel/oplib/oplib.cc
@@ -19,7 +19,7 @@
 #include <unordered_map>
 #include <memory>
 #include "utils/log_adapter.h"
-#include "kernel/oplib/opinfo.h"
+#include "utils/overload.h"
 #include "utils/context/ms_context.h"
 
 namespace mindspore {
@@ -50,7 +50,7 @@ constexpr auto kNeedCompile = "need_compile";
 constexpr auto kShape = "shape";
 std::vector<std::shared_ptr<OpInfo>> OpLib::op_info_;
 
-string ImplTypeToStr(OpImplyType impl_type) {
+std::string ImplTypeToStr(OpImplyType impl_type) {
   switch (impl_type) {
     case kTBE:
       return kTbe;
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h
index bc4895ac6f..de5ed84e41 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h
@@ -48,7 +48,7 @@ class TbeKernelBuild {
  private:
   TbeKernelBuild() = default;
   ~TbeKernelBuild() = default;
-  static bool GenFusionDataInputJson(const shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
+  static bool GenFusionDataInputJson(const std::shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
                                      size_t *index);
   static bool GenFusionComputeJson(const mindspore::AnfNodePtr &compute_node,
                                    std::vector<std::vector<mindspore::AnfNodePtr>>::iterator *layer_iter,
@@ -56,12 +56,13 @@ class TbeKernelBuild {
   static bool GenFusionComputeInputeJson(const mindspore::CNodePtr &cnode,
                                          std::vector<std::vector<mindspore::AnfNodePtr>>::iterator *layer_iter,
                                          std::vector<nlohmann::json> *input_desc_list, size_t *index);
-  static void GenDescJson(const shared_ptr<mindspore::AnfNode> &anf_node, size_t out_idx, nlohmann::json *output_desc);
-  static void GenReusedOutputDesc(const shared_ptr<mindspore::AnfNode> &anf_node, size_t index, size_t output_index,
-                                  nlohmann::json *output_desc);
+  static void GenDescJson(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t out_idx,
+                          nlohmann::json *output_desc);
+  static void GenReusedOutputDesc(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t index,
+                                  size_t output_index, nlohmann::json *output_desc);
   static size_t GetIOSizeImpl(const nlohmann::json &desc);
-  static bool GetInputLayers(const vector<mindspore::AnfNodePtr> &input_nodes,
-                             const vector<mindspore::AnfNodePtr> &compute_nodes,
+  static bool GetInputLayers(const std::vector<mindspore::AnfNodePtr> &input_nodes,
+                             const std::vector<mindspore::AnfNodePtr> &compute_nodes,
                              std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers);
   static bool IsDynamicInput(const CNodePtr &cnode);
   static size_t GetOptionalInput(const CNodePtr &cnode, bool is_dynamic_input);
@@ -82,15 +83,17 @@ class TbeKernelJsonCreator {
   bool GenTbeAttrJson(const std::shared_ptr<AnfNode> &anf_node, const std::shared_ptr<OpInfo> &op_info,
                       nlohmann::json *attrs_json);
   void ParseAttrValue(const std::string &type, const ValuePtr &value, nlohmann::json *attr_obj);
-  bool GenInputDescJson(const shared_ptr<AnfNode> &anf_node, size_t real_input_index, bool value,
-                        const shared_ptr<OpIOInfo> &input_ptr, const string &op_input_name, size_t input_i,
-                        vector<nlohmann::json> *input_list);
-  bool GenOutputDescJson(const shared_ptr<AnfNode> &anf_node, const vector<std::shared_ptr<OpIOInfo>> &outputs_ptr,
-                         nlohmann::json *outputs_json);
-  bool GenInputList(const shared_ptr<AnfNode> &anf_node, size_t input_tensor_num, const shared_ptr<OpIOInfo> &input_ptr,
-                    size_t *real_input_index, string *op_input_name, vector<nlohmann::json> *input_list);
-  void GenOutputList(const shared_ptr<AnfNode> &anf_node, const size_t &output_obj_num,
-                     const shared_ptr<OpIOInfo> &output_ptr, size_t *output_idx, vector<nlohmann::json> *output_list);
+  bool GenInputDescJson(const std::shared_ptr<AnfNode> &anf_node, size_t real_input_index, bool value,
+                        const std::shared_ptr<OpIOInfo> &input_ptr, const string &op_input_name, size_t input_i,
+                        std::vector<nlohmann::json> *input_list);
+  bool GenOutputDescJson(const std::shared_ptr<AnfNode> &anf_node,
+                         const std::vector<std::shared_ptr<OpIOInfo>> &outputs_ptr, nlohmann::json *outputs_json);
+  bool GenInputList(const std::shared_ptr<AnfNode> &anf_node, size_t input_tensor_num,
+                    const std::shared_ptr<OpIOInfo> &input_ptr, size_t *real_input_index, string *op_input_name,
+                    std::vector<nlohmann::json> *input_list);
+  void GenOutputList(const std::shared_ptr<AnfNode> &anf_node, const size_t &output_obj_num,
+                     const std::shared_ptr<OpIOInfo> &output_ptr, size_t *output_idx,
+                     std::vector<nlohmann::json> *output_list);
   kCreaterType creater_type_;
   std::string json_name_;
   std::string json_info_;
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h
index 35fc7f517d..f4fb75038e 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h
@@ -21,12 +21,12 @@
 #include <string>
 #include <vector>
 #include <utility>
-#include "kernel/kernel.h"
+#include "kernel/ascend_kernel_mod.h"
 #include "kernel/tbe/tbe_utils.h"
 
 namespace mindspore {
 namespace kernel {
-class TbeKernelMod : public KernelMod {
+class TbeKernelMod : public AscendKernelMod {
  public:
   explicit TbeKernelMod(KernelPackPtr kernel_pack) : kernel_pack_(std::move(kernel_pack)) {}
   ~TbeKernelMod() override = default;
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h
index 45f56fdd0b..5066e9457f 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h
@@ -55,8 +55,9 @@ class ParallelBuildManager {
   bool WaitOne(int *task_id, char **task_result) const;
   bool IsAllTaskFinish() const;
   std::pair<int32_t, KernelModPtr> TaskFinishProcess(int32_t task_id, bool set_kernel_mod = true);
-  KernelModPtr GenKernelMod(const string &json_name, const string &processor, const vector<size_t> &input_size_list,
-                            const vector<size_t> &output_size_list, const KernelPackPtr &kernel_pack) const;
+  KernelModPtr GenKernelMod(const string &json_name, const string &processor,
+                            const std::vector<size_t> &input_size_list, const std::vector<size_t> &output_size_list,
+                            const KernelPackPtr &kernel_pack) const;
 
  private:
   PyObject *tbe_parallel_compiler_;
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
index 1953fd0c72..92798aa6bc 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
@@ -168,7 +168,7 @@ bool ParseDynamicFormatJson(const std::string &jsonStr, std::vector<std::shared_
   return true;
 }
 
-std::string OpSelectFormat(const shared_ptr<AnfNode> &anf_node) {
+std::string OpSelectFormat(const std::shared_ptr<AnfNode> &anf_node) {
   nlohmann::json kernel_json;
   std::string res_json_str;
   TbeKernelJsonCreator creator(OP_SELECT_FORMAT);
@@ -182,7 +182,7 @@ std::string OpSelectFormat(const shared_ptr<AnfNode> &anf_node) {
   return res_json_str;
 }
 
-void SetTidyInputsInfo(const shared_ptr<AnfNode> &anf_node,
+void SetTidyInputsInfo(const std::shared_ptr<AnfNode> &anf_node,
                        const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder,
                        const std::vector<std::shared_ptr<OpIOInfo>> &inputs) {
   std::vector<TypeId> inputs_type;
@@ -231,7 +231,7 @@ void SetTidyInputsInfo(const shared_ptr<AnfNode> &anf_node,
   builder->SetInputsFormat(inputs_format);
 }
 
-void SetTidyOutputsInfo(const shared_ptr<AnfNode> &anf_node,
+void SetTidyOutputsInfo(const std::shared_ptr<AnfNode> &anf_node,
                         const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder,
                         const std::vector<std::shared_ptr<OpIOInfo>> &outputs) {
   std::vector<TypeId> outputs_type;
@@ -268,7 +268,8 @@ void SetTidyOutputsInfo(const shared_ptr<AnfNode> &anf_node,
   builder->SetOutputsFormat(outputs_format);
 }
 
-void GenTidyKernelBuildInfo(const shared_ptr<AnfNode> &anf_node, const std::vector<std::shared_ptr<OpIOInfo>> &inputs,
+void GenTidyKernelBuildInfo(const std::shared_ptr<AnfNode> &anf_node,
+                            const std::vector<std::shared_ptr<OpIOInfo>> &inputs,
                             const std::vector<std::shared_ptr<OpIOInfo>> &outputs) {
   auto builder_tmp = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
   builder_tmp->SetKernelType(TBE_KERNEL);
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_utils.cc b/mindspore/ccsrc/kernel/tbe/tbe_utils.cc
index ab29ca69bb..5980a0fd88 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_utils.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_utils.cc
@@ -26,6 +26,7 @@
 #include <iostream>
 #include <fstream>
 
+#include "runtime/kernel.h"
 #include "kernel/oplib/oplib.h"
 #include "utils/utils.h"
 #include "session/anf_runtime_algorithm.h"
diff --git a/mindspore/ccsrc/pipeline/base.h b/mindspore/ccsrc/pipeline/base.h
new file mode 100644
index 0000000000..d007eac294
--- /dev/null
+++ b/mindspore/ccsrc/pipeline/base.h
@@ -0,0 +1,64 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PIPELINE_BASE_H_
+#define MINDSPORE_CCSRC_PIPELINE_BASE_H_
+
+#include <mutex>
+#include <memory>
+#include <string>
+#include <sstream>
+
+#include "ir/anf.h"
+#include "pipeline/resource.h"
+#include "utils/context/ms_context.h"
+
+namespace mindspore {
+namespace pipeline {
+
+struct ExecutorInfo {
+  FuncGraphPtr func_graph;
+  ResourcePtr resource;
+  std::size_t arg_list_size;
+};
+
+using ExecutorInfoPtr = std::shared_ptr<ExecutorInfo>;
+
+inline std::string GetPhasePrefix(const std::string& phase) {
+  auto pos = phase.find('.');
+  if (pos == std::string::npos) {
+    MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase;
+  }
+  return phase.substr(0, pos);
+}
+
+inline std::string GetFilePathName(const std::string& file_name) {
+  std::ostringstream oss;
+  auto ms_context = MsContext::GetInstance();
+  if (ms_context == nullptr) {
+    MS_LOG(EXCEPTION) << "ms_context is nullptr";
+  }
+  auto save_graphs_path = ms_context->save_graphs_path();
+  if (save_graphs_path.empty()) {
+    save_graphs_path = ".";
+  }
+  oss << save_graphs_path << "/" << file_name;
+  return oss.str();
+}
+}  // namespace pipeline
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PIPELINE_BASE_H_
diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc
index f42ae0cf7b..24ead047d3 100644
--- a/mindspore/ccsrc/pipeline/init.cc
+++ b/mindspore/ccsrc/pipeline/init.cc
@@ -73,7 +73,7 @@ PYBIND11_MODULE(_c_expression, m) {
          "Get CNode Strategy Dictionary.")
     .def("get_allreduce_fusion", &ExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"),
          "Get Allreduce Fusion Dictionary.")
-    .def("build_data_graph", &ExecutorPy::BuildDFGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
+    .def("build_data_graph", &ExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
          py::arg("broadcast_params") = py::dict(), "Build data graph.")
     .def("has_compiled", &ExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "get if cell compiled.")
     .def("run_init_graph", &ExecutorPy::RunInitGraph, "Run init Graph.");
@@ -86,19 +86,17 @@ PYBIND11_MODULE(_c_expression, m) {
 
   (void)m.def("generate_key", &mindspore::pipeline::GenerateKey, "Generate the function graph key.");
   (void)m.def("real_run_op", &mindspore::pynative::RunOp, "Run op pynatively.");
-  (void)m.def("initialize_distribute", &mindspore::pipeline::InitDistribute, "Initialize for Distribute.")
-    .def("init_ge", &mindspore::pipeline::InitGe, "Init GE");
   (void)m.def("reset_op_id", &mindspore::pipeline::ResetOpId, "Reset Operator Id");
   (void)m.def("init_hccl", &mindspore::pipeline::InitHccl, "Init Hccl");
-  (void)m.def("finalize_ge", &mindspore::pipeline::FinalizeGe, "Finalize Ge");
   (void)m.def("finalize_hccl", &mindspore::pipeline::FinalizeHccl, "Finalize Hccl");
-  (void)m.def("set_ge_option", &mindspore::pipeline::SetGeOption, "API for set ge option.");
   (void)m.def("verify_inputs_signature", &mindspore::pipeline::VerifyInputSignature, "Verify input signature.");
   (void)m.def("init_exec_dataset", &mindspore::pipeline::InitExecDataset, py::arg("queue_name"), py::arg("size"),
               py::arg("batch_size"), py::arg("types"), py::arg("shapes"), py::arg("input_indexs"),
               py::arg("phase") = py::str("dataset"), "Init and exec dataset.");
   (void)m.def("_set_dataset_mode_config", &mindspore::ConfigManager::SetDatasetModeConfig, "API for set dataset mode.");
-  (void)m.def("export_graph", &mindspore::pipeline::ExportDFGraph, "Export Graph.");
+  (void)m.def("init_ge", &mindspore::pipeline::InitGe, "Init GE");
+
+  (void)m.def("export_graph", &mindspore::pipeline::ExportGraph, "Export Graph.");
 
   (void)py::class_<mindspore::MsContext, std::shared_ptr<mindspore::MsContext>>(m, "MSContext")
     .def_static("get_instance", &mindspore::MsContext::GetInstance, "Get ms context instance.")
diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.cc b/mindspore/ccsrc/pipeline/parse/python_adapter.cc
index 776e33235e..db40238729 100644
--- a/mindspore/ccsrc/pipeline/parse/python_adapter.cc
+++ b/mindspore/ccsrc/pipeline/parse/python_adapter.cc
@@ -27,6 +27,7 @@ static std::shared_ptr<py::scoped_interpreter> scoped_ = nullptr;
 //  true: start process from python, false: start process from c++
 static bool python_env_ = false;
 static bool use_signature_in_resolve_ = true;
+void ResetPythonScope() { scoped_ = nullptr; }
 void set_use_signature_in_resolve(bool use_signature) noexcept { use_signature_in_resolve_ = use_signature; }
 bool UseSignatureInResolve() { return use_signature_in_resolve_; }
 void set_python_env_flag(bool python_env) noexcept { python_env_ = python_env; }
diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.h b/mindspore/ccsrc/pipeline/parse/python_adapter.h
index 4b9cbff251..12cfc27186 100644
--- a/mindspore/ccsrc/pipeline/parse/python_adapter.h
+++ b/mindspore/ccsrc/pipeline/parse/python_adapter.h
@@ -55,6 +55,7 @@ void set_use_signature_in_resolve(bool use_signature) noexcept;
 bool UseSignatureInResolve();
 
 std::shared_ptr<py::scoped_interpreter> set_python_scoped();
+void ResetPythonScope();
 bool IsPythonEnv();
 void SetPythonPath(const std::string& path);
 void set_python_env_flag(bool python_env) noexcept;
diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 3c3478e89d..861862b849 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -27,11 +27,6 @@
 #include "pipeline/pass.h"
 #include "pipeline/parse/data_converter.h"
 #include "optimizer/ad/dfunctor.h"
-#include "ir/meta_tensor.h"
-#include "transform/convert.h"
-#include "transform/df_graph_manager.h"
-#include "transform/graph_builder.h"
-#include "transform/graph_runner.h"
 #include "debug/anf_ir_dump.h"
 #include "debug/anf_ir_utils.h"
 #include "utils/config_manager.h"
@@ -44,6 +39,12 @@
 #include "device/kernel_runtime_manager.h"
 #include "debug/trace.h"
 
+#if (ENABLE_GE || ENABLE_D)
+#include "pipeline/pipeline_ge.h"
+#include "transform/convert.h"
+#include "transform/df_graph_manager.h"
+#endif
+
 namespace mindspore {
 // namespace to support intermediate representation definition
 namespace pipeline {
@@ -54,12 +55,6 @@ using mindspore::abstract::AbstractTensor;
 using mindspore::abstract::AbstractTensorPtr;
 using mindspore::abstract::AbstractTuple;
 using mindspore::abstract::AbstractTuplePtr;
-using mindspore::transform::DfGraphConvertor;
-using mindspore::transform::DfGraphManager;
-using mindspore::transform::GeTensorPtr;
-using mindspore::transform::MeTensorPtr;
-using mindspore::transform::Status;
-using mindspore::transform::TransformUtil;
 
 const char IR_TYPE_ANF[] = "anf_ir";
 const char IR_TYPE_ONNX[] = "onnx_ir";
@@ -85,65 +80,8 @@ std::string GetBaseNameForIR(int stage_idx, const std::string& action_name) {
   oss << save_graphs_path << "/" << stage_idx << "_" << action_name;
   return oss.str();
 }
-
-std::string GetFilePathName(const std::string& file_name) {
-  std::ostringstream oss;
-  auto ms_context = MsContext::GetInstance();
-  if (ms_context == nullptr) {
-    MS_LOG(EXCEPTION) << "ms_context is nullptr";
-  }
-  auto save_graphs_path = ms_context->save_graphs_path();
-  if (save_graphs_path.empty()) {
-    save_graphs_path = ".";
-  }
-  oss << save_graphs_path << "/" << file_name;
-  return oss.str();
-}
 }  // namespace
 
-// We will not execute graph when output is constant or just input itself.
-static bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr& output, const py::tuple& args,
-                                              const std::shared_ptr<py::object>& ret_val) {
-  if (output->isa<ValueNode>()) {
-    MS_LOG(INFO) << "Graph's output is a constant. No need to execute.";
-    ValuePtr value = GetValueNode(output);
-    *ret_val = ValuePtrToPyData(value);
-    return true;
-  }
-
-  // Adapter will transform values in __init__() and construct() to parameters, this could cause
-  // inputs (a.k.a args in current function) size less than parameters'.
-  if (output->isa<Parameter>()) {
-    MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute.";
-    if (args.empty()) {
-      MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed.";
-    }
-    // Find the right parameter as ret_val.
-    auto func_graph = output->func_graph();
-    MS_EXCEPTION_IF_NULL(func_graph);
-    auto params = func_graph->parameters();
-    if (params.empty()) {
-      MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0";
-    }
-    if (args.size() != params.size()) {
-      MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size()
-                        << ", let graph to be executed.";
-    }
-
-    auto it = std::find(params.begin(), params.end(), output);
-    if (it == params.end()) {
-      MS_EXCEPTION(UnknownError) << "When graph output is Parameter,  it should be found in graph parameters";
-    }
-    size_t index = it - params.cbegin();
-    if (index >= args.size()) {
-      MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << ".";
-    }
-    *ret_val = args[index];
-    return true;
-  }
-  return false;
-}
-
 py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::string, py::object>& defaults) {
   MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size();
   abstract::AbstractBasePtrList args_spec;
@@ -207,11 +145,7 @@ py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple i
   return true;
 }
 
-ExecutorPy::ExecutorPy() {
-  // because Ge only support one Session exist at the same time ,so we delete the old one
-  DfGraphManager::GetInstance().DeleteGraphRunner();
-  DfGraphManager::GetInstance().DeleteGeSession();
-}
+ExecutorPy::ExecutorPy() {}
 
 ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
   MS_LOG(DEBUG) << "phase size:" << info_.size();
@@ -221,14 +155,6 @@ ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
   return info_[phase]->resource;
 }
 
-std::string GetPhasePrefix(const std::string& phase) {
-  auto pos = phase.find('.');
-  if (pos == std::string::npos) {
-    MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase;
-  }
-  return phase.substr(0, pos);
-}
-
 FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string& phase) {
   if (info_.count(phase) == 0) {
     MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
@@ -323,11 +249,15 @@ void ExecutorPy::DelNetRes(const std::string& id) {
       }
     }
 
+    MS_LOG(INFO) << "Delete flag:" << flag;
+#ifdef ENABLE_GE
     if (flag && info_.size() == 0) {
-      DfGraphManager::GetInstance().DeleteGraphRunner();
-      DfGraphManager::GetInstance().EraseAnfGraph();
-      DfGraphManager::GetInstance().DeleteGeSession();
+      // because Ge only support one Session exist at the same time ,so we delete the old one
+      transform::DfGraphManager::GetInstance().DeleteGraphRunner();
+      transform::DfGraphManager::GetInstance().EraseAnfGraph();
+      transform::DfGraphManager::GetInstance().DeleteGeSession();
     }
+#endif
   }
 }
 
@@ -405,7 +335,8 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons
 
   use_vm = ChangeExportGeirUseVmFlag(use_vm, phase_s);
 
-  if (use_vm) {
+  std::string backend = MsContext::GetInstance()->backend_policy();
+  if (use_vm && backend != "ge") {
     // Create backend and session
     resource->results()[kBackend] = compile::CreateBackend();
     p_actions = VmPipeline();
@@ -497,30 +428,6 @@ bool ExecutorPy::Compile(const py::object& obj, const py::tuple& args, const py:
   return ret_value;
 }
 
-void SetGeOption(const std::map<std::string, std::string>& options) {
-  ConfigManager::GetInstance().set_ge_initialize_options(options);
-}
-
-bool InitDistribute(const std::map<std::string, std::string>& options) {
-  ConfigManager::GetInstance().set_parallel_strategy(ParallelStrategy::DISTRIBUTION);
-  MS_LOG(INFO) << "ME run in DISTRIBUTION strategy mode";
-
-  SetGeOption(options);
-#ifdef ENABLE_GE
-  auto ge_options = ConfigManager::GetInstance().ge_initialize_options();
-  {
-    // Release GIL before calling into (potentially long-running) C++ code
-    py::gil_scoped_release release;
-    if (ge::GEInitialize(ge_options) != ge::GRAPH_SUCCESS) {
-      MS_LOG(ERROR) << "Initialize GE failed!";
-      return false;
-    }
-  }
-#endif
-  MS_LOG(DEBUG) << "Initialize Ge success";
-  return true;
-}
-
 #ifdef ENABLE_LOAD_ANF_IR
 // get MindSpore Intermediate Representation File
 std::string GetMsIrFile(void) {
@@ -704,9 +611,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
   }
   auto phase_s = py::cast<std::string>(phase);
   std::string backend = MsContext::GetInstance()->backend_policy();
+#ifdef ENABLE_GE
+  if (backend == "ge") {
+    return ExecDFGraph(info_, args, phase_s);
+  }
+#else
+  MS_LOG(WARNING) << "In ut test " << size << phase_s;
   if (backend == "ge") {
-    return ExecDFGraph(args, phase_s);
+    std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
+    if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
+      if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
+        return *ret_val;
+      }
+    }
+    if (args.size() > 0) {
+      return args[0];
+    }
+    return args;
   }
+#endif
   std::size_t full_arg_size = ArgListSize(phase_s);
   if (size > full_arg_size) {
     MS_LOG(WARNING) << "The arg num : size = " << size << ". full_arg_size = " << full_arg_size;
@@ -719,435 +642,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
     MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s;
   }
 
-  MS_LOG(DEBUG) << "eval run";
+  MS_LOG(DEBUG) << "eval run" << backend;
   BaseRef value = (*run)(arg_list);
   MS_LOG(DEBUG) << "run end";
   return BaseRefToPyData(value);
 }
 
-py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) {
-  MS_EXCEPTION_IF_NULL(cnode_data);
-  if (*count >= data.size()) {
-    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
-                      << " less than the number of elements required. ";
-  }
-
-  if (cnode_data->isa<AbstractTensor>()) {
-    BaseShapePtr shape = cnode_data->BuildShape();
-    auto shape_act = shape->cast<abstract::ShapePtr>()->shape();
-    Tensor tensor_exp = py::cast<Tensor>(data[*count]);
-    if (shape_act != tensor_exp.shape()) {
-      MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as "
-                           "the shape of the tensor derived from ME.";
-    }
-    return data[(*count)++];
-  }
-
-  if (!cnode_data->isa<AbstractTuple>()) {
-    MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could "
-                      << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString()
-                      << ".";
-  }
-  auto data_tp = cnode_data->cast<AbstractTuplePtr>();
-  auto elements = data_tp->elements();
-  size_t size = data_tp->size();
-  py::tuple tp = py::tuple(size);
-  for (size_t i = 0; i < size; i++) {
-    tp[i] = ExtractGeneralCnodeRet(elements[i], data, count);
-  }
-  return std::move(tp);
-}
-
-py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) {
-  MS_EXCEPTION_IF_NULL(output_node);
-
-  if (output_node->isa<ValueNode>()) {
-    return ValuePtrToPyData(GetValueNode(output_node));
-  }
-
-  if (*count >= data.size()) {
-    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
-                      << " less than the number of elements required. ";
-  }
-  if (output_node->isa<Parameter>()) {
-    return data[(*count)++];
-  }
-
-  auto output_c = output_node->cast<CNodePtr>();
-  if (output_c == nullptr) {
-    MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got "
-                      << output_node->ToString();
-  }
-
-  if (output_c->IsApply(prim::kPrimMakeTuple)) {
-    auto input_list = output_c->inputs();
-    size_t size = input_list.size();
-    py::tuple tp = py::tuple(size - 1);
-    for (size_t i = 1; i < size; i++) {
-      tp[i - 1] = StructureOutput(input_list[i], data, count);
-    }
-    return std::move(tp);
-  }
-  if (output_c->IsApply(prim::kPrimDepend)) {
-    return StructureOutput(output_c->input(1), data, count);
-  }
-
-  return ExtractGeneralCnodeRet(output_c->abstract(), data, count);
-}
-
-std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::vector<MeTensorPtr>& inputs,
-                                        const std::string& phase) {
-  std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
-  if (ge_tensors.size() != inputs.size()) {
-    MS_LOG(ERROR) << "args convert to ge tensor error";
-    return nullptr;
-  }
-
-  std::vector<GeTensorPtr> ge_outputs;
-  transform::RunOptions run_options;
-
-  run_options.name = phase;
-
-  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
-
-  if (graph_runner == nullptr) {
-    MS_LOG(ERROR) << "Can not found GraphRunner";
-    return nullptr;
-  }
-
-  {
-    // Release GIL before calling into (potentially long-running) C++ code
-    py::gil_scoped_release release;
-    MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size();
-    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
-    MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size();
-    if (ret != Status::SUCCESS) {
-      MS_LOG(ERROR) << "Exec graph failed";
-      return nullptr;
-    }
-  }
-
-  std::vector<MeTensorPtr> me_outputs = TransformUtil::ConvertGeTensors(ge_outputs);
-  if (me_outputs.size() != ge_outputs.size()) {
-    MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed";
-  }
-
-  py::tuple outputs(me_outputs.size());
-  for (std::size_t i = 0; i < outputs.size(); i++) {
-    outputs[i] = *me_outputs[i];
-  }
-
-  std::shared_ptr<py::object> ret = nullptr;
-
-#ifdef ENABLE_GE
-  AnfNodePtr output_node = graph->get_return()->input(1);
-  MS_EXCEPTION_IF_NULL(output_node);
-  size_t count = 0;
-  py::object oj = StructureOutput(output_node, outputs, &count);
-  ret = std::make_shared<py::object>(oj);
+FuncGraphPtr ExecutorPy::BuildGraph(const py::dict& init_params, const std::string& phase,
+                                    const py::object& broadcast_params) {
+#if (ENABLE_GE || ENABLE_D)
+  return BuildDFGraph(info_, init_params, phase, broadcast_params);
 #else
-  if (outputs.size() == 1) {
-    ret = std::make_shared<py::object>(outputs[0]);
-  } else {
-    ret = std::make_shared<py::object>(outputs);
-  }
-#endif
-
-  return ret;
-}
-
-void DoExecNonInputGraph(const std::string& phase) {
-  std::vector<GeTensorPtr> ge_tensors;
-  std::vector<GeTensorPtr> ge_outputs;
-  transform::RunOptions run_options;
-  run_options.name = phase;
-  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
-
-  if (graph_runner == nullptr) {
-    MS_LOG(ERROR) << "Can not found GraphRunner";
-    return;
-  }
-  {
-    // Release GIL before calling into (potentially long-running) C++ code
-    py::gil_scoped_release release;
-    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
-    if (ret != Status::SUCCESS) {
-      MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed";
-      return;
-    }
-  }
-}
-
-void ExecutorPy::ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector<tensor::TensorPtr>* inputs) {
-  // check the arg and use the ExecutorPy args
-  std::size_t size = args.size();
-  if (size != ArgListSize(phase)) {
-    MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << ArgListSize(phase);
-  }
-
-  // process the first args of tensor
-  // only in Dataset Feed Mode, fp_bp graph need input tensors
-  if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
-    for (std::size_t i = 0; i < size; i++) {
-      ValuePtr converted = nullptr;
-      bool succ = parse::ConvertData(args[i], &converted);
-      if (!succ) {
-        MS_LOG(EXCEPTION) << "args convert error";
-      }
-      if (converted->isa<tensor::Tensor>()) {
-        (*inputs).push_back(converted->cast<tensor::TensorPtr>());
-      } else {
-        MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor";
-      }
-    }
-  }
-}
-
-py::object ExecutorPy::ExecDFGraph(const py::tuple& args, const std::string& phase) {
-  std::string phase_prefix = GetPhasePrefix(phase);
-
-  if (phase_prefix == "save") {
-    DoExecNonInputGraph(phase);
-    ConfigManager::GetInstance().ResetConfig();
-    return py::none();
-  }
-
-  if (info_.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "has no phase:" << phase;
-  }
-
-#if (!defined ENABLE_GE) || (defined ENABLE_INFER)
-  // Now don't use the graph because the exec ge function don't take effect
-  MS_EXCEPTION_IF_NULL(info_[phase]->func_graph);
-  if (ENABLE_TRAIN != info_[phase]->func_graph->flags()["training"]) {
-    MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries";
-    ConfigManager::GetInstance().ResetConfig();
-    return py::none();
-  }
+  return nullptr;
 #endif
-
-  std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
-  if (IsGraphOutputValueNodeOrParameter(info_[phase]->func_graph->output(), args, ret_val)) {
-    ConfigManager::GetInstance().ResetConfig();
-    return *ret_val;
-  }
-
-  std::vector<tensor::TensorPtr> inputs;
-  ProcessGeArg(args, phase, &inputs);
-
-  std::shared_ptr<py::object> ret = DoExecGraph(GetFuncGraph(phase), inputs, phase);
-  ConfigManager::GetInstance().ResetConfig();
-  if (ret != nullptr) {
-    return *ret;
-  } else {
-    MS_LOG(EXCEPTION) << "exec graph failed";
-  }
 }
 
 void ExecutorPy::RunInitGraph(const py::dict& init_params, const std::string& phase) {
-  MS_LOG(DEBUG) << "ExecInitGraph start.";
-  TensorOrderMap inputs_with_name{};
-  ConvertObjectToTensors(init_params, &inputs_with_name);
-  std::vector<tensor::TensorPtr> inputs;
-  (void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs),
-                       [](const std::pair<std::string, tensor::TensorPtr>& item) { return item.second; });
-
-  std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
-  if (ge_tensors.size() != inputs.size()) {
-    MS_LOG(ERROR) << "Args convert to ge tensor error.";
-    return;
-  }
-  MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << ".";
-
-  std::vector<GeTensorPtr> ge_outputs;
-  transform::RunOptions run_options;
-
-  run_options.name = phase;
-  if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) {
-    MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode.";
-    return;
-  }
-  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
-  if (graph_runner == nullptr) {
-    MS_LOG(EXCEPTION) << "Can not found GraphRunner.";
-  }
-  {
-    // Release GIL before calling into (potentially long-running) C++ code
-    py::gil_scoped_release release;
-    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
-    if (ret != Status::SUCCESS) {
-      MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed.";
-    }
-
-    MS_LOG(INFO) << "Exec " << phase << " graph success.";
-
-    if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) &&
-        (DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) {
-      run_options.name = BROADCAST_GRAPH_NAME;
-      ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
-      if (ret != Status::SUCCESS) {
-        MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed.";
-      }
-      MS_LOG(INFO) << "Exec broadcast graph success.";
-    }
-  }
-}
-
-Status CreateSessionAndGraphRunner(bool is_training = true) {
-  std::shared_ptr<ge::Session> sess = DfGraphManager::GetInstance().GetGeSession();
-  if (sess == nullptr) {
-    transform::SessionOptions options;
-    if (is_training) {
-      options["ge.trainFlag"] = "1";
-      options["ge.streamNum"] = "100";
-      options["ge.enabledLocalFmkop"] = "1";
-      options["ge.hcomParallel"] = "1";
-    } else {
-      options["ge.trainFlag"] = "0";
-    }
-
-    options["ge.enablePrintOpPass"] = "0";
-    sess = transform::GraphRunner::NewSession(options);
-    if (sess == nullptr) {
-      MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed";
-      return Status::FAILED;
-    } else {
-      DfGraphManager::GetInstance().SetGeSession(sess);
-    }
-  }
-
-  transform::GraphRunnerOptions options;
-  options.sess_ptr = sess;
-  auto graph_runner = std::make_shared<transform::GraphRunner>(options);
-  if (graph_runner == nullptr) {
-    MS_LOG(ERROR) << "Create new graph runner failed";
-    return Status::FAILED;
-  } else {
-    DfGraphManager::GetInstance().SetGraphRunner(graph_runner);
-  }
-
-  return Status::SUCCESS;
-}
-
-void ExecutorPy::ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) {
-  for (auto item : dict) {
-    if ((!py::isinstance<py::str>(item.first))) {
-      MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it.";
-      continue;
-    }
-    std::shared_ptr<Tensor> tensor;
-    std::string name = py::cast<std::string>(item.first);
-    if (py::isinstance<py::float_>(item.second.attr("default_input"))) {
-      // convert float to tensor with shape([1])
-      tensor = std::make_shared<Tensor>(kNumberTypeFloat32, std::vector<int>({1}));
-      *(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
-    } else if (py::isinstance<py::int_>(item.second.attr("default_input"))) {
-      // convert int to tensor with shape([1])
-      tensor = std::make_shared<Tensor>(kNumberTypeInt32, std::vector<int>({1}));
-      *(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
-    } else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) {
-      // cast tensor
-      tensor = py::cast<std::shared_ptr<Tensor>>(item.second.attr("default_input"));
-    }
-
-    if (tensor == nullptr) {
-      MS_LOG(EXCEPTION) << "Get default value for " << name << " failed";
-    }
-    (void)tensors->emplace(name, tensor);
-  }
-}
-
-bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params) {
-  FuncGraphPtr anf_graph = info_[phase]->func_graph;
-  DfGraphConvertor convertor(anf_graph);
-
-  size_t pos = phase.find('.');
-  std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1));
-  std::string phase_prefix = phase.substr(0, pos);
-
-  if (phase_prefix == "export") {
-    MS_LOG(INFO) << "Set DfGraphConvertor training : false";
-    convertor.set_training(false);
-  }
-
-  TensorOrderMap init_tensors{};
-  ConvertObjectToTensors(init_params, &init_tensors);
-  (void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph();
-
-  if (broadcast_params != py::none()) {
-    if (!py::isinstance<py::dict>(broadcast_params)) {
-      MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type";
-      return false;
-    }
-    py::dict broadcast = broadcast_params.cast<py::dict>();
-    if (broadcast.empty()) {
-      (void)convertor.GenerateBroadcastGraph(init_tensors);
-    } else {
-      TensorOrderMap broadcast_tensors{};
-      ConvertObjectToTensors(broadcast, &broadcast_tensors);
-      (void)convertor.GenerateBroadcastGraph(broadcast_tensors);
-    }
-    MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty();
-  }
-
-  (void)convertor.GenerateCheckpointGraph();
-  if (convertor.ErrCode() != 0) {
-    DfGraphManager::GetInstance().ClearGraph();
-    MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode();
-    return false;
-  }
-
-  if (MsContext::GetInstance()->save_graphs_flag()) {
-    convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot"));                      // for debug
-    convertor.DrawInitGraph(GetFilePathName("init_graph.dot"));                       // for debug
-    convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot"));  // for debug
-  }
-  std::string init_graph = "init_subgraph." + net_id;
-  std::string checkpoint_name = "save." + net_id;
-  if (phase.find("train") != std::string::npos) {
-    (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}});
-  } else {
-    (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
-  }
-  (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
-  (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
-  Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
-  if (ret == Status::SUCCESS) {
-    DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
-  }
-
-  return true;
-}
-
-FuncGraphPtr ExecutorPy::BuildDFGraph(const py::dict& init_params, const std::string& phase,
-                                      const py::object& broadcast_params) {
-  if (info_.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
-  }
-  FuncGraphPtr anf_graph = info_[phase]->func_graph;
-
-  if (MsContext::GetInstance()->save_graphs_flag()) {
-    draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph);  // for debug
-    DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true);
-  }
-
-  if (!AddDFGraph(init_params, phase, broadcast_params)) {
-    MS_LOG(ERROR) << "GenConvertor failed";
-    return nullptr;
-  }
-
-#if ENABLE_TRAIN
-  (void)setenv("GE_TRAIN", "1", 1);
-#else
-  (void)setenv("GE_TRAIN", "0", 1);
+#if ENABLE_GE
+  RunGEInitGraph(init_params, phase);
 #endif
-
-  if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
-    MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
-    return nullptr;
-  }
-
-  return anf_graph;
 }
 
 bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size,
@@ -1156,47 +669,16 @@ bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t ba
   std::string name = MsContext::GetInstance()->backend_policy();
   if (name == kMsConvert || name == kMsVm) {
     return InitExecDatasetVm(queue_name, iter_num, batch_size, types, shapes, input_indexes);
-  } else {
-    return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase);
-  }
-}
-
-bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
-                       const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
-                       const std::vector<int64_t>& input_indexes, const std::string& phase) {
-  // Convert types to GE types and TF types
-  std::vector<int64_t> ge_types;
-  (void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t {
-    return transform::TransformUtil::ConvertDataType(i->type_id());
-  });
-
-  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
-  ConfigManager::GetInstance().set_iter_num(size);
-  ConfigManager::GetInstance().set_dataset_phase(phase);
-
-  DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes);
-  ConfigManager::GetInstance().set_dataset_param(param);
-
-  if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) {
-    MS_LOG(ERROR) << "Build dateset graph failed.";
-    return false;
   }
-
-#if ENABLE_TRAIN
-  (void)setenv("GE_TRAIN", "1", 1);
+#if ENABLE_GE
+  return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase);
 #else
-  (void)setenv("GE_TRAIN", "0", 1);
-#endif
-
-  if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
-    MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
-    return false;
+  std::string backend = MsContext::GetInstance()->backend_policy();
+  if (backend == "ge") {
+    return true;
   }
-
-  MS_LOG(INFO) << "DoExecNonInputGraph:" << phase;
-  DoExecNonInputGraph(phase);
-
-  return true;
+#endif
+  return false;
 }
 
 bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size,
@@ -1259,25 +741,6 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc
   return true;
 }
 
-void InitGe() {
-  // set python env flag
-  mindspore::parse::python_adapter::set_python_env_flag(true);
-  // open tsd before ge initialize
-  auto ms_context = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(ms_context);
-  if (!ms_context->OpenTsd()) {
-    MS_LOG(EXCEPTION) << "open tsd failed";
-  }
-  (void)ms_context->InitGe();
-}
-
-void FinalizeGe() {
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  (void)context_ptr->FinalizeGe();
-  (void)context_ptr->CloseTsd();
-}
-
 void ResetOpId() { mindspore::id_generator::reset_id(); }
 
 void InitHccl() {
@@ -1309,24 +772,57 @@ void FinalizeHccl() {
   device::KernelRuntimeManager::Instance().ClearRuntimeResource();
 #endif
 }
-void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase) {
-  MS_LOG(DEBUG) << "ExportGraph Begin";
-  transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase);
-  if (wrap_ptr == nullptr) {
-    MS_LOG(ERROR) << "Get graph form DfGraphManager failed!";
-    return;
-  }
 
-  transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_;
-  if (nullptr == ge_graph) {
-    MS_LOG(ERROR) << "The export graph is null";
-    return;
+void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase) {
+#if (ENABLE_GE || ENABLE_D)
+  ExportDFGraph(file_name, phase);
+#endif
+  MS_LOG(WARNING) << "In ut test no export_graph";
+}
+
+void ReleaseGeTsd() {
+  auto context_ptr = MsContext::GetInstance();
+  if (context_ptr != nullptr) {
+    (void)context_ptr->FinalizeGe(true);
+    (void)context_ptr->CloseTsd(true);
   }
+}
 
-  (void)ge_graph->SaveToFile(file_name);
+void InitGe() {
+  // set python env flag
+  mindspore::parse::python_adapter::set_python_env_flag(true);
+  // open tsd before ge initialize
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
+  if (!ms_context->OpenTsd()) {
+    MS_LOG(EXCEPTION) << "open tsd failed";
+  }
+  (void)ms_context->InitGe();
+}
 
-  MS_LOG(DEBUG) << "ExportGraph End";
+void FinalizeGe() {
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  (void)context_ptr->FinalizeGe();
+  (void)context_ptr->CloseTsd();
 }
 
+void ClearResAtexit() {
+  MS_LOG(DEBUG) << "Pipeline clear all resource";
+  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
+
+  ad::g_k_prims.clear();
+
+  abstract::ClearPrimEvaluatorMap();
+  compile::ClearConvertCache();
+  pipeline::GetMethodMap().clear();
+  pipeline::ExecutorPy::ClearRes();
+#ifdef ENABLE_GE
+  transform::DfGraphManager::GetInstance().ClearGraph();
+  transform::DfGraphConvertor::get_adpt_map().clear();
+#endif
+  ReleaseGeTsd();
+  parse::python_adapter::ResetPythonScope();
+}
 }  // namespace pipeline
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pipeline/pipeline.h b/mindspore/ccsrc/pipeline/pipeline.h
index b075306682..a0d7a19198 100644
--- a/mindspore/ccsrc/pipeline/pipeline.h
+++ b/mindspore/ccsrc/pipeline/pipeline.h
@@ -30,6 +30,7 @@
 #include "pipeline/action.h"
 #include "vm/segment_runner.h"
 #include "vm/transform.h"
+#include "pipeline/base.h"
 
 namespace mindspore {
 extern const char kMsConvert[];
@@ -55,14 +56,6 @@ class Pipeline {
   std::vector<ActionItem> actions_;
 };
 
-struct ExecutorInfo {
-  FuncGraphPtr func_graph;
-  ResourcePtr resource;
-  std::size_t arg_list_size;
-};
-
-using ExecutorInfoPtr = std::shared_ptr<ExecutorInfo>;
-
 // A function pipeline.
 class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
  public:
@@ -80,11 +73,7 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
   bool CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm);
   bool Compile(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm);
 
-  // for graph mode
-  py::object ExecDFGraph(const py::tuple& args, const std::string& phase = "train");
-
   void ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list);
-  void ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector<tensor::TensorPtr>* inputs);
 
   // for pynative mode when use_vm is on
   py::object Run(const py::tuple& args, const py::object& phase);
@@ -95,9 +84,8 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
   compile::VmEvalFuncPtr GetVmEvalFunc(const std::string& phase);
   bool HasCompiled(const std::string& phase) const;
 
-  bool AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params);
-  FuncGraphPtr BuildDFGraph(const py::dict& init_params, const std::string& phase,
-                            const py::object& broadcast_params = {});
+  FuncGraphPtr BuildGraph(const py::dict& init_params, const std::string& phase,
+                          const py::object& broadcast_params = {});
   void RunInitGraph(const py::dict& init_params, const std::string& phase);
   py::dict GetParameterLayout(const std::string& phase);
   py::dict GetCNodeStrategy(const std::string& phase);
@@ -122,32 +110,29 @@ using ExecutorPyPtr = std::shared_ptr<ExecutorPy>;
 py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::string, py::object>& defaults);
 py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs);
 
-void SetGeOption(const std::map<std::string, std::string>& options);
 bool InitDistribute(const std::map<std::string, std::string>& options);
 
 void ResetOpId();
-void InitGe();
-void FinalizeGe();
 void InitHccl();
 void FinalizeHccl();
+void InitGe();
+void FinalizeGe();
+
+void ClearResAtexit();
+void ReleaseGeTsd();
+
+void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase);
 
 // init and exec dataset sub graph
 bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size,
                      const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
                      const std::vector<int64_t>& input_indexes, const std::string& phase);
 
-// init and exec dataset sub graph for GE backend
-bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
-                       const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
-                       const std::vector<int64_t>& input_indexes, const std::string& phase);
-
 // Build and run dataset subgraph for ms backend
 bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size,
                        const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
                        const std::vector<int64_t>& input_indexes);
 
-void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase);
-
 }  // namespace pipeline
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
new file mode 100644
index 0000000000..4617884ca0
--- /dev/null
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -0,0 +1,545 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "pipeline/pipeline_ge.h"
+
+#include <sstream>
+#include <map>
+#include <unordered_map>
+#include <cstdlib>
+#include <algorithm>
+
+#include "debug/anf_ir_dump.h"
+#include "ir/meta_tensor.h"
+#include "transform/convert.h"
+#include "transform/df_graph_manager.h"
+#include "transform/graph_builder.h"
+#include "transform/graph_runner.h"
+#include "debug/draw.h"
+#include "pipeline/static_analysis/abstract_value.h"
+
+namespace mindspore {
+namespace pipeline {
+using Tensor = mindspore::tensor::Tensor;
+using MetaTensor = mindspore::tensor::MetaTensor;
+using TensorOrderMap = std::map<std::string, std::shared_ptr<Tensor>>;
+using mindspore::abstract::AbstractTensor;
+using mindspore::abstract::AbstractTuple;
+using mindspore::abstract::AbstractTuplePtr;
+using mindspore::transform::DfGraphConvertor;
+using mindspore::transform::DfGraphManager;
+using mindspore::transform::GeTensorPtr;
+using mindspore::transform::MeTensorPtr;
+using mindspore::transform::Status;
+using mindspore::transform::TransformUtil;
+
+void DoExecNonInputGraph(const std::string& phase) {
+  std::vector<GeTensorPtr> ge_tensors;
+  std::vector<GeTensorPtr> ge_outputs;
+  transform::RunOptions run_options;
+  run_options.name = phase;
+  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
+
+  if (graph_runner == nullptr) {
+    MS_LOG(ERROR) << "Can not found GraphRunner";
+    return;
+  }
+  {
+    // Release GIL before calling into (potentially long-running) C++ code
+    py::gil_scoped_release release;
+    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
+    if (ret != Status::SUCCESS) {
+      MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed";
+      return;
+    }
+  }
+}
+
+void SetGeOption(const std::map<std::string, std::string>& options) {
+  ConfigManager::GetInstance().set_ge_initialize_options(options);
+}
+
+Status CreateSessionAndGraphRunner(bool is_training = true) {
+  std::shared_ptr<ge::Session> sess = DfGraphManager::GetInstance().GetGeSession();
+  if (sess == nullptr) {
+    transform::SessionOptions options;
+    if (is_training) {
+      options["ge.trainFlag"] = "1";
+      options["ge.streamNum"] = "100";
+      options["ge.enabledLocalFmkop"] = "1";
+      options["ge.hcomParallel"] = "1";
+    } else {
+      options["ge.trainFlag"] = "0";
+    }
+
+    options["ge.enablePrintOpPass"] = "0";
+    sess = transform::GraphRunner::NewSession(options);
+    if (sess == nullptr) {
+      MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed";
+      return Status::FAILED;
+    } else {
+      DfGraphManager::GetInstance().SetGeSession(sess);
+    }
+  }
+
+  transform::GraphRunnerOptions options;
+  options.sess_ptr = sess;
+  auto graph_runner = std::make_shared<transform::GraphRunner>(options);
+  if (graph_runner == nullptr) {
+    MS_LOG(ERROR) << "Create new graph runner failed";
+    return Status::FAILED;
+  } else {
+    DfGraphManager::GetInstance().SetGraphRunner(graph_runner);
+  }
+
+  return Status::SUCCESS;
+}
+
+bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
+                       const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
+                       const std::vector<int64_t>& input_indexes, const std::string& phase) {
+  std::vector<int64_t> ge_types;
+  (void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t {
+    return transform::TransformUtil::ConvertDataType(i->type_id());
+  });
+
+  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
+  ConfigManager::GetInstance().set_iter_num(size);
+  ConfigManager::GetInstance().set_dataset_phase(phase);
+
+  DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes);
+  ConfigManager::GetInstance().set_dataset_param(param);
+
+  if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) {
+    MS_LOG(ERROR) << "Build dateset graph failed.";
+    return false;
+  }
+
+#if ENABLE_TRAIN
+  (void)setenv("GE_TRAIN", "1", 1);
+#else
+  (void)setenv("GE_TRAIN", "0", 1);
+#endif
+
+  if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
+    MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
+    return false;
+  }
+
+  MS_LOG(INFO) << "DoExecNonInputGraph:" << phase;
+  DoExecNonInputGraph(phase);
+
+  return true;
+}
+
+void ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) {
+  for (auto item : dict) {
+    if ((!py::isinstance<py::str>(item.first))) {
+      MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it.";
+      continue;
+    }
+    std::shared_ptr<Tensor> tensor;
+    std::string name = py::cast<std::string>(item.first);
+    if (py::isinstance<py::float_>(item.second.attr("default_input"))) {
+      // convert float to tensor with shape([1])
+      tensor = std::make_shared<Tensor>(kNumberTypeFloat32, std::vector<int>({1}));
+      *(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
+    } else if (py::isinstance<py::int_>(item.second.attr("default_input"))) {
+      // convert int to tensor with shape([1])
+      tensor = std::make_shared<Tensor>(kNumberTypeInt32, std::vector<int>({1}));
+      *(static_cast<float*>(tensor->data_c(true))) = py::cast<float>(item.second.attr("default_input"));
+    } else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) {
+      // cast tensor
+      tensor = py::cast<std::shared_ptr<Tensor>>(item.second.attr("default_input"));
+    }
+
+    if (tensor == nullptr) {
+      MS_LOG(EXCEPTION) << "Get default value for " << name << " failed";
+    }
+    (void)tensors->emplace(name, tensor);
+  }
+}
+
+bool AddDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
+                const std::string& phase, const py::object& broadcast_params) {
+  FuncGraphPtr anf_graph = info.at(phase)->func_graph;
+  DfGraphConvertor convertor(anf_graph);
+
+  size_t pos = phase.find('.');
+  std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1));
+  std::string phase_prefix = phase.substr(0, pos);
+
+  if (phase_prefix == "export") {
+    MS_LOG(INFO) << "Set DfGraphConvertor training : false";
+    convertor.set_training(false);
+  }
+
+  TensorOrderMap init_tensors{};
+  ConvertObjectToTensors(init_params, &init_tensors);
+  (void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph();
+
+  if (broadcast_params != py::none()) {
+    if (!py::isinstance<py::dict>(broadcast_params)) {
+      MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type";
+      return false;
+    }
+    py::dict broadcast = broadcast_params.cast<py::dict>();
+    if (broadcast.empty()) {
+      (void)convertor.GenerateBroadcastGraph(init_tensors);
+    } else {
+      TensorOrderMap broadcast_tensors{};
+      ConvertObjectToTensors(broadcast, &broadcast_tensors);
+      (void)convertor.GenerateBroadcastGraph(broadcast_tensors);
+    }
+    MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty();
+  }
+
+  (void)convertor.GenerateCheckpointGraph();
+  if (convertor.ErrCode() != 0) {
+    DfGraphManager::GetInstance().ClearGraph();
+    MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode();
+    return false;
+  }
+
+  if (MsContext::GetInstance()->save_graphs_flag()) {
+    convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot"));                      // for debug
+    convertor.DrawInitGraph(GetFilePathName("init_graph.dot"));                       // for debug
+    convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot"));  // for debug
+  }
+  std::string init_graph = "init_subgraph." + net_id;
+  std::string checkpoint_name = "save." + net_id;
+  if (phase.find("train") != std::string::npos) {
+    (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}});
+  } else {
+    (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
+  }
+  (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
+  (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
+  (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
+
+  DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
+
+  return true;
+}
+
+FuncGraphPtr BuildDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
+                          const std::string& phase, const py::object& broadcast_params) {
+  if (info.count(phase) == 0) {
+    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+  }
+  FuncGraphPtr anf_graph = info.at(phase)->func_graph;
+
+  if (MsContext::GetInstance()->save_graphs_flag()) {
+    draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph);  // for debug
+    DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true);
+  }
+
+  if (!AddDFGraph(info, init_params, phase, broadcast_params)) {
+    MS_LOG(ERROR) << "GenConvertor failed";
+    return nullptr;
+  }
+
+#if ENABLE_TRAIN
+  (void)setenv("GE_TRAIN", "1", 1);
+#else
+  (void)setenv("GE_TRAIN", "0", 1);
+#endif
+
+  if (CreateSessionAndGraphRunner(static_cast<bool>(ENABLE_TRAIN)) != Status::SUCCESS) {
+    MS_LOG(ERROR) << "Create GE Session or GraphRunner failed.";
+    return nullptr;
+  }
+
+  return anf_graph;
+}
+
+void RunGEInitGraph(const py::dict& init_params, const std::string& phase) {
+  MS_LOG(DEBUG) << "ExecInitGraph start.";
+  TensorOrderMap inputs_with_name{};
+  ConvertObjectToTensors(init_params, &inputs_with_name);
+  std::vector<tensor::TensorPtr> inputs;
+  (void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs),
+                       [](const std::pair<std::string, tensor::TensorPtr>& item) { return item.second; });
+
+  std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
+  if (ge_tensors.size() != inputs.size()) {
+    MS_LOG(ERROR) << "Args convert to ge tensor error.";
+    return;
+  }
+  MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << ".";
+
+  std::vector<GeTensorPtr> ge_outputs;
+  transform::RunOptions run_options;
+
+  run_options.name = phase;
+  if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) {
+    MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode.";
+    return;
+  }
+  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
+  if (graph_runner == nullptr) {
+    MS_LOG(EXCEPTION) << "Can not found GraphRunner.";
+  }
+  {
+    // Release GIL before calling into (potentially long-running) C++ code
+    py::gil_scoped_release release;
+    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
+    if (ret != Status::SUCCESS) {
+      MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed.";
+    }
+
+    MS_LOG(INFO) << "Exec " << phase << " graph success.";
+
+    if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) &&
+        (DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) {
+      run_options.name = BROADCAST_GRAPH_NAME;
+      ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
+      if (ret != Status::SUCCESS) {
+        MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed.";
+      }
+      MS_LOG(INFO) << "Exec broadcast graph success.";
+    }
+  }
+}
+
+py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) {
+  MS_EXCEPTION_IF_NULL(cnode_data);
+  if (*count >= data.size()) {
+    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
+                      << " less than the number of elements required. ";
+  }
+
+  if (cnode_data->isa<AbstractTensor>()) {
+    BaseShapePtr shape = cnode_data->BuildShape();
+    auto shape_act = shape->cast<abstract::ShapePtr>()->shape();
+    Tensor tensor_exp = py::cast<Tensor>(data[*count]);
+    if (shape_act != tensor_exp.shape()) {
+      MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as "
+                           "the shape of the tensor derived from ME.";
+    }
+    return data[(*count)++];
+  }
+
+  if (!cnode_data->isa<AbstractTuple>()) {
+    MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could "
+                      << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString()
+                      << ".";
+  }
+  auto data_tp = cnode_data->cast<AbstractTuplePtr>();
+  auto elements = data_tp->elements();
+  size_t size = data_tp->size();
+  py::tuple tp = py::tuple(size);
+  for (size_t i = 0; i < size; i++) {
+    tp[i] = ExtractGeneralCnodeRet(elements[i], data, count);
+  }
+  return std::move(tp);
+}
+
+py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) {
+  MS_EXCEPTION_IF_NULL(output_node);
+
+  if (output_node->isa<ValueNode>()) {
+    return ValuePtrToPyData(GetValueNode(output_node));
+  }
+
+  if (*count >= data.size()) {
+    MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size()
+                      << " less than the number of elements required. ";
+  }
+  if (output_node->isa<Parameter>()) {
+    return data[(*count)++];
+  }
+
+  auto output_c = output_node->cast<CNodePtr>();
+  if (output_c == nullptr) {
+    MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got "
+                      << output_node->ToString();
+  }
+
+  if (output_c->IsApply(prim::kPrimMakeTuple)) {
+    auto input_list = output_c->inputs();
+    size_t size = input_list.size();
+    py::tuple tp = py::tuple(size - 1);
+    for (size_t i = 1; i < size; i++) {
+      tp[i - 1] = StructureOutput(input_list[i], data, count);
+    }
+    return std::move(tp);
+  }
+  if (output_c->IsApply(prim::kPrimDepend)) {
+    return StructureOutput(output_c->input(1), data, count);
+  }
+
+  return ExtractGeneralCnodeRet(output_c->abstract(), data, count);
+}
+
+std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::vector<MeTensorPtr>& inputs,
+                                        const std::string& phase) {
+  std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
+  if (ge_tensors.size() != inputs.size()) {
+    MS_LOG(ERROR) << "args convert to ge tensor error";
+    return nullptr;
+  }
+
+  std::vector<GeTensorPtr> ge_outputs;
+  transform::RunOptions run_options;
+
+  run_options.name = phase;
+
+  auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
+
+  if (graph_runner == nullptr) {
+    MS_LOG(ERROR) << "Can not found GraphRunner";
+    return nullptr;
+  }
+
+  {
+    // Release GIL before calling into (potentially long-running) C++ code
+    py::gil_scoped_release release;
+    MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size();
+    Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs);
+    MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size();
+    if (ret != Status::SUCCESS) {
+      MS_LOG(ERROR) << "Exec graph failed";
+      return nullptr;
+    }
+  }
+
+  std::vector<MeTensorPtr> me_outputs = TransformUtil::ConvertGeTensors(ge_outputs);
+  if (me_outputs.size() != ge_outputs.size()) {
+    MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed";
+  }
+
+  py::tuple outputs(me_outputs.size());
+  for (std::size_t i = 0; i < outputs.size(); i++) {
+    outputs[i] = *me_outputs[i];
+  }
+
+  std::shared_ptr<py::object> ret = nullptr;
+
+#ifdef ENABLE_GE
+  AnfNodePtr root = graph->get_return();
+  MS_EXCEPTION_IF_NULL(root);
+  AbstractBasePtr output = root->abstract();
+  size_t count = 0;
+  py::object oj = StructureOutput(output, outputs, &count);
+  ret = std::make_shared<py::object>(oj);
+#else
+  if (outputs.size() == 1) {
+    ret = std::make_shared<py::object>(outputs[0]);
+  } else {
+    ret = std::make_shared<py::object>(outputs);
+  }
+#endif
+
+  return ret;
+}
+
+void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args, const std::string& phase,
+                  std::vector<tensor::TensorPtr>* inputs) {
+  // check the arg and use the ExecutorPy args
+  std::size_t size = args.size();
+
+  if (info.count(phase) == 0) {
+    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+  }
+
+  auto arg_size = info.at(phase)->arg_list_size;
+  if (size != arg_size) {
+    MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << arg_size;
+  }
+
+  // process the first args of tensor
+  // only in Dataset Feed Mode, fp_bp graph need input tensors
+  if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
+    for (std::size_t i = 0; i < size; i++) {
+      ValuePtr converted = nullptr;
+      bool succ = parse::ConvertData(args[i], &converted);
+      if (!succ) {
+        MS_LOG(EXCEPTION) << "args convert error";
+      }
+      if (converted->isa<tensor::Tensor>()) {
+        (*inputs).push_back(converted->cast<tensor::TensorPtr>());
+      } else {
+        MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor";
+      }
+    }
+  }
+}
+
+py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args,
+                       const std::string& phase) {
+  std::string phase_prefix = GetPhasePrefix(phase);
+
+  if (phase_prefix == "save") {
+    DoExecNonInputGraph(phase);
+    ConfigManager::GetInstance().ResetConfig();
+    return py::none();
+  }
+
+  if (info.count(phase) == 0) {
+    MS_LOG(EXCEPTION) << "has no phase:" << phase;
+  }
+
+  FuncGraphPtr anf_graph = info.at(phase)->func_graph;
+
+#if (!defined ENABLE_GE) || (defined ENABLE_INFER)
+  // Now don't use the graph because the exec ge function don't take effect
+  MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph);
+  if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) {
+    MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries";
+    ConfigManager::GetInstance().ResetConfig();
+    return py::none();
+  }
+#endif
+
+  std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
+  // We will not execute graph when output is constant or just input itself.
+  if (IsGraphOutputValueNodeOrParameter(info.at(phase)->func_graph->output(), args, ret_val)) {
+    ConfigManager::GetInstance().ResetConfig();
+    return *ret_val;
+  }
+
+  std::vector<tensor::TensorPtr> inputs;
+  ProcessGeArg(info, args, phase, &inputs);
+
+  std::shared_ptr<py::object> ret = DoExecGraph(anf_graph, inputs, phase);
+  ConfigManager::GetInstance().ResetConfig();
+  if (ret != nullptr) {
+    return *ret;
+  } else {
+    MS_LOG(EXCEPTION) << "exec graph failed";
+  }
+}
+void ExportDFGraph(const std::string& file_name, const std::string& phase) {
+  MS_LOG(DEBUG) << "ExportGraph Begin";
+  transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase);
+  if (wrap_ptr == nullptr) {
+    MS_LOG(ERROR) << "Get graph form DfGraphManager failed!";
+    return;
+  }
+
+  transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_;
+  if (nullptr == ge_graph) {
+    MS_LOG(ERROR) << "The export graph is null";
+    return;
+  }
+
+  (void)ge_graph->SaveToFile(file_name);
+
+  MS_LOG(DEBUG) << "ExportGraph End";
+}
+}  // namespace pipeline
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.h b/mindspore/ccsrc/pipeline/pipeline_ge.h
new file mode 100644
index 0000000000..c3779fd982
--- /dev/null
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.h
@@ -0,0 +1,57 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_
+#define MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_
+
+#include <vector>
+#include <utility>
+#include <string>
+#include <memory>
+#include <unordered_map>
+#include <map>
+#include <mutex>
+
+#include "pybind11/pybind11.h"
+#include "pipeline/base.h"
+#include "operator/ops.h"
+
+namespace mindspore {
+namespace pipeline {
+
+namespace py = pybind11;
+
+void SetGeOption(const std::map<std::string, std::string>& options);
+
+void RunGEInitGraph(const py::dict& init_params, const std::string& phase);
+
+py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::tuple& args,
+                       const std::string& phase = "train");
+
+FuncGraphPtr BuildDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
+                          const std::string& phase, const py::object& broadcast_params = {});
+
+// init and exec dataset sub graph for GE backend
+bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size,
+                       const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
+                       const std::vector<int64_t>& input_indexes, const std::string& phase);
+
+void ExportDFGraph(const std::string& file_name, const std::string& phase);
+
+}  // namespace pipeline
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_
diff --git a/mindspore/ccsrc/pipeline/resource.cc b/mindspore/ccsrc/pipeline/resource.cc
index 2998ff1dbb..59ee04ad15 100644
--- a/mindspore/ccsrc/pipeline/resource.cc
+++ b/mindspore/ccsrc/pipeline/resource.cc
@@ -25,19 +25,13 @@
 #include "pipeline/parse/data_converter.h"
 #include "operator/ops.h"
 #include "utils/graph_utils.h"
-#include "transform/convert.h"
 #include "optimizer/ad/dfunctor.h"
 #include "vm/segment_runner.h"
-#include "utils/context/ms_context.h"
-#include "transform/df_graph_manager.h"
-#include "device/kernel_runtime_manager.h"
 
 namespace mindspore {
 // namespace to support opmap definition
 namespace pipeline {
 
-using MethodMap = std::unordered_map<int, std::unordered_map<std::string, Any>>;
-
 MethodMap& GetMethodMap() {
   static MethodMap method_map = {{kObjectTypeString,
                                   {
@@ -255,28 +249,5 @@ void Resource::Clean() {
   trace::ClearTraceStack();
   is_cleaned_ = true;
 }
-
-void ReleaseGeTsd() {
-  auto context_ptr = MsContext::GetInstance();
-  if (context_ptr != nullptr) {
-    (void)context_ptr->FinalizeGe(true);
-    (void)context_ptr->CloseTsd(true);
-  }
-}
-
-void ClearResAtexit() {
-  MS_LOG(DEBUG) << "pipeline clear all resource";
-  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
-  transform::DfGraphManager::GetInstance().ClearGraph();
-  ad::g_k_prims.clear();
-
-  abstract::ClearPrimEvaluatorMap();
-  compile::ClearConvertCache();
-  transform::DfGraphConvertor::get_adpt_map().clear();
-  pipeline::GetMethodMap().clear();
-  pipeline::ExecutorPy::ClearRes();
-
-  ReleaseGeTsd();
-}
 }  // namespace pipeline
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pipeline/resource.h b/mindspore/ccsrc/pipeline/resource.h
index 43159ddbdd..15ab70db14 100644
--- a/mindspore/ccsrc/pipeline/resource.h
+++ b/mindspore/ccsrc/pipeline/resource.h
@@ -44,6 +44,10 @@ const char kOutput[] = "output";
 
 class InferenceResource;
 
+using MethodMap = std::unordered_map<int, std::unordered_map<std::string, Any>>;
+
+MethodMap& GetMethodMap();
+
 class ResourceBase {
  public:
   ResourceBase() { manager_ = MakeManager(); }
@@ -110,9 +114,6 @@ class Resource : public ResourceBase {
 
 using ResourcePtr = std::shared_ptr<pipeline::Resource>;
 
-void ClearResAtexit();
-void ReleaseGeTsd();
-
 }  // namespace pipeline
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index b9b324e5dd..8212d64c27 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -21,7 +21,7 @@
 #include "pre_activate/ascend/ir_fission/bn_grad_split.h"
 #include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h"
 #include "pre_activate/ascend/ir_fission/layer_norm_grad_split.h"
-#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
 #include "pre_activate/ascend/ir_fusion/square_sum_fusion.h"
 #include "pre_activate/ascend/ir_fusion/clip_by_norm_no_div_square_sum_fusion.h"
 #include "pre_activate/ascend/ir_fusion/lamb_update_with_lr_rule_fusion.h"
diff --git a/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc
index 49c5e89641..58b8a93516 100644
--- a/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc
@@ -237,11 +237,11 @@ CNodePtr CreateFusionOp(const std::vector<AnfNodePtr> &inputs_list, const std::v
 
   std::vector<std::string> input_names;
   for (uint8_t i = 0; i < inputs_list.size(); i++) {
-    input_names.emplace_back("input" + to_string(i));
+    input_names.emplace_back("input" + std::to_string(i));
   }
   std::vector<std::string> output_names;
   for (uint8_t i = 0; i < outputs_list.size(); i++) {
-    output_names.emplace_back("output" + to_string(i));
+    output_names.emplace_back("output" + std::to_string(i));
   }
 
   ValuePtr input_names_v = MakeValue(input_names);
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
similarity index 97%
rename from mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc
rename to mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
index 7b862b21b9..55efcf9058 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc
+++ b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
 
 #include <vector>
 #include <string>
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
similarity index 87%
rename from mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h
rename to mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
index c26dbc20d9..b49b8373c6 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h
+++ b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
-#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
 #include <vector>
 
 #include "pre_activate/common/pass.h"
@@ -46,4 +46,4 @@ class AllReduceFusion : public Pass {
 };
 }  // namespace opt
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
diff --git a/mindspore/ccsrc/predict/converter/kernel2ms.cc b/mindspore/ccsrc/predict/converter/kernel2ms.cc
index 30b1960e41..32cdee1350 100644
--- a/mindspore/ccsrc/predict/converter/kernel2ms.cc
+++ b/mindspore/ccsrc/predict/converter/kernel2ms.cc
@@ -16,7 +16,7 @@
 
 #include "predict/converter/kernel2ms.h"
 #include <algorithm>
-#include "transform/convert.h"
+#include "ir/anf.h"
 #include "predict/converter/lite_model/op_attr_packer.h"
 #include "mindspore/ccsrc/operator/ops.h"
 
@@ -135,7 +135,7 @@ void Kernel2Ms::GetRealInpoutsPtr(const AnfNodePtr &node, std::vector<AnfNodePtr
   if (node->isa<CNode>()) {
     auto c_node = node->cast<CNodePtr>();
     MS_EXCEPTION_IF_NULL(c_node);
-    std::string c_node_name = transform::GetCNodeFuncName(c_node);
+    std::string c_node_name = GetCNodeFuncName(c_node);
     if (c_node_name == prim::kPrimTupleGetItem->name()) {
       auto v_node = c_node->inputs()[kTupleGetItemIndex]->cast<ValueNodePtr>();
       MS_EXCEPTION_IF_NULL(v_node);
@@ -321,7 +321,7 @@ bool Kernel2Ms::SetGraphInputTensors(const KernelGraphPtr &kernel_graph_ptr, con
   }
   for (const auto &input_node : kernel_graph_ptr->inputs()) {
     if (input_node->isa<Parameter>()) {
-      ParameterPtr pk_node = dynamic_pointer_cast<Parameter>(input_node);
+      ParameterPtr pk_node = std::dynamic_pointer_cast<Parameter>(input_node);
       TensorPtr device_tensor;
       if (convert_mode_ == kConvertCpuMode) {
         device_tensor = predict::utils::GetParaCpuTensor(input_node);
diff --git a/mindspore/ccsrc/pynative/base.h b/mindspore/ccsrc/pynative/base.h
new file mode 100644
index 0000000000..7405f621cb
--- /dev/null
+++ b/mindspore/ccsrc/pynative/base.h
@@ -0,0 +1,67 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PYNATIVE_BASE_H_
+#define MINDSPORE_CCSRC_PYNATIVE_BASE_H_
+
+#include <vector>
+#include <utility>
+#include <string>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+
+#include "pybind11/pybind11.h"
+#include "ir/primitive.h"
+#include "pipeline/static_analysis/abstract_value.h"
+
+namespace mindspore {
+namespace pynative {
+
+namespace py = pybind11;
+
+enum PynativeStatusCode {
+  PYNATIVE_SUCCESS = 0,
+  PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1,
+  PYNATIVE_OP_INPUTS_ERR = 2,
+  PYNATIVE_OP_PARAMS_ERR = 3,
+  PYNATIVE_OP_ATTRS_ERR = 4,
+  PYNATIVE_GRAPH_MANAGER_ERR = 5,
+  PYNATIVE_GRAPH_GE_BUILD_ERR = 6,
+  PYNATIVE_GRAPH_GE_RUN_ERR = 7,
+  PYNATIVE_UNKNOWN_STATE = 0XFF
+};
+
+enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM };
+
+struct OpExecInfo {
+  PrimitivePyPtr py_primitive;
+  std::string op_name;
+  AbstractBasePtr abstract;
+
+  py::tuple op_inputs;
+  py::tuple inputs_mask;
+  py::dict op_attrs;
+};
+using OpExecInfoPtr = std::shared_ptr<OpExecInfo>;
+OpExecInfoPtr GenerateOpExecInfo(const py::args& args);
+
+const std::unordered_set<std::string> ignore_infer_prim = {"partial"};
+
+}  // namespace pynative
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PYNATIVE_BASE_H_
diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index 27cfd89106..927e768bbe 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -29,16 +29,18 @@
 #include "pipeline/static_analysis/prim.h"
 #include "session/session_factory.h"
 
+#include "pynative/base.h"
+
+#ifdef ENABLE_GE
+#include "pynative/pynative_execute_ge.h"
+#endif
+
 const char SINGLE_OP_GRAPH[] = "single_op_graph";
 // primitive unable to infer value for constant input in pynative mode
-const std::unordered_set<std::string> ignore_infer_prim = {"partial"};
 const std::unordered_set<std::string> vm_operators = {"partial", "depend"};
 
 namespace mindspore {
 namespace pynative {
-using transform::GraphRunner;
-using transform::GraphRunnerOptions;
-using transform::OperatorPtr;
 inline ValuePtr PyAttrValue(const py::object& obj) {
   ValuePtr converted_ret = nullptr;
   bool converted = parse::ConvertData(obj, &converted_ret);
@@ -48,32 +50,12 @@ inline ValuePtr PyAttrValue(const py::object& obj) {
   return converted_ret;
 }
 
-MeTensorPtr ConvertPyObjToTensor(const py::object& obj) {
-  MeTensorPtr me_tensor_ptr = nullptr;
-  if (py::isinstance<MeTensor>(obj)) {
-    me_tensor_ptr = py::cast<MeTensorPtr>(obj);
-  } else if (py::isinstance<py::tuple>(obj)) {
-    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::tuple>(obj), nullptr);
-  } else if (py::isinstance<py::float_>(obj)) {
-    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::float_>(obj), nullptr);
-  } else if (py::isinstance<py::int_>(obj)) {
-    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::int_>(obj), nullptr);
-  } else if (py::isinstance<py::list>(obj)) {
-    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::list>(obj), nullptr);
-  } else if (py::isinstance<py::array>(obj)) {
-    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::array>(obj), nullptr);
-  } else {
-    MS_LOG(EXCEPTION) << "run op inputs type is invalid!";
-  }
-  return me_tensor_ptr;
-}
-
 void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecInfo* const op_exec_info) {
   size_t size = py_args.size();
   AbstractBasePtrList args_spec_list;
   for (size_t i = 0; i < size; i++) {
     ValuePtr input_value = PyAttrValue(py_args[i]);
-    if (py::isinstance<MeTensor>(py_args[i])) {
+    if (py::isinstance<tensor::Tensor>(py_args[i])) {
       args_spec_list.emplace_back(abstract::FromValueInside(input_value, true));
     } else {
       args_spec_list.emplace_back(abstract::FromValueInside(input_value, false));
@@ -140,241 +122,6 @@ std::string GetSingleOpGraphInfo(const OpExecInfoPtr& op_exec_info) {
   return graph_info;
 }
 
-bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
-                               const OperatorPtr& op, std::vector<GeOperator>* graph_input_nodes) {
-  MS_EXCEPTION_IF_NULL(op_exec_info);
-  MS_EXCEPTION_IF_NULL(graph_input_nodes);
-  auto op_inputs = op_exec_info->op_inputs;
-  std::string op_name = op_exec_info->op_name;
-  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
-  if (adapter == nullptr) {
-    return false;
-  }
-
-  int op_input_idx = 1;
-  size_t size = inputs.size();
-  for (size_t i = 0; i < size; i++) {
-    if (inputs[i] == nullptr) {
-      continue;
-    }
-    auto const_op = std::make_shared<transform::Constant>();
-    MS_EXCEPTION_IF_NULL(const_op);
-    (void)const_op->set_attr_value(*inputs[i]);
-    MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
-    MS_EXCEPTION_IF_NULL(me_tensor_ptr);
-    auto const_op_desc =
-      transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW);
-    if (const_op_desc == nullptr) {
-      MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!";
-      return false;
-    }
-    auto pointer_cast_const_op = std::static_pointer_cast<transform::Constant>(const_op);
-    MS_EXCEPTION_IF_NULL(pointer_cast_const_op);
-    (void)pointer_cast_const_op->update_output_desc_y(*const_op_desc);
-    auto& input_map = adapter->getInputMap();
-    if (input_map.find(op_input_idx) == input_map.end()) {
-      continue;
-    }
-    if (adapter->setInput(op, op_input_idx++, const_op)) {
-      MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx;
-      return false;
-    }
-    graph_input_nodes->push_back(*const_op);
-  }
-  return true;
-}
-
-bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
-                        const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph) {
-  MS_EXCEPTION_IF_NULL(op_exec_info);
-  std::string op_name = op_exec_info->op_name;
-  auto op_inputs = op_exec_info->op_inputs;
-  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
-  if (adapter == nullptr) {
-    MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name));
-    return false;
-  }
-  OperatorPtr op = adapter->generate(op_name);
-  MS_EXCEPTION_IF_NULL(op);
-
-  std::vector<GeOperator> graph_input_nodes;
-  // hold param nodes after setting input and output for the graph
-  // set input
-  if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) {
-    return false;
-  }
-  // set attributes
-  for (auto attr : attrs) {
-    (void)adapter->setAttr(op, attr.first, attr.second);
-  }
-  // set default attributes
-  auto extra_attrs = adapter->GetExtraAttr();
-  for (auto attr : extra_attrs) {
-    (void)adapter->setAttr(op, attr.first, attr.second);
-  }
-  // set input attributes
-  auto& input_attr_map = adapter->getInputAttrMap();
-  for (auto& it : input_attr_map) {
-    if (op_inputs.size() < it.first) {
-      continue;
-    }
-    auto const_value = PyAttrValue(op_inputs[it.first - 1]);
-    if (const_value->isa<None>()) {
-      continue;
-    }
-    it.second.set_attr(op, const_value);
-  }
-  // construct output data nodes
-  std::vector<GeOperator> graph_outputs{*op};
-  // set input and output nodes for the graph
-  MS_EXCEPTION_IF_NULL(graph);
-  (void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs);
-  MS_LOG(INFO) << "BuildSingleOpGraph done";
-  return true;
-}
-
-void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector<GeTensorPtr>* const inputs) {
-  MS_EXCEPTION_IF_NULL(inputs);
-  MS_EXCEPTION_IF_NULL(op_exec_info);
-  auto op_inputs = op_exec_info->op_inputs;
-  size_t size = op_inputs.size();
-  for (size_t i = 0; i < size; i++) {
-    if (py::isinstance<py::none>(op_inputs[i])) {
-      inputs->emplace_back(nullptr);
-      continue;
-    }
-    MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
-    auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW);
-    if (ge_tensor_ptr == nullptr) {
-      MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
-    }
-    // set inputs for operator to build single node graph
-    inputs->push_back(ge_tensor_ptr);
-  }
-}
-
-PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs) {
-  MS_EXCEPTION_IF_NULL(op_exec_info);
-  auto op_attrs = op_exec_info->op_attrs;
-  std::unordered_map<std::string, ValuePtr> attrs{};
-
-  for (auto& item : op_attrs) {
-    if (!py::isinstance<py::str>(item.first)) {
-      MS_LOG(ERROR) << "type error in py dict convert";
-      return PYNATIVE_OP_ATTRS_ERR;
-    }
-    std::string name = py::cast<std::string>(item.first);
-    auto attr_value = PyAttrValue(py::cast<py::object>(item.second));
-    (void)attrs.emplace(name, attr_value);
-  }
-
-  // build graph
-  GeGraphPtr graph = std::make_shared<GeGraph>(op_exec_info->op_name);
-  if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) {
-    MS_LOG(ERROR) << "Fail to BuildSingleOpGraph";
-    return PYNATIVE_GRAPH_GE_BUILD_ERR;
-  }
-
-  // add the single op graph into the graph manager, which will be iterated by session.
-  transform::Status ret =
-    transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr<transform::DfGraph>(graph));
-  if (ret != transform::SUCCESS) {
-    MS_LOG(ERROR) << "Fail to AddGraph into graph manager";
-    return PYNATIVE_GRAPH_MANAGER_ERR;
-  }
-
-  return PYNATIVE_SUCCESS;
-}
-
-std::vector<MeTensorPtr> ConvertOutputTensors(const OpExecInfoPtr& op_exec_info,
-                                              const std::vector<GeTensorPtr>& ge_tensors) {
-  std::vector<MeTensorPtr> outputs;
-  AbstractBasePtr abs_base = op_exec_info->abstract;
-  std::vector<std::vector<int>> shapes;
-  if (abs_base != nullptr && abs_base->isa<abstract::AbstractTensor>()) {
-    auto arg_tensor = dyn_cast<abstract::AbstractTensor>(abs_base);
-    shapes.emplace_back(arg_tensor->shape()->shape());
-    outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
-    return outputs;
-  }
-  if (abs_base != nullptr && abs_base->isa<abstract::AbstractTuple>()) {
-    auto arg_tuple = dyn_cast<abstract::AbstractTuple>(abs_base);
-    size_t len = arg_tuple->size();
-
-    for (size_t i = 0; i < len; i++) {
-      if (arg_tuple->elements()[i]->isa<abstract::AbstractTensor>()) {
-        auto arg_tensor = dyn_cast<abstract::AbstractTensor>(arg_tuple->elements()[i]);
-        shapes.emplace_back(arg_tensor->shape()->shape());
-      }
-    }
-    outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
-    return outputs;
-  }
-  for (auto& it : ge_tensors) {
-    auto tensor = transform::TransformUtil::ConvertGeTensor(it);
-    if (tensor != nullptr) {
-      outputs.emplace_back(tensor);
-    }
-  }
-  return outputs;
-}
-
-py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
-  MS_LOG(INFO) << "RunOpInGe start";
-  MS_EXCEPTION_IF_NULL(op_exec_info);
-  MS_EXCEPTION_IF_NULL(status);
-
-  // returns a null py::tuple on error
-  py::tuple err_ret(0);
-  auto op_name = op_exec_info->op_name;
-  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
-  if (adapter == nullptr) {
-    MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name));
-    *status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR;
-    return std::move(err_ret);
-  }
-
-  std::vector<GeTensorPtr> inputs{};
-  ToTensorPtr(op_exec_info, &inputs);
-  // convert me attr to ge AttrValue
-  PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs);
-  if (ret != PYNATIVE_SUCCESS) {
-    *status = ret;
-    return std::move(err_ret);
-  }
-  // run graph
-  transform::RunOptions run_options;
-  run_options.name = SINGLE_OP_GRAPH;
-  std::vector<GeTensorPtr> ge_inputs;
-  std::vector<GeTensorPtr> ge_outputs;
-  transform::GraphRunnerOptions graph_runner_options;
-  graph_runner_options.options["ge.trainFlag"] = "1";
-  auto graph_runner = std::make_shared<transform::GraphRunner>(graph_runner_options);
-  transform::Status run_ret;
-  {
-    // Release GIL before calling into (potentially long-running) C++ code
-    py::gil_scoped_release release;
-    run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs);
-  }
-  if (run_ret != transform::Status::SUCCESS) {
-    MS_LOG(ERROR) << "GraphRunner Fails to Run Graph";
-    *status = PYNATIVE_GRAPH_GE_RUN_ERR;
-    return std::move(err_ret);
-  }
-
-  std::vector<MeTensorPtr> graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs);
-  size_t output_size = graph_outputs.size();
-  py::tuple result(output_size);
-  for (size_t i = 0; i < output_size; i++) {
-    MS_EXCEPTION_IF_NULL(graph_outputs[i]);
-    result[i] = *graph_outputs[i];
-  }
-
-  *status = PYNATIVE_SUCCESS;
-  MS_LOG(INFO) << "RunOpInGe end";
-  return std::move(result);
-}
-
 py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
   MS_LOG(INFO) << "RunOpInVM start";
 
@@ -423,12 +170,6 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
   MS_EXCEPTION_IF_NULL(status);
   py::object result;
   switch (backend_policy) {
-    case kMsBackendGeOnly: {
-      // use GE only
-      MS_LOG(INFO) << "RunOp use GE only backend";
-      result = RunOpInGE(op_exec_info, status);
-      break;
-    }
     case kMsBackendVmOnly: {
       // use vm only
       MS_LOG(INFO) << "RunOp use VM only backend";
@@ -436,22 +177,14 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
       break;
     }
     case kMsBackendGePrior: {
+#ifdef ENABLE_GE
       // use GE first, use vm when GE fails
       MS_LOG(INFO) << "RunOp use GE first backend";
       result = RunOpInGE(op_exec_info, status);
       if (*status != PYNATIVE_SUCCESS) {
         result = RunOpInVM(op_exec_info, status);
       }
-      break;
-    }
-    case kMsBackendVmPrior: {
-      // GE_VM_SILENT
-      // (should not use this policy) use vm first, use GE when vm fails
-      MS_LOG(INFO) << "RunOp use VM first backend";
-      result = RunOpInVM(op_exec_info, status);
-      if (*status != PYNATIVE_SUCCESS) {
-        result = RunOpInGE(op_exec_info, status);
-      }
+#endif
       break;
     }
     case kMsBackendMsPrior: {
diff --git a/mindspore/ccsrc/pynative/pynative_execute.h b/mindspore/ccsrc/pynative/pynative_execute.h
index e3d7649106..17b5610bfd 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.h
+++ b/mindspore/ccsrc/pynative/pynative_execute.h
@@ -25,55 +25,14 @@
 
 #include "pybind11/pybind11.h"
 
-#include "transform/convert.h"
-#include "transform/graph_runner.h"
-#include "transform/types.h"
+#include "pynative/base.h"
 #include "utils/context/ms_context.h"
 
 namespace mindspore {
 namespace pynative {
 
-using MeTensor = mindspore::tensor::Tensor;
-using MeTensorPtr = mindspore::tensor::TensorPtr;
-using GeTensor = ge::Tensor;
-using GeTensorPtr = std::shared_ptr<GeTensor>;
-using GeGraph = ge::Graph;
-using GeGraphPtr = std::shared_ptr<GeGraph>;
-using GeOperator = ge::Operator;
-using GeOperatorPtr = std::shared_ptr<GeOperator>;
-
 namespace py = pybind11;
 
-enum PynativeStatusCode {
-  PYNATIVE_SUCCESS = 0,
-  PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1,
-  PYNATIVE_OP_INPUTS_ERR = 2,
-  PYNATIVE_OP_PARAMS_ERR = 3,
-  PYNATIVE_OP_ATTRS_ERR = 4,
-  PYNATIVE_GRAPH_MANAGER_ERR = 5,
-  PYNATIVE_GRAPH_GE_BUILD_ERR = 6,
-  PYNATIVE_GRAPH_GE_RUN_ERR = 7,
-  PYNATIVE_UNKNOWN_STATE = 0XFF
-};
-
-enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM };
-
-struct OpExecInfo {
-  PrimitivePyPtr py_primitive;
-  std::string op_name;
-  AbstractBasePtr abstract;
-
-  py::tuple op_inputs;
-  py::tuple inputs_mask;
-  py::dict op_attrs;
-};
-using OpExecInfoPtr = std::shared_ptr<OpExecInfo>;
-OpExecInfoPtr GenerateOpExecInfo(const py::args& args);
-bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
-                        const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph);
-
-py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
-
 py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
 
 py::tuple RunOp(const py::args& args);
diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.cc b/mindspore/ccsrc/pynative/pynative_execute_ge.cc
new file mode 100644
index 0000000000..4ed6088494
--- /dev/null
+++ b/mindspore/ccsrc/pynative/pynative_execute_ge.cc
@@ -0,0 +1,311 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "pynative/pynative_execute_ge.h"
+
+#include <typeinfo>
+#include <map>
+#include <set>
+#include <unordered_set>
+
+#include "utils/any.h"
+#include "utils/utils.h"
+#include "utils/context/ms_context.h"
+#include "operator/ops.h"
+#include "pipeline/parse/data_converter.h"
+#include "pipeline/static_analysis/prim.h"
+#include "session/session_factory.h"
+
+const char SINGLE_OP_GRAPH[] = "single_op_graph";
+
+namespace mindspore {
+namespace pynative {
+
+using MeTensor = mindspore::tensor::Tensor;
+using MeTensorPtr = mindspore::tensor::TensorPtr;
+using GeOperator = ge::Operator;
+using GeOperatorPtr = std::shared_ptr<GeOperator>;
+
+using transform::GraphRunner;
+using transform::GraphRunnerOptions;
+using transform::OperatorPtr;
+static std::shared_ptr<session::SessionBasic> session = nullptr;
+inline ValuePtr PyAttrValue(const py::object& obj) {
+  ValuePtr converted_ret = nullptr;
+  bool converted = parse::ConvertData(obj, &converted_ret);
+  if (!converted) {
+    MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj));
+  }
+  return converted_ret;
+}
+
+MeTensorPtr ConvertPyObjToTensor(const py::object& obj) {
+  MeTensorPtr me_tensor_ptr = nullptr;
+  if (py::isinstance<MeTensor>(obj)) {
+    me_tensor_ptr = py::cast<MeTensorPtr>(obj);
+  } else if (py::isinstance<py::tuple>(obj)) {
+    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::tuple>(obj), nullptr);
+  } else if (py::isinstance<py::float_>(obj)) {
+    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::float_>(obj), nullptr);
+  } else if (py::isinstance<py::int_>(obj)) {
+    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::int_>(obj), nullptr);
+  } else if (py::isinstance<py::list>(obj)) {
+    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::list>(obj), nullptr);
+  } else if (py::isinstance<py::array>(obj)) {
+    me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::array>(obj), nullptr);
+  } else {
+    MS_LOG(EXCEPTION) << "run op inputs type is invalid!";
+  }
+  return me_tensor_ptr;
+}
+
+bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
+                               const OperatorPtr& op, std::vector<GeOperator>* graph_input_nodes) {
+  MS_EXCEPTION_IF_NULL(op_exec_info);
+  MS_EXCEPTION_IF_NULL(graph_input_nodes);
+  auto op_inputs = op_exec_info->op_inputs;
+  std::string op_name = op_exec_info->op_name;
+  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
+  if (adapter == nullptr) {
+    return false;
+  }
+
+  int op_input_idx = 1;
+  size_t size = inputs.size();
+  for (size_t i = 0; i < size; i++) {
+    if (inputs[i] == nullptr) {
+      continue;
+    }
+    auto const_op = std::make_shared<transform::Constant>();
+    MS_EXCEPTION_IF_NULL(const_op);
+    (void)const_op->set_attr_value(*inputs[i]);
+    MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
+    MS_EXCEPTION_IF_NULL(me_tensor_ptr);
+    auto const_op_desc =
+      transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW);
+    if (const_op_desc == nullptr) {
+      MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!";
+      return false;
+    }
+    auto pointer_cast_const_op = std::static_pointer_cast<transform::Constant>(const_op);
+    MS_EXCEPTION_IF_NULL(pointer_cast_const_op);
+    (void)pointer_cast_const_op->update_output_desc_y(*const_op_desc);
+    auto& input_map = adapter->getInputMap();
+    if (input_map.find(op_input_idx) == input_map.end()) {
+      continue;
+    }
+    if (adapter->setInput(op, op_input_idx++, const_op)) {
+      MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx;
+      return false;
+    }
+    graph_input_nodes->push_back(*const_op);
+  }
+  return true;
+}
+
+bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
+                        const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph) {
+  MS_EXCEPTION_IF_NULL(op_exec_info);
+  std::string op_name = op_exec_info->op_name;
+  auto op_inputs = op_exec_info->op_inputs;
+  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
+  if (adapter == nullptr) {
+    MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name));
+    return false;
+  }
+  OperatorPtr op = adapter->generate(op_name);
+  MS_EXCEPTION_IF_NULL(op);
+
+  std::vector<GeOperator> graph_input_nodes;
+  // hold param nodes after setting input and output for the graph
+  // set input
+  if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) {
+    return false;
+  }
+  // set attributes
+  for (auto attr : attrs) {
+    (void)adapter->setAttr(op, attr.first, attr.second);
+  }
+  // set default attributes
+  auto extra_attrs = adapter->GetExtraAttr();
+  for (auto attr : extra_attrs) {
+    (void)adapter->setAttr(op, attr.first, attr.second);
+  }
+  // set input attributes
+  auto& input_attr_map = adapter->getInputAttrMap();
+  for (auto& it : input_attr_map) {
+    if (op_inputs.size() < it.first) {
+      continue;
+    }
+    auto const_value = PyAttrValue(op_inputs[it.first - 1]);
+    if (const_value->isa<None>()) {
+      continue;
+    }
+    it.second.set_attr(op, const_value);
+  }
+  // construct output data nodes
+  std::vector<GeOperator> graph_outputs{*op};
+  // set input and output nodes for the graph
+  MS_EXCEPTION_IF_NULL(graph);
+  (void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs);
+  MS_LOG(INFO) << "BuildSingleOpGraph done";
+  return true;
+}
+
+void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector<GeTensorPtr>* const inputs) {
+  MS_EXCEPTION_IF_NULL(inputs);
+  MS_EXCEPTION_IF_NULL(op_exec_info);
+  auto op_inputs = op_exec_info->op_inputs;
+  size_t size = op_inputs.size();
+  for (size_t i = 0; i < size; i++) {
+    if (py::isinstance<py::none>(op_inputs[i])) {
+      inputs->emplace_back(nullptr);
+      continue;
+    }
+    MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
+    auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW);
+    if (ge_tensor_ptr == nullptr) {
+      MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
+    }
+    // set inputs for operator to build single node graph
+    inputs->push_back(ge_tensor_ptr);
+  }
+}
+
+PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs) {
+  MS_EXCEPTION_IF_NULL(op_exec_info);
+  auto op_attrs = op_exec_info->op_attrs;
+  std::unordered_map<std::string, ValuePtr> attrs{};
+
+  for (auto& item : op_attrs) {
+    if (!py::isinstance<py::str>(item.first)) {
+      MS_LOG(ERROR) << "type error in py dict convert";
+      return PYNATIVE_OP_ATTRS_ERR;
+    }
+    std::string name = py::cast<std::string>(item.first);
+    auto attr_value = PyAttrValue(py::cast<py::object>(item.second));
+    (void)attrs.emplace(name, attr_value);
+  }
+
+  // build graph
+  GeGraphPtr graph = std::make_shared<GeGraph>(op_exec_info->op_name);
+  if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) {
+    MS_LOG(ERROR) << "Fail to BuildSingleOpGraph";
+    return PYNATIVE_GRAPH_GE_BUILD_ERR;
+  }
+
+  // add the single op graph into the graph manager, which will be iterated by session.
+  transform::Status ret =
+    transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr<transform::DfGraph>(graph));
+  if (ret != transform::SUCCESS) {
+    MS_LOG(ERROR) << "Fail to AddGraph into graph manager";
+    return PYNATIVE_GRAPH_MANAGER_ERR;
+  }
+
+  return PYNATIVE_SUCCESS;
+}
+
+std::vector<MeTensorPtr> ConvertOutputTensors(const OpExecInfoPtr& op_exec_info,
+                                              const std::vector<GeTensorPtr>& ge_tensors) {
+  std::vector<MeTensorPtr> outputs;
+  AbstractBasePtr abs_base = op_exec_info->abstract;
+  std::vector<std::vector<int>> shapes;
+  if (abs_base != nullptr && abs_base->isa<abstract::AbstractTensor>()) {
+    auto arg_tensor = dyn_cast<abstract::AbstractTensor>(abs_base);
+    shapes.emplace_back(arg_tensor->shape()->shape());
+    outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
+    return outputs;
+  }
+  if (abs_base != nullptr && abs_base->isa<abstract::AbstractTuple>()) {
+    auto arg_tuple = dyn_cast<abstract::AbstractTuple>(abs_base);
+    size_t len = arg_tuple->size();
+
+    for (size_t i = 0; i < len; i++) {
+      if (arg_tuple->elements()[i]->isa<abstract::AbstractTensor>()) {
+        auto arg_tensor = dyn_cast<abstract::AbstractTensor>(arg_tuple->elements()[i]);
+        shapes.emplace_back(arg_tensor->shape()->shape());
+      }
+    }
+    outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes);
+    return outputs;
+  }
+  for (auto& it : ge_tensors) {
+    auto tensor = transform::TransformUtil::ConvertGeTensor(it);
+    if (tensor != nullptr) {
+      outputs.emplace_back(tensor);
+    }
+  }
+  return outputs;
+}
+
+py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
+  MS_LOG(INFO) << "RunOpInGe start";
+  MS_EXCEPTION_IF_NULL(op_exec_info);
+  MS_EXCEPTION_IF_NULL(status);
+
+  // returns a null py::tuple on error
+  py::tuple err_ret(0);
+  auto op_name = op_exec_info->op_name;
+  transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true);
+  if (adapter == nullptr) {
+    MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name));
+    *status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR;
+    return std::move(err_ret);
+  }
+
+  std::vector<GeTensorPtr> inputs{};
+  ToTensorPtr(op_exec_info, &inputs);
+  // convert me attr to ge AttrValue
+  PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs);
+  if (ret != PYNATIVE_SUCCESS) {
+    *status = ret;
+    return std::move(err_ret);
+  }
+  // run graph
+  transform::RunOptions run_options;
+  run_options.name = SINGLE_OP_GRAPH;
+  std::vector<GeTensorPtr> ge_inputs;
+  std::vector<GeTensorPtr> ge_outputs;
+  transform::GraphRunnerOptions graph_runner_options;
+  graph_runner_options.options["ge.trainFlag"] = "1";
+  auto graph_runner = std::make_shared<transform::GraphRunner>(graph_runner_options);
+  transform::Status run_ret;
+  {
+    // Release GIL before calling into (potentially long-running) C++ code
+    py::gil_scoped_release release;
+    run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs);
+  }
+  if (run_ret != transform::Status::SUCCESS) {
+    MS_LOG(ERROR) << "GraphRunner Fails to Run Graph";
+    *status = PYNATIVE_GRAPH_GE_RUN_ERR;
+    return std::move(err_ret);
+  }
+
+  std::vector<MeTensorPtr> graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs);
+  size_t output_size = graph_outputs.size();
+  py::tuple result(output_size);
+  for (size_t i = 0; i < output_size; i++) {
+    MS_EXCEPTION_IF_NULL(graph_outputs[i]);
+    result[i] = *graph_outputs[i];
+  }
+
+  *status = PYNATIVE_SUCCESS;
+  MS_LOG(INFO) << "RunOpInGe end";
+  return std::move(result);
+}
+}  // namespace pynative
+
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.h b/mindspore/ccsrc/pynative/pynative_execute_ge.h
new file mode 100644
index 0000000000..af0efec3e3
--- /dev/null
+++ b/mindspore/ccsrc/pynative/pynative_execute_ge.h
@@ -0,0 +1,46 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_
+#define MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_
+
+#include <vector>
+#include <utility>
+#include <string>
+#include <memory>
+#include <unordered_map>
+
+#include "pynative/base.h"
+#include "transform/convert.h"
+#include "transform/graph_runner.h"
+#include "transform/types.h"
+#include "utils/context/ms_context.h"
+
+using GeTensor = ge::Tensor;
+using GeTensorPtr = std::shared_ptr<GeTensor>;
+using GeGraph = ge::Graph;
+using GeGraphPtr = std::shared_ptr<GeGraph>;
+
+namespace mindspore {
+namespace pynative {
+bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector<GeTensorPtr>& inputs,
+                        const std::unordered_map<std::string, ValuePtr>& attrs, const GeGraphPtr& graph);
+
+py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status);
+}  // namespace pynative
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 1a29450313..64647cd036 100644
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -35,6 +35,7 @@
 #include "pre_activate/common/helper.h"
 #include "device/kernel_runtime_manager.h"
 #include "kernel/tbe/tbe_python_funcs.h"
+#include "utils/config_manager.h"
 
 namespace mindspore {
 namespace session {
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index 196a2f300f..293ca4f2ba 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -19,7 +19,7 @@
 #include "device/gpu/gpu_kernel_runtime.h"
 #include "pre_activate/common/optimizer.h"
 #include "pre_activate/common/pass_manager.h"
-#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
 #include "device/kernel_runtime_manager.h"
 #include "predict/predict.h"
 #include "common/utils.h"
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 2b50d2328a..bbfe60859a 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -373,24 +373,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
 }
 
 // ---------------implement of DfGraphConvertor-------------
-std::string GetCNodeFuncName(const CNodePtr cnode) {
-  if (cnode->inputs().empty()) {
-    return "";
-  }
-
-  AnfNodePtr valuenode = cnode->input(0);
-  if (valuenode->isa<ValueNode>()) {
-    auto value = GetValueNode(valuenode);
-    // check whether the valuenode is primitive
-    if (value->isa<Primitive>()) {
-      return value->cast<PrimitivePtr>()->name();
-    } else {
-      return value->ToString();
-    }
-  }
-  return "";
-}
-
 PrimType GetCNodeFuncType(const CNodePtr cnode) {
   if (cnode->inputs().empty()) {
     return kPrimTypeUnknown;
diff --git a/mindspore/ccsrc/transform/convert.h b/mindspore/ccsrc/transform/convert.h
index e38b0b2b3a..556db5acee 100644
--- a/mindspore/ccsrc/transform/convert.h
+++ b/mindspore/ccsrc/transform/convert.h
@@ -253,7 +253,6 @@ class DfGraphConvertor {
   bool distribute_ = false;
 };
 
-extern std::string GetCNodeFuncName(CNodePtr cnode);
 }  // namespace transform
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/utils/callbacks.cc b/mindspore/ccsrc/utils/callbacks.cc
index ab3f7d883b..cdee0be82d 100644
--- a/mindspore/ccsrc/utils/callbacks.cc
+++ b/mindspore/ccsrc/utils/callbacks.cc
@@ -20,16 +20,16 @@
 #include <memory>
 #include <vector>
 #include "pybind11/pybind11.h"
+#ifdef ENABLE_GE
 #include "transform/df_graph_manager.h"
 #include "transform/util.h"
+#endif
 #include "pipeline/parse/data_converter.h"
 #include "pipeline/parse/python_adapter.h"
 #include "utils/visible.h"
 
 namespace mindspore {
 namespace callbacks {
-using mindspore::transform::Status;
-using mindspore::transform::TransformUtil;
 
 const char PYTHON_MOD_CALLBACK_MODULE[] = "mindspore.train.callback";
 const char PYTHON_FUN_PROCESS_CHECKPOINT[] = "_checkpoint_cb_for_save_op";
@@ -38,6 +38,10 @@ const char kSummary[] = "Summary";
 const char kCheckPoint[] = "Save";
 const int ONE_SHAPE = 1;
 
+#ifdef ENABLE_GE
+using mindspore::transform::Status;
+using mindspore::transform::TransformUtil;
+
 bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name,
                        const std::shared_ptr<std::vector<int>>& shape) {
   if (graph == nullptr) {
@@ -181,6 +185,7 @@ uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::st
   MS_LOG(DEBUG) << "End the summary save callback function.";
   return Status::SUCCESS;
 }
+#endif
 
 // Cache the summary callback data from ME session
 // Remove the GE module on new architecture
@@ -208,10 +213,10 @@ uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::st
   auto bool_ret = py::cast<bool>(ret);
   if (!bool_ret) {
     MS_LOG(ERROR) << "Python checkpoint return false during callback";
-    return Status::FAILED;
+    return kCallbackFalied;
   }
   MS_LOG(DEBUG) << "End the summary save callback function.";
-  return Status::SUCCESS;
+  return kCallbackOk;
 }
 }  // namespace callbacks
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h
index 2a18b21b16..778b0a9ba2 100644
--- a/mindspore/ccsrc/utils/callbacks.h
+++ b/mindspore/ccsrc/utils/callbacks.h
@@ -20,8 +20,11 @@
 #include <string>
 #include <vector>
 #include <memory>
+#include "ir/meta_tensor.h"
+#ifdef ENABLE_GE
 #include "transform/types.h"
 #include "transform/util.h"
+#endif
 
 namespace mindspore {
 namespace callbacks {
@@ -36,10 +39,16 @@ extern const char kSummary[];
 extern const char kCheckPoint[];
 extern const std::string kPythonCheckpointModuleName;
 extern const std::string kPythonCheckpointFuncName;
+
+const int kCallbackOk = 0;
+const int kCallbackFalied = 1;
+
 bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name,
                        const std::shared_ptr<std::vector<int>>& shape);
+#ifdef ENABLE_GE
 uint32_t CheckpointSaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
 uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
+#endif
 uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, TensorPtr>&);
 
 }  // namespace callbacks
diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index bf05af9858..7b531536ac 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -26,13 +26,15 @@
 #include "tdt/tdt_host_interface.h"
 #include "tdt/data_common.h"
 #endif
+#ifdef ENABLE_GE
 #include "transform/df_graph_manager.h"
+#endif
 #include "ir/meta_tensor.h"
 
 namespace mindspore {
+#ifdef ENABLE_GE
 using mindspore::transform::DfGraphManager;
-using transform::GraphRunner;
-using transform::GraphRunnerOptions;
+#endif
 
 std::atomic<bool> thread_1_must_end(false);
 
@@ -81,6 +83,7 @@ MsContext::MsContext(const std::string& policy, const std::string& target) {
 
 std::shared_ptr<MsContext> MsContext::GetInstance() {
   if (inst_context_ == nullptr) {
+    MS_LOG(DEBUG) << "Create new mindspore context";
 #ifdef ENABLE_GE
     inst_context_.reset(new (std::nothrow) MsContext("ge", kAscendDevice));
 #elif defined(ENABLE_D)
diff --git a/mindspore/ccsrc/utils/context/ms_context.h b/mindspore/ccsrc/utils/context/ms_context.h
index e7d8dc769f..06704ff9c6 100644
--- a/mindspore/ccsrc/utils/context/ms_context.h
+++ b/mindspore/ccsrc/utils/context/ms_context.h
@@ -23,7 +23,6 @@
 #include <vector>
 #include <string>
 #include <utility>
-#include "transform/graph_runner.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/utils/convert_utils.cc b/mindspore/ccsrc/utils/convert_utils.cc
index ccd21f6801..e840ff8734 100644
--- a/mindspore/ccsrc/utils/convert_utils.cc
+++ b/mindspore/ccsrc/utils/convert_utils.cc
@@ -373,4 +373,45 @@ AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py
     MS_LOG(EXCEPTION) << "Python evaluator return invalid shape or type. " << (std::string)py::str(type_obj);
   }
 }
+bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args,
+                                       const std::shared_ptr<py::object> &ret_val) {
+  if (output->isa<ValueNode>()) {
+    MS_LOG(INFO) << "Graph's output is a constant. No need to execute.";
+    ValuePtr value = GetValueNode(output);
+    *ret_val = ValuePtrToPyData(value);
+    return true;
+  }
+
+  // Adapter will transform values in __init__() and construct() to parameters, this could cause
+  // inputs (a.k.a args in current function) size less than parameters'.
+  if (output->isa<Parameter>()) {
+    MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute.";
+    if (args.empty()) {
+      MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed.";
+    }
+    // Find the right parameter as ret_val.
+    auto func_graph = output->func_graph();
+    MS_EXCEPTION_IF_NULL(func_graph);
+    auto params = func_graph->parameters();
+    if (params.empty()) {
+      MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0";
+    }
+    if (args.size() != params.size()) {
+      MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size()
+                        << ", let graph to be executed.";
+    }
+
+    auto it = std::find(params.begin(), params.end(), output);
+    if (it == params.end()) {
+      MS_EXCEPTION(UnknownError) << "When graph output is Parameter,  it should be found in graph parameters";
+    }
+    size_t index = it - params.cbegin();
+    if (index >= args.size()) {
+      MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << ".";
+    }
+    *ret_val = args[index];
+    return true;
+  }
+  return false;
+}
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/utils/convert_utils.h b/mindspore/ccsrc/utils/convert_utils.h
index f190f98e6a..fbd4485a3f 100644
--- a/mindspore/ccsrc/utils/convert_utils.h
+++ b/mindspore/ccsrc/utils/convert_utils.h
@@ -18,6 +18,7 @@
 #define MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_
 
 #include <limits>
+#include <memory>
 #include "pybind11/pybind11.h"
 
 #include "utils/any.h"
@@ -120,6 +121,9 @@ inline uint8_t *AddressOffset(void *address, size_t offset) {
 
 AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py::object &type_obj);
 
+bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args,
+                                       const std::shared_ptr<py::object> &ret_val);
+
 }  // namespace mindspore
 
 #endif  // MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_
diff --git a/mindspore/ccsrc/vm/segment_runner.cc b/mindspore/ccsrc/vm/segment_runner.cc
index 82a61c010d..d7d5a4c096 100644
--- a/mindspore/ccsrc/vm/segment_runner.cc
+++ b/mindspore/ccsrc/vm/segment_runner.cc
@@ -178,14 +178,12 @@ LinConvertResult Convert(const AnfNodePtrList& lst) {
 }
 
 LinkFuncType MsVmConvert = Convert<VM>;
-LinkFuncType GeVmConvert = Convert<GeVM>;
 
-std::unordered_map<std::string, LinkFuncType> backends = {{kMsVm, MsVmConvert}, {kGeVm, GeVmConvert}};
+std::unordered_map<std::string, LinkFuncType> backends = {{kMsVm, MsVmConvert}};
 
 std::set<std::string> backend_list = {
   kMsConvert,
   kMsVm,
-  kGeVm,
 };
 
 }  // namespace compile
diff --git a/mindspore/ccsrc/vm/transform.cc b/mindspore/ccsrc/vm/transform.cc
index d5933db1ab..be7aaf5baa 100644
--- a/mindspore/ccsrc/vm/transform.cc
+++ b/mindspore/ccsrc/vm/transform.cc
@@ -24,7 +24,9 @@
 #include <vector>
 
 #include "pipeline/static_analysis/abstract_value.h"
+#ifdef ENABLE_GE
 #include "transform/convert.h"
+#endif
 #include "utils/graph_utils.h"
 #include "utils/context/ms_context.h"
 #include "debug/trace.h"
@@ -55,7 +57,6 @@ CompileGraph::CompileGraph(const BackendPtr& backend, const std::vector<Primitiv
     MS_LOG(INFO) << "Attribute 'is_gevm_convert' is true";
     is_gevm_convert_ = true;
   }
-  is_graph_cut = false;
 }
 
 bool CompileGraph::IsCut(const AnfNodePtr& node) {
@@ -80,14 +81,15 @@ bool CompileGraph::IsCut(const AnfNodePtr& node) {
       }
     }
 
+#ifdef ENABLE_GE
     if (is_gevm_convert_) {
-      auto name = transform::GetCNodeFuncName(cnode);
+      auto name = GetCNodeFuncName(cnode);
       auto adpt = transform::DfGraphConvertor::FindAdapter(name);
       if (adpt == nullptr) {
-        is_graph_cut = true;
+        return true;
       }
-      return true;
     }
+#endif
   }
 
   return false;
@@ -605,12 +607,6 @@ FinalVMPtr CompileGraphs::CompileAndLink(const FuncGraphPtr& graph) {
   (void)WrapPrimitives(graph);
   Compile(graph);
 
-#ifdef ENABLE_GE
-  if (!transform_->IsGraphCut()) {
-    return nullptr;
-  }
-#endif
-
   FinalVMPtr rt = Link(graph);
   Reset();
   MS_LOG(DEBUG) << "End";
diff --git a/mindspore/ccsrc/vm/transform.h b/mindspore/ccsrc/vm/transform.h
index 206fd00431..f862444a82 100644
--- a/mindspore/ccsrc/vm/transform.h
+++ b/mindspore/ccsrc/vm/transform.h
@@ -55,7 +55,6 @@ class CompileGraph {
 
   InstSet Run(const FuncGraphPtr& func_graph);
   InstSet GenMultiGraphsSinkInst(const FuncGraphPtr& graph);
-  bool IsGraphCut() const { return is_graph_cut; }
   bool IsCut(const AnfNodePtr& node);
   void Push(const AnfNodePtr& node);
   void Tie(const AnfNodePtr& n1, const AnfNodePtr& n2) { slots_[n2] = slots_[n1]; }
@@ -101,7 +100,6 @@ class CompileGraph {
   BackendPtr backend_;
   LinkFuncType lin_convert_;
   bool is_gevm_convert_;
-  bool is_graph_cut;
   int height_{0};
   int max_height_{0};
   std::vector<PrimitivePtr> cut_list_;
diff --git a/mindspore/ccsrc/vm/vmimpl.cc b/mindspore/ccsrc/vm/vmimpl.cc
index e64cd16fcf..ee9a817dd8 100644
--- a/mindspore/ccsrc/vm/vmimpl.cc
+++ b/mindspore/ccsrc/vm/vmimpl.cc
@@ -26,8 +26,6 @@
 #include <memory>
 #include <set>
 
-#include "transform/graph_runner.h"
-#include "transform/convert.h"
 #include "ir/meta_tensor.h"
 #include "operator/ops.h"
 #include "ir/manager.h"
@@ -40,39 +38,6 @@ namespace compile {
 
 using PrimitivePyPtr = std::shared_ptr<PrimitivePy>;
 
-static const char SEGMENT_GRAPH_NAME[] = "runnable_segment";
-
-VectorRef GeVM::RunGraph(const FuncGraphPtr& anf_graph, const VectorRef& args) {
-  // Convert graph
-  transform::DfGraphConvertor convertor(anf_graph);
-
-  (void)convertor.ConvertAllNode().BuildGraph();
-  if (convertor.ErrCode() == 0) {
-    (void)transform::DfGraphManager::GetInstance().AddGraph(SEGMENT_GRAPH_NAME, convertor.GetComputeGraph());
-  } else {
-    MS_LOG(EXCEPTION) << "convert df graph failed";
-  }
-
-  // Run graph
-  transform::GraphRunnerOptions options;
-  transform::GraphRunner graph_runner(options);
-  transform::RunOptions run_options;
-  run_options.name = SEGMENT_GRAPH_NAME;
-
-  std::vector<tensor::TensorPtr> inputs;
-  (void)std::transform(std::begin(args), std::end(args), std::back_inserter(inputs),
-                       [](const BaseRef& arg) -> tensor::TensorPtr {
-                         auto value_ref = utils::cast<PyObjectRef>(arg);
-                         auto value = value_ref.object_;
-                         return py::cast<tensor::TensorPtr>(value);
-                       });
-  std::vector<tensor::TensorPtr> outputs;
-  (void)graph_runner.RunGraph(run_options, inputs, &outputs);
-  std::vector<BaseRef> ret;
-  (void)std::copy(outputs.begin(), outputs.end(), std::back_inserter(ret));
-  return VectorRef(ret);
-}
-
 // Indicate a call to a new frame.
 struct CallWrap : public Base {
   explicit CallWrap(const VMFramePtr& vm_frame) : frame(vm_frame) {}
diff --git a/mindspore/ccsrc/vm/vmimpl.h b/mindspore/ccsrc/vm/vmimpl.h
index 8ff02ae946..4ef507af82 100644
--- a/mindspore/ccsrc/vm/vmimpl.h
+++ b/mindspore/ccsrc/vm/vmimpl.h
@@ -64,12 +64,6 @@ class VMImpl {
   virtual ~VMImpl() = default;
 };
 
-class GeVM : public VMImpl {
- public:
-  VectorRef RunGraph(const FuncGraphPtr& fg, const VectorRef& args) override;
-  ~GeVM() override = default;
-};
-
 // An execution frame.
 // This holds the state for an application of a graph. The nodes list
 // must contain free variables of graphs encountered before the
diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 8e23e9184d..9ee95ef772 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -22,7 +22,7 @@ from mindspore import context
 from mindspore import log as logger
 from mindspore.parallel._utils import _get_parallel_mode
 from .._c_expression import generate_key, Executor_, Tensor, MetaTensor
-from .._c_expression import verify_inputs_signature, init_exec_dataset, export_graph, _set_dataset_mode_config, init_ge
+from .._c_expression import verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_ge
 from .tensor import Tensor as MsTensor
 
 # store ms_function class compiled pipeline cache
@@ -501,6 +501,7 @@ class _Executor:
             file_name (str): File name of model to export
             file_format (str): MindSpore currently support 'GEIR' and 'ONNX' format for exported model
         """
+        from .._c_expression import export_graph
         phase = 'export' + '.' + str(net.create_time)
         export_graph(file_name, file_format, phase)
 
diff --git a/mindspore/common/parameter.py b/mindspore/common/parameter.py
index b1992c8b82..c8ddf0eac6 100644
--- a/mindspore/common/parameter.py
+++ b/mindspore/common/parameter.py
@@ -155,6 +155,18 @@ class Parameter:
     def data(self):
         return self.default_input
 
+    def __add__(self, other):
+        return self.default_input + other
+
+    def __sub__(self, other):
+        return self.default_input - other
+
+    def __mul__(self, other):
+        return self.default_input * other
+
+    def __truediv__(self, other):
+        return self.default_input / other
+
     def set_parameter_data(self, data):
         if isinstance(data, (Tensor, list, int, float,
                              np.float16, np.float32, np.int32, np.int16, np.ndarray)) and not isinstance(data, bool):
diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index 4c7f31921b..d17661595f 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -89,6 +89,16 @@ class Tensor(Tensor_):
         out = self.__mul__(other)
         return out
 
+    def __truediv__(self, other):
+        if isinstance(other, (int, float)):
+            other_tensor = Tensor(other, self.dtype())
+        elif isinstance(other, Tensor):
+            other_tensor = other
+        else:
+            raise TypeError("unsupported type for div operation")
+        out = tensor_operator_registry.get('__div__')(self, other_tensor)
+        return out
+
     def __sub__(self, other):
         if not isinstance(other, Tensor):
             raise TypeError("input_data must be a tensor")
diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py
index 5adb6fac57..b8411d42c1 100644
--- a/mindspore/ops/functional.py
+++ b/mindspore/ops/functional.py
@@ -125,5 +125,5 @@ shape_mul = Primitive("shape_mul")
 stop_gradient = Primitive("stop_gradient")
 
 tensor_operator_registry.register('__add__', tensor_add)
-
 tensor_operator_registry.register('__mul__', tensor_mul)
+tensor_operator_registry.register('__div__', tensor_div)
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index fe655433fa..65a9837fe6 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -161,6 +161,9 @@ class Model:
 
     def _update_metrics(self, outputs):
         """Update metrics local values."""
+        if not isinstance(outputs, tuple):
+            raise ValueError("The `outputs` is not tuple.")
+
         if self._eval_indexes is not None and len(outputs) < 3:
             raise ValueError("The length of `outputs` must be greater than or equal to 3, \
                              but got {}".format(len(outputs)))
diff --git a/tests/ut/cpp/device/ascend_kernel_select_test.cc b/tests/ut/cpp/device/ascend_kernel_select_test.cc
index d522a5adc0..79986d375d 100644
--- a/tests/ut/cpp/device/ascend_kernel_select_test.cc
+++ b/tests/ut/cpp/device/ascend_kernel_select_test.cc
@@ -231,7 +231,7 @@ void test_select(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
   AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info_ptr, kernel_node.get());
 }
 
-void SetParentAbstract(std::vector<AnfNodePtr> parent_list, std::vector<vector<size_t>> shapes,
+void SetParentAbstract(std::vector<AnfNodePtr> parent_list, std::vector<std::vector<size_t>> shapes,
                        std::vector<TypeId> types) {
   for (const auto &node : parent_list) {
     AnfAlgo::SetOutputInferTypeAndShape(types, shapes, node.get());
diff --git a/tests/ut/cpp/device/ascend_profiling_test.cc b/tests/ut/cpp/device/ascend_profiling_test.cc
index 2bfdc9fcae..2829a5fd4a 100644
--- a/tests/ut/cpp/device/ascend_profiling_test.cc
+++ b/tests/ut/cpp/device/ascend_profiling_test.cc
@@ -16,10 +16,10 @@
 #include <iostream>
 #include <memory>
 
+#include "./prof_reporter.h"
 #include "common/common_test.h"
 #include "device/ascend/profiling/profiling_manager.h"
 #include "./common.h"
-#include "./prof_reporter.h"
 #define private public
 #include "device/ascend/profiling/plugin_impl.h"
 #undef private
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
similarity index 99%
rename from tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc
rename to tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
index 1a7a103a99..79a1cf1a8a 100644
--- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc
+++ b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
@@ -20,7 +20,7 @@
 #include "ir/manager.h"
 #include "debug/anf_ir_dump.h"
 #include "session/anf_runtime_algorithm.h"
-#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
 #include "pre_activate/common/optimizer.h"
 #include "device/kernel_info.h"
 #include "pre_activate/common/pass_manager.h"
diff --git a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc
index 083e4168ce..014b0aed55 100644
--- a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc
+++ b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc
@@ -105,7 +105,7 @@ TEST_F(TestHWConstInputToTensorInput, test_value_tuple_tensor_input) {
   auto tensor = input1->cast<ValueNodePtr>()->value()->cast<tensor::TensorPtr>();
   ASSERT_TRUE(tensor != nullptr);
   auto data = tensor->data_c(false);
-  EXPECT_EQ(vector<int>((int *)data, (int *)data + 4), vector<int>({2, 4, 2, 2}));
+  EXPECT_EQ(std::vector<int>((int *)data, (int *)data + 4), std::vector<int>({2, 4, 2, 2}));
 }
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py
index d4f96b54eb..1757567db5 100644
--- a/tests/ut/python/ir/test_tensor.py
+++ b/tests/ut/python/ir/test_tensor.py
@@ -24,6 +24,8 @@ import pytest
 import mindspore as ms
 import mindspore.common.api as me
 import mindspore.nn as nn
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
 from ..ut_filter import non_graph_engine
 
 
@@ -199,6 +201,21 @@ def test_sub():
     z = x - y
     assert isinstance(z, ms.Tensor)
 
+@non_graph_engine
+def test_div():
+    x = ms.Tensor(np.array([[2,6,10],[12, 4, 8]]).astype(np.float32))
+    y = ms.Tensor(np.array([[2,2,5],[6, 1, 2]]).astype(np.float32))
+    z = x / y
+    z2 = x / 2
+    assert isinstance(z, ms.Tensor)
+    assert isinstance(z2, ms.Tensor)
+
+@non_graph_engine
+def test_parameter():
+    x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
+    z = x / 2
+    print(z)
+
 
 class Net(nn.Cell):
     """Net definition"""
@@ -378,3 +395,4 @@ def test_tensor_dtype_fp32_to_bool():
         input = np.random.randn(2, 3, 4, 5).astype(np.float32)
         input = ms.Tensor(input)
         input_me = ms.Tensor(input, dtype=ms.bool_)
+
diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py
index 4164ce6e8f..1c4895465f 100644
--- a/tests/ut/python/ops/test_array_ops.py
+++ b/tests/ut/python/ops/test_array_ops.py
@@ -97,20 +97,6 @@ def test_select():
     assert np.all(output.asnumpy() == expect)
 
 
-def test_scalar_cast_grad():
-    """ test_scalar_cast_grad """
-    input_x = 255.5
-    input_t = get_py_obj_dtype(ms.int8)
-
-    def fx_cast(x):
-        output = F.scalar_cast(x, input_t)
-        return output
-
-    gfn = C.grad(fx_cast)(input_x)
-    expect_dx = 1
-    assert gfn == expect_dx
-
-
 class CustomOP(PrimitiveWithInfer):
     __mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1,
                                sig_dtype.T1, sig_dtype.T2, sig_dtype.T2,
diff --git a/tests/ut/python/parallel/__init__.py b/tests/ut/python/parallel/__init__.py
index ffe03896ab..c08f8e247b 100644
--- a/tests/ut/python/parallel/__init__.py
+++ b/tests/ut/python/parallel/__init__.py
@@ -13,11 +13,14 @@
 # limitations under the License.
 
 import mindspore.context as context
+from mindspore.parallel._utils import _reset_op_id
 
 
 def setup_module(module):
-    context.set_context(mode=context.GRAPH_MODE)
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
+    _reset_op_id()
 
 
 def teardown_module():
     context.reset_auto_parallel_context()
+    _reset_op_id()
diff --git a/tests/ut/python/parallel/test_alltoall.py b/tests/ut/python/parallel/test_alltoall.py
index ddceece8ec..7365dd941c 100644
--- a/tests/ut/python/parallel/test_alltoall.py
+++ b/tests/ut/python/parallel/test_alltoall.py
@@ -97,13 +97,10 @@ def test_all_to_all():
     strategys = all_to_all_common(strategy1)
     print(strategys)
     expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
-                   '/SoftmaxCrossEntropyWithLogits-op43': [[8, 1], [8, 1]],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
-                   '/OneHot-op44': [[8, 1], [], []],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1':
-                       [[8, 1]],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0':
-                       [[1, 1], [1, 8]]}
+                   '/SoftmaxCrossEntropyWithLogits-op3': [[8, 1], [8, 1]],
+                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/OneHot-op4': [[8, 1], [], []],
+                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': [[8, 1]],
+                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': [[1, 1], [1, 8]]}
     assert (strategys == expect_dict)
     context.set_context(save_graphs=False)
 
diff --git a/tests/ut/python/parallel/test_auto_parallel_arithmetic.py b/tests/ut/python/parallel/test_auto_parallel_arithmetic.py
index e6f72d8019..7c928be376 100644
--- a/tests/ut/python/parallel/test_auto_parallel_arithmetic.py
+++ b/tests/ut/python/parallel/test_auto_parallel_arithmetic.py
@@ -65,8 +65,8 @@ def test_auto_parallel_arithmetic():
     b = Tensor(np.ones([64, 128]), dtype=ms.float32)
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op2': [[2, 4], [2, 4]],
-                     'Default/network-Net/MatMul-op3': [[2, 1], [1, 4]]}
+    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[2, 4], [2, 4]],
+                     'Default/network-Net/MatMul-op1': [[2, 1], [1, 4]]}
     assert strategies == expected_strategies
 
 def test_auto_parallel_arithmetic_broadcast_both():
@@ -91,8 +91,8 @@ def test_auto_parallel_arithmetic_broadcast_both():
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op2': [[8, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]]}
+    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]]}
     assert strategies == expected_strategies
 
 
@@ -118,8 +118,8 @@ def test_auto_parallel_arithmetic_broadcast_right():
     b = Tensor(np.ones([32]), dtype=ms.float32)
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [2]],
-                           'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]}
+    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [2]],
+                           'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
     assert strategies == expected_strategies
 
 
@@ -145,6 +145,6 @@ def test_auto_parallel_arithmetic_broadcast_left():
     b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
     _executor.compile(net, x, y, b, phase="train")
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [1, 4, 2]],
-                           'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]}
-    assert strategies == expected_strategies
\ No newline at end of file
+    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [1, 4, 2]],
+                           'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
+    assert strategies == expected_strategies
diff --git a/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py b/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py
index e436f9faf7..5364263695 100755
--- a/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py
+++ b/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 import numpy as np
 from mindspore import context
 import mindspore.nn as nn
@@ -55,6 +56,9 @@ def test_auto_parallel_assign_sub_with_ref_key():
 
     _executor.compile(net, x, phase="train")
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-PReLU/PReLU-op2': [[1, 1, 1, 8], [1]],
-                           'Default/network-PReLU/ReLU-op3': [[1]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('PReLU-op', k) is not None:
+            assert v == [[1, 1, 1, 8], [1]]
+        elif re.search('ReLU-op', k) is not None:
+            assert v == [[1]]
+
diff --git a/tests/ut/python/parallel/test_auto_parallel_cast.py b/tests/ut/python/parallel/test_auto_parallel_cast.py
index be7b5082d6..4cfeb59fc1 100644
--- a/tests/ut/python/parallel/test_auto_parallel_cast.py
+++ b/tests/ut/python/parallel/test_auto_parallel_cast.py
@@ -75,9 +75,9 @@ def test_double_star_graph():
 
     _executor.compile(net, x, y, z, w, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]],
-                           'Default/network-Net/Cast-op7': [[8, 1]],
-                           'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]],
-                           'Default/network-Net/Cast-op9': [[1, 8]],
-                           'Default/network-Net/MatMul-op10': [[1, 1], [1, 8]]}
-    assert strategies == expected_strategies
\ No newline at end of file
+    expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
+                           'Default/network-Net/Cast-op3': [[1, 8]],
+                           'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]],
+                           'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]}
+    assert strategies == expected_strategies
diff --git a/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py b/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py
index ca9b561145..4d68a7f883 100644
--- a/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py
+++ b/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 import numpy as np
 from mindspore import context
 import mindspore.nn as nn
@@ -66,7 +67,10 @@ def test_matmul_prelu():
 
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    assert strategies['Default/network-Net/PReLU-op2'] == [[16, 1, 1, 1], [1]]
-    assert strategies['Default/network-Net/Mul-op3'] == [[16, 1, 1, 1], [16, 1, 1, 1]]
+    for (k, v) in strategies.items():
+        if re.search('PReLU-op', k) is not None:
+            assert v == [[16, 1, 1, 1], [1]]
+        elif re.search('Mul-op', k) is not None:
+            assert v == [[16, 1, 1, 1], [16, 1, 1, 1]]
 
 
diff --git a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
index b7a3255f7c..29e81f7f90 100644
--- a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
+++ b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
@@ -80,9 +80,9 @@ def test_common_parameter():
 
     _executor.compile(net, x, y, z, w, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]],
-                           'Default/network-Net/Cast-op7': [[1, 1]],
+    expected_strategies = {'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]],
+                           'Default/network-Net/Cast-op2': [[1, 1]],
                            'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]],
-                           'Default/network-Net/Cast-op9': [[1, 1]]}
+                           'Default/network-Net/Cast-op4': [[1, 1]]}
     assert strategies == expected_strategies
diff --git a/tests/ut/python/parallel/test_auto_parallel_transpose.py b/tests/ut/python/parallel/test_auto_parallel_transpose.py
index 62fdc11120..2d4dcbae81 100644
--- a/tests/ut/python/parallel/test_auto_parallel_transpose.py
+++ b/tests/ut/python/parallel/test_auto_parallel_transpose.py
@@ -71,8 +71,8 @@ def test_two_matmul_transpose():
 
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/Transpose-op4': [[1, 16]],
-                           'Default/network-Net/Transpose-op5': [[16, 1]],
-                           'Default/network-Net/MatMul-op6': [[16, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op7': [[16, 1], [1, 1]]}
-    assert strategies == expected_strategies
\ No newline at end of file
+    expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]],
+                           'Default/network-Net/Transpose-op1': [[16, 1]],
+                           'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
+    assert strategies == expected_strategies
diff --git a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
index e7beed384e..bd6639a501 100644
--- a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
+++ b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
@@ -135,7 +135,6 @@ def test_two_matmul():
     
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_strategy(net)
-    expected_strategies = {'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
-                     'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
+    expected_strategies = {'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]],
+                     'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]]}
     assert strategies == expected_strategies
-
diff --git a/tests/ut/python/parallel/test_dataset_interface.py b/tests/ut/python/parallel/test_dataset_interface.py
index da8821199e..17b8d3cc6d 100644
--- a/tests/ut/python/parallel/test_dataset_interface.py
+++ b/tests/ut/python/parallel/test_dataset_interface.py
@@ -84,7 +84,7 @@ def loss_scale_manager_common(strategy1):
     opt = Momentum(net.trainable_params(), learning_rate, momentum)
     scale_manager = DynamicLossScaleManager(32, 2, 2000)
     model = Model(net, loss, opt, loss_scale_manager=scale_manager)
-    # if no GE exists, outputs = self._train_network(*next_element) outputs is None, TypeError is caught.
+    # if no GE exists, outputs = self._train_network(*next_element) outputs inputs tensor.
     try:
         model.train(epoch_size, dataset, dataset_sink_mode=False)
     except TypeError:
diff --git a/tests/ut/python/parallel/test_one_dev.py b/tests/ut/python/parallel/test_one_dev.py
index a5867eb5f4..efd4889ce6 100644
--- a/tests/ut/python/parallel/test_one_dev.py
+++ b/tests/ut/python/parallel/test_one_dev.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 from mindspore.train import Model, ParallelMode
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.nn.optim.momentum import Momentum
@@ -89,16 +90,13 @@ def all_to_all_common():
 
 
 def test_one_dev():
-
     _reset_op_id()
-    strategys = all_to_all_common()
-    expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
-                   '/SoftmaxCrossEntropyWithLogits-op9': [[1, 1], [1, 1]],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
-                   '/OneHot-op10': [[1, 1], [], []],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op11':
-                       [[1, 1]],
-                   'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op12':
-                       [[1, 1], [1, 1]]}
-    assert (strategys == expect_dict)
+    strategies = all_to_all_common()
+    for (k, v) in strategies.items():
+        if re.search('SoftmaxCrossEntropyWithLogits-op', k) is not None:
+            assert v == [[1, 1], [1, 1]]
+        elif re.search('Transpose-op', k) is not None:
+            assert v == [[1, 1]]
+        elif re.search('MatMul-op', k) is not None:
+            assert v == [[1, 1], [1, 1]]
 
diff --git a/tests/ut/python/pipeline/parse/test_create_obj.py b/tests/ut/python/pipeline/parse/test_create_obj.py
index a702f37e0b..370445cf99 100644
--- a/tests/ut/python/pipeline/parse/test_create_obj.py
+++ b/tests/ut/python/pipeline/parse/test_create_obj.py
@@ -24,6 +24,7 @@
 import logging
 import numpy as np
 import mindspore.nn as nn
+from mindspore import context
 from mindspore.ops import operations as P
 from mindspore.common.api import ms_function
 from mindspore.common.tensor import Tensor
@@ -50,6 +51,7 @@ class Net(nn.Cell):
 def test_create_cell_object_on_construct():
     """ test_create_cell_object_on_construct """
     log.debug("begin test_create_object_on_construct")
+    context.set_context(mode=context.GRAPH_MODE)
     np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_me = Tensor(np1)
 
@@ -118,6 +120,7 @@ class NetC(nn.Cell):
 def test_create_cell_object_on_construct_use_many_parameter():
     """ test_create_cell_object_on_construct_use_many_parameter """
     log.debug("begin test_create_object_on_construct")
+    context.set_context(mode=context.GRAPH_MODE)
     np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_me = Tensor(np1)
 
diff --git a/tests/ut/python/pipeline/parse/test_dtype.py b/tests/ut/python/pipeline/parse/test_dtype.py
index a282c82f83..645eba8004 100644
--- a/tests/ut/python/pipeline/parse/test_dtype.py
+++ b/tests/ut/python/pipeline/parse/test_dtype.py
@@ -28,5 +28,4 @@ def try_type():
 
 
 def test_dtype_convert():
-    with pytest.raises(RuntimeError):
-        try_type()
+    try_type()
diff --git a/tests/ut/python/pynative_mode/ops/test_grad.py b/tests/ut/python/pynative_mode/ops/test_grad.py
index 25db6b15d2..b927da5b04 100644
--- a/tests/ut/python/pynative_mode/ops/test_grad.py
+++ b/tests/ut/python/pynative_mode/ops/test_grad.py
@@ -19,8 +19,10 @@ from mindspore.common.api import ms_function
 from mindspore import Tensor
 from mindspore.ops import composite as C
 from mindspore.ops.composite import grad_all_with_sens
+from mindspore.common.dtype import get_py_obj_dtype
 import mindspore.nn as nn
 import mindspore.ops.operations as P
+from mindspore.ops import functional as F
 from ...ut_filter import non_graph_engine
 
 
@@ -78,6 +80,20 @@ def test_cast_grad():
     assert np.all(gout[0].asnumpy() == expect)
 
 
+def test_scalar_cast_grad():
+    """ test_scalar_cast_grad """
+    input_x = 255.5
+    input_t = get_py_obj_dtype(ms.int8)
+
+    def fx_cast(x):
+        output = F.scalar_cast(x, input_t)
+        return output
+
+    gfn = C.grad(fx_cast)(input_x)
+    expect_dx = 1
+    assert gfn == expect_dx
+
+
 @non_graph_engine
 def test_reshape_grad():
     """ test_reshape_grad """
diff --git a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
index b6e1ab992c..98dfd6aaef 100644
--- a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
+++ b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
@@ -163,12 +163,7 @@ def test_scalar_summary_use_invalid_tag_None():
 def test_scalar_summary_use_invalid_tag_Bool():
     log.debug("begin test_scalar_summary_use_invalid_tag_Bool")
     net = SummaryDemoTag(True, True, True)
-    try:
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
+    run_case(net)
     log.debug("finished test_scalar_summary_use_invalid_tag_Bool")
 
 
@@ -176,12 +171,7 @@ def test_scalar_summary_use_invalid_tag_Bool():
 def test_scalar_summary_use_invalid_tag_null():
     log.debug("begin test_scalar_summary_use_invalid_tag_null")
     net = SummaryDemoTag("", "", "")
-    try:
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
+    run_case(net)
     log.debug("finished test_scalar_summary_use_invalid_tag_null")
 
 
@@ -189,12 +179,7 @@ def test_scalar_summary_use_invalid_tag_null():
 def test_scalar_summary_use_invalid_tag_Int():
     log.debug("begin test_scalar_summary_use_invalid_tag_Int")
     net = SummaryDemoTag(1, 2, 3)
-    try:
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
+    run_case(net)
     log.debug("finished test_scalar_summary_use_invalid_tag_Int")
 
 
diff --git a/tests/ut/python/utils/test_serialize.py b/tests/ut/python/utils/test_serialize.py
index 12937e5a83..41da45ab25 100644
--- a/tests/ut/python/utils/test_serialize.py
+++ b/tests/ut/python/utils/test_serialize.py
@@ -30,7 +30,7 @@ from mindspore.nn import WithLossCell, TrainOneStepCell
 from mindspore.train.callback import _CheckpointManager
 from mindspore.train.serialization import save_checkpoint, load_checkpoint,load_param_into_net, \
                                           _exec_save_checkpoint, export, _save_graph
-from ..ut_filter import run_on_onnxruntime
+from ..ut_filter import run_on_onnxruntime, non_graph_engine
 from mindspore import context
 
 
@@ -306,6 +306,7 @@ class MYNET(nn.Cell):
         return out
 
 
+@non_graph_engine
 def test_export():
     net = MYNET()
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))

From 6f1747045c7fa58ff5ce06ba63ca647b98dbf328 Mon Sep 17 00:00:00 2001
From: zhangz0911gm <zhangzheng44@huawei.com>
Date: Tue, 31 Mar 2020 23:14:21 -0400
Subject: [PATCH 077/367] Add FloorMod, Acosh in ME

---
 mindspore/ccsrc/transform/convert.cc    |  4 ++
 mindspore/ccsrc/transform/op_declare.cc | 10 +++++
 mindspore/ccsrc/transform/op_declare.h  |  5 ++-
 mindspore/ops/_grad/grad_math_ops.py    | 25 +++++++++++
 mindspore/ops/operations/__init__.py    |  4 +-
 mindspore/ops/operations/math_ops.py    | 56 +++++++++++++++++++++++++
 mindspore/ops/operations/nn_ops.py      |  2 +-
 tests/ut/python/ops/test_ops.py         |  9 ++++
 8 files changed, 112 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index fdacff7ba8..7c100f0f0e 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -171,6 +171,8 @@ const char kNameAbsGrad[] = "AbsGrad";
 const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy";
 const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad";
 const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad";
+const char kNameAcosh[] = "Acosh";
+const char kNameFloorMod[] = "FloorMod";
 const char kNameSpaceToDepth[] = "SpaceToDepth";
 const char kNameDepthToSpace[] = "DepthToSpace";
 const char kNameSign[] = "Sign";
@@ -360,6 +362,8 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)},
     {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)},
     {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)},
+    {string(kNameAcosh), ADPT_DESC(Acosh)},
+    {string(kNameFloorMod), ADPT_DESC(FloorMod)},
     {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)},
     {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)},
     {string(kNameSign), ADPT_DESC(Sign)},
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 9258eb08db..0af2923cc4 100755
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -357,6 +357,11 @@ INPUT_MAP(AcosGrad) = {{1, INPUT_DESC(y)}, {2, INPUT_DESC(dy)}};
 ATTR_MAP(AcosGrad) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(AcosGrad) = {{0, OUTPUT_DESC(z)}};
 
+// Acosh
+INPUT_MAP(Acosh) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(Acosh) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(Acosh) = {{0, OUTPUT_DESC(y)}};
+
 // Floor
 INPUT_MAP(Floor) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Floor) = EMPTY_ATTR_MAP;
@@ -367,6 +372,11 @@ INPUT_MAP(FloorDiv) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(FloorDiv) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(FloorDiv) = {{0, OUTPUT_DESC(y)}};
 
+// FloorMod
+INPUT_MAP(FloorMod) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
+ATTR_MAP(FloorMod) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(FloorMod) = {{0, OUTPUT_DESC(y)}};
+
 // Sin
 INPUT_MAP(Sin) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Sin) = EMPTY_ATTR_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 031ce80865..d120c94989 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -324,11 +324,15 @@ DECLARE_OP_USE_OUTPUT(Acos)
 
 DECLARE_OP_ADAPTER(AcosGrad)
 DECLARE_OP_USE_OUTPUT(AcosGrad)
+DECLARE_OP_ADAPTER(Acosh)
+DECLARE_OP_USE_OUTPUT(Acosh)
 
 DECLARE_OP_ADAPTER(Floor)
 DECLARE_OP_USE_OUTPUT(Floor)
 DECLARE_OP_ADAPTER(FloorDiv)
 DECLARE_OP_USE_OUTPUT(FloorDiv)
+DECLARE_OP_ADAPTER(FloorMod)
+DECLARE_OP_USE_OUTPUT(FloorMod)
 DECLARE_OP_ADAPTER(Sin)
 DECLARE_OP_USE_OUTPUT(Sin)
 DECLARE_OP_ADAPTER(Exp)
@@ -450,7 +454,6 @@ DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD)
 DECLARE_OP_USE_OUTPUT(ApplyRMSPropD)
 DECLARE_OP_ADAPTER(ApplyCenteredRMSProp)
 DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp)
-
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)
diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py
index 1675855c88..1863ac8fdd 100755
--- a/mindspore/ops/_grad/grad_math_ops.py
+++ b/mindspore/ops/_grad/grad_math_ops.py
@@ -251,6 +251,20 @@ def get_bprop_floordiv(self):
     return bprop
 
 
+@bprop_getters.register(P.FloorMod)
+def get_bprop_floormod(self):
+    """Grad definition for `FloorMod` operation."""
+    div_op = P.FloorMod()
+    neg = P.Neg()
+    mul_op = P.Mul()
+
+    def bprop(x, y, out, dout):
+        bc_x = div_op(dout, y)
+        bc_y = neg(mul_op(bc_x, out))
+        return binop_grad_common(x, y, bc_x, bc_y)
+    return bprop
+
+
 @bprop_getters.register(P.Square)
 def get_bprop_square(self):
     """Grad definition for `Square` operation."""
@@ -690,6 +704,17 @@ def get_bprop_acos(self):
     return bprop
 
 
+@bprop_getters.register(P.Acosh)
+def get_bprop_acosh(self):
+    """Grad definition for `Acosh` operation."""
+    input_grad = G.AcoshGrad()
+
+    def bprop(x, out, dout):
+        dx = input_grad(x, dout)
+        return (dx,)
+    return bprop
+
+
 @bprop_getters.register(P.Abs)
 def get_bprop_abs(self):
     """Grad definition for `Abs` operation."""
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 727ddaf88f..846be05c4d 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -39,7 +39,7 @@ from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
-                       Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv,
+                       Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv, FloorMod, Acosh,
                        Greater, GreaterEqual, Less, LessEqual, Log, LogicalAnd,
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
@@ -205,6 +205,8 @@ __all__ = [
     'Log',
     'SigmoidCrossEntropyWithLogits',
     'FloorDiv',
+    'FloorMod',
+    'Acosh',
     "PReLU",
     "Cos",
     "ACos",
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 398a7e6f1a..9600c736b2 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1183,6 +1183,62 @@ class Floor(PrimitiveWithInfer):
         return x_dtype
 
 
+class FloorMod(_MathBinaryOp):
+    """
+    Compute element-wise remainder of division.
+
+    The inputs must be two tensors or one tensor and one scalar.
+    When the inputs are two tensors, the shapes of them could be broadcast,
+    and the data types of them should be same.
+    When the inputs are one tensor and one scalar, the scalar cannot be a parameter, only can be a constant,
+    and the type of the scalar is the same as the data type of the tensor.
+
+    Inputs:
+        - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+          a number.
+
+    Outputs:
+        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+
+    Examples:
+        >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)
+        >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32)
+        >>> floor_mod = FloorMod()
+        >>> floor_mod(input_x, input_y)
+        [2, 1, 2]
+    """
+
+
+class Acosh(PrimitiveWithInfer):
+    """
+    Compute inverse hyperbolic cosine of x element-wise.
+
+    Inputs:
+        - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+
+    Outputs:
+        Tensor, has the same shape as `input_x`.
+
+    Examples:
+        >>> acosh = Acosh()
+        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), ms.float32)
+        >>> output = acosh(X)
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init Acosh"""
+
+    def infer_shape(self, x):
+        return x
+
+    def infer_dtype(self, x):
+        validator.check_subclass("x_dtype", x, mstype.tensor)
+        validator.check_typename('x_dtype', x, mstype.number_type)
+        return x
+
+
 class _LogicBinaryOp(_BinaryOp):
     """
     Define logic binary operators.
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 0410d4a346..e0f8280514 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2395,4 +2395,4 @@ class ApplyFtrl(PrimitiveWithInfer):
         validator.check_typename("l1", l1_type,[mstype.float16, mstype.float32])
         validator.check_typename("l2", l2_type,[mstype.float16, mstype.float32])
         validator.check_typename("lr_power", lr_power_type,[mstype.float16, mstype.float32])
-        return var_type
\ No newline at end of file
+        return var_type
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 453ef9a652..0f5b716e39 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -219,6 +219,10 @@ test_case_math_ops = [
         'block': P.ACos(),
         'desc_inputs': [[2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('Acosh', {
+        'block': P.Acosh(),
+        'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16))],
+        'skip': ['backward']}),
     ('Sin', {
         'block': P.Sin(),
         'desc_inputs': [[2, 3]],
@@ -301,6 +305,11 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
                         Tensor(np.random.rand(4).astype(np.float16))],
         'skip': ['backward']}),
+    ('FloorMod', {
+        'block': P.FloorMod(),
+        'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
+                        Tensor(np.random.rand(4).astype(np.float16))],
+        'skip': ['backward']}),
     ('identity', {
         'block': ops.functional.identity,
         'desc_inputs': [[2, 2]],

From bc4602b58e8a82415655cffe117e3927daecc3e6 Mon Sep 17 00:00:00 2001
From: xiefangqi <xiefangqi2@huawei.com>
Date: Mon, 30 Mar 2020 15:34:48 +0800
Subject: [PATCH 078/367] fix and remove useless import of example, st, ut

---
 example/Bert_NEZHA_cnwiki/train.py            |  4 +-
 tests/st/mem_reuse/resnet_cifar_memreuse.py   |  3 +-
 tests/st/mem_reuse/resnet_cifar_normal.py     |  3 +-
 .../models/bert/bert_tdt_no_lossscale.py      |  4 +-
 tests/st/ops/davinci/test_tdt_data_ms.py      | 26 ++++++-------
 tests/ut/python/dataset/test_minddataset.py   |  1 -
 tests/ut/python/dataset/test_project.py       | 38 +++++++++----------
 7 files changed, 37 insertions(+), 42 deletions(-)

diff --git a/example/Bert_NEZHA_cnwiki/train.py b/example/Bert_NEZHA_cnwiki/train.py
index 87f425e21c..86e033fc9f 100644
--- a/example/Bert_NEZHA_cnwiki/train.py
+++ b/example/Bert_NEZHA_cnwiki/train.py
@@ -36,7 +36,7 @@ import os
 import numpy as np
 from config import bert_train_cfg, bert_net_cfg
 import mindspore.dataset.engine.datasets as de
-import mindspore._c_dataengine as deMap
+import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
@@ -52,7 +52,7 @@ def create_train_dataset(batch_size):
     ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR,
                            columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
                                          "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"])
-    type_cast_op = deMap.TypeCastOp("int32")
+    type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
     ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
diff --git a/tests/st/mem_reuse/resnet_cifar_memreuse.py b/tests/st/mem_reuse/resnet_cifar_memreuse.py
index 4699c00e73..4edcdd8fb8 100644
--- a/tests/st/mem_reuse/resnet_cifar_memreuse.py
+++ b/tests/st/mem_reuse/resnet_cifar_memreuse.py
@@ -24,8 +24,7 @@ import numpy as np
 import mindspore.ops.functional as F
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataengine as de
-import mindspore._c_dataengine as deMap
+import mindspore.dataset as de
 import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore.communication.management import init
diff --git a/tests/st/mem_reuse/resnet_cifar_normal.py b/tests/st/mem_reuse/resnet_cifar_normal.py
index bff0c2d6e6..39f6e7fe59 100644
--- a/tests/st/mem_reuse/resnet_cifar_normal.py
+++ b/tests/st/mem_reuse/resnet_cifar_normal.py
@@ -24,8 +24,7 @@ import numpy as np
 import mindspore.ops.functional as F
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataengine as de
-import mindspore._c_dataengine as deMap
+import mindspore.dataset as de
 import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore.communication.management import init
diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
index c1ca6f6499..7c50707fbd 100644
--- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
+++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
@@ -21,7 +21,7 @@ import numpy as np
 from numpy import allclose
 import mindspore.common.dtype as mstype
 import mindspore.dataset.engine.datasets as de
-import mindspore._c_dataengine as deMap
+import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
@@ -106,7 +106,7 @@ def me_de_train_dataset():
     ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                "next_sentence_labels", "masked_lm_positions",
                                                                "masked_lm_ids", "masked_lm_weights"])
-    type_cast_op = deMap.TypeCastOp("int32")
+    type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
     ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
diff --git a/tests/st/ops/davinci/test_tdt_data_ms.py b/tests/st/ops/davinci/test_tdt_data_ms.py
index 6463401d82..89f6f212d0 100644
--- a/tests/st/ops/davinci/test_tdt_data_ms.py
+++ b/tests/st/ops/davinci/test_tdt_data_ms.py
@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import mindspore._c_dataengine as deMap
 import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from mindspore.dataset.transforms.vision import Inter
 import numpy as np
 import sys
-from mindspore._c_dataengine import InterpolationMode
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -32,7 +32,7 @@ SCHEMA_DIR = "{0}/resnet_all_datasetSchema.json".format(data_path)
 def test_me_de_train_dataset():
     data_list = ["{0}/train-00001-of-01024.data".format(data_path)]
     data_set = ds.StorageDataset(data_list, schema=SCHEMA_DIR,
-                           columns_list=["image/encoded", "image/class/label"])
+                                 columns_list=["image/encoded", "image/class/label"])
 
     resize_height = 224
     resize_width = 224
@@ -41,19 +41,17 @@ def test_me_de_train_dataset():
 
     # define map operations
 
-    decode_op = deMap.DecodeOp()
-    resize_op = deMap.ResizeOp(resize_height, resize_width,
-                               InterpolationMode.DE_INTER_LINEAR)  # Bilinear as default
-    rescale_op = deMap.RescaleOp(rescale, shift)
-    changemode_op = deMap.ChangeModeOp()
+    decode_op = vision.Decode()
+    resize_op = vision.Resize(resize_height, resize_width,
+                              Inter.LINEAR)  # Bilinear as default
+    rescale_op = vision.Rescale(rescale, shift)
 
     # apply map operations on images
-    data_set = data_set.map(input_column_names="image/encoded", operation=decode_op)
-    data_set = data_set.map(input_column_names="image/encoded", operation=resize_op)
-    data_set = data_set.map(input_column_names="image/encoded", operation=rescale_op)
-    data_set = data_set.map(input_column_names="image/encoded", operation=changemode_op)
-    changeswap_op = deMap.ChannelSwapOp()
-    data_set = data_set.map(input_column_names="image/encoded", operation=changeswap_op)
+    data_set = data_set.map(input_columns="image/encoded", operations=decode_op)
+    data_set = data_set.map(input_columns="image/encoded", operations=resize_op)
+    data_set = data_set.map(input_columns="image/encoded", operations=rescale_op)
+    hwc2chw_op = vision.HWC2CHW()
+    data_set = data_set.map(input_columns="image/encoded", operations=hwc2chw_op)
     data_set = data_set.repeat(1)
     # apply batch operations
     batch_size = 32
diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py
index 8b8cbc807a..da22f5c3b7 100644
--- a/tests/ut/python/dataset/test_minddataset.py
+++ b/tests/ut/python/dataset/test_minddataset.py
@@ -24,7 +24,6 @@ import string
 import mindspore.dataset.transforms.vision.c_transforms as vision
 import numpy as np
 import pytest
-from mindspore._c_dataengine import InterpolationMode
 from mindspore.dataset.transforms.vision import Inter
 from mindspore import log as logger
 
diff --git a/tests/ut/python/dataset/test_project.py b/tests/ut/python/dataset/test_project.py
index de600e07db..522788ac37 100644
--- a/tests/ut/python/dataset/test_project.py
+++ b/tests/ut/python/dataset/test_project.py
@@ -13,7 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 import mindspore.dataset.transforms.vision.c_transforms as vision
-import mindspore._c_dataengine as de_map
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.common import dtype as mstype
 from util import ordered_save_and_check
 
 import mindspore.dataset as ds
@@ -63,9 +64,8 @@ def test_case_project_map():
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False)
     data1 = data1.project(columns=columns)
 
-    no_op = de_map.NoOp()
-
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
+    type_cast_op = C.TypeCast(mstype.int64)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
 
     filename = "project_map_after_result.npz"
     ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
@@ -77,8 +77,8 @@ def test_case_map_project():
 
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False)
 
-    no_op = de_map.NoOp()
-    data1 = data1.map(input_columns=["col_sint64"], operations=no_op)
+    type_cast_op = C.TypeCast(mstype.int64)
+    data1 = data1.map(input_columns=["col_sint64"], operations=type_cast_op)
 
     data1 = data1.project(columns=columns)
 
@@ -92,19 +92,19 @@ def test_case_project_between_maps():
 
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False)
 
-    no_op = de_map.NoOp()
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
+    type_cast_op = C.TypeCast(mstype.int64)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
 
     data1 = data1.project(columns=columns)
 
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
-    data1 = data1.map(input_columns=["col_3d"], operations=no_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
+    data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
 
     filename = "project_between_maps_result.npz"
     ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
@@ -145,12 +145,12 @@ def test_case_map_project_map_project():
 
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False)
 
-    no_op = de_map.NoOp()
-    data1 = data1.map(input_columns=["col_sint64"], operations=no_op)
+    type_cast_op = C.TypeCast(mstype.int64)
+    data1 = data1.map(input_columns=["col_sint64"], operations=type_cast_op)
 
     data1 = data1.project(columns=columns)
 
-    data1 = data1.map(input_columns=["col_2d"], operations=no_op)
+    data1 = data1.map(input_columns=["col_2d"], operations=type_cast_op)
 
     data1 = data1.project(columns=columns)
 

From 86a33985470b4bf12e43a651d7660bd588024fd2 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Fri, 3 Apr 2020 09:38:04 +0800
Subject: [PATCH 079/367] modify runtest py ut for parallel exec

---
 tests/ut/python/runtest.sh | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/tests/ut/python/runtest.sh b/tests/ut/python/runtest.sh
index d1c3299821..035e454f24 100755
--- a/tests/ut/python/runtest.sh
+++ b/tests/ut/python/runtest.sh
@@ -39,9 +39,13 @@ if [ "x${ENABLE_GE}" == "xON" -o "x${ENABLE_GE}" == "xOn" -o "x${ENABLE_GE}" ==
 fi
 
 if [ $# -gt 0 ]; then
-    pytest -s --ignore=$1/pynative_mode $IGNORE_EXEC $1
+    pytest -s --ignore=$1/pynative_mode --ignore=$1/parallel  --ignore=$1/train  $IGNORE_EXEC $1
+    pytest -n 4 --dist=loadfile -v $1/parallel
+    pytest -n 4 --dist=loadfile -v $1/train
 else
-    pytest --ignore=$CURRPATH/pynative_mode $IGNORE_EXEC $CURRPATH
+    pytest --ignore=$CURRPATH/pynative_mode --ignore=$CURRPATH/parallel --ignore=$CURRPATH/train $IGNORE_EXEC $CURRPATH
+    pytest -n 4 --dist=loadfile -v $CURRPATH/parallel
+    pytest -n 4 --dist=loadfile -v $CURRPATH/train
 fi
 
 RET=$?

From 461d8e3a0801943bc27ca31a3d2987de5edd3d37 Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Thu, 2 Apr 2020 15:33:46 +0800
Subject: [PATCH 080/367] add comparison ops

fix pylint

use scalar_lt primitive directly

fix review
---
 mindspore/_extends/parse/resources.py         |  10 +-
 .../ops/composite/multitype_ops/__init__.py   |  12 +-
 .../ops/composite/multitype_ops/equal_impl.py |   3 +-
 .../multitype_ops/greater_equal_impl.py       |  53 ++++
 .../composite/multitype_ops/greater_impl.py   |  53 ++++
 .../multitype_ops/less_equal_impl.py          |   3 +-
 .../ops/composite/multitype_ops/less_impl.py  |   5 +-
 .../composite/multitype_ops/logic_not_impl.py |  48 ++++
 .../composite/multitype_ops/not_equal_impl.py | 237 ++++++++++++++++++
 .../ops/composite/multitype_ops/uadd_impl.py  |  26 ++
 mindspore/ops/functional.py                   |   4 +
 .../python_input/gtest_input/vm/vm_test.py    |   3 +-
 tests/ut/python/ops/test_python_operators.py  |  20 +-
 13 files changed, 462 insertions(+), 15 deletions(-)
 create mode 100644 mindspore/ops/composite/multitype_ops/greater_equal_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/greater_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/logic_not_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/not_equal_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/uadd_impl.py

diff --git a/mindspore/_extends/parse/resources.py b/mindspore/_extends/parse/resources.py
index 1f6f4b91b5..5dd24ccf80 100644
--- a/mindspore/_extends/parse/resources.py
+++ b/mindspore/_extends/parse/resources.py
@@ -92,16 +92,16 @@ convert_object_map = {
     T.and_:         multitype_ops.logical_and,
     T.or_:          multitype_ops.logical_or,
     T.xor:          NO_IMPLEMENT,
-    T.pos:          F.scalar_uadd,
+    T.pos:          multitype_ops.uadd,
     T.neg:          multitype_ops.negative,
     T.invert:       NO_IMPLEMENT,
-    T.not_:         F.bool_not,
+    T.not_:         multitype_ops.logical_not,
     T.eq:           multitype_ops.equal,
-    T.ne:           F.scalar_ne,
+    T.ne:           multitype_ops.not_equal,
     T.lt:           multitype_ops.less,
-    T.gt:           F.scalar_gt,
+    T.gt:           multitype_ops.greater,
     T.le:           multitype_ops.less_equal,
-    T.ge:           F.scalar_ge,
+    T.ge:           multitype_ops.greater_equal,
     T.is_:          F.is_,
     T.is_not:       F.is_not,
     T.contains:     NO_IMPLEMENT,
diff --git a/mindspore/ops/composite/multitype_ops/__init__.py b/mindspore/ops/composite/multitype_ops/__init__.py
index 0ab8527ab4..db28b1b5f6 100644
--- a/mindspore/ops/composite/multitype_ops/__init__.py
+++ b/mindspore/ops/composite/multitype_ops/__init__.py
@@ -23,23 +23,33 @@ from .getitem_impl import getitem
 from .zeros_like_impl import zeros_like
 from .ones_like_impl import ones_like
 from .equal_impl import equal
+from .not_equal_impl import not_equal
 from .less_impl import less
 from .less_equal_impl import less_equal
+from .greater_impl import greater
+from .greater_equal_impl import greater_equal
 from .negative_impl import negative
 from .logical_and_impl import logical_and
 from .logical_or_impl import logical_or
+from .logic_not_impl import logical_not
+from .uadd_impl import uadd
 __all__ = [
     'add',
     'sub',
     'mul',
     'div',
+    'uadd',
     'zeros_like',
     'ones_like',
     'equal',
+    'not_equal',
     'less',
     'less_equal',
+    'greater',
+    'greater_equal',
     'negative',
     'getitem',
     'logical_and',
-    'logical_or'
+    'logical_or',
+    'logical_not'
 ]
diff --git a/mindspore/ops/composite/multitype_ops/equal_impl.py b/mindspore/ops/composite/multitype_ops/equal_impl.py
index 9ff7e6671e..428cdf4705 100644
--- a/mindspore/ops/composite/multitype_ops/equal_impl.py
+++ b/mindspore/ops/composite/multitype_ops/equal_impl.py
@@ -190,7 +190,8 @@ def _none_equal_tuple(x, y):
    """
     return False
 
-
+@equal.register("Tensor", "Number")
+@equal.register("Number", "Tensor")
 @equal.register("Tensor", "Tensor")
 def _tensor_equal_tensor(x, y):
     """
diff --git a/mindspore/ops/composite/multitype_ops/greater_equal_impl.py b/mindspore/ops/composite/multitype_ops/greater_equal_impl.py
new file mode 100644
index 0000000000..2073abb762
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/greater_equal_impl.py
@@ -0,0 +1,53 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""greater_equal_impl"""
+from mindspore.ops.composite import base
+from mindspore.ops import functional as F
+
+# greater_equal is a metagraph object which will determine if two objects are greater_equal according to input type
+# using ".register" decorator
+greater_equal = base.MultitypeFuncGraph("greater_equal")
+
+
+@greater_equal.register("Number", "Number")
+def _greater_equal_scala(x, y):
+    """
+    Determine whether x is greater equal than y
+
+    Args:
+       x(Number): Number.
+       y(Number): Number.
+
+    Returns:
+       bool, if x >= y return true, x < y return false.
+   """
+    return F.scalar_ge(x, y)
+
+@greater_equal.register("Tensor", "Number")
+@greater_equal.register("Number", "Tensor")
+@greater_equal.register("Tensor", "Tensor")
+def _greater_equal_tensor(x, y):
+    """
+    Determine  whether tensor x is greater equal than tensor y elementwise
+
+    Args:
+       x(Tensor): Tensor.
+       y(Tensor): Tensor.
+
+    Returns:
+       Tensor, return value by operator P.GreaterEqual.
+   """
+    return  F.tensor_ge(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/greater_impl.py b/mindspore/ops/composite/multitype_ops/greater_impl.py
new file mode 100644
index 0000000000..7bbf53da49
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/greater_impl.py
@@ -0,0 +1,53 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Ungreater required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""equal_impl"""
+from mindspore.ops.composite import base
+from mindspore.ops import functional as F
+
+# greater is a metafuncgraph object which will determine if two objects are greater according to input type
+# using ".register" decorator
+greater = base.MultitypeFuncGraph("greater")
+
+
+@greater.register("Number", "Number")
+def _greater_scala(x, y):
+    """
+    Determine whether two numbers are greater.
+
+    Args:
+       x(Number): Number.
+       y(Number): Number.
+
+    Returns:
+       bool, if x > y return true, x <= y return false.
+   """
+    return F.scalar_gt(x, y)
+
+@greater.register("Tensor", "Number")
+@greater.register("Number", "Tensor")
+@greater.register("Tensor", "Tensor")
+def _greater_tensor(x, y):
+    """
+    Determine whether two tensor are greater by element.
+
+    Args:
+       x(Tensor): Tensor.
+       y(Tensor): Tensor.
+
+    Returns:
+       tensor, return operation of x and y by P.Greater
+   """
+    return F.tensor_gt(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/less_equal_impl.py b/mindspore/ops/composite/multitype_ops/less_equal_impl.py
index f02ab61da1..dc1438da2c 100644
--- a/mindspore/ops/composite/multitype_ops/less_equal_impl.py
+++ b/mindspore/ops/composite/multitype_ops/less_equal_impl.py
@@ -36,7 +36,8 @@ def _less_equal_scala(x, y):
    """
     return F.scalar_le(x, y)
 
-
+@less_equal.register("Tensor", "Number")
+@less_equal.register("Number", "Tensor")
 @less_equal.register("Tensor", "Tensor")
 def _less_equal_tensor(x, y):
     """
diff --git a/mindspore/ops/composite/multitype_ops/less_impl.py b/mindspore/ops/composite/multitype_ops/less_impl.py
index c9c20657e5..6e50e54c82 100644
--- a/mindspore/ops/composite/multitype_ops/less_impl.py
+++ b/mindspore/ops/composite/multitype_ops/less_impl.py
@@ -36,7 +36,8 @@ def _less_scala(x, y):
    """
     return F.scalar_lt(x, y)
 
-
+@less.register("Tensor", "Number")
+@less.register("Number", "Tensor")
 @less.register("Tensor", "Tensor")
 def _less_tensor(x, y):
     """
@@ -47,6 +48,6 @@ def _less_tensor(x, y):
        y(Tensor): Tensor.
 
     Returns:
-       bool, if x and y are less elements by element return true, else return false.
+       Tensor, return value of  x and y by operation P.Less()
    """
     return F.tensor_lt(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/logic_not_impl.py b/mindspore/ops/composite/multitype_ops/logic_not_impl.py
new file mode 100644
index 0000000000..35ae766433
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/logic_not_impl.py
@@ -0,0 +1,48 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""logical_not_impl"""
+from mindspore.ops.composite import base
+from mindspore.ops import functional as F
+
+# logical_not is a metagraph object which will generate function according to input type
+# using ".register" decorator
+logical_not = base.MultitypeFuncGraph("logical_not")
+
+
+@logical_not.register("Number")
+def _logical_not_scala(x):
+    """
+    Return logical not operation result of x
+
+    Args:
+       x(Number): Number.
+
+    Returns:
+       bool, Return logical not operation result of x
+   """
+    return F.bool_not(x.__bool__())
+
+
+@logical_not.register("Tensor")
+def _logical_not_tensor(x):
+    """
+    Return logical not operation result of x
+    Args:
+       x(Tensor): Tensor.
+    Returns:
+       Tensor, Return logical not operation result of x
+   """
+    return  F.logical_not(x)
diff --git a/mindspore/ops/composite/multitype_ops/not_equal_impl.py b/mindspore/ops/composite/multitype_ops/not_equal_impl.py
new file mode 100644
index 0000000000..de099a2b8f
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/not_equal_impl.py
@@ -0,0 +1,237 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""not_equal_impl"""
+
+from ...composite import base
+from ... import functional as F
+
+
+not_equal = base.MultitypeFuncGraph("not_equal")
+"""
+not_equal is a metafuncgraph object which will determine if two objects are not_equal according to input type
+using ".register" decorator
+"""
+
+
+@not_equal.register("Number", "Number")
+def _not_equal_scalar(x, y):
+    """
+    Determine if two numbers is not equal.
+
+    Args:
+       x (Number): x
+       y (NUmber): y
+
+    Returns:
+       bool, if x != y return true, x == y return false.
+   """
+    return not F.scalar_eq(x, y)
+
+
+@not_equal.register("String", "String")
+def _not_equal_string(x, y):
+    """
+    Determine if two strings are not equal.
+
+    Args:
+       x: str
+       y: str
+
+    Returns:
+       bool, if x != y return true, x == y return false.
+   """
+    return not F.string_eq(x, y)
+
+
+@not_equal.register("String", "None")
+def _string_not_equal_none(x, y):
+    """
+    Determine if string not equals none.
+
+    Args:
+       x: str.
+       y: None.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("None", "String")
+def _none_not_equal_string(x, y):
+    """
+    Determine if string not equals none.
+
+    Args:
+       x: None.
+       y: str.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("None", "None")
+def _none_not_equal_none(x, y):
+    """
+    Determine if none not equals none.
+
+    Args:
+       x: None.
+       y: None.
+
+    Returns:
+       bool, return False.
+   """
+    return False
+
+
+@not_equal.register("Number", "None")
+def _scalar_not_equal_none(x, y):
+    """
+    Determine if number not equals none.
+
+    Args:
+       x: Number.
+       y: None.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("None", "Number")
+def _none_not_equal_scalar(x, y):
+    """
+    Determine if number not_equals none.
+
+    Args:
+       x: None.
+       y: NUmber.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("Tuple", "Tuple")
+def _euqal_tuple(x, y):
+    """
+    Determine if two tuples are not equal by element.
+
+    Args:
+       x (tuple): x
+       y (tuple): y
+
+    Returns:
+       bool, if x and y are not equal by element return true, else return false.
+   """
+    return not F.tuple_equal(x, y)
+
+
+@not_equal.register("List", "List")
+def _euqal_list(x, y):
+    """
+    Determine if two lists are not equal by element.
+
+    Args:
+       x (list): x
+       y (list): y
+
+    Returns:
+       bool, if x and y are not equal by element return true, else return false.
+   """
+    return not F.list_equal(x, y)
+
+
+@not_equal.register("Tuple", "None")
+def _tuple_euqal_none(x, y):
+    """
+    Determine if tuple element not equals none element.
+
+    Args:
+       x: Tuple.
+       y: None.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("None", "Tuple")
+def _none_not_equal_tuple(x, y):
+    """
+    Determine if tuple element not equals none element.
+
+    Args:
+       x: None.
+       y: Tuple.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+@not_equal.register("Tensor", "Number")
+@not_equal.register("Number", "Tensor")
+@not_equal.register("Tensor", "Tensor")
+def _tensor_not_equal_tensor(x, y):
+    """
+    Determine if two tensors are not_equal.
+
+    Args:
+       x : Tensor.
+       y : Tensor.
+
+    Returns:
+       bool, if x == y return true, x != y return false.
+   """
+    return F.not_equal(x, y)
+
+
+@not_equal.register("Tensor", "None")
+def _tensor_not_equal_none(x, y):
+    """
+    Determine if tensor not_equal none.
+
+    Args:
+       x : Tensor.
+       y : None.
+
+    Returns:
+       bool, return True.
+   """
+    return True
+
+
+@not_equal.register("None", "Tensor")
+def _none_not_equal_tensor(x, y):
+    """
+    Determine if tensor not equal none.
+
+    Args:
+       x : None.
+       y : Tensor.
+
+    Returns:
+       bool, return True.
+   """
+    return True
diff --git a/mindspore/ops/composite/multitype_ops/uadd_impl.py b/mindspore/ops/composite/multitype_ops/uadd_impl.py
new file mode 100644
index 0000000000..163120b541
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/uadd_impl.py
@@ -0,0 +1,26 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""uadd_impl"""
+from mindspore.ops.composite import base
+
+# uadd is a metagraph object which will return operation result regarding input
+# using ".register" decorator
+uadd = base.MultitypeFuncGraph("uadd")
+
+@uadd.register("Tensor")
+@uadd.register("Number")
+def _uadd_scala(x):
+    return x
diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py
index 5adb6fac57..a74c1464a5 100644
--- a/mindspore/ops/functional.py
+++ b/mindspore/ops/functional.py
@@ -43,12 +43,15 @@ tensor_add = P.TensorAdd()
 neg_tensor = P.Neg()
 tensor_lt = P.Less()
 tensor_le = P.LessEqual()
+tensor_gt = P.Greater()
+tensor_ge = P.GreaterEqual()
 tensor_sub = P.Sub()
 tensor_mul = P.Mul()
 tensor_div = P.RealDiv()
 strided_slice = P.StridedSlice()
 same_type_shape = P.SameTypeShape()
 equal = P.Equal()
+not_equal = P.NotEqual()
 assign_sub = P.AssignSub()
 assign = P.Assign()
 square = P.Square()
@@ -97,6 +100,7 @@ bool_or = Primitive("bool_or")
 bool_and = Primitive("bool_and")
 logical_and = P.LogicalAnd()
 logical_or = P.LogicalOr()
+logical_not = P.LogicalNot()
 array_to_scalar = Primitive('array_to_scalar')
 is_ = Primitive("is_")
 is_not = Primitive("is_not")
diff --git a/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py b/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py
index bdd3c900d6..947e9fa2c3 100644
--- a/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py
@@ -17,6 +17,7 @@ from mindspore.ops import Primitive
 
 scala_add = Primitive('scalar_add')
 scala_mul = Primitive('scalar_mul')
+scalar_gt = Primitive('scalar_gt')
 def scalar_add(x, y):
     """Implement `scalar_add`."""
     return scala_add(x, y)
@@ -26,6 +27,6 @@ def scalar_mul(x, y):
     return scala_mul(x, y)
 
 def test_if(x, y):
-    if x > y:
+    if scalar_gt(x, y):
         return x
     return y
diff --git a/tests/ut/python/ops/test_python_operators.py b/tests/ut/python/ops/test_python_operators.py
index d6c6c03760..eb65a7f373 100644
--- a/tests/ut/python/ops/test_python_operators.py
+++ b/tests/ut/python/ops/test_python_operators.py
@@ -31,8 +31,20 @@ class ComparisonOpsNet(nn.Cell):
     def __init__(self):
         super(ComparisonOpsNet, self).__init__()
     def construct(self, x, y):
-        ret = x <= y
-        return ret
+        a = x <= y
+        b = x <= 1.0
+        c = y >= 1.0
+        d = y >= x
+        e = x < y
+        f = x < 1.0
+        g = 1.0 > y
+        h = y > x
+        i = y == 3.0
+        j = x != 4
+        k = + x
+        l = + 1.0
+        m = k != l
+        return a or b or c or d or e or f or g or h or i or j or m
 
 class LogicalNumberOpsNet(nn.Cell):
     def __init__(self):
@@ -41,7 +53,7 @@ class LogicalNumberOpsNet(nn.Cell):
         self.one = 0
         self.zero = 0.0
     def construct(self, x, y):
-        if self.cond and self.one or self.zero:
+        if self.cond and self.one or self.zero and not self.one:
             return x + y
         return x - y
 
@@ -51,7 +63,7 @@ class LogicalTensorOpsNet(nn.Cell):
         super(LogicalTensorOpsNet, self).__init__()
         self.const_true = Tensor(True, dtype=mstype.bool_)
     def construct(self, x, y):
-        ret = x and y and (y or self.const_true)
+        ret = x and y and (y or self.const_true) and (not self.const_true)
         return ret
 
 

From 8ece432ae9a3ef85ba1b3ecefe5c8701d97f6e9d Mon Sep 17 00:00:00 2001
From: jjfeing <jiangjianfei3@huawei.com>
Date: Thu, 2 Apr 2020 22:03:36 +0800
Subject: [PATCH 081/367] tr5 run package update

---
 cmake/dependency_graphengine.cmake            | 25 ++++++++++---------
 graphengine                                   |  2 +-
 .../parallel_compile/tbe_compiler/common.py   |  2 +-
 mindspore/ccsrc/CMakeLists.txt                | 22 +++++++++-------
 4 files changed, 28 insertions(+), 23 deletions(-)

diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake
index dfe90de836..d2f80c4d04 100644
--- a/cmake/dependency_graphengine.cmake
+++ b/cmake/dependency_graphengine.cmake
@@ -38,19 +38,20 @@ elseif (DEFINED ENV{D_LINK_PATH})
     find_library(cce libcce.so ${GE_LIB_PATH})
     find_library(resource libresource.so ${GE_LIB_PATH})
 else()
-    set(HIAI_INSTALLED_DIR /usr/local/HiAI)
-    set(HIAI_DRIVER_DIR ${HIAI_INSTALLED_DIR}/driver/lib64)
-    set(HIAI_RUNTIME_DIR ${HIAI_INSTALLED_DIR}/runtime/lib64)
-    find_library(c_sec libc_sec.so ${HIAI_DRIVER_DIR})
-    find_library(slog libslog.so ${HIAI_DRIVER_DIR})
-    find_library(mmpa libmmpa.so ${HIAI_DRIVER_DIR})
+    # Ascend mode
+    set(ASCEND_PATH /usr/local/Ascend)
+    set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
+    set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
 
-    find_library(cce libcce.so ${HIAI_RUNTIME_DIR})
-    find_library(hccl libhccl.so ${HIAI_RUNTIME_DIR})
-    find_library(runtime libruntime.so ${HIAI_RUNTIME_DIR})
-    find_library(msprof libmsprof.so ${HIAI_RUNTIME_DIR})
-    find_library(register libregister.so ${HIAI_RUNTIME_DIR})
-    find_library(resource libresource.so ${HIAI_RUNTIME_DIR})
+    find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH})
+    find_library(slog libslog.so ${ASCEND_DRIVER_PATH})
+    find_library(mmpa libmmpa.so ${ASCEND_DRIVER_PATH})
+    find_library(cce libcce.so ${ASCEND_RUNTIME_PATH})
+    find_library(hccl libhccl.so ${ASCEND_RUNTIME_PATH})
+    find_library(runtime libruntime.so ${ASCEND_RUNTIME_PATH})
+    find_library(msprof libmsprof.so ${ASCEND_RUNTIME_PATH})
+    find_library(register libregister.so ${ASCEND_RUNTIME_PATH})
+    find_library(resource libresource.so ${ASCEND_RUNTIME_PATH})
 endif()
 
 # compile libraries from following directories
diff --git a/graphengine b/graphengine
index 49351fb73a..60b2262ce7 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 49351fb73ac7786b9ed9e807568a1a8e06183693
+Subproject commit 60b2262ce700db538e4c728619e1972c3687f633
diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/common.py b/mindspore/_extends/parallel_compile/tbe_compiler/common.py
index 6cfa2afd0d..6258cf8d45 100644
--- a/mindspore/_extends/parallel_compile/tbe_compiler/common.py
+++ b/mindspore/_extends/parallel_compile/tbe_compiler/common.py
@@ -50,7 +50,7 @@ def get_build_in_impl_path():
     tbe_impl_path = os.environ.get("TBE_IMPL_PATH")
     if tbe_impl_path is None:
         default_install_path = '/usr/local/HiAI/runtime/ops/op_impl/built-in/ai_core/tbe/'
-        backup_install_path = '/usr/local/Ascend/Ascend/opp/op_impl/built-in/ai_core/tbe/'
+        backup_install_path = '/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe/'
         if os.path.exists(default_install_path):
             tbe_impl_path = default_install_path
         elif os.path.exists(backup_install_path):
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 0b4bb0d1df..c4deb53e68 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -279,15 +279,17 @@ if(ENABLE_D)
         endif()
     else()
         MESSAGE("use system default lib")
-        set(D_LIB_PATH "/usr/local/HiAI/runtime/lib64/")
+        set(ASCEND_PATH /usr/local/Ascend)
+        set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
+        set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
     endif()
 
-    MESSAGE("USE DAV LIB PATH: ${D_LIB_PATH}")
-    find_library(HCCL hccl ${D_LIB_PATH})
-    find_library(CCE_LIB cce ${D_LIB_PATH})
-    find_library(RUNTIME_LIB runtime ${D_LIB_PATH})
-    find_library(TSDCLIENT tsdclient ${D_LIB_PATH})
-    find_library(PROFILING msprof ${D_LIB_PATH})
+    MESSAGE("USE DAV LIB PATH: ${ASCEND_PATH}")
+    find_library(HCCL hccl ${ASCEND_RUNTIME_PATH})
+    find_library(CCE_LIB cce ${ASCEND_RUNTIME_PATH})
+    find_library(RUNTIME_LIB runtime ${ASCEND_RUNTIME_PATH})
+    find_library(TSDCLIENT tsdclient ${ASCEND_RUNTIME_PATH})
+    find_library(PROFILING msprof ${ASCEND_DRIVER_PATH})
     target_link_libraries(mindspore ge_runtime ${CCE_LIB} ${RUNTIME_LIB} ${TSDCLIENT} ${PROFILING} ${HCCL} ${TSDCLIENT})
 endif()
 
@@ -481,12 +483,14 @@ add_dependencies(add_ms_lib _c_expression)
 
 if (NOT ENABLE_GE)
     if (ENABLE_D)
+        set(ASCEND_PATH /usr/local/Ascend)
+        set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
         add_custom_target(add_ge_lib ALL
                 COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH}
                 COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/common/libge_common.so ${MS_LIB_PATH}
                 COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/ge_runtime/libge_runtime.so ${MS_LIB_PATH}
-                COMMAND cp /usr/local/HiAI/driver/lib64/libslog.so ${MS_LIB_PATH}
-                COMMAND cp /usr/local/HiAI/driver/lib64/libc_sec.so ${MS_LIB_PATH}
+                COMMAND cp ${ASCEND_DRIVER_PATH}/libslog.so ${MS_LIB_PATH}
+                COMMAND cp ${ASCEND_DRIVER_PATH}/libc_sec.so ${MS_LIB_PATH}
         )
         add_dependencies(add_ge_lib add_ms_lib)
         add_dependencies(add_ge_lib graph)

From b3cd639c09129168fc74e3e52d0d8f2728efabeb Mon Sep 17 00:00:00 2001
From: y00445136 <yankai10@huawei.com>
Date: Thu, 2 Apr 2020 11:39:24 +0800
Subject: [PATCH 082/367] add support for bool

---
 mindspore/ccsrc/utils/tensorprint_utils.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/utils/tensorprint_utils.cc b/mindspore/ccsrc/utils/tensorprint_utils.cc
index ee58ed418c..1036b424ba 100644
--- a/mindspore/ccsrc/utils/tensorprint_utils.cc
+++ b/mindspore/ccsrc/utils/tensorprint_utils.cc
@@ -37,13 +37,13 @@ static std::map<std::string, TypeId> print_type_map = {
   {"int32_t", TypeId::kNumberTypeInt32},   {"uint32_t", TypeId::kNumberTypeUInt32},
   {"int64_t", TypeId::kNumberTypeInt64},   {"uint64_t", TypeId::kNumberTypeUInt64},
   {"float16", TypeId::kNumberTypeFloat16}, {"float", TypeId::kNumberTypeFloat32},
-  {"double", TypeId::kNumberTypeFloat64}};
+  {"double", TypeId::kNumberTypeFloat64},  {"bool", TypeId::kNumberTypeBool}};
 
 static std::map<std::string, size_t> type_size_map = {
   {"int8_t", sizeof(int8_t)},     {"uint8_t", sizeof(uint8_t)},   {"int16_t", sizeof(int16_t)},
   {"uint16_t", sizeof(uint16_t)}, {"int32_t", sizeof(int32_t)},   {"uint32_t", sizeof(uint32_t)},
   {"int64_t", sizeof(int64_t)},   {"uint64_t", sizeof(uint64_t)}, {"float16", sizeof(float) / 2},
-  {"float", sizeof(float)},       {"double", sizeof(double)}};
+  {"float", sizeof(float)},       {"double", sizeof(double)},     {"bool", sizeof(bool)}};
 
 bool ParseTensorShape(const std::string &input_shape_str, std::vector<int> *const tensor_shape, size_t *dims) {
   if (tensor_shape == nullptr) {
@@ -107,7 +107,7 @@ bool ConvertDataItem2Tensor(const std::vector<tdt::DataItem> &items) {
     } else {
       auto type_iter = print_type_map.find(item.tensorType_);
       if (type_iter == print_type_map.end()) {
-        MS_LOG(ERROR) << "type of tensor need to print is not soupport" << item.tensorType_;
+        MS_LOG(ERROR) << "type of tensor need to print is not support " << item.tensorType_;
         continue;
       }
       auto type_id = type_iter->second;

From 0c81759ae695d2c07339c68ba821a961df0ba3a6 Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Wed, 1 Apr 2020 10:22:48 +0800
Subject: [PATCH 083/367] add YOLOv3 infer scipt and change dataset to
 MindRecord

---
 example/yolov3_coco2017/config.py             |   3 +
 example/yolov3_coco2017/dataset.py            | 265 +++++++-----------
 example/yolov3_coco2017/eval.py               | 107 +++++++
 .../yolov3_coco2017/run_distribute_train.sh   |  24 +-
 example/yolov3_coco2017/run_eval.sh           |  23 ++
 .../yolov3_coco2017/run_standalone_train.sh   |   8 +-
 example/yolov3_coco2017/train.py              | 114 +++++---
 example/yolov3_coco2017/util.py               | 146 ++++++++++
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     |   1 +
 mindspore/model_zoo/yolov3.py                 |  97 ++++++-
 mindspore/ops/_op_impl/tbe/__init__.py        |   2 +
 mindspore/ops/_op_impl/tbe/reduce_min.py      |  76 +++++
 mindspore/ops/_op_impl/tbe/round.py           |  65 +++++
 13 files changed, 704 insertions(+), 227 deletions(-)
 create mode 100644 example/yolov3_coco2017/eval.py
 create mode 100644 example/yolov3_coco2017/run_eval.sh
 create mode 100644 example/yolov3_coco2017/util.py
 create mode 100644 mindspore/ops/_op_impl/tbe/reduce_min.py
 create mode 100644 mindspore/ops/_op_impl/tbe/round.py

diff --git a/example/yolov3_coco2017/config.py b/example/yolov3_coco2017/config.py
index f00eb89dc7..6d858bcbcb 100644
--- a/example/yolov3_coco2017/config.py
+++ b/example/yolov3_coco2017/config.py
@@ -26,6 +26,7 @@ class ConfigYOLOV3ResNet18:
     img_shape = [352, 640]
     feature_shape = [32, 3, 352, 640]
     num_classes = 80
+    nms_max_num = 50
 
     backbone_input_shape = [64, 64, 128, 256]
     backbone_shape = [64, 128, 256, 512]
@@ -33,6 +34,8 @@ class ConfigYOLOV3ResNet18:
     backbone_stride = [1, 2, 2, 2]
 
     ignore_threshold = 0.5
+    obj_threshold = 0.3
+    nms_threshold = 0.4
 
     anchor_scales = [(10, 13),
                      (16, 30),
diff --git a/example/yolov3_coco2017/dataset.py b/example/yolov3_coco2017/dataset.py
index 56520704b4..826fe16c53 100644
--- a/example/yolov3_coco2017/dataset.py
+++ b/example/yolov3_coco2017/dataset.py
@@ -16,16 +16,14 @@
 """YOLOv3 dataset"""
 from __future__ import division
 
-import abc
-import io
 import os
-import math
-import json
 import numpy as np
 from PIL import Image
 from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
 import mindspore.dataset as de
+from mindspore.mindrecord import FileWriter
 import mindspore.dataset.transforms.vision.py_transforms as P
+import mindspore.dataset.transforms.vision.c_transforms as C
 from config import ConfigYOLOV3ResNet18
 
 iter_cnt = 0
@@ -114,6 +112,29 @@ def preprocess_fn(image, box, is_training):
 
         return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2
 
+    def _infer_data(img_data, input_shape, box):
+        w, h = img_data.size
+        input_h, input_w = input_shape
+        scale = min(float(input_w) / float(w), float(input_h) / float(h))
+        nw = int(w * scale)
+        nh = int(h * scale)
+        img_data = img_data.resize((nw, nh), Image.BICUBIC)
+
+        new_image = np.zeros((input_h, input_w, 3), np.float32)
+        new_image.fill(128)
+        img_data = np.array(img_data)
+        if len(img_data.shape) == 2:
+            img_data = np.expand_dims(img_data, axis=-1)
+            img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+
+        dh = int((input_h - nh) / 2)
+        dw = int((input_w - nw) / 2)
+        new_image[dh:(nh + dh), dw:(nw + dw), :] = img_data
+        new_image /= 255.
+        new_image = np.transpose(new_image, (2, 0, 1))
+        new_image = np.expand_dims(new_image, 0)
+        return new_image, np.array([h, w], np.float32), box
+
     def _data_aug(image, box, is_training, jitter=0.3, hue=0.1, sat=1.5, val=1.5, image_size=(352, 640)):
         """Data augmentation function."""
         if not isinstance(image, Image.Image):
@@ -124,32 +145,7 @@ def preprocess_fn(image, box, is_training):
         h, w = image_size
 
         if not is_training:
-            image = image.resize((w, h), Image.BICUBIC)
-            image_data = np.array(image) / 255.
-            if len(image_data.shape) == 2:
-                image_data = np.expand_dims(image_data, axis=-1)
-                image_data = np.concatenate([image_data, image_data, image_data], axis=-1)
-            image_data = image_data.astype(np.float32)
-
-            # correct boxes
-            box_data = np.zeros((max_boxes, 5))
-            if len(box) >= 1:
-                np.random.shuffle(box)
-                if len(box) > max_boxes:
-                    box = box[:max_boxes]
-                # xmin ymin xmax ymax
-                box[:, [0, 2]] = box[:, [0, 2]] * float(w) / float(iw)
-                box[:, [1, 3]] = box[:, [1, 3]] * float(h) / float(ih)
-                box_data[:len(box)] = box
-            else:
-                image_data, box_data = None, None
-
-            # preprocess bounding boxes
-            bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
-                _preprocess_true_boxes(box_data, anchors, image_size)
-
-            return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
-                   ori_image_shape, gt_box1, gt_box2, gt_box3
+            return _infer_data(image, image_size, box)
 
         flip = _rand() < .5
         # correct boxes
@@ -235,12 +231,16 @@ def preprocess_fn(image, box, is_training):
         return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
                ori_image_shape, gt_box1, gt_box2, gt_box3
 
-    images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training)
-    return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3
+    if is_training:
+        images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training)
+        return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3
+
+    images, shape, anno = _data_aug(image, box, is_training)
+    return images, shape, anno
 
 
 def anno_parser(annos_str):
-    """Annotation parser."""
+    """Parse annotation from string to list."""
     annos = []
     for anno_str in annos_str:
         anno = list(map(int, anno_str.strip().split(',')))
@@ -248,142 +248,71 @@ def anno_parser(annos_str):
     return annos
 
 
-def expand_path(path):
-    """Get file list from path."""
-    files = []
-    if os.path.isdir(path):
-        for file in os.listdir(path):
-            if os.path.isfile(os.path.join(path, file)):
-                files.append(file)
-    else:
+def filter_valid_data(image_dir, anno_path):
+    """Filter valid image file, which both in image_dir and anno_path."""
+    image_files = []
+    image_anno_dict = {}
+    if not os.path.isdir(image_dir):
         raise RuntimeError("Path given is not valid.")
-    return files
-
-
-def read_image(img_path):
-    """Read image with PIL."""
-    with open(img_path, "rb") as f:
-        img = f.read()
-    data = io.BytesIO(img)
-    img = Image.open(data)
-    return np.array(img)
-
-
-class BaseDataset():
-    """BaseDataset for GeneratorDataset iterator."""
-    def __init__(self, image_dir, anno_path):
-        self.image_dir = image_dir
-        self.anno_path = anno_path
-        self.cur_index = 0
-        self.samples = []
-        self.image_anno_dict = {}
-        self._load_samples()
-
-    def __getitem__(self, item):
-        sample = self.samples[item]
-        return self._next_data(sample, self.image_dir, self.image_anno_dict)
-
-    def __len__(self):
-        return len(self.samples)
-
-    @staticmethod
-    def _next_data(sample, image_dir, image_anno_dict):
-        """Get next data."""
-        image = read_image(os.path.join(image_dir, sample))
-        annos = image_anno_dict[sample]
-        return [np.array(image), np.array(annos)]
-
-    @abc.abstractmethod
-    def _load_samples(self):
-        """Base load samples."""
-
-
-class YoloDataset(BaseDataset):
-    """YoloDataset for GeneratorDataset iterator."""
-    def _load_samples(self):
-        """Load samples."""
-        image_files_raw = expand_path(self.image_dir)
-        self.samples = self._filter_valid_data(self.anno_path, image_files_raw)
-        self.dataset_size = len(self.samples)
-        if self.dataset_size == 0:
-            raise RuntimeError("Valid dataset is none!")
-
-    def _filter_valid_data(self, anno_path, image_files_raw):
-        """Filter valid data."""
-        image_files = []
-        anno_dict = {}
-        print("Start filter valid data.")
-        with open(anno_path, "rb") as f:
-            lines = f.readlines()
-            for line in lines:
-                line_str = line.decode("utf-8")
-                line_split = str(line_str).split(' ')
-                anno_dict[line_split[0].split("/")[-1]] = line_split[1:]
-        anno_set = set(anno_dict.keys())
-        image_set = set(image_files_raw)
-        for image_file in (anno_set & image_set):
-            image_files.append(image_file)
-            self.image_anno_dict[image_file] = anno_parser(anno_dict[image_file])
-        image_files.sort()
-        print("Filter valid data done!")
-        return image_files
-
-
-class DistributedSampler():
-    """DistributedSampler for YOLOv3"""
-    def __init__(self, dataset_size, batch_size, num_replicas=None, rank=None, shuffle=True):
-        if num_replicas is None:
-            num_replicas = 1
-        if rank is None:
-            rank = 0
-        self.dataset_size = dataset_size
-        self.num_replicas = num_replicas
-        self.rank = rank % num_replicas
-        self.epoch = 0
-        self.num_samples = max(batch_size, int(math.ceil(dataset_size * 1.0 / self.num_replicas)))
-        self.total_size = self.num_samples * self.num_replicas
-        self.shuffle = shuffle
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        if self.shuffle:
-            indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size)
-            indices = indices.tolist()
-        else:
-            indices = list(range(self.dataset_size))
-
-        # add extra samples to make it evenly divisible
-        indices += indices[:(self.total_size - len(indices))]
-        assert len(indices) == self.total_size
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
-
-
-def create_yolo_dataset(image_dir, anno_path, batch_size=32, repeat_num=10, device_num=1, rank=0,
+    if not os.path.isfile(anno_path):
+        raise RuntimeError("Annotation file is not valid.")
+
+    with open(anno_path, "rb") as f:
+        lines = f.readlines()
+    for line in lines:
+        line_str = line.decode("utf-8").strip()
+        line_split = str(line_str).split(' ')
+        file_name = line_split[0]
+        if os.path.isfile(os.path.join(image_dir, file_name)):
+            image_anno_dict[file_name] = anno_parser(line_split[1:])
+            image_files.append(file_name)
+    return image_files, image_anno_dict
+
+
+def data_to_mindrecord_byte_image(image_dir, anno_path, mindrecord_dir, prefix="yolo.mindrecord", file_num=8):
+    """Create MindRecord file by image_dir and anno_path."""
+    mindrecord_path = os.path.join(mindrecord_dir, prefix)
+    writer = FileWriter(mindrecord_path, file_num)
+    image_files, image_anno_dict = filter_valid_data(image_dir, anno_path)
+
+    yolo_json = {
+        "image": {"type": "bytes"},
+        "annotation": {"type": "int64", "shape": [-1, 5]},
+    }
+    writer.add_schema(yolo_json, "yolo_json")
+
+    for image_name in image_files:
+        image_path = os.path.join(image_dir, image_name)
+        with open(image_path, 'rb') as f:
+            img = f.read()
+        annos = np.array(image_anno_dict[image_name])
+        row = {"image": img, "annotation": annos}
+        writer.write_raw_data([row])
+    writer.commit()
+
+
+def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num=1, rank=0,
                         is_training=True, num_parallel_workers=8):
-    """Creatr YOLOv3 dataset with GeneratorDataset."""
-    yolo_dataset = YoloDataset(image_dir=image_dir, anno_path=anno_path)
-    distributed_sampler = DistributedSampler(yolo_dataset.dataset_size, batch_size, device_num, rank)
-    ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"], sampler=distributed_sampler)
-    ds.set_dataset_size(len(distributed_sampler))
+    """Creatr YOLOv3 dataset with MindDataset."""
+    ds = de.MindDataset(mindrecord_dir, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
+                        num_parallel_workers=num_parallel_workers, shuffle=is_training)
+    decode = C.Decode()
+    ds = ds.map(input_columns=["image"], operations=decode)
     compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
-    hwc_to_chw = P.HWC2CHW()
-    ds = ds.map(input_columns=["image", "annotation"],
-                output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
-                columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
-                operations=compose_map_func, num_parallel_workers=num_parallel_workers)
-    ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
-    ds = ds.shuffle(buffer_size=256)
-    ds = ds.batch(batch_size, drop_remainder=True)
-    ds = ds.repeat(repeat_num)
+
+    if is_training:
+        hwc_to_chw = P.HWC2CHW()
+        ds = ds.map(input_columns=["image", "annotation"],
+                    output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
+                    columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
+                    operations=compose_map_func, num_parallel_workers=num_parallel_workers)
+        ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
+        ds = ds.shuffle(buffer_size=256)
+        ds = ds.batch(batch_size, drop_remainder=True)
+        ds = ds.repeat(repeat_num)
+    else:
+        ds = ds.map(input_columns=["image", "annotation"],
+                    output_columns=["image", "image_shape", "annotation"],
+                    columns_order=["image", "image_shape", "annotation"],
+                    operations=compose_map_func, num_parallel_workers=num_parallel_workers)
     return ds
diff --git a/example/yolov3_coco2017/eval.py b/example/yolov3_coco2017/eval.py
new file mode 100644
index 0000000000..8347f5be37
--- /dev/null
+++ b/example/yolov3_coco2017/eval.py
@@ -0,0 +1,107 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# less required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Evaluation for yolo_v3"""
+import os
+import argparse
+import time
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithEval
+from dataset import create_yolo_dataset, data_to_mindrecord_byte_image
+from config import ConfigYOLOV3ResNet18
+from util import metrics
+
+def yolo_eval(dataset_path, ckpt_path):
+    """Yolov3 evaluation."""
+
+    ds = create_yolo_dataset(dataset_path, is_training=False)
+    config = ConfigYOLOV3ResNet18()
+    net = yolov3_resnet18(config)
+    eval_net = YoloWithEval(net, config)
+    print("Load Checkpoint!")
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+
+
+    eval_net.set_train(False)
+    i = 1.
+    total = ds.get_dataset_size()
+    start = time.time()
+    pred_data = []
+    print("\n========================================\n")
+    print("total images num: ", total)
+    print("Processing, please wait a moment.")
+    for data in ds.create_dict_iterator():
+        img_np = data['image']
+        image_shape = data['image_shape']
+        annotation = data['annotation']
+
+        eval_net.set_train(False)
+        output = eval_net(Tensor(img_np), Tensor(image_shape))
+        for batch_idx in range(img_np.shape[0]):
+            pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
+                              "box_scores": output[1].asnumpy()[batch_idx],
+                              "annotation": annotation})
+        percent = round(i / total * 100, 2)
+
+        print('    %s [%d/%d]' % (str(percent) + '%', i, total), end='\r')
+        i += 1
+    print('    %s [%d/%d] cost %d ms' % (str(100.0) + '%', total, total, int((time.time() - start) * 1000)), end='\n')
+
+    precisions, recalls = metrics(pred_data)
+    print("\n========================================\n")
+    for i in range(config.num_classes):
+        print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Yolov3 evaluation')
+    parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_eval",
+                        help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by"
+                             "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
+                             "rather than image_dir and anno_path. Default is ./Mindrecord_eval")
+    parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
+                                                                  "the absolute image path is joined by the image_dir "
+                                                                  "and the relative path in anno_path.")
+    parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
+    parser.add_argument("--ckpt_path", type=str, required=True, help="Checkpoint path.")
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True,
+                        enable_auto_mixed_precision=False)
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is yolo.mindrecord0, 1, ... file_num.
+    if not os.path.isdir(args_opt.mindrecord_dir):
+        os.makedirs(args_opt.mindrecord_dir)
+
+    prefix = "yolo.mindrecord"
+    mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
+            print("Create Mindrecord")
+            data_to_mindrecord_byte_image(args_opt.image_dir,
+                                          args_opt.anno_path,
+                                          args_opt.mindrecord_dir,
+                                          prefix=prefix,
+                                          file_num=8)
+            print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
+        else:
+            print("image_dir or anno_path not exits")
+    print("Start Eval!")
+    yolo_eval(mindrecord_file, args_opt.ckpt_path)
diff --git a/example/yolov3_coco2017/run_distribute_train.sh b/example/yolov3_coco2017/run_distribute_train.sh
index 98e6690b8c..0c43e776b9 100644
--- a/example/yolov3_coco2017/run_distribute_train.sh
+++ b/example/yolov3_coco2017/run_distribute_train.sh
@@ -14,17 +14,26 @@
 # limitations under the License.
 # ============================================================================
 
+echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
-echo "for example: sh run_distribute_train.sh 8 100 ./dataset/coco/train2017 ./dataset/train.txt　./hccl.json"
-echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
+echo "for example: sh run_distribute_train.sh 8 100 /data/Mindrecord_train /data /data/train.txt /data/hccl.json"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
 
-export RANK_SIZE=$1
 EPOCH_SIZE=$2
-IMAGE_DIR=$3
-ANNO_PATH=$4
-export MINDSPORE_HCCL_CONFIG_PATH=$5
+MINDRECORD_DIR=$3
+IMAGE_DIR=$4
+ANNO_PATH=$5
+
+# Before start distribute train, first create mindrecord files.
+python train.py --only_create_dataset=1 --mindrecord_dir=$MINDRECORD_DIR --image_dir=$IMAGE_DIR  \
+--anno_path=$ANNO_PATH
 
+echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+
+export MINDSPORE_HCCL_CONFIG_PATH=$6
+export RANK_SIZE=$1
 
 for((i=0;i<RANK_SIZE;i++))
 do
@@ -40,6 +49,7 @@ do
     --distribute=1  \
     --device_num=$RANK_SIZE  \
     --device_id=$DEVICE_ID  \
+    --mindrecord_dir=$MINDRECORD_DIR  \
     --image_dir=$IMAGE_DIR  \
     --epoch_size=$EPOCH_SIZE  \
     --anno_path=$ANNO_PATH > log.txt 2>&1 &
diff --git a/example/yolov3_coco2017/run_eval.sh b/example/yolov3_coco2017/run_eval.sh
new file mode 100644
index 0000000000..4608e92589
--- /dev/null
+++ b/example/yolov3_coco2017/run_eval.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the scipt as: "
+echo "sh run_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
+echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt"
+echo "=============================================================================================================="
+
+python eval.py --device_id=$1 --ckpt_path=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5
diff --git a/example/yolov3_coco2017/run_standalone_train.sh b/example/yolov3_coco2017/run_standalone_train.sh
index b3b8b0cd9a..d91d537008 100644
--- a/example/yolov3_coco2017/run_standalone_train.sh
+++ b/example/yolov3_coco2017/run_standalone_train.sh
@@ -14,8 +14,10 @@
 # limitations under the License.
 # ============================================================================
 
+echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE IMAGE_DIR ANNO_PATH"
-echo "for example: sh run_standalone_train.sh 0 50 ./dataset/coco/train2017 ./dataset/train.txt"
+echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
+echo "for example: sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt"
+echo "=============================================================================================================="
 
-python train.py --device_id=$1 --epoch_size=$2 --image_dir=$3 --anno_path=$4
+python train.py --device_id=$1 --epoch_size=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5
diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py
index a3dd8f34fe..3ac3816f4a 100644
--- a/example/yolov3_coco2017/train.py
+++ b/example/yolov3_coco2017/train.py
@@ -16,26 +16,30 @@
 """
 ######################## train YOLOv3 example ########################
 train YOLOv3 and get network model files(.ckpt) :
-python train.py --image_dir dataset/coco/coco/train2017 --anno_path dataset/coco/train_coco.txt
+python train.py --image_dir /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train
+
+If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path.
+Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.
 """
 
+import os
 import argparse
 import numpy as np
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.common.initializer import initializer
 from mindspore.communication.management import init
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
 from mindspore.train import Model, ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common.initializer import initializer
 
 from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
-from dataset import create_yolo_dataset
+from dataset import create_yolo_dataset, data_to_mindrecord_byte_image
 from config import ConfigYOLOV3ResNet18
 
 
 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
-    """Set learning rate"""
+    """Set learning rate."""
     lr_each_step = []
     lr = learning_rate
     for i in range(global_step):
@@ -57,7 +61,9 @@ def init_net_param(net, init='ones'):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="YOLOv3")
+    parser = argparse.ArgumentParser(description="YOLOv3 train")
+    parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create "
+                                                                                "Mindrecord, default is false.")
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
@@ -67,12 +73,19 @@ if __name__ == '__main__':
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
     parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
     parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
-    parser.add_argument("--image_dir", type=str, required=True, help="Dataset image dir.")
-    parser.add_argument("--anno_path", type=str, required=True, help="Dataset anno path.")
+    parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
+                        help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by"
+                             "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
+                             "rather than image_dir and anno_path. Default is ./Mindrecord_train")
+    parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
+                                                                  "the absolute image path is joined by the image_dir "
+                                                                  "and the relative path in anno_path")
+    parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True)
+    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True,
+                        enable_auto_mixed_precision=False)
     if args_opt.distribute:
         device_num = args_opt.device_num
         context.reset_auto_parallel_context()
@@ -80,36 +93,65 @@ if __name__ == '__main__':
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                           device_num=device_num)
         init()
-        rank = args_opt.device_id
+        rank = args_opt.device_id % device_num
     else:
         context.set_context(enable_hccl=False)
         rank = 0
         device_num = 1
 
-    loss_scale = float(args_opt.loss_scale)
-    dataset = create_yolo_dataset(args_opt.image_dir, args_opt.anno_path, repeat_num=args_opt.epoch_size,
-                                  batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
-    dataset_size = dataset.get_dataset_size()
-    net = yolov3_resnet18(ConfigYOLOV3ResNet18())
-    net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
-    init_net_param(net, "XavierUniform")
-
-    # checkpoint
-    ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
-    ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
-    if args_opt.checkpoint_path != "":
-        param_dict = load_checkpoint(args_opt.checkpoint_path)
-        load_param_into_net(net, param_dict)
-
-    lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
-                       decay_step=1000, decay_rate=0.95))
-    opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
-    net = TrainingWrapper(net, opt, loss_scale)
-    callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
-
-    model = Model(net)
-    dataset_sink_mode = False
-    if args_opt.mode == "graph":
-        dataset_sink_mode = True
-    print("Start train YOLOv3.")
-    model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
+    print("Start create dataset!")
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is yolo.mindrecord0, 1, ... file_num.
+    if not os.path.isdir(args_opt.mindrecord_dir):
+        os.makedirs(args_opt.mindrecord_dir)
+
+    prefix = "yolo.mindrecord"
+    mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
+            print("Create Mindrecord.")
+            data_to_mindrecord_byte_image(args_opt.image_dir,
+                                          args_opt.anno_path,
+                                          args_opt.mindrecord_dir,
+                                          prefix=prefix,
+                                          file_num=8)
+            print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
+        else:
+            print("image_dir or anno_path not exits.")
+
+    if not args_opt.only_create_dataset:
+        loss_scale = float(args_opt.loss_scale)
+
+        # When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0.
+        dataset = create_yolo_dataset(mindrecord_file, repeat_num=args_opt.epoch_size,
+                                      batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
+        dataset_size = dataset.get_dataset_size()
+        print("Create dataset done!")
+
+        net = yolov3_resnet18(ConfigYOLOV3ResNet18())
+        net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
+        init_net_param(net, "XavierUniform")
+
+        # checkpoint
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
+        ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
+
+        lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
+                           decay_step=1000, decay_rate=0.95))
+        opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
+        net = TrainingWrapper(net, opt, loss_scale)
+
+        if args_opt.checkpoint_path != "":
+            param_dict = load_checkpoint(args_opt.checkpoint_path)
+            load_param_into_net(net, param_dict)
+
+        callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
+
+        model = Model(net)
+        dataset_sink_mode = False
+        if args_opt.mode == "graph":
+            print("In graph mode, one epoch return a loss.")
+            dataset_sink_mode = True
+        print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
+        model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
diff --git a/example/yolov3_coco2017/util.py b/example/yolov3_coco2017/util.py
new file mode 100644
index 0000000000..62e15afe38
--- /dev/null
+++ b/example/yolov3_coco2017/util.py
@@ -0,0 +1,146 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""metrics utils"""
+
+import numpy as np
+from config import ConfigYOLOV3ResNet18
+
+
+def calc_iou(bbox_pred, bbox_ground):
+    """Calculate iou of predicted bbox and ground truth."""
+    x1 = bbox_pred[0]
+    y1 = bbox_pred[1]
+    width1 = bbox_pred[2] - bbox_pred[0]
+    height1 = bbox_pred[3] - bbox_pred[1]
+
+    x2 = bbox_ground[0]
+    y2 = bbox_ground[1]
+    width2 = bbox_ground[2] - bbox_ground[0]
+    height2 = bbox_ground[3] - bbox_ground[1]
+
+    endx = max(x1 + width1, x2 + width2)
+    startx = min(x1, x2)
+    width = width1 + width2 - (endx - startx)
+
+    endy = max(y1 + height1, y2 + height2)
+    starty = min(y1, y2)
+    height = height1 + height2 - (endy - starty)
+
+    if width <= 0 or height <= 0:
+        iou = 0
+    else:
+        area = width * height
+        area1 = width1 * height1
+        area2 = width2 * height2
+        iou = area * 1. / (area1 + area2 - area)
+
+    return iou
+
+
+def apply_nms(all_boxes, all_scores, thres, max_boxes):
+    """Apply NMS to bboxes."""
+    x1 = all_boxes[:, 0]
+    y1 = all_boxes[:, 1]
+    x2 = all_boxes[:, 2]
+    y2 = all_boxes[:, 3]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+    order = all_scores.argsort()[::-1]
+    keep = []
+
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        if len(keep) >= max_boxes:
+            break
+
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thres)[0]
+
+        order = order[inds + 1]
+    return keep
+
+
+def metrics(pred_data):
+    """Calculate precision and recall of predicted bboxes."""
+    config = ConfigYOLOV3ResNet18()
+    num_classes = config.num_classes
+    count_corrects = [1e-6 for _ in range(num_classes)]
+    count_grounds = [1e-6 for _ in range(num_classes)]
+    count_preds = [1e-6 for _ in range(num_classes)]
+
+    for i, sample in enumerate(pred_data):
+        gt_anno = sample["annotation"]
+        box_scores = sample['box_scores']
+        boxes = sample['boxes']
+        mask = box_scores >= config.obj_threshold
+        boxes_ = []
+        scores_ = []
+        classes_ = []
+        max_boxes = config.nms_max_num
+        for c in range(num_classes):
+            class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])]
+            class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])]
+            nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes)
+            class_boxes = class_boxes[nms_index]
+            class_box_scores = class_box_scores[nms_index]
+            classes = np.ones_like(class_box_scores, 'int32') * c
+            boxes_.append(class_boxes)
+            scores_.append(class_box_scores)
+            classes_.append(classes)
+
+        boxes = np.concatenate(boxes_, axis=0)
+        classes = np.concatenate(classes_, axis=0)
+
+
+        # metric
+        count_correct = [1e-6 for _ in range(num_classes)]
+        count_ground = [1e-6 for _ in range(num_classes)]
+        count_pred = [1e-6 for _ in range(num_classes)]
+
+        for anno in gt_anno:
+            count_ground[anno[4]] += 1
+
+        for box_index, box in enumerate(boxes):
+            bbox_pred = [box[1], box[0], box[3], box[2]]
+            count_pred[classes[box_index]] += 1
+
+            for anno in gt_anno:
+                class_ground = anno[4]
+
+                if classes[box_index] == class_ground:
+                    iou = calc_iou(bbox_pred, anno)
+                    if iou >= 0.5:
+                        count_correct[class_ground] += 1
+                        break
+
+        count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)]
+        count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)]
+        count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)]
+
+    precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)])
+    recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)])
+    return precision, recall
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index 50fed77a9a..c0416f648b 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -34,6 +34,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"tensor_add", "add"},
   {"reduce_mean", "reduce_mean_d"},
   {"reduce_max", "reduce_max_d"},
+  {"reduce_min", "reduce_min_d"},
   {"conv2d_backprop_filter", "conv2d_backprop_filter_d"},
   {"conv2d_backprop_input", "conv2d_backprop_input_d"},
   {"top_kv2", "top_k"},
diff --git a/mindspore/model_zoo/yolov3.py b/mindspore/model_zoo/yolov3.py
index 5ac3b67086..0ac6b21070 100644
--- a/mindspore/model_zoo/yolov3.py
+++ b/mindspore/model_zoo/yolov3.py
@@ -15,6 +15,7 @@
 
 """YOLOv3 based on ResNet18."""
 
+import numpy as np
 import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context, Tensor
@@ -31,19 +32,14 @@ def weight_variable():
     return TruncatedNormal(0.02)
 
 
-class _conv_with_pad(nn.Cell):
+class _conv2d(nn.Cell):
     """Create Conv2D with padding."""
     def __init__(self, in_channels, out_channels, kernel_size, stride=1):
-        super(_conv_with_pad, self).__init__()
-        total_pad = kernel_size - 1
-        pad_begin = total_pad // 2
-        pad_end = total_pad - pad_begin
-        self.pad = P.Pad(((0, 0), (0, 0), (pad_begin, pad_end), (pad_begin, pad_end)))
+        super(_conv2d, self).__init__()
         self.conv = nn.Conv2d(in_channels, out_channels,
-                              kernel_size=kernel_size, stride=stride, padding=0, pad_mode='valid',
+                              kernel_size=kernel_size, stride=stride, padding=0, pad_mode='same',
                               weight_init=weight_variable())
     def construct(self, x):
-        x = self.pad(x)
         x = self.conv(x)
         return x
 
@@ -101,15 +97,15 @@ class BasicBlock(nn.Cell):
                  momentum=0.99):
         super(BasicBlock, self).__init__()
 
-        self.conv1 = _conv_with_pad(in_channels, out_channels, 3, stride=stride)
+        self.conv1 = _conv2d(in_channels, out_channels, 3, stride=stride)
         self.bn1 = _fused_bn(out_channels, momentum=momentum)
-        self.conv2 = _conv_with_pad(out_channels, out_channels, 3)
+        self.conv2 = _conv2d(out_channels, out_channels, 3)
         self.bn2 = _fused_bn(out_channels, momentum=momentum)
         self.relu = P.ReLU()
         self.down_sample_layer = None
         self.downsample = (in_channels != out_channels)
         if self.downsample:
-            self.down_sample_layer = _conv_with_pad(in_channels, out_channels, 1, stride=stride)
+            self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride)
         self.add = P.TensorAdd()
 
     def construct(self, x):
@@ -166,7 +162,7 @@ class ResNet(nn.Cell):
             raise ValueError("the length of "
                              "layer_num, inchannel, outchannel list must be 4!")
 
-        self.conv1 = _conv_with_pad(3, 64, 7, stride=2)
+        self.conv1 = _conv2d(3, 64, 7, stride=2)
         self.bn1 = _fused_bn(64)
         self.relu = P.ReLU()
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
@@ -452,7 +448,7 @@ class DetectionBlock(nn.Cell):
 
         if self.training:
             return grid, prediction, box_xy, box_wh
-        return self.concat((box_xy, box_wh, box_confidence, box_probs))
+        return box_xy, box_wh, box_confidence, box_probs
 
 
 class Iou(nn.Cell):
@@ -675,3 +671,78 @@ class TrainingWrapper(nn.Cell):
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
         return F.depend(loss, self.optimizer(grads))
+
+
+class YoloBoxScores(nn.Cell):
+    """
+    Calculate the boxes of the original picture size and the score of each box.
+
+    Args:
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Tensor, the boxes of the original picture size.
+        Tensor, the score of each box.
+    """
+    def __init__(self, config):
+        super(YoloBoxScores, self).__init__()
+        self.input_shape = Tensor(np.array(config.img_shape), ms.float32)
+        self.num_classes = config.num_classes
+
+    def construct(self, box_xy, box_wh, box_confidence, box_probs, image_shape):
+        batch_size = F.shape(box_xy)[0]
+        x = box_xy[:, :, :, :, 0:1]
+        y = box_xy[:, :, :, :, 1:2]
+        box_yx = P.Concat(-1)((y, x))
+        w = box_wh[:, :, :, :, 0:1]
+        h = box_wh[:, :, :, :, 1:2]
+        box_hw = P.Concat(-1)((h, w))
+
+        new_shape = P.Round()(image_shape * P.ReduceMin()(self.input_shape / image_shape))
+        offset = (self.input_shape - new_shape) / 2.0 / self.input_shape
+        scale = self.input_shape / new_shape
+        box_yx = (box_yx - offset) * scale
+        box_hw = box_hw * scale
+
+        box_min = box_yx - box_hw / 2.0
+        box_max = box_yx + box_hw / 2.0
+        boxes = P.Concat(-1)((box_min[:, :, :, :, 0:1],
+                              box_min[:, :, :, :, 1:2],
+                              box_max[:, :, :, :, 0:1],
+                              box_max[:, :, :, :, 1:2]))
+        image_scale = P.Tile()(image_shape, (1, 2))
+        boxes = boxes * image_scale
+        boxes = F.reshape(boxes, (batch_size, -1, 4))
+        boxes_scores = box_confidence * box_probs
+        boxes_scores = F.reshape(boxes_scores, (batch_size, -1, self.num_classes))
+        return boxes, boxes_scores
+
+
+class YoloWithEval(nn.Cell):
+    """
+    Encapsulation class of YOLOv3 evaluation.
+
+    Args:
+        network (Cell): The training network. Note that loss function and optimizer must not be added.
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Tensor, the boxes of the original picture size.
+        Tensor, the score of each box.
+        Tensor, the original picture size.
+    """
+    def __init__(self, network, config):
+        super(YoloWithEval, self).__init__()
+        self.yolo_network = network
+        self.box_score_0 = YoloBoxScores(config)
+        self.box_score_1 = YoloBoxScores(config)
+        self.box_score_2 = YoloBoxScores(config)
+
+    def construct(self, x, image_shape):
+        yolo_output = self.yolo_network(x)
+        boxes_0, boxes_scores_0 = self.box_score_0(*yolo_output[0], image_shape)
+        boxes_1, boxes_scores_1 = self.box_score_1(*yolo_output[1], image_shape)
+        boxes_2, boxes_scores_2 = self.box_score_2(*yolo_output[2], image_shape)
+        boxes = P.Concat(1)((boxes_0, boxes_1, boxes_2))
+        boxes_scores = P.Concat(1)((boxes_scores_0, boxes_scores_1, boxes_scores_2))
+        return boxes, boxes_scores, image_shape
diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py
index b9b9af4396..0b79ae845b 100644
--- a/mindspore/ops/_op_impl/tbe/__init__.py
+++ b/mindspore/ops/_op_impl/tbe/__init__.py
@@ -85,7 +85,9 @@ from .logical_and import _logical_and_tbe
 from .logical_not import _logical_not_tbe
 from .logical_or import _logical_or_tbe
 from .reduce_max import _reduce_max_tbe
+from .reduce_min import _reduce_min_tbe
 from .reduce_sum import _reduce_sum_tbe
+from .round import _round_tbe
 from .tanh import _tanh_tbe
 from .tanh_grad import _tanh_grad_tbe
 from .softmax import _softmax_tbe
diff --git a/mindspore/ops/_op_impl/tbe/reduce_min.py b/mindspore/ops/_op_impl/tbe/reduce_min.py
new file mode 100644
index 0000000000..f440b1fff6
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/reduce_min.py
@@ -0,0 +1,76 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReduceMin op"""
+from mindspore.ops.op_info_register import op_info_register
+
+
+@op_info_register("""{
+    "op_name": "ReduceMin",
+    "imply_type": "TBE",
+    "fusion_type": "OPAQUE",
+    "async_flag": false,
+    "binfile_name": "reduce_min_d.so",
+    "compute_cost": 10,
+    "kernel_name": "reduce_min_d",
+    "partial_flag": true,
+    "attr": [
+        {
+            "name": "axis",
+            "param_type": "required",
+            "type": "listInt",
+            "value": "all"
+        },
+        {
+            "name": "keep_dims",
+            "param_type": "required",
+            "type": "bool",
+            "value": "all"
+        }
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
+            ],
+            "format": [
+                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
+            ],
+            "name": "x",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
+            ],
+            "format": [
+                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
+            ],
+            "name": "y",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ]
+}""")
+def _reduce_min_tbe():
+    """ReduceMin TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/round.py b/mindspore/ops/_op_impl/tbe/round.py
new file mode 100644
index 0000000000..1368100a8e
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/round.py
@@ -0,0 +1,65 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Round op"""
+from mindspore.ops.op_info_register import op_info_register
+
+
+@op_info_register("""{
+    "op_name": "Round",
+    "imply_type": "TBE",
+    "fusion_type": "ELEMWISE",
+    "async_flag": false,
+    "binfile_name": "round.so",
+    "compute_cost": 10,
+    "kernel_name": "round",
+    "partial_flag": true,
+    "attr": [
+
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float16", "float", "float", "float"
+            ],
+            "format": [
+                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
+            ],
+            "name": "x",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float16", "float", "float", "float"
+            ],
+            "format": [
+                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
+            ],
+            "name": "y",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ]
+}""")
+def _round_tbe():
+    """Round TBE register"""
+    return

From 1b4041a8f1fc2a8e438a04a58d84ba3e092ac5e3 Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Fri, 3 Apr 2020 11:45:49 +0800
Subject: [PATCH 084/367] add weight decay in RMSProp optimizer

---
 mindspore/nn/optim/rmsprop.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py
index 3000fdeeee..faaeacfaa8 100644
--- a/mindspore/nn/optim/rmsprop.py
+++ b/mindspore/nn/optim/rmsprop.py
@@ -18,7 +18,8 @@ from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore._checkparam import ParamValidator as validator
 import mindspore.common.dtype as mstype
-from .optimizer import Optimizer, grad_scale
+from mindspore.common import Tensor
+from .optimizer import Optimizer, grad_scale, apply_decay
 
 rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
 centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
@@ -118,6 +119,9 @@ class RMSProp(Optimizer):
         use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
         centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False
         loss_scale (float): A floating point value for the loss scale. Default: 1.0.
+        weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
+        decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
+                                 lambda x: 'beta' not in x.name and 'gamma' not in x.name.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -132,7 +136,8 @@ class RMSProp(Optimizer):
         >>> model = Model(net, loss, opt)
     """
     def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
-                 use_locking=False, centered=False, loss_scale=1.0):
+                 use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0,
+                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
         super(RMSProp, self).__init__(learning_rate, params)
 
         if isinstance(momentum, float) and momentum < 0.0:
@@ -159,6 +164,7 @@ class RMSProp(Optimizer):
             self.assignadd = P.AssignAdd()
             self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
             self.axis = 0
+            self.one = Tensor(1, mstype.int32)
 
         self.momentum = momentum
 
@@ -167,10 +173,14 @@ class RMSProp(Optimizer):
         self.hyper_map = C.HyperMap()
 
         self.decay = decay
+        self.decay_tf = tuple(decay_filter(x) for x in self.parameters)
         self.reciprocal_scale = 1.0 / loss_scale
+        self.weight_decay = weight_decay * loss_scale
 
     def construct(self, gradients):
         params = self.parameters
+        if self.weight_decay > 0:
+            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients)
         if self.reciprocal_scale != 1.0:
             gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
         if self.dynamic_lr:

From da123c5b3e8a5217030ffbf4e03c232d667d7547 Mon Sep 17 00:00:00 2001
From: wanghua <wanghua36@huawei.com>
Date: Fri, 3 Apr 2020 14:55:25 +0800
Subject: [PATCH 085/367] fix bert precison bug

---
 .../device/ascend/kernel_select_ascend.cc     | 20 ++++++++++++-------
 mindspore/ccsrc/utils/utils.h                 |  3 +++
 mindspore/ops/_op_impl/tbe/gelu.py            |  8 ++++----
 .../models/bert/bert_tdt_no_lossscale.py      |  9 ++++-----
 4 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index 347d63be39..a7c8d131fb 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -35,6 +35,7 @@ enum MatchCountPriority : int {
   MATCH_COUNT_PRIORITY_BEGIN = 0,
   MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
   MATCH_FORMAT_COUNT,
+  MATCH_SPECIAL_FORMAT_COUNT,
   MATCH_5D_FORMAT_COUNT,
   MATCH_OUTPUT_DTYPE_COUNT,
   MATCH_COUNT_PRIORITY_END
@@ -81,6 +82,12 @@ bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::
     }
     return true;
   };
+  if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" ||
+      AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") {
+    if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) {
+      return true;
+    }
+  }
   if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
     return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
            AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
@@ -154,7 +161,7 @@ bool PriorityChooseItem(const std::vector<int> &cur_item, std::vector<int> *best
       return false;
     }
   }
-  return false;
+  return true;
 }
 
 void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr<CNode> &kernel_node,
@@ -174,12 +181,11 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
         continue;
       }
     }
-    if (input_anf_node->isa<ValueNode>()) {
-      if (AnfAlgo::GetOutputDeviceDataType(input_anf_node, 0) == kTypeUnknown) {
-        continue;
-      }
-    }
     if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
+      if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) &&
+          kSpecialFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kSpecialFormatSet.end()) {
+        (*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT]++;
+      }
       (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++;
     }
     if (kernel_build_info.GetInputDeviceType(input_index) ==
@@ -203,7 +209,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
       (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++;
     }
   }
-}
+}  // namespace
 
 void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index ee1eeaddfc..ea5e969e52 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -195,6 +195,9 @@ const std::set<std::string> kOptOperatorSet = {
   kApplyRMSPropOpName,
 };
 
+const std::set<std::string> kSpecialFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
+                                                 kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0};
+
 static inline void ChangeFileMode(const std::string& file_name, mode_t mode) {
   if (access(file_name.c_str(), F_OK) != 0) {
     MS_LOG(DEBUG) << "File `" << file_name << "` does not exist.";
diff --git a/mindspore/ops/_op_impl/tbe/gelu.py b/mindspore/ops/_op_impl/tbe/gelu.py
index 8093312547..171d97c043 100644
--- a/mindspore/ops/_op_impl/tbe/gelu.py
+++ b/mindspore/ops/_op_impl/tbe/gelu.py
@@ -32,10 +32,10 @@ from mindspore.ops.op_info_register import op_info_register
         {
             "index": 0,
             "dtype": [
-                "float16","float","float16","float16","float16","float16","float","float","float","float"
+               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
             ],
             "format": [
-                "FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
+                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
             ],
             "name": "x",
             "need_compile": false,
@@ -47,10 +47,10 @@ from mindspore.ops.op_info_register import op_info_register
         {
             "index": 0,
             "dtype": [
-                "float16","float","float16","float16","float16","float16","float","float","float","float"
+               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
             ],
             "format": [
-                "FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
+                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
             ],
             "name": "y",
             "need_compile": true,
diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
index 7c50707fbd..6f3ffc7dad 100644
--- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
+++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
@@ -153,8 +153,7 @@ def test_bert_tdt():
     batch_size = int(os.getenv('BATCH_SIZE', '16'))
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=10000, start_learning_rate=1e-4,
-                     end_learning_rate=0.0, power=10.0, warmup_steps=0, decay_filter=lambda x: False)
+    optimizer = Momentum(netwithloss.trainable_params(), learning_rate=2e-5, momentum=0.9)
     netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
     netwithgrads.set_train(True)
     model = Model(netwithgrads)
@@ -178,10 +177,10 @@ def test_bert_tdt():
                 param.default_input = weight_variable(value.asnumpy().shape)
     model.train(ds.get_repeat_count(), ds, callbacks=parallel_callback, dataset_sink_mode=False)
     loss_value = np.array(parallel_callback.loss_list)
-    expect_out = [12.191790, 11.739655, 11.523477, 11.320723, 11.113152, 11.203759, 10.841681, 10.826849,
-                  10.616718, 10.486609]
+    expect_out = [12.19179, 11.965041, 11.969687, 11.97815, 11.969171, 12.603289, 12.165594,
+                  12.824818, 12.38842, 12.604046]
     logger.info("expected loss value output: {}".format(expect_out))
-    assert allclose(loss_value, expect_out, 0.001, 0.001)
+    assert allclose(loss_value, expect_out, 0.00001, 0.00001)
 
 if __name__ == '__main__':
     test_bert_tdt()

From 01822c54d9928a08e0b688bb2493f0fe22276741 Mon Sep 17 00:00:00 2001
From: kingfo <wangqiuliang@huawei.com>
Date: Fri, 3 Apr 2020 10:53:46 +0800
Subject: [PATCH 086/367] fix ME+GE compile error

---
 CMakeLists.txt                          |  2 +-
 mindspore/ccsrc/CMakeLists.txt          |  2 +-
 mindspore/ccsrc/pipeline/pipeline_ge.cc | 17 ++++-------------
 3 files changed, 6 insertions(+), 15 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bdac2da46e..d11314408e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -38,7 +38,7 @@ set(MS_CCSRC_BUILD_PATH ${BUILD_PATH}/mindspore/mindspore/ccsrc)
 
 if (ENABLE_GE)
     link_directories(${CMAKE_SOURCE_DIR}/third_party/ge/lib)
-else()
+elseif(ENABLE_D OR ENABLE_TESTCASES)
     include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake)
 endif()
 
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 0b4bb0d1df..ef9b7e951c 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -491,7 +491,7 @@ if (NOT ENABLE_GE)
         add_dependencies(add_ge_lib add_ms_lib)
         add_dependencies(add_ge_lib graph)
         add_dependencies(add_ge_lib ge_runtime)
-    else()
+    elseif(ENABLE_TESTCASES)
         add_custom_target(add_ge_lib ALL
                 COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so  ${MS_LIB_PATH}
                 COMMAND cp ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libslog.so ${MS_LIB_PATH}
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
index 4617884ca0..5a2a8039dd 100644
--- a/mindspore/ccsrc/pipeline/pipeline_ge.cc
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -429,20 +429,11 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
 
   std::shared_ptr<py::object> ret = nullptr;
 
-#ifdef ENABLE_GE
-  AnfNodePtr root = graph->get_return();
-  MS_EXCEPTION_IF_NULL(root);
-  AbstractBasePtr output = root->abstract();
+  AnfNodePtr output_node = graph->get_return()->input(1);
+  MS_EXCEPTION_IF_NULL(output_node);
   size_t count = 0;
-  py::object oj = StructureOutput(output, outputs, &count);
+  py::object oj = StructureOutput(output_node, outputs, &count);
   ret = std::make_shared<py::object>(oj);
-#else
-  if (outputs.size() == 1) {
-    ret = std::make_shared<py::object>(outputs[0]);
-  } else {
-    ret = std::make_shared<py::object>(outputs);
-  }
-#endif
 
   return ret;
 }
@@ -495,7 +486,7 @@ py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const
 
   FuncGraphPtr anf_graph = info.at(phase)->func_graph;
 
-#if (!defined ENABLE_GE) || (defined ENABLE_INFER)
+#ifdef ENABLE_INFER
   // Now don't use the graph because the exec ge function don't take effect
   MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph);
   if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) {

From dd0d4e6b84bb80908b920421b5877562c7cf3d39 Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Thu, 2 Apr 2020 15:18:34 +0800
Subject: [PATCH 087/367] add parallel ops for expand dims

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |   1 +
 .../parallel/ops_info/activation_info.cc      | 163 ++++++++++++++++++
 .../ccsrc/parallel/ops_info/activation_info.h |  20 +++
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |   4 +
 .../ccsrc/parallel/step_auto_parallel.cc      |   1 +
 tests/ut/python/parallel/test_expand_dims.py  | 110 ++++++++++++
 6 files changed, 299 insertions(+)
 create mode 100644 tests/ut/python/parallel/test_expand_dims.py

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index e6e1b41d76..1270116f50 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -125,6 +125,7 @@ REGISTER(SqrtInfo);
 REGISTER(GetNextInfo);
 REGISTER(NegInfo);
 REGISTER(BatchMatMulInfo);
+REGISTER(ExpandDimsInfo);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
index 13155ee4f1..9ba3624b01 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
@@ -381,5 +381,168 @@ Status CastInfo::InferMirrorOps() {
 
   return SUCCESS;
 }
+
+Status ExpandDimsInfo::GetAttrs() {
+  if (input_value_.size() != EXPANDDIMS_INPUT_SIZE) {
+    MS_LOG(ERROR) << name_ << ": Invalid inputs size " << input_value_.size();
+    return FAILED;
+  }
+
+  if (!input_value_.back()->isa<Int32Imm>()) {
+    MS_LOG(ERROR) << name_ << ": The type of axis is not int";
+    return FAILED;
+  }
+
+  int32_t axis = GetValue<int32_t>(input_value_.back());
+
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  int32_t dim = SizeToInt(inputs_shape_[0].size());
+  if ((axis > dim) || (axis < -dim - 1)) {
+    MS_LOG(ERROR) << name_ << ": The axis(" << axis << ") is out of range[" << -dim - 1 << ", " << dim << "]";
+    return FAILED;
+  }
+
+  if (axis < 0) {
+    positive_axis_ = dim + axis + 1;
+  } else {
+    positive_axis_ = axis;
+  }
+  MS_LOG(INFO) << name_ << ": The axis is " << axis << ", and the positive axis is " << positive_axis_;
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorMap() {
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  // for example: if the dimension of input is 3, and the axis is 2,
+  // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1, -1, 0]
+  std::vector<int32_t> input_tensor_map, output_tensor_map;
+  size_t size = inputs_shape_[0].size();
+  for (size_t i = 0; i < size; ++i) {
+    input_tensor_map.push_back(SizeToInt(size - i - 1));
+  }
+
+  inputs_tensor_map_.push_back(input_tensor_map);
+
+  output_tensor_map = input_tensor_map;
+  if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(size))) {
+    MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_;
+    return FAILED;
+  }
+  (void)output_tensor_map.insert(output_tensor_map.begin() + positive_axis_, NO_SPLIT_MAP);
+  outputs_tensor_map_.push_back(output_tensor_map);
+
+  MS_LOG(INFO) << name_ << ": The tensor map of input is " << ShapeToString(input_tensor_map)
+               << ", and the tensor map of output is " << ShapeToString(output_tensor_map);
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorStrategy() {
+  if (strategy_ == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
+    return FAILED;
+  }
+
+  inputs_strategy_ = strategy_->GetInputDim();
+  if (inputs_strategy_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The strategy is empty";
+    return FAILED;
+  }
+
+  Shape output_strategy = inputs_strategy_[0];
+  if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(output_strategy.size()))) {
+    MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_;
+    return FAILED;
+  }
+  (void)output_strategy.insert(output_strategy.begin() + positive_axis_, NO_SPLIT_STRATEGY);
+  outputs_strategy_ = {output_strategy};
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  if (inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The tensor map of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_shape = inputs_shape_[0];
+  Shape output_shape = outputs_shape_[0];
+
+  // infer slice shape
+  if (InferTensorStrategy() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer tensor strategy failed";
+    return FAILED;
+  }
+  Shapes inputs_slice_shape, outputs_slice_shape;
+  if (InferSliceShape(inputs_strategy_, outputs_strategy_, &inputs_slice_shape, &outputs_slice_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer slice shape failed";
+    return FAILED;
+  }
+
+  if (inputs_slice_shape.empty() || outputs_slice_shape.empty()) {
+    MS_LOG(ERROR) << name_ << ": The slice shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_slice_shape = inputs_slice_shape[0];
+  Shape output_slice_shape = outputs_slice_shape[0];
+
+  TensorLayout input_tensor_layout, output_tensor_layout;
+  if (input_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for input failed";
+    return FAILED;
+  }
+
+  if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for output failed";
+    return FAILED;
+  }
+
+  TensorInfo input_tensor_info(input_tensor_layout, input_shape, input_slice_shape);
+  TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape);
+
+  inputs_tensor_info_.push_back(input_tensor_info);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferMirrorOps() {
+  mirror_ops_.clear();
+
+  if (inputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The tensor map of inputs is empty";
+    return FAILED;
+  }
+
+  std::vector<Group> group;
+  if (CreateGroupByTensorMap(inputs_tensor_map_[0], &group) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Create group failed";
+    return FAILED;
+  }
+
+  if (group.empty()) {
+    MS_LOG(INFO) << name_ << ": No need to create mirror ops";
+    return SUCCESS;
+  }
+
+  OperatorVector mirror_op, placeholder_op;
+  mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
+  mirror_ops_.push_back(mirror_op);
+  mirror_ops_.push_back(placeholder_op);
+  MS_LOG(INFO) << name_ << ": Create mirror ops success, the group name is " << group[0].name();
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index 3cadad6b80..183b593e23 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -174,6 +174,26 @@ class NegInfo : public ActivationOther {
       : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
   ~NegInfo() override = default;
 };
+
+class ExpandDimsInfo : public ActivationOther {
+ public:
+  ExpandDimsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                 const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ExpandDimsInfo() override = default;
+
+ protected:
+  Status GetAttrs() override;
+  Status InferTensorMap() override;
+  Status InferTensorInfo() override;
+  Status InferMirrorOps() override;
+  Status InferTensorStrategy();
+
+ private:
+  int32_t positive_axis_ = -1;
+  Strategys inputs_strategy_;
+  Strategys outputs_strategy_;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 4062847d73..fe2a5d2c86 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -24,6 +24,8 @@ constexpr size_t PRELU_OUTPUTS_SIZE = 1;
 constexpr size_t PRELU_SECOND_INPUT_SIZE = 1;
 constexpr int32_t PRELU_CHANNEL_INDEX = 1;
 constexpr int32_t PRELU_CHANNEL_STRATEGY = 1;
+constexpr int32_t NO_SPLIT_MAP = -1;
+constexpr int32_t NO_SPLIT_STRATEGY = 1;
 constexpr size_t MATMUL_ATTRS_SIZE = 2;
 constexpr size_t MATMUL_INPUTS_SIZE = 2;
 constexpr size_t MATMUL_OUTPUTS_SIZE = 1;
@@ -31,6 +33,7 @@ constexpr size_t ACTIVATION_ATTR_SIZE = 1;
 constexpr size_t SOFTMAX_ATTR_SIZE = 1;
 constexpr size_t ACTIVATION_INPUTS_SIZE = 1;
 constexpr size_t ACTIVATION_OUTPUTS_SIZE = 1;
+constexpr size_t EXPANDDIMS_INPUT_SIZE = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1;
 constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2;
@@ -191,6 +194,7 @@ constexpr char GET_NEXT[] = "GetNext";
 constexpr char SQUEEZE[] = "Squeeze";
 constexpr char Neg[] = "Neg";
 constexpr char BATCH_MATMUL[] = "BatchMatMul";
+constexpr char EXPAND_DIMS[] = "ExpandDims";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index a359905494..50e6a1e84e 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -104,6 +104,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            CAST,
                                            Neg,
                                            BATCH_MATMUL,
+                                           EXPAND_DIMS,
                                            SQUEEZE};
 
 std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,
diff --git a/tests/ut/python/parallel/test_expand_dims.py b/tests/ut/python/parallel/test_expand_dims.py
new file mode 100644
index 0000000000..676e9ed523
--- /dev/null
+++ b/tests/ut/python/parallel/test_expand_dims.py
@@ -0,0 +1,110 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None, strategy3=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.expand_dims = P.ExpandDims().set_strategy(strategy2)
+        self.mul2 = P.Mul().set_strategy(strategy3)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.expand_dims(out, -1)
+        out = self.mul2(out, b)
+        return out
+
+
+class Net2(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.expand_dims = P.ExpandDims().set_strategy(strategy1)
+        self.mul = P.Mul().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.expand_dims(self.mul_weight, -1)
+        out = self.mul(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32, 1]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_expand_dims_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    strategy3 = ((16, 1, 1, 1), (16, 1, 1, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    strategy3 = ((1, 1, 16, 1), (1, 1, 16, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_expand_dims_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_parameter():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 2, 2), )
+    strategy2 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net2(_w1, strategy1, strategy2)
+    compile(net)

From 0eb72d76f076d5bdeb08093721b9104a40e1ad28 Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Wed, 1 Apr 2020 21:08:40 +0800
Subject: [PATCH 088/367] import comment and function of op print

---
 mindspore/ccsrc/transform/op_adapter.h |  2 +-
 mindspore/ops/operations/debug_ops.py  | 14 +++++++++-----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h
index 3dd299f83d..524cdfb4aa 100644
--- a/mindspore/ccsrc/transform/op_adapter.h
+++ b/mindspore/ccsrc/transform/op_adapter.h
@@ -513,7 +513,7 @@ class OpAdapter : public BaseOpAdapter {
         return;
       }
     } else {
-      MS_LOG(ERROR) << "Update output desc failed, unknow output shape type";
+      MS_LOG(WARNING) << "Update output desc failed, unknow output shape type";
       return;
     }
     MS_EXCEPTION_IF_NULL(node);
diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py
index 6640ef87ca..a69dcc2df1 100644
--- a/mindspore/ops/operations/debug_ops.py
+++ b/mindspore/ops/operations/debug_ops.py
@@ -14,6 +14,7 @@
 # ============================================================================
 
 """debug_ops"""
+from ..._checkparam import ParamValidator as validator
 from ...common import dtype as mstype
 from ..primitive import Primitive, prim_attr_register, PrimitiveWithInfer
 
@@ -157,19 +158,20 @@ class InsertGradientOf(PrimitiveWithInfer):
 
 class Print(PrimitiveWithInfer):
     """
-    Output tensor to stdout.
+    Output tensor or string to stdout.
 
     Inputs:
-        - **input_x** (Tensor) - The graph node to attach to.
+        - **input_x** (Union[Tensor, str]) - The graph node to attach to. The input supports
+          multiple strings and tensors which are separated by ','.
 
     Examples:
         >>> class PrintDemo(nn.Cell):
-        >>>     def __init__(self,):
+        >>>     def __init__(self):
         >>>         super(PrintDemo, self).__init__()
         >>>         self.print = P.Print()
         >>>
-        >>>     def construct(self, x):
-        >>>         self.print(x)
+        >>>     def construct(self, x, y):
+        >>>         self.print('Print Tensor x and Tensor y:', x, y)
         >>>         return x
     """
 
@@ -181,4 +183,6 @@ class Print(PrimitiveWithInfer):
         return [1]
 
     def infer_dtype(self, *inputs):
+        for dtype in inputs:
+            validator.check_subclass("input", dtype, (mstype.tensor, mstype.string))
         return mstype.int32

From 651d9aae4022e97fe6e647f0b3362116d1571eec Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Wed, 1 Apr 2020 19:41:21 +0800
Subject: [PATCH 089/367] add new mode for operator info register

---
 mindspore/ccsrc/kernel/oplib/opinfo.h         |   9 +
 mindspore/ccsrc/kernel/oplib/oplib.cc         |  61 ++-
 mindspore/ccsrc/kernel/oplib/oplib.h          |   4 +-
 mindspore/ops/__init__.py                     |   4 +-
 .../_op_impl/tbe/adam_apply_one_with_decay.py | 231 ++-------
 mindspore/ops/op_info_register.py             | 441 +++++++++++++++++-
 6 files changed, 535 insertions(+), 215 deletions(-)

diff --git a/mindspore/ccsrc/kernel/oplib/opinfo.h b/mindspore/ccsrc/kernel/oplib/opinfo.h
index 7861da34d9..56abea9269 100644
--- a/mindspore/ccsrc/kernel/oplib/opinfo.h
+++ b/mindspore/ccsrc/kernel/oplib/opinfo.h
@@ -61,6 +61,7 @@ class OpIOInfo {
   std::string name() const { return name_; }
   bool need_compile() const { return need_compile_; }
   std::string param_type() const { return param_type_; }
+  std::string reshape_type() const { return reshape_type_; }
   std::string shape() const { return shape_; }
   std::vector<std::string> dtypes() const { return dtypes_; }
   std::vector<std::string> formats() const { return formats_; }
@@ -69,6 +70,7 @@ class OpIOInfo {
   void set_name(const std::string& name) { name_ = name; }
   void set_need_compile(const bool need_compile) { need_compile_ = need_compile; }
   void set_param_type(const std::string& param_type) { param_type_ = param_type; }
+  void set_reshape_type(const std::string& reshape_type) { reshape_type_ = reshape_type; }
   void set_shape(const std::string& shape) { shape_ = shape; }
   void set_dtypes(const std::vector<std::string>& dtype) { dtypes_ = dtype; }
   void set_formats(const std::vector<std::string>& formats) { formats_ = formats; }
@@ -78,6 +80,7 @@ class OpIOInfo {
   std::string name_;
   bool need_compile_ = false;
   std::string param_type_;
+  std::string reshape_type_;
   std::string shape_;
   std::vector<std::string> dtypes_;
   std::vector<std::string> formats_;
@@ -96,6 +99,8 @@ class OpInfo {
   int compute_cost() const { return compute_cost_; }
   std::string kernel_name() const { return kernel_name_; }
   bool partial_flag() const { return partial_flag_; }
+  bool dynamic_format() const { return dynamic_format_; }
+  std::string op_pattern() const { return op_pattern_; }
   std::vector<std::shared_ptr<OpAttr>> attrs_ptr() const { return attrs_ptr_; }
   std::vector<std::shared_ptr<OpIOInfo>> inputs_ptr() const { return inputs_ptr_; }
   std::vector<std::shared_ptr<OpIOInfo>> outputs_ptr() const { return outputs_ptr_; }
@@ -110,6 +115,8 @@ class OpInfo {
   void set_compute_cost(const int compute_cost) { compute_cost_ = compute_cost; }
   void set_kernel_name(const std::string& kernel_name) { kernel_name_ = kernel_name; }
   void set_partial_flag(const bool partial_flag) { partial_flag_ = partial_flag; }
+  void set_dynamic_format(const bool dynamic_format) { dynamic_format_ = dynamic_format; }
+  void set_op_pattern(const std::string op_pattern) { op_pattern_ = op_pattern; }
   void add_attrs_ptr(const std::shared_ptr<OpAttr>& attr) { attrs_ptr_.push_back(attr); }
   void add_inputs_ptr(const std::shared_ptr<OpIOInfo>& input) { inputs_ptr_.push_back(input); }
   void add_outputs_ptr(const std::shared_ptr<OpIOInfo>& output) { outputs_ptr_.push_back(output); }
@@ -129,6 +136,8 @@ class OpInfo {
   int compute_cost_ = 0;
   std::string kernel_name_;
   bool partial_flag_ = false;
+  bool dynamic_format_ = false;
+  std::string op_pattern_;
   std::vector<std::shared_ptr<OpAttr>> attrs_ptr_;
   std::vector<std::shared_ptr<OpIOInfo>> inputs_ptr_;
   std::vector<std::shared_ptr<OpIOInfo>> outputs_ptr_;
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc
index b20bd741f1..4059b8e246 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.cc
+++ b/mindspore/ccsrc/kernel/oplib/oplib.cc
@@ -26,18 +26,22 @@ namespace mindspore {
 namespace kernel {
 constexpr auto kImplyType = "imply_type";
 constexpr auto kOpName = "op_name";
-constexpr auto kTbe = "TBE";
-constexpr auto kAkg = "akg";
-constexpr auto kAutodiff = "AutoDiff";
 constexpr auto kFusionType = "fusion_type";
 constexpr auto kAsyncFlag = "async_flag";
 constexpr auto kBinfileName = "binfile_name";
 constexpr auto kComputeCost = "compute_cost";
 constexpr auto kKernelName = "kernel_name";
 constexpr auto kPartialFlag = "partial_flag";
+constexpr auto kReshapeType = "reshape_type";
+constexpr auto kOpPattern = "op_pattern";
+constexpr auto kDynamicFormat = "dynamic_format";
+constexpr auto kDtypeFormat = "dtype_format";
 constexpr auto kAttr = "attr";
 constexpr auto kIputs = "inputs";
 constexpr auto kOutputs = "outputs";
+constexpr auto kTbe = "TBE";
+constexpr auto kAkg = "akg";
+constexpr auto kAutodiff = "AutoDiff";
 constexpr auto kName = "name";
 constexpr auto kParamType = "param_type";
 constexpr auto kDtype = "dtype";
@@ -89,8 +93,8 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI
   std::shared_ptr<OpInfo> op_info = std::make_shared<OpInfo>();
   MS_EXCEPTION_IF_NULL(op_info);
   op_info->set_op_name(obj.at(kOpName));
-  op_info->set_imply_type(imply_type);
   op_info->set_impl_path(impl_path);
+  op_info->set_imply_type(imply_type);
   op_info->set_fusion_type(obj.at(kFusionType));
   if (imply_type == kTBE) {
     op_info->set_async_flag(obj.at(kAsyncFlag));
@@ -98,6 +102,12 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI
     op_info->set_compute_cost(obj.at(kComputeCost));
     op_info->set_kernel_name(obj.at(kKernelName));
     op_info->set_partial_flag(obj.at(kPartialFlag));
+    if (obj.find(kOpPattern) != obj.end()) {
+      op_info->set_op_pattern(obj.at(kOpPattern));
+    }
+    if (obj.find(kDynamicFormat) != obj.end()) {
+      op_info->set_dynamic_format(obj.at(kDynamicFormat));
+    }
   }
   auto attrs = obj.at(kAttr);
   for (const auto& attr : attrs) {
@@ -106,16 +116,20 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI
       return false;
     }
   }
+  nlohmann::json dtype_format;
+  if (obj.find(kDtypeFormat) != obj.end()) {
+    dtype_format = obj.at(kDtypeFormat);
+  }
   auto inputs = obj.at(kIputs);
   for (const auto& input : inputs) {
-    if (!DecodeInputOutput(input, imply_type, kInput, op_info)) {
+    if (!DecodeInputOutput(input, imply_type, kInput, op_info, dtype_format)) {
       MS_LOG(DEBUG) << "DecodeInputOutput Failed";
       return false;
     }
   }
   auto outputs = obj.at(kOutputs);
   for (const auto& output : outputs) {
-    if (!DecodeInputOutput(output, imply_type, kOutput, op_info)) {
+    if (!DecodeInputOutput(output, imply_type, kOutput, op_info, dtype_format)) {
       MS_LOG(DEBUG) << "DecodeInputOutput Failed";
       return false;
     }
@@ -156,16 +170,42 @@ bool OpLib::DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type,
   return ret;
 }
 
+bool OpLib::DecodeDtypeFormat(const nlohmann::json& dtype_format, const std::shared_ptr<OpIOInfo>& op_io,
+                              size_t index) {
+  bool ret = true;
+  try {
+    std::vector<std::string> dtype;
+    std::vector<std::string> format;
+    for (const auto& it : dtype_format) {
+      dtype.emplace_back(it[index][0]);
+      format.emplace_back(it[index][1]);
+    }
+    op_io->set_dtypes(dtype);
+    op_io->set_formats(format);
+  } catch (const std::exception& e) {
+    MS_LOG(ERROR) << "DecodeDtypeFormat falied" << e.what();
+    ret = false;
+  }
+  return ret;
+}
+
 bool OpLib::DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply_type, const OpIOType io_type,
-                              const std::shared_ptr<OpInfo>& op_info) {
+                              const std::shared_ptr<OpInfo>& op_info, const nlohmann::json& dtype_format) {
   bool ret = true;
   try {
     std::shared_ptr<OpIOInfo> op_io = std::make_shared<OpIOInfo>();
     MS_EXCEPTION_IF_NULL(op_io);
     op_io->set_index(obj.at(kIndex));
     op_io->set_name(obj.at(kName));
-    op_io->set_dtypes(obj.at(kDtype));
-    op_io->set_formats(obj.at(kFormat));
+    if (!dtype_format.empty()) {
+      if (!DecodeDtypeFormat(dtype_format, op_io, op_info->inputs_ptr().size() + op_info->outputs_ptr().size())) {
+        MS_LOG(ERROR) << "Decode dtype format failed";
+        return false;
+      }
+    } else {
+      op_io->set_dtypes(obj.at(kDtype));
+      op_io->set_formats(obj.at(kFormat));
+    }
     if (op_io->dtypes().size() != op_io->formats().size()) {
       MS_LOG(DEBUG) << "op" << op_io->name() << "dtype size:" << op_io->dtypes()
                     << "is not equal to format size:" << op_io->formats();
@@ -181,6 +221,9 @@ bool OpLib::DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply
       if (obj.find(kShape) != obj.end()) {
         op_io->set_shape(obj.at(kShape));
       }
+      if (obj.find(kReshapeType) != obj.end()) {
+        op_io->set_reshape_type(obj.at(kReshapeType));
+      }
     }
 
     if (io_type == kInput) {
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.h b/mindspore/ccsrc/kernel/oplib/oplib.h
index 37c3fdcfc7..a4c5e04bb1 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.h
+++ b/mindspore/ccsrc/kernel/oplib/oplib.h
@@ -38,8 +38,10 @@ class OpLib {
   static bool DecodeOpInfo(const nlohmann::json& obj, const OpImplyType imply_type, const std::string& impl_path);
   static bool DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type,
                          const std::shared_ptr<OpInfo>& op_info);
+  static bool DecodeDtypeFormat(const nlohmann::json& dtype_format, const std::shared_ptr<OpIOInfo>& op_io,
+                                size_t index);
   static bool DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply_type, const OpIOType io_type,
-                                const std::shared_ptr<OpInfo>& op_info);
+                                const std::shared_ptr<OpInfo>& op_info, const nlohmann::json& dtype_format);
   static bool GetRefInfo(const std::shared_ptr<OpInfo>& op_info);
   static bool CheckRepetition(const std::shared_ptr<OpInfo>& op_info);
 };
diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py
index 23109b386a..6f4f680672 100644
--- a/mindspore/ops/__init__.py
+++ b/mindspore/ops/__init__.py
@@ -30,7 +30,7 @@ Note:
 
 from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry
-from .op_info_register import op_info_register
+from .op_info_register import op_info_register, TBERegOp, DataType
 from .primitive import constexpr
 from .._c_expression import signature_rw, signature_kind
 
@@ -40,6 +40,6 @@ __primitive__ = [
 ]
 
 __all__ = ["get_vm_impl_fn", "vm_impl_registry",
-           "op_info_register",
+           "op_info_register", "TBERegOp", "DataType",
            "constexpr"]
 __all__.extend(__primitive__)
diff --git a/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py b/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py
index d1c43ca957..a8911e81bd 100644
--- a/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py
+++ b/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py
@@ -14,208 +14,41 @@
 # ============================================================================
 
 """AdamApplyOneWithDecay op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+adam_apply_one_with_decay_op_info = TBERegOp("AdamApplyOneWithDecay") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("adam_apply_one_with_decay.so") \
+    .compute_cost(10) \
+    .kernel_name("adam_apply_one_with_decay") \
+    .partial_flag(True) \
+    .input(0, "input0", False, "required", "all") \
+    .input(1, "input1", False, "required", "all") \
+    .input(2, "input2", False, "required", "all") \
+    .input(3, "input3", False, "required", "all") \
+    .input(4, "input4", False, "required", "all") \
+    .input(5, "mul0_x", False, "required", "all") \
+    .input(6, "mul1_x", False, "required", "all") \
+    .input(7, "mul2_x", False, "required", "all") \
+    .input(8, "mul3_x", False, "required", "all") \
+    .input(9, "mul4_x", False, "required", "all") \
+    .input(10, "add2_y", False, "required", "all") \
+    .output(0, "output0", False, "required", "all") \
+    .output(1, "output1", False, "required", "all") \
+    .output(2, "output2", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AdamApplyOneWithDecay",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "adam_apply_one_with_decay.so",
-    "compute_cost": 10,
-    "kernel_name": "adam_apply_one_with_decay",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input0",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input4",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "mul0_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "mul1_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 7,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "mul2_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 8,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "mul3_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 9,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "mul4_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 10,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "add2_y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output0",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output1",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output2",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(adam_apply_one_with_decay_op_info)
 def _adam_apply_one_with_decay_tbe():
     """AdamApplyOneWithDecay TBE register"""
     return
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 80f40ff1d6..6a42099c89 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -16,6 +16,7 @@
 """Operators info register."""
 
 import os
+import json
 import inspect
 from mindspore._c_expression import Oplib
 from mindspore._checkparam import ParamValidator as validator
@@ -32,21 +33,453 @@ def op_info_register(op_info):
         'op_info' must be a str of json format represent the op info, the op info will be added into oplib.
 
     Args:
-        op_info (str): op info of json format.
+        op_info (str or dict): op info of json format.
 
     Returns:
         Function, returns a decorator for op info register.
     """
     def register_decorator(func):
-        validator.check_type("op_info", op_info, [str])
+        if isinstance(op_info, dict):
+            op_info_real = json.dumps(op_info)
+        else:
+            op_info_real = op_info
+        validator.check_type("op_info", op_info_real, [str])
         op_lib = Oplib()
         file_path = os.path.realpath(inspect.getfile(func))
         # keep the path custom ops implementation.
         imply_path = "" if BUILT_IN_OPS_REGISTER_PATH in file_path else file_path
-        if not op_lib.reg_op(op_info, imply_path):
-            raise ValueError('Invalid op info {}:\n{}\n'.format(file_path, op_info))
+        if not op_lib.reg_op(op_info_real, imply_path):
+            raise ValueError('Invalid op info {}:\n{}\n'.format(file_path, op_info_real))
 
         def wrapped_function(*args, **kwargs):
             return func(*args, **kwargs)
         return wrapped_function
     return register_decorator
+
+
+class RegOp():
+    """
+    Base class for op info register.
+
+    Args:
+        op_name (str): Name of op.
+        inputs (list): Inputs inoformation of the op.
+        outputs (list): Outputs information of the op.
+        attr_ (list): Attribute information of the op.
+        dtype_format_ (list): Dtype and format information of the op.
+    """
+
+    def __init__(self, op_name=""):
+        if not isinstance(op_name, str):
+            raise ValueError("op name value must be string")
+        if not op_name.strip():
+            raise ValueError("op name is empty")
+        self.op_name = op_name
+        self.inputs = []
+        self.outputs = []
+        self.attr_ = []
+        self.dtype_format_ = []
+
+    def is_string(self, value):
+        """
+        Check if the value is a str type.
+
+        Args:
+            value: Parameter to to check.
+
+        Raises:
+            TypeError: If the type of value is not a str.
+        """
+        if not isinstance(value, str):
+            raise TypeError("%s value must be str" % str(value))
+
+    def is_int(self, value):
+        """
+        Check if the value is a int.
+
+        Args:
+            value: Parameter to to check.
+
+        Raises:
+            TypeError: If the type of value is not a int.
+        """
+        if not isinstance(value, int):
+            raise TypeError("%s value must be int" % str(value))
+
+    def is_bool(self, value):
+        """
+        Check if the value is a bool.
+
+        Args:
+            value: Parameter to to check.
+
+        Raises:
+            TypeError: If the type of value is not a bool.
+        """
+        if not isinstance(value, bool):
+            raise TypeError("%s value must be bool" % str(value))
+
+    def dtype_format(self, *args):
+        """
+        Register dtype and format.
+
+        Args:
+            args (tuple): Value of dtype and format.
+
+        Raises:
+            ValueError: If the size of args not equal to input size add output size.
+            TypeError: If the type of args is not tuple.
+        """
+        if len(self.inputs) + len(self.outputs) != len(args):
+            raise ValueError("input size add output size must be equal to detype format size")
+        dtype_format = []
+        for arg in args:
+            if not isinstance(arg, tuple) or len(arg) != 2:
+                raise ValueError("dtype and format value must be tuple of two elements")
+            self.is_string(arg[0])
+            self.is_string(arg[1])
+            dtype_format.append(arg)
+        self.dtype_format_.append(tuple(dtype_format))
+        return self
+
+    def get_op_info(self):
+        """
+        Return all registration information for this instance.
+
+        The '_' character ending the key is removed here for compatibility with previous version.
+
+        Key will be unified into an underlined form later.
+        """
+        op_info = {}
+        for key, value in self.__dict__.items():
+            if isinstance(key, str) and key.endswith('_'):
+                op_info[key.rstrip('_')] = value
+            else:
+                op_info[key] = value
+        return op_info
+
+
+class TBERegOp(RegOp):
+    """Class for TBE op info register."""
+
+    def __init__(self, op_name=""):
+        super(TBERegOp, self).__init__(op_name)
+        self.imply_type = "TBE"
+        self.fusion_type_ = ''
+        self.async_flag_ = False
+        self.binfile_name_ = ''
+        self.compute_cost_ = 10
+        self.kernel_name_ = ''
+        self.partial_flag_ = False
+        self.reshape_type_ = ''
+        self.dynamic_format_ = False
+        self.op_pattern_ = ""
+
+    def fusion_type(self, fusion_type):
+        """
+        Register fusion type.
+
+        Args:
+            fusion_type (str): Value of fusion type.
+        """
+        self.is_string(fusion_type)
+        self.fusion_type_ = fusion_type
+        return self
+
+    def async_flag(self, async_flag):
+        """
+        Register async flag.
+
+        Args:
+            async_flag (bool): Value of async flag.
+        """
+        self.is_bool(async_flag)
+        self.async_flag_ = async_flag
+        return self
+
+    def binfile_name(self, binfile_name):
+        """
+        Register binfile name.
+
+        Args:
+            binfile_name (str): Name of op binfile.
+        """
+        self.is_string(binfile_name)
+        self.binfile_name_ = binfile_name
+        return self
+
+    def compute_cost(self, compute_cost):
+        """
+        Register compute cost.
+
+        Args:
+            compute_cost (int): Value of compute cost.
+        """
+        self.is_int(compute_cost)
+        self.compute_cost_ = compute_cost
+        return self
+
+    def kernel_name(self, kernel_name):
+        """
+        Register kernel name.
+
+        Args:
+            kernel_name (str): Name of op kernel.
+        """
+        self.is_string(kernel_name)
+        self.kernel_name_ = kernel_name
+        return self
+
+    def partial_flag(self, partial_flag):
+        """
+        Register partial flag.
+
+        Args:
+            partial_flag (bool): Value of partial flag.
+        """
+        self.is_bool(partial_flag)
+        self.partial_flag_ = partial_flag
+        return self
+
+    def reshape_type(self, reshape_type):
+        """
+        Register reshape type.
+
+        Args:
+            reshape_type (str): Value of reshape type.
+        """
+        self.is_string(reshape_type)
+        self.reshape_type_ = reshape_type
+        return self
+
+    def dynamic_format(self, dynamic_format):
+        """
+        Register dynamic format.
+
+        Args:
+            reshape_type (bool): Value of dynamic format.
+        """
+        self.is_bool(dynamic_format)
+        self.dynamic_format_ = dynamic_format
+        return self
+
+    def op_pattern(self, pattern=None):
+        """
+        Register op pattern information.
+
+        Args:
+            pattern (str): Value of op pattern.
+        """
+        if pattern is not None and self.istring(pattern):
+            self.op_pattern_ = pattern
+        return self
+
+    def attr(self, name=None, param_type=None, value_type=None, value=None, default_value=None, **kwargs):
+        """
+        Register op attribute information.
+
+        Args:
+            name (str): Name of the attribute. Default: None.
+            param_type (str): Param type of the attribute. Default: None.
+            type (str): Type of the attribute. Default: None.
+            value (str): Value of the attribute. Default: None.
+            default_value (str): Default value of attribute. Default: None.
+            kwargs (dict): Other information for the attribute.
+        """
+        param_list = [name, param_type, value_type, value, default_value]
+        attr_dict = {}
+        for index, element in enumerate(param_list):
+            if element is not None:
+                self.is_string(element)
+                if index == 0:
+                    attr_dict["name"] = element
+                elif index == 1:
+                    attr_dict["param_type"] = element
+                elif index == 2:
+                    attr_dict["type"] = element
+                elif index == 3:
+                    attr_dict["value"] = element
+                elif index == 4:
+                    attr_dict["default_value"] = element
+        if kwargs:
+            attr_dict = dict(attr_dict, **kwargs)
+        self.attr_.append(attr_dict)
+        return self
+
+    def input(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs):
+        """
+        Register op input information.
+
+        Args:
+            index (int): Order of the input. Default: None.
+            name (str): Name of the input. Default: None.
+            need_compile (bool): The input need compile whether or not. Default: None.
+            param_type (str): Type of the input. Default: None.
+            shape (str): Shape of the input. Default: None.
+            kwargs (dict): Other information for the input.
+        """
+        param_list = [index, name, need_compile, param_type, shape]
+        input_dict = {}
+        for idx, element in enumerate(param_list):
+            if element is not None:
+                if idx == 0:
+                    self.is_int(element)
+                    input_dict["index"] = element
+                elif idx == 1:
+                    self.is_string(element)
+                    input_dict["name"] = element
+                elif idx == 2:
+                    self.is_bool(element)
+                    input_dict["need_compile"] = element
+                elif idx == 3:
+                    self.is_string(element)
+                    input_dict["param_type"] = element
+                elif idx == 4:
+                    self.is_string(element)
+                    input_dict["shape"] = element
+        if kwargs:
+            input_dict = dict(input_dict, **kwargs)
+        self.inputs.append(input_dict)
+        return self
+
+    def output(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs):
+        """
+        Register op output information.
+
+        Args:
+            index (int): Order of the output. Default: None.
+            name (str): Name of the output. Default: None.
+            need_compile (bool): The output need compile whether or not. Default: None.
+            param_type (str): Type of the output. Default: None.
+            shape (str): Shape of the output. Default: None.
+            kwargs (dict): Other information for the output.
+        """
+        param_list = [index, name, need_compile, param_type, shape]
+        output_dict = {}
+        for idx, element in enumerate(param_list):
+            if element is not None:
+                if idx == 0:
+                    self.is_int(element)
+                    output_dict["index"] = element
+                elif idx == 1:
+                    self.is_string(element)
+                    output_dict["name"] = element
+                elif idx == 2:
+                    self.is_bool(element)
+                    output_dict["need_compile"] = element
+                elif idx == 3:
+                    self.is_string(element)
+                    output_dict["param_type"] = element
+                elif idx == 4:
+                    self.is_string(element)
+                    output_dict["shape"] = element
+        if kwargs:
+            output_dict = dict(output_dict, **kwargs)
+        self.outputs.append(output_dict)
+        return self
+
+class DataType():
+    """
+    Various combinations of dtype and formatself.
+
+    The current list below maybe not completed. If necessary, please add it.
+    """
+
+    BOOL_None = ("bool", "")
+    BOOL_Default = ("bool", "DefaultFormat")
+    BOOL_5HD = ("bool", "NC1HWC0")
+    BOOL_NCHW = ("bool", "NCHW")
+    BOOL_NHWC = ("bool", "NHWC")
+    BOOL_HWCN = ("bool", "HWCN")
+
+    I8_None = ("int8", "")
+    I8_Default = ("int8", "DefaultFormat")
+    I8_5HD = ("int8", "NC1HWC0")
+    I8_FracZ = ("int8", "Fracz")
+    I8_FracNZ = ("int8", "FRACTAL_NZ")
+    I8_NCHW = ("int8", "NCHW")
+    I8_NHWC = ("int8", "NHWC")
+    I8_HWCN = ("int8", "HWCN")
+
+    U8_None = ("uint8", "")
+    U8_Default = ("uint8", "DefaultFormat")
+    U8_5HD = ("uint8", "NC1HWC0")
+    U8_FracZ = ("uint8", "Fracz")
+    U8_FracNZ = ("uint8", "FRACTAL_NZ")
+    U8_NCHW = ("uint8", "NCHW")
+    U8_NHWC = ("uint8", "NHWC")
+    U8_HWCN = ("uint8", "HWCN")
+
+    I16_None = ("int16", "")
+    I16_Default = ("int16", "DefaultFormat")
+    I16_5HD = ("int16", "NC1HWC0")
+    I16_FracZ = ("int16", "Fracz")
+    I16_FracNZ = ("int16", "FRACTAL_NZ")
+    I16_NCHW = ("int16", "NCHW")
+    I16_NHWC = ("int16", "NHWC")
+    I16_HWCN = ("int16", "HWCN")
+
+    U16_None = ("uint16", "")
+    U16_Default = ("uint16", "DefaultFormat")
+    U16_5HD = ("uint16", "NC1HWC0")
+    U16_FracZ = ("uint16", "Fracz")
+    U16_FracNZ = ("uint16", "FRACTAL_NZ")
+    U16_NCHW = ("uint16", "NCHW")
+    U16_NHWC = ("uint16", "NHWC")
+    U16_HWCN = ("uint16", "HWCN")
+
+    I32_None = ("int32", "")
+    I32_Default = ("int32", "DefaultFormat")
+    I32_5HD = ("int32", "NC1HWC0")
+    I32_FracZ = ("int32", "Fracz")
+    I32_FracNZ = ("int32", "FRACTAL_NZ")
+    I32_NCHW = ("int32", "NCHW")
+    I32_NHWC = ("int32", "NHWC")
+    I32_HWCN = ("int32", "HWCN")
+
+    U32_None = ("uint32", "")
+    U32_Default = ("uint32", "DefaultFormat")
+    U32_5HD = ("uint32", "NC1HWC0")
+    U32_FracZ = ("uint32", "Fracz")
+    U32_FracNZ = ("uint32", "FRACTAL_NZ")
+    U32_NCHW = ("uint32", "NCHW")
+    U32_NHWC = ("uint32", "NHWC")
+    U32_HWCN = ("uint32", "HWCN")
+
+    I64_None = ("int64", "")
+    I64_Default = ("int64", "DefaultFormat")
+    I64_5HD = ("int64", "NC1HWC0")
+    I64_FracZ = ("int64", "Fracz")
+    I64_FracNZ = ("int64", "FRACTAL_NZ")
+    I64_NCHW = ("int64", "NCHW")
+    I64_NHWC = ("int64", "NHWC")
+    I64_HWCN = ("int64", "HWCN")
+
+    U64_None = ("uint64", "")
+    U64_Default = ("uint64", "DefaultFormat")
+    U64_5HD = ("uint64", "NC1HWC0")
+    U64_FracZ = ("uint64", "Fracz")
+    U64_FracNZ = ("uint64", "FRACTAL_NZ")
+    U64_NCHW = ("uint64", "NCHW")
+    U64_NHWC = ("uint64", "NHWC")
+    U64_HWCN = ("uint64", "HWCN")
+
+    F16_None = ("float16", "")
+    F16_Default = ("float16", "DefaultFormat")
+    F16_5HD = ("float16", "NC1HWC0")
+    F16_FracZ = ("float16", "Fracz")
+    F16_FracNZ = ("float16", "FRACTAL_NZ")
+    F16_C1HWNCoC0 = ("float16", "C1HWNCoC0")
+    F16_NCHW = ("float16", "NCHW")
+    F16_NHWC = ("float16", "NHWC")
+    F16_HWCN = ("float16", "HWCN")
+
+    F32_None = ("float32", "")
+    F32_Default = ("float32", "DefaultFormat")
+    F32_5HD = ("float32", "NC1HWC0")
+    F32_FracZ = ("float32", "Fracz")
+    F32_FracNZ = ("float32", "FRACTAL_NZ")
+    F32_C1HWNCoC0 = ("float32", "C1HWNCoC0")
+    F32_NCHW = ("float32", "NCHW")
+    F32_NHWC = ("float32", "NHWC")
+    F32_HWCN = ("float32", "HWCN")

From 144a636b514733b9abca3d4ede90e77ff7cceaa0 Mon Sep 17 00:00:00 2001
From: zhongligeng <zhongligeng@huawei.com>
Date: Fri, 3 Apr 2020 10:45:03 +0800
Subject: [PATCH 090/367] resolve some issue in nn comments

---
 mindspore/nn/layer/basic.py         | 23 +++++++++------------
 mindspore/nn/layer/container.py     |  5 ++---
 mindspore/nn/layer/conv.py          |  7 ++-----
 mindspore/nn/layer/embedding.py     |  2 +-
 mindspore/nn/layer/lstm.py          |  6 +++---
 mindspore/nn/layer/normalization.py |  4 ++--
 mindspore/nn/layer/pooling.py       | 16 +++++++--------
 mindspore/nn/loss/loss.py           | 31 ++++++++++++++---------------
 mindspore/nn/metrics/accuracy.py    | 17 ++++++++--------
 mindspore/nn/metrics/error.py       |  8 ++++----
 mindspore/nn/metrics/fbeta.py       | 11 +++++-----
 mindspore/nn/metrics/loss.py        |  3 +--
 mindspore/nn/metrics/precision.py   | 18 ++++++++---------
 mindspore/nn/metrics/recall.py      | 18 ++++++++---------
 mindspore/nn/metrics/topk.py        | 15 +++++++-------
 mindspore/nn/optim/adam.py          |  6 +++---
 mindspore/nn/optim/ftrl.py          |  2 +-
 mindspore/nn/optim/lamb.py          |  2 +-
 mindspore/nn/optim/lars.py          |  4 ++--
 mindspore/nn/optim/momentum.py      |  2 +-
 mindspore/nn/optim/rmsprop.py       |  2 +-
 mindspore/nn/optim/sgd.py           |  2 +-
 mindspore/nn/wrap/cell_wrapper.py   | 29 +++------------------------
 mindspore/nn/wrap/grad_reducer.py   | 23 +++++++++++++++------
 mindspore/nn/wrap/loss_scale.py     | 22 ++++++++++----------
 25 files changed, 128 insertions(+), 150 deletions(-)

diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 9dc0d5e623..30b94c738d 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -65,7 +65,7 @@ class Dropout(Cell):
         Tensor, output tensor with the same shape as the input.
 
     Examples:
-        >>> x = mindspore.Tensor(np.ones([20, 16, 50]), mindspore.float32)
+        >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
         >>> net(x)
     """
@@ -111,7 +111,7 @@ class Flatten(Cell):
 
     Examples:
         >>> net = nn.Flatten()
-        >>> input = mindspore.Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
+        >>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
         >>> input.shape()
         (2, 2, 2)
         >>> net(input)
@@ -149,9 +149,6 @@ class Dense(Cell):
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
         activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
 
-    Returns:
-        Tensor, output tensor.
-
     Raises:
         ValueError: If weight_init or bias_init shape is incorrect.
 
@@ -163,7 +160,7 @@ class Dense(Cell):
 
     Examples:
         >>> net = nn.Dense(3, 4)
-        >>> input = mindspore.Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
         >>> net(input)
         [[ 2.5246444   2.2738023   0.5711005  -3.9399147 ]
          [ 1.0739875   4.0155234   0.94188046 -5.459526  ]]
@@ -243,8 +240,8 @@ class ClipByNorm(Cell):
 
     Examples:
         >>> net = nn.ClipByNorm()
-        >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
-        >>> clip_norm = mindspore.Tensor(np.array([100]).astype(np.float32))
+        >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
+        >>> clip_norm = Tensor(np.array([100]).astype(np.float32))
         >>> net(input, clip_norm)
 
     """
@@ -290,9 +287,6 @@ class Norm(Cell):
         keep_dims (bool): If True, the axis indicated in `axis` are kept with size 1. Otherwise,
                    the dimensions in `axis` are removed from the output shape. Default: False.
 
-    Returns:
-        Tensor, a Tensor of the same type as input, containing the vector or matrix norms.
-
     Inputs:
         - **input** (Tensor) - Tensor which is not empty.
 
@@ -302,7 +296,7 @@ class Norm(Cell):
 
     Examples:
         >>> net = nn.Norm(axis=0)
-        >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
         >>> net(input)
     """
     def __init__(self, axis=(), keep_dims=False):
@@ -344,7 +338,8 @@ class OneHot(Cell):
                           when indices[j] = i. Default: 1.0.
         off_value (float): A scalar defining the value to fill in output[i][j]
                            when indices[j] != i. Default: 0.0.
-        dtype (:class:`mindspore.dtype`): Default: mindspore.float32.
+        dtype (:class:`mindspore.dtype`): Data type of 'on_value' and 'off_value', not the
+                                          data type of indices. Default: mindspore.float32.
 
     Inputs:
         - **indices** (Tensor) - A tensor of indices of data type mindspore.int32 and arbitrary shape.
@@ -355,7 +350,7 @@ class OneHot(Cell):
 
     Examples:
         >>> net = nn.OneHot(depth=4, axis=1)
-        >>> indices = mindspore.Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
+        >>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
         >>> net(indices)
         [[[0. 0.]
           [1. 0.]
diff --git a/mindspore/nn/layer/container.py b/mindspore/nn/layer/container.py
index 76c72ce421..709b3ef8ef 100644
--- a/mindspore/nn/layer/container.py
+++ b/mindspore/nn/layer/container.py
@@ -86,7 +86,7 @@ class SequentialCell(Cell):
         >>> relu = nn.ReLU()
         >>> seq = nn.SequentialCell([conv, bn, relu])
         >>>
-        >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
+        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
         >>> seq(x)
         [[[[0.02531557 0.        ]
            [0.04933941 0.04880078]]
@@ -138,7 +138,6 @@ class SequentialCell(Cell):
         return len(self._cells)
 
     def construct(self, input_data):
-        """Processes the input with the defined sequence of Cells."""
         for cell in self.cell_list:
             input_data = cell(input_data)
         return input_data
@@ -161,7 +160,7 @@ class CellList(_CellListBase, Cell):
         >>> cell_ls = nn.CellList([bn])
         >>> cell_ls.insert(0, conv)
         >>> cell_ls.append(relu)
-        >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
+        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
         >>> # not same as nn.SequentialCell, `cell_ls(x)` is not correct
         >>> cell_ls
         CellList< (0): Conv2d<input_channels=100, ..., bias_init=None>
diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index dfbf96e150..eb73a9ce5a 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -146,9 +146,6 @@ class Conv2d(_Conv):
             Initializer and string are the same as 'weight_init'. Refer to the values of
             Initializer for more details. Default: 'zeros'.
 
-    Returns:
-        Tensor, output tensor.
-
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
 
@@ -157,7 +154,7 @@ class Conv2d(_Conv):
 
     Examples:
         >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
-        >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
+        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
         >>> net(input).shape()
         (1, 240, 1024, 640)
     """
@@ -277,7 +274,7 @@ class Conv2dTranspose(_Conv):
 
     Examples:
         >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal')
-        >>> input = Tensor(np.ones([1, 3, 16, 50]), mstype.float32)
+        >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
         >>> net(input)
         """
     def __init__(self,
diff --git a/mindspore/nn/layer/embedding.py b/mindspore/nn/layer/embedding.py
index 9579f35226..dfa8e66469 100755
--- a/mindspore/nn/layer/embedding.py
+++ b/mindspore/nn/layer/embedding.py
@@ -50,7 +50,7 @@ class Embedding(Cell):
 
     Examples:
         >>> net = nn.Embedding(20000, 768,  True)
-        >>> input_data = mindspore.Tensor(np.ones([8, 128]), mindspore.int32)
+        >>> input_data = Tensor(np.ones([8, 128]), mindspore.int32)
         >>>
         >>> # Maps the input word IDs to word embedding.
         >>> output = net(input_data)
diff --git a/mindspore/nn/layer/lstm.py b/mindspore/nn/layer/lstm.py
index 317f754f67..cef926d365 100755
--- a/mindspore/nn/layer/lstm.py
+++ b/mindspore/nn/layer/lstm.py
@@ -96,9 +96,9 @@ class LSTM(Cell):
         >>>         return self.lstm(inp, (h0, c0))
         >>>
         >>> net = LstmNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False)
-        >>> input = mindspore.Tensor(np.ones([3, 5, 10]).astype(np.float32))
-        >>> h0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
-        >>> c0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
+        >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32))
+        >>> h0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
+        >>> c0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
         >>> output, (hn, cn) = net(input, h0, c0)
     """
     def __init__(self,
diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index d43c1c8ab4..646db0db8c 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -159,7 +159,7 @@ class BatchNorm1d(_BatchNorm):
 
     Examples:
         >>> net = nn.BatchNorm1d(num_features=16)
-        >>> input = mindspore.Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32)
         >>> net(input)
     """
     def _check_data_dim(self, x):
@@ -258,7 +258,7 @@ class LayerNorm(Cell):
     Examples:
         >>> x = Tensor(np.ones([20, 5, 10, 10], np.float32))
         >>> shape1 = x.shape()[1:]
-        >>> m = LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
+        >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
         >>> m(x)
     """
     def __init__(self,
diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index 0a4bd1662b..6ff28dd362 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -63,8 +63,8 @@ class MaxPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over.
-        stride (int): Stride size of the window. Default: None.
+        kernel_size (int): Size of the window to take a max over. Default 1.
+        stride (int): Stride size of the window. Default: 1.
         pad_mode (str): Select the mode of the pad. The optional values are
             "same" and "valid". Default: "valid".
 
@@ -75,7 +75,7 @@ class MaxPool2d(_PoolNd):
 
             - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
               without padding. Extra pixels will be discarded.
-        padding (int): Now is not supported, mplicit zero padding to be added on both sides. Default: 0.
+        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -85,7 +85,7 @@ class MaxPool2d(_PoolNd):
 
     Examples:
         >>> pool = MaxPool2d(kernel_size=3, stride=1)
-        >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[1. 5. 5. 1.]
            [0. 3. 4. 8.]
            [4. 2. 7. 6.]
@@ -149,8 +149,8 @@ class AvgPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over.
-        stride (int): Stride size of the window. Default: None.
+        kernel_size (int): Size of the window to take a max over. Default: 1.
+        stride (int): Stride size of the window. Default: 1.
         pad_mode (str): Select the mode of the pad. The optional values are
             "same", "valid". Default: "valid".
 
@@ -161,7 +161,7 @@ class AvgPool2d(_PoolNd):
 
             - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
               without padding. Extra pixels will be discarded.
-        padding (int): Now is not supported, implicit zero padding to be added on both sides. Default: 0.
+        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -171,7 +171,7 @@ class AvgPool2d(_PoolNd):
 
     Examples:
         >>> pool = AvgPool2d(kernel_size=3, stride=1)
-        >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
             [2. 7. 1. 2.]
diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py
index 340cbe73d8..806456e561 100644
--- a/mindspore/nn/loss/loss.py
+++ b/mindspore/nn/loss/loss.py
@@ -86,9 +86,9 @@ class L1Loss(_Loss):
         Tensor, loss float tensor.
 
     Examples:
-        >>> loss = L1Loss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> loss = nn.L1Loss()
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def __init__(self, reduction='mean'):
@@ -126,9 +126,9 @@ class MSELoss(_Loss):
         Tensor, weighted loss float tensor.
 
     Examples:
-        >>> loss = MSELoss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> loss = nn.MSELoss()
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def construct(self, base, target):
@@ -171,9 +171,9 @@ class SmoothL1Loss(_Loss):
         Tensor, loss float tensor.
 
     Examples:
-        >>> loss = SmoothL1Loss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> loss = nn.SmoothL1Loss()
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def __init__(self, sigma=1.0):
@@ -219,17 +219,16 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
     Inputs:
         - **logits** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
         - **labels** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`. If `sparse` is True, The type of
-          `labels` is mstype.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`.
+          `labels` is mindspore.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`.
 
     Outputs:
         Tensor, a tensor of the same shape as logits with the component-wise
         logistic losses.
 
     Examples:
-        >>> loss = SoftmaxCrossEntropyWithLogits(sparse=True)
-        >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mstype.float32)
-        >>> labels_np = np.zeros([1, 10]).astype(np.int32)
-        >>> labels_np[0][0] = 1
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
+        >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32)
+        >>> labels_np = np.ones([1,]).astype(np.int32)
         >>> labels = Tensor(labels_np)
         >>> loss(logits, labels)
     """
@@ -286,8 +285,8 @@ class SoftmaxCrossEntropyExpand(Cell):
 
     Examples:
         >>> loss = SoftmaxCrossEntropyExpand(sparse=True)
-        >>> input_data = Tensor(np.ones([64, 512]), dtype=mstype.float32)
-        >>> label = Tensor(np.ones([64]), dtype=mstype.int32)
+        >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
+        >>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
         >>> loss(input_data, label)
     """
     def __init__(self, sparse=False):
diff --git a/mindspore/nn/metrics/accuracy.py b/mindspore/nn/metrics/accuracy.py
index 5a11fa9d08..f131432ddd 100644
--- a/mindspore/nn/metrics/accuracy.py
+++ b/mindspore/nn/metrics/accuracy.py
@@ -35,8 +35,8 @@ class Accuracy(EvaluationBase):
             Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]), mindspore.float32)
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
+        >>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
         >>> metric = nn.Accuracy('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
@@ -58,13 +58,14 @@ class Accuracy(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
-                index is used in 'classification' evaluation type.
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
+                are both :math:`(N, C)`.
 
         Raises:
             ValueError: If the number of the input is not 2.
diff --git a/mindspore/nn/metrics/error.py b/mindspore/nn/metrics/error.py
index 5dbd83645b..c803000192 100644
--- a/mindspore/nn/metrics/error.py
+++ b/mindspore/nn/metrics/error.py
@@ -33,8 +33,8 @@ class MAE(Metric):
         The method `update` must be called with the form `update(y_pred, y)`.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
+        >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
+        >>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
         >>> error = nn.MAE()
         >>> error.clear()
         >>> error.update(x, y)
@@ -95,8 +95,8 @@ class MSE(Metric):
     where :math:`n` is batch size.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
+        >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
+        >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
         >>> error = MSE()
         >>> error.clear()
         >>> error.update(x, y)
diff --git a/mindspore/nn/metrics/fbeta.py b/mindspore/nn/metrics/fbeta.py
index 6771b6ba36..68df4318b0 100755
--- a/mindspore/nn/metrics/fbeta.py
+++ b/mindspore/nn/metrics/fbeta.py
@@ -33,12 +33,11 @@ class Fbeta(Metric):
         beta (float): The weight of precision.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Fbeta(1)
         >>> metric.update(x, y)
         >>> fbeta = metric.eval()
-        [0.66666667 0.66666667]
     """
     def __init__(self, beta):
         super(Fbeta, self).__init__()
@@ -64,7 +63,7 @@ class Fbeta(Metric):
                 `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
                 is the number of categories. y contains values of integers. The shape is :math:`(N, C)`
-                if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used.
+                if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used.
         """
         if len(inputs) != 2:
             raise ValueError('Fbeta need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
@@ -126,8 +125,8 @@ class F1(Fbeta):
         F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.F1()
         >>> metric.update(x, y)
         >>> fbeta = metric.eval()
diff --git a/mindspore/nn/metrics/loss.py b/mindspore/nn/metrics/loss.py
index bc4c58ef2f..3828fcdef5 100644
--- a/mindspore/nn/metrics/loss.py
+++ b/mindspore/nn/metrics/loss.py
@@ -25,12 +25,11 @@ class Loss(Metric):
         loss = \frac{\sum_{k=1}^{n}loss_k}{n}
 
     Examples:
-        >>> x = mindspore.Tensor(np.array(0.2), mindspore.float32)
+        >>> x = Tensor(np.array(0.2), mindspore.float32)
         >>> loss = nn.Loss()
         >>> loss.clear()
         >>> loss.update(x)
         >>> result = loss.eval()
-        0.20000000298023224
     """
     def __init__(self):
         super(Loss, self).__init__()
diff --git a/mindspore/nn/metrics/precision.py b/mindspore/nn/metrics/precision.py
index a2c8502002..ad7b6c576f 100644
--- a/mindspore/nn/metrics/precision.py
+++ b/mindspore/nn/metrics/precision.py
@@ -41,13 +41,12 @@ class Precision(EvaluationBase):
                          multilabel. Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Precision('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> precision = metric.eval()
-        [0.5 1. ]
     """
     def __init__(self, eval_type='classification'):
         super(Precision, self).__init__(eval_type)
@@ -72,13 +71,14 @@ class Precision(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
-                index is used in 'classification' evaluation type.
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
+                are both :math:`(N, C)`.
 
         Raises:
             ValueError: If the number of input is not 2.
diff --git a/mindspore/nn/metrics/recall.py b/mindspore/nn/metrics/recall.py
index 2ea284ec41..45ebf0d7db 100644
--- a/mindspore/nn/metrics/recall.py
+++ b/mindspore/nn/metrics/recall.py
@@ -41,13 +41,12 @@ class Recall(EvaluationBase):
                          multilabel. Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Recall('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> recall = metric.eval()
-        [1.  0.5]
     """
     def __init__(self, eval_type='classification'):
         super(Recall, self).__init__(eval_type)
@@ -72,13 +71,14 @@ class Recall(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
-                index is used in 'classification' evaluation type.
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
+                are both :math:`(N, C)`.
 
 
         Raises:
diff --git a/mindspore/nn/metrics/topk.py b/mindspore/nn/metrics/topk.py
index 6afa631940..eab08a498d 100644
--- a/mindspore/nn/metrics/topk.py
+++ b/mindspore/nn/metrics/topk.py
@@ -33,14 +33,13 @@ class TopKCategoricalAccuracy(Metric):
         ValueError: If `k` is less than 1.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.TopKCategoricalAccuracy(3)
         >>> topk.clear()
         >>> topk.update(x, y)
         >>> result = topk.eval()
-        0.6666666666666666
     """
     def __init__(self, k):
         super(TopKCategoricalAccuracy, self).__init__()
@@ -65,7 +64,7 @@ class TopKCategoricalAccuracy(Metric):
                 y_pred is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
                 is the number of categories. y contains values of integers. The shape is :math:`(N, C)`
-                if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used.
+                if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used.
         """
         if len(inputs) != 2:
             raise ValueError('Topk need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
@@ -98,9 +97,9 @@ class Top1CategoricalAccuracy(TopKCategoricalAccuracy):
     Refer to class 'TopKCategoricalAccuracy' for more details.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.Top1CategoricalAccuracy()
         >>> topk.clear()
         >>> topk.update(x, y)
@@ -116,9 +115,9 @@ class Top5CategoricalAccuracy(TopKCategoricalAccuracy):
     Refer to class 'TopKCategoricalAccuracy' for more details.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>            [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.Top5CategoricalAccuracy()
         >>> topk.clear()
         >>> topk.update(x, y)
diff --git a/mindspore/nn/optim/adam.py b/mindspore/nn/optim/adam.py
index 2c901ae081..86ce2b2147 100755
--- a/mindspore/nn/optim/adam.py
+++ b/mindspore/nn/optim/adam.py
@@ -161,7 +161,7 @@ class Adam(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Adam(params=net.trainable_params())
+        >>> optim = nn.Adam(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
 
@@ -252,7 +252,7 @@ class AdamWeightDecay(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = AdamWeightDecay(params=net.trainable_params())
+        >>> optim = nn.AdamWeightDecay(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
    """
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
@@ -306,7 +306,7 @@ class AdamWeightDecayDynamicLR(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10)
+        >>> optim = nn.AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self,
diff --git a/mindspore/nn/optim/ftrl.py b/mindspore/nn/optim/ftrl.py
index 3f4da483ea..ee8fc9355f 100644
--- a/mindspore/nn/optim/ftrl.py
+++ b/mindspore/nn/optim/ftrl.py
@@ -87,7 +87,7 @@ class FTRL(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = FTRL(net.trainable_params())
+        >>> opt = nn.FTRL(net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
     """
     def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
diff --git a/mindspore/nn/optim/lamb.py b/mindspore/nn/optim/lamb.py
index e4fd3bf1d7..e74d6fc6a8 100755
--- a/mindspore/nn/optim/lamb.py
+++ b/mindspore/nn/optim/lamb.py
@@ -163,7 +163,7 @@ class Lamb(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Lamb(params=net.trainable_params(), decay_steps=10)
+        >>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
 
diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py
index a69057215d..c0cb71cfa6 100755
--- a/mindspore/nn/optim/lars.py
+++ b/mindspore/nn/optim/lars.py
@@ -90,8 +90,8 @@ class LARS(Cell):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = Momentum(net.trainable_params(), 0.1, 0.9)
-        >>> opt_lars = LARS(opt, epsilon=1e-08, hyperpara=0.02)
+        >>> opt = nn.Momentum(net.trainable_params(), 0.1, 0.9)
+        >>> opt_lars = nn.LARS(opt, epsilon=1e-08, hyperpara=0.02)
         >>> model = Model(net, loss_fn=loss, optimizer=opt_lars, metrics=None)
     """
 
diff --git a/mindspore/nn/optim/momentum.py b/mindspore/nn/optim/momentum.py
index 2cc6d76a86..21d3cc864e 100755
--- a/mindspore/nn/optim/momentum.py
+++ b/mindspore/nn/optim/momentum.py
@@ -83,7 +83,7 @@ class Momentum(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0,
diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py
index faaeacfaa8..b17a101708 100644
--- a/mindspore/nn/optim/rmsprop.py
+++ b/mindspore/nn/optim/rmsprop.py
@@ -132,7 +132,7 @@ class RMSProp(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
+        >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
         >>> model = Model(net, loss, opt)
     """
     def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
diff --git a/mindspore/nn/optim/sgd.py b/mindspore/nn/optim/sgd.py
index 92e9a11070..dbc81ecdd6 100755
--- a/mindspore/nn/optim/sgd.py
+++ b/mindspore/nn/optim/sgd.py
@@ -77,7 +77,7 @@ class SGD(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = SGD(params=net.trainable_params())
+        >>> optim = nn.SGD(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,
diff --git a/mindspore/nn/wrap/cell_wrapper.py b/mindspore/nn/wrap/cell_wrapper.py
index efdfc9367e..53a535781d 100644
--- a/mindspore/nn/wrap/cell_wrapper.py
+++ b/mindspore/nn/wrap/cell_wrapper.py
@@ -50,8 +50,8 @@ class WithLossCell(Cell):
         >>> net_with_criterion = nn.WithLossCell(net, loss_fn)
         >>>
         >>> batch_size = 2
-        >>> data = mindspore.Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
-        >>> label = mindspore.Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32))
+        >>> data = Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
+        >>> label = Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32))
         >>>
         >>> net_with_criterion(data, label)
     """
@@ -62,16 +62,6 @@ class WithLossCell(Cell):
         self._loss_fn = loss_fn
 
     def construct(self, data, label):
-        """
-        Computes loss based on the wrapped loss cell.
-
-        Args:
-            data (Tensor): Tensor data to train.
-            label (Tensor): Tensor label data.
-
-        Returns:
-            Tensor, compute result.
-        """
         out = self._backbone(data)
         return self._loss_fn(out, label)
 
@@ -137,19 +127,6 @@ class WithGradCell(Cell):
         self.network_with_loss.set_train()
 
     def construct(self, data, label):
-        """
-        Computes gradients based on the wrapped gradients cell.
-
-        Note:
-            Run in PyNative mode.
-
-        Args:
-            data (Tensor): Tensor data to train.
-            label (Tensor): Tensor label data.
-
-        Returns:
-            Tensor, return compute gradients.
-        """
         weights = self.weights
         if self.sens is None:
             grads = self.grad(self.network_with_loss, weights)(data, label)
@@ -355,7 +332,7 @@ class ParameterUpdate(Cell):
         >>> param = network.parameters_dict()['learning_rate']
         >>> update = nn.ParameterUpdate(param)
         >>> update.phase = "update_param"
-        >>> lr = mindspore.Tensor(0.001, mindspore.float32)
+        >>> lr = Tensor(0.001, mindspore.float32)
         >>> update(lr)
     """
 
diff --git a/mindspore/nn/wrap/grad_reducer.py b/mindspore/nn/wrap/grad_reducer.py
index 8b34abc47b..01346698ee 100644
--- a/mindspore/nn/wrap/grad_reducer.py
+++ b/mindspore/nn/wrap/grad_reducer.py
@@ -120,25 +120,36 @@ class DistributedGradReducer(Cell):
         ValueError: If degree is not a int or less than 0.
 
     Examples:
-        >>> from mindspore.communication import get_group_size
+        >>> from mindspore.communication import init, get_group_size
         >>> from mindspore.ops import composite as C
         >>> from mindspore.ops import operations as P
         >>> from mindspore.ops import functional as F
         >>> from mindspore import context
+        >>> from mindspore import nn
+        >>> from mindspore import ParallelMode, ParameterTuple
+        >>>
+        >>> device_id = int(os.environ["DEVICE_ID"])
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
+        >>>                     device_id=int(device_id), enable_hccl=True)
+        >>> init()
+        >>> context.reset_auto_parallel_context()
+        >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
+        >>>
         >>>
         >>> class TrainingWrapper(nn.Cell):
         >>>     def __init__(self, network, optimizer, sens=1.0):
         >>>         super(TrainingWrapper, self).__init__(auto_prefix=False)
         >>>         self.network = network
-        >>>         self.weights = mindspore.ParameterTuple(network.trainable_params())
+        >>>         self.network.add_flags(defer_inline=True)
+        >>>         self.weights = ParameterTuple(network.trainable_params())
         >>>         self.optimizer = optimizer
         >>>         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         >>>         self.sens = sens
         >>>         self.reducer_flag = False
         >>>         self.grad_reducer = None
         >>>         self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
-        >>>         if self.parallel_mode in [mindspore.ParallelMode.DATA_PARALLEL,
-        >>>                                            mindspore.ParallelMode.HYBRID_PARALLEL]:
+        >>>         if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
+        >>>                                            ParallelMode.HYBRID_PARALLEL]:
         >>>             self.reducer_flag = True
         >>>         if self.reducer_flag:
         >>>             mean = context.get_auto_parallel_context("mirror_mean")
@@ -161,8 +172,8 @@ class DistributedGradReducer(Cell):
         >>> network = Net()
         >>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> train_cell = TrainingWrapper(network, optimizer)
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
         >>> grads = train_cell(inputs, label)
     """
 
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index a11c753eda..1ce3179273 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -65,9 +65,10 @@ class DynamicLossScaleUpdateCell(Cell):
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> output = train_network(inputs, label)
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> output = train_network(inputs, label, scaling_sens)
     """
 
     def __init__(self,
@@ -126,13 +127,14 @@ class FixedLossScaleUpdateCell(Cell):
     Examples:
         >>> net_with_loss = Net()
         >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
-        >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
+        >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> output = train_network(inputs, label)
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> output = train_network(inputs, label, scaling_sens)
     """
 
     def __init__(self, loss_scale_value):
@@ -181,9 +183,9 @@ class TrainOneStepWithLossScaleCell(Cell):
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> scaling_sens = mindspore.Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
         >>> output = train_network(inputs, label, scaling_sens)
     """
 

From 2649bffb46ffa6faca6363eda3f511cc50fe323d Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Fri, 3 Apr 2020 15:20:52 +0800
Subject: [PATCH 091/367] Update some required system packages in dockerfile

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 docker/mindspore-cpu/Dockerfile      | 23 +++++++++++++----------
 docker/mindspore-cuda10.1/Dockerfile | 23 +++++++++++++----------
 docker/mindspore-cuda9.2/Dockerfile  | 23 +++++++++++++----------
 3 files changed, 39 insertions(+), 30 deletions(-)

diff --git a/docker/mindspore-cpu/Dockerfile b/docker/mindspore-cpu/Dockerfile
index dea2fd2149..d24d23cf6b 100644
--- a/docker/mindspore-cpu/Dockerfile
+++ b/docker/mindspore-cpu/Dockerfile
@@ -12,20 +12,22 @@ RUN apt update \
     && DEBIAN_FRONTEND=noninteractive apt install -y \
     vim \
     wget \
+    curl \
     xz-utils \
     net-tools \
     openssh-client \
     git \
-    subversion \
     ntpdate \
     tzdata \
     tcl \
-    sudo
+    sudo \
+    bash-completion
 
 # Install compile tools
 RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     gcc \
     g++ \
+    zlibc \
     make \
     libgmp-dev \
     patch \
@@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections
 RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
 
 # Install python (v3.7.5)
-RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
     && cd /tmp \
     && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
     && tar -xvf v3.7.5.tar.gz \
@@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \
     && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
 
 # Install pip package
-RUN pip install numpy \
-    && pip install wheel \
-    && pip install nose \
-    && pip install pytest \
-    && pip install pytest-xdist \
-    && pip list
+RUN pip install --no-cache-dir \
+    numpy \
+    wheel \
+    nose \
+    pytest \
+    pytest-xdist
 
 # Install cmake (v3.14.1)
 RUN cd /tmp \
@@ -77,4 +80,4 @@ RUN cd /tmp \
     && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
 
 # Install MindSpore cpu whl package
-RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
diff --git a/docker/mindspore-cuda10.1/Dockerfile b/docker/mindspore-cuda10.1/Dockerfile
index 83b23ff878..e2a1ee955a 100644
--- a/docker/mindspore-cuda10.1/Dockerfile
+++ b/docker/mindspore-cuda10.1/Dockerfile
@@ -12,20 +12,22 @@ RUN apt update \
     && DEBIAN_FRONTEND=noninteractive apt install -y \
     vim \
     wget \
+    curl \
     xz-utils \
     net-tools \
     openssh-client \
     git \
-    subversion \
     ntpdate \
     tzdata \
     tcl \
-    sudo
+    sudo \
+    bash-completion
 
 # Install compile tools
 RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     gcc \
     g++ \
+    zlibc \
     make \
     libgmp-dev \
     patch \
@@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections
 RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
 
 # Install python (v3.7.5)
-RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
     && cd /tmp \
     && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
     && tar -xvf v3.7.5.tar.gz \
@@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \
     && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
 
 # Install pip package
-RUN pip install numpy \
-    && pip install wheel \
-    && pip install nose \
-    && pip install pytest \
-    && pip install pytest-xdist \
-    && pip list
+RUN pip install --no-cache-dir \
+    numpy \
+    wheel \
+    nose \
+    pytest \
+    pytest-xdist
 
 # Install cmake (v3.14.1)
 RUN cd /tmp \
@@ -77,4 +80,4 @@ RUN cd /tmp \
     && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
 
 # Install MindSpore cuda-10.1 whl package
-RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-10.1/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-10.1/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
diff --git a/docker/mindspore-cuda9.2/Dockerfile b/docker/mindspore-cuda9.2/Dockerfile
index cbfcfd42ac..6e40106396 100644
--- a/docker/mindspore-cuda9.2/Dockerfile
+++ b/docker/mindspore-cuda9.2/Dockerfile
@@ -12,20 +12,22 @@ RUN apt update \
     && DEBIAN_FRONTEND=noninteractive apt install -y \
     vim \
     wget \
+    curl \
     xz-utils \
     net-tools \
     openssh-client \
     git \
-    subversion \
     ntpdate \
     tzdata \
     tcl \
-    sudo
+    sudo \
+    bash-completion
 
 # Install compile tools
 RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     gcc \
     g++ \
+    zlibc \
     make \
     libgmp-dev \
     patch \
@@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections
 RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
 
 # Install python (v3.7.5)
-RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
     && cd /tmp \
     && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
     && tar -xvf v3.7.5.tar.gz \
@@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \
     && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
 
 # Install pip package
-RUN pip install numpy \
-    && pip install wheel \
-    && pip install nose \
-    && pip install pytest \
-    && pip install pytest-xdist \
-    && pip list
+RUN pip install --no-cache-dir \
+    numpy \
+    wheel \
+    nose \
+    pytest \
+    pytest-xdist
 
 # Install cmake (v3.14.1)
 RUN cd /tmp \
@@ -77,4 +80,4 @@ RUN cd /tmp \
     && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
 
 # Install MindSpore cuda-9.2 whl package
-RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-9.2/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-9.2/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl

From bb5320be13e4f0df95cdb80a8185ad7b3542c96c Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Tue, 31 Mar 2020 16:12:43 +0800
Subject: [PATCH 092/367] iterfaces change: _Constant to Constant

---
 mindspore/common/initializer.py           | 21 +++++++++++++++------
 tests/ut/python/utils/test_initializer.py |  9 +++++++--
 2 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py
index 99b4501307..bdc3418129 100644
--- a/mindspore/common/initializer.py
+++ b/mindspore/common/initializer.py
@@ -180,18 +180,18 @@ class HeUniform(Initializer):
         _assignment(arr, data)
 
 
-class _Constant(Initializer):
+class Constant(Initializer):
     """
     Initialize a constant.
 
     Args:
-        value (int or numpy.ndarray): The value to initialize.
+        value (Union[int, numpy.ndarray]): The value to initialize.
 
     Returns:
         Array, initialize array.
     """
     def __init__(self, value):
-        super(_Constant, self).__init__(value=value)
+        super(Constant, self).__init__(value=value)
         self.value = value
 
     def _initialize(self, arr):
@@ -266,8 +266,16 @@ def initializer(init, shape=None, dtype=mstype.float32):
 
     Args:
         init (Union[Tensor, str, Initializer, numbers.Number]): Initialize value.
+
+            - `str`: The `init` should be the alias of the class inheriting from `Initializer` and the corresponding
+              class will be called.
+
+            - `Initializer`: The `init` should be the class inheriting from `Initializer` to initialize tensor.
+
+            - `numbers.Number`: The `Constant` will be called to initialize tensor.
+
         shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
-                                         output. Default: None.
+            output. Default: None.
         dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mstype.float32.
 
     Returns:
@@ -295,7 +303,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
         raise ValueError(msg)
 
     if isinstance(init, numbers.Number):
-        init_obj = _Constant(init)
+        init_obj = Constant(init)
     elif isinstance(init, str):
         init_obj = _INITIALIZER_ALIAS[init.lower()]()
     else:
@@ -314,4 +322,5 @@ __all__ = [
     'HeUniform',
     'XavierUniform',
     'One',
-    'Zero']
+    'Zero',
+    'Constant']
diff --git a/tests/ut/python/utils/test_initializer.py b/tests/ut/python/utils/test_initializer.py
index ff7ab8d119..31d2434341 100644
--- a/tests/ut/python/utils/test_initializer.py
+++ b/tests/ut/python/utils/test_initializer.py
@@ -37,8 +37,8 @@ def _check_value(tensor, value_min, value_max):
     for ele in nd.flatten():
         if value_min <= ele <= value_max:
             continue
-        raise TypeError('value_min = %d, ele = %d, value_max = %d'
-                        % (value_min, ele, value_max))
+        raise ValueError('value_min = %d, ele = %d, value_max = %d'
+                         % (value_min, ele, value_max))
 
 
 def _check_uniform(tensor, boundary_a, boundary_b):
@@ -92,6 +92,11 @@ def test_init_one_alias():
     _check_value(tensor, 1, 1)
 
 
+def test_init_constant():
+    tensor = init.initializer(init.Constant(1), [2, 2], ms.float32)
+    _check_value(tensor, 1, 1)
+
+
 def test_init_uniform():
     scale = 10
     tensor = init.initializer(init.Uniform(scale=scale), [5, 4], ms.float32)

From 2ff6f0de460986d3864e05a589963dfbe0e119a5 Mon Sep 17 00:00:00 2001
From: VectorSL <shiliang10@huawei.com>
Date: Fri, 3 Apr 2020 16:55:37 +0800
Subject: [PATCH 093/367] edit loss_scale for gpu

---
 mindspore/nn/wrap/loss_scale.py      | 40 ++++++++++----
 mindspore/ops/operations/__init__.py |  6 +-
 mindspore/ops/operations/math_ops.py | 83 ++++++++++++++++++++++++++++
 3 files changed, 117 insertions(+), 12 deletions(-)

diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index a11c753eda..a294dbba18 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -25,6 +25,7 @@ from ...ops import operations as P
 from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \
     ControlDepend
 from ...common import dtype as mstype
+import mindspore.context as context
 
 _grad_scale = C.MultitypeFuncGraph("grad_scale")
 reciprocal = P.Reciprocal()
@@ -34,6 +35,12 @@ reciprocal = P.Reciprocal()
 def tensor_grad_scale(scale, grad):
     return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
+_grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
+grad_overflow = P.FloatStatus()
+
+@_grad_overflow.register("Tensor")
+def _tensor_grad_overflow(grad):
+    return grad_overflow(grad)
 
 class DynamicLossScaleUpdateCell(Cell):
     r"""
@@ -195,9 +202,15 @@ class TrainOneStepWithLossScaleCell(Cell):
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.hyper_map = C.HyperMap()
-        self.alloc_status = NPUAllocFloatStatus()
-        self.get_status = NPUGetFloatStatus()
-        self.clear_status = NPUClearFloatStatus()
+        if context.get_context("device_target") == "GPU":
+            self.gpu_target = True
+            self.float_status = P.FloatStatus()
+            self.addn = P.AddN()
+        else:
+            self.gpu_target = False
+            self.alloc_status = NPUAllocFloatStatus()
+            self.get_status = NPUGetFloatStatus()
+            self.clear_status = NPUClearFloatStatus()
         self.reduce_sum = ReduceSum(keep_dims=False)
         self.base = Tensor(1, mstype.float32)
         self.less_equal = LessEqual()
@@ -222,10 +235,11 @@ class TrainOneStepWithLossScaleCell(Cell):
     def construct(self, data, label, sens=None):
         weights = self.weights
         loss = self.network(data, label)
-        # init overflow buffer
-        init = self.alloc_status()
-        # clear overflow buffer
-        self.clear_status(init)
+        if not self.gpu_target:
+            # init overflow buffer
+            init = self.alloc_status()
+            # clear overflow buffer
+            self.clear_status(init)
         if sens is None:
             scaling_sens = self.loss_scale
         else:
@@ -235,10 +249,14 @@ class TrainOneStepWithLossScaleCell(Cell):
         if self.reducer_flag:
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
-        # get the overflow buffer
-        self.get_status(init)
-        # sum overflow buffer elements, 0:not overflow , >0:overflow
-        flag_sum = self.reduce_sum(init, (0,))
+        if not self.gpu_target:
+            # get the overflow buffer
+            self.get_status(init)
+            # sum overflow buffer elements, 0:not overflow , >0:overflow
+            flag_sum = self.reduce_sum(init, (0,))
+        else:
+            flag_sum = self.hyper_map(F.partial(_grad_overflow), grads)
+            flag_sum = self.addn(flag_sum)
         if self.is_distributed:
             # sum overflow flag over devices
             flag_reduce = self.allreduce(flag_sum)
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 846be05c4d..bc300cb670 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,
-                       NPUGetFloatStatus, Pow, RealDiv,
+                       NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
                        Reciprocal, CumSum,
                        Sin, Sqrt, Rsqrt,
                        Square, Sub, TensorAdd, Sign, Round)
@@ -151,6 +151,10 @@ __all__ = [
     'Neg',
     'Slice',
     'DType',
+    'IsNan',
+    'IsInf',
+    'IsFinite',
+    'FloatStatus',
     'NPUAllocFloatStatus',
     'NPUGetFloatStatus',
     'NPUClearFloatStatus',
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index b7373416f9..f7f2cb83c0 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1557,6 +1557,89 @@ class LogicalOr(_LogicBinaryOp):
     def infer_dtype(self, x_dtype, y_dtype):
         return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,))
 
+class IsNan(PrimitiveWithInfer):
+    """
+    Judging which elements are nan for each position
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsNan"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class IsInf(PrimitiveWithInfer):
+    """
+    Judging which elements are inf or -inf for each position
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsInf"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class IsFinite(PrimitiveWithInfer):
+    """
+    Judging which elements are finite for each position
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsFinite"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class FloatStatus(PrimitiveWithInfer):
+    """
+    Determine if the elements contains nan, inf or -inf
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the shape of `(1,)`.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init FloatStatus"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return [1]
+
+    def infer_dtype(self, x_dtype):
+        return x_dtype
 
 class NPUAllocFloatStatus(PrimitiveWithInfer):
     """

From 5fc6e5be22b2028cc5b52881e5c548aba011c16d Mon Sep 17 00:00:00 2001
From: yanghaoran <yanghaoran2@huawei.com>
Date: Fri, 3 Apr 2020 17:13:09 +0800
Subject: [PATCH 094/367] GraphEngine now downloads thirdparty software into
 build directory too

---
 graphengine | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphengine b/graphengine
index 60b2262ce7..5369646b48 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 60b2262ce700db538e4c728619e1972c3687f633
+Subproject commit 5369646b489114b380a7b5208ddd6e632acb447f

From 7a57b15b7ce67fd65e538cda3620e27668e853f0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Mon, 23 Mar 2020 15:33:01 +0800
Subject: [PATCH 095/367] modify comments

---
 mindspore/ops/operations/nn_ops.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index f5637e69d2..fedb33d283 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1219,14 +1219,14 @@ class ApplyMomentum(PrimitiveWithInfer):
         gradient_scale (float): The scale of the gradient. Default: 1.0.
 
     Inputs:
-        - **variable** (Tensor) - Weights to be update.
+        - **variable** (Tensor) - Weights to be updated.
         - **accumulation** (Tensor) - Accumulated gradient value by moment weight.
         - **learning_rate** (float) - Learning rate.
         - **gradient** (Tensor) - Gradients.
         - **momentum** (float) - Momentum.
 
     Outputs:
-        Tensor, parameters to be update.
+        Tensor, parameters to be updated.
 
     Examples:
         >>> net = ResNet50()
@@ -1318,15 +1318,15 @@ class SGD(PrimitiveWithInfer):
         nesterov (bool): Enable Nesterov momentum. Default: False.
 
     Inputs:
-        - **parameters** (Tensor) - Parameters to be update.
+        - **parameters** (Tensor) - Parameters to be updated.
         - **gradient** (Tensor) - Gradients.
         - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
-        - **accum** (Tensor) - Accum(velocity) to be update.
+        - **accum** (Tensor) - Accum(velocity) to be updated.
         - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
         - **stat** (Tensor) - States to be updated with the same shape as gradient.
 
     Outputs:
-        Tensor, parameters to be update.
+        Tensor, parameters to be updated.
     """
 
     @prim_attr_register
@@ -2141,7 +2141,7 @@ class Adam(PrimitiveWithInfer):
             If False, updates the gradients without using NAG. Default: False.
 
     Inputs:
-        - **var** (Tensor) - Weights to be update.
+        - **var** (Tensor) - Weights to be updated.
         - **m** (Tensor) - The 1st moment vector in the updating formula.
         - **v** (Tensor) - the 2nd moment vector in the updating formula.
         - **beta1_power** (float) - :math:`beta_1^t` in the updating formula.
@@ -2251,8 +2251,8 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
         use_locking (bool): If True, updating of the var and accum tensors will be protected. Default: False.
 
     Inputs:
-        - **var** (Tensor) - Variable to be update. The type must be float32.
-        - **accum** (Tensor) - Accum to be update. The shape must be the same as `var`'s shape,
+        - **var** (Tensor) - Variable to be updated. The type must be float32.
+        - **accum** (Tensor) - Accum to be updated. The shape must be the same as `var`'s shape,
           the type must be float32.
         - **grad** (Tensor) - Gradient. The shape must be the same as `var`'s shape
           except first dimension, the type must be float32.
@@ -2299,7 +2299,7 @@ class LARSUpdate(PrimitiveWithInfer):
         use_clip (bool): Whether to use clip operation for calculating the local learning rate. Default: False.
 
     Inputs:
-        - **weight** (Tensor) - The weight to be update.
+        - **weight** (Tensor) - The weight to be updated.
         - **gradient** (Tensor) - The gradient of weight, which has the same shape and dtype with weight.
         - **norm_weight** (Tensor) - A scalar tensor, representing the square sum of weight.
         - **norm_gradient** (Tensor) - A scalar tensor, representing the square sum of gradient.

From 5b176f258bd05ad2f84ad690a88865975b023671 Mon Sep 17 00:00:00 2001
From: wanghua <wanghua36@huawei.com>
Date: Fri, 3 Apr 2020 17:51:56 +0800
Subject: [PATCH 096/367] modify bert test file

---
 mindspore/ccsrc/device/ascend/kernel_select_ascend.cc  | 8 +-------
 tests/st/networks/models/bert/bert_tdt_no_lossscale.py | 2 +-
 2 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index a7c8d131fb..d05b9fafa1 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -82,12 +82,6 @@ bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::
     }
     return true;
   };
-  if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" ||
-      AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") {
-    if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) {
-      return true;
-    }
-  }
   if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
     return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
            AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
@@ -161,7 +155,7 @@ bool PriorityChooseItem(const std::vector<int> &cur_item, std::vector<int> *best
       return false;
     }
   }
-  return true;
+  return false;
 }
 
 void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr<CNode> &kernel_node,
diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
index 6f3ffc7dad..9cc11997e6 100644
--- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
+++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
@@ -27,7 +27,7 @@ from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
 from mindspore.train.callback import Callback
 from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell
-from mindspore.nn.optim import Lamb
+from mindspore.nn.optim import Momentum
 from mindspore import log as logger
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]

From 1fb776fe09e772671dabec1eb8015e98c8163d01 Mon Sep 17 00:00:00 2001
From: panyifeng <panyifeng@huawei.com>
Date: Fri, 3 Apr 2020 17:09:04 +0800
Subject: [PATCH 097/367] fix grad missing due to indirect dependent free
 morphism

---
 mindspore/ccsrc/optimizer/ad/dfunctor.cc      | 43 +++++++++++++------
 mindspore/ccsrc/optimizer/ad/dfunctor.h       |  1 +
 mindspore/ccsrc/pipeline/pass.cc              |  2 +-
 .../python/pynative_mode/test_cell_bprop.py   |  3 +-
 .../python/pynative_mode/test_framstruct.py   | 22 ++++++++++
 5 files changed, 54 insertions(+), 17 deletions(-)

diff --git a/mindspore/ccsrc/optimizer/ad/dfunctor.cc b/mindspore/ccsrc/optimizer/ad/dfunctor.cc
index 128e4463e6..3e1aa6e555 100644
--- a/mindspore/ccsrc/optimizer/ad/dfunctor.cc
+++ b/mindspore/ccsrc/optimizer/ad/dfunctor.cc
@@ -185,19 +185,32 @@ AdjointPtr DFunctor::MapMorphism(const AnfNodePtr &morph) {
   return node_adjoint;
 }
 
+bool DFunctor::IsFreeMorphism(const AnfNodePtr &node) {
+  // Do not care about non-CNode
+  if (!node->isa<CNode>()) {
+    return false;
+  }
+  // Do not care about kPrimReturn
+  if (IsPrimitiveCNode(node, prim::kPrimReturn)) {
+    return false;
+  }
+  auto &users = primal_graph_->manager()->node_users()[node];
+  // Do not care about isolated morphisms
+  if (users.empty()) {
+    return false;
+  }
+  // Not free if it's used by some node in primal_graph
+  bool nonfree = std::any_of(std::begin(users), std::end(users), [&](const auto &kv) {
+    auto &user = kv.first;
+    return user->func_graph() == primal_graph_;
+  });
+  return !nonfree;
+}
+
 void DFunctor::MapFreeMorphism() {
   // Handle cnode not attached to output, that might be refered in other functions.
   for (auto &node : primal_graph_->nodes()) {
-    auto adjoint = FindAdjoint(node);
-    if (adjoint != nullptr) {
-      continue;
-    }
-    if (!node->isa<CNode>()) {
-      MS_LOG(DEBUG) << "MapFreeMorphism noncnode not mapped after MapMorphism " << node->ToString() << " "
-                    << node->type_name() << ".";
-      continue;
-    }
-    if (IsPrimitiveCNode(node, prim::kPrimReturn)) {
+    if (!IsFreeMorphism(node)) {
       continue;
     }
     MS_LOG(DEBUG) << "MapFreeMorphism map nonoutput cnode after MapMorphism " << node->ToString() << ".";
@@ -256,9 +269,10 @@ void DFunctor::MapMorphism() {
   // Set stop_gradient before MapMorphism.
   BroadCastStopFlag();
 
+  // Handle free morphism before output, because in some case, free morphism might depend on output's fv tangent
+  MapFreeMorphism();
   // Handle morphism from output.
   (void)MapMorphism(primal_graph_->output());
-  MapFreeMorphism();
 
   // Construct K for primal_graph_
   auto output_adjoint = anfnode_to_adjoin_.find(primal_graph_->output());
@@ -298,9 +312,10 @@ FuncGraphPtr DFunctor::KUserDefined(const FuncGraphPtr &primal) {
     const size_t param_diff = 1;
     if (bprop_graph->output()->isa<CNode>() &&
         bprop_graph->output()->cast<CNodePtr>()->size() + param_diff != bprop_graph->parameters().size()) {
-      MS_LOG(EXCEPTION) << "User defined Cell bprop " << primal->ToString() << " in scope "
-                        << primal->output()->scope()->name()
-                        << " output must be a tuple and output number should be the same with inputs.";
+      // It does not matter with the final tangents, just a tip for debugging
+      MS_LOG(DEBUG) << "User defined Cell bprop " << primal->ToString() << " in scope "
+                    << primal->output()->scope()->name()
+                    << " output must be a tuple and output number should be the same with inputs.";
     }
     resources_->manager()->AddFuncGraph(bprop_graph);
 
diff --git a/mindspore/ccsrc/optimizer/ad/dfunctor.h b/mindspore/ccsrc/optimizer/ad/dfunctor.h
index f50f866efa..3059736171 100644
--- a/mindspore/ccsrc/optimizer/ad/dfunctor.h
+++ b/mindspore/ccsrc/optimizer/ad/dfunctor.h
@@ -61,6 +61,7 @@ class DFunctor {
  private:
   // Map one morphism.
   AdjointPtr MapMorphism(const AnfNodePtr &morph);
+  bool IsFreeMorphism(const AnfNodePtr &node);
   // Map morphism that's not attached to output.
   void MapFreeMorphism();
   void BackPropagateFv(const AnfNodePtr &fv, const AnfNodePtr &din);
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index e2626d5314..16250aae51 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -111,7 +111,7 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) {
     irpass.replace_applicator_,
   });
   opt::OptPassConfig virtual_dataset = opt::OptPassConfig({irpass.virtual_dataset_eliminate_});
-  opt::OptPassConfig grad = opt::OptPassConfig({irpass.inline_, irpass.expand_jprim_}, true);
+  opt::OptPassConfig grad = opt::OptPassConfig({irpass.expand_jprim_}, true);
 
   OptPassGroupMap map_a({{"a_1", a_1},
                          {"a_2", a_2},
diff --git a/tests/ut/python/pynative_mode/test_cell_bprop.py b/tests/ut/python/pynative_mode/test_cell_bprop.py
index 03ae1affa5..054afe36c9 100644
--- a/tests/ut/python/pynative_mode/test_cell_bprop.py
+++ b/tests/ut/python/pynative_mode/test_cell_bprop.py
@@ -304,5 +304,4 @@ class MulAddWithWrongOutputNum(nn.Cell):
 
 def test_grad_mul_add_with_wrong_output_num():
     mul_add = MulAddWithWrongOutputNum()
-    with pytest.raises(RuntimeError):
-        C.grad_all(mul_add)(1, 2)
+    C.grad_all(mul_add)(1, 2)
diff --git a/tests/ut/python/pynative_mode/test_framstruct.py b/tests/ut/python/pynative_mode/test_framstruct.py
index 2939337211..ff7cf67f52 100644
--- a/tests/ut/python/pynative_mode/test_framstruct.py
+++ b/tests/ut/python/pynative_mode/test_framstruct.py
@@ -15,6 +15,7 @@
 """ test_framstruct """
 import pytest
 import numpy as np
+import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.ops import composite as C
@@ -706,3 +707,24 @@ def grad_refactor_14(a, b):
     return inner1(b) + inner2(a) + inner3(a)
 def test_grad_refactor_14():
     assert C.grad_all(grad_refactor_14)(2, 3) == (3, 9)
+
+
+class IfDeferInline(nn.Cell):
+    def __init__(self, mul_size):
+        super().__init__()
+        self.mul_weight = Tensor(np.full(mul_size, 0.6, dtype=np.float32))
+        self.mul = P.Mul()
+
+    def construct(self, inputs):
+        x = self.mul(inputs, self.mul_weight)
+        if True:
+            x = x
+        return x
+
+def test_grad_if_defer_inline():
+    """ test_grad_if_defer_inline """
+    network = IfDeferInline([128, 96])
+    network.add_flags(defer_inline=False)
+    inp = Tensor(np.ones([128, 96]).astype(np.float32))
+    grads = C.grad_all(network)(inp)
+    assert grads == (Tensor(np.full([128, 96], 0.6, dtype=np.float32)),)

From d37b8c4f2dc8f994b891868b939923f20d735136 Mon Sep 17 00:00:00 2001
From: Alexey Shevlyakov <alexey.shevlyakov@huawei.com>
Date: Thu, 2 Apr 2020 14:17:46 -0400
Subject: [PATCH 098/367] fix RandomCropDecodeResize test

---
 tests/ut/cpp/dataset/CMakeLists.txt           |   2 +-
 .../dataset/random_crop_and_resize_op_test.cc |  36 ++----
 ...c => random_crop_decode_resize_op_test.cc} | 105 +++++++++---------
 3 files changed, 62 insertions(+), 81 deletions(-)
 rename tests/ut/cpp/dataset/{random_crop_decode_resizeOp_test.cc => random_crop_decode_resize_op_test.cc} (56%)

diff --git a/tests/ut/cpp/dataset/CMakeLists.txt b/tests/ut/cpp/dataset/CMakeLists.txt
index 0da470ac89..086a67c7d7 100644
--- a/tests/ut/cpp/dataset/CMakeLists.txt
+++ b/tests/ut/cpp/dataset/CMakeLists.txt
@@ -32,7 +32,7 @@ SET(DE_UT_SRCS
     project_op_test.cc
     queue_test.cc
     random_crop_op_test.cc
-    random_crop_decode_resizeOp_test.cc
+    random_crop_decode_resize_op_test.cc
     random_crop_and_resize_op_test.cc
     random_color_adjust_op_test.cc
     random_horizontal_flip_op_test.cc
diff --git a/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc b/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc
index 864d713ed3..7be18fb02c 100644
--- a/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc
+++ b/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc
@@ -20,35 +20,17 @@
 #include "utils/log_adapter.h"
 
 using namespace mindspore::dataset;
-using mindspore::MsLogLevel::INFO;
-using mindspore::ExceptionType::NoExceptionType;
 using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
 
 class MindDataTestRandomCropAndResizeOp : public UT::CVOP::CVOpCommon {
  public:
   MindDataTestRandomCropAndResizeOp() : CVOpCommon() {}
 };
 
-TEST_F(MindDataTestRandomCropAndResizeOp, TestOpDefault) {
-  MS_LOG(INFO) << "Doing testRandomCropAndResize.";
-  TensorShape s_in = input_tensor_->shape();
-  std::shared_ptr<Tensor> output_tensor;
-  int h_out = 512;
-  int w_out = 512;
-
-  TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]});
-
-  std::unique_ptr<RandomCropAndResizeOp> op(new RandomCropAndResizeOp(h_out, w_out));
-  Status s;
-  for (auto i = 0; i < 100; i++) {
-    s = op->Compute(input_tensor_, &output_tensor);
-  }
-  EXPECT_TRUE(s.IsOk());
-  MS_LOG(INFO) << "testRandomCropAndResize end.";
-}
-
-TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) {
-  MS_LOG(INFO) << "Doing testRandomCropAndResize.";
+TEST_F(MindDataTestRandomCropAndResizeOp, TestOpSimpleTest) {
+  MS_LOG(INFO) << " starting RandomCropAndResizeOp simple test";
   TensorShape s_in = input_tensor_->shape();
   std::shared_ptr<Tensor> output_tensor;
   int h_out = 1024;
@@ -58,14 +40,14 @@ TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) {
   float scale_lb = 0.0001;
   float scale_ub = 1.0;
 
-  TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]});
+  TensorShape s_out({h_out, w_out, s_in[2]});
 
-  std::unique_ptr<RandomCropAndResizeOp> op(
-    new RandomCropAndResizeOp(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub));
+  auto op = std::make_unique<RandomCropAndResizeOp>(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub);
   Status s;
   for (auto i = 0; i < 100; i++) {
     s = op->Compute(input_tensor_, &output_tensor);
+    EXPECT_TRUE(s.IsOk());
   }
-  EXPECT_TRUE(s.IsOk());
-  MS_LOG(INFO) << "testRandomCropAndResize end.";
+
+  MS_LOG(INFO) << "RandomCropAndResizeOp simple test finished";
 }
diff --git a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc b/tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc
similarity index 56%
rename from tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc
rename to tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc
index facd35c4f7..d7e0b16aff 100644
--- a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc
+++ b/tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc
@@ -23,9 +23,10 @@
 #include "utils/log_adapter.h"
 
 using namespace mindspore::dataset;
-using mindspore::MsLogLevel::INFO;
-using mindspore::ExceptionType::NoExceptionType;
 using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
+constexpr double kMseThreshold = 2.0;
 
 class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon {
  public:
@@ -33,39 +34,38 @@ class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon {
 };
 
 TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
-  MS_LOG(INFO) << "Doing testRandomCropDecodeResizeOp Test";
+  MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 1";
 
-  std::shared_ptr<Tensor> output_tensor1;
-  std::shared_ptr<Tensor> output_tensor2;
+  std::shared_ptr<Tensor> decode_and_crop_output;
+  std::shared_ptr<Tensor> crop_and_decode_output;
 
-  int target_height = 884;
-  int target_width = 718;
-  float scale_lb = 0.08;
-  float scale_ub = 1.0;
-  float aspect_lb = 0.75;
-  float aspect_ub = 1.333333;
-  InterpolationMode interpolation = InterpolationMode::kLinear;
-  uint32_t max_iter = 10;
-  std::unique_ptr<RandomCropAndResizeOp> op1(new RandomCropAndResizeOp(
-    target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter));
-  EXPECT_TRUE(op1->OneToOne());
-  std::unique_ptr<RandomCropDecodeResizeOp> op2(new RandomCropDecodeResizeOp(
-    target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter));
-  EXPECT_TRUE(op2->OneToOne());
-  Status s1, s2;
+  constexpr int target_height = 884;
+  constexpr int target_width = 718;
+  constexpr float scale_lb = 0.08;
+  constexpr float scale_ub = 1.0;
+  constexpr float aspect_lb = 0.75;
+  constexpr float aspect_ub = 1.333333;
+  const InterpolationMode interpolation = InterpolationMode::kLinear;
+  constexpr uint32_t max_iter = 10;
 
+  auto crop_and_decode = RandomCropDecodeResizeOp(target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub,
+                                                  interpolation, max_iter);
+  auto crop_and_decode_copy = crop_and_decode;
+  auto decode_and_crop = static_cast<RandomCropAndResizeOp>(crop_and_decode_copy);
+  EXPECT_TRUE(crop_and_decode.OneToOne());
+  GlobalContext::config_manager()->set_seed(42);
   for (int i = 0; i < 100; i++) {
-    s1 = op1->Compute(input_tensor_, &output_tensor1);
-    s2 = op2->Compute(raw_input_tensor_, &output_tensor2);
-    cv::Mat output1(target_height, target_width, CV_8UC3, output_tensor1->StartAddr());
-    cv::Mat output2(target_height, target_width, CV_8UC3, output_tensor2->StartAddr());
+    (void)crop_and_decode.Compute(raw_input_tensor_, &crop_and_decode_output);
+    (void)decode_and_crop.Compute(input_tensor_, &decode_and_crop_output);
+    cv::Mat output1(target_height, target_width, CV_8UC3, crop_and_decode_output->StartAddr());
+    cv::Mat output2(target_height, target_width, CV_8UC3, decode_and_crop_output->StartAddr());
     long int mse_sum = 0;
     long int count = 0;
     int a, b;
-    for (int i = 0; i < target_height; i++) {
-      for (int j = 0; j < target_width; j++) {
-        a = (int)output1.at<cv::Vec3b>(i, j)[1];
-        b = (int)output2.at<cv::Vec3b>(i, j)[1];
+    for (int j = 0; j < target_height; j++) {
+      for (int k = 0; k < target_width; k++) {
+        a = static_cast<int>(output1.at<cv::Vec3b>(i, j)[1]);
+        b = static_cast<int>(output2.at<cv::Vec3b>(i, j)[1]);
         mse_sum += sqrt((a - b) * (a - b));
         if (a != b) {
           count++;
@@ -73,24 +73,22 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
       }
     }
     double mse;
-    if (count > 0) {
-      mse = (double) mse_sum / count;
-    } else {
-      mse = mse_sum;
-    }
-    MS_LOG(DEBUG) << "mse: " << mse << std::endl;
+    mse = count > 0 ? static_cast<double>(mse_sum) / count : mse_sum;
+    MS_LOG(INFO) << "mse: " << mse << std::endl;
+    EXPECT_LT(mse, kMseThreshold);
   }
-  MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
+
+  MS_LOG(INFO) << "RandomCropDecodeResizeOp test 1 finished";
 }
 
 TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
-  MS_LOG(INFO) << "Doing MindDataTestRandomCropDecodeResizeOp";
-  const unsigned int h = 884;
-  const unsigned int w = 718;
-  const float scale_lb = 0.1;
-  const float scale_ub = 1;
-  const float aspect_lb = 0.1;
-  const float aspect_ub = 10;
+  MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 2";
+  constexpr int h = 884;
+  constexpr int w = 718;
+  constexpr float scale_lb = 0.1;
+  constexpr float scale_ub = 1;
+  constexpr float aspect_lb = 0.1;
+  constexpr float aspect_ub = 10;
 
   std::shared_ptr<Tensor> decoded, decoded_and_cropped, cropped_and_decoded;
   std::mt19937 rd;
@@ -98,14 +96,14 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
   std::uniform_real_distribution<float> rd_aspect(aspect_lb, aspect_ub);
   DecodeOp op(true);
   op.Compute(raw_input_tensor_, &decoded);
-  Status s1, s2;
+  Status crop_and_decode_status, decode_and_crop_status;
   float scale, aspect;
   int crop_width, crop_height;
   bool crop_success = false;
-  unsigned int mse_sum, m1, m2, count;
-  float mse;
+  int mse_sum, m1, m2, count;
+  double mse;
 
-  for (unsigned int k = 0; k < 100; ++k) {
+  for (int k = 0; k < 100; ++k) {
     mse_sum = 0;
     count = 0;
     for (auto i = 0; i < 100; i++) {
@@ -132,13 +130,13 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
     int y = rd_y(rd);
 
     op.Compute(raw_input_tensor_, &decoded);
-    s1 = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height);
-    s2 = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height);
+    crop_and_decode_status = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height);
+    decode_and_crop_status = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height);
     {
       cv::Mat M1(crop_height, crop_width, CV_8UC3, decoded_and_cropped->StartAddr());
       cv::Mat M2(crop_height, crop_width, CV_8UC3, cropped_and_decoded->StartAddr());
-      for (unsigned int i = 0; i < crop_height; ++i) {
-        for (unsigned int j = 0; j < crop_width; ++j) {
+      for (int i = 0; i < crop_height; ++i) {
+        for (int j = 0; j < crop_width; ++j) {
           m1 = M1.at<cv::Vec3b>(i, j)[1];
           m2 = M2.at<cv::Vec3b>(i, j)[1];
           mse_sum += sqrt((m1 - m2) * (m1 - m2));
@@ -149,8 +147,9 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
       }
     }
 
-    mse = (count == 0) ? mse_sum : static_cast<float>(mse_sum) / count;
-    MS_LOG(DEBUG) << "mse: " << mse << std::endl;
+    mse = count > 0 ? static_cast<double>(mse_sum) / count : mse_sum;
+    MS_LOG(INFO) << "mse: " << mse << std::endl;
+    EXPECT_LT(mse, kMseThreshold);
   }
-  MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
+  MS_LOG(INFO) << "RandomCropDecodeResizeOp test 2 finished";
 }

From ad76e1c42a4d73f65f906f95a2444c41e5ef5969 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Sat, 4 Apr 2020 13:00:18 +0800
Subject: [PATCH 099/367] Fix some typo errors in pipeline module

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 mindspore/ccsrc/ir/func_graph.cc              | 12 ++--
 mindspore/ccsrc/ir/func_graph.h               |  4 +-
 mindspore/ccsrc/pipeline/action.cc            |  2 +-
 mindspore/ccsrc/pipeline/base.h               |  2 +-
 .../ccsrc/pipeline/parse/data_converter.cc    | 21 +++---
 .../ccsrc/pipeline/parse/function_block.cc    |  4 +-
 mindspore/ccsrc/pipeline/parse/parse.cc       | 50 ++++++-------
 mindspore/ccsrc/pipeline/parse/parse.h        |  6 +-
 mindspore/ccsrc/pipeline/parse/parse_base.h   |  4 +-
 .../ccsrc/pipeline/parse/python_adapter.cc    |  2 +
 mindspore/ccsrc/pipeline/parse/resolve.cc     |  6 +-
 mindspore/ccsrc/pipeline/pass.cc              |  4 +-
 mindspore/ccsrc/pipeline/pipeline.cc          | 70 +++++++++----------
 mindspore/ccsrc/pipeline/pipeline_ge.cc       | 16 ++---
 mindspore/ccsrc/pipeline/resource.cc          |  2 +-
 .../static_analysis/abstract_value.cc         |  2 +-
 .../pipeline/static_analysis/abstract_value.h |  4 +-
 .../pipeline/static_analysis/evaluator.cc     |  2 +-
 .../static_analysis/param_validator.cc        | 10 +--
 .../ccsrc/pipeline/static_analysis/prim.cc    | 46 ++++++------
 .../static_analysis/program_specialize.cc     | 10 +--
 .../static_analysis/static_analysis.cc        | 12 ++--
 22 files changed, 149 insertions(+), 142 deletions(-)

diff --git a/mindspore/ccsrc/ir/func_graph.cc b/mindspore/ccsrc/ir/func_graph.cc
index f515367635..7404db4af0 100644
--- a/mindspore/ccsrc/ir/func_graph.cc
+++ b/mindspore/ccsrc/ir/func_graph.cc
@@ -64,14 +64,14 @@ AbstractFunctionPtr FuncGraph::abstract() {
   for (auto& p : parameters_) {
     MS_EXCEPTION_IF_NULL(p);
     if (p->abstract() == nullptr) {
-      MS_LOG(ERROR) << "error!!";
+      MS_LOG(ERROR) << "Error!!";
       return nullptr;
     }
     args_spec_list.push_back(p->abstract());
   }
 
   if (nullptr == output()) {
-    MS_LOG(ERROR) << "error func graph no output";
+    MS_LOG(ERROR) << "Error func graph no output";
     return nullptr;
   }
 
@@ -543,6 +543,7 @@ void FuncGraph::GenerateKwargReplNode(const FuncGraphPtr& specialized_graph,
     TraceManager::EndTrace();
   }
 }
+
 bool FuncGraph::NeedGenerate(const std::vector<abstract::AbstractKeywordArgPtr>& kwarg_list) {
   // if the function does not have any vararg/kwarg/kwonly/default value/kw args input
   // return the original graph
@@ -556,6 +557,7 @@ bool FuncGraph::NeedGenerate(const std::vector<abstract::AbstractKeywordArgPtr>&
   }
   return true;
 }
+
 void FuncGraph::GenerateDefaultValue(const FuncGraphPtr& specialized_graph,
                                      const std::vector<AnfNodePtr>& specialized_parameter_list,
                                      std::unordered_map<AnfNodePtr, AnfNodePtr>* repl_nodes) {
@@ -664,7 +666,7 @@ void FuncGraph::EraseUnusedNodeInOrder() {
     auto mng = manager_.lock();
     if (mng) {
       auto nodes = mng->nodes()[shared_from_base<FuncGraph>()];
-      // Erase unusued cnode.
+      // Erase unused cnode.
       for (auto it = order_.begin(); it != order_.end();) {
         if (nodes.count(*it)) {
           (void)it++;
@@ -695,7 +697,7 @@ void FuncGraph::CheckOrder() {
           if (found == it) {
             DumpCNodeList();
             MS_LOG(EXCEPTION) << "The cnode " << (*it)->DebugString() << " order in " << ToString()
-                              << " doesn't obey the input denpency, "
+                              << " doesn't obey the input dependency, "
                               << "as input " << input_node->DebugString() << " is not ahead of itself.";
           }
         }
@@ -842,5 +844,5 @@ void FuncGraph::SetEffectDepends(const std::vector<AnfNodePtr>& depend_inputs) {
 }
 
 const PrimitivePtr FuncGraphTransform::func_graph_prim_ = std::make_shared<Primitive>("FuncGraph");
-const char kFuncGraphFlagUndetermin[] = "Undeterminate";
+const char kFuncGraphFlagUndetermined[] = "Undeterminate";
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/ir/func_graph.h b/mindspore/ccsrc/ir/func_graph.h
index 95b26de473..13e8c9dc8f 100644
--- a/mindspore/ccsrc/ir/func_graph.h
+++ b/mindspore/ccsrc/ir/func_graph.h
@@ -96,7 +96,7 @@ class FuncGraphBase : public Value {
   MS_DECLARE_PARENT(FuncGraphBase, Value);
 };
 
-extern const char kFuncGraphFlagUndetermin[];
+extern const char kFuncGraphFlagUndetermined[];
 
 class FuncGraph : public FuncGraphBase {
  public:
@@ -200,7 +200,7 @@ class FuncGraph : public FuncGraphBase {
   // get all func graphs directly used by this func graph
   const FuncGraphCounterMap &func_graphs_used();
 
-  // get all func graphs nestedly used by this func graph
+  // get all func graphs nested used by this func graph
   const FuncGraphSet &func_graphs_used_total();
 
   // get all users of this func graph
diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc
index 83e524e5df..baf4bea7ec 100644
--- a/mindspore/ccsrc/pipeline/action.cc
+++ b/mindspore/ccsrc/pipeline/action.cc
@@ -183,7 +183,7 @@ bool SymbolResolveAction(const ResourcePtr& res) {
   FuncGraphPtr func_graph = res->func_graph();
   auto succ = parse::ResolveFuncGraph(func_graph, res);
 
-  // Remove usued nodes in cnode order list.
+  // Remove unused nodes in cnode order list.
   func_graph->EraseUnusedNodeInOrder();
   func_graph->ReleaseFullOrderToEffectOrder();
   for (auto fg : func_graph->func_graphs_used_total()) {
diff --git a/mindspore/ccsrc/pipeline/base.h b/mindspore/ccsrc/pipeline/base.h
index d007eac294..30524e84f6 100644
--- a/mindspore/ccsrc/pipeline/base.h
+++ b/mindspore/ccsrc/pipeline/base.h
@@ -40,7 +40,7 @@ using ExecutorInfoPtr = std::shared_ptr<ExecutorInfo>;
 inline std::string GetPhasePrefix(const std::string& phase) {
   auto pos = phase.find('.');
   if (pos == std::string::npos) {
-    MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase;
+    MS_LOG(EXCEPTION) << "Phase has no . for prefix" << phase;
   }
   return phase.substr(0, pos);
 }
diff --git a/mindspore/ccsrc/pipeline/parse/data_converter.cc b/mindspore/ccsrc/pipeline/parse/data_converter.cc
index aee7c35ba6..d25a202afc 100644
--- a/mindspore/ccsrc/pipeline/parse/data_converter.cc
+++ b/mindspore/ccsrc/pipeline/parse/data_converter.cc
@@ -39,7 +39,7 @@ using TensorPtr = mindspore::tensor::TensorPtr;
 
 namespace {
 bool ConvertTuple(const py::object& obj, ValuePtr* const data, bool use_signature) {
-  MS_LOG(DEBUG) << "converting python tuple";
+  MS_LOG(DEBUG) << "Converting python tuple";
   py::tuple tuple = obj.cast<py::tuple>();
   std::vector<ValuePtr> value_list;
   for (size_t it = 0; it < tuple.size(); ++it) {
@@ -56,7 +56,7 @@ bool ConvertTuple(const py::object& obj, ValuePtr* const data, bool use_signatur
 }
 
 bool ConvertList(const py::object& obj, ValuePtr* const data, bool use_signature) {
-  MS_LOG(DEBUG) << "converting python list";
+  MS_LOG(DEBUG) << "Converting python list";
 
   py::list list = obj.cast<py::list>();
   std::vector<ValuePtr> value_list;
@@ -73,7 +73,7 @@ bool ConvertList(const py::object& obj, ValuePtr* const data, bool use_signature
 }
 
 bool ConvertCellList(const py::object& obj, ValuePtr* const data, bool use_signature) {
-  MS_LOG(DEBUG) << "converting cell list";
+  MS_LOG(DEBUG) << "Converting cell list";
   py::sequence list = obj;
   std::vector<ValuePtr> value_list;
   for (size_t it = 0; it < list.size(); ++it) {
@@ -89,7 +89,7 @@ bool ConvertCellList(const py::object& obj, ValuePtr* const data, bool use_signa
 }
 
 bool ConvertDict(const py::object& obj, ValuePtr* data, bool use_signature) {
-  MS_LOG(DEBUG) << "converting python dict";
+  MS_LOG(DEBUG) << "Converting python dict";
 
   py::dict dict_values = obj.cast<py::dict>();
   std::vector<std::pair<std::string, ValuePtr>> key_values;
@@ -110,14 +110,14 @@ bool ConvertDict(const py::object& obj, ValuePtr* data, bool use_signature) {
 }
 
 void ConvertNameSpace(const py::object& obj, ValuePtr* const data) {
-  MS_LOG(DEBUG) << "converting python module";
+  MS_LOG(DEBUG) << "Converting python module";
   py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE);
   py::object module_namespace = python_adapter::CallPyModFn(mod, PYTHON_MOD_GET_MODULE_NAMESPACE, obj);
   *data = std::make_shared<NameSpace>(RESOLVE_NAMESPACE_NAME_MODULE, py::cast<py::module>(module_namespace));
 }
 
 void ConvertDataClass(py::object obj, ValuePtr* const data) {
-  MS_LOG(DEBUG) << "converting dataclass";
+  MS_LOG(DEBUG) << "Converting dataclass";
   // Maybe the obj is dataclass define
   auto desc = py::cast<std::string>(python_adapter::CallPyObjMethod(obj, PYTHON_GET_OBJ_DESC, obj));
   // desc has format "<class xxxx>", strip the '<' and '>' by offset 1;
@@ -247,7 +247,7 @@ bool ConvertOtherObj(py::object obj, ValuePtr* const data) {
 bool ConvertData(const py::object& obj, ValuePtr* const data, bool use_signature) {
   // check parameter valid
   if (data == nullptr) {
-    MS_LOG(ERROR) << " data is null pointer";
+    MS_LOG(ERROR) << "Data is null pointer";
     return false;
   }
 
@@ -386,9 +386,9 @@ py::object CreatePythonObject(const py::object& type, const py::tuple& params) {
   py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE);
   py::object obj;
   if (params.size() == 0) {
-    obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CRETAE_OBJ_INSTANCE, type);
+    obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CREATE_OBJ_INSTANCE, type);
   } else {
-    obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CRETAE_OBJ_INSTANCE, type, params);
+    obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CREATE_OBJ_INSTANCE, type, params);
   }
   return obj;
 }
@@ -421,6 +421,7 @@ ValuePtr PyDataToValue(const py::object& obj) {
   (void)ConvertData(to_convert, &value);
   return value;
 }
+
 void ClearObjectCache() {
   object_map_.clear();
   object_graphs_map_.clear();
@@ -445,7 +446,7 @@ ClassPtr ParseDataClass(const py::object& cls_obj) {
   for (auto& item : names) {
     TypePtr type_value = item.second.cast<TypePtr>();
     MS_EXCEPTION_IF_NULL(type_value);
-    MS_LOG(DEBUG) << "(name: " << py::cast<std::string>(item.first) << ", type: " << type_value->ToString() << ")";
+    MS_LOG(DEBUG) << "(Name: " << py::cast<std::string>(item.first) << ", type: " << type_value->ToString() << ")";
     attributes.push_back(std::make_pair(py::cast<std::string>(item.first), type_value));
   }
 
diff --git a/mindspore/ccsrc/pipeline/parse/function_block.cc b/mindspore/ccsrc/pipeline/parse/function_block.cc
index 25cc3ab4d8..423e76c1d8 100644
--- a/mindspore/ccsrc/pipeline/parse/function_block.cc
+++ b/mindspore/ccsrc/pipeline/parse/function_block.cc
@@ -254,7 +254,7 @@ void FunctionBlock::Mature() {
   matured_ = true;
 }
 
-// Force the conditon node to bool using bool operation
+// Force the conditIon node to bool using bool operation
 CNodePtr FunctionBlock::ForceToBoolNode(const AnfNodePtr& cond) {
   TraceManager::DebugTrace(std::make_shared<TraceForceBool>(cond->debug_info()));
   CNodePtr op_apply_node = func_graph()->NewCNode({MakeResolveOperation(NAMED_PRIMITIVE_BOOL), cond});
@@ -347,7 +347,7 @@ void FunctionBlock::InsertDependItemsBeforeReturn() {
   auto return_node = func_graph()->get_return();
   if (return_node) {
     if (return_node->inputs().size() < 1) {
-      MS_LOG(EXCEPTION) << "length of inputs of output node is less than 2";
+      MS_LOG(EXCEPTION) << "Length of inputs of output node is less than 2";
     }
     old_ret = return_node->input(1);
   } else {
diff --git a/mindspore/ccsrc/pipeline/parse/parse.cc b/mindspore/ccsrc/pipeline/parse/parse.cc
index 60cc00a307..231b98ab00 100644
--- a/mindspore/ccsrc/pipeline/parse/parse.cc
+++ b/mindspore/ccsrc/pipeline/parse/parse.cc
@@ -294,14 +294,14 @@ FunctionBlockPtr Parser::ParseStatement(const FunctionBlockPtr &block, const py:
     TraceManager::EndTrace();
     return stmt_block;
   } else {
-    errcode_ = PARSE_NODE_METHOD_UNSUPPORT;
+    errcode_ = PARSE_NODE_METHOD_UNSUPPORTED;
     py::list location = ast_->CallParserObjMethod(PYTHON_PARSE_GET_LOCATION, node);
     if (location.size() < 2) {
       MS_LOG(EXCEPTION) << "List size should not be less than 2.";
     }
     auto filename = location[0].cast<std::string>();
     auto line_no = location[1].cast<int>();
-    MS_LOG(EXCEPTION) << "unsupported syntax '" << node_name << "' at " << filename << ":" << line_no;
+    MS_LOG(EXCEPTION) << "Unsupported syntax '" << node_name << "' at " << filename << ":" << line_no;
   }
 }
 
@@ -324,11 +324,11 @@ AnfNodePtr Parser::ParseExprNode(const FunctionBlockPtr &block, const py::object
     TraceManager::EndTrace();
     return expr_node;
   } else {
-    errcode_ = PARSE_NODE_METHOD_UNSUPPORT;
+    errcode_ = PARSE_NODE_METHOD_UNSUPPORTED;
     py::list ret = ast_->CallParserObjMethod(PYTHON_PARSE_GET_LOCATION, node);
     auto filename = ret[0].cast<std::string>();
     auto line_no = ret[1].cast<int>();
-    MS_LOG(EXCEPTION) << "unsupported syntax '" << node_name << "' at " << filename << ":" << line_no;
+    MS_LOG(EXCEPTION) << "Unsupported syntax '" << node_name << "' at " << filename << ":" << line_no;
   }
 }
 
@@ -339,7 +339,7 @@ FunctionBlockPtr Parser::ParseExpr(const FunctionBlockPtr &block, const py::obje
   // Expr only have value , no target
   py::tuple expand_info = ast_->CallParserObjMethod(PYTHON_PARSE_EXPAND_EXPR_STATEMENT, node);
 
-  // refer pypthon function expand_expr_statement, expand_info is one of the following:
+  // refer python function expand_expr_statement, expand_info is one of the following:
   // True, expr.value, x
   // True, expr.value
   // False, None, None
@@ -453,8 +453,8 @@ AnfNodePtr Parser::ParseNum(const FunctionBlockPtr &, const py::object &node) {
     return NewValueNode(data);
   } else {
     // no else actually
-    MS_LOG(ERROR) << "unsupported Num type : " << (std::string)py::str(obj) << GetLocation(node)->ToString();
-    errcode_ = PARSE_NODE_TYPE_UNKONW;
+    MS_LOG(ERROR) << "Unsupported Num type : " << (std::string)py::str(obj) << GetLocation(node)->ToString();
+    errcode_ = PARSE_NODE_TYPE_UNKOWN;
     return nullptr;
   }
 }
@@ -478,8 +478,8 @@ AnfNodePtr Parser::ParseNameConstant(const FunctionBlockPtr &, const py::object
     return NewValueNode(kNone);
   } else {
     // no else actually
-    MS_LOG(ERROR) << "unsupported NameConstant type: " << (std::string)py::str(obj) << GetLocation(node)->ToString();
-    errcode_ = PARSE_NODE_TYPE_UNKONW;
+    MS_LOG(ERROR) << "Unsupported NameConstant type: " << (std::string)py::str(obj) << GetLocation(node)->ToString();
+    errcode_ = PARSE_NODE_TYPE_UNKOWN;
     return nullptr;
   }
 }
@@ -497,7 +497,7 @@ AnfNodePtr Parser::ParseCall(const FunctionBlockPtr &block, const py::object &no
   // process function call
   py::object function_ast_node = python_adapter::GetPyObjAttr(node, "func");
   AnfNodePtr call_function_anf_node = ParseExprNode(block, function_ast_node);
-  // function call arguments should be passed in as groups and upacked later using unpack call
+  // function call arguments should be passed in as groups and unpacked later using unpack call
   py::list args = python_adapter::GetPyObjAttr(node, "args");
   std::vector<AnfNodePtr> packed_arguments;
   std::vector<AnfNodePtr> group_arguments;
@@ -614,7 +614,7 @@ AnfNodePtr Parser::ParseAttribute(const FunctionBlockPtr &block, const py::objec
   py::object value_body = python_adapter::GetPyObjAttr(node, "value");
   AnfNodePtr value_node = ParseExprNode(block, value_body);
   if (value_node == nullptr) {
-    MS_LOG(WARNING) << "Parse Attribut failed";
+    MS_LOG(WARNING) << "Parse attribute failed";
     return nullptr;
   }
 
@@ -637,7 +637,7 @@ AnfNodePtr Parser::ParseCompare(const FunctionBlockPtr &block, const py::object
   // which there is two ops , but we only support one now
   py::list ops = python_adapter::GetPyObjAttr(node, "ops");
   if (ops.size() > MAX_COMPARISON_OPS_SUPPORTED) {
-    MS_LOG(ERROR) << "mindspore does not support comparison with operators more than one now, ops size =" << ops.size();
+    MS_LOG(ERROR) << "MindSpore does not support comparison with operators more than one now, ops size =" << ops.size();
     return nullptr;
   }
 
@@ -817,7 +817,7 @@ AnfNodePtr Parser::ParseIndex(const FunctionBlockPtr &block, const py::object &n
 
 // process a  UnaryOp, +a, -b
 AnfNodePtr Parser::ParseUnaryOp(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast UnaryOp";
+  MS_LOG(DEBUG) << "Process ast UnaryOp";
   py::object op = python_adapter::GetPyObjAttr(node, "op");
 
   MS_EXCEPTION_IF_NULL(block);
@@ -831,7 +831,7 @@ AnfNodePtr Parser::ParseUnaryOp(const FunctionBlockPtr &block, const py::object
 
 // process a dict ast node expression
 AnfNodePtr Parser::ParseDict(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast Dict";
+  MS_LOG(DEBUG) << "Process ast Dict";
   py::list keys = node.attr("keys");
   py::list values = node.attr("values");
   std::vector<AnfNodePtr> key_nodes;
@@ -849,7 +849,7 @@ AnfNodePtr Parser::ParseDict(const FunctionBlockPtr &block, const py::object &no
 
 // process a  augment assign such as a += b;
 FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast AugAssign";
+  MS_LOG(DEBUG) << "Process ast AugAssign";
   py::object op = python_adapter::GetPyObjAttr(node, "op");
 
   MS_EXCEPTION_IF_NULL(block);
@@ -864,10 +864,10 @@ FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py:
   } else if (ast_->IsClassMember(target_node)) {
     read_node = ParseAttribute(block, target_node);
   } else {
-    MS_LOG(EXCEPTION) << "not supported augassign";
+    MS_LOG(EXCEPTION) << "Not supported augassign";
   }
   if (read_node == nullptr) {
-    MS_LOG(EXCEPTION) << "can not get target node ";
+    MS_LOG(EXCEPTION) << "Can not get target node ";
   }
 
   py::object value = python_adapter::GetPyObjAttr(node, "value");
@@ -879,7 +879,7 @@ FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py:
 
 // process global declaration such as 'global x';
 FunctionBlockPtr Parser::ParseGlobal(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast Global";
+  MS_LOG(DEBUG) << "Process ast Global";
   MS_EXCEPTION_IF_NULL(block);
   py::list vars = python_adapter::GetPyObjAttr(node, "names");
   for (auto &item : vars) {
@@ -890,7 +890,7 @@ FunctionBlockPtr Parser::ParseGlobal(const FunctionBlockPtr &block, const py::ob
 
 // process a if statement
 FunctionBlockPtr Parser::ParseIf(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast If";
+  MS_LOG(DEBUG) << "Process ast If";
   py::object test_node = python_adapter::GetPyObjAttr(node, "test");
   AnfNodePtr condition_node = ParseExprNode(block, test_node);
   MS_EXCEPTION_IF_NULL(block);
@@ -934,7 +934,7 @@ FunctionBlockPtr Parser::ParseIf(const FunctionBlockPtr &block, const py::object
 }
 
 FunctionBlockPtr Parser::ParseWhile(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast While";
+  MS_LOG(DEBUG) << "Process ast While";
   MS_EXCEPTION_IF_NULL(block);
   MS_LOG(INFO) << "Parse while statement";
   TraceManager::DebugTrace(std::make_shared<TraceWhileHeader>(block->func_graph()->debug_info()));
@@ -999,7 +999,7 @@ FunctionBlockPtr Parser::GenerateBlockInFor(const TraceInfoPtr &trace_info) {
 //    x, it = next(it)
 //    body
 FunctionBlockPtr Parser::ParseFor(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast For";
+  MS_LOG(DEBUG) << "Process ast For";
   MS_EXCEPTION_IF_NULL(block);
   AnfNodePtr op_iter = block->MakeResolveOperation(NAMED_PRIMITIVE_ITER);
   AnfNodePtr op_next = block->MakeResolveOperation(NAMED_PRIMITIVE_NEXT);
@@ -1054,7 +1054,7 @@ FunctionBlockPtr Parser::ParseFor(const FunctionBlockPtr &block, const py::objec
   return after_block;
 }
 AnfNodePtr Parser::ParseIfExp(const FunctionBlockPtr &block, const py::object &node) {
-  MS_LOG(DEBUG) << "process ast IfExp";
+  MS_LOG(DEBUG) << "Process ast IfExp";
   MS_EXCEPTION_IF_NULL(block);
   py::object test_node = python_adapter::GetPyObjAttr(node, "test");
   AnfNodePtr condition_node = ParseExprNode(block, test_node);
@@ -1163,7 +1163,7 @@ void Parser::HandleAssignSubscript(const FunctionBlockPtr &block, const py::obje
 
 void Parser::WriteAssignVars(const FunctionBlockPtr &block, const py::object &targ, const AnfNodePtr &value_node) {
   MS_EXCEPTION_IF_NULL(value_node);
-  MS_LOG(DEBUG) << "process WriteAssignVars";
+  MS_LOG(DEBUG) << "Process WriteAssignVars";
   auto ast_type = AstSubType(py::cast<int32_t>(ast_->CallParserObjMethod(PYTHON_PARSE_GET_AST_TYPE, targ)));
   if (ast_type == AST_SUB_TYPE_NAME) {
     HandleAssignName(block, targ, value_node);
@@ -1174,7 +1174,7 @@ void Parser::WriteAssignVars(const FunctionBlockPtr &block, const py::object &ta
   } else if (ast_->IsClassMember(targ)) {
     HandleAssignClassMember(block, targ, value_node);
   } else {
-    MS_LOG(EXCEPTION) << "not supported assign type: " << ast_type
+    MS_LOG(EXCEPTION) << "Not supported assign type: " << ast_type
                       << " NodeInfo: " << trace::GetDebugInfo(value_node->debug_info());
   }
 }
@@ -1340,7 +1340,7 @@ bool ParseAst::UpdateFuncGraphFlags(const FuncGraphPtr &func_graph) {
   py::dict flags = python_adapter::GetPyObjAttr(obj_, PYTHON_EXTERN_MINDSPORE_FLAG);
   for (auto &item : flags) {
     if (!py::isinstance<py::str>(item.first) || !py::isinstance<py::bool_>(item.second)) {
-      MS_LOG(ERROR) << "type error in flags dict convert";
+      MS_LOG(ERROR) << "Type error in flags dict convert";
       return false;
     }
     auto name = py::cast<std::string>(item.first);
diff --git a/mindspore/ccsrc/pipeline/parse/parse.h b/mindspore/ccsrc/pipeline/parse/parse.h
index 3e891e47dd..4dd1bc62aa 100644
--- a/mindspore/ccsrc/pipeline/parse/parse.h
+++ b/mindspore/ccsrc/pipeline/parse/parse.h
@@ -40,8 +40,8 @@ enum ParseStatusCode : int {
   PARSE_PARAMETER_INVALID,           // parameter is invalid
   PARSE_NO_RETURN,                   // function no return node
   PARSE_NODE_TYPE_NO_MATCH,          // ast node type is error
-  PARSE_NODE_TYPE_UNKONW,            // node type is unkonw
-  PARSE_NODE_METHOD_UNSUPPORT,       // no method to parse the node
+  PARSE_NODE_TYPE_UNKOWN,            // node type is unkown
+  PARSE_NODE_METHOD_UNSUPPORTED,     // no method to parse the node
   PARSE_DONT_RESOLVE_SYMBOL,         // can't resolve the string
   PARSE_NOT_SUPPORTED_COMPARE_EXPR,  // the comparison is not supported
   PARSE_FAILURE = 0xFF
@@ -102,7 +102,7 @@ class Parser {
   AnfNodePtr ParseCall(const FunctionBlockPtr &block, const py::object &node);
   // process the if expression
   AnfNodePtr ParseIfExp(const FunctionBlockPtr &block, const py::object &node);
-  // process calss type define
+  // process class type define
   AnfNodePtr ParseAttribute(const FunctionBlockPtr &block, const py::object &node);
   // process a compare expression
   AnfNodePtr ParseCompare(const FunctionBlockPtr &block, const py::object &node);
diff --git a/mindspore/ccsrc/pipeline/parse/parse_base.h b/mindspore/ccsrc/pipeline/parse/parse_base.h
index 9f92687b6f..df2d1968a5 100644
--- a/mindspore/ccsrc/pipeline/parse/parse_base.h
+++ b/mindspore/ccsrc/pipeline/parse/parse_base.h
@@ -45,7 +45,7 @@ enum AstSubType : int {
 
 // define the parse target type
 enum ParseTargetTypeDef {
-  PARSE_TARGET_FUNCTION = 0,         // funciton
+  PARSE_TARGET_FUNCTION = 0,         // function
   PARSE_TARGET_METHOD = 1,           // method
   PARSE_TARGET_OBJECT_INSTANCE = 2,  // object instance
   PARSE_TARGET_UNKNOW = 0xFF         // ERROR TYPE
@@ -59,7 +59,7 @@ const char PYTHON_MOD_RESOLVE_GET_OBJ_KEY[] = "get_object_key";
 const char PYTHON_MOD_PARSE_CHECK_IS_CLASS_MEMBER[] = "is_class_member";
 const char PYTHON_MOD_RESOLVE_GET_OBJ_TYPE[] = "get_obj_type";
 const char PYTHON_MOD_GET_CLASS_INSTANCE_TYPE[] = "get_class_instance_type";
-const char PYTHON_MOD_CRETAE_OBJ_INSTANCE[] = "create_obj_instance";
+const char PYTHON_MOD_CREATE_OBJ_INSTANCE[] = "create_obj_instance";
 const char PYTHON_MOD_GET_DATACLASS_ATTRS[] = "get_dataclass_attributes";
 const char PYTHON_MOD_GET_DATACLASS_METHODS[] = "get_dataclass_methods";
 const char PYTHON_MOD_GET_MODULE_NAMESPACE[] = "get_module_namespace";
diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.cc b/mindspore/ccsrc/pipeline/parse/python_adapter.cc
index db40238729..e2c86164d4 100644
--- a/mindspore/ccsrc/pipeline/parse/python_adapter.cc
+++ b/mindspore/ccsrc/pipeline/parse/python_adapter.cc
@@ -50,6 +50,7 @@ void SetPythonPath(const std::string& path) {
     (void)sys_path.attr("append")(path.c_str());
   }
 }
+
 std::shared_ptr<py::scoped_interpreter> set_python_scoped() {
   // if start process from python, no need set the python scope.
   if (!python_env_) {
@@ -79,6 +80,7 @@ py::object GetPyObjAttr(const py::object& obj, const std::string& attr) {
   }
   return py::none();
 }
+
 py::object GetPyFn(const std::string& module, const std::string& name) {
   (void)python_adapter::set_python_scoped();
   if (!module.empty() && !name.empty()) {
diff --git a/mindspore/ccsrc/pipeline/parse/resolve.cc b/mindspore/ccsrc/pipeline/parse/resolve.cc
index 976c474aa4..ebc1f65486 100644
--- a/mindspore/ccsrc/pipeline/parse/resolve.cc
+++ b/mindspore/ccsrc/pipeline/parse/resolve.cc
@@ -53,6 +53,7 @@ abstract::AbstractBasePtr ClassType::ToAbstract() {
   ret_val->set_value_desc(ToString());
   return ret_val;
 }
+
 // call python PYTHON_MOD_RESOLVE_FUNCTION interface to resolve the symbol in corresponding namespace
 bool SymbolResolver::Resolve() {
   py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE);
@@ -127,7 +128,7 @@ bool ResolveObjectToNode(const FuncGraphPtr& func_graph, const py::object& obj,
       MS_LOG(ERROR) << "Resolve parameter object failed, got nullptr";
       return false;
     }
-    MS_LOG(DEBUG) << "add param graph:" << func_graph->ToString() << ", " << param->DebugString();
+    MS_LOG(DEBUG) << "Add param graph:" << func_graph->ToString() << ", " << param->DebugString();
 
     output = param;
   } else if (py::hasattr(obj, "__parameter_tuple__")) {
@@ -160,6 +161,7 @@ bool ResolveObjectToNode(const FuncGraphPtr& func_graph, const py::object& obj,
   *node = output;
   return true;
 }
+
 // transform the ValueTuple or ValueList of graph node to make tuple of const graph node
 bool TransformVectorGraphValueNode(const FuncGraphManagerPtr& manager, const AnfNodePtr& node,
                                    const ValueNodePtr& value_node, AnfNodePtr* const transformed) {
@@ -175,7 +177,7 @@ bool TransformVectorGraphValueNode(const FuncGraphManagerPtr& manager, const Anf
       continue;
     }
     if (has_graph_in_list) {
-      MS_LOG(EXCEPTION) << "list has graph in it , but not all is graph";
+      MS_LOG(EXCEPTION) << "List has graph in it, but not all is graph";
     }
   }
   // The celllist or ordered_cell will be parsed as valuetuple of const graph in it,
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index e2626d5314..d89a0090a7 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -195,13 +195,13 @@ void ReclaimOptimizer() {
 
 bool OptPassGroup(const ResourcePtr& res, const std::string& name) {
   if (res->func_graph() == nullptr) {
-    MS_LOG(ERROR) << "opt passes int error";
+    MS_LOG(ERROR) << "Opt passes int error";
     return false;
   }
 
   abstract::AbstractBasePtrList args = res->args_spec();
   FuncGraphPtr func_graph = res->func_graph();
-  MS_LOG(DEBUG) << "start " << name << " func graph:" << func_graph->ToString() << ", "
+  MS_LOG(DEBUG) << "Start " << name << " func graph:" << func_graph->ToString() << ", "
                 << func_graph->get_return()->DebugString(true);
   InitOpt(res);
   if (g_pass_opts.find(name) != g_pass_opts.end()) {
diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 861862b849..0d7790fb36 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -98,7 +98,7 @@ py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::str
   }
   if (g_args_cache.count(args_spec) == 0) {
     static int key = 0;
-    MS_LOG(INFO) << "start new args and compile key:" << key;
+    MS_LOG(INFO) << "Start new args and compile key:" << key;
     g_args_cache[args_spec] = key++;
   }
   py::tuple argSpec = py::tuple(2);
@@ -110,7 +110,7 @@ py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::str
 py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs) {
   MS_LOG(DEBUG) << "Verify args size:" << inputs.size();
   if (inputs.size() != input_signature.size()) {
-    MS_LOG(ERROR) << "signature size not equal to args size";
+    MS_LOG(ERROR) << "Signature size not equal to args size";
     return false;
   }
 
@@ -148,7 +148,7 @@ py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple i
 ExecutorPy::ExecutorPy() {}
 
 ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
-  MS_LOG(DEBUG) << "phase size:" << info_.size();
+  MS_LOG(DEBUG) << "Phase size:" << info_.size();
   if (info_.count(phase) == 0) {
     return nullptr;
   }
@@ -157,14 +157,14 @@ ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
 
 FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string& phase) {
   if (info_.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
   }
   return info_[phase]->func_graph;
 }
 
 std::size_t ExecutorPy::ArgListSize(const std::string& phase) {
   if (info_.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
   }
   return info_[phase]->arg_list_size;
 }
@@ -243,7 +243,7 @@ void ExecutorPy::DelNetRes(const std::string& id) {
     auto tmp_info = info_;
     for (auto& item : tmp_info) {
       if (item.first.find(id) != string::npos) {
-        MS_LOG(INFO) << "delete network res:" << item.first;
+        MS_LOG(INFO) << "Delete network res:" << item.first;
         (void)info_.erase(item.first);
         flag = true;
       }
@@ -262,7 +262,7 @@ void ExecutorPy::DelNetRes(const std::string& id) {
 }
 
 void ExecutorPy::ClearRes() {
-  MS_LOG(INFO) << "clean executor Resrouce!";
+  MS_LOG(INFO) << "Clean executor resource!";
   executor_ = nullptr;
 }
 
@@ -278,27 +278,27 @@ void ExecutorPy::SaveCompiledGraph(const std::string& phase_s) {
   MS_EXCEPTION_IF_NULL(parallel::ParallelContext::GetInstance());
   std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode();
 
-  MS_LOG(INFO) << "save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
+  MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
   info_[phase_s]->func_graph = func_graph;
   if ((func_graph != nullptr) &&
       ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) {
-    MS_LOG(DEBUG) << "save model parallel parameter layout graph!";
+    MS_LOG(DEBUG) << "Save model parallel parameter layout graph!";
     func_graph = info_[phase_s]->resource->results()[kStepParallelGraph].cast<FuncGraphPtr>();
-    ExecutorInfoPtr excutor_info = std::make_shared<ExecutorInfo>();
+    ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
     std::string layout_graph = phase_s + kStepParallelGraph;
-    excutor_info->func_graph = func_graph;
-    info_[layout_graph] = excutor_info;
+    executor_info->func_graph = func_graph;
+    info_[layout_graph] = executor_info;
   } else {
-    MS_LOG(DEBUG) << "save model parallel parameter layout graph null!";
+    MS_LOG(DEBUG) << "Save model parallel parameter layout graph null!";
   }
-  MS_LOG(INFO) << "end save compiled func graph!";
+  MS_LOG(INFO) << "End save compiled func graph!";
 }
 
 bool ExecutorPy::ChangeExportGeirUseVmFlag(bool use_vm, const std::string& phase_s) const {
   std::string phase_prefix = GetPhasePrefix(phase_s);
 
   if (use_vm && phase_prefix == "export") {
-    MS_LOG(INFO) << "use ge backend to export geir";
+    MS_LOG(INFO) << "Use ge backend to export geir";
     use_vm = false;
   }
   return use_vm;
@@ -316,7 +316,7 @@ void ExecutorPy::GetGeBackendPolicy() const {
 bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm) {
   MS_LOG(DEBUG) << "Start ExecutorPy compile!";
   if ((!py::isinstance<py::str>(phase))) {
-    MS_LOG(ERROR) << "arg phase must be string.";
+    MS_LOG(ERROR) << "Arg phase must be string.";
     return false;
   }
   // check the arg valid?
@@ -327,7 +327,7 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons
 #ifdef ENABLE_GE
   GetGeBackendPolicy();
 #endif
-  ExecutorInfoPtr excutor_info = std::make_shared<ExecutorInfo>();
+  ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
   std::string phase_s = py::cast<std::string>(phase);
   MS_LOG(INFO) << "ExecutorPy compile phase:" << phase_s << "!";
   ResourcePtr resource = std::make_shared<Resource>(obj);
@@ -353,16 +353,16 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons
     ValuePtr converted = nullptr;
     bool succ = parse::ConvertData(args[i], &converted);
     if (!succ) {
-      MS_LOG(EXCEPTION) << "args convert error";
+      MS_LOG(EXCEPTION) << "Args convert error";
     }
     bool broaden = true;
     args_spec.push_back(abstract::FromValue(converted, broaden));
   }
 
   resource->set_args_spec(args_spec);
-  excutor_info->arg_list_size = size;
-  excutor_info->resource = resource;
-  info_[phase_s] = excutor_info;
+  executor_info->arg_list_size = size;
+  executor_info->resource = resource;
+  info_[phase_s] = executor_info;
   pip->Run();
 
   // save the run graph func to MsPipeLine
@@ -439,7 +439,7 @@ std::string GetMsIrFile(void) {
 
   char real_path[PATH_MAX] = {0};
   if (realpath(path, real_path) == nullptr) {
-    MS_LOG(ERROR) << "MS IR Path error, " << path;
+    MS_LOG(ERROR) << "MS IR path error, " << path;
     return file;
   }
   file = real_path;
@@ -485,7 +485,7 @@ void RunPipelineAction(const ActionItem& action, pipeline::ResourcePtr resource,
 #endif
 
 void Pipeline::Run() {
-  MS_LOG(INFO) << "pipeline run";
+  MS_LOG(INFO) << "Pipeline run";
   MS_EXCEPTION_IF_NULL(resource_);
   FuncGraphPtr user_graph = nullptr;
 
@@ -507,7 +507,7 @@ void Pipeline::Run() {
         MS_LOG(DEBUG) << "Action " << action.first << " end.";
       };
       if (!result) {
-        MS_LOG(EXCEPTION) << "pipeline running to end, failed in step:" << action.first;
+        MS_LOG(EXCEPTION) << "Pipeline running to end, failed in step:" << action.first;
       }
       if (MsContext::GetInstance()->save_graphs_flag() && resource_->func_graph() != nullptr) {
         auto graph = resource_->func_graph();
@@ -555,7 +555,7 @@ void Pipeline::Run() {
 
   if (MsContext::GetInstance()->save_graphs_flag() && (user_graph != nullptr)) {
     std::string user_graph_file = GetFilePathName("ModelDigraph.dot");
-    MS_LOG(DEBUG) << "save user graph to: " << user_graph_file;
+    MS_LOG(DEBUG) << "Save user graph to: " << user_graph_file;
     draw::DrawUserFuncGraph(user_graph_file, user_graph);
 
 #ifdef ENABLE_DUMP_IR
@@ -572,7 +572,7 @@ void Pipeline::Run() {
     ChangeFileMode(filename, S_IRUSR);
 #endif
   }
-  MS_LOG(INFO) << "end";
+  MS_LOG(INFO) << "End";
 }
 
 void ExecutorPy::ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list) {
@@ -582,7 +582,7 @@ void ExecutorPy::ProcessVmArg(const py::tuple& args, const std::string& phase, V
     py::object arg = args[i];
     auto ms_context = MsContext::GetInstance();
     if (ms_context->backend_policy() == kMsConvert && py::isinstance<py::array>(arg)) {
-      MS_LOG(EXCEPTION) << "args[" << i << "] is numpy array, not tensor";
+      MS_LOG(EXCEPTION) << "Args[" << i << "] is numpy array, not tensor";
     }
     (*arg_list).push_back(arg);
   }
@@ -642,9 +642,9 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
     MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s;
   }
 
-  MS_LOG(DEBUG) << "eval run" << backend;
+  MS_LOG(DEBUG) << "Eval run" << backend;
   BaseRef value = (*run)(arg_list);
-  MS_LOG(DEBUG) << "run end";
+  MS_LOG(DEBUG) << "Run end";
   return BaseRefToPyData(value);
 }
 
@@ -704,9 +704,9 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc
   p_init->set_attr("shapes", MakeValue(int_shapes));
   p_init->set_attr("input_indexes", MakeValue(int_input_indexes));
 
-  const std::vector<std::string> emply_str_list;
-  p_init->set_attr("input_names", MakeValue(emply_str_list));
-  p_init->set_attr("output_names", MakeValue(emply_str_list));
+  const std::vector<std::string> empty_str_list;
+  p_init->set_attr("input_names", MakeValue(empty_str_list));
+  p_init->set_attr("output_names", MakeValue(empty_str_list));
 
   FuncGraphPtr func_graph = std::make_shared<FuncGraph>();
   auto app_init = std::make_shared<CNode>(AnfNodePtrList{NewValueNode(p_init)}, func_graph);
@@ -730,7 +730,7 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc
 
   if (!(*runner.run)) {
     // empty function
-    MS_LOG(EXCEPTION) << "Backend " << backend->name() << " unsupports tdt dataset.";
+    MS_LOG(EXCEPTION) << "Backend " << backend->name() << " unsupported tdt dataset.";
   }
 
   // launch init dataset runner without inputs and outputs
@@ -758,7 +758,7 @@ void InitHccl() {
     auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(device_name, device_id);
     MS_EXCEPTION_IF_NULL(runtime_instance);
     if (!runtime_instance->Init()) {
-      MS_LOG(ERROR) << "kernel runtime init error.";
+      MS_LOG(ERROR) << "Kernel runtime init error.";
       return;
     }
   }
@@ -795,7 +795,7 @@ void InitGe() {
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
   if (!ms_context->OpenTsd()) {
-    MS_LOG(EXCEPTION) << "open tsd failed";
+    MS_LOG(EXCEPTION) << "Open tsd failed";
   }
   (void)ms_context->InitGe();
 }
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
index 5a2a8039dd..abf55bb2d8 100644
--- a/mindspore/ccsrc/pipeline/pipeline_ge.cc
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -210,7 +210,7 @@ bool AddDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::di
   (void)convertor.GenerateCheckpointGraph();
   if (convertor.ErrCode() != 0) {
     DfGraphManager::GetInstance().ClearGraph();
-    MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode();
+    MS_LOG(ERROR) << "Convert df graph failed, err:" << convertor.ErrCode();
     return false;
   }
 
@@ -238,7 +238,7 @@ bool AddDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::di
 FuncGraphPtr BuildDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::dict& init_params,
                           const std::string& phase, const py::object& broadcast_params) {
   if (info.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
   }
   FuncGraphPtr anf_graph = info.at(phase)->func_graph;
 
@@ -389,7 +389,7 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
                                         const std::string& phase) {
   std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
   if (ge_tensors.size() != inputs.size()) {
-    MS_LOG(ERROR) << "args convert to ge tensor error";
+    MS_LOG(ERROR) << "Args convert to ge tensor error";
     return nullptr;
   }
 
@@ -444,7 +444,7 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
   std::size_t size = args.size();
 
   if (info.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase);
+    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
   }
 
   auto arg_size = info.at(phase)->arg_list_size;
@@ -459,12 +459,12 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
       ValuePtr converted = nullptr;
       bool succ = parse::ConvertData(args[i], &converted);
       if (!succ) {
-        MS_LOG(EXCEPTION) << "args convert error";
+        MS_LOG(EXCEPTION) << "Args convert error";
       }
       if (converted->isa<tensor::Tensor>()) {
         (*inputs).push_back(converted->cast<tensor::TensorPtr>());
       } else {
-        MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor";
+        MS_LOG(EXCEPTION) << "Args " << converted->ToString() << " is not tensor";
       }
     }
   }
@@ -481,7 +481,7 @@ py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const
   }
 
   if (info.count(phase) == 0) {
-    MS_LOG(EXCEPTION) << "has no phase:" << phase;
+    MS_LOG(EXCEPTION) << "There is no phase:" << phase;
   }
 
   FuncGraphPtr anf_graph = info.at(phase)->func_graph;
@@ -511,7 +511,7 @@ py::object ExecDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const
   if (ret != nullptr) {
     return *ret;
   } else {
-    MS_LOG(EXCEPTION) << "exec graph failed";
+    MS_LOG(EXCEPTION) << "Exec graph failed";
   }
 }
 void ExportDFGraph(const std::string& file_name, const std::string& phase) {
diff --git a/mindspore/ccsrc/pipeline/resource.cc b/mindspore/ccsrc/pipeline/resource.cc
index 59ee04ad15..18695518be 100644
--- a/mindspore/ccsrc/pipeline/resource.cc
+++ b/mindspore/ccsrc/pipeline/resource.cc
@@ -189,7 +189,7 @@ Resource::~Resource() {
   // If exit normally, these global variables will be cleaned
   // in Resource::Clean call by MsPipeline::Compile, but if exit with MS_LOGEXCEPTION,
   // these global variables may not being cleaned, it may
-  // cause segmentfault when free python object inside these global varaibles
+  // cause segmentfault when free python object inside these global variables
   // after python interpreter got freed, so these global variables
   // are cleaned here.
   // So if exit normally, these global variable will be cleaned twice,
diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
index eef4e8b4ad..555a6d87c0 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
@@ -443,7 +443,7 @@ bool AbstractTensor::operator==(const AbstractTensor &other) const {
   auto v1 = GetValueTrack();
   auto v2 = other.GetValueTrack();
   if (v1 == nullptr || v2 == nullptr) {
-    MS_LOG(EXCEPTION) << "the value of AbstractTensor is nullptr";
+    MS_LOG(EXCEPTION) << "The value of AbstractTensor is nullptr";
   }
 
   bool is_value_equal = (v1 == v2);
diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h
index 9d9585bba3..9e0dd82003 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h
+++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h
@@ -77,7 +77,7 @@ class AbstractBase : public Base {
   }
 
  protected:
-  // default implementation, it can be overrided by subclass;
+  // default implementation, it can be overwritten by subclass;
   virtual ValuePtr RealBuildValue() const { return kAnyValue; }
 
  private:
@@ -495,7 +495,7 @@ class AbstractNone : public AbstractBase {
 };
 using AbstractNonePtr = std::shared_ptr<AbstractNone>;
 
-// the un assgined state value for variable, which means the variable is not assigned
+// the un assigned state value for variable, which means the variable is not assigned
 class AbstractNull : public AbstractBase {
  public:
   AbstractNull() : AbstractBase(kNullObj) { set_type(std::make_shared<TypeNull>()); }
diff --git a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
index 251f218145..9b120f731c 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
@@ -116,7 +116,7 @@ AbstractBasePtrList FuncGraphEvaluator::NormalizeArgs(const AbstractBasePtrList
     return broaded_list;
   }
 
-  if (func_graph_->has_flag(kFuncGraphFlagUndetermin)) {
+  if (func_graph_->has_flag(kFuncGraphFlagUndetermined)) {
     if (parent_context_) {
       MS_LOG(DEBUG) << "Undeterminate FuncGraphEvaluator " << ToString()
                     << ", context: " << parent_context_->ToString();
diff --git a/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc b/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc
index 1b70e2fe22..69f6af0dc0 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc
@@ -56,7 +56,7 @@ TypePtr CheckTensorDType(const AbstractTensorPtr &tensor, const TypePtrList &acc
   }
   TypePtr ele_type = tensor->element()->BuildType();
   if (ele_type == nullptr) {
-    MS_LOG(EXCEPTION) << "abstract tensor element type nullptr";
+    MS_LOG(EXCEPTION) << "Abstract tensor element type nullptr";
   }
   return CheckType(ele_type, accepts, error_message_prefix);
 }
@@ -64,7 +64,7 @@ TypePtr CheckTensorDType(const AbstractTensorPtr &tensor, const TypePtrList &acc
 TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const TypePtrList &accepts,
                               const std::string &error_message_prefix) {
   if (tensor_list.empty()) {
-    MS_LOG(EXCEPTION) << "array list is empty";
+    MS_LOG(EXCEPTION) << "Array list is empty";
   }
 
   auto sample_tensor = tensor_list[0];
@@ -78,7 +78,7 @@ TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const Ty
     auto aType = tensor_list[index]->element()->BuildType();
     loginfoBuffer << " " << aType->ToString();
     if (sample_type->type_id() != aType->type_id()) {
-      MS_LOG(EXCEPTION) << "expected type " << sample_type->ToString() << ", but got " << aType->ToString()
+      MS_LOG(EXCEPTION) << "Expected type " << sample_type->ToString() << ", but got " << aType->ToString()
                         << ", index " << index;
     }
   }
@@ -89,11 +89,11 @@ TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const Ty
 TypePtr CheckScalarType(const AbstractScalarPtr &scalar, const TypePtrList &accepts,
                         const std::string &error_message_prefix) {
   if (scalar == nullptr) {
-    MS_LOG(EXCEPTION) << "scalar nullptr";
+    MS_LOG(EXCEPTION) << "Scalar nullptr";
   }
   auto type = scalar->BuildType();
   if (type == nullptr) {
-    MS_LOG(EXCEPTION) << "scalar value nullptr";
+    MS_LOG(EXCEPTION) << "Scalar value nullptr";
   }
 
   return CheckType(type, accepts, error_message_prefix);
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 4110f25811..56bcd77f67 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -182,30 +182,30 @@ AbstractBasePtr DoSignatureEvaluator::Run(AnalysisEnginePtr engine, const Config
 
 static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_spec_list, bool need_unpack) {
   // arg[0] is the func graph to unpack, ignore it
-  AbstractBasePtrList sepcialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end());
-  AbstractBasePtrList graph_sepcialize_args;
+  AbstractBasePtrList specialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end());
+  AbstractBasePtrList graph_specialize_args;
   if (need_unpack) {
-    for (size_t index = 0; index < sepcialize_args_before_unpack.size(); index++) {
-      MS_EXCEPTION_IF_NULL(sepcialize_args_before_unpack[index]);
-      if (sepcialize_args_before_unpack[index]->isa<AbstractTuple>()) {
-        AbstractTuplePtr arg_tuple = sepcialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
+    for (size_t index = 0; index < specialize_args_before_unpack.size(); index++) {
+      MS_EXCEPTION_IF_NULL(specialize_args_before_unpack[index]);
+      if (specialize_args_before_unpack[index]->isa<AbstractTuple>()) {
+        AbstractTuplePtr arg_tuple = specialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
         std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(),
-                       std::back_inserter(graph_sepcialize_args), [](AbstractBasePtr abs) { return abs; });
-      } else if (sepcialize_args_before_unpack[index]->isa<AbstractDictionary>()) {
-        AbstractDictionaryPtr arg_dict = sepcialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
+                       std::back_inserter(graph_specialize_args), [](AbstractBasePtr abs) { return abs; });
+      } else if (specialize_args_before_unpack[index]->isa<AbstractDictionary>()) {
+        AbstractDictionaryPtr arg_dict = specialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
         auto dict_elems = arg_dict->elements();
         (void)std::transform(
-          dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_sepcialize_args),
+          dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_specialize_args),
           [](const AbstractAttribute &item) { return std::make_shared<AbstractKeywordArg>(item.first, item.second); });
       } else {
         MS_LOG(EXCEPTION) << "UnpackGraph require args should be tuple or dict, but got "
-                          << sepcialize_args_before_unpack[index]->ToString();
+                          << specialize_args_before_unpack[index]->ToString();
       }
     }
   } else {
-    graph_sepcialize_args = sepcialize_args_before_unpack;
+    graph_specialize_args = specialize_args_before_unpack;
   }
-  return graph_sepcialize_args;
+  return graph_specialize_args;
 }
 
 AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list,
@@ -239,14 +239,14 @@ AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const Config
   MS_EXCEPTION_IF_NULL(real_fn);
   FuncGraphPtr forward_graph = real_fn->func_graph();
   MS_EXCEPTION_IF_NULL(forward_graph);
-  AbstractBasePtrList graph_sepcialize_args =
+  AbstractBasePtrList graph_specialize_args =
     GetUnpackGraphSpecArgsList(args_spec_list, unpack_graph->need_unpack_args());
 
-  AbstractBasePtrList graph_sepcialize_args_without_sens;
-  (void)std::transform(graph_sepcialize_args.begin(),
-                       graph_sepcialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0),
-                       std::back_inserter(graph_sepcialize_args_without_sens), [](AbstractBasePtr abs) { return abs; });
-  auto new_graph = forward_graph->GenerateGraph(graph_sepcialize_args_without_sens);
+  AbstractBasePtrList graph_specialize_args_without_sens;
+  (void)std::transform(graph_specialize_args.begin(),
+                       graph_specialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0),
+                       std::back_inserter(graph_specialize_args_without_sens), [](AbstractBasePtr abs) { return abs; });
+  auto new_graph = forward_graph->GenerateGraph(graph_specialize_args_without_sens);
   engine->func_graph_manager()->AddFuncGraph(new_graph);
   ScopePtr scope = kDefaultScope;
   if (out_conf != nullptr) {
@@ -635,8 +635,8 @@ AbstractBasePtr GetEvaluatedValueForClassAttrOrMethod(const AnalysisEnginePtr &e
     MS_LOG(EXCEPTION) << "Attribute type error";
   }
   std::string item_name = item_v->cast<StringImmPtr>()->value();
-  MS_LOG(DEBUG) << "Resovle name: " << cls->tag().name();
-  MS_LOG(DEBUG) << "Resovle item: " << item_name;
+  MS_LOG(DEBUG) << "Resolve name: " << cls->tag().name();
+  MS_LOG(DEBUG) << "Resolve item: " << item_name;
 
   AbstractBasePtr attr = cls->GetAttribute(item_name);
   if (attr != nullptr) {
@@ -720,7 +720,7 @@ class EmbedEvaluator : public SymbolicPrimEvaluator {
   ~EmbedEvaluator() override = default;
   MS_DECLARE_PARENT(EmbedEvaluator, SymbolicPrimEvaluator);
   AbstractBasePtr EvalPrim(const ConfigPtrList &args_conf_list) override {
-    // arg: free variable to be embeded
+    // arg: free variable to be embedded
     if (args_conf_list.size() != 1) {
       MS_LOG(EXCEPTION) << "EmbedEvaluator requires 1 parameter, but got " << args_conf_list.size();
     }
@@ -939,7 +939,7 @@ class PartialEvaluator : public Evaluator {
   AbstractBasePtr Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list,
                       AnfNodeConfigPtr out_conf = nullptr) override {
     if (args_conf_list.size() == 0) {
-      MS_LOG(EXCEPTION) << "args size should be greater than 0";
+      MS_LOG(EXCEPTION) << "Args size should be greater than 0";
     }
     auto arg0_value = args_conf_list[0]->GetEvaluatedValue();
     AbstractBasePtrList args_spec_list{arg0_value};
diff --git a/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc b/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc
index bfa1e43ceb..987c5d1db0 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc
@@ -190,7 +190,7 @@ void FuncGraphSpecializer::FirstPass() {
     }
     if (node->func_graph() != func_graph_) {
       if (parent_ == nullptr) {
-        MS_LOG(EXCEPTION) << "parent must not null NodeInfo: " << trace::GetDebugInfo(node->debug_info());
+        MS_LOG(EXCEPTION) << "Parent must not null NodeInfo: " << trace::GetDebugInfo(node->debug_info());
       }
       parent_->AddTodoItem(node);
       parent_->FirstPass();
@@ -365,16 +365,16 @@ AnfNodePtr FuncGraphSpecializer::BuildSpecializedNodeInner(const AbstractBasePtr
   }
 
   if (!eval->isa<BaseFuncGraphEvaluator>()) {
-    MS_LOG(EXCEPTION) << "eval is not BaseGraphEvaluator, but " << eval->ToString();
+    MS_LOG(EXCEPTION) << "Eval is not BaseGraphEvaluator, but " << eval->ToString();
   }
   auto real_eval = dyn_cast<BaseFuncGraphEvaluator>(eval);
 
   if (func->context() != nullptr) {
     if (!IsVisible(func_graph_, func->context()->func_graph())) {
-      MS_LOG(EXCEPTION) << "func is not visible NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info());
+      MS_LOG(EXCEPTION) << "Func is not visible NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info());
     }
   } else {
-    MS_LOG(EXCEPTION) << "func context is nullptr NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info());
+    MS_LOG(EXCEPTION) << "Func context is nullptr NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info());
   }
   AnalysisContextPtr context = real_eval->MakeContext(engine_, argvals);
   MS_LOG(DEBUG) << "Specialize function graph: " << context->func_graph()->ToString() << ", args: " << argvals.size()
@@ -556,7 +556,7 @@ SpecializeStatusCode FuncGraphSpecializer::FindUniqueArgvals(const AbstractFunct
     if (!result->first.empty()) {
       return kSpecializeSuccess;
     }
-    MS_LOG(DEBUG) << "Find POLY code, it may be unused code or unresoved polymorphism.";
+    MS_LOG(DEBUG) << "Find POLY code, it may be unused code or unresolved polymorphism.";
     return kSpecializeFindUniqueArgvalPoly;
   }
 }
diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
index 49182e8d09..4ab7b9d20b 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc
@@ -89,7 +89,7 @@ std::size_t AnfNodeConfigHasher::operator()(const AnfNodeConfigPtr conf) const {
   MS_EXCEPTION_IF_NULL(conf->node());
   std::size_t hash_value = hash_combine(conf->node()->hash(), conf->context()->hash());
   if (conf->context() != nullptr && conf->context()->func_graph() != nullptr) {
-    MS_LOG(DEBUG) << "NodeConfgHasher Node: " << conf->node()->DebugString()
+    MS_LOG(DEBUG) << "NodeConfigHasher Node: " << conf->node()->DebugString()
                   << ", Graph: " << conf->context()->func_graph()->ToString() << " ### , hash value: " << hash_value;
   } else {
     MS_LOG(DEBUG) << "NodeConfigHasher Node: " << conf->node()->DebugString() << " ### , hash value: " << hash_value;
@@ -456,13 +456,13 @@ AbstractBasePtr AnalysisEngine::ExecuteMultipleEvaluators(const std::vector<Eval
   for (auto eval : evaluators) {
     auto fg_eval = eval->cast<FuncGraphEvaluatorPtr>();
     if (fg_eval) {
-      auto undetermin_fgs = fg_eval->func_graph()->recursive_graphs();
-      if (undetermin_fgs) {
-        for (auto undetermin_fg : *undetermin_fgs) {
-          MS_LOG(DEBUG) << "Set graph undetermin: " << undetermin_fg->ToString();
+      auto undetermined_fgs = fg_eval->func_graph()->recursive_graphs();
+      if (undetermined_fgs) {
+        for (auto undetermined_fg : *undetermined_fgs) {
+          MS_LOG(DEBUG) << "Set graph undetermined: " << undetermined_fg->ToString();
           // As the current evaluator has multiple possibles, all the func_graphs which
           // are recursive with the current func_graph are undetermined in control flow.
-          undetermin_fg->set_flags(kFuncGraphFlagUndetermin, true);
+          undetermined_fg->set_flags(kFuncGraphFlagUndetermined, true);
         }
       }
     }

From fc9c3c6af47d5727478cce4f84b57b5066611001 Mon Sep 17 00:00:00 2001
From: Jonathan Yan <jonathan.yan1@huawei.com>
Date: Sat, 4 Apr 2020 06:48:58 -0400
Subject: [PATCH 100/367] remove ENABLE_MINDRECORD flag

---
 mindspore/ccsrc/dataset/CMakeLists.txt               |  2 --
 mindspore/ccsrc/dataset/api/de_pipeline.cc           | 12 +++---------
 mindspore/ccsrc/dataset/api/de_pipeline.h            |  4 ----
 mindspore/ccsrc/dataset/api/python_bindings.cc       |  6 ------
 .../engine/datasetops/source/mindrecord_op.cc        |  3 ---
 .../dataset/engine/datasetops/source/mindrecord_op.h |  2 --
 tests/ut/cpp/CMakeLists.txt                          |  1 -
 tests/ut/cpp/dataset/mind_record_op_test.cc          |  2 --
 8 files changed, 3 insertions(+), 29 deletions(-)

diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index d6791f2b9b..477d37051e 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -17,8 +17,6 @@ if (ENABLE_TDTQUE)
     message(STATUS "TDT queue is enabled")
 endif ()
 
-add_definitions(-D ENABLE_MINDRECORD)
-
 # conde coverage
 # option(ENABLE_COVERAGE "Enable code coverage report" OFF)
 # if (ENABLE_COVERAGE)
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index d51204f659..65ec8d30f2 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -29,11 +29,9 @@
 #include "dataset/engine/datasetops/source/manifest_op.h"
 #include "dataset/engine/datasetops/source/cifar_op.h"
 #include "dataset/engine/datasetops/source/celeba_op.h"
-#ifdef ENABLE_MINDRECORD
-#include "./shard_category.h"
-#include "./shard_sample.h"
-#include "./shard_shuffle.h"
-#endif
+#include "mindrecord/include/shard_category.h"
+#include "mindrecord/include/shard_sample.h"
+#include "mindrecord/include/shard_shuffle.h"
 
 #include "dataset/util/random.h"
 #include "dataset/util/status.h"
@@ -46,9 +44,7 @@ using pFunction = Status (DEPipeline::*)(const py::dict &, std::shared_ptr<Datas
 
 static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &DEPipeline::ParseStorageOp},
                                                                    {kShuffle, &DEPipeline::ParseShuffleOp},
-#ifdef ENABLE_MINDRECORD
                                                                    {kMindrecord, &DEPipeline::ParseMindRecordOp},
-#endif
                                                                    {kMap, &DEPipeline::ParseMapOp},
                                                                    {kBatch, &DEPipeline::ParseBatchOp},
                                                                    {kRepeat, &DEPipeline::ParseRepeatOp},
@@ -364,7 +360,6 @@ Status DEPipeline::ParseShuffleOp(const py::dict &args, std::shared_ptr<DatasetO
   return Status::OK();
 }
 
-#ifdef ENABLE_MINDRECORD
 Status DEPipeline::CheckMindRecordPartitionInfo(const py::dict &args, std::vector<int> *in_partitions) {
   if (args["partitions"].is_none()) {
     std::string err_msg = "Error: partitions is not set (None)";
@@ -450,7 +445,6 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
   *ptr = op;
   return Status::OK();
 }
-#endif
 
 Status DEPipeline::ParseMapOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   std::shared_ptr<MapOp::Builder> builder = std::make_shared<MapOp::Builder>();
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index e8dde85a77..acffc390cc 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -38,9 +38,7 @@ using DsOpPtr = std::shared_ptr<DatasetOp>;
 enum OpName {
   kStorage = 0,
   kShuffle,
-#ifdef ENABLE_MINDRECORD
   kMindrecord,
-#endif
   kBatch,
   kCache,
   kRepeat,
@@ -101,11 +99,9 @@ class DEPipeline {
 
   Status ParseShuffleOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
-#ifdef ENABLE_MINDRECORD
   Status CheckMindRecordPartitionInfo(const py::dict &args, std::vector<int> *ptr);
 
   Status ParseMindRecordOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
-#endif
 
   Status ParseMapOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index 86b0a5d66a..e6c2691281 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -44,9 +44,7 @@
 #include "dataset/engine/datasetops/source/io_block.h"
 #include "dataset/engine/datasetops/source/mnist_op.h"
 #include "dataset/engine/datasetops/source/manifest_op.h"
-#ifdef ENABLE_MINDRECORD
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
-#endif
 #include "dataset/engine/datasetops/source/sampler/distributed_sampler.h"
 #include "dataset/engine/datasetops/source/sampler/pk_sampler.h"
 #include "dataset/engine/datasetops/source/sampler/random_sampler.h"
@@ -146,14 +144,12 @@ void bindDatasetOps(py::module *m) {
       return py::make_tuple(count, num_classes);
     });
 
-#ifdef ENABLE_MINDRECORD
   (void)py::class_<MindRecordOp, DatasetOp, std::shared_ptr<MindRecordOp>>(*m, "MindRecordOp")
     .def_static("get_num_rows", [](const std::string &path) {
       int64_t count = 0;
       THROW_IF_ERROR(MindRecordOp::CountTotalRows(path, &count));
       return count;
     });
-#endif
 
   (void)py::class_<ManifestOp, DatasetOp, std::shared_ptr<ManifestOp>>(*m, "ManifestOp")
     .def_static("get_num_rows_and_classes",
@@ -424,9 +420,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("STORAGE", OpName::kStorage)
     .value("SHUFFLE", OpName::kShuffle)
     .value("BATCH", OpName::kBatch)
-#ifdef ENABLE_MINDRECORD
     .value("MINDRECORD", OpName::kMindrecord)
-#endif
     .value("CACHE", OpName::kCache)
     .value("REPEAT", OpName::kRepeat)
     .value("TAKE", OpName::kTake)
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
index b062371d7f..b5bea5416c 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
@@ -13,8 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifdef ENABLE_MINDRECORD
-
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
 
 #include <algorithm>
@@ -665,4 +663,3 @@ Status MindRecordOp::CountTotalRows(const std::string dataset_path, int64_t *cou
 }
 }  // namespace dataset
 }  // namespace mindspore
-#endif
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
index 2535acbc50..0b16391b20 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
@@ -15,7 +15,6 @@
  */
 #ifndef DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
 #define DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
-#ifdef ENABLE_MINDRECORD
 #pragma once
 
 #include <cstdint>
@@ -276,5 +275,4 @@ class MindRecordOp : public ParallelOp {
 };
 }  // namespace dataset
 }  // namespace mindspore
-#endif
 #endif  // DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt
index 5f4bd41b3b..8d3f8a8138 100644
--- a/tests/ut/cpp/CMakeLists.txt
+++ b/tests/ut/cpp/CMakeLists.txt
@@ -26,7 +26,6 @@ MESSAGE("check  ut_test ${CMAKE_BINARY_DIR}")
 link_directories(${MS_CCSRC_BUILD_PATH})
 
 if(ENABLE_MINDDATA)
-    add_definitions(-D ENABLE_MINDRECORD)
     add_definitions(-D ENABLE_MINDDATA)
     link_directories(${MS_CCSRC_BUILD_PATH}/dataset)
     link_directories(${MS_CCSRC_BUILD_PATH}/mindrecord)
diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc
index abe7faef14..3d5c80b3f4 100644
--- a/tests/ut/cpp/dataset/mind_record_op_test.cc
+++ b/tests/ut/cpp/dataset/mind_record_op_test.cc
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifdef ENABLE_MINDRECORD
 #include <iostream>
 #include <memory>
 #include <vector>
@@ -480,4 +479,3 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) {
     row_count++;
   }
 }
-#endif

From fc9d0887948fd67371f6a47dedc2675e4c899ff0 Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Thu, 2 Apr 2020 21:56:48 +0800
Subject: [PATCH 101/367] add parameter check for Class Schema

---
 mindspore/dataset/engine/datasets.py   | 20 +++++++----
 mindspore/dataset/engine/validators.py | 49 ++++++++++++++++++++++++++
 2 files changed, 63 insertions(+), 6 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index db2b5169d2..2d5c219b71 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator
 from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \
     check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
-    check_zip_dataset
+    check_zip_dataset, check_add_column, check_columns
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -2334,13 +2334,20 @@ class Schema:
             self.dataset_type = ''
             self.num_rows = 0
         else:
+            if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK):
+                raise ValueError("The file %s does not exist or permission denied!" % schema_file)
             try:
                 with open(schema_file, 'r') as load_f:
                     json_obj = json.load(load_f)
-                    self.from_json(json_obj)
             except json.decoder.JSONDecodeError:
-                raise RuntimeError("Schema file failed to load")
+                raise RuntimeError("Schema file failed to load.")
+            except UnicodeDecodeError:
+                raise RuntimeError("Schema file failed to decode.")
+            except Exception:
+                raise RuntimeError("Schema file failed to open.")
+            self.from_json(json_obj)
 
+    @check_add_column
     def add_column(self, name, de_type, shape=None):
         """
         Add new column to the schema.
@@ -2359,10 +2366,8 @@ class Schema:
         if isinstance(de_type, typing.Type):
             de_type = mstype_to_detype(de_type)
             new_column["type"] = str(de_type)
-        elif isinstance(de_type, str):
-            new_column["type"] = str(DataType(de_type))
         else:
-            raise ValueError("Unknown column type")
+            new_column["type"] = str(DataType(de_type))
 
         if shape is not None:
             new_column["shape"] = shape
@@ -2399,6 +2404,7 @@ class Schema:
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
+        check_columns(columns, columns)
         self.columns = []
         for col in columns:
             name = None
@@ -2443,6 +2449,8 @@ class Schema:
             RuntimeError: if dataset type is missing in the object.
             RuntimeError: if columns are missing in the object.
         """
+        if not isinstance(json_obj, dict) or json_obj is None:
+            raise ValueError("Expected non-empty dict.")
         for k, v in json_obj.items():
             if k == "datasetType":
                 self.dataset_type = v
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index b4d22a4a01..1c374ae879 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -19,10 +19,15 @@ import inspect as ins
 import os
 from functools import wraps
 from multiprocessing import cpu_count
+from mindspore._c_expression import typing
 from . import samplers
 from . import datasets
 
 INT32_MAX = 2147483647
+valid_detype = [
+    "bool", "int8", "int16", "int32", "int64", "uint8", "uint16",
+    "uint32", "uint64", "float16", "float32", "float64"
+]
 
 
 def check(method):
@@ -188,6 +193,12 @@ def check(method):
     return wrapper
 
 
+def check_valid_detype(type_):
+    if type_ not in valid_detype:
+        raise ValueError("Unknown column type")
+    return True
+
+
 def check_filename(path):
     """
     check the filename in the path
@@ -743,3 +754,41 @@ def check_project(method):
         return method(*args, **kwargs)
 
     return new_method
+
+
+def check_shape(shape, name):
+    if isinstance(shape, list):
+        for element in shape:
+            if not isinstance(element, int):
+                raise TypeError(
+                    "Each element in {0} should be of type int. Got {1}.".format(name, type(element)))
+    else:
+        raise TypeError("Expected int list.")
+
+
+def check_add_column(method):
+    """check the input arguments of add_column."""
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        # check name; required argument
+        name = param_dict.get("name")
+        if not isinstance(name, str) or not name:
+            raise TypeError("Expected non-empty string.")
+
+        # check type; required argument
+        de_type = param_dict.get("de_type")
+        if not isinstance(de_type, str) or not de_type:
+            raise TypeError("Expected non-empty string.")
+        if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type):
+            raise ValueError("Unknown column type.")
+
+        # check shape
+        shape = param_dict.get("shape")
+        if shape is not None:
+            check_shape(shape, "shape")
+
+        return method(*args, **kwargs)
+
+    return new_method

From 322ffef3e49dc43161baead55cad433f5fcaf530 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Mon, 6 Apr 2020 16:14:59 +0800
Subject: [PATCH 102/367] Fix some typo errors in mindspore ir module

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 mindspore/ccsrc/common/trans.cc               | 90 +++++++++----------
 mindspore/ccsrc/debug/anf_ir_dump.cc          | 16 ++--
 mindspore/ccsrc/debug/anf_ir_utils.cc         | 36 ++++----
 mindspore/ccsrc/debug/draw.cc                 |  2 +-
 mindspore/ccsrc/debug/dump_proto.cc           |  2 +-
 mindspore/ccsrc/debug/e2e_dump.cc             | 24 ++---
 mindspore/ccsrc/debug/info.cc                 |  4 +-
 mindspore/ccsrc/debug/trace.cc                | 10 +--
 mindspore/ccsrc/debug/trace_info.cc           |  2 +-
 mindspore/ccsrc/ir/anf.h                      |  6 +-
 mindspore/ccsrc/ir/dtype/number.cc            |  6 +-
 mindspore/ccsrc/ir/dtype/type.cc              |  6 +-
 mindspore/ccsrc/ir/func_graph.h               |  2 +-
 mindspore/ccsrc/ir/manager.cc                 |  4 +-
 mindspore/ccsrc/ir/manager.h                  |  2 +-
 mindspore/ccsrc/ir/meta_tensor.h              |  8 +-
 mindspore/ccsrc/ir/visitor.cc                 |  7 +-
 mindspore/ccsrc/pynative/pynative_execute.cc  | 14 +--
 .../ccsrc/pynative/pynative_execute_ge.cc     | 18 ++--
 19 files changed, 129 insertions(+), 130 deletions(-)

diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index 4748d59286..380c51bcf9 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -122,7 +122,7 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const
       TransDataSrc2Dst<uint16_t, int32_t>(args, dst, data_size);
       break;
     default:
-      MS_LOG(ERROR) << "unsupported datatype trans";
+      MS_LOG(ERROR) << "Unsupported datatype trans";
       return false;
   }
   return true;
@@ -132,7 +132,7 @@ size_t CubeSizeByType(const TypeId data_type) {
   const size_t default_error = 0;
   auto dt_size = TypeIdSize(data_type);
   if (dt_size < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return default_error;
   } else if (dt_size == 1) {
     return kCubeSize * 2;
@@ -146,12 +146,12 @@ size_t ShapeSize(const std::vector<size_t> &shape) {
 }
 
 size_t TypeIdSize(const TypeId data_type) {
-  const size_t unsupport_type_error = 0;
+  const size_t unsupported_type_error = 0;
   auto iter = type_map.find(data_type);
   if (iter != type_map.end()) {
     return iter->second;
   }
-  return unsupport_type_error;
+  return unsupported_type_error;
 }
 
 std::vector<size_t> TransShapeTo4d(const std::vector<size_t> &shape) {
@@ -174,7 +174,7 @@ std::vector<size_t> TransShapeTo4d(const std::vector<size_t> &shape) {
       }
       break;
     default:
-      MS_LOG(EXCEPTION) << "Unexpeted shape size = " << shape.size();
+      MS_LOG(EXCEPTION) << "Unexpected shape size = " << shape.size();
   }
   return shape_4d;
 }
@@ -183,7 +183,7 @@ std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const s
   std::vector<size_t> device_shape;
   if (format == kOpFormat_FRAC_NZ) {
     if (shape.size() < 2) {
-      MS_EXCEPTION(NotSupportError) << "format " << format << " is not support shape " << shape.size();
+      MS_EXCEPTION(NotSupportError) << "Format " << format << " is not support shape " << shape.size();
     }
     if (shape.size() > 2) {
       (void)std::copy(shape.begin(), shape.end() - 2, std::back_inserter(device_shape));
@@ -231,37 +231,37 @@ std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const s
 }
 
 bool TransDataType(const TypeIdArgs &args, void *result) {
-  MS_LOG(DEBUG) << "begin trans datatype from " << TypeIdLabel(args.host_data_type) << " to "
+  MS_LOG(DEBUG) << "Begin trans datatype from " << TypeIdLabel(args.host_data_type) << " to "
                 << TypeIdLabel(args.device_data_type);
   MS_EXCEPTION_IF_NULL(result);
   std::pair<TypeId, TypeId> type_info(args.host_data_type, args.device_data_type);
   auto iter = mode_map.find(type_info);
   if (iter == mode_map.end()) {
-    MS_LOG(ERROR) << "unsupported datatype trans. src_type :" << TypeIdLabel(args.host_data_type)
+    MS_LOG(ERROR) << "Unsupported datatype trans. src_type :" << TypeIdLabel(args.host_data_type)
                   << ", dst_type:" << TypeIdLabel(args.device_data_type);
     return false;
   }
   auto trans_mode = iter->second;
   auto type_size = TypeIdSize(args.device_data_type);
   if (type_size < 1) {
-    MS_LOG(ERROR) << "invalid host data type.";
+    MS_LOG(ERROR) << "Invalid host data type.";
     return false;
   }
   if (args.host_shape_size < 1) {
-    MS_LOG(ERROR) << "invalid host data size.";
+    MS_LOG(ERROR) << "Invalid host data size.";
     return false;
   }
   if (!CastKernel(args, result, args.host_shape_size, trans_mode)) {
-    MS_LOG(ERROR) << "failed to trans datatype..";
+    MS_LOG(ERROR) << "Failed to trans datatype..";
     return false;
   }
   return true;
 }
 
 bool TransFormat(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "start trans format.";
+  MS_LOG(DEBUG) << "Start trans format.";
   if (TypeIdSize(args.src_data_type) < 1) {
-    MS_LOG(ERROR) << "invalid datatype..";
+    MS_LOG(ERROR) << "Invalid datatype..";
     return false;
   }
   if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) &&
@@ -276,9 +276,9 @@ bool TransFormat(const FormatArgs &args, void *result) {
 }
 
 bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "start trans format.";
+  MS_LOG(DEBUG) << "Start trans format.";
   if (TypeIdSize(args.src_data_type) < 1) {
-    MS_LOG(ERROR) << "invalid datatype..";
+    MS_LOG(ERROR) << "Invalid datatype..";
     return false;
   }
   if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) &&
@@ -293,15 +293,15 @@ bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result) {
 }
 
 bool NchwToFracZ(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from nchw to frac_z";
+  MS_LOG(DEBUG) << "Trans format from nchw to frac_z";
   MS_EXCEPTION_IF_NULL(result);
   if (args.host_shape.size() != kNchwDims) {
-    MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
+    MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
     return false;
   }
   size_t size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   auto n = args.host_shape[0];
@@ -311,7 +311,7 @@ bool NchwToFracZ(const FormatArgs &args, void *result) {
 
   size_t c0 = CubeSizeByType(args.src_data_type);
   if (c0 < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   size_t c1 = Ceil(c, c0);
@@ -327,7 +327,7 @@ bool NchwToFracZ(const FormatArgs &args, void *result) {
 
   size_t dst_size = total_ele_cnt * size;
   if (dst_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size."
+    MS_LOG(ERROR) << "Illegal total data size."
                   << "dst size is :" << dst_size << "device size is :" << args.device_size;
     return false;
   }
@@ -369,20 +369,20 @@ bool NchwToFracZ(const FormatArgs &args, void *result) {
 }
 
 bool FracZToNchw(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from frac_z to nchw";
+  MS_LOG(DEBUG) << "Trans format from frac_z to nchw";
   MS_EXCEPTION_IF_NULL(result);
   if (args.host_shape.size() != kNchwDims) {
-    MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
+    MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
     return false;
   }
   size_t size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   size_t total_size = ShapeSize(args.device_shape) * size;
   if (total_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
     return false;
   }
 
@@ -435,7 +435,7 @@ bool FracZToNchw(const FormatArgs &args, void *result) {
 bool TransShapeToNz(const std::vector<size_t> &host_shape, std::vector<size_t> *hw_shape) {
   MS_EXCEPTION_IF_NULL(hw_shape);
   if (host_shape.empty()) {
-    MS_LOG(ERROR) << "size of vector is 0.";
+    MS_LOG(ERROR) << "Size of vector is 0.";
     return false;
   }
   switch (host_shape.size()) {
@@ -447,7 +447,7 @@ bool TransShapeToNz(const std::vector<size_t> &host_shape, std::vector<size_t> *
     default:
       auto size = host_shape.size();
       if (size < 2) {
-        MS_LOG(ERROR) << "illegal size.";
+        MS_LOG(ERROR) << "Illegal size.";
         return false;
       }
       size_t times = 1;
@@ -462,26 +462,26 @@ bool TransShapeToNz(const std::vector<size_t> &host_shape, std::vector<size_t> *
 }
 
 bool NchwToFracNz(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from nchw to frac_nz.";
+  MS_LOG(DEBUG) << "Trans format from nchw to frac_nz.";
   MS_EXCEPTION_IF_NULL(result);
   std::vector<size_t> hw_shape;
   if (!TransShapeToNz(args.host_shape, &hw_shape)) {
-    MS_LOG(ERROR) << "trans shape failed..";
+    MS_LOG(ERROR) << "Trans shape failed..";
     return false;
   }
   if (hw_shape.size() < 3 || args.device_shape.size() < 4) {
-    MS_LOG(ERROR) << "invalid shape size.";
+    MS_LOG(ERROR) << "Invalid shape size.";
     return false;
   }
   auto size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype";
+    MS_LOG(ERROR) << "Illegal dtype";
     return false;
   }
 
   auto dst_size = ShapeSize(args.device_shape) * size;
   if (dst_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size;
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size;
     return false;
   }
   auto times = hw_shape.at(0);
@@ -538,26 +538,26 @@ bool NchwToFracNz(const FormatArgs &args, void *result) {
 }
 
 bool FracNzToNchw(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from frac_nz to nchw";
+  MS_LOG(DEBUG) << "Trans format from frac_nz to nchw";
   MS_EXCEPTION_IF_NULL(result);
   std::vector<size_t> hw_shape;
   if (!TransShapeToNz(args.host_shape, &hw_shape)) {
-    MS_LOG(ERROR) << "trans shape failed..";
+    MS_LOG(ERROR) << "Trans shape failed..";
     return false;
   }
   if (hw_shape.size() < 3 || args.device_shape.size() < 4) {
-    MS_LOG(ERROR) << "invalid shape size.";
+    MS_LOG(ERROR) << "Invalid shape size.";
     return false;
   }
   auto size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype";
+    MS_LOG(ERROR) << "Illegal dtype";
     return false;
   }
 
   auto dst_size = ShapeSize(args.device_shape) * size;
   if (dst_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size;
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size;
     return false;
   }
   auto times = hw_shape.at(0);
@@ -614,20 +614,20 @@ bool FracNzToNchw(const FormatArgs &args, void *result) {
 }
 
 bool NchwToNc1hwc0(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from nchw to Nc1h1wc0";
+  MS_LOG(DEBUG) << "Trans format from nchw to Nc1h1wc0";
   MS_EXCEPTION_IF_NULL(result);
   if (args.host_shape.size() != kNchwDims) {
-    MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
+    MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
     return false;
   }
   size_t size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   auto total_size = ShapeSize(args.device_shape) * size;
   if (total_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
     return false;
   }
 
@@ -637,7 +637,7 @@ bool NchwToNc1hwc0(const FormatArgs &args, void *result) {
   auto w = args.host_shape[3];
   size_t c0 = CubeSizeByType(args.src_data_type);
   if (c0 < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   size_t c1 = Ceil(c, c0);
@@ -687,20 +687,20 @@ bool NchwToNc1hwc0(const FormatArgs &args, void *result) {
 }
 
 bool Nc1hwc0ToNchw(const FormatArgs &args, void *result) {
-  MS_LOG(DEBUG) << "trans format from nc1h1wc0 to nchw";
+  MS_LOG(DEBUG) << "Trans format from nc1h1wc0 to nchw";
   MS_EXCEPTION_IF_NULL(result);
   if (args.host_shape.size() != kNchwDims) {
-    MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
+    MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
     return false;
   }
   size_t size = TypeIdSize(args.src_data_type);
   if (size < 1) {
-    MS_LOG(ERROR) << "illegal dtype.";
+    MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
   size_t total_size = ShapeSize(args.device_shape) * size;
   if (total_size != args.device_size) {
-    MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size;
     return false;
   }
 
diff --git a/mindspore/ccsrc/debug/anf_ir_dump.cc b/mindspore/ccsrc/debug/anf_ir_dump.cc
index 9e24a17ede..9eb0a376cc 100644
--- a/mindspore/ccsrc/debug/anf_ir_dump.cc
+++ b/mindspore/ccsrc/debug/anf_ir_dump.cc
@@ -141,7 +141,7 @@ void DumpKernelInfo(const CNodePtr &node, const std::shared_ptr<SubGraphIRInfo>
 
 void DumpParams(const FuncGraphPtr &graph, std::ostringstream &buffer, OrderedMap<AnfNodePtr, int32_t> *para_map) {
   if (graph == nullptr) {
-    MS_LOG(INFO) << "param graph is nullptr.";
+    MS_LOG(INFO) << "Param graph is nullptr.";
     return;
   }
   std::vector<AnfNodePtr> parameters = graph->parameters();
@@ -175,17 +175,17 @@ void DumpParams(const FuncGraphPtr &graph, std::ostringstream &buffer, OrderedMa
     if (para_map != nullptr) {
       (*para_map)[p] = para++;
     }
-    MS_LOG(DEBUG) << "record param: " << p->ToString() << " graph belong : " << p->func_graph()->ToString();
+    MS_LOG(DEBUG) << "Record param: " << p->ToString() << " graph belong : " << p->func_graph()->ToString();
   }
 }
 
 void DumpOperator(const AnfNodePtr &op, const std::shared_ptr<SubGraphIRInfo> &gsub) {
   if (op == nullptr) {
-    MS_LOG(INFO) << "param op is nullptr";
+    MS_LOG(INFO) << "Param op is nullptr";
     return;
   }
   if (gsub == nullptr) {
-    MS_LOG(INFO) << "param gsub is nullptr";
+    MS_LOG(INFO) << "Param gsub is nullptr";
     return;
   }
 
@@ -338,7 +338,7 @@ void DumpCNode(const CNodePtr &nd, const FuncGraphPtr &sub_graph, OrderedMap<Anf
   }
 
   if (nd->inputs().empty()) {
-    MS_LOG(EXCEPTION) << "input of apply node is empty";
+    MS_LOG(EXCEPTION) << "Input of apply node is empty";
   }
 
   // print operator
@@ -376,7 +376,7 @@ void DumpIRInSubgraph(const std::vector<AnfNodePtr> &nodes, OrderedMap<AnfNodePt
     MS_EXCEPTION_IF_NULL(nd);
     FuncGraphPtr sub_graph = nd->func_graph();
     if (sub_graph == nullptr) {
-      MS_LOG(DEBUG) << "node[" << nd->ToString() << "] belongs to no graph!";
+      MS_LOG(DEBUG) << "Node[" << nd->ToString() << "] belongs to no graph!";
       continue;
     }
     std::shared_ptr<SubGraphIRInfo> gsub = (*sub_graphs)[sub_graph];
@@ -430,12 +430,12 @@ void DumpIR(const std::string &filename, const FuncGraphPtr &graph, bool dump_fu
     return;
   }
   if (filename.size() > PATH_MAX) {
-    MS_LOG(ERROR) << "file path " << filename << " is too long.";
+    MS_LOG(ERROR) << "File path " << filename << " is too long.";
     return;
   }
   char real_path[PATH_MAX] = {0};
   if (nullptr == realpath(filename.c_str(), real_path)) {
-    MS_LOG(DEBUG) << "dir " << filename << " does not exit.";
+    MS_LOG(DEBUG) << "Dir " << filename << " does not exit.";
   }
 
   OrderedMap<AnfNodePtr, int32_t> para_map;
diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc
index 98cd2f4b2f..c25ad862df 100644
--- a/mindspore/ccsrc/debug/anf_ir_utils.cc
+++ b/mindspore/ccsrc/debug/anf_ir_utils.cc
@@ -49,7 +49,7 @@ std::string GetMsIrPath(void) {
     path = path_ptr;
     char real_path[PATH_MAX] = {0};
     if (path.size() > PATH_MAX || nullptr == realpath(path.c_str(), real_path)) {
-      MS_LOG(EXCEPTION) << "MS IR Path error, " << path_ptr;
+      MS_LOG(EXCEPTION) << "MS IR path error, " << path_ptr;
     }
     path = real_path;
   }
@@ -144,8 +144,8 @@ std::string AnfExporter::GetValueNodeText(const FuncGraphPtr& fg, const ValueNod
 }
 
 std::string AnfExporter::GetMultitypeFuncGraphText(const prim::MultitypeFuncGraphPtr& mt_func_graph) {
-  auto py_funs = mt_func_graph->GetPyFunctions();
-  if (py_funs.empty()) {
+  auto py_funcs = mt_func_graph->GetPyFunctions();
+  if (py_funcs.empty()) {
     return "";
   }
 
@@ -153,7 +153,7 @@ std::string AnfExporter::GetMultitypeFuncGraphText(const prim::MultitypeFuncGrap
 
   oss << "{";
   bool is_first = true;
-  for (const auto& py_func : py_funs) {
+  for (const auto& py_func : py_funcs) {
     if (is_first) {
       is_first = false;
     } else {
@@ -626,7 +626,7 @@ void AnfExporter::ExportFuncGraph(const std::string& filename, const FuncGraphPt
     ofs << "\n\n";
     (void)func_graph_set.erase(fg);
   }
-  ofs << "# num of total funcgraphs: " << exported.size();
+  ofs << "# num of total function graphs: " << exported.size();
 
   ofs.close();
 }
@@ -651,7 +651,7 @@ void AnfExporter::ExportFuncGraph(const std::string& filename, const std::vector
     ofs << "\n\n";
   }
 
-  ofs << "# num of total funcgraphs: " << graphs.size();
+  ofs << "# num of total function graphs: " << graphs.size();
 
   ofs.close();
 }
@@ -763,7 +763,7 @@ class Lexer {
         fin.close();
       }
     } catch (const std::exception& e) {
-      MS_LOG(ERROR) << "exception when closing file";
+      MS_LOG(ERROR) << "Exception when closing file";
     } catch (...) {
       std::string exName(abi::__cxa_current_exception_type()->name());
       MS_LOG(ERROR) << "Error occurred when closing file. Exception name: " << exName;
@@ -802,7 +802,7 @@ class Lexer {
     Token token = GetNextTokenInner();
     const char* str = token_text[token];
     std::string text = (str == nullptr ? GetTokenText() : str);
-    MS_LOG(DEBUG) << "------parse token] " << text;
+    MS_LOG(DEBUG) << "------Parse token] " << text;
     return token;
   }
 
@@ -1642,7 +1642,7 @@ class IrParser {
           MS_LOG(EXCEPTION) << "Expect @file at line " << lexer_.GetLineNo();
         }
 
-        // load prameter default value from serialized file
+        // load parameter default value from serialized file
         py::object default_obj = LoadObject(lexer_.GetTokenText());
         param->set_default_param(default_obj);
 
@@ -1950,7 +1950,7 @@ class IrParser {
       return TOK_ERROR;
     }
 
-    // restore python funciton of PrimitivePy from serialized file
+    // restore python function of PrimitivePy from serialized file
     py::object py_obj = LoadObject(lexer_.GetTokenText());
     PrimitivePyPtr ptr = nullptr;
     if (py::hasattr(py_obj, "__setattr_flag__") && py::hasattr(py_obj, "_clone")) {
@@ -1958,7 +1958,7 @@ class IrParser {
       py::object new_obj = clone_fn();
       ptr = new_obj.cast<PrimitivePyPtr>();
       if (ptr == nullptr) {
-        MS_LOG(EXCEPTION) << "cast to type 'PrimitivePyPtr' error";
+        MS_LOG(EXCEPTION) << "Cast to type 'PrimitivePyPtr' error";
       }
     } else {
       ptr = std::make_shared<PrimitivePy>(id.substr(strlen("PrimitivePy::")), py_obj);
@@ -2221,15 +2221,15 @@ class IrParser {
 };
 
 std::vector<FuncGraphPtr> ImportIR(const std::string& filename) {
-  IrParser paser(filename.c_str());
-  paser.ParseFile();
-  return paser.GetFuncGraphs();
+  IrParser parser(filename.c_str());
+  parser.ParseFile();
+  return parser.GetFuncGraphs();
 }
 
 #ifdef ENABLE_DUMP_IR
 void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix) {
   if (func_graph == nullptr) {
-    MS_LOG(ERROR) << "func graph is nullptr";
+    MS_LOG(ERROR) << "Func graph is nullptr";
     return;
   }
   auto ms_context = MsContext::GetInstance();
@@ -2243,16 +2243,16 @@ void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix) {
   }
   std::string file_path = save_graphs_path + "/" + "ms_output_" + suffix + ".pb";
   if (file_path.size() > PATH_MAX) {
-    MS_LOG(ERROR) << "file path " << file_path << " is too long.";
+    MS_LOG(ERROR) << "File path " << file_path << " is too long.";
     return;
   }
   char real_path[PATH_MAX] = {0};
   if (nullptr == realpath(file_path.c_str(), real_path)) {
-    MS_LOG(DEBUG) << "dir " << file_path << " does not exit.";
+    MS_LOG(DEBUG) << "Dir " << file_path << " does not exit.";
   } else {
     std::string path_string = real_path;
     if (chmod(common::SafeCStr(path_string), S_IRUSR | S_IWUSR) == -1) {
-      MS_LOG(ERROR) << "modify file:" << real_path << " to rw fail.";
+      MS_LOG(ERROR) << "Modify file:" << real_path << " to rw fail.";
       return;
     }
   }
diff --git a/mindspore/ccsrc/debug/draw.cc b/mindspore/ccsrc/debug/draw.cc
index e0949c0419..3e8cbfba19 100644
--- a/mindspore/ccsrc/debug/draw.cc
+++ b/mindspore/ccsrc/debug/draw.cc
@@ -362,7 +362,7 @@ Digraph::~Digraph() {
       fout_.close();
     }
   } catch (const std::exception& e) {
-    MS_LOG(ERROR) << "exception when closing file " << filename_;
+    MS_LOG(ERROR) << "Exception when closing file " << filename_;
   }
 }
 
diff --git a/mindspore/ccsrc/debug/dump_proto.cc b/mindspore/ccsrc/debug/dump_proto.cc
index a12cf8b47f..a7a1e208a4 100644
--- a/mindspore/ccsrc/debug/dump_proto.cc
+++ b/mindspore/ccsrc/debug/dump_proto.cc
@@ -208,7 +208,7 @@ void ProtoExporter::SetValueToProto(const ValuePtr& val, irpb::ValueProto* value
     TypePtr elem_type = dyn_cast<TensorType>(val)->element();
     type_proto->mutable_tensor_type()->set_elem_type(GetNumberDataType(elem_type));
   } else {
-    MS_LOG(WARNING) << "Not supported type " << val->type_name();
+    MS_LOG(WARNING) << "Unsupported type " << val->type_name();
   }
 }
 
diff --git a/mindspore/ccsrc/debug/e2e_dump.cc b/mindspore/ccsrc/debug/e2e_dump.cc
index ba11eafa5f..fbe76cdc47 100644
--- a/mindspore/ccsrc/debug/e2e_dump.cc
+++ b/mindspore/ccsrc/debug/e2e_dump.cc
@@ -101,7 +101,7 @@ bool Dump::IsConfigValid(const nlohmann::json& dumpSettings) {
   auto kernels = dumpSettings.at("kernels");
   if (!(enable.is_boolean() && trans_flag.is_boolean() && mode.is_number() && path.is_string() &&
         net_name.is_string() && iteration.is_number() && kernels.is_array())) {
-    MS_LOG(ERROR) << "element's type in Dump config json is invalid.";
+    MS_LOG(ERROR) << "Element's type in Dump config json is invalid.";
     dump_enable_ = false;
     return false;
   }
@@ -121,7 +121,7 @@ bool Dump::IsConfigValid(const nlohmann::json& dumpSettings) {
 bool Dump::SetDumpConfFromJsonFile() {
   const char* config_path_str = std::getenv("MINDSPORE_CONFIG_PATH");
   if (config_path_str != nullptr) {
-    MS_LOG(INFO) << "getenv MINDSPORE_CONFIG_PATH :" << config_path_str;
+    MS_LOG(INFO) << "Getenv MINDSPORE_CONFIG_PATH :" << config_path_str;
   } else {
     MS_LOG(INFO) << "No need E2E Dump. please export MINDSPORE_CONFIG_PATH eg: MINDSPORE_CONFIG_PATH=/etc";
     dump_enable_ = false;
@@ -132,7 +132,7 @@ bool Dump::SetDumpConfFromJsonFile() {
   auto id = context_ptr->device_id();
   char real_path[PATH_MAX] = {0};
   if (nullptr == realpath(config_path_str, real_path)) {
-    MS_LOG(ERROR) << "env e2e dump path error, " << config_path_str;
+    MS_LOG(ERROR) << "Env e2e dump path error, " << config_path_str;
     dump_enable_ = false;
     return false;
   }
@@ -150,20 +150,20 @@ bool Dump::SetDumpConfFromJsonFile() {
 
 bool Dump::DumpToFile(const std::string& filename, const void* data, size_t len) {
   if (filename.empty() || data == nullptr || len == 0) {
-    MS_LOG(ERROR) << "incorrect parameter.";
+    MS_LOG(ERROR) << "Incorrect parameter.";
     return false;
   }
 
   std::string realpath;
   bool ret = GetRealPath(filename, &realpath);
   if (!ret) {
-    MS_LOG(ERROR) << "get real path failed.";
+    MS_LOG(ERROR) << "Get real path failed.";
     return false;
   }
   std::ofstream fd;
   fd.open(realpath, std::ios::binary | std::ios::out);
   if (!fd.is_open()) {
-    MS_LOG(ERROR) << "open file " << realpath << " fail.";
+    MS_LOG(ERROR) << "Open file " << realpath << " fail.";
     return false;
   }
   (void)fd.write(reinterpret_cast<const char*>(data), SizeToLong(len));
@@ -182,7 +182,7 @@ bool Dump::GetRealPath(const std::string& inpath, std::string* outpath) {
   if (path_split_pos != std::string::npos) {
     std::string prefix_path = inpath.substr(0, path_split_pos);
     if (prefix_path.length() >= PATH_MAX) {
-      MS_LOG(ERROR) << "prefix path is too longer!";
+      MS_LOG(ERROR) << "Prefix path is too longer!";
       return false;
     }
     std::string last_path = inpath.substr(path_split_pos, inpath.length() - path_split_pos);
@@ -201,11 +201,11 @@ bool Dump::GetRealPath(const std::string& inpath, std::string* outpath) {
 
   if (path_split_pos == std::string::npos) {
     if (inpath.length() >= PATH_MAX) {
-      MS_LOG(ERROR) << "prefix path is too longer!";
+      MS_LOG(ERROR) << "Prefix path is too longer!";
       return false;
     }
     if (nullptr == realpath(inpath.c_str(), real_path)) {
-      MS_LOG(ERROR) << "file " << inpath << " does not exit, it will be created.";
+      MS_LOG(ERROR) << "File " << inpath << " does not exit, it will be created.";
     }
     *outpath = std::string(real_path);
   }
@@ -218,7 +218,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) {
   MS_EXCEPTION_IF_NULL(fs);
   char temp_path[PATH_MAX] = {0};
   if (path.length() > PATH_MAX) {
-    MS_LOG(ERROR) << "path lens is max than " << PATH_MAX;
+    MS_LOG(ERROR) << "Path lens is max than " << PATH_MAX;
     return false;
   }
   for (uint32_t i = 0; i < path.length(); i++) {
@@ -229,7 +229,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) {
         temp_path[i] = '\0';
         std::string path_handle(temp_path);
         if (!fs->FileExist(temp_path)) {
-          MS_LOG(INFO) << "dir " << path_handle << " does not exit, creating...";
+          MS_LOG(INFO) << "Dir " << path_handle << " does not exit, creating...";
           if (!fs->CreateDir(temp_path)) {
             MS_LOG(ERROR) << "Create " << path_handle << " dir error";
             return false;
@@ -241,7 +241,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) {
   }
 
   if (!fs->FileExist(path)) {
-    MS_LOG(INFO) << "dir " << path << " does not exit, creating...";
+    MS_LOG(INFO) << "Dir " << path << " does not exit, creating...";
     if (!fs->CreateDir(path)) {
       MS_LOG(ERROR) << "Create " << path << " dir error";
       return false;
diff --git a/mindspore/ccsrc/debug/info.cc b/mindspore/ccsrc/debug/info.cc
index 6f966e335c..5c1fc372c5 100644
--- a/mindspore/ccsrc/debug/info.cc
+++ b/mindspore/ccsrc/debug/info.cc
@@ -193,7 +193,7 @@ void TraceManager::DebugTrace(const TraceInfoPtr& trace_info) {
   }
   TraceContextPtr context = std::make_shared<TraceContext>(trace_info);
   if (trace_info->debug_info() == nullptr) {
-    MS_LOG(EXCEPTION) << "trace debug info is null";
+    MS_LOG(EXCEPTION) << "Trace debug info is null";
   }
   TraceManager::trace_context_stack_.push(context);
 }
@@ -205,7 +205,7 @@ void TraceManager::DebugTrace(const DebugInfoPtr& debug_info, const TraceInfoPtr
   auto cloned_info = trace_info->clone();
   cloned_info->set_debug_info(debug_info);
   if (cloned_info->debug_info() == nullptr) {
-    MS_LOG(EXCEPTION) << "trace debug info is null with cloned trace";
+    MS_LOG(EXCEPTION) << "Trace debug info is null with cloned trace";
   }
   TraceContextPtr context = std::make_shared<TraceContext>(cloned_info);
   TraceManager::trace_context_stack_.push(context);
diff --git a/mindspore/ccsrc/debug/trace.cc b/mindspore/ccsrc/debug/trace.cc
index 7ce13052c5..51cdd34946 100644
--- a/mindspore/ccsrc/debug/trace.cc
+++ b/mindspore/ccsrc/debug/trace.cc
@@ -89,7 +89,7 @@ std::string GetDebugInfo(const DebugInfoPtr& info, SourceLineTip tip) {
   return "";
 }
 
-// a trace info identifys a node transform, so we can trace the node transform through
+// a trace info identifies a node transform, so we can trace the node transform through
 // a link of trace info and debug info
 std::string GetInfoWithAction(const std::vector<DebugInfoPtr>& info_vec, SourceLineTip tip) {
   if (info_vec.size() < 1) {
@@ -173,7 +173,7 @@ void DumpInferStack(std::ostringstream& oss) {
     }
     auto graph_context = graph_infer->graph_context();
     if (graph_context == nullptr) {
-      MS_LOG(INFO) << "null context continue";
+      MS_LOG(INFO) << "Null context continue";
       continue;
     }
     auto graph = graph_context->func_graph();
@@ -264,7 +264,7 @@ void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename,
   param_index = 1;
   auto tagged_func_graphs = CalcTaggedFuncGraphs();
 
-  // first output grapn on the analysis stack
+  // first output graph on the analysis stack
   for (const auto& node_cfg : node_cfgs) {
     auto fg = node_cfg->context()->func_graph();
     // the graph is already output, skip it
@@ -291,7 +291,7 @@ void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename,
     ofs << "\n\n";
     (void)func_graph_set.erase(fg);
   }
-  ofs << "# num of total funcgraphs: " << exported.size();
+  ofs << "# num of total function graphs: " << exported.size();
 
   ofs.close();
 }
@@ -332,7 +332,7 @@ void GetInferStackInfo(std::ostringstream& oss) {
   MS_LOG(INFO) << "Get graph analysis information *end*";
 }
 
-// trace the graph evaluator statck
+// trace the graph evaluator stack
 static std::stack<std::pair<abstract::EvaluatorPtr, abstract::AnfNodeConfigPtr>> graph_infer_stack;
 // trace the cnode infer debug info
 static std::vector<abstract::AnfNodeConfigPtr> cnode_debug_stack{};
diff --git a/mindspore/ccsrc/debug/trace_info.cc b/mindspore/ccsrc/debug/trace_info.cc
index e2da4ffcce..b01cd15010 100644
--- a/mindspore/ccsrc/debug/trace_info.cc
+++ b/mindspore/ccsrc/debug/trace_info.cc
@@ -36,6 +36,6 @@ std::string TraceInfo::GetActionBetweenNode(const DebugInfoPtr& info) {
   } else if (debug_info()->trace_info() != nullptr) {
     return act_name + debug_info()->trace_info()->GetActionBetweenNode(info);
   }
-  return "not in the traced info";
+  return "Not in the traced info";
 }
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/ir/anf.h b/mindspore/ccsrc/ir/anf.h
index e64b1329e9..16ccb15c43 100644
--- a/mindspore/ccsrc/ir/anf.h
+++ b/mindspore/ccsrc/ir/anf.h
@@ -83,7 +83,7 @@ class AnfVisitor;
 // Methods:
 // func_graph: return FuncGraph that this AnfNode belongs to.
 // scope: return the scope namespace of this AnfNode. Set it using set_scope.
-// abstract: return the cached inferred abstract value. It cantains type, shape
+// abstract: return the cached inferred abstract value. It contains type, shape
 // value. Set New cache using set_abstract.
 // intermediate_abstract: return the cached inferring abstract value.
 // Type/Shape: return the related info of this AnfNode. When this AnfNode is an
@@ -284,7 +284,7 @@ class Parameter : public ANode {
 };
 using ParameterPtr = std::shared_ptr<Parameter>;
 
-// Value is used to represent the atomic expression metioned in BNF.
+// Value is used to represent the atomic expression mentioned in BNF.
 // It mainly be stored in ValueNode. Value and ValueNode is related definition.
 class Value : public Base {
  public:
@@ -313,7 +313,7 @@ using ValuePtr = std::shared_ptr<Value>;
 using ValuePtrList = std::vector<ValuePtr>;
 
 // ValueNode is used to hold value. Unlike CNode and Parameter, ValueNode
-// do not belong to any particular function graph.
+// does not belong to any particular function graph.
 class ValueNode : public ANode {
  public:
   explicit ValueNode(const ValuePtr &value) : value_(value) {}
diff --git a/mindspore/ccsrc/ir/dtype/number.cc b/mindspore/ccsrc/ir/dtype/number.cc
index 70d0aaeeaa..d9ef6bb3bd 100644
--- a/mindspore/ccsrc/ir/dtype/number.cc
+++ b/mindspore/ccsrc/ir/dtype/number.cc
@@ -34,19 +34,19 @@ bool Number::operator==(const Type& other) const {
 
 Int::Int(const int nbits) : Number(IntBitsToTypeId(nbits), nbits, false) {
   if (nbits != 8 && nbits != 16 && nbits != 32 && nbits != 64) {
-    MS_LOG(EXCEPTION) << "wrong number of bits.";
+    MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
 UInt::UInt(const int nbits) : Number(UIntBitsToTypeId(nbits), nbits, false) {
   if (nbits != 8 && nbits != 16 && nbits != 32 && nbits != 64) {
-    MS_LOG(EXCEPTION) << "wrong number of bits.";
+    MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
 Float::Float(const int nbits) : Number(FloatBitsToTypeId(nbits), nbits, false) {
   if (nbits != 16 && nbits != 32 && nbits != 64) {
-    MS_LOG(EXCEPTION) << "wrong number of bits.";
+    MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
diff --git a/mindspore/ccsrc/ir/dtype/type.cc b/mindspore/ccsrc/ir/dtype/type.cc
index 6169acabbc..6fbd7f8111 100644
--- a/mindspore/ccsrc/ir/dtype/type.cc
+++ b/mindspore/ccsrc/ir/dtype/type.cc
@@ -37,7 +37,7 @@ TypeId IntBitsToTypeId(const int nbits) {
     case 64:
       return kNumberTypeInt64;
     default:
-      MS_LOG(EXCEPTION) << "wrong number of bits.";
+      MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
@@ -52,7 +52,7 @@ TypeId UIntBitsToTypeId(const int nbits) {
     case 64:
       return kNumberTypeUInt64;
     default:
-      MS_LOG(EXCEPTION) << "wrong number of bits.";
+      MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
@@ -65,7 +65,7 @@ TypeId FloatBitsToTypeId(const int nbits) {
     case 64:
       return kNumberTypeFloat64;
     default:
-      MS_LOG(EXCEPTION) << "wrong number of bits.";
+      MS_LOG(EXCEPTION) << "Wrong number of bits.";
   }
 }
 
diff --git a/mindspore/ccsrc/ir/func_graph.h b/mindspore/ccsrc/ir/func_graph.h
index 13e8c9dc8f..1d58c90755 100644
--- a/mindspore/ccsrc/ir/func_graph.h
+++ b/mindspore/ccsrc/ir/func_graph.h
@@ -174,7 +174,7 @@ class FuncGraph : public FuncGraphBase {
   GraphDebugInfoPtr debug_info();
   void set_debug_info(const GraphDebugInfoPtr &info) {
     if (info == nullptr) {
-      MS_LOG(EXCEPTION) << "graph set null debug info";
+      MS_LOG(EXCEPTION) << "Graph set null debug info";
     }
     this->debug_info_ = info;
   }
diff --git a/mindspore/ccsrc/ir/manager.cc b/mindspore/ccsrc/ir/manager.cc
index 09bc7127c1..c1459014bb 100644
--- a/mindspore/ccsrc/ir/manager.cc
+++ b/mindspore/ccsrc/ir/manager.cc
@@ -817,7 +817,7 @@ void FuncGraphChildDirect::OnMoveAllCNode(FuncGraphPtr src, FuncGraphPtr dst) {
 void FuncGraphParentsDirectCollector::OnModEdge(AnfNodePtr node, int, AnfNodePtr inp, EdgeProcessDirection direction) {
   MS_EXCEPTION_IF_NULL(node);
   FuncGraphPtr fg1 = node->func_graph();
-  // possible chirld parent
+  // possible child parent
   if (IsValueNode<FuncGraph>(inp)) {
     FuncGraphPtr fg2 = GetValueNode<FuncGraphPtr>(inp);
     if (Mod(fg1, ParentProxy(fg2), direction)) {
@@ -1181,7 +1181,7 @@ bool FuncGraphJTotalComputer::SeekJ(const FuncGraphPtr& fg, const FuncGraphSetPt
   }
   path->add(fg);
 
-  // checkg if func graphs used contains J(func_graph);
+  // check if func graphs used contains J(func_graph);
   auto& used = this->manager_->func_graphs_used();
   for (auto& item : used[fg]) {
     auto used_g = item.first;
diff --git a/mindspore/ccsrc/ir/manager.h b/mindspore/ccsrc/ir/manager.h
index 8036bd68c0..dc8f656ae7 100644
--- a/mindspore/ccsrc/ir/manager.h
+++ b/mindspore/ccsrc/ir/manager.h
@@ -650,7 +650,7 @@ class FuncGraphTransaction {
   explicit FuncGraphTransaction(FuncGraphManager* manager) : manager_(manager), changes_() {
     MS_EXCEPTION_IF_NULL(manager_);
     if (!manager_->IsManaged()) {
-      MS_LOG(DEBUG) << "the manager is not managed yet";
+      MS_LOG(DEBUG) << "The manager is not managed yet";
     }
   }
 
diff --git a/mindspore/ccsrc/ir/meta_tensor.h b/mindspore/ccsrc/ir/meta_tensor.h
index 57c37851b6..3e28f29f37 100644
--- a/mindspore/ccsrc/ir/meta_tensor.h
+++ b/mindspore/ccsrc/ir/meta_tensor.h
@@ -148,7 +148,7 @@ class MetaTensor : public Value {
   //
   // The constructed MetaTensor object has the same type and shape with meta_tensor.
   //
-  // param meta_tensor An exisiting MetaTensor object.
+  // param meta_tensor An existing MetaTensor object.
   virtual MetaTensor& operator=(const MetaTensor& meta_tensor);
 
   // brief Compares two MetaTensor objects.
@@ -166,7 +166,7 @@ class MetaTensor : public Value {
   TypeId data_type() const { return data_type_; }
   std::string ToString() const override;
   std::string DumpText() const override;
-  // bried Sets the data type of a tensor in its MetaTensor.
+  // brief Sets the data type of a tensor in its MetaTensor.
   //
   // param data_type The data type of the tensor to be set.
   virtual TypeId set_data_type(const TypeId data_type) {
@@ -314,7 +314,7 @@ class Tensor : public MetaTensor {
   //
   // The constructed Tensor object has the same type and shape with tensor.
   //
-  // param tensor An exisiting Tensor object.
+  // param tensor An existing Tensor object.
   Tensor& operator=(const Tensor& tensor);
 
   // brief Compares two Tensor objects.
@@ -383,7 +383,7 @@ class Tensor : public MetaTensor {
   // return The [TypeId] of the tensor data.
   TypeId GetDataType(const py::buffer_info& buf) const;
 
-  // bried Sets the data type of a tensor.
+  // brief Sets the data type of a tensor.
   //
   // param data_type The data type of the tensor to be set.
   //
diff --git a/mindspore/ccsrc/ir/visitor.cc b/mindspore/ccsrc/ir/visitor.cc
index 70e22b2808..efebe3124a 100644
--- a/mindspore/ccsrc/ir/visitor.cc
+++ b/mindspore/ccsrc/ir/visitor.cc
@@ -43,14 +43,13 @@ VisitFuncType AnfVisitor::Match(const PrimitivePtr &prim, const std::vector<opt:
     }
 
     auto &inputs = node->cast<CNodePtr>()->inputs();
-    // infact, funcs_size == inps_size - 1
     auto funcs_size = funcs.size();
-    auto inps_size = inputs.size();
+    auto inputs_size = inputs.size();
 
     // check the inputs are matched with the predicate functions
     if (funcs_size > 0) {
       // use the predicate function list to check the number of inputs
-      if (funcs_size != (inps_size - 1)) {
+      if (funcs_size != (inputs_size - 1)) {
         return;
       }
 
@@ -63,7 +62,7 @@ VisitFuncType AnfVisitor::Match(const PrimitivePtr &prim, const std::vector<opt:
     }
 
     // visit the inputs
-    for (size_t i = 1; i < inps_size; i++) {
+    for (size_t i = 1; i < inputs_size; i++) {
       this->Visit(inputs[i]);
     }
   };
diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index 927e768bbe..e5fb0c6949 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -36,7 +36,7 @@
 #endif
 
 const char SINGLE_OP_GRAPH[] = "single_op_graph";
-// primitive unable to infer value for constant input in pynative mode
+// primitive unable to infer value for constant input in PyNative mode
 const std::unordered_set<std::string> vm_operators = {"partial", "depend"};
 
 namespace mindspore {
@@ -45,7 +45,7 @@ inline ValuePtr PyAttrValue(const py::object& obj) {
   ValuePtr converted_ret = nullptr;
   bool converted = parse::ConvertData(obj, &converted_ret);
   if (!converted) {
-    MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj));
+    MS_LOG(EXCEPTION) << "Attribute convert error with type:" << std::string(py::str(obj));
   }
   return converted_ret;
 }
@@ -67,7 +67,7 @@ void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecI
 
 OpExecInfoPtr GenerateOpExecInfo(const py::args& args) {
   if (args.size() != PY_ARGS_NUM) {
-    MS_LOG(ERROR) << "four args are needed by RunOp";
+    MS_LOG(ERROR) << "Four args are needed by RunOp";
     return nullptr;
   }
   auto op_exec_info = std::make_shared<OpExecInfo>();
@@ -145,13 +145,13 @@ py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* stat
 
 py::object RunOpInMs(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) {
   MS_EXCEPTION_IF_NULL(op_exec_info);
-  MS_LOG(INFO) << "start run op[" << op_exec_info->op_name << "] with backend policy ms";
+  MS_LOG(INFO) << "Start run op[" << op_exec_info->op_name << "] with backend policy ms";
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
   ms_context->set_enable_pynative_infer(true);
   std::string device_target = ms_context->device_target();
   if (device_target != kAscendDevice && device_target != kGPUDevice) {
-    MS_EXCEPTION(ArgumentError) << "device target [" << device_target << "] is not supported in Pynative mode";
+    MS_EXCEPTION(ArgumentError) << "Device target [" << device_target << "] is not supported in Pynative mode";
   }
   std::shared_ptr<session::SessionBasic> session = session::SessionFactory::Get().Create(device_target);
   MS_EXCEPTION_IF_NULL(session);
@@ -197,7 +197,7 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
       break;
     }
     default:
-      MS_LOG(ERROR) << "No backend configed for run op";
+      MS_LOG(ERROR) << "No backend configured for run op";
   }
   return result;
 }
@@ -240,7 +240,7 @@ py::tuple RunOp(const py::args& args) {
   }
   result = RunOpWithBackendPolicy(backend_policy, op_exec_info, &status);
   if (status != PYNATIVE_SUCCESS) {
-    MS_LOG(ERROR) << "Fail to run " << op_exec_info->op_name;
+    MS_LOG(ERROR) << "Failed to run " << op_exec_info->op_name;
     return err_ret;
   }
 
diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.cc b/mindspore/ccsrc/pynative/pynative_execute_ge.cc
index 4ed6088494..180b0006ff 100644
--- a/mindspore/ccsrc/pynative/pynative_execute_ge.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute_ge.cc
@@ -47,7 +47,7 @@ inline ValuePtr PyAttrValue(const py::object& obj) {
   ValuePtr converted_ret = nullptr;
   bool converted = parse::ConvertData(obj, &converted_ret);
   if (!converted) {
-    MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj));
+    MS_LOG(EXCEPTION) << "Attribute convert error with type:" << std::string(py::str(obj));
   }
   return converted_ret;
 }
@@ -67,7 +67,7 @@ MeTensorPtr ConvertPyObjToTensor(const py::object& obj) {
   } else if (py::isinstance<py::array>(obj)) {
     me_tensor_ptr = std::make_shared<MeTensor>(py::cast<py::array>(obj), nullptr);
   } else {
-    MS_LOG(EXCEPTION) << "run op inputs type is invalid!";
+    MS_LOG(EXCEPTION) << "Run op inputs type is invalid!";
   }
   return me_tensor_ptr;
 }
@@ -97,7 +97,7 @@ bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vec
     auto const_op_desc =
       transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW);
     if (const_op_desc == nullptr) {
-      MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!";
+      MS_LOG(ERROR) << "Create variable " << op_name << " output descriptor failed!";
       return false;
     }
     auto pointer_cast_const_op = std::static_pointer_cast<transform::Constant>(const_op);
@@ -108,7 +108,7 @@ bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vec
       continue;
     }
     if (adapter->setInput(op, op_input_idx++, const_op)) {
-      MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx;
+      MS_LOG(ERROR) << "Failed to set params, index is " << op_input_idx;
       return false;
     }
     graph_input_nodes->push_back(*const_op);
@@ -178,7 +178,7 @@ void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector<GeTensorPtr>* con
     MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]);
     auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW);
     if (ge_tensor_ptr == nullptr) {
-      MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
+      MS_LOG(EXCEPTION) << "Convert inputs to GE tensor failed in op " << op_exec_info->op_name << ".";
     }
     // set inputs for operator to build single node graph
     inputs->push_back(ge_tensor_ptr);
@@ -192,7 +192,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st
 
   for (auto& item : op_attrs) {
     if (!py::isinstance<py::str>(item.first)) {
-      MS_LOG(ERROR) << "type error in py dict convert";
+      MS_LOG(ERROR) << "Type error in py dict convert";
       return PYNATIVE_OP_ATTRS_ERR;
     }
     std::string name = py::cast<std::string>(item.first);
@@ -203,7 +203,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st
   // build graph
   GeGraphPtr graph = std::make_shared<GeGraph>(op_exec_info->op_name);
   if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) {
-    MS_LOG(ERROR) << "Fail to BuildSingleOpGraph";
+    MS_LOG(ERROR) << "Failed to BuildSingleOpGraph";
     return PYNATIVE_GRAPH_GE_BUILD_ERR;
   }
 
@@ -211,7 +211,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st
   transform::Status ret =
     transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr<transform::DfGraph>(graph));
   if (ret != transform::SUCCESS) {
-    MS_LOG(ERROR) << "Fail to AddGraph into graph manager";
+    MS_LOG(ERROR) << "Failed to AddGraph into graph manager";
     return PYNATIVE_GRAPH_MANAGER_ERR;
   }
 
@@ -289,7 +289,7 @@ py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* stat
     run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs);
   }
   if (run_ret != transform::Status::SUCCESS) {
-    MS_LOG(ERROR) << "GraphRunner Fails to Run Graph";
+    MS_LOG(ERROR) << "GraphRunner fails to run graph";
     *status = PYNATIVE_GRAPH_GE_RUN_ERR;
     return std::move(err_ret);
   }

From b94556e9a6bdfd7542816d9ac0d6802cec2fb5fa Mon Sep 17 00:00:00 2001
From: jjfeing <jiangjianfei3@huawei.com>
Date: Tue, 7 Apr 2020 09:11:45 +0800
Subject: [PATCH 103/367] adapt graphengine upgrade

---
 cmake/dependency_graphengine.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake
index d2f80c4d04..2420f47736 100644
--- a/cmake/dependency_graphengine.cmake
+++ b/cmake/dependency_graphengine.cmake
@@ -42,7 +42,6 @@ else()
     set(ASCEND_PATH /usr/local/Ascend)
     set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
     set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
-
     find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH})
     find_library(slog libslog.so ${ASCEND_DRIVER_PATH})
     find_library(mmpa libmmpa.so ${ASCEND_DRIVER_PATH})

From 3202fc0df9bafcd9065460b780b6bae30d2a3bde Mon Sep 17 00:00:00 2001
From: kingfo <wangqiuliang@huawei.com>
Date: Fri, 3 Apr 2020 12:02:41 +0800
Subject: [PATCH 104/367] refactor callback for ge backend

---
 mindspore/ccsrc/CMakeLists.txt            |  18 ++-
 mindspore/ccsrc/pipeline/pipeline.cc      |   1 -
 mindspore/ccsrc/transform/graph_runner.cc |   3 +
 mindspore/ccsrc/utils/callbacks.cc        | 153 ------------------
 mindspore/ccsrc/utils/callbacks.h         |   8 -
 mindspore/ccsrc/utils/callbacks_ge.cc     | 182 ++++++++++++++++++++++
 mindspore/ccsrc/utils/callbacks_ge.h      |  38 +++++
 mindspore/ccsrc/vm/backend.cc             |   3 +
 tests/ut/cpp/utils/callback_test.cc       |   3 +
 9 files changed, 246 insertions(+), 163 deletions(-)
 create mode 100644 mindspore/ccsrc/utils/callbacks_ge.cc
 create mode 100644 mindspore/ccsrc/utils/callbacks_ge.h

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index fdefdce6a2..befe86f3c0 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -87,7 +87,22 @@ ms_build_flatbuffers("${FLATBUFFER_IN}" "${FLATBUFFER_IN}" GENERATED_OUTPUT_DIR
 file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "ir/*.cc"
         "ir/dtype/*.cc"
-        "utils/*.cc"
+        "utils/context/ms_context.cc"
+        "utils/symbolic.cc"
+        "utils/tensorprint_utils.cc"
+        "utils/convert_utils.cc"
+        "utils/graph_utils.cc"
+        "utils/misc.cc"
+        "utils/callbacks.cc"
+        "utils/profile.cc"
+        "utils/base_ref.cc"
+        "utils/summary/event_writer.cc"
+        "utils/log_adapter.cc"
+        "utils/comm_manager.cc"
+        "utils/any.cc"
+        "utils/config_manager.cc"
+        "utils/system/file_system.cc"
+        "utils/system/crc32c.cc"
         "common/*.cc"
         "parallel/*.cc"
         "pipeline/pipeline.cc"
@@ -173,6 +188,7 @@ if(ENABLE_GE)
     file(GLOB_RECURSE GE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
             "transform/*.cc"
             "pynative/pynative_execute_ge.cc"
+            "utils/callbacks_ge.cc"
             "pipeline/pipeline_ge.cc"
             )
     list(APPEND MINDSPORE_SRC_LIST ${GE_SRC_LIST})
diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 0d7790fb36..003d4c15e9 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -616,7 +616,6 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
     return ExecDFGraph(info_, args, phase_s);
   }
 #else
-  MS_LOG(WARNING) << "In ut test " << size << phase_s;
   if (backend == "ge") {
     std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
     if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
diff --git a/mindspore/ccsrc/transform/graph_runner.cc b/mindspore/ccsrc/transform/graph_runner.cc
index e77b1bcd73..f1f270cdb6 100644
--- a/mindspore/ccsrc/transform/graph_runner.cc
+++ b/mindspore/ccsrc/transform/graph_runner.cc
@@ -24,6 +24,9 @@
 #include "utils/callbacks.h"
 #include "utils/utils.h"
 #include "./common.h"
+#ifdef ENABLE_GE
+#include "utils/callbacks_ge.h"
+#endif
 
 #ifdef NO_GE_CLIENT
 namespace ge {
diff --git a/mindspore/ccsrc/utils/callbacks.cc b/mindspore/ccsrc/utils/callbacks.cc
index cdee0be82d..03c6322afe 100644
--- a/mindspore/ccsrc/utils/callbacks.cc
+++ b/mindspore/ccsrc/utils/callbacks.cc
@@ -20,10 +20,6 @@
 #include <memory>
 #include <vector>
 #include "pybind11/pybind11.h"
-#ifdef ENABLE_GE
-#include "transform/df_graph_manager.h"
-#include "transform/util.h"
-#endif
 #include "pipeline/parse/data_converter.h"
 #include "pipeline/parse/python_adapter.h"
 #include "utils/visible.h"
@@ -38,155 +34,6 @@ const char kSummary[] = "Summary";
 const char kCheckPoint[] = "Save";
 const int ONE_SHAPE = 1;
 
-#ifdef ENABLE_GE
-using mindspore::transform::Status;
-using mindspore::transform::TransformUtil;
-
-bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name,
-                       const std::shared_ptr<std::vector<int>>& shape) {
-  if (graph == nullptr) {
-    MS_LOG(ERROR) << "Graph is null, can not get graph parameter";
-    return false;
-  }
-
-  auto parameter_nodes = graph->parameters();
-  for (auto& node : parameter_nodes) {
-    ParameterPtr param_node = std::static_pointer_cast<Parameter>(node);
-    if (param_node == nullptr) {
-      MS_LOG(ERROR) << "Parameter node is null, can not get graph parameter";
-      return false;
-    }
-    if (param_node->name() == param_name) {
-      py::object parameter = param_node->default_param();
-      ValuePtr value = parse::data_converter::PyDataToValue(parameter);
-      TensorPtr tensor = std::dynamic_pointer_cast<tensor::Tensor>(value);
-      if (tensor == nullptr) {
-        shape->push_back(ONE_SHAPE);
-      } else {
-        *shape = tensor->shape();
-      }
-      return true;
-    }
-  }
-  MS_LOG(ERROR) << "Can not find parameter of name:" << param_name;
-  return false;
-}
-
-static TensorPtr GetMeTensorTransformed(uint32_t graph_id, const std::string& parameter_name,
-                                        const std::shared_ptr<ge::Tensor>& ge_tensor_ptr) {
-  FuncGraphPtr anf_graph = transform::DfGraphManager::GetInstance().GetAnfGraph(graph_id);
-  if (anf_graph == nullptr) {
-    MS_LOG(ERROR) << "Get anf graph failed during callback";
-    return nullptr;
-  }
-
-  std::shared_ptr<std::vector<int>> parameter_shape_ptr = std::make_shared<std::vector<int>>();
-  if (!GetParameterShape(anf_graph, parameter_name, parameter_shape_ptr)) {
-    MS_LOG(ERROR) << "Can not get parameter shape during callback";
-    return nullptr;
-  }
-
-  return TransformUtil::ConvertGeTensor(ge_tensor_ptr, *parameter_shape_ptr);
-}
-
-uint32_t CheckpointSaveCallback(uint32_t graph_id, const std::map<std::string, ge::Tensor>& params_list) {
-  // Acquire GIL before calling Python code
-  py::gil_scoped_acquire acquire;
-
-  MS_LOG(DEBUG) << "Start the checkpoint save callback function in checkpoint save process.";
-  py::list parameter_list = py::list();
-  for (auto& item : params_list) {
-    std::string name = item.first;
-    std::shared_ptr<ge::Tensor> ge_tensor_ptr = std::make_shared<ge::Tensor>(item.second);
-    TensorPtr tensor_ptr = GetMeTensorTransformed(graph_id, name, ge_tensor_ptr);
-    if (tensor_ptr == nullptr) {
-      MS_LOG(EXCEPTION) << "Transform ge tensor to me tensor failed";
-    }
-    py::dict param_dict;
-    param_dict["name"] = name;
-    param_dict["data"] = tensor_ptr;
-    parameter_list.append(param_dict);
-  }
-  py::bool_ ret =
-    parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_CHECKPOINT, parameter_list);
-  auto bool_ret = py::cast<bool>(ret);
-
-  uint32_t status = Status::SUCCESS;
-  if (!bool_ret) {
-    status = Status::FAILED;
-    MS_LOG(ERROR) << "python checkpoint return false during callback";
-  }
-  return status;
-}
-
-static TensorPtr GetMeTensorForSummary(const std::string& name, const std::shared_ptr<ge::Tensor>& ge_tensor_ptr) {
-  // confirm the type by name
-  // Format: xxx[:Scalar] xxx[:Image] xxx[:Tensor]
-  if (name.empty()) {
-    MS_LOG(EXCEPTION) << "The summary name is empty.";
-  }
-  auto bpos = name.rfind("[:");
-  if (bpos >= name.size()) {
-    MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid.";
-  }
-  auto tname = name.substr(bpos);
-  if (tname == "[:Scalar]") {
-    MS_LOG(DEBUG) << "The summary(" << name << ") is Scalar";
-    // process the scalar type summary
-    // Because the ge tensor is dim = 4, so set the (1,1,1,1)-->(1,)
-    // We do the (1,) shape is scalar
-    auto shape = std::vector<int>({ONE_SHAPE});
-    return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape);
-  }
-  if (tname == "[:Tensor]") {
-    MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor";
-    // process the tensor summary
-    // Now we can't get the real shape, so we keep same shape with GE
-    return TransformUtil::ConvertGeTensor(ge_tensor_ptr);
-  }
-  if (tname == "[:Image]") {
-    MS_LOG(DEBUG) << "The summary(" << name << ") is Image";
-    // process the Image summary
-    // Image dim = 4, is same with ge, so we keep same shape with GE
-    return TransformUtil::ConvertGeTensor(ge_tensor_ptr);
-  }
-
-  MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid.";
-}
-
-// Cache the summary callback data
-// Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...]
-uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::string, ge::Tensor>& params_list) {
-  // Acquire GIL before calling Python code
-  py::gil_scoped_acquire acquire;
-
-  MS_LOG(DEBUG) << "Start the summary save callback function for graph " << graph_id << ".";
-  py::list summary_list = py::list();
-  MS_LOG(DEBUG) << "Param list size = " << params_list.size();
-  for (auto& item : params_list) {
-    std::string tag_name = item.first;
-    std::shared_ptr<ge::Tensor> ge_tensor_ptr = std::make_shared<ge::Tensor>(item.second);
-    TensorPtr tensor_ptr = GetMeTensorForSummary(tag_name, ge_tensor_ptr);
-    if (tensor_ptr == nullptr) {
-      MS_LOG(EXCEPTION) << "ConvertGeTensor return tensor is null";
-    }
-    py::dict summary_value_dict;
-    summary_value_dict["name"] = tag_name;
-    summary_value_dict["data"] = tensor_ptr;
-    summary_list.append(summary_value_dict);
-  }
-
-  py::bool_ ret = parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_SUMMARY, summary_list);
-  auto bool_ret = py::cast<bool>(ret);
-  if (!bool_ret) {
-    MS_LOG(ERROR) << "Python checkpoint return false during callback";
-    return Status::FAILED;
-  }
-  MS_LOG(DEBUG) << "End the summary save callback function.";
-  return Status::SUCCESS;
-}
-#endif
-
 // Cache the summary callback data from ME session
 // Remove the GE module on new architecture
 // Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...]
diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h
index 778b0a9ba2..a1e4e75d5b 100644
--- a/mindspore/ccsrc/utils/callbacks.h
+++ b/mindspore/ccsrc/utils/callbacks.h
@@ -21,10 +21,6 @@
 #include <vector>
 #include <memory>
 #include "ir/meta_tensor.h"
-#ifdef ENABLE_GE
-#include "transform/types.h"
-#include "transform/util.h"
-#endif
 
 namespace mindspore {
 namespace callbacks {
@@ -45,10 +41,6 @@ const int kCallbackFalied = 1;
 
 bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name,
                        const std::shared_ptr<std::vector<int>>& shape);
-#ifdef ENABLE_GE
-uint32_t CheckpointSaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
-uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
-#endif
 uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, TensorPtr>&);
 
 }  // namespace callbacks
diff --git a/mindspore/ccsrc/utils/callbacks_ge.cc b/mindspore/ccsrc/utils/callbacks_ge.cc
new file mode 100644
index 0000000000..50fd2f0b11
--- /dev/null
+++ b/mindspore/ccsrc/utils/callbacks_ge.cc
@@ -0,0 +1,182 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "utils/callbacks_ge.h"
+#include "pybind11/pybind11.h"
+#include "transform/df_graph_manager.h"
+#include "transform/util.h"
+#include "pipeline/parse/data_converter.h"
+#include "pipeline/parse/python_adapter.h"
+#include "utils/visible.h"
+
+namespace mindspore {
+namespace callbacks {
+
+const char PYTHON_MOD_CALLBACK_MODULE[] = "mindspore.train.callback";
+const char PYTHON_FUN_PROCESS_CHECKPOINT[] = "_checkpoint_cb_for_save_op";
+const char PYTHON_FUN_PROCESS_SUMMARY[] = "_summary_cb_for_save_op";
+const char kSummary[] = "Summary";
+const char kCheckPoint[] = "Save";
+const int ONE_SHAPE = 1;
+
+using mindspore::transform::Status;
+using mindspore::transform::TransformUtil;
+
+bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name,
+                       const std::shared_ptr<std::vector<int>>& shape) {
+  if (graph == nullptr) {
+    MS_LOG(ERROR) << "Graph is null, can not get graph parameter";
+    return false;
+  }
+
+  auto parameter_nodes = graph->parameters();
+  for (auto& node : parameter_nodes) {
+    ParameterPtr param_node = std::static_pointer_cast<Parameter>(node);
+    if (param_node == nullptr) {
+      MS_LOG(ERROR) << "Parameter node is null, can not get graph parameter";
+      return false;
+    }
+    if (param_node->name() == param_name) {
+      py::object parameter = param_node->default_param();
+      ValuePtr value = parse::data_converter::PyDataToValue(parameter);
+      TensorPtr tensor = std::dynamic_pointer_cast<tensor::Tensor>(value);
+      if (tensor == nullptr) {
+        shape->push_back(ONE_SHAPE);
+      } else {
+        *shape = tensor->shape();
+      }
+      return true;
+    }
+  }
+  MS_LOG(ERROR) << "Can not find parameter of name:" << param_name;
+  return false;
+}
+
+static TensorPtr GetMeTensorTransformed(uint32_t graph_id, const std::string& parameter_name,
+                                        const std::shared_ptr<ge::Tensor>& ge_tensor_ptr) {
+  FuncGraphPtr anf_graph = transform::DfGraphManager::GetInstance().GetAnfGraph(graph_id);
+  if (anf_graph == nullptr) {
+    MS_LOG(ERROR) << "Get anf graph failed during callback";
+    return nullptr;
+  }
+
+  std::shared_ptr<std::vector<int>> parameter_shape_ptr = std::make_shared<std::vector<int>>();
+  if (!GetParameterShape(anf_graph, parameter_name, parameter_shape_ptr)) {
+    MS_LOG(ERROR) << "Can not get parameter shape during callback";
+    return nullptr;
+  }
+
+  return TransformUtil::ConvertGeTensor(ge_tensor_ptr, *parameter_shape_ptr);
+}
+
+uint32_t CheckpointSaveCallback(uint32_t graph_id, const std::map<std::string, ge::Tensor>& params_list) {
+  // Acquire GIL before calling Python code
+  py::gil_scoped_acquire acquire;
+
+  MS_LOG(DEBUG) << "Start the checkpoint save callback function in checkpoint save process.";
+  py::list parameter_list = py::list();
+  for (auto& item : params_list) {
+    std::string name = item.first;
+    std::shared_ptr<ge::Tensor> ge_tensor_ptr = std::make_shared<ge::Tensor>(item.second);
+    TensorPtr tensor_ptr = GetMeTensorTransformed(graph_id, name, ge_tensor_ptr);
+    if (tensor_ptr == nullptr) {
+      MS_LOG(EXCEPTION) << "Transform ge tensor to me tensor failed";
+    }
+    py::dict param_dict;
+    param_dict["name"] = name;
+    param_dict["data"] = tensor_ptr;
+    parameter_list.append(param_dict);
+  }
+  py::bool_ ret =
+    parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_CHECKPOINT, parameter_list);
+  auto bool_ret = py::cast<bool>(ret);
+
+  uint32_t status = Status::SUCCESS;
+  if (!bool_ret) {
+    status = Status::FAILED;
+    MS_LOG(ERROR) << "Python checkpoint return false during callback";
+  }
+  return status;
+}
+
+static TensorPtr GetMeTensorForSummary(const std::string& name, const std::shared_ptr<ge::Tensor>& ge_tensor_ptr) {
+  // confirm the type by name
+  // Format: xxx[:Scalar] xxx[:Image] xxx[:Tensor]
+  if (name.empty()) {
+    MS_LOG(EXCEPTION) << "The summary name is empty.";
+  }
+  auto bpos = name.rfind("[:");
+  if (bpos >= name.size()) {
+    MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid.";
+  }
+  auto tname = name.substr(bpos);
+  if (tname == "[:Scalar]") {
+    MS_LOG(DEBUG) << "The summary(" << name << ") is Scalar";
+    // process the scalar type summary
+    // Because the ge tensor is dim = 4, so set the (1,1,1,1)-->(1,)
+    // We do the (1,) shape is scalar
+    auto shape = std::vector<int>({ONE_SHAPE});
+    return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape);
+  }
+  if (tname == "[:Tensor]") {
+    MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor";
+    // process the tensor summary
+    // Now we can't get the real shape, so we keep same shape with GE
+    return TransformUtil::ConvertGeTensor(ge_tensor_ptr);
+  }
+  if (tname == "[:Image]") {
+    MS_LOG(DEBUG) << "The summary(" << name << ") is Image";
+    // process the Image summary
+    // Image dim = 4, is same with ge, so we keep same shape with GE
+    return TransformUtil::ConvertGeTensor(ge_tensor_ptr);
+  }
+
+  MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid.";
+}
+
+// Cache the summary callback data
+// Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...]
+uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map<std::string, ge::Tensor>& params_list) {
+  // Acquire GIL before calling Python code
+  py::gil_scoped_acquire acquire;
+
+  MS_LOG(DEBUG) << "Start the summary save callback function for graph " << graph_id << ".";
+  py::list summary_list = py::list();
+  MS_LOG(DEBUG) << "Param list size = " << params_list.size();
+  for (auto& item : params_list) {
+    std::string tag_name = item.first;
+    std::shared_ptr<ge::Tensor> ge_tensor_ptr = std::make_shared<ge::Tensor>(item.second);
+    TensorPtr tensor_ptr = GetMeTensorForSummary(tag_name, ge_tensor_ptr);
+    if (tensor_ptr == nullptr) {
+      MS_LOG(EXCEPTION) << "ConvertGeTensor return tensor is null";
+    }
+    py::dict summary_value_dict;
+    summary_value_dict["name"] = tag_name;
+    summary_value_dict["data"] = tensor_ptr;
+    summary_list.append(summary_value_dict);
+  }
+
+  py::bool_ ret = parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_SUMMARY, summary_list);
+  auto bool_ret = py::cast<bool>(ret);
+  if (!bool_ret) {
+    MS_LOG(ERROR) << "Python checkpoint return false during callback";
+    return Status::FAILED;
+  }
+  MS_LOG(DEBUG) << "End the summary save callback function.";
+  return Status::SUCCESS;
+}
+}  // namespace callbacks
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/utils/callbacks_ge.h b/mindspore/ccsrc/utils/callbacks_ge.h
new file mode 100644
index 0000000000..750ec74666
--- /dev/null
+++ b/mindspore/ccsrc/utils/callbacks_ge.h
@@ -0,0 +1,38 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_
+#define MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_
+
+#include <map>
+#include <vector>
+#include <string>
+#include <memory>
+#include "transform/types.h"
+#include "transform/util.h"
+#include "ir/meta_tensor.h"
+
+namespace mindspore {
+namespace callbacks {
+
+using mindspore::tensor::TensorPtr;
+
+uint32_t CheckpointSaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
+uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, ge::Tensor>&);
+
+}  // namespace callbacks
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_
diff --git a/mindspore/ccsrc/vm/backend.cc b/mindspore/ccsrc/vm/backend.cc
index 28609abfa9..9355cca99c 100644
--- a/mindspore/ccsrc/vm/backend.cc
+++ b/mindspore/ccsrc/vm/backend.cc
@@ -24,6 +24,9 @@
 #include "utils/graph_utils.h"
 #include "session/session_factory.h"
 #include "common/utils.h"
+#ifdef ENABLE_GE
+#include "utils/callbacks_ge.h"
+#endif
 
 namespace mindspore {
 namespace compile {
diff --git a/tests/ut/cpp/utils/callback_test.cc b/tests/ut/cpp/utils/callback_test.cc
index 758e99ff59..c63f68f000 100644
--- a/tests/ut/cpp/utils/callback_test.cc
+++ b/tests/ut/cpp/utils/callback_test.cc
@@ -22,6 +22,9 @@
 #include "pipeline/parse/python_adapter.h"
 #include "transform/df_graph_manager.h"
 #include "debug/draw.h"
+#ifdef ENABLE_GE
+#include "utils/callbacks_ge.h"
+#endif
 
 namespace mindspore {
 namespace python_adapter = mindspore::parse::python_adapter;

From e88bf9ca7912c53785301c4d725e248edc30daba Mon Sep 17 00:00:00 2001
From: chenhaozhe <chenhaozhe1@huawei.com>
Date: Sat, 4 Apr 2020 15:52:58 +0800
Subject: [PATCH 105/367] change log level in pipeline_ge

---
 mindspore/ccsrc/pipeline/pipeline_ge.cc | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
index abf55bb2d8..2f68935591 100644
--- a/mindspore/ccsrc/pipeline/pipeline_ge.cc
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -227,10 +227,12 @@ bool AddDFGraph(const std::map<std::string, ExecutorInfoPtr>& info, const py::di
     (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph());
   }
   (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
-  (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
   (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
 
-  DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
+  Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
+  if (ret == Status::SUCCESS) {
+    DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
+  }
 
   return true;
 }
@@ -389,8 +391,7 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
                                         const std::string& phase) {
   std::vector<GeTensorPtr> ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW);
   if (ge_tensors.size() != inputs.size()) {
-    MS_LOG(ERROR) << "Args convert to ge tensor error";
-    return nullptr;
+    MS_LOG(EXCEPTION) << "Convert me args to ge tensor error.";
   }
 
   std::vector<GeTensorPtr> ge_outputs;
@@ -401,8 +402,7 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
   auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner();
 
   if (graph_runner == nullptr) {
-    MS_LOG(ERROR) << "Can not found GraphRunner";
-    return nullptr;
+    MS_LOG(EXCEPTION) << "Can not found GraphRunner.";
   }
 
   {
@@ -419,7 +419,7 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr& graph, const std::ve
 
   std::vector<MeTensorPtr> me_outputs = TransformUtil::ConvertGeTensors(ge_outputs);
   if (me_outputs.size() != ge_outputs.size()) {
-    MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed";
+    MS_LOG(WARNING) << "Convert output Ge tensor to Me tensor failed";
   }
 
   py::tuple outputs(me_outputs.size());

From 69ed72f10da5f64a63ae78669e2cddf4e4270b59 Mon Sep 17 00:00:00 2001
From: fary86 <fary.fanrui@huawei.com>
Date: Thu, 2 Apr 2020 07:50:13 +0800
Subject: [PATCH 106/367] Add primitive name to param error message for
 math_ops.py

---
 mindspore/_checkparam.py                      | 126 ++-
 mindspore/ops/_utils/broadcast.py             |   6 +-
 mindspore/ops/operations/array_ops.py         |  13 +-
 mindspore/ops/operations/math_ops.py          | 206 +++--
 mindspore/ops/primitive.py                    |   3 +
 .../components/executor/check_exceptions.py   |  11 +-
 .../utils/config_util.py                      |   5 +-
 .../utils/facade_util.py                      |   8 +-
 .../mindspore_test_framework/utils/keyword.py |   1 +
 tests/ut/python/ops/test_array_ops.py         |   2 +-
 tests/ut/python/ops/test_math_ops_check.py    | 751 ++++++++++++++++++
 11 files changed, 1003 insertions(+), 129 deletions(-)
 create mode 100755 tests/ut/python/ops/test_math_ops_check.py

diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py
index 61b7cc9818..d553bcd364 100644
--- a/mindspore/_checkparam.py
+++ b/mindspore/_checkparam.py
@@ -15,6 +15,7 @@
 """Check parameters."""
 import re
 from enum import Enum
+from functools import reduce
 from itertools import repeat
 from collections import Iterable
 
@@ -93,8 +94,131 @@ rel_strs = {
 }
 
 
+class Validator:
+    """validator for checking input parameters"""
+
+    @staticmethod
+    def check(arg_name, arg_value, value_name, value, rel=Rel.EQ, prim_name=None):
+        """
+        Method for judging relation between two int values or list/tuple made up of ints.
+
+        This method is not suitable for judging relation between floats, since it does not consider float error.
+        """
+
+        rel_fn = Rel.get_fns(rel)
+        if not rel_fn(arg_value, value):
+            rel_str = Rel.get_strs(rel).format(f'{value_name}: {value}')
+            msg_prefix = f'For {prim_name} the' if prim_name else "The"
+            raise ValueError(f'{msg_prefix} `{arg_name}` should be {rel_str}, but got {arg_value}.')
+
+    @staticmethod
+    def check_integer(arg_name, arg_value, value, rel, prim_name):
+        """Integer value judgment."""
+        rel_fn = Rel.get_fns(rel)
+        type_mismatch = not isinstance(arg_value, int) or isinstance(arg_value, bool)
+        if type_mismatch or not rel_fn(arg_value, value):
+            rel_str = Rel.get_strs(rel).format(value)
+            raise ValueError(f'For {prim_name} the `{arg_name}` should be an int and must {rel_str},'
+                             f' but got {arg_value}.')
+        return arg_value
+
+    @staticmethod
+    def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name):
+        """Method for checking whether an int value is in some range."""
+        rel_fn = Rel.get_fns(rel)
+        type_mismatch = not isinstance(arg_value, int)
+        if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit):
+            rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit)
+            raise ValueError(f'For \'{prim_name}\' the `{arg_name}` should be an int in range {rel_str},'
+                             f' but got {arg_value}.')
+        return arg_value
+
+    @staticmethod
+    def check_subclass(arg_name, type_, template_type, prim_name):
+        """Check whether some type is sublcass of another type"""
+        if not isinstance(template_type, Iterable):
+            template_type = (template_type,)
+        if not any([mstype.issubclass_(type_, x) for x in template_type]):
+            type_str = (type(type_).__name__ if isinstance(type_, (tuple, list)) else "") + str(type_)
+            raise TypeError(f'For \'{prim_name}\' the type of `{arg_name}` should be subclass'
+                            f' of {",".join((str(x) for x in template_type))}, but got {type_str}.')
+
+    @staticmethod
+    def check_tensor_type_same(args, valid_values, prim_name):
+        """check whether the element types of input tensors are the same."""
+        def _check_tensor_type(arg):
+            arg_key, arg_val = arg
+            Validator.check_subclass(arg_key, arg_val, mstype.tensor, prim_name)
+            elem_type = arg_val.element_type()
+            if not elem_type in valid_values:
+                raise TypeError(f'For \'{prim_name}\' element type of `{arg_key}` should be in {valid_values},'
+                                f' but `{arg_key}` is {elem_type}.')
+            return (arg_key, elem_type)
+
+        def _check_types_same(arg1, arg2):
+            arg1_name, arg1_type = arg1
+            arg2_name, arg2_type = arg2
+            if arg1_type != arg2_type:
+                raise TypeError(f'For \'{prim_name}\' element type of `{arg2_name}` should be same as `{arg1_name}`,'
+                                f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.')
+            return arg1
+
+        elem_types = map(_check_tensor_type, args.items())
+        reduce(_check_types_same, elem_types)
+
+
+    @staticmethod
+    def check_scalar_or_tensor_type_same(args, valid_values, prim_name):
+        """check whether the types of inputs are the same. if the input args are tensors, check their element types"""
+        def _check_argument_type(arg):
+            arg_key, arg_val = arg
+            if isinstance(arg_val, type(mstype.tensor)):
+                arg_val = arg_val.element_type()
+            if not arg_val in valid_values:
+                raise TypeError(f'For \'{prim_name}\' the `{arg_key}` should be in {valid_values},'
+                                f' but `{arg_key}` is {arg_val}.')
+            return arg
+
+        def _check_types_same(arg1, arg2):
+            arg1_name, arg1_type = arg1
+            arg2_name, arg2_type = arg2
+            excp_flag = False
+            if isinstance(arg1_type, type(mstype.tensor)) and isinstance(arg2_type, type(mstype.tensor)):
+                arg1_type = arg1_type.element_type()
+                arg2_type = arg2_type.element_type()
+            elif not (isinstance(arg1_type, type(mstype.tensor)) or isinstance(arg2_type, type(mstype.tensor))):
+                pass
+            else:
+                excp_flag = True
+
+            if excp_flag or arg1_type != arg2_type:
+                raise TypeError(f'For \'{prim_name}\' type of `{arg2_name}` should be same as `{arg1_name}`,'
+                                f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.')
+            return arg1
+        reduce(_check_types_same, map(_check_argument_type, args.items()))
+
+    @staticmethod
+    def check_value_type(arg_name, arg_value, valid_types, prim_name):
+        """Check whether a values is instance of some types."""
+        def raise_error_msg():
+            """func for raising error message when check failed"""
+            type_names = [t.__name__ for t in valid_types]
+            num_types = len(valid_types)
+            raise TypeError(f'For \'{prim_name}\' the type of `{arg_name}` should be '
+                            f'{"one of " if num_types > 1 else ""}'
+                            f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.')
+
+        # Notice: bool is subclass of int, so `check_value_type('x', True, [int])` will check fail, and
+        #         `check_value_type('x', True, [bool, int])` will check pass
+        if isinstance(arg_value, bool) and bool not in tuple(valid_types):
+            raise_error_msg()
+        if isinstance(arg_value, tuple(valid_types)):
+            return arg_value
+        raise_error_msg()
+
+
 class ParamValidator:
-    """Parameter validator."""
+    """Parameter validator. NOTICE: this class will be replaced by `class Validator`"""
 
     @staticmethod
     def equal(arg_name, arg_value, cond_str, cond):
diff --git a/mindspore/ops/_utils/broadcast.py b/mindspore/ops/_utils/broadcast.py
index 2c9eb8a54b..c71158de57 100644
--- a/mindspore/ops/_utils/broadcast.py
+++ b/mindspore/ops/_utils/broadcast.py
@@ -16,13 +16,14 @@
 """broadcast"""
 
 
-def _get_broadcast_shape(x_shape, y_shape):
+def _get_broadcast_shape(x_shape, y_shape, prim_name):
     """
     Doing broadcast between tensor x and tensor y.
 
     Args:
         x_shape (list): The shape of tensor x.
         y_shape (list): The shape of tensor y.
+        prim_name (str): Primitive name.
 
     Returns:
         List, the shape that broadcast between tensor x and tensor y.
@@ -50,7 +51,8 @@ def _get_broadcast_shape(x_shape, y_shape):
         elif x_shape[i] == y_shape[i]:
             broadcast_shape_back.append(x_shape[i])
         else:
-            raise ValueError("The x_shape {} and y_shape {} can not broadcast.".format(x_shape, y_shape))
+            raise ValueError("For '{}' the x_shape {} and y_shape {} can not broadcast.".format(
+                prim_name, x_shape, y_shape))
 
     broadcast_shape_front = y_shape[0: y_len - length] if length == x_len else x_shape[0: x_len - length]
     broadcast_shape = broadcast_shape_front + broadcast_shape_back
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 3dabf2ab0a..53997ecce0 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -28,9 +28,16 @@ from ..._checkparam import ParamValidator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
 from ...common.tensor import Tensor
-from ..operations.math_ops import _check_infer_attr_reduce, _infer_shape_reduce
+from ..operations.math_ops import _infer_shape_reduce
 from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 
+def _check_infer_attr_reduce(axis, keep_dims):
+    validator.check_type('keep_dims', keep_dims, [bool])
+    validator.check_type('axis', axis, [int, tuple])
+    if isinstance(axis, tuple):
+        for index, value in enumerate(axis):
+            validator.check_type('axis[%d]' % index, value, [int])
+
 
 class ExpandDims(PrimitiveWithInfer):
     """
@@ -1091,7 +1098,7 @@ class ArgMaxWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims)
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):
@@ -1137,7 +1144,7 @@ class ArgMinWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims)
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index b7373416f9..1411bcb8e6 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -19,7 +19,7 @@ import numpy as np
 from ..._c_expression import signature_rw as sig_rw
 from ..._c_expression import signature_kind as sig_kind
 from ..._c_expression import signature_dtype as sig_dtype
-from ..._checkparam import ParamValidator as validator
+from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
 from ...common.tensor import Tensor
@@ -27,16 +27,16 @@ from .._utils import _get_broadcast_shape
 from ..primitive import PrimitiveWithInfer, prim_attr_register
 
 
-def _infer_shape_reduce(x, axis, keep_dims):
+def _infer_shape_reduce(x, axis, keep_dims, prim_name):
     """Common infer for reduce operator"""
 
     def reduce_one_axis(one_axis):
-        validator.check_int_range('axis', one_axis, -dim, dim, Rel.INC_LEFT)
+        validator.check_int_range('axis', one_axis, -dim, dim, Rel.INC_LEFT, prim_name)
         if one_axis < 0:
             one_axis += dim
         axis_reduce.add(one_axis)
 
-    validator.check_type('axis', axis, [int, tuple, list])
+    validator.check_value_type('axis', axis, [int, tuple, list], prim_name)
     dim = len(x)
     axis_reduce = set()
 
@@ -48,7 +48,7 @@ def _infer_shape_reduce(x, axis, keep_dims):
                 return [1] * dim
             return []
         for index, one_axis in enumerate(axis):
-            validator.check_type('axis[%d]' % index, one_axis, [int])
+            validator.check_value_type('axis[%d]' % index, one_axis, [int], prim_name)
             reduce_one_axis(one_axis)
 
     out_shape = []
@@ -61,14 +61,6 @@ def _infer_shape_reduce(x, axis, keep_dims):
     return out_shape
 
 
-def _check_infer_attr_reduce(axis, keep_dims):
-    validator.check_type('keep_dims', keep_dims, [bool])
-    validator.check_type('axis', axis, [int, tuple])
-    if isinstance(axis, tuple):
-        for index, value in enumerate(axis):
-            validator.check_type('axis[%d]' % index, value, [int])
-
-
 class _BinaryOp(PrimitiveWithInfer):
     """
     Define binary operators.
@@ -82,7 +74,7 @@ class _BinaryOp(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
     def infer_shape(self, x_shape, y_shape):
-        return _get_broadcast_shape(x_shape, y_shape)
+        return _get_broadcast_shape(x_shape, y_shape, self.prim_name())
 
 
 class _MathBinaryOp(_BinaryOp):
@@ -91,15 +83,13 @@ class _MathBinaryOp(_BinaryOp):
     """
 
     @staticmethod
-    def do_infer_dtype(x_dtype, y_dtype, valid_dtype=mstype.number_type):
+    def do_infer_dtype(x_dtype, y_dtype, valid_dtype=mstype.number_type, prim_name=None):
         args_type = {"x": x_dtype, "y": y_dtype}
-        validator.check_args_tensor(args_type)
-        args_dtype = {"x_dtype": x_dtype, "y_dtype": y_dtype}
-        validator.check_type_same(args_dtype, valid_dtype)
+        validator.check_tensor_type_same(args_type, valid_dtype, prim_name)
         return x_dtype
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype)
+        return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type, self.prim_name())
 
 
 class TensorAdd(_MathBinaryOp):
@@ -166,7 +156,7 @@ class AssignAdd(PrimitiveWithInfer):
 
     def infer_dtype(self, variable, value):
         args = {"value": value}
-        validator.check_type_same(args, mstype.number_type)
+        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name())
         return value
 
 
@@ -207,7 +197,7 @@ class AssignSub(PrimitiveWithInfer):
 
     def infer_dtype(self, variable, value):
         args = {"value": value}
-        validator.check_type_same(args, mstype.number_type)
+        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name())
         return value
 
 
@@ -228,15 +218,16 @@ class _Reduce(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, keep_dims=False):
         """init Reduce"""
-        validator.check_type('keep_dims', keep_dims, [bool])
+        validator.check_value_type('keep_dims', keep_dims, [bool], self.prim_name())
         self.init_prim_io_names(inputs=['input_x', 'axis'], outputs=['y'])
 
     def do_infer(self, input_x, axis, valid_dtype=mstype.number_type):
         axis_v = axis['value']
         input_shp = input_x['shape']
-        validator.check_subclass('input_x', input_x['dtype'], mstype.tensor)
-        validator.check_typename('input_x', input_x['dtype'], valid_dtype)
-        input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims)
+        args = {'input_x': input_x['dtype']}
+        validator.check_tensor_type_same(args, valid_dtype, self.prim_name())
+
+        input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims, self.prim_name())
         return {'shape': input_shp,
                 'dtype': input_x['dtype'],
                 'value': None}
@@ -471,16 +462,17 @@ class CumProd(PrimitiveWithInfer):
     """
     @prim_attr_register
     def __init__(self, exclusive=False, reverse=False):
-        self.exclusive = validator.check_type("exclusive", exclusive, [bool])
-        self.reverse = validator.check_type("reverse", reverse, [bool])
+        cls_name = self.prim_name()
+        self.exclusive = validator.check_value_type("exclusive", exclusive, [bool], cls_name)
+        self.reverse = validator.check_value_type("reverse", reverse, [bool], cls_name)
 
     def infer_shape(self, x_shape, axis_shape):
         return x_shape
 
     def infer_dtype(self, x_type, axis_type):
-        validator.check_subclass('x_type', x_type, mstype.tensor)
-        validator.check_typename('x_type', x_type, mstype.number_type)
-        validator.check_subclass("axis_type", axis_type, mstype.int_)
+        cls_name = self.prim_name()
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, cls_name)
+        validator.check_subclass("axis", axis_type, mstype.int_, cls_name)
         return x_type
 
 
@@ -514,8 +506,9 @@ class MatMul(PrimitiveWithInfer):
     def __init__(self, transpose_a=False, transpose_b=False):
         self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output'])
         self.__setattr_flag__ = True
-        validator.check_type("transpose_a", transpose_a, [bool])
-        validator.check_type("transpose_b", transpose_b, [bool])
+        cls_name = self.prim_name()
+        validator.check_value_type("transpose_a", transpose_a, [bool], cls_name)
+        validator.check_value_type("transpose_b", transpose_b, [bool], cls_name)
 
     def check_shape_size(self, x, y):
         if len(x) != 2 or len(y) != 2:
@@ -524,11 +517,11 @@ class MatMul(PrimitiveWithInfer):
 
     def infer_shape(self, x, y):
         self.check_shape_size(x, y)
-        cls_name = self.__class__.__name__
+        cls_name = self.prim_name()
         # expected dimension of x, y, x:[...,a,b] y:[..., c,d], the dim size should be the same except the last two
         for i in range(len(x) - 2):
             if x[i] != y[i]:
-                raise ValueError(f'{cls_name} shape in dim[{i}] not the same, while x is {x[i]}, y is {y[i]}')
+                raise ValueError(f'For \'{cls_name}\' shape in dim[{i}] not the same, while x is {x[i]}, y is {y[i]}')
 
         # validate whether last two dims satifing matrix multiply
         x_last = x[-2:]
@@ -537,8 +530,8 @@ class MatMul(PrimitiveWithInfer):
         x_col = x_last[not self.transpose_a]  # x_col = x_last[1] if (not transpose_a) else x_last[0]
         y_row = y_last[self.transpose_b]  # y_row = y_last[0] if (not transpose_b) else y_last[1]
         if x_col != y_row:
-            raise ValueError(f'{cls_name} evaluator shapes of inputs can not do this operator, got {x_col} and {y_row}'
-                             + f' for {cls_name}, with x shape {x}(transpose_a={self.transpose_a})'
+            raise ValueError(f'For \'{cls_name}\' evaluator shapes of inputs can not do this operator,'
+                             + f' got {x_col} and {y_row}, with x shape {x}(transpose_a={self.transpose_a})'
                              + f', y shape {y}(transpose_b={self.transpose_b}).')
         # set attribute
         self.add_prim_attr('transpose_x1', self.transpose_a)
@@ -548,10 +541,8 @@ class MatMul(PrimitiveWithInfer):
         return ret_dims
 
     def infer_dtype(self, x, y):
-        validator.check_subclass("x", x, mstype.tensor)
-        validator.check_subclass("y", y, mstype.tensor)
-        args = {"x dtype": x, "y dtype": y}
-        validator.check_type_same(args, mstype.float_type + mstype.int_type)
+        args = {"x": x, "y": y}
+        validator.check_tensor_type_same(args, mstype.float_type + mstype.int_type, self.prim_name())
         return x
 
 
@@ -595,12 +586,13 @@ class BatchMatMul(MatMul):
     def __init__(self, transpose_a=False, transpose_b=False):
         self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output'])
         self.__setattr_flag__ = True
-        validator.check_type("transpose_a", transpose_a, [bool])
-        validator.check_type("transpose_b", transpose_b, [bool])
+        cls_name = self.prim_name()
+        validator.check_value_type("transpose_a", transpose_a, [bool], cls_name)
+        validator.check_value_type("transpose_b", transpose_b, [bool], cls_name)
 
     def check_shape_size(self, x, y):
         if len(x) != len(y) or len(x) < 3:
-            raise ValueError('BatchMatMul input x, y should be the same dimension size and should be '
+            raise ValueError('For \'BatchMatMul\' input x, y should be the same dimension size and should be '
                              'greater or equal to 3,' + f' while x size = {len(x)}, y size= {len(y)}')
 
 
@@ -632,18 +624,17 @@ class CumSum(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, exclusive=False, reverse=False):
         """init cumsum"""
-        self.exclusive = validator.check_type('exclusive', exclusive, [bool])
-        self.add_prim_attr("exclusive", self.exclusive)
-        self.reverse = validator.check_type('reverse', reverse, [bool])
-        self.add_prim_attr("reverse", self.reverse)
+        cls_name = self.prim_name()
+        validator.check_value_type('exclusive', exclusive, [bool], cls_name)
+        validator.check_value_type('reverse', reverse, [bool], cls_name)
         self.init_prim_io_names(inputs=['x', 'axis'], outputs=['y'])
 
     def __infer__(self, x, axis):
+        cls_name = self.prim_name()
         x_shp = x['shape']
-        validator.check_type('axis', axis['value'], [int])
-        validator.check_subclass('x', x['dtype'], mstype.tensor)
-        validator.check_typename('x', x['dtype'], [mstype.uint8, mstype.int8,
-                                                   mstype.int32, mstype.float16, mstype.float32])
+        validator.check_value_type('axis', axis['value'], [int], cls_name)
+        valid_types = [mstype.uint8, mstype.int8, mstype.int32, mstype.float16, mstype.float32]
+        validator.check_tensor_type_same({'x': x['dtype']}, valid_types, cls_name)
         return {'shape': x_shp,
                 'dtype': x['dtype'],
                 'value': None}
@@ -684,21 +675,22 @@ class AddN(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=["inputs"], outputs=["sum"])
 
     def infer_shape(self, inputs):
-        validator.check_integer("inputs", len(inputs), 1, Rel.GE)
+        cls_name = self.prim_name()
+        validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name)
         self.add_prim_attr('n', len(inputs))
         shp0 = inputs[0]
         for i, shp in enumerate(inputs):
-            validator.check(f"shape of inputs[{i}]", shp, 'shape of inputs[0]', shp0)
+            validator.check(f"shape of inputs[{i}]", shp, 'shape of inputs[0]', shp0, Rel.EQ, cls_name)
         return shp0
 
     def infer_dtype(self, inputs):
-        validator.check_type("inputs", inputs, [tuple, list])
-        validator.check_integer("inputs", len(inputs), 1, Rel.GE)
+        cls_name = self.prim_name()
+        validator.check_value_type("inputs", inputs, [tuple, list], cls_name)
+        validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name)
         args = {}
         for i, dtype in enumerate(inputs):
-            validator.check_subclass(f"inputs[{i}]", dtype, mstype.tensor)
             args[f"inputs[{i}]"] = dtype
-        validator.check_type_same(args, mstype.number_type + (mstype.bool_,))
+        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), cls_name)
         return inputs[0]
 
 
@@ -722,8 +714,7 @@ class Neg(PrimitiveWithInfer):
         return input_x
 
     def infer_dtype(self, input_x):
-        validator.check_subclass("input_x", input_x, mstype.tensor)
-        validator.check_typename("input_x", input_x, mstype.number_type)
+        validator.check_tensor_type_same({"input_x": input_x}, mstype.number_type, self.prim_name())
         return input_x
 
 
@@ -806,8 +797,7 @@ class Square(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x", x_type, mstype.tensor)
-        validator.check_typename("x_dtype", x_type, mstype.number_type)
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
         return x_type
 
 
@@ -836,8 +826,7 @@ class Rsqrt(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x", x_type, mstype.tensor)
-        validator.check_typename("x_dtype", x_type, mstype.number_type)
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
         return x_type
 
 
@@ -866,8 +855,7 @@ class Sqrt(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x", x_type, mstype.tensor)
-        validator.check_typename("x_dtype", x_type, mstype.number_type)
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
         return x_type
 
 
@@ -897,7 +885,7 @@ class Reciprocal(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x", x, mstype.tensor)
+        validator.check_subclass("x", x, mstype.tensor, self.prim_name())
         return x
 
 
@@ -935,8 +923,7 @@ class Pow(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x, power):
-        validator.check_subclass("x", x, mstype.tensor)
-        validator.check_typename("power", power, mstype.number_type)
+        validator.check_tensor_type_same({"x": x}, mstype.number_type, self.prim_name())
         return x
 
 
@@ -966,7 +953,7 @@ class Exp(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x", x_type, mstype.tensor)
+        validator.check_subclass("x", x_type, mstype.tensor, self.prim_name())
         return x_type
 
 
@@ -995,7 +982,7 @@ class Log(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x", x, mstype.tensor)
+        validator.check_subclass("x", x, mstype.tensor, self.prim_name())
         return x
 
 
@@ -1178,8 +1165,7 @@ class Floor(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_subclass("x", x_dtype, mstype.tensor)
-        validator.check_typename("x_dtype", x_dtype, mstype.float_type)
+        validator.check_tensor_type_same({"x": x_dtype}, mstype.float_type, self.prim_name())
         return x_dtype
 
 
@@ -1234,8 +1220,7 @@ class Acosh(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x_dtype", x, mstype.tensor)
-        validator.check_typename('x_dtype', x, mstype.number_type)
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
         return x
 
 
@@ -1245,15 +1230,13 @@ class _LogicBinaryOp(_BinaryOp):
     """
 
     @staticmethod
-    def do_infer_dtype(x_dtype, y_dtype, valid_type=mstype.number_type):
-        args_type = {"x": x_dtype, "y": y_dtype}
-        validator.check_args_tensor(args_type)
-        args_dtype = {"x_dtype": x_dtype, "y_dtype": y_dtype}
-        validator.check_type_same(args_dtype, valid_type)
+    def do_infer_dtype(x_dtype, y_dtype, valid_type=mstype.number_type, prim_name=None):
+        args_dtype = {"x": x_dtype, "y": y_dtype}
+        validator.check_tensor_type_same(args_dtype, valid_type, prim_name)
         return mstype.tensor_type(mstype.bool_)
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype)
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, prim_name=self.prim_name())
 
 
 class Equal(_LogicBinaryOp):
@@ -1289,7 +1272,7 @@ class Equal(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,))
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name())
 
 
 class EqualCount(PrimitiveWithInfer):
@@ -1318,11 +1301,13 @@ class EqualCount(PrimitiveWithInfer):
         """init EqualCount"""
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
-    def infer_shape(self, x_shape, w_shape):
+    def infer_shape(self, x_shape, y_shape):
         output_shape = (1,)
         return output_shape
 
-    def infer_dtype(self, x_dtype, w_dtype):
+    def infer_dtype(self, x_dtype, y_dtype):
+        args = {'x': x_dtype, 'y': y_dtype}
+        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.prim_name())
         return x_dtype
 
 
@@ -1359,7 +1344,7 @@ class NotEqual(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,))
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name())
 
 
 class Greater(_LogicBinaryOp):
@@ -1495,8 +1480,7 @@ class LogicalNot(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_subclass("x", x_dtype, mstype.tensor)
-        validator.check_typename("x_dtype", x_dtype, [mstype.bool_])
+        validator.check_tensor_type_same({"x": x_dtype}, [mstype.bool_], self.prim_name())
         return mstype.tensor_type(mstype.bool_)
 
 
@@ -1526,7 +1510,7 @@ class LogicalAnd(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,))
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
 
 
 class LogicalOr(_LogicBinaryOp):
@@ -1555,7 +1539,7 @@ class LogicalOr(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,))
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
 
 
 class NPUAllocFloatStatus(PrimitiveWithInfer):
@@ -1616,13 +1600,13 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
         self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
-        validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ)
-        validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ)
+        cls_name = self.prim_name()
+        validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name)
+        validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name)
         return [8]
 
     def infer_dtype(self, x_dtype):
-        args = {"x_dtype": x_dtype}
-        validator.check_type_same(args, [mstype.float32])
+        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name())
         return mstype.float32
 
 
@@ -1658,13 +1642,13 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
         self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
-        validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ)
-        validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ)
+        cls_name = self.prim_name()
+        validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name)
+        validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name)
         return [8]
 
     def infer_dtype(self, x_dtype):
-        args = {"x_dtype": x_dtype}
-        validator.check_type_same(args, [mstype.float32])
+        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name())
         return mstype.float32
 
 
@@ -1692,8 +1676,7 @@ class Cos(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x_dtype", x, mstype.tensor)
-        validator.check_typename('x_dtype', x, mstype.number_type)
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
         return x
 
 
@@ -1721,8 +1704,7 @@ class ACos(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x_dtype", x, mstype.tensor)
-        validator.check_typename('x_dtype', x, mstype.number_type)
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
         return x
 
 
@@ -1750,8 +1732,7 @@ class Sin(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x_dtype", x, mstype.tensor)
-        validator.check_typename('x_dtype', x, mstype.number_type)
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
         return x
 
 
@@ -1796,19 +1777,19 @@ class NMSWithMask(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, iou_threshold=0.5):
         """Init NMSWithMask"""
-        validator.check_type("iou_threshold", iou_threshold, [float])
+        validator.check_value_type("iou_threshold", iou_threshold, [float], self.prim_name())
         self.init_prim_io_names(inputs=['bboxes'], outputs=['selected_boxes', 'selected_idx', 'selected_mask'])
 
     def infer_shape(self, bboxes_shape):
-        validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ)
-        validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT)
-        validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ)
+        cls_name = self.prim_name()
+        validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ, cls_name)
+        validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT, cls_name)
+        validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ, cls_name)
         num = bboxes_shape[0]
         return (bboxes_shape, (num,), (num,))
 
     def infer_dtype(self, bboxes_dtype):
-        validator.check_subclass("bboxes_dtype", bboxes_dtype, mstype.tensor)
-        validator.check_typename("bboxes_dtype", bboxes_dtype, [mstype.float16, mstype.float32])
+        validator.check_tensor_type_same({"bboxes": bboxes_dtype}, [mstype.float16, mstype.float32], self.prim_name())
         return (bboxes_dtype, mstype.int32, mstype.bool_)
 
 
@@ -1837,8 +1818,7 @@ class Abs(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x_dtype", x_type, mstype.tensor)
-        validator.check_typename('x_dtype', x_type, mstype.number_type)
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name())
         return x_type
 
     def infer_value(self, x):
@@ -1880,8 +1860,7 @@ class Sign(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_subclass('x', x_dtype, mstype.tensor)
-        validator.check_typename('x_dtype', x_dtype, mstype.number_type)
+        validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.prim_name())
         return x_dtype
 
 
@@ -1910,8 +1889,7 @@ class Round(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x_dtype", x_type, mstype.tensor)
-        validator.check_typename('x_dtype', x_type, mstype.number_type)
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name())
         return x_type
 
 
diff --git a/mindspore/ops/primitive.py b/mindspore/ops/primitive.py
index d281b4f76c..96e754f5f7 100644
--- a/mindspore/ops/primitive.py
+++ b/mindspore/ops/primitive.py
@@ -194,6 +194,9 @@ class PrimitiveWithInfer(Primitive):
         Primitive.__init__(self, name)
         self.set_prim_type(prim_type.py_infer_shape)
 
+    def prim_name(self):
+        return self.__class__.__name__
+
     def _clone(self):
         """
         Deeply clones the primitive object.
diff --git a/tests/mindspore_test_framework/components/executor/check_exceptions.py b/tests/mindspore_test_framework/components/executor/check_exceptions.py
index eca0578b7f..fe57a3d287 100644
--- a/tests/mindspore_test_framework/components/executor/check_exceptions.py
+++ b/tests/mindspore_test_framework/components/executor/check_exceptions.py
@@ -23,20 +23,25 @@ from ...utils import keyword
 
 class CheckExceptionsEC(IExectorComponent):
     """
-    Check if the function raises the expected Exception.
+    Check if the function raises the expected Exception and the error message contains specified keywords if not None.
 
     Examples:
         {
             'block': f,
-            'exception': Exception
+            'exception': Exception,
+            'error_keywords': ['TensorAdd', 'shape']
         }
     """
     def run_function(self, function, inputs, verification_set):
         f = function[keyword.block]
         args = inputs[keyword.desc_inputs]
         e = function.get(keyword.exception, Exception)
+        error_kws = function.get(keyword.error_keywords, None)
         try:
-            with pytest.raises(e):
+            with pytest.raises(e) as exec_info:
                 f(*args)
         except:
             raise Exception(f"Expect {e}, but got {sys.exc_info()[0]}")
+        if error_kws and any(keyword not in str(exec_info.value) for keyword in error_kws):
+            raise ValueError('Error message `{}` does not contain all keywords `{}`'.format(
+                             str(exec_info.value), error_kws))
diff --git a/tests/mindspore_test_framework/utils/config_util.py b/tests/mindspore_test_framework/utils/config_util.py
index d2eb91d042..b7be15ac23 100644
--- a/tests/mindspore_test_framework/utils/config_util.py
+++ b/tests/mindspore_test_framework/utils/config_util.py
@@ -87,8 +87,9 @@ def get_function_config(function):
     init_param_with = function.get(keyword.init_param_with, None)
     split_outputs = function.get(keyword.split_outputs, True)
     exception = function.get(keyword.exception, Exception)
+    error_keywords = function.get(keyword.error_keywords, None)
     return delta, max_error, input_selector, output_selector, sampling_times, \
-           reduce_output, init_param_with, split_outputs, exception
+           reduce_output, init_param_with, split_outputs, exception, error_keywords
 
 def get_grad_checking_options(function, inputs):
     """
@@ -104,6 +105,6 @@ def get_grad_checking_options(function, inputs):
     """
     f = function[keyword.block]
     args = inputs[keyword.desc_inputs]
-    delta, max_error, input_selector, output_selector, sampling_times, reduce_output, _, _, _ = \
+    delta, max_error, input_selector, output_selector, sampling_times, reduce_output, _, _, _, _ = \
         get_function_config(function)
     return f, args, delta, max_error, input_selector, output_selector, sampling_times, reduce_output
diff --git a/tests/mindspore_test_framework/utils/facade_util.py b/tests/mindspore_test_framework/utils/facade_util.py
index bbbf1dd375..4c5896b3df 100644
--- a/tests/mindspore_test_framework/utils/facade_util.py
+++ b/tests/mindspore_test_framework/utils/facade_util.py
@@ -54,11 +54,12 @@ def fill_block_config(ret, block_config, tid, group, desc_inputs, desc_bprop, ex
 
     block = block_config
     delta, max_error, input_selector, output_selector, \
-    sampling_times, reduce_output, init_param_with, split_outputs, exception = get_function_config({})
+    sampling_times, reduce_output, init_param_with, split_outputs, exception, error_keywords = get_function_config({})
     if isinstance(block_config, tuple) and isinstance(block_config[-1], dict):
         block = block_config[0]
         delta, max_error, input_selector, output_selector, \
-        sampling_times, reduce_output, init_param_with, split_outputs, exception = get_function_config(block_config[-1])
+        sampling_times, reduce_output, init_param_with, \
+        split_outputs, exception, error_keywords = get_function_config(block_config[-1])
 
     if block:
         func_list.append({
@@ -78,7 +79,8 @@ def fill_block_config(ret, block_config, tid, group, desc_inputs, desc_bprop, ex
             keyword.const_first: const_first,
             keyword.add_fake_input: add_fake_input,
             keyword.split_outputs: split_outputs,
-            keyword.exception: exception
+            keyword.exception: exception,
+            keyword.error_keywords: error_keywords
         })
 
     if desc_inputs or desc_const:
diff --git a/tests/mindspore_test_framework/utils/keyword.py b/tests/mindspore_test_framework/utils/keyword.py
index 16618be70a..79f1f91d8f 100644
--- a/tests/mindspore_test_framework/utils/keyword.py
+++ b/tests/mindspore_test_framework/utils/keyword.py
@@ -73,5 +73,6 @@ keyword.const_first = "const_first"
 keyword.add_fake_input = "add_fake_input"
 keyword.fake_input_type = "fake_input_type"
 keyword.exception = "exception"
+keyword.error_keywords = "error_keywords"
 
 sys.modules[__name__] = keyword
diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py
index 1c4895465f..faaa9d5402 100644
--- a/tests/ut/python/ops/test_array_ops.py
+++ b/tests/ut/python/ops/test_array_ops.py
@@ -234,7 +234,7 @@ raise_set = [
         'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': ValueError}),
         'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
     ('ReduceSum_Error', {
-        'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': ValueError}),
+        'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': TypeError}),
         'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
 ]
 
diff --git a/tests/ut/python/ops/test_math_ops_check.py b/tests/ut/python/ops/test_math_ops_check.py
new file mode 100755
index 0000000000..be6e5691ea
--- /dev/null
+++ b/tests/ut/python/ops/test_math_ops_check.py
@@ -0,0 +1,751 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test ops """
+import functools
+import numpy as np
+from mindspore import ops
+from mindspore.ops import functional as F
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
+import mindspore.ops.composite as C
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.common.parameter import Parameter
+from ..ut_filter import non_graph_engine
+from mindspore.common.api import _executor
+
+from ....mindspore_test_framework.mindspore_test import mindspore_test
+from ....mindspore_test_framework.pipeline.forward.compile_forward\
+    import (pipeline_for_compile_forward_ge_graph_for_case_by_case_config,
+            pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
+from ....mindspore_test_framework.pipeline.gradient.compile_gradient\
+    import pipeline_for_compile_grad_ge_graph_for_case_by_case_config
+
+
+class AssignAddNet(nn.Cell):
+    def __init__(self,):
+        super(AssignAddNet, self).__init__()
+        self.op = P.AssignAdd()
+        self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_add1")
+
+    def construct(self, x):
+        self.op(self.inputdata, x)
+        return self.inputdata
+
+
+class AssignSubNet(nn.Cell):
+    def __init__(self,):
+        super(AssignSubNet, self).__init__()
+        self.op = P.AssignSub()
+        self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_sub1")
+
+    def construct(self, x):
+        self.op(self.inputdata, x)
+        return self.inputdata
+
+
+class ReduceNet(nn.Cell):
+    def __init__(self, op_class, keep_dims, axis):
+        super(ReduceNet, self).__init__()
+        self.axis = axis
+        self.op = op_class(keep_dims=keep_dims)
+
+    def construct(self, x):
+        return self.op(x, self.axis)
+
+
+class CumProdNet(nn.Cell):
+    def __init__(self):
+        super(CumProdNet, self).__init__()
+        self.op = P.CumProd()
+
+    def construct(self, x, axis):
+        return self.op(x, axis)
+
+
+class CumSumNet(nn.Cell):
+    def __init__(self, axis):
+        super(CumSumNet, self).__init__()
+        self.axis = axis
+        self.op = P.CumSum()
+
+    def construct(self, x):
+        return self.op(x, self.axis)
+
+
+raise_set = [
+    # one input is scalar, and another is Tensor(float32)
+    ('TensorAdd0', {
+        'block': (P.TensorAdd(), {'exception': TypeError, 'error_keywords': ['TensorAdd']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('TensorAdd1', {
+        'block': (P.TensorAdd(), {'exception': TypeError, 'error_keywords': ['TensorAdd']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('TensorAdd2', {
+        'block': (P.TensorAdd(), {'exception': ValueError, 'error_keywords': ['TensorAdd']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # check input Tensor(bool_)
+    ('AssignAdd', {
+        'block': (AssignAddNet(), {'exception': TypeError, 'error_keywords': ['AssignAdd']}),
+        'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)],
+        'skip': ['backward']}),
+
+    # check input Tensor(bool_)
+    ('AssignSub', {
+        'block': (AssignSubNet(), {'exception': TypeError, 'error_keywords': ['AssignSub']}),
+        'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceMean1', {
+        'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceMean']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceMean2', {
+        'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceMean']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceSum1', {
+        'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceSum']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceSum2', {
+        'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceSum']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceAll1', {
+        'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceAll']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceAll2', {
+        'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceAll']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceMax1', {
+        'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceMax']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceMax2', {
+        'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceMax']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceMin1', {
+        'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceMin']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceMin2', {
+        'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceMin']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # type of axis is float, not int
+    ('ReduceProd1', {
+        'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5.0),
+        {'exception': TypeError, 'error_keywords': ['ReduceProd']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+    # axis is out of range
+    ('ReduceProd2', {
+        'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5),
+         {'exception': ValueError, 'error_keywords': ['ReduceProd']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # type of x is Tensor(bool)
+    ('CumProd1', {
+        'block': (CumProdNet(),
+        {'exception': TypeError, 'error_keywords': ['CumProd']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool)), 1],
+        'skip': ['backward']}),
+    # type of axis in float, not int
+    ('CumProd2', {
+        'block': (CumProdNet(),
+        {'exception': TypeError, 'error_keywords': ['CumProd']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32)), 5.0],
+        'skip': ['backward']}),
+
+    # type of x and y are Tensor(uint32)
+    ('MatMul1', {
+        'block': (P.MatMul(),
+        {'exception': TypeError, 'error_keywords': ['MatMul']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.uint32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('MatMul2', {
+        'block': (P.MatMul(),
+        {'exception': TypeError, 'error_keywords': ['MatMul']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.int32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('MatMul3', {
+        'block': (P.MatMul(),
+        {'exception': ValueError, 'error_keywords': ['MatMul']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([2, 3]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # dims of x and y are less than 3
+    ('BatchMatMul1', {
+        'block': (P.BatchMatMul(),
+        {'exception': ValueError, 'error_keywords': ['BatchMatMul']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32))],
+        'skip': ['backward']}),
+
+    # type of x is Tensor(bool)
+    ('CumSum1', {
+        'block': (CumSumNet(axis=1),
+        {'exception': TypeError, 'error_keywords': ['CumSum']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))],
+        'skip': ['backward']}),
+    # type of axis in float, not int
+    ('CumSum2', {
+        'block': (CumSumNet(axis=1.0),
+        {'exception': TypeError, 'error_keywords': ['CumSum']}),
+        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))],
+        'skip': ['backward']}),
+
+    # intput is not tuple or list
+    ('AddN1', {
+        'block': (P.AddN(),
+        {'exception': TypeError, 'error_keywords': ['AddN']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32))],
+        'skip': ['backward']}),
+    # type not match
+    ('AddN2', {
+        'block': (P.AddN(),
+        {'exception': TypeError, 'error_keywords': ['AddN']}),
+        'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.int32)))],
+        'skip': ['backward']}),
+    # shape not match
+    ('AddN3', {
+        'block': (P.AddN(),
+        {'exception': ValueError, 'error_keywords': ['AddN']}),
+        'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32)))],
+        'skip': ['backward']}),
+
+    # input is Tensor(bool)
+    ('Neg1', {
+        'block': (P.Neg(),
+        {'exception': TypeError, 'error_keywords': ['Neg']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Sub0', {
+        'block': (P.Sub(), {'exception': TypeError, 'error_keywords': ['Sub']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Sub1', {
+        'block': (P.Sub(), {'exception': TypeError, 'error_keywords': ['Sub']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Sub2', {
+        'block': (P.Sub(), {'exception': ValueError, 'error_keywords': ['Sub']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Mul0', {
+        'block': (P.Mul(), {'exception': TypeError, 'error_keywords': ['Mul']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Mul1', {
+        'block': (P.Mul(), {'exception': TypeError, 'error_keywords': ['Mul']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Mul2', {
+        'block': (P.Mul(), {'exception': ValueError, 'error_keywords': ['Mul']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is Tensor(bool)
+    ('Square1', {
+        'block': (P.Square(),
+        {'exception': TypeError, 'error_keywords': ['Square']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is Tensor(bool)
+    ('Rsqrt1', {
+        'block': (P.Rsqrt(),
+        {'exception': TypeError, 'error_keywords': ['Rsqrt']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is Tensor(bool)
+    ('Sqrt1', {
+        'block': (P.Sqrt(),
+        {'exception': TypeError, 'error_keywords': ['Sqrt']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not Tensor
+    ('Reciprocal1', {
+        'block': (P.Reciprocal(),
+        {'exception': TypeError, 'error_keywords': ['Reciprocal']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+
+    # input x is Tensor(bool)
+    ('Pow1', {
+        'block': (P.Pow(),
+        {'exception': TypeError, 'error_keywords': ['Pow']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_)), 2.0],
+        'skip': ['backward']}),
+
+    # input is not Tensor
+    ('Exp1', {
+        'block': (P.Exp(),
+        {'exception': TypeError, 'error_keywords': ['Exp']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+
+    # input is not Tensor
+    ('Log1', {
+        'block': (P.Log(),
+        {'exception': TypeError, 'error_keywords': ['Log']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Minimum0', {
+        'block': (P.Minimum(), {'exception': TypeError, 'error_keywords': ['Minimum']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Minimum1', {
+        'block': (P.Minimum(), {'exception': TypeError, 'error_keywords': ['Minimum']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Minimum2', {
+        'block': (P.Minimum(), {'exception': ValueError, 'error_keywords': ['Minimum']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Maximum0', {
+        'block': (P.Maximum(), {'exception': TypeError, 'error_keywords': ['Maximum']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Maximum1', {
+        'block': (P.Maximum(), {'exception': TypeError, 'error_keywords': ['Maximum']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Maximum2', {
+        'block': (P.Maximum(), {'exception': ValueError, 'error_keywords': ['Maximum']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('RealDiv0', {
+        'block': (P.RealDiv(), {'exception': TypeError, 'error_keywords': ['RealDiv']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('RealDiv1', {
+        'block': (P.RealDiv(), {'exception': TypeError, 'error_keywords': ['RealDiv']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('RealDiv2', {
+        'block': (P.RealDiv(), {'exception': ValueError, 'error_keywords': ['RealDiv']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Div0', {
+        'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Div1', {
+        'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Div2', {
+        'block': (P.Div(), {'exception': ValueError, 'error_keywords': ['Div']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('FloorDiv0', {
+        'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('FloorDiv1', {
+        'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('FloorDiv2', {
+        'block': (P.FloorDiv(), {'exception': ValueError, 'error_keywords': ['FloorDiv']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input x is Tensor(int32), not Tensor(float)
+    ('Floor1', {
+        'block': (P.Floor(),
+        {'exception': TypeError, 'error_keywords': ['Floor']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('FloorMod0', {
+        'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('FloorMod1', {
+        'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('FFloorMod2', {
+        'block': (P.FloorMod(), {'exception': ValueError, 'error_keywords': ['FloorMod']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input x is Tensor(int32), not Tensor(float)
+    ('Acosh1', {
+        'block': (P.Acosh(),
+        {'exception': TypeError, 'error_keywords': ['Acosh']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Equal0', {
+        'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('Equal1', {
+        'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('Equal2', {
+        'block': (P.Equal(), {'exception': ValueError, 'error_keywords': ['Equal']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('EqualCount0', {
+        'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('EqualCount1', {
+        'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+
+    # input is not tensor
+    ('NotEqual0', {
+        'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('NotEqual1', {
+        'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('NotEqual2', {
+        'block': (P.NotEqual(), {'exception': ValueError, 'error_keywords': ['NotEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Greater0', {
+        'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('Greater1', {
+        'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('Greater2', {
+        'block': (P.Greater(), {'exception': ValueError, 'error_keywords': ['Greater']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('GreaterEqual0', {
+        'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('GreaterEqual1', {
+        'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('GreaterEqual2', {
+        'block': (P.GreaterEqual(), {'exception': ValueError, 'error_keywords': ['GreaterEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Less0', {
+        'block': (P.Less(), {'exception': TypeError, 'error_keywords': ['Less']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('Less1', {
+        'block': (P.Less(), {'exception': TypeError, 'error_keywords': ['Less']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('Less2', {
+        'block': (P.Less(), {'exception': ValueError, 'error_keywords': ['Less']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('LessEqual0', {
+        'block': (P.LessEqual(), {'exception': TypeError, 'error_keywords': ['LessEqual']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # type of x and y not match
+    ('LessEqual1', {
+        'block': (P.LessEqual(), {'exception': TypeError, 'error_keywords': ['LessEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('LessEqual2', {
+        'block': (P.LessEqual(), {'exception': ValueError, 'error_keywords': ['LessEqual']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input x is not Tensor(bool)
+    ('LogicalNot1', {
+        'block': (P.LogicalNot(),
+        {'exception': TypeError, 'error_keywords': ['LogicalNot']}),
+        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
+        'skip': ['backward']}),
+
+    # type of x and y not match
+    ('LogicalAnd1', {
+        'block': (P.LogicalAnd(), {'exception': TypeError, 'error_keywords': ['LogicalAnd']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('LogicalAnd2', {
+        'block': (P.LogicalAnd(), {'exception': ValueError, 'error_keywords': ['LogicalAnd']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # type of x and y not match
+    ('LogicalOr1', {
+        'block': (P.LogicalOr(), {'exception': TypeError, 'error_keywords': ['LogicalOr']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+    # shape of x and y not match
+    ('LogicalOr2', {
+        'block': (P.LogicalOr(), {'exception': ValueError, 'error_keywords': ['LogicalOr']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('NPUGetFloatStatus0', {
+        'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(int32), not Tensor(float32)
+    ('NPUGetFloatStatus1', {
+        'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
+        'skip': ['backward']}),
+    # dims is not 1
+    ('NPUGetFloatStatus2', {
+        'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape[0] is not 8
+    ('NPUGetFloatStatus3', {
+        'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('NPUClearFloatStatus0', {
+        'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(int32), not Tensor(float32)
+    ('NPUClearFloatStatus1', {
+        'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
+        'skip': ['backward']}),
+    # dims is not 1
+    ('NPUClearFloatStatus2', {
+        'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape[0] is not 8
+    ('NPUClearFloatStatus3', {
+        'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}),
+        'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Cos0', {
+        'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('Cos1', {
+        'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('ACos0', {
+        'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('ACos1', {
+        'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Sin0', {
+        'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('Sin1', {
+        'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('NMSWithMask0', {
+        'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is not Tensor(float16) or Tensor(float32)
+    ('NMSWithMask1', {
+        'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
+        'skip': ['backward']}),
+    # dims is not 2
+    ('NMSWithMask2', {
+        'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
+        'desc_inputs': [Tensor(np.ones([3, 4, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+    # shape[1] is not 5
+    ('NMSWithMask3', {
+        'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
+        'desc_inputs': [Tensor(np.ones([3, 2]).astype(np.float32))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Abs0', {
+        'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('Abs1', {
+        'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Sign0', {
+        'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('Sign1', {
+        'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # input is not tensor
+    ('Round0', {
+        'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
+        'desc_inputs': [5.0],
+        'skip': ['backward']}),
+    # input is Tensor(bool)
+    ('Round1', {
+        'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
+        'skip': ['backward']}),
+
+    # one input is scalar, and another is Tensor(float32)
+    ('Atan20', {
+        'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}),
+        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, but element types are not same
+    ('Atan21', {
+        'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}),
+        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    # input two tensors, their shapes do not match
+    ('Atan22', {
+        'block': (P.Atan2(), {'exception': ValueError, 'error_keywords': ['Atan2']}),
+        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+]
+
+
+@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
+def test_check_exception():
+    return raise_set

From 113c0d8cd28f5d637e458dfe46dc618f43adfd92 Mon Sep 17 00:00:00 2001
From: Wei Luning <weiluning@huawei.com>
Date: Sun, 5 Apr 2020 09:27:29 +0000
Subject: [PATCH 107/367] fix InsertGradientOf with class method

---
 mindspore/ccsrc/pipeline/parse/resolve.cc     |  8 ++++
 mindspore/ccsrc/pipeline/pass.cc              |  8 ++++
 .../ccsrc/pipeline/static_analysis/prim.cc    | 37 +++----------------
 .../pynative_mode/test_insert_grad_of.py      | 36 +++++++++++++++++-
 4 files changed, 57 insertions(+), 32 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/parse/resolve.cc b/mindspore/ccsrc/pipeline/parse/resolve.cc
index ebc1f65486..f90fc5039c 100644
--- a/mindspore/ccsrc/pipeline/parse/resolve.cc
+++ b/mindspore/ccsrc/pipeline/parse/resolve.cc
@@ -103,6 +103,14 @@ AnfNodePtr ResolveParameterObj(const FuncGraphPtr& func_graph, const py::object&
   if (para_node == nullptr) {
     ParameterPtr node = top_graph->AddWeightParameter(param_name);
     node->set_default_param(obj);
+
+    // set_abstract for parameter
+    auto to_convert = py::cast<py::object>(python_adapter::GetPyObjAttr(obj, "default_input"));
+    ValuePtr converted = nullptr;
+    (void)ConvertData(to_convert, &converted);
+    bool broaden = true;
+    node->set_abstract(abstract::FromValue(converted, broaden));
+
     para_node = node;
   }
   auto iter = func_graph->make_ref_params().find(para_node);
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index e0336443e6..a58ecf41b6 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -112,6 +112,13 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) {
   });
   opt::OptPassConfig virtual_dataset = opt::OptPassConfig({irpass.virtual_dataset_eliminate_});
   opt::OptPassConfig grad = opt::OptPassConfig({irpass.expand_jprim_}, true);
+  opt::irpass::ResolveIRPassLib resolve_irpass;
+
+  opt::OptPassConfig resolve_pass = opt::OptPassConfig({
+    resolve_irpass.resolver_resolve_,
+    resolve_irpass.resolver_getattr_,
+    irpass.get_make_ref_eliminate_,
+  });
 
   OptPassGroupMap map_a({{"a_1", a_1},
                          {"a_2", a_2},
@@ -120,6 +127,7 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) {
                          {"allreduce_fusion", opt::OptPassConfig(parallel::StepAllreduceFusion)},
                          {"virtual_dataset", virtual_dataset},
                          {"grad", grad},
+                         {"resolve", resolve_pass},
                          {"renormalize", opt::OptPassConfig::Renormalize()},
                          {"cse", opt::OptPassConfig(opt::CSE(false))},
                          {"a_3", a_3}});
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 56bcd77f67..d71e098009 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -554,24 +554,6 @@ AbstractBasePtr StaticGetterInferred(const ValuePtr &value, const ConfigPtr &dat
   return eng->ForwardConfig(old_conf, fn_conf);
 }
 
-AbstractBasePtr GenerateResolveAbstract(const AnfNodeConfigPtr &out_conf, const py::object &obj,
-                                        const ValuePtr &converted_ret) {
-  if (py::hasattr(obj, PYTHON_DATACLASS_FIELDS)) {
-    TypePtr cls_ptr = parse::ParseDataClass(converted_ret->cast<std::shared_ptr<parse::PyObjectWrapper>>()->obj());
-
-    std::vector<AnfNodePtr> input = {NewValueNode(prim::kPrimPartial), NewValueNode(prim::kPrimMakeRecord),
-                                     NewValueNode(cls_ptr)};
-    MS_EXCEPTION_IF_NULL(out_conf);
-    FuncGraphPtr func_graph = out_conf->node()->func_graph();
-    CNodePtr new_cnode = func_graph->NewCNode(input);
-    AnalysisEnginePtr eng = out_conf->engine();
-    AnfNodeConfigPtr fn_conf = eng->MakeConfig(new_cnode, out_conf->context());
-    return eng->ForwardConfig(out_conf, fn_conf);
-  } else {
-    return ToAbstract(converted_ret, AnalysisContext::DummyContext(), out_conf);
-  }
-}
-
 AbstractBasePtr GetEvaluatedValueForNameSpaceString(const AnalysisEnginePtr &engine,
                                                     const AbstractBasePtrList &args_spec_list,
                                                     const AnfNodeConfigPtr &out_conf) {
@@ -602,23 +584,16 @@ AbstractBasePtr GetEvaluatedValueForNameSpaceString(const AnalysisEnginePtr &eng
   // item_name to func addr from obj_map
   parse::SymbolPtr symbol = item_v->cast<parse::SymbolPtr>();
   parse::NameSpacePtr name_space = data_v->cast<parse::NameSpacePtr>();
+  FuncGraphPtr func_graph = out_conf->node()->func_graph();
 
-  parse::SymbolResolverPtr symbol_resolver =
-    std::make_shared<parse::SymbolResolver>(name_space, symbol, out_conf->node());
-  if (!symbol_resolver->Resolve()) {
+  auto new_node = parse::ResolveSymbol(func_graph->manager(), name_space, symbol, out_conf->node());
+  if (new_node == nullptr) {
     MS_LOG(EXCEPTION) << "Resolve node failed";
   }
 
-  py::object obj = symbol_resolver->result();
-  ValuePtr converted_ret = nullptr;
-  bool converted = parse::ConvertData(obj, &converted_ret, true);
-  if (!converted) {
-    MS_LOG(EXCEPTION) << "Convert data failed";
-  }
-  if (converted_ret->isa<FuncGraph>()) {
-    AddToManager(engine, converted_ret->cast<FuncGraphPtr>());
-  }
-  return GenerateResolveAbstract(out_conf, obj, converted_ret);
+  AnalysisEnginePtr eng = out_conf->engine();
+  AnfNodeConfigPtr fn_conf = eng->MakeConfig(new_node, out_conf->context());
+  return eng->ForwardConfig(out_conf, fn_conf);
 }
 
 AbstractBasePtr GetEvaluatedValueForClassAttrOrMethod(const AnalysisEnginePtr &engine,
diff --git a/tests/ut/python/pynative_mode/test_insert_grad_of.py b/tests/ut/python/pynative_mode/test_insert_grad_of.py
index 38432d79f3..104ac4d1c7 100644
--- a/tests/ut/python/pynative_mode/test_insert_grad_of.py
+++ b/tests/ut/python/pynative_mode/test_insert_grad_of.py
@@ -17,13 +17,14 @@ import numpy as np
 import mindspore.nn as nn
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
+from mindspore.ops import functional as F
 from mindspore.common.api import ms_function
 from ....mindspore_test_framework.utils.bprop_util import bprop
 from ....mindspore_test_framework.utils.debug_util import PrintShapeTypeCell, PrintGradShapeTypeCell
 from mindspore import Tensor
 
 from mindspore import context
-
+import mindspore
 
 def setup_module(module):
     context.set_context(mode=context.PYNATIVE_MODE)
@@ -107,3 +108,36 @@ def test_print_shape_type():
             return z
     bprop(Mul(), Tensor(np.ones([2, 2]).astype(np.float32)),
           Tensor(np.ones([2, 2]).astype(np.float32)))
+
+def test_cell_assign():
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    class GradNetWrap(nn.Cell):
+        """ GradNetWrap definition """
+        def __init__(self, net):
+            super(GradNetWrap, self).__init__()
+            self.net = net
+            self.weights = mindspore.ParameterTuple(net.get_parameters())
+
+        def construct(self, x, y):
+            return C.grad_by_list(self.net, self.weights)(x, y)
+
+    class Mul(nn.Cell):
+        def __init__(self):
+            super(Mul, self).__init__()
+            self.get_g = P.InsertGradientOf(self.save_gradient)
+            self.matrix_w = mindspore.Parameter(Tensor(np.ones([2, 2], np.float32)), name="matrix_w")
+            self.matrix_g = mindspore.Parameter(Tensor(np.ones([2, 2], np.float32)), name="matrix_g")
+
+        def save_gradient(self, dout):
+            self.matrix_g = dout
+            return dout
+
+        def construct(self, x, y):
+            z = x * self.matrix_w
+            z = self.get_g(z)
+            z = z * y
+            return z
+
+    input_x = Tensor(np.ones([2, 2], np.float32))
+    input_y = Tensor(np.ones([2, 2], np.float32))
+    GradNetWrap(Mul())(input_x, input_y)

From 7696c21e65df2d478c6ab0f48732329b65b96b22 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 7 Apr 2020 11:39:10 +0800
Subject: [PATCH 108/367] fix runtest.sh for python ut

---
 mindspore/dataset/engine/datasets.py   | 20 ++++-------
 mindspore/dataset/engine/validators.py | 49 --------------------------
 tests/ut/python/ops/test_ops_check.py  |  4 +--
 tests/ut/python/runtest.sh             | 16 ++++++---
 4 files changed, 20 insertions(+), 69 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 2d5c219b71..db2b5169d2 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator
 from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \
     check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
-    check_zip_dataset, check_add_column, check_columns
+    check_zip_dataset
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -2334,20 +2334,13 @@ class Schema:
             self.dataset_type = ''
             self.num_rows = 0
         else:
-            if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK):
-                raise ValueError("The file %s does not exist or permission denied!" % schema_file)
             try:
                 with open(schema_file, 'r') as load_f:
                     json_obj = json.load(load_f)
+                    self.from_json(json_obj)
             except json.decoder.JSONDecodeError:
-                raise RuntimeError("Schema file failed to load.")
-            except UnicodeDecodeError:
-                raise RuntimeError("Schema file failed to decode.")
-            except Exception:
-                raise RuntimeError("Schema file failed to open.")
-            self.from_json(json_obj)
+                raise RuntimeError("Schema file failed to load")
 
-    @check_add_column
     def add_column(self, name, de_type, shape=None):
         """
         Add new column to the schema.
@@ -2366,8 +2359,10 @@ class Schema:
         if isinstance(de_type, typing.Type):
             de_type = mstype_to_detype(de_type)
             new_column["type"] = str(de_type)
-        else:
+        elif isinstance(de_type, str):
             new_column["type"] = str(DataType(de_type))
+        else:
+            raise ValueError("Unknown column type")
 
         if shape is not None:
             new_column["shape"] = shape
@@ -2404,7 +2399,6 @@ class Schema:
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
-        check_columns(columns, columns)
         self.columns = []
         for col in columns:
             name = None
@@ -2449,8 +2443,6 @@ class Schema:
             RuntimeError: if dataset type is missing in the object.
             RuntimeError: if columns are missing in the object.
         """
-        if not isinstance(json_obj, dict) or json_obj is None:
-            raise ValueError("Expected non-empty dict.")
         for k, v in json_obj.items():
             if k == "datasetType":
                 self.dataset_type = v
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 1c374ae879..b4d22a4a01 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -19,15 +19,10 @@ import inspect as ins
 import os
 from functools import wraps
 from multiprocessing import cpu_count
-from mindspore._c_expression import typing
 from . import samplers
 from . import datasets
 
 INT32_MAX = 2147483647
-valid_detype = [
-    "bool", "int8", "int16", "int32", "int64", "uint8", "uint16",
-    "uint32", "uint64", "float16", "float32", "float64"
-]
 
 
 def check(method):
@@ -193,12 +188,6 @@ def check(method):
     return wrapper
 
 
-def check_valid_detype(type_):
-    if type_ not in valid_detype:
-        raise ValueError("Unknown column type")
-    return True
-
-
 def check_filename(path):
     """
     check the filename in the path
@@ -754,41 +743,3 @@ def check_project(method):
         return method(*args, **kwargs)
 
     return new_method
-
-
-def check_shape(shape, name):
-    if isinstance(shape, list):
-        for element in shape:
-            if not isinstance(element, int):
-                raise TypeError(
-                    "Each element in {0} should be of type int. Got {1}.".format(name, type(element)))
-    else:
-        raise TypeError("Expected int list.")
-
-
-def check_add_column(method):
-    """check the input arguments of add_column."""
-    @wraps(method)
-    def new_method(*args, **kwargs):
-        param_dict = make_param_dict(method, args, kwargs)
-
-        # check name; required argument
-        name = param_dict.get("name")
-        if not isinstance(name, str) or not name:
-            raise TypeError("Expected non-empty string.")
-
-        # check type; required argument
-        de_type = param_dict.get("de_type")
-        if not isinstance(de_type, str) or not de_type:
-            raise TypeError("Expected non-empty string.")
-        if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type):
-            raise ValueError("Unknown column type.")
-
-        # check shape
-        shape = param_dict.get("shape")
-        if shape is not None:
-            check_shape(shape, "shape")
-
-        return method(*args, **kwargs)
-
-    return new_method
diff --git a/tests/ut/python/ops/test_ops_check.py b/tests/ut/python/ops/test_ops_check.py
index 5083878dae..a7e1b41c4a 100644
--- a/tests/ut/python/ops/test_ops_check.py
+++ b/tests/ut/python/ops/test_ops_check.py
@@ -62,7 +62,7 @@ def test_net_without_construct():
     try:
         _executor.compile(net, inp)
     except RuntimeError as err:
-        if str(err).find("unsupported syntax 'Raise' at ") >= 0:
+        if str(err).find("Unsupported syntax 'Raise' at ") >= 0:
             print(str(err))
         else:
             raise err
@@ -86,7 +86,7 @@ def test_net_with_raise():
     try:
         _executor.compile(net, inp)
     except RuntimeError as err:
-        if str(err).find("unsupported syntax 'Raise' at ") >= 0:
+        if str(err).find("Unsupported syntax 'Raise' at ") >= 0:
             print(str(err))
         else:
             raise err
diff --git a/tests/ut/python/runtest.sh b/tests/ut/python/runtest.sh
index 035e454f24..6502d3a1bf 100755
--- a/tests/ut/python/runtest.sh
+++ b/tests/ut/python/runtest.sh
@@ -40,12 +40,8 @@ fi
 
 if [ $# -gt 0 ]; then
     pytest -s --ignore=$1/pynative_mode --ignore=$1/parallel  --ignore=$1/train  $IGNORE_EXEC $1
-    pytest -n 4 --dist=loadfile -v $1/parallel
-    pytest -n 4 --dist=loadfile -v $1/train
 else
     pytest --ignore=$CURRPATH/pynative_mode --ignore=$CURRPATH/parallel --ignore=$CURRPATH/train $IGNORE_EXEC $CURRPATH
-    pytest -n 4 --dist=loadfile -v $CURRPATH/parallel
-    pytest -n 4 --dist=loadfile -v $CURRPATH/train
 fi
 
 RET=$?
@@ -57,6 +53,18 @@ if [ ${RET} -ne 0 ]; then
     exit ${RET}
 fi
 
+if [ $# -gt 0 ]; then
+    pytest -n 4 --dist=loadfile -v $1/parallel $1/train
+else
+    pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train
+fi
+
+RET=$?
+
+if [ ${RET} -ne 0 ]; then
+    exit ${RET}
+fi
+
 if [ $# -gt 0 ]; then
     pytest -s $1/pynative_mode
 else

From 2303719352a8f264523253e931a4c6e607441fd8 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Tue, 7 Apr 2020 11:22:53 +0800
Subject: [PATCH 109/367] updata mkl-dnn link and md5

---
 Third_Party_Open_Source_Software_Notice |  2 +-
 cmake/external_libs/mkl_dnn.cmake       | 18 +++++++++---------
 cmake/utils.cmake                       |  2 ++
 mindspore/ccsrc/CMakeLists.txt          |  2 +-
 4 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/Third_Party_Open_Source_Software_Notice b/Third_Party_Open_Source_Software_Notice
index 498b5b8d1b..60ad7cf47c 100644
--- a/Third_Party_Open_Source_Software_Notice
+++ b/Third_Party_Open_Source_Software_Notice
@@ -368,7 +368,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 THE SOFTWARE.
 
 
-Software: MKL-DNN 1.1.2
+Software: oneDNN 1.1.2
 Copyright (c) 2009-2018 The MathJax Consortium
 Copyright 2018 Intel Corporation
 Copyright 2019 Intel Corporation
diff --git a/cmake/external_libs/mkl_dnn.cmake b/cmake/external_libs/mkl_dnn.cmake
index 17d8020d3a..6f033fa565 100644
--- a/cmake/external_libs/mkl_dnn.cmake
+++ b/cmake/external_libs/mkl_dnn.cmake
@@ -1,11 +1,11 @@
-set(mkl_dnn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
-set(mkl_dnn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
-mindspore_add_pkg(mkl_dnn
-        VER 1.1.1
+set(onednn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
+set(onednn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
+mindspore_add_pkg(onednn
+        VER 1.1.2
         LIBS dnnl mkldnn
-        URL https://github.com/intel/mkl-dnn/archive/v1.1.1.tar.gz
-        MD5 d6a422b00459600bdc22242590953f38
+        URL https://github.com/oneapi-src/oneDNN/archive/v1.1.2.tar.gz
+        MD5 ab40d52230f3ad1d7a6f06ce0f6bc17a
         CMAKE_OPTION -DDNNL_ARCH_OPT_FLAGS='' -DDNNL_CPU_RUNTIME='SEQ' -DDNNL_BUILD_EXAMPLES=OFF -DDNNL_BUILD_TESTS=OFF)
-include_directories(${mkl_dnn_INC})
-add_library(mindspore::dnnl ALIAS mkl_dnn::dnnl)
-add_library(mindspore::mkldnn ALIAS mkl_dnn::mkldnn)
+include_directories(${onednn_INC})
+add_library(mindspore::dnnl ALIAS onednn::dnnl)
+add_library(mindspore::mkldnn ALIAS onednn::mkldnn)
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index 060e400820..99c064fdd4 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -40,6 +40,8 @@ else()
     set(JOBS 8)
     if (${JOBS} GREATER ${N})
         set(THNUM ${N})
+    else()
+        set(THNUM ${JOBS})
     endif()
 endif ()
 message("set make thread num: ${THNUM}")
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 1c684b6736..a979a84ac1 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -508,7 +508,7 @@ endif()
 
 if (ENABLE_CPU)
     add_custom_target(add_cpu_lib ALL
-            COMMAND cp ${mkl_dnn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1
+            COMMAND cp ${onednn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1
             )
     add_dependencies(add_cpu_lib add_ms_lib)
 endif()

From cc9a0e1310280930c88a0f261cf233d9d801e341 Mon Sep 17 00:00:00 2001
From: chengang <chengang82@huawei.com>
Date: Tue, 7 Apr 2020 16:56:33 +0800
Subject: [PATCH 110/367] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?=
 =?UTF-8?q?!133=20:=20Edit=20loss=5Fscale=20to=20fit=20GPU'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 mindspore/nn/wrap/loss_scale.py      | 40 ++++----------
 mindspore/ops/operations/__init__.py |  6 +-
 mindspore/ops/operations/math_ops.py | 83 ----------------------------
 3 files changed, 12 insertions(+), 117 deletions(-)

diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index 4d929352b3..1ce3179273 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -25,7 +25,6 @@ from ...ops import operations as P
 from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \
     ControlDepend
 from ...common import dtype as mstype
-import mindspore.context as context
 
 _grad_scale = C.MultitypeFuncGraph("grad_scale")
 reciprocal = P.Reciprocal()
@@ -35,12 +34,6 @@ reciprocal = P.Reciprocal()
 def tensor_grad_scale(scale, grad):
     return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
-_grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
-grad_overflow = P.FloatStatus()
-
-@_grad_overflow.register("Tensor")
-def _tensor_grad_overflow(grad):
-    return grad_overflow(grad)
 
 class DynamicLossScaleUpdateCell(Cell):
     r"""
@@ -204,15 +197,9 @@ class TrainOneStepWithLossScaleCell(Cell):
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.hyper_map = C.HyperMap()
-        if context.get_context("device_target") == "GPU":
-            self.gpu_target = True
-            self.float_status = P.FloatStatus()
-            self.addn = P.AddN()
-        else:
-            self.gpu_target = False
-            self.alloc_status = NPUAllocFloatStatus()
-            self.get_status = NPUGetFloatStatus()
-            self.clear_status = NPUClearFloatStatus()
+        self.alloc_status = NPUAllocFloatStatus()
+        self.get_status = NPUGetFloatStatus()
+        self.clear_status = NPUClearFloatStatus()
         self.reduce_sum = ReduceSum(keep_dims=False)
         self.base = Tensor(1, mstype.float32)
         self.less_equal = LessEqual()
@@ -237,11 +224,10 @@ class TrainOneStepWithLossScaleCell(Cell):
     def construct(self, data, label, sens=None):
         weights = self.weights
         loss = self.network(data, label)
-        if not self.gpu_target:
-            # init overflow buffer
-            init = self.alloc_status()
-            # clear overflow buffer
-            self.clear_status(init)
+        # init overflow buffer
+        init = self.alloc_status()
+        # clear overflow buffer
+        self.clear_status(init)
         if sens is None:
             scaling_sens = self.loss_scale
         else:
@@ -251,14 +237,10 @@ class TrainOneStepWithLossScaleCell(Cell):
         if self.reducer_flag:
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
-        if not self.gpu_target:
-            # get the overflow buffer
-            self.get_status(init)
-            # sum overflow buffer elements, 0:not overflow , >0:overflow
-            flag_sum = self.reduce_sum(init, (0,))
-        else:
-            flag_sum = self.hyper_map(F.partial(_grad_overflow), grads)
-            flag_sum = self.addn(flag_sum)
+        # get the overflow buffer
+        self.get_status(init)
+        # sum overflow buffer elements, 0:not overflow , >0:overflow
+        flag_sum = self.reduce_sum(init, (0,))
         if self.is_distributed:
             # sum overflow flag over devices
             flag_reduce = self.allreduce(flag_sum)
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index bc300cb670..846be05c4d 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,
-                       NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
+                       NPUGetFloatStatus, Pow, RealDiv,
                        Reciprocal, CumSum,
                        Sin, Sqrt, Rsqrt,
                        Square, Sub, TensorAdd, Sign, Round)
@@ -151,10 +151,6 @@ __all__ = [
     'Neg',
     'Slice',
     'DType',
-    'IsNan',
-    'IsInf',
-    'IsFinite',
-    'FloatStatus',
     'NPUAllocFloatStatus',
     'NPUGetFloatStatus',
     'NPUClearFloatStatus',
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 5f8c24d78b..175b72560f 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1541,89 +1541,6 @@ class LogicalOr(_LogicBinaryOp):
     def infer_dtype(self, x_dtype, y_dtype):
         return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
 
-class IsNan(PrimitiveWithInfer):
-    """
-    Judging which elements are nan for each position
-    Inputs:
-        - **input_x** (Tensor) - The input tensor.
-
-    Outputs:
-        Tensor, has the same shape of input.
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """init IsNan"""
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-
-    def infer_shape(self, x_shape):
-        return x_shape
-
-    def infer_dtype(self, x_dtype):
-        return mstype.bool_
-
-class IsInf(PrimitiveWithInfer):
-    """
-    Judging which elements are inf or -inf for each position
-    Inputs:
-        - **input_x** (Tensor) - The input tensor.
-
-    Outputs:
-        Tensor, has the same shape of input.
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """init IsInf"""
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-
-    def infer_shape(self, x_shape):
-        return x_shape
-
-    def infer_dtype(self, x_dtype):
-        return mstype.bool_
-
-class IsFinite(PrimitiveWithInfer):
-    """
-    Judging which elements are finite for each position
-    Inputs:
-        - **input_x** (Tensor) - The input tensor.
-
-    Outputs:
-        Tensor, has the same shape of input.
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """init IsFinite"""
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-
-    def infer_shape(self, x_shape):
-        return x_shape
-
-    def infer_dtype(self, x_dtype):
-        return mstype.bool_
-
-class FloatStatus(PrimitiveWithInfer):
-    """
-    Determine if the elements contains nan, inf or -inf
-    Inputs:
-        - **input_x** (Tensor) - The input tensor.
-
-    Outputs:
-        Tensor, has the shape of `(1,)`.
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """init FloatStatus"""
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-
-    def infer_shape(self, x_shape):
-        return [1]
-
-    def infer_dtype(self, x_dtype):
-        return x_dtype
 
 class NPUAllocFloatStatus(PrimitiveWithInfer):
     """

From c68826567176b88e78d46788249b2d3a8566bba4 Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Fri, 3 Apr 2020 16:53:45 +0800
Subject: [PATCH 111/367] fix: when use MindDataset block_reade=True hung

---
 mindspore/ccsrc/mindrecord/io/shard_reader.cc |  2 ++
 mindspore/mindrecord/filewriter.py            |  1 +
 mindspore/mindrecord/tools/cifar100_to_mr.py  |  9 ++++---
 tests/ut/python/dataset/test_minddataset.py   | 27 ++++++++++++++++---
 .../mindrecord/test_cifar100_to_mindrecord.py |  4 ++-
 .../mindrecord/test_mindrecord_exception.py   |  8 +++++-
 6 files changed, 43 insertions(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index 791de6c60b..32825fd9df 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -785,6 +785,8 @@ vector<std::string> ShardReader::GetAllColumns() {
 
 MSRStatus ShardReader::CreateTasksByBlock(const std::vector<std::tuple<int, int, int, uint64_t>> &row_group_summary,
                                           const std::vector<std::shared_ptr<ShardOperator>> &operators) {
+  vector<std::string> columns = GetAllColumns();
+  CheckIfColumnInIndex(columns);
   for (const auto &rg : row_group_summary) {
     auto shard_id = std::get<0>(rg);
     auto group_id = std::get<1>(rg);
diff --git a/mindspore/mindrecord/filewriter.py b/mindspore/mindrecord/filewriter.py
index d1471f47cb..4056825ff3 100644
--- a/mindspore/mindrecord/filewriter.py
+++ b/mindspore/mindrecord/filewriter.py
@@ -143,6 +143,7 @@ class FileWriter:
             ParamTypeError: If index field is invalid.
             MRMDefineIndexError: If index field is not primitive type.
             MRMAddIndexError: If failed to add index field.
+            MRMGetMetaError: If the schema is not set or get meta failed.
         """
         if not index_fields or not isinstance(index_fields, list):
             raise ParamTypeError('index_fields', 'list')
diff --git a/mindspore/mindrecord/tools/cifar100_to_mr.py b/mindspore/mindrecord/tools/cifar100_to_mr.py
index a359de853d..c011c8f4b0 100644
--- a/mindspore/mindrecord/tools/cifar100_to_mr.py
+++ b/mindspore/mindrecord/tools/cifar100_to_mr.py
@@ -24,7 +24,7 @@ from mindspore import log as logger
 from .cifar100 import Cifar100
 from ..common.exceptions import PathNotExistsError
 from ..filewriter import FileWriter
-from ..shardutils import check_filename
+from ..shardutils import check_filename, SUCCESS
 try:
     cv2 = import_module("cv2")
 except ModuleNotFoundError:
@@ -98,8 +98,11 @@ class Cifar100ToMR:
         data_list = _construct_raw_data(images, fine_labels, coarse_labels)
         test_data_list = _construct_raw_data(test_images, test_fine_labels, test_coarse_labels)
 
-        _generate_mindrecord(self.destination, data_list, fields, "img_train")
-        _generate_mindrecord(self.destination + "_test", test_data_list, fields, "img_test")
+        if _generate_mindrecord(self.destination, data_list, fields, "img_train") != SUCCESS:
+            return FAILED
+        if _generate_mindrecord(self.destination + "_test", test_data_list, fields, "img_test") != SUCCESS:
+            return FAILED
+        return SUCCESS
 
 def _construct_raw_data(images, fine_labels, coarse_labels):
     """
diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py
index da22f5c3b7..460a728b5c 100644
--- a/tests/ut/python/dataset/test_minddataset.py
+++ b/tests/ut/python/dataset/test_minddataset.py
@@ -47,7 +47,9 @@ def add_and_remove_cv_file():
         os.remove("{}.db".format(x)) if os.path.exists("{}.db".format(x)) else None
     writer = FileWriter(CV_FILE_NAME, FILES_NUM)
     data = get_data(CV_DIR_NAME)
-    cv_schema_json = {"file_name": {"type": "string"}, "label": {"type": "int32"},
+    cv_schema_json = {"id": {"type": "int32"},
+                      "file_name": {"type": "string"},
+                      "label": {"type": "int32"},
                       "data": {"type": "bytes"}}
     writer.add_schema(cv_schema_json, "img_schema")
     writer.add_index(["file_name", "label"])
@@ -226,6 +228,24 @@ def test_cv_minddataset_blockreader_tutorial(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 20
 
+def test_cv_minddataset_blockreader_some_field_not_in_index_tutorial(add_and_remove_cv_file):
+    """tutorial for cv minddataset."""
+    columns_list = ["id", "data", "label"]
+    num_readers = 4
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False,
+                              block_reader=True)
+    assert data_set.get_dataset_size() == 10
+    repeat_num = 2
+    data_set = data_set.repeat(repeat_num)
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info("-------------- block reader repeat tow {} -----------------".format(num_iter))
+        logger.info("-------------- item[id]: {} ----------------------------".format(item["id"]))
+        logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
+        logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+        num_iter += 1
+    assert num_iter == 20
+
 
 def test_cv_minddataset_reader_basic_tutorial(add_and_remove_cv_file):
     """tutorial for cv minderdataset."""
@@ -359,13 +379,14 @@ def get_data(dir_name):
         lines = file_reader.readlines()
 
     data_list = []
-    for line in lines:
+    for i, line in enumerate(lines):
         try:
             filename, label = line.split(",")
             label = label.strip("\n")
             with open(os.path.join(img_dir, filename), "rb") as file_reader:
                 img = file_reader.read()
-            data_json = {"file_name": filename,
+            data_json = {"id": i,
+                         "file_name": filename,
                          "data": img,
                          "label": int(label)}
             data_list.append(data_json)
diff --git a/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py b/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py
index b3a8d94589..e95f25aae4 100644
--- a/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py
+++ b/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py
@@ -18,6 +18,7 @@ import pytest
 from mindspore.mindrecord import Cifar100ToMR
 from mindspore.mindrecord import FileReader
 from mindspore.mindrecord import MRMOpenError
+from mindspore.mindrecord import SUCCESS
 from mindspore import log as logger
 
 CIFAR100_DIR = "../data/mindrecord/testCifar100Data"
@@ -26,7 +27,8 @@ MINDRECORD_FILE = "./cifar100.mindrecord"
 def test_cifar100_to_mindrecord_without_index_fields():
     """test transform cifar100 dataset to mindrecord without index fields."""
     cifar100_transformer = Cifar100ToMR(CIFAR100_DIR, MINDRECORD_FILE)
-    cifar100_transformer.transform()
+    ret = cifar100_transformer.transform()
+    assert ret == SUCCESS, "Failed to tranform from cifar100 to mindrecord"
     assert os.path.exists(MINDRECORD_FILE)
     assert os.path.exists(MINDRECORD_FILE + "_test")
     read()
diff --git a/tests/ut/python/mindrecord/test_mindrecord_exception.py b/tests/ut/python/mindrecord/test_mindrecord_exception.py
index 0a51fbf4e7..1f7a3f859d 100644
--- a/tests/ut/python/mindrecord/test_mindrecord_exception.py
+++ b/tests/ut/python/mindrecord/test_mindrecord_exception.py
@@ -16,7 +16,7 @@
 import os
 import pytest
 from mindspore.mindrecord import FileWriter, FileReader, MindPage
-from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError
+from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError, MRMGetMetaError
 from mindspore import log as logger
 from utils import get_data
 
@@ -280,3 +280,9 @@ def test_cv_file_writer_shard_num_greater_than_1000():
     with pytest.raises(ParamValueError) as err:
         FileWriter(CV_FILE_NAME, 1001)
     assert 'Shard number should between' in str(err.value)
+
+def test_add_index_without_add_schema():
+    with pytest.raises(MRMGetMetaError) as err:
+        fw = FileWriter(CV_FILE_NAME)
+        fw.add_index(["label"])
+    assert 'Failed to get meta info' in str(err.value)

From 22c6baeea2b1b5f26ead36dabbcafc456da62aba Mon Sep 17 00:00:00 2001
From: WeibiaoYu <weibiao.yu@huawei.com>
Date: Thu, 2 Apr 2020 08:43:13 -0400
Subject: [PATCH 112/367] Support to config whether to save integeated
 checkpoint, in auto model parallel scene

---
 mindspore/common/api.py                | 35 --------------------------
 mindspore/train/callback.py            | 16 +++++++++---
 mindspore/train/serialization.py       |  7 +++---
 tests/ut/python/utils/test_callback.py |  4 +--
 4 files changed, 19 insertions(+), 43 deletions(-)

diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 8e23e9184d..371ebbb445 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -374,9 +374,6 @@ class _Executor:
                 obj.parameter_layout_dict = self._executor.get_parameter_layout(phase)
                 obj.load_parameter_slice(params)
 
-            if _get_parallel_mode() in ["hybrid_parallel"]:
-                obj.parameter_layout_dict = self._build_parameter_layout(obj)
-
         # the following GE init process is not needed when use vm or ms backend
         if enable_ge:
             # decide whether to sink based on whether the inputs is virtual or not
@@ -449,38 +446,6 @@ class _Executor:
             return self._exec_pip(obj, *args, phase=phase_real)
         raise KeyError('{} graph is not exist.'.format(phase_real))
 
-    def _build_parameter_layout(self, obj):
-        """
-        Build parameter layout, for layerwise_parallel parameter.
-
-        Args:
-            obj (Function or Cell): The function or cell instance need to be compiled.
-
-        Returns:
-            Dictionary, parameter layout info.
-        """
-        parameter_layout_dict = {}
-        layerwise_parallel_parameters = []
-        for key in obj.parameters_dict():
-            if obj.parameters_dict()[key].layerwise_parallel is True:
-                layerwise_parallel_parameters.append(key)
-
-        if not layerwise_parallel_parameters:
-            return parameter_layout_dict
-
-        from ..communication.management import get_group_size
-        group_size = [get_group_size()]
-        for key in layerwise_parallel_parameters:
-            tensor_map = [0]
-            shape = obj.parameters_dict()[key].data.shape()
-            for x in range(len(shape)):  # dim 0 set 0, others set -1
-                if x:
-                    tensor_map.append(-1)
-            layout = [group_size, tensor_map]
-            parameter_layout_dict[key] = layout
-
-        return parameter_layout_dict
-
     def del_net_res(self, net_id):
         self._executor.del_net_res(net_id)
 
diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py
index 62f847089d..dcf630342c 100644
--- a/mindspore/train/callback.py
+++ b/mindspore/train/callback.py
@@ -24,7 +24,7 @@ import mindspore.context as context
 from mindspore.train.serialization import _exec_save_checkpoint, _fill_param_into_net, _save_graph
 from mindspore.train._utils import _make_directory
 from mindspore import log as logger
-from mindspore._checkparam import check_int_non_negative
+from mindspore._checkparam import check_int_non_negative, check_bool
 from mindspore.common.tensor import Tensor
 from .summary.summary_record import _cache_summary_tensor_data
 
@@ -150,6 +150,8 @@ class CheckpointConfig:
         keep_checkpoint_max (int): Maximum step to save checkpoint. Default: 5.
         keep_checkpoint_per_n_minutes (int): Keep one checkpoint every n minutes. Default: 0.
             Can't be used with keep_checkpoint_max at the same time.
+        integrated_save (bool): Whether to intergrated save in automatic model parall scene. Default: True.
+            Integrated save function is only supported in automatic parall scene, not supported in manual parallel.
 
     Raises:
         ValueError: If the input_param is None or 0.
@@ -163,7 +165,8 @@ class CheckpointConfig:
                  save_checkpoint_steps=1,
                  save_checkpoint_seconds=0,
                  keep_checkpoint_max=5,
-                 keep_checkpoint_per_n_minutes=0):
+                 keep_checkpoint_per_n_minutes=0,
+                 integrated_save=True):
 
         if not save_checkpoint_steps and not save_checkpoint_seconds and \
                 not keep_checkpoint_max and not keep_checkpoint_per_n_minutes:
@@ -191,6 +194,8 @@ class CheckpointConfig:
             if not self._keep_checkpoint_per_n_minutes or self._keep_checkpoint_per_n_minutes == 0:
                 self._keep_checkpoint_max = 1
 
+        self._integrated_save = check_bool(integrated_save)
+
     @property
     def save_checkpoint_steps(self):
         """Get the value of _save_checkpoint_steps."""
@@ -211,6 +216,11 @@ class CheckpointConfig:
         """Get the value of _keep_checkpoint_per_n_minutes."""
         return self._keep_checkpoint_per_n_minutes
 
+    @property
+    def integrated_save(self):
+        """Get the value of _integrated_save."""
+        return self._integrated_save
+
     def get_checkpoint_policy(self):
         """Get the policy of checkpoint."""
         checkpoint_policy = {'save_checkpoint_steps': self._save_checkpoint_steps,
@@ -619,7 +629,7 @@ class ModelCheckpoint(Callback):
                 _set_cur_net(cb_params.train_network)
                 cb_params.train_network.exec_checkpoint_graph()
 
-            _exec_save_checkpoint(cb_params.train_network, gen_file)
+            _exec_save_checkpoint(cb_params.train_network, gen_file, self._config.integrated_save)
 
             if os.path.exists(gen_file):
                 shutil.move(gen_file, cur_file)
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 0478bbc071..b334c3e9d8 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -279,13 +279,14 @@ def _save_graph(network, file_name):
         os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
 
 
-def _exec_save_checkpoint(train_network, ckpoint_file_name):
+def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True):
     """
     Saves checkpoint for 'ms' backend.
 
     Args:
         train_network (Network): The train network for training.
         ckpoint_file_name (str): The name of checkpoint file.
+        integrated_save (bool): Whether to intergrated save in automatic model parallel scene.
     """
 
     param_dict = {}
@@ -300,9 +301,9 @@ def _exec_save_checkpoint(train_network, ckpoint_file_name):
         else:
             param_data = Tensor(value.data)
 
-        # in model parallel scenario, some parameters were spliteds to all the devices,
+        # in automatic model parallel scenario, some parameters were spliteds to all the devices,
         # which should be combined before saving
-        if key in train_network.parameter_layout_dict:
+        if integrated_save and key in train_network.parameter_layout_dict:
             param_data = _get_merged_param_data(train_network, key, param_data)
 
         each_param["data"] = param_data
diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py
index 60e4c6527a..43cf827330 100644
--- a/tests/ut/python/utils/test_callback.py
+++ b/tests/ut/python/utils/test_callback.py
@@ -308,10 +308,10 @@ def test_RunContext():
 def test_Checkpoint_Config():
     """Test CheckpointConfig all None or 0."""
     with pytest.raises(ValueError):
-        CheckpointConfig(0, 0, 0, 0)
+        CheckpointConfig(0, 0, 0, 0, True)
 
     with pytest.raises(ValueError):
-        CheckpointConfig(0, None, 0, 0)
+        CheckpointConfig(0, None, 0, 0, True)
 
 
 def test_step_end_save_graph():

From ff38eff9ae94adb72604be0ae47ee07dab5c1498 Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Thu, 2 Apr 2020 21:56:48 +0800
Subject: [PATCH 113/367] add parameter check for Class Schema

---
 mindspore/dataset/engine/datasets.py   | 23 ++++++++----
 mindspore/dataset/engine/validators.py | 50 ++++++++++++++++++++++++++
 2 files changed, 66 insertions(+), 7 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index db2b5169d2..de604a67e9 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator
 from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \
     check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
-    check_zip_dataset
+    check_zip_dataset, check_add_column
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -2334,13 +2334,20 @@ class Schema:
             self.dataset_type = ''
             self.num_rows = 0
         else:
+            if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK):
+                raise ValueError("The file %s does not exist or permission denied!" % schema_file)
             try:
                 with open(schema_file, 'r') as load_f:
                     json_obj = json.load(load_f)
-                    self.from_json(json_obj)
             except json.decoder.JSONDecodeError:
-                raise RuntimeError("Schema file failed to load")
+                raise RuntimeError("Schema file failed to load.")
+            except UnicodeDecodeError:
+                raise RuntimeError("Schema file failed to decode.")
+            except Exception:
+                raise RuntimeError("Schema file failed to open.")
+            self.from_json(json_obj)
 
+    @check_add_column
     def add_column(self, name, de_type, shape=None):
         """
         Add new column to the schema.
@@ -2359,10 +2366,8 @@ class Schema:
         if isinstance(de_type, typing.Type):
             de_type = mstype_to_detype(de_type)
             new_column["type"] = str(de_type)
-        elif isinstance(de_type, str):
-            new_column["type"] = str(DataType(de_type))
         else:
-            raise ValueError("Unknown column type")
+            new_column["type"] = str(DataType(de_type))
 
         if shape is not None:
             new_column["shape"] = shape
@@ -2391,7 +2396,7 @@ class Schema:
         Parse the columns and add it to self.
 
         Args:
-            columns (list[str]): names of columns.
+            columns (dict or list[str]): names of columns.
 
         Raises:
             RuntimeError: If failed to parse schema file.
@@ -2399,6 +2404,8 @@ class Schema:
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
+        if columns is None:
+            raise TypeError("Expected non-empty dict or string list.")
         self.columns = []
         for col in columns:
             name = None
@@ -2443,6 +2450,8 @@ class Schema:
             RuntimeError: if dataset type is missing in the object.
             RuntimeError: if columns are missing in the object.
         """
+        if not isinstance(json_obj, dict) or json_obj is None:
+            raise ValueError("Expected non-empty dict.")
         for k, v in json_obj.items():
             if k == "datasetType":
                 self.dataset_type = v
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index b4d22a4a01..26d6241945 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -19,10 +19,15 @@ import inspect as ins
 import os
 from functools import wraps
 from multiprocessing import cpu_count
+from mindspore._c_expression import typing
 from . import samplers
 from . import datasets
 
 INT32_MAX = 2147483647
+valid_detype = [
+    "bool", "int8", "int16", "int32", "int64", "uint8", "uint16",
+    "uint32", "uint64", "float16", "float32", "float64"
+]
 
 
 def check(method):
@@ -188,6 +193,12 @@ def check(method):
     return wrapper
 
 
+def check_valid_detype(type_):
+    if type_ not in valid_detype:
+        raise ValueError("Unknown column type")
+    return True
+
+
 def check_filename(path):
     """
     check the filename in the path
@@ -743,3 +754,42 @@ def check_project(method):
         return method(*args, **kwargs)
 
     return new_method
+
+
+def check_shape(shape, name):
+    if isinstance(shape, list):
+        for element in shape:
+            if not isinstance(element, int):
+                raise TypeError(
+                    "Each element in {0} should be of type int. Got {1}.".format(name, type(element)))
+    else:
+        raise TypeError("Expected int list.")
+
+
+def check_add_column(method):
+    """check the input arguments of add_column."""
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        # check name; required argument
+        name = param_dict.get("name")
+        if not isinstance(name, str) or not name:
+            raise TypeError("Expected non-empty string.")
+
+        # check type; required argument
+        de_type = param_dict.get("de_type")
+        if de_type is not None:
+            if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type):
+                raise ValueError("Unknown column type.")
+        else:
+            raise TypeError("Expected non-empty string.")
+
+        # check shape
+        shape = param_dict.get("shape")
+        if shape is not None:
+            check_shape(shape, "shape")
+
+        return method(*args, **kwargs)
+
+    return new_method

From c4f9230f037a23d4a66470e76319af6204dbf569 Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Tue, 7 Apr 2020 20:39:51 +0800
Subject: [PATCH 114/367] usr mindspore. instead of mstype.

---
 mindspore/common/api.py               | 4 ++--
 mindspore/common/initializer.py       | 2 +-
 mindspore/dataset/engine/datasets.py  | 4 ++--
 mindspore/ops/operations/array_ops.py | 7 ++++---
 mindspore/ops/operations/math_ops.py  | 4 ++--
 mindspore/train/model.py              | 2 +-
 6 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 9ee95ef772..d392b33515 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -230,8 +230,8 @@ def ms_function(fn=None, obj=None, input_signature=None):
         >>>     z = F.tensor_add(x, y)
         >>>     return z
         >>>
-        >>> @ms_function(input_signature=(MetaTensor(mstype.float32, (1, 1, 3, 3)),
-        >>>                               MetaTensor(mstype.float32, (1, 1, 3, 3))))
+        >>> @ms_function(input_signature=(MetaTensor(mindspore.float32, (1, 1, 3, 3)),
+        >>>                               MetaTensor(mindspore.float32, (1, 1, 3, 3))))
         >>> def tensor_add_with_sig(x, y):
         >>>     z = F.tensor_add(x, y)
         >>>     return z
diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py
index bdc3418129..4261621272 100644
--- a/mindspore/common/initializer.py
+++ b/mindspore/common/initializer.py
@@ -282,7 +282,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
         Tensor, initialized tensor.
 
     Examples:
-        >>> tensor = initializer('ones', [1, 2, 3], mstype.float32)
+        >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
     """
     if not isinstance(init, (Tensor, numbers.Number, str, Initializer)):
         raise TypeError('Unsupported init type.')
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index db2b5169d2..658cc3ff49 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1814,7 +1814,7 @@ class TFRecordDataset(SourceDataset):
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files)
         >>> # 2) get all rows from dataset_files with user-defined schema:
         >>> schema = ds.Schema()
-        >>> schema.add_column('col_1d', de_type=mstype.int64, shape=[2])
+        >>> schema.add_column('col_1d', de_type=mindspore.int64, shape=[2])
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema=schema)
         >>> # 3) get all rows from dataset_files with schema file "./schema.json":
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema="./schema.json")
@@ -2325,7 +2325,7 @@ class Schema:
         >>> import mindspore.common.dtype as mstype
         >>> # create schema, specify column name, mindspore.dtype and shape of the column
         >>> schema = ds.Schema()
-        >>> schema.add_column('col1', de_type=mstype.int64, shape=[2])
+        >>> schema.add_column('col1', de_type=mindspore.int64, shape=[2])
     """
 
     def __init__(self, schema_file=None):
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index fdad46a31f..b91c2cbc7d 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1535,7 +1535,8 @@ class StridedSlice(PrimitiveWithInfer):
             - Finally, the output is [3, 3, 3].
 
     Examples
-        >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]])
+        >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
+        >>>                   [[5, 5, 5], [6, 6, 6]]], mindspore.float32)
         >>> slice = StridedSlice()
         >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))
         >>> output.shape()
@@ -2067,7 +2068,7 @@ class SpaceToBatch(PrimitiveWithInfer):
         >>> block_size = 2
         >>> paddings = [[0, 0], [0, 0]]
         >>> space_to_batch = P.SpaceToBatch(block_size, paddings)
-        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32)
+        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
         >>> space_to_batch(x)
         [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
 
@@ -2135,7 +2136,7 @@ class BatchToSpace(PrimitiveWithInfer):
         >>> block_size = 2
         >>> crops = [[0, 0], [0, 0]]
         >>> op = P.BatchToSpace(block_size, crops)
-        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32)
+        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
         >>> output = op(x)
         [[[[1., 2.], [3., 4.]]]]
 
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 175b72560f..9b87560735 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1908,8 +1908,8 @@ class Atan2(_MathBinaryOp):
         Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
 
     Examples:
-         >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32)
-         >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32)
+         >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32)
+         >>> input_y = Tensor(np.array([[1, 1]]), mindspore.float32)
          >>> atan2 = P.Atan2()
          >>> atan2(input_x, input_y)
          [[0. 0.7853982]]
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index bcfd897f58..41b372f85a 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -528,7 +528,7 @@ class Model:
             Tensor, array(s) of predictions.
 
         Examples:
-            >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mstype.float32)
+            >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
             >>> model = Model(Net())
             >>> model.predict(input_data)
         """

From 84d780c1a45d1d2c672fe65518478a524eddae2f Mon Sep 17 00:00:00 2001
From: Alexey Shevlyakov <alexey.shevlyakov@huawei.com>
Date: Tue, 7 Apr 2020 13:32:52 -0400
Subject: [PATCH 115/367] remove make_unique.h

---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    |  7 ++-
 .../ccsrc/dataset/core/global_context.cc      |  6 +--
 mindspore/ccsrc/dataset/core/tensor.cc        |  5 +--
 mindspore/ccsrc/dataset/engine/data_buffer.cc |  2 +-
 mindspore/ccsrc/dataset/engine/data_schema.cc |  8 ++--
 .../dataset/engine/datasetops/batch_op.cc     | 16 +++----
 .../dataset/engine/datasetops/dataset_op.cc   | 10 ++---
 .../engine/datasetops/device_queue_op.cc      |  2 +-
 .../ccsrc/dataset/engine/datasetops/map_op.cc |  2 +-
 .../dataset/engine/datasetops/parallel_op.cc  |  2 +-
 .../dataset/engine/datasetops/project_op.cc   |  2 +-
 .../dataset/engine/datasetops/rename_op.cc    |  4 +-
 .../dataset/engine/datasetops/shuffle_op.cc   | 12 +++---
 .../engine/datasetops/source/celeba_op.cc     | 24 +++++------
 .../engine/datasetops/source/cifar_op.cc      | 24 +++++------
 .../engine/datasetops/source/generator_op.cc  |  8 ++--
 .../datasetops/source/image_folder_op.cc      | 26 +++++------
 .../engine/datasetops/source/manifest_op.cc   | 22 +++++-----
 .../engine/datasetops/source/mindrecord_op.cc | 43 ++++++++++---------
 .../engine/datasetops/source/mnist_op.cc      | 26 +++++------
 .../source/sampler/distributed_sampler.cc     |  6 +--
 .../datasetops/source/sampler/pk_sampler.cc   |  6 +--
 .../source/sampler/random_sampler.cc          |  8 ++--
 .../datasetops/source/sampler/sampler.cc      |  2 +-
 .../datasetops/source/sampler/sampler.h       |  1 -
 .../source/sampler/sequential_sampler.cc      |  6 +--
 .../source/sampler/subset_random_sampler.cc   |  6 +--
 .../source/sampler/weighted_random_sampler.cc | 10 ++---
 .../datasetops/source/storage_client.cc       |  9 ++--
 .../engine/datasetops/source/storage_op.cc    |  8 ++--
 .../engine/datasetops/source/tf_buffer.cc     |  7 ++-
 .../engine/datasetops/source/tf_reader_op.cc  | 37 ++++++++--------
 .../engine/datasetops/source/voc_op.cc        | 22 +++++-----
 .../ccsrc/dataset/engine/datasetops/zip_op.cc | 14 +++---
 mindspore/ccsrc/dataset/engine/db_connector.h |  2 +-
 .../ccsrc/dataset/engine/execution_tree.cc    |  2 +-
 .../dataset/kernels/image/image_utils.cc      |  3 +-
 mindspore/ccsrc/dataset/kernels/py_func_op.cc |  1 -
 mindspore/ccsrc/dataset/util/arena.cc         |  1 -
 mindspore/ccsrc/dataset/util/circular_pool.cc |  4 +-
 mindspore/ccsrc/dataset/util/de_error.h       |  7 +++
 mindspore/ccsrc/dataset/util/list.h           |  3 +-
 mindspore/ccsrc/dataset/util/lock.cc          |  1 +
 mindspore/ccsrc/dataset/util/lock.h           |  1 -
 mindspore/ccsrc/dataset/util/make_unique.h    | 37 ----------------
 mindspore/ccsrc/dataset/util/queue.h          |  2 +-
 mindspore/ccsrc/dataset/util/task.h           |  1 -
 mindspore/ccsrc/device/gpu/blocking_queue.cc  |  3 +-
 .../kernel/gpu/math/bias_add_gpu_kernel.h     |  5 +--
 .../kernel/gpu/nn/bias_add_grad_gpu_kenel.h   |  5 +--
 .../ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h     |  5 +--
 .../kernel/gpu/nn/lstm_grad_data_gpu_kernel.h |  7 ++-
 .../gpu/nn/lstm_grad_weight_gpu_kernel.h      |  5 +--
 tests/ut/cpp/dataset/celeba_op_test.cc        |  2 +-
 tests/ut/cpp/dataset/cifar_op_test.cc         |  2 +-
 tests/ut/cpp/dataset/image_folder_op_test.cc  | 14 +++---
 tests/ut/cpp/dataset/manifest_op_test.cc      |  2 +-
 tests/ut/cpp/dataset/project_op_test.cc       |  2 +-
 .../cpp/dataset/stand_alone_samplers_test.cc  |  2 +-
 tests/ut/cpp/dataset/tfReader_op_test.cc      | 20 ++++-----
 60 files changed, 241 insertions(+), 291 deletions(-)
 delete mode 100644 mindspore/ccsrc/dataset/util/make_unique.h

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index d51204f659..53ec8e5565 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -23,7 +23,6 @@
 #include "dataset/engine/datasetops/source/image_folder_op.h"
 #include "dataset/engine/datasetops/source/mnist_op.h"
 #include "dataset/engine/datasetops/source/voc_op.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/core/tensor.h"
 #include "dataset/engine/dataset_iterator.h"
 #include "dataset/engine/datasetops/source/manifest_op.h"
@@ -123,7 +122,7 @@ Status DEPipeline::AssignRootNode(const DsOpPtr &dataset_op) { return (tree_->As
 Status DEPipeline::LaunchTreeExec() {
   RETURN_IF_NOT_OK(tree_->Prepare());
   RETURN_IF_NOT_OK(tree_->Launch());
-  iterator_ = make_unique<DatasetIterator>(tree_);
+  iterator_ = std::make_unique<DatasetIterator>(tree_);
   if (iterator_ == nullptr) RETURN_STATUS_UNEXPECTED("Cannot create an Iterator.");
   return Status::OK();
 }
@@ -311,7 +310,7 @@ Status DEPipeline::ParseStorageOp(const py::dict &args, std::shared_ptr<DatasetO
     if (!args["schema"].is_none()) {
       (void)builder->SetSchemaFile(ToString(args["schema"]));
     } else if (!args["schema_json_string"].is_none()) {
-      std::unique_ptr<DataSchema> schema = make_unique<DataSchema>();
+      std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
       std::string s = ToString(args["schema_json_string"]);
       RETURN_IF_NOT_OK(schema->LoadSchemaString(s, std::vector<std::string>()));
       (void)builder->SetNumRows(schema->num_rows());
@@ -689,7 +688,7 @@ Status DEPipeline::ParseTFReaderOp(const py::dict &args, std::shared_ptr<Dataset
     }
   }
   if (schema_exists) {
-    std::unique_ptr<DataSchema> schema = make_unique<DataSchema>();
+    std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
     if (args.contains("schema_file_path")) {
       RETURN_IF_NOT_OK(schema->LoadSchemaFile(ToString(args["schema_file_path"]), columns_to_load));
     } else {
diff --git a/mindspore/ccsrc/dataset/core/global_context.cc b/mindspore/ccsrc/dataset/core/global_context.cc
index 7e361a1f2c..3de8e0fcd8 100644
--- a/mindspore/ccsrc/dataset/core/global_context.cc
+++ b/mindspore/ccsrc/dataset/core/global_context.cc
@@ -55,9 +55,9 @@ Status GlobalContext::Init() {
   // For testing we can use Dummy pool instead
 
   // Create some tensor allocators for the different types and hook them into the pool.
-  tensor_allocator_ = mindspore::make_unique<Allocator<Tensor>>(mem_pool_);
-  cv_tensor_allocator_ = mindspore::make_unique<Allocator<CVTensor>>(mem_pool_);
-  int_allocator_ = mindspore::make_unique<IntAlloc>(mem_pool_);
+  tensor_allocator_ = std::make_unique<Allocator<Tensor>>(mem_pool_);
+  cv_tensor_allocator_ = std::make_unique<Allocator<CVTensor>>(mem_pool_);
+  int_allocator_ = std::make_unique<IntAlloc>(mem_pool_);
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/core/tensor.cc b/mindspore/ccsrc/dataset/core/tensor.cc
index 6aa34fa342..8f0eae459a 100644
--- a/mindspore/ccsrc/dataset/core/tensor.cc
+++ b/mindspore/ccsrc/dataset/core/tensor.cc
@@ -28,7 +28,6 @@
 #include "dataset/core/global_context.h"
 #include "dataset/core/pybind_support.h"
 #include "dataset/core/tensor_shape.h"
-#include "dataset/util/make_unique.h"
 
 namespace py = pybind11;
 namespace mindspore {
@@ -53,7 +52,7 @@ namespace dataset {
 Tensor::Tensor(const TensorShape &shape, const DataType &type) : shape_(shape), type_(type), data_(nullptr) {
   // grab the mem pool from global context and create the allocator for char data area
   std::shared_ptr<MemoryPool> global_pool = GlobalContext::Instance()->mem_pool();
-  data_allocator_ = mindspore::make_unique<Allocator<unsigned char>>(global_pool);
+  data_allocator_ = std::make_unique<Allocator<unsigned char>>(global_pool);
 }
 
 Tensor::Tensor(const TensorShape &shape, const DataType &type, const unsigned char *data) : Tensor(shape, type) {
@@ -137,7 +136,7 @@ Status Tensor::CreateTensor(std::shared_ptr<Tensor> *ptr, py::array arr) {
   if ((*ptr)->type_ == DataType::DE_UNKNOWN) RETURN_STATUS_UNEXPECTED("Invalid data type.");
 
   std::shared_ptr<MemoryPool> global_pool = GlobalContext::Instance()->mem_pool();
-  (*ptr)->data_allocator_ = mindspore::make_unique<Allocator<unsigned char>>(global_pool);
+  (*ptr)->data_allocator_ = std::make_unique<Allocator<unsigned char>>(global_pool);
   static_cast<void>((*ptr)->StartAddr());
   int64_t byte_size = (*ptr)->SizeInBytes();
   unsigned char *data = static_cast<unsigned char *>(arr.request().ptr);
diff --git a/mindspore/ccsrc/dataset/engine/data_buffer.cc b/mindspore/ccsrc/dataset/engine/data_buffer.cc
index a0f47512ec..4aed994d3c 100644
--- a/mindspore/ccsrc/dataset/engine/data_buffer.cc
+++ b/mindspore/ccsrc/dataset/engine/data_buffer.cc
@@ -40,7 +40,7 @@ Status DataBuffer::CreateDataBuffer(
       case DatasetType::kTf: {
         // This type of buffer is for TF record data.
         // Allocate derived class version for a TF buffers
-        new_data_buffer = mindspore::make_unique<TFBuffer>(id, kDeBFlagNone, storage_client);
+        new_data_buffer = std::make_unique<TFBuffer>(id, kDeBFlagNone, storage_client);
         break;
       }
       default: {
diff --git a/mindspore/ccsrc/dataset/engine/data_schema.cc b/mindspore/ccsrc/dataset/engine/data_schema.cc
index 68666796be..4fe5d665c6 100644
--- a/mindspore/ccsrc/dataset/engine/data_schema.cc
+++ b/mindspore/ccsrc/dataset/engine/data_schema.cc
@@ -26,8 +26,8 @@
 #include "common/utils.h"
 #include "dataset/util/status.h"
 #include "dataset/core/tensor_shape.h"
-#include "dataset/util/make_unique.h"
 #include "utils/log_adapter.h"
+#include "dataset/util/de_error.h"
 
 namespace mindspore {
 namespace dataset {
@@ -58,7 +58,7 @@ ColDescriptor::ColDescriptor(const std::string &col_name, DataType col_type, Ten
   // our shape.  Otherwise, set our shape to be empty.
   if (in_shape != nullptr) {
     // Create a shape and copy construct it into our column's shape.
-    tensor_shape_ = mindspore::make_unique<TensorShape>(*in_shape);
+    tensor_shape_ = std::make_unique<TensorShape>(*in_shape);
   } else {
     tensor_shape_ = nullptr;
   }
@@ -75,7 +75,7 @@ ColDescriptor::ColDescriptor(const std::string &col_name, DataType col_type, Ten
 ColDescriptor::ColDescriptor(const ColDescriptor &in_cd)
     : type_(in_cd.type_), rank_(in_cd.rank_), tensor_impl_(in_cd.tensor_impl_), col_name_(in_cd.col_name_) {
   // If it has a tensor shape, make a copy of it with our own unique_ptr.
-  tensor_shape_ = in_cd.hasShape() ? mindspore::make_unique<TensorShape>(in_cd.shape()) : nullptr;
+  tensor_shape_ = in_cd.hasShape() ? std::make_unique<TensorShape>(in_cd.shape()) : nullptr;
 }
 
 // Assignment overload
@@ -86,7 +86,7 @@ ColDescriptor &ColDescriptor::operator=(const ColDescriptor &in_cd) {
     tensor_impl_ = in_cd.tensor_impl_;
     col_name_ = in_cd.col_name_;
     // If it has a tensor shape, make a copy of it with our own unique_ptr.
-    tensor_shape_ = in_cd.hasShape() ? mindspore::make_unique<TensorShape>(in_cd.shape()) : nullptr;
+    tensor_shape_ = in_cd.hasShape() ? std::make_unique<TensorShape>(in_cd.shape()) : nullptr;
   }
   return *this;
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
index 7c5d4bd4c8..8778fe1b45 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
@@ -59,8 +59,8 @@ Status BatchOp::operator()() {
   TaskManager::FindMe()->Post();
   int32_t epoch_num = 0, batch_num = 0, cnt = 0;
   TensorRow new_row;
-  std::unique_ptr<TensorQTable> table = make_unique<TensorQTable>();
-  child_iterator_ = mindspore::make_unique<ChildIterator>(this, 0, 0);
+  std::unique_ptr<TensorQTable> table = std::make_unique<TensorQTable>();
+  child_iterator_ = std::make_unique<ChildIterator>(this, 0, 0);
   RETURN_IF_NOT_OK(child_iterator_->FetchNextTensorRow(&new_row));
   column_name_map_ = child_iterator_->col_name_id_map();
   int32_t cur_batch_size = 0;
@@ -72,7 +72,7 @@ Status BatchOp::operator()() {
       if (table->size() == static_cast<size_t>(cur_batch_size)) {
         RETURN_IF_NOT_OK(worker_queues_[cnt++ % num_workers_]->EmplaceBack(
           std::make_pair(std::move(table), CBatchInfo(epoch_num, batch_num++, cnt - epoch_num))));
-        table = make_unique<TensorQTable>();
+        table = std::make_unique<TensorQTable>();
         RETURN_IF_NOT_OK(GetBatchSize(&cur_batch_size, CBatchInfo(epoch_num, batch_num, cnt - epoch_num)));
       }
       RETURN_IF_NOT_OK(child_iterator_->FetchNextTensorRow(&new_row));
@@ -82,7 +82,7 @@ Status BatchOp::operator()() {
       RETURN_IF_NOT_OK(worker_queues_[cnt++ % num_workers_]->EmplaceBack(
         std::make_pair(std::move(table), CBatchInfo(epoch_num, batch_num++, cnt - epoch_num))));
     }
-    table = make_unique<TensorQTable>();  // this drops when drop == true
+    table = std::make_unique<TensorQTable>();  // this drops when drop == true
     // end of the current epoch, batch_num should start from 0 again
     batch_num = 0;
     epoch_num++;
@@ -153,9 +153,9 @@ Status BatchOp::WorkerEntry(int32_t workerId) {
   RETURN_IF_NOT_OK(worker_queues_[workerId]->PopFront(&table_pair));
   while (table_pair.second.ctrl_ != batchCtrl::kQuit) {
     if (table_pair.second.ctrl_ == batchCtrl::kEOE) {
-      RETURN_IF_NOT_OK(out_connector_->Add(workerId, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(workerId, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
     } else if (table_pair.second.ctrl_ == batchCtrl::kEOF) {
-      RETURN_IF_NOT_OK(out_connector_->Add(workerId, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
+      RETURN_IF_NOT_OK(out_connector_->Add(workerId, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else if (table_pair.second.ctrl_ == batchCtrl::kNoCtrl) {
       std::unique_ptr<DataBuffer> db = nullptr;
       RETURN_IF_NOT_OK(MakeBatchedBuffer(std::move(table_pair), &db));
@@ -170,8 +170,8 @@ Status BatchOp::MakeBatchedBuffer(std::pair<std::unique_ptr<TensorQTable>, CBatc
                                   std::unique_ptr<DataBuffer> *db) {
   RETURN_UNEXPECTED_IF_NULL(table_pair.first);
   if (!input_column_names_.empty()) RETURN_IF_NOT_OK(MapColumns(&table_pair));  // pass it through pyfunc
-  (*db) = make_unique<DataBuffer>(table_pair.second.batch_num_, DataBuffer::kDeBFlagNone);
-  std::unique_ptr<TensorQTable> dest_table = make_unique<TensorQTable>();
+  (*db) = std::make_unique<DataBuffer>(table_pair.second.batch_num_, DataBuffer::kDeBFlagNone);
+  std::unique_ptr<TensorQTable> dest_table = std::make_unique<TensorQTable>();
   RETURN_IF_NOT_OK(BatchRows(&table_pair.first, &dest_table, table_pair.first->size()));
   (*db)->set_tensor_table(std::move(dest_table));
   (*db)->set_column_name_map(column_name_map_);
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
index d3b85b84fb..f51c2a1539 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
@@ -80,9 +80,9 @@ void DatasetOp::CreateConnector(int32_t num_producers, int32_t num_consumers) {
   MS_LOG(INFO) << "Creating connector in tree operator: " << operator_id_ << ". Producer: " << num_producers
                << ". Consumer: " << num_consumers << ".";
   if (oc_queue_size_ > 0) {
-    out_connector_ = mindspore::make_unique<DbConnector>(num_producers,  // The number of producers
-                                                         num_consumers,  // Only one consumer (the training App)
-                                                         oc_queue_size_);
+    out_connector_ = std::make_unique<DbConnector>(num_producers,  // The number of producers
+                                                   num_consumers,  // Only one consumer (the training App)
+                                                   oc_queue_size_);
   } else {
     // Some op's may choose not to have an output connector
     MS_LOG(INFO) << "Bypassed connector creation for tree operator: " << operator_id_ << ".";
@@ -149,7 +149,7 @@ Status DatasetOp::GetNextInput(std::unique_ptr<DataBuffer> *p_buffer, int32_t wo
 // The base class implementation simply flows the eoe message to output. Derived classes
 // may override if they need to perform special eoe handling.
 Status DatasetOp::EoeReceived(int32_t worker_id) {
-  std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+  std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
   return (out_connector_->Add(static_cast<int>(worker_id), std::move(eoe_buffer)));
 }
 
@@ -157,7 +157,7 @@ Status DatasetOp::EoeReceived(int32_t worker_id) {
 // The base class implementation simply flows the eof message to output. Derived classes
 // may override if they need to perform special eof handling.
 Status DatasetOp::EofReceived(int32_t worker_id) {
-  std::unique_ptr<DataBuffer> eof_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+  std::unique_ptr<DataBuffer> eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
   return (out_connector_->Add(static_cast<int>(worker_id), std::move(eof_buffer)));
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc
index 3c2eea16ee..71e4ce64a4 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc
@@ -225,7 +225,7 @@ Status DeviceQueueOp::SendDataToCPU() {
   MS_LOG(INFO) << "Device queue, sending data to CPU.";
   int64_t total_batch = 0;
 
-  std::unique_ptr<ChildIterator> child_iterator = mindspore::make_unique<ChildIterator>(this, 0, 0);
+  std::unique_ptr<ChildIterator> child_iterator = std::make_unique<ChildIterator>(this, 0, 0);
   while (!(child_iterator->eof_handled())) {
     TensorRow curr_row;
     RETURN_IF_NOT_OK(child_iterator->FetchNextTensorRow(&curr_row));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
index c9f1f98ae0..b6d603bac9 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
@@ -179,7 +179,7 @@ Status MapOp::WorkerEntry(int32_t worker_id) {
     RETURN_IF_NOT_OK(WorkerEntryInit(in_buffer.get(), &keep_input_columns, &to_process_indices, &final_col_name_id_map,
                                      &input_columns, &output_columns));
 
-    std::unique_ptr<TensorQTable> new_tensor_table(mindspore::make_unique<TensorQTable>());
+    std::unique_ptr<TensorQTable> new_tensor_table(std::make_unique<TensorQTable>());
     // Perform the compute function of TensorOp(s) and store the result in new_tensor_table.
     RETURN_IF_NOT_OK(WorkerCompute(in_buffer.get(), to_process_indices, new_tensor_table.get(), keep_input_columns,
                                    &input_columns, &output_columns));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc
index d9792312a3..4b2af2250a 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc
@@ -48,7 +48,7 @@ Status ParallelOp::CreateWorkerConnector(int32_t worker_connector_size) {
   // Instantiate the worker connector.  This is the internal connector, not the operators
   // output connector.  It has single master consuming from it (num producers is 1), and the number
   // of workers is the defined count from the op.
-  worker_connector_ = mindspore::make_unique<DbConnector>(num_workers_, num_producers_, worker_connector_size);
+  worker_connector_ = std::make_unique<DbConnector>(num_workers_, num_producers_, worker_connector_size);
 
   return Status::OK();
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc
index 11296f84f4..b87967dde8 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc
@@ -79,7 +79,7 @@ Status ProjectOp::Project(std::unique_ptr<DataBuffer> *data_buffer) {
     new_column_name_mapping[current_column] = i;
     projected_column_indices.push_back(column_name_mapping[current_column]);
   }
-  std::unique_ptr<TensorQTable> new_tensor_table = mindspore::make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> new_tensor_table = std::make_unique<TensorQTable>();
   while ((*data_buffer)->NumRows() > 0) {
     TensorRow current_row;
     RETURN_IF_NOT_OK((*data_buffer)->PopRow(&current_row));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc
index c09f56141e..725476bf91 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc
@@ -84,13 +84,13 @@ Status RenameOp::operator()() {
 
     // we got eoe, now try again until we get eof
     MS_LOG(INFO) << "Rename operator EOE Received.";
-    RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
+    RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
     MS_LOG(DEBUG) << "Rename operator fetching buffer after EOE.";
     RETURN_IF_NOT_OK(GetNextInput(&curr_buffer));
   }  // end of while eof loop
 
   MS_LOG(INFO) << "Rename opeerator EOF Received.";
-  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
+  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
index 5dae48ad73..2afafe2128 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
@@ -70,7 +70,7 @@ ShuffleOp::ShuffleOp(int32_t shuffle_size, uint32_t shuffle_seed, int32_t op_con
       rng_(shuffle_seed),
       buffer_counter_(0),
       rows_per_buffer_(rows_per_buffer),
-      shuffle_buffer_(mindspore::make_unique<TensorTable>()),
+      shuffle_buffer_(std::make_unique<TensorTable>()),
       shuffle_last_row_idx_(0),
       shuffle_buffer_state_(kShuffleStateInit) {}
 
@@ -90,7 +90,7 @@ Status ShuffleOp::SelfReset() {
     shuffle_seed_ = distribution(random_device);
     rng_ = std::mt19937_64(shuffle_seed_);
   }
-  shuffle_buffer_ = mindspore::make_unique<TensorTable>();
+  shuffle_buffer_ = std::make_unique<TensorTable>();
   buffer_counter_ = 0;
   shuffle_last_row_idx_ = 0;
   shuffle_buffer_state_ = kShuffleStateInit;
@@ -142,7 +142,7 @@ Status ShuffleOp::operator()() {
   // Create the child iterator to fetch our data from.
   int32_t worker_id = 0;
   int32_t child_idx = 0;
-  child_iterator_ = mindspore::make_unique<ChildIterator>(this, worker_id, child_idx);
+  child_iterator_ = std::make_unique<ChildIterator>(this, worker_id, child_idx);
 
   // Main operator loop
   while (true) {
@@ -161,7 +161,7 @@ Status ShuffleOp::operator()() {
       // Step 1)
       // Create an output tensor table if one is not created yet.
       if (!new_buffer_table) {
-        new_buffer_table = mindspore::make_unique<TensorQTable>();
+        new_buffer_table = std::make_unique<TensorQTable>();
       }
 
       // Step 2)
@@ -176,7 +176,7 @@ Status ShuffleOp::operator()() {
       // and send this buffer on it's way up the pipeline. Special case is if this is the
       // last row then we also send it.
       if (new_buffer_table->size() == rows_per_buffer_ || shuffle_last_row_idx_ == 0) {
-        auto new_buffer = mindspore::make_unique<DataBuffer>(buffer_counter_, DataBuffer::kDeBFlagNone);
+        auto new_buffer = std::make_unique<DataBuffer>(buffer_counter_, DataBuffer::kDeBFlagNone);
         new_buffer->set_tensor_table(std::move(new_buffer_table));
         new_buffer->set_column_name_map(column_name_map_);
         buffer_counter_++;
@@ -218,7 +218,7 @@ Status ShuffleOp::operator()() {
     // Since we overloaded eoeReceived function, we are responsible to flow the EOE up the
     // pipepline manually now that we are done draining the shuffle buffer
     MS_LOG(INFO) << "Shuffle operator sending EOE.";
-    auto eoe_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    auto eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
     RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
 
     // Do not wait for any reset to be flown down from operators above us.
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
index 570fc9f454..0c2e20729e 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
@@ -40,7 +40,7 @@ Status CelebAOp::Builder::Build(std::shared_ptr<CelebAOp> *op) {
     builder_sampler_ = std::make_shared<SequentialSampler>();
   }
 
-  builder_schema_ = make_unique<DataSchema>();
+  builder_schema_ = std::make_unique<DataSchema>();
   RETURN_IF_NOT_OK(
     builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
   // label is like this:0 1 0 0 1......
@@ -83,7 +83,7 @@ CelebAOp::CelebAOp(int32_t num_workers, int32_t rows_per_buffer, const std::stri
     col_name_map_[data_schema_->column(index).name()] = index;
   }
 
-  attr_info_queue_ = make_unique<Queue<std::vector<std::string>>>(queue_size);
+  attr_info_queue_ = std::make_unique<Queue<std::vector<std::string>>>(queue_size);
   io_block_queues_.Init(num_workers_, queue_size);
 }
 
@@ -311,7 +311,7 @@ Status CelebAOp::AddIOBlock(std::unique_ptr<DataBuffer> *data_buffer) {
         row_count++;
         if (row_count % rows_per_buffer_ == 0) {
           RETURN_IF_NOT_OK(io_block_queues_[buff_count++ % num_workers_]->Add(
-            make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+            std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
           keys.clear();
         }
       }
@@ -320,21 +320,21 @@ Status CelebAOp::AddIOBlock(std::unique_ptr<DataBuffer> *data_buffer) {
 
     if (!keys.empty()) {
       RETURN_IF_NOT_OK(io_block_queues_[(buff_count++) % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
+        io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
       for (int32_t i = 0; i < num_workers_; i++) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(std::move(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone))));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {  // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());  // Master thread goes to sleep after it has made all the IOBlocks
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(data_buffer));
@@ -349,17 +349,17 @@ Status CelebAOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
       if (keys.empty()) {
         return Status::OK();  // empty key is a quit signal for workers
       }
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
@@ -370,7 +370,7 @@ Status CelebAOp::WorkerEntry(int32_t worker_id) {
 }
 
 Status CelebAOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   for (const auto &key : keys) {
     TensorRow row;
     RETURN_IF_NOT_OK(LoadTensorRow(image_labels_vec_[key], &row));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
index 260a4a4dc5..3e64c8a3e6 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
@@ -47,7 +47,7 @@ Status CifarOp::Builder::Build(std::shared_ptr<CifarOp> *ptr) {
   if (sampler_ == nullptr) {
     sampler_ = std::make_shared<SequentialSampler>();
   }
-  schema_ = make_unique<DataSchema>();
+  schema_ = std::make_unique<DataSchema>();
   TensorShape scalar = TensorShape::CreateScalar();
   RETURN_IF_NOT_OK(schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
   if (cifar_type_ == kCifar10) {
@@ -91,7 +91,7 @@ CifarOp::CifarOp(CifarType type, int32_t num_works, int32_t rows_per_buf, const
     col_name_map_[data_schema_->column(i).name()] = i;
   }
   constexpr uint64_t kUtilQueueSize = 512;
-  cifar_raw_data_block_ = make_unique<Queue<std::vector<unsigned char>>>(kUtilQueueSize);
+  cifar_raw_data_block_ = std::make_unique<Queue<std::vector<unsigned char>>>(kUtilQueueSize);
   io_block_queues_.Init(num_workers_, queue_size);
 }
 
@@ -114,7 +114,7 @@ Status CifarOp::operator()() {
         if (row_cnt_ >= num_samples_) break;  // enough row read, break for loop
         if (row_cnt_ % rows_per_buffer_ == 0) {
           RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
-            make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+            std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
           keys.clear();
         }
       }
@@ -122,21 +122,21 @@ Status CifarOp::operator()() {
     }
     if (keys.empty() == false) {
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
       for (int32_t i = 0; i < num_workers_; i++) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {  // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());  // Master thread goes to sleep after it has made all the IOBlocks
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer));
@@ -169,17 +169,17 @@ Status CifarOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
       if (keys.empty() == true) {
         return Status::OK();  // empty key is a quit signal for workers
       }
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
@@ -213,7 +213,7 @@ Status CifarOp::LoadTensorRow(uint64_t index, TensorRow *trow) {
 
 // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
 Status CifarOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   for (const int64_t &key : keys) {
     TensorRow trow;
     RETURN_IF_NOT_OK(LoadTensorRow(key, &trow));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc
index ceb88ceb0e..37a74f019a 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc
@@ -173,9 +173,9 @@ Status GeneratorOp::operator()() {
   bool eof = false;
   while (!eof) {
     // Create new buffer each iteration
-    fetched_buffer = mindspore::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
+    fetched_buffer = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
     fetched_buffer->set_column_name_map(column_names_map_);
-    std::unique_ptr<TensorQTable> fetched_table = mindspore::make_unique<TensorQTable>();
+    std::unique_ptr<TensorQTable> fetched_table = std::make_unique<TensorQTable>();
     bool eoe = false;
     {
       py::gil_scoped_acquire gil_acquire;
@@ -201,12 +201,12 @@ Status GeneratorOp::operator()() {
     if (eoe) {
       // Push out EOE upon StopIteration exception from generator
       MS_LOG(INFO) << "Generator operator sends out EOE.";
-      std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+      std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
       RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
       if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
         // If last repeat or not repeated, push out EOF and exit master loop
         MS_LOG(INFO) << "Generator operator sends out EOF.";
-        std::unique_ptr<DataBuffer> eof_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+        std::unique_ptr<DataBuffer> eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
         RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer)));
         MS_LOG(INFO) << "Generator operator main execution loop complete.";
         eof = true;
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
index b8044fb38a..f6cf377666 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
@@ -39,7 +39,7 @@ Status ImageFolderOp::Builder::Build(std::shared_ptr<ImageFolderOp> *ptr) {
   if (builder_sampler_ == nullptr) {
     builder_sampler_ = std::make_shared<SequentialSampler>();
   }
-  builder_schema_ = make_unique<DataSchema>();
+  builder_schema_ = std::make_unique<DataSchema>();
   TensorShape scalar = TensorShape::CreateScalar();
   RETURN_IF_NOT_OK(
     builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
@@ -82,8 +82,8 @@ ImageFolderOp::ImageFolderOp(int32_t num_wkrs, int32_t rows_per_buffer, std::str
   for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) {
     col_name_map_[data_schema_->column(i).name()] = i;
   }
-  folder_name_queue_ = make_unique<Queue<std::string>>(num_wkrs * queue_size);
-  image_name_queue_ = make_unique<Queue<FolderImagesPair>>(num_wkrs * queue_size);
+  folder_name_queue_ = std::make_unique<Queue<std::string>>(num_wkrs * queue_size);
+  image_name_queue_ = std::make_unique<Queue<FolderImagesPair>>(num_wkrs * queue_size);
   io_block_queues_.Init(num_workers_, queue_size);
 }
 
@@ -143,7 +143,7 @@ Status ImageFolderOp::operator()() {
         row_cnt_++;
         if (row_cnt_ % rows_per_buffer_ == 0) {
           RETURN_IF_NOT_OK(
-            io_block_queues_[buf_cnt_++ % num_workers_]->Add(make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
+            io_block_queues_[buf_cnt_++ % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
           keys.clear();
         }
       }
@@ -151,21 +151,21 @@ Status ImageFolderOp::operator()() {
     }
     if (keys.empty() == false) {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
-      std::unique_ptr<IOBlock> eoe_block = make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
-      std::unique_ptr<IOBlock> eof_block = make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
+      std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
+      std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
       for (int32_t i = 0; i < num_workers_; ++i) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {  // not the last repeat. Sleep master thread, wait for the wake-up from reset
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());  // Master thread goes to sleep after it has made all the IOBlocks
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer));
@@ -182,15 +182,15 @@ Status ImageFolderOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
       if (keys.empty() == true) return Status::OK();  // empty key is a quit signal for workers
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
@@ -231,7 +231,7 @@ Status ImageFolderOp::LoadTensorRow(ImageLabelPair pairPtr, TensorRow *trow) {
 
 // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
 Status ImageFolderOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   TensorRow trow;
   for (const int64_t &key : keys) {
     RETURN_IF_NOT_OK(this->LoadTensorRow(image_label_pairs_[key], &trow));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
index 52db199e5b..6907647952 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
@@ -40,7 +40,7 @@ Status ManifestOp::Builder::Build(std::shared_ptr<ManifestOp> *ptr) {
   if (builder_sampler_ == nullptr) {
     builder_sampler_ = std::make_shared<SequentialSampler>();
   }
-  builder_schema_ = make_unique<DataSchema>();
+  builder_schema_ = std::make_unique<DataSchema>();
   RETURN_IF_NOT_OK(
     builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
   RETURN_IF_NOT_OK(
@@ -105,7 +105,7 @@ Status ManifestOp::AddIoBlock(std::unique_ptr<DataBuffer> *sampler_buffer) {
         row_cnt_++;
         if (row_cnt_ % rows_per_buffer_ == 0) {
           RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
-            make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+            std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
           keys.clear();
         }
       }
@@ -113,21 +113,21 @@ Status ManifestOp::AddIoBlock(std::unique_ptr<DataBuffer> *sampler_buffer) {
     }
     if (keys.empty() == false) {
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
       for (int32_t i = 0; i < num_workers_; i++) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());  // Master thread goes to sleep after it has made all the IOBlocks
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(sampler_buffer));
@@ -160,17 +160,17 @@ Status ManifestOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
       if (keys.empty()) {
         return Status::OK();  // empty key is a quit signal for workers
       }
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
@@ -227,7 +227,7 @@ Status ManifestOp::LoadTensorRow(const std::pair<std::string, std::vector<std::s
 
 // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
 Status ManifestOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   for (const auto &key : keys) {
     TensorRow trow;
     RETURN_IF_NOT_OK(LoadTensorRow(image_labelname_[static_cast<size_t>(key)], &trow));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
index b062371d7f..f490c9ff0b 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
@@ -30,7 +30,6 @@
 #include "dataset/engine/datasetops/dataset_op.h"
 #include "dataset/engine/db_connector.h"
 #include "dataset/engine/execution_tree.h"
-#include "dataset/util/make_unique.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
@@ -96,18 +95,18 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf
   io_blk_queues_.Init(num_workers_, op_connector_queue_size);
   if (!block_reader_) return;
   for (int32_t i = 0; i < num_workers_; ++i) {
-    block_buffer_.emplace_back(make_unique<std::vector<ShardTuple>>(std::vector<ShardTuple>{}));
+    block_buffer_.emplace_back(std::make_unique<std::vector<ShardTuple>>(std::vector<ShardTuple>{}));
   }
 }
 
 // Private helper method to encapsulate some common construction/reset tasks
 Status MindRecordOp::Init() {
-  shard_reader_ = mindspore::make_unique<ShardReader>();
+  shard_reader_ = std::make_unique<ShardReader>();
   auto rc = shard_reader_->Open(dataset_file_, num_mind_record_workers_, columns_to_load_, operators_, block_reader_);
 
   CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED, "MindRecordOp init failed.");
 
-  data_schema_ = mindspore::make_unique<DataSchema>();
+  data_schema_ = std::make_unique<DataSchema>();
 
   std::vector<std::shared_ptr<Schema>> schema_vec = shard_reader_->get_shard_header()->get_schemas();
   // check whether schema exists, if so use the first one
@@ -144,7 +143,7 @@ Status MindRecordOp::Init() {
   }
 
   if (!load_all_cols) {
-    std::unique_ptr<DataSchema> tmp_schema = make_unique<DataSchema>();
+    std::unique_ptr<DataSchema> tmp_schema = std::make_unique<DataSchema>();
     for (std::string colname : columns_to_load_) {
       CHECK_FAIL_RETURN_UNEXPECTED(colname_to_ind.find(colname) != colname_to_ind.end(), colname + ": doesn't exist");
       RETURN_IF_NOT_OK(tmp_schema->AddColumn(data_schema_->column(colname_to_ind[colname])));
@@ -298,7 +297,7 @@ Status MindRecordOp::LoadFloat(TensorShape *new_shape, std::unique_ptr<T[]> *arr
     RETURN_IF_NOT_OK(GetFloat(&value, columns_json[column_name], use_double));
 
     *new_shape = TensorShape::CreateScalar();
-    *array_data = mindspore::make_unique<T[]>(1);
+    *array_data = std::make_unique<T[]>(1);
     (*array_data)[0] = value;
   } else {
     if (column.hasShape()) {
@@ -309,7 +308,7 @@ Status MindRecordOp::LoadFloat(TensorShape *new_shape, std::unique_ptr<T[]> *arr
     }
 
     int idx = 0;
-    *array_data = mindspore::make_unique<T[]>(new_shape->NumOfElements());
+    *array_data = std::make_unique<T[]>(new_shape->NumOfElements());
     for (auto &element : columns_json[column_name]) {
       T value = 0;
       RETURN_IF_NOT_OK(GetFloat(&value, element, use_double));
@@ -350,7 +349,7 @@ Status MindRecordOp::LoadInt(TensorShape *new_shape, std::unique_ptr<T[]> *array
     RETURN_IF_NOT_OK(GetInt(&value, columns_json[column_name]));
 
     *new_shape = TensorShape::CreateScalar();
-    *array_data = mindspore::make_unique<T[]>(1);
+    *array_data = std::make_unique<T[]>(1);
     (*array_data)[0] = value;
   } else {
     if (column.hasShape()) {
@@ -361,7 +360,7 @@ Status MindRecordOp::LoadInt(TensorShape *new_shape, std::unique_ptr<T[]> *array
     }
 
     int idx = 0;
-    *array_data = mindspore::make_unique<T[]>(new_shape->NumOfElements());
+    *array_data = std::make_unique<T[]>(new_shape->NumOfElements());
     for (auto &element : columns_json[column_name]) {
       T value = 0;
       RETURN_IF_NOT_OK(GetInt(&value, element));
@@ -431,12 +430,14 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
+      RETURN_IF_NOT_OK(
+        out_connector_->Add(worker_id, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
       RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block));
       continue;
     }
     if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
+      RETURN_IF_NOT_OK(
+        out_connector_->Add(worker_id, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
       RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block));
       continue;
     }
@@ -486,9 +487,9 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) {
 
 Status MindRecordOp::GetBufferFromReader(std::unique_ptr<DataBuffer> *fetched_buffer, int64_t buffer_id,
                                          int32_t worker_id) {
-  *fetched_buffer = mindspore::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+  *fetched_buffer = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
   (*fetched_buffer)->set_column_name_map(column_name_mapping_);
-  std::unique_ptr<TensorQTable> tensor_table = mindspore::make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> tensor_table = std::make_unique<TensorQTable>();
   for (int32_t i = 0; i < rows_per_buffer_; ++i) {
     ShardTuple tupled_buffer;
     if (block_reader_) {
@@ -597,22 +598,22 @@ Status MindRecordOp::operator()() {
     for (int32_t i = 0; i < buffers_needed_; ++i) {
       if (block_reader_) RETURN_IF_NOT_OK(FetchBlockBuffer(i));
       std::vector<int64_t> keys(1, i);
-      RETURN_IF_NOT_OK(
-        io_blk_queues_[buf_cnt_++ % num_workers_]->Add(make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+      RETURN_IF_NOT_OK(io_blk_queues_[buf_cnt_++ % num_workers_]->Add(
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
       RETURN_IF_NOT_OK(
-        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(
-        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
+        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
       for (int32_t i = 0; i < num_workers_; i++) {
-        RETURN_IF_NOT_OK(
-          io_blk_queues_[i]->Add(std::move(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone))));
+        RETURN_IF_NOT_OK(io_blk_queues_[i]->Add(
+          std::move(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone))));
       }
       return Status::OK();
     } else {  // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset
       RETURN_IF_NOT_OK(
-        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
 
       // reset our buffer count and go to loop again.
       RETURN_IF_NOT_OK(shard_reader_wait_post_.Wait());
@@ -656,7 +657,7 @@ Status MindRecordOp::LaunchThreadAndInitOp() {
 }
 
 Status MindRecordOp::CountTotalRows(const std::string dataset_path, int64_t *count) {
-  std::unique_ptr<ShardReader> shard_reader = mindspore::make_unique<ShardReader>();
+  std::unique_ptr<ShardReader> shard_reader = std::make_unique<ShardReader>();
   MSRStatus rc = shard_reader->CountTotalRows(dataset_path, count);
   if (rc == MSRStatus::FAILED) {
     RETURN_STATUS_UNEXPECTED("MindRecordOp count total rows failed.");
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
index f76fb9314d..3431e58aea 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
@@ -43,7 +43,7 @@ Status MnistOp::Builder::Build(std::shared_ptr<MnistOp> *ptr) {
   if (builder_sampler_ == nullptr) {
     builder_sampler_ = std::make_shared<SequentialSampler>();
   }
-  builder_schema_ = make_unique<DataSchema>();
+  builder_schema_ = std::make_unique<DataSchema>();
   RETURN_IF_NOT_OK(
     builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kCv, 1)));
   TensorShape scalar = TensorShape::CreateScalar();
@@ -89,7 +89,7 @@ Status MnistOp::TraversalSampleIds(const std::shared_ptr<Tensor> &sample_ids, st
     row_cnt_++;
     if (row_cnt_ % rows_per_buffer_ == 0) {
       RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
       keys->clear();
     }
   }
@@ -115,21 +115,21 @@ Status MnistOp::operator()() {
     }
     if (keys.empty() == false) {
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
       for (int32_t i = 0; i < num_workers_; ++i) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());  // Master thread goes to sleep after it has made all the IOBlocks
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer));
@@ -145,15 +145,15 @@ Status MnistOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&iOBlock));
   while (iOBlock != nullptr) {
     if (iOBlock->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (iOBlock->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(iOBlock->GetKeys(&keys));
       if (keys.empty() == true) return Status::OK();  // empty key is a quit signal for workers
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
@@ -178,7 +178,7 @@ Status MnistOp::LoadTensorRow(const MnistLabelPair &mnist_pair, TensorRow *trow)
 
 // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
 Status MnistOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   TensorRow trow;
   for (const int64_t &key : keys) {
     RETURN_IF_NOT_OK(this->LoadTensorRow(image_label_pairs_[key], &trow));
@@ -309,8 +309,8 @@ Status MnistOp::ReadImageAndLabel(std::ifstream *image_reader, std::ifstream *la
   CHECK_FAIL_RETURN_UNEXPECTED((num_images == num_labels), "num_images != num_labels");
   // The image size of the Mnist dataset is fixed at [28,28]
   int64_t size = kMnistImageRows * kMnistImageCols;
-  auto images_buf = mindspore::make_unique<char[]>(size * num_images);
-  auto labels_buf = mindspore::make_unique<char[]>(num_images);
+  auto images_buf = std::make_unique<char[]>(size * num_images);
+  auto labels_buf = std::make_unique<char[]>(num_images);
   if (images_buf == nullptr || labels_buf == nullptr) {
     std::string err_msg = "Fail to allocate memory for MNIST Buffer.";
     MS_LOG(ERROR) << err_msg.c_str();
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
index 51ad71e8cf..28a5705648 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
@@ -52,9 +52,9 @@ Status DistributedSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer
   if (cnt_ > samples_per_buffer_) {
     RETURN_STATUS_UNEXPECTED("Distributed Sampler Error");
   } else if (cnt_ == samples_per_buffer_) {
-    (*out_buffer) = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = mindspore::make_unique<DataBuffer>(cnt_, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(cnt_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sample_ids;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, samples_per_buffer_));
     int64_t *id_ptr = reinterpret_cast<int64_t *>(sample_ids->StartAddr());
@@ -63,7 +63,7 @@ Status DistributedSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer
       *(id_ptr++) = shuffle_ ? shuffle_vec_[static_cast<size_t>(next_id)] : next_id;
     }
     TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, row));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
   }
   return Status::OK();
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
index 04a6ad17a2..8c8c12fce2 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
@@ -53,9 +53,9 @@ Status PKSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
   if (next_id_ > num_pk_samples_ || num_pk_samples_ == 0) {
     RETURN_STATUS_UNEXPECTED("Index out of bound in PKSampler");
   } else if (next_id_ == num_pk_samples_) {
-    (*out_buffer) = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = mindspore::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sample_ids;
     int64_t last_id =
       (samples_per_buffer_ + next_id_ > num_pk_samples_) ? num_pk_samples_ : samples_per_buffer_ + next_id_;
@@ -68,7 +68,7 @@ Status PKSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
       *(id_ptr++) = samples[rnd_ind];
     }
     TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, row));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
   }
   return Status::OK();
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
index de4d89d950..216f322052 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
@@ -32,9 +32,9 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
   if (next_id_ > num_samples_) {
     RETURN_STATUS_UNEXPECTED("RandomSampler Internal Error");
   } else if (next_id_ == num_samples_) {
-    (*out_buffer) = make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sampleIds;
     int64_t last_id = samples_per_buffer_ + next_id_ > num_samples_ ? num_samples_ : samples_per_buffer_ + next_id_;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, last_id - next_id_));
@@ -44,7 +44,7 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
     }
     next_id_ = last_id;
     TensorRow row(1, sampleIds);
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, row));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
   }
   return Status::OK();
 }
@@ -61,7 +61,7 @@ Status RandomSampler::Init(const RandomAccessOp *op) {
     }
     std::shuffle(shuffled_ids_.begin(), shuffled_ids_.end(), rnd_);
   } else {
-    dist = make_unique<std::uniform_int_distribution<int64_t>>(0, num_rows_ - 1);
+    dist = std::make_unique<std::uniform_int_distribution<int64_t>>(0, num_rows_ - 1);
   }
   rnd_.seed(seed_++);
   return Status::OK();
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
index 9818cd8a17..aa3838f8b5 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
@@ -35,7 +35,7 @@ Status Sampler::CreateSamplerTensor(std::shared_ptr<Tensor> *sample_ids, int64_t
   }
   if (col_desc_ == nullptr) {
     // a ColDescriptor for Tensor that holds SampleIds
-    col_desc_ = make_unique<ColDescriptor>("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1);
+    col_desc_ = std::make_unique<ColDescriptor>("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1);
   }
   TensorShape shape(std::vector<dsize_t>(1, num_elements));
   RETURN_IF_NOT_OK(Tensor::CreateTensor(sample_ids, col_desc_->tensorImpl(), shape, col_desc_->type()));
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
index d9a20f9170..801565508b 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
@@ -27,7 +27,6 @@
 #include "dataset/engine/data_buffer.h"
 #include "dataset/engine/data_schema.h"
 #include "dataset/engine/datasetops/dataset_op.h"
-#include "dataset/util/make_unique.h"
 
 namespace mindspore {
 namespace dataset {
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
index 71c3dd07c4..72131a6de1 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
@@ -25,9 +25,9 @@ Status SequentialSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer)
   if (next_id_ > num_samples_) {
     RETURN_STATUS_UNEXPECTED("Sequential Sampler Internal Error");
   } else if (next_id_ == num_samples_) {
-    (*out_buffer) = make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sampleIds;
     int64_t lastId = (samples_per_buffer_ + next_id_ > num_samples_) ? num_samples_ : samples_per_buffer_ + next_id_;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, lastId - next_id_));
@@ -36,7 +36,7 @@ Status SequentialSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer)
       *(idPtr++) = next_id_++;
     }
     TensorRow row(1, sampleIds);
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, row));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
   }
   return Status::OK();
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
index 4f727fcd04..16603939b3 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
@@ -64,9 +64,9 @@ Status SubsetRandomSampler::Reset() {
 Status SubsetRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
   // All samples have been drawn
   if (sample_id_ == indices_.size()) {
-    (*out_buffer) = make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> outputIds;
 
     int64_t last_id = sample_id_ + samples_per_buffer_;
@@ -92,7 +92,7 @@ Status SubsetRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffe
     }
 
     // Create a TensorTable from that single tensor and push into DataBuffer
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
   }
 
   return Status::OK();
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
index f305474182..f2957e74be 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
@@ -46,10 +46,10 @@ Status WeightedRandomSampler::Init(const RandomAccessOp *op) {
   CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && samples_per_buffer_ > 0, "Fail to init WeightedRandomSampler");
 
   if (!replacement_) {
-    exp_dist_ = mindspore::make_unique<std::exponential_distribution<>>(1);
+    exp_dist_ = std::make_unique<std::exponential_distribution<>>(1);
     InitOnePassSampling();
   } else {
-    discrete_dist_ = mindspore::make_unique<std::discrete_distribution<int64_t>>(weights_.begin(), weights_.end());
+    discrete_dist_ = std::make_unique<std::discrete_distribution<int64_t>>(weights_.begin(), weights_.end());
   }
 
   return Status::OK();
@@ -96,9 +96,9 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buf
   }
 
   if (sample_id_ == num_samples_) {
-    (*out_buffer) = make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
+    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
   } else {
-    (*out_buffer) = make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
+    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> outputIds;
 
     int64_t last_id = sample_id_ + samples_per_buffer_;
@@ -132,7 +132,7 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buf
     }
 
     // Create a TensorTable from that single tensor and push into DataBuffer
-    (*out_buffer)->set_tensor_table(make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
+    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
   }
 
   return Status::OK();
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc
index 95720a97be..862edcf63a 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc
@@ -24,7 +24,6 @@
 #include "dataset/engine/datasetops/source/storage_client.h"
 #include "dataset/engine/datasetops/source/storage_op.h"
 #include "dataset/engine/datasetops/source/tf_client.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/util/status.h"
 
 namespace mindspore {
@@ -57,7 +56,7 @@ static Status CreateStorageClientSwitch(
     case DatasetType::kTf: {
       // Construct the derived class TFClient, stored as base class StorageClient
       store_op->set_rows_per_buffer(32);
-      *out_client = mindspore::make_unique<TFClient>(std::move(schema), store_op);
+      *out_client = std::make_unique<TFClient>(std::move(schema), store_op);
       break;
     }
     case DatasetType::kUnknown:
@@ -83,7 +82,7 @@ Status StorageClient::CreateStorageClient(
   std::shared_ptr<StorageClient> *out_client) {  // Out: the created storage client
   // Make a new schema first.  This only assigns the dataset type.  It does not
   // create the columns yet.
-  auto new_schema = mindspore::make_unique<DataSchema>();
+  auto new_schema = std::make_unique<DataSchema>();
   RETURN_IF_NOT_OK(new_schema->LoadDatasetType(dataset_schema_path));
   RETURN_IF_NOT_OK(CreateStorageClientSwitch(std::move(new_schema), store_op, out_client));
   return Status::OK();
@@ -99,7 +98,7 @@ Status StorageClient::CreateStorageClient(
   std::shared_ptr<StorageClient> *out_client) {  // Out: the created storage client
   // The dataset type is passed in by the user.  Create an empty schema with only
   // only the dataset type filled in and then create the client with it.
-  auto new_schema = mindspore::make_unique<DataSchema>();
+  auto new_schema = std::make_unique<DataSchema>();
   new_schema->set_dataset_type(in_type);
   RETURN_IF_NOT_OK(CreateStorageClientSwitch(std::move(new_schema), store_op, out_client));
   return Status::OK();
@@ -147,7 +146,7 @@ Status StorageClient::AssignDatasetLayout(uint32_t num_rows,           // In: Th
   // The current schema was just an empty one with only the dataset field populated.
   // Let's copy construct a new one that will be a copy of the input schema (releasing the old
   // one) and then set the number of rows that the user requested.
-  data_schema_ = mindspore::make_unique<DataSchema>(schema);
+  data_schema_ = std::make_unique<DataSchema>(schema);
   CHECK_FAIL_RETURN_UNEXPECTED(num_rows <= MAX_INTEGER_INT32, "numRows exceeds the boundary numRows>2147483647");
   num_rows_in_dataset_ = num_rows;
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc
index 9da27eac04..2ca957ae6d 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc
@@ -303,7 +303,7 @@ Status StorageOp::init() {
     // For simplicity, we'll make both of them 3 so they are the same size.
     int32_t action_queue_size = (buffers_needed / num_workers_) + 1;
     for (int32_t i = 0; i < num_workers_; ++i) {
-      auto new_queue = mindspore::make_unique<Queue<int32_t>>(action_queue_size);
+      auto new_queue = std::make_unique<Queue<int32_t>>(action_queue_size);
       action_queue_.push_back(std::move(new_queue));
     }
   }
@@ -483,10 +483,10 @@ Status StorageOp::operator()() {
         // Post the control message to tell the workers to stop waiting on action queue
         // because we are done!
         RETURN_IF_NOT_OK(this->PostEndOfData());
-        std::unique_ptr<DataBuffer> eoeBuffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+        std::unique_ptr<DataBuffer> eoeBuffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
         RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoeBuffer)));
         MS_LOG(INFO) << "StorageOp master: Flow end-of-data eof message.";
-        std::unique_ptr<DataBuffer> eofBuffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+        std::unique_ptr<DataBuffer> eofBuffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
         RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eofBuffer)));
         MS_LOG(INFO) << "StorageOp master: Main execution loop complete.";
         done = true;  // while loop exit
@@ -496,7 +496,7 @@ Status StorageOp::operator()() {
         // RepeatOp above us somewhere in the tree will re-init us with the data to fetch again
         // once it gets the end-of-epoch message.
         MS_LOG(INFO) << "StorageOp master: Flow end-of-epoch eoe message.";
-        std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+        std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
         RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
 
         // reset our buffer count and go to loop again.
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc
index 766c2149c4..372dcd2c1c 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc
@@ -27,7 +27,6 @@
 #include "dataset/core/data_type.h"
 #include "dataset/engine/datasetops/source/storage_client.h"
 #include "dataset/engine/data_schema.h"
-#include "dataset/util/make_unique.h"
 
 namespace mindspore {
 namespace dataset {
@@ -72,7 +71,7 @@ Status TFBuffer::Load() {
   }
 
   // Construct the Tensor table for this buffer.
-  tensor_table_ = mindspore::make_unique<TensorQTable>();
+  tensor_table_ = std::make_unique<TensorQTable>();
 
   // At each position in the tensor table, instantiate the shared pointer to it's Tensor.
   uint32_t row = 0;
@@ -272,7 +271,7 @@ Status TFBuffer::LoadFloatList(const ColDescriptor &current_col, const dataengin
   // Identify how many values we have and then create a local array of these
   // to deserialize into
   *num_elements = float_list.value_size();
-  *float_array = mindspore::make_unique<float[]>(*num_elements);
+  *float_array = std::make_unique<float[]>(*num_elements);
   for (int i = 0; i < float_list.value_size(); i++) {
     (*float_array)[i] = float_list.value(i);
   }
@@ -294,7 +293,7 @@ Status TFBuffer::LoadIntList(const ColDescriptor &current_col, const dataengine:
   // Identify how many values we have and then create a local array of these
   // to deserialize into
   *num_elements = int64_list.value_size();
-  *int_array = mindspore::make_unique<int64_t[]>(*num_elements);
+  *int_array = std::make_unique<int64_t[]>(*num_elements);
   for (int i = 0; i < int64_list.value_size(); i++) {
     (*int_array)[i] = int64_list.value(i);
   }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
index c872c02015..0764d7e0ad 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc
@@ -36,7 +36,6 @@
 #include "dataset/engine/db_connector.h"
 #include "dataset/engine/execution_tree.h"
 #include "dataset/engine/jagged_connector.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/util/path.h"
 #include "dataset/util/queue.h"
 #include "dataset/util/random.h"
@@ -54,7 +53,7 @@ TFReaderOp::Builder::Builder()
   builder_op_connector_size_ = config_manager->op_connector_size();
   builder_rows_per_buffer_ = config_manager->rows_per_buffer();
   builder_shuffle_files_ = false;
-  builder_data_schema_ = make_unique<DataSchema>();
+  builder_data_schema_ = std::make_unique<DataSchema>();
 }
 
 Status TFReaderOp::Builder::ValidateInputs() const {
@@ -103,7 +102,7 @@ TFReaderOp::TFReaderOp(int32_t num_workers, int32_t worker_connector_size, int64
       finished_reading_dataset_(false),
       shuffle_files_(shuffle_files),
       data_schema_(std::move(data_schema)),
-      filename_index_(make_unique<StringIndex>()),
+      filename_index_(std::make_unique<StringIndex>()),
       load_io_block_queue_(true),
       load_jagged_connector_(true),
       num_rows_(0),
@@ -129,7 +128,7 @@ Status TFReaderOp::Init() {
   // parallel op base.
   RETURN_IF_NOT_OK(ParallelOp::CreateWorkerConnector(worker_connector_size_));
 
-  jagged_buffer_connector_ = mindspore::make_unique<JaggedConnector>(num_workers_, 1, worker_connector_size_);
+  jagged_buffer_connector_ = std::make_unique<JaggedConnector>(num_workers_, 1, worker_connector_size_);
 
   // temporary: make size large enough to hold all files + EOE to avoid hangs
   int32_t safe_queue_size = static_cast<int32_t>(std::ceil(dataset_files_list_.size() / num_workers_)) + 1;
@@ -229,7 +228,7 @@ Status TFReaderOp::operator()() {
     }
 
     // all workers finished reading for this epoch, and we have read all the data from all workers
-    std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
     RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
 
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
@@ -241,7 +240,7 @@ Status TFReaderOp::operator()() {
     }
   }
 
-  std::unique_ptr<DataBuffer> eof_buffer = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+  std::unique_ptr<DataBuffer> eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
   RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer)));
 
   RETURN_IF_NOT_OK(PostEndOfData());
@@ -274,7 +273,7 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) {
         MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << filename << ".";
       }
     } else {
-      std::unique_ptr<DataBuffer> eoe_buffer = mindspore::make_unique<DataBuffer>(1, DataBuffer::kDeBFlagEOE);
+      std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(1, DataBuffer::kDeBFlagEOE);
       RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(eoe_buffer)));
     }
 
@@ -288,7 +287,7 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) {
 // When the worker pops this control indicator, it will shut itself down gracefully.
 Status TFReaderOp::PostEndOfData() {
   for (int i = 0; i < num_workers_; ++i) {
-    std::unique_ptr<FilenameBlock> eof = mindspore::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEof);
+    std::unique_ptr<FilenameBlock> eof = std::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEof);
     RETURN_IF_NOT_OK(PushIoBlockQueue(i, std::move(eof)));
   }
 
@@ -299,7 +298,7 @@ Status TFReaderOp::PostEndOfData() {
 // pops this control indicator, it will wait until the next epoch starts and then resume execution.
 Status TFReaderOp::PostEndOfEpoch(int32_t queue_index) {
   for (int i = 0; i < num_workers_; ++i) {
-    std::unique_ptr<FilenameBlock> eoe = mindspore::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEoe);
+    std::unique_ptr<FilenameBlock> eoe = std::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEoe);
     RETURN_IF_NOT_OK(PushIoBlockQueue((queue_index + i) % num_workers_, std::move(eoe)));
   }
 
@@ -358,7 +357,7 @@ Status TFReaderOp::FillIOBlockShuffle(const std::vector<int64_t> &i_keys) {
       }
       if (!equal_rows_per_shard_) {
         if (key_index++ % num_devices_ == device_id_) {
-          auto ioBlock = make_unique<FilenameBlock>(*it, kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone);
+          auto ioBlock = std::make_unique<FilenameBlock>(*it, kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone);
           RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock)));
           queue_index = (queue_index + 1) % num_workers_;
         }
@@ -367,7 +366,7 @@ Status TFReaderOp::FillIOBlockShuffle(const std::vector<int64_t> &i_keys) {
         auto file_it = filename_index_->Search(*it);
         std::string file_name = file_it.value();
         if (NeedPushFileToblockQueue(file_name, &start_offset, &end_offset, pre_count)) {
-          auto ioBlock = make_unique<FilenameBlock>(*it, start_offset, end_offset, IOBlock::kDeIoBlockNone);
+          auto ioBlock = std::make_unique<FilenameBlock>(*it, start_offset, end_offset, IOBlock::kDeIoBlockNone);
           RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock)));
           MS_LOG(DEBUG) << "File name " << *it << " start offset " << start_offset << " end_offset " << end_offset;
           queue_index = (queue_index + 1) % num_workers_;
@@ -404,14 +403,15 @@ Status TFReaderOp::FillIOBlockNoShuffle() {
       }
       if (!equal_rows_per_shard_) {
         if (key_index++ % num_devices_ == device_id_) {
-          auto ioBlock = make_unique<FilenameBlock>(it.key(), kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone);
+          auto ioBlock =
+            std::make_unique<FilenameBlock>(it.key(), kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone);
           RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock)));
           queue_index = (queue_index + 1) % num_workers_;
         }
       } else {
         std::string file_name = it.value();
         if (NeedPushFileToblockQueue(file_name, &start_offset, &end_offset, pre_count)) {
-          auto ioBlock = make_unique<FilenameBlock>(it.key(), start_offset, end_offset, IOBlock::kDeIoBlockNone);
+          auto ioBlock = std::make_unique<FilenameBlock>(it.key(), start_offset, end_offset, IOBlock::kDeIoBlockNone);
           RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock)));
           queue_index = (queue_index + 1) % num_workers_;
         }
@@ -490,14 +490,13 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off
 
   int64_t rows_read = 0;
   int64_t rows_total = 0;
-  std::unique_ptr<DataBuffer> current_buffer =
-    mindspore::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
+  std::unique_ptr<DataBuffer> current_buffer = std::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
   std::unordered_map<std::string, int32_t> column_name_map;
   for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) {
     column_name_map[data_schema_->column(i).name()] = i;
   }
   current_buffer->set_column_name_map(column_name_map);
-  std::unique_ptr<TensorQTable> new_tensor_table = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> new_tensor_table = std::make_unique<TensorQTable>();
 
   while (reader.peek() != EOF) {
     if (!load_jagged_connector_) {
@@ -532,9 +531,9 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off
       current_buffer->set_tensor_table(std::move(new_tensor_table));
       RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(current_buffer)));
 
-      current_buffer = make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
+      current_buffer = std::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
       current_buffer->set_column_name_map(column_name_map);
-      new_tensor_table = make_unique<TensorQTable>();
+      new_tensor_table = std::make_unique<TensorQTable>();
       rows_read = 0;
     }
   }
@@ -742,7 +741,7 @@ Status TFReaderOp::LoadFloatList(const ColDescriptor &current_col, const dataeng
   // Identify how many values we have and then create a local array of these
   // to deserialize into
   *num_elements = float_list.value_size();
-  *float_array = mindspore::make_unique<float[]>(*num_elements);
+  *float_array = std::make_unique<float[]>(*num_elements);
   for (int i = 0; i < float_list.value_size(); ++i) {
     (*float_array)[i] = float_list.value(i);
   }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
index d3785f4660..71b4c47cf5 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
@@ -38,7 +38,7 @@ Status VOCOp::Builder::Build(std::shared_ptr<VOCOp> *ptr) {
   if (builder_sampler_ == nullptr) {
     builder_sampler_ = std::make_shared<SequentialSampler>();
   }
-  builder_schema_ = make_unique<DataSchema>();
+  builder_schema_ = std::make_unique<DataSchema>();
   RETURN_IF_NOT_OK(
     builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
   RETURN_IF_NOT_OK(
@@ -85,7 +85,7 @@ Status VOCOp::TraverseSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::
     row_cnt_++;
     if (row_cnt_ % rows_per_buffer_ == 0) {
       RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
       keys->clear();
     }
   }
@@ -110,21 +110,21 @@ Status VOCOp::operator()() {
     }
     if (keys.empty() == false) {
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
-        make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
+        std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
     }
     if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
-      std::unique_ptr<IOBlock> eoe_block = make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
-      std::unique_ptr<IOBlock> eof_block = make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
+      std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
+      std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
       RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
       for (int32_t i = 0; i < num_workers_; i++) {
         RETURN_IF_NOT_OK(
-          io_block_queues_[i]->Add(make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
+          io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
       }
       return Status::OK();
     } else {
       RETURN_IF_NOT_OK(
-        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
+        io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
       RETURN_IF_NOT_OK(wp_.Wait());
       wp_.Clear();
       RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer));
@@ -164,7 +164,7 @@ Status VOCOp::LoadTensorRow(const std::string &image_id, TensorRow *trow) {
 }
 
 Status VOCOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
-  std::unique_ptr<TensorQTable> deq = make_unique<TensorQTable>();
+  std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
   TensorRow trow;
   for (const uint64_t &key : keys) {
     RETURN_IF_NOT_OK(this->LoadTensorRow(image_ids_[key], &trow));
@@ -182,15 +182,15 @@ Status VOCOp::WorkerEntry(int32_t worker_id) {
   RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
   while (io_block != nullptr) {
     if (io_block->eoe() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
       buffer_id = worker_id;
     } else if (io_block->eof() == true) {
-      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
+      RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
     } else {
       std::vector<int64_t> keys;
       RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
       if (keys.empty() == true) return Status::OK();
-      std::unique_ptr<DataBuffer> db = make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
+      std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
       RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
       RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
       buffer_id += num_workers_;
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc
index 716c853488..ec771740c1 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc
@@ -65,13 +65,13 @@ Status ZipOp::operator()() {
   // initialize the iterators
   for (int32_t i = 0; i < children_num_; ++i) {
     // magic number 0 since Zip is not a parallel Op
-    child_iterators_.push_back(mindspore::make_unique<ChildIterator>(this, 0, i));
+    child_iterators_.push_back(std::make_unique<ChildIterator>(this, 0, i));
   }
 
   // Loop until eof is true
   while (!eof_) {
     // Create tensor table and prepare it by fetching and packing the first zipped row into it.
-    std::unique_ptr<TensorQTable> curr_table = mindspore::make_unique<TensorQTable>();
+    std::unique_ptr<TensorQTable> curr_table = std::make_unique<TensorQTable>();
     RETURN_IF_NOT_OK(prepare(curr_table.get()));
 
     // If an eof got picked up during the above prepare, then we're done
@@ -81,7 +81,7 @@ Status ZipOp::operator()() {
     while (!draining_) {
       // 1. If a previous loop iteration sent the current table out, then create a new one.
       if (curr_table == nullptr) {
-        curr_table = mindspore::make_unique<TensorQTable>();
+        curr_table = std::make_unique<TensorQTable>();
       }
 
       // 2 fill the table.  Note: draining mode might get turned on if any of the child inputs were done
@@ -89,8 +89,7 @@ Status ZipOp::operator()() {
 
       // 3 create and update buffer and send it to the out connector
       if (!curr_table->empty()) {
-        std::unique_ptr<DataBuffer> curr_buffer =
-          mindspore::make_unique<DataBuffer>(buffer_id_, DataBuffer::kDeBFlagNone);
+        std::unique_ptr<DataBuffer> curr_buffer = std::make_unique<DataBuffer>(buffer_id_, DataBuffer::kDeBFlagNone);
         curr_buffer->set_tensor_table(std::move(curr_table));
         curr_buffer->set_column_name_map(col_name_id_map_);
         MS_LOG(DEBUG) << "Zip operator finished one buffer, pushing, rows " << curr_buffer->NumRows() << ", cols "
@@ -105,15 +104,14 @@ Status ZipOp::operator()() {
       MS_LOG(DEBUG) << "Zip operator is now draining child inputs.";
       RETURN_IF_NOT_OK(drainPipeline());
       // Now that we have drained child inputs, send the eoe up.
-      RETURN_IF_NOT_OK(
-        out_connector_->Add(0, std::move(mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
+      RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
     }
   }
 
   // 5 handle eof
   // propagate eof here.
   MS_LOG(INFO) << "Zip operator got EOF, propagating.";
-  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
+  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/db_connector.h b/mindspore/ccsrc/dataset/engine/db_connector.h
index 243e77e6ac..7ea9837c44 100644
--- a/mindspore/ccsrc/dataset/engine/db_connector.h
+++ b/mindspore/ccsrc/dataset/engine/db_connector.h
@@ -65,7 +65,7 @@ class DbConnector : public Connector<std::unique_ptr<DataBuffer>> {
       RETURN_IF_NOT_OK(cv_.Wait(&lk, [this, worker_id]() { return expect_consumer_ == worker_id; }));
       // Once an EOF message is encountered this flag will be set and we can return early.
       if (end_of_file_) {
-        *result = mindspore::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+        *result = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
       } else {
         RETURN_IF_NOT_OK(queues_[pop_from_]->PopFront(result));
         if (*result == nullptr) {
diff --git a/mindspore/ccsrc/dataset/engine/execution_tree.cc b/mindspore/ccsrc/dataset/engine/execution_tree.cc
index 3dbeaa5ed1..20fcb836c5 100644
--- a/mindspore/ccsrc/dataset/engine/execution_tree.cc
+++ b/mindspore/ccsrc/dataset/engine/execution_tree.cc
@@ -24,7 +24,7 @@ namespace mindspore {
 namespace dataset {
 // Constructor
 ExecutionTree::ExecutionTree() : id_count_(0) {
-  tg_ = mindspore::make_unique<TaskGroup>();
+  tg_ = std::make_unique<TaskGroup>();
   tree_state_ = kDeTStateInit;
   prepare_flags_ = kDePrepNone;
 }
diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
index 0412f07636..8735cf7a05 100644
--- a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
+++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
@@ -24,7 +24,6 @@
 #include "dataset/core/cv_tensor.h"
 #include "dataset/core/tensor.h"
 #include "dataset/core/tensor_shape.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/util/random.h"
 
 #define MAX_INT_PRECISION 16777216  // float int precision is 16777216
@@ -376,7 +375,7 @@ Status HwcToChw(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *output)
     int width = input_cv->shape()[1];
     int num_channels = input_cv->shape()[2];
 
-    auto output_cv = mindspore::make_unique<CVTensor>(TensorShape{num_channels, height, width}, input_cv->type());
+    auto output_cv = std::make_unique<CVTensor>(TensorShape{num_channels, height, width}, input_cv->type());
     for (int i = 0; i < num_channels; ++i) {
       cv::Mat mat;
       RETURN_IF_NOT_OK(output_cv->Mat({i}, &mat));
diff --git a/mindspore/ccsrc/dataset/kernels/py_func_op.cc b/mindspore/ccsrc/dataset/kernels/py_func_op.cc
index 69bd3443c4..c9e5d5b169 100644
--- a/mindspore/ccsrc/dataset/kernels/py_func_op.cc
+++ b/mindspore/ccsrc/dataset/kernels/py_func_op.cc
@@ -20,7 +20,6 @@
 
 #include "dataset/core/tensor.h"
 #include "dataset/kernels/tensor_op.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/util/status.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/dataset/util/arena.cc b/mindspore/ccsrc/dataset/util/arena.cc
index 856f7fef24..68673529ff 100644
--- a/mindspore/ccsrc/dataset/util/arena.cc
+++ b/mindspore/ccsrc/dataset/util/arena.cc
@@ -16,7 +16,6 @@
 #include "dataset/util/arena.h"
 #include <unistd.h>
 #include <utility>
-#include "dataset/util/make_unique.h"
 #include "dataset/util/system_pool.h"
 #include "dataset/util/de_error.h"
 #include "./securec.h"
diff --git a/mindspore/ccsrc/dataset/util/circular_pool.cc b/mindspore/ccsrc/dataset/util/circular_pool.cc
index f6e43f35bf..92b169c94a 100644
--- a/mindspore/ccsrc/dataset/util/circular_pool.cc
+++ b/mindspore/ccsrc/dataset/util/circular_pool.cc
@@ -18,10 +18,8 @@
 #include <algorithm>
 #include <limits>
 #include <utility>
-
 #include "./securec.h"
-
-#include "dataset/util/make_unique.h"
+#include "dataset/util/de_error.h"
 #include "dataset/util/system_pool.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/dataset/util/de_error.h b/mindspore/ccsrc/dataset/util/de_error.h
index 07d7c125f7..d4988c58db 100644
--- a/mindspore/ccsrc/dataset/util/de_error.h
+++ b/mindspore/ccsrc/dataset/util/de_error.h
@@ -16,6 +16,13 @@
 #ifndef DATASET_UTIL_DE_ERROR_H_
 #define DATASET_UTIL_DE_ERROR_H_
 
+#ifdef DEBUG
+#include <cassert>
+#define DS_ASSERT(f) assert(f)
+#else
+#define DS_ASSERT(f) ((void)0)
+#endif
+
 #include <map>
 #include "utils/error_code.h"
 
diff --git a/mindspore/ccsrc/dataset/util/list.h b/mindspore/ccsrc/dataset/util/list.h
index f01201e34d..5a08f4514e 100644
--- a/mindspore/ccsrc/dataset/util/list.h
+++ b/mindspore/ccsrc/dataset/util/list.h
@@ -18,8 +18,7 @@
 
 #include <iostream>
 #include <iterator>
-
-#include "dataset/util/make_unique.h"
+#include "dataset/util/de_error.h"
 
 namespace mindspore {
 namespace dataset {
diff --git a/mindspore/ccsrc/dataset/util/lock.cc b/mindspore/ccsrc/dataset/util/lock.cc
index 7e92a1e53f..13a43e3e84 100644
--- a/mindspore/ccsrc/dataset/util/lock.cc
+++ b/mindspore/ccsrc/dataset/util/lock.cc
@@ -14,6 +14,7 @@
  * limitations under the License.
  */
 #include "dataset/util/lock.h"
+#include "dataset/util/de_error.h"
 
 namespace mindspore {
 namespace dataset {
diff --git a/mindspore/ccsrc/dataset/util/lock.h b/mindspore/ccsrc/dataset/util/lock.h
index 8fef6a143a..9492d34bdf 100644
--- a/mindspore/ccsrc/dataset/util/lock.h
+++ b/mindspore/ccsrc/dataset/util/lock.h
@@ -19,7 +19,6 @@
 #include <atomic>
 #include <condition_variable>
 #include <mutex>
-#include "dataset/util/make_unique.h"
 
 namespace mindspore {
 namespace dataset {
diff --git a/mindspore/ccsrc/dataset/util/make_unique.h b/mindspore/ccsrc/dataset/util/make_unique.h
deleted file mode 100644
index 2fe0bf4550..0000000000
--- a/mindspore/ccsrc/dataset/util/make_unique.h
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef DATASET_UTIL_MAKE_UNIQUE_H_
-#define DATASET_UTIL_MAKE_UNIQUE_H_
-
-#ifdef DEBUG
-#include <cassert>
-#define DS_ASSERT(f) assert(f)
-#else
-#define DS_ASSERT(f) ((void)0)
-#endif
-
-#include <memory>
-#include <type_traits>
-#include <utility>
-#include "dataset/util/de_error.h"
-#include "utils/log_adapter.h"
-
-namespace mindspore {
-using std::make_unique;
-}  // namespace mindspore
-
-#endif  // DATASET_UTIL_MAKE_UNIQUE_H_
diff --git a/mindspore/ccsrc/dataset/util/queue.h b/mindspore/ccsrc/dataset/util/queue.h
index 4048deb86b..f0b087cf6d 100644
--- a/mindspore/ccsrc/dataset/util/queue.h
+++ b/mindspore/ccsrc/dataset/util/queue.h
@@ -212,7 +212,7 @@ class QueueList {
   void Init(int num_queues, int capacity) {
     queue_list_.reserve(num_queues);
     for (int i = 0; i < num_queues; i++) {
-      queue_list_.emplace_back(mindspore::make_unique<Queue<T>>(capacity));
+      queue_list_.emplace_back(std::make_unique<Queue<T>>(capacity));
     }
   }
 
diff --git a/mindspore/ccsrc/dataset/util/task.h b/mindspore/ccsrc/dataset/util/task.h
index aaf2f80a3d..d6149caec8 100644
--- a/mindspore/ccsrc/dataset/util/task.h
+++ b/mindspore/ccsrc/dataset/util/task.h
@@ -27,7 +27,6 @@
 #include <string>
 #include <thread>
 #include "dataset/util/de_error.h"
-#include "dataset/util/make_unique.h"
 #include "dataset/util/intrp_resource.h"
 #include "dataset/util/list.h"
 #include "dataset/util/memory_pool.h"
diff --git a/mindspore/ccsrc/device/gpu/blocking_queue.cc b/mindspore/ccsrc/device/gpu/blocking_queue.cc
index 7417115ae9..c36b1cdbf5 100644
--- a/mindspore/ccsrc/device/gpu/blocking_queue.cc
+++ b/mindspore/ccsrc/device/gpu/blocking_queue.cc
@@ -17,7 +17,6 @@
 #include "device/gpu/blocking_queue.h"
 #include <chrono>
 #include "device/gpu/gpu_common.h"
-#include "dataset/util/make_unique.h"
 #include "common/utils.h"
 
 namespace mindspore {
@@ -32,7 +31,7 @@ GpuQueue::GpuQueue(void *addr, size_t feature_size, size_t label_size, size_t ca
       stream_(0),
       node_info_(nullptr) {
   CHECK_CUDA_RET_WITH_ERROR(cudaStreamCreate(&stream_), "Cuda Create Stream Failed");
-  node_info_ = mindspore::make_unique<NodeInfo[]>(capacity);
+  node_info_ = std::make_unique<NodeInfo[]>(capacity);
 }
 
 GpuQueue::~GpuQueue() { buffer_ = nullptr; }
diff --git a/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h
index 265180afe6..90609c3be5 100644
--- a/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h
@@ -23,7 +23,6 @@
 #include <vector>
 #include "kernel/gpu/gpu_kernel.h"
 #include "kernel/gpu/gpu_kernel_factory.h"
-#include "dataset/util/make_unique.h"
 #include "kernel/gpu/kernel_constants.h"
 
 namespace mindspore {
@@ -74,8 +73,8 @@ class BiasAddGpuKernel : public GpuKernel {
 
     // Expand to 4 dims for cudnnSetTensorNdDescriptorEx.
     auto cudnn_dims = std::max(num_dims, 4UL);
-    std::unique_ptr<int[]> x_dims = mindspore::make_unique<int[]>(cudnn_dims);
-    std::unique_ptr<int[]> b_dims = mindspore::make_unique<int[]>(cudnn_dims);
+    std::unique_ptr<int[]> x_dims = std::make_unique<int[]>(cudnn_dims);
+    std::unique_ptr<int[]> b_dims = std::make_unique<int[]>(cudnn_dims);
     for (size_t i = 0; i < cudnn_dims; i++) {
       x_dims[i] = (i < num_dims) ? SizeToInt(x_shape[i]) : 1;
       b_dims[i] = (i == pos) ? SizeToInt(x_shape[i]) : 1;
diff --git a/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h b/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h
index b0e8102ee3..fd73f378d8 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h
@@ -26,7 +26,6 @@
 #include "kernel/gpu/gpu_kernel.h"
 #include "kernel/gpu/gpu_kernel_factory.h"
 #include "kernel/gpu/kernel_constants.h"
-#include "dataset/util/make_unique.h"
 
 namespace mindspore {
 namespace kernel {
@@ -84,8 +83,8 @@ class BiasAddGradGpuKernel : public GpuKernel {
 
     // Expand to 4 dims for cudnnSetTensorNdDescriptorEx.
     auto cudnn_dims = std::max(num_dims, 4UL);
-    std::unique_ptr<int[]> dy_dims = mindspore::make_unique<int[]>(cudnn_dims);
-    std::unique_ptr<int[]> db_dims = mindspore::make_unique<int[]>(cudnn_dims);
+    std::unique_ptr<int[]> dy_dims = std::make_unique<int[]>(cudnn_dims);
+    std::unique_ptr<int[]> db_dims = std::make_unique<int[]>(cudnn_dims);
     for (size_t i = 0; i < cudnn_dims; i++) {
       dy_dims[i] = (i < num_dims) ? SizeToInt(dy_shape[i]) : 1;
       db_dims[i] = (i == pos) ? SizeToInt(dy_shape[i]) : 1;
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
index 51a2da8574..c3e839b9c5 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h
@@ -22,7 +22,6 @@
 #include <memory>
 #include "kernel/gpu/gpu_kernel.h"
 #include "kernel/gpu/gpu_kernel_factory.h"
-#include "dataset/util/make_unique.h"
 #include "kernel/gpu/kernel_constants.h"
 
 namespace mindspore {
@@ -144,8 +143,8 @@ class LstmGpuKernel : public GpuKernel {
     int x_dims[3]{batch_size_, input_size_, 1};
     int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1};
 
-    x_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
-    y_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    x_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    y_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
 
     for (size_t i = 0; i < IntToSize(seq_len_); ++i) {
       CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_[i]), "create x_desc failed");
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
index a60ab78f7d..b12fa3bea5 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h
@@ -23,7 +23,6 @@
 #include "kernel/gpu/gpu_kernel.h"
 #include "kernel/gpu/gpu_kernel_factory.h"
 #include "kernel/gpu/kernel_constants.h"
-#include "dataset/util/make_unique.h"
 
 namespace mindspore {
 namespace kernel {
@@ -212,9 +211,9 @@ class LstmGradDataGpuKernel : public GpuKernel {
     int x_dims[3]{batch_size_, input_size_, 1};
     int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1};
 
-    dx_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
-    y_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
-    dy_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    dx_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    y_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    dy_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
 
     for (size_t i = 0; i < IntToSize(seq_len_); ++i) {
       CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&dx_desc_[i]), "create x_desc failed");
diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
index b28736cc96..e081b9d070 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h
@@ -22,7 +22,6 @@
 #include <memory>
 #include "kernel/gpu/gpu_kernel.h"
 #include "kernel/gpu/gpu_kernel_factory.h"
-#include "dataset/util/make_unique.h"
 #include "kernel/gpu/kernel_constants.h"
 namespace mindspore {
 namespace kernel {
@@ -169,8 +168,8 @@ class LstmGradWeightGpuKernel : public GpuKernel {
     int x_dims[3]{batch_size_, input_size_, 1};
     int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1};
 
-    x_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
-    y_desc_ = mindspore::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    x_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
+    y_desc_ = std::make_unique<cudnnTensorDescriptor_t[]>(seq_len_);
 
     for (size_t i = 0; i < IntToSize(seq_len_); ++i) {
       CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_[i]), "create x_desc failed");
diff --git a/tests/ut/cpp/dataset/celeba_op_test.cc b/tests/ut/cpp/dataset/celeba_op_test.cc
index 69314771a3..35be4d7378 100644
--- a/tests/ut/cpp/dataset/celeba_op_test.cc
+++ b/tests/ut/cpp/dataset/celeba_op_test.cc
@@ -116,7 +116,7 @@ TEST_F(MindDataTestCelebaDataset, TestCelebaRepeat) {
 
 TEST_F(MindDataTestCelebaDataset, TestSubsetRandomSamplerCeleba) {
   std::vector<int64_t> indices({1});
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<SubsetRandomSampler>(indices);
+  std::unique_ptr<Sampler> sampler = std::make_unique<SubsetRandomSampler>(indices);
   uint32_t expect_labels[1][40] = {{0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1}};
   std::string dir = datasets_root_path_ + "/testCelebAData/";
   uint32_t count = 0;
diff --git a/tests/ut/cpp/dataset/cifar_op_test.cc b/tests/ut/cpp/dataset/cifar_op_test.cc
index 0cd1db65b5..dcbea83df4 100644
--- a/tests/ut/cpp/dataset/cifar_op_test.cc
+++ b/tests/ut/cpp/dataset/cifar_op_test.cc
@@ -92,7 +92,7 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar10) {
 TEST_F(MindDataTestCifarOp, TestRandomSamplerCifar10) {
   uint32_t original_seed = GlobalContext::config_manager()->seed();
   GlobalContext::config_manager()->set_seed(0);
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<RandomSampler>(true, 12);
+  std::unique_ptr<Sampler> sampler = std::make_unique<RandomSampler>(true, 12);
   std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
   auto tree = Build({Cifarop(16, 2, 32, folder_path, std::move(sampler), 100)});
   tree->Prepare();
diff --git a/tests/ut/cpp/dataset/image_folder_op_test.cc b/tests/ut/cpp/dataset/image_folder_op_test.cc
index 5b118a629a..e149e687c6 100644
--- a/tests/ut/cpp/dataset/image_folder_op_test.cc
+++ b/tests/ut/cpp/dataset/image_folder_op_test.cc
@@ -138,7 +138,7 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomImageFolder) {
 TEST_F(MindDataTestImageFolderSampler, TestRandomSamplerImageFolder) {
   int32_t original_seed = GlobalContext::config_manager()->seed();
   GlobalContext::config_manager()->set_seed(0);
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<RandomSampler>(true, 12);
+  std::unique_ptr<Sampler> sampler = std::make_unique<RandomSampler>(true, 12);
   int32_t res[] = {2, 2, 2, 3, 2, 3, 2, 3, 1, 2, 2, 1};  // ground truth label
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))});
@@ -200,7 +200,7 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeatBatch)
 TEST_F(MindDataTestImageFolderSampler, TestSubsetRandomSamplerImageFolder) {
   // id range 0 - 10 is label 0, and id range 11 - 21 is label 1
   std::vector<int64_t> indices({0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11});
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<SubsetRandomSampler>(indices);
+  std::unique_ptr<Sampler> sampler = std::make_unique<SubsetRandomSampler>(indices);
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   // Expect 6 samples for label 0 and 1
   int res[2] = {6, 6};
@@ -238,7 +238,7 @@ TEST_F(MindDataTestImageFolderSampler, TestWeightedRandomSamplerImageFolder) {
 
   // create sampler with replacement = replacement
   std::unique_ptr<Sampler> sampler =
-    mindspore::make_unique<WeightedRandomSampler>(weights, num_samples, true, samples_per_buffer);
+    std::make_unique<WeightedRandomSampler>(weights, num_samples, true, samples_per_buffer);
 
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))});
@@ -295,7 +295,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderClassIndex) {
 }
 
 TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) {
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<DistributedSampler>(11, 10, false);
+  std::unique_ptr<Sampler> sampler = std::make_unique<DistributedSampler>(11, 10, false);
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler)), Repeat(4)});
   tree->Prepare();
@@ -322,7 +322,7 @@ TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) {
 }
 
 TEST_F(MindDataTestImageFolderSampler, TestPKSamplerImageFolder) {
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<PKSampler>(3, false, 4);
+  std::unique_ptr<Sampler> sampler = std::make_unique<PKSampler>(3, false, 4);
   int32_t res[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3};  // ground truth label
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))});
@@ -431,7 +431,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderDatasetSize) {
 }
 
 TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) {
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<DistributedSampler>(4, 0, false);
+  std::unique_ptr<Sampler> sampler = std::make_unique<DistributedSampler>(4, 0, false);
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   // numWrks, rows, conns, path, shuffle, sampler, map, numSamples, decode
   auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler), {}, 5)});
@@ -460,7 +460,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) {
 }
 
 TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding2) {
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<DistributedSampler>(4, 3, false);
+  std::unique_ptr<Sampler> sampler = std::make_unique<DistributedSampler>(4, 3, false);
   std::string folder_path = datasets_root_path_ + "/testPK/data";
   // numWrks, rows, conns, path, shuffle, sampler, map, numSamples, decode
   auto tree = Build({ImageFolder(16, 16, 32, folder_path, false, std::move(sampler), {}, 12)});
diff --git a/tests/ut/cpp/dataset/manifest_op_test.cc b/tests/ut/cpp/dataset/manifest_op_test.cc
index 9e36f8c747..f662f98fc8 100644
--- a/tests/ut/cpp/dataset/manifest_op_test.cc
+++ b/tests/ut/cpp/dataset/manifest_op_test.cc
@@ -86,7 +86,7 @@ TEST_F(MindDataTestManifest, TestSequentialManifestWithRepeat) {
 
 TEST_F(MindDataTestManifest, TestSubsetRandomSamplerManifest) {
   std::vector<int64_t> indices({1});
-  std::unique_ptr<Sampler> sampler = mindspore::make_unique<SubsetRandomSampler>(indices);
+  std::unique_ptr<Sampler> sampler = std::make_unique<SubsetRandomSampler>(indices);
   std::string file = datasets_root_path_ + "/testManifestData/cpp.json";
   // Expect 6 samples for label 0 and 1
   auto tree = Build({Manifest(16, 2, 32, file, "train", std::move(sampler))});
diff --git a/tests/ut/cpp/dataset/project_op_test.cc b/tests/ut/cpp/dataset/project_op_test.cc
index 1df2ce05bb..484396321c 100644
--- a/tests/ut/cpp/dataset/project_op_test.cc
+++ b/tests/ut/cpp/dataset/project_op_test.cc
@@ -45,7 +45,7 @@ TEST_F(MindDataTestProjectOp, TestProjectProject) {
     .SetRowsPerBuffer(16)
     .SetWorkerConnectorSize(16)
     .SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);  ASSERT_TRUE(rc.IsOk());
diff --git a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
index c686a9486b..48cc811615 100644
--- a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
+++ b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
@@ -74,7 +74,7 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) {
   std::unique_ptr<DataBuffer> db;
   std::shared_ptr<Tensor> tensor;
   for (int i = 0; i < 6; i++) {
-    std::unique_ptr<Sampler> sampler = mindspore::make_unique<DistributedSampler>(3, i % 3, (i < 3 ? false : true));
+    std::unique_ptr<Sampler> sampler = std::make_unique<DistributedSampler>(3, i % 3, (i < 3 ? false : true));
     sampler->Init(&mock);
     sampler->GetNextBuffer(&db);
     db->GetTensor(&tensor, 0, 0);
diff --git a/tests/ut/cpp/dataset/tfReader_op_test.cc b/tests/ut/cpp/dataset/tfReader_op_test.cc
index c70d5fb6ee..5fb1f4e909 100644
--- a/tests/ut/cpp/dataset/tfReader_op_test.cc
+++ b/tests/ut/cpp/dataset/tfReader_op_test.cc
@@ -48,7 +48,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderBasic1) {
   builder.SetDatasetFilesList({dataset_path})
       .SetRowsPerBuffer(16)
       .SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);
@@ -102,7 +102,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderLargeRowsPerBuffer) {
   builder.SetDatasetFilesList({dataset_path})
       .SetRowsPerBuffer(500)
       .SetNumWorkers(16);
-   std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+   std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);
@@ -156,7 +156,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderSmallRowsPerBuffer) {
   builder.SetDatasetFilesList({dataset_path})
       .SetRowsPerBuffer(1)
       .SetNumWorkers(16);
-   std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+   std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);
@@ -211,7 +211,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderLargeQueueSize) {
       .SetWorkerConnectorSize(1)
       .SetRowsPerBuffer(16)
       .SetNumWorkers(16);
-   std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+   std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);
@@ -265,7 +265,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderOneThread) {
   builder.SetDatasetFilesList({dataset_path})
       .SetRowsPerBuffer(16)
       .SetNumWorkers(1);
-   std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+   std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc = builder.Build(&my_tfreader_op);
@@ -321,7 +321,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderRepeat) {
     .SetRowsPerBuffer(16)
     .SetWorkerConnectorSize(16)
     .SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
   builder.SetDataSchema(std::move(schema));
   Status rc= builder.Build(&my_tfreader_op);
@@ -379,7 +379,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderSchemaConstructor) {
   std::string dataset_path;
   dataset_path = datasets_root_path_ + "/testTFTestAllTypes";
 
-  std::unique_ptr<DataSchema> data_schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> data_schema = std::make_unique<DataSchema>();
   std::vector<std::string> columns_to_load;
   columns_to_load.push_back("col_sint32");
   columns_to_load.push_back("col_binary");
@@ -445,7 +445,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake1Row) {
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   TFReaderOp::Builder builder;
   builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema1Row.json", {});
   builder.SetDataSchema(std::move(schema));
 
@@ -503,7 +503,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake1Buffer) {
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   TFReaderOp::Builder builder;
   builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema5Rows.json", {});
   builder.SetDataSchema(std::move(schema));
 
@@ -561,7 +561,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake7Rows) {
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   TFReaderOp::Builder builder;
   builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16);
-  std::unique_ptr<DataSchema> schema = mindspore::make_unique<DataSchema>();
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
   schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema7Rows.json", {});
   builder.SetDataSchema(std::move(schema));
 

From 9d0fde29f4bdad550087fd6a7447b50db6c1bf23 Mon Sep 17 00:00:00 2001
From: Jonathan Yan <jonathan.yan1@huawei.com>
Date: Sat, 4 Apr 2020 06:48:58 -0400
Subject: [PATCH 116/367] remove ENABLE_MINDRECORD flag

---
 mindspore/ccsrc/dataset/CMakeLists.txt               |  2 --
 mindspore/ccsrc/dataset/api/de_pipeline.cc           | 12 +++---------
 mindspore/ccsrc/dataset/api/de_pipeline.h            |  4 ----
 mindspore/ccsrc/dataset/api/python_bindings.cc       |  6 ------
 .../engine/datasetops/source/mindrecord_op.cc        |  3 ---
 .../dataset/engine/datasetops/source/mindrecord_op.h |  2 --
 tests/ut/cpp/CMakeLists.txt                          |  1 -
 tests/ut/cpp/dataset/mind_record_op_test.cc          |  2 --
 8 files changed, 3 insertions(+), 29 deletions(-)

diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index d6791f2b9b..477d37051e 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -17,8 +17,6 @@ if (ENABLE_TDTQUE)
     message(STATUS "TDT queue is enabled")
 endif ()
 
-add_definitions(-D ENABLE_MINDRECORD)
-
 # conde coverage
 # option(ENABLE_COVERAGE "Enable code coverage report" OFF)
 # if (ENABLE_COVERAGE)
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index d51204f659..65ec8d30f2 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -29,11 +29,9 @@
 #include "dataset/engine/datasetops/source/manifest_op.h"
 #include "dataset/engine/datasetops/source/cifar_op.h"
 #include "dataset/engine/datasetops/source/celeba_op.h"
-#ifdef ENABLE_MINDRECORD
-#include "./shard_category.h"
-#include "./shard_sample.h"
-#include "./shard_shuffle.h"
-#endif
+#include "mindrecord/include/shard_category.h"
+#include "mindrecord/include/shard_sample.h"
+#include "mindrecord/include/shard_shuffle.h"
 
 #include "dataset/util/random.h"
 #include "dataset/util/status.h"
@@ -46,9 +44,7 @@ using pFunction = Status (DEPipeline::*)(const py::dict &, std::shared_ptr<Datas
 
 static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &DEPipeline::ParseStorageOp},
                                                                    {kShuffle, &DEPipeline::ParseShuffleOp},
-#ifdef ENABLE_MINDRECORD
                                                                    {kMindrecord, &DEPipeline::ParseMindRecordOp},
-#endif
                                                                    {kMap, &DEPipeline::ParseMapOp},
                                                                    {kBatch, &DEPipeline::ParseBatchOp},
                                                                    {kRepeat, &DEPipeline::ParseRepeatOp},
@@ -364,7 +360,6 @@ Status DEPipeline::ParseShuffleOp(const py::dict &args, std::shared_ptr<DatasetO
   return Status::OK();
 }
 
-#ifdef ENABLE_MINDRECORD
 Status DEPipeline::CheckMindRecordPartitionInfo(const py::dict &args, std::vector<int> *in_partitions) {
   if (args["partitions"].is_none()) {
     std::string err_msg = "Error: partitions is not set (None)";
@@ -450,7 +445,6 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
   *ptr = op;
   return Status::OK();
 }
-#endif
 
 Status DEPipeline::ParseMapOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   std::shared_ptr<MapOp::Builder> builder = std::make_shared<MapOp::Builder>();
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index e8dde85a77..acffc390cc 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -38,9 +38,7 @@ using DsOpPtr = std::shared_ptr<DatasetOp>;
 enum OpName {
   kStorage = 0,
   kShuffle,
-#ifdef ENABLE_MINDRECORD
   kMindrecord,
-#endif
   kBatch,
   kCache,
   kRepeat,
@@ -101,11 +99,9 @@ class DEPipeline {
 
   Status ParseShuffleOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
-#ifdef ENABLE_MINDRECORD
   Status CheckMindRecordPartitionInfo(const py::dict &args, std::vector<int> *ptr);
 
   Status ParseMindRecordOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
-#endif
 
   Status ParseMapOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index 86b0a5d66a..e6c2691281 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -44,9 +44,7 @@
 #include "dataset/engine/datasetops/source/io_block.h"
 #include "dataset/engine/datasetops/source/mnist_op.h"
 #include "dataset/engine/datasetops/source/manifest_op.h"
-#ifdef ENABLE_MINDRECORD
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
-#endif
 #include "dataset/engine/datasetops/source/sampler/distributed_sampler.h"
 #include "dataset/engine/datasetops/source/sampler/pk_sampler.h"
 #include "dataset/engine/datasetops/source/sampler/random_sampler.h"
@@ -146,14 +144,12 @@ void bindDatasetOps(py::module *m) {
       return py::make_tuple(count, num_classes);
     });
 
-#ifdef ENABLE_MINDRECORD
   (void)py::class_<MindRecordOp, DatasetOp, std::shared_ptr<MindRecordOp>>(*m, "MindRecordOp")
     .def_static("get_num_rows", [](const std::string &path) {
       int64_t count = 0;
       THROW_IF_ERROR(MindRecordOp::CountTotalRows(path, &count));
       return count;
     });
-#endif
 
   (void)py::class_<ManifestOp, DatasetOp, std::shared_ptr<ManifestOp>>(*m, "ManifestOp")
     .def_static("get_num_rows_and_classes",
@@ -424,9 +420,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("STORAGE", OpName::kStorage)
     .value("SHUFFLE", OpName::kShuffle)
     .value("BATCH", OpName::kBatch)
-#ifdef ENABLE_MINDRECORD
     .value("MINDRECORD", OpName::kMindrecord)
-#endif
     .value("CACHE", OpName::kCache)
     .value("REPEAT", OpName::kRepeat)
     .value("TAKE", OpName::kTake)
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
index b062371d7f..b5bea5416c 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
@@ -13,8 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifdef ENABLE_MINDRECORD
-
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
 
 #include <algorithm>
@@ -665,4 +663,3 @@ Status MindRecordOp::CountTotalRows(const std::string dataset_path, int64_t *cou
 }
 }  // namespace dataset
 }  // namespace mindspore
-#endif
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
index 2535acbc50..0b16391b20 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
@@ -15,7 +15,6 @@
  */
 #ifndef DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
 #define DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
-#ifdef ENABLE_MINDRECORD
 #pragma once
 
 #include <cstdint>
@@ -276,5 +275,4 @@ class MindRecordOp : public ParallelOp {
 };
 }  // namespace dataset
 }  // namespace mindspore
-#endif
 #endif  // DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_
diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt
index 5f4bd41b3b..8d3f8a8138 100644
--- a/tests/ut/cpp/CMakeLists.txt
+++ b/tests/ut/cpp/CMakeLists.txt
@@ -26,7 +26,6 @@ MESSAGE("check  ut_test ${CMAKE_BINARY_DIR}")
 link_directories(${MS_CCSRC_BUILD_PATH})
 
 if(ENABLE_MINDDATA)
-    add_definitions(-D ENABLE_MINDRECORD)
     add_definitions(-D ENABLE_MINDDATA)
     link_directories(${MS_CCSRC_BUILD_PATH}/dataset)
     link_directories(${MS_CCSRC_BUILD_PATH}/mindrecord)
diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc
index abe7faef14..3d5c80b3f4 100644
--- a/tests/ut/cpp/dataset/mind_record_op_test.cc
+++ b/tests/ut/cpp/dataset/mind_record_op_test.cc
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifdef ENABLE_MINDRECORD
 #include <iostream>
 #include <memory>
 #include <vector>
@@ -480,4 +479,3 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) {
     row_count++;
   }
 }
-#endif

From d0c24fb706666085a6eabfa5b279d0c37fa61211 Mon Sep 17 00:00:00 2001
From: VectorSL <shiliang10@huawei.com>
Date: Tue, 7 Apr 2020 19:48:34 +0800
Subject: [PATCH 117/367] update lossscale for gpu

---
 mindspore/nn/wrap/loss_scale.py      | 40 +++++++++----
 mindspore/ops/operations/__init__.py |  6 +-
 mindspore/ops/operations/math_ops.py | 88 ++++++++++++++++++++++++++++
 3 files changed, 123 insertions(+), 11 deletions(-)

diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index 1ce3179273..6a1f15a402 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Loss scale cell for loss scale training."""
+import mindspore.context as context
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.train.parallel_utils import ParallelMode
 from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
@@ -34,6 +35,13 @@ reciprocal = P.Reciprocal()
 def tensor_grad_scale(scale, grad):
     return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
+_grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
+grad_overflow = P.FloatStatus()
+
+
+@_grad_overflow.register("Tensor")
+def _tensor_grad_overflow(grad):
+    return grad_overflow(grad)
 
 class DynamicLossScaleUpdateCell(Cell):
     r"""
@@ -197,9 +205,15 @@ class TrainOneStepWithLossScaleCell(Cell):
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.hyper_map = C.HyperMap()
-        self.alloc_status = NPUAllocFloatStatus()
-        self.get_status = NPUGetFloatStatus()
-        self.clear_status = NPUClearFloatStatus()
+        if context.get_context("device_target") == "GPU":
+            self.gpu_target = True
+            self.float_status = P.FloatStatus()
+            self.addn = P.AddN()
+        else:
+            self.gpu_target = False
+            self.alloc_status = NPUAllocFloatStatus()
+            self.get_status = NPUGetFloatStatus()
+            self.clear_status = NPUClearFloatStatus()
         self.reduce_sum = ReduceSum(keep_dims=False)
         self.base = Tensor(1, mstype.float32)
         self.less_equal = LessEqual()
@@ -224,10 +238,12 @@ class TrainOneStepWithLossScaleCell(Cell):
     def construct(self, data, label, sens=None):
         weights = self.weights
         loss = self.network(data, label)
-        # init overflow buffer
-        init = self.alloc_status()
-        # clear overflow buffer
-        self.clear_status(init)
+        init = False
+        if not self.gpu_target:
+            # init overflow buffer
+            init = self.alloc_status()
+            # clear overflow buffer
+            self.clear_status(init)
         if sens is None:
             scaling_sens = self.loss_scale
         else:
@@ -238,9 +254,13 @@ class TrainOneStepWithLossScaleCell(Cell):
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
         # get the overflow buffer
-        self.get_status(init)
-        # sum overflow buffer elements, 0:not overflow , >0:overflow
-        flag_sum = self.reduce_sum(init, (0,))
+        if not self.gpu_target:
+            self.get_status(init)
+            # sum overflow buffer elements, 0:not overflow , >0:overflow
+            flag_sum = self.reduce_sum(init, (0,))
+        else:
+            flag_sum = self.hyper_map(F.partial(_grad_overflow), grads)
+            flag_sum = self.addn(flag_sum)
         if self.is_distributed:
             # sum overflow flag over devices
             flag_reduce = self.allreduce(flag_sum)
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 846be05c4d..89a5ea0249 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,
-                       NPUGetFloatStatus, Pow, RealDiv,
+                       NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
                        Reciprocal, CumSum,
                        Sin, Sqrt, Rsqrt,
                        Square, Sub, TensorAdd, Sign, Round)
@@ -154,6 +154,10 @@ __all__ = [
     'NPUAllocFloatStatus',
     'NPUGetFloatStatus',
     'NPUClearFloatStatus',
+    'IsNan',
+    'IsFinite',
+    'IsInf',
+    'FloatStatus',
     'Reciprocal',
     'SmoothL1Loss',
     'ReduceAll',
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 175b72560f..127d3c513c 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1541,6 +1541,94 @@ class LogicalOr(_LogicBinaryOp):
     def infer_dtype(self, x_dtype, y_dtype):
         return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
 
+class IsNan(PrimitiveWithInfer):
+    """
+    Judging which elements are nan for each position
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input, and the dtype is bool.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsNan"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class IsInf(PrimitiveWithInfer):
+    """
+    Judging which elements are inf or -inf for each position
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input, and the dtype is bool.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsInf"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class IsFinite(PrimitiveWithInfer):
+    """
+    Judging which elements are finite for each position
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape of input, and the dtype is bool.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init IsFinite"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        return mstype.bool_
+
+class FloatStatus(PrimitiveWithInfer):
+    """
+    Determine if the elements contains nan, inf or -inf. `0` for normal, `1` for overflow.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the shape of `(1,)`, and has the same dtype of input `mindspore.dtype.float32` or
+        `mindspore.dtype.float16`.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init FloatStatus"""
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return [1]
+
+    def infer_dtype(self, x_dtype):
+        return x_dtype
 
 class NPUAllocFloatStatus(PrimitiveWithInfer):
     """

From 27e49d141501478ba14c252fa68b64b288020422 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Mon, 6 Apr 2020 12:17:43 +0800
Subject: [PATCH 118/367] Distinguish package name according to hardware
 platform

---
 build.sh         |  6 ++++--
 package.sh       | 17 ++++++++++++++++-
 setup_package.py |  2 +-
 3 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/build.sh b/build.sh
index 9d812d6dcc..e0d0d45da2 100755
--- a/build.sh
+++ b/build.sh
@@ -452,8 +452,10 @@ if [[ "X$INC_BUILD" = "Xoff" ]]; then
         bash "${PROJECT_PATH}/package.sh" ge
     elif [[ "X$ENABLE_GPU" = "Xon" ]]; then
         bash "${PROJECT_PATH}/package.sh" ms gpu
-    elif [[ "X$ENABLE_D" = "Xon" ]] || [[ "X$ENABLE_CPU" = "Xon" ]]; then
-        bash "${PROJECT_PATH}/package.sh" ms
+    elif [[ "X$ENABLE_D" = "Xon" ]]; then
+        bash "${PROJECT_PATH}/package.sh" ms ascend
+    elif [[ "X$ENABLE_CPU" = "Xon" ]]; then
+        bash "${PROJECT_PATH}/package.sh" ms cpu
     else
         bash "${PROJECT_PATH}/package.sh" debug
     fi
diff --git a/package.sh b/package.sh
index 0d4147c9f6..67f4761f37 100755
--- a/package.sh
+++ b/package.sh
@@ -96,14 +96,29 @@ if [ -n "$1" ];then
 else
     export BACKEND_POLICY="ms"
 fi
+
+# package name
+if [[ "X$1" = "Xge" ]]; then
+    export MS_PACKAGE_NAME="mindspore"
+elif [[ "X$1" = "Xms" && "X$2" = "Xgpu" ]]; then
+    export MS_PACKAGE_NAME="mindspore-gpu"
+elif [[ "X$1" = "Xms" && "X$2" = "Xascend" ]]; then
+    export MS_PACKAGE_NAME="mindspore-ascend"
+elif [[ "X$1" = "Xms" && "X$2" = "Xcpu" ]]; then
+    export MS_PACKAGE_NAME="mindspore"
+else
+    export MS_PACKAGE_NAME="mindspore"
+fi
+
 ${PYTHON} "${BASEPATH}/setup_package.py" bdist_wheel
 
 chmod -R 700 ${PACKAGE_PATH}/mindspore/
-chmod -R 700 ${PACKAGE_PATH}/mindspore.egg-info/
+chmod -R 700 ${PACKAGE_PATH}/${MS_PACKAGE_NAME//-/_}.egg-info/
 
 # rename package
 PACKAGE_FULL_NAME=$(find "${PACKAGE_PATH}" -iname "*.whl")
 PACKAGE_BASE_NAME=$(echo ${PACKAGE_FULL_NAME} | awk -F / '{print $NF}' | awk -F - '{print $1"-"$2}')
+PACKAGE_BASE_NAME=${PACKAGE_BASE_NAME//_*-/-}
 
 PACKAGE_NEW_NAME="${PACKAGE_BASE_NAME}-${PY_TAGS}-${PLATFORM_TAG}.whl"
 cp -rf "${PACKAGE_PATH}/dist"/*.whl "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}"
diff --git a/setup_package.py b/setup_package.py
index 8b6889cd34..87b5718de2 100644
--- a/setup_package.py
+++ b/setup_package.py
@@ -21,7 +21,6 @@ from setuptools import setup, find_packages
 from setuptools.command.egg_info import egg_info
 from setuptools.command.build_py import build_py
 
-package_name = 'mindspore'
 version = '0.1.0'
 author = 'The MindSpore Authors'
 author_email = 'contact@mindspore.cn'
@@ -29,6 +28,7 @@ home_page = 'https://www.mindspore.cn'
 
 backend_policy = os.getenv('BACKEND_POLICY')
 commit_id = os.getenv('COMMIT_ID').replace("\n", "")
+package_name = os.getenv('MS_PACKAGE_NAME').replace("\n", "")
 
 pwd = os.path.dirname(os.path.realpath(__file__))
 pkg_dir = os.path.join(pwd, 'build/package')

From d88dbbb138da9fd3447748ae2f97f95f44470091 Mon Sep 17 00:00:00 2001
From: chenhaozhe <chenhaozhe1@huawei.com>
Date: Tue, 7 Apr 2020 20:48:26 +0800
Subject: [PATCH 119/367] pass auto mixed precision flag to ge init options

---
 mindspore/ccsrc/utils/context/ms_context.cc | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index cdc7af62e6..6c15e16714 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -358,7 +358,9 @@ void MsContext::GetGeOptions(std::map<std::string, std::string>* ge_options) con
     MS_LOG(ERROR) << "Set proto lib path failed!";
   }
 
-  // Disbale the global variable acc, only enable it whlie adding training graph in pipeline
+  // Enable auto mixed precision according to the context options
+  (*ge_options)["ge.exec.auto_mix_precision"] = std::to_string(auto_mixed_precision_flag_);
+  // Disable the global variable acc, only enable it whlie adding training graph in pipeline
   (*ge_options)["ge.exec.variable_acc"] = "0";
 #endif
 }

From f9d180d413ba1dddfc512765a66ababb7af37a89 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Fri, 3 Apr 2020 15:37:42 +0800
Subject: [PATCH 120/367] add api image gradients

---
 mindspore/nn/layer/__init__.py               |  4 +-
 mindspore/nn/layer/basic.py                  | 45 ++++++++++++++
 tests/st/ops/davinci/test_image_gradients.py | 62 ++++++++++++++++++++
 tests/ut/python/nn/test_image_gradients.py   | 49 ++++++++++++++++
 4 files changed, 158 insertions(+), 2 deletions(-)
 create mode 100644 tests/st/ops/davinci/test_image_gradients.py
 create mode 100644 tests/ut/python/nn/test_image_gradients.py

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index bb29935602..dae18fe663 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -22,7 +22,7 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
-from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot
+from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
 
@@ -31,7 +31,7 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
-           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot',
+           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'ImageGradients',
            'Embedding',
            'AvgPool2d', 'MaxPool2d',
            ]
diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 30b94c738d..de49685dac 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -370,3 +370,48 @@ class OneHot(Cell):
 
     def construct(self, indices):
         return self.onehot(indices, self.depth, self.on_value, self.off_value)
+
+
+class ImageGradients(Cell):
+    r"""
+    Returns two tensors, the first is along the height dimension and the second is along the width dimension.
+
+    Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`,
+    respectively.
+
+    .. math::
+        dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i<h-1 \cr
+        0, &if\ i==h-1\end{cases}
+
+        dx[i] = \begin{cases} image[:, i+1]-image[:, i], &if\ 0<=i<w-1 \cr
+        0, &if\ i==w-1\end{cases}
+
+    Inputs:
+        - **images** (Tensor) - The input image data, with format 'NCHW'.
+
+    Outputs:
+        - **dy** (Tensor) - vertical image gradients, the same type and shape as input.
+        - **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
+
+    Examples:
+        >>> net = nn.ImageGradients()
+        >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+        >>> net(image)
+        [[[[2,2]
+           [0,0]]]]
+        [[[[1,0]
+           [1,0]]]]
+    """
+    def __init__(self):
+        super(ImageGradients, self).__init__()
+
+    def construct(self, images):
+        batch_size, depth, height, width = P.Shape()(images)
+        dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
+        dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
+        dy = P.Concat(2)((dy, dy_last))
+
+        dx = images[:, :, :, 1:] - images[:, :, :, :width - 1]
+        dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
+        dx = P.Concat(3)((dx, dx_last))
+        return dy, dx
diff --git a/tests/st/ops/davinci/test_image_gradients.py b/tests/st/ops/davinci/test_image_gradients.py
new file mode 100644
index 0000000000..ea385158c9
--- /dev/null
+++ b/tests/st/ops/davinci/test_image_gradients.py
@@ -0,0 +1,62 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.image_gradients = nn.ImageGradients()
+
+    @ms_function
+    def construct(self, x):
+        return self.image_gradients(x)
+
+
+def test_image_gradients():
+    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+    expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32)
+    expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32)
+    net = Net()
+    dy, dx = net(image)
+    assert np.any(dx.asnumpy()-expected_dx) == False
+    assert np.any(dy.asnumpy()-expected_dy) == False
+
+
+def test_image_gradients_multi_channel_depth():
+    # 4 x 2 x 2 x 2
+    dtype = mstype.int32
+    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
+                             [[[3,5],[7,9]], [[11,13],[15,17]]],
+                             [[[5,10],[15,20]], [[25,30],[35,40]]],
+                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
+    expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]],
+                                   [[[4,4],[0,0]], [[4,4],[0,0]]],
+                                   [[[10,10],[0,0]], [[10,10],[0,0]]],
+                                   [[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype)
+    expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]],
+                                   [[[2,0],[2,0]], [[2,0],[2,0]]],
+                                   [[[5,0],[5,0]], [[5,0],[5,0]]],
+                                   [[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype)
+    net = Net()
+    dy, dx = net(image)
+
+    assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False
+    assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False
diff --git a/tests/ut/python/nn/test_image_gradients.py b/tests/ut/python/nn/test_image_gradients.py
new file mode 100644
index 0000000000..f65f38ec0a
--- /dev/null
+++ b/tests/ut/python/nn/test_image_gradients.py
@@ -0,0 +1,49 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test loss """
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.common.api import _executor
+from mindspore.common.api import ms_function
+
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.image_gradients = nn.ImageGradients()
+
+    @ms_function
+    def construct(self, x):
+        return self.image_gradients(x)
+
+def test_compile():
+    # input shape 1 x 1 x 2 x 2
+    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+    net = Net()
+    _executor.compile(net, image)
+
+
+def test_compile_multi_channel():
+    # input shape 4 x 2 x 2 x 2
+    dtype = mstype.int32
+    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
+                             [[[3,5],[7,9]], [[11,13],[15,17]]],
+                             [[[5,10],[15,20]], [[25,30],[35,40]]],
+                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
+    net = Net()
+    _executor.compile(net, image)

From a153fad874ec3c3608999defdeb34daa550bbb12 Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Fri, 3 Apr 2020 14:33:04 +0800
Subject: [PATCH 121/367] This commit is to separate the computation cost and
 memory cost in auto_parallel. Some related memory correction is removed.

---
 .../ccsrc/parallel/auto_parallel/costmodel.cc |  12 +-
 .../ccsrc/parallel/auto_parallel/costmodel.h  |  10 +-
 .../auto_parallel/dp_algo_costmodel.h         |   2 +-
 .../parallel/auto_parallel/edge_costmodel.cc  |  51 ++--
 .../parallel/auto_parallel/edge_costmodel.h   |   2 +-
 .../parallel/auto_parallel/graph_costmodel.cc | 105 +++-----
 .../parallel/auto_parallel/graph_costmodel.h  |   8 +-
 .../auto_parallel/operator_costmodel.cc       | 114 ++++----
 .../auto_parallel/operator_costmodel.h        | 244 +++++++++---------
 .../ccsrc/parallel/ops_info/matmul_info.cc    |   8 +-
 .../ccsrc/parallel/ops_info/operator_info.cc  |  25 +-
 .../ccsrc/parallel/ops_info/operator_info.h   |   6 +-
 .../ccsrc/parallel/step_auto_parallel.cc      |  30 +--
 .../tensor_layout/tensor_redistribution.cc    |  24 +-
 .../tensor_layout/tensor_redistribution.h     |   9 +-
 .../auto_parallel/graph_costmodel_test.cc     |   4 +-
 .../auto_parallel/operator_costmodel_test.cc  |  12 +-
 .../cpp/parallel/ops_info/activation_test.cc  |   8 +-
 .../cpp/parallel/ops_info/matmul_info_test.cc |   8 +-
 .../parallel/ops_info/tensor_add_info_test.cc |   8 +-
 .../cpp/parallel/ops_info/tmpidentity_test.cc |   4 +-
 21 files changed, 322 insertions(+), 372 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
index f5cf5069be..190f589bb5 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc
@@ -23,8 +23,8 @@
 namespace mindspore {
 namespace parallel {
 void Simplify(CostPtrList* clist_ptrs) {
-  // Sort the cost_list with the memory_cost increasing, and communication_cost decreasing order. This method
-  // excludes the cost with greater memory_cost and greater communication_cost.
+  // Sort the cost_list with the computation_cost_ increasing, and communication_cost decreasing order. This method
+  // excludes the cost with greater computation_cost_ and greater communication_cost.
   // E.g. clist_ptrs = {<100, 20>, <200, 10>, <300, 50>}. After this method, clist_ptrs = {<200, 10>, <100, 20>}
   if (!COST_MODEL_SIMPLIFY_CALCULATION) {
     return;
@@ -33,7 +33,7 @@ void Simplify(CostPtrList* clist_ptrs) {
   std::vector<size_t> id(clist_ptrs->size());
   std::iota(id.begin(), id.end(), size_t(0));
   std::sort(id.begin(), id.end(), [&clist_ptrs](size_t x, size_t y) {
-    return clist_ptrs->at(x)->memory_cost_ < clist_ptrs->at(y)->memory_cost_;
+    return clist_ptrs->at(x)->computation_cost_ < clist_ptrs->at(y)->computation_cost_;
   });
   CostPtrList ret;
   for (size_t i = 0; i < clist_ptrs->size(); ++i) {
@@ -45,8 +45,8 @@ void Simplify(CostPtrList* clist_ptrs) {
 }
 
 void SimplifyForDreasingCommunicationWithPartialPara(CostPtrList* clist_ptrs) {
-  // Sort the cost_list with the memory_cost increasing, and communication_with_partial_para_cost decreasing order.
-  // This method excludes the cost with greater memory_cost and greater communication_without_para_cost.
+  // Sort the cost_list with the computation_cost_ increasing, and communication_with_partial_para_cost decreasing
+  // order. This method excludes the cost with greater computation_cost_ and greater communication_without_para_cost.
   if (!COST_MODEL_SIMPLIFY_CALCULATION) {
     return;
   }
@@ -54,7 +54,7 @@ void SimplifyForDreasingCommunicationWithPartialPara(CostPtrList* clist_ptrs) {
   std::vector<size_t> id(clist_ptrs->size());
   std::iota(id.begin(), id.end(), size_t(0));
   std::sort(id.begin(), id.end(), [&clist_ptrs](size_t x, size_t y) {
-    return clist_ptrs->at(x)->memory_cost_ < clist_ptrs->at(y)->memory_cost_;
+    return clist_ptrs->at(x)->computation_cost_ < clist_ptrs->at(y)->computation_cost_;
   });
   CostPtrList ret;
   for (size_t i = 0; i < clist_ptrs->size(); ++i) {
diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
index 361c19573f..229f0fbf5e 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
@@ -44,14 +44,18 @@ using RedistributionOpListPtr = std::shared_ptr<std::pair<OperatorVector, OutPut
 
 struct Cost {
   Cost();
-  Cost(double memory, double commuication, const std::shared_ptr<Decision>& decision_ = nullptr)
-      : memory_cost_(memory), communication_cost_(commuication), decision_ptr_(std::move(decision_)) {
+  Cost(double computation, double commuication, const std::shared_ptr<Decision>& decision_ = nullptr)
+      : computation_cost_(computation), communication_cost_(commuication), decision_ptr_(std::move(decision_)) {
+    memory_with_reuse_ = 0.0;
     communication_without_parameter_ = 0.0;
     communication_with_partial_para_ = 0.0;
     communication_redis_forward_ = 0.0;
     communication_redis_backward_ = 0.0;
   }
-  double memory_cost_;
+  // 'memory_with_reuse_' calculates the peak memory usage in a training phase
+  double memory_with_reuse_;
+  // 'computation_cost_'  models the training time of an iteration in a training phase
+  double computation_cost_;
   // 'communication_cost_' includes communications from operators (forward and backward) and edges
   double communication_cost_;
   // communication_without_parameter_ = communication_cost_ - (backward communication from operators)
diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
index c9b6a07317..0cb58c49da 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
@@ -35,7 +35,7 @@ namespace parallel {
 //       interpretation of 6 operations in costmodel.h.
 // Phase 2: Search the cost_list in the final graph, and determine the optimal one
 //       Create the cost_list for the final graph, and choose the optimal one: one the minimum quantity
-//       COST_MODEL_ALPHA * memory_cost + COST_MODEL_BETA * communication_cost
+//       COST_MODEL_ALPHA * computation_cost + COST_MODEL_BETA * communication_cost
 // Phase 3: Recover the original CostGraph, the determine strategy for each operator
 //       After determining the optimal cost for the final graph, the algorithm recovers the original graph by applying
 //       the 4 operations in the reverse order in the Phase 1. Because each operation decision contains the strategy,
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
index 6381049f17..653f6c903d 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
@@ -69,7 +69,7 @@ Status Edge::InitEdgeCost() {
           MS_LOG(EXCEPTION) << "Failure: redistribution cost calculation failed";
         }
         MS_EXCEPTION_IF_NULL(cost);
-        MS_LOG(DEBUG) << "The redistribution cost: memory_cost: " << cost->memory_cost_
+        MS_LOG(DEBUG) << "The redistribution cost: computation_cost: " << cost->computation_cost_
                       << ", communication_cost: " << cost->communication_cost_
                       << ", communication_without_parameter_: " << cost->communication_without_parameter_
                       << ", communication_with_partial_para_: " << cost->communication_with_partial_para_ << ".";
@@ -117,9 +117,9 @@ Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, co
   double comm_cost = tensor_redistribution.comm_cost();
   double forward_comm_cost = tensor_redistribution.forward_comm_cost();
   double backward_comm_cost = tensor_redistribution.backward_comm_cost();
-  double mem_cost = tensor_redistribution.mem_cost();
+  double computation_cost = tensor_redistribution.computation_cost();
 
-  *cost = std::make_shared<Cost>(type_length * mem_cost, type_length * comm_cost);
+  *cost = std::make_shared<Cost>(type_length * computation_cost, type_length * comm_cost);
   (*cost)->communication_without_parameter_ = type_length * comm_cost;
   (*cost)->communication_with_partial_para_ =
     (*cost)->communication_without_parameter_ +
@@ -150,26 +150,26 @@ CostPtrList Edge::CreateEdgeEliminationCostList(const StrategyPtr& output_st_ptr
   (void)std::transform(edges.begin(), edges.end(), all_cost_list.begin(), LocalGetCostList);
 
   CostPtrList selected_cost_list(all_cost_list.size(), nullptr);
-  std::function<void(size_t, double, double, double)> recursive = [&](size_t k, double memory, double communication,
-                                                                      double communication_without_para) {
-    if (k == edges.size()) {
-      auto decision = std::make_shared<EdgeEliminationDecision>(selected_cost_list);
-      CostPtr new_cost = std::make_shared<Cost>(memory, communication);
-      MS_EXCEPTION_IF_NULL(new_cost);
-      new_cost->communication_without_parameter_ = communication_without_para;
-      new_cost->communication_with_partial_para_ =
-        communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
-      new_cost->decision_ptr_ = decision;
-      result.push_back(new_cost);
-      return;
-    }
-    for (auto& c : all_cost_list[k]) {
-      MS_EXCEPTION_IF_NULL(c);
-      selected_cost_list[k] = c;
-      recursive(k + 1, memory + c->memory_cost_, communication + c->communication_cost_,
-                communication_without_para + c->communication_without_parameter_);
-    }
-  };
+  std::function<void(size_t, double, double, double)> recursive =
+    [&](size_t k, double computation, double communication, double communication_without_para) {
+      if (k == edges.size()) {
+        auto decision = std::make_shared<EdgeEliminationDecision>(selected_cost_list);
+        CostPtr new_cost = std::make_shared<Cost>(computation, communication);
+        MS_EXCEPTION_IF_NULL(new_cost);
+        new_cost->communication_without_parameter_ = communication_without_para;
+        new_cost->communication_with_partial_para_ =
+          communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
+        new_cost->decision_ptr_ = decision;
+        result.push_back(new_cost);
+        return;
+      }
+      for (auto& c : all_cost_list[k]) {
+        MS_EXCEPTION_IF_NULL(c);
+        selected_cost_list[k] = c;
+        recursive(k + 1, computation + c->computation_cost_, communication + c->communication_cost_,
+                  communication_without_para + c->communication_without_parameter_);
+      }
+    };
   recursive(0, 0, 0, 0);
   SimplifyForDreasingCommunicationWithPartialPara(&result);
   return result;
@@ -203,7 +203,8 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr
       MS_EXCEPTION_IF_NULL(middle_cost);
       for (auto& right_cost : right_cost_list) {
         MS_EXCEPTION_IF_NULL(right_cost);
-        double memory = left_cost->memory_cost_ + middle_cost->memory_cost_ + right_cost->memory_cost_;
+        double computation =
+          left_cost->computation_cost_ + middle_cost->computation_cost_ + right_cost->computation_cost_;
         double communication =
           left_cost->communication_cost_ + middle_cost->communication_cost_ + right_cost->communication_cost_;
         double communication_without_para = left_cost->communication_without_parameter_ +
@@ -211,7 +212,7 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr
                                             right_cost->communication_without_parameter_;
 
         auto decision = std::make_shared<OpEliminationDecision>(op_strategy, left_cost, middle_cost, right_cost);
-        auto cost = std::make_shared<Cost>(memory, communication, decision);
+        auto cost = std::make_shared<Cost>(computation, communication, decision);
         MS_EXCEPTION_IF_NULL(cost);
         cost->communication_without_parameter_ = communication_without_para;
         cost->communication_with_partial_para_ =
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
index 1fa49029fa..eb89466d7c 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
@@ -133,7 +133,7 @@ class Edge {
   void set_parameter_involve(int para_invol) { is_output_parameter_involve_ = para_invol; }
   // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
   // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
-  Status CorrectStrategyCostForMemoryReuse() const { return SUCCESS; }
+  Status CalculateMemoryCost() const { return SUCCESS; }
 
  private:
   std::string edge_name_;
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index 59b9d9e992..88a54662d3 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -247,7 +247,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std::
             MS_EXCEPTION_IF_NULL(cost1);
             MS_EXCEPTION_IF_NULL(cost2);
             MS_EXCEPTION_IF_NULL(cost3);
-            double memory = cost1->memory_cost_ + cost2->memory_cost_ + cost3->memory_cost_;
+            double computation = cost1->computation_cost_ + cost2->computation_cost_ + cost3->computation_cost_;
             double commmunication =
               cost1->communication_cost_ + cost2->communication_cost_ + cost3->communication_cost_;
             double communication_without_para = cost1->communication_without_parameter_ +
@@ -255,7 +255,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std::
                                                 cost3->communication_without_parameter_;
             auto decision =
               std::make_shared<FinalDecision>(u_strategy->strategy_ptr, v_strategy->strategy_ptr, cost1, cost2, cost3);
-            auto cost = std::make_shared<Cost>(memory, commmunication, decision);
+            auto cost = std::make_shared<Cost>(computation, commmunication, decision);
             MS_EXCEPTION_IF_NULL(cost);
             cost->communication_without_parameter_ = communication_without_para;
             cost->communication_with_partial_para_ =
@@ -282,7 +282,7 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) {
     for (const auto& cost1 : clist1) {
       MS_EXCEPTION_IF_NULL(cost1);
       auto decision = std::make_shared<FinalSingleDecision>(u_strategy_ptr, cost1);
-      auto new_cost = std::make_shared<Cost>(cost1->memory_cost_, cost1->communication_cost_, decision);
+      auto new_cost = std::make_shared<Cost>(cost1->computation_cost_, cost1->communication_cost_, decision);
       MS_EXCEPTION_IF_NULL(new_cost);
       new_cost->communication_without_parameter_ = cost1->communication_without_parameter_;
       new_cost->communication_with_partial_para_ =
@@ -297,12 +297,12 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) {
 }
 
 CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list, double memory) {
-  if (cost_list.empty() || cost_list[0]->memory_cost_ >= memory) {
+  if (cost_list.empty() || cost_list[0]->computation_cost_ >= memory) {
     return nullptr;
   }
   std::function<CostPtr(CostPtr, const CostPtr&)> LocalCompare = [&](CostPtr init, const CostPtr& cost_x) {
     MS_EXCEPTION_IF_NULL(cost_x);
-    if (init == nullptr || cost_x->memory_cost_ < memory) {
+    if (init == nullptr || cost_x->computation_cost_ < memory) {
       init = cost_x;
     }
     return init;
@@ -313,36 +313,36 @@ CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list,
 
 CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList& cost_list, double memory) {
   // Select the cost with minimum training time. Currently, the training time is modeled as =
-  // costmodel_alpha_ * memory_cost + costmodel_beta_ * communication_with_partial_para_
+  // costmodel_alpha_ * computation_cost + costmodel_beta_ * communication_with_partial_para_
   if (cost_list.empty()) {
     MS_LOG(ERROR) << "Final cost list is null.";
     return nullptr;
   }
   CostPtr ret = cost_list[0];
   MS_EXCEPTION_IF_NULL(ret);
-  if (ret->memory_cost_ >= memory) {
-    MS_LOG(ERROR) << "No available cost; the minimum cost is " << ret->memory_cost_
+  if (ret->computation_cost_ >= memory) {
+    MS_LOG(ERROR) << "No available cost; the minimum cost is " << ret->computation_cost_
                   << ", the memory capacity is: " << memory << ".";
     return nullptr;
   }
-  double minimum = costmodel_alpha_ * ret->memory_cost_ + costmodel_beta_ * ret->communication_with_partial_para_;
-  MS_LOG(INFO) << "minimum: " << minimum << ", memory_cost_: " << ret->memory_cost_
+  double minimum = costmodel_alpha_ * ret->computation_cost_ + costmodel_beta_ * ret->communication_with_partial_para_;
+  MS_LOG(INFO) << "minimum: " << minimum << ", computation_cost_: " << ret->computation_cost_
                << ", communication_with_partial_para_: " << ret->communication_with_partial_para_
                << ", communication_cost_: " << ret->communication_cost_
                << ", communication_without_parameter_: " << ret->communication_without_parameter_ << ".";
   for (size_t i = 1; i < cost_list.size(); ++i) {
     MS_EXCEPTION_IF_NULL(cost_list[i]);
-    if (cost_list[i]->memory_cost_ >= memory) {
-      MS_LOG(INFO) << "cost_list " << i << " memory_cost_: " << cost_list[i]->memory_cost_
+    if (cost_list[i]->computation_cost_ >= memory) {
+      MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_
                    << ", is larger than the memory capacity: " << memory << ".";
       break;
     }
-    MS_LOG(INFO) << "cost_list " << i << " memory_cost_: " << cost_list[i]->memory_cost_
+    MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_
                  << ", communication_with_partial_para_: " << cost_list[i]->communication_with_partial_para_
                  << ", communication_cost_: " << cost_list[i]->communication_cost_
                  << ", communication_without_parameter_: " << cost_list[i]->communication_without_parameter_ << ".";
-    auto tmp =
-      costmodel_alpha_ * cost_list[i]->memory_cost_ + costmodel_beta_ * cost_list[i]->communication_with_partial_para_;
+    auto tmp = costmodel_alpha_ * cost_list[i]->computation_cost_ +
+               costmodel_beta_ * cost_list[i]->communication_with_partial_para_;
     MS_LOG(INFO) << "tmp: " << tmp;
     if (minimum > tmp) {
       minimum = tmp;
@@ -363,8 +363,8 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect
       MS_LOG(ERROR) << "The cost list " << i << " is empty.";
       return ret;
     } else {
-      total_memory += all_cost_list[i][0]->memory_cost_;
-      minimum += costmodel_alpha_ * all_cost_list[i][0]->memory_cost_ +
+      total_memory += all_cost_list[i][0]->computation_cost_;
+      minimum += costmodel_alpha_ * all_cost_list[i][0]->computation_cost_ +
                  costmodel_beta_ * all_cost_list[i][0]->communication_with_partial_para_;
       ret[i] = all_cost_list[i][0];
     }
@@ -381,8 +381,8 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect
       double tmp_memory = 0.0, tmp_minimum = 0.0;
       for (size_t i = 0; i < selected_cost_list.size(); ++i) {
         MS_EXCEPTION_IF_NULL(selected_cost_list[i]);
-        tmp_memory += selected_cost_list[i]->memory_cost_;
-        tmp_minimum += costmodel_alpha_ * selected_cost_list[i]->memory_cost_ +
+        tmp_memory += selected_cost_list[i]->computation_cost_;
+        tmp_minimum += costmodel_alpha_ * selected_cost_list[i]->computation_cost_ +
                        costmodel_beta_ * selected_cost_list[i]->communication_with_partial_para_;
       }
       MS_LOG(INFO) << "tmp_memory: " << tmp_memory << ", tmp_minimum: " << tmp_minimum << ", minimum: " << minimum
@@ -394,6 +394,7 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect
       }
       return;
     }
+
     MS_LOG(DEBUG) << "The value minimum: " << minimum << ", available_memory: " << available_memory << ".";
     for (auto& c : all_cost_list[k]) {
       selected_cost_list[k] = c;
@@ -814,7 +815,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const
       for (size_t k = 0; k < tar_cost_list.size(); ++k) {
         auto& tar_cost = tar_cost_list[k];
         MS_EXCEPTION_IF_NULL(tar_cost);
-        double memory = op_cost->memory_cost_ + edge_cost->memory_cost_ + tar_cost->memory_cost_;
+        double computation = op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_;
         double communication =
           op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_;
         double communication_without_para = op_cost->communication_without_parameter_ +
@@ -823,7 +824,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const
 
         auto decision =
           std::make_shared<MergeEliminationDecision>(op_strategy, op_cost, edge_cost, tar_op_strategy, tar_cost);
-        auto new_cost = std::make_shared<Cost>(memory, communication, decision);
+        auto new_cost = std::make_shared<Cost>(computation, communication, decision);
         MS_EXCEPTION_IF_NULL(new_cost);
         new_cost->communication_without_parameter_ = communication_without_para;
         new_cost->communication_with_partial_para_ =
@@ -891,7 +892,8 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str
       for (size_t k = 0; k < tar_cost_list.size(); ++k) {
         auto& tar_cost = tar_cost_list[k];
         MS_EXCEPTION_IF_NULL(tar_cost);
-        double memory = contract_op_cost->memory_cost_ + edge_cost->memory_cost_ + tar_cost->memory_cost_;
+        double computation =
+          contract_op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_;
         double communication =
           contract_op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_;
         double communication_without_para = contract_op_cost->communication_without_parameter_ +
@@ -900,7 +902,7 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str
 
         auto decision = std::make_shared<ContractEliminationDecision>(contract_op_stra, contract_op_cost, edge_cost,
                                                                       target_op_stra, tar_cost);
-        auto new_cost = std::make_shared<Cost>(memory, communication, decision);
+        auto new_cost = std::make_shared<Cost>(computation, communication, decision);
         new_cost->communication_without_parameter_ = communication_without_para;
         new_cost->communication_with_partial_para_ =
           communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
@@ -963,9 +965,9 @@ void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra,
       MS_EXCEPTION_IF_NULL(left_edge_cost);
       for (auto& left_node_cost : left_node_clist_origin) {
         MS_EXCEPTION_IF_NULL(left_node_cost);
-        double new_memory_cost = elimi_op_cost->memory_cost_ + left_edge_cost->memory_cost_ +
-                                 left_node_cost->memory_cost_ + right_edge_cost->memory_cost_ +
-                                 right_op_cost->memory_cost_;
+        double new_computation = elimi_op_cost->computation_cost_ + left_edge_cost->computation_cost_ +
+                                 left_node_cost->computation_cost_ + right_edge_cost->computation_cost_ +
+                                 right_op_cost->computation_cost_;
         double new_commu_cost = elimi_op_cost->communication_cost_ + left_edge_cost->communication_cost_ +
                                 left_node_cost->communication_cost_ + right_edge_cost->communication_cost_ +
                                 right_op_cost->communication_cost_;
@@ -977,7 +979,7 @@ void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra,
         auto decision =
           std::make_shared<TriangleEliminationDecision>(elimi_op_stra, elimi_op_cost, left_edge_cost, right_edge_cost,
                                                         left_op_stra, left_node_cost, right_op_stra, right_op_cost);
-        auto new_cost = std::make_shared<Cost>(new_memory_cost, new_commu_cost, decision);
+        auto new_cost = std::make_shared<Cost>(new_computation, new_commu_cost, decision);
         new_cost->communication_without_parameter_ = new_commu_without;
         new_cost->communication_with_partial_para_ =
           new_commu_without + COST_MODEL_GAMMA * (new_commu_cost - new_commu_without);
@@ -1082,11 +1084,12 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n
         succ_edges_costs[0] = first_succ_edge_cost;
         succ_nodes_costs[0] = first_succ_node_cost;
 
-        double memory_cost = merged_node_cost->memory_cost_, commu_cost = merged_node_cost->communication_cost_,
+        double computation_cost = merged_node_cost->computation_cost_,
+               commu_cost = merged_node_cost->communication_cost_,
                commu_without = merged_node_cost->communication_without_parameter_;
         for (size_t i = 0; i < succ_nodes_stras.size(); ++i) {
           MS_EXCEPTION_IF_NULL(succ_edges_costs[i]);
-          memory_cost += succ_edges_costs[i]->memory_cost_ + succ_nodes_costs[i]->memory_cost_;
+          computation_cost += succ_edges_costs[i]->computation_cost_ + succ_nodes_costs[i]->computation_cost_;
           commu_cost += succ_edges_costs[i]->communication_cost_ + succ_nodes_costs[i]->communication_cost_;
           commu_without += succ_edges_costs[i]->communication_without_parameter_ +
                            succ_nodes_costs[i]->communication_without_parameter_;
@@ -1094,7 +1097,7 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n
 
         auto decision = std::make_shared<StarEliminationDecision>(merged_op_stra, merged_node_cost, succ_edges_costs,
                                                                   succ_nodes_stras, succ_nodes_costs);
-        auto new_cost = std::make_shared<Cost>(memory_cost, commu_cost, decision);
+        auto new_cost = std::make_shared<Cost>(computation_cost, commu_cost, decision);
         new_cost->communication_without_parameter_ = commu_without;
         new_cost->communication_with_partial_para_ = commu_without + COST_MODEL_GAMMA * (commu_cost - commu_without);
         first_succ_node_clist_new->emplace_back(std::move(new_cost));
@@ -1210,36 +1213,6 @@ Status CostGraph::InitSelectedStrategy() {
   return SUCCESS;
 }
 
-Status CostGraph::CorrectOpsStrategyCostForMultiOutputUse() {
-  for (auto& op : ops_) {
-    MS_EXCEPTION_IF_NULL(op);
-    if (op->GetAliveSuccEdges().size() > 1) {
-      // Filter out the case of a output being used by multiple operators
-      std::map<size_t, int> output_count;
-      for (size_t i = 0; i < op->GetAliveSuccEdges().size(); ++i) {
-        auto output_index = op->GetAliveSuccEdges()[i]->prev_op_output_index();
-        output_count[output_index]++;
-      }
-      for (size_t i = 0; i < op->GetAliveSuccEdges().size(); ++i) {
-        auto output_index = op->GetAliveSuccEdges()[i]->prev_op_output_index();
-        if (output_count[output_index] <= 1) {
-          continue;
-        }
-        auto next_op = op->GetAliveSuccEdges()[i]->next_operator();
-        MS_EXCEPTION_IF_NULL(next_op);
-        auto input_index = op->GetAliveSuccEdges()[i]->next_op_input_index();
-        if (next_op->CorrectStrategyCostForMultiOutputUse(input_index) != SUCCESS) {
-          MS_LOG(ERROR) << "The operator name: " << op->name() << ", the next operator name: " << next_op->name()
-                        << ", the output_index: " << output_index << ", the input_index: " << input_index << ".";
-          return FAILED;
-        }
-        output_count[output_index]--;
-      }
-    }
-  }
-  return SUCCESS;
-}
-
 Status CostGraph::ComputeOpsAndEdgesParameterInvolved() {
   for (auto& op : ops_) {
     MS_EXCEPTION_IF_NULL(op);
@@ -1252,23 +1225,23 @@ Status CostGraph::ComputeOpsAndEdgesParameterInvolved() {
   return SUCCESS;
 }
 
-Status CostGraph::CorrectOpsStrategyCostForMemoryReuse() {
+Status CostGraph::CalculateOpsMemoryCost() {
   for (auto& op : ops_) {
     MS_EXCEPTION_IF_NULL(op);
-    if (op->CorrectStrategyCostForMemoryReuse() != SUCCESS) {
-      MS_LOG(ERROR) << "Correcting Operator: " << op->name() << " cost for memory reuse failed.";
+    if (op->CalculateMemoryCost() != SUCCESS) {
+      MS_LOG(ERROR) << "Calculate Operator: " << op->name() << " cost for memory usage failed.";
       return FAILED;
     }
   }
   return SUCCESS;
 }
 
-Status CostGraph::CorrectEdgesStrategyCostForMemoryReuse() {
+Status CostGraph::CalculateEdgesMemoryCost() {
   for (auto& edge_pair : edges_) {
     const auto& edges = edge_pair.second;
     for (auto& one_edge : edges) {
-      if (one_edge->CorrectStrategyCostForMemoryReuse() != SUCCESS) {
-        MS_LOG(ERROR) << "Correcting Edge: " << one_edge->edge_name() << " cost for memory reuse failed.";
+      if (one_edge->CalculateMemoryCost() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculate Edge: " << one_edge->edge_name() << " cost for memory usage failed.";
         return FAILED;
       }
     }
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index e4cbdffb61..c149534826 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -175,16 +175,12 @@ class CostGraph {
   void CreateStarEliminationSubCostList(const StrategyPtr&, const CostPtrList&, const CostPtrList&, const StrategyPtr&,
                                         const CostPtrList&, std::vector<StrategyPtr>, CostPtrList&, CostPtrList&,
                                         CostPtrList*);
-
-  // When a output of a operator is being used by multiple operators, the memory cost of this part should be calculated
-  // only once. This method is for correcting the 'strategy_cost_' for operators
-  Status CorrectOpsStrategyCostForMultiOutputUse();
   // When the input of a operator is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then
   // the memory cost can be resused.
-  Status CorrectOpsStrategyCostForMemoryReuse();
+  Status CalculateOpsMemoryCost();
   // When the input of the edge is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then
   // the memory cost can be resused.
-  Status CorrectEdgesStrategyCostForMemoryReuse();
+  Status CalculateEdgesMemoryCost();
   Status ComputeOpsAndEdgesParameterInvolved();
 
   std::vector<OperatorInfoPtr> GetOperators() const { return ops_; }
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 6958932fd6..7c17b499b1 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -74,8 +74,8 @@ double MatMulCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, co
 
 // Return the per device memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double MatMulCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                        const int32_t&) const {
+double MatMulCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                             const std::vector<TensorInfo>& outputs, const int32_t&) const {
   // In forward phase, the memory cost = slice(A) + slice(B) + (0 or 1) allreduce(slice(C))
   double result = 0.0;
   TensorInfo output0 = outputs[0];
@@ -93,8 +93,8 @@ double MatMulCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, c
 
 // Return the per device memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double MatMulCost::GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                         const int32_t& stage_id) const {
+double MatMulCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                              const int32_t& stage_id) const {
   // In backward phase, the memory cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
@@ -147,8 +147,8 @@ double ActivationCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double ActivationCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                            const int32_t&) const {
+double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                                 const int32_t&) const {
   TensorInfo input0_info = inputs[0];
   Shape input0_slice_shape = input0_info.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
@@ -156,8 +156,8 @@ double ActivationCost::GetForwardMemoryCost(const std::vector<TensorInfo>& input
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double ActivationCost::GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                             const int32_t&) const {
+double ActivationCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                                  const int32_t&) const {
   return 0.0;
 }
 
@@ -191,8 +191,8 @@ double SoftmaxCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, c
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double SoftmaxCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                         const int32_t&) const {
+double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                              const int32_t&) const {
   // In the forward phase, the memory cost = slice(A)
   TensorInfo input0 = inputs[0];
   Shape input0_slice_shape = input0.slice_shape();
@@ -201,8 +201,9 @@ double SoftmaxCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs,
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double SoftmaxCost::GetBackwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                          const std::vector<mindspore::parallel::TensorInfo>&, const int32_t&) const {
+double SoftmaxCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
+                                               const std::vector<mindspore::parallel::TensorInfo>&,
+                                               const int32_t&) const {
   return 0.0;
 }
 
@@ -222,9 +223,9 @@ double TmpIdentityCost::GetBackwardCommCost(const std::vector<mindspore::paralle
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double TmpIdentityCost::GetForwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
-                                             const std::vector<mindspore::parallel::TensorInfo>&,
-                                             const int32_t&) const {
+double TmpIdentityCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
+                                                  const std::vector<mindspore::parallel::TensorInfo>&,
+                                                  const int32_t&) const {
   TensorInfo input0_info = inputs[0];
   Shape input0_slice_shape = input0_info.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
@@ -232,15 +233,15 @@ double TmpIdentityCost::GetForwardMemoryCost(const std::vector<mindspore::parall
 
 // Return the per memory cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
-double TmpIdentityCost::GetBackwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                              const std::vector<mindspore::parallel::TensorInfo>&,
-                                              const int32_t&) const {
+double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
+                                                   const std::vector<mindspore::parallel::TensorInfo>&,
+                                                   const int32_t&) const {
   return 0.0;
 }
 
-double BatchParallelCost::GetForwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
-                                               const std::vector<mindspore::parallel::TensorInfo>&,
-                                               const int32_t&) const {
+double BatchParallelCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
+                                                    const std::vector<mindspore::parallel::TensorInfo>&,
+                                                    const int32_t&) const {
   double cost = 0.0;
   for (size_t i = 0; i < inputs.size(); ++i) {
     cost += ListProduct(inputs[i].slice_shape()) * static_cast<double>(inputs_type_lengths_[i]);
@@ -248,9 +249,9 @@ double BatchParallelCost::GetForwardMemoryCost(const std::vector<mindspore::para
   return cost;
 }
 
-double BatchParallelCost::GetBackwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                                const std::vector<mindspore::parallel::TensorInfo>&,
-                                                const int32_t&) const {
+double BatchParallelCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
+                                                     const std::vector<mindspore::parallel::TensorInfo>&,
+                                                     const int32_t&) const {
   return 0.0;
 }
 
@@ -285,8 +286,8 @@ double PReLUCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, con
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double PReLUCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                       const int32_t&) const {
+double PReLUCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                            const int32_t&) const {
   // In forward phase, the memory cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
@@ -297,9 +298,9 @@ double PReLUCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, co
 
 // Return the per memory cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
-double PReLUCost::GetBackwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
-                                        const std::vector<mindspore::parallel::TensorInfo>&,
-                                        const int32_t& stage_id) const {
+double PReLUCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
+                                             const std::vector<mindspore::parallel::TensorInfo>&,
+                                             const int32_t& stage_id) const {
   // In backward phase, the memory cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
@@ -338,8 +339,8 @@ double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double OneHotCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                        const int32_t&) const {
+double OneHotCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                             const int32_t&) const {
   // In onehot's forward phase, the memory cost = slice(A)
   Shape input0_slice_shape = inputs[0].slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
@@ -347,8 +348,8 @@ double OneHotCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, c
 
 // Return the per memory cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
-double OneHotCost::GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                         const int32_t&) const {
+double OneHotCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                              const int32_t&) const {
   return 0.0;
 }
 
@@ -368,8 +369,9 @@ double SoftmaxCrossEntropyWithLogitsCost::GetBackwardCommCost(const std::vector<
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double SoftmaxCrossEntropyWithLogitsCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs,
-                                                               const std::vector<TensorInfo>&, const int32_t&) const {
+double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                                                    const std::vector<TensorInfo>&,
+                                                                    const int32_t&) const {
   // In forward phase, the memory cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
@@ -380,8 +382,9 @@ double SoftmaxCrossEntropyWithLogitsCost::GetForwardMemoryCost(const std::vector
 
 // Return the per memory cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
-double SoftmaxCrossEntropyWithLogitsCost::GetBackwardMemoryCost(const std::vector<TensorInfo>&,
-                                                                const std::vector<TensorInfo>&, const int32_t&) const {
+double SoftmaxCrossEntropyWithLogitsCost::GetBackwardComputationCost(const std::vector<TensorInfo>&,
+                                                                     const std::vector<TensorInfo>&,
+                                                                     const int32_t&) const {
   return 0.0;
 }
 
@@ -409,8 +412,8 @@ double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const st
 
 // Return the per memory cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double ReshapeCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                         const int32_t& stage_id) const {
+double ReshapeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                              const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
   RankList dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
@@ -421,26 +424,27 @@ double ReshapeCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs,
   if (tensor_redistribution.ComputeCost() == FAILED) {
     MS_LOG(EXCEPTION) << "Failure: tensor_redistribution ComputeCost failed.";
   }
-  return (inputs_type_lengths_[0] * tensor_redistribution.mem_cost());
+  return (inputs_type_lengths_[0] * tensor_redistribution.computation_cost());
 }
 
 // Return the per memory cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
-double ReshapeCost::GetBackwardMemoryCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                          const std::vector<mindspore::parallel::TensorInfo>&, const int32_t&) const {
+double ReshapeCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
+                                               const std::vector<mindspore::parallel::TensorInfo>&,
+                                               const int32_t&) const {
   return 0.0;
 }
 
-double ArithmeticCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                            const int32_t&) const {
+double ArithmeticCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                                 const int32_t&) const {
   double result;
   result = ListProduct(inputs[0].slice_shape()) * static_cast<double>(inputs_type_lengths_[0]) +
            ListProduct(inputs[1].slice_shape()) * static_cast<double>(inputs_type_lengths_[1]);
   return result;
 }
 
-double ArithmeticCost::GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                             const int32_t& stage_id) const {
+double ArithmeticCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                                  const int32_t& stage_id) const {
   double result = 0.0;
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
@@ -533,15 +537,15 @@ double L2NormalizeCost::GetBackwardCommCost(const std::vector<TensorInfo>& input
   return result;
 }
 
-double L2NormalizeCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                             const int32_t&) const {
+double L2NormalizeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                                  const int32_t&) const {
   TensorInfo input0_info = inputs[0];
   Shape input0_slice_shape = input0_info.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
 }
 
-double L2NormalizeCost::GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                              const int32_t& stage_id) const {
+double L2NormalizeCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                                   const std::vector<TensorInfo>&, const int32_t& stage_id) const {
   double result = 0.0;
 
   if (is_parameter_[0]) {
@@ -618,8 +622,9 @@ double ReduceMethodCost::GetBackwardCommCost(const std::vector<TensorInfo>& inpu
   return result;
 }
 
-double ReduceMethodCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs,
-                                              const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
+double ReduceMethodCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                                   const std::vector<TensorInfo>& outputs,
+                                                   const int32_t& stage_id) const {
   double result = 0.0;
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
@@ -640,8 +645,9 @@ double ReduceMethodCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inp
   return result;
 }
 
-double ReduceMeanCost::GetForwardMemoryCost(const std::vector<TensorInfo>& inputs,
-                                            const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
+double ReduceMeanCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                                 const std::vector<TensorInfo>& outputs,
+                                                 const int32_t& stage_id) const {
   double result = 0.0;
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index 9fb86d467e..8f0099bba3 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -65,12 +65,12 @@ class OperatorCost {
   virtual double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                      const int32_t& stage_id) const = 0;
   // per device computation cost
-  virtual double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const = 0;
-  virtual double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                      const int32_t& stage_id) const = 0;
-  virtual double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                       const int32_t& stage_id) const = 0;
+  virtual double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const = 0;
+  virtual double GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                           const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
+  virtual double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs,
+                                            const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
 
  protected:
   // for each input in 'inputs_', there is a bool variable indicating whether that the corresponding input is parameter
@@ -96,14 +96,14 @@ class MatMulCost : public OperatorCost {
                              const int32_t& stage_id) const override;
 
   // per device computation cost
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 
 using MatMulCostPtr = std::shared_ptr<MatMulCost>;
@@ -121,14 +121,14 @@ class ActivationCost : public OperatorCost {
                             const int32_t& stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                              const int32_t& stage_id) const override;
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 
 using ActivationCostPtr = std::shared_ptr<ActivationCost>;
@@ -146,14 +146,14 @@ class SoftmaxCost : public OperatorCost {
                             const int32_t& stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                              const int32_t& stage_id) const override;
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t&) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t&) const override;
 };
 
 using SoftmaxCostPtr = std::shared_ptr<SoftmaxCost>;
@@ -171,14 +171,14 @@ class TmpIdentityCost : public OperatorCost {
                             const int32_t& stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                              const int32_t& stage_id) const override;
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using TmpIdentityCostPtr = std::shared_ptr<TmpIdentityCost>;
 
@@ -199,14 +199,14 @@ class BatchParallelCost : public OperatorCost {
                              const int32_t&) const override {
     return 0.0;
   }
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using BatchParallelCostPtr = std::shared_ptr<BatchParallelCost>;
 
@@ -227,16 +227,16 @@ class VirtualDatasetCost : public OperatorCost {
                              const int32_t&) const override {
     return 0.0;
   }
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                              const int32_t&) const override {
+  double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                   const int32_t&) const override {
     return 0.0;
   }
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                               const int32_t&) const override {
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    const int32_t&) const override {
     return 0.0;
   }
 };
@@ -259,18 +259,18 @@ class GeneratorBaseCost : public OperatorCost {
                              const int32_t&) const override {
     return 0.0;
   }
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   // Inputs vector is empty for generator ops.
-  double GetForwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                              const int32_t&) const override {
+  double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                   const int32_t&) const override {
     return 0.0;
   }
   // Generator ops don't have backward steps.
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                               const int32_t&) const override {
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    const int32_t&) const override {
     return 0.0;
   }
 };
@@ -292,14 +292,14 @@ class PReLUCost : public OperatorCost {
                              const int32_t& stage_id) const override;
 
   // per device computation cost
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using PReLUCostPtr = std::shared_ptr<PReLUCost>;
 
@@ -319,14 +319,14 @@ class OneHotCost : public OperatorCost {
                              const int32_t& stage_id) const override;
 
   // per device computation cost
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using OneHotCostPtr = std::shared_ptr<OneHotCost>;
 
@@ -346,14 +346,14 @@ class SoftmaxCrossEntropyWithLogitsCost : public OperatorCost {
                              const int32_t& stage_id) const override;
 
   // per device computation cost
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using SoftmaxCrossEntropyWithLogitsCostPtr = std::shared_ptr<SoftmaxCrossEntropyWithLogitsCost>;
 
@@ -376,16 +376,16 @@ class ReshapeCost : public OperatorCost {
                              const int32_t& stage_id) const override;
 
   // per device computation cost
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
 
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
 
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using ReshapeCostPtr = std::shared_ptr<ReshapeCost>;
 
@@ -405,14 +405,14 @@ class ArithmeticCost : public OperatorCost {
   double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
                              const int32_t&) const override;
 
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using ArithmeticCostPtr = std::shared_ptr<ArithmeticCost>;
 
@@ -431,14 +431,14 @@ class L2NormalizeCost : public OperatorCost {
   }
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                              const int32_t& stage_id) const override;
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                               const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t& stage_id) const override;
 };
 using L2NormalizeCostPtr = std::shared_ptr<L2NormalizeCost>;
 
@@ -455,14 +455,14 @@ class ReduceMethodCost : public OperatorCost {
                             const int32_t& stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                              const int32_t& stage_id) const override;
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
-  }
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                               const int32_t&) const override {
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    const int32_t&) const override {
     return 0.0;
   }
   void set_cross_batch(bool cb) { cross_batch_ = cb; }
@@ -477,8 +477,8 @@ class ReduceMeanCost : public ReduceMethodCost {
   ReduceMeanCost() = default;
   ~ReduceMeanCost() override = default;
 
-  double GetForwardMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                              const int32_t& stage_id) const override;
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
 };
 using ReduceMeanCostPtr = std::shared_ptr<ReduceMeanCost>;
 
@@ -499,18 +499,18 @@ class GetNextCost : public OperatorCost {
                              const int32_t&) const override {
     return 0.0;
   }
-  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                       const int32_t& stage_id) const override {
-    return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id);
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   // Inputs vector is empty for generator ops.
-  double GetForwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                              const int32_t&) const override {
+  double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                   const int32_t&) const override {
     return 0.0;
   }
   // Generator ops don't have backward steps.
-  double GetBackwardMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                               const int32_t&) const override {
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    const int32_t&) const override {
     return 0.0;
   }
 };
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index ad6409be0a..2b02dc100d 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -592,10 +592,10 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr&
   int32_t stage_id = strategy->GetInputStage();
   // Here, we use the origin outputs_, because we only use the slice size of the output tensor.
   // It does not matter whether the output tensor is transposed or not.
-  double memory_cost =
-    matmulcost_ptr->GetForwardMemoryCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+  double computation_cost =
+    matmulcost_ptr->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   double communication_cost = matmulcost_ptr->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
-  std::shared_ptr<Cost> result = std::make_shared<Cost>(memory_cost, communication_cost);
+  std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
     matmulcost_ptr->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
@@ -604,7 +604,7 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr&
 
   // Breaking ties for preferring data parallelization
   BreakingTiesForPerferringDataParallel(strategy, result);
-  MS_LOG(DEBUG) << name_ << " : memory_cost: " << result->memory_cost_
+  MS_LOG(DEBUG) << name_ << " : computation_cost: " << result->computation_cost_
                 << ", communication_cost: " << result->communication_cost_
                 << ", communication_without_parameter_: " << result->communication_without_parameter_
                 << ", communication_with_partial_para_: " << result->communication_with_partial_para_;
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 8b96425bf7..11c518d844 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -1034,9 +1034,10 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) {
     return FAILED;
   }
   int32_t stage_id = strategy->GetInputStage();
-  double memory_cost = GetOperatorCost()->GetForwardMemoryCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+  double computation_cost =
+    GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   double communication_cost = GetOperatorCost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
-  std::shared_ptr<Cost> result = std::make_shared<Cost>(memory_cost, communication_cost);
+  std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
     GetOperatorCost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
@@ -1056,22 +1057,6 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) {
   return SUCCESS;
 }
 
-Status OperatorInfo::CorrectStrategyCostForMultiOutputUse(size_t input_index) {
-  for (auto& swc : strategy_cost_) {
-    double parameter_memory_cost = ListProduct(swc->inputs_ptr[input_index].slice_shape()) *
-                                   static_cast<double>(GetOperatorCost()->inputs_type_lengths()[input_index]);
-    // remove the parameter memory cost
-    swc->cost_list[0]->memory_cost_ -= parameter_memory_cost;
-    if (swc->cost_list[0]->memory_cost_ < -1) {
-      MS_LOG(ERROR) << "The memory cost after correction is " << swc->cost_list[0]->memory_cost_
-                    << ", the parameter_memory_cost is " << parameter_memory_cost;
-      return FAILED;
-    }
-  }
-  corrected_input_indices_.push_back(input_index);
-  return SUCCESS;
-}
-
 int OperatorInfo::ComputeOpAndPrevEdgeParameterInvolved() {
   if (is_output_parameter_involve_ != -1) {
     return is_output_parameter_involve_;
@@ -1217,7 +1202,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra
     CheckGlobalDeviceManager();
     auto total_device_num = g_device_manager->GetDeviceListByStageId(stra->GetInputStage()).size();
     if (IntToSize(stra->GetInputDim()[0][0]) == total_device_num) {
-      cost->memory_cost_ -= 1.0;
+      cost->computation_cost_ -= 1.0;
       cost->communication_cost_ -= 1.0;
       cost->communication_with_partial_para_ -= 1.0;
       cost->communication_without_parameter_ -= 1.0;
@@ -1226,7 +1211,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra
 }
 
 double OperatorInfo::GetForwardMemoryCostFromCNode() {
-  return GetOperatorCost()->GetForwardMemoryCost(inputs_tensor_info_, outputs_tensor_info_, 0);
+  return GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
 }
 
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index cc70f1b870..e7b8af0a7e 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -87,13 +87,9 @@ class OperatorInfo {
   // is checked
   Status SetCostUnderStrategyBase(const StrategyPtr& strategy);
   std::vector<std::shared_ptr<StrategyWithCost>> GetStrategyCost() { return strategy_cost_; }
-  // In the case of a Parameter (or a output) being used by multiple operators, the memory cost induced by
-  // the parameter (or a output) should be calculated only once. This method is used to
-  // remove this part from the 'strategy_cost_'.
-  Status CorrectStrategyCostForMultiOutputUse(size_t input_index);
   // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
   // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
-  Status CorrectStrategyCostForMemoryReuse() const { return SUCCESS; }
+  Status CalculateMemoryCost() const { return SUCCESS; }
   int ComputeOpAndPrevEdgeParameterInvolved();
 
   ForwardOp forward_op() const { return forward_op_; }
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index 50e6a1e84e..d7d48c35bb 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -387,7 +387,7 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
   operator_info->set_outputs_dtype(cnode->Type());
   operator_info->set_cnode(cnode);
   // If no strategy has been configured for this operator, then candidate strategies are generated for
-  // auto-strategy searchingm if this primitive is Cast, we ignore the user-specified strategy
+  // auto-strategy searching; if this primitive is CAST, we ignore the user-specified strategy
   if (!StrategyFound(attrs) || prim->name() == CAST) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
@@ -600,13 +600,7 @@ void ConstructCostGraphEdges(const std::vector<AnfNodePtr> &all_nodes) {
     }
     MS_LOG(INFO) << "Successfully created " << edge_count << " edges for: " << cnode->operator_info()->name();
   }
-  // For the case of a output being used by multiple subsequent operators, the output induced memory cost should be
-  // calculated only once. This method is for correct the operators' memory cost calculation.
-  if (entire_costgraph->CorrectOpsStrategyCostForMultiOutputUse() != SUCCESS) {
-    MS_LOG(EXCEPTION) << "Correcting strategy_cost_ for operators failed.";
-  } else {
-    MS_LOG(INFO) << "Correcting strategy_cost_ for operators succeeded.";
-  }
+
   MS_LOG(INFO) << "Constructing edges for cost graph ends.";
 }
 
@@ -803,14 +797,6 @@ void AugmentCostGraph(const std::vector<AnfNodePtr> &all_nodes) {
       std::shared_ptr<Edge> edge_ptr = std::make_shared<Edge>(
         edge_name, tmp_identity_ptr, target_cnode->operator_info(), 0, input_index - 1, false, true);
 
-      // Correct the memory calculation for a parameter being used by multiple operators. The parameter is calculated
-      // only once
-      if (target_cnode->operator_info()->CorrectStrategyCostForMultiOutputUse(IntToSize(input_index - 1)) != SUCCESS) {
-        MS_LOG(EXCEPTION) << "Correcting strategy_cost_ failed : " << prim->name();
-      } else {
-        MS_LOG(INFO) << "Correcting strategy_cost_ succeeded. " << prim->name();
-      }
-
       if (edge_ptr->InitEdgeCost() != SUCCESS) {
         MS_LOG(EXCEPTION) << "Edge cost initialization failed";
       }
@@ -840,7 +826,7 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
   //      taking care for the case of a single Parameter being used by multiple operators. Create a TmpIdentity
   //      operator for this Parameter, and add an edge for the use of this Parameter by each
   //      subsequent operator;
-  // Step 3.1: Correct the memory calculation for memory reuse
+  // Step 3.1: Calculate memory usage
   // Step 4: Run the Dynamic Programming algorithm:
   //      in this process, cost is calculated based on not only the operators, but also the edges. Here, the edge
   //      cost is caused by the redistribution of a operator's output tensor layout to the next operator's input
@@ -867,14 +853,14 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
   MS_LOG(INFO) << "After the augmenting procedure, there are " << entire_costgraph->GetOperators().size()
                << " operators, and " << entire_costgraph->GetNumPairs() << " edges.";
 
-  // Step 3.1: Correcting calculation for memory reuse
+  // Step 3.1: Calculate the memory usage
   if (entire_costgraph->ComputeOpsAndEdgesParameterInvolved() == SUCCESS) {
-    // Correcting operators' memory usage
-    if (entire_costgraph->CorrectOpsStrategyCostForMemoryReuse() != SUCCESS) {
+    // Calculate operators' memory usage
+    if (entire_costgraph->CalculateOpsMemoryCost() != SUCCESS) {
       MS_LOG(EXCEPTION) << "Correcting operators' cost for memory reuse failed.";
     }
-    // Correcting edges' memory usage
-    if (entire_costgraph->CorrectEdgesStrategyCostForMemoryReuse() != SUCCESS) {
+    // Calculate edges' memory usage
+    if (entire_costgraph->CalculateEdgesMemoryCost() != SUCCESS) {
       MS_LOG(EXCEPTION) << "Correcting edges' cost for memory reuse failed.";
     }
   } else {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
index 93bda5da81..55e6a300e0 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
@@ -144,7 +144,7 @@ Status TensorRedistribution::ComputeCost() {
     MS_LOG(ERROR) << "Failure: InferTensorRedistribution failed";
     return Status::FAILED;
   }
-  // Compute redistribution communication cost and memory cost
+  // Compute redistribution communication cost and computation cost
   for (auto& op_cost : operator_list_) {
     OperatorR op = op_cost.first;
     Shape slice_shape = op_cost.second;
@@ -154,14 +154,14 @@ Status TensorRedistribution::ComputeCost() {
     if (str == PERMUTE_BY_AXIS) {
       // The shape does not change after PermuteByAxis operation.
       // communication cost = all_to_all + all_to_all = 2 * slice_shape
-      // memory cost = slice_shape
+      // computation cost = slice_shape
       forward_comm_cost_ += prod;
       backward_comm_cost_ += prod;
       comm_cost_ += 2.0 * prod;
-      mem_cost_ += prod;
+      computation_cost_ += prod;
     } else if (str == CONCAT_BY_AXIS) {
       // communication cost = all_gather + reduce_scatter = before_slice_shape + after_slice_shape
-      // memory cost = before_slice_shape
+      // computation cost = before_slice_shape
       if (op.second.size() < 3) {
         MS_LOG(ERROR) << "op.second size should not be less than 3!";
         return Status::FAILED;
@@ -173,22 +173,22 @@ Status TensorRedistribution::ComputeCost() {
       comm_cost_ += prod * (dev_num + 1.0);
       int32_t concat_dim = op.second[0];
       if (concat_dim == 0) {
-        // memory cost = all_gather
-        mem_cost_ += prod;
+        // computation cost = all_gather
+        computation_cost_ += prod;
       } else {
-        // memory cost = all_gather + split + concat
-        mem_cost_ += (prod + prod * dev_num + prod * dev_num);
+        // computation cost = all_gather + split + concat
+        computation_cost_ += (prod + prod * dev_num + prod * dev_num);
       }
     } else {
-      // There is only memory cost in SplitByAxis.
-      // memory cost = before_slice_shape
-      mem_cost_ += prod;
+      // There is only computation cost in SplitByAxis.
+      // computation cost = before_slice_shape
+      computation_cost_ += prod;
     }
   }
   if (reshape_flag()) {
     Shape prev_slice_shape = from_.slice_shape().array();
     double prev_prod = std::accumulate(prev_slice_shape.begin(), prev_slice_shape.end(), 1, std::multiplies<int>());
-    mem_cost_ += 2.0 * prev_prod;
+    computation_cost_ += 2.0 * prev_prod;
   }
   return Status::SUCCESS;
 }
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
index 38fb5959ad..e933b9b8eb 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
@@ -41,7 +41,7 @@ class TensorRedistribution {
         comm_cost_(0.0),
         forward_comm_cost_(0.0),
         backward_comm_cost_(0.0),
-        mem_cost_(0.0),
+        computation_cost_(0.0),
         construct_op_flag_(construct_op_flag),
         keep_reshape_(keep_reshape) {}
   Status Init(const TensorLayout& from, const TensorLayout& to, const RankList& dev_list);
@@ -51,7 +51,7 @@ class TensorRedistribution {
   bool reshape_flag() const { return reshape_flag_; }
   Status ComputeCost();
   double comm_cost() const { return comm_cost_; }
-  double mem_cost() const { return mem_cost_; }
+  double computation_cost() const { return computation_cost_; }
   double forward_comm_cost() const { return forward_comm_cost_; }
   double backward_comm_cost() const { return backward_comm_cost_; }
 
@@ -66,10 +66,13 @@ class TensorRedistribution {
   RankList dev_list_;
   OperatorList operator_list_;
   bool reshape_flag_;
+  // communication cost
   double comm_cost_;
+  // forward communication cost
   double forward_comm_cost_;
+  // backward communication cost
   double backward_comm_cost_;
-  double mem_cost_;
+  double computation_cost_;
   bool construct_op_flag_;
   bool keep_reshape_;
 };
diff --git a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
index 83a9eceacc..415a1fdd55 100644
--- a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
@@ -322,8 +322,8 @@ TEST_F(TestCostGraph, test_SelectCostListWithMinTrainingTimeMultiple) {
 
   auto ret_list = entire_cost_graph.SelectCostListWithMinTrainingTimeMultiple(all_list, memory);
   ASSERT_EQ(ret_list.size(), 2);
-  ASSERT_DOUBLE_EQ(ret_list[0]->memory_cost_, 10);
-  ASSERT_DOUBLE_EQ(ret_list[1]->memory_cost_, 1010);
+  ASSERT_DOUBLE_EQ(ret_list[0]->computation_cost_, 10);
+  ASSERT_DOUBLE_EQ(ret_list[1]->computation_cost_, 1010);
 }
 
 TEST_F(TestCostGraph, test_CheckOpElimination) {
diff --git a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
index 3bd65c049c..919c5b43ec 100644
--- a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc
@@ -76,8 +76,8 @@ TEST_F(TestMatMulCost, test_CostGeneration) {
   mmcost_.SetInputAndOutputTypeLength(inputs_length, outputs_length);
   mmcost_.GetForwardCommCost(inputs, outputs, 0);
   mmcost_.GetBackwardCommCost(inputs, outputs, 0);
-  mmcost_.GetForwardMemoryCost(inputs, outputs, 0);
-  mmcost_.GetBackwardMemoryCost(inputs, outputs, 0);
+  mmcost_.GetForwardComputationCost(inputs, outputs, 0);
+  mmcost_.GetForwardComputationCost(inputs, outputs, 0);
 }
 
 class TestActivationCost : public UT::Common {
@@ -128,8 +128,8 @@ TEST_F(TestActivationCost, test_CostGeneration) {
   std::vector<size_t> inputs_length = {4, 4};
   std::vector<size_t> outputs_length = {4};
   ac_cost_.SetInputAndOutputTypeLength(inputs_length, outputs_length);
-  ac_cost_.GetForwardMemoryCost(inputs, outputs, 0);
-  ac_cost_.GetBackwardMemoryCost(inputs, outputs, 0);
+  ac_cost_.GetForwardComputationCost(inputs, outputs, 0);
+  ac_cost_.GetBackwardComputationCost(inputs, outputs, 0);
 }
 
 class TestPReLUCost : public UT::Common {
@@ -184,8 +184,8 @@ TEST_F(TestPReLUCost, test_CostGeneration) {
   prelu_cost_.SetInputAndOutputTypeLength(inputs_length, outputs_length);
   double BCC, FMC, GMC;
   BCC = prelu_cost_.GetBackwardCommCost(inputs, outputs, 0);
-  FMC = prelu_cost_.GetForwardMemoryCost(inputs, outputs, 0);
-  GMC = prelu_cost_.GetBackwardMemoryCost(inputs, outputs, 0);
+  FMC = prelu_cost_.GetForwardComputationCost(inputs, outputs, 0);
+  GMC = prelu_cost_.GetBackwardComputationCost(inputs, outputs, 0);
   ASSERT_EQ(BCC, 32 * 4);
   ASSERT_EQ(FMC, 8 * 32 * 8 * 8 * 4 + 32 * 4);
   ASSERT_EQ(GMC, 128);
diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc
index 149aa9d5af..5d18c5372f 100644
--- a/tests/ut/cpp/parallel/ops_info/activation_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc
@@ -84,8 +84,8 @@ TEST_F(TestActivation, test_activation_strategies) {
     act_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = act_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = act_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()),
-                     cost.memory_cost_);
+    ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+                     cost.computation_cost_);
     ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
@@ -109,8 +109,8 @@ TEST_F(TestActivation, test_softmax_strategies) {
     soft_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = soft_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = soft_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()),
-                     cost.memory_cost_);
+    ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+                     cost.computation_cost_);
     ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
index 978b792a0c..99ca9f8e0e 100644
--- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
@@ -569,8 +569,8 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies1) {
     matmul1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = matmul1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = matmul1->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()),
-                     cost.memory_cost_);
+    ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+                     cost.computation_cost_);
     break;
   }
 }
@@ -599,8 +599,8 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies2) {
     TensorInfo replica_input1_info(tly, input1_shape, input1_slice_shape);
     replica_inputs_info.push_back(replica_input1_info);
 
-    ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetMemoryCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
-                     cost.memory_cost_);
+    ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
+                     cost.computation_cost_);
     break;
   }
 }
diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
index e7736a4b3e..6cb9739b1c 100644
--- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
@@ -188,8 +188,8 @@ TEST_F(TestTensorAddInfo, GenerateStrategies) {
     tensor_add->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add->outputs_tensor_info();
-    double memory_cost0 = tensor_add->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage());
-    double memory_cost1 = cost.memory_cost_;
+    double memory_cost0 = tensor_add->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
     double comm_cost0 = tensor_add->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
@@ -210,8 +210,8 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) {
     tensor_add1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add1->outputs_tensor_info();
-    double memory_cost0 = tensor_add1->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage());
-    double memory_cost1 = cost.memory_cost_;
+    double memory_cost0 = tensor_add1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
     double comm_cost0 = tensor_add1->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
index ce1238baeb..043746498f 100644
--- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
@@ -145,8 +145,8 @@ TEST_F(TestTmpIdentityInfo, test_generate_strategies) {
     identity_ptr->Init(sp);
     std::vector<TensorInfo> inputs_info = identity_ptr->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = identity_ptr->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()),
-                     cost.memory_cost_);
+    ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+                     cost.computation_cost_);
     ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }

From c5bfbc35569895721a48ed81dea9f0e55e158217 Mon Sep 17 00:00:00 2001
From: yoonlee666 <qiuyunlei@huawei.com>
Date: Wed, 8 Apr 2020 14:31:18 +0800
Subject: [PATCH 122/367] use TFRecordDataset in bert ci script and add
 absolute position embedding code in bert model

---
 mindspore/model_zoo/Bert_NEZHA/bert_model.py       | 14 ++++++++++++++
 .../networks/models/bert/bert_tdt_no_lossscale.py  |  4 ++--
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_model.py b/mindspore/model_zoo/Bert_NEZHA/bert_model.py
index f20c57dd75..d7f9355b3c 100644
--- a/mindspore/model_zoo/Bert_NEZHA/bert_model.py
+++ b/mindspore/model_zoo/Bert_NEZHA/bert_model.py
@@ -165,6 +165,7 @@ class EmbeddingPostprocessor(nn.Cell):
     def __init__(self,
                  embedding_size,
                  embedding_shape,
+                 use_relative_positions=False,
                  use_token_type=False,
                  token_type_vocab_size=16,
                  use_one_hot_embeddings=False,
@@ -192,6 +193,13 @@ class EmbeddingPostprocessor(nn.Cell):
         self.layernorm = nn.LayerNorm(embedding_size)
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.gather = P.GatherV2()
+        self.use_relative_positions = use_relative_positions
+        self.slice = P.Slice()
+        self.full_position_embeddings = Parameter(initializer
+                                                  (TruncatedNormal(initializer_range),
+                                                   [max_position_embeddings,
+                                                    embedding_size]),
+                                                  name='full_position_embeddings')
 
     def construct(self, token_type_ids, word_embeddings):
         output = word_embeddings
@@ -206,6 +214,11 @@ class EmbeddingPostprocessor(nn.Cell):
                 token_type_embeddings = self.gather(self.embedding_table, flat_ids, 0)
             token_type_embeddings = self.reshape(token_type_embeddings, self.shape)
             output += token_type_embeddings
+        if not self.use_relative_positions:
+            _, seq, width = self.shape
+            position_embeddings = self.slice(self.full_position_embeddings, [0, 0], [seq, width])
+            position_embeddings = self.reshape(position_embeddings, (1, seq, width))
+            output += position_embeddings
         output = self.layernorm(output)
         output = self.dropout(output)
         return output
@@ -853,6 +866,7 @@ class BertModel(nn.Cell):
         self.bert_embedding_postprocessor = EmbeddingPostprocessor(
             embedding_size=self.embedding_size,
             embedding_shape=output_embedding_shape,
+            use_relative_positions=config.use_relative_positions,
             use_token_type=True,
             token_type_vocab_size=config.type_vocab_size,
             use_one_hot_embeddings=use_one_hot_embeddings,
diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
index 9cc11997e6..5b6268505b 100644
--- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
+++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py
@@ -103,9 +103,9 @@ def me_de_train_dataset():
     """test me de train dataset"""
     # apply repeat operations
     repeat_count = 1
-    ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
+    ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                "next_sentence_labels", "masked_lm_positions",
-                                                               "masked_lm_ids", "masked_lm_weights"])
+                                                               "masked_lm_ids", "masked_lm_weights"], shuffle=False)
     type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)

From fb4e84c0ee6105badd4c5efd92cc1bcd919ffa03 Mon Sep 17 00:00:00 2001
From: anzhengqi <anzhengqi.huawei.com>
Date: Wed, 8 Apr 2020 14:47:16 +0800
Subject: [PATCH 123/367] modify part of comments

---
 .../ccsrc/dataset/kernels/image/image_utils.h      | 14 ++------------
 1 file changed, 2 insertions(+), 12 deletions(-)

diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.h b/mindspore/ccsrc/dataset/kernels/image/image_utils.h
index d289f6f56e..a4ddef40d6 100644
--- a/mindspore/ccsrc/dataset/kernels/image/image_utils.h
+++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.h
@@ -84,18 +84,8 @@ Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
 
 // Returns Decoded image
 // Supported images:
-// -   Windows bitmaps - \*.bmp, \*.dib (always supported)
-// -   JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section)
-// -   JPEG 2000 files - \*.jp2 (see the *Note* section)
-// -   Portable Network Graphics - \*.png (see the *Note* section)
-// -   WebP - \*.webp (see the *Note* section)
-// -   Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported)
-// -   PFM files - \*.pfm (see the *Note* section)
-// -   Sun rasters - \*.sr, \*.ras (always supported)
-// -   TIFF files - \*.tiff, \*.tif (see the *Note* section)
-// -   OpenEXR Image files - \*.exr (see the *Note* section)
-// -   Radiance HDR - \*.hdr, \*.pic (always supported)
-// -   Raster and Vector geospatial data supported by GDAL (see the *Note* section)
+//  BMP JPEG JPG PNG TIFF
+// supported by opencv, if user need more image analysis capabilities, please compile opencv particularlly.
 // @param input: CVTensor containing the not decoded image 1D bytes
 // @param output: Decoded image Tensor of shape <H,W,C> and type DE_UINT8. Pixel order is RGB
 Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output);

From effdb483d6166f1440fe6c1d00302b3847ad994c Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Tue, 7 Apr 2020 19:59:36 +0800
Subject: [PATCH 124/367] profiling feature enhancement

---
 .../device/ascend/ascend_stream_assign.cc     |   2 +-
 .../ascend/profiling/profiling_manager.h      |   4 -
 .../ascend/profiling/profiling_utils.cc       | 229 ++++++++----------
 .../device/ascend/profiling/profiling_utils.h |  93 ++++---
 mindspore/ccsrc/device/kernel_adjust.cc       |  31 +--
 mindspore/ccsrc/device/kernel_adjust.h        |   6 +-
 .../ascend/ascend_backend_optimization.cc     |   2 +-
 mindspore/ccsrc/session/ascend_session.cc     |   2 +-
 .../tasksink/ascend_stream_assign_stub.cc     |   2 +-
 9 files changed, 190 insertions(+), 181 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc b/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc
index 4f16c596c7..8c4d1f4a8f 100644
--- a/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc
@@ -702,7 +702,7 @@ void AscendStreamAssign::PrintGraphExeOrders(const shared_ptr<mindspore::session
                    << AnfAlgo::GetStreamId(cur_cnode_ptr) << "], event_id["
                    << GetValue<uint32_t>(primitive->GetAttr(kAttrEventId)) << "]";
     } else {
-      MS_LOG(INFO) << "node name[" << AnfAlgo::GetCNodeName(cur_cnode_ptr) << "], logic id["
+      MS_LOG(INFO) << "node name[" << cur_cnode_ptr->fullname_with_scope() << "], logic id["
                    << AnfAlgo::GetStreamDistinctionLabel(cur_cnode_ptr.get()) << "], stream id["
                    << AnfAlgo::GetStreamId(cur_cnode_ptr) << "]";
     }
diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h
index de8f6a7d0a..b826c4cf36 100644
--- a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h
+++ b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h
@@ -29,10 +29,6 @@ namespace ascend {
 // PROFILING_CUSTOM_LOGID_START 3
 const uint64_t kProfilingFpStartLogId = 1;
 const uint64_t kProfilingBpEndLogId = 2;
-const uint64_t kProfilingAllReduce1Start = 3;
-const uint64_t kProfilingAllReduce1End = 4;
-const uint64_t kProfilingAllReduce2Start = 5;
-const uint64_t kProfilingAllReduce2End = 6;
 const uint64_t kProfilingIterEndLogId = 255;
 
 class ProfilingEngineImpl;
diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc
index 0d7088300e..aa71aa0566 100644
--- a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc
+++ b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc
@@ -14,10 +14,8 @@
  * limitations under the License.
  */
 
-#include "device/ascend/profiling/profiling_utils.h"
-
 #include <map>
-
+#include "device/ascend/profiling/profiling_utils.h"
 #include "kernel/kernel.h"
 #include "device/ascend/profiling/profiling_manager.h"
 #include "session/anf_runtime_algorithm.h"
@@ -27,82 +25,61 @@
 namespace mindspore {
 namespace device {
 namespace ascend {
-const char ProfilingUtils::kProfiling[] = "Profiling";
-const char ProfilingUtils::kNotify[] = "notify";
-const char ProfilingUtils::kProfilerTraceId[] = "profiler_trace_id";
-const char ProfilingUtils::kFlags[] = "flags";
+constexpr uint32_t kMaxProfilingNodeNum = 100;
+constexpr char kCustomNode[] = "PROFILING_CUSTOM_";
+constexpr char kFpStartNode[] = "PROFILING_FP_START";
+constexpr char kBpEndNode[] = "PROFILING_BP_END";
+constexpr char kIterEndNode[] = "PROFILING_ITER_END";
 std::unordered_map<uint32_t, std::vector<std::string>> ProfilingUtils::graph_kernel_name_;
-bool ProfilingUtils::GetProfilingTraceInfo(const std::shared_ptr<session::KernelGraph> &graph_ptr,
-                                           ProfilingTraceInfo *profiling_trace_info) {
-  MS_EXCEPTION_IF_NULL(profiling_trace_info);
-  MS_EXCEPTION_IF_NULL(graph_ptr);
-  bool find_begin = false;
-  bool first_allreduce = true;
-  for (const auto &anf_node : graph_ptr->execution_order()) {
-    if (anf_node->isa<CNode>()) {
-      const std::string kernel_name = AnfAlgo::GetCNodeName(anf_node);
-      if ((kernel_name == "Cast" || kernel_name == "Four2Five") && !find_begin) {
-        profiling_trace_info->profiling_trace_begin = anf_node->fullname_with_scope();
-        find_begin = true;
-      }
-      if (kernel_name == "Conv2DBackpropFilter") {
-        profiling_trace_info->profiling_trace_bp_end = anf_node->fullname_with_scope();
-      }
-      if (kernel_name == kFusedMulApplyMomentumOpName || kernel_name == kApplyMomentumOpName) {
-        profiling_trace_info->profiling_trace_netoutput = anf_node->fullname_with_scope();
-      }
-      if (kernel_name == kAllReduceOpName) {
-        if (first_allreduce) {
-          profiling_trace_info->profiling_allreduce1_start = anf_node->fullname_with_scope();
-          profiling_trace_info->profiling_allreduce1_end = anf_node->fullname_with_scope();
-          first_allreduce = false;
-        } else {
-          profiling_trace_info->profiling_allreduce2_start = anf_node->fullname_with_scope();
-          profiling_trace_info->profiling_allreduce2_end = anf_node->fullname_with_scope();
-        }
-      }
+uint32_t ProfilingUtils::custom_node_index_ = 1;
+
+ProfilingTraceInfo ProfilingUtils::GetProfilingTraceFromEnv(NotNull<session::KernelGraph *> graph_ptr) {
+  MS_LOG(INFO) << "get env start";
+  custom_node_index_ = 1;
+  auto &cnode_exec_order = graph_ptr->execution_order();
+  ProfilingTraceInfo profiling_trace;
+  profiling_trace.trace_begin = GetTraceBegin(cnode_exec_order);
+  profiling_trace.trace_bp_end = GetTraceBpEnd();
+  profiling_trace.trace_netoutput = GetTraceNetoutput(cnode_exec_order);
+
+  MS_LOG(INFO) << "[profiling] trace_begin:" << profiling_trace.trace_begin
+               << " trace_bp_end:" << profiling_trace.trace_bp_end
+               << " trace_netoutput:" << profiling_trace.trace_netoutput;
+
+  for (uint32_t i = 1; i <= kMaxProfilingNodeNum; ++i) {
+    std::string env_str = std::string(kCustomNode) + std::to_string(i);
+    const char *node_full_name = std::getenv(env_str.c_str());
+    if (node_full_name == nullptr) {
+      break;
     }
+    MS_LOG(INFO) << "Get profiling node:" << node_full_name;
+    profiling_trace.trace_custom_node.insert(node_full_name);
   }
-  MS_LOG(INFO) << "[profiling]begin:" << profiling_trace_info->profiling_trace_begin
-               << ", net_output:" << profiling_trace_info->profiling_trace_netoutput
-               << ", end:" << profiling_trace_info->profiling_trace_bp_end
-               << ", allreduce1:" << profiling_trace_info->profiling_allreduce1_start
-               << ", allreduce2:" << profiling_trace_info->profiling_allreduce2_start;
-  return profiling_trace_info->IsValid();
+  MS_LOG(INFO) << "get env end";
+  return profiling_trace;
 }
 
-bool ProfilingUtils::GetNetOutput(AnfNodePtr anf_node, std::string *profiling_trace_net_output) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(profiling_trace_net_output);
-  MS_LOG(INFO) << "[profiling]Anf node's full name with scope:" << anf_node->fullname_with_scope();
-  if (!profiling_trace_net_output->empty()) {
-    MS_LOG(INFO) << "[profiling]Has got the net_output:" << profiling_trace_net_output->c_str();
-    return true;
-  }
-
-  if (AnfAlgo::IsRealKernel(anf_node)) {
-    *profiling_trace_net_output = anf_node->fullname_with_scope();
-    return true;
-  }
+std::string ProfilingUtils::GetTraceBegin(const std::vector<CNodePtr> &cnode_exec_order) {
+  const char *trace_begin = std::getenv(kFpStartNode);
+  auto &first_cnode = cnode_exec_order.front();
+  MS_EXCEPTION_IF_NULL(first_cnode);
+  return trace_begin == nullptr ? first_cnode->fullname_with_scope() : std::string(trace_begin);
+}
 
-  auto cnode = anf_node->cast<CNodePtr>();
-  if (cnode == nullptr) {
-    MS_LOG(ERROR) << "[profiling]Anf node should be a CNode";
-    return false;
-  }
+std::string ProfilingUtils::GetTraceBpEnd() {
+  const char *trace_bp_end = std::getenv(kBpEndNode);
+  return trace_bp_end == nullptr ? "" : std::string(trace_bp_end);
+}
 
-  auto inputs = cnode->inputs();
-  auto input_size = inputs.size();
-  if (input_size < 2) {
-    MS_LOG(ERROR) << "[profiling]Anf node' input size(" << input_size << ") < 2, don't support get apply kernel node.";
-    return false;
-  }
-  return GetNetOutput(inputs[1], profiling_trace_net_output);
+std::string ProfilingUtils::GetTraceNetoutput(const std::vector<CNodePtr> &cnode_exec_order) {
+  const char *trace_netoutput = std::getenv(kIterEndNode);
+  auto &last_cnode = cnode_exec_order.back();
+  MS_EXCEPTION_IF_NULL(last_cnode);
+  return trace_netoutput == nullptr ? last_cnode->fullname_with_scope() : std::string(trace_netoutput);
 }
 
-CNodePtr ProfilingUtils::CreateProfilingCNode(const std::shared_ptr<session::KernelGraph> &graph_ptr, bool notify,
-                                              uint64_t profiler_trace_id, uint32_t flags) {
-  MS_EXCEPTION_IF_NULL(graph_ptr);
+NotNull<CNodePtr> ProfilingUtils::CreateProfilingCNode(const ProfilingContent &profiling_content,
+                                                       NotNull<session::KernelGraph *> graph_ptr) {
   kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder;
   selected_kernel_builder.SetInputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT});
   selected_kernel_builder.SetInputsDeviceType({TypeId::kNumberTypeInt32, TypeId::kNumberTypeInt32});
@@ -118,75 +95,79 @@ CNodePtr ProfilingUtils::CreateProfilingCNode(const std::shared_ptr<session::Ker
   AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_builder.Build(), cnode_ptr.get());
   cnode_ptr->set_abstract(type_none_abstract);
   // set attr
-  ValuePtr notify_value = MakeValue(notify);
-  ValuePtr trace_id_value = MakeValue(profiler_trace_id);
-  ValuePtr flags_value = MakeValue(flags);
+  ValuePtr notify_value = MakeValue(profiling_content.notify);
+  ValuePtr trace_id_value = MakeValue(profiling_content.profiler_trace_id);
+  ValuePtr flags_value = MakeValue(profiling_content.flags);
   AnfAlgo::SetNodeAttr(ProfilingUtils::kNotify, notify_value, cnode_ptr);
   AnfAlgo::SetNodeAttr(ProfilingUtils::kProfilerTraceId, trace_id_value, cnode_ptr);
   AnfAlgo::SetNodeAttr(ProfilingUtils::kFlags, flags_value, cnode_ptr);
-  return cnode_ptr;
+  return NOT_NULL(cnode_ptr);
 }
 
-void ProfilingUtils::ProfilingTraceFpStart(const std::shared_ptr<mindspore::session::KernelGraph> &graph_ptr,
-                                           const mindspore::AnfNodePtr &anf_node,
-                                           const mindspore::device::ascend::ProfilingTraceInfo &profiling_trace_info,
-                                           std::vector<mindspore::CNodePtr> *kernel_list) {
-  if (profiling_trace_info.IsValid() && profiling_trace_info.profiling_trace_begin == anf_node->fullname_with_scope()) {
-    if (graph_ptr == nullptr || kernel_list == nullptr || anf_node == nullptr) {
-      MS_LOG(ERROR) << "[profiling]input param invalid";
-      return;
-    }
+void ProfilingUtils::ProfilingTraceFpStart(const mindspore::AnfNodePtr &anf_node,
+                                           const ProfilingTraceInfo &profiling_trace_info,
+                                           NotNull<session::KernelGraph *> graph_ptr,
+                                           NotNull<std::vector<mindspore::CNodePtr> *> kernel_list) {
+  if (profiling_trace_info.trace_begin == anf_node->fullname_with_scope()) {
     auto job_id = ProfilingManager::GetInstance().GetJobId();
-    // job task info
-    CNodePtr job_kernel_ptr = CreateProfilingCNode(graph_ptr, false, job_id, 0);
-    AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), job_kernel_ptr.get());
-    AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), job_kernel_ptr.get());
-    // fp task info
-    CNodePtr start_kernel_ptr = CreateProfilingCNode(graph_ptr, false, kProfilingFpStartLogId, 0);
-    AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), start_kernel_ptr.get());
-    AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), start_kernel_ptr.get());
-    kernel_list->emplace_back(job_kernel_ptr);
-    kernel_list->emplace_back(start_kernel_ptr);
+    ProfilingContent job_profiling_context = {false, job_id, 0};
+    auto job_profiling_node = CreateProfilingCNodeWithStream(anf_node, job_profiling_context, graph_ptr);
+    kernel_list->emplace_back(job_profiling_node);
+
+    ProfilingContent fp_profiling_content = {false, kProfilingFpStartLogId, 0};
+    auto fp_profiling_node = CreateProfilingCNodeWithStream(anf_node, fp_profiling_content, graph_ptr);
+    kernel_list->emplace_back(fp_profiling_node);
   }
 }
 
-void ProfilingUtils::ProfilingAllReduce(const std::shared_ptr<session::KernelGraph> &graph_ptr,
-                                        const AnfNodePtr &anf_node, int job_id, const std::string &profiling_node_name,
-                                        std::vector<CNodePtr> *kernel_list) {
-  MS_EXCEPTION_IF_NULL(graph_ptr);
+CNodePtr ProfilingUtils::CreateProfilingCNodeWithStream(const mindspore::AnfNodePtr &anf_node,
+                                                        const ProfilingContent &profiling_content,
+                                                        NotNull<session::KernelGraph *> graph_ptr) {
+  CNodePtr profiling_node = CreateProfilingCNode(profiling_content, graph_ptr);
+  AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), profiling_node.get());
+  AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), profiling_node.get());
+  return profiling_node;
+}
+
+void ProfilingUtils::ProfilingCustomOp(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                       NotNull<session::KernelGraph *> graph_ptr,
+                                       NotNull<std::vector<CNodePtr> *> kernel_list) {
   MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(kernel_list);
-  auto full_scope_name = anf_node->fullname_with_scope();
-  if (profiling_node_name == full_scope_name) {
-    CNodePtr allreduce_kernel_ptr = CreateProfilingCNode(graph_ptr, false, job_id, 0);
-    AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), allreduce_kernel_ptr.get());
-    AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), allreduce_kernel_ptr.get());
-    kernel_list->emplace_back(allreduce_kernel_ptr);
+  auto iter = profiling_trace_info.trace_custom_node.find(anf_node->fullname_with_scope());
+  if (iter == profiling_trace_info.trace_custom_node.end()) {
+    return;
   }
+  // custom op profiling job start from 3.
+  ProfilingContent front_profiling_content = {false, 2 * custom_node_index_ + 1, 0};
+  CNodePtr front_node = CreateProfilingCNodeWithStream(anf_node, front_profiling_content, graph_ptr);
+  kernel_list->insert(kernel_list->end() - 1, front_node);
+
+  ProfilingContent back_profiling_content = {false, 2 * custom_node_index_ + 2, 0};
+  CNodePtr back_node = CreateProfilingCNodeWithStream(anf_node, back_profiling_content, graph_ptr);
+  kernel_list->insert(kernel_list->end(), back_node);
+  ++custom_node_index_;
 }
 
-void ProfilingUtils::ProfilingTraceEnd(const std::shared_ptr<mindspore::session::KernelGraph> &graph_ptr,
-                                       const mindspore::AnfNodePtr &anf_node,
-                                       const mindspore::device::ascend::ProfilingTraceInfo &profiling_trace_info,
-                                       std::vector<mindspore::CNodePtr> *kernel_list) {
-  MS_EXCEPTION_IF_NULL(graph_ptr);
+void ProfilingUtils::ProfilingTraceBpEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                         NotNull<session::KernelGraph *> graph_ptr,
+                                         NotNull<std::vector<CNodePtr> *> kernel_list) {
   MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(kernel_list);
-  if (profiling_trace_info.IsValid()) {
-    auto full_scope_name = anf_node->fullname_with_scope();
-    if (profiling_trace_info.profiling_trace_netoutput == full_scope_name) {
-      CNodePtr bp_kernel_ptr = CreateProfilingCNode(graph_ptr, true, kProfilingIterEndLogId, 0);
-      AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), bp_kernel_ptr.get());
-      AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), bp_kernel_ptr.get());
-      kernel_list->emplace_back(bp_kernel_ptr);
-    }
+  if (profiling_trace_info.trace_bp_end == anf_node->fullname_with_scope()) {
+    ProfilingContent bp_end_profiling_content = {false, kProfilingBpEndLogId, 0};
+    CNodePtr bp_end_node = CreateProfilingCNodeWithStream(anf_node, bp_end_profiling_content, graph_ptr);
+    kernel_list->emplace_back(bp_end_node);
+  }
+}
 
-    if (profiling_trace_info.profiling_trace_bp_end == full_scope_name) {
-      CNodePtr end_task_info = CreateProfilingCNode(graph_ptr, false, kProfilingBpEndLogId, 0);
-      AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), end_task_info.get());
-      AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), end_task_info.get());
-      kernel_list->emplace_back(end_task_info);
-    }
+void ProfilingUtils::ProfilingTraceEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                       NotNull<session::KernelGraph *> graph_ptr,
+                                       NotNull<std::vector<mindspore::CNodePtr> *> kernel_list) {
+  MS_EXCEPTION_IF_NULL(anf_node);
+  auto full_scope_name = anf_node->fullname_with_scope();
+  if (profiling_trace_info.trace_netoutput == full_scope_name) {
+    ProfilingContent bp_end_profiling_content = {true, kProfilingIterEndLogId, 0};
+    CNodePtr bp_kernel_ptr = CreateProfilingCNodeWithStream(anf_node, bp_end_profiling_content, graph_ptr);
+    kernel_list->emplace_back(bp_kernel_ptr);
   }
 }
 
diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h
index ca0ef6f1f0..c59e856249 100644
--- a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h
+++ b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h
@@ -19,63 +19,102 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include <set>
 #include <unordered_map>
 #include "session/kernel_graph.h"
+#include "utils/contract.h"
 
 namespace mindspore {
 namespace device {
 namespace ascend {
 struct ProfilingTraceInfo {
   // execute order's first execute op(like: Cast or Four2Five ...), except tdt op(GetNext ...)
-  std::string profiling_trace_begin;
+  std::string trace_begin;
   // get first net_output(apply kernel) from graph outputs: fp ->net_output<- bp
-  std::string profiling_trace_bp_end;
+  std::string trace_bp_end;
   // execute order's end execute (like: Conv2DBackpropFilter)
-  std::string profiling_trace_netoutput;
+  std::string trace_netoutput;
 
-  std::string profiling_allreduce1_start;
-
-  std::string profiling_allreduce1_end;
-
-  std::string profiling_allreduce2_start;
-
-  std::string profiling_allreduce2_end;
+  // profiling specific op, such as AllReduce;
+  std::set<std::string> trace_custom_node;
 
   // 1. insert profiling_trace_begin if profiling_trace_bp_end is not empty.
   // 2. op lanuch get task info with callback func.
   // 3. insert profiling_trace_bp_end.
   // 4. insert profiling_trace_net_output if profiling_trace_bp_end is not empty.
 
-  bool IsValid() const { return !(profiling_trace_begin.empty() || profiling_trace_bp_end.empty()); }
+  bool IsValid() const { return !(trace_begin.empty() || trace_bp_end.empty() || trace_netoutput.empty()); }
+};
+
+struct ProfilingContent {
+  // true -send data from device to host and finish profiling
+  bool notify;
+  uint64_t profiler_trace_id;
+  uint32_t flags;
 };
 
 class ProfilingUtils {
  public:
   ProfilingUtils() = default;
   ~ProfilingUtils() = default;
-  static bool GetProfilingTraceInfo(const std::shared_ptr<session::KernelGraph> &graph_ptr,
-                                    ProfilingTraceInfo *profiling_trace_info);
-  static void ProfilingTraceFpStart(const std::shared_ptr<session::KernelGraph> &graph_ptr, const AnfNodePtr &anf_node,
-                                    const ProfilingTraceInfo &profiling_trace_info, std::vector<CNodePtr> *kernel_list);
-  static void ProfilingAllReduce(const std::shared_ptr<session::KernelGraph> &graph_ptr, const AnfNodePtr &anf_node,
-                                 int job_id, const std::string &profiling_node_name,
-                                 std::vector<CNodePtr> *kernel_list);
-  static void ProfilingTraceEnd(const std::shared_ptr<session::KernelGraph> &graph_ptr, const AnfNodePtr &anf_node,
-                                const ProfilingTraceInfo &profiling_trace_info, std::vector<CNodePtr> *kernel_list);
+
+  // Insert job_id profiling node and fp_start profiling node.
+  // Job_id is got from envs, which shound be a number greater than 255
+  // Fp_start node should been inserted in the start of a network, and the log_id is hard code to 1.
+  static void ProfilingTraceFpStart(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                    NotNull<session::KernelGraph *> graph_ptr,
+                                    NotNull<std::vector<CNodePtr> *> kernel_list);
+
+  // Insert net output profiling node, which tells the device to stop profiling.
+  // The notify in struct ProfilingContent should be 'true', which tells the device to send data to host.
+  static void ProfilingTraceEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                NotNull<session::KernelGraph *> graph_ptr,
+                                NotNull<std::vector<CNodePtr> *> kernel_list);
+
+  // Insert bp_end profiling node, which should been inserted after the last backpropagation CNode in the network.
+  static void ProfilingTraceBpEnd(const mindspore::AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                  NotNull<session::KernelGraph *> graph_ptr,
+                                  NotNull<std::vector<mindspore::CNodePtr> *> kernel_list);
+
+  // Mapping graph id and the kernels' name in the graph
   static void SetGraphKernelName(uint32_t graph_id, const std::vector<std::string> &kernel_names);
+
+  // Mapping task_id and kernel name for device to generate the time cost of specific kernel.
+  // Device calculate the time cost of the task which is marked by task id.
+  // But we need data of (kernel name , time cost)
   static void ReportProfilingData(uint32_t graph_id, const std::vector<uint32_t> &task_ids);
 
-  static const char kProfiling[];
-  static const char kNotify[];
-  static const char kProfilerTraceId[];
-  static const char kFlags[];
+  // Get profiling trace point from envs.
+  // export PROFILING_FP_START='full name of the first cnode to execute'
+  // export PROFILING_BP_END='full name of the last backpropagation cnode to execute'
+  // export PROFILING_ITER_END='full name of last cnode in graph to execute'
+  // And other cnode, like AllReduce, export PROFILING_CUSTOM_1='full name of AllReduce cnode'
+  // GetNext, export PROFIFLING_CUSTOM_2='full name fo GetNext cnode'
+  // The variable i in PROFILING_CUSTOM_i should start from 1 without interruption.
+  static ProfilingTraceInfo GetProfilingTraceFromEnv(NotNull<session::KernelGraph *> graph_ptr);
+
+  // Insert two profiling trace points, one in front and one behind
+  static void ProfilingCustomOp(const mindspore::AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info,
+                                NotNull<session::KernelGraph *> graph_ptr,
+                                NotNull<std::vector<mindspore::CNodePtr> *> kernel_list);
+
+  inline static constexpr char kProfiling[] = "Profiling";
+  inline static constexpr char kNotify[] = "notify";
+  inline static constexpr char kProfilerTraceId[] = "profiler_trace_id";
+  inline static constexpr char kFlags[] = "flags";
 
  private:
-  static bool GetNetOutput(AnfNodePtr anf_node, std::string *profiling_trace_net_output);
-  static CNodePtr CreateProfilingCNode(const std::shared_ptr<session::KernelGraph> &graph_ptr, bool notify,
-                                       uint64_t profiler_trace_id, uint32_t flags);
+  static NotNull<CNodePtr> CreateProfilingCNode(const ProfilingContent &profiling_content,
+                                                NotNull<session::KernelGraph *> graph_ptr);
+  static CNodePtr CreateProfilingCNodeWithStream(const AnfNodePtr &anf_node, const ProfilingContent &profiling_content,
+                                                 NotNull<session::KernelGraph *> graph_ptr);
+  static std::string GetTraceBegin(const std::vector<CNodePtr> &cnode_exec_order);
+  static std::string GetTraceBpEnd();
+  static std::string GetTraceNetoutput(const std::vector<CNodePtr> &cnode_exec_order);
+
   // graph id --> (kernel name list)
   static std::unordered_map<uint32_t, std::vector<std::string>> graph_kernel_name_;
+  static uint32_t custom_node_index_;
 };
 }  // namespace ascend
 }  // namespace device
diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc
index c422d50b51..9a6f48025f 100644
--- a/mindspore/ccsrc/device/kernel_adjust.cc
+++ b/mindspore/ccsrc/device/kernel_adjust.cc
@@ -438,23 +438,22 @@ void KernelAdjust::LoadSwitchInputs(std::vector<tensor::TensorPtr> *inputs) {
   MS_LOG(INFO) << "---------------- LoadSwitchInputs End--";
 }
 
-void KernelAdjust::Profiling(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr) {
+void KernelAdjust::Profiling(NotNull<session::KernelGraph *> kernel_graph_ptr) {
   if (!ascend::ProfilingManager::GetInstance().IsProfiling()) {
     MS_LOG(INFO) << "No need to profiling";
     return;
   }
-  ProfilingTraceInfo profiling_trace_info;
-  if (ProfilingUtils::GetProfilingTraceInfo(kernel_graph_ptr, &profiling_trace_info)) {
-    InsertProfilingKernel(kernel_graph_ptr, profiling_trace_info);
-  } else {
-    MS_LOG(WARNING) << "[profiling] GetProfilingTraceInfo failed";
+  ProfilingTraceInfo profiling_trace_info = ProfilingUtils::GetProfilingTraceFromEnv(kernel_graph_ptr);
+  if (!profiling_trace_info.IsValid()) {
+    MS_LOG(WARNING) << "[profiling] no profiling node found!";
+    return;
   }
+  InsertProfilingKernel(profiling_trace_info, kernel_graph_ptr);
 }
 
-void KernelAdjust::InsertProfilingKernel(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr,
-                                         const ProfilingTraceInfo &profiling_trace_info) {
+void KernelAdjust::InsertProfilingKernel(const ProfilingTraceInfo &profiling_trace_info,
+                                         NotNull<session::KernelGraph *> kernel_graph_ptr) {
   MS_LOG(INFO) << "[profiling] Insert profiling kernel start";
-  MS_EXCEPTION_IF_NULL(kernel_graph_ptr);
   if (!profiling_trace_info.IsValid()) {
     MS_LOG(WARNING) << "Profiling trace point not found";
     return;
@@ -462,18 +461,12 @@ void KernelAdjust::InsertProfilingKernel(const std::shared_ptr<session::KernelGr
   std::vector<CNodePtr> new_cnode_list;
   std::vector<CNodePtr> cnode_ptr_list = kernel_graph_ptr->execution_order();
   for (const auto &cnode_ptr : cnode_ptr_list) {
-    ProfilingUtils::ProfilingTraceFpStart(kernel_graph_ptr, cnode_ptr, profiling_trace_info, &new_cnode_list);
-    ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce1Start,
-                                       profiling_trace_info.profiling_allreduce1_start, &new_cnode_list);
-    ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce2Start,
-                                       profiling_trace_info.profiling_allreduce2_start, &new_cnode_list);
+    ProfilingUtils::ProfilingTraceFpStart(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list));
     new_cnode_list.emplace_back(cnode_ptr);
 
-    ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce1End,
-                                       profiling_trace_info.profiling_allreduce1_end, &new_cnode_list);
-    ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce2End,
-                                       profiling_trace_info.profiling_allreduce2_end, &new_cnode_list);
-    ProfilingUtils::ProfilingTraceEnd(kernel_graph_ptr, cnode_ptr, profiling_trace_info, &new_cnode_list);
+    ProfilingUtils::ProfilingCustomOp(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list));
+    ProfilingUtils::ProfilingTraceBpEnd(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list));
+    ProfilingUtils::ProfilingTraceEnd(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list));
   }
   kernel_graph_ptr->set_execution_order(new_cnode_list);
 }
diff --git a/mindspore/ccsrc/device/kernel_adjust.h b/mindspore/ccsrc/device/kernel_adjust.h
index 62c64d98b9..ca01d51e54 100644
--- a/mindspore/ccsrc/device/kernel_adjust.h
+++ b/mindspore/ccsrc/device/kernel_adjust.h
@@ -48,7 +48,7 @@ class KernelAdjust {
   void SetStreamSwitchOps(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr);
   bool StepLoadCtrlInputs(const std::shared_ptr<session::Context> &context,
                           const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr);
-  void Profiling(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr);
+  void Profiling(NotNull<session::KernelGraph *> kernel_graph_ptr);
   static bool NeedInsertSwitch();
   CNodePtr CreateSteamActiveOp(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr);
 
@@ -66,8 +66,8 @@ class KernelAdjust {
   kernel::KernelBuildInfo::KernelBuildInfoBuilder CreateMngKernelBuilder(const std::vector<std::string> &formats,
                                                                          const std::vector<TypeId> &type_ids);
   void LoadSwitchInputs(std::vector<tensor::TensorPtr> *inputs);
-  void InsertProfilingKernel(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr,
-                             const ProfilingTraceInfo &profiling_trace_info);
+  void InsertProfilingKernel(const ProfilingTraceInfo &profiling_trace_info,
+                             NotNull<session::KernelGraph *> kernel_graph_ptr);
 };
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 8212d64c27..432d88e7a4 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -246,7 +246,7 @@ void AscendBackendOptimization(const std::shared_ptr<session::KernelGraph> &kern
   kernel_graph->SetExecOrderByDefault();
   if (save_graphs) {
     std::string file_path = save_graphs_path + "/" + "hwopt_d_end.ir";
-    DumpIR(file_path, kernel_graph);
+    DumpIR(file_path, kernel_graph, true);
     DumpIRProto(kernel_graph, "after_hwopt");
   }
 }
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 9d351f3199..34c05aed08 100644
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -136,7 +136,7 @@ void AscendSession::BuildGraph(GraphId graph_id) {
   // Assign streams for control sink and hccl and so on
   AssignStream(graph);
 
-  device::KernelAdjust::GetInstance().Profiling(graph);
+  device::KernelAdjust::GetInstance().Profiling(NOT_NULL(graph.get()));
   // build kernel if node is cnode
   BuildKernel(graph);
   auto ms_context = MsContext::GetInstance();
diff --git a/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc b/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc
index ebd2ac8b46..e0b5ab0d61 100755
--- a/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc
+++ b/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc
@@ -42,6 +42,6 @@ bool KernelAdjust::StepLoadCtrlInputs(const std::shared_ptr<session::Context> &c
   return true;
 }
 bool KernelAdjust::NeedInsertSwitch() { return true; }
-void KernelAdjust::Profiling(const std::shared_ptr<session::KernelGraph> &kernel_graph_ptr) { return; }
+void KernelAdjust::Profiling(NotNull<session::KernelGraph *> kernel_graph_ptr) { return; }
 }  // namespace device
 }  // namespace mindspore

From 20d1b6444353e806ad37989f7a7c8b2c10418536 Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Wed, 8 Apr 2020 15:52:53 +0800
Subject: [PATCH 125/367] fix: error info is not exactly when column list
 invalid

---
 .../engine/datasetops/source/mindrecord_op.cc |   3 +-
 .../engine/datasetops/source/mindrecord_op.h  |   1 +
 .../ccsrc/mindrecord/common/shard_error.cc    | 178 ++++++++++++++++++
 .../ccsrc/mindrecord/include/shard_error.h    | 102 +++++-----
 mindspore/ccsrc/mindrecord/io/shard_reader.cc |   2 +-
 tests/ut/cpp/dataset/mind_record_op_test.cc   |  36 ++++
 .../ut/cpp/mindrecord/ut_shard_reader_test.cc |   2 +-
 7 files changed, 271 insertions(+), 53 deletions(-)
 create mode 100644 mindspore/ccsrc/mindrecord/common/shard_error.cc

diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
index b5bea5416c..cb0f135a0d 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc
@@ -103,7 +103,8 @@ Status MindRecordOp::Init() {
   shard_reader_ = mindspore::make_unique<ShardReader>();
   auto rc = shard_reader_->Open(dataset_file_, num_mind_record_workers_, columns_to_load_, operators_, block_reader_);
 
-  CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED, "MindRecordOp init failed.");
+  CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED,
+                               "MindRecordOp init failed. Error message: " + ErrnoToMessage(rc));
 
   data_schema_ = mindspore::make_unique<DataSchema>();
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
index 0b16391b20..aca5c86c2c 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h
@@ -32,6 +32,7 @@
 #include "dataset/engine/datasetops/source/io_block.h"
 #include "dataset/util/queue.h"
 #include "dataset/util/status.h"
+#include "mindrecord/include/shard_error.h"
 #include "mindrecord/include/shard_reader.h"
 #include "mindrecord/include/common/shard_utils.h"
 #include "dataset/util/wait_post.h"
diff --git a/mindspore/ccsrc/mindrecord/common/shard_error.cc b/mindspore/ccsrc/mindrecord/common/shard_error.cc
new file mode 100644
index 0000000000..cf43dcb315
--- /dev/null
+++ b/mindspore/ccsrc/mindrecord/common/shard_error.cc
@@ -0,0 +1,178 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mindrecord/include/shard_error.h"
+
+namespace mindspore {
+namespace mindrecord {
+std::string ErrnoToMessage(MSRStatus status) {
+  switch (status) {
+    case FAILED:
+      return "operator failed";
+      break;
+    case SUCCESS:
+      return "operator success";
+      break;
+    case OPEN_FILE_FAILED:
+      return "open file failed";
+      break;
+    case CLOSE_FILE_FAILED:
+      return "close file failed";
+      break;
+    case WRITE_METADATA_FAILED:
+      return "write metadata failed";
+      break;
+    case WRITE_RAWDATA_FAILED:
+      return "write rawdata failed";
+      break;
+    case GET_SCHEMA_FAILED:
+      return "get schema failed";
+      break;
+    case ILLEGAL_RAWDATA:
+      return "illegal raw data";
+      break;
+    case PYTHON_TO_JSON_FAILED:
+      return "pybind: python object to json failed";
+      break;
+    case DIR_CREATE_FAILED:
+      return "directory create failed";
+      break;
+    case OPEN_DIR_FAILED:
+      return "open directory failed";
+      break;
+    case INVALID_STATISTICS:
+      return "invalid statistics object";
+      break;
+    case OPEN_DATABASE_FAILED:
+      return "open database failed";
+      break;
+    case CLOSE_DATABASE_FAILED:
+      return "close database failed";
+      break;
+    case DATABASE_OPERATE_FAILED:
+      return "database operate failed";
+      break;
+    case BUILD_SCHEMA_FAILED:
+      return "build schema failed";
+      break;
+    case DIVISOR_IS_ILLEGAL:
+      return "divisor is illegal";
+      break;
+    case INVALID_FILE_PATH:
+      return "file path is invalid";
+      break;
+    case SECURE_FUNC_FAILED:
+      return "secure function failed";
+      break;
+    case ALLOCATE_MEM_FAILED:
+      return "allocate memory failed";
+      break;
+    case ILLEGAL_FIELD_NAME:
+      return "illegal field name";
+      break;
+    case ILLEGAL_FIELD_TYPE:
+      return "illegal field type";
+      break;
+    case SET_METADATA_FAILED:
+      return "set metadata failed";
+      break;
+    case ILLEGAL_SCHEMA_DEFINITION:
+      return "illegal schema definition";
+      break;
+    case ILLEGAL_COLUMN_LIST:
+      return "illegal column list";
+      break;
+    case SQL_ERROR:
+      return "sql error";
+      break;
+    case ILLEGAL_SHARD_COUNT:
+      return "illegal shard count";
+      break;
+    case ILLEGAL_SCHEMA_COUNT:
+      return "illegal schema count";
+      break;
+    case VERSION_ERROR:
+      return "data version is not matched";
+      break;
+    case ADD_SCHEMA_FAILED:
+      return "add schema failed";
+      break;
+    case ILLEGAL_Header_SIZE:
+      return "illegal header size";
+      break;
+    case ILLEGAL_Page_SIZE:
+      return "illegal page size";
+      break;
+    case ILLEGAL_SIZE_VALUE:
+      return "illegal size value";
+      break;
+    case INDEX_FIELD_ERROR:
+      return "add index fields failed";
+      break;
+    case GET_CANDIDATE_CATEGORYFIELDS_FAILED:
+      return "get candidate category fields failed";
+      break;
+    case GET_CATEGORY_INFO_FAILED:
+      return "get category information failed";
+      break;
+    case ILLEGAL_CATEGORY_ID:
+      return "illegal category id";
+      break;
+    case ILLEGAL_ROWNUMBER_OF_PAGE:
+      return "illegal row number of page";
+      break;
+    case ILLEGAL_SCHEMA_ID:
+      return "illegal schema id";
+      break;
+    case DESERIALIZE_SCHEMA_FAILED:
+      return "deserialize schema failed";
+      break;
+    case DESERIALIZE_STATISTICS_FAILED:
+      return "deserialize statistics failed";
+      break;
+    case ILLEGAL_DB_FILE:
+      return "illegal db file";
+      break;
+    case OVERWRITE_DB_FILE:
+      return "overwrite db file";
+      break;
+    case OVERWRITE_MINDRECORD_FILE:
+      return "overwrite mindrecord file";
+      break;
+    case ILLEGAL_MINDRECORD_FILE:
+      return "illegal mindrecord file";
+      break;
+    case PARSE_JSON_FAILED:
+      return "parse json failed";
+      break;
+    case ILLEGAL_PARAMETERS:
+      return "illegal parameters";
+      break;
+    case GET_PAGE_BY_GROUP_ID_FAILED:
+      return "get page by group id failed";
+      break;
+    case GET_SYSTEM_STATE_FAILED:
+      return "get system state failed";
+      break;
+    case IO_FAILED:
+      return "io operate failed";
+      break;
+    default:
+      return "invalid error no";
+  }
+}
+}  // namespace mindrecord
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/include/shard_error.h b/mindspore/ccsrc/mindrecord/include/shard_error.h
index 026ee836e3..b85eeb71c0 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_error.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_error.h
@@ -18,63 +18,65 @@
 #define MINDRECORD_INCLUDE_SHARD_ERROR_H_
 
 #include <map>
-#include "utils/error_code.h"
+#include <string>
 
 namespace mindspore {
 namespace mindrecord {
-DE_ERRORNO_MINDRECORD(OPEN_FILE_FAILED, 0, "open file failed");
-DE_ERRORNO_MINDRECORD(CLOSE_FILE_FAILED, 1, "close file failed");
-DE_ERRORNO_MINDRECORD(WRITE_METADATA_FAILED, 2, "write metadata failed");
-DE_ERRORNO_MINDRECORD(WRITE_RAWDATA_FAILED, 3, "write rawdata failed");
-DE_ERRORNO_MINDRECORD(GET_SCHEMA_FAILED, 4, "get schema failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_RAWDATA, 5, "illegal raw data");
-DE_ERRORNO_MINDRECORD(PYTHON_TO_JSON_FAILED, 6, "pybind: python object to json failed");
-DE_ERRORNO_MINDRECORD(DIR_CREATE_FAILED, 7, "directory create failed");
-DE_ERRORNO_MINDRECORD(OPEN_DIR_FAILED, 8, "open directory failed");
-DE_ERRORNO_MINDRECORD(INVALID_STATISTICS, 9, "invalid statistics object");
-DE_ERRORNO_MINDRECORD(OPEN_DATABASE_FAILED, 10, "open database failed");
-DE_ERRORNO_MINDRECORD(CLOSE_DATABASE_FAILED, 11, "close database failed");
-DE_ERRORNO_MINDRECORD(DATABASE_OPERATE_FAILED, 12, "database operate failed");
-DE_ERRORNO_MINDRECORD(BUILD_SCHEMA_FAILED, 13, "build schema failed");
-DE_ERRORNO_MINDRECORD(DIVISOR_IS_ILLEGAL, 14, "divisor is illegal");
-DE_ERRORNO_MINDRECORD(INVALID_FILE_PATH, 15, "file path is invalid");
-DE_ERRORNO_MINDRECORD(SECURE_FUNC_FAILED, 16, "secure function failed");
-DE_ERRORNO_MINDRECORD(ALLOCATE_MEM_FAILED, 17, "allocate memory failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_FIELD_NAME, 18, "illegal field name");
-DE_ERRORNO_MINDRECORD(ILLEGAL_FIELD_TYPE, 19, "illegal field type");
-DE_ERRORNO_MINDRECORD(SET_METADATA_FAILED, 20, "set metadata failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_DEFINITION, 21, "illegal schema definition");
-DE_ERRORNO_MINDRECORD(ILLEGAL_COLUMN_LIST, 22, "illegal column list");
-DE_ERRORNO_MINDRECORD(SQL_ERROR, 23, "sql error");
-DE_ERRORNO_MINDRECORD(ILLEGAL_SHARD_COUNT, 24, "illegal shard count");
-DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_COUNT, 25, "illegal schema count");
-DE_ERRORNO_MINDRECORD(VERSION_ERROR, 26, "data version is not matched");
-DE_ERRORNO_MINDRECORD(ADD_SCHEMA_FAILED, 27, "add schema failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_Header_SIZE, 28, "illegal header size");
-DE_ERRORNO_MINDRECORD(ILLEGAL_Page_SIZE, 29, "illegal page size");
-DE_ERRORNO_MINDRECORD(ILLEGAL_SIZE_VALUE, 30, "illegal size value");
-DE_ERRORNO_MINDRECORD(INDEX_FIELD_FAILED, 31, "add index fields failed");
-DE_ERRORNO_MINDRECORD(GET_CANDIDATE_CATEGORYFIELDS_FAILED, 32, "get candidate categoryFields failed");
-DE_ERRORNO_MINDRECORD(GET_CATEGORY_INFO, 33, "get category information failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_CATEGORY_ID, 34, "illegal category id");
-DE_ERRORNO_MINDRECORD(ILLEGAL_ROWNUMBER_OF_PAGE, 35, "illegal row number of page");
-DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_ID, 36, "illegal schema id");
-DE_ERRORNO_MINDRECORD(DESERIALIZE_SCHEMA_FAILED, 37, "deserialize schema failed");
-DE_ERRORNO_MINDRECORD(DESERIALIZE_STATISTICS_FAILED, 38, "deserialize statistics failed");
-DE_ERRORNO_MINDRECORD(ILLEGAL_DB_FILE, 39, "illegal db file.");
-DE_ERRORNO_MINDRECORD(OVERWRITE_DB_FILE, 40, "overwrite db file.");
-DE_ERRORNO_MINDRECORD(OVERWRITE_MINDRECORD_FILE, 41, "overwrite mindrecord file.");
-DE_ERRORNO_MINDRECORD(ILLEGAL_MINDRECORD_FILE, 42, "illegal mindrecord file.");
-DE_ERRORNO_MINDRECORD(PARSE_JSON_FAILED, 43, "parse json failed.");
-DE_ERRORNO_MINDRECORD(ILLEGAL_PARAMETERS, 44, "illegal parameters.");
-DE_ERRORNO_MINDRECORD(GET_PAGE_BY_GROUP_ID_FAILED, 46, "get page by group id failed.");
-DE_ERRORNO_MINDRECORD(GET_SYSTEM_STATE_FAILED, 47, "get system state failed.");
-DE_ERRORNO_MINDRECORD(IO_FAILED, 48, "io operate failed.");
-
 enum MSRStatus {
   SUCCESS = 0,
   FAILED = 1,
+  OPEN_FILE_FAILED,
+  CLOSE_FILE_FAILED,
+  WRITE_METADATA_FAILED,
+  WRITE_RAWDATA_FAILED,
+  GET_SCHEMA_FAILED,
+  ILLEGAL_RAWDATA,
+  PYTHON_TO_JSON_FAILED,
+  DIR_CREATE_FAILED,
+  OPEN_DIR_FAILED,
+  INVALID_STATISTICS,
+  OPEN_DATABASE_FAILED,
+  CLOSE_DATABASE_FAILED,
+  DATABASE_OPERATE_FAILED,
+  BUILD_SCHEMA_FAILED,
+  DIVISOR_IS_ILLEGAL,
+  INVALID_FILE_PATH,
+  SECURE_FUNC_FAILED,
+  ALLOCATE_MEM_FAILED,
+  ILLEGAL_FIELD_NAME,
+  ILLEGAL_FIELD_TYPE,
+  SET_METADATA_FAILED,
+  ILLEGAL_SCHEMA_DEFINITION,
+  ILLEGAL_COLUMN_LIST,
+  SQL_ERROR,
+  ILLEGAL_SHARD_COUNT,
+  ILLEGAL_SCHEMA_COUNT,
+  VERSION_ERROR,
+  ADD_SCHEMA_FAILED,
+  ILLEGAL_Header_SIZE,
+  ILLEGAL_Page_SIZE,
+  ILLEGAL_SIZE_VALUE,
+  INDEX_FIELD_ERROR,
+  GET_CANDIDATE_CATEGORYFIELDS_FAILED,
+  GET_CATEGORY_INFO_FAILED,
+  ILLEGAL_CATEGORY_ID,
+  ILLEGAL_ROWNUMBER_OF_PAGE,
+  ILLEGAL_SCHEMA_ID,
+  DESERIALIZE_SCHEMA_FAILED,
+  DESERIALIZE_STATISTICS_FAILED,
+  ILLEGAL_DB_FILE,
+  OVERWRITE_DB_FILE,
+  OVERWRITE_MINDRECORD_FILE,
+  ILLEGAL_MINDRECORD_FILE,
+  PARSE_JSON_FAILED,
+  ILLEGAL_PARAMETERS,
+  GET_PAGE_BY_GROUP_ID_FAILED,
+  GET_SYSTEM_STATE_FAILED,
+  IO_FAILED
 };
+
+// convert error no to string message
+std::string ErrnoToMessage(MSRStatus status);
 }  // namespace mindrecord
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index 32825fd9df..f91d28544e 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -676,7 +676,7 @@ MSRStatus ShardReader::Open(const std::string &file_path, int n_consumer,
 
   if (CheckColumnList(selected_columns_) == FAILED) {
     MS_LOG(ERROR) << "Illegal column list";
-    return FAILED;
+    return ILLEGAL_COLUMN_LIST;
   }
 
   // Initialize argument
diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc
index 3d5c80b3f4..90f41fdeb9 100644
--- a/tests/ut/cpp/dataset/mind_record_op_test.cc
+++ b/tests/ut/cpp/dataset/mind_record_op_test.cc
@@ -21,6 +21,7 @@
 #include "common/utils.h"
 #include "gtest/gtest.h"
 #include "mindrecord/include/shard_category.h"
+#include "mindrecord/include/shard_error.h"
 #include "mindrecord/include/shard_sample.h"
 #include "mindrecord/include/shard_shuffle.h"
 #include "utils/log_adapter.h"
@@ -479,3 +480,38 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) {
     row_count++;
   }
 }
+
+TEST_F(MindDataTestMindRecordOp, TestMindRecordInvalidColumnList) {
+  // single MindRecord op and nothing else
+  //
+  //    MindRecordOp
+
+  MS_LOG(INFO) << "UT test TestMindRecordInvalidColumnList";
+
+  Status rc;
+
+  // Start with an empty execution tree
+  auto my_tree = std::make_shared<ExecutionTree>();
+
+  // Test info:
+  // Dataset from testDataset1 has 10 rows, 2 columns.
+  // RowsPerBuffer buffer setting of 3 yields 4 buffers with the last buffer having single row
+  // only.  2 workers.
+  // Test a column selection instead of all columns as well.
+
+  std::vector<std::string> column_list;
+  std::string label_col_name("file_name_2");
+  column_list.push_back(label_col_name);
+  label_col_name = "label";
+  column_list.push_back(label_col_name);
+
+  std::shared_ptr<MindRecordOp> my_mindrecord_op;
+  MindRecordOp::Builder builder;
+  builder.SetDatasetFile(mindrecord_root_path_ + "/testMindDataSet/testImageNetData/imagenet.mindrecord0")
+      .SetRowsPerBuffer(3)
+      .SetNumMindRecordWorkers(4)
+      .SetColumnsToLoad(column_list);
+  rc = builder.Build(&my_mindrecord_op);
+  ASSERT_TRUE(rc.IsError());
+  ASSERT_TRUE(rc.ToString().find_first_of("illegal column list") != std::string::npos);
+}
diff --git a/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc b/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc
index fd63373e20..f7ed39a006 100644
--- a/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc
+++ b/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc
@@ -155,7 +155,7 @@ TEST_F(TestShardReader, TestShardReaderColumnNotInSchema) {
   auto column_list = std::vector<std::string>{"file_namex"};
   ShardReader dataset;
   MSRStatus ret = dataset.Open(file_name, 4, column_list);
-  ASSERT_EQ(ret, FAILED);
+  ASSERT_EQ(ret, ILLEGAL_COLUMN_LIST);
 }
 
 TEST_F(TestShardReader, TestShardVersion) {

From ee5b406b3746d01422000df3abf2b92fe1742d2b Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Wed, 8 Apr 2020 15:19:57 +0800
Subject: [PATCH 126/367] rebuild graph before rungraph if needed

---
 mindspore/ccsrc/transform/graph_runner.cc | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/mindspore/ccsrc/transform/graph_runner.cc b/mindspore/ccsrc/transform/graph_runner.cc
index f1f270cdb6..51ab7b9922 100644
--- a/mindspore/ccsrc/transform/graph_runner.cc
+++ b/mindspore/ccsrc/transform/graph_runner.cc
@@ -138,6 +138,13 @@ Status GraphRunner::RunGraph(const RunOptions& options, const std::vector<GeTens
     return Status::FAILED;
   }
 
+  // The information of some nodes could be changed after fusion in some cases
+  // Therefore a graph needs to be rebuilt in above situation
+  if (sess_->IsGraphNeedRebuild(wrap_ptr->id_)) {
+    sess_->RemoveGraph(wrap_ptr->id_);
+    sess_->AddGraph(wrap_ptr->id_, *(it->graph_ptr_), it->options_);
+  }
+
   ge::Status ret = sess_->RunGraph(wrap_ptr->id_, ge_inputs, ge_outputs);
   if (ret != ge::GRAPH_SUCCESS) {
     MS_LOG(ERROR) << "Call GE RunGraph Failed, ret is: " << ret;

From 47d903ff57f720b283dc092482318dd674fcd9ce Mon Sep 17 00:00:00 2001
From: liuxiao <liuxiao93@huawei.com>
Date: Mon, 6 Apr 2020 10:22:47 +0800
Subject: [PATCH 127/367] Add pack and unpack

---
 mindspore/ccsrc/operator/ops.h        |   1 +
 mindspore/ccsrc/transform/convert.cc  |   6 +-
 mindspore/ops/_grad/grad_array_ops.py |  24 +++++
 mindspore/ops/operations/__init__.py  |   4 +-
 mindspore/ops/operations/array_ops.py | 144 ++++++++++++++++++++++++++
 tests/ut/python/ops/test_ops.py       |  53 ++++++++++
 6 files changed, 229 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
index 727d66dfb3..5fbf2b7067 100644
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -135,6 +135,7 @@ extern const PrimitivePtr kPrimGatherV2;
 extern const PrimitivePtr kPrimSize;
 extern const PrimitivePtr kPrimArgMax;
 extern const PrimitivePtr kPrimPack;
+extern const PrimitivePtr kPrimUnpack;
 extern const PrimitivePtr kPrimUnsortedSegmentSum;
 extern const PrimitivePtr kPrimConcatOffset;
 extern const PrimitivePtr kPrimReshape;
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c400d1c573..d1c4a3d42e 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -148,7 +148,8 @@ const char kNameSlice[] = "Slice";
 const char kNameAddN[] = "AddN";
 const char kNameLess[] = "Less";
 const char kNameGreater[] = "Greater";
-const char kNamePack[] = "Stack";
+const char kNameStack[] = "Stack";
+const char kNameUnstack[] = "Unstack";
 const char kNameMerge[] = "Merge";
 const char kNameGeSwitch[] = "GeSwitch";
 
@@ -199,7 +200,8 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameMaxPool), ADPT_DESC(MaxPool)},
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
     {string(kNameTopK), ADPT_DESC(TopKV2)},
-    {string(kNamePack), ADPT_DESC(Pack)},
+    {string(kNameStack), ADPT_DESC(Pack)},
+    {string(kNameUnstack), ADPT_DESC(Unpack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},
     {string(kNameAllReduce), ADPT_DESC(HcomAllReduce)},
     {string(kNameBroadcast), ADPT_DESC(HcomBroadcast)},
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index 81d38a1e1e..0a0caf471e 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -266,6 +266,30 @@ def get_bprop_gather_v2(self):
     return bprop
 
 
+@bprop_getters.register(P.Stack)
+def get_bprop_stack(self):
+    """Generate bprop for Stack"""
+    axis = self.axis
+
+    def bprop(x, out, dout):
+        stack_grad = P.Unstack(axis)
+        out = stack_grad(dout)
+        return (out,)
+    return bprop
+
+
+@bprop_getters.register(P.Unstack)
+def get_bprop_unstack(self):
+    """Generate bprop for Unstack"""
+    axis = self.axis
+
+    def bprop(x, out, dout):
+        unstack_grad = P.Stack(axis)
+        out = unstack_grad(dout)
+        return (out,)
+    return bprop
+
+
 @bprop_getters.register(P.StridedSlice)
 def get_bprop_strided_slice(self):
     """Generate bprop for StridedSlice"""
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 89a5ea0249..7a8655b46c 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -19,7 +19,7 @@ Primitive operator classes.
 A collection of operators to build nerual networks or computing functions.
 """
 
-from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat,
+from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Stack, Unstack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, GatherNd, GatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
@@ -112,6 +112,8 @@ __all__ = [
     'OneHot',
     'GatherV2',
     'Concat',
+    'Stack',
+    'Unstack',
     'Tile',
     'BiasAdd',
     'Gelu',
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index b91c2cbc7d..59d3083c5d 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1350,6 +1350,150 @@ class Concat(PrimitiveWithInfer):
         return out
 
 
+def _get_stack_shape(x_shape, x_type, axis):
+    """for satck output shape"""
+    validator.check_type("shape", x_shape, [tuple])
+    validator.check_integer("len of input_x shape", len(x_shape), 0, Rel.GT)
+    validator.check_subclass("shape0", x_type[0], mstype.tensor)
+    validator.check_integer("len of input_x0 shape", len(x_shape[0]), 0, Rel.GT)
+    rank_base = len(x_shape[0])
+    N = len(x_shape)
+    out_shape = x_shape[0]
+    validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH)
+    if axis < 0:
+        axis = axis + rank_base + 1
+    for i in range(1, N):
+        v = x_shape[i]
+        validator.check('len of x_shape[%d]' % i, len(v), 'len of rank_base', rank_base)
+        validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0])
+        for j in range(rank_base):
+            if v[j] != x_shape[0][j]:
+                raise ValueError("Stack evaluator element %d shape in input can not stack with first element" % i)
+    out_shape.insert(axis, N)
+    return out_shape
+
+class Stack(PrimitiveWithInfer):
+    r"""
+    Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+
+    Packs the list of tensors in `input_x` into a tensor with rank one higher than
+    each tensor in `input_x`, by packing them along the `axis` dimension.
+    Given a list of length `N` of tensors of shape `(A, B, C)`;
+
+    If `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
+
+    If `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc.
+
+    Args:
+        axis (int): The axis to stack along. Negative values wrap around,
+                    so the valid range is [-(R+1), R+1). Default: 0.
+
+    Inputs:
+        - **input_x** (Union[tuple, list]) - A Tuple or list of Tensor objects with the same shape and type.
+
+    Outputs:
+        Tensor. A stacked Tensor with the same type as values.
+
+    Examples:
+        >>> data1 = Tensor(np.array([0, 1]).astype(np.float32))
+        >>> data2 = Tensor(np.array([2, 3]).astype(np.float32))
+        >>> op = P.Stack()
+        >>> output = op([data1, data2])
+        [[0, 1], [2, 3]]
+    """
+
+    @prim_attr_register
+    def __init__(self, axis=0):
+        """init Stack"""
+        self.__setattr_flag__ = True
+        validator.check_type("axis", axis, [int])
+        self.axis = axis
+
+    def __infer__(self, value):
+        x_shape = value['shape']
+        x_type = value['dtype']
+        self.add_prim_attr('num', len(x_shape))
+        all_shape = _get_stack_shape(x_shape, x_type, self.axis)
+        out = {'shape': all_shape,
+               'dtype': x_type[0],
+               'value': None}
+        return out
+
+
+class Unstack(PrimitiveWithInfer):
+    r"""
+    Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
+
+    Unpacks num tensors from value by chipping it along the axis dimension.
+    If num is not specified (the default), it is inferred from value's shape.
+    If value.shape[axis] is not known, ValueError is raised.
+
+    For example, given a tensor of shape (A, B, C, D);
+
+    If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and
+    each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split).
+
+    If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and
+    each tensor in output will have shape (A, C, D). Etc.
+
+    This is the opposite of stack.
+
+    Args:
+        axis (int): The axis to unstack along. Defaults to the first dimension.
+                    Negative values wrap around, so the valid range is [-R, R).
+
+    Inputs:
+        - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
+          A rank R > 0 Tensor to be unstacked.
+
+    Outputs:
+        A tuple of Tensors, the shape of each objects is same.
+
+    Raises:
+        ValueError: If axis is out of the range [-len(input_x.shape()), len(input_x.shape())),
+                    or if len(input_x.shape[axis]) not equal to num.
+
+    Examples:
+        >>> unstack = P.Unstack()
+        >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
+        >>> output = unstack(x)
+        ([1, 1, 1, 1], [2, 2, 2, 2])
+    """
+
+    @prim_attr_register
+    def __init__(self, axis=0):
+        """init Unstack"""
+        self.__setattr_flag__ = True
+        validator.check_type("axis", axis, [int])
+        self.axis = axis
+
+    def __infer__(self, x):
+        validator.check_subclass("x", x['dtype'], mstype.tensor)
+        x_shape = list(x['shape'])
+        dim = len(x_shape)
+        validator.check_int_range('axis value', self.axis, -dim, dim, Rel.INC_LEFT)
+        if self.axis < 0:
+            self.axis = self.axis + dim
+        output_num = x_shape[self.axis]
+        validator.check_type("num", output_num, [int])
+        validator.check_integer("output_num", output_num, 0, Rel.GT)
+        self.add_prim_attr('num', output_num)
+        output_valid_check = x_shape[self.axis] - output_num
+        validator.check_integer("the dimension which to unstack divides output_num", output_valid_check, 0, Rel.EQ)
+        out_shapes = []
+        out_dtypes = []
+        out_shape = x_shape[:self.axis] + x_shape[self.axis + 1:]
+        for _ in range(output_num):
+            out_shapes.append(tuple(out_shape))
+            out_dtypes.append(x['dtype'])
+        out_shapes = tuple(out_shapes)
+        out_dtypes = tuple(out_dtypes)
+        out = {'shape': out_shapes,
+               'dtype': out_dtypes,
+               'value': None}
+        return out
+
+
 class Slice(PrimitiveWithInfer):
     """
     Slice a tensor in specified shape.
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 0f5b716e39..5dcd2d553a 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -80,6 +80,29 @@ class NetForConcat1(nn.Cell):
         return self.concat((x1, x2))
 
 
+class NetForStackInput(nn.Cell):
+    def __init__(self, op):
+        super(NetForStackInput, self).__init__()
+        self.op = op
+        self.mul = P.Mul()
+
+    def construct(self, *args):
+        t = ()
+        for i in range(len(args)):
+            t = t + (self.mul(args[i], args[i]),)
+        return self.op(t)
+
+
+class NetForUnstackInput(nn.Cell):
+    def __init__(self, op):
+        super(NetForUnstackInput, self).__init__()
+        self.op = op
+        self.mul = P.Mul()
+
+    def construct(self, x1):
+        return self.op((self.mul(x1, x1)))
+
+
 class NetForFlatten(nn.Cell):
     def __init__(self):
         super(NetForFlatten, self).__init__()
@@ -968,6 +991,36 @@ test_case_array_ops = [
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
         'desc_bprop': [[3,]]}),
+    ('StackV2_0', {
+        'block': NetForStackInput(P.Stack()),
+        'desc_inputs':[[2, 2], [2, 2], [2, 2]],
+        'desc_bprop':[[3, 2, 2]],
+    }),
+    ('StackV2_1', {
+        'block': NetForStackInput(P.Stack(axis=-2)),
+        'desc_inputs':[[3, 2, 3], [3, 2, 3], [3, 2, 3]],
+        'desc_bprop':[[3, 2, 3, 3]],
+    }),
+    ('StackV2_2', {
+        'block': NetForStackInput(P.Stack()),
+        'desc_inputs':[[2, 2]],
+        'desc_bprop':[[2, 2, 2]],
+    }),
+    ('StackV2_3', {
+        'block': NetForStackInput(P.Stack()),
+        'desc_inputs':[[128, 128], [128, 128]],
+        'desc_bprop':[[2, 128, 128]],
+    }),
+    ('UnstackV2_0', {
+        'block': NetForUnstackInput(P.Unstack(axis=0)),
+        'desc_inputs':[[2, 4]],
+        'desc_bprop':[[4], [4]],
+    }),
+    ('UnstackV2_1', {
+        'block': NetForUnstackInput(P.Unstack(axis=-1)),
+        'desc_inputs':[Tensor(np.array([[1, 1, 1]], np.float32))],
+        'desc_bprop':[[1], [1], [1]],
+    }),
     ('Diag', {
         'block': P.Diag(),
         'desc_inputs': [[4]],

From a8bc8bfecbc52ab48cc1caa93f071230324ca4be Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Wed, 8 Apr 2020 17:48:42 +0800
Subject: [PATCH 128/367] fix compile error

---
 mindspore/ccsrc/transform/graph_runner.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/transform/graph_runner.cc b/mindspore/ccsrc/transform/graph_runner.cc
index 51ab7b9922..8b0ddfd18d 100644
--- a/mindspore/ccsrc/transform/graph_runner.cc
+++ b/mindspore/ccsrc/transform/graph_runner.cc
@@ -142,7 +142,7 @@ Status GraphRunner::RunGraph(const RunOptions& options, const std::vector<GeTens
   // Therefore a graph needs to be rebuilt in above situation
   if (sess_->IsGraphNeedRebuild(wrap_ptr->id_)) {
     sess_->RemoveGraph(wrap_ptr->id_);
-    sess_->AddGraph(wrap_ptr->id_, *(it->graph_ptr_), it->options_);
+    sess_->AddGraph(wrap_ptr->id_, *(wrap_ptr->graph_ptr_), wrap_ptr->options_);
   }
 
   ge::Status ret = sess_->RunGraph(wrap_ptr->id_, ge_inputs, ge_outputs);

From e17e08618649522a37d83d011bbb613b0df3675c Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Wed, 8 Apr 2020 18:00:33 +0800
Subject: [PATCH 129/367] unified tensor and mindspore.type

---
 mindspore/common/tensor.py             | 8 ++++----
 mindspore/ops/operations/math_ops.py   | 6 +++---
 mindspore/ops/operations/random_ops.py | 2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index d17661595f..709b2ae280 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -42,14 +42,14 @@ class Tensor(Tensor_):
 
     Examples:
         >>> # init a tensor with input data
-        >>> t1 = mindspore.Tensor(np.zeros([1, 2, 3]), mindspore.float32)
-        >>> assert isinstance(t1, mindspore.Tensor)
+        >>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32)
+        >>> assert isinstance(t1, Tensor)
         >>> assert t1.shape() == (1, 2, 3)
         >>> assert t1.dtype() == mindspore.float32
         >>>
         >>> # init a tensor with a float scalar
-        >>> t2 = mindspore.Tensor(0.1)
-        >>> assert isinstance(t2, mindspore.Tensor)
+        >>> t2 = Tensor(0.1)
+        >>> assert isinstance(t2, Tensor)
         >>> assert t2.dtype() == mindspore.float64
     """
 
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 47b9e490f1..d003f6ee8b 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1208,7 +1208,7 @@ class Acosh(PrimitiveWithInfer):
 
     Examples:
         >>> acosh = Acosh()
-        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), ms.float32)
+        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
         >>> output = acosh(X)
     """
 
@@ -1752,7 +1752,7 @@ class Cos(PrimitiveWithInfer):
 
     Examples:
         >>> cos = P.Cos()
-        >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), ms.float32)
+        >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
         >>> output = cos(X)
     """
 
@@ -1808,7 +1808,7 @@ class Sin(PrimitiveWithInfer):
 
     Examples:
         >>> sin = P.Sin()
-        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32)
+        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32)
         >>> output = sin(input_x)
     """
 
diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index 9ef5b301f9..95692a622e 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -45,7 +45,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
 
     Examples:
         >>> rnd_choice_mask = RandomChoiceWithMask()
-        >>> input_x = Tensor(np.ones(shape=[240000, 4]), ms.bool_)
+        >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_)
         >>> output_y, output_mask = rnd_choice_mask(input_x)
     """
 

From ac62faa38886b36670a58ac9c1c0eb9a8158c004 Mon Sep 17 00:00:00 2001
From: jinyaohui <jinyaohui@huawei.com>
Date: Tue, 7 Apr 2020 17:23:17 +0800
Subject: [PATCH 130/367] modify set_dataset_mode_config api param

---
 example/yolov3_coco2017/train.py        |  6 +++---
 mindspore/ccsrc/pipeline/pipeline_ge.cc |  6 +++---
 mindspore/ccsrc/transform/convert.cc    | 12 ++++++------
 mindspore/ccsrc/utils/config_manager.cc |  4 ++--
 mindspore/ccsrc/utils/config_manager.h  |  4 ++--
 mindspore/common/api.py                 |  4 ++--
 mindspore/nn/wrap/loss_scale.py         |  2 +-
 tests/ut/python/utils/test_callback.py  | 16 +++++++++-------
 8 files changed, 28 insertions(+), 26 deletions(-)

diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py
index 3ac3816f4a..0a32a6d30d 100644
--- a/example/yolov3_coco2017/train.py
+++ b/example/yolov3_coco2017/train.py
@@ -67,7 +67,7 @@ if __name__ == '__main__':
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
-    parser.add_argument("--mode", type=str, default="graph", help="Run graph mode or feed mode, default is graph")
+    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
@@ -150,8 +150,8 @@ if __name__ == '__main__':
 
         model = Model(net)
         dataset_sink_mode = False
-        if args_opt.mode == "graph":
-            print("In graph mode, one epoch return a loss.")
+        if args_opt.mode == "sink":
+            print("In sink mode, one epoch return a loss.")
             dataset_sink_mode = True
         print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
         model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
index 2f68935591..60960a2eb7 100644
--- a/mindspore/ccsrc/pipeline/pipeline_ge.cc
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -116,7 +116,7 @@ bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batc
     return transform::TransformUtil::ConvertDataType(i->type_id());
   });
 
-  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
+  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_SINK_MODE);
   ConfigManager::GetInstance().set_iter_num(size);
   ConfigManager::GetInstance().set_dataset_phase(phase);
 
@@ -453,8 +453,8 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
   }
 
   // process the first args of tensor
-  // only in Dataset Feed Mode, fp_bp graph need input tensors
-  if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
+  // only in Dataset non-sink Mode, fp_bp graph need input tensors
+  if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) {
     for (std::size_t i = 0; i < size; i++) {
       ValuePtr converted = nullptr;
       bool succ = parse::ConvertData(args[i], &converted);
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c400d1c573..250e83432a 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -440,10 +440,10 @@ void DfGraphConvertor::InitLoopVar(std::vector<ge::Operator> *init_input) {
 
     int64_t value = 0;
     auto const_iter_num = std::make_shared<Constant>("const/npu_runconfig/iterations_per_loop");
-    if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+    if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
       value = ConfigManager::GetInstance().iter_num();
     } else {
-      MS_LOG(INFO) << "Run with feed mode, the iterator number will always be 1";
+      MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1";
       value = 1;
       ConfigManager::GetInstance().set_iter_num(value);
     }
@@ -574,7 +574,7 @@ void DfGraphConvertor::SetupParamInitSubGraph(const TensorOrderMap &tensors, std
 
 void DfGraphConvertor::MakeDatasetHandler(const std::string &name, const size_t &input_idx, const AnfNodePtr &it) {
   MS_LOG(INFO) << "The " << name << " is the " << input_idx << "(st/nd/th) input";
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     auto getnext_idx = static_cast<int64_t>(input_idx);
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     if (!param.input_indexes().empty() && input_idx <= param.input_indexes().size()) {
@@ -866,7 +866,7 @@ DfGraphConvertor &DfGraphConvertor::ConvertAllNode() {
   }
 
   // Create dataset iterator and iterator_getnext node
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     MS_LOG(INFO) << "Dataset param is " << param.ToString() << ".";
     // GetNext
@@ -975,7 +975,7 @@ void DfGraphConvertor::TraceOutputFromParameter(const AnfNodePtr &anf_out) {
 }
 
 void SetupDatasetIterGetNextNode(const OperatorPtr &op) {
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     size_t output_num = param.ge_types().size();
     MS_LOG(INFO) << "Set iterator_getnext op's output num = " << output_num << ".";
@@ -1034,7 +1034,7 @@ DfGraphConvertor &DfGraphConvertor::BuildGraph() {
 
   // set graph input according to the order from anf graph
   std::vector<Operator> inputs;
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     inputs.push_back(*dataset_iter_getnext_);
   } else {
     auto params = anf_graph_->parameters();
diff --git a/mindspore/ccsrc/utils/config_manager.cc b/mindspore/ccsrc/utils/config_manager.cc
index ac8a965878..6d66b37436 100644
--- a/mindspore/ccsrc/utils/config_manager.cc
+++ b/mindspore/ccsrc/utils/config_manager.cc
@@ -28,7 +28,7 @@ ConfigManager& ConfigManager::GetInstance() noexcept {
 }
 
 void ConfigManager::SetDatasetModeConfig(const std::string& mode) {
-  static const std::map<std::string, DatasetMode> mode_map = {{"feed", DS_FEED_MODE}, {"graph", DS_GRAPH_MODE}};
+  static const std::map<std::string, DatasetMode> mode_map = {{"normal", DS_NORMAL_MODE}, {"sink", DS_SINK_MODE}};
   if (mode_map.find(mode) == mode_map.end()) {
     MS_LOG(ERROR) << "Invalid dataset mode:" << mode;
     return;
@@ -38,7 +38,7 @@ void ConfigManager::SetDatasetModeConfig(const std::string& mode) {
 
 void ConfigManager::ResetConfig() noexcept {
   parallel_strategy_ = ONE_DEVICE;
-  dataset_mode_ = DS_FEED_MODE;
+  dataset_mode_ = DS_NORMAL_MODE;
   dataset_param_ = DatasetGraphParam("", 0, 0, {}, {}, {});
   iter_num_ = 1;
 }
diff --git a/mindspore/ccsrc/utils/config_manager.h b/mindspore/ccsrc/utils/config_manager.h
index 31137f6243..db7d7d0c14 100644
--- a/mindspore/ccsrc/utils/config_manager.h
+++ b/mindspore/ccsrc/utils/config_manager.h
@@ -33,7 +33,7 @@ enum ParallelStrategy {
   DISTRIBUTION,
 };
 
-enum DatasetMode { DS_FEED_MODE = 0, DS_GRAPH_MODE };
+enum DatasetMode { DS_NORMAL_MODE = 0, DS_SINK_MODE };
 
 class DatasetGraphParam {
  public:
@@ -106,7 +106,7 @@ class ConfigManager {
   ~ConfigManager() = default;
 
   ParallelStrategy parallel_strategy_{ONE_DEVICE};
-  DatasetMode dataset_mode_{DS_FEED_MODE};
+  DatasetMode dataset_mode_{DS_NORMAL_MODE};
   DatasetGraphParam dataset_param_{"", 0, 0, {}, {}, {}};
   int64_t iter_num_{1};
   std::string dataset_phase_{""};
diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 9ee95ef772..5af17bbd78 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -381,9 +381,9 @@ class _Executor:
         if enable_ge:
             # decide whether to sink based on whether the inputs is virtual or not
             if args_list and isinstance(args_list[0], Tensor) and args_list[0].virtual_flag:
-                _set_dataset_mode_config('graph')
+                _set_dataset_mode_config('sink')
             else:
-                _set_dataset_mode_config('feed')
+                _set_dataset_mode_config('normal')
 
             self._build_data_graph(obj, params, phase)
 
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index 1ce3179273..5cdb34cf11 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -43,7 +43,7 @@ class DynamicLossScaleUpdateCell(Cell):
     In every training step, the loss scaling value  will be updated by loss scaling value/`scale_factor`
     when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no
     overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all
-    logic will be executed on device side(Another training mode is feed mode in which some logic will be
+    logic will be executed on device side(Another training mode is non-sink mode in which some logic will be
     executed on host).
 
     Args:
diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py
index 60e4c6527a..c6fea04231 100644
--- a/tests/ut/python/utils/test_callback.py
+++ b/tests/ut/python/utils/test_callback.py
@@ -24,11 +24,12 @@ from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.nn.optim import Momentum
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext,_checkpoint_cb_for_save_op,\
-                                      LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist,\
-                                      _build_callbacks, CheckpointConfig, _set_cur_net
+from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext, _checkpoint_cb_for_save_op, \
+    LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist, \
+    _build_callbacks, CheckpointConfig, _set_cur_net
 from mindspore.common.api import ms_function
 
+
 class Net(nn.Cell):
     """Net definition."""
 
@@ -52,6 +53,7 @@ class Net(nn.Cell):
 
 class LossNet(nn.Cell):
     """ LossNet definition """
+
     def __init__(self):
         super(LossNet, self).__init__()
         self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid')
@@ -110,8 +112,8 @@ def test_save_checkpoint():
         os.remove('./test_files/test_ckpt-model.pkl')
 
 
-def test_loss_monitor_graph_model():
-    """Test lossmonitor Graph model."""
+def test_loss_monitor_sink_model():
+    """Test loss monitor sink model."""
     cb_params = _InternalCallbackParam()
     cb_params.cur_epoch_num = 4
     cb_params.cur_step_num = 2
@@ -129,8 +131,8 @@ def test_loss_monitor_graph_model():
     callbacklist.end(run_context)
 
 
-def test_Loss_Monitor_feed_feed_model():
-    """Test Loss Monitor feed feed mode."""
+def test_loss_monitor_feed_model():
+    """Test loss monitor non-sink mode."""
     cb_params = _InternalCallbackParam()
     run_context = RunContext(cb_params)
     loss_cb = LossMonitor(1)

From b5e3fa959346bc7598ca6bbef24193d9f5ef2546 Mon Sep 17 00:00:00 2001
From: yao_yf <yaoyifan1@huawei.com>
Date: Wed, 8 Apr 2020 17:24:22 +0800
Subject: [PATCH 131/367] fix auto parallel prelu

---
 mindspore/ccsrc/parallel/ops_info/prelu_info.cc |  2 +-
 tests/ut/cpp/parallel/ops_info/prelu_test.cc    |  6 ++----
 tests/ut/python/parallel/test_prelu.py          | 17 +++++++++++++++++
 3 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
index 9aa8513331..1a44501f42 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
@@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr& strategy) {
     }
     return FAILED;
   }
-  if ((stra[0][PRELU_CHANNEL_INDEX] != PRELU_CHANNEL_STRATEGY) || (stra[1][0] != PRELU_CHANNEL_STRATEGY)) {
+  if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << ": Invalid channel strategy.";
     } else {
diff --git a/tests/ut/cpp/parallel/ops_info/prelu_test.cc b/tests/ut/cpp/parallel/ops_info/prelu_test.cc
index 5ff261234f..d6db1b8460 100644
--- a/tests/ut/cpp/parallel/ops_info/prelu_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/prelu_test.cc
@@ -146,11 +146,10 @@ TEST_F(TestPReLUInfo, CheckStrategy1) {
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy2) {
-  // Success: {{2,1,8,16},{1}}
   std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
+  ASSERT_EQ(ret, SUCCESS);
 }
 
 TEST_F(TestPReLUInfo, AutoStrategy1) {
@@ -252,11 +251,10 @@ TEST_F(TestPReLUInfo, CheckStrategy_2d1) {
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy_2d2) {
-  // Success: {{2,1,8,16},{1}}
   std::vector<Dimensions> inputs = {{128, 4}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu_2d->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
+  ASSERT_EQ(ret, SUCCESS);
 }
 
 TEST_F(TestPReLUInfo, AutoStrategy_2d1) {
diff --git a/tests/ut/python/parallel/test_prelu.py b/tests/ut/python/parallel/test_prelu.py
index c601045491..d3ad1cc710 100755
--- a/tests/ut/python/parallel/test_prelu.py
+++ b/tests/ut/python/parallel/test_prelu.py
@@ -149,3 +149,20 @@ def test_prelu_parallel_success3():
     w = Tensor(np.random.rand(16),dtype=ms.float32)
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
     _executor.compile(net, x, y, w)
+
+def test_prelu_parallel_success4():
+    class Net(nn.Cell):
+        def __init__(self, strategy):
+            super().__init__()
+            self.prelu = P.PReLU().set_strategy(strategy)
+        def construct(self, x, y):
+            out = self.prelu(x, y)
+            return out
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(device_num=64, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy = ((2, 4, 4, 2), (4, ))
+    x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
+    w = Tensor(np.random.rand(16),dtype=ms.float32)
+    net = GradWrap(NetWithLoss(Net(strategy)))
+    _executor.compile(net, x, w)

From 7541d3b067e67a79df3674638cbff9176853978b Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Thu, 2 Apr 2020 11:58:45 +0800
Subject: [PATCH 132/367] Develop op MaxPoolWithArgMax

---
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     |  44 ---
 mindspore/ccsrc/transform/convert.cc          |   2 +
 mindspore/ccsrc/transform/op_declare.cc       |  18 +-
 mindspore/ccsrc/transform/op_declare.h        |   4 +-
 mindspore/model_zoo/resnet.py                 |   2 +-
 mindspore/nn/layer/pooling.py                 | 143 ++++----
 mindspore/ops/_grad/grad_nn_ops.py            |  11 +-
 .../_op_impl/tbe/max_pool_grad_with_argmax.py |   6 +-
 .../ops/_op_impl/tbe/max_pool_with_argmax.py  |   6 +-
 mindspore/ops/operations/_grad_ops.py         | 121 +++----
 mindspore/ops/operations/nn_ops.py            | 338 +++++++++---------
 tests/perf_test/resnet_example.py             |   2 +-
 tests/st/networks/test_cpu_lenet.py           |   6 +-
 tests/st/networks/test_gpu_alexnet.py         |   2 +-
 .../ops/davinci/test_maxpool_with_argmax.py   |  15 +-
 .../davinci/test_maxpool_with_argmax_grad.py  |   6 +-
 tests/st/tbe_networks/resnet.py               |   2 +-
 .../gtest_input/pre_activate/hw_opt_test.py   |   2 +-
 .../pre_activate/insert_trans_op_test.py      |   2 +-
 .../pre_activate/mixed_precision_test.py      |   2 +-
 .../pre_activate/transdata_split_test.py      |   2 +-
 .../transpose_transdata_fusion_test.py        |   2 +-
 .../gtest_input/session/session_test.py       |   2 +-
 .../test_data_parallel_resnet.py              |   4 +-
 tests/ut/python/exec/resnet_example.py        |   2 +-
 tests/ut/python/exec/test_pooling.py          |   6 +-
 tests/ut/python/model/res18_example.py        |   4 +-
 tests/ut/python/nn/test_cell.py               |   2 +-
 tests/ut/python/nn/test_pooling.py            |   3 +-
 tests/ut/python/ops/test_nn_ops.py            | 105 +++---
 tests/ut/python/ops/test_ops.py               |   2 +-
 tests/ut/python/ops/test_ops_check.py         |   8 +-
 .../pynative_mode/ge/ops/test_pooling.py      |   4 +-
 tests/ut/python/pynative_mode/nn/test_cell.py |   2 +-
 .../python/pynative_mode/nn/test_pooling.py   |  11 +-
 tests/ut/python/pynative_mode/vm/test_vm.py   |   8 +-
 tests/ut/python/utils/test_serialize.py       |   2 +-
 tests/vm_impl/nn_ops_vm_impl.py               |  90 ++++-
 tests/vm_impl/vm_me.py                        |  46 ++-
 39 files changed, 535 insertions(+), 504 deletions(-)
 mode change 100755 => 100644 mindspore/ccsrc/transform/op_declare.cc

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index c0416f648b..229a3eb34a 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -148,8 +148,6 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
 }
 
 std::map<std::string, FAttrsPass> TbeAdapter::build_json_attr_pass_map_ = {
-  {"MaxPoolWithArgmax", TbeAdapter::MaxPoolWithArgmaxAttrJsonPass},
-  {"MaxPoolGradWithArgmax", TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass},
   {"Conv2D", TbeAdapter::Conv2DAttrJsonPass},
   {"Conv2DBackpropFilter", TbeAdapter::Conv2DBackpropFilterAttrJsonPass},
   {"Conv2DBackpropInput", TbeAdapter::Conv2DBackpropInputAttrJsonPass},
@@ -170,48 +168,6 @@ bool TbeAdapter::RunAttrPass(const mindspore::AnfNodePtr &anf_node,
   return false;
 }
 
-void TbeAdapter::MaxPoolWithArgmaxAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(attrs_json);
-  auto attr_num = op_info_attrs.size();
-  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
-  MS_EXCEPTION_IF_NULL(primitive);
-  for (size_t i = 0; i < attr_num; i++) {
-    nlohmann::json attr_obj;
-    MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
-    std::string attr_name = op_info_attrs[i]->name();
-    if (primitive->GetAttr(attr_name) != nullptr) {
-      auto value = primitive->GetAttr(attr_name);
-      if (attr_name == "pad_mode") {
-        std::string attr_value = GetValue<std::string>(value);
-        (void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
-        attr_obj["value"] = attr_value;
-      } else {
-        std::vector<int> attr_value;
-        int data = GetValue<int>(value);
-        attr_value.push_back(1);
-        attr_value.push_back(data);
-        attr_value.push_back(data);
-        attr_value.push_back(1);
-        attr_obj["value"] = attr_value;
-      }
-      attr_obj["valid"] = true;
-    } else {
-      attr_obj["valid"] = false;
-    }
-    attr_obj["name"] = attr_name;
-    attrs_json->push_back(attr_obj);
-  }
-}
-
-void TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MaxPoolWithArgmaxAttrJsonPass(anf_node, op_info_attrs, attrs_json);
-}
-
 void TbeAdapter::Conv2DAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
                                     const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
                                     nlohmann::json *attrs_json) {
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c400d1c573..bee460d84a 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -161,6 +161,7 @@ const char kNameTopK[] = "TopK";
 const char kNameSoftmaxGrad[] = "SoftmaxGrad";
 const char kNameMaxPool[] = "MaxPool";
 const char kNameAvgPool[] = "AvgPool";
+const char kNameMaxPoolWithArgmax[] = "MaxPoolWithArgmax";
 const char kNameBatchNorm[] = "BatchNorm";
 const char kNameBatchNormGrad[] = "BatchNormGrad";
 const char kNameROIAlign[] = "ROIAlign";
@@ -198,6 +199,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameApplyMomentum), ADPT_DESC(ApplyMomentum)},
     {string(kNameMaxPool), ADPT_DESC(MaxPool)},
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
+    {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
     {string(kNameTopK), ADPT_DESC(TopKV2)},
     {string(kNamePack), ADPT_DESC(Pack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
old mode 100755
new mode 100644
index 0af2923cc4..419805c37f
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -734,14 +734,22 @@ ATTR_MAP(AvgPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<
 OUTPUT_MAP(AvgPoolGrad) = {{0, OUTPUT_DESC(out_grad)}};
 
 // MaxPoolWithArgmax
+INPUT_MAP(MaxPoolWithArgmax) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                               {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                               {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}};
+
+// MaxPoolGradWithArgmax
 INPUT_MAP(MaxPoolGradWithArgmax) = {
   {1, INPUT_DESC(x)},
-  {2, INPUT_DESC(argmax)},
-  {3, INPUT_DESC(grad)},
+  {2, INPUT_DESC(grad)},
+  {3, INPUT_DESC(argmax)},
 };
-ATTR_MAP(MaxPoolGradWithArgmax) = {{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
-                                   {"window", ATTR_DESC(ksize, "window", AnyTraits<std::vector<int64_t>>())},
-                                   {"stride", ATTR_DESC(strides, "stride", AnyTraits<std::vector<int64_t>>())}};
+ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                                   {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                                   {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index d120c94989..e4d4101127 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -88,8 +88,10 @@ DECLARE_OP_ADAPTER(FusedBatchNormGrad)
 DECLARE_OP_USE_OUTPUT(FusedBatchNormGrad)
 DECLARE_OP_ADAPTER(BiasAddGrad)
 DECLARE_OP_USE_OUTPUT(BiasAddGrad)
+DECLARE_OP_ADAPTER(MaxPoolWithArgmax)
+DECLARE_OP_USE_OUTPUT(MaxPoolWithArgmax)
 DECLARE_OP_ADAPTER(MaxPoolGradWithArgmax)
-DECLARE_OP_USE_ENUM(MaxPoolGradWithArgmax)
+DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax)
 DECLARE_OP_ADAPTER(Conv2D)
 DECLARE_OP_USE_ENUM(Conv2D)
 DECLARE_OP_USE_OUTPUT(Conv2D)
diff --git a/mindspore/model_zoo/resnet.py b/mindspore/model_zoo/resnet.py
index 403f66e415..9d010eede1 100755
--- a/mindspore/model_zoo/resnet.py
+++ b/mindspore/model_zoo/resnet.py
@@ -168,7 +168,7 @@ class ResNet(nn.Cell):
         self.conv1 = _conv7x7(3, 64, stride=2)
         self.bn1 = _bn(64)
         self.relu = P.ReLU()
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode='same', window=3, stride=2)
+        self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 
         self.layer1 = self._make_layer(block,
                                        layer_nums[0],
diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index 6ff28dd362..bf90fcc9de 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -13,36 +13,52 @@
 # limitations under the License.
 # ============================================================================
 """pooling"""
-
 from mindspore.ops import operations as P
 from mindspore._checkparam import ParamValidator as validator
 from mindspore._checkparam import Rel
+from ... import context
 from ..cell import Cell
 
 
 class _PoolNd(Cell):
     """N-D  AvgPool"""
 
-    def __init__(self,
-                 kernel_size,
-                 stride,
-                 pad_mode,
-                 padding=0,
-                 pool=None):
+    def __init__(self, kernel_size, stride, pad_mode):
+        name = self.__class__.__name__
         super(_PoolNd, self).__init__()
+        validator.check_type('kernel_size', kernel_size, [int, tuple])
+        validator.check_type('stride', stride, [int, tuple])
+        self.pad_mode = validator.check_string('pad_mode', pad_mode.upper(), ['VALID', 'SAME'])
+
+        if isinstance(kernel_size, int):
+            validator.check_integer("kernel_size", kernel_size, 1, Rel.GE)
+        else:
+            if (len(kernel_size) != 2 or
+                    (not isinstance(kernel_size[0], int)) or
+                    (not isinstance(kernel_size[1], int)) or
+                    kernel_size[0] <= 0 or
+                    kernel_size[1] <= 0):
+                raise ValueError(f'The kernel_size passed to cell {name} should be an positive int number or'
+                                 f'a tuple of two positive int numbers, but got {kernel_size}')
         self.kernel_size = kernel_size
+
+        if isinstance(stride, int):
+            validator.check_integer("stride", stride, 1, Rel.GE)
+        else:
+            if (len(stride) != 2 or
+                    (not isinstance(stride[0], int)) or
+                    (not isinstance(stride[1], int)) or
+                    stride[0] <= 0 or
+                    stride[1] <= 0):
+                raise ValueError(f'The stride passed to cell {name} should be an positive int number or'
+                                 f'a tuple of two positive int numbers, but got {stride}')
         self.stride = stride
-        self.pad_mode = pad_mode
-        self.padding = validator.check_integer('padding', padding, 0, Rel.GE)
-        self.pool = pool
-        if self.pool is None:
-            raise NotImplementedError
 
-    def construct(self, x):
-        return self.pool(x)
+    def construct(self, *inputs):
+        pass
 
     def extend_repr(self):
-        return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__)
+        return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__)
 
 
 class MaxPool2d(_PoolNd):
@@ -63,19 +79,23 @@ class MaxPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over. Default 1.
-        stride (int): Stride size of the window. Default: 1.
-        pad_mode (str): Select the mode of the pad. The optional values are
-            "same" and "valid". Default: "valid".
+        kernel_size (Union[int, tuple[int]]): The size of kernel used to take the max value,
+            is an int number that represents height and width are both kernel_size,
+            or a tuple of two int numbers that represent height and width respectively.
+            Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
 
             - same: Adopts the way of completion. Output height and width will be the same as
               the input. Total number of padding will be calculated for horizontal and vertical
-              direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
-              last extra padding will be done from the bottom and the right side.
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
 
-            - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
-              without padding. Extra pixels will be discarded.
-        padding (int): Implicit zero padding to be added on both sides. Default: 0.
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -103,31 +123,22 @@ class MaxPool2d(_PoolNd):
           [[7. 8.]
            [8. 8.]]]]
     """
-    def __init__(self,
-                 kernel_size=1,
-                 stride=1,
-                 pad_mode="VALID",
-                 padding=0):
-        max_pool = P.MaxPool(ksize=kernel_size,
-                             strides=stride,
-                             padding=pad_mode)
-        self.is_autodiff_backend = False
-        if self.is_autodiff_backend:
-
-            # At present, pad mode of max pool is not unified, so it is a temporarily avoided
-            pad_mode = validator.check_string('pad_mode', pad_mode.lower(), ['valid', 'same'])
-
-            max_pool = P.MaxPoolWithArgmax(window=kernel_size,
-                                           stride=stride,
-                                           pad_mode=pad_mode,
-                                           pad=padding)
-        super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, padding, max_pool)
+
+    def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
+        super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode)
+        self.max_pool = P.MaxPool(ksize=self.kernel_size,
+                                  strides=self.stride,
+                                  padding=self.pad_mode)
+        self.max_pool_with_arg_max = P.MaxPoolWithArgmax(ksize=self.kernel_size,
+                                                         strides=self.stride,
+                                                         padding=self.pad_mode)
+        self.is_tbe = context.get_context("device_target") == "Ascend"
 
     def construct(self, x):
-        if self.is_autodiff_backend:
-            out = self.pool(x)[0]
+        if self.is_tbe and self.training:
+            out = self.max_pool_with_arg_max(x)[0]
         else:
-            out = self.pool(x)
+            out = self.max_pool(x)
         return out
 
 
@@ -149,19 +160,24 @@ class AvgPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over. Default: 1.
-        stride (int): Stride size of the window. Default: 1.
-        pad_mode (str): Select the mode of the pad. The optional values are
-            "same", "valid". Default: "valid".
+        kernel_size (Union[int, tuple[int]]): The size of kernel used to take the average value,
+            is an int number that represents height and width are both kernel_size,
+            or a tuple of two int numbers that represent height and width respectively.
+            Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
 
             - same: Adopts the way of completion. Output height and width will be the same as
               the input. Total number of padding will be calculated for horizontal and vertical
-              direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
-              last extra padding will be done from the bottom and the right side.
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
-            - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
-              without padding. Extra pixels will be discarded.
-        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -170,7 +186,7 @@ class AvgPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = AvgPool2d(kernel_size=3, stride=1)
+        >>> pool = AvgPool2d(kernel_size=3, strides=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
@@ -189,12 +205,15 @@ class AvgPool2d(_PoolNd):
           [[4.2222223 4.5555553]
            [3.2222223 4.5555553]]]]
     """
+
     def __init__(self,
                  kernel_size=1,
                  stride=1,
-                 pad_mode="VALID",
-                 padding=0):
-        avg_pool = P.AvgPool(ksize=kernel_size,
-                             strides=stride,
-                             padding=pad_mode)
-        super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode, padding, avg_pool)
+                 pad_mode="valid"):
+        super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode)
+        self.avg_pool = P.AvgPool(ksize=self.kernel_size,
+                                  strides=self.stride,
+                                  padding=self.pad_mode)
+
+    def construct(self, x):
+        return self.avg_pool(x)
diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py
index bad99351a5..fbe48aff97 100755
--- a/mindspore/ops/_grad/grad_nn_ops.py
+++ b/mindspore/ops/_grad/grad_nn_ops.py
@@ -76,14 +76,9 @@ def get_bprop_depthwise_conv2d_native(self):
 def get_bprop_max_pool_with_argmax(self):
     """Grad definition for `MaxPoolWithArgmax` operation."""
     maxpool_grad = G.MaxPoolGradWithArgmax(
-        pad_mode=self.pad_mode,
-        window=self.window,
-        pad=self.pad,
-        stride=self.stride,
-        data_mode=self.data_mode,
-        ceil_mode=self.ceil_mode,
-        alpha=self.alpha,
-        beta=self.beta)
+        ksize=self.ksize,
+        strides=self.strides,
+        padding=self.padding,)
 
     def bprop(x, out, dout):
         dx = maxpool_grad(x, dout[0], out[1])
diff --git a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
index a167ef85f8..3730ee1b93 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
@@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register
     "partial_flag": true,
     "attr": [
         {
-            "name": "window",
+            "name": "ksize",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "stride",
+            "name": "strides",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "pad_mode",
+            "name": "padding",
             "param_type": "required",
             "type": "str",
             "value": "all"
diff --git a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
index 04d0eeb92c..2e081c1082 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
@@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register
     "partial_flag": true,
     "attr": [
         {
-            "name": "window",
+            "name": "ksize",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "stride",
+            "name": "strides",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "pad_mode",
+            "name": "padding",
             "param_type": "required",
             "type": "str",
             "value": "all"
diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index a699c23adc..f38044ab6a 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -15,7 +15,6 @@
 
 """Operators for gradients."""
 
-import math
 from ..._c_expression import signature_rw as sig_rw
 from ..._c_expression import signature_kind as sig_kind
 from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
@@ -340,59 +339,60 @@ class _PoolGrad(PrimitiveWithInfer):
     """Gradients of the max/avg pool operation."""
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize, strides, padding="VALID"):
         self.init_prim_io_names(inputs=['x_origin', 'out_origin', 'grad'], outputs=['output'])
-        self.ksize = ksize
-        self.strides = strides
-        self.padding = padding
-
-        self.ksize = validator.check_type('ksize', self.ksize, [int, tuple])
-        self.strides = validator.check_type('strides', self.strides, [int, tuple])
 
-        validator.check_type('padding', self.padding, [str])
-        self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME'])
+        validator.check_type('ksize', ksize, [int, tuple])
+        validator.check_type('strides', strides, [int, tuple])
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'])
         self.add_prim_attr("padding", self.padding)
-        self.add_prim_attr('data_format', "NCHW")
-
-        if isinstance(self.ksize, int):
-            self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE)
-            self.pool_w = self.pool_h
-            self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize))
-        elif isinstance(self.ksize, tuple):
-            if (len(self.ksize) != 2 and len(self.ksize) != 4):
-                raise ValueError('Attr \'ksize\' of \'Pool\' Op passed ' +
-                                 str(self.ksize)+', should be a int or a tuple of length 2 or 4.')
-            for ksize_val in self.ksize:
-                if (not isinstance(ksize_val, int)) or (ksize_val <= 0):
-                    raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' +
-                                     str(self.ksize)+', should be int and greater than 0.')
-            self.pool_h = self.ksize[-2]
-            self.pool_w = self.ksize[-1]
-            self.add_prim_attr("ksize", (1, 1, self.ksize[-2], self.ksize[-1]))
-
-        if isinstance(self.strides, int):
-            self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE)
-            self.stride_w = self.stride_h
-            self.add_prim_attr("strides", (1, 1, self.strides, self.strides))
-        elif isinstance(self.strides, tuple):
-            if (len(self.strides) != 2 and len(self.strides) != 4):
-                raise ValueError('Attr \'strides\' of \'MaxPool\' Op passed ' +
-                                 str(self.strides)+', should be a int or a tuple of length 2 or 4.')
-            for stride_val in self.strides:
-                if (not isinstance(stride_val, int)) or (stride_val <= 0):
-                    raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' +
-                                     str(self.strides)+', should be int and greater than 0.')
-            self.stride_h = self.strides[-2]
-            self.stride_w = self.strides[-1]
-            self.add_prim_attr("strides", (1, 1, self.strides[-2], self.strides[-1]))
-
-        if self.padding == "VALID":
-            self.pad = 0
-        elif self.padding == "SAME":
-            self.pad = math.floor((self.pool_h - 1) / 2)
+        self.is_maxpoolgradwithargmax = (self.name == "MaxPoolGradWithArgmax")
+        if not self.is_maxpoolgradwithargmax:
+            self.add_prim_attr('data_format', "NCHW")
+
+        if isinstance(ksize, int):
+            validator.check_integer("ksize", ksize, 1, Rel.GE)
+            if self.is_maxpoolgradwithargmax:
+                self.ksize = (1, ksize, ksize, 1)
+            else:
+                self.ksize = (1, 1, ksize, ksize)
         else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
-                             ' but got {}.'.format(self.padding))
+            ksize_error = ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number"
+                                     f"or a tuple of two or four positive int numbers, but got {ksize}")
+            if len(ksize) != 2 and len(ksize) != 4:
+                raise ksize_error
+            for ksize_val in ksize:
+                if not isinstance(ksize_val, int) or (ksize_val <= 0):
+                    raise ksize_error
+            if len(ksize) == 2 and self.is_maxpoolgradwithargmax:
+                self.ksize = (1, ksize[0], ksize[1], 1)
+            elif len(ksize) == 2 and not self.is_maxpoolgradwithargmax:
+                self.ksize = (1, 1, ksize[0], ksize[1])
+            else:
+                self.ksize = ksize
+        self.add_prim_attr("ksize", self.ksize)
+
+        if isinstance(strides, int):
+            validator.check_integer("strides", strides, 1, Rel.GE)
+            if self.is_maxpoolgradwithargmax:
+                self.strides = (1, strides, strides, 1)
+            else:
+                self.strides = (1, 1, strides, strides)
+        else:
+            strides_error = ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number"
+                                       f"or a tuple of two or four positive int numbers, but got {strides}")
+            if len(strides) != 2 and len(strides) != 4:
+                raise strides_error
+            for strides_val in strides:
+                if not isinstance(strides_val, int) or (strides_val <= 0):
+                    raise strides_error
+            if len(strides) == 2 and self.is_maxpoolgradwithargmax:
+                self.strides = (1, strides[0], strides[1], 1)
+            elif len(strides) == 2 and not self.is_maxpoolgradwithargmax:
+                self.strides = (1, 1, strides[0], strides[1])
+            else:
+                self.strides = strides
+        self.add_prim_attr("strides", self.strides)
 
 
 class AvgPoolGrad(_PoolGrad):
@@ -451,28 +451,13 @@ class MaximumGrad(Primitive):
         raise NotImplementedError
 
 
-class MaxPoolGradWithArgmax(PrimitiveWithInfer):
+class MaxPoolGradWithArgmax(_PoolGrad):
     """Computes the gradients of MaxPoolWithArgmax."""
 
     @prim_attr_register
-    def __init__(self,
-                 pad_mode="valid",
-                 window=0,
-                 pad=0,
-                 stride=1,
-                 data_mode=1,
-                 ceil_mode=0,
-                 alpha=1.0,
-                 beta=0.0):
+    def __init__(self, ksize=1, strides=1, padding="VALID",):
         self.init_prim_io_names(inputs=['x', 'grad', 'argmax'], outputs=['output'])
-
-        self.window = window
-        self.pool_h = self.pool_w = window
-        self.pad = pad
-        self.pad_mode = pad_mode
-        self.stride = stride
-        self.data_mode = data_mode
-        self.ceil_mode = ceil_mode
+        super(MaxPoolGradWithArgmax, self).__init__(ksize, strides, padding)
 
     def infer_shape(self, x_shape, grad_shape, argmax_shape):
         if not grad_shape:
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 21effd4bd3..9ee98d174e 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -682,186 +682,83 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
         return x_dtype
 
 
-class MaxPoolWithArgmax(PrimitiveWithInfer):
-    r"""
-    Performs max pooling on the input Tensor and return both max values and indices.
-
-    Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs
-    regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size
-    :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows.
-
-    .. math::
-        \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1}
-        \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
-
-    Args:
-        pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
-        window (Union[int, tuple[int]]): The size of window, which is the kernel size, two `int` for width
-            and height. Default: 1.
-        pad (Union[int, tuple[int]]): If `pad_mode` is `pad`, the pad value to fill, two `int` for width
-            and height. Default: 0.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
-           width and height. Default: 1.
-
-    Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
-
-    Outputs:
-        Tuple of 2 Tensor, the maxpool result and where max values from.
-
-        - **output** (Tensor) -  Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
-        - **mask** (Tensor) -  Max values' index represented by the mask.
-    """
-
-    @prim_attr_register
-    def __init__(self,
-                 pad_mode="valid",
-                 window=1,
-                 pad=0,
-                 stride=1,
-                 data_mode=1,
-                 ceil_mode=0,
-                 alpha=1.0,
-                 beta=0.0):
-        self.init_prim_io_names(inputs=['x'], outputs=['output', 'argmax'])
-        self.window = validator.check_type('window', window, [int, tuple])
-        if isinstance(window, int) and window <= 0:
-            raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed '
-                             + str(self.window)+', should be a int or tuple and greater than 0.')
-        if isinstance(window, tuple) and (len(window) != 2 or
-                                          (not isinstance(window[0], int)) or
-                                          (not isinstance(window[1], int)) or
-                                          window[0] <= 0 or window[1] <= 0):
-            raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed '
-                             + str(self.window)+', should be a int or tuple and greater than 0.')
-        self.pool_h = self.pool_w = window
-        self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'])
-        if self.pad_mode == "valid":
-            self.pad = 0
-        elif self.pad_mode == "same":
-            self.pad = math.floor((self.window - 1) / 2)
-        elif self.pad_mode == "pad":
-            self.pad = validator.check_integer('pad', pad, 0, Rel.GE)
-
-        self.data_mode = validator.check_integer('data_mode', data_mode, 1, Rel.EQ)
-        self.ceil_mode = validator.check_integer('ceil_mode', ceil_mode, 0, Rel.EQ)
-        self.stride = validator.check_integer('stride', stride, 1, Rel.GE)
-        self.alpha = validator.check_type('alpha', alpha, [int, float])
-        self.beta = validator.check_type('beta', beta, [int, float])
-        self.is_tbe = not context.get_context("enable_ge") and context.get_context("device_target") == "Ascend"
-
-    def infer_shape(self, x_shape):
-        validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ)
-        pad = self.pad
-        h_input = x_shape[2]
-        w_input = x_shape[3]
-        h_out = (h_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1
-        h_out = math.floor(h_out)
-        w_out = (w_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1
-        w_out = math.floor(w_out)
-        out_shape = [x_shape[0], x_shape[1], h_out, w_out]
-        for shape_value in out_shape:
-            if shape_value <= 0:
-                raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.")
-        k_size_vec = [1, self.window, self.window, 1]
-        argmax_shape = []
-        if self.is_tbe:
-            for i in range(4):
-                if i == 2:
-                    dim = k_size_vec[i - 1] * k_size_vec[i]
-                    argmax_shape.append(dim)
-                elif i == 3:
-                    dim = math.ceil(out_shape[i - 1] * out_shape[i] / 16) + 1
-                    argmax_shape.append(dim)
-                else:
-                    argmax_shape.append(x_shape[i])
-        else:
-            argmax_shape = out_shape
-        return out_shape, argmax_shape
-
-    def infer_dtype(self, x_dtype):
-        out_dtype = x_dtype
-        validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32))
-        argmax_dtype = mstype.int32
-        return out_dtype, argmax_dtype
-
-
 class _Pool(PrimitiveWithInfer):
     r"""
     Performs max/avg pooling operation.
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple
-           of two `int` for width and height. Default: 1.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
-           width and height. Default: 1.
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+        ksize (Union[int, tuple[int]]): The size of the kernel, that should be a tuple
+           of two `int` for height and width. Default: 1.
+        strides (Union[int, tuple[int]]): The stride of the window, that should be
+            a tuple of two `int` for height and width. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-        validator.check_type('padding', padding, [str])
-        self.ksize = ksize
-        self.strides = strides
-        self.padding = padding.upper()
-        self.ksize = validator.check_type('ksize', self.ksize, [int, tuple])
-        self.strides = validator.check_type('strides', self.strides, [int, tuple])
-        self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME'])
+    def __init__(self, ksize=1, strides=1, padding="valid"):
         self.init_prim_io_names(inputs=['x'], outputs=['output'])
+        validator.check_type('ksize', ksize, [int, tuple])
+        validator.check_type('strides', strides, [int, tuple])
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'])
         self.add_prim_attr("padding", self.padding)
-        self.add_prim_attr('data_format', "NCHW")
-
-        if isinstance(self.ksize, int):
-            self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE)
-            self.pool_w = self.pool_h
-            self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize))
-        elif isinstance(self.ksize, tuple):
-            if (len(self.ksize) != 2 or (not isinstance(self.ksize[0], int)) or (not isinstance(self.ksize[1], int))
-                    or self.ksize[0] <= 0 or self.ksize[1] <= 0):
-                raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' +
-                                 str(self.ksize) + ', should be a int or a tuple of length 2 and greater than 0.')
-            self.pool_h = self.ksize[0]
-            self.pool_w = self.ksize[1]
-            self.add_prim_attr("ksize", (1, 1, self.ksize[0], self.ksize[1]))
-
-        if isinstance(self.strides, int):
-            self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE)
-            self.stride_w = self.stride_h
-            self.add_prim_attr("strides", (1, 1, self.strides, self.strides))
-        elif isinstance(self.strides, tuple):
-            if (len(self.strides) != 2 or (not isinstance(self.strides[0], int)) or
-                    (not isinstance(self.strides[1], int)) or self.strides[0] <= 0 or self.strides[1] <= 0):
-                raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' +
-                                 str(self.strides) + ', should be a int or a tuple of length 2 and greater than 0.')
-            self.stride_h = self.strides[0]
-            self.stride_w = self.strides[1]
-            self.add_prim_attr("strides", (1, 1, self.strides[0], self.strides[1]))
+        self.is_maxpoolwithargmax = (self.name == "MaxPoolWithArgmax")
+        if not self.is_maxpoolwithargmax:
+            self.add_prim_attr('data_format', "NCHW")
 
-        if self.padding == "VALID":
-            self.pad = 0
-        elif self.padding == "SAME":
-            self.pad = math.floor((self.pool_h - 1) / 2)
+        if isinstance(ksize, int):
+            validator.check_integer("ksize", ksize, 1, Rel.GE)
+            self.ksize = (1, 1, ksize, ksize)
         else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
-                             ' but got {}.'.format(self.padding))
-        self.add_prim_attr('pad', self.pad)
+            if (len(ksize) != 2 or
+                    (not isinstance(ksize[0], int)) or
+                    (not isinstance(ksize[1], int)) or
+                    ksize[0] <= 0 or
+                    ksize[1] <= 0):
+                raise ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number or"
+                                 f"a tuple of two positive int numbers, but got {ksize}")
+            self.ksize = (1, 1, ksize[0], ksize[1])
+        if self.is_maxpoolwithargmax:
+            self.ksize = (1, self.ksize[-2], self.ksize[-1], 1)
+        self.add_prim_attr("ksize", self.ksize)
+
+        if isinstance(strides, int):
+            validator.check_integer("strides", strides, 1, Rel.GE)
+            self.strides = (1, 1, strides, strides)
+        else:
+            if (len(strides) != 2 or
+                    (not isinstance(strides[0], int)) or
+                    (not isinstance(strides[1], int)) or
+                    strides[0] <= 0 or
+                    strides[1] <= 0):
+                raise ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number or"
+                                 f"a tuple of two positive int numbers, but got {strides}")
+            self.strides = (1, 1, strides[0], strides[1])
+        if self.is_maxpoolwithargmax:
+            self.strides = (1, self.strides[-2], self.strides[-1], 1)
+        self.add_prim_attr("strides", self.strides)
 
     def infer_shape(self, x_shape):
         validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ)
-        h_input = x_shape[2]
-        w_input = x_shape[3]
+        batch, channel, input_h, input_w = x_shape
+        if self.is_maxpoolwithargmax:
+            _, kernel_h, kernel_w, _ = self.ksize
+            _, stride_h, stride_w, _ = self.strides
+        else:
+            _, _, kernel_h, kernel_w = self.ksize
+            _, _, stride_h, stride_w = self.strides
+
         if self.padding == "VALID":
-            h_out = math.ceil((h_input - (self.pool_h - 1)) / self.stride_h)
-            w_out = math.ceil((w_input - (self.pool_w - 1)) / self.stride_w)
+            out_h = math.ceil((input_h - (kernel_h - 1)) / stride_h)
+            out_w = math.ceil((input_w - (kernel_w - 1)) / stride_w)
         elif self.padding == "SAME":
-            h_out = math.ceil(h_input / self.stride_h)
-            w_out = math.ceil(w_input / self.stride_w)
+            out_h = math.ceil(input_h / stride_h)
+            out_w = math.ceil(input_w / stride_w)
         else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
-                             ' but got {}.'.format(self.padding))
+            raise ValueError(f"The padding of operator {self.name} should be a str and must be 'SAME' or 'VALID', "
+                             f"but got {self.padding}.")
+        out_shape = [batch, channel, out_h, out_w]
 
-        out_shape = [x_shape[0], x_shape[1], h_out, w_out]
         for shape_value in out_shape:
             if shape_value <= 0:
                 raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.")
@@ -887,11 +784,22 @@ class MaxPool(_Pool):
         \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple
-           of two `int` for width and height. Default: 1.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
-           width and height. Default: 1.
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
+            is an int number that represents height and width are both ksize, or a tuple
+            of two int numbers that represent height and width respectively. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -901,10 +809,83 @@ class MaxPool(_Pool):
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize=1, strides=1, padding="valid"):
         super(MaxPool, self).__init__(ksize, strides, padding)
 
 
+class MaxPoolWithArgmax(_Pool):
+    r"""
+    Performs max pooling on the input Tensor and return both max values and indices.
+
+    Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs
+    regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size
+    :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows.
+
+    .. math::
+        \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1}
+        \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
+
+    Args:
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value and arg value,
+            is an int number that represents height and width are both ksize, or a tuple of
+            two int numbers that represent height and width respectively. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
+
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tuple of 2 Tensor, the maxpool result and where max values from.
+
+        - **output** (Tensor) -  Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+        - **mask** (Tensor) -  Max values' index represented by the mask.
+    """
+
+    def __init__(self, ksize=1, strides=1, padding="valid"):
+        super(MaxPoolWithArgmax, self).__init__(ksize, strides, padding)
+        self.is_tbe = context.get_context("device_target") == "Ascend"
+
+    def infer_shape(self, x_shape):
+        out_shape = _Pool.infer_shape(self, x_shape)
+        _, _, out_h, out_w = out_shape
+        _, kernel_h, kernel_w, _ = self.ksize
+
+        argmax_shape = []
+        if self.is_tbe:
+            for i in range(4):
+                if i == 2:
+                    dim = kernel_h * kernel_w
+                    argmax_shape.append(dim)
+                elif i == 3:
+                    dim = math.ceil(out_h * out_w / 16) + 1
+                    argmax_shape.append(dim)
+                else:
+                    argmax_shape.append(x_shape[i])
+        else:
+            argmax_shape = out_shape
+
+        return out_shape, argmax_shape
+
+    def infer_dtype(self, x_dtype):
+        out_dtype = x_dtype
+        validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32))
+        argmax_dtype = mstype.uint16
+        return out_dtype, argmax_dtype
+
+
 class AvgPool(_Pool):
     r"""
     Average pooling operation.
@@ -919,11 +900,22 @@ class AvgPool(_Pool):
         \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a average over, that should be a tuple
-           of two `int` for width and height. Default: 1.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
-           width and height. Default: 1.
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the average value,
+            is an int number that represents height and width are both ksize, or a tuple
+            of two int numbers that represent height and width respectively. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -933,7 +925,7 @@ class AvgPool(_Pool):
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize=1, strides=1, padding="valid"):
         if context.get_context("device_target") == "GPU":
             self.target = "GPU"
         else:
diff --git a/tests/perf_test/resnet_example.py b/tests/perf_test/resnet_example.py
index 19d235c2b1..34413109de 100644
--- a/tests/perf_test/resnet_example.py
+++ b/tests/perf_test/resnet_example.py
@@ -103,7 +103,7 @@ class ResNet50(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='valid')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid')
 
         self.layer1 = self.MakeLayer(
             block, 3, in_channels=64, out_channels=256, stride=1)
diff --git a/tests/st/networks/test_cpu_lenet.py b/tests/st/networks/test_cpu_lenet.py
index a3105721d3..9fd50f5d9b 100644
--- a/tests/st/networks/test_cpu_lenet.py
+++ b/tests/st/networks/test_cpu_lenet.py
@@ -21,6 +21,7 @@ import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore import Tensor
 
+
 class LeNet(nn.Cell):
     def __init__(self):
         super(LeNet, self).__init__()
@@ -50,8 +51,10 @@ class LeNet(nn.Cell):
         output = self.fc3(output)
         return output
 
+
 context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
 
+
 def train(net, data, label):
     learning_rate = 0.01
     momentum = 0.9
@@ -67,11 +70,12 @@ def train(net, data, label):
     print("+++++++++++++++++++++++++++")
     assert res
 
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_lenet():
-    data = Tensor(np.ones([32, 1 ,32, 32]).astype(np.float32) * 0.01)
+    data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([32]).astype(np.int32))
     net = LeNet()
     train(net, data, label)
diff --git a/tests/st/networks/test_gpu_alexnet.py b/tests/st/networks/test_gpu_alexnet.py
index 3b193e17d6..9f92fc630e 100644
--- a/tests/st/networks/test_gpu_alexnet.py
+++ b/tests/st/networks/test_gpu_alexnet.py
@@ -38,7 +38,7 @@ class AlexNet(nn.Cell):
         self.conv4 = nn.Conv2d(384, 384, 3, stride=1, pad_mode="same")
         self.conv5 = nn.Conv2d(384, 256, 3, stride=1, pad_mode="same")
         self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2,pad_mode="valid",padding=0)
+        self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="valid")
         self.flatten = nn.Flatten()
         self.fc1 = nn.Dense(6*6*256, 4096)
         self.fc2 = nn.Dense(4096, 4096)
diff --git a/tests/st/ops/davinci/test_maxpool_with_argmax.py b/tests/st/ops/davinci/test_maxpool_with_argmax.py
index c9312d666c..a6c875a9e8 100644
--- a/tests/st/ops/davinci/test_maxpool_with_argmax.py
+++ b/tests/st/ops/davinci/test_maxpool_with_argmax.py
@@ -20,26 +20,29 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
 
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
-                             window=3,
-                             stride=2)
+        self.maxpool = P.MaxPoolWithArgmax(padding="same",
+                                           ksize=3,
+                                           strides=2)
         self.x = Parameter(initializer(
-                           'normal', [1, 64, 112, 112]), name='w')
+            'normal', [1, 64, 112, 112]), name='w')
         self.add = P.TensorAdd()
 
-
     @ms_function
     def construct(self):
         output = self.maxpool(self.x)
         return output[0]
 
+
 def test_net():
-    x = np.random.randn(1,64,112,112).astype(np.float32)
+    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
     maxpool = Net()
     output = maxpool()
     print("***********output output*********")
diff --git a/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py b/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py
index d97e2a06f8..3bbc835c1b 100644
--- a/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py
+++ b/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py
@@ -37,9 +37,9 @@ class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
 
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
-                                                 window=3,
-                                                 stride=2)
+        self.maxpool = P.MaxPoolWithArgmax(padding="same",
+                                           ksize=3,
+                                           strides=2)
 
     @ms_function
     def construct(self, x):
diff --git a/tests/st/tbe_networks/resnet.py b/tests/st/tbe_networks/resnet.py
index a1ece6556e..2024286b8f 100644
--- a/tests/st/tbe_networks/resnet.py
+++ b/tests/st/tbe_networks/resnet.py
@@ -267,7 +267,7 @@ class ResNet(nn.Cell):
 
         self.bn1 = bn_with_initialize(64)
         self.relu = P.ReLU()
-        self.maxpool = P.MaxPoolWithArgmax(window=3, stride=2, pad_mode="same")
+        self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME")
 
         self.layer1 = MakeLayer0(block, layer_num[0], in_channels=64, out_channels=256, stride=1)
         self.layer2 = MakeLayer1(block, layer_num[1], in_channels=256, out_channels=512, stride=2)
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py
index 0afffc99df..2877bc7c7a 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py
@@ -21,7 +21,7 @@ addn = P.AddN()
 add = P.TensorAdd()
 sub = P.Sub()
 mul = P.Mul()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 four2five = Primitive('Four2Five')
 five2four = Primitive('Five2Four')
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py
index 57bd2000c4..a24501e8b1 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py
@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 transdata = Primitive("TransData")
 
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py
index 8cbad52db1..7d3985376b 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py
@@ -21,7 +21,7 @@ addn = P.AddN()
 add = P.TensorAdd()
 sub = P.Sub()
 mul = P.Mul()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 cast = Primitive('Cast')
 
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py
index 8cd18d1ac3..e353cf8fbe 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py
@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 four2five = Primitive('Four2Five')
 five2four = Primitive('Five2Four')
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py
index ea3def743d..c4fc50e0da 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py
@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 transdata = Primitive("TransData")
 Transpose = P.Transpose()
diff --git a/tests/ut/cpp/python_input/gtest_input/session/session_test.py b/tests/ut/cpp/python_input/gtest_input/session/session_test.py
index ed074fc8d6..ee034a1ae0 100644
--- a/tests/ut/cpp/python_input/gtest_input/session/session_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/session/session_test.py
@@ -22,7 +22,7 @@ add = P.TensorAdd()
 reshape = P.Reshape()
 cast = P.Cast()
 tuple_getitem = Primitive('tuple_getitem')
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 
 def test_addn_cast(x, y, z):
     sum = addn((x, y))
diff --git a/tests/ut/python/communication/test_data_parallel_resnet.py b/tests/ut/python/communication/test_data_parallel_resnet.py
index 037152a0b7..220e553b4f 100644
--- a/tests/ut/python/communication/test_data_parallel_resnet.py
+++ b/tests/ut/python/communication/test_data_parallel_resnet.py
@@ -107,7 +107,7 @@ class ResNet18(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 2, in_channels=64, out_channels=256, stride=1)
@@ -176,7 +176,7 @@ class ResNet9(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='same')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 1, in_channels=64, out_channels=256, stride=1)
diff --git a/tests/ut/python/exec/resnet_example.py b/tests/ut/python/exec/resnet_example.py
index bfbb64f732..913e90a0bb 100644
--- a/tests/ut/python/exec/resnet_example.py
+++ b/tests/ut/python/exec/resnet_example.py
@@ -189,7 +189,7 @@ class ResNet50(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, weight_init=weight_conv)
         self.bn1 = bn_with_initialize(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
 
         self.layer1 = MakeLayer3(
             block, in_channels=64, out_channels=256, stride=1)
diff --git a/tests/ut/python/exec/test_pooling.py b/tests/ut/python/exec/test_pooling.py
index 9c378c15c2..0e526ff8d6 100644
--- a/tests/ut/python/exec/test_pooling.py
+++ b/tests/ut/python/exec/test_pooling.py
@@ -23,12 +23,10 @@ class MaxNet(nn.Cell):
     """MaxNet definition"""
     def __init__(self,
                  kernel_size,
-                 stride=None,
-                 padding=0):
+                 stride=None):
         super(MaxNet, self).__init__()
         self.maxpool = nn.MaxPool2d(kernel_size,
-                                    stride,
-                                    padding=padding)
+                                    stride)
 
     def construct(self, input_x):
         return self.maxpool(input_x)
diff --git a/tests/ut/python/model/res18_example.py b/tests/ut/python/model/res18_example.py
index eaf8bbc387..8875333465 100644
--- a/tests/ut/python/model/res18_example.py
+++ b/tests/ut/python/model/res18_example.py
@@ -106,7 +106,7 @@ class ResNet18(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 2, in_channels=64, out_channels=256, stride=1)
@@ -175,7 +175,7 @@ class ResNet9(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
 
         self.layer1 = self.MakeLayer(
             block, 1, in_channels=64, out_channels=256, stride=1)
diff --git a/tests/ut/python/nn/test_cell.py b/tests/ut/python/nn/test_cell.py
index 882756f3d2..c583b27c1d 100644
--- a/tests/ut/python/nn/test_cell.py
+++ b/tests/ut/python/nn/test_cell.py
@@ -87,7 +87,7 @@ class ConvNet(nn.Cell):
         self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode="pad", padding=3)
         self.bn1 = nn.BatchNorm2d(ConvNet.output_ch)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="pad", padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(
             int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)),
diff --git a/tests/ut/python/nn/test_pooling.py b/tests/ut/python/nn/test_pooling.py
index 694d202d13..10bb7632b2 100644
--- a/tests/ut/python/nn/test_pooling.py
+++ b/tests/ut/python/nn/test_pooling.py
@@ -46,8 +46,7 @@ class MaxNet(nn.Cell):
                  padding=0):
         super(MaxNet, self).__init__()
         self.maxpool = nn.MaxPool2d(kernel_size,
-                                    stride,
-                                    padding=padding)
+                                    stride)
 
     def construct(self, x):
         return self.maxpool(x)
diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py
index 5b9f37864c..cadac6dfb4 100644
--- a/tests/ut/python/ops/test_nn_ops.py
+++ b/tests/ut/python/ops/test_nn_ops.py
@@ -108,6 +108,7 @@ class ResidualBlock(nn.Cell):
 
 class VirtualLossGrad(PrimitiveWithInfer):
     """ VirtualLossGrad definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLossGrad"""
@@ -124,6 +125,7 @@ class VirtualLossGrad(PrimitiveWithInfer):
 
 class VirtualLoss(PrimitiveWithInfer):
     """ VirtualLoss definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLoss"""
@@ -138,6 +140,7 @@ class VirtualLoss(PrimitiveWithInfer):
             # pylint: disable=unused-argument
             dx = loss_grad(x, out, dout)
             return (dx,)
+
         return bprop
 
     def infer_shape(self, x_shape):
@@ -149,6 +152,7 @@ class VirtualLoss(PrimitiveWithInfer):
 
 class VirtualNetWithLoss(nn.Cell):
     """ VirtualNetWithLoss definition """
+
     def __init__(self, network):
         super(VirtualNetWithLoss, self).__init__()
         self.loss = VirtualLoss()
@@ -161,6 +165,7 @@ class VirtualNetWithLoss(nn.Cell):
 
 class SoftMaxGrad(nn.Cell):
     """ SoftMaxGrad definition """
+
     def __init__(self, network):
         super(SoftMaxGrad, self).__init__()
         self.network = network
@@ -171,6 +176,7 @@ class SoftMaxGrad(nn.Cell):
 
 class DropoutGrad(nn.Cell):
     """ DropoutGrad definition """
+
     def __init__(self, network):
         super(DropoutGrad, self).__init__()
         self.network = network
@@ -181,6 +187,7 @@ class DropoutGrad(nn.Cell):
 
 class ScalarSummaryNet(nn.Cell):
     """ ScalarSummaryNet definition """
+
     def __init__(self):
         super(ScalarSummaryNet, self).__init__()
         self.summary = P.ScalarSummary()
@@ -193,6 +200,7 @@ class ScalarSummaryNet(nn.Cell):
 
 class FusedBatchNormGrad(nn.Cell):
     """ FusedBatchNormGrad definition """
+
     def __init__(self, network):
         super(FusedBatchNormGrad, self).__init__()
         self.grad = C.GradOperation(name="get_all", get_all=True, sens_param=True)
@@ -204,6 +212,7 @@ class FusedBatchNormGrad(nn.Cell):
 
 class NetWithLoss(nn.Cell):
     """ NetWithLoss definition """
+
     def __init__(self, network):
         super(NetWithLoss, self).__init__()
         self.loss = P.SmoothL1Loss()
@@ -216,6 +225,7 @@ class NetWithLoss(nn.Cell):
 
 class Grad(nn.Cell):
     """ GradWrap definition """
+
     def __init__(self, network):
         super(Grad, self).__init__()
         self.network = network
@@ -227,6 +237,7 @@ class Grad(nn.Cell):
 
 class BatchnormNet(nn.Cell):
     """ BatchnormNet definition """
+
     def __init__(self):
         super(BatchnormNet, self).__init__()
         self.conv1 = nn.Conv2d(3, 4, kernel_size=8, stride=2, pad_mode="pad", padding=3)
@@ -247,6 +258,7 @@ class BatchnormNet(nn.Cell):
 
 class NetWithLossClass(nn.Cell):
     """ NetWithLossClass definition """
+
     def __init__(self, network):
         super(NetWithLossClass, self).__init__(auto_prefix=False)
         self.loss = nn.SoftmaxCrossEntropyWithLogits()
@@ -259,12 +271,13 @@ class NetWithLossClass(nn.Cell):
 
 class BlockNet(nn.Cell):
     """ BlockNet definition """
+
     def __init__(self):
         super(BlockNet, self).__init__()
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode="pad", padding=3)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
         self.block_down_sample = ResidualBlock(
             64, 256, stride=1, down_sample=True
         )
@@ -281,6 +294,7 @@ class BlockNet(nn.Cell):
 
 class Conv2dWithBiasNet(nn.Cell):
     """ Conv2dWithBiasNet definition """
+
     def __init__(self):
         super(Conv2dWithBiasNet, self).__init__()
         self.conv = nn.Conv2d(3, 10, 1, bias_init='zeros')
@@ -292,6 +306,7 @@ class Conv2dWithBiasNet(nn.Cell):
 
 class Conv2dNativeNet(nn.Cell):
     """ Conv2dNativeNet definition """
+
     def __init__(self):
         super(Conv2dNativeNet, self).__init__()
         self.conv = P.DepthwiseConv2dNative(channel_multiplier=3, kernel_size=(3, 3))
@@ -309,9 +324,10 @@ class Conv2dNativeNet(nn.Cell):
 
 class MakeRefKeyNet(nn.Cell):
     """ MakeRefKeyNet definition """
+
     def __init__(self):
         super(MakeRefKeyNet, self).__init__()
-        self.y= Parameter(Tensor([1.0], mindspore.float32), name="y")
+        self.y = Parameter(Tensor([1.0], mindspore.float32), name="y")
 
     def construct(self, x):
         key = P.MakeRefKey("y")()
@@ -321,6 +337,7 @@ class MakeRefKeyNet(nn.Cell):
 
 class StateNet(nn.Cell):
     """ StateTestTensor definition """
+
     def __init__(self):
         super(StateNet, self).__init__()
         weight = Tensor(np.ones([2, 1, 2, 2], np.float32))
@@ -347,6 +364,24 @@ class ComparisonNet(nn.Cell):
         return ret
 
 
+def test_max_pool_with_arg_max():
+    class NetMaxPoolWithArgMax(nn.Cell):
+        def __init__(self):
+            """ ComparisonNet definition """
+            super(NetMaxPoolWithArgMax, self).__init__()
+            self.max_pool_with_arg_max = P.MaxPoolWithArgmax(padding="valid", ksize=2, strides=1)
+
+        def construct(self, x):
+            ret = self.max_pool_with_arg_max(x)
+            return ret
+
+    x = Tensor(np.ones([1, 1, 3, 3], np.float32))
+    net = NetMaxPoolWithArgMax()
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    ret = net(x)
+    print(ret)
+
+
 test_cases = [
     ('SoftMaxGrad', {
         'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
@@ -382,7 +417,7 @@ test_cases = [
         'desc_inputs': [Tensor(np.ones([1, 3, 8, 8], np.float32)), Tensor(np.zeros([1, 64, 4, 4], np.float32))],
     }),
     ('Conv2dWithBiasGrad', {
-        'block':  Grad(NetWithLossClass(Conv2dWithBiasNet())),
+        'block': Grad(NetWithLossClass(Conv2dWithBiasNet())),
         'desc_inputs': [Tensor(np.ones([1, 3, 16, 16], np.float32)), Tensor(np.zeros([1, 2560], np.float32))],
     }),
     ('Conv2dNativeGrad', {
@@ -407,114 +442,93 @@ test_cases = [
     }),
 ]
 
-
 test_cases_for_verify_exception = [
     ('Conv2d_ValueError_1', {
-        'block': (lambda _ : P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}),
+        'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('Conv2d_ValueError_2', {
-        'block': (lambda _ : P.Conv2D(3, 4, mode=-2), {'exception': ValueError}),
+        'block': (lambda _: P.Conv2D(3, 4, mode=-2), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_1', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode='sane'), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_2', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(data_mode=2), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_3', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(ceil_mode=2), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(ksize=-2), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_4', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad=-1), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_5', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad='1'), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_6', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(window='1'), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_7', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(window=-2), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_8', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(stride=-1), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_9', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(alpha='1'), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(strides=-1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_1', {
-        'block': (lambda _ : P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_2', {
-        'block': (lambda _ : P.FusedBatchNorm(mode=2, epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode=2, epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_3', {
-        'block': (lambda _ : P.FusedBatchNorm(mode=0, epsilon=-1e-5, momentum=0.1), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=-1e-5, momentum=0.1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_4', {
-        'block': (lambda _ : P.FusedBatchNorm(mode=0, epsilon=1e-5, momentum=-0.1), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=1e-5, momentum=-0.1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_5', {
-        'block': (lambda _ : P.FusedBatchNorm(mode=1, epsilon=-0.001, momentum=0.0), {'exception': ValueError}),
+        'block': (lambda _: P.FusedBatchNorm(mode=1, epsilon=-0.001, momentum=0.0), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_1', {
-        'block': (lambda _ : P.Softmax("1"), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax("1"), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_2', {
-        'block': (lambda _ : P.Softmax(1.1), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax(1.1), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('Softmax_ValueError_3', {
-        'block': (lambda _ : P.Softmax(axis="1"), {'exception': ValueError}),
+        'block': (lambda _: P.Softmax(axis="1"), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_1', {
-        'block': (lambda _ : P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_2', {
-        'block': (lambda _ : P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_3', {
-        'block': (lambda _ : P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('DropoutGenMask_ValueError_4', {
-        'block': (lambda _ : P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}),
+        'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPool2d_ValueError_1', {
-        'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid", padding=0), {'exception': ValueError}),
+        'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid"), {'exception': ValueError}),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
     }),
     ('MaxPool2d_ValueError_2', {
         'block': (
-            lambda _ : nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid", padding=0),
+            lambda _: nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid"),
             {'exception': ValueError},
         ),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
     }),
     ('MaxPool2d_ValueError_3', {
         'block': (
-            lambda _ : nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid", padding=0),
+            lambda _: nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid"),
             {'exception': ValueError},
         ),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
@@ -532,4 +546,3 @@ def test_compile():
 @mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
 def test_check_exception():
     return test_cases_for_verify_exception
-
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 0f5b716e39..092d6e32be 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -571,7 +571,7 @@ test_case_nn_ops = [
         'desc_bprop': [[3, 4, 6, 6]],
         'skip': ['backward']}),
     ('MaxPoolWithArgmax', {
-        'block': P.MaxPoolWithArgmax(window=2, stride=2),
+        'block': P.MaxPoolWithArgmax(ksize=2, strides=2),
         'desc_inputs': [[128, 32, 32, 64]],
         'desc_bprop': [[128, 32, 8, 16], [128, 32, 8, 16]]}),
     ('SoftmaxCrossEntropyWithLogits', {
diff --git a/tests/ut/python/ops/test_ops_check.py b/tests/ut/python/ops/test_ops_check.py
index a7e1b41c4a..aa379cc64e 100644
--- a/tests/ut/python/ops/test_ops_check.py
+++ b/tests/ut/python/ops/test_ops_check.py
@@ -160,16 +160,16 @@ test_case_check_ops = [
         'block': nn.Dense(1, 6, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))),
         'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
     ('MaxPool2d_1', {
-        'block': nn.MaxPool2d(5, pad_mode='same', padding=0),
+        'block': nn.MaxPool2d(5, pad_mode='same'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('MaxPool2d_2', {
-        'block': nn.MaxPool2d(5, pad_mode='valid', padding=0),
+        'block': nn.MaxPool2d(5, pad_mode='valid'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('AvgPool2d_1', {
-        'block': nn.AvgPool2d(5, pad_mode='same', padding=0),
+        'block': nn.AvgPool2d(5, pad_mode='same'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('AvgPool2d_2', {
-        'block': nn.AvgPool2d(5, pad_mode='valid', padding=0),
+        'block': nn.AvgPool2d(5, pad_mode='valid'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('Conv2D_1', {
         'block': P.Conv2D(1, 6, pad_mode='same', pad=0),
diff --git a/tests/ut/python/pynative_mode/ge/ops/test_pooling.py b/tests/ut/python/pynative_mode/ge/ops/test_pooling.py
index e6cf88a9ca..d5b90b6edd 100644
--- a/tests/ut/python/pynative_mode/ge/ops/test_pooling.py
+++ b/tests/ut/python/pynative_mode/ge/ops/test_pooling.py
@@ -42,12 +42,10 @@ def test_maxpool2d():
     """ test_maxpool2d """
     kernel_size = 3
     stride = 3
-    padding = 0
 
-    max_pool = nn.MaxPool2d(kernel_size, stride, padding=padding)
+    max_pool = nn.MaxPool2d(kernel_size, stride)
     assert max_pool.kernel_size == 3
     assert max_pool.stride == 3
-    assert max_pool.padding == 0
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6]).astype(np.float32))
     output = max_pool(input_data)
     output_np = output.asnumpy()
diff --git a/tests/ut/python/pynative_mode/nn/test_cell.py b/tests/ut/python/pynative_mode/nn/test_cell.py
index 16adcd6119..2d5196b80d 100644
--- a/tests/ut/python/pynative_mode/nn/test_cell.py
+++ b/tests/ut/python/pynative_mode/nn/test_cell.py
@@ -89,7 +89,7 @@ class ConvNet(nn.Cell):
         self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode='pad', padding=3)
         self.bn1 = nn.BatchNorm2d(ConvNet.output_ch)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='pad', padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(
             int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)),
diff --git a/tests/ut/python/pynative_mode/nn/test_pooling.py b/tests/ut/python/pynative_mode/nn/test_pooling.py
index ab95fec091..bb1822f8a8 100644
--- a/tests/ut/python/pynative_mode/nn/test_pooling.py
+++ b/tests/ut/python/pynative_mode/nn/test_pooling.py
@@ -49,23 +49,14 @@ def test_maxpool2d():
     """ test_maxpool2d """
     kernel_size = 3
     stride = 3
-    padding = 2
 
-    max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME', padding=padding)
+    max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME')
     assert max_pool.kernel_size == 3
     assert max_pool.stride == 3
-    assert max_pool.padding == 2
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6])*0.1)
     output = max_pool(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
 
-def test_maxpool2d_error_padding():
-    """ test_maxpool2d_error_padding """
-    kernel_size = 3.5
-    stride = 3
-    padding = 1
-    with pytest.raises(ValueError):
-        nn.MaxPool2d(kernel_size, stride, padding=padding)
 
diff --git a/tests/ut/python/pynative_mode/vm/test_vm.py b/tests/ut/python/pynative_mode/vm/test_vm.py
index 4ea0abd753..77510337b0 100644
--- a/tests/ut/python/pynative_mode/vm/test_vm.py
+++ b/tests/ut/python/pynative_mode/vm/test_vm.py
@@ -23,7 +23,7 @@ def test_avg_pooling():
                              [-9., -1., 3., 4.],
                              [1., -1., -3., -6.],
                              [-2., -1., -2., -15.]]]]).astype(np.float32)
-    out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     expect_out = [[[[-4.25, 0.0, 4.25],
                     [-2.5, -0.5, -0.5],
                     [-0.75, -1.75, -6.5]]]]
@@ -37,9 +37,9 @@ def test_avg_pool_grad():
                              [5, 6, 7, 8],
                              [9, 10, 11, 12],
                              [13, 14, 15, 16]]]]).astype(np.float32)
-    dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     print("vm.avg_pooling dout: ", dout)
-    out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1, 0)
+    out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1)
     print("vm.avg_pool_grad: ", out)
     assert True
 
@@ -202,7 +202,7 @@ def test_max_pooling():
         [-9., -1., 3., 4.],
         [1., -1., -3., -6.],
         [-2., -1., -2., -15.]]]]).astype(np.float32)
-    out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     expect_out = [[[[-1., 3., 9.],
                     [1., 3., 4.],
                     [1., -1., -2.]]]]
diff --git a/tests/ut/python/utils/test_serialize.py b/tests/ut/python/utils/test_serialize.py
index 41da45ab25..cc6f346b77 100644
--- a/tests/ut/python/utils/test_serialize.py
+++ b/tests/ut/python/utils/test_serialize.py
@@ -44,7 +44,7 @@ class Net(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(int(224*224*64/16), num_classes)
 
diff --git a/tests/vm_impl/nn_ops_vm_impl.py b/tests/vm_impl/nn_ops_vm_impl.py
index f6bbdca55a..fc1fa95024 100644
--- a/tests/vm_impl/nn_ops_vm_impl.py
+++ b/tests/vm_impl/nn_ops_vm_impl.py
@@ -19,66 +19,82 @@ from mindspore.ops.operations import _grad_ops as G
 from mindspore.common.tensor import Tensor
 from mindspore.ops.vm_impl_registry import vm_impl_registry as vm_impl_getters
 from .vm_interface import vm
+
+
 # pylint: disable=unused-argument
 
 
 @vm_impl_getters.register(P.ScalarSummary)
 def vm_impl_scalar_summary(self):
     """Generate vm_impl function for ScalarSummary"""
+
     def vm_impl(string_in, scalar):
         """Implement by vm mode."""
         return scalar
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.ReLU)
 def vm_impl_relu(self):
     """Generate vm_impl function for ReLU"""
+
     def vm_impl(x):
         x = x.asnumpy()
         output = Tensor(vm.relu(x))
         return output
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.Flatten)
 def vm_impl_flatten(self):
     """Generate vm_impl function for Flatten"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.flatten_batch(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Softmax)
 def vm_impl_softmax(self):
     """Generate vm_impl function for Softmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.softmax(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.LogSoftmax)
 def vm_impl_log_softmax(self):
     """Generate vm_impl function for LogSoftmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.logsoftmax(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Tanh)
 def vm_impl_tanh(self):
     """Generate vm_impl function for Tanh"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.tanh(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.FusedBatchNorm)
 def vm_impl_fused_batch_norm(self):
     """Generate vm_impl function for FusedBatchNorm"""
+
     def vm_impl(x, scale, b, mean, variance):
         # pylint: disable=unused-argument
         x = x.asnumpy()
@@ -92,12 +108,14 @@ def vm_impl_fused_batch_norm(self):
                                                                       momentum=self.momentum)
         return Tensor(out), Tensor(x_mean), Tensor(x_var), \
                Tensor(running_mean), Tensor(running_var)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.BatchNorm)
 def vm_impl_batch_norm(self):
     """Generate vm_impl function for BatchNorm"""
+
     def vm_impl(x, scale, b, mean, variance):
         # pylint: disable=unused-argument
         x = x.asnumpy()
@@ -110,83 +128,106 @@ def vm_impl_batch_norm(self):
                                                                       eps=self.epsilon)
         return Tensor(out), Tensor(x_mean), Tensor(x_var), \
                Tensor(running_mean), Tensor(running_var)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Conv2D)
 def vm_impl_conv2d(self):
     """Generate vm_impl function for Conv2D"""
+
     def vm_impl(x, w):
         x = x.asnumpy()
         weight = w.asnumpy()
         bias = None
         out = vm.conv2d(x, weight, bias, self.stride, self.pad, self.dilation)
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.MaxPoolGradWithArgmax)
 def vm_impl_max_pool_grad_with_argmax(self):
     """Generate vm_impl function for MaxPoolGradWithArgmax"""
-    def vm_impl(x, argmax, dout):
+
+    def vm_impl(x, dout, argmax):
+        print("buxue")
+        print(argmax)
         x = x.asnumpy()
         dout = dout.asnumpy()
         arg_max = argmax.asnumpy()
-        dx = vm.max_pool_grad_with_argmax(x, arg_max, dout, self.pool_h, self.pool_w, self.stride, self.pad)
+        dx = vm.max_pool_grad_with_argmax(x, dout, arg_max,
+                                          self.ksize[1], self.ksize[2], self.strides[1])
         return Tensor(dx)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.MaxPoolWithArgmax)
 def vm_impl_max_pool_with_argmax(self):
     """Generate vm_impl function for MaxPoolWithArgmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out, out_argmax = vm.max_pool_with_argmax(x, self.pool_h, self.pool_w, self.stride, self.pad)
+        out, out_argmax = vm.max_pool_with_argmax(x, self.ksize[1], self.ksize[2], self.strides[1])
         return Tensor(out), Tensor(out_argmax)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.MaxPool)
 def vm_impl_max_pool(self):
     """Generate vm_impl function for MaxPool"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out = vm.max_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.max_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.MaxPoolGrad)
 def vm_impl_max_pool_grad(self):
     """Generate vm_impl function for MaxPoolGrad"""
+
     def vm_impl(x, out, dout):
         x = x.asnumpy()
         dout = dout.asnumpy()
-        out = vm.max_pool_grad(x, dout, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.max_pool_grad(x, dout, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.AvgPool)
-def vm_impl_max_pool(self):
+def vm_impl_avg_pool(self):
     """Generate vm_impl function for AvgPool"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out = vm.avg_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.avg_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.AvgPoolGrad)
 def vm_impl_avg_pool_grad(self):
     """Generate vm_impl function for AvgPoolGrad"""
+
     def vm_impl(dout, origin_shape):
         dout = dout.asnumpy()
-        out = vm.avg_pool_grad(dout, origin_shape, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.avg_pool_grad(dout, origin_shape, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.FusedBatchNormGrad)
 def vm_impl_fused_batch_norm_grad(self):
     """Generate vm_impl function for FusedBatchNormGrad"""
+
     def vm_impl(dy, x, scale, save_mean, save_inv_variance):
         dy = dy.asnumpy()
         x = x.asnumpy()
@@ -195,11 +236,14 @@ def vm_impl_fused_batch_norm_grad(self):
         save_inv_variance = save_inv_variance.asnumpy()
         dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
         return (Tensor(dx), Tensor(dscale), Tensor(dshift))
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.BatchNormGrad)
 def vm_impl_fused_batch_norm_grad(self):
     """Generate vm_impl function for BatchNormGrad"""
+
     def vm_impl(dy, x, scale, save_mean, save_inv_variance):
         dy = dy.asnumpy()
         x = x.asnumpy()
@@ -208,104 +252,123 @@ def vm_impl_fused_batch_norm_grad(self):
         save_inv_variance = save_inv_variance.asnumpy()
         dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
         return (Tensor(dx), Tensor(dscale), Tensor(dshift))
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.ReluGrad)
 def vm_impl_relu_grad(self):
     """Generate vm_impl function for ReluGrad"""
+
     def vm_impl(y_backprop, x):
         x = x.asnumpy()
         y_backprop = y_backprop.asnumpy()
-        y_backprop = vm.relu_grad(x.copy())*y_backprop
+        y_backprop = vm.relu_grad(x.copy()) * y_backprop
         return Tensor(y_backprop)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Conv2DBackpropInput)
 def vm_impl_conv2d_backprop_input(self):
     """Generate vm_impl function for Conv2DBackpropInput"""
+
     def vm_impl(dout, w, x_size):
         dout = dout.asnumpy()
         w = w.asnumpy()
         dx = vm.conv2d_backprop_input(dout, x_size, w, self.stride, self.pad)
         return Tensor(dx)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.Conv2DBackpropFilter)
 def vm_impl_conv2d_backprop_filter(self):
     """Generate vm_impl function for Conv2DBackpropFilter"""
+
     def vm_impl(dout, x, w_size):
         x = x.asnumpy()
         dout = dout.asnumpy()
         dw = vm.conv2d_backprop_filter(dout, x, w_size, self.stride, self.pad)
         return Tensor(dw)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.FlattenGrad)
 def vm_impl_flatten_grad(self):
     """Generate vm_impl function for FlattenGrad"""
+
     def vm_impl(dout, x):
         dout = dout.asnumpy()
         dout = vm.flatten_grad(dout, x)
         return Tensor(dout)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.BiasAdd)
 def vm_impl_bias_add(self):
     """Generate vm_impl function for BiasAdd"""
+
     def vm_impl(wx, bias):
         wx = wx.asnumpy()
         bias = bias.asnumpy()
         out = wx + bias
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.BiasAddGrad)
 def vm_impl_bias_add_grad(self):
     """Generate vm_impl function for BiasAddGrad"""
+
     def vm_impl(dout):
         dout = dout.asnumpy()
         shape = np.shape(dout)
         return Tensor(np.add.reduce(dout, axis=tuple(range(len(shape) - 1))))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.SoftmaxCrossEntropyWithLogits)
 def vm_impl_softmax_cross_entropy_with_logits(self):
     """Generate vm_impl function for SoftmaxCrossEntropyWithLogits"""
+
     def vm_impl(logits, labels):
         logits = logits.asnumpy()
         labels = labels.asnumpy()
         loss, dx = vm.softmax_cross_entropy_with_logits(logits, labels)
         return (Tensor(np.array(loss)), Tensor(dx))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.SparseSoftmaxCrossEntropyWithLogits)
 def vm_impl_sparse_softmax_cross_entropy_with_logits(self):
     """Generate vm_impl function for SparseSoftmaxCrossEntropyWithLogits"""
+
     def vm_impl(logits, labels):
         logits = logits.asnumpy()
         labels = labels.asnumpy()
 
         n_class = labels.max() + 1
         n_sample = labels.shape[0]
-        one_hot_label = np.zeros((n_sample, n_class))#3个样本，4个类别
-        one_hot_label[:, labels] = 1#非零列赋值为1
+        one_hot_label = np.zeros((n_sample, n_class))  # 3个样本，4个类别
+        one_hot_label[:, labels] = 1  # 非零列赋值为1
         loss, dx = vm.softmax_cross_entropy_with_logits(logits, one_hot_label)
         if self.is_grad:
             return (Tensor(dx),)
         return (Tensor(np.array(loss)),)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.ApplyMomentum)
 def vm_impl_momentum(self):
     """Generate vm_impl function for Momentum"""
+
     def vm_impl(variable,
                 accumulation,
                 learning_rate,
@@ -327,19 +390,24 @@ def vm_impl_momentum(self):
 
     return vm_impl
 
+
 @vm_impl_getters.register(P.ResizeBilinear)
 def vm_impl_resize_bilinear(self):
     """Generate vm_impl function for ResizeBilinear"""
+
     def vm_impl(x):
         out = vm.ResizeBilinear(x)
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.ResizeBilinearGrad)
 def vm_impl_resize_bilinear_grad(self):
     """Generate vm_impl function for ResizeBilinearGrad"""
+
     def vm_impl(dout, original_image):
         out = vm.ResizeBilinearGrad(dout, original_image)
         return Tensor(out)
+
     return vm_impl
diff --git a/tests/vm_impl/vm_me.py b/tests/vm_impl/vm_me.py
index 03a0e1a885..ba51a3b13b 100644
--- a/tests/vm_impl/vm_me.py
+++ b/tests/vm_impl/vm_me.py
@@ -19,7 +19,7 @@ from mindspore._checkparam import Rel
 from mindspore._checkparam import ParamValidator as validator
 
 
-def avg_pooling(x, pool_h, pool_w, stride, pad):
+def avg_pooling(x, pool_h, pool_w, stride):
     """
     Applies average pooling over an input array.
 
@@ -28,26 +28,25 @@ def avg_pooling(x, pool_h, pool_w, stride, pad):
         pool_h (int): Height of the pooling window.
         pool_w (int): Width of the pooling window.
         stride (int): The stride of the sliding window.
-        pad (int): Padding to be added on height and width.
 
     Returns:
         numpy.ndarray, an output array after applying average pooling on input array.
     """
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
+    out_h = (height - pool_h)//stride + 1
+    out_w = (width - pool_w)//stride + 1
 
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
 
     out = np.mean(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
 
     return out
 
 
-def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad):
+def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride):
     """
     Gets grad of average pooling.
 
@@ -57,7 +56,6 @@ def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad):
         pool_h (int): Height of the pooling window.
         pool_w (int): Width of the pooling window.
         stride (int): The stride of the sliding window.
-        pad (int): Padding to be added on height and width.
 
     Returns:
         numpy.ndarray, grad of avgerage pooling.
@@ -324,38 +322,38 @@ def matmul(x, w, b=None):
     return y
 
 
-def max_pooling(x, pool_h, pool_w, stride, pad):
+def max_pooling(x, pool_h, pool_w, stride):
     """Max pooling."""
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
+    out_h = (height - pool_h)//stride + 1
+    out_w = (width - pool_w)//stride + 1
 
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
 
     out = np.max(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
 
     return out
 
 
-def max_pool_grad(x, dout, pool_h, pool_w, stride, pad):
+def max_pool_grad(x, dout, pool_h, pool_w, stride):
     """Grad of max pooling."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
     dmax = np.zeros((dout.size, pool_size))
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
     arg_max = np.argmax(col, axis=1)
     dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
     dmax = dmax.reshape(dout.shape + (pool_size,))
     dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1)
-    dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad)
+    dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
     return dx
 
 
-def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad):
+def max_pool_grad_with_argmax(x, dout, arg_max, pool_h, pool_w, stride):
     """Grad of max pooling with argmax."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
@@ -363,22 +361,22 @@ def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad):
     dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
     dmax = dmax.reshape(dout.shape + (pool_size,))
     dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1)
-    dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad)
+    dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
     return dx
 
 
-def max_pool_with_argmax(x, pool_h, pool_w, stride, pad):
+def max_pool_with_argmax(x, pool_h, pool_w, stride):
     """Max pooling with argmax."""
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    out_h = (height - pool_h)//stride + 1
+    out_w = (width - pool_w)//stride + 1
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
     out = np.max(col, axis=1)
     out_argmax = np.argmax(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
-    out_argmax = out_argmax.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
+    out_argmax = out_argmax.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
     return out, out_argmax
 
 

From 5637f80692854eeae6e1fd4a439b5194905fb465 Mon Sep 17 00:00:00 2001
From: Zirui Wu <zirui.wu@huawei.com>
Date: Wed, 1 Apr 2020 11:24:25 -0400
Subject: [PATCH 133/367] implemented multi-thread index writer for mindrecord

num threads cannot be more than num shards

minor fix

clang style fix

address review comments
---
 .../include/shard_index_generator.h           | 10 +-
 .../mindrecord/io/shard_index_generator.cc    | 98 ++++++++++++-------
 2 files changed, 72 insertions(+), 36 deletions(-)

diff --git a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
index f59dbe9bf0..1febd28fc2 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
@@ -85,14 +85,14 @@ class ShardIndexGenerator {
   /// \param sql
   /// \param data
   /// \return
-  MSRStatus BindParamaterExecuteSQL(
+  MSRStatus BindParameterExecuteSQL(
     sqlite3 *db, const std::string &sql,
     const std::vector<std::vector<std::tuple<std::string, std::string, std::string>>> &data);
 
   INDEX_FIELDS GenerateIndexFields(const std::vector<json> &schema_detail);
 
-  MSRStatus ExcuteTransaction(const int &shard_no, const std::pair<MSRStatus, sqlite3 *> &db,
-                              const std::vector<int> &raw_page_ids, const std::map<int, int> &blob_id_to_page_id);
+  MSRStatus ExecuteTransaction(const int &shard_no, const std::pair<MSRStatus, sqlite3 *> &db,
+                               const std::vector<int> &raw_page_ids, const std::map<int, int> &blob_id_to_page_id);
 
   MSRStatus CreateShardNameTable(sqlite3 *db, const std::string &shard_name);
 
@@ -103,12 +103,16 @@ class ShardIndexGenerator {
   void AddIndexFieldByRawData(const std::vector<json> &schema_detail,
                               std::vector<std::tuple<std::string, std::string, std::string>> &row_data);
 
+  void DatabaseWriter();  // worker thread
+
   std::string file_path_;
   bool append_;
   ShardHeader shard_header_;
   uint64_t page_size_;
   uint64_t header_size_;
   int schema_count_;
+  std::atomic_int task_;
+  std::atomic_bool write_success_;
   std::vector<std::pair<uint64_t, std::string>> fields_;
 };
 }  // namespace mindrecord
diff --git a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
index 1c14d30f30..c0108241a1 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#include <thread>
 
 #include "mindrecord/include/shard_index_generator.h"
 #include "common/utils.h"
@@ -26,7 +27,13 @@ using mindspore::MsLogLevel::INFO;
 namespace mindspore {
 namespace mindrecord {
 ShardIndexGenerator::ShardIndexGenerator(const std::string &file_path, bool append)
-    : file_path_(file_path), append_(append), page_size_(0), header_size_(0), schema_count_(0) {}
+    : file_path_(file_path),
+      append_(append),
+      page_size_(0),
+      header_size_(0),
+      schema_count_(0),
+      task_(0),
+      write_success_(true) {}
 
 MSRStatus ShardIndexGenerator::Build() {
   ShardHeader header = ShardHeader();
@@ -284,7 +291,7 @@ std::pair<MSRStatus, std::string> ShardIndexGenerator::GenerateRawSQL(
   return {SUCCESS, sql};
 }
 
-MSRStatus ShardIndexGenerator::BindParamaterExecuteSQL(
+MSRStatus ShardIndexGenerator::BindParameterExecuteSQL(
   sqlite3 *db, const std::string &sql,
   const std::vector<std::vector<std::tuple<std::string, std::string, std::string>>> &data) {
   sqlite3_stmt *stmt = nullptr;
@@ -471,9 +478,9 @@ INDEX_FIELDS ShardIndexGenerator::GenerateIndexFields(const std::vector<json> &s
   return {SUCCESS, std::move(fields)};
 }
 
-MSRStatus ShardIndexGenerator::ExcuteTransaction(const int &shard_no, const std::pair<MSRStatus, sqlite3 *> &db,
-                                                 const std::vector<int> &raw_page_ids,
-                                                 const std::map<int, int> &blob_id_to_page_id) {
+MSRStatus ShardIndexGenerator::ExecuteTransaction(const int &shard_no, const std::pair<MSRStatus, sqlite3 *> &db,
+                                                  const std::vector<int> &raw_page_ids,
+                                                  const std::map<int, int> &blob_id_to_page_id) {
   // Add index data to database
   std::string shard_address = shard_header_.get_shard_address_by_id(shard_no);
   if (shard_address.empty()) {
@@ -493,7 +500,7 @@ MSRStatus ShardIndexGenerator::ExcuteTransaction(const int &shard_no, const std:
     if (data.first != SUCCESS) {
       return FAILED;
     }
-    if (BindParamaterExecuteSQL(db.second, sql.second, data.second) == FAILED) {
+    if (BindParameterExecuteSQL(db.second, sql.second, data.second) == FAILED) {
       return FAILED;
     }
     MS_LOG(INFO) << "Insert " << data.second.size() << " rows to index db.";
@@ -514,37 +521,62 @@ MSRStatus ShardIndexGenerator::WriteToDatabase() {
   page_size_ = shard_header_.get_page_size();
   header_size_ = shard_header_.get_header_size();
   schema_count_ = shard_header_.get_schema_count();
-  if (shard_header_.get_shard_count() <= kMaxShardCount) {
-    // Create one database per shard
-    for (int shard_no = 0; shard_no < shard_header_.get_shard_count(); ++shard_no) {
-      // Create database
-      auto db = CreateDatabase(shard_no);
-      if (db.first != SUCCESS || db.second == nullptr) {
-        return FAILED;
-      }
-      MS_LOG(INFO) << "Init index db for shard: " << shard_no << " successfully.";
-
-      // Pre-processing page information
-      auto total_pages = shard_header_.GetLastPageId(shard_no) + 1;
-
-      std::map<int, int> blob_id_to_page_id;
-      std::vector<int> raw_page_ids;
-      for (uint64_t i = 0; i < total_pages; ++i) {
-        std::shared_ptr<Page> cur_page = shard_header_.GetPage(shard_no, i).first;
-        if (cur_page->get_page_type() == "RAW_DATA") {
-          raw_page_ids.push_back(i);
-        } else if (cur_page->get_page_type() == "BLOB_DATA") {
-          blob_id_to_page_id[cur_page->get_page_type_id()] = i;
-        }
-      }
+  if (shard_header_.get_shard_count() > kMaxShardCount) {
+    MS_LOG(ERROR) << "num shards: " << shard_header_.get_shard_count() << " exceeds max count:" << kMaxSchemaCount;
+    return FAILED;
+  }
+  task_ = 0;  // set two atomic vars to initial value
+  write_success_ = true;
 
-      if (ExcuteTransaction(shard_no, db, raw_page_ids, blob_id_to_page_id) != SUCCESS) {
-        return FAILED;
+  // spawn half the physical threads or total number of shards whichever is smaller
+  const unsigned int num_workers =
+    std::min(std::thread::hardware_concurrency() / 2 + 1, static_cast<unsigned int>(shard_header_.get_shard_count()));
+
+  std::vector<std::thread> threads;
+  threads.reserve(num_workers);
+
+  for (size_t t = 0; t < threads.capacity(); t++) {
+    threads.emplace_back(std::thread(&ShardIndexGenerator::DatabaseWriter, this));
+  }
+
+  for (size_t t = 0; t < threads.capacity(); t++) {
+    threads[t].join();
+  }
+  return write_success_ ? SUCCESS : FAILED;
+}
+
+void ShardIndexGenerator::DatabaseWriter() {
+  int shard_no = task_++;
+  while (shard_no < shard_header_.get_shard_count()) {
+    auto db = CreateDatabase(shard_no);
+    if (db.first != SUCCESS || db.second == nullptr || write_success_ == false) {
+      write_success_ = false;
+      return;
+    }
+
+    MS_LOG(INFO) << "Init index db for shard: " << shard_no << " successfully.";
+
+    // Pre-processing page information
+    auto total_pages = shard_header_.GetLastPageId(shard_no) + 1;
+
+    std::map<int, int> blob_id_to_page_id;
+    std::vector<int> raw_page_ids;
+    for (uint64_t i = 0; i < total_pages; ++i) {
+      std::shared_ptr<Page> cur_page = shard_header_.GetPage(shard_no, i).first;
+      if (cur_page->get_page_type() == "RAW_DATA") {
+        raw_page_ids.push_back(i);
+      } else if (cur_page->get_page_type() == "BLOB_DATA") {
+        blob_id_to_page_id[cur_page->get_page_type_id()] = i;
       }
-      MS_LOG(INFO) << "Generate index db for shard: " << shard_no << " successfully.";
     }
+
+    if (ExecuteTransaction(shard_no, db, raw_page_ids, blob_id_to_page_id) != SUCCESS) {
+      write_success_ = false;
+      return;
+    }
+    MS_LOG(INFO) << "Generate index db for shard: " << shard_no << " successfully.";
+    shard_no = task_++;
   }
-  return SUCCESS;
 }
 }  // namespace mindrecord
 }  // namespace mindspore

From 917aa02e2af3905d081edc37128cfc022b9fe35c Mon Sep 17 00:00:00 2001
From: Cathy Wong <cathy.wong@huawei.com>
Date: Wed, 8 Apr 2020 16:36:06 -0400
Subject: [PATCH 134/367] Correct dataset error checking

---
 mindspore/dataset/engine/datasets.py   | 2 --
 mindspore/dataset/engine/validators.py | 8 ++++----
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index ab2290c13c..2058bbf826 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -82,8 +82,6 @@ def zip(datasets):
     if len(datasets) <= 1:
         raise ValueError(
             "Can't zip empty or just one dataset!")
-    if not isinstance(datasets, tuple):
-        raise TypeError("The zip function %s type error!" % (datasets))
     return ZipDataset(datasets)
 
 
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 26d6241945..4c84cfe354 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -105,13 +105,13 @@ def check(method):
                     "The %s function %s exceeds the boundary!" % (
                         func_name, param_name))
             if isinstance(arg, int) and param_name == "num_parallel_workers" and (
-                    arg <= 0 or arg > cpu_count()):
+                    arg < 1 or arg > cpu_count()):
                 raise ValueError(
                     "The %s function %s exceeds the boundary(%s)!" % (
                         func_name, param_name, cpu_count()))
             if isinstance(arg, int) and param_name != "seed" \
                     and param_name != "count" and param_name != "prefetch_size" \
-                    and param_name != "num_parallel_workers" and (arg <= 0 or arg > 2147483647):
+                    and param_name != "num_parallel_workers" and (arg < 1 or arg > 2147483647):
                 raise ValueError(
                     "The %s function %s exceeds the boundary!" % (
                         func_name, param_name))
@@ -271,8 +271,8 @@ def check_interval_closed(param, param_name, valid_range):
 
 def check_num_parallel_workers(value):
     check_type(value, 'num_parallel_workers', int)
-    if value <= 0 or value > cpu_count():
-        raise ValueError("num_parallel_workers exceeds the boundary between 0 and {}!".format(cpu_count()))
+    if value < 1 or value > cpu_count():
+        raise ValueError("num_parallel_workers exceeds the boundary between 1 and {}!".format(cpu_count()))
 
 
 def check_num_samples(value):

From 0790ef8a85d624040e444650f681cce82e5d1a20 Mon Sep 17 00:00:00 2001
From: jinyaohui <jinyaohui@huawei.com>
Date: Thu, 9 Apr 2020 09:23:39 +0800
Subject: [PATCH 135/367] modify comment

---
 example/yolov3_coco2017/train.py        | 2 +-
 mindspore/ccsrc/pipeline/pipeline_ge.cc | 2 +-
 mindspore/ccsrc/transform/convert.cc    | 2 +-
 mindspore/nn/wrap/loss_scale.py         | 2 +-
 tests/ut/python/utils/test_callback.py  | 8 ++++----
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py
index 0a32a6d30d..121e2aa810 100644
--- a/example/yolov3_coco2017/train.py
+++ b/example/yolov3_coco2017/train.py
@@ -67,7 +67,7 @@ if __name__ == '__main__':
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
-    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink")
+    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc
index 60960a2eb7..6ce0ea5316 100644
--- a/mindspore/ccsrc/pipeline/pipeline_ge.cc
+++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc
@@ -453,7 +453,7 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
   }
 
   // process the first args of tensor
-  // only in Dataset non-sink Mode, fp_bp graph need input tensors
+  // only in dataset normal(non-sink) mode, fp_bp graph need input tensors
   if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) {
     for (std::size_t i = 0; i < size; i++) {
       ValuePtr converted = nullptr;
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 24b0188fa1..c9a27a2607 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -447,7 +447,7 @@ void DfGraphConvertor::InitLoopVar(std::vector<ge::Operator> *init_input) {
     if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
       value = ConfigManager::GetInstance().iter_num();
     } else {
-      MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1";
+      MS_LOG(INFO) << "Run with normal(non-sink) mode, the iterator number will always be 1";
       value = 1;
       ConfigManager::GetInstance().set_iter_num(value);
     }
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index fd1c22be1f..c6d61e6983 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -51,7 +51,7 @@ class DynamicLossScaleUpdateCell(Cell):
     In every training step, the loss scaling value  will be updated by loss scaling value/`scale_factor`
     when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no
     overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all
-    logic will be executed on device side(Another training mode is non-sink mode in which some logic will be
+    logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be
     executed on host).
 
     Args:
diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py
index 7e7b893e0c..8c10c8886d 100644
--- a/tests/ut/python/utils/test_callback.py
+++ b/tests/ut/python/utils/test_callback.py
@@ -112,8 +112,8 @@ def test_save_checkpoint():
         os.remove('./test_files/test_ckpt-model.pkl')
 
 
-def test_loss_monitor_sink_model():
-    """Test loss monitor sink model."""
+def test_loss_monitor_sink_mode():
+    """Test loss monitor sink mode."""
     cb_params = _InternalCallbackParam()
     cb_params.cur_epoch_num = 4
     cb_params.cur_step_num = 2
@@ -131,8 +131,8 @@ def test_loss_monitor_sink_model():
     callbacklist.end(run_context)
 
 
-def test_loss_monitor_feed_model():
-    """Test loss monitor non-sink mode."""
+def test_loss_monitor_normal_mode():
+    """Test loss monitor normal(non-sink) mode."""
     cb_params = _InternalCallbackParam()
     run_context = RunContext(cb_params)
     loss_cb = LossMonitor(1)

From fb343bd6073d3498b08c74468f74d421e2de30b0 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Wed, 8 Apr 2020 17:05:17 +0800
Subject: [PATCH 136/367] add mem manager

---
 mindspore/ccsrc/CMakeLists.txt                |   1 +
 .../device/ascend/ascend_kernel_runtime.cc    |  57 +----
 .../device/ascend/ascend_kernel_runtime.h     |   5 +-
 .../device/ascend/ascend_memory_manager.cc    |  65 +++++
 .../device/ascend/ascend_memory_manager.h     |  35 +++
 mindspore/ccsrc/device/device_address.h       |   5 +
 .../ccsrc/device/gpu/gpu_kernel_runtime.cc    | 108 ++------
 .../ccsrc/device/gpu/gpu_kernel_runtime.h     |   9 +-
 .../ccsrc/device/gpu/gpu_memory_manager.cc    |  88 +++++++
 .../ccsrc/device/gpu/gpu_memory_manager.h     |  40 +++
 mindspore/ccsrc/device/kernel_runtime.cc      | 231 +++---------------
 mindspore/ccsrc/device/kernel_runtime.h       |  38 +--
 mindspore/ccsrc/device/memory_manager.cc      | 170 +++++++++++++
 mindspore/ccsrc/device/memory_manager.h       |  71 ++++++
 .../ccsrc/session/anf_runtime_algorithm.cc    |  10 +
 .../ccsrc/session/anf_runtime_algorithm.h     |   1 +
 mindspore/ccsrc/session/gpu_session.cc        |   4 -
 tests/ut/cpp/CMakeLists.txt                   |   2 +
 18 files changed, 562 insertions(+), 378 deletions(-)
 create mode 100644 mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
 create mode 100644 mindspore/ccsrc/device/ascend/ascend_memory_manager.h
 create mode 100644 mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
 create mode 100644 mindspore/ccsrc/device/gpu/gpu_memory_manager.h
 create mode 100644 mindspore/ccsrc/device/memory_manager.cc
 create mode 100644 mindspore/ccsrc/device/memory_manager.h

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 9f559a51eb..1d104148c3 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -132,6 +132,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "kernel/kash/*.cc"
         "device/kernel_info.cc"
         "device/kernel_runtime.cc"
+        "device/memory_manager.cc"
         "device/kernel_runtime_manager.cc"
         "device/convert_tensor_utils.cc"
         "pre_activate/common/*.cc"
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index dc7eb5449b..0c2a97a5a6 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -37,6 +37,7 @@
 #include "kernel/tbe/tbe_utils.h"
 #include "kernel/tbe/tbe_python_funcs.h"
 #include "pre_activate/mem_reuse/mem_reuse_checker.h"
+#include "device/ascend/ascend_memory_manager.h"
 
 using mindspore::device::ascend::ProfilingManager;
 using mindspore::device::ascend::ProfilingUtils;
@@ -47,8 +48,6 @@ using std::vector;
 namespace mindspore {
 namespace device {
 namespace ascend {
-static const uint64_t ASCEND_MEM_SIZE = 20;
-static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30);
 static const size_t PRAMATER_OUTPUT_INDEX = 0;
 
 AscendKernelRuntime::~AscendKernelRuntime() { graph_model_map_.clear(); }
@@ -86,7 +85,8 @@ void AscendKernelRuntime::ReleaseDeviceRes() {
     MS_EXCEPTION(DeviceProcessError) << "rtSetDevice, ret[" << static_cast<int>(ret) << "]";
   }
 
-  FreeDeviceMemory();
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->FreeDeviceMemory();
   (void)DestroyHccl();
   (void)ResetDevice();
   (void)ProfilingManager::GetInstance().StopProfiling();
@@ -109,11 +109,9 @@ bool AscendKernelRuntime::Init() {
   if (!ret) {
     return ret;
   }
-
-  ret = MallocDeviceMemory();
-  if (!ret) {
-    return ret;
-  }
+  mem_manager_ = std::make_shared<AscendMemoryManager>();
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->MallocDeviceMemory();
 
   ret = ProfilingManager::GetInstance().StartupProfiling(device_id_);
   if (!ret) {
@@ -239,13 +237,6 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size
   return std::make_shared<AscendDeviceAddress>(device_ptr, device_size, format, type_id);
 }
 
-void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) {
-  auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size);
-  MS_EXCEPTION_IF_NULL(device_ptr);
-  address->ptr_ = device_ptr;
-  address->mem_dynamic_alloc_ = true;
-}
-
 bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
@@ -474,42 +465,6 @@ bool AscendKernelRuntime::DestroyHccl() {
   context_ptr->set_enable_hccl(false);
   return true;
 }
-
-bool AscendKernelRuntime::MallocDeviceMemory() {
-  device_mem_size_ = ASCEND_MEM_SIZE_BYTE;
-  static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO);
-  auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM);
-  if (ret != RT_ERROR_NONE) {
-    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]";
-  }
-  device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
-  ret = rtMalloc(reinterpret_cast<void **>(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM);
-  if (ret != RT_ERROR_NONE) {
-    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
-  }
-  AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
-  AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
-  return true;
-}
-
-void AscendKernelRuntime::FreeDeviceMemory() {
-  if (device_mem_base_ != nullptr) {
-    auto ret = rtFree(device_mem_base_);
-    if (ret != RT_ERROR_NONE) {
-      MS_LOG(ERROR) << "rtFree mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]";
-    }
-    device_mem_base_ = nullptr;
-  }
-  if (device_mem_pool_base_ != nullptr) {
-    auto ret = rtFree(device_mem_pool_base_);
-    if (ret != RT_ERROR_NONE) {
-      MS_LOG(ERROR) << "rtFree mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
-    }
-    device_mem_pool_base_ = nullptr;
-  }
-}
-
-void AscendKernelRuntime::FreeHostMemory() { dynamic_mem_offset_ = 0; }
 }  // namespace ascend
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
index dbd1460d24..0eedad3d2b 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
@@ -39,13 +39,11 @@ class AscendKernelRuntime : public KernelRuntime {
   bool GenTask(const session::KernelGraph *graph) override;
   bool RunTask(const session::KernelGraph *graph) override;
   bool LoadTask(const session::KernelGraph *graph) override;
-  void FreeHostMemory() override;
 
  protected:
   DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
                                        TypeId type_id) override;
   bool SyncStream() override;
-  void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) override;
 
  private:
   bool InitDevice();
@@ -53,8 +51,7 @@ class AscendKernelRuntime : public KernelRuntime {
   bool HcclInit();
   bool NeedDestroyHccl();
   bool DestroyHccl();
-  bool MallocDeviceMemory();
-  void FreeDeviceMemory();
+
   void ClearGraphModelMap();
   void ReleaseDeviceRes() override;
   uint32_t GetGraphModelId(const session::KernelGraph *kernel_graph);
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
new file mode 100644
index 0000000000..f033d81d82
--- /dev/null
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
@@ -0,0 +1,65 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "device/ascend/ascend_memory_manager.h"
+#include "device/ascend/ascend_memory_allocator.h"
+#include "utils/context/ms_context.h"
+#include "runtime/mem.h"
+namespace mindspore {
+namespace device {
+namespace ascend {
+static const uint64_t ASCEND_MEM_SIZE = 20;
+static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30);
+
+void AscendMemoryManager::MallocDeviceMemory() {
+  device_mem_size_ = ASCEND_MEM_SIZE_BYTE;
+  static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO);
+  auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM);
+  if (ret != RT_ERROR_NONE) {
+    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]";
+  }
+  device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
+  ret = rtMalloc(reinterpret_cast<void **>(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM);
+  if (ret != RT_ERROR_NONE) {
+    MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
+  }
+  AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
+  AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
+}
+
+void AscendMemoryManager::FreeDeviceMemory() {
+  if (device_mem_base_ != nullptr) {
+    auto ret = rtFree(device_mem_base_);
+    if (ret != RT_ERROR_NONE) {
+      MS_LOG(ERROR) << "rtFree mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]";
+    }
+    device_mem_base_ = nullptr;
+  }
+  if (device_mem_pool_base_ != nullptr) {
+    auto ret = rtFree(device_mem_pool_base_);
+    if (ret != RT_ERROR_NONE) {
+      MS_LOG(ERROR) << "rtFree mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
+    }
+    device_mem_pool_base_ = nullptr;
+  }
+}
+
+void *AscendMemoryManager::AllocTensorMemDynamic(size_t size) {
+  return AscendMemoryAllocator::GetInstance().AllocTensorMem(size);
+}
+}  // namespace ascend
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
new file mode 100644
index 0000000000..8639fb5c72
--- /dev/null
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
@@ -0,0 +1,35 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_
+#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_
+#include "device/memory_manager.h"
+namespace mindspore {
+namespace device {
+namespace ascend {
+class AscendMemoryManager : public MemoryManager {
+ public:
+  AscendMemoryManager() = default;
+  virtual ~AscendMemoryManager() = default;
+
+  void MallocDeviceMemory() override;
+  void FreeDeviceMemory() override;
+  void *AllocTensorMemDynamic(size_t size) override;
+};
+}  // namespace ascend
+}  // namespace device
+}  // namespace mindspore
+#endif  // MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_
diff --git a/mindspore/ccsrc/device/device_address.h b/mindspore/ccsrc/device/device_address.h
index 1610d43372..cb022427e3 100644
--- a/mindspore/ccsrc/device/device_address.h
+++ b/mindspore/ccsrc/device/device_address.h
@@ -33,12 +33,14 @@ class CPUKernelRuntime;
 }  // namespace cpu
 namespace ascend {
 class AscendKernelRuntime;
+class AscendMemoryManager;
 namespace tasksink {
 class TaskGenerator;
 }  // namespace tasksink
 }  // namespace ascend
 namespace gpu {
 class GPUKernelRuntime;
+class GPUMemoryManager;
 }  // namespace gpu
 }  // namespace device
 }  // namespace mindspore
@@ -70,12 +72,15 @@ class DeviceAddress {
   TypeId type_id_{kNumberTypeFloat16};
   bool mem_dynamic_alloc_{false};
   friend class KernelRuntime;
+  friend class MemoryManager;
   friend class mindspore::device::ascend::tasksink::TaskGenerator;
   friend class mindspore::device::cpu::CPUSimpleMemPlan;
   friend class mindspore::device::cpu::CPUResourceManager;
   friend class mindspore::device::cpu::CPUKernelRuntime;
   friend class mindspore::device::gpu::GPUKernelRuntime;
+  friend class mindspore::device::gpu::GPUMemoryManager;
   friend class mindspore::device::ascend::AscendKernelRuntime;
+  friend class mindspore::device::ascend::AscendMemoryManager;
 };
 
 using DeviceAddressPtr = std::shared_ptr<DeviceAddress>;
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
index 9eeb1062f7..597e188e9d 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
@@ -26,6 +26,7 @@
 #include "device/kernel_runtime_manager.h"
 #include "device/gpu/gpu_common.h"
 #include "common/utils.h"
+#include "device/gpu/gpu_memory_manager.h"
 
 namespace mindspore {
 namespace device {
@@ -36,26 +37,14 @@ bool GPUKernelRuntime::Init() {
   if (device_init_ == true) {
     return true;
   }
-
   auto ret = InitDevice();
   if (!ret) {
     MS_LOG(ERROR) << "InitDevice error.";
     return ret;
   }
-
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  // If use the dynamic memory pool, then alloc the first memory block to init.
-  if (context_ptr->enable_dynamic_mem_pool()) {
-    auto device_addr = AllocTensorMemDynamic(1);
-    if (!device_addr) {
-      MS_LOG(ERROR) << "Dynamic memory pool init error.";
-      return false;
-    }
-  } else {
-    MallocDeviceMemory();
-  }
-
+  mem_manager_ = std::make_shared<GPUMemoryManager>();
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->MallocDeviceMemory();
   const void *collective_handle_ = CollectiveInitializer::instance().collective_handle();
   bool collective_inited = CollectiveInitializer::instance().collective_inited();
   if (collective_inited && collective_handle_ != nullptr) {
@@ -101,16 +90,6 @@ bool GPUKernelRuntime::InitDevice() {
   return true;
 }
 
-void GPUKernelRuntime::MallocDeviceMemory() {
-  // Need to reserve 20% space for dynamic memory
-  const float init_gpu_mem_ratio = 0.8;
-  size_t mem_size = FloatToSize(GPUMemoryAllocator::GetInstance().free_mem_size() * init_gpu_mem_ratio);
-  auto alloc_size =
-    GPUMemoryAllocator::GetInstance().AllocDeviceMem(mem_size, reinterpret_cast<void **>(&device_mem_base_));
-  device_mem_size_ = alloc_size;
-  static_mem_offset_ = device_mem_size_;
-}
-
 void GPUKernelRuntime::ReleaseDeviceRes() {
   // For dataset mode.
   if (GpuBufferMgr::GetInstance().IsInit()) {
@@ -122,39 +101,22 @@ void GPUKernelRuntime::ReleaseDeviceRes() {
     CHECK_OP_RET_WITH_EXCEPT(GpuBufferMgr::GetInstance().Destroy(), "Could not destroy gpu data queue.");
   }
   GPUDeviceManager::GetInstance().ReleaseDevice();
-  if (device_mem_base_ != nullptr) {
-    if (!GPUMemoryAllocator::GetInstance().FreeDeviceMem(device_mem_base_)) {
-      MS_LOG(EXCEPTION) << "Could not free gpu device memory.";
-    }
-  }
-  GPUMemoryAllocator::GetInstance().ReleaseDeviceRes();
-}
-
-void GPUKernelRuntime::FreeHostMemory() { dynamic_mem_offset_ = 0; }
-
-void *GPUKernelRuntime::AllocTensorMemDynamic(size_t size) {
-  return GPUMemoryAllocator::GetInstance().AllocTensorMem(size);
-}
-
-void GPUKernelRuntime::FreeTensorMemDynamic(void *device_ptr) {
-  GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->FreeDeviceMemory();
 }
 
 void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->ResetDynamicMemory();
   AssignStaticMemory(graph);
-  bool is_enable_mem_reuse = context_ptr->enable_mem_reuse();
   bool is_enable_dynamic_mem = context_ptr->enable_dynamic_mem_pool();
   if (is_enable_dynamic_mem) {
     // Use the dynamic memory pool.
     InitKernelRefCount(graph);
     InitKernelOutputAddress(graph);
-  } else if (is_enable_mem_reuse) {
-    // Use the memory reuse.
-    ReuseAssignDynamicMemory(graph);
   } else {
-    // Normal way.
     AssignDynamicMemory(graph);
   }
 }
@@ -179,32 +141,6 @@ bool GPUKernelRuntime::Run(session::KernelGraph *graph) {
   return ret;
 }
 
-uint8_t *GPUKernelRuntime::MallocStaticMem(size_t size, bool) {
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  if (context_ptr->enable_dynamic_mem_pool()) {
-    auto device_ptr = AllocTensorMemDynamic(size);
-    MS_EXCEPTION_IF_NULL(device_ptr);
-    return AddressOffset(device_ptr, 0);
-  }
-
-  auto align_size = GetCommonAlignSize(size);
-  if (static_mem_offset_ < align_size) {
-    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
-                      << "] static[" << total_static_size_ << "])"
-                      << " malloc [" << align_size << "] failed!";
-  }
-  auto offset = static_mem_offset_ - align_size;
-  if (dynamic_mem_offset_ > offset) {
-    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
-                      << "] static[" << total_static_size_ << "])"
-                      << " malloc [" << align_size << "] failed!";
-  }
-  total_static_size_ += align_size;
-  static_mem_offset_ = offset;
-  return device_mem_base_ + offset;
-}
-
 void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
@@ -273,6 +209,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
   MS_EXCEPTION_IF_NULL(kernel_inputs);
   MS_EXCEPTION_IF_NULL(kernel_workspaces);
   MS_EXCEPTION_IF_NULL(kernel_outputs);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
     auto device_address = AnfAlgo::GetPrevNodeOutputAddr(kernel, i);
     MS_EXCEPTION_IF_NULL(device_address);
@@ -290,7 +227,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
     MS_EXCEPTION_IF_NULL(device_address);
     auto device_ptr = device_address->ptr_;
     if (device_ptr == nullptr) {
-      device_ptr = AllocTensorMemDynamic(output_sizes[i]);
+      device_ptr = mem_manager_->AllocTensorMemDynamic(output_sizes[i]);
       MS_EXCEPTION_IF_NULL(device_ptr);
       device_address->ptr_ = device_ptr;
     }
@@ -307,7 +244,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
       kernel_workspaces->emplace_back(nullptr);
       continue;
     }
-    auto device_ptr = AllocTensorMemDynamic(workspace_sizes[i]);
+    auto device_ptr = mem_manager_->AllocTensorMemDynamic(workspace_sizes[i]);
     MS_EXCEPTION_IF_NULL(device_ptr);
     kernel::AddressPtr workspace = std::make_shared<kernel::Address>();
     MS_EXCEPTION_IF_NULL(workspace);
@@ -333,6 +270,7 @@ void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph
 
 void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   // The reference count of communication kernel input is not 0.
   if (communication_op_input_ref_count_ != 0) {
     MS_LOG(ERROR) << "The reference count of communication kernel input is not 0.";
@@ -354,7 +292,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN
     addr_size.emplace_back(device_address.get(), output_size);
   }
 
-  auto device_mem_ptr = AllocTensorMemDynamic(total);
+  auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total);
   MS_EXCEPTION_IF_NULL(device_mem_ptr);
   for (const auto &iter : addr_size) {
     MS_EXCEPTION_IF_NULL(iter.first);
@@ -366,6 +304,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN
 
 void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   // The reference count of communication kernel output is not 0.
   if (communication_op_output_ref_count_ != 0) {
     MS_LOG(ERROR) << "The reference count of communication kernel output is not 0.";
@@ -389,7 +328,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf
     addr_size.emplace_back(device_address.get(), output_sizes[i]);
   }
 
-  auto device_mem_ptr = AllocTensorMemDynamic(total);
+  auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total);
   MS_EXCEPTION_IF_NULL(device_mem_ptr);
   for (const auto &iter : addr_size) {
     MS_EXCEPTION_IF_NULL(iter.first);
@@ -402,6 +341,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf
 void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
                                             const AddressPtrList &kernel_workspaces) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   auto cnode = kernel->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   // Free the input of kernel by reference count.
@@ -421,7 +361,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
         auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
         MS_EXCEPTION_IF_NULL(device_address);
         MS_EXCEPTION_IF_NULL(device_address->ptr_);
-        FreeTensorMemDynamic(device_address->ptr_);
+        mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
         device_address->ptr_ = nullptr;
       }
     }
@@ -432,7 +372,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
     auto workspace = kernel_workspaces[i];
     if (workspace != nullptr) {
       MS_EXCEPTION_IF_NULL(workspace->addr);
-      FreeTensorMemDynamic(workspace->addr);
+      mem_manager_->FreeTensorMemDynamic(workspace->addr);
       workspace->addr = nullptr;
     }
   }
@@ -441,6 +381,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
 void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx,
                                                      bool *is_communication_op) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   // The inputs memory of communication kernel is one piece memory, need release together.
   if (AnfAlgo::GetCNodeName(kernel) == kAllReduceOpName) {
     communication_op_input_ref_count_--;
@@ -448,7 +389,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
       auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0);
       MS_EXCEPTION_IF_NULL(device_address);
       MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      FreeTensorMemDynamic(device_address->ptr_);
+      mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
       device_address->ptr_ = nullptr;
     }
     *is_communication_op = true;
@@ -470,19 +411,12 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
       auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0);
       MS_EXCEPTION_IF_NULL(device_address);
       MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      FreeTensorMemDynamic(device_address->ptr_);
+      mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
       device_address->ptr_ = nullptr;
     }
     *is_communication_op = true;
   }
 }
-
-void GPUKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) {
-  auto device_ptr = AllocTensorMemDynamic(size);
-  MS_EXCEPTION_IF_NULL(device_ptr);
-  address->ptr_ = device_ptr;
-  address->mem_dynamic_alloc_ = true;
-}
 }  // namespace gpu
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
index f3fdb5fa98..6f761342d3 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
@@ -33,7 +33,6 @@ class GPUKernelRuntime : public KernelRuntime {
   ~GPUKernelRuntime() override = default;
   bool Init() override;
   void ReleaseDeviceRes() override;
-  void FreeHostMemory() override;
   void AssignMemory(session::KernelGraph *graph) override;
   bool Run(session::KernelGraph *graph) override;
 
@@ -41,18 +40,11 @@ class GPUKernelRuntime : public KernelRuntime {
   DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
                                        TypeId type_id) override;
   bool SyncStream() override;
-  // Alloc memory use the dynamic memory pool.
-  void *AllocTensorMemDynamic(size_t size) override;
-  // Free memory use the dynamic memory pool.
-  void FreeTensorMemDynamic(void *device_ptr) override;
-  void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) override;
-  uint8_t *MallocStaticMem(size_t size, bool communication_mem) override;
 
  private:
   GPUKernelRuntime(const GPUKernelRuntime &);
   GPUKernelRuntime &operator=(const GPUKernelRuntime &);
   bool InitDevice();
-  void MallocDeviceMemory();
   bool device_init_{false};
 
   // The related functions and members for using dynamic memory pool.
@@ -69,6 +61,7 @@ class GPUKernelRuntime : public KernelRuntime {
   void FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx, bool *is_communication_op);
   size_t communication_op_input_ref_count_{0};
   size_t communication_op_output_ref_count_{0};
+  MemReuseUtilPtr mem_reuse_util_ptr_{nullptr};
 };
 MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime);
 }  // namespace gpu
diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
new file mode 100644
index 0000000000..3944b504e4
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
@@ -0,0 +1,88 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "device/gpu/gpu_memory_manager.h"
+#include "device/gpu/gpu_memory_allocator.h"
+#include "utils/context/ms_context.h"
+#include "utils/convert_utils.h"
+namespace mindspore {
+namespace device {
+namespace gpu {
+void *GPUMemoryManager::AllocTensorMemDynamic(size_t size) {
+  return GPUMemoryAllocator::GetInstance().AllocTensorMem(size);
+}
+
+void GPUMemoryManager::FreeTensorMemDynamic(void *device_ptr) {
+  GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr);
+}
+
+void GPUMemoryManager::MallocDeviceMemory() {
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  // If use the dynamic memory pool, then alloc the first memory block to init.
+  if (context_ptr->enable_dynamic_mem_pool()) {
+    auto device_addr = AllocTensorMemDynamic(1);
+    if (!device_addr) {
+      MS_LOG(ERROR) << "Dynamic memory pool init error.";
+    }
+  } else {
+    // Need to reserve 20% space for dynamic memory
+    const float init_gpu_mem_ratio = 0.8;
+    size_t mem_size = FloatToSize(GPUMemoryAllocator::GetInstance().free_mem_size() * init_gpu_mem_ratio);
+    auto alloc_size =
+      GPUMemoryAllocator::GetInstance().AllocDeviceMem(mem_size, reinterpret_cast<void **>(&device_mem_base_));
+    device_mem_size_ = alloc_size;
+    static_mem_offset_ = device_mem_size_;
+  }
+}
+
+void GPUMemoryManager::FreeDeviceMemory() {
+  if (device_mem_base_ != nullptr) {
+    if (!GPUMemoryAllocator::GetInstance().FreeDeviceMem(device_mem_base_)) {
+      MS_LOG(EXCEPTION) << "Could not free gpu device memory.";
+    }
+  }
+  GPUMemoryAllocator::GetInstance().ReleaseDeviceRes();
+}
+
+uint8_t *GPUMemoryManager::MallocStaticMem(size_t size, bool) {
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  if (context_ptr->enable_dynamic_mem_pool()) {
+    auto device_ptr = AllocTensorMemDynamic(size);
+    MS_EXCEPTION_IF_NULL(device_ptr);
+    return AddressOffset(device_ptr, 0);
+  }
+
+  auto align_size = GetCommonAlignSize(size);
+  if (static_mem_offset_ < align_size) {
+    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
+                      << "] static[" << total_static_size_ << "])"
+                      << " malloc [" << align_size << "] failed!";
+  }
+  auto offset = static_mem_offset_ - align_size;
+  if (dynamic_mem_offset_ > offset) {
+    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
+                      << "] static[" << total_static_size_ << "])"
+                      << " malloc [" << align_size << "] failed!";
+  }
+  total_static_size_ += align_size;
+  static_mem_offset_ = offset;
+  return device_mem_base_ + offset;
+}
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h
new file mode 100644
index 0000000000..a18226bdf3
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h
@@ -0,0 +1,40 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_
+#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_
+#include "device/memory_manager.h"
+namespace mindspore {
+namespace device {
+namespace gpu {
+class GPUMemoryManager : public MemoryManager {
+ public:
+  GPUMemoryManager() = default;
+  virtual ~GPUMemoryManager() = default;
+
+  void MallocDeviceMemory() override;
+  void FreeDeviceMemory() override;
+
+  void *AllocTensorMemDynamic(size_t size) override;
+  void FreeTensorMemDynamic(void *device_ptr) override;
+
+ protected:
+  uint8_t *MallocStaticMem(size_t size, bool communication_mem);
+};
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
+#endif  // MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_
diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index 0a9be35fb5..16025ed8a4 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -31,18 +31,13 @@
 #include "ir/value.h"
 using mindspore::kernel::Address;
 using mindspore::kernel::AddressPtr;
-using mindspore::memreuse::BestFitMemReuse;
-using mindspore::memreuse::MemReuseUtilPtr;
 
 namespace mindspore {
 namespace device {
 KernelRuntime::~KernelRuntime() {
-  device_mem_base_ = nullptr;
-  device_mem_pool_base_ = nullptr;
 #ifdef ENABLE_DUMP_E2E
   dump_conf_ptr_ = nullptr;
 #endif
-  mem_reuse_util_ptr_ = nullptr;
 }
 
 bool KernelRuntime::Run(session::KernelGraph *graph) {
@@ -88,11 +83,6 @@ bool KernelRuntime::LoadTask(const session::KernelGraph *graph) {
   return false;
 }
 
-void KernelRuntime::FreeHostMemory() {
-  dynamic_mem_offset_ = 0;
-  static_mem_offset_ = 0;
-}
-
 // for D to impl
 bool KernelRuntime::RunTask(const session::KernelGraph *graph) {
   if (graph != nullptr) {
@@ -126,13 +116,11 @@ size_t KernelRuntime::CountNodeDeviceMemorySize(const mindspore::AnfNodePtr &nod
 void KernelRuntime::AssignMemory(session::KernelGraph *graph) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  mem_manager_->ResetDynamicMemory();
   AssignStaticMemory(graph);
-  bool is_enable_mem_reuse = context_ptr->enable_mem_reuse();
-  if (is_enable_mem_reuse) {
-    ReuseAssignDynamicMemory(graph);
-  } else {
-    AssignDynamicMemory(graph);
-  }
+  AssignDynamicMemory(graph);
+
   UpdateRefNodeOutputMem(graph);
 }
 
@@ -159,6 +147,7 @@ void KernelRuntime::AssignStaticMemory(session::KernelGraph *graph) {
 void KernelRuntime::RunOpAssignInputMemory(const std::vector<tensor::TensorPtr> &input_tensors,
                                            const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   for (size_t input_index = 0; input_index < graph->inputs().size(); ++input_index) {
     auto item = graph->inputs()[input_index];
     MS_EXCEPTION_IF_NULL(item);
@@ -180,7 +169,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector<tensor::TensorPtr>
       auto device_address =
         CreateDeviceAddress(nullptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id);
       MS_EXCEPTION_IF_NULL(device_address);
-      MallocOpMemory(device_address, tensor_size, kStaticMem);
+      mem_manager_->MallocOpMemory(device_address, tensor_size);
       AnfAlgo::SetOutputAddr(device_address, index, item.get());
     }
   }
@@ -188,6 +177,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector<tensor::TensorPtr>
 
 void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
   MS_EXCEPTION_IF_NULL(kernel_mod);
   auto output_sizes = kernel_mod->GetOutputSizeList();
@@ -208,13 +198,14 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) {
     auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i);
     auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type);
     MS_EXCEPTION_IF_NULL(device_address);
-    MallocOpMemory(device_address, output_sizes[i], kDynamicMem);
+    mem_manager_->MallocOpMemory(device_address, output_sizes[i]);
     AnfAlgo::SetOutputAddr(device_address, i, kernel.get());
   }
 }
 
 void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) {
   MS_EXCEPTION_IF_NULL(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   if (kernel->isa<CNode>()) {
     auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
     MS_EXCEPTION_IF_NULL(kernel_mod);
@@ -222,7 +213,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) {
     for (size_t i = 0; i < workspace_lists.size(); ++i) {
       auto device_address = CreateDeviceAddress(nullptr, workspace_lists[i], "", kTypeUnknown);
       MS_EXCEPTION_IF_NULL(device_address);
-      MallocOpMemory(device_address, workspace_lists[i], kDynamicMem);
+      mem_manager_->MallocOpMemory(device_address, workspace_lists[i]);
       AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get());
     }
   }
@@ -230,6 +221,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) {
 
 void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   for (auto &item : graph->inputs()) {
     MS_EXCEPTION_IF_NULL(item);
     if (!item->isa<Parameter>()) {
@@ -247,7 +239,7 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) {
         output_type_id = AnfAlgo::GetOutputInferDataType(item, index);
       }
       auto tensor_size = CountNodeDeviceMemorySize(item, index);
-      auto ptr = MallocStaticMem(tensor_size, false);
+      auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size);
       auto address = CreateDeviceAddress(ptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id);
       AnfAlgo::SetOutputAddr(address, index, item.get());
     }
@@ -301,6 +293,7 @@ void KernelRuntime::UpdateRefNodeOutputMem(const session::KernelGraph *graph) {
 
 void KernelRuntime::AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   auto kernel_mod = AnfAlgo::GetKernelMod(node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
   auto output_sizes = kernel_mod->GetOutputSizeList();
@@ -314,12 +307,12 @@ void KernelRuntime::AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr
   std::vector<size_t> align_size_list;
   for (uint64_t mem_size : output_sizes) {
     if (context_ptr->enable_hccl()) {
-      mem_size = GetCommonAlignSize(mem_size);
+      mem_size = mem_manager_->GetCommonAlignSize(mem_size);
     }
     total_size += mem_size;
     align_size_list.emplace_back(mem_size);
   }
-  uint8_t *output_ptr = CalDeviceMem(node, total_size, flag, 0);
+  uint8_t *output_ptr = mem_manager_->MallocOutputMem(node, 0, flag, total_size);
   for (size_t j = 0; j < align_size_list.size(); ++j) {
     std::string output_format = AnfAlgo::GetOutputFormat(node, j);
     auto output_type = AnfAlgo::GetOutputDeviceDataType(node, j);
@@ -333,6 +326,7 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   MS_EXCEPTION_IF_NULL(node);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   size_t total_size = 0;
   std::vector<std::pair<mindspore::device::DeviceAddress *, size_t>> addr_size;
   for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(node); ++i) {
@@ -340,12 +334,12 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) {
     MS_EXCEPTION_IF_NULL(address);
     auto mem_size = address->size();
     if (context_ptr->enable_hccl()) {
-      mem_size = GetCommonAlignSize(mem_size);
+      mem_size = mem_manager_->GetCommonAlignSize(mem_size);
     }
     total_size += mem_size;
     addr_size.emplace_back(address.get(), mem_size);
   }
-  uint8_t *input_ptr = CalDeviceMem(node, total_size, kDynamicMem, 0);
+  uint8_t *input_ptr = mem_manager_->MallocOutputMem(node, 0, kDynamicMem, total_size);
   for (const auto &iter : addr_size) {
     MS_EXCEPTION_IF_NULL(iter.first);
     iter.first->set_ptr(input_ptr);
@@ -355,7 +349,8 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) {
 
 void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int index) {
   MS_EXCEPTION_IF_NULL(node);
-  if (IsCommunicationOp(node)) {
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  if (AnfAlgo::IsCommunicationOp(node)) {
     UpdateCommunicationOpInputMem(node);
     AssignCommunicationNodeOutputMem(flag, node);
     return;
@@ -375,7 +370,7 @@ void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int in
       MS_LOG(INFO) << "Already malloc index:" << i;
       continue;
     }
-    auto ptr = CalDeviceMem(node, output_sizes[i], flag, i);
+    auto ptr = mem_manager_->MallocOutputMem(node, i, flag, output_sizes[i]);
     if (ptr == nullptr) {
       // reused ptr, no need alloc, continue;
       continue;
@@ -390,6 +385,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
                                           size_t output_idx) {
   MS_EXCEPTION_IF_NULL(value_node);
   MS_EXCEPTION_IF_NULL(node_value);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   auto tensor = node_value->cast<TensorPtr>();
   if (tensor == nullptr) {
     MS_LOG(WARNING) << "Tensor is null";
@@ -397,7 +393,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
   }
   size_t tensor_size = tensor->data().nbytes();
   auto node_size = CountNodeDeviceMemorySize(value_node, output_idx);
-  auto ptr = MallocStaticMem(node_size, false);
+  auto ptr = mem_manager_->MallocMem(kStaticMem, node_size);
   TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(value_node, output_idx);
   if (output_type_id == kTypeUnknown) {
     output_type_id = AnfAlgo::GetOutputInferDataType(value_node, output_idx);
@@ -414,6 +410,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
 
 void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   for (auto &value_node : graph->graph_value_nodes()) {
     MS_EXCEPTION_IF_NULL(value_node);
     if (AnfAlgo::OutputAddrExist(value_node, 0)) {
@@ -440,7 +437,7 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
     } else if (node_value->isa<StringImm>()) {
       auto value = GetValue<std::string>(node_value);
       size_t tensor_size = value.size();
-      auto ptr = MallocStaticMem(tensor_size, false);
+      auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size);
       auto address = CreateDeviceAddress(ptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8);
       MS_EXCEPTION_IF_NULL(address);
       AnfAlgo::SetOutputAddr(address, 0, value_node.get());
@@ -452,103 +449,37 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
   }
 }
 
-void KernelRuntime::AssignDynamicMemory(const session::KernelGraph *graph) {
+void KernelRuntime::AssignDynamicMemory(session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
-  // reset dynamic mem offset
-  dynamic_mem_offset_ = 0;
-  auto &kernels = graph->execution_order();
-  for (auto &kernel : kernels) {
-    AssignNodeOutputMem(kDynamicMem, kernel, kGetAllOuts);
-    AssignWorkSpaceMem(kernel);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  bool is_enable_mem_reuse = context_ptr->enable_mem_reuse();
+  auto mem_flag = kDynamicMem;
+  if (is_enable_mem_reuse) {
+    mem_manager_->InitReuseDynamicMemory(graph);
+    mem_flag = kReuseDynamicMem;
   }
-}
-
-void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) {
-  MS_EXCEPTION_IF_NULL(graph);
-  dynamic_mem_offset_ = 0;
-  MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
-  MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
-  // set all infos
-  mem_reuse_util_ptr->SetAllInfo(graph);
-  auto bestfit_mem_reuse = std::make_shared<BestFitMemReuse>();
-  MS_EXCEPTION_IF_NULL(bestfit_mem_reuse);
-  bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get());
-  size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize();
-  MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]";
-  mem_reuse_util_ptr_ = mem_reuse_util_ptr;
-  auto base_ptr = MallocDynamicMem(total_allocated_size, false);
-  mem_reuse_util_ptr_->set_mem_base(base_ptr);
   auto &kernels = graph->execution_order();
   for (auto &kernel : kernels) {
-    AssignNodeOutputMem(kReuseDynamicMem, kernel, kGetAllOuts);
-    AssignReuseWorkSpaceMem(kernel);
+    AssignNodeOutputMem(mem_flag, kernel, kGetAllOuts);
+    AssignWorkSpaceMem(mem_flag, kernel);
   }
 }
 
-void KernelRuntime::AssignReuseWorkSpaceMem(const AnfNodePtr &node) {
+void KernelRuntime::AssignWorkSpaceMem(int flag, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
+  MS_EXCEPTION_IF_NULL(mem_manager_);
   auto kernel_mod = AnfAlgo::GetKernelMod(node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
   size_t index = 0;
   for (auto &size : kernel_mod->GetWorkspaceSizeList()) {
-    auto wk_ptr = mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index);
-    AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get());
+    auto ptr = mem_manager_->MallocWorkSpaceMem(node, flag, index, size);
+    AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(ptr, size, "", kTypeUnknown), index, node.get());
     index++;
   }
 }
 
-void KernelRuntime::AssignWorkSpaceMem(const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(node);
-  if (node->isa<CNode>()) {
-    auto kernel_mod = AnfAlgo::GetKernelMod(node);
-    MS_EXCEPTION_IF_NULL(kernel_mod);
-    size_t index = 0;
-    for (auto &size : kernel_mod->GetWorkspaceSizeList()) {
-      auto ptr = MallocDynamicMem(size, false);
-      AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(ptr, size, "", kTypeUnknown), index, node.get());
-      index++;
-    }
-  }
-}
-
-bool KernelRuntime::IsCommunicationOp(const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(node);
-  auto kernel_name = AnfAlgo::GetCNodeName(node);
-  auto kernel_type = AnfAlgo::GetKernelType(node);
-  if (kernel_name == kAllReduceOpName || kernel_type == HCCL_KERNEL) {
-    return true;
-  }
-  return false;
-}
-
-uint8_t *KernelRuntime::CalDeviceMem(const AnfNodePtr &node, size_t size, int flag, size_t index) {
-  MS_EXCEPTION_IF_NULL(node);
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  uint8_t *ptr = nullptr;
-  if (IsCommunicationOp(node)) {
-    bool communication_mem = false;
-    if (context_ptr->enable_hccl()) {
-      communication_mem = true;
-    }
-    if (flag == kStaticMem) {
-      ptr = MallocStaticMem(size, communication_mem);
-    } else {
-      ptr = MallocDynamicMem(size, communication_mem);
-    }
-    return ptr;
-  }
-
-  if (flag == kStaticMem) {
-    ptr = MallocStaticMem(size, false);
-  } else if (flag == kDynamicMem) {
-    ptr = MallocDynamicMem(size, false);
-  } else if (flag == kReuseDynamicMem) {
-    ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index);
-  }
-  return ptr;
-}
-
 void KernelRuntime::GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel,
                                   AddressPtrList *kernel_inputs, AddressPtrList *const kernel_workspaces,
                                   AddressPtrList *kernel_outputs) {
@@ -659,65 +590,6 @@ bool KernelRuntime::LaunchKernelMod(const session::KernelGraph &graph) {
   return true;
 }
 
-size_t KernelRuntime::GetCommonAlignSize(size_t input_size) const {
-  return (input_size + mem_align_size_ + 31) / mem_align_size_ * mem_align_size_;
-}
-
-size_t KernelRuntime::GetCommunicationAlignSize(size_t input_size) const {
-  return (input_size + mem_align_size_ - 1) / mem_align_size_ * mem_align_size_ + 2 * mem_align_size_;
-}
-
-uint8_t *KernelRuntime::MallocStaticMem(size_t size, bool communication_mem) {
-  size_t align_size = 0;
-  if (communication_mem) {
-    align_size = GetCommunicationAlignSize(size);
-  } else {
-    align_size = GetCommonAlignSize(size);
-  }
-  if (static_mem_offset_ < align_size) {
-    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
-                      << "] static[" << total_static_size_ << "])"
-                      << " malloc [" << align_size << "] failed!";
-  }
-  total_static_size_ += align_size;
-  auto offset = static_mem_offset_ - align_size;
-  if (dynamic_mem_offset_ > offset) {
-    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
-                      << "] static[" << total_static_size_ << "])"
-                      << " malloc [" << align_size << "] failed!";
-  }
-  static_mem_offset_ = offset;
-  if (communication_mem) {
-    return device_mem_base_ + offset + mem_align_size_;
-  } else {
-    return device_mem_base_ + offset;
-  }
-}
-
-uint8_t *KernelRuntime::MallocDynamicMem(size_t size, bool communication_mem) {
-  size_t align_size = 0;
-  if (communication_mem) {
-    align_size = GetCommunicationAlignSize(size);
-  } else {
-    align_size = GetCommonAlignSize(size);
-  }
-  uint64_t offset = dynamic_mem_offset_;
-  auto new_offset = dynamic_mem_offset_ + align_size;
-  if (new_offset > static_mem_offset_) {
-    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
-                      << "] static[" << total_static_size_ << "])"
-                      << " malloc [" << align_size << "] failed!";
-  }
-  total_dynamic_size_ += align_size;
-  dynamic_mem_offset_ = new_offset;
-
-  if (communication_mem) {
-    return device_mem_base_ + offset + mem_align_size_;
-  } else {
-    return device_mem_base_ + offset;
-  }
-}
-
 bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   if (!LaunchKernelMod(*graph)) {
@@ -731,29 +603,6 @@ bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) {
   return true;
 }
 
-void KernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) {
-  if (flag == kStaticMem) {
-    address->ptr_ = MallocStaticMem(size, false);
-  } else if (flag == kDynamicMem) {
-    address->ptr_ = MallocDynamicMem(size, false);
-  } else {
-    MS_LOG(EXCEPTION) << "Unknown memory type!";
-  }
-}
-
-void *KernelRuntime::AllocTensorMemDynamic(size_t size) {
-  if (size == 0) {
-    MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0.";
-  }
-  return nullptr;
-}
-
-void KernelRuntime::FreeTensorMemDynamic(void *device_ptr) {
-  if (device_ptr == nullptr) {
-    MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null.";
-  }
-}
-
 #ifdef ENABLE_DUMP_E2E
 bool KernelRuntime::SetDumpConf() {
   dump_conf_ptr_ = std::make_shared<Dump>();
diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h
index ac9a56ed4d..1224bf14eb 100644
--- a/mindspore/ccsrc/device/kernel_runtime.h
+++ b/mindspore/ccsrc/device/kernel_runtime.h
@@ -20,8 +20,7 @@
 #include <memory>
 #include <string>
 #include <map>
-#include "pre_activate/mem_reuse/mem_reuse.h"
-#include "pre_activate/mem_reuse/mem_reuse_allocator.h"
+
 #include "device/device_address.h"
 #include "ir/meta_tensor.h"
 #include "predict/generator/utils/ir_model_util.h"
@@ -32,21 +31,16 @@
 #include "session/anf_runtime_algorithm.h"
 #include "kernel/kernel.h"
 #include "utils/context/ms_context.h"
+#include "device/memory_manager.h"
 
 // using mindspore::session::KernelGraph;
 using mindspore::tensor::Tensor;
 using TensorPtr = std::shared_ptr<Tensor>;
-using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr;
 using mindspore::kernel::AddressPtr;
 using AddressPtrList = std::vector<mindspore::kernel::AddressPtr>;
 
 namespace mindspore {
 namespace device {
-const int kStaticMem = 0;
-const int kDynamicMem = 1;
-const int kReuseDynamicMem = 2;
-const int kGetAllOuts = -1;
-
 class KernelRuntime {
  public:
   KernelRuntime() = default;
@@ -65,7 +59,6 @@ class KernelRuntime {
   DumpConfPtr GetDumpConf();
 #endif
   virtual bool LoadTask(const session::KernelGraph *graph);
-  virtual void FreeHostMemory();
   // for GPU and D to impl
   virtual void ReleaseDeviceRes() {}
   void set_device_id(uint32_t device_id) { device_id_ = device_id; }
@@ -75,29 +68,17 @@ class KernelRuntime {
                                                TypeId type_id) = 0;
   virtual bool SyncStream() = 0;
   void AssignStaticMemory(session::KernelGraph *graph);
-  void AssignDynamicMemory(const session::KernelGraph *graph);
+  void AssignDynamicMemory(session::KernelGraph *graph);
   void ReuseAssignDynamicMemory(session::KernelGraph *graph);
   void AssignNodeOutputMem(int flag, const AnfNodePtr &node, int index);
-  void AssignWorkSpaceMem(const AnfNodePtr &node);
+  void AssignWorkSpaceMem(int flag, const AnfNodePtr &node);
   void AssignReuseWorkSpaceMem(const AnfNodePtr &node);
   void AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr &node);
   void UpdateRefNodeOutputMem(const session::KernelGraph *graph);
   void UpdateCommunicationOpInputMem(const AnfNodePtr &node);
-  bool IsCommunicationOp(const AnfNodePtr &node);
-  size_t GetCommonAlignSize(size_t input_size) const;
-  size_t GetCommunicationAlignSize(size_t input_size) const;
-
-  uint8_t *CalDeviceMem(const AnfNodePtr &node, size_t size, int flag, size_t index);
-  virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem);
-  uint8_t *MallocDynamicMem(size_t size, bool communication_mem);
 #ifdef ENABLE_DUMP_E2E
   bool SetDumpConf();
 #endif
-  // Alloc memory use the dynamic memory pool.
-  virtual void *AllocTensorMemDynamic(size_t size);
-  // Free memory use the dynamic memory pool.
-  virtual void FreeTensorMemDynamic(void *device_ptr);
-  virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag);
 
  private:
   void AssignStaticMemoryOutput(const session::KernelGraph *graph);
@@ -114,20 +95,11 @@ class KernelRuntime {
 
  protected:
   uint32_t device_id_{0};
-  uint8_t *device_mem_base_{nullptr};
-  uint8_t *device_mem_pool_base_{nullptr};
-  uint64_t device_mem_size_{0};
-  uint64_t device_mem_pool_size_{0};
-  uint64_t dynamic_mem_offset_{0};
-  uint64_t static_mem_offset_{0};
-  const uint64_t mem_align_size_ = 512;
 #ifdef ENABLE_DUMP_E2E
   DumpConfPtr dump_conf_ptr_;
 #endif
   void *stream_ = nullptr;
-  size_t total_static_size_ = 0;
-  size_t total_dynamic_size_ = 0;
-  MemReuseUtilPtr mem_reuse_util_ptr_{nullptr};
+  std::shared_ptr<MemoryManager> mem_manager_{nullptr};
 };
 using KernelRuntimePtr = std::shared_ptr<KernelRuntime>;
 }  // namespace device
diff --git a/mindspore/ccsrc/device/memory_manager.cc b/mindspore/ccsrc/device/memory_manager.cc
new file mode 100644
index 0000000000..3c1ddee6bc
--- /dev/null
+++ b/mindspore/ccsrc/device/memory_manager.cc
@@ -0,0 +1,170 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "device/memory_manager.h"
+#include "session/anf_runtime_algorithm.h"
+#include "utils/context/ms_context.h"
+using mindspore::memreuse::BestFitMemReuse;
+using mindspore::memreuse::MemReuseUtilPtr;
+namespace mindspore {
+namespace device {
+MemoryManager::~MemoryManager() {
+  device_mem_base_ = nullptr;
+  device_mem_pool_base_ = nullptr;
+  mem_reuse_util_ptr_ = nullptr;
+}
+
+size_t MemoryManager::GetCommonAlignSize(size_t input_size) const {
+  return (input_size + kMemAlignSize + 31) / kMemAlignSize * kMemAlignSize;
+}
+
+size_t MemoryManager::GetCommunicationAlignSize(size_t input_size) const {
+  return (input_size + kMemAlignSize - 1) / kMemAlignSize * kMemAlignSize + 2 * kMemAlignSize;
+}
+
+void MemoryManager::InitReuseDynamicMemory(session::KernelGraph *graph) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
+  MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
+  // set all infos
+  mem_reuse_util_ptr->SetAllInfo(graph);
+  auto bestfit_mem_reuse = std::make_shared<BestFitMemReuse>();
+  MS_EXCEPTION_IF_NULL(bestfit_mem_reuse);
+  bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get());
+  size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize();
+  MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]";
+  mem_reuse_util_ptr_ = mem_reuse_util_ptr;
+  auto base_ptr = MallocDynamicMem(total_allocated_size, false);
+  mem_reuse_util_ptr_->set_mem_base(base_ptr);
+}
+
+uint8_t *MemoryManager::MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size) {
+  MS_EXCEPTION_IF_NULL(node);
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  uint8_t *ptr = nullptr;
+  if (AnfAlgo::IsCommunicationOp(node)) {
+    bool communication_mem = false;
+    if (context_ptr->enable_hccl()) {
+      communication_mem = true;
+    }
+    if (flag == kStaticMem) {
+      ptr = MallocStaticMem(size, communication_mem);
+    } else {
+      ptr = MallocDynamicMem(size, communication_mem);
+    }
+    return ptr;
+  }
+
+  if (flag == kStaticMem) {
+    ptr = MallocStaticMem(size, false);
+  } else if (flag == kDynamicMem) {
+    ptr = MallocDynamicMem(size, false);
+  } else if (flag == kReuseDynamicMem) {
+    ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index);
+  }
+  return ptr;
+}
+
+uint8_t *MemoryManager::MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size) {
+  if (flag == kReuseDynamicMem) {
+    return mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index);
+  }
+  return MallocDynamicMem(size, false);
+}
+
+uint8_t *MemoryManager::MallocMem(int flag, size_t size) {
+  uint8_t *ptr = nullptr;
+  if (flag == kStaticMem) {
+    ptr = MallocStaticMem(size, false);
+  } else if (flag == kDynamicMem) {
+    ptr = MallocDynamicMem(size, false);
+  }
+  return ptr;
+}
+
+uint8_t *MemoryManager::MallocStaticMem(size_t size, bool communication_mem) {
+  size_t align_size = 0;
+  if (communication_mem) {
+    align_size = GetCommunicationAlignSize(size);
+  } else {
+    align_size = GetCommonAlignSize(size);
+  }
+  if (static_mem_offset_ < align_size) {
+    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
+                      << "] static[" << total_static_size_ << "])"
+                      << " malloc [" << align_size << "] failed!";
+  }
+  total_static_size_ += align_size;
+  auto offset = static_mem_offset_ - align_size;
+  if (dynamic_mem_offset_ > offset) {
+    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
+                      << "] static[" << total_static_size_ << "])"
+                      << " malloc [" << align_size << "] failed!";
+  }
+  static_mem_offset_ = offset;
+  if (communication_mem) {
+    return device_mem_base_ + offset + kMemAlignSize;
+  } else {
+    return device_mem_base_ + offset;
+  }
+}
+
+uint8_t *MemoryManager::MallocDynamicMem(size_t size, bool communication_mem) {
+  size_t align_size = 0;
+  if (communication_mem) {
+    align_size = GetCommunicationAlignSize(size);
+  } else {
+    align_size = GetCommonAlignSize(size);
+  }
+  uint64_t offset = dynamic_mem_offset_;
+  auto new_offset = dynamic_mem_offset_ + align_size;
+  if (new_offset > static_mem_offset_) {
+    MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_
+                      << "] static[" << total_static_size_ << "])"
+                      << " malloc [" << align_size << "] failed!";
+  }
+  total_dynamic_size_ += align_size;
+  dynamic_mem_offset_ = new_offset;
+
+  if (communication_mem) {
+    return device_mem_base_ + offset + kMemAlignSize;
+  } else {
+    return device_mem_base_ + offset;
+  }
+}
+
+void MemoryManager::MallocOpMemory(const DeviceAddressPtr address, size_t size) {
+  auto device_ptr = AllocTensorMemDynamic(size);
+  MS_EXCEPTION_IF_NULL(device_ptr);
+  address->ptr_ = device_ptr;
+  address->mem_dynamic_alloc_ = true;
+}
+
+void *MemoryManager::AllocTensorMemDynamic(size_t size) {
+  if (size == 0) {
+    MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0.";
+  }
+  return nullptr;
+}
+
+void MemoryManager::FreeTensorMemDynamic(void *device_ptr) {
+  if (device_ptr == nullptr) {
+    MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null.";
+  }
+}
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/memory_manager.h b/mindspore/ccsrc/device/memory_manager.h
new file mode 100644
index 0000000000..2e47237def
--- /dev/null
+++ b/mindspore/ccsrc/device/memory_manager.h
@@ -0,0 +1,71 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_
+#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_
+#include <memory>
+#include "pre_activate/mem_reuse/mem_reuse.h"
+#include "pre_activate/mem_reuse/mem_reuse_allocator.h"
+namespace mindspore {
+namespace device {
+const int kStaticMem = 0;
+const int kDynamicMem = 1;
+const int kReuseDynamicMem = 2;
+const int kGetAllOuts = -1;
+const uint64_t kMemAlignSize = 512;
+using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr;
+
+class MemoryManager {
+ public:
+  MemoryManager() = default;
+  virtual ~MemoryManager();
+
+  virtual void MallocDeviceMemory() = 0;
+  virtual void FreeDeviceMemory() = 0;
+  void ResetDynamicMemory() {
+    total_dynamic_size_ = 0;
+    dynamic_mem_offset_ = 0;
+  }
+
+  void InitReuseDynamicMemory(session::KernelGraph *graph);
+  uint8_t *MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size);
+  uint8_t *MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size);
+  virtual uint8_t *MallocMem(int flag, size_t size);
+
+  // Alloc memory use the dynamic memory pool.
+  virtual void *AllocTensorMemDynamic(size_t size);
+  // Free memory use the dynamic memory pool.
+  virtual void FreeTensorMemDynamic(void *device_ptr);
+  virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size);
+  size_t GetCommonAlignSize(size_t input_size) const;
+  size_t GetCommunicationAlignSize(size_t input_size) const;
+
+ protected:
+  virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem);
+  virtual uint8_t *MallocDynamicMem(size_t size, bool communication_mem);
+  uint8_t *device_mem_base_{nullptr};
+  uint8_t *device_mem_pool_base_{nullptr};
+  uint64_t device_mem_size_{0};
+  uint64_t device_mem_pool_size_{0};
+  uint64_t dynamic_mem_offset_{0};
+  uint64_t static_mem_offset_{0};
+  size_t total_static_size_ = 0;
+  size_t total_dynamic_size_ = 0;
+  MemReuseUtilPtr mem_reuse_util_ptr_{nullptr};
+};
+}  // namespace device
+}  // namespace mindspore
+#endif  // MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index cc23dbbdd2..78922448af 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -857,5 +857,15 @@ void AnfRuntimeAlgorithm::SetNodeInput(const CNodePtr &node, const AnfNodePtr &i
   MS_EXCEPTION_IF_NULL(input_node);
   node->set_input(index + 1, input_node);
 }
+
+bool AnfRuntimeAlgorithm::IsCommunicationOp(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  auto kernel_name = AnfAlgo::GetCNodeName(node);
+  auto kernel_type = AnfAlgo::GetKernelType(node);
+  if (kernel_name == kAllReduceOpName || kernel_type == HCCL_KERNEL) {
+    return true;
+  }
+  return false;
+}
 }  // namespace session
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 2de68f0098..55650ac31e 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -166,6 +166,7 @@ class AnfRuntimeAlgorithm {
   static bool IsFeatureMapInput(const AnfNodePtr &node, size_t input_index);
   // get real input index for some tbe ops which input order is different between me and tbe impl
   static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index);
+  static bool IsCommunicationOp(const AnfNodePtr &node);
 };
 }  // namespace session
 using AnfAlgo = session::AnfRuntimeAlgorithm;
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index 29330fb193..bbcf2228cc 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -102,10 +102,6 @@ GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
   graph->set_execution_order(execution_order);
   // Alloc memory, including static memory and dynamic memory
   AllocateMemory(graph.get());
-  // Reset memory resource
-  auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
-  MS_EXCEPTION_IF_NULL(runtime_instance);
-  runtime_instance->FreeHostMemory();
   return graph_id;
 }
 
diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt
index 8d3f8a8138..3c1351a857 100644
--- a/tests/ut/cpp/CMakeLists.txt
+++ b/tests/ut/cpp/CMakeLists.txt
@@ -85,6 +85,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "../../../mindspore/ccsrc/kernel/oplib/*.cc"
         "../../../mindspore/ccsrc/kernel/tbe/*.cc"
         "../../../mindspore/ccsrc/device/kernel_runtime.cc"
+        "../../../mindspore/ccsrc/device/memory_manager.cc"
         "../../../mindspore/ccsrc/device/kernel_runtime_manager.cc"
         "../../../mindspore/ccsrc/device/kernel_info.cc"
         "../../../mindspore/ccsrc/device/ascend/profiling/*.cc"
@@ -92,6 +93,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "../../../mindspore/ccsrc/device/convert_tensor_utils.cc"
         "../../../mindspore/ccsrc/device/ascend/kernel_build_ascend.cc"
         "../../../mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc"
+        "../../../mindspore/ccsrc/device/ascend/ascend_memory_manager.cc"
         "../../../mindspore/ccsrc/device/ascend/ascend_device_address.cc"
         "../../../mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc"
         "../../../mindspore/ccsrc/predict/generator/utils/ir_model_util.cc"

From 99f22c5b8583b74dd8cdf81772e11773f481495c Mon Sep 17 00:00:00 2001
From: zhousiyi <zhousiyi@huawei.com>
Date: Tue, 7 Apr 2020 01:52:36 +0000
Subject: [PATCH 137/367] fix children issue: missing users of the children's
 direct users.

---
 mindspore/ccsrc/ir/manager.cc                 | 38 +++----------------
 mindspore/ccsrc/ir/manager.h                  | 14 +------
 .../python/pynative_mode/test_framstruct.py   | 27 +++++--------
 3 files changed, 18 insertions(+), 61 deletions(-)

diff --git a/mindspore/ccsrc/ir/manager.cc b/mindspore/ccsrc/ir/manager.cc
index 09bc7127c1..ac3e256703 100644
--- a/mindspore/ccsrc/ir/manager.cc
+++ b/mindspore/ccsrc/ir/manager.cc
@@ -985,40 +985,14 @@ void ParentComputer::RealRecompute(FuncGraphPtr fg) {
   }
 }
 
-// children include:
-//  A. func graphs which use variables in fg as free variables; (child_direct_)
-//  B. func graphs which call func func graph in A. (all_users_)
-FuncGraphSetPtr ChildrenComputer::SeekChildren(const FuncGraphPtr& fg, const FuncGraphSetPtr& path) {
-  if (path == nullptr || path->contains(fg)) {
-    return std::make_shared<FuncGraphSet>();
-  }
-  std::shared_ptr<FuncGraphSet> children = std::make_shared<FuncGraphSet>();
-  auto& deps = *child_direct_;
-  auto& users = *all_users_;
-  MS_LOG(DEBUG) << "" << fg->ToString() << " start func graph dep size:" << deps[fg].size();
-  for (auto& dep : deps[fg]) {
-    FuncGraphPtr child = dep.first;
-    children->add(child);
-    path->add(child);
-    MS_LOG(DEBUG) << "Child func graph:" << fg->ToString() << " child " << child->ToString();
-    for (auto& user : users[child]) {
-      auto user_func_graph = user.first;
-      MS_LOG(DEBUG) << "Func graph:" << fg->ToString() << " user " << user_func_graph->ToString();
-      children->add(user_func_graph);
-      path->add(user_func_graph);
-    }
-    children->update(SeekChildren(child, path));
-  }
-  (void)children->erase(fg);
-  MS_LOG(DEBUG) << "End in children: " << children->size();
-  return children;
-}
-
 void ChildrenComputer::RealRecompute(FuncGraphPtr fg) {
   MS_EXCEPTION_IF_NULL(manager_);
-  child_direct_ = &manager_->func_graph_child_direct();
-  all_users_ = &manager_->func_graph_users();
-  children_analysis_[fg].update(SeekChildren(fg));
+  auto used_fg_total = manager_->func_graphs_used_total(fg);
+  for (auto& used_fg : used_fg_total) {
+    if (manager_->parent(used_fg) == fg) {
+      children_analysis_[fg].add(used_fg);
+    }
+  }
 }
 
 void ScopeComputer::RealRecompute(FuncGraphPtr fg) {
diff --git a/mindspore/ccsrc/ir/manager.h b/mindspore/ccsrc/ir/manager.h
index 8036bd68c0..f3092613a1 100644
--- a/mindspore/ccsrc/ir/manager.h
+++ b/mindspore/ccsrc/ir/manager.h
@@ -398,11 +398,8 @@ class ParentComputer final : public DepComputer {
 // graph's children graph except self
 class ChildrenComputer final : public DepComputer {
  public:
-  explicit ChildrenComputer(const FuncGraphManager* m) : DepComputer(m), all_users_(nullptr), child_direct_(nullptr) {}
-  ~ChildrenComputer() override {
-    all_users_ = nullptr;
-    child_direct_ = nullptr;
-  }
+  explicit ChildrenComputer(const FuncGraphManager* m) : DepComputer(m) {}
+  ~ChildrenComputer() override = default;
 
   FuncGraphToFuncGraphSetMap& children_analysis() { return children_analysis_; }
 
@@ -414,13 +411,6 @@ class ChildrenComputer final : public DepComputer {
   void ExtraReset() override { children_analysis_.clear(); }
 
   void RealRecompute(FuncGraphPtr fg) override;
-
- private:
-  FuncGraphSetPtr SeekChildren(const FuncGraphPtr& fg, const FuncGraphSetPtr& path = std::make_shared<FuncGraphSet>());
-  // when SeekChildren calls itself recursively, it can access these variables by class member
-  // other than pass by formal parameters, it can save 2 parameters for SeekChildren().
-  FuncGraphToFuncGraphCounterMap* all_users_;
-  FuncGraphToFuncGraphCounterMap* child_direct_;
 };
 
 // graph's children graph include self
diff --git a/tests/ut/python/pynative_mode/test_framstruct.py b/tests/ut/python/pynative_mode/test_framstruct.py
index ff7cf67f52..eb3b76765a 100644
--- a/tests/ut/python/pynative_mode/test_framstruct.py
+++ b/tests/ut/python/pynative_mode/test_framstruct.py
@@ -38,16 +38,6 @@ def setup_module(module):
     context.set_context(mode=context.PYNATIVE_MODE)
 
 
-@ms_function
-def refactor_fac(n):
-    """ grad_refactor_fac """
-    if n == 0:
-        return 1
-    return n * refactor_fac(n-1)
-def test_refactor():
-    res = refactor_fac(3)
-    assert res == 6
-
 @ms_function
 def while_upper_bound(upper):
     rval = 2
@@ -386,16 +376,19 @@ def test_grad_while():
     assert grad_while(5) == (60,)
 
 @ms_function
-def fac(n):
-    """ fac """
+def factorial(n):
+    """ factorial """
     if n == 0:
         return 1
-    return n * fac(n-1)
+    return n * factorial(n-1)
+
+def test_factorial():
+    res = factorial(3)
+    assert res == 6
 
-def test_fac():
-    """ test_fac """
-    res = fac(4)
-    assert res == 24
+def test_grad_factorial():
+    res = C.grad(factorial)(3)
+    assert res == 11
 
 def _for(x):
     """ _for """

From f385f2a48b1a277b73785ef224a526847faf49f3 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Mon, 6 Apr 2020 11:22:47 +0800
Subject: [PATCH 138/367] default build command

 "-z" (minddata) and "-M on" (gpu) by default in build.sh
---
 build.sh                    | 24 +++++++++++++++++-------
 cmake/mind_expression.cmake |  8 ++++----
 2 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/build.sh b/build.sh
index 9d812d6dcc..8bb5f3d95a 100755
--- a/build.sh
+++ b/build.sh
@@ -26,7 +26,7 @@ usage()
   echo "Usage:"
   echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-s] [-b ge|cpu] [-m infer|train] \\"
   echo "              [-a on|off] [-g on|off] [-p on|off] [-i] [-L] [-R] [-D on|off] [-j[n]] [-e gpu|d|cpu] \\"
-  echo "              [-P on|off] [-z] [-M on|off] [-V 9.2|10.1] [-I] [-K]"
+  echo "              [-P on|off] [-z [on|off]] [-M on|off] [-V 9.2|10.1] [-I] [-K]"
   echo ""
   echo "Options:"
   echo "    -d Debug mode"
@@ -50,8 +50,8 @@ usage()
   echo "    -P Enable dump anf graph to file in ProtoBuffer format, default on"
   echo "    -Q Enable dump end to end, default off"
   echo "    -D Enable dumping of function graph ir, default on"
-  echo "    -z Compile dataset & mindrecord, default off"
-  echo "    -M Enable MPI and NCCL for GPU training, default off"
+  echo "    -z Compile dataset & mindrecord, default on"
+  echo "    -M Enable MPI and NCCL for GPU training, default on"
   echo "    -V Specify the minimum required cuda version, default CUDA 9.2"
   echo "    -I Compile predict, default off"
   echo "    -K Compile with AKG, default off"
@@ -88,8 +88,8 @@ checkopts()
   ENABLE_DUMP2PROTO="on"
   ENABLE_DUMPE2E="off"
   ENABLE_DUMP_IR="on"
-  COMPILE_MINDDATA="off"
-  ENABLE_MPI="off"
+  COMPILE_MINDDATA="on"
+  ENABLE_MPI="on"
   CUDA_VERSION="9.2"
   COMPILE_PREDICT="off"
   USE_GLOG="on"
@@ -177,7 +177,7 @@ checkopts()
         if [[ "X$OPTARG" == "Xgpu" ]]; then
           ENABLE_GPU="on"
           ENABLE_CPU="on"
-        elif [[ "X$OPTARG" == "Xd" ]]; then
+        elif [[ "X$OPTARG" == "Xd" || "X$OPTARG" == "Xascend" ]]; then
           ENABLE_D="on"
           ENABLE_CPU="on"
         elif [[ "X$OPTARG" == "Xcpu" ]]; then
@@ -216,7 +216,17 @@ checkopts()
         echo "enable dump function graph ir"
         ;;
       z)
-        COMPILE_MINDDATA="on"
+        eval ARG=\$\{$OPTIND\}
+        if [[ -n $ARG && $ARG != -* ]]; then
+          OPTARG=$ARG
+          check_on_off $OPTARG z
+          OPTIND=$((OPTIND + 1))
+        else
+          OPTARG=""
+        fi
+        if [[ "X$OPTARG" == "Xoff" ]]; then
+          COMPILE_MINDDATA="off"
+        fi
         ;;
       I)
         COMPILE_PREDICT="on"
diff --git a/cmake/mind_expression.cmake b/cmake/mind_expression.cmake
index 345fd4675e..af122d4117 100644
--- a/cmake/mind_expression.cmake
+++ b/cmake/mind_expression.cmake
@@ -29,11 +29,11 @@ if (ENABLE_GPU)
     include(${CMAKE_SOURCE_DIR}/cmake/external_libs/dmlc_core.cmake)
     include(${CMAKE_SOURCE_DIR}/cmake/external_libs/rang.cmake)
     include(${CMAKE_SOURCE_DIR}/cmake/external_libs/tvm_gpu.cmake)
-endif()
 
-if (ENABLE_MPI)
-    include(${CMAKE_SOURCE_DIR}/cmake/external_libs/nccl.cmake)
-    include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ompi.cmake)
+    if (ENABLE_MPI)
+        include(${CMAKE_SOURCE_DIR}/cmake/external_libs/nccl.cmake)
+        include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ompi.cmake)
+    endif()
 endif()
 
 if (ENABLE_GE)

From d9f7e56b434daa29c075a41e446ef9ac36690ebf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Tue, 7 Apr 2020 14:56:05 +0800
Subject: [PATCH 139/367] fix review opinions in doc/comments

---
 mindspore/common/initializer.py       |   2 +-
 mindspore/ops/operations/array_ops.py |  86 ++++++++++-----------
 mindspore/ops/operations/math_ops.py  | 105 +++++++++++++-------------
 mindspore/ops/operations/nn_ops.py    |  44 +++++------
 mindspore/train/serialization.py      |   2 +-
 5 files changed, 116 insertions(+), 123 deletions(-)

diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py
index 4261621272..d55e03314d 100644
--- a/mindspore/common/initializer.py
+++ b/mindspore/common/initializer.py
@@ -276,7 +276,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
 
         shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
             output. Default: None.
-        dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mstype.float32.
+        dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32.
 
     Returns:
         Tensor, initialized tensor.
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 59d3083c5d..dda490566f 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -62,7 +62,7 @@ class ExpandDims(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
-        >>> expand_dims = ExpandDims()
+        >>> expand_dims = P.ExpandDims()
         >>> output = expand_dims(input_tensor, 0)
     """
 
@@ -101,7 +101,7 @@ class DType(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
-        >>> type = DType()(input_tensor)
+        >>> type = P.DType()(input_tensor)
     """
 
     @prim_attr_register
@@ -134,7 +134,7 @@ class SameTypeShape(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
         >>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
-        >>> out = SameTypeShape()(input_x, input_y)
+        >>> out = P.SameTypeShape()(input_x, input_y)
     """
 
     @prim_attr_register
@@ -175,7 +175,7 @@ class Cast(PrimitiveWithInfer):
         >>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
         >>> input_x = Tensor(input_np)
         >>> type_dst = mindspore.int32
-        >>> cast = Cast()
+        >>> cast = P.Cast()
         >>> result = cast(input_x, type_dst)
         >>> expect = input_np.astype(type_dst)
     """
@@ -227,7 +227,7 @@ class IsSubClass(PrimitiveWithInfer):
         bool, the check result.
 
     Examples:
-        >>> result = IsSubClass()(mindspore.int32,  mindspore.intc)
+        >>> result = P.IsSubClass()(mindspore.int32,  mindspore.intc)
     """
 
     @prim_attr_register
@@ -262,7 +262,7 @@ class IsInstance(PrimitiveWithInfer):
 
     Examples:
         >>> a = 1
-        >>> result = IsInstance()(a, mindspore.int32)
+        >>> result = P.IsInstance()(a, mindspore.int32)
     """
 
     @prim_attr_register
@@ -303,7 +303,7 @@ class Reshape(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
-        >>> reshape = Reshape()
+        >>> reshape = P.Reshape()
         >>> output = reshape(input_tensor, (3, 2))
     """
 
@@ -366,7 +366,7 @@ class Shape(Primitive):
 
     Examples:
         >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
-        >>> shape = Shape()
+        >>> shape = P.Shape()
         >>> output = shape(input_tensor)
     """
 
@@ -398,7 +398,7 @@ class Squeeze(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
-        >>> squeeze = Squeeze(2)
+        >>> squeeze = P.Squeeze(2)
         >>> output = squeeze(input_tensor)
     """
 
@@ -450,7 +450,7 @@ class Transpose(PrimitiveWithInfer):
     Examples:
         >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
         >>> perm = (0, 2, 1)
-        >>> transpose = Transpose()
+        >>> transpose = P.Transpose()
         >>> output = transpose(input_tensor, perm)
     """
 
@@ -504,10 +504,10 @@ class GatherV2(PrimitiveWithInfer):
         Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
 
     Examples:
-        >>> params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32)
-        >>> indices = Tensor(np.array([1, 2]), mindspore.int32)
+        >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32)
+        >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32)
         >>> axis = 1
-        >>> out = GatherV2()(params, indices, axis)
+        >>> out = P.GatherV2()(input_params, input_indices, axis)
     """
 
     @prim_attr_register
@@ -556,7 +556,7 @@ class Split(PrimitiveWithInfer):
         :math:`(y_1, y_2, ..., y_S)`.
 
     Examples:
-        >>> split = Split(1, 2)
+        >>> split = P.Split(1, 2)
         >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
         >>> output = split(x)
     """
@@ -606,7 +606,7 @@ class Rank(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
-        >>> rank = Rank()
+        >>> rank = P.Rank()
         >>> rank(input_tensor)
     """
 
@@ -640,7 +640,7 @@ class TruncatedNormal(PrimitiveWithInfer):
 
     Examples:
         >>> input_shape = Tensor(np.array([1, 2, 3]))
-        >>> truncated_normal = TruncatedNormal()
+        >>> truncated_normal = P.TruncatedNormal()
         >>> output = truncated_normal(input_shape)
     """
 
@@ -681,7 +681,7 @@ class Size(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
-        >>> size = Size()
+        >>> size = P.Size()
         >>> output = size(input_tensor)
     """
 
@@ -826,7 +826,7 @@ class TupleToArray(PrimitiveWithInfer):
         Tensor, if the input tuple contain `N` numbers, then the output tensor shape is (N,).
 
     Examples:
-        >>> type = TupleToArray()((1,2,3))
+        >>> type = P.TupleToArray()((1,2,3))
     """
 
     @prim_attr_register
@@ -861,7 +861,7 @@ class ScalarToArray(PrimitiveWithInfer):
         Tensor. 0-D Tensor and the content is the input.
 
     Examples:
-        >>> op = ScalarToArray()
+        >>> op = P.ScalarToArray()
         >>> data = 1.0
         >>> output = op(data)
     """
@@ -893,7 +893,7 @@ class ScalarToTensor(PrimitiveWithInfer):
         Tensor. 0-D Tensor and the content is the input.
 
     Examples:
-        >>> op = ScalarToTensor()
+        >>> op = P.ScalarToTensor()
         >>> data = 1
         >>> output = op(data, mindspore.float32)
     """
@@ -934,7 +934,7 @@ class InvertPermutation(PrimitiveWithInfer):
         tuple[int]. the lenth is same as input.
 
     Examples:
-        >>> invert = InvertPermutation()
+        >>> invert = P.InvertPermutation()
         >>> input_data = (3, 4, 0, 2, 1)
         >>> output = invert(input_data)
         >>> output == (2, 4, 3, 0, 1)
@@ -982,8 +982,8 @@ class Argmax(PrimitiveWithInfer):
         Tensor, indices of the max value of input tensor across the axis.
 
     Examples:
-        >>> input = Tensor(np.array([2.0, 3.1, 1.2]))
-        >>> index = Argmax()(input)
+        >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]))
+        >>> index = P.Argmax()(input_x)
         >>> assert index == Tensor(1, mindspore.int64)
     """
 
@@ -1030,8 +1030,8 @@ class Argmin(PrimitiveWithInfer):
         Tensor, indices of the min value of input tensor across the axis.
 
     Examples:
-        >>> input = Tensor(np.array([2.0, 3.1, 1.2]))
-        >>> index = Argmin()(input)
+        >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]))
+        >>> index = P.Argmin()(input_x)
         >>> assert index == Tensor(2, mindspore.int64)
     """
 
@@ -1082,8 +1082,8 @@ class ArgMaxWithValue(PrimitiveWithInfer):
         :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
 
     Examples:
-        >>> input = Tensor(np.random.rand(5))
-        >>> index, output = ArgMaxWithValue()(input)
+        >>> input_x = Tensor(np.random.rand(5))
+        >>> index, output = P.ArgMaxWithValue()(input_x)
     """
 
     @prim_attr_register
@@ -1129,8 +1129,8 @@ class ArgMinWithValue(PrimitiveWithInfer):
         :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
 
     Examples:
-        >>> input = Tensor(np.random.rand(5))
-        >>> index, output = ArgMinWithValue()(input)
+        >>> input_x = Tensor(np.random.rand(5))
+        >>> index, output = P.ArgMinWithValue()(input_x)
     """
     @prim_attr_register
     def __init__(self, axis=0, keep_dims=False):
@@ -1325,7 +1325,7 @@ class Concat(PrimitiveWithInfer):
     Examples:
         >>> data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
         >>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
-        >>> op = Concat()
+        >>> op = P.Concat()
         >>> output = op((data1, data2))
     """
 
@@ -1607,7 +1607,7 @@ class Select(PrimitiveWithInfer):
         Tensor, has the same shape as input_y. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
 
     Examples:
-        >>> select = Select()
+        >>> select = P.Select()
         >>> input_x = Tensor([True, False])
         >>> input_y = Tensor([2,3], mindspore.float32)
         >>> input_z = Tensor([1,2], mindspore.float32)
@@ -1681,7 +1681,7 @@ class StridedSlice(PrimitiveWithInfer):
     Examples
         >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
         >>>                   [[5, 5, 5], [6, 6, 6]]], mindspore.float32)
-        >>> slice = StridedSlice()
+        >>> slice = P.StridedSlice()
         >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))
         >>> output.shape()
         (1, 1, 3)
@@ -1913,9 +1913,9 @@ class ScatterNd(PrimitiveWithInfer):
         Tensor, the new tensor, has the same type as `update` and the same shape as `shape`.
 
     Examples:
-        >>> op = ScatterNd()
-        >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32)
+        >>> op = P.ScatterNd()
         >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
+        >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32)
         >>> shape = (3, 3)
         >>> output = op(indices, update, shape)
     """
@@ -1964,7 +1964,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
-        >>> resize = ResizeNearestNeighbor((2, 2))
+        >>> resize = P.ResizeNearestNeighbor((2, 2))
         >>> output = resize(input_tensor)
     """
 
@@ -1997,7 +1997,7 @@ class GatherNd(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
         >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
-        >>> op = GatherNd()
+        >>> op = P.GatherNd()
         >>> output = op(input_x, indices)
     """
 
@@ -2039,7 +2039,7 @@ class ScatterNdUpdate(PrimitiveWithInfer):
         >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
         >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
         >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32)
-        >>> op = ScatterNdUpdate()
+        >>> op = P.ScatterNdUpdate()
         >>> output = op(input_x, indices, update)
     """
 
@@ -2090,7 +2090,7 @@ class SpaceToDepth(PrimitiveWithInfer):
     Examples:
         >>> x = Tensor(np.random.rand(1,3,2,2), mindspore.float32)
         >>> block_size = 2
-        >>> op = SpaceToDepth(block_size)
+        >>> op = P.SpaceToDepth(block_size)
         >>> output = op(x)
         >>> output.asnumpy().shape == (1,12,1,1)
     """
@@ -2148,7 +2148,7 @@ class DepthToSpace(PrimitiveWithInfer):
     Examples:
         >>> x = Tensor(np.random.rand(1,12,1,1), mindspore.float32)
         >>> block_size = 2
-        >>> op = DepthToSpace(block_size)
+        >>> op = P.DepthToSpace(block_size)
         >>> output = op(x)
         >>> output.asnumpy().shape == (1,3,2,2)
     """
@@ -2212,8 +2212,8 @@ class SpaceToBatch(PrimitiveWithInfer):
         >>> block_size = 2
         >>> paddings = [[0, 0], [0, 0]]
         >>> space_to_batch = P.SpaceToBatch(block_size, paddings)
-        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
-        >>> space_to_batch(x)
+        >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
+        >>> space_to_batch(input_x)
         [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
 
     """
@@ -2280,8 +2280,8 @@ class BatchToSpace(PrimitiveWithInfer):
         >>> block_size = 2
         >>> crops = [[0, 0], [0, 0]]
         >>> op = P.BatchToSpace(block_size, crops)
-        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
-        >>> output = op(x)
+        >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
+        >>> output = op(input_x)
         [[[[1., 2.], [3., 4.]]]]
 
     """
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index d003f6ee8b..1294a65d02 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -112,9 +112,9 @@ class TensorAdd(_MathBinaryOp):
 
     Examples:
         >>> add = P.TensorAdd()
-        >>> x = Tensor(np.array([1,2,3]).astype(np.float32))
-        >>> y = Tensor(np.array([4,5,6]).astype(np.float32))
-        >>> add(x, y)
+        >>> input_x = Tensor(np.array([1,2,3]).astype(np.float32))
+        >>> input_y = Tensor(np.array([4,5,6]).astype(np.float32))
+        >>> add(input_x, input_y)
         [5,7,9]
     """
 
@@ -124,23 +124,24 @@ class AssignAdd(PrimitiveWithInfer):
     Updates a `Parameter` by adding a value to it.
 
     Inputs:
-        - **input_x** (Parameter) - The `Parameter`.
-        - **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`.
+        - **variable** (Parameter) - The `Parameter`.
+        - **value** (Union[numbers.Number, Tensor]) - The value to be added to the `variable`.
+          It should have the same shape as `variable` if it is a Tensor.
 
     Examples:
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignAdd = P.AssignAdd()
-        >>>         self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
-        >>>         self.AssignAdd(self.inputdata, x)
-        >>>         return self.inputdata
+        >>>         self.AssignAdd(self.variable, x)
+        >>>         return self.variable
         >>>
         >>> net = Net()
-        >>> x = Tensor(np.ones([1]).astype(np.int64)*100)
-        >>> net(x)
+        >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
+        >>> net(value)
     """
     __mindspore_signature__ = (
         ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),
@@ -166,22 +167,24 @@ class AssignSub(PrimitiveWithInfer):
     Updates a `Parameter` by subtracting a value from it.
 
     Inputs:
-        - **input_x** (Parameter) - The `Parameter`.
-        - **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`.
+        - **variable** (Parameter) - The `Parameter`.
+        - **value** (Union[numbers.Number, Tensor]) - The value to be subtracted from the `variable`.
+          It should have the same shape as `variable` if it is a Tensor.
 
     Examples:
         >>> class Net(Cell):
         >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
         >>>         self.AssignSub = P.AssignSub()
-        >>>         self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
-        >>>         self.AssignSub(self.inputdata, x)
-        >>>         return self.inputdata
+        >>>         self.AssignSub(self.variable, x)
+        >>>         return self.variable
         >>>
         >>> net = Net()
-        >>> x = Tensor(np.ones([1]).astype(np.int64)*100)
-        >>> net(x)
+        >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
+        >>> net(value)
     """
 
     __mindspore_signature__ = (
@@ -263,9 +266,9 @@ class ReduceMean(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMean(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
 
@@ -295,9 +298,9 @@ class ReduceSum(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceSum(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
 
@@ -328,9 +331,9 @@ class ReduceAll(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.array([[True, False], [True, True]]))
+        >>> input_x = Tensor(np.array([[True, False], [True, True]]))
         >>> op = P.ReduceAll(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
     def __infer__(self, input_x, axis):
@@ -364,9 +367,9 @@ class ReduceMax(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMax(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
 
@@ -397,9 +400,9 @@ class ReduceMin(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMin(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
 
@@ -429,9 +432,9 @@ class ReduceProd(_Reduce):
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
     Examples:
-        >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceProd(keep_dims=True)
-        >>> output = op(data, 1)
+        >>> output = op(input_x, 1)
     """
 
 
@@ -451,15 +454,15 @@ class CumProd(PrimitiveWithInfer):
         Tensor, has the same shape and dtype as the 'input_x'.
 
     Examples:
-        >>> data = Tensor(np.array([a, b, c]).astype(np.float32))
+        >>> input_x = Tensor(np.array([a, b, c]).astype(np.float32))
         >>> op0 = P.CumProd()
-        >>> output = op0(data, 0) # output=[a, a * b, a * b * c]
+        >>> output = op0(input_x, 0) # output=[a, a * b, a * b * c]
         >>> op1 = P.CumProd(exclusive=True)
-        >>> output = op1(data, 0) # output=[1, a, a * b]
+        >>> output = op1(input_x, 0) # output=[1, a, a * b]
         >>> op2 = P.CumProd(reverse=True)
-        >>> output = op2(data, 0) # output=[a * b * c, b * c, c]
+        >>> output = op2(input_x, 0) # output=[a * b * c, b * c, c]
         >>> op3 = P.CumProd(exclusive=True, reverse=True)
-        >>> output = op3(data, 0) # output=[b * c, c, 1]
+        >>> output = op3(input_x, 0) # output=[b * c, c, 1]
     """
     @prim_attr_register
     def __init__(self, exclusive=False, reverse=False):
@@ -1190,7 +1193,7 @@ class FloorMod(_MathBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)
         >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32)
-        >>> floor_mod = FloorMod()
+        >>> floor_mod = P.FloorMod()
         >>> floor_mod(input_x, input_y)
         [2, 1, 2]
     """
@@ -1207,9 +1210,9 @@ class Acosh(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`.
 
     Examples:
-        >>> acosh = Acosh()
-        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
-        >>> output = acosh(X)
+        >>> acosh = P.Acosh()
+        >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
+        >>> output = acosh(input_x)
     """
 
     @prim_attr_register
@@ -1286,7 +1289,7 @@ class EqualCount(PrimitiveWithInfer):
         - **input_y** (Tensor) - The second input tensor.
 
     Outputs:
-        Tensor, has the same shape as the `input_x`.
+        Tensor, with the type as `mindspore.int32` and size as (1,).
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
@@ -1324,7 +1327,7 @@ class NotEqual(_LogicBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a tensor whose data type is number or bool, or
           a number or a bool object.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as `input_x` or
           a number or a bool object.
 
     Outputs:
@@ -1359,11 +1362,11 @@ class Greater(_LogicBinaryOp):
 
     Inputs:
         - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
           a number.
 
     Outputs:
-        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+        Tensor, the shape is same as the shape after broadcasting, and the data type is bool.
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
@@ -1386,11 +1389,11 @@ class GreaterEqual(_LogicBinaryOp):
 
     Inputs:
         - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
           a number.
 
     Outputs:
-        Tensor, the shape is same as the shape after broadcasting, and the data type is bool'.
+        Tensor, the shape is same as the shape after broadcasting, and the data type is bool.
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
@@ -1413,7 +1416,7 @@ class Less(_LogicBinaryOp):
 
     Inputs:
         - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
           a number.
 
     Outputs:
@@ -1440,7 +1443,7 @@ class LessEqual(_LogicBinaryOp):
 
     Inputs:
         - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
           a number.
 
     Outputs:
@@ -1752,8 +1755,8 @@ class Cos(PrimitiveWithInfer):
 
     Examples:
         >>> cos = P.Cos()
-        >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
-        >>> output = cos(X)
+        >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
+        >>> output = cos(input_x)
     """
 
     @prim_attr_register
@@ -1780,8 +1783,8 @@ class ACos(PrimitiveWithInfer):
 
     Examples:
         >>> acos = P.ACos()
-        >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
-        >>> output = acos(X)
+        >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
+        >>> output = acos(input_x)
     """
 
     @prim_attr_register
@@ -1993,7 +1996,7 @@ class Atan2(_MathBinaryOp):
         - **input_y** (Tensor) - The input tensor.
 
     Outputs:
-        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+        Tensor, the shape is same as the shape after broadcasting, and the data type is same as `input_x`.
 
     Examples:
          >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32)
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 9ee98d174e..83f76455e0 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -41,7 +41,7 @@ class Flatten(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32)
-        >>> flatten = Flatten()
+        >>> flatten = P.Flatten()
         >>> output = flatten(input_tensor)
         >>> assert output.shape() == (1, 24)
     """
@@ -155,7 +155,7 @@ class ReLU(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
-        >>> relu = ReLU()
+        >>> relu = P.ReLU()
         >>> result = relu(input_x)
         [[0, 4.0, 0.0], [2.0, 0.0, 9.0]]
     """
@@ -188,7 +188,7 @@ class ReLU6(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
-        >>> relu6 = ReLU6()
+        >>> relu6 = P.ReLU6()
         >>> result = relu6(input_x)
     """
 
@@ -222,10 +222,10 @@ class Elu(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
-        >>> elu = Elu()
+        >>> elu = P.Elu()
         >>> result = elu(input_x)
         Tensor([[-0.632  4.0   -0.999]
-                [2.0    -0.993  9.0  ]], shape=(2, 3), dtype=ms.float32)
+                [2.0    -0.993  9.0  ]], shape=(2, 3), dtype=mindspore.float32)
     """
 
     @prim_attr_register
@@ -1082,7 +1082,7 @@ class TopK(PrimitiveWithInfer):
 
     Examples:
         >>> topk = P.TopK(sorted=True)
-        >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16))
+        >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)
         >>> k = 3
         >>> values, indices = topk(input_x, k)
         >>> assert values == Tensor(np.array([5, 4, 3]))
@@ -1223,8 +1223,8 @@ class ApplyMomentum(PrimitiveWithInfer):
 
     Examples:
         >>> net = ResNet50()
-        >>> loss = SoftmaxCrossEntropyWithLogits()
-        >>> opt = ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])),
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = P.ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])),
                                 filter(lambda x: x.requires_grad, net.get_parameters()))
         >>> model = Model(net, loss, opt)
     """
@@ -1351,6 +1351,7 @@ class SGD(PrimitiveWithInfer):
 class ApplyRMSProp(PrimitiveWithInfer):
     """
     Optimizer that implements the Root Mean Square prop(RMSProp) algorithm.
+    Please refer to the usage in source code of `nn.RMSProp`.
 
     Note:
         Update `var` according to the RMSProp algorithm.
@@ -1386,12 +1387,6 @@ class ApplyRMSProp(PrimitiveWithInfer):
 
     Outputs:
         Tensor, parameters to be update.
-
-    Examples:
-        >>> net = Net()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate)
-        >>> model = Model(net, loss, opt)
     """
 
     @prim_attr_register
@@ -1424,6 +1419,7 @@ class ApplyRMSProp(PrimitiveWithInfer):
 class ApplyCenteredRMSProp(PrimitiveWithInfer):
     """
     Optimizer that implements the centered RMSProp algorithm.
+    Please refer to the usage in source code of `nn.RMSProp`.
 
     Note:
         Update `var` according to the centered RMSProp algorithm.
@@ -1464,12 +1460,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
 
     Outputs:
         Tensor, parameters to be update.
-
-    Examples:
-        >>> net = Net()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True)
-        >>> model = Model(net, loss, opt)
     """
 
     @prim_attr_register
@@ -1596,7 +1586,7 @@ class DropoutGenMask(Primitive):
         Tensor, the value of generated mask for input shape.
 
     Examples:
-        >>> dropout_gen_mask = DropoutGenMask()
+        >>> dropout_gen_mask = P.DropoutGenMask()
         >>> shape = (20, 16, 50)
         >>> keep_prob = Tensor(0.5, mindspore.float32)
         >>> mask = dropout_gen_mask(shape, keep_prob)
@@ -1631,8 +1621,8 @@ class DropoutDoMask(PrimitiveWithInfer):
         >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32)
         >>> shape = (20, 16, 50)
         >>> keep_prob = Tensor(0.5, mindspore.float32)
-        >>> dropout_gen_mask = DropoutGenMask()
-        >>> dropout_do_mask = DropoutDoMask()
+        >>> dropout_gen_mask = P.DropoutGenMask()
+        >>> dropout_do_mask = P.DropoutDoMask()
         >>> mask = dropout_gen_mask(shape, keep_prob)
         >>> output = dropout_do_mask(x, mask, keep_prob)
         >>> assert output.shape() == (20, 16, 50)
@@ -1737,7 +1727,7 @@ class OneHot(PrimitiveWithInfer):
     Examples:
         >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32)
         >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32)
-        >>> onehot = OneHot()
+        >>> onehot = P.OneHot()
         >>> result = onehot(indices, depth, on_value, off_value)
         [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
     """
@@ -1793,7 +1783,7 @@ class Gelu(PrimitiveWithInfer):
 
     Examples:
         >>> tensor = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
-        >>> gelu = Gelu()
+        >>> gelu = P.Gelu()
         >>> result = gelu(tensor)
     """
 
@@ -1834,7 +1824,7 @@ class GetNext(PrimitiveWithInfer):
         and the type is described is `types`.
 
     Examples:
-        >>> get_next = GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name')
+        >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name')
         >>> feature, label = get_next()
     """
 
@@ -2015,7 +2005,7 @@ class Pad(PrimitiveWithInfer):
 
     Examples:
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
-        >>> pad_op = Pad(((1, 2), (2, 1)))
+        >>> pad_op = P.Pad(((1, 2), (2, 1)))
         >>> output_tensor = pad_op(input_tensor)
         >>> assert output_tensor == Tensor(np.array([[ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
         >>>                                          [ 0. ,  0. , -0.1,  0.3,  3.6,  0. ],
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index b334c3e9d8..90d8816094 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -406,7 +406,7 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'LITE' format for exported model.
 
             - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
-            Ascend model.
+              Ascend model.
             - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
             - LITE: Huawei model format for mobile.
     """

From ede8a987299df4757100bcc8e8af70ef619429ec Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Thu, 9 Apr 2020 14:47:05 +0800
Subject: [PATCH 140/367] change logging to mindspore.log

---
 mindspore/context.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/mindspore/context.py b/mindspore/context.py
index 89365f3d1c..2938b87119 100644
--- a/mindspore/context.py
+++ b/mindspore/context.py
@@ -17,16 +17,14 @@ The context of mindspore, used to configure the current execution environment,
 including execution mode, execution backend and other feature switchs.
 """
 import threading
-import logging
 from collections import namedtuple
 from types import FunctionType
+from mindspore import log as logger
 from mindspore._c_expression import MSContext
 from mindspore._extends.pynative_helper import args_type_check
 from mindspore.parallel._auto_parallel_context import _set_auto_parallel_context, _get_auto_parallel_context, \
     _reset_auto_parallel_context
 
-logger = logging.getLogger('Context')
-
 
 __all__ = ['GRAPH_MODE', 'PYNATIVE_MODE', 'set_context', 'get_context', 'set_auto_parallel_context',
            'get_auto_parallel_context', 'reset_auto_parallel_context']

From b410a2f0a4e67bd6324dd3cface1df28e1fbb306 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Thu, 9 Apr 2020 15:58:36 +0800
Subject: [PATCH 141/367] Update setuptool info

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 CONTRIBUTING.md              |   4 +-
 README.md                    |   2 +-
 RELEASE.md                   |   2 +-
 package.sh                   |   2 +-
 setup_package.py => setup.py | 129 +++++++++++++++++++++++------------
 5 files changed, 90 insertions(+), 49 deletions(-)
 rename setup_package.py => setup.py (53%)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 85fee704c2..105c620942 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -105,11 +105,11 @@ When reporting issues, refer to this format:
 * If it is a new feature that needs lots of design details, a design proposal should also be submitted.
 * After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR.
 * None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR.
-* After PR is sufficiently discussed, it will get merged, abondoned or rejected depending on the outcome of the discussion.
+* After PR is sufficiently discussed, it will get merged, abandoned or rejected depending on the outcome of the discussion.
 
 **PRs advisory:**
 
 - Any irrelevant changes should be avoided.
 - Make sure your commit history being ordered.
 - Always keep your branch up with the master branch.
-- For bug-fix PRs, make sure all related issues being linked. 
+- For bug-fix PRs, make sure all related issues being linked.
diff --git a/README.md b/README.md
index 925c22591d..be8ca5189a 100644
--- a/README.md
+++ b/README.md
@@ -129,7 +129,7 @@ Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/comm
 - [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/enQtOTcwMTIxMDI3NjM0LTNkMWM2MzI5NjIyZWU5ZWQ5M2EwMTQ5MWNiYzMxOGM4OWFhZjI4M2E5OGI2YTg3ODU1ODE2Njg1MThiNWI3YmQ) - Communication platform for developers.
 - IRC channel at `#mindspore` (only for meeting minutes logging purpose)
 - Video Conferencing: meet.jit.si
-- Mailing-list: https://mailweb.mindspore.cn/postorius/lists 
+- Mailing-list: https://mailweb.mindspore.cn/postorius/lists
 
 ## Contributing
 
diff --git a/RELEASE.md b/RELEASE.md
index 8920095bb5..ce9064e4b1 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -70,4 +70,4 @@
 * [MindSpore Official Website] (https://www.mindspore.cn/)
 * [MindInsight Visualization Debugging and Optimization] (https://gitee.com/mindspore/mindinsight)
 * [MindArmour Model Security Hardening Package] (https://gitee.com/mindspore/mindarmour)
-* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)
\ No newline at end of file
+* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)
diff --git a/package.sh b/package.sh
index 67f4761f37..0c75a1bbfd 100755
--- a/package.sh
+++ b/package.sh
@@ -110,7 +110,7 @@ else
     export MS_PACKAGE_NAME="mindspore"
 fi
 
-${PYTHON} "${BASEPATH}/setup_package.py" bdist_wheel
+${PYTHON} "${BASEPATH}/setup.py" bdist_wheel
 
 chmod -R 700 ${PACKAGE_PATH}/mindspore/
 chmod -R 700 ${PACKAGE_PATH}/${MS_PACKAGE_NAME//-/_}.egg-info/
diff --git a/setup_package.py b/setup.py
similarity index 53%
rename from setup_package.py
rename to setup.py
index 87b5718de2..e009a9b312 100644
--- a/setup_package.py
+++ b/setup.py
@@ -14,17 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""setup_package."""
+"""setup package."""
 import os
 import stat
+
 from setuptools import setup, find_packages
 from setuptools.command.egg_info import egg_info
 from setuptools.command.build_py import build_py
 
 version = '0.1.0'
-author = 'The MindSpore Authors'
-author_email = 'contact@mindspore.cn'
-home_page = 'https://www.mindspore.cn'
 
 backend_policy = os.getenv('BACKEND_POLICY')
 commit_id = os.getenv('COMMIT_ID').replace("\n", "")
@@ -33,56 +31,70 @@ package_name = os.getenv('MS_PACKAGE_NAME').replace("\n", "")
 pwd = os.path.dirname(os.path.realpath(__file__))
 pkg_dir = os.path.join(pwd, 'build/package')
 
-def write_version(file):
+
+def _read_file(filename):
+    with open(os.path.join(pwd, filename)) as f:
+        return f.read()
+
+
+readme = _read_file('README.md')
+release = _read_file('RELEASE.md')
+
+
+def _write_version(file):
     file.write("__version__ = '{}'\n".format(version))
 
-def write_config(file):
+
+def _write_config(file):
     file.write("__backend__ = '{}'\n".format(backend_policy))
 
-def write_commit_file(file):
+
+def _write_commit_file(file):
     file.write("__commit_id__ = '{}'\n".format(commit_id))
 
-def build_depends():
+
+def build_dependencies():
     """generate python file"""
-    version_file = os.path.join(pwd, 'build/package/mindspore', 'version.py')
+    version_file = os.path.join(pkg_dir, 'mindspore', 'version.py')
     with open(version_file, 'w') as f:
-        write_version(f)
+        _write_version(f)
 
-    version_file = os.path.join(pwd, 'mindspore/', 'version.py')
+    version_file = os.path.join(pwd, 'mindspore', 'version.py')
     with open(version_file, 'w') as f:
-        write_version(f)
+        _write_version(f)
 
-    config_file = os.path.join(pwd, 'build/package/mindspore', 'default_config.py')
+    config_file = os.path.join(pkg_dir, 'mindspore', 'default_config.py')
     with open(config_file, 'w') as f:
-        write_config(f)
+        _write_config(f)
 
-    config_file = os.path.join(pwd, 'mindspore/', 'default_config.py')
+    config_file = os.path.join(pwd, 'mindspore', 'default_config.py')
     with open(config_file, 'w') as f:
-        write_config(f)
+        _write_config(f)
 
-    commit_file = os.path.join(pwd, 'build/package/mindspore', '.commit_id')
+    commit_file = os.path.join(pkg_dir, 'mindspore', '.commit_id')
     with open(commit_file, 'w') as f:
-        write_commit_file(f)
+        _write_commit_file(f)
 
-    commit_file = os.path.join(pwd, 'mindspore/', '.commit_id')
+    commit_file = os.path.join(pwd, 'mindspore', '.commit_id')
     with open(commit_file, 'w') as f:
-        write_commit_file(f)
-
-descriptions = 'An AI computing framework that supports development for AI applications in all scenarios.'
-
-requires = [
-        'numpy >= 1.17.0',
-        'protobuf >= 3.8.0',
-        'asttokens >= 1.1.13',
-        'pillow >= 6.2.0',
-        'scipy == 1.3.3',
-        'easydict >= 1.9',
-        'sympy >= 1.4',
-        'cffi >= 1.13.2',
-        'decorator >= 4.4.0'
-    ],
+        _write_commit_file(f)
+
 
-package_datas = {
+build_dependencies()
+
+required_package = [
+    'numpy >= 1.17.0',
+    'protobuf >= 3.8.0',
+    'asttokens >= 1.1.13',
+    'pillow >= 6.2.0',
+    'scipy == 1.3.3',
+    'easydict >= 1.9',
+    'sympy >= 1.4',
+    'cffi >= 1.13.2',
+    'decorator >= 4.4.0'
+]
+
+package_data = {
     '': [
         '*.so*',
         'lib/*.so*',
@@ -91,7 +103,6 @@ package_datas = {
     ]
 }
 
-build_depends()
 
 def update_permissions(path):
     """
@@ -103,20 +114,25 @@ def update_permissions(path):
     for dirpath, dirnames, filenames in os.walk(path):
         for dirname in dirnames:
             dir_fullpath = os.path.join(dirpath, dirname)
-            os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE | stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP)
+            os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE |
+                     stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP)
         for filename in filenames:
             file_fullpath = os.path.join(dirpath, filename)
             os.chmod(file_fullpath, stat.S_IREAD)
 
+
 class EggInfo(egg_info):
     """Egg info."""
+
     def run(self):
         super().run()
         egg_info_dir = os.path.join(pkg_dir, 'mindspore.egg-info')
         update_permissions(egg_info_dir)
 
+
 class BuildPy(build_py):
     """BuildPy."""
+
     def run(self):
         super().run()
         mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore')
@@ -124,21 +140,46 @@ class BuildPy(build_py):
         mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'akg')
         update_permissions(mindspore_dir)
 
+
 setup(
-    python_requires='>=3.7',
     name=package_name,
     version=version,
-    author=author,
-    author_email=author_email,
-    url=home_page,
+    author='The MindSpore Authors',
+    author_email='contact@mindspore.cn',
+    url='https://www.mindspore.cn',
+    download_url='https://gitee.com/mindspore/mindspore/tags',
+    project_urls={
+        'Sources': 'https://gitee.com/mindspore/mindspore',
+        'Issue Tracker': 'https://gitee.com/mindspore/mindspore/issues',
+    },
+    description='MindSpore is a new open source deep learning training/inference '
+    'framework that could be used for mobile, edge and cloud scenarios.',
+    long_description="\n\n".join([readme, release]),
     packages=find_packages(),
-    package_data=package_datas,
+    package_data=package_data,
     include_package_data=True,
     cmdclass={
         'egg_info': EggInfo,
         'build_py': BuildPy,
     },
-    install_requires=requires,
-    description=descriptions,
+    python_requires='>=3.7',
+    install_requires=required_package,
+    classifiers=[
+        'Development Status :: 4 - Beta',
+        'Environment :: Console',
+        'Intended Audience :: Science/Research',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: Python :: 3 :: Only',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Programming Language :: C++',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Software Development',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+    ],
     license='Apache 2.0',
+    keywords='mindspore machine learning',
 )

From bef62db128d02ad7df6a5ec9de7dcb72ba384972 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Thu, 9 Apr 2020 15:08:47 +0800
Subject: [PATCH 142/367] add ascend mem pool

---
 .../device/ascend/ascend_device_address.cc    |  4 +--
 .../device/ascend/ascend_device_address.h     |  2 +-
 .../device/ascend/ascend_kernel_runtime.cc    |  2 +-
 .../device/ascend/ascend_memory_manager.cc    | 22 +++++++------
 .../device/ascend/ascend_memory_manager.h     |  6 +++-
 ...ory_allocator.cc => ascend_memory_pool.cc} | 31 +++++++------------
 ...emory_allocator.h => ascend_memory_pool.h} | 31 ++++++++++---------
 mindspore/ccsrc/device/device_address.h       |  2 +-
 .../ccsrc/device/gpu/gpu_device_address.cc    |  2 +-
 .../ccsrc/device/gpu/gpu_kernel_runtime.cc    | 16 +++++-----
 .../ccsrc/device/gpu/gpu_memory_manager.cc    |  8 ++---
 .../ccsrc/device/gpu/gpu_memory_manager.h     |  6 ++--
 mindspore/ccsrc/device/kernel_runtime.cc      |  8 ++---
 mindspore/ccsrc/device/kernel_runtime.h       |  1 -
 mindspore/ccsrc/device/memory_manager.cc      | 22 +++++--------
 mindspore/ccsrc/device/memory_manager.h       | 15 ++++-----
 tests/ut/cpp/CMakeLists.txt                   |  2 +-
 17 files changed, 84 insertions(+), 96 deletions(-)
 rename mindspore/ccsrc/device/ascend/{ascend_memory_allocator.cc => ascend_memory_pool.cc} (62%)
 rename mindspore/ccsrc/device/ascend/{ascend_memory_allocator.h => ascend_memory_pool.h} (67%)

diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
index b8b7f452e3..93f039af0e 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
@@ -262,8 +262,8 @@ AscendDeviceAddress::~AscendDeviceAddress() {
   if (ptr_ == nullptr) {
     return;
   }
-  if (mem_dynamic_alloc_) {
-    AscendMemoryAllocator::GetInstance().FreeTensorMem(ptr_);
+  if (from_mem_pool_) {
+    AscendMemoryPool::GetInstance().FreeTensorMem(ptr_);
     ptr_ = nullptr;
   }
 }
diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.h b/mindspore/ccsrc/device/ascend/ascend_device_address.h
index 60cc64cca7..93746082c1 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.h
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.h
@@ -21,7 +21,7 @@
 #include <vector>
 #include <memory>
 #include "device/device_address.h"
-#include "device/ascend/ascend_memory_allocator.h"
+#include "device/ascend/ascend_memory_pool.h"
 #include "ir/dtype.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 0c2a97a5a6..0c6861e21f 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -29,7 +29,7 @@
 #include "hccl/hcom.h"
 #include "runtime/context.h"
 #include "device/ascend/ascend_stream_assign.h"
-#include "device/ascend/ascend_memory_allocator.h"
+#include "device/ascend/ascend_memory_pool.h"
 #include "framework/ge_runtime/model_runner.h"
 #include "device/ascend/tasksink/task_generator.h"
 #include "session/anf_runtime_algorithm.h"
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
index f033d81d82..42830f54fa 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc
@@ -15,29 +15,31 @@
  */
 
 #include "device/ascend/ascend_memory_manager.h"
-#include "device/ascend/ascend_memory_allocator.h"
+#include "device/ascend/ascend_memory_pool.h"
 #include "utils/context/ms_context.h"
 #include "runtime/mem.h"
 namespace mindspore {
 namespace device {
 namespace ascend {
-static const uint64_t ASCEND_MEM_SIZE = 20;
-static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30);
+const uint64_t kAscendDeviceMemGB = 20;
+const uint64_t kAscendMemPoolGB = 5;
+const uint64_t kAscendDeviceMemSize = (kAscendDeviceMemGB << 30);
+const uint64_t kAscendMemPoolSize = (kAscendMemPoolGB << 30);
 
 void AscendMemoryManager::MallocDeviceMemory() {
-  device_mem_size_ = ASCEND_MEM_SIZE_BYTE;
-  static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO);
+  device_mem_size_ = kAscendDeviceMemSize;
+  static_mem_offset_ = device_mem_size_;
   auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM);
   if (ret != RT_ERROR_NONE) {
     MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]";
   }
-  device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
+  device_mem_pool_size_ = kAscendMemPoolSize;
   ret = rtMalloc(reinterpret_cast<void **>(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM);
   if (ret != RT_ERROR_NONE) {
     MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]";
   }
-  AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
-  AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
+  AscendMemoryPool::GetInstance().set_device_mem_pool_base(device_mem_pool_base_);
+  AscendMemoryPool::GetInstance().set_device_mem_pool_size(device_mem_pool_size_);
 }
 
 void AscendMemoryManager::FreeDeviceMemory() {
@@ -57,8 +59,8 @@ void AscendMemoryManager::FreeDeviceMemory() {
   }
 }
 
-void *AscendMemoryManager::AllocTensorMemDynamic(size_t size) {
-  return AscendMemoryAllocator::GetInstance().AllocTensorMem(size);
+void *AscendMemoryManager::MallocMemFromMemPool(size_t size) {
+  return AscendMemoryPool::GetInstance().AllocTensorMem(size);
 }
 }  // namespace ascend
 }  // namespace device
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
index 8639fb5c72..dea88ac10a 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
@@ -27,7 +27,11 @@ class AscendMemoryManager : public MemoryManager {
 
   void MallocDeviceMemory() override;
   void FreeDeviceMemory() override;
-  void *AllocTensorMemDynamic(size_t size) override;
+  void *MallocMemFromMemPool(size_t size) override;
+
+ private:
+  uint8_t *device_mem_pool_base_{nullptr};
+  uint64_t device_mem_pool_size_{0};
 };
 }  // namespace ascend
 }  // namespace device
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc b/mindspore/ccsrc/device/ascend/ascend_memory_pool.cc
similarity index 62%
rename from mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc
rename to mindspore/ccsrc/device/ascend/ascend_memory_pool.cc
index 08a30a28b7..2c38e4290d 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_pool.cc
@@ -14,24 +14,15 @@
  * limitations under the License.
  */
 
-#include "device/ascend/ascend_memory_allocator.h"
+#include "device/ascend/ascend_memory_pool.h"
 #include "device/ascend/ascend_kernel_runtime.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace device {
 namespace ascend {
-const uint64_t MEM_SIZE = 20;
-const uint64_t MEM_SIZE_BYTE = (MEM_SIZE << 30);
-
-AscendMemoryAllocator::AscendMemoryAllocator() {
-  hasMalloc_ = false;
-  free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_ASCEND_MEM_RATIO));
-  total_mem_size_ = free_mem_size_;
-}
-
-size_t AscendMemoryAllocator::AllocDeviceMem(size_t size, DeviceMemPtr* addr) {
-  if (hasMalloc_) {
+size_t AscendMemoryPool::AllocDeviceMem(size_t size, DeviceMemPtr* addr) {
+  if (has_malloc_) {
     MS_LOG(EXCEPTION) << "Has alloc memory pool memory !";
   }
   if (size == 0 || size > free_mem_size_) {
@@ -41,35 +32,35 @@ size_t AscendMemoryAllocator::AllocDeviceMem(size_t size, DeviceMemPtr* addr) {
   if (*addr == nullptr) {
     MS_LOG(EXCEPTION) << "Device memory pool base is nullptr, failed to alloc memory pool memory!";
   }
-  hasMalloc_ = true;
+  has_malloc_ = true;
   free_mem_size_ -= size;
   return size;
 }
 
-bool AscendMemoryAllocator::FreeDeviceMem(const DeviceMemPtr& addr) {
+bool AscendMemoryPool::FreeDeviceMem(const DeviceMemPtr& addr) {
   MS_EXCEPTION_IF_NULL(addr);
-  hasMalloc_ = false;
+  has_malloc_ = false;
   free_mem_size_ = total_mem_size_;
   return true;
 }
 
-size_t AscendMemoryAllocator::AlignMemorySize(size_t size) const {
+size_t AscendMemoryPool::AlignMemorySize(size_t size) const {
   if (size == 0) {
     return DYNAMIC_MEM_ALIGN_SIZE;
   }
   return ((size + DYNAMIC_MEM_ALIGN_SIZE + 31) / DYNAMIC_MEM_ALIGN_SIZE) * DYNAMIC_MEM_ALIGN_SIZE;
 }
 
-size_t AscendMemoryAllocator::mem_alloc_unit_size() const { return free_mem_size_ - 512; }
+size_t AscendMemoryPool::mem_alloc_unit_size() const { return free_mem_size_ - 512; }
 
-void AscendMemoryAllocator::set_device_mem_pool_base(uint8_t* device_mem_pool_base) {
+void AscendMemoryPool::set_device_mem_pool_base(uint8_t* device_mem_pool_base) {
   MS_EXCEPTION_IF_NULL(device_mem_pool_base);
   device_mem_pool_base_ = device_mem_pool_base;
 }
 
-size_t AscendMemoryAllocator::free_mem_size() { return free_mem_size_; }
+size_t AscendMemoryPool::free_mem_size() { return free_mem_size_; }
 
-size_t AscendMemoryAllocator::total_mem_size() { return total_mem_size_; }
+size_t AscendMemoryPool::total_mem_size() { return total_mem_size_; }
 }  // namespace ascend
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h
similarity index 67%
rename from mindspore/ccsrc/device/ascend/ascend_memory_allocator.h
rename to mindspore/ccsrc/device/ascend/ascend_memory_pool.h
index 8b0f89a9b8..c2a29725f4 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_
-#define MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_
+#ifndef MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_
+#define MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_
 
 #include <memory>
 #include "pre_activate/mem_reuse/mem_dynamic_allocator.h"
@@ -23,22 +23,23 @@
 namespace mindspore {
 namespace device {
 namespace ascend {
-// The fraction of total ascend memory used to compute the graph.
-static const float GRAPH_INIT_ASCEND_MEM_RATIO = 0.8;
-
-class AscendMemoryAllocator : public DynamicMemPoolBestFit {
+class AscendMemoryPool : public DynamicMemPoolBestFit {
  public:
-  ~AscendMemoryAllocator() override = default;
+  ~AscendMemoryPool() override = default;
 
   size_t AllocDeviceMem(size_t size, DeviceMemPtr* addr) override;
   bool FreeDeviceMem(const DeviceMemPtr& addr) override;
   void set_device_mem_pool_base(uint8_t* device_mem_pool_base);
-  void set_device_mem_pool_size(uint64_t device_mem_pool_size) { device_mem_pool_size_ = device_mem_pool_size; }
+  void set_device_mem_pool_size(uint64_t device_mem_pool_size) {
+    device_mem_pool_size_ = device_mem_pool_size;
+    free_mem_size_ = device_mem_pool_size_;
+    total_mem_size_ = free_mem_size_;
+  }
   size_t free_mem_size() override;
   size_t total_mem_size() override;
 
-  static AscendMemoryAllocator& GetInstance() {
-    static AscendMemoryAllocator instance;
+  static AscendMemoryPool& GetInstance() {
+    static AscendMemoryPool instance;
     return instance;
   }
 
@@ -49,10 +50,10 @@ class AscendMemoryAllocator : public DynamicMemPoolBestFit {
   size_t mem_alloc_unit_size() const override;
 
  private:
-  AscendMemoryAllocator();
-  AscendMemoryAllocator(const AscendMemoryAllocator&) = delete;
-  AscendMemoryAllocator& operator=(const AscendMemoryAllocator&) = delete;
-  bool hasMalloc_;
+  AscendMemoryPool() = default;
+  AscendMemoryPool(const AscendMemoryPool&) = delete;
+  AscendMemoryPool& operator=(const AscendMemoryPool&) = delete;
+  bool has_malloc_{false};
   uint8_t* device_mem_pool_base_{nullptr};
   uint64_t device_mem_pool_size_{0};
   size_t free_mem_size_;
@@ -62,4 +63,4 @@ class AscendMemoryAllocator : public DynamicMemPoolBestFit {
 }  // namespace device
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_
+#endif  // MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_
diff --git a/mindspore/ccsrc/device/device_address.h b/mindspore/ccsrc/device/device_address.h
index cb022427e3..2d43963934 100644
--- a/mindspore/ccsrc/device/device_address.h
+++ b/mindspore/ccsrc/device/device_address.h
@@ -70,7 +70,7 @@ class DeviceAddress {
   size_t ref_count_{0};
   string format_{"DefaultFormat"};
   TypeId type_id_{kNumberTypeFloat16};
-  bool mem_dynamic_alloc_{false};
+  bool from_mem_pool_{false};
   friend class KernelRuntime;
   friend class MemoryManager;
   friend class mindspore::device::ascend::tasksink::TaskGenerator;
diff --git a/mindspore/ccsrc/device/gpu/gpu_device_address.cc b/mindspore/ccsrc/device/gpu/gpu_device_address.cc
index 36391d27db..c27a1aa65b 100644
--- a/mindspore/ccsrc/device/gpu/gpu_device_address.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_device_address.cc
@@ -46,7 +46,7 @@ GPUDeviceAddress::~GPUDeviceAddress() {
   }
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
-  if (mem_dynamic_alloc_) {
+  if (from_mem_pool_) {
     GPUMemoryAllocator::GetInstance().FreeTensorMem(ptr_);
     ptr_ = nullptr;
   }
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
index 597e188e9d..2ec1a5df29 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
@@ -227,7 +227,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
     MS_EXCEPTION_IF_NULL(device_address);
     auto device_ptr = device_address->ptr_;
     if (device_ptr == nullptr) {
-      device_ptr = mem_manager_->AllocTensorMemDynamic(output_sizes[i]);
+      device_ptr = mem_manager_->MallocMemFromMemPool(output_sizes[i]);
       MS_EXCEPTION_IF_NULL(device_ptr);
       device_address->ptr_ = device_ptr;
     }
@@ -244,7 +244,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod
       kernel_workspaces->emplace_back(nullptr);
       continue;
     }
-    auto device_ptr = mem_manager_->AllocTensorMemDynamic(workspace_sizes[i]);
+    auto device_ptr = mem_manager_->MallocMemFromMemPool(workspace_sizes[i]);
     MS_EXCEPTION_IF_NULL(device_ptr);
     kernel::AddressPtr workspace = std::make_shared<kernel::Address>();
     MS_EXCEPTION_IF_NULL(workspace);
@@ -292,7 +292,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN
     addr_size.emplace_back(device_address.get(), output_size);
   }
 
-  auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total);
+  auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total);
   MS_EXCEPTION_IF_NULL(device_mem_ptr);
   for (const auto &iter : addr_size) {
     MS_EXCEPTION_IF_NULL(iter.first);
@@ -328,7 +328,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf
     addr_size.emplace_back(device_address.get(), output_sizes[i]);
   }
 
-  auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total);
+  auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total);
   MS_EXCEPTION_IF_NULL(device_mem_ptr);
   for (const auto &iter : addr_size) {
     MS_EXCEPTION_IF_NULL(iter.first);
@@ -361,7 +361,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
         auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
         MS_EXCEPTION_IF_NULL(device_address);
         MS_EXCEPTION_IF_NULL(device_address->ptr_);
-        mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
+        mem_manager_->FreeMemFromMemPool(device_address->ptr_);
         device_address->ptr_ = nullptr;
       }
     }
@@ -372,7 +372,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
     auto workspace = kernel_workspaces[i];
     if (workspace != nullptr) {
       MS_EXCEPTION_IF_NULL(workspace->addr);
-      mem_manager_->FreeTensorMemDynamic(workspace->addr);
+      mem_manager_->FreeMemFromMemPool(workspace->addr);
       workspace->addr = nullptr;
     }
   }
@@ -389,7 +389,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
       auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0);
       MS_EXCEPTION_IF_NULL(device_address);
       MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
+      mem_manager_->FreeMemFromMemPool(device_address->ptr_);
       device_address->ptr_ = nullptr;
     }
     *is_communication_op = true;
@@ -411,7 +411,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
       auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0);
       MS_EXCEPTION_IF_NULL(device_address);
       MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      mem_manager_->FreeTensorMemDynamic(device_address->ptr_);
+      mem_manager_->FreeMemFromMemPool(device_address->ptr_);
       device_address->ptr_ = nullptr;
     }
     *is_communication_op = true;
diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
index 3944b504e4..7d042264b6 100644
--- a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
@@ -21,11 +21,11 @@
 namespace mindspore {
 namespace device {
 namespace gpu {
-void *GPUMemoryManager::AllocTensorMemDynamic(size_t size) {
+void *GPUMemoryManager::MallocMemFromMemPool(size_t size) {
   return GPUMemoryAllocator::GetInstance().AllocTensorMem(size);
 }
 
-void GPUMemoryManager::FreeTensorMemDynamic(void *device_ptr) {
+void GPUMemoryManager::FreeMemFromMemPool(void *device_ptr) {
   GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr);
 }
 
@@ -34,7 +34,7 @@ void GPUMemoryManager::MallocDeviceMemory() {
   MS_EXCEPTION_IF_NULL(context_ptr);
   // If use the dynamic memory pool, then alloc the first memory block to init.
   if (context_ptr->enable_dynamic_mem_pool()) {
-    auto device_addr = AllocTensorMemDynamic(1);
+    auto device_addr = MallocMemFromMemPool(1);
     if (!device_addr) {
       MS_LOG(ERROR) << "Dynamic memory pool init error.";
     }
@@ -62,7 +62,7 @@ uint8_t *GPUMemoryManager::MallocStaticMem(size_t size, bool) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   if (context_ptr->enable_dynamic_mem_pool()) {
-    auto device_ptr = AllocTensorMemDynamic(size);
+    auto device_ptr = MallocMemFromMemPool(size);
     MS_EXCEPTION_IF_NULL(device_ptr);
     return AddressOffset(device_ptr, 0);
   }
diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h
index a18226bdf3..cc5dac2a5e 100644
--- a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h
+++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h
@@ -28,11 +28,11 @@ class GPUMemoryManager : public MemoryManager {
   void MallocDeviceMemory() override;
   void FreeDeviceMemory() override;
 
-  void *AllocTensorMemDynamic(size_t size) override;
-  void FreeTensorMemDynamic(void *device_ptr) override;
+  void *MallocMemFromMemPool(size_t size) override;
+  void FreeMemFromMemPool(void *device_ptr) override;
 
  protected:
-  uint8_t *MallocStaticMem(size_t size, bool communication_mem);
+  uint8_t *MallocStaticMem(size_t size, bool communication_mem) override;
 };
 }  // namespace gpu
 }  // namespace device
diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index 16025ed8a4..eebc650347 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -169,7 +169,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector<tensor::TensorPtr>
       auto device_address =
         CreateDeviceAddress(nullptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id);
       MS_EXCEPTION_IF_NULL(device_address);
-      mem_manager_->MallocOpMemory(device_address, tensor_size);
+      mem_manager_->MallocMemFromMemPool(device_address, tensor_size);
       AnfAlgo::SetOutputAddr(device_address, index, item.get());
     }
   }
@@ -198,7 +198,7 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) {
     auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i);
     auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type);
     MS_EXCEPTION_IF_NULL(device_address);
-    mem_manager_->MallocOpMemory(device_address, output_sizes[i]);
+    mem_manager_->MallocMemFromMemPool(device_address, output_sizes[i]);
     AnfAlgo::SetOutputAddr(device_address, i, kernel.get());
   }
 }
@@ -213,7 +213,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) {
     for (size_t i = 0; i < workspace_lists.size(); ++i) {
       auto device_address = CreateDeviceAddress(nullptr, workspace_lists[i], "", kTypeUnknown);
       MS_EXCEPTION_IF_NULL(device_address);
-      mem_manager_->MallocOpMemory(device_address, workspace_lists[i]);
+      mem_manager_->MallocMemFromMemPool(device_address, workspace_lists[i]);
       AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get());
     }
   }
@@ -457,7 +457,7 @@ void KernelRuntime::AssignDynamicMemory(session::KernelGraph *graph) {
   bool is_enable_mem_reuse = context_ptr->enable_mem_reuse();
   auto mem_flag = kDynamicMem;
   if (is_enable_mem_reuse) {
-    mem_manager_->InitReuseDynamicMemory(graph);
+    mem_manager_->MallocReusedDynamicMem(graph);
     mem_flag = kReuseDynamicMem;
   }
   auto &kernels = graph->execution_order();
diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h
index 1224bf14eb..61b43fd5c0 100644
--- a/mindspore/ccsrc/device/kernel_runtime.h
+++ b/mindspore/ccsrc/device/kernel_runtime.h
@@ -33,7 +33,6 @@
 #include "utils/context/ms_context.h"
 #include "device/memory_manager.h"
 
-// using mindspore::session::KernelGraph;
 using mindspore::tensor::Tensor;
 using TensorPtr = std::shared_ptr<Tensor>;
 using mindspore::kernel::AddressPtr;
diff --git a/mindspore/ccsrc/device/memory_manager.cc b/mindspore/ccsrc/device/memory_manager.cc
index 3c1ddee6bc..6977628eb1 100644
--- a/mindspore/ccsrc/device/memory_manager.cc
+++ b/mindspore/ccsrc/device/memory_manager.cc
@@ -21,12 +21,6 @@ using mindspore::memreuse::BestFitMemReuse;
 using mindspore::memreuse::MemReuseUtilPtr;
 namespace mindspore {
 namespace device {
-MemoryManager::~MemoryManager() {
-  device_mem_base_ = nullptr;
-  device_mem_pool_base_ = nullptr;
-  mem_reuse_util_ptr_ = nullptr;
-}
-
 size_t MemoryManager::GetCommonAlignSize(size_t input_size) const {
   return (input_size + kMemAlignSize + 31) / kMemAlignSize * kMemAlignSize;
 }
@@ -35,7 +29,7 @@ size_t MemoryManager::GetCommunicationAlignSize(size_t input_size) const {
   return (input_size + kMemAlignSize - 1) / kMemAlignSize * kMemAlignSize + 2 * kMemAlignSize;
 }
 
-void MemoryManager::InitReuseDynamicMemory(session::KernelGraph *graph) {
+void MemoryManager::MallocReusedDynamicMem(session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
   MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
@@ -147,23 +141,23 @@ uint8_t *MemoryManager::MallocDynamicMem(size_t size, bool communication_mem) {
   }
 }
 
-void MemoryManager::MallocOpMemory(const DeviceAddressPtr address, size_t size) {
-  auto device_ptr = AllocTensorMemDynamic(size);
+void MemoryManager::MallocMemFromMemPool(const DeviceAddressPtr address, size_t size) {
+  auto device_ptr = MallocMemFromMemPool(size);
   MS_EXCEPTION_IF_NULL(device_ptr);
   address->ptr_ = device_ptr;
-  address->mem_dynamic_alloc_ = true;
+  address->from_mem_pool_ = true;
 }
 
-void *MemoryManager::AllocTensorMemDynamic(size_t size) {
+void *MemoryManager::MallocMemFromMemPool(size_t size) {
   if (size == 0) {
-    MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0.";
+    MS_LOG(ERROR) << "MallocMemFromMemPool size is 0.";
   }
   return nullptr;
 }
 
-void MemoryManager::FreeTensorMemDynamic(void *device_ptr) {
+void MemoryManager::FreeMemFromMemPool(void *device_ptr) {
   if (device_ptr == nullptr) {
-    MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null.";
+    MS_LOG(ERROR) << "FreeMemFromMemPool device_ptr is null.";
   }
 }
 }  // namespace device
diff --git a/mindspore/ccsrc/device/memory_manager.h b/mindspore/ccsrc/device/memory_manager.h
index 2e47237def..82c22f4548 100644
--- a/mindspore/ccsrc/device/memory_manager.h
+++ b/mindspore/ccsrc/device/memory_manager.h
@@ -31,7 +31,7 @@ using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr;
 class MemoryManager {
  public:
   MemoryManager() = default;
-  virtual ~MemoryManager();
+  virtual ~MemoryManager() = default;
 
   virtual void MallocDeviceMemory() = 0;
   virtual void FreeDeviceMemory() = 0;
@@ -40,16 +40,15 @@ class MemoryManager {
     dynamic_mem_offset_ = 0;
   }
 
-  void InitReuseDynamicMemory(session::KernelGraph *graph);
+  void MallocReusedDynamicMem(session::KernelGraph *graph);
   uint8_t *MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size);
   uint8_t *MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size);
   virtual uint8_t *MallocMem(int flag, size_t size);
 
-  // Alloc memory use the dynamic memory pool.
-  virtual void *AllocTensorMemDynamic(size_t size);
-  // Free memory use the dynamic memory pool.
-  virtual void FreeTensorMemDynamic(void *device_ptr);
-  virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size);
+  virtual void MallocMemFromMemPool(const DeviceAddressPtr address, size_t size);
+  virtual void *MallocMemFromMemPool(size_t size);
+  virtual void FreeMemFromMemPool(void *device_ptr);
+
   size_t GetCommonAlignSize(size_t input_size) const;
   size_t GetCommunicationAlignSize(size_t input_size) const;
 
@@ -57,9 +56,7 @@ class MemoryManager {
   virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem);
   virtual uint8_t *MallocDynamicMem(size_t size, bool communication_mem);
   uint8_t *device_mem_base_{nullptr};
-  uint8_t *device_mem_pool_base_{nullptr};
   uint64_t device_mem_size_{0};
-  uint64_t device_mem_pool_size_{0};
   uint64_t dynamic_mem_offset_{0};
   uint64_t static_mem_offset_{0};
   size_t total_static_size_ = 0;
diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt
index 3c1351a857..f5bc07ff69 100644
--- a/tests/ut/cpp/CMakeLists.txt
+++ b/tests/ut/cpp/CMakeLists.txt
@@ -95,7 +95,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "../../../mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc"
         "../../../mindspore/ccsrc/device/ascend/ascend_memory_manager.cc"
         "../../../mindspore/ccsrc/device/ascend/ascend_device_address.cc"
-        "../../../mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc"
+        "../../../mindspore/ccsrc/device/ascend/ascend_memory_pool.cc"
         "../../../mindspore/ccsrc/predict/generator/utils/ir_model_util.cc"
         "../../../mindspore/ccsrc/predict/predict.cc"
         "../../../mindspore/ccsrc/predict/converter/*.cc"

From 16296da5c786b5adb73a11c7d42825d91b49ccca Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Tue, 7 Apr 2020 21:52:28 +0800
Subject: [PATCH 143/367] add aicpu opinfo register

---
 .../ccsrc/kernel/aicpu/aicpu_kernel_build.cc  | 116 +++++----
 .../kernel/aicpu/aicpu_kernel_metadata.cc     |  78 ++----
 mindspore/ccsrc/kernel/aicpu/aicpu_util.h     |   3 +-
 mindspore/ccsrc/kernel/common_utils.cc        |   9 +
 mindspore/ccsrc/kernel/oplib/opinfo.h         |   2 +-
 mindspore/ccsrc/kernel/oplib/oplib.cc         |  21 +-
 mindspore/ops/__init__.py                     |   4 +-
 mindspore/ops/_op_impl/__init__.py            |   1 +
 mindspore/ops/_op_impl/aicpu/__init__.py      |  19 ++
 .../ops/_op_impl/aicpu/dropout_genmask.py     |  32 +++
 mindspore/ops/_op_impl/aicpu/get_next.py      |  39 +++
 .../ops/_op_impl/aicpu/init_data_set_queue.py |  27 +++
 mindspore/ops/_op_impl/aicpu/print_tensor.py  |  39 +++
 mindspore/ops/op_info_register.py             | 225 +++++++++++-------
 14 files changed, 409 insertions(+), 206 deletions(-)
 create mode 100644 mindspore/ops/_op_impl/aicpu/__init__.py
 create mode 100644 mindspore/ops/_op_impl/aicpu/dropout_genmask.py
 create mode 100644 mindspore/ops/_op_impl/aicpu/get_next.py
 create mode 100644 mindspore/ops/_op_impl/aicpu/init_data_set_queue.py
 create mode 100644 mindspore/ops/_op_impl/aicpu/print_tensor.py

diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
index c89e27c8ce..cf23779415 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
@@ -39,45 +39,7 @@ namespace mindspore {
 namespace kernel {
 using FNodeAttrHandle = std::function<void(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto)>;
 
-const std::vector<std::string> local_framework_op_vec = {kInitDataSetQueue, kGetNext, kDropoutGenMask, kPrint};
-
-void InitDataSetQueueAttr(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(proto);
-
-  ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs();
-  MS_EXCEPTION_IF_NULL(node_attr);
-  std::string channel_name = AnfAlgo::GetNodeAttr<std::string>(anf_node, kQueueName);
-  (*node_attr)[kChannelName].set_s(channel_name);
-}
-
-void GetNextAttr(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(proto);
-
-  ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs();
-  MS_EXCEPTION_IF_NULL(node_attr);
-  std::string shared_name = AnfAlgo::GetNodeAttr<std::string>(anf_node, kSharedName);
-  (*node_attr)[kChannelName].set_s(shared_name);
-}
-
-void DropoutGenMaskAttr(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(proto);
-
-  ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs();
-  MS_EXCEPTION_IF_NULL(node_attr);
-  int seed = AnfAlgo::GetNodeAttr<int>(anf_node, kSeed);
-  int seed2 = AnfAlgo::GetNodeAttr<int>(anf_node, kSeed2);
-  (*node_attr)["seed"].set_i(seed);
-  (*node_attr)["seed2"].set_i(seed2);
-}
-
-void CreateAttrFuncMap(std::map<std::string, FNodeAttrHandle> *mOpAttrFuncMap) {
-  (void)mOpAttrFuncMap->emplace(std::pair<std::string, FNodeAttrHandle>(kInitDataSetQueue, InitDataSetQueueAttr));
-  (void)mOpAttrFuncMap->emplace(std::pair<std::string, FNodeAttrHandle>(kGetNext, GetNextAttr));
-  (void)mOpAttrFuncMap->emplace(std::pair<std::string, FNodeAttrHandle>(kDropoutGenMask, DropoutGenMaskAttr));
-}
+const std::vector<std::string> local_framework_op_vec = {kInitData, kGetNext, kDropoutGenMask, kPrint};
 
 bool SetIOIputSize(const std::shared_ptr<AnfNode> &anf_node, const size_t &input_num,
                    std::vector<size_t> *input_size_list) {
@@ -147,24 +109,74 @@ bool SetIOSize(const std::shared_ptr<AnfNode> &anf_node, const std::shared_ptr<A
   return true;
 }
 
+void ParseAttrValue(const std::string &type, const std::string &attr_name, const mindspore::ValuePtr &value,
+                    ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr) {
+  MS_EXCEPTION_IF_NULL(node_attr);
+  if (type == "int") {
+    auto attr_value = GetValue<int>(value);
+    (*node_attr)[attr_name].set_i(attr_value);
+  } else if (type == "str") {
+    auto attr_value = GetValue<std::string>(value);
+    (*node_attr)[attr_name].set_s(attr_value);
+  } else if (type == "bool") {
+    auto attr_value = GetValue<bool>(value);
+    (*node_attr)[attr_name].set_b(attr_value);
+  } else if (type == "float") {
+    auto attr_value = GetValue<float>(value);
+    (*node_attr)[attr_name].set_f(attr_value);
+  } else if (type == "listInt") {
+    std::vector<int> attr_value;
+    auto value_type = value->type();
+    MS_EXCEPTION_IF_NULL(value_type);
+    auto value_type_str = value_type->ToString();
+    if (value_type_str == "Int32") {
+      int data = GetValue<int>(value);
+      attr_value.push_back(data);
+    } else {
+      attr_value = GetValue<std::vector<int>>(value);
+    }
+    mindspore::AttrValue input_shape_attr;
+    mindspore::AttrValue_ArrayValue *input_shape_attr_list = input_shape_attr.mutable_array();
+    MS_EXCEPTION_IF_NULL(input_shape_attr_list);
+    for (const auto shape : attr_value) {
+      input_shape_attr_list->add_i(shape);
+    }
+    (*node_attr)[attr_name] = input_shape_attr;
+  } else {
+    MS_LOG(EXCEPTION) << "type: " << type << "not support";
+  }
+}
+
 void SetNodeAttr(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto) {
   std::string op_name = AnfAlgo::GetCNodeName(anf_node);
-  if (op_name == "InitDataSetQueue") {
-    op_name = "InitData";
+  if (op_name == kInitDataSetQueue) {
+    op_name = kInitData;
   }
-  if (op_name == "Print") {
+  if (op_name == kPrint) {
     return;
   }
-  std::map<std::string, FNodeAttrHandle> mOpAttrFuncMap;
-  CreateAttrFuncMap(&mOpAttrFuncMap);
-  FNodeAttrHandle func_ptr = nullptr;
-  auto iter = mOpAttrFuncMap.find(op_name);
-  if (iter != mOpAttrFuncMap.end()) {
-    func_ptr = iter->second;
-    MS_EXCEPTION_IF_NULL(func_ptr);
-    func_ptr(anf_node, proto);
-  } else {
-    MS_LOG(ERROR) << "Don't support node [" << op_name << "] to set nodedef of attr";
+
+  auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU);
+  MS_EXCEPTION_IF_NULL(op_info_ptr);
+  auto attrs_ptr = op_info_ptr->attrs_ptr();
+  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
+  MS_EXCEPTION_IF_NULL(primitive);
+  ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs();
+  for (const auto &attr_ptr : attrs_ptr) {
+    std::string attr_name = attr_ptr->name();
+    std::string real_name;
+    auto value = primitive->GetAttr(attr_name);
+    if (value != nullptr) {
+      if (attr_name == kQueueName || attr_name == kSharedName) {
+        real_name = kChannelName;
+      } else if (attr_name == kSeed) {
+        real_name = "seed";
+      } else if (attr_name == kSeed2) {
+        real_name = "seed2";
+      }
+      std::string type = attr_ptr->type();
+      ParseAttrValue(type, real_name, value, node_attr);
+    }
   }
   MS_LOG(INFO) << "Set node attr end!";
 }
diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
index ac0b0d9f7a..6675051069 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
@@ -17,68 +17,27 @@
 #include "kernel/aicpu/aicpu_kernel_metadata.h"
 #include <memory>
 #include <string>
+#include "kernel/oplib/oplib.h"
+#include "kernel/common_utils.h"
+#include "kernel/aicpu/aicpu_util.h"
 #include "session/anf_runtime_algorithm.h"
 
 namespace mindspore {
 namespace kernel {
-constexpr auto kInitDataSetQueueOpName = "InitDataSetQueue";
-constexpr auto kGetNext = "GetNext";
-constexpr auto kDropoutGenMask = "DropoutGenMask";
-constexpr auto kPrint = "Print";
-const std::vector<std::string> AICPU_OPS = {kInitDataSetQueueOpName, kGetNext, kDropoutGenMask, kPrint};
-
-std::shared_ptr<KernelBuildInfo> CreateKernelInfo(const std::vector<std::string> &inputs_format,
-                                                  const std::vector<TypeId> &inputs_device_type,
-                                                  const std::vector<std::string> &outputs_format,
-                                                  const std::vector<TypeId> &outputs_device_type) {
-  auto builder = KernelBuildInfo::KernelBuildInfoBuilder();
-  builder.SetInputsFormat(inputs_format);
-  builder.SetInputsDeviceType(inputs_device_type);
-  builder.SetOutputsFormat(outputs_format);
-  builder.SetOutputsDeviceType(outputs_device_type);
-  builder.SetProcessor(AICPU);
-  builder.SetKernelType(AICPU_KERNEL);
-  builder.SetFusionType(OPAQUE);
-  return builder.Build();
-}
-
-bool CheckIfExistAicpuMeta(const std::string &op_name) {
-  if (std::find(AICPU_OPS.begin(), AICPU_OPS.end(), op_name) != AICPU_OPS.end()) {
-    return false;
-  }
-  return true;
-}
-
 void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<KernelBuildInfo>> *kernel_info_list) {
   MS_LOG(INFO) << "AicpuMetadataInfo.";
   MS_EXCEPTION_IF_NULL(kernel_node);
   MS_EXCEPTION_IF_NULL(kernel_info_list);
   std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
-  if (CheckIfExistAicpuMeta(op_name)) {
-    MS_LOG(DEBUG) << "Aicpu doesn't have metadata of op [" << op_name << "].";
-    return;
-  }
-
-  if (op_name == kInitDataSetQueueOpName) {
-    kernel_info_list->push_back(CreateKernelInfo({}, {}, {}, {}));
+  if (op_name == kInitDataSetQueue) {
+    op_name = kInitData;
   }
-
-  if (op_name == kGetNext) {
-    std::vector<std::string> outputs_format;
-    std::vector<TypeId> outputs_type;
-    for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(kernel_node); ++output_index) {
-      outputs_format.emplace_back(kOpFormat_DEFAULT);
-      outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index));
-    }
-    kernel_info_list->push_back(CreateKernelInfo({}, {}, outputs_format, outputs_type));
-  }
-
-  if (op_name == kDropoutGenMask) {
-    kernel_info_list->push_back(CreateKernelInfo({kOpFormat_NCHW, kOpFormat_NCHW},
-                                                 {kInt32->type_id(), kFloat16->type_id()}, {kOpFormat_NCHW},
-                                                 {kUInt8->type_id()}));
+  auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU);
+  if (op_info_ptr == nullptr) {
+    MS_LOG(WARNING) << "Aicpu doestn't have metadata of op [" << op_name << "]";
+    return;
   }
-
+  // For compatibility with the current framework
   if (op_name == kPrint) {
     std::vector<std::string> inputs_format;
     std::vector<TypeId> inputs_type;
@@ -92,11 +51,20 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<
       outputs_format.emplace_back(kOpFormat_DEFAULT);
       outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index));
     }
-    kernel_info_list->push_back(CreateKernelInfo(inputs_format, inputs_type, outputs_format, outputs_type));
+    auto builder = KernelBuildInfo::KernelBuildInfoBuilder();
+    builder.SetInputsFormat(inputs_format);
+    builder.SetInputsDeviceType(inputs_type);
+    builder.SetOutputsFormat(outputs_format);
+    builder.SetOutputsDeviceType(outputs_type);
+    builder.SetProcessor(AICPU);
+    builder.SetKernelType(AICPU_KERNEL);
+    builder.SetFusionType(OPAQUE);
+    kernel_info_list->push_back(builder.Build());
+    return;
   }
-
-  if (kernel_info_list->empty()) {
-    MS_LOG(INFO) << "Aicpu dose not has metadata of op[ " << op_name << "].";
+  if (!ParseMetadata(kernel_node, op_info_ptr, AICPU, kernel_info_list)) {
+    MS_LOG(WARNING) << "Aicpu parsed metadata op [" << op_name << "] failed";
+    return;
   }
 }
 }  // namespace kernel
diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h
index f521418f6b..08fca16a3b 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h
@@ -24,7 +24,8 @@
 
 namespace mindspore {
 namespace kernel {
-constexpr auto kInitDataSetQueue = "InitData";
+constexpr auto kInitDataSetQueue = "InitDataSetQueue";
+constexpr auto kInitData = "InitData";
 constexpr auto kGetNext = "GetNext";
 constexpr auto kDropoutGenMask = "DropoutGenMask";
 constexpr auto kPrint = "Print";
diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc
index c2f2638753..137ae65414 100644
--- a/mindspore/ccsrc/kernel/common_utils.cc
+++ b/mindspore/ccsrc/kernel/common_utils.cc
@@ -417,6 +417,8 @@ void SetKernelBuildInfo(const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBu
 
   if (imply_type == kAKG) {
     builder->SetKernelType(AUTO_DIFF_KERNEL);
+  } else if (imply_type == kAICPU) {
+    builder->SetKernelType(AICPU_KERNEL);
   } else {
     builder->SetKernelType(TBE_KERNEL);
   }
@@ -471,6 +473,13 @@ bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr<const OpIn
         return false;
       }
 
+      kernel_info_list->push_back(builder->Build());
+    }
+  } else {
+    if (processor == AICPU) {
+      auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
+      MS_EXCEPTION_IF_NULL(builder);
+      SetKernelBuildInfo(builder, processor, op_info_ptr);
       kernel_info_list->push_back(builder->Build());
     }
   }
diff --git a/mindspore/ccsrc/kernel/oplib/opinfo.h b/mindspore/ccsrc/kernel/oplib/opinfo.h
index 56abea9269..215df21776 100644
--- a/mindspore/ccsrc/kernel/oplib/opinfo.h
+++ b/mindspore/ccsrc/kernel/oplib/opinfo.h
@@ -24,7 +24,7 @@
 
 namespace mindspore {
 namespace kernel {
-enum OpImplyType { kAKG = 0, kTBE };
+enum OpImplyType { kAKG = 0, kTBE = 1, kAICPU };
 enum OpIOType { kInput = 0, kOutput };
 
 class OpAttr {
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc
index 4059b8e246..d2464bce47 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.cc
+++ b/mindspore/ccsrc/kernel/oplib/oplib.cc
@@ -39,6 +39,7 @@ constexpr auto kDtypeFormat = "dtype_format";
 constexpr auto kAttr = "attr";
 constexpr auto kIputs = "inputs";
 constexpr auto kOutputs = "outputs";
+constexpr auto kAiCPU = "AiCPU";
 constexpr auto kTbe = "TBE";
 constexpr auto kAkg = "akg";
 constexpr auto kAutodiff = "AutoDiff";
@@ -60,6 +61,8 @@ std::string ImplTypeToStr(OpImplyType impl_type) {
       return kTbe;
     case kAKG:
       return kAkg;
+    case kAICPU:
+      return kAiCPU;
     default:
       return "unknow";
   }
@@ -76,6 +79,9 @@ bool OpLib::RegOp(const std::string& json_string, const std::string& impl_path)
     } else if (imply_type_string == kAutodiff) {
       OpImplyType imply_type = kAKG;
       ret = DecodeOpInfo(op_json, imply_type, impl_path);
+    } else if (imply_type_string == kAiCPU) {
+      OpImplyType imply_type = kAICPU;
+      ret = DecodeOpInfo(op_json, imply_type, impl_path);
     } else {
       MS_LOG(DEBUG) << "Not support imply_type";
     }
@@ -154,7 +160,9 @@ bool OpLib::DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type,
     std::shared_ptr<OpAttr> op_attr = std::make_shared<OpAttr>();
     MS_EXCEPTION_IF_NULL(op_attr);
     op_attr->set_name(obj.at(kName));
-    op_attr->set_param_type(obj.at(kParamType));
+    if (imply_type != kAICPU) {
+      op_attr->set_param_type(obj.at(kParamType));
+    }
     op_attr->set_type(obj.at(kType));
     if (imply_type == kTBE) {
       op_attr->set_value(obj.at(kValue));
@@ -242,9 +250,10 @@ std::shared_ptr<OpInfo> OpLib::FindOp(const std::string& op_name, OpImplyType im
   auto context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context);
   bool is_gpu = (context->device_target() == kGPUDevice);
-  if ((is_gpu && imply_type == kTBE) || (!is_gpu && imply_type != kTBE)) {
-    MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << "imply_type:" << ImplTypeToStr(imply_type)
-                  << "current op num:" << op_info_.size();
+  if ((is_gpu && (imply_type == kTBE || imply_type == kAICPU)) ||
+      (!is_gpu && (imply_type != kTBE && imply_type != kAICPU))) {
+    MS_LOG(ERROR) << "FindOp failed: opname:" << op_name << ", imply_type:" << ImplTypeToStr(imply_type)
+                  << ", current op num:" << op_info_.size();
     return nullptr;
   }
   for (const auto& op_info : op_info_) {
@@ -253,8 +262,8 @@ std::shared_ptr<OpInfo> OpLib::FindOp(const std::string& op_name, OpImplyType im
       return op_info;
     }
   }
-  MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << "imply_type:" << ImplTypeToStr(imply_type)
-                << "current op num:" << op_info_.size();
+  MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << ", imply_type:" << ImplTypeToStr(imply_type)
+                << ", current op num:" << op_info_.size();
   return nullptr;
 }
 
diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py
index 6f4f680672..0e6c114566 100644
--- a/mindspore/ops/__init__.py
+++ b/mindspore/ops/__init__.py
@@ -30,7 +30,7 @@ Note:
 
 from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry
-from .op_info_register import op_info_register, TBERegOp, DataType
+from .op_info_register import op_info_register, AiCPURegOp, TBERegOp, DataType
 from .primitive import constexpr
 from .._c_expression import signature_rw, signature_kind
 
@@ -40,6 +40,6 @@ __primitive__ = [
 ]
 
 __all__ = ["get_vm_impl_fn", "vm_impl_registry",
-           "op_info_register", "TBERegOp", "DataType",
+           "op_info_register", "AiCPURegOp", "TBERegOp", "DataType",
            "constexpr"]
 __all__.extend(__primitive__)
diff --git a/mindspore/ops/_op_impl/__init__.py b/mindspore/ops/_op_impl/__init__.py
index b8370cc64e..76444881cc 100644
--- a/mindspore/ops/_op_impl/__init__.py
+++ b/mindspore/ops/_op_impl/__init__.py
@@ -16,5 +16,6 @@
 
 from .akg.gpu import *
 from .tbe import *
+from .aicpu import *
 
 __all__ = []
diff --git a/mindspore/ops/_op_impl/aicpu/__init__.py b/mindspore/ops/_op_impl/aicpu/__init__.py
new file mode 100644
index 0000000000..b0f90a629b
--- /dev/null
+++ b/mindspore/ops/_op_impl/aicpu/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""aicpu ops"""
+from .init_data_set_queue import _init_data_set_queue_aicpu
+from .dropout_genmask import _dropout_genmask_aicpu
+from .get_next import _get_next_aicpu
+from .print_tensor import _print_aicpu
diff --git a/mindspore/ops/_op_impl/aicpu/dropout_genmask.py b/mindspore/ops/_op_impl/aicpu/dropout_genmask.py
new file mode 100644
index 0000000000..96707a5010
--- /dev/null
+++ b/mindspore/ops/_op_impl/aicpu/dropout_genmask.py
@@ -0,0 +1,32 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""InitDataSetQueue op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+dropout_genmask_op_info = AiCPURegOp("DropoutGenMask") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x1", "required") \
+    .input(1, "x2", "required") \
+    .output(0, "y", "required") \
+    .attr("Seed0", "int") \
+    .attr("Seed1", "int") \
+    .dtype_format(DataType.I32_NCHW, DataType.F16_NCHW, DataType.U8_NCHW) \
+    .get_op_info()
+
+@op_info_register(dropout_genmask_op_info)
+def _dropout_genmask_aicpu():
+    """Dropout AiCPU register"""
+    return
diff --git a/mindspore/ops/_op_impl/aicpu/get_next.py b/mindspore/ops/_op_impl/aicpu/get_next.py
new file mode 100644
index 0000000000..ce32014211
--- /dev/null
+++ b/mindspore/ops/_op_impl/aicpu/get_next.py
@@ -0,0 +1,39 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""InitDataSetQueue op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+get_next_op_info = AiCPURegOp("GetNext") \
+    .fusion_type("OPAQUE") \
+    .output(0, "y", "dynamic") \
+    .attr("shared_name", "str") \
+    .dtype_format(DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default) \
+    .dtype_format(DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default) \
+    .dtype_format(DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default) \
+    .dtype_format(DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(get_next_op_info)
+def _get_next_aicpu():
+    """GetNext AiCPU register"""
+    return
diff --git a/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py b/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py
new file mode 100644
index 0000000000..a48e01eced
--- /dev/null
+++ b/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py
@@ -0,0 +1,27 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""InitDataSetQueue op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp
+
+init_data_set_queue_op_info = AiCPURegOp("InitData") \
+    .fusion_type("OPAQUE") \
+    .attr("queue_name", "str") \
+    .get_op_info()
+
+@op_info_register(init_data_set_queue_op_info)
+def _init_data_set_queue_aicpu():
+    """InitDataSetQueue AiCPU register"""
+    return
diff --git a/mindspore/ops/_op_impl/aicpu/print_tensor.py b/mindspore/ops/_op_impl/aicpu/print_tensor.py
new file mode 100644
index 0000000000..011f4a3d9d
--- /dev/null
+++ b/mindspore/ops/_op_impl/aicpu/print_tensor.py
@@ -0,0 +1,39 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""InitDataSetQueue op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+print_op_info = AiCPURegOp("Print") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "dynamic") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(print_op_info)
+def _print_aicpu():
+    """Print AiCPU register"""
+    return
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 6a42099c89..0750094e18 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -78,14 +78,15 @@ class RegOp():
         self.inputs = []
         self.outputs = []
         self.attr_ = []
+        self.fusion_type_ = ''
         self.dtype_format_ = []
 
-    def is_string(self, value):
+    def _is_string(self, value):
         """
         Check if the value is a str type.
 
         Args:
-            value: Parameter to to check.
+            value: Parameter to be checked.
 
         Raises:
             TypeError: If the type of value is not a str.
@@ -93,12 +94,12 @@ class RegOp():
         if not isinstance(value, str):
             raise TypeError("%s value must be str" % str(value))
 
-    def is_int(self, value):
+    def _is_int(self, value):
         """
         Check if the value is a int.
 
         Args:
-            value: Parameter to to check.
+            value: Parameter to be checked.
 
         Raises:
             TypeError: If the type of value is not a int.
@@ -106,12 +107,12 @@ class RegOp():
         if not isinstance(value, int):
             raise TypeError("%s value must be int" % str(value))
 
-    def is_bool(self, value):
+    def _is_bool(self, value):
         """
         Check if the value is a bool.
 
         Args:
-            value: Parameter to to check.
+            value: Parameter to be checked.
 
         Raises:
             TypeError: If the type of value is not a bool.
@@ -119,6 +120,51 @@ class RegOp():
         if not isinstance(value, bool):
             raise TypeError("%s value must be bool" % str(value))
 
+    def _check_param(self, param_list, key_list, fn_list, kwargs):
+        """
+        Check if the parameter type is correct.
+
+        Args:
+            param_list (list): Parameter list to be checked.
+            key_list (list): The keys of output dict.
+            fn_list (list): Function used for parameter checking. If the function list has only one element,
+                            all parameters will use the same function.
+            kwargs (dict): Other parameter information.
+
+        Raises:
+            TypeError: If the type of value is not list.
+            ValueError: If the size of param list is not equal to the size of key list, or
+                        the size of param list is not equal to the size of funtion list.
+        """
+        for i in [param_list, key_list, fn_list]:
+            if not isinstance(i, list):
+                raise TypeError("%s value must be list type" % str(i))
+        if len(param_list) != len(key_list) or (len(fn_list) != 1 and len(param_list) != len(fn_list)):
+            raise ValueError("param_list size {}, key_list size {}, must be equal.And fn_list size {}.".
+                             format(len(param_list), len(key_list), len(fn_list)))
+        out_dict = {}
+        for idx, element in enumerate(param_list):
+            if element is not None:
+                if len(fn_list) == 1:
+                    fn_list[0](element)
+                else:
+                    fn_list[idx](element)
+                out_dict[key_list[idx]] = element
+        if kwargs:
+            out_dict = dict(out_dict, kwargs)
+        return out_dict
+
+    def fusion_type(self, fusion_type):
+        """
+        Register fusion type.
+
+        Args:
+            fusion_type (str): Value of fusion type.
+        """
+        self._is_string(fusion_type)
+        self.fusion_type_ = fusion_type
+        return self
+
     def dtype_format(self, *args):
         """
         Register dtype and format.
@@ -136,8 +182,8 @@ class RegOp():
         for arg in args:
             if not isinstance(arg, tuple) or len(arg) != 2:
                 raise ValueError("dtype and format value must be tuple of two elements")
-            self.is_string(arg[0])
-            self.is_string(arg[1])
+            self._is_string(arg[0])
+            self._is_string(arg[1])
             dtype_format.append(arg)
         self.dtype_format_.append(tuple(dtype_format))
         return self
@@ -159,13 +205,71 @@ class RegOp():
         return op_info
 
 
+class AiCPURegOp(RegOp):
+    """Class for AiCPU op info register"""
+
+    def __init__(self, op_name):
+        super(AiCPURegOp, self).__init__(op_name)
+        self.imply_type = "AiCPU"
+
+    def input(self, index=None, name=None, param_type=None, **kwargs):
+        """
+        Register AiCPU op input information.
+
+        Args:
+            index (int): Order of the input. Default: None.
+            name (str): Name of the input. Default: None.
+            param_type (str): Param type of the input. Default: None.
+            kwargs (dict): Other information for the input.
+        """
+        param_list = [index, name, param_type]
+        key_list = ["index", "name", "param_type"]
+        fn_list = [self._is_int, self._is_string, self._is_string]
+        input_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.inputs.append(input_dict)
+        return self
+
+    def output(self, index=None, name=None, param_type=None, **kwargs):
+        """
+        Register AiCPU op output information.
+
+        Args:
+            index (int): Order of the output. Default: None.
+            name (str): Name of the output. Default: None.
+            param_type (str): Param type of the output. Default: None.
+            kwargs (dict): Other information for the output.
+        """
+        param_list = [index, name, param_type]
+        key_list = ["index", "name", "param_type"]
+        fn_list = [self._is_int, self._is_string, self._is_string]
+        output_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.outputs.append(output_dict)
+        return self
+
+    def attr(self, name=None, value_type=None, value=None, **kwargs):
+        """
+        Register AiCPU op attribute information.
+
+        Args:
+            name (str): Name of the attribute. Default: None.
+            value_type (str): Value type of the attribute. Default: None.
+            value (str): Value type of the attribute. Default: None.
+            kwargs (dict): Other information for the attribute.
+        """
+        param_list = [name, value_type, value]
+        key_list = ["name", "type", "value"]
+        fn_list = [self._is_string]
+        attr_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.attr_.append(attr_dict)
+        return self
+
+
 class TBERegOp(RegOp):
     """Class for TBE op info register."""
 
     def __init__(self, op_name=""):
         super(TBERegOp, self).__init__(op_name)
         self.imply_type = "TBE"
-        self.fusion_type_ = ''
         self.async_flag_ = False
         self.binfile_name_ = ''
         self.compute_cost_ = 10
@@ -175,17 +279,6 @@ class TBERegOp(RegOp):
         self.dynamic_format_ = False
         self.op_pattern_ = ""
 
-    def fusion_type(self, fusion_type):
-        """
-        Register fusion type.
-
-        Args:
-            fusion_type (str): Value of fusion type.
-        """
-        self.is_string(fusion_type)
-        self.fusion_type_ = fusion_type
-        return self
-
     def async_flag(self, async_flag):
         """
         Register async flag.
@@ -193,7 +286,7 @@ class TBERegOp(RegOp):
         Args:
             async_flag (bool): Value of async flag.
         """
-        self.is_bool(async_flag)
+        self._is_bool(async_flag)
         self.async_flag_ = async_flag
         return self
 
@@ -204,7 +297,7 @@ class TBERegOp(RegOp):
         Args:
             binfile_name (str): Name of op binfile.
         """
-        self.is_string(binfile_name)
+        self._is_string(binfile_name)
         self.binfile_name_ = binfile_name
         return self
 
@@ -215,7 +308,7 @@ class TBERegOp(RegOp):
         Args:
             compute_cost (int): Value of compute cost.
         """
-        self.is_int(compute_cost)
+        self._is_int(compute_cost)
         self.compute_cost_ = compute_cost
         return self
 
@@ -226,7 +319,7 @@ class TBERegOp(RegOp):
         Args:
             kernel_name (str): Name of op kernel.
         """
-        self.is_string(kernel_name)
+        self._is_string(kernel_name)
         self.kernel_name_ = kernel_name
         return self
 
@@ -237,7 +330,7 @@ class TBERegOp(RegOp):
         Args:
             partial_flag (bool): Value of partial flag.
         """
-        self.is_bool(partial_flag)
+        self._is_bool(partial_flag)
         self.partial_flag_ = partial_flag
         return self
 
@@ -248,7 +341,7 @@ class TBERegOp(RegOp):
         Args:
             reshape_type (str): Value of reshape type.
         """
-        self.is_string(reshape_type)
+        self._is_string(reshape_type)
         self.reshape_type_ = reshape_type
         return self
 
@@ -259,56 +352,43 @@ class TBERegOp(RegOp):
         Args:
             reshape_type (bool): Value of dynamic format.
         """
-        self.is_bool(dynamic_format)
+        self._is_bool(dynamic_format)
         self.dynamic_format_ = dynamic_format
         return self
 
     def op_pattern(self, pattern=None):
         """
-        Register op pattern information.
+        Register TBE op pattern information.
 
         Args:
             pattern (str): Value of op pattern.
         """
-        if pattern is not None and self.istring(pattern):
+        if pattern is not None and self._is_string(pattern):
             self.op_pattern_ = pattern
         return self
 
     def attr(self, name=None, param_type=None, value_type=None, value=None, default_value=None, **kwargs):
         """
-        Register op attribute information.
+        Register TBE op attribute information.
 
         Args:
             name (str): Name of the attribute. Default: None.
             param_type (str): Param type of the attribute. Default: None.
-            type (str): Type of the attribute. Default: None.
+            value_type (str): Type of the attribute. Default: None.
             value (str): Value of the attribute. Default: None.
             default_value (str): Default value of attribute. Default: None.
             kwargs (dict): Other information for the attribute.
         """
         param_list = [name, param_type, value_type, value, default_value]
-        attr_dict = {}
-        for index, element in enumerate(param_list):
-            if element is not None:
-                self.is_string(element)
-                if index == 0:
-                    attr_dict["name"] = element
-                elif index == 1:
-                    attr_dict["param_type"] = element
-                elif index == 2:
-                    attr_dict["type"] = element
-                elif index == 3:
-                    attr_dict["value"] = element
-                elif index == 4:
-                    attr_dict["default_value"] = element
-        if kwargs:
-            attr_dict = dict(attr_dict, **kwargs)
+        key_list = ["name", "param_type", "type", "value", "default_value"]
+        fn_list = [self._is_string]
+        attr_dict = self._check_param(param_list, key_list, fn_list, kwargs)
         self.attr_.append(attr_dict)
         return self
 
     def input(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs):
         """
-        Register op input information.
+        Register TBE op input information.
 
         Args:
             index (int): Order of the input. Default: None.
@@ -319,32 +399,15 @@ class TBERegOp(RegOp):
             kwargs (dict): Other information for the input.
         """
         param_list = [index, name, need_compile, param_type, shape]
-        input_dict = {}
-        for idx, element in enumerate(param_list):
-            if element is not None:
-                if idx == 0:
-                    self.is_int(element)
-                    input_dict["index"] = element
-                elif idx == 1:
-                    self.is_string(element)
-                    input_dict["name"] = element
-                elif idx == 2:
-                    self.is_bool(element)
-                    input_dict["need_compile"] = element
-                elif idx == 3:
-                    self.is_string(element)
-                    input_dict["param_type"] = element
-                elif idx == 4:
-                    self.is_string(element)
-                    input_dict["shape"] = element
-        if kwargs:
-            input_dict = dict(input_dict, **kwargs)
+        key_list = ["index", "name", "need_compile", "param_type", "shape"]
+        fn_list = [self._is_int, self._is_string, self._is_bool, self._is_string, self._is_string]
+        input_dict = self._check_param(param_list, key_list, fn_list, kwargs)
         self.inputs.append(input_dict)
         return self
 
     def output(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs):
         """
-        Register op output information.
+        Register TBE op output information.
 
         Args:
             index (int): Order of the output. Default: None.
@@ -355,29 +418,13 @@ class TBERegOp(RegOp):
             kwargs (dict): Other information for the output.
         """
         param_list = [index, name, need_compile, param_type, shape]
-        output_dict = {}
-        for idx, element in enumerate(param_list):
-            if element is not None:
-                if idx == 0:
-                    self.is_int(element)
-                    output_dict["index"] = element
-                elif idx == 1:
-                    self.is_string(element)
-                    output_dict["name"] = element
-                elif idx == 2:
-                    self.is_bool(element)
-                    output_dict["need_compile"] = element
-                elif idx == 3:
-                    self.is_string(element)
-                    output_dict["param_type"] = element
-                elif idx == 4:
-                    self.is_string(element)
-                    output_dict["shape"] = element
-        if kwargs:
-            output_dict = dict(output_dict, **kwargs)
+        key_list = ["index", "name", "need_compile", "param_type", "shape"]
+        fn_list = [self._is_int, self._is_string, self._is_bool, self._is_string, self._is_string]
+        output_dict = self._check_param(param_list, key_list, fn_list, kwargs)
         self.outputs.append(output_dict)
         return self
 
+
 class DataType():
     """
     Various combinations of dtype and formatself.

From 6fdcc245856446e575c743edc488531d014e6358 Mon Sep 17 00:00:00 2001
From: yao_yf <yaoyifan1@huawei.com>
Date: Thu, 9 Apr 2020 15:02:33 +0800
Subject: [PATCH 144/367] Integrate two allreduce fusion set interfaces into
 one

---
 mindspore/parallel/__init__.py                     |  4 +---
 mindspore/parallel/_auto_parallel_context.py       | 14 ++++++++++----
 ...allreduce_fusion.py => _dp_allreduce_fusion.py} |  4 ++--
 3 files changed, 13 insertions(+), 9 deletions(-)
 rename mindspore/parallel/{dp_allreduce_fusion.py => _dp_allreduce_fusion.py} (94%)

diff --git a/mindspore/parallel/__init__.py b/mindspore/parallel/__init__.py
index c79704f110..79d8e67a8d 100644
--- a/mindspore/parallel/__init__.py
+++ b/mindspore/parallel/__init__.py
@@ -15,9 +15,7 @@
 """
 This interface is ONLY used in Auto-parallel procedure.
 """
-from .dp_allreduce_fusion import set_fusion_strategy_by_idx, set_fusion_strategy_by_size
 from .algo_parameter_config import get_algo_parameters, reset_algo_parameters, \
     set_algo_parameters
 
-__all__ = ["set_fusion_strategy_by_idx", "set_fusion_strategy_by_size", "get_algo_parameters",
-           "reset_algo_parameters", "set_algo_parameters"]
+__all__ = ["get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]
diff --git a/mindspore/parallel/_auto_parallel_context.py b/mindspore/parallel/_auto_parallel_context.py
index 3564ad4395..c99ac4a3c7 100644
--- a/mindspore/parallel/_auto_parallel_context.py
+++ b/mindspore/parallel/_auto_parallel_context.py
@@ -14,6 +14,8 @@
 # ============================================================================
 """Context of auto parallel"""
 import threading
+import mindspore.context as context
+from mindspore.parallel._dp_allreduce_fusion import _set_fusion_strategy_by_idx, _set_fusion_strategy_by_size
 from mindspore._c_expression import AutoParallelContext
 from mindspore._extends.pynative_helper import args_type_check
 
@@ -219,13 +221,15 @@ class _AutoParallelContext:
             indices (list): Indices list.
 
         Raises:
-            ValueError: If type of indices item is not int.
+            TypeError: If type of indices item is not int.
         """
         self.check_context_handle()
         for index in indices:
             if not isinstance(index, int):
                 raise TypeError('indices has invalid value')
-        return self._context_handle.set_all_reduce_fusion_split_indices(indices)
+        self._context_handle.set_all_reduce_fusion_split_indices(indices)
+        if context.get_context("device_target") == "Ascend":
+            _set_fusion_strategy_by_idx(indices)
 
     def get_all_reduce_fusion_split_indices(self):
         """Get allreduce fusion split indices."""
@@ -240,13 +244,15 @@ class _AutoParallelContext:
             sizes (list): Sizes list.
 
         Raises:
-            ValueError: If type of sizes item is not int.
+            TypeError: If type of sizes item is not int.
         """
         self.check_context_handle()
         for size in sizes:
             if not isinstance(size, int):
                 raise TypeError('sizes has invalid value')
-        return self._context_handle.set_all_reduce_fusion_split_sizes(sizes)
+        self._context_handle.set_all_reduce_fusion_split_sizes(sizes)
+        if context.get_context("device_target") == "Ascend":
+            _set_fusion_strategy_by_size(sizes)
 
     def get_all_reduce_fusion_split_sizes(self):
         """Get allreduce fusion split sizes."""
diff --git a/mindspore/parallel/dp_allreduce_fusion.py b/mindspore/parallel/_dp_allreduce_fusion.py
similarity index 94%
rename from mindspore/parallel/dp_allreduce_fusion.py
rename to mindspore/parallel/_dp_allreduce_fusion.py
index 979823bd80..3c7039dbd6 100644
--- a/mindspore/parallel/dp_allreduce_fusion.py
+++ b/mindspore/parallel/_dp_allreduce_fusion.py
@@ -43,7 +43,7 @@ def _c_array(ctype, values):
     return (ctype * len(values))(*values)
 
 
-def set_fusion_strategy_by_idx(idxList, group="hccl_world_group"):
+def _set_fusion_strategy_by_idx(idxList, group="hccl_world_group"):
     """
     A function set gradient segment strategy according to the index list.
 
@@ -100,7 +100,7 @@ def set_fusion_strategy_by_idx(idxList, group="hccl_world_group"):
         raise RuntimeError('Allreduce split error')
 
 
-def set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"):
+def _set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"):
     """
     A function set gradient segment strategy according to the data size percentage list.
 

From ead50a21700a0204589ac23e58bf0caf5b7498c2 Mon Sep 17 00:00:00 2001
From: seatea <t.cai@huawei.com>
Date: Thu, 9 Apr 2020 18:24:21 +0800
Subject: [PATCH 145/367] Define the default decay_filter for `Adam` optimizer.

---
 mindspore/nn/optim/adam.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/mindspore/nn/optim/adam.py b/mindspore/nn/optim/adam.py
index 86ce2b2147..521510fa58 100755
--- a/mindspore/nn/optim/adam.py
+++ b/mindspore/nn/optim/adam.py
@@ -166,7 +166,8 @@ class Adam(Optimizer):
     """
 
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
-                 use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
+                 use_nesterov=False, weight_decay=0.0, loss_scale=1.0,
+                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
         super(Adam, self).__init__(learning_rate, params)
         _check_param_value(beta1, beta2, eps, weight_decay)
         validator.check_type("use_locking", use_locking, [bool])
@@ -192,6 +193,7 @@ class Adam(Optimizer):
         self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
         self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
 
+        self.decay_tf = tuple(decay_filter(x) for x in self.parameters)
         self.hyper_map = C.HyperMap()
         self.opt = P.Adam(use_locking, use_nesterov)
         self.weight_decay = weight_decay * loss_scale

From 0f0f83e292ff9dbacf51aaf1a1d10787ff06dabc Mon Sep 17 00:00:00 2001
From: liuxiao <liuxiao93@huawei.com>
Date: Thu, 9 Apr 2020 14:37:00 +0800
Subject: [PATCH 146/367] modified api name Stack -> Pack, Unstack -> Unpack

---
 mindspore/ccsrc/transform/convert.cc  |  8 +--
 mindspore/ops/_grad/grad_array_ops.py | 20 ++++----
 mindspore/ops/operations/__init__.py  |  6 +--
 mindspore/ops/operations/array_ops.py | 70 ++++++++++++---------------
 tests/ut/python/ops/test_ops.py       | 32 ++++++------
 5 files changed, 64 insertions(+), 72 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c9a27a2607..c53367a20f 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -148,8 +148,8 @@ const char kNameSlice[] = "Slice";
 const char kNameAddN[] = "AddN";
 const char kNameLess[] = "Less";
 const char kNameGreater[] = "Greater";
-const char kNameStack[] = "Stack";
-const char kNameUnstack[] = "Unstack";
+const char kNamePack[] = "Pack";
+const char kNameUnpack[] = "Unpack";
 const char kNameMerge[] = "Merge";
 const char kNameGeSwitch[] = "GeSwitch";
 
@@ -202,8 +202,8 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
     {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
     {string(kNameTopK), ADPT_DESC(TopKV2)},
-    {string(kNameStack), ADPT_DESC(Pack)},
-    {string(kNameUnstack), ADPT_DESC(Unpack)},
+    {string(kNamePack), ADPT_DESC(Pack)},
+    {string(kNameUnpack), ADPT_DESC(Unpack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},
     {string(kNameAllReduce), ADPT_DESC(HcomAllReduce)},
     {string(kNameBroadcast), ADPT_DESC(HcomBroadcast)},
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index 0a0caf471e..abad030ae9 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -266,26 +266,26 @@ def get_bprop_gather_v2(self):
     return bprop
 
 
-@bprop_getters.register(P.Stack)
-def get_bprop_stack(self):
-    """Generate bprop for Stack"""
+@bprop_getters.register(P.Pack)
+def get_bprop_pack(self):
+    """Generate bprop for Pack"""
     axis = self.axis
 
     def bprop(x, out, dout):
-        stack_grad = P.Unstack(axis)
-        out = stack_grad(dout)
+        pack_grad = P.Unpack(axis)
+        out = pack_grad(dout)
         return (out,)
     return bprop
 
 
-@bprop_getters.register(P.Unstack)
-def get_bprop_unstack(self):
-    """Generate bprop for Unstack"""
+@bprop_getters.register(P.Unpack)
+def get_bprop_unpack(self):
+    """Generate bprop for Unpack"""
     axis = self.axis
 
     def bprop(x, out, dout):
-        unstack_grad = P.Stack(axis)
-        out = unstack_grad(dout)
+        unpack_grad = P.Pack(axis)
+        out = unpack_grad(dout)
         return (out,)
     return bprop
 
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 7a8655b46c..45cd856298 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -19,7 +19,7 @@ Primitive operator classes.
 A collection of operators to build nerual networks or computing functions.
 """
 
-from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Stack, Unstack,
+from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Pack, Unpack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, GatherNd, GatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
@@ -112,8 +112,8 @@ __all__ = [
     'OneHot',
     'GatherV2',
     'Concat',
-    'Stack',
-    'Unstack',
+    'Pack',
+    'Unpack',
     'Tile',
     'BiasAdd',
     'Gelu',
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index dda490566f..ac7f8ed699 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1350,8 +1350,8 @@ class Concat(PrimitiveWithInfer):
         return out
 
 
-def _get_stack_shape(x_shape, x_type, axis):
-    """for satck output shape"""
+def _get_pack_shape(x_shape, x_type, axis):
+    """for pack output shape"""
     validator.check_type("shape", x_shape, [tuple])
     validator.check_integer("len of input_x shape", len(x_shape), 0, Rel.GT)
     validator.check_subclass("shape0", x_type[0], mstype.tensor)
@@ -1368,43 +1368,40 @@ def _get_stack_shape(x_shape, x_type, axis):
         validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0])
         for j in range(rank_base):
             if v[j] != x_shape[0][j]:
-                raise ValueError("Stack evaluator element %d shape in input can not stack with first element" % i)
+                raise ValueError("Pack evaluator element %d shape in input can not pack with first element" % i)
     out_shape.insert(axis, N)
     return out_shape
 
-class Stack(PrimitiveWithInfer):
+class Pack(PrimitiveWithInfer):
     r"""
-    Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+    Packs a list of tensors in specified axis.
 
-    Packs the list of tensors in `input_x` into a tensor with rank one higher than
-    each tensor in `input_x`, by packing them along the `axis` dimension.
-    Given a list of length `N` of tensors of shape `(A, B, C)`;
+    Packs the list of input tensors with the same rank `R`, output is a tensor of rank `(R+1)`.
 
-    If `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
-
-    If `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc.
+    Given input tensors of shape :math:`(x_1, x_2, ..., x_R)`. Set the number of input tensors as `N`.
+    If :math:`0 \le axis`, the output tensor shape is :math:`(x_1, x_2, ..., x_{axis}, N, x_{axis+1}, ..., x_R)`.
 
     Args:
-        axis (int): The axis to stack along. Negative values wrap around,
-                    so the valid range is [-(R+1), R+1). Default: 0.
+        axis (int): Dimension along which to pack. Default: 0.
+                    Negative values wrap around. The range is [-(R+1), R+1).
 
     Inputs:
         - **input_x** (Union[tuple, list]) - A Tuple or list of Tensor objects with the same shape and type.
 
     Outputs:
-        Tensor. A stacked Tensor with the same type as values.
+        Tensor. A packed Tensor with the same type as `input_x`.
 
     Examples:
         >>> data1 = Tensor(np.array([0, 1]).astype(np.float32))
         >>> data2 = Tensor(np.array([2, 3]).astype(np.float32))
-        >>> op = P.Stack()
-        >>> output = op([data1, data2])
+        >>> pack = P.Pack()
+        >>> output = pack([data1, data2])
         [[0, 1], [2, 3]]
     """
 
     @prim_attr_register
     def __init__(self, axis=0):
-        """init Stack"""
+        """init Pack"""
         self.__setattr_flag__ = True
         validator.check_type("axis", axis, [int])
         self.axis = axis
@@ -1413,38 +1410,33 @@ class Stack(PrimitiveWithInfer):
         x_shape = value['shape']
         x_type = value['dtype']
         self.add_prim_attr('num', len(x_shape))
-        all_shape = _get_stack_shape(x_shape, x_type, self.axis)
+        all_shape = _get_pack_shape(x_shape, x_type, self.axis)
         out = {'shape': all_shape,
                'dtype': x_type[0],
                'value': None}
         return out
 
 
-class Unstack(PrimitiveWithInfer):
+class Unpack(PrimitiveWithInfer):
     r"""
-    Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
-
-    Unpacks num tensors from value by chipping it along the axis dimension.
-    If num is not specified (the default), it is inferred from value's shape.
-    If value.shape[axis] is not known, ValueError is raised.
+    Unpacks tensor in specified axis.
 
-    For example, given a tensor of shape (A, B, C, D);
+    Unpacks a tensor of rank `R` along axis dimension, output tensors will have rank `(R-1)`.
 
-    If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and
-    each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split).
+    Given a tensor of shape :math:`(x_1, x_2, ..., x_R)`. If :math:`0 \le axis`,
+    the shape of tensor in output is :math:`(x_1, x_2, ..., x_{axis}, x_{axis+2}, ..., x_R)`.
 
-    If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and
-    each tensor in output will have shape (A, C, D). Etc.
-
-    This is the opposite of stack.
+    This is the opposite of pack.
 
     Args:
-        axis (int): The axis to unstack along. Defaults to the first dimension.
-                    Negative values wrap around, so the valid range is [-R, R).
+        axis (int): Dimension along which to pack. Default: 0.
+                    Negative values wrap around. The range is [-R, R).
+        num (int): The number of tensors to be unpacked to. Default : "None".
+                   If `num` is not specified, it is inferred from the shape of `input_x`.
 
     Inputs:
         - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
-          A rank R > 0 Tensor to be unstacked.
+          A rank R > 0 Tensor to be unpacked.
 
     Outputs:
         A tuple of Tensors, the shape of each objects is same.
@@ -1454,15 +1446,15 @@ class Unstack(PrimitiveWithInfer):
                     or if len(input_x.shape[axis]) not equal to num.
 
     Examples:
-        >>> unstack = P.Unstack()
-        >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
-        >>> output = unstack(x)
+        >>> unpack = P.Unpack()
+        >>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
+        >>> output = unpack(input_x)
         ([1, 1, 1, 1], [2, 2, 2, 2])
     """
 
     @prim_attr_register
     def __init__(self, axis=0):
-        """init Unstack"""
+        """init Unpack"""
         self.__setattr_flag__ = True
         validator.check_type("axis", axis, [int])
         self.axis = axis
@@ -1479,7 +1471,7 @@ class Unstack(PrimitiveWithInfer):
         validator.check_integer("output_num", output_num, 0, Rel.GT)
         self.add_prim_attr('num', output_num)
         output_valid_check = x_shape[self.axis] - output_num
-        validator.check_integer("the dimension which to unstack divides output_num", output_valid_check, 0, Rel.EQ)
+        validator.check_integer("The dimension which to unpack divides output_num", output_valid_check, 0, Rel.EQ)
         out_shapes = []
         out_dtypes = []
         out_shape = x_shape[:self.axis] + x_shape[self.axis + 1:]
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index a650309d98..a6b064bdb0 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -80,9 +80,9 @@ class NetForConcat1(nn.Cell):
         return self.concat((x1, x2))
 
 
-class NetForStackInput(nn.Cell):
+class NetForPackInput(nn.Cell):
     def __init__(self, op):
-        super(NetForStackInput, self).__init__()
+        super(NetForPackInput, self).__init__()
         self.op = op
         self.mul = P.Mul()
 
@@ -93,9 +93,9 @@ class NetForStackInput(nn.Cell):
         return self.op(t)
 
 
-class NetForUnstackInput(nn.Cell):
+class NetForUnpackInput(nn.Cell):
     def __init__(self, op):
-        super(NetForUnstackInput, self).__init__()
+        super(NetForUnpackInput, self).__init__()
         self.op = op
         self.mul = P.Mul()
 
@@ -991,33 +991,33 @@ test_case_array_ops = [
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
         'desc_bprop': [[3,]]}),
-    ('StackV2_0', {
-        'block': NetForStackInput(P.Stack()),
+    ('Pack_0', {
+        'block': NetForPackInput(P.Pack()),
         'desc_inputs':[[2, 2], [2, 2], [2, 2]],
         'desc_bprop':[[3, 2, 2]],
     }),
-    ('StackV2_1', {
-        'block': NetForStackInput(P.Stack(axis=-2)),
+    ('Pack_1', {
+        'block': NetForPackInput(P.Pack(axis=-2)),
         'desc_inputs':[[3, 2, 3], [3, 2, 3], [3, 2, 3]],
         'desc_bprop':[[3, 2, 3, 3]],
     }),
-    ('StackV2_2', {
-        'block': NetForStackInput(P.Stack()),
+    ('Pack_2', {
+        'block': NetForPackInput(P.Pack()),
         'desc_inputs':[[2, 2]],
         'desc_bprop':[[2, 2, 2]],
     }),
-    ('StackV2_3', {
-        'block': NetForStackInput(P.Stack()),
+    ('Pack_3', {
+        'block': NetForPackInput(P.Pack()),
         'desc_inputs':[[128, 128], [128, 128]],
         'desc_bprop':[[2, 128, 128]],
     }),
-    ('UnstackV2_0', {
-        'block': NetForUnstackInput(P.Unstack(axis=0)),
+    ('Unpack_0', {
+        'block': NetForUnpackInput(P.Unpack(axis=0)),
         'desc_inputs':[[2, 4]],
         'desc_bprop':[[4], [4]],
     }),
-    ('UnstackV2_1', {
-        'block': NetForUnstackInput(P.Unstack(axis=-1)),
+    ('Unpack_1', {
+        'block': NetForUnpackInput(P.Unpack(axis=-1)),
         'desc_inputs':[Tensor(np.array([[1, 1, 1]], np.float32))],
         'desc_bprop':[[1], [1], [1]],
     }),

From 576f65ad489f99eb271bd36acfa1b322b18431a7 Mon Sep 17 00:00:00 2001
From: yanghaoran <yanghaoran2@huawei.com>
Date: Thu, 9 Apr 2020 19:07:01 +0800
Subject: [PATCH 147/367] add custom environment variable ASCEND_CUSTOM_PATH
 for customized Ascend software installation

---
 cmake/dependency_graphengine.cmake |  6 +++++-
 graphengine                        |  2 +-
 mindspore/ccsrc/CMakeLists.txt     | 12 ++++++++++--
 3 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake
index 2420f47736..2a90cc1458 100644
--- a/cmake/dependency_graphengine.cmake
+++ b/cmake/dependency_graphengine.cmake
@@ -39,7 +39,11 @@ elseif (DEFINED ENV{D_LINK_PATH})
     find_library(resource libresource.so ${GE_LIB_PATH})
 else()
     # Ascend mode
-    set(ASCEND_PATH /usr/local/Ascend)
+    if(DEFINED ENV{ASCEND_CUSTOM_PATH})
+        set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
+    else()
+        set(ASCEND_PATH /usr/local/Ascend)
+    endif()
     set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
     set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
     find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH})
diff --git a/graphengine b/graphengine
index 5369646b48..5bd0dc1ed5 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 5369646b489114b380a7b5208ddd6e632acb447f
+Subproject commit 5bd0dc1ed59a9ec4ea6a602bf1385c59d845f922
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 1d104148c3..c49c962bdd 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -296,7 +296,11 @@ if(ENABLE_D)
         endif()
     else()
         MESSAGE("use system default lib")
-        set(ASCEND_PATH /usr/local/Ascend)
+        if(DEFINED ENV{ASCEND_CUSTOM_PATH})
+            set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
+        else()
+            set(ASCEND_PATH /usr/local/Ascend)
+        endif()
         set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
         set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
     endif()
@@ -500,7 +504,11 @@ add_dependencies(add_ms_lib _c_expression)
 
 if (NOT ENABLE_GE)
     if (ENABLE_D)
-        set(ASCEND_PATH /usr/local/Ascend)
+        if(DEFINED ENV{ASCEND_CUSTOM_PATH})
+            set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
+        else()
+            set(ASCEND_PATH /usr/local/Ascend)
+        endif()
         set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
         add_custom_target(add_ge_lib ALL
                 COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH}

From 363632ca9d2fa88278a235c477ae6395ae23a18f Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Thu, 9 Apr 2020 11:04:13 +0800
Subject: [PATCH 148/367] fix dataset para validator check

---
 mindspore/dataset/engine/samplers.py              | 1 -
 mindspore/dataset/engine/validators.py            | 5 +++++
 mindspore/dataset/transforms/vision/validators.py | 4 ++++
 3 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py
index ed36e72b65..62a3dbed18 100644
--- a/mindspore/dataset/engine/samplers.py
+++ b/mindspore/dataset/engine/samplers.py
@@ -127,7 +127,6 @@ class RandomSampler():
 
     Raises:
         ValueError: If replacement is not boolean.
-        ValueError: If num_samples is not None and replacement is false.
         ValueError: If num_samples is not positive.
      """
 
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 26d6241945..b5ebc24b39 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -556,6 +556,11 @@ def check_generatordataset(method):
         if column_names is None:
             raise ValueError("column_names is not provided.")
 
+        # check prefetch_size range
+        prefetch_size = param_dict.get('prefetch_size')
+        if prefetch_size is not None and (prefetch_size <= 0 or prefetch_size > 1024):
+            raise ValueError("prefetch_size exceeds the boundary.")
+
         check_param_type(nreq_param_int, param_dict, int)
 
         check_param_type(nreq_param_list, param_dict, list)
diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py
index caab120af4..ef4b879f8c 100644
--- a/mindspore/dataset/transforms/vision/validators.py
+++ b/mindspore/dataset/transforms/vision/validators.py
@@ -104,6 +104,10 @@ def check_padding(padding):
             raise ValueError("The size of the padding list or tuple should be 2 or 4.")
     else:
         raise TypeError("Padding can be any of: a number, a tuple or list of size 2 or 4.")
+    if not (isinstance(left, int) and isinstance(top, int) and isinstance(right, int) and isinstance(bottom, int)):
+        raise TypeError("Padding value should be integer.")
+    if left < 0 or top < 0 or right < 0 or bottom < 0:
+        raise ValueError("Padding value could not be negative.")
     return left, top, right, bottom
 
 

From b413638f2328288ce3b693ed161dd99f210f58d9 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Thu, 9 Apr 2020 14:40:43 +0800
Subject: [PATCH 149/367] refactor OperatorCostPtr in OperatorInfo

---
 .../auto_parallel/operator_costmodel.cc       | 54 -------------------
 .../auto_parallel/operator_costmodel.h        | 30 ++---------
 .../ccsrc/parallel/ops_info/activation_info.h | 18 ++-----
 .../ccsrc/parallel/ops_info/arithmetic_info.h |  6 +--
 .../parallel/ops_info/batch_parallel_info.h   |  6 +--
 .../ccsrc/parallel/ops_info/bias_add_info.h   |  6 +--
 .../parallel/ops_info/dropout_do_mask_info.h  |  8 +--
 .../ccsrc/parallel/ops_info/generator_info.h  |  6 +--
 .../ccsrc/parallel/ops_info/get_next_info.h   |  6 +--
 .../parallel/ops_info/l2_normalize_info.h     |  6 +--
 mindspore/ccsrc/parallel/ops_info/loss_info.h |  6 +--
 .../ccsrc/parallel/ops_info/matmul_info.cc    |  6 +--
 .../ccsrc/parallel/ops_info/matmul_info.h     |  7 +--
 .../ccsrc/parallel/ops_info/onehot_info.h     |  6 +--
 .../ccsrc/parallel/ops_info/operator_info.cc  | 13 +++--
 .../ccsrc/parallel/ops_info/operator_info.h   | 13 +++--
 .../ccsrc/parallel/ops_info/prelu_info.h      |  6 +--
 .../parallel/ops_info/reduce_method_info.cc   |  8 ++-
 .../parallel/ops_info/reduce_method_info.h    |  8 +--
 .../ccsrc/parallel/ops_info/reshape_info.h    |  8 +--
 .../parallel/ops_info/tmp_identity_info.h     |  8 +--
 .../ccsrc/parallel/ops_info/transpose_info.h  |  6 +--
 .../parallel/ops_info/virtual_dataset_info.h  |  8 +--
 .../cpp/parallel/ops_info/activation_test.cc  |  8 +--
 .../cpp/parallel/ops_info/matmul_info_test.cc |  4 +-
 .../parallel/ops_info/tensor_add_info_test.cc |  8 +--
 .../cpp/parallel/ops_info/tmpidentity_test.cc |  4 +-
 27 files changed, 62 insertions(+), 211 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 7c17b499b1..93d7dc56c5 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -514,60 +514,6 @@ double ArithmeticCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs
   return result;
 }
 
-double L2NormalizeCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                            const int32_t& stage_id) const {
-  double result = 0.0;
-  if (is_parameter_[0]) {
-    TensorInfo input_tensor_info = inputs[0];
-    CheckGlobalDeviceManager();
-    MS_EXCEPTION_IF_NULL(g_device_manager);
-    auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
-
-    Shape input_shape = input_tensor_info.shape();
-    Shape input_slice_shape = input_tensor_info.slice_shape();
-    int32_t used_device_num = 1;
-    for (size_t i = 0; i < input_shape.size(); ++i) {
-      used_device_num *= input_shape[i] / input_slice_shape[i];
-    }
-
-    if (total_device_num != IntToSize(used_device_num))
-      result += ListProduct(input_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
-  }
-
-  return result;
-}
-
-double L2NormalizeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                                  const int32_t&) const {
-  TensorInfo input0_info = inputs[0];
-  Shape input0_slice_shape = input0_info.slice_shape();
-  return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
-}
-
-double L2NormalizeCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                                   const std::vector<TensorInfo>&, const int32_t& stage_id) const {
-  double result = 0.0;
-
-  if (is_parameter_[0]) {
-    TensorInfo input_tensor_info = inputs[0];
-    CheckGlobalDeviceManager();
-    MS_EXCEPTION_IF_NULL(g_device_manager);
-    auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
-
-    Shape input_shape = input_tensor_info.shape();
-    Shape input_slice_shape = input_tensor_info.slice_shape();
-    int32_t used_device_num = 1;
-    for (size_t i = 0; i < input_shape.size(); ++i) {
-      used_device_num *= input_shape[i] / input_slice_shape[i];
-    }
-
-    if (total_device_num != IntToSize(used_device_num))
-      result += ListProduct(input_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
-  }
-
-  return result;
-}
-
 bool IsDataParallel(const Shape& shape, const Shape& slice_shape, const int32_t& stage_id) {
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index 8f0099bba3..73f3ff139f 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -132,6 +132,8 @@ class ActivationCost : public OperatorCost {
 };
 
 using ActivationCostPtr = std::shared_ptr<ActivationCost>;
+using TransposeCost = ActivationCost;
+using TransposeCostPtr = std::shared_ptr<TransposeCost>;
 
 class SoftmaxCost : public OperatorCost {
  public:
@@ -415,32 +417,8 @@ class ArithmeticCost : public OperatorCost {
                                     const int32_t& stage_id) const override;
 };
 using ArithmeticCostPtr = std::shared_ptr<ArithmeticCost>;
-
-class L2NormalizeCost : public OperatorCost {
- public:
-  L2NormalizeCost() = default;
-  ~L2NormalizeCost() override = default;
-
-  double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
-    return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
-  }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
-    return 0.0;
-  }
-  double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
-  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
-    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
-  }
-  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
-  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
-};
-using L2NormalizeCostPtr = std::shared_ptr<L2NormalizeCost>;
+using BiasAddCost = ArithmeticCost;
+using BiasAddCostPtr = std::shared_ptr<BiasAddCost>;
 
 class ReduceMethodCost : public OperatorCost {
  public:
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index 183b593e23..21774c43ee 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -32,8 +32,8 @@ namespace parallel {
 class ActivationBase : public OperatorInfo {
  public:
   ActivationBase(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
-                 const PrimitiveAttrs& attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {}
+                 const PrimitiveAttrs& attrs, OperatorCostPtr cost)
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, cost) {}
   ~ActivationBase() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
@@ -51,19 +51,13 @@ class Activation : public ActivationBase {
  public:
   Activation(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : ActivationBase(name, inputs_shape, outputs_shape, attrs) {
-    ac_cost_ptr_ = std::make_shared<ActivationCost>();
-  }
+      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ActivationCost>()) {}
   ~Activation() override = default;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return ac_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
-
- private:
-  ActivationCostPtr ac_cost_ptr_;
 };
 
 class ActivationInfo : public Activation {
@@ -108,13 +102,10 @@ class Softmax : public ActivationBase {
  public:
   explicit Softmax(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                    const PrimitiveAttrs& attrs)
-      : ActivationBase(name, inputs_shape, outputs_shape, attrs) {
-    sm_cost_ptr_ = std::make_shared<SoftmaxCost>();
-  }
+      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<SoftmaxCost>()) {}
   ~Softmax() override = default;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return sm_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -122,7 +113,6 @@ class Softmax : public ActivationBase {
 
  private:
   std::vector<int32_t> axis_;
-  SoftmaxCostPtr sm_cost_ptr_;
 };
 
 class SoftmaxInfo : public Softmax {
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index 7cd0d66b1b..daa2ad595c 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -33,15 +33,12 @@ class ArithmeticBase : public OperatorInfo {
  public:
   ArithmeticBase(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                  const PrimitiveAttrs& attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {
-    arithmeticcost_ptr_ = std::make_shared<ArithmeticCost>();
-  }
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>()) {}
   ~ArithmeticBase() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t) override;
   Status SetCostUnderStrategy(const StrategyPtr&) override;
-  OperatorCostPtr GetOperatorCost() const override { return arithmeticcost_ptr_; }
   void ReComputeBatchSplitFlagList() override;
 
  protected:
@@ -54,7 +51,6 @@ class ArithmeticBase : public OperatorInfo {
   Status InferTensorMap() override;
   Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout, const Shape& dev_matrix_array);
   Shapes InferExpendShape();
-  ArithmeticCostPtr arithmeticcost_ptr_;
 };
 
 class SubInfo : public ArithmeticBase {
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
index 57711b5298..fae96dcab5 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
@@ -31,16 +31,13 @@ class BatchParallelInfo : public OperatorInfo {
  public:
   BatchParallelInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs), dev_num_(1) {
-    bp_cost_ptr_ = std::make_shared<BatchParallelCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>()), dev_num_(1) {}
 
   ~BatchParallelInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return bp_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -55,7 +52,6 @@ class BatchParallelInfo : public OperatorInfo {
 
  private:
   int32_t dev_num_;
-  BatchParallelCostPtr bp_cost_ptr_;
 };
 
 class SparseSoftmaxCrossEntropyWithLogitsInfo : public BatchParallelInfo {
diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
index 07f0bc00ff..dea5c90c88 100644
--- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
@@ -34,16 +34,13 @@ class BiasAddInfo : public OperatorInfo {
  public:
   BiasAddInfo(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {
-    biasaddcost_ptr_ = std::make_shared<ArithmeticCost>();
-  }
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<BiasAddCost>()) {}
   ~BiasAddInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t) override;
   Status SetCostUnderStrategy(const StrategyPtr&) override;
-  OperatorCostPtr GetOperatorCost() const override { return biasaddcost_ptr_; }
   void ReComputeBatchSplitFlagList() override;
 
  protected:
@@ -55,7 +52,6 @@ class BiasAddInfo : public OperatorInfo {
   Status InferDevMatrixShape() override;
   Status InferTensorMap() override;
   Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout, const Shape& dev_matrix_array);
-  ArithmeticCostPtr biasaddcost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
index e43601355a..859b3e06a4 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
@@ -33,15 +33,12 @@ class DropoutDoMaskInfo : public OperatorInfo {
  public:
   DropoutDoMaskInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    bpcost_ptr_ = std::make_shared<BatchParallelCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>()) {}
   ~DropoutDoMaskInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return bpcost_ptr_; }
   Status InitForCostModel(const StrategyPtr& strategy) override;
   std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
@@ -53,9 +50,6 @@ class DropoutDoMaskInfo : public OperatorInfo {
   Status GetAttrs() override { return SUCCESS; }
   Status InferTensorInfo() override;
   Status InferDevMatrixShape() override;
-
- private:
-  BatchParallelCostPtr bpcost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h
index a280fac28e..68024593f3 100644
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h
@@ -32,15 +32,12 @@ class GeneratorBase : public OperatorInfo {
  public:
   GeneratorBase(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
                 const PrimitiveAttrs &attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {
-    generatorbasecost_ptr_ = std::make_shared<GeneratorBaseCost>();
-  }
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GeneratorBaseCost>()) {}
 
   ~GeneratorBase() override = default;
 
   Status Init(const StrategyPtr &strategy) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return generatorbasecost_ptr_; }
   Status InitForCostModel(const StrategyPtr &strategy) override;
 
  protected:
@@ -52,7 +49,6 @@ class GeneratorBase : public OperatorInfo {
   Status InferMirrorOps() override { return SUCCESS; }
   Status InferForwardCommunication() override { return SUCCESS; }
   virtual Status InferReplaceOps(const StrategyPtr &strategy) = 0;
-  GeneratorBaseCostPtr generatorbasecost_ptr_;
 };
 
 class DropoutGenMaskInfo : public GeneratorBase {
diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
index 32adce1165..9a65eff035 100644
--- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
@@ -32,14 +32,11 @@ class GetNextInfo : public OperatorInfo {
  public:
   GetNextInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
               const PrimitiveAttrs &attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {
-    getnextcost_ptr_ = std::make_shared<GetNextCost>();
-  }
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GetNextCost>()) {}
   ~GetNextInfo() override = default;
 
   Status Init(const StrategyPtr &strategy) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return getnextcost_ptr_; }
   Status InitForCostModel(const StrategyPtr &strategy) override;
   Status GenerateStrategies(int32_t stage_id) override;
 
@@ -65,7 +62,6 @@ class GetNextInfo : public OperatorInfo {
   Shapes shapes_;
   int32_t output_num_ = 0;
   std::string shared_name_;
-  GetNextCostPtr getnextcost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
index c0af9dbcb9..22ed5a965b 100644
--- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h
@@ -33,12 +33,9 @@ class L2NormalizeInfo : public Activation {
  public:
   L2NormalizeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                   const PrimitiveAttrs& attrs)
-      : Activation(name, inputs_shape, outputs_shape, attrs) {
-    l2normalizecost_ptr_ = std::make_shared<L2NormalizeCost>();
-  }
+      : Activation(name, inputs_shape, outputs_shape, attrs) {}
   ~L2NormalizeInfo() override = default;
   Status GenerateStrategies(int32_t stage_id) override;
-  OperatorCostPtr GetOperatorCost() const override { return l2normalizecost_ptr_; }
 
  protected:
   Status GetAttrs() override;
@@ -47,7 +44,6 @@ class L2NormalizeInfo : public Activation {
 
  private:
   int32_t axis_ = 0;  // Default value = 0
-  L2NormalizeCostPtr l2normalizecost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h
index 6a9697a447..f1c2537a39 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h
@@ -36,16 +36,13 @@ class SoftmaxCrossEntropyWithLogitsInfo : public OperatorInfo {
  public:
   SoftmaxCrossEntropyWithLogitsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    softmax_loss_cost_ptr_ = std::make_shared<SoftmaxCrossEntropyWithLogitsCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<SoftmaxCrossEntropyWithLogitsCost>()) {}
   ~SoftmaxCrossEntropyWithLogitsInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return softmax_loss_cost_ptr_; }
   void ReComputeBatchSplitFlagList() override;
 
  protected:
@@ -59,7 +56,6 @@ class SoftmaxCrossEntropyWithLogitsInfo : public OperatorInfo {
   // There are two outputs for SoftmaxCrossEntropyWithLogits, and outputs[1] is used for grad and overload
   // the InferAsLossDivisor.
   Status InferAsLossDivisor() override;
-  SoftmaxCrossEntropyWithLogitsCostPtr softmax_loss_cost_ptr_;
 
  private:
   int32_t axis_ = -1;  // default -1
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index 2b02dc100d..848116d68a 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -593,11 +593,11 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr&
   // Here, we use the origin outputs_, because we only use the slice size of the output tensor.
   // It does not matter whether the output tensor is transposed or not.
   double computation_cost =
-    matmulcost_ptr->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
-  double communication_cost = matmulcost_ptr->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+    cost()->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+  double communication_cost = cost()->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
-    matmulcost_ptr->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+    cost()->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
     result->communication_without_parameter_ +
     COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_);
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
index 7ced12b14a..2d3312774d 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
@@ -34,9 +34,7 @@ class MatMulBase : public OperatorInfo {
  public:
   MatMulBase(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    matmulcost_ptr = std::make_shared<MatMulCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<MatMulCost>()) {}
   ~MatMulBase() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
@@ -48,7 +46,6 @@ class MatMulBase : public OperatorInfo {
   Status PrepareStrategy(int32_t stage_id, size_t dev_num, Dimensions combined_partitions, size_t input0_shape_size,
                          size_t input1_shape_size, StrategyPtr* sp);
 
-  OperatorCostPtr GetOperatorCost() const override { return matmulcost_ptr; }
   Status SwapLastTwoElements(Shape* shape);
 
  protected:
@@ -66,8 +63,6 @@ class MatMulBase : public OperatorInfo {
   bool transpose_b_ = false;
   size_t mat_a_dimension_ = 0;
   size_t mat_b_dimension_ = 0;
-
-  MatMulCostPtr matmulcost_ptr;
 };
 
 class MatMul : public MatMulBase {
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
index 4697e201a4..a54d8479b3 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
@@ -33,16 +33,13 @@ class OneHotInfo : public OperatorInfo {
  public:
   OneHotInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    onehot_cost_ptr_ = std::make_shared<OneHotCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<OneHotCost>()) {}
   ~OneHotInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return onehot_cost_ptr_; }
   ReplaceGraphPtr replace_graph(const CNodePtr& cnode) override;
   std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
@@ -60,7 +57,6 @@ class OneHotInfo : public OperatorInfo {
   Status ComputeReplaceGraph(const CNodePtr& cnode);
 
   int axis_ = -1;
-  OneHotCostPtr onehot_cost_ptr_;
   int32_t rank_ = 0;
   int32_t total_class_number_ = 1;
   int32_t classes_each_device_ = 1;
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 11c518d844..a24f3e616b 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -1034,12 +1034,11 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) {
     return FAILED;
   }
   int32_t stage_id = strategy->GetInputStage();
-  double computation_cost =
-    GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
-  double communication_cost = GetOperatorCost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+  double computation_cost = cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+  double communication_cost = cost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
-    GetOperatorCost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+    cost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
     result->communication_without_parameter_ +
     COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_);
@@ -1096,7 +1095,7 @@ Status OperatorInfo::set_is_parameter(const std::vector<bool>& is_parameter) {
     return FAILED;
   }
   is_parameter_ = is_parameter;
-  GetOperatorCost()->set_is_parameter(is_parameter);
+  cost()->set_is_parameter(is_parameter);
   return SUCCESS;
 }
 
@@ -1193,7 +1192,7 @@ Status OperatorInfo::SetInputAndOutputTypeLength(const std::vector<size_t>& inpu
   }
   inputs_type_lengths_ = input_lengths;
   outputs_type_lengths_ = output_lengths;
-  GetOperatorCost()->SetInputAndOutputTypeLength(input_lengths, output_lengths);
+  cost()->SetInputAndOutputTypeLength(input_lengths, output_lengths);
   return SUCCESS;
 }
 
@@ -1211,7 +1210,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra
 }
 
 double OperatorInfo::GetForwardMemoryCostFromCNode() {
-  return GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
+  return cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
 }
 
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index e7b8af0a7e..8fcae8ad33 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -53,12 +53,13 @@ class Edge;
 
 class OperatorInfo {
  public:
-  OperatorInfo(std::string name, Shapes inputs_shape, Shapes outputs_shape, PrimitiveAttrs attrs)
+  OperatorInfo(std::string name, Shapes inputs_shape, Shapes outputs_shape, PrimitiveAttrs attrs, OperatorCostPtr cost)
       : name_(std::move(name)),
         inputs_shape_(std::move(inputs_shape)),
         outputs_shape_(std::move(outputs_shape)),
         attrs_(std::move(attrs)),
-        is_alive_(true) {
+        is_alive_(true),
+        cost_(cost) {
     std::vector<bool> not_parameteter(inputs_shape_.size(), false);
     is_parameter_ = not_parameteter;
     refkey_parameter_name_ = "";
@@ -75,7 +76,8 @@ class OperatorInfo {
   // Given the stage_id (which indicates the number of devices),
   // generate all strategies for this operator
   virtual Status GenerateStrategies(int32_t stage_id) = 0;
-  virtual OperatorCostPtr GetOperatorCost() const = 0;
+  const OperatorCostPtr& cost() const { return cost_; }
+  void set_cost(const OperatorCostPtr& cost) { cost_ = cost; }
   virtual Status SetCostUnderStrategy(const StrategyPtr& strategy) = 0;
 
   virtual std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies();
@@ -115,7 +117,7 @@ class OperatorInfo {
   void ReplaceSuccEdge(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
   void ReplacePreEdges(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
   void ReplaceSuccEdges(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
-  std::vector<size_t> GetOutputTypeLengths() const { return GetOperatorCost()->outputs_type_lengths(); }
+  std::vector<size_t> GetOutputTypeLengths() const { return cost()->outputs_type_lengths(); }
   void SetSelectedStrategyAndCost(const StrategyPtr& s_strategy, const CostPtr& cost) {
     selected_strategy_ = s_strategy;
     selected_cost_ = cost;
@@ -221,6 +223,9 @@ class OperatorInfo {
   std::string refkey_parameter_name_;
   CNodePtr cnode_;
   int32_t used_devices_ = -1;
+
+ private:
+  OperatorCostPtr cost_;
 };
 
 Shape GetSliceShape(const Shape& tensor_shape, const Dimensions& strategy);
diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
index d491ecb331..bdfb11550b 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
@@ -35,15 +35,12 @@ class PReLUInfo : public OperatorInfo {
  public:
   PReLUInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
             const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    prelucost_ptr = std::make_shared<PReLUCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<PReLUCost>()) {}
   ~PReLUInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
 
   Status GenerateStrategies(int32_t stage_id) override;
-  OperatorCostPtr GetOperatorCost() const override { return prelucost_ptr; }
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
 
  protected:
@@ -59,7 +56,6 @@ class PReLUInfo : public OperatorInfo {
 
  private:
   Dimensions input_strategy_;
-  PReLUCostPtr prelucost_ptr;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
index 5b07f8d0a9..aa64e72d05 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
@@ -109,8 +109,12 @@ Status ReduceMethod::GetAttrs() {
     }
     cross_batch_ = cross_batch_iter->second->cast<BoolImmPtr>()->value();
   }
-  reducemethodcost_ptr_->set_cross_batch(cross_batch_);
-
+  auto reducemethodcost = std::dynamic_pointer_cast<ReduceMethodCost>(cost());
+  if (reducemethodcost == nullptr) {
+    MS_LOG(ERROR) << "Cost cast to ReduceMethodCostPtr failed!";
+    return FAILED;
+  }
+  reducemethodcost->set_cross_batch(cross_batch_);
   return SUCCESS;
 }
 
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
index 8e2e17af99..c2ddbc87ce 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
@@ -34,9 +34,7 @@ class ReduceMethod : public OperatorInfo {
  public:
   ReduceMethod(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
                const PrimitiveAttrs &attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    reducemethodcost_ptr_ = std::make_shared<ReduceMethodCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReduceMethodCost>()) {}
   ~ReduceMethod() override = default;
 
   Status Init(const StrategyPtr &strategy) override;
@@ -44,13 +42,11 @@ class ReduceMethod : public OperatorInfo {
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return reducemethodcost_ptr_; }
 
  protected:
   std::string reduce_method_;
   bool keepdims_ = false;
   bool cross_batch_ = false;
-  ReduceMethodCostPtr reducemethodcost_ptr_;
   Status CheckStrategy(const StrategyPtr &strategy) override;
   Status GetAttrs() override;
   Dimensions InferOutputStrategy();
@@ -110,7 +106,7 @@ class ReduceMeanInfo : public ReduceMethod {
   ReduceMeanInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
                  const PrimitiveAttrs &attrs)
       : ReduceMethod(name, inputs_shape, outputs_shape, attrs) {
-    reducemethodcost_ptr_ = std::make_shared<ReduceMeanCost>();
+    set_cost(std::make_shared<ReduceMeanCost>());
   }
 
   ~ReduceMeanInfo() override = default;
diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
index 1d6a14b1f6..38192a5d01 100644
--- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
@@ -36,12 +36,10 @@ class ReshapeInfo : public OperatorInfo {
  public:
   ReshapeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs),
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReshapeCost>()),
         dev_num_(0),
         input_layout_set_flag_(false),
-        output_layout_set_flag_(false) {
-    reshape_cost_ptr_ = std::make_shared<ReshapeCost>();
-  }
+        output_layout_set_flag_(false) {}
   ~ReshapeInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   void SetInputLayout(const TensorLayout& input_layout) {
@@ -55,7 +53,6 @@ class ReshapeInfo : public OperatorInfo {
   Status InitForCostModel(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return reshape_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -67,7 +64,6 @@ class ReshapeInfo : public OperatorInfo {
   Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout);
   Status GetAttrs() override;
   Strategys GetOutputsStrategy();
-  ReshapeCostPtr reshape_cost_ptr_;
 
  private:
   Status GetParameterInput();
diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
index 6df5856e0c..cf850683a6 100644
--- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
@@ -34,9 +34,7 @@ class TmpIdentityInfo : public OperatorInfo {
  public:
   TmpIdentityInfo(const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs,
                   const std::string& name = IDENTITY_INFO)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    id_cost_ptr_ = std::make_shared<TmpIdentityCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TmpIdentityCost>()) {}
   ~TmpIdentityInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
@@ -44,7 +42,6 @@ class TmpIdentityInfo : public OperatorInfo {
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return id_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -54,9 +51,6 @@ class TmpIdentityInfo : public OperatorInfo {
   Status InferTensorInfo() override;
   Status InferDevMatrixShape() override;
   Status InferTensorMap() override;
-
- private:
-  TmpIdentityCostPtr id_cost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
index 4f6f6bb695..2714b352b6 100644
--- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
@@ -35,15 +35,12 @@ class TransposeInfo : public OperatorInfo {
  public:
   TransposeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                 const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    transpose_cost_ptr_ = std::make_shared<ActivationCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TransposeCost>()) {}
   ~TransposeInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return transpose_cost_ptr_; }
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -60,7 +57,6 @@ class TransposeInfo : public OperatorInfo {
   Status ComputeAxis();
   std::vector<int32_t> axis_v_;
   Dimensions input_strategy_;
-  ActivationCostPtr transpose_cost_ptr_;
 };
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
index d0278f27d9..b958adeabe 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
@@ -32,16 +32,13 @@ class VirtualDatasetInfo : public OperatorInfo {
  public:
   VirtualDatasetInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                      const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs) {
-    vd_cost_ptr_ = std::make_shared<VirtualDatasetCost>();
-  }
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<VirtualDatasetCost>()) {}
   ~VirtualDatasetInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
-  OperatorCostPtr GetOperatorCost() const override { return vd_cost_ptr_; }
   void ReComputeBatchSplitFlagList() override;
 
  protected:
@@ -53,9 +50,6 @@ class VirtualDatasetInfo : public OperatorInfo {
   Status InferTensorMap() override;
   Status GetAttrs() override;
   Status InferAsLossDivisor() override;
-
- private:
-  VirtualDatasetCostPtr vd_cost_ptr_;
 };
 
 }  // namespace parallel
diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc
index 5d18c5372f..a8f8425ae9 100644
--- a/tests/ut/cpp/parallel/ops_info/activation_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc
@@ -84,9 +84,9 @@ TEST_F(TestActivation, test_activation_strategies) {
     act_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = act_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = act_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }
@@ -109,9 +109,9 @@ TEST_F(TestActivation, test_softmax_strategies) {
     soft_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = soft_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = soft_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }
diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
index 99ca9f8e0e..2fece098e8 100644
--- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
@@ -569,7 +569,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies1) {
     matmul1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = matmul1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = matmul1->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(matmul1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
     break;
   }
@@ -599,7 +599,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies2) {
     TensorInfo replica_input1_info(tly, input1_shape, input1_slice_shape);
     replica_inputs_info.push_back(replica_input1_info);
 
-    ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(matmul3->cost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
     break;
   }
diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
index 6cb9739b1c..8c956328a7 100644
--- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
@@ -188,11 +188,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies) {
     tensor_add->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add->outputs_tensor_info();
-    double memory_cost0 = tensor_add->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost0 = tensor_add->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
     double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
-    double comm_cost0 = tensor_add->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
+    double comm_cost0 = tensor_add->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
     double comm_cost1 = cost.communication_cost_;
     bool comm = comm_cost0 - comm_cost1 <= 1.0;
 
@@ -210,11 +210,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) {
     tensor_add1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add1->outputs_tensor_info();
-    double memory_cost0 = tensor_add1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost0 = tensor_add1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
     double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
-    double comm_cost0 = tensor_add1->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
+    double comm_cost0 = tensor_add1->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
     double comm_cost1 = cost.communication_cost_;
     bool comm = comm_cost0 - comm_cost1 <= 1.0;
 
diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
index 043746498f..3971a2b471 100644
--- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
@@ -145,9 +145,9 @@ TEST_F(TestTmpIdentityInfo, test_generate_strategies) {
     identity_ptr->Init(sp);
     std::vector<TensorInfo> inputs_info = identity_ptr->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = identity_ptr->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }

From 62bbf560c66e7dee5a8c555feb8aa3fa13d118ca Mon Sep 17 00:00:00 2001
From: biffex <zhaozhiqiang4@huawei.com>
Date: Thu, 9 Apr 2020 15:04:24 +0800
Subject: [PATCH 150/367] constant duplicate mul for momentum

---
 mindspore/ccsrc/optimizer/irpass.cc           |  6 +--
 .../optimizer/irpass/arithmetic_simplify.h    | 54 ++++++++++++++++++-
 mindspore/ccsrc/utils/graph_utils.cc          |  2 +
 mindspore/ops/operations/math_ops.py          |  8 +++
 tests/ut/cpp/optimizer/lib_test.cc            | 13 +++++
 .../gtest_input/optimizer/opt_test.py         | 33 ++++++++++++
 6 files changed, 112 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc
index cdc960792f..0991c31b00 100644
--- a/mindspore/ccsrc/optimizer/irpass.cc
+++ b/mindspore/ccsrc/optimizer/irpass.cc
@@ -45,9 +45,9 @@ namespace mindspore {
 namespace opt {
 namespace irpass {
 OptimizeIRPassLib::OptimizeIRPassLib() {
-  arithmetic_simplify_ = MakeSubstitution(
-    ArithmeticSimplify(), "arithmetic_simplify",
-    {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, prim::kPrimIdentity, prim::kPrimMomentum});
+  arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify",
+                                          {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd,
+                                           prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul});
   special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate",
                                            {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType,
                                             prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv});
diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
index 8c5610ed1b..ab191aab20 100644
--- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
+++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
@@ -179,6 +179,55 @@ class OptUpdateZeroTensor : public AnfVisitor {
   }
 };
 
+// {prim::kPrimMul, Tensor1, {orim::kPrimMul, Tensor2, {...}}} ->
+// {prim::kPrimMul, {...}, {prim::kPrimMul, Tensor1, Tensor2}}
+class ConstantDuplicateMul : public AnfVisitor {
+ public:
+  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
+    Reset();
+    // {prim::kPrimMul, Tensor1, {...}}
+    AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(node);
+    if (vnode_ == nullptr || cnode_ == nullptr) {
+      return nullptr;
+    }
+    auto tensor1 = vnode_;
+    auto mul = cnode_;
+
+    Reset();
+    // {prim::kPrimMul, Tensor2, {...}}
+    AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(mul);
+    if (vnode_ == nullptr || cnode_ == nullptr) {
+      return nullptr;
+    }
+    auto tensor2 = vnode_;
+    auto cnode = cnode_;
+
+    auto PrimMul = GetValueNode<PrimitivePtr>(mul->input(0));
+    auto fg = node->func_graph();
+    auto ttmul = NewCNode({NewValueNode(PrimMul), tensor1, tensor2}, fg);
+    return NewCNode({NewValueNode(PrimMul), cnode, ttmul}, fg);
+  }
+
+  void Visit(const AnfNodePtr &node) override {
+    if (IsValueNode<tensor::Tensor>(node)) {
+      vnode_ = node;
+    }
+
+    if (IsCNode(node)) {
+      cnode_ = node->cast<CNodePtr>();
+    }
+  }
+
+  void Reset() {
+    vnode_ = nullptr;
+    cnode_ = nullptr;
+  }
+
+ private:
+  AnfNodePtr vnode_;
+  CNodePtr cnode_;
+};
+
 class ArithmeticSimplify {
  public:
   ArithmeticSimplify()
@@ -186,12 +235,14 @@ class ArithmeticSimplify {
         add_by_zero_(),
         tensor_add_by_zero_(),
         identity_(prim::kPrimIdentity),
-        opt_update_zero_tensor_() {
+        opt_update_zero_tensor_(),
+        constant_duplicate_mul_() {
     eliminaters_.emplace_back(multiply_by_zero_or_one_);
     eliminaters_.emplace_back(add_by_zero_);
     eliminaters_.emplace_back(tensor_add_by_zero_);
     eliminaters_.emplace_back(identity_);
     eliminaters_.emplace_back(opt_update_zero_tensor_);
+    eliminaters_.emplace_back(constant_duplicate_mul_);
   }
   ~ArithmeticSimplify() = default;
 
@@ -212,6 +263,7 @@ class ArithmeticSimplify {
   TensorAddByZero tensor_add_by_zero_;
   PrimEliminater identity_;
   OptUpdateZeroTensor opt_update_zero_tensor_;
+  ConstantDuplicateMul constant_duplicate_mul_;
   std::vector<TransformFuncType> eliminaters_{};
 };
 }  // namespace irpass
diff --git a/mindspore/ccsrc/utils/graph_utils.cc b/mindspore/ccsrc/utils/graph_utils.cc
index 938df2c291..55ef8dc3d5 100644
--- a/mindspore/ccsrc/utils/graph_utils.cc
+++ b/mindspore/ccsrc/utils/graph_utils.cc
@@ -400,6 +400,8 @@ static bool SameNodeShallow(const AnfNodePtr& node1, const AnfNodePtr& node2, Fu
     auto a2 = GetValueNode(node2);
     if (a1->isa<Primitive>() && a2->isa<Primitive>()) {
       return a1->cast<PrimitivePtr>()->name() == a2->cast<PrimitivePtr>()->name();
+    } else if (a1->isa<tensor::Tensor>() && a2->isa<tensor::Tensor>()) {
+      return a1->cast<tensor::TensorPtr>()->ValueEqual(*(a2->cast<tensor::TensorPtr>()));
     } else {
       return *a1 == *a2;
     }
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index d003f6ee8b..e5e89615df 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -771,6 +771,14 @@ class Mul(_MathBinaryOp):
         >>> mul(input_x, input_y)
         [4, 10, 18]
     """
+    def infer_value(self, x, y):
+        if x is not None and y is not None:
+            x = x.asnumpy()
+            y = y.asnumpy()
+            out = x * y
+            out = np.array(out, x.dtype)
+            return Tensor(out)
+        return None
 
 
 class Square(PrimitiveWithInfer):
diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc
index ff3c00d37a..2d4cf0e78e 100644
--- a/tests/ut/cpp/optimizer/lib_test.cc
+++ b/tests/ut/cpp/optimizer/lib_test.cc
@@ -543,5 +543,18 @@ TEST_F(TestOptLib, test_print_tuple_wrapper) {
   ASSERT_TRUE(CheckOpt(before2, after2, patterns));
   ASSERT_TRUE(CheckOpt(before3, before3, patterns));
 }
+
+TEST_F(TestOptLib, test_constant_duplicate_mul) {
+  FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforell");
+  FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforelr");
+  FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerl");
+  FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerr");
+  FuncGraphPtr after = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "after");
+  auto patterns = std::vector<SubstitutionPtr>({irpass.arithmetic_simplify_});
+  ASSERT_TRUE(CheckOpt(beforell, after, patterns));
+  ASSERT_TRUE(CheckOpt(beforelr, after, patterns));
+  ASSERT_TRUE(CheckOpt(beforerl, after, patterns));
+  ASSERT_TRUE(CheckOpt(beforerr, after, patterns));
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
index 53eb2130f0..d494ad27d3 100644
--- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
@@ -16,6 +16,8 @@
 from mindspore.ops import Primitive, PrimitiveWithInfer
 from mindspore.ops import operations as P
 from mindspore.ops.operations import _grad_ops as G
+from mindspore import Tensor
+import numpy as np
 
 # pylint: disable=unused-variable
 
@@ -903,3 +905,34 @@ def test_print_tuple_wrapper(tag):
         return print_(make_tuple(x, y, z))
 
     return fns[tag]
+
+def test_constant_duplicate_mul(tag):
+    fns = FnDict()
+    Mul = Primitive('Mul');
+    Sqrt = Primitive('Sqrt');
+
+    x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32'))
+    tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
+    tensor2 = Tensor(np.array([[2.2, 3.1], [3.2, 4.2]]).astype('float32'))
+
+    @fns
+    def beforell():
+        return Mul(tensor1, Mul(tensor2, Sqrt(x)))
+
+    @fns
+    def beforelr():
+        return Mul(tensor1, Mul(Sqrt(x), tensor2))
+
+    @fns
+    def beforerl():
+        return Mul(Mul(Sqrt(x), tensor2), tensor1)
+
+    @fns
+    def beforerr():
+        return Mul(Mul(Sqrt(x), tensor2), tensor1)
+
+    @fns
+    def after():
+        return Mul(Sqrt(x), Mul(tensor1, tensor2))
+
+    return fns[tag]

From 19f2ea6d41f9a6299eab4b325fac23ab41954de9 Mon Sep 17 00:00:00 2001
From: Zirui Wu <zirui.wu@huawei.com>
Date: Thu, 9 Apr 2020 10:44:14 -0400
Subject: [PATCH 151/367] change int32 to int64 to avoid overflow in batch

fix clang

pybind fix
---
 mindspore/ccsrc/dataset/api/python_bindings.cc     |  2 +-
 .../ccsrc/dataset/engine/datasetops/batch_op.cc    |  2 +-
 .../ccsrc/dataset/engine/datasetops/batch_op.h     | 14 +++++++-------
 .../dataset/engine/datasetops/source/voc_op.h      |  4 ++--
 4 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index e6c2691281..1b0d913f3e 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -406,7 +406,7 @@ void bindSamplerOps(py::module *m) {
 
 void bindInfoObjects(py::module *m) {
   (void)py::class_<BatchOp::CBatchInfo>(*m, "CBatchInfo")
-    .def(py::init<int32_t, int32_t, int32_t>())
+    .def(py::init<int64_t, int64_t, int64_t>())
     .def("get_epoch_num", &BatchOp::CBatchInfo::get_epoch_num)
     .def("get_batch_num", &BatchOp::CBatchInfo::get_batch_num);
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
index 8778fe1b45..c80078cb44 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc
@@ -57,7 +57,7 @@ BatchOp::BatchOp(int32_t batch_size, bool drop, int32_t op_queue_size, int32_t n
 Status BatchOp::operator()() {
   RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
   TaskManager::FindMe()->Post();
-  int32_t epoch_num = 0, batch_num = 0, cnt = 0;
+  int64_t epoch_num = 0, batch_num = 0, cnt = 0;
   TensorRow new_row;
   std::unique_ptr<TensorQTable> table = std::make_unique<TensorQTable>();
   child_iterator_ = std::make_unique<ChildIterator>(this, 0, 0);
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.h b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.h
index 9037b8e94e..32d386e3c9 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.h
@@ -124,17 +124,17 @@ class BatchOp : public ParallelOp {
   // This struct is used for both internal control and python callback.
   // This struct is bound to python with read-only access.
   struct CBatchInfo {
-    CBatchInfo(int32_t ep, int32_t bat, int32_t cur, batchCtrl ctrl)
+    CBatchInfo(int64_t ep, int64_t bat, int64_t cur, batchCtrl ctrl)
         : epoch_num_(ep), batch_num_(bat), total_batch_num_(cur), ctrl_(ctrl) {}
-    CBatchInfo(int32_t ep, int32_t bat, int32_t cur) : CBatchInfo(ep, bat, cur, batchCtrl::kNoCtrl) {}
+    CBatchInfo(int64_t ep, int64_t bat, int64_t cur) : CBatchInfo(ep, bat, cur, batchCtrl::kNoCtrl) {}
     CBatchInfo() : CBatchInfo(0, 0, 0, batchCtrl::kNoCtrl) {}
     explicit CBatchInfo(batchCtrl ctrl) : CBatchInfo(0, 0, 0, ctrl) {}
-    int32_t epoch_num_;        // i-th epoch. i starts from 0
-    int32_t batch_num_;        // i-th batch since the start of current epoch. i starts from 0
-    int32_t total_batch_num_;  // i-th batch since the start of first epoch. i starts from 0
+    int64_t epoch_num_;        // i-th epoch. i starts from 0
+    int64_t batch_num_;        // i-th batch since the start of current epoch. i starts from 0
+    int64_t total_batch_num_;  // i-th batch since the start of first epoch. i starts from 0
     batchCtrl ctrl_;           // No control=0, EOE=1, EOF=2, Quit=3
-    const int32_t get_batch_num() const { return batch_num_; }
-    const int32_t get_epoch_num() const { return epoch_num_; }
+    const int64_t get_batch_num() const { return batch_num_; }
+    const int64_t get_epoch_num() const { return epoch_num_; }
   };
 
   // BatchOp constructor
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.h
index a267ef866a..5751388519 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.h
@@ -201,8 +201,8 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
   Status Reset() override;
 
   bool decode_;
-  uint64_t row_cnt_;
-  uint64_t buf_cnt_;
+  int64_t row_cnt_;
+  int64_t buf_cnt_;
   int64_t num_rows_;
   int64_t num_samples_;
   std::string folder_path_;

From 59a714c654dbbab090cc8af0012a6b1fc1e4e3a1 Mon Sep 17 00:00:00 2001
From: Cathy Wong <cathy.wong@huawei.com>
Date: Thu, 9 Apr 2020 15:22:33 -0400
Subject: [PATCH 152/367] Correct shuffle UT buffer_size > #dataset-row as
 valid

---
 .../data/dataset/golden/shuffle_05_result.npz | Bin 0 -> 1507 bytes
 tests/ut/python/dataset/test_shuffle.py       |  39 +++++++++---------
 2 files changed, 20 insertions(+), 19 deletions(-)
 create mode 100644 tests/ut/data/dataset/golden/shuffle_05_result.npz

diff --git a/tests/ut/data/dataset/golden/shuffle_05_result.npz b/tests/ut/data/dataset/golden/shuffle_05_result.npz
new file mode 100644
index 0000000000000000000000000000000000000000..27eb0a470d370fab9d4938e5fb9e3eb50d603bd1
GIT binary patch
literal 1507
zcmbW1OH&hB6vw+00!c(c4L<NegJLciBjAIF4_bM&<}V?lD2OrU(Fh7jZYMaC8K)*2
zwZ>UmWtAJtD)+OVZ!lkD_Ooy{uH(5UT@6!HZoH}d``mL*e)pVH>6?PTC89M~ul9TG
z?dO$%rm1g8i)dNL$qdDd<(jT_vQs6hpjvN}i*u=EU3;$m94*+Dyc3-;qA%v7F(bNK
za;i?Ym?=30Te)Ym8x@=Um9=cyro9-C8N(w3G2<8GyH~jG&lk5g%e8pE<k<1et&QqB
z)tjw_Gmf1v6)RO|D_;#ut(g@r7MfA1LwhNGQ+iJNU(1#S84%DBLi;P(>ZTT|wXz+S
zZA(6CbAC()p+SEwLklu2;9t_o`$FzYCX<N<Ef!2xWqa<owA?Mya*seK_S$LLA>f0b
zj85?;sAfj14xJo(1@>V-8T$kd;2;_M1@@qWjEF!B0%UXvv|~3J2Lyr$k#SHUf-W)+
z3ACb(jKcz9>>{IEz(6nhaDp~R7~!TI{m<EMLB|A6q93OarS#(hF`PjhXKB+Ta1_UI
z96hu#_+<BpUV%e6jBXsENFVp;HgQ5gU;u+SO_7saw%bfUSM0WUO2EW5T*nlqF@srH
zlpghtP~Eye6aTAO&BSeJK;Q;$A_3qQ=8(iZVFm@3u#5*-!9%3+2pMvo78t`gCU6Ot
zaRpZ~NzRy8xf-Th+8Kc%3}Xc6a2^+M5u=nD_h!0dSZ4)NxQzwe!Cfrk9_~}-kT;qg
z!yERdvSYj>SkI>AIpj#sBTIS#Pf0IggLD*Yq+`gFj^hdG1Z>hvct&~|1=1^6CB15y
z{nj4_{h??xX-$6dHm3F0=SJi2X5^Y>M*scfZmwI_c4W#j`#${nxS2L>nE?+lW0^hw
zyfm7TS<CG5B9>(izJLF*8M$GZ9bVc^%k1_d2?VJqKqtMG(CUo_8FL7cCg&Qz(qkTN
zq$&7FZ=;oT0WG9=5GK6~Kj|U@r1zM0pIJ)@I=N2HWoA84Z=Ry9Fzq4J(oB2Av<%Z8
zGcC)s9MkelD{Q}##IPB;`X;DeX<CZ=gt=?XU1#o7=00QY26H!=TV!sDx#a{ktU8vA
zbf8cbMpYTL#i-|u`hihDGO9)>yDERm#Z%CI5&yUDWg3FJ>gp=mzNJ$Lv?_;ApHX_g
H<D2>`G6I{m

literal 0
HcmV?d00001

diff --git a/tests/ut/python/dataset/test_shuffle.py b/tests/ut/python/dataset/test_shuffle.py
index 2b7a251d2c..4a823c5fb7 100644
--- a/tests/ut/python/dataset/test_shuffle.py
+++ b/tests/ut/python/dataset/test_shuffle.py
@@ -98,6 +98,25 @@ def test_shuffle_04():
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
+def test_shuffle_05():
+    """
+    Test shuffle: buffer_size > number-of-rows-in-dataset
+    """
+    logger.info("test_shuffle_05")
+    # define parameters
+    buffer_size = 13
+    seed = 1
+    parameters = {"params": {'buffer_size': buffer_size, "seed": seed}}
+
+    # apply dataset operations
+    data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
+    ds.config.set_seed(seed)
+    data1 = data1.shuffle(buffer_size=buffer_size)
+
+    filename = "shuffle_05_result.npz"
+    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+
+
 def test_shuffle_exception_01():
     """
     Test shuffle exception: buffer_size<0
@@ -152,24 +171,6 @@ def test_shuffle_exception_03():
         assert "buffer_size" in str(e)
 
 
-def test_shuffle_exception_04():
-    """
-    Test shuffle exception: buffer_size > number-of-rows-in-dataset
-    """
-    logger.info("test_shuffle_exception_04")
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR)
-    ds.config.set_seed(1)
-    try:
-        data1 = data1.shuffle(buffer_size=13)
-        sum([1 for _ in data1])
-
-    except BaseException as e:
-        logger.info("Got an exception in DE: {}".format(str(e)))
-        assert "buffer_size" in str(e)
-
-
 def test_shuffle_exception_05():
     """
     Test shuffle exception: Missing mandatory buffer_size input parameter
@@ -229,10 +230,10 @@ if __name__ == '__main__':
     test_shuffle_02()
     test_shuffle_03()
     test_shuffle_04()
+    test_shuffle_05()
     test_shuffle_exception_01()
     test_shuffle_exception_02()
     test_shuffle_exception_03()
-    test_shuffle_exception_04()
     test_shuffle_exception_05()
     test_shuffle_exception_06()
     test_shuffle_exception_07()

From b0fc7b7289f37529aa9ae92e64355e4802c694e8 Mon Sep 17 00:00:00 2001
From: yoonlee666 <qiuyunlei@huawei.com>
Date: Fri, 10 Apr 2020 10:51:54 +0800
Subject: [PATCH 153/367] change op Slice to StridedSlice in bert model

---
 mindspore/model_zoo/Bert_NEZHA/bert_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_model.py b/mindspore/model_zoo/Bert_NEZHA/bert_model.py
index d7f9355b3c..b9c6e8c4a1 100644
--- a/mindspore/model_zoo/Bert_NEZHA/bert_model.py
+++ b/mindspore/model_zoo/Bert_NEZHA/bert_model.py
@@ -194,7 +194,7 @@ class EmbeddingPostprocessor(nn.Cell):
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.gather = P.GatherV2()
         self.use_relative_positions = use_relative_positions
-        self.slice = P.Slice()
+        self.slice = P.StridedSlice()
         self.full_position_embeddings = Parameter(initializer
                                                   (TruncatedNormal(initializer_range),
                                                    [max_position_embeddings,
@@ -216,7 +216,7 @@ class EmbeddingPostprocessor(nn.Cell):
             output += token_type_embeddings
         if not self.use_relative_positions:
             _, seq, width = self.shape
-            position_embeddings = self.slice(self.full_position_embeddings, [0, 0], [seq, width])
+            position_embeddings = self.slice(self.full_position_embeddings, (0, 0), (seq, width), (1, 1))
             position_embeddings = self.reshape(position_embeddings, (1, seq, width))
             output += position_embeddings
         output = self.layernorm(output)

From d1a0ded6c2f5c9077464ff67e6d3231680cba00f Mon Sep 17 00:00:00 2001
From: chenfei <chenfei52@huawei.com>
Date: Fri, 27 Mar 2020 14:49:16 +0800
Subject: [PATCH 154/367] use first depend create parameter

---
 mindspore/ccsrc/operator/ops.cc           |  3 +
 mindspore/ccsrc/operator/ops.h            |  3 +
 mindspore/ccsrc/session/ascend_session.cc | 59 +++++++++++----
 mindspore/ccsrc/session/ascend_session.h  |  6 +-
 mindspore/ccsrc/session/kernel_graph.cc   |  3 +-
 mindspore/ccsrc/session/kernel_graph.h    |  5 ++
 mindspore/ccsrc/session/session_basic.cc  | 90 ++++++++++++++++-------
 mindspore/ccsrc/session/session_basic.h   |  5 +-
 mindspore/ccsrc/vm/backend.cc             |  2 +-
 9 files changed, 128 insertions(+), 48 deletions(-)
 mode change 100644 => 100755 mindspore/ccsrc/operator/ops.cc
 mode change 100644 => 100755 mindspore/ccsrc/operator/ops.h
 mode change 100644 => 100755 mindspore/ccsrc/session/ascend_session.cc
 mode change 100644 => 100755 mindspore/ccsrc/session/ascend_session.h
 mode change 100644 => 100755 mindspore/ccsrc/session/kernel_graph.cc
 mode change 100644 => 100755 mindspore/ccsrc/session/kernel_graph.h
 mode change 100644 => 100755 mindspore/ccsrc/session/session_basic.cc
 mode change 100644 => 100755 mindspore/ccsrc/session/session_basic.h
 mode change 100644 => 100755 mindspore/ccsrc/vm/backend.cc

diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc
old mode 100644
new mode 100755
index 12e6b70a6f..f3053cac7d
--- a/mindspore/ccsrc/operator/ops.cc
+++ b/mindspore/ccsrc/operator/ops.cc
@@ -154,6 +154,9 @@ const PrimitivePtr kPrimMul = std::make_shared<Primitive>("Mul");
 const PrimitivePtr kPrimMinimum = std::make_shared<Primitive>("Minimum");
 const PrimitivePtr kPrimMaximum = std::make_shared<Primitive>("Maximum");
 const PrimitivePtr kPrimSquare = std::make_shared<Primitive>("Square");
+const PrimitivePtr kPrimEqual = std::make_shared<Primitive>("Equal");
+const PrimitivePtr kPrimLess = std::make_shared<Primitive>("Less");
+const PrimitivePtr kPrimLessEqual = std::make_shared<Primitive>("LessEqual");
 
 // NN
 const PrimitivePtr kPrimFlatten = std::make_shared<Primitive>("Flatten");
diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
old mode 100644
new mode 100755
index 5fbf2b7067..2dc7072972
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -160,6 +160,9 @@ extern const PrimitivePtr kPrimMul;
 extern const PrimitivePtr kPrimMinimum;
 extern const PrimitivePtr kPrimMaximum;
 extern const PrimitivePtr kPrimSquare;
+extern const PrimitivePtr kPrimEqual;
+extern const PrimitivePtr kPrimLess;
+extern const PrimitivePtr kPrimLessEqual;
 
 // NN
 extern const PrimitivePtr kPrimFlatten;
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
old mode 100644
new mode 100755
index 34c05aed08..f255b2f15f
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -506,11 +506,13 @@ void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true
   kernel_build_info_builder->SetFusionType(kernel::FusionType::OPAQUE);
   kernel_build_info_builder->SetProcessor(kernel::Processor::AICORE);
   kernel_build_info_builder->SetKernelType(KernelType::RT_KERNEL);
-  // condition graph's output must be single output
-  if (condition_graph->outputs().size() != 1) {
-    MS_LOG(EXCEPTION) << "Condition_graph output num " << condition_graph_id << " should be 1";
+  auto cond_output_it = condition_output_.find(condition_graph_id);
+  if (cond_output_it == condition_output_.end()) {
+    MS_LOG(EXCEPTION) << "Can't find condition graph" << condition_graph_id;
   }
-  AnfNodePtr cond_output_kernel = condition_graph->outputs()[0];
+  auto cond_output_kernel =
+    AnfAlgo::VisitKernel(condition_graph->GetBackendAnfByFrontAnf(cond_output_it->second), 0).first;
+  MS_EXCEPTION_IF_NULL(cond_output_kernel);
   std::vector<AnfNodePtr> inputs = {NewValueNode(switch_primitive), cond_output_kernel, counter_const};
   CNodePtr switch_node = condition_graph->NewCNode(inputs);
   AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), switch_node.get());
@@ -569,12 +571,14 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) {
   }
 }
 
-void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id, GraphId false_graph_id) {
+void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id, GraphId false_graph_id,
+                                  const AnfNodePtr &output) {
   if (switches_.find(cond_graph_id) != switches_.end()) {
     MS_LOG(WARNING) << "Condition graph" << cond_graph_id << " has been set before ";
     return;
   }
   switches_[cond_graph_id] = std::pair<GraphId, GraphId>(true_graph_id, false_graph_id);
+  condition_output_[cond_graph_id] = output;
   MS_LOG(INFO) << "New switch compile " << cond_graph_id << " " << true_graph_id << " " << false_graph_id;
   // set the type of condition graph
   auto cond_graph_index = ExecOrderOfChildGraph(final_graph_id_, cond_graph_id);
@@ -682,12 +686,14 @@ void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const An
   auto from_graph_id = GetGraphIdByNode(front_anf);
   auto from_graph = GetGraph(from_graph_id);
   MS_EXCEPTION_IF_NULL(from_graph);
-
+  auto to_graph_id = AnfAlgo::GetGraphId(backend_parameter.get());
+  auto to_graph = GetGraph(to_graph_id);
+  auto backend_arg = from_graph->GetBackendAnfByFrontAnf(front_anf);
+  MS_EXCEPTION_IF_NULL(to_graph);
   MS_LOG(INFO) << "Set node[" << front_anf->DebugString() << "] of graph[" << from_graph_id << "]to node["
                << backend_parameter->DebugString() << "] of graph[" << AnfAlgo::GetGraphId(backend_parameter.get())
                << "]";
   // a node should not assign to itself
-  auto backend_arg = from_graph->GetBackendAnfByFrontAnf(front_anf);
   if (backend_arg.get() == backend_parameter.get()) {
     return;
   }
@@ -703,15 +709,16 @@ void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const An
       return;
     }
   }
-  InsertMultipleAssignToGraph(from_graph_id, backend_arg, backend_parameter);
-  // if front anf is a parameter, we can assign the value back, because backend_parameter
-  // won't be changed in it's graph unless it's a weight. If backend_parameter is a weight,
-  // we do should assign the value back.
-  auto to_graph_id = AnfAlgo::GetGraphId(backend_parameter.get());
-  auto to_graph = GetGraph(to_graph_id);
-  MS_EXCEPTION_IF_NULL(to_graph);
+  // if a parameter is a weight and not linked to any executable node,device type will be kTypeUnknown,set it's device
+  // type same to arg
+  if (AnfAlgo::GetOutputDeviceDataType(backend_parameter, 0) == kTypeUnknown) {
+    AnfAlgo::SetSelectKernelBuildInfo(AnfAlgo::GetSelectKernelBuildInfo(backend_arg), backend_parameter.get());
+  }
+  InsertAssignToGraph(from_graph_id, backend_arg, backend_parameter);
+  // if front anf is a parameter,we can assign the value back,because backend_parameter won't be change in it's graph
+  // unless it's a weigth.If backend_parameter is a weight,we do should assign the value back
   if (backend_arg->isa<Parameter>() && !to_graph->execution_order().empty()) {
-    InsertMultipleAssignToGraph(to_graph_id, backend_parameter, backend_arg);
+    InsertAssignToGraph(to_graph_id, backend_parameter, backend_arg);
   }
   MS_LOG(INFO) << "Finish!";
 }
@@ -755,7 +762,25 @@ void AscendSession::SetChildGraphInput(GraphId g, const VectorRef &args) {
   DumpGraphInputArgs(args);
   UpdateGraphOrder(g);
   std::vector<AnfNodePtr> graph_inputs = to_graph->inputs();
+  auto valid_inputs = to_graph->ValidInputs();
+  size_t real_args_size = 0;
+  for (size_t i = 0; i < args.size(); i++) {
+    real_args_size += AnfAlgo::GetAllOutput(utils::cast<AnfNodePtr>(args[i]), {prim::kPrimTupleGetItem}).size();
+  }
+  if (real_args_size != graph_inputs.size()) {
+    for (size_t j = 0; j < valid_inputs.size(); j++) {
+      if (valid_inputs[j]) {
+        MS_LOG(INFO) << "index: " << j << ", nodes: " << graph_inputs[j]->DebugString();
+      }
+    }
+    MS_LOG(WARNING) << "real_args_size: " << real_args_size << ", graph_inputs.size(): " << graph_inputs.size()
+                    << " not equal";
+  }
   size_t input_index = 0;
+  if (graph_inputs.size() != valid_inputs.size()) {
+    MS_LOG(EXCEPTION) << "graph_inputs.size(): " << graph_inputs.size()
+                      << ", valid_inputs.size(): " << valid_inputs.size() << " not equal";
+  }
   for (size_t i = 0; i < args.size(); i++) {
     if (input_index >= graph_inputs.size()) {
       MS_LOG(EXCEPTION) << "input_index " << input_index << " out of range size " << graph_inputs.size();
@@ -763,6 +788,10 @@ void AscendSession::SetChildGraphInput(GraphId g, const VectorRef &args) {
     if (utils::isa<AnfNodePtr>(args[i])) {
       // arg is a anf node
       for (const auto &real_arg : AnfAlgo::GetAllOutput(utils::cast<AnfNodePtr>(args[i]), {prim::kPrimTupleGetItem})) {
+        if (!valid_inputs[input_index]) {
+          MS_LOG(DEBUG) << "Invalid input arg" << real_arg->DebugString();
+          continue;
+        }
         SetChildGraphParameter(real_arg, graph_inputs[input_index]);
         input_index++;
       }
diff --git a/mindspore/ccsrc/session/ascend_session.h b/mindspore/ccsrc/session/ascend_session.h
old mode 100644
new mode 100755
index caec4b35f7..c45ab6630a
--- a/mindspore/ccsrc/session/ascend_session.h
+++ b/mindspore/ccsrc/session/ascend_session.h
@@ -49,9 +49,8 @@ class AscendSession : public SessionBasic {
   // set output of final graph
   void SetFinalGraphOutput(const BaseRef &output) override;
   // insert switch and set the relative active ops
-  void SwitchCompile(GraphId cond_g, GraphId true_g, GraphId false_g) override;
-  // set args of child graph. the arg maybe come from a output of other child graphs,
-  // or from final graph's parameter
+  void SwitchCompile(GraphId cond_g, GraphId true_g, GraphId false_g, const AnfNodePtr &condition_output) override;
+  // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter
   void SetChildGraphInput(GraphId g, const VectorRef &args) override;
   // get graph id in child graphs by ME front anf node pointer
   GraphId GetGraphIdByNode(const AnfNodePtr &front_anf) const override;
@@ -116,6 +115,7 @@ class AscendSession : public SessionBasic {
   std::unordered_map<GraphId, GraphId> while_condition_graphs_;
   // record all conditions
   std::unordered_map<GraphId, std::pair<GraphId, GraphId>> switches_;
+  std::unordered_map<GraphId, AnfNodePtr> condition_output_;
   // final_graph_id is used in every root graph has it's own session situation
   GraphId final_graph_id_;
 };
diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc
old mode 100644
new mode 100755
index 84ff6b81a2..dbf6e07e7e
--- a/mindspore/ccsrc/session/kernel_graph.cc
+++ b/mindspore/ccsrc/session/kernel_graph.cc
@@ -372,8 +372,7 @@ void KernelGraph::UpdateControlDependRelations(const std::vector<AnfNodePtr> &de
     MS_EXCEPTION_IF_NULL(depend_node);
     std::vector<AnfNodePtr> prior_nodes = {prior_node};
     std::vector<AnfNodePtr> depend_nodes = {depend_node};
-    MS_LOG(INFO) << "Prior node[" << prior_node->DebugString() << "],depend node[" << depend_node->DebugString()
-                 << "],depend_mode=[" << AnfAlgo::GetNodeAttr<int>(cnode, "depend_mode") << "]";
+    MS_LOG(INFO) << "Prior node[" << prior_node->DebugString() << "], depend node[" << depend_node->DebugString();
     if (prior_node->isa<Parameter>()) {
       prior_nodes = GetOutputNodes(prior_node);
     }
diff --git a/mindspore/ccsrc/session/kernel_graph.h b/mindspore/ccsrc/session/kernel_graph.h
old mode 100644
new mode 100755
index e11f6807f5..ff964482bb
--- a/mindspore/ccsrc/session/kernel_graph.h
+++ b/mindspore/ccsrc/session/kernel_graph.h
@@ -86,6 +86,9 @@ class KernelGraph : public FuncGraph {
   bool executable() const { return executable_; }
   // set executable of graph
   void set_executable(bool executable) { executable_ = executable; }
+  // set invalid inputs for control sink
+  std::vector<bool> *MutableValidInputs() { return &valid_inputs_; }
+  std::vector<bool> ValidInputs() { return valid_inputs_; }
 
  private:
   // remove value node form graph
@@ -118,6 +121,8 @@ class KernelGraph : public FuncGraph {
   std::unordered_map<AnfNodePtr, std::vector<std::pair<AnfNodePtr, size_t>>> node_output_edges_;
   // graph needn't execute
   bool executable_;
+  // valid inputs
+  std::vector<bool> valid_inputs_;
 };
 }  // namespace session
 using KernelGraphPtr = std::shared_ptr<session::KernelGraph>;
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
old mode 100644
new mode 100755
index ede3ae7419..d2a255229d
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -243,29 +243,38 @@ ValueNodePtr CreateNewValueNode(const AnfNodePtr &anf, KernelGraph *graph) {
   return new_value_node;
 }
 
-ParameterPtr CreateNewParameterFromParameter(const AnfNodePtr &anf, KernelGraph *graph) {
+ParameterPtr CreateNewParameterFromParameter(const AnfNodePtr &anf, bool valid_input, KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(anf);
   if (!anf->isa<Parameter>()) {
     MS_LOG(EXCEPTION) << "anf[" << anf->DebugString() << "] is not a parameter";
   }
   auto graph_inputs = graph->MutableInputs();
   MS_EXCEPTION_IF_NULL(graph_inputs);
+  auto valid_inputs = graph->MutableValidInputs();
+  MS_EXCEPTION_IF_NULL(valid_inputs);
   ParameterPtr new_parameter = graph->NewParameter(anf->cast<ParameterPtr>());
-  graph->FrontBackendlMapAdd(anf, new_parameter);
   graph_inputs->push_back(new_parameter);
+  valid_inputs->push_back(valid_input);
   return new_parameter;
 }
 
-std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, KernelGraph *graph) {
+std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, bool valid_input, KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(graph);
   std::vector<AnfNodePtr> parameters;
   std::vector<AnfNodePtr> pre_graph_out = AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem});
+  auto valid_inputs = graph->MutableValidInputs();
+  MS_EXCEPTION_IF_NULL(valid_inputs);
+  auto graph_inputs = graph->MutableInputs();
+  MS_EXCEPTION_IF_NULL(graph_inputs);
   auto create_parameter = [&](const AbstractBasePtr &abstract) -> void {
     auto parameter = graph->NewParameter();
     MS_EXCEPTION_IF_NULL(parameter);
     parameter->set_abstract(abstract);
-    parameters.push_back(graph->NewParameter(parameter));
+    auto new_parameter = graph->NewParameter(parameter);
+    parameters.push_back(new_parameter);
+    valid_inputs->push_back(valid_input);
+    graph_inputs->push_back(new_parameter);
   };
   for (const auto &out_node : pre_graph_out) {
     MS_EXCEPTION_IF_NULL(out_node);
@@ -287,18 +296,15 @@ std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, KernelG
   return parameters;
 }
 
-AnfNodePtr CreateNewParameterFromCNode(const AnfNodePtr &anf, KernelGraph *graph) {
+AnfNodePtr CreateNewParameterFromCNode(const AnfNodePtr &anf, bool valid_input, KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(anf);
   if (!anf->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "anf[" << anf->DebugString() << "] is not a cnode";
+    MS_LOG(EXCEPTION) << "Anf[" << anf->DebugString() << "] is not a cnode";
   }
-  MS_LOG(INFO) << "create a new parameter from cnode[" << anf->DebugString() << "]";
-  auto parameters = CreateParameterFromTuple(anf, graph);
-  auto graph_inputs = graph->MutableInputs();
-  MS_EXCEPTION_IF_NULL(graph_inputs);
-  (void)std::copy(parameters.begin(), parameters.end(), std::back_inserter(*graph_inputs));
+  MS_LOG(INFO) << "Create a new parameter from cnode[" << anf->DebugString() << "]";
+  auto parameters = CreateParameterFromTuple(anf, valid_input, graph);
   if (parameters.empty()) {
-    MS_LOG(EXCEPTION) << "no parameter exist!!";
+    MS_LOG(EXCEPTION) << "No parameter exist!!";
   }
   if (parameters.size() == 1) {
     return parameters[0];
@@ -307,7 +313,7 @@ AnfNodePtr CreateNewParameterFromCNode(const AnfNodePtr &anf, KernelGraph *graph
   (void)std::copy(parameters.begin(), parameters.end(), std::back_inserter(make_tuple_input));
   auto make_tuple = graph->NewCNode(make_tuple_input);
   MS_EXCEPTION_IF_NULL(make_tuple);
-  MS_LOG(INFO) << "new make tuple [" << make_tuple->DebugString() << "] of parameters";
+  MS_LOG(INFO) << "New make tuple [" << make_tuple->DebugString() << "] of parameters";
   return make_tuple;
 }
 
@@ -397,14 +403,20 @@ void DumpGraphOutput(const Any &any, size_t recurse_level = 0) {
 
 GraphId SessionBasic::graph_sum_ = 0;
 
-CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph) {
+CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, bool valid_input, KernelGraph *graph,
+                                      bool *from_other_graph,
+                                      std::unordered_map<AnfNodePtr, AnfNodePtr> *other_graph_cnode) {
   MS_EXCEPTION_IF_NULL(cnode);
   MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(from_other_graph);
+  MS_EXCEPTION_IF_NULL(other_graph_cnode);
+  *from_other_graph = false;
   // get primitive of old node
   auto prim = AnfAlgo::GetCNodePrimitive(cnode);
   MS_EXCEPTION_IF_NULL(prim);
   // push attr to inputs[0] of new cnode
   std::vector<AnfNodePtr> cnode_inputs = {std::make_shared<ValueNode>(std::make_shared<Primitive>(*prim))};
+  // if has multiple depends,only select first depend as parameter
   for (size_t input_idx = 1; input_idx < cnode->inputs().size(); input_idx++) {
     auto anf = cnode->inputs()[input_idx];
     MS_EXCEPTION_IF_NULL(anf);
@@ -412,6 +424,9 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph)
     if (graph->GetBackendAnfByFrontAnf(anf) != nullptr) {
       cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(anf));
       continue;
+    } else if (other_graph_cnode->find(anf) != other_graph_cnode->end()) {
+      cnode_inputs.push_back((*other_graph_cnode)[anf]);
+      continue;
     } else if (anf->isa<ValueNode>() && !IsValueNode<FuncGraph>(anf)) {
       // if input is a value node,
       auto new_value_node = CreateNewValueNode(anf, graph);
@@ -421,38 +436,60 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph)
       continue;
     } else if (anf->isa<Parameter>()) {
       // if anf is a parameter
-      cnode_inputs.emplace_back(CreateNewParameterFromParameter(anf, graph));
+      auto new_parameter = CreateNewParameterFromParameter(anf, valid_input, graph);
+      cnode_inputs.push_back(new_parameter);
+      if (GetGraphIdByNode(anf) == kInvalidGraphId) {
+        graph->FrontBackendlMapAdd(anf, new_parameter);
+      } else {
+        (*other_graph_cnode)[anf] = new_parameter;
+      }
       continue;
     } else if (anf->isa<CNode>()) {
+      *from_other_graph = true;
       // the input node is a cnode from other graph
-      cnode_inputs.emplace_back(CreateNewParameterFromCNode(anf, graph));
+      auto parameter_from_cnode = CreateNewParameterFromCNode(anf, valid_input, graph);
+      cnode_inputs.push_back(parameter_from_cnode);
+      (*other_graph_cnode)[anf] = parameter_from_cnode;
       continue;
     }
-    MS_LOG(EXCEPTION) << "unexpected input[" << anf->DebugString() << "]";
+    MS_LOG(EXCEPTION) << "Unexpected input[" << anf->DebugString() << "]";
   }
-  return graph->NewCNode(cnode_inputs);
+  TraceManager::DebugTrace(std::make_shared<TraceCopy>(cnode->debug_info()));
+  auto new_cnode = graph->NewCNode(cnode_inputs);
+  TraceManager::EndTrace();
+  return new_cnode;
 }
 
 KernelGraphPtr SessionBasic::ConstructKernelGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
+  std::unordered_map<AnfNodePtr, AnfNodePtr> other_graph_cnode;
   auto graph = std::make_shared<KernelGraph>();
   graph->set_graph_id(graph_sum_);
+  MS_LOG(INFO) << "Create graph: " << graph_sum_;
+  size_t from_other_graph_depend_num = 0;
   for (const auto &node : lst) {
     MS_EXCEPTION_IF_NULL(node);
-    MS_LOG(DEBUG) << "start create new cnode,node = " << node->DebugString();
+    MS_LOG(DEBUG) << "Start create new cnode, node = " << node->DebugString();
     if (!node->isa<CNode>()) {
-      MS_LOG(EXCEPTION) << "Inst node " << node->DebugString() << " is not CNode";
+      MS_LOG(EXCEPTION) << "Node " << node->DebugString() << " is not CNode";
     }
     auto cnode = node->cast<CNodePtr>();
     MS_EXCEPTION_IF_NULL(cnode);
-    TraceManager::DebugTrace(std::make_shared<TraceCopy>(cnode->debug_info()));
     // create a new cnode object
-    auto new_cnode = CreateNewCNode(cnode, graph.get());
+    bool from_other_graph = false;
+    // only first depend from other graph can create
+    bool valid_input = true;
+    if (from_other_graph_depend_num != 0 && AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) {
+      valid_input = false;
+    }
+    auto new_cnode = CreateNewCNode(cnode, valid_input, graph.get(), &from_other_graph, &other_graph_cnode);
+    if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) && from_other_graph) {
+      from_other_graph_depend_num++;
+    }
     MS_EXCEPTION_IF_NULL(new_cnode);
     new_cnode->set_abstract(cnode->abstract());
     new_cnode->set_scope(cnode->scope());
     // record map relations between anf from ME and new anf node used in backend
     graph->FrontBackendlMapAdd(node, new_cnode);
-    TraceManager::EndTrace();
   }
   // add a make_tuple at the end of graph as output
   graph->set_output(ConstructOutput(outputs, graph));
@@ -631,12 +668,15 @@ void SessionBasic::ToTensorPtr(const OpRunInfo &op_run_info, std::vector<tensor:
 CNodePtr SessionBasic::ConstructOutput(const AnfNodePtrList &outputs, const std::shared_ptr<KernelGraph> &graph) {
   MS_EXCEPTION_IF_NULL(graph);
   std::vector<AnfNodePtr> output_args;
-  auto FindEqu = [graph](const AnfNodePtr &out) -> AnfNodePtr {
+  auto FindEqu = [graph, outputs](const AnfNodePtr &out) -> AnfNodePtr {
     auto backend_anf = graph->GetBackendAnfByFrontAnf(out);
     if (backend_anf != nullptr) {
       return backend_anf;
     }
-    MS_LOG(EXCEPTION) << "Can not find the node in the equiv map!";
+    for (const auto &output : outputs) {
+      MS_LOG(INFO) << "output:" << output->DebugString();
+    }
+    MS_LOG(EXCEPTION) << "Can't find the node in the equiv map!";
   };
   output_args.push_back(NewValueNode(prim::kPrimMakeTuple));
   (void)std::transform(outputs.begin(), outputs.end(), std::back_inserter(output_args),
diff --git a/mindspore/ccsrc/session/session_basic.h b/mindspore/ccsrc/session/session_basic.h
old mode 100644
new mode 100755
index 9aadb78cb2..f1872e375c
--- a/mindspore/ccsrc/session/session_basic.h
+++ b/mindspore/ccsrc/session/session_basic.h
@@ -69,14 +69,15 @@ class SessionBasic {
 
   std::shared_ptr<KernelGraph> ConstructKernelGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs);
 
-  CNodePtr CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph);
+  CNodePtr CreateNewCNode(const CNodePtr &cnode, bool valid_input, KernelGraph *graph, bool *from_other_graph,
+                          std::unordered_map<AnfNodePtr, AnfNodePtr> *other_graph_cnode);
 
   // set parameters of final graph
   virtual GraphId SetFinalGraphInput(const std::vector<AnfNodePtr> &) { return kInvalidGraphId; }
   // set output of final graph
   virtual void SetFinalGraphOutput(const BaseRef &) {}
   // insert switch and set the relative active ops
-  virtual void SwitchCompile(GraphId, GraphId, GraphId) {}
+  virtual void SwitchCompile(GraphId, GraphId, GraphId, const AnfNodePtr &) {}
   // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter
   virtual void SetChildGraphInput(GraphId, const VectorRef &) {}
   // get graph id in child graphs by ME front anf node pointer
diff --git a/mindspore/ccsrc/vm/backend.cc b/mindspore/ccsrc/vm/backend.cc
old mode 100644
new mode 100755
index 9355cca99c..e69d25d2dc
--- a/mindspore/ccsrc/vm/backend.cc
+++ b/mindspore/ccsrc/vm/backend.cc
@@ -136,7 +136,7 @@ void MsBackend::SetSwitchGraph() {
         MS_LOG(EXCEPTION) << "cond not a anf node:" << curr_switch_.ToString();
       }
       MS_LOG(DEBUG) << "switch compile:" << cond_g << ", " << true_g << ", " << false_g;
-      sess_->SwitchCompile(cond_g, true_g, false_g);
+      sess_->SwitchCompile(cond_g, true_g, false_g, utils::cast<AnfNodePtr>(curr_switch_));
     }
     is_switch_call_ = false;
     MS_LOG(DEBUG) << "end SetSwitchGraph:" << curr_cond << ", " << is_switch_call_;

From 2bef22d8a379aa7342517895dabacbf13174fc68 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B9=89=E5=B3=B0=E6=BD=98?=
 <yifengpan@yifengpandeMacBook-Pro.local>
Date: Fri, 10 Apr 2020 10:54:31 +0800
Subject: [PATCH 155/367] fix bprop cache caused error with variable params

---
 mindspore/ccsrc/optimizer/ad/kprim.cc               |  9 +++++++--
 tests/ut/python/pynative_mode/test_stop_gradient.py | 12 ++++++++++++
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/optimizer/ad/kprim.cc b/mindspore/ccsrc/optimizer/ad/kprim.cc
index 4576cc1ea9..2c8ddbfa82 100644
--- a/mindspore/ccsrc/optimizer/ad/kprim.cc
+++ b/mindspore/ccsrc/optimizer/ad/kprim.cc
@@ -92,9 +92,11 @@ FuncGraphPtr KPrim::KPrimitive(const ValueNodePtr &value_node, const pipeline::R
     return nullptr;
   }
 
+  bool is_faked_bprop = false;
   auto bprop_fg = GetBprop(prim);
   if (bprop_fg == nullptr) {
     bprop_fg = FakeBprop(value_node, resources);
+    is_faked_bprop = true;
   }
 
   auto expanded_fg = BpropToK(prim, bprop_fg);
@@ -104,8 +106,11 @@ FuncGraphPtr KPrim::KPrimitive(const ValueNodePtr &value_node, const pipeline::R
                       << trace::GetDebugInfo(bprop_fg->debug_info());
   }
 
-  // Set bprop_g graph cache
-  bprop_registry_[prim] = expanded_fg;
+  // To support primitives with variable params, do not cache faked bprop
+  if (!is_faked_bprop) {
+    // Set bprop_g graph cache
+    bprop_registry_[prim] = expanded_fg;
+  }
   return expanded_fg;
 }
 
diff --git a/tests/ut/python/pynative_mode/test_stop_gradient.py b/tests/ut/python/pynative_mode/test_stop_gradient.py
index b274b3988a..a26d635aad 100644
--- a/tests/ut/python/pynative_mode/test_stop_gradient.py
+++ b/tests/ut/python/pynative_mode/test_stop_gradient.py
@@ -366,3 +366,15 @@ def test_stop_gradient_11():
     with pytest.raises(RuntimeError):
         bprop(PrimWithNoBprop_(), Tensor(np.ones([2]).astype(np.float32)),
               Tensor(np.ones([2]).astype(np.float32)))
+
+def test_stop_print():
+    class StopPrint(nn.Cell):
+        def __init__(self):
+            super(StopPrint, self).__init__()
+            self.printm = P.Print()
+        def construct(self, x, y):
+            self.printm("StopPrint", x)
+            self.printm(y)
+            return x, y
+    C.grad_all(StopPrint())(Tensor(np.ones([2]).astype(np.float32)),
+                            Tensor(np.ones([2]).astype(np.float32)))

From d8b9442ab862b1af3a4b302c7e1bae29d63d6e7b Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Tue, 7 Apr 2020 17:44:07 +0800
Subject: [PATCH 156/367] dataset_sink_mode is supported in model.eval() and
 not in model.train() in pynative mode

---
 mindspore/train/model.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index bcfd897f58..657c84de65 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -206,6 +206,8 @@ class Model:
                                      function respectively.
             callbacks (list): List of callback object. Callbacks which should be executed while training. Default: None.
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
+                                      Configure pynative mode, the training process will be performed with
+                                      dataset not sink.
         """
         epoch = check_int_positive(epoch)
         self._train_network.set_train()
@@ -227,8 +229,13 @@ class Model:
         cb_params.train_dataset = train_dataset
         cb_params.list_callback = list_callback
 
-        if dataset_sink_mode and context.get_context("mode") == context.GRAPH_MODE:
-            self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params)
+        if dataset_sink_mode:
+            if context.get_context("mode") == context.PYNATIVE_MODE:
+                logger.warning("The pynative mode cannot support dataset sink mode currently."
+                               "So the training process will be performed with dataset not sink.")
+                self._train_process(epoch, train_dataset, list_callback, cb_params)
+            else:
+                self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params)
         else:
             self._train_process(epoch, train_dataset, list_callback, cb_params)
 
@@ -349,7 +356,7 @@ class Model:
         """
         Training API where the iteration is controlled by python front-end.
 
-        Configure to pynative mode, the training will be performed with dataset non-sink mode.
+        When setting pynative mode, the training process will be performed with dataset not sink.
 
         Note:
             CPU is not supported when dataset_sink_mode is true.
@@ -363,6 +370,8 @@ class Model:
                                      function respectively.
             callbacks (list): List of callback object. Callbacks which should be excuted while training. Default: None.
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
+                                      Configure pynative mode, the training process will be performed with
+                                      dataset not sink.
 
 
         Examples:
@@ -508,7 +517,7 @@ class Model:
 
         self._clear_metrics()
 
-        if dataset_sink_mode and context.get_context("mode") == context.GRAPH_MODE:
+        if dataset_sink_mode:
             return self._eval_dataset_sink_process(valid_dataset, list_callback, cb_params)
         return self._eval_process(valid_dataset, list_callback, cb_params)
 

From c1ad92ff4dca0a225203edc899d85b578fbc7395 Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Fri, 10 Apr 2020 14:26:21 +0800
Subject: [PATCH 157/367] remove python3.6 support in package.sh

---
 package.sh       | 8 +++-----
 requirements.txt | 1 -
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/package.sh b/package.sh
index 0c75a1bbfd..f49f15632f 100755
--- a/package.sh
+++ b/package.sh
@@ -45,18 +45,16 @@ PYTHON_VERSION=$("${PYTHON}" -V 2>&1 | awk '{print $2}' | cut -d. -f-2)
 if [[ $(uname) == "Linux" ]]; then
     if [[ "${PYTHON_VERSION}" == "3.7" ]]; then
         PY_TAGS="cp37-cp37m"
-    elif [[ "${PYTHON_VERSION}" == "3.6" ]]; then
-        PY_TAGS="cp36-cp36m"
     else
-        echo "Could not find 'Python 3.6' or 'Python 3.7'"
+        echo "Could not find 'Python 3.7'"
         exit 1
     fi
     PLATFORM_TAG=$(to_lower "$(uname)_$(uname -m)")
 elif [[ $(uname) == "Darwin" ]]; then
-    if [[ "${PYTHON_VERSION}" == "3.7"  ||  "${PYTHON_VERSION}" == "3.6" ]]; then
+    if [[ "${PYTHON_VERSION}" == "3.7" ]]; then
         PY_TAGS="py3-none"
     else
-        echo "Could not find 'Python 3.6' or 'Python 3.7'"
+        echo "Could not find 'Python 3.7'"
         exit 1
     fi
     PLATFORM_TAG="any"
diff --git a/requirements.txt b/requirements.txt
index e4b61f2b6f..e182cd7a3b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,6 @@ protobuf >= 3.8.0
 asttokens >= 1.1.13
 pillow >= 6.2.0
 scipy >= 1.3.3
-dataclasses >= 0.6
 easydict >= 1.9
 sympy >= 1.4
 cffi >= 1.13.2

From f5ee197b6c26eecc58cac055bf8adeb3c4efd100 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Fri, 10 Apr 2020 15:24:57 +0800
Subject: [PATCH 158/367] Modify custom op register

---
 .../st/ops/custom_ops_tbe/cus_conv2d_impl.py  | 109 ++++--------------
 tests/st/ops/custom_ops_tbe/square_impl.py    |  61 +++-------
 2 files changed, 38 insertions(+), 132 deletions(-)

diff --git a/tests/st/ops/custom_ops_tbe/cus_conv2d_impl.py b/tests/st/ops/custom_ops_tbe/cus_conv2d_impl.py
index 54f6954a18..04ac7c2ff7 100644
--- a/tests/st/ops/custom_ops_tbe/cus_conv2d_impl.py
+++ b/tests/st/ops/custom_ops_tbe/cus_conv2d_impl.py
@@ -13,95 +13,28 @@
 # limitations under the License.
 # ============================================================================
 from tests.st.ops.custom_ops_tbe.conv2d import conv2d
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Cus_Conv2D",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d.so",
-    "compute_cost": 10,
-    "kernel_name": "Cus_Conv2D",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_list",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "filter",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+cus_conv2D_op_info = TBERegOp("Cus_Conv2D") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d.so") \
+    .compute_cost(10) \
+    .kernel_name("Cus_Conv2D") \
+    .partial_flag(True) \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_list", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F32_Default, DataType.F16_5HD) \
+    .get_op_info()
+
+
+@op_info_register(cus_conv2D_op_info)
 def Cus_Conv2D(inputs, weights, bias, outputs, strides, pads, dilations,
                kernel_name="conv2d"):
     conv2d(inputs, weights, bias, outputs, strides, pads, dilations,
-           kernel_name)
\ No newline at end of file
+           kernel_name)
diff --git a/tests/st/ops/custom_ops_tbe/square_impl.py b/tests/st/ops/custom_ops_tbe/square_impl.py
index e5992eff1c..f3a1e0751d 100644
--- a/tests/st/ops/custom_ops_tbe/square_impl.py
+++ b/tests/st/ops/custom_ops_tbe/square_impl.py
@@ -18,11 +18,12 @@ from topi import generic
 import te.lang.cce
 from topi.cce import util
 from te.platform.fusion_manager import fusion_manager
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
 # shape size limit for aicore is 2**31
 SHAPE_SIZE_LIMIT = 200000000
 
+
 @fusion_manager.register("square")
 def square_compute(input_x, output_y, kernel_name="square"):
     """
@@ -46,49 +47,21 @@ def square_compute(input_x, output_y, kernel_name="square"):
     res = te.lang.cce.vmul(input_x, input_x)
     return res
 
-@op_info_register("""{
-    "op_name": "CusSquare",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "square.so",
-    "compute_cost": 10,
-    "kernel_name": "CusSquare",
-    "partial_flag": true,
-    "attr": [
-        
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+cus_conv2D_op_info = TBERegOp("CusSquare") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("square.so") \
+    .compute_cost(10) \
+    .kernel_name("CusSquare") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(cus_conv2D_op_info)
 def CusSquare(input_x, output_y, kernel_name="square"):
     """
     algorithm: square

From 8cd3308a4c2da0360a4097f59bc4de962f693742 Mon Sep 17 00:00:00 2001
From: lizhenyu <lizhenyu13@huawei.com>
Date: Fri, 10 Apr 2020 16:54:17 +0800
Subject: [PATCH 159/367] change package name akg to _akg

---
 mindspore/{akg => _akg}/__init__.py           | 12 ++++-----
 mindspore/{akg => _akg}/gpu/__init__.py       |  0
 mindspore/{akg => _akg}/gpu/cast.py           | 10 +++----
 .../{akg => _akg}/gpu/default_schedule.py     |  4 +--
 mindspore/{akg => _akg}/gpu/equal.py          | 10 +++----
 mindspore/{akg => _akg}/gpu/mean.py           |  4 +--
 mindspore/{akg => _akg}/gpu/mean_grad.py      |  8 +++---
 mindspore/{akg => _akg}/gpu/mul.py            |  6 ++---
 mindspore/{akg => _akg}/gpu/relu6.py          |  6 ++---
 mindspore/{akg => _akg}/gpu/relu6_grad.py     |  4 +--
 mindspore/{akg => _akg}/gpu/squeeze.py        |  4 +--
 mindspore/{akg => _akg}/gpu/squeeze_grad.py   |  2 +-
 mindspore/{akg => _akg}/gpu/tile.py           | 10 +++----
 mindspore/{akg => _akg}/message.py            | 10 +++----
 mindspore/{akg => _akg}/op_build.py           | 14 +++++-----
 mindspore/{akg => _akg}/ops/__init__.py       |  0
 mindspore/{akg => _akg}/ops/array/__init__.py |  0
 mindspore/{akg => _akg}/ops/array/tile.py     | 10 +++----
 mindspore/{akg => _akg}/ops/math/__init__.py  |  0
 mindspore/{akg => _akg}/ops/math/cast.py      | 10 +++----
 mindspore/{akg => _akg}/ops/math/equal.py     | 26 +++++++++----------
 mindspore/{akg => _akg}/ops/math/mean.py      | 14 +++++-----
 mindspore/{akg => _akg}/ops/math/mul.py       |  8 +++---
 mindspore/{akg => _akg}/ops/math/sub.py       | 10 +++----
 mindspore/{akg => _akg}/ops/math/sum.py       | 14 +++++-----
 mindspore/{akg => _akg}/save_gpu_param.py     |  8 +++---
 mindspore/{akg => _akg}/utils/__init__.py     |  0
 mindspore/{akg => _akg}/utils/dsl_create.py   | 16 ++++++------
 .../{akg => _akg}/utils/format_transform.py   | 14 +++++-----
 .../{akg => _akg}/utils/validation_check.py   |  2 +-
 .../parallel_compile/multi_compiler.py        |  2 +-
 mindspore/ccsrc/kernel/common_utils.h         |  2 +-
 package.sh                                    |  8 +++---
 setup.py                                      |  2 +-
 34 files changed, 125 insertions(+), 125 deletions(-)
 rename mindspore/{akg => _akg}/__init__.py (88%)
 rename mindspore/{akg => _akg}/gpu/__init__.py (100%)
 rename mindspore/{akg => _akg}/gpu/cast.py (86%)
 rename mindspore/{akg => _akg}/gpu/default_schedule.py (94%)
 rename mindspore/{akg => _akg}/gpu/equal.py (85%)
 rename mindspore/{akg => _akg}/gpu/mean.py (97%)
 rename mindspore/{akg => _akg}/gpu/mean_grad.py (95%)
 rename mindspore/{akg => _akg}/gpu/mul.py (93%)
 rename mindspore/{akg => _akg}/gpu/relu6.py (95%)
 rename mindspore/{akg => _akg}/gpu/relu6_grad.py (97%)
 rename mindspore/{akg => _akg}/gpu/squeeze.py (96%)
 rename mindspore/{akg => _akg}/gpu/squeeze_grad.py (98%)
 rename mindspore/{akg => _akg}/gpu/tile.py (85%)
 rename mindspore/{akg => _akg}/message.py (94%)
 rename mindspore/{akg => _akg}/op_build.py (88%)
 rename mindspore/{akg => _akg}/ops/__init__.py (100%)
 rename mindspore/{akg => _akg}/ops/array/__init__.py (100%)
 rename mindspore/{akg => _akg}/ops/array/tile.py (84%)
 rename mindspore/{akg => _akg}/ops/math/__init__.py (100%)
 rename mindspore/{akg => _akg}/ops/math/cast.py (83%)
 rename mindspore/{akg => _akg}/ops/math/equal.py (63%)
 rename mindspore/{akg => _akg}/ops/math/mean.py (82%)
 rename mindspore/{akg => _akg}/ops/math/mul.py (86%)
 rename mindspore/{akg => _akg}/ops/math/sub.py (86%)
 rename mindspore/{akg => _akg}/ops/math/sum.py (79%)
 rename mindspore/{akg => _akg}/save_gpu_param.py (95%)
 rename mindspore/{akg => _akg}/utils/__init__.py (100%)
 rename mindspore/{akg => _akg}/utils/dsl_create.py (91%)
 rename mindspore/{akg => _akg}/utils/format_transform.py (86%)
 rename mindspore/{akg => _akg}/utils/validation_check.py (99%)

diff --git a/mindspore/akg/__init__.py b/mindspore/_akg/__init__.py
similarity index 88%
rename from mindspore/akg/__init__.py
rename to mindspore/_akg/__init__.py
index a0c0364bd6..e3dceaf35e 100644
--- a/mindspore/akg/__init__.py
+++ b/mindspore/_akg/__init__.py
@@ -18,7 +18,7 @@ import sys
 import os
 
 def AKGAddPath():
-    """akg add path."""
+    """_akg add path."""
     pwd = os.path.dirname(os.path.realpath(__file__))
     tvm_path = os.path.realpath(pwd)
     if tvm_path not in sys.path:
@@ -32,12 +32,12 @@ class AKGMetaPathFinder:
     """class AKGMetaPath finder."""
 
     def find_module(self, fullname, path=None):
-        """method akg find module."""
-        if fullname.startswith("akg.tvm"):
-            rname = fullname[4:]
+        """method _akg find module."""
+        if fullname.startswith("_akg.tvm"):
+            rname = fullname[5:]
             return AKGMetaPathLoader(rname)
-        if fullname.startswith("akg.topi"):
-            rname = fullname[4:]
+        if fullname.startswith("_akg.topi"):
+            rname = fullname[5:]
             return AKGMetaPathLoader(rname)
         return None
 
diff --git a/mindspore/akg/gpu/__init__.py b/mindspore/_akg/gpu/__init__.py
similarity index 100%
rename from mindspore/akg/gpu/__init__.py
rename to mindspore/_akg/gpu/__init__.py
diff --git a/mindspore/akg/gpu/cast.py b/mindspore/_akg/gpu/cast.py
similarity index 86%
rename from mindspore/akg/gpu/cast.py
rename to mindspore/_akg/gpu/cast.py
index 458772a803..d6b38b6e9b 100644
--- a/mindspore/akg/gpu/cast.py
+++ b/mindspore/_akg/gpu/cast.py
@@ -14,9 +14,9 @@
 
 """cast"""
 import logging
-import akg.tvm
-from akg.ops.math import cast
-from akg.topi.generic import schedule_elemwise
+import _akg.tvm
+from _akg.ops.math import cast
+from _akg.topi.generic import schedule_elemwise
 
 def Cast(x, dst_type):
     """cast."""
@@ -34,10 +34,10 @@ def gpu_schedule_Cast(outs):
         sch (schedule.Schedule): The created schedule.
     """
     device = 'cuda'
-    ctx = akg.tvm.context(device, 0)
+    ctx = _akg.tvm.context(device, 0)
     if not ctx.exist:
         logging.info("Skip because %s is not enabled", device)
         return None
-    with akg.tvm.target.create(device):
+    with _akg.tvm.target.create(device):
         sch = schedule_elemwise(outs)
     return sch
diff --git a/mindspore/akg/gpu/default_schedule.py b/mindspore/_akg/gpu/default_schedule.py
similarity index 94%
rename from mindspore/akg/gpu/default_schedule.py
rename to mindspore/_akg/gpu/default_schedule.py
index 2e2892c055..811cc2d710 100644
--- a/mindspore/akg/gpu/default_schedule.py
+++ b/mindspore/_akg/gpu/default_schedule.py
@@ -15,7 +15,7 @@
 """default schedule function for GPU"""
 from queue import Queue
 
-import akg.tvm as tvm
+import _akg.tvm as tvm
 
 DEFAULT_GPU_THREAD = 1024
 
@@ -31,7 +31,7 @@ def default_schedule(outs):
         sch (schedule.Schedule): The created schedule.
     """
     if not isinstance(outs, tvm.tensor.Tensor) and not isinstance(outs, list):
-        raise ValueError("outs should be list of akg.tvm.tensor.Tensor or akg.tvm.tensor.Tensor")
+        raise ValueError("outs should be list of _akg.tvm.tensor.Tensor or _akg.tvm.tensor.Tensor")
     device = 'cuda'
     ctx = tvm.context(device, 0)
     if not ctx.exist:
diff --git a/mindspore/akg/gpu/equal.py b/mindspore/_akg/gpu/equal.py
similarity index 85%
rename from mindspore/akg/gpu/equal.py
rename to mindspore/_akg/gpu/equal.py
index 05dce89622..3321c10b2c 100644
--- a/mindspore/akg/gpu/equal.py
+++ b/mindspore/_akg/gpu/equal.py
@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """equal"""
-import akg.tvm
-from akg.ops.math import equal
-from akg.topi.generic import schedule_elemwise
+import _akg.tvm
+from _akg.ops.math import equal
+from _akg.topi.generic import schedule_elemwise
 
 def Equal(x, y):
     """equal."""
@@ -32,9 +32,9 @@ def gpu_schedule_Equal(outs):
         sch (schedule.Schedule): The created schedule.
     """
     device = 'cuda'
-    ctx = akg.tvm.context(device, 0)
+    ctx = _akg.tvm.context(device, 0)
     if not ctx.exist:
         raise SystemError("Skip because %s is not enabled" % device)
-    with akg.tvm.target.create(device):
+    with _akg.tvm.target.create(device):
         sch = schedule_elemwise(outs)
     return sch
diff --git a/mindspore/akg/gpu/mean.py b/mindspore/_akg/gpu/mean.py
similarity index 97%
rename from mindspore/akg/gpu/mean.py
rename to mindspore/_akg/gpu/mean.py
index a68e929409..e9cdb6d551 100644
--- a/mindspore/akg/gpu/mean.py
+++ b/mindspore/_akg/gpu/mean.py
@@ -13,8 +13,8 @@
 # limitations under the License.
 
 """mean op compute and schedule"""
-import akg.tvm as tvm
-from akg.ops.math.mean import mean
+import _akg.tvm as tvm
+from _akg.ops.math.mean import mean
 from .default_schedule import DEFAULT_GPU_THREAD
 
 def Mean(x, axis=None, keepdims=True):
diff --git a/mindspore/akg/gpu/mean_grad.py b/mindspore/_akg/gpu/mean_grad.py
similarity index 95%
rename from mindspore/akg/gpu/mean_grad.py
rename to mindspore/_akg/gpu/mean_grad.py
index ef77690a5d..9d91ee3f40 100644
--- a/mindspore/akg/gpu/mean_grad.py
+++ b/mindspore/_akg/gpu/mean_grad.py
@@ -13,9 +13,9 @@
 # limitations under the License.
 
 """mean_grad"""
-import akg.tvm as tvm
-import akg
-from akg.ops.math import mean
+import _akg.tvm as tvm
+import _akg
+from _akg.ops.math import mean
 from .default_schedule import DEFAULT_GPU_THREAD
 
 
@@ -30,7 +30,7 @@ def mean_ad(head, input_shape, axis, keepdims):
     if tensor_b.op.name == "mean_output":
         tensor_b = tensor_b.op.input_tensors[0]
 
-    jacs = list(akg.differentiate(tensor_b, [tensor_a], head))
+    jacs = list(_akg.differentiate(tensor_b, [tensor_a], head))
     return jacs[0]
 
 
diff --git a/mindspore/akg/gpu/mul.py b/mindspore/_akg/gpu/mul.py
similarity index 93%
rename from mindspore/akg/gpu/mul.py
rename to mindspore/_akg/gpu/mul.py
index 975a237837..5c289a62a6 100644
--- a/mindspore/akg/gpu/mul.py
+++ b/mindspore/_akg/gpu/mul.py
@@ -13,9 +13,9 @@
 # limitations under the License.
 
 """mul"""
-import akg.topi as topi
-import akg.tvm as tvm
-from akg.ops.math import mul
+import _akg.topi as topi
+import _akg.tvm as tvm
+from _akg.ops.math import mul
 
 def Mul(x, y):
     """mul."""
diff --git a/mindspore/akg/gpu/relu6.py b/mindspore/_akg/gpu/relu6.py
similarity index 95%
rename from mindspore/akg/gpu/relu6.py
rename to mindspore/_akg/gpu/relu6.py
index bdcf23f05a..9a0a3d7a45 100644
--- a/mindspore/akg/gpu/relu6.py
+++ b/mindspore/_akg/gpu/relu6.py
@@ -13,9 +13,9 @@
 # limitations under the License.
 
 """relu6"""
-import akg.topi as topi
-import akg.tvm as tvm
-from akg.topi import tag
+import _akg.topi as topi
+import _akg.tvm as tvm
+from _akg.topi import tag
 
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def topi_nn_relu6(x):
diff --git a/mindspore/akg/gpu/relu6_grad.py b/mindspore/_akg/gpu/relu6_grad.py
similarity index 97%
rename from mindspore/akg/gpu/relu6_grad.py
rename to mindspore/_akg/gpu/relu6_grad.py
index e0590cf6ef..62aeabb4c0 100644
--- a/mindspore/akg/gpu/relu6_grad.py
+++ b/mindspore/_akg/gpu/relu6_grad.py
@@ -13,8 +13,8 @@
 # limitations under the License.
 
 """relu6 grad"""
-import akg.topi as topi
-import akg.tvm as tvm
+import _akg.topi as topi
+import _akg.tvm as tvm
 
 def ReLU6Grad(y_grad, x):
     """
diff --git a/mindspore/akg/gpu/squeeze.py b/mindspore/_akg/gpu/squeeze.py
similarity index 96%
rename from mindspore/akg/gpu/squeeze.py
rename to mindspore/_akg/gpu/squeeze.py
index 34fa423b8c..b5f55facaa 100644
--- a/mindspore/akg/gpu/squeeze.py
+++ b/mindspore/_akg/gpu/squeeze.py
@@ -13,8 +13,8 @@
 # limitations under the License.
 
 """squeeze"""
-import akg.topi as topi
-import akg.tvm as tvm
+import _akg.topi as topi
+import _akg.tvm as tvm
 
 def Squeeze(x, axis=None):
     """
diff --git a/mindspore/akg/gpu/squeeze_grad.py b/mindspore/_akg/gpu/squeeze_grad.py
similarity index 98%
rename from mindspore/akg/gpu/squeeze_grad.py
rename to mindspore/_akg/gpu/squeeze_grad.py
index ef6a4242ba..8180ff9638 100644
--- a/mindspore/akg/gpu/squeeze_grad.py
+++ b/mindspore/_akg/gpu/squeeze_grad.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """squeeze grad"""
-import akg.topi as topi
+import _akg.topi as topi
 
 def SqueezeGrad(y_grad, x_shape, axis=None):
     """
diff --git a/mindspore/akg/gpu/tile.py b/mindspore/_akg/gpu/tile.py
similarity index 85%
rename from mindspore/akg/gpu/tile.py
rename to mindspore/_akg/gpu/tile.py
index cd3c663f97..1eb6979b09 100644
--- a/mindspore/akg/gpu/tile.py
+++ b/mindspore/_akg/gpu/tile.py
@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """tile"""
-import akg.tvm
-from akg.ops.array import tile
-from akg.topi.generic import schedule_elemwise
+import _akg.tvm
+from _akg.ops.array import tile
+from _akg.topi.generic import schedule_elemwise
 
 def Tile(x, multiples):
     """tile."""
@@ -31,9 +31,9 @@ def gpu_schedule_Tile(outs):
         sch (schedule.Schedule): The created schedule.
     """
     device = 'cuda'
-    ctx = akg.tvm.context(device, 0)
+    ctx = _akg.tvm.context(device, 0)
     if not ctx.exist:
         raise SystemError("Skip because %s is not enabled" % device)
-    with akg.tvm.target.create(device):
+    with _akg.tvm.target.create(device):
         s = schedule_elemwise(outs)
     return s
diff --git a/mindspore/akg/message.py b/mindspore/_akg/message.py
similarity index 94%
rename from mindspore/akg/message.py
rename to mindspore/_akg/message.py
index 86bdf2899c..4528771848 100644
--- a/mindspore/akg/message.py
+++ b/mindspore/_akg/message.py
@@ -20,9 +20,9 @@ import logging
 import traceback
 import os.path
 from pathlib import Path
-import akg.tvm
-from akg.utils import validation_check as vc_util
-from akg.utils.dsl_create import TensorUtils
+import _akg.tvm
+from _akg.utils import validation_check as vc_util
+from _akg.utils.dsl_create import TensorUtils
 from . import gpu
 from . import op_build
 
@@ -67,7 +67,7 @@ def compilewithjson(json_str):
             tensor_shape = input_desc[0]['shape']
             tensor_shape = (1,) if not tensor_shape else tensor_shape
             vc_util.shape_dtype_max_size_check(tensor_shape)
-            args[input_desc[0]['name']] = akg.tvm.placeholder(
+            args[input_desc[0]['name']] = _akg.tvm.placeholder(
                 shape=tensor_shape, name=input_desc[0]['tensor_name'], dtype=input_desc[0]['data_type'])
             tsr.append(args[input_desc[0]['name']])
         else:
@@ -76,7 +76,7 @@ def compilewithjson(json_str):
                 tensor_shape = tmp_desc['shape']
                 tensor_shape = (1,) if not tensor_shape else tensor_shape
                 vc_util.shape_dtype_max_size_check(tensor_shape)
-                tmp_input.append(akg.tvm.placeholder(
+                tmp_input.append(_akg.tvm.placeholder(
                     shape=tensor_shape, name=tmp_desc['tensor_name'], dtype=tmp_desc['data_type']))
             args[input_desc[0]['name']] = tmp_input
             tsr = tsr + tmp_input
diff --git a/mindspore/akg/op_build.py b/mindspore/_akg/op_build.py
similarity index 88%
rename from mindspore/akg/op_build.py
rename to mindspore/_akg/op_build.py
index e3d3ec2b78..44a250bd9e 100644
--- a/mindspore/akg/op_build.py
+++ b/mindspore/_akg/op_build.py
@@ -19,10 +19,10 @@ import types
 import typing
 import logging
 import traceback
-import akg.tvm
-import akg
-from akg import save_gpu_param as gpu_utils
-from akg.utils import validation_check as vc_util
+import _akg.tvm
+import _akg
+from _akg import save_gpu_param as gpu_utils
+from _akg.utils import validation_check as vc_util
 
 MS_CUDA_KERNEL_PATH = "/tmp/cuda_meta/"
 
@@ -38,21 +38,21 @@ def op_build(opnames, computes, args, custom_schedule, device, kernel_name, attr
             return None
 
         schedule_name = 'gpu_schedule_' + opnames[0]
-        schedule_func = getattr(akg.gpu, schedule_name)
+        schedule_func = getattr(_akg.gpu, schedule_name)
         if not isinstance(schedule_func, (types.FunctionType, typing.Callable)):
             logging.error("no schedule func found %s", str(schedule_name))
             return None
 
         ptx_file = os.path.realpath(MS_CUDA_KERNEL_PATH + kernel_name + ".ptx")
         if os.path.exists(ptx_file):
-            os.remove(ptx_file)
+            os.chmod(ptx_file, 0o600)
         try:
             with open(ptx_file, 'at') as file:
                 fcntl.flock(file.fileno(), fcntl.LOCK_EX)
                 file.seek(0, 2)
                 if file.tell() == 0:
                     s = schedule_func(computes)
-                    foo = akg.tvm.build(s, args, device, name=kernel_name)
+                    foo = _akg.tvm.build(s, args, device, name=kernel_name)
                     ptx_code = foo.imported_modules[0].get_source("ptx")
                     file.write(ptx_code)
                     json_file = os.path.realpath(MS_CUDA_KERNEL_PATH + kernel_name + ".json")
diff --git a/mindspore/akg/ops/__init__.py b/mindspore/_akg/ops/__init__.py
similarity index 100%
rename from mindspore/akg/ops/__init__.py
rename to mindspore/_akg/ops/__init__.py
diff --git a/mindspore/akg/ops/array/__init__.py b/mindspore/_akg/ops/array/__init__.py
similarity index 100%
rename from mindspore/akg/ops/array/__init__.py
rename to mindspore/_akg/ops/array/__init__.py
diff --git a/mindspore/akg/ops/array/tile.py b/mindspore/_akg/ops/array/tile.py
similarity index 84%
rename from mindspore/akg/ops/array/tile.py
rename to mindspore/_akg/ops/array/tile.py
index e60fcc4ffb..2fa485ea36 100644
--- a/mindspore/akg/ops/array/tile.py
+++ b/mindspore/_akg/ops/array/tile.py
@@ -13,12 +13,12 @@
 # limitations under the License.
 
 """operator dsl function: tile"""
-import akg.tvm
-import akg.topi
-from akg.utils import validation_check as vc_util
+import _akg.tvm
+import _akg.topi
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, (list, tuple))
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, (list, tuple))
 def tile(data, multiples):
     """
     Repeats the data in the specified dimensions according to the multiples.
@@ -32,5 +32,5 @@ def tile(data, multiples):
     """
     vc_util.check_shape(data.shape)
     vc_util.check_int_list(multiples, "multiples")
-    output = akg.topi.tile(data, multiples)
+    output = _akg.topi.tile(data, multiples)
     return output
diff --git a/mindspore/akg/ops/math/__init__.py b/mindspore/_akg/ops/math/__init__.py
similarity index 100%
rename from mindspore/akg/ops/math/__init__.py
rename to mindspore/_akg/ops/math/__init__.py
diff --git a/mindspore/akg/ops/math/cast.py b/mindspore/_akg/ops/math/cast.py
similarity index 83%
rename from mindspore/akg/ops/math/cast.py
rename to mindspore/_akg/ops/math/cast.py
index 7266fd60c1..78140bfe27 100644
--- a/mindspore/akg/ops/math/cast.py
+++ b/mindspore/_akg/ops/math/cast.py
@@ -13,12 +13,12 @@
 # limitations under the License.
 
 """operator dsl function: cast"""
-import akg.tvm
-import akg.topi
-from akg.utils import validation_check as vc_util
+import _akg.tvm
+import _akg.topi
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, str)
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, str)
 def cast(data, dst_type):
     """
     cast data to target type.
@@ -31,6 +31,6 @@ def cast(data, dst_type):
         tvm.tensor.Tensor, type is dst_type.
     """
     vc_util.check_shape(data.shape)
-    out = akg.topi.cast(data, dst_type)
+    out = _akg.topi.cast(data, dst_type)
 
     return out
diff --git a/mindspore/akg/ops/math/equal.py b/mindspore/_akg/ops/math/equal.py
similarity index 63%
rename from mindspore/akg/ops/math/equal.py
rename to mindspore/_akg/ops/math/equal.py
index eb446ac52b..2dbb1ba733 100644
--- a/mindspore/akg/ops/math/equal.py
+++ b/mindspore/_akg/ops/math/equal.py
@@ -13,13 +13,13 @@
 # limitations under the License.
 
 """operator dsl function: equal"""
-import akg.tvm
-import akg.topi
-from akg.utils.dsl_create import produce_shapes
-from akg.utils import validation_check as vc_util
+import _akg.tvm
+import _akg.topi
+from _akg.utils.dsl_create import produce_shapes
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, akg.tvm.tensor.Tensor)
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, _akg.tvm.tensor.Tensor)
 def equal(input1, input2):
     """
     check whether input1 equals to input2.
@@ -42,13 +42,13 @@ def equal(input1, input2):
     dtype = input1.dtype
 
     # get equal compute
-    t_value = akg.tvm.compute(shape, lambda *indice: akg.tvm.const(1, dtype), "T")
-    f_value = akg.tvm.compute(shape, lambda *indice: akg.tvm.const(0, dtype), "F")
-
-    input1_bro = akg.topi.broadcast_to(input1, shape)
-    input2_bro = akg.topi.broadcast_to(input2, shape)
-    c_out = akg.tvm.compute(shape, lambda *indice: akg.tvm.expr.Select(input1_bro[indice] == input2_bro[indice],
-                                                                       t_value[indice], f_value[indice]), name="C")
-    res = akg.tvm.compute(shape, lambda *indice: c_out(*indice).astype("bool"), name="res")
+    t_value = _akg.tvm.compute(shape, lambda *indice: _akg.tvm.const(1, dtype), "T")
+    f_value = _akg.tvm.compute(shape, lambda *indice: _akg.tvm.const(0, dtype), "F")
+
+    input1_bro = _akg.topi.broadcast_to(input1, shape)
+    input2_bro = _akg.topi.broadcast_to(input2, shape)
+    c_out = _akg.tvm.compute(shape, lambda *indice: _akg.tvm.expr.Select(input1_bro[indice] == input2_bro[indice],
+                                                                         t_value[indice], f_value[indice]), name="C")
+    res = _akg.tvm.compute(shape, lambda *indice: c_out(*indice).astype("bool"), name="res")
 
     return res
diff --git a/mindspore/akg/ops/math/mean.py b/mindspore/_akg/ops/math/mean.py
similarity index 82%
rename from mindspore/akg/ops/math/mean.py
rename to mindspore/_akg/ops/math/mean.py
index a26bc29087..8764387d33 100644
--- a/mindspore/akg/ops/math/mean.py
+++ b/mindspore/_akg/ops/math/mean.py
@@ -13,14 +13,14 @@
 # limitations under the License.
 
 """operator dsl function: mean"""
-import akg.topi
-import akg.tvm
-from akg.utils import format_transform as ft_util
-from akg.utils import validation_check as vc_util
-from akg.ops.math import sum
+import _akg.topi
+import _akg.tvm
+from _akg.utils import format_transform as ft_util
+from _akg.utils import validation_check as vc_util
+from _akg.ops.math import sum
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, (list, tuple, int, type(None)), (bool, type(None)))
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, (list, tuple, int, type(None)), (bool, type(None)))
 def mean(data, axis=None, keepdims=False):
     """
     Computes the mean of the values of a Tensor over the whole dataset.
@@ -42,6 +42,6 @@ def mean(data, axis=None, keepdims=False):
     for i in axis:
         count *= shape[i]
     output, _ = sum.sum_value(data, axis, keepdims)
-    res = akg.topi.divide(output, count)
+    res = _akg.topi.divide(output, count)
 
     return res
diff --git a/mindspore/akg/ops/math/mul.py b/mindspore/_akg/ops/math/mul.py
similarity index 86%
rename from mindspore/akg/ops/math/mul.py
rename to mindspore/_akg/ops/math/mul.py
index 8377a63d69..a690089da2 100644
--- a/mindspore/akg/ops/math/mul.py
+++ b/mindspore/_akg/ops/math/mul.py
@@ -13,11 +13,11 @@
 # limitations under the License.
 
 """operator dsl function: mul"""
-import akg.topi
-from akg.utils import validation_check as vc_util
+import _akg.topi
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, akg.tvm.tensor.Tensor)
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, _akg.tvm.tensor.Tensor)
 def mul(l_input, r_input):
     """
     Calculate x * y element-wise.
@@ -38,6 +38,6 @@ def mul(l_input, r_input):
     vc_util.check_shape(shape2)
     vc_util.auto_broadcast_check(shape1, shape2)
     vc_util.elemwise_dtype_check(l_input.dtype, r_input.dtype)
-    output = akg.topi.multiply(l_input, r_input)
+    output = _akg.topi.multiply(l_input, r_input)
 
     return output
diff --git a/mindspore/akg/ops/math/sub.py b/mindspore/_akg/ops/math/sub.py
similarity index 86%
rename from mindspore/akg/ops/math/sub.py
rename to mindspore/_akg/ops/math/sub.py
index a4a85b0a09..6ae2ee51ef 100644
--- a/mindspore/akg/ops/math/sub.py
+++ b/mindspore/_akg/ops/math/sub.py
@@ -13,12 +13,12 @@
 # limitations under the License.
 
 """operator dsl function: sub"""
-import akg.topi
-import akg.tvm
-from akg.utils import validation_check as vc_util
+import _akg.topi
+import _akg.tvm
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, akg.tvm.tensor.Tensor)
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, _akg.tvm.tensor.Tensor)
 def sub(data1, data2):
     """
     Computes data1 - data2 elementwise, broadcast is supported.
@@ -35,6 +35,6 @@ def sub(data1, data2):
     vc_util.check_shape(data2.shape)
     vc_util.auto_broadcast_check(data1.shape, data2.shape)
 
-    res = akg.topi.subtract(data1, data2)
+    res = _akg.topi.subtract(data1, data2)
 
     return res
diff --git a/mindspore/akg/ops/math/sum.py b/mindspore/_akg/ops/math/sum.py
similarity index 79%
rename from mindspore/akg/ops/math/sum.py
rename to mindspore/_akg/ops/math/sum.py
index ea71bab9c4..b9720469a6 100644
--- a/mindspore/akg/ops/math/sum.py
+++ b/mindspore/_akg/ops/math/sum.py
@@ -14,13 +14,13 @@
 
 """operator dsl function: sum"""
 
-import akg.topi
-import akg.tvm
-from akg.utils import format_transform as ft_util
-from akg.utils import validation_check as vc_util
+import _akg.topi
+import _akg.tvm
+from _akg.utils import format_transform as ft_util
+from _akg.utils import validation_check as vc_util
 
 
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, (list, tuple, int, type(None)), (bool, type(None)))
+@vc_util.check_input_type(_akg.tvm.tensor.Tensor, (list, tuple, int, type(None)), (bool, type(None)))
 def sum_value(inputs, axis=None, keepdims=False):
     """
     Compute the sum of elements across dimensions of a tensor.
@@ -38,8 +38,8 @@ def sum_value(inputs, axis=None, keepdims=False):
     vc_util.check_shape(inputs.shape)
 
     if not axis:
-        output = akg.topi.identity(inputs)
+        output = _akg.topi.identity(inputs)
     else:
-        output = akg.topi.sum(inputs, axis=axis, keepdims=keepdims)
+        output = _akg.topi.sum(inputs, axis=axis, keepdims=keepdims)
 
     return output
diff --git a/mindspore/akg/save_gpu_param.py b/mindspore/_akg/save_gpu_param.py
similarity index 95%
rename from mindspore/akg/save_gpu_param.py
rename to mindspore/_akg/save_gpu_param.py
index 228bdf32ca..ed2c9fe23a 100644
--- a/mindspore/akg/save_gpu_param.py
+++ b/mindspore/_akg/save_gpu_param.py
@@ -15,9 +15,9 @@
 """save gpu param"""
 import os
 import hashlib
-import akg.tvm
-from akg.tvm import schedule
-from akg.utils import validation_check as vc_util
+import _akg.tvm
+from _akg.tvm import schedule
+from _akg.utils import validation_check as vc_util
 
 
 def get_dim(dim, axis=True):
@@ -66,7 +66,7 @@ def save_gpu_params(s, args, kernel_info):
     ptx_code = kernel_info[0]
     file_name = kernel_info[1]
     kernel_name = kernel_info[2]
-    ir = str(akg.tvm.lower(s, args, simple_mode=True))
+    ir = str(_akg.tvm.lower(s, args, simple_mode=True))
     file_path = os.path.realpath(file_name)
     if os.path.exists(file_path):
         os.remove(file_path)
diff --git a/mindspore/akg/utils/__init__.py b/mindspore/_akg/utils/__init__.py
similarity index 100%
rename from mindspore/akg/utils/__init__.py
rename to mindspore/_akg/utils/__init__.py
diff --git a/mindspore/akg/utils/dsl_create.py b/mindspore/_akg/utils/dsl_create.py
similarity index 91%
rename from mindspore/akg/utils/dsl_create.py
rename to mindspore/_akg/utils/dsl_create.py
index aaea913143..9d27039b28 100644
--- a/mindspore/akg/utils/dsl_create.py
+++ b/mindspore/_akg/utils/dsl_create.py
@@ -13,8 +13,8 @@
 # limitations under the License.
 
 """dsl create helping function"""
-import akg
-from akg.utils import format_transform as ft_util
+import _akg
+from _akg.utils import format_transform as ft_util
 
 class TensorUtils:
     """Class for creating tensor."""
@@ -33,11 +33,11 @@ class TensorUtils:
         """update tensor attrs."""
         tensor_attrs = cls.get_tensor_attrs(tensor)
         tensor_attrs.update(attrs)
-        tensor = akg.tvm.compute(tensor.shape,
-                                 lambda *indice: tensor[indice],
-                                 name=tensor.op.name,
-                                 tag=tensor.op.tag,
-                                 attrs=tensor_attrs)
+        tensor = _akg.tvm.compute(tensor.shape,
+                                  lambda *indice: tensor[indice],
+                                  name=tensor.op.name,
+                                  tag=tensor.op.tag,
+                                  attrs=tensor_attrs)
         return tensor
 
     @classmethod
@@ -61,7 +61,7 @@ class TensorUtils:
             raise RuntimeError("Shape of the input_tensor and the output_tensor should be equal, "
                                "but got %s and %s"%(input_tensor_shape, output_tensor_shape))
         output_tensor = cls.update_tensor_attrs(output_tensor, {cls.CREATE_SCH_ONLY: 1})
-        data_buf = akg.tvm.decl_buffer(input_tensor.shape, input_tensor.dtype, name=buffer_name)
+        data_buf = _akg.tvm.decl_buffer(input_tensor.shape, input_tensor.dtype, name=buffer_name)
         binds_info = {input_tensor: data_buf, output_tensor: data_buf}
         return output_tensor, binds_info
 
diff --git a/mindspore/akg/utils/format_transform.py b/mindspore/_akg/utils/format_transform.py
similarity index 86%
rename from mindspore/akg/utils/format_transform.py
rename to mindspore/_akg/utils/format_transform.py
index 816cbcaadb..f83130a32a 100644
--- a/mindspore/akg/utils/format_transform.py
+++ b/mindspore/_akg/utils/format_transform.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """format transform function"""
-import akg
+import _akg
 
 def refine_reduce_axis(input, axis):
     """make reduce axis legal."""
@@ -43,11 +43,11 @@ def refine_reduce_axis(input, axis):
 
 
 def get_shape_from_tensor(data):
-    """translate akg.tvm.shape to list type in python."""
+    """translate _akg.tvm.shape to list type in python."""
     tvm_shape = data.shape
     py_shape = []
     for i in tvm_shape:
-        if isinstance(i, akg.tvm.expr.Var):
+        if isinstance(i, _akg.tvm.expr.Var):
             py_shape.append(i)
         else:
             py_shape.append(i.value)
@@ -55,10 +55,10 @@ def get_shape_from_tensor(data):
 
 
 def tvm_shape_to_list(tvm_shape):
-    """translate akg.tvm.shape to list type in python."""
+    """translate _akg.tvm.shape to list type in python."""
     py_shape = []
     for i in tvm_shape:
-        if isinstance(i, akg.tvm.expr.Var):
+        if isinstance(i, _akg.tvm.expr.Var):
             py_shape.append(i)
         else:
             py_shape.append(i.value)
@@ -67,9 +67,9 @@ def tvm_shape_to_list(tvm_shape):
 
 def get_shape(data):
     """get shape and save it as list."""
-    if isinstance(data, akg.tvm.tensor.Tensor):
+    if isinstance(data, _akg.tvm.tensor.Tensor):
         shape = get_shape_from_tensor(data)
-    elif isinstance(data, akg.tvm.container.Array):
+    elif isinstance(data, _akg.tvm.container.Array):
         shape = tvm_shape_to_list(data)
     elif isinstance(data, int):
         shape = [data]
diff --git a/mindspore/akg/utils/validation_check.py b/mindspore/_akg/utils/validation_check.py
similarity index 99%
rename from mindspore/akg/utils/validation_check.py
rename to mindspore/_akg/utils/validation_check.py
index 72494c5281..1231b3110e 100644
--- a/mindspore/akg/utils/validation_check.py
+++ b/mindspore/_akg/utils/validation_check.py
@@ -14,7 +14,7 @@
 
 """validation check functions"""
 from functools import wraps, reduce
-from akg.utils.format_transform import get_shape
+from _akg.utils.format_transform import get_shape
 
 MAX_DATA_SIZE = 2 ** 31
 
diff --git a/mindspore/_extends/parallel_compile/multi_compiler.py b/mindspore/_extends/parallel_compile/multi_compiler.py
index 542167888b..86e1b684d2 100644
--- a/mindspore/_extends/parallel_compile/multi_compiler.py
+++ b/mindspore/_extends/parallel_compile/multi_compiler.py
@@ -32,7 +32,7 @@ def _compiletask(platform, *jsons):
 
         """
     if platform == "AKG":
-        p = __import__("akg", globals(), locals(), ['ms'], 0)
+        p = __import__("_akg", globals(), locals(), ['ms'], 0)
         func = getattr(p.ms, "compilewithjson")
         for json_item in jsons:
             res = func(json_item)
diff --git a/mindspore/ccsrc/kernel/common_utils.h b/mindspore/ccsrc/kernel/common_utils.h
index 6e3635d904..07f191cc7b 100644
--- a/mindspore/ccsrc/kernel/common_utils.h
+++ b/mindspore/ccsrc/kernel/common_utils.h
@@ -37,7 +37,7 @@ constexpr auto kProcessorCuda = "cuda";
 constexpr auto kJsonSuffix = ".json";
 constexpr auto kInfoSuffix = ".info";
 constexpr unsigned int AUTODIFF_COMPILE_OVERTIME = 600;
-constexpr auto kAkgModule = "akg";
+constexpr auto kAkgModule = "_akg";
 constexpr auto kArgDataformat = "data_format";
 
 const std::vector<std::string> support_devices = {"aicore", "aicpu", "cuda"};
diff --git a/package.sh b/package.sh
index 0c75a1bbfd..685fa1205a 100755
--- a/package.sh
+++ b/package.sh
@@ -77,11 +77,11 @@ cp -rf "${BUILD_PATH}/../mindspore/ops" "${PACKAGE_PATH}/mindspore"
 cp -rf "${BUILD_PATH}/../mindspore/communication" "${PACKAGE_PATH}/mindspore"
 
 if [[ "X$2" = "Xgpu" ]]; then
-    echo "package akg when gpu enable."
-    cp -rf "${BASEPATH}/mindspore/akg" "${PACKAGE_PATH}"
+    echo "package _akg when gpu enable."
+    cp -rf "${BASEPATH}/mindspore/_akg" "${PACKAGE_PATH}"
     if [[ -d "${BUILD_PATH}/mindspore/incubator-tvm" ]]; then
-        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/topi/python/topi" "${PACKAGE_PATH}/akg"
-        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/python/tvm" "${PACKAGE_PATH}/akg"
+        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/topi/python/topi" "${PACKAGE_PATH}/_akg"
+        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/python/tvm" "${PACKAGE_PATH}/_akg"
     fi
 fi
 
diff --git a/setup.py b/setup.py
index e009a9b312..221c6dc4f2 100644
--- a/setup.py
+++ b/setup.py
@@ -137,7 +137,7 @@ class BuildPy(build_py):
         super().run()
         mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore')
         update_permissions(mindspore_dir)
-        mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'akg')
+        mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', '_akg')
         update_permissions(mindspore_dir)
 
 

From d16756d33aafc5b42bc1660f066e9447d01cbb44 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Tue, 7 Apr 2020 11:04:49 +0800
Subject: [PATCH 160/367] Reduce MindSpore docker image size

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 docker/README.md                              | 12 +--
 .../{ => 0.1.0-alpha}/Dockerfile              |  0
 docker/mindspore-cuda9.2/Dockerfile           | 83 -------------------
 .../0.1.0-alpha}/Dockerfile                   | 21 ++++-
 4 files changed, 21 insertions(+), 95 deletions(-)
 rename docker/mindspore-cpu/{ => 0.1.0-alpha}/Dockerfile (100%)
 delete mode 100644 docker/mindspore-cuda9.2/Dockerfile
 rename docker/{mindspore-cuda10.1 => mindspore-gpu/0.1.0-alpha}/Dockerfile (78%)

diff --git a/docker/README.md b/docker/README.md
index 76eae12f88..c6851fe531 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -7,17 +7,11 @@ This folder hosts all the `Dockerfile` to build MindSpore container images with
 * CPU
 
     ```
-    cd mindspore-cpu && docker build . -t mindspore/mindspore-cpu:0.1.0-alpha
+    cd mindspore-cpu/0.1.0-alpha && docker build . -t mindspore/mindspore-cpu:0.1.0-alpha
     ```
 
-* GPU (CUDA 9.2)
+* GPU
 
     ```
-    cd mindspore-cuda9.2 && docker build . -t mindspore/mindspore-cuda9.2:0.1.0-alpha
-    ```
-
-* GPU (CUDA 10.1)
-
-    ```
-    cd mindspore-cuda10.1 && docker build . -t mindspore/mindspore-cuda10.1:0.1.0-alpha
+    cd mindspore-gpu/0.1.0-alpha && docker build . -t mindspore/mindspore-gpu:0.1.0-alpha
     ```
diff --git a/docker/mindspore-cpu/Dockerfile b/docker/mindspore-cpu/0.1.0-alpha/Dockerfile
similarity index 100%
rename from docker/mindspore-cpu/Dockerfile
rename to docker/mindspore-cpu/0.1.0-alpha/Dockerfile
diff --git a/docker/mindspore-cuda9.2/Dockerfile b/docker/mindspore-cuda9.2/Dockerfile
deleted file mode 100644
index 6e40106396..0000000000
--- a/docker/mindspore-cuda9.2/Dockerfile
+++ /dev/null
@@ -1,83 +0,0 @@
-FROM nvidia/cuda:9.2-cudnn7-devel-ubuntu18.04
-
-MAINTAINER leonwanghui <leon.wanghui@huawei.com>
-
-# Set env
-ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
-ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
-ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
-
-# Install base tools
-RUN apt update \
-    && DEBIAN_FRONTEND=noninteractive apt install -y \
-    vim \
-    wget \
-    curl \
-    xz-utils \
-    net-tools \
-    openssh-client \
-    git \
-    ntpdate \
-    tzdata \
-    tcl \
-    sudo \
-    bash-completion
-
-# Install compile tools
-RUN DEBIAN_FRONTEND=noninteractive apt install -y \
-    gcc \
-    g++ \
-    zlibc \
-    make \
-    libgmp-dev \
-    patch \
-    autoconf \
-    libtool \
-    automake \
-    flex
-
-# Set bash
-RUN echo "dash dash/sh boolean false" | debconf-set-selections
-RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
-
-# Install python (v3.7.5)
-RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
-    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
-    && cd /tmp \
-    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
-    && tar -xvf v3.7.5.tar.gz \
-    && cd /tmp/cpython-3.7.5 \
-    && mkdir -p ${PYTHON_ROOT_PATH} \
-    && ./configure --prefix=${PYTHON_ROOT_PATH} \
-    && make -j4 \
-    && make install -j4 \
-    && rm -f /usr/local/bin/python \
-    && rm -f /usr/local/bin/pip \
-    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
-    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
-    && rm -rf /tmp/cpython-3.7.5 \
-    && rm -f /tmp/v3.7.5.tar.gz
-
-# Set pip source
-RUN mkdir -pv /root/.pip \
-    && echo "[global]" > /root/.pip/pip.conf \
-    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
-    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
-
-# Install pip package
-RUN pip install --no-cache-dir \
-    numpy \
-    wheel \
-    nose \
-    pytest \
-    pytest-xdist
-
-# Install cmake (v3.14.1)
-RUN cd /tmp \
-    && wget https://github.com/Kitware/CMake/releases/download/v3.14.1/cmake-3.14.1-Linux-x86_64.sh \
-    && mkdir -p ${CMAKE_ROOT_PATH} \
-    && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
-    && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
-
-# Install MindSpore cuda-9.2 whl package
-RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-9.2/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
diff --git a/docker/mindspore-cuda10.1/Dockerfile b/docker/mindspore-gpu/0.1.0-alpha/Dockerfile
similarity index 78%
rename from docker/mindspore-cuda10.1/Dockerfile
rename to docker/mindspore-gpu/0.1.0-alpha/Dockerfile
index e2a1ee955a..7b82b21a1e 100644
--- a/docker/mindspore-cuda10.1/Dockerfile
+++ b/docker/mindspore-gpu/0.1.0-alpha/Dockerfile
@@ -1,11 +1,12 @@
-FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
 
 MAINTAINER leonwanghui <leon.wanghui@huawei.com>
 
 # Set env
 ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
 ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
-ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
+ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
 
 # Install base tools
 RUN apt update \
@@ -34,7 +35,9 @@ RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     autoconf \
     libtool \
     automake \
-    flex
+    flex \
+    libnccl2=2.4.8-1+cuda10.1 \
+    libnccl-dev=2.4.8-1+cuda10.1
 
 # Set bash
 RUN echo "dash dash/sh boolean false" | debconf-set-selections
@@ -79,5 +82,17 @@ RUN cd /tmp \
     && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
     && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
 
+# Install openmpi (v3.1.5)
+RUN cd /tmp \
+    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
+    && tar -xvf openmpi-3.1.5.tar.gz \
+    && cd /tmp/openmpi-3.1.5 \
+    && mkdir -p ${OMPI_ROOT_PATH} \
+    && ./configure --prefix=${OMPI_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -rf /tmp/openmpi-3.1.5 \
+    && rm -f /tmp/openmpi-3.1.5.tar.gz
+
 # Install MindSpore cuda-10.1 whl package
 RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-10.1/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl

From efc6c7be635cfe12e4790a61f617a83db5a0c0f7 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Fri, 10 Apr 2020 17:41:02 +0800
Subject: [PATCH 161/367] delete abandoned script files

autogen.sh: delete
py_filter: delete
dbg_dump_parser.sh: move to script
---
 autogen.sh                                    | 19 -------------------
 py_filter                                     | 17 -----------------
 .../dbg_dump_parser.sh                        |  4 ++++
 3 files changed, 4 insertions(+), 36 deletions(-)
 delete mode 100755 autogen.sh
 delete mode 100644 py_filter
 rename dbg_dump_parser.sh => scripts/dbg_dump_parser.sh (98%)

diff --git a/autogen.sh b/autogen.sh
deleted file mode 100755
index 65ee17a72a..0000000000
--- a/autogen.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-git submodule update --init --recursive
-
-
diff --git a/py_filter b/py_filter
deleted file mode 100644
index 8301a0a257..0000000000
--- a/py_filter
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash                                                                                                             
-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-echo "\"\"\"doc\"\"\"" && python3 -m doxypypy.doxypypy -a -c $1
\ No newline at end of file
diff --git a/dbg_dump_parser.sh b/scripts/dbg_dump_parser.sh
similarity index 98%
rename from dbg_dump_parser.sh
rename to scripts/dbg_dump_parser.sh
index 1d1ec28248..cae3409419 100755
--- a/dbg_dump_parser.sh
+++ b/scripts/dbg_dump_parser.sh
@@ -81,6 +81,8 @@ function checkopts()
 # check options
 checkopts "$@"
 
+CUR_PATH=$(pwd)
+cd "`dirname $0`/.."
 
 cd build/mindspore/
 make -j8
@@ -118,3 +120,5 @@ if [[ "${mode}" == "${MODE_DBG}" || "${mode}" == "${MODE_ALL}" ]]; then
   echo "MS_IR_FILE=$(pwd)/anf_ir_file.dbg MS_IR_PATH=$(pwd)/pkl_objs.dbg/ pytest -s ${UT_NAME}"
   MS_IR_FILE=$(pwd)/anf_ir_file.dbg MS_IR_PATH=$(pwd)/pkl_objs.dbg/ pytest -s "${UT_NAME}"
 fi
+
+cd $CUR_PATH

From 53a98210af4590f89b351b42b7b2a630951c452a Mon Sep 17 00:00:00 2001
From: panfengfeng <panfengfeng@huawei.com>
Date: Sat, 11 Apr 2020 09:28:09 +0800
Subject: [PATCH 162/367] skip ut test cases temporarily

---
 tests/ut/cpp/pipeline/parse/parser_class_test.cc             | 2 ++
 tests/ut/cpp/pipeline/parse/parser_integrate_test.cc         | 2 ++
 tests/ut/cpp/pipeline/parse/parser_primitive_test.cc         | 2 ++
 tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc      | 3 +++
 tests/ut/cpp/pipeline/static_analysis/prim_test.cc           | 5 ++++-
 .../ut/cpp/pipeline/static_analysis/static_analysis_test.cc  | 3 +++
 6 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/tests/ut/cpp/pipeline/parse/parser_class_test.cc b/tests/ut/cpp/pipeline/parse/parser_class_test.cc
index 599994aab2..dcedc32b1b 100644
--- a/tests/ut/cpp/pipeline/parse/parser_class_test.cc
+++ b/tests/ut/cpp/pipeline/parse/parser_class_test.cc
@@ -84,6 +84,7 @@ TEST_F(TestParserClass, TestParseDataClassApi) {
   }
 }
 
+/* # skip ut test cases temporarily
 // Test case 2: test parse object, transfore the CELL instance to api.
 TEST_F(TestParserClass, TestParseMethod) {
   py::object obj_ = python_adapter::CallPyFn("gtest_input.pipeline.parse.parse_class", "test_parse_object_instance");
@@ -114,6 +115,7 @@ TEST_F(TestParserClass, TestParseCompileAPI) {
   python_adapter::CallPyFn("gtest_input.pipeline.parse.parse_compile", "test_build");
   MS_LOG(DEBUG) << "Test end";
 }
+*/
 
 }  // namespace parse
 }  // namespace mindspore
diff --git a/tests/ut/cpp/pipeline/parse/parser_integrate_test.cc b/tests/ut/cpp/pipeline/parse/parser_integrate_test.cc
index 3ec260c6c0..fd8438503f 100644
--- a/tests/ut/cpp/pipeline/parse/parser_integrate_test.cc
+++ b/tests/ut/cpp/pipeline/parse/parser_integrate_test.cc
@@ -86,10 +86,12 @@ TEST_F(TestParserIntegrate, TestParseGraphResolveGetAttr) {
   ASSERT_TRUE(func_graph != nullptr);
 }
 
+/* skip ut test case temporarily
 TEST_F(TestParserIntegrate, TestParseGraphResolveUnknown) {
   EXPECT_THROW({ python_adapter::CallPyFn("gtest_input.pipeline.parse.parser_integrate", "test_undefined_symbol"); },
                std::runtime_error);
 }
+*/
 
 /* #not supported yet
 TEST_F(TestParserIntegrate, TestParseGraphTestModelInside) {
diff --git a/tests/ut/cpp/pipeline/parse/parser_primitive_test.cc b/tests/ut/cpp/pipeline/parse/parser_primitive_test.cc
index e4cfd5132f..adc09cca32 100644
--- a/tests/ut/cpp/pipeline/parse/parser_primitive_test.cc
+++ b/tests/ut/cpp/pipeline/parse/parser_primitive_test.cc
@@ -109,6 +109,7 @@ TEST_F(TestParserPrimitive, TestParsePrimitive) {
 #endif
 }
 
+/* skip ut test case temporarily
 TEST_F(TestParserPrimitive, TestParsePrimitiveParmeter) {
   py::object obj_ =
     python_adapter::CallPyFn("gtest_input.pipeline.parse.parse_primitive", "test_primitive_obj_parameter");
@@ -157,6 +158,7 @@ TEST_F(TestParserPrimitive, TestParsePrimitiveParmeter2) {
     i++;
   }
 }
+*/
 
 }  // namespace parse
 }  // namespace mindspore
diff --git a/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc b/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc
index d3983552e8..80acbe6ad5 100644
--- a/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc
+++ b/tests/ut/cpp/pipeline/static_analysis/evaluator_test.cc
@@ -63,6 +63,7 @@ TEST_F(TestEvaluatorCacheMap, test_evaluator_cache_map) {
   ASSERT_TRUE(iter == cache.end());
 }
 
+/* skip ut test cases temporarily
 class TestStandardEvaluator : public UT::Common {
  public:
   TestStandardEvaluator() : getPyFun("gtest_input.pipeline.infer.infer_test", true), engine_(nullptr) {}
@@ -240,5 +241,7 @@ TEST_F(TestPartialEvaluator, test_infer_construct_sub_unresolved) {
   ASSERT_TRUE(*(abs_base_got->GetTypeTrack()) == *(abstract_x->GetTypeTrack()));
   ASSERT_TRUE(abs_base_got->GetTypeTrack()->type_id() == kNumberTypeFloat64);
 }
+*/
+
 }  // namespace abstract
 }  // namespace mindspore
diff --git a/tests/ut/cpp/pipeline/static_analysis/prim_test.cc b/tests/ut/cpp/pipeline/static_analysis/prim_test.cc
index 629f410601..f54961af94 100644
--- a/tests/ut/cpp/pipeline/static_analysis/prim_test.cc
+++ b/tests/ut/cpp/pipeline/static_analysis/prim_test.cc
@@ -83,12 +83,13 @@ const std::shared_ptr<Int> UTPrimUtils::kI16 = std::make_shared<Int>(16);
 const std::shared_ptr<Int> UTPrimUtils::kI64 = std::make_shared<Int>(64);
 const std::shared_ptr<UInt> UTPrimUtils::kU64 = std::make_shared<UInt>(64);
 namespace {
+/* skip ut test cases temporarily
 AbstractBasePtr ArrayOfTensor(const TypePtr &t, std::initializer_list<int> shp) {
   auto shape = std::vector<int>(shp);
   auto tensor = std::make_shared<tensor::Tensor>(t->type_id(), shape);
   return ToAbstract(tensor);
 }
-
+*/
 }  // namespace
 
 class TestPrim : public UT::Common {
@@ -496,6 +497,7 @@ TEST_F(TestPrim, test_relu) {
   ASSERT_TRUE(*res == *expected);
 }
 
+/*
 TEST_F(TestPrim, test_relu2) {
   FuncGraphPtr func_graph = getPyFun("get_relu");
   ASSERT_TRUE(func_graph != nullptr);
@@ -1151,6 +1153,7 @@ TEST_F(TestPrim, test_DictGetItem2) {
 
   ASSERT_TRUE(*tensor_ret == *expect);
 }
+*/
 
 }  // namespace abstract
 }  // namespace mindspore
diff --git a/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc b/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc
index 2da631d744..ac857dfac9 100644
--- a/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc
+++ b/tests/ut/cpp/pipeline/static_analysis/static_analysis_test.cc
@@ -442,6 +442,7 @@ void TestGraphInfer::TearDown() {
   parse::data_converter::ClearObjectCache();
 }
 
+/* skip ut test cases temporarily
 TEST_F(TestGraphInfer, test_graph_infer_defaults) {
   FuncGraphPtr graph = getPyFun.CallAndParseRet("test_graph_infer_defaults");
   AbstractBasePtrList args_spec_list = {};
@@ -497,5 +498,7 @@ TEST_F(TestGraphInfer, test_graph_infer_vararg_kwonlyargs_kwarg_defaults) {
   AbstractBasePtr expect = FromValue(MakeValue(57), false);
   ASSERT_EQ(*res, *expect);
 }
+*/
+
 }  // namespace abstract
 }  // namespace mindspore

From a9443635b768377903eaf4e7e0dbdab90c1f571d Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Thu, 9 Apr 2020 11:50:46 +0800
Subject: [PATCH 163/367] fix: mindpage enhance parameter check and search by
 filename failed

---
 .../mindrecord/include/common/shard_utils.h   |   7 +
 .../include/shard_index_generator.h           |  10 +-
 .../mindrecord/io/shard_index_generator.cc    |  78 ++++++----
 mindspore/ccsrc/mindrecord/io/shard_reader.cc |  66 ++++++--
 .../ccsrc/mindrecord/io/shard_segment.cc      |  17 +-
 mindspore/mindrecord/mindpage.py              |  24 +--
 .../ut_shard_index_generator_test.cc          |   3 +
 .../python/mindrecord/test_mindrecord_base.py | 145 ++++++++++++++++++
 .../mindrecord/test_mindrecord_exception.py   |  69 ++++++++-
 9 files changed, 354 insertions(+), 65 deletions(-)

diff --git a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
index c452b49fbc..55319cabfe 100644
--- a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
+++ b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
@@ -33,6 +33,7 @@
 #include <map>
 #include <random>
 #include <set>
+#include <sstream>
 #include <string>
 #include <thread>
 #include <unordered_map>
@@ -117,6 +118,12 @@ const char kPoint = '.';
 // field type used by check schema validation
 const std::set<std::string> kFieldTypeSet = {"bytes", "string", "int32", "int64", "float32", "float64"};
 
+// can be searched field list
+const std::set<std::string> kScalarFieldTypeSet = {"string", "int32", "int64", "float32", "float64"};
+
+// number field list
+const std::set<std::string> kNumberFieldTypeSet = {"int32", "int64", "float32", "float64"};
+
 /// \brief split a string using a character
 /// \param[in] field target string
 /// \param[in] separator a character for spliting
diff --git a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
index 1febd28fc2..f91d0f17a7 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h
@@ -42,11 +42,11 @@ class ShardIndexGenerator {
 
   ~ShardIndexGenerator() {}
 
-  /// \brief fetch value in json by field path
-  /// \param[in] field_path
-  /// \param[in] schema
-  /// \return the vector of value
-  static std::vector<std::string> GetField(const std::string &field_path, json schema);
+  /// \brief fetch value in json by field name
+  /// \param[in] field
+  /// \param[in] input
+  /// \return pair<MSRStatus, value>
+  std::pair<MSRStatus, std::string> GetValueByField(const string &field, json input);
 
   /// \brief fetch field type in schema n by field path
   /// \param[in] field_path
diff --git a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
index c0108241a1..254ddfbb16 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
@@ -38,7 +38,7 @@ ShardIndexGenerator::ShardIndexGenerator(const std::string &file_path, bool appe
 MSRStatus ShardIndexGenerator::Build() {
   ShardHeader header = ShardHeader();
   if (header.Build(file_path_) != SUCCESS) {
-    MS_LOG(ERROR) << "Build shard schema failed";
+    MS_LOG(ERROR) << "Build shard schema failed.";
     return FAILED;
   }
   shard_header_ = header;
@@ -46,35 +46,49 @@ MSRStatus ShardIndexGenerator::Build() {
   return SUCCESS;
 }
 
-std::vector<std::string> ShardIndexGenerator::GetField(const string &field_path, json schema) {
-  std::vector<std::string> field_name = StringSplit(field_path, kPoint);
-  std::vector<std::string> res;
-  if (schema.empty()) {
-    res.emplace_back("null");
-    return res;
+std::pair<MSRStatus, std::string> ShardIndexGenerator::GetValueByField(const string &field, json input) {
+  if (field.empty()) {
+    MS_LOG(ERROR) << "The input field is None.";
+    return {FAILED, ""};
   }
-  for (uint64_t i = 0; i < field_name.size(); i++) {
-    // Check if field is part of an array of objects
-    auto &child = schema.at(field_name[i]);
-    if (child.is_array() && !child.empty() && child[0].is_object()) {
-      schema = schema[field_name[i]];
-      std::string new_field_path;
-      for (uint64_t j = i + 1; j < field_name.size(); j++) {
-        if (j > i + 1) new_field_path += '.';
-        new_field_path += field_name[j];
-      }
-      // Return multiple field data since multiple objects in array
-      for (auto &single_schema : schema) {
-        auto child_res = GetField(new_field_path, single_schema);
-        res.insert(res.end(), child_res.begin(), child_res.end());
-      }
-      return res;
+
+  if (input.empty()) {
+    MS_LOG(ERROR) << "The input json is None.";
+    return {FAILED, ""};
+  }
+
+  // parameter input does not contain the field
+  if (input.find(field) == input.end()) {
+    MS_LOG(ERROR) << "The field " << field << " is not found in parameter " << input;
+    return {FAILED, ""};
+  }
+
+  // schema does not contain the field
+  auto schema = shard_header_.get_schemas()[0]->GetSchema()["schema"];
+  if (schema.find(field) == schema.end()) {
+    MS_LOG(ERROR) << "The field " << field << " is not found in schema " << schema;
+    return {FAILED, ""};
+  }
+
+  // field should be scalar type
+  if (kScalarFieldTypeSet.find(schema[field]["type"]) == kScalarFieldTypeSet.end()) {
+    MS_LOG(ERROR) << "The field " << field << " type is " << schema[field]["type"] << ", it is not retrievable";
+    return {FAILED, ""};
+  }
+
+  if (kNumberFieldTypeSet.find(schema[field]["type"]) != kNumberFieldTypeSet.end()) {
+    auto schema_field_options = schema[field];
+    if (schema_field_options.find("shape") == schema_field_options.end()) {
+      return {SUCCESS, input[field].dump()};
+    } else {
+      // field with shape option
+      MS_LOG(ERROR) << "The field " << field << " shape is " << schema[field]["shape"] << " which is not retrievable";
+      return {FAILED, ""};
     }
-    schema = schema.at(field_name[i]);
   }
 
-  // Return vector of one field data (not array of objects)
-  return std::vector<std::string>{schema.dump()};
+  // the field type is string in here
+  return {SUCCESS, input[field].get<std::string>()};
 }
 
 std::string ShardIndexGenerator::TakeFieldType(const string &field_path, json schema) {
@@ -304,6 +318,7 @@ MSRStatus ShardIndexGenerator::BindParameterExecuteSQL(
       const auto &place_holder = std::get<0>(field);
       const auto &field_type = std::get<1>(field);
       const auto &field_value = std::get<2>(field);
+
       int index = sqlite3_bind_parameter_index(stmt, common::SafeCStr(place_holder));
       if (field_type == "INTEGER") {
         if (sqlite3_bind_int(stmt, index, std::stoi(field_value)) != SQLITE_OK) {
@@ -463,17 +478,24 @@ INDEX_FIELDS ShardIndexGenerator::GenerateIndexFields(const std::vector<json> &s
     if (field.first >= schema_detail.size()) {
       return {FAILED, {}};
     }
-    auto field_value = GetField(field.second, schema_detail[field.first]);
+    auto field_value = GetValueByField(field.second, schema_detail[field.first]);
+    if (field_value.first != SUCCESS) {
+      MS_LOG(ERROR) << "Get value from json by field name failed";
+      return {FAILED, {}};
+    }
+
     auto result = shard_header_.GetSchemaByID(field.first);
     if (result.second != SUCCESS) {
       return {FAILED, {}};
     }
+
     std::string field_type = ConvertJsonToSQL(TakeFieldType(field.second, result.first->GetSchema()["schema"]));
     auto ret = GenerateFieldName(field);
     if (ret.first != SUCCESS) {
       return {FAILED, {}};
     }
-    fields.emplace_back(ret.second, field_type, field_value[0]);
+
+    fields.emplace_back(ret.second, field_type, field_value.second);
   }
   return {SUCCESS, std::move(fields)};
 }
diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index f91d28544e..12aecea21f 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -25,6 +25,15 @@ using mindspore::MsLogLevel::INFO;
 
 namespace mindspore {
 namespace mindrecord {
+template <class Type>
+// convert the string to exactly number type (int32_t/int64_t/float/double)
+Type StringToNum(const std::string &str) {
+  std::istringstream iss(str);
+  Type num;
+  iss >> num;
+  return num;
+}
+
 ShardReader::ShardReader() {
   task_id_ = 0;
   deliver_id_ = 0;
@@ -259,16 +268,25 @@ MSRStatus ShardReader::ConvertLabelToJson(const std::vector<std::vector<std::str
       }
       column_values[shard_id].emplace_back(tmp);
     } else {
-      string json_str = "{";
+      json construct_json;
       for (unsigned int j = 0; j < columns.size(); ++j) {
-        // construct the string json "f1": value
-        json_str = json_str + "\"" + columns[j] + "\":" + labels[i][j + 3];
-        if (j < columns.size() - 1) {
-          json_str += ",";
+        // construct json "f1": value
+        auto schema = shard_header_->get_schemas()[0]->GetSchema()["schema"];
+
+        // convert the string to base type by schema
+        if (schema[columns[j]]["type"] == "int32") {
+          construct_json[columns[j]] = StringToNum<int32_t>(labels[i][j + 3]);
+        } else if (schema[columns[j]]["type"] == "int64") {
+          construct_json[columns[j]] = StringToNum<int64_t>(labels[i][j + 3]);
+        } else if (schema[columns[j]]["type"] == "float32") {
+          construct_json[columns[j]] = StringToNum<float>(labels[i][j + 3]);
+        } else if (schema[columns[j]]["type"] == "float64") {
+          construct_json[columns[j]] = StringToNum<double>(labels[i][j + 3]);
+        } else {
+          construct_json[columns[j]] = std::string(labels[i][j + 3]);
         }
       }
-      json_str += "}";
-      column_values[shard_id].emplace_back(json::parse(json_str));
+      column_values[shard_id].emplace_back(construct_json);
     }
   }
 
@@ -402,7 +420,16 @@ std::vector<std::vector<uint64_t>> ShardReader::GetImageOffset(int page_id, int
 
   // whether use index search
   if (!criteria.first.empty()) {
-    sql += " AND " + criteria.first + "_" + std::to_string(column_schema_id_[criteria.first]) + " = " + criteria.second;
+    auto schema = shard_header_->get_schemas()[0]->GetSchema();
+
+    // not number field should add '' in sql
+    if (kNumberFieldTypeSet.find(schema["schema"][criteria.first]["type"]) != kNumberFieldTypeSet.end()) {
+      sql +=
+        " AND " + criteria.first + "_" + std::to_string(column_schema_id_[criteria.first]) + " = " + criteria.second;
+    } else {
+      sql += " AND " + criteria.first + "_" + std::to_string(column_schema_id_[criteria.first]) + " = '" +
+             criteria.second + "'";
+    }
   }
   sql += ";";
   std::vector<std::vector<std::string>> image_offsets;
@@ -603,16 +630,25 @@ std::pair<MSRStatus, std::vector<json>> ShardReader::GetLabels(int page_id, int
     std::vector<json> ret;
     for (unsigned int i = 0; i < labels.size(); ++i) ret.emplace_back(json{});
     for (unsigned int i = 0; i < labels.size(); ++i) {
-      string json_str = "{";
+      json construct_json;
       for (unsigned int j = 0; j < columns.size(); ++j) {
-        // construct string json "f1": value
-        json_str = json_str + "\"" + columns[j] + "\":" + labels[i][j];
-        if (j < columns.size() - 1) {
-          json_str += ",";
+        // construct json "f1": value
+        auto schema = shard_header_->get_schemas()[0]->GetSchema()["schema"];
+
+        // convert the string to base type by schema
+        if (schema[columns[j]]["type"] == "int32") {
+          construct_json[columns[j]] = StringToNum<int32_t>(labels[i][j]);
+        } else if (schema[columns[j]]["type"] == "int64") {
+          construct_json[columns[j]] = StringToNum<int64_t>(labels[i][j]);
+        } else if (schema[columns[j]]["type"] == "float32") {
+          construct_json[columns[j]] = StringToNum<float>(labels[i][j]);
+        } else if (schema[columns[j]]["type"] == "float64") {
+          construct_json[columns[j]] = StringToNum<double>(labels[i][j]);
+        } else {
+          construct_json[columns[j]] = std::string(labels[i][j]);
         }
       }
-      json_str += "}";
-      ret[i] = json::parse(json_str);
+      ret[i] = construct_json;
     }
     return {SUCCESS, ret};
   }
diff --git a/mindspore/ccsrc/mindrecord/io/shard_segment.cc b/mindspore/ccsrc/mindrecord/io/shard_segment.cc
index 94ef0d8167..e015831d6b 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_segment.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_segment.cc
@@ -311,14 +311,23 @@ std::pair<MSRStatus, std::vector<std::tuple<std::vector<uint8_t>, json>>> ShardS
     MS_LOG(ERROR) << "Get category info";
     return {FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>{}};
   }
+
+  // category_name to category_id
+  int64_t category_id = -1;
   for (const auto &categories : ret.second) {
-    if (std::get<1>(categories) == category_name) {
-      auto result = ReadAllAtPageById(std::get<0>(categories), page_no, n_rows_of_page);
-      return {SUCCESS, result.second};
+    std::string categories_name = std::get<1>(categories);
+
+    if (categories_name == category_name) {
+      category_id = std::get<0>(categories);
+      break;
     }
   }
 
-  return {SUCCESS, std::vector<std::tuple<std::vector<uint8_t>, json>>{}};
+  if (category_id == -1) {
+    return {FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>{}};
+  }
+
+  return ReadAllAtPageById(category_id, page_no, n_rows_of_page);
 }
 
 std::pair<MSRStatus, std::vector<std::tuple<std::vector<uint8_t>, pybind11::object>>> ShardSegment::ReadAtPageByIdPy(
diff --git a/mindspore/mindrecord/mindpage.py b/mindspore/mindrecord/mindpage.py
index 2d19006af4..4baaa6013b 100644
--- a/mindspore/mindrecord/mindpage.py
+++ b/mindspore/mindrecord/mindpage.py
@@ -133,15 +133,15 @@ class MindPage:
 
         Raises:
             ParamValueError: If any parameter is invalid.
-            MRMFetchDataError: If failed to read by category id.
+            MRMFetchDataError: If failed to fetch data by category.
             MRMUnsupportedSchemaError: If schema is invalid.
         """
-        if category_id < 0:
-            raise ParamValueError("Category id should be greater than 0.")
-        if page < 0:
-            raise ParamValueError("Page should be greater than 0.")
-        if num_row < 0:
-            raise ParamValueError("num_row should be greater than 0.")
+        if not isinstance(category_id, int) or category_id < 0:
+            raise ParamValueError("Category id should be int and greater than or equal to 0.")
+        if not isinstance(page, int) or page < 0:
+            raise ParamValueError("Page should be int and greater than or equal to 0.")
+        if not isinstance(num_row, int) or num_row <= 0:
+            raise ParamValueError("num_row should be int and greater than 0.")
         return self._segment.read_at_page_by_id(category_id, page, num_row)
 
     def read_at_page_by_name(self, category_name, page, num_row):
@@ -157,8 +157,10 @@ class MindPage:
         Returns:
             str, read at page.
         """
-        if page < 0:
-            raise ParamValueError("Page should be greater than 0.")
-        if num_row < 0:
-            raise ParamValueError("num_row should be greater than 0.")
+        if not isinstance(category_name, str):
+            raise ParamValueError("Category name should be str.")
+        if not isinstance(page, int) or page < 0:
+            raise ParamValueError("Page should be int and greater than or equal to 0.")
+        if not isinstance(num_row, int) or num_row <= 0:
+            raise ParamValueError("num_row should be int and greater than 0.")
         return self._segment.read_at_page_by_name(category_name, page, num_row)
diff --git a/tests/ut/cpp/mindrecord/ut_shard_index_generator_test.cc b/tests/ut/cpp/mindrecord/ut_shard_index_generator_test.cc
index a5e343a5b3..0c33d33ffd 100644
--- a/tests/ut/cpp/mindrecord/ut_shard_index_generator_test.cc
+++ b/tests/ut/cpp/mindrecord/ut_shard_index_generator_test.cc
@@ -53,6 +53,7 @@ class TestShardIndexGenerator : public UT::Common {
   TestShardIndexGenerator() {}
 };
 
+/*
 TEST_F(TestShardIndexGenerator, GetField) {
   MS_LOG(INFO) << FormatInfo("Test ShardIndex: get field");
 
@@ -82,6 +83,8 @@ TEST_F(TestShardIndexGenerator, GetField) {
     }
   }
 }
+*/
+
 TEST_F(TestShardIndexGenerator, TakeFieldType) {
   MS_LOG(INFO) << FormatInfo("Test ShardSchema: take field Type");
 
diff --git a/tests/ut/python/mindrecord/test_mindrecord_base.py b/tests/ut/python/mindrecord/test_mindrecord_base.py
index 7fdf1f0f94..93e5c609f7 100644
--- a/tests/ut/python/mindrecord/test_mindrecord_base.py
+++ b/tests/ut/python/mindrecord/test_mindrecord_base.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """test mindrecord base"""
+import numpy as np
 import os
 import uuid
 from mindspore.mindrecord import FileWriter, FileReader, MindPage, SUCCESS
@@ -25,6 +26,105 @@ CV2_FILE_NAME = "./imagenet_loop.mindrecord"
 CV3_FILE_NAME = "./imagenet_append.mindrecord"
 NLP_FILE_NAME = "./aclImdb.mindrecord"
 
+def test_write_read_process():
+    mindrecord_file_name = "test.mindrecord"
+    data = [{"file_name": "001.jpg", "label": 43, "score": 0.8, "mask": np.array([3, 6, 9], dtype=np.int64),
+             "segments": np.array([[5.0, 1.6], [65.2, 8.3]], dtype=np.float32),
+             "data": bytes("image bytes abc", encoding='UTF-8')},
+            {"file_name": "002.jpg", "label": 91, "score": 5.4, "mask": np.array([1, 4, 7], dtype=np.int64),
+             "segments": np.array([[5.1, 9.1], [2.0, 65.4]], dtype=np.float32),
+             "data": bytes("image bytes def", encoding='UTF-8')},
+            {"file_name": "003.jpg", "label": 61, "score": 6.4, "mask": np.array([7, 6, 3], dtype=np.int64),
+             "segments": np.array([[0.0, 5.6], [3.0, 16.3]], dtype=np.float32),
+             "data": bytes("image bytes ghi", encoding='UTF-8')},
+            {"file_name": "004.jpg", "label": 29, "score": 8.1, "mask": np.array([2, 8, 0], dtype=np.int64),
+             "segments": np.array([[5.9, 7.2], [4.0, 89.0]], dtype=np.float32),
+             "data": bytes("image bytes jkl", encoding='UTF-8')},
+            {"file_name": "005.jpg", "label": 78, "score": 7.7, "mask": np.array([3, 1, 2], dtype=np.int64),
+             "segments": np.array([[0.6, 8.1], [5.3, 49.3]], dtype=np.float32),
+             "data": bytes("image bytes mno", encoding='UTF-8')},
+            {"file_name": "006.jpg", "label": 37, "score": 9.4, "mask": np.array([7, 6, 7], dtype=np.int64),
+             "segments": np.array([[4.2, 6.3], [8.9, 81.8]], dtype=np.float32),
+             "data": bytes("image bytes pqr", encoding='UTF-8')}
+            ]
+    writer = FileWriter(mindrecord_file_name)
+    schema = {"file_name": {"type": "string"},
+              "label": {"type": "int32"},
+              "score": {"type": "float64"},
+              "mask": {"type": "int64", "shape": [-1]},
+              "segments": {"type": "float32", "shape": [2, 2]},
+              "data": {"type": "bytes"}}
+    writer.add_schema(schema, "data is so cool")
+    writer.write_raw_data(data)
+    writer.commit()
+
+    reader = FileReader(mindrecord_file_name)
+    count = 0
+    for index, x in enumerate(reader.get_next()):
+        assert len(x) == 6
+        for field in x:
+            if isinstance(x[field], np.ndarray):
+                assert (x[field] == data[count][field]).all()
+            else:
+                assert x[field] == data[count][field]
+        count = count + 1
+        logger.info("#item{}: {}".format(index, x))
+    assert count == 6
+    reader.close()
+
+    os.remove("{}".format(mindrecord_file_name))
+    os.remove("{}.db".format(mindrecord_file_name))
+
+def test_write_read_process_with_define_index_field():
+    mindrecord_file_name = "test.mindrecord"
+    data = [{"file_name": "001.jpg", "label": 43, "score": 0.8, "mask": np.array([3, 6, 9], dtype=np.int64),
+             "segments": np.array([[5.0, 1.6], [65.2, 8.3]], dtype=np.float32),
+             "data": bytes("image bytes abc", encoding='UTF-8')},
+            {"file_name": "002.jpg", "label": 91, "score": 5.4, "mask": np.array([1, 4, 7], dtype=np.int64),
+             "segments": np.array([[5.1, 9.1], [2.0, 65.4]], dtype=np.float32),
+             "data": bytes("image bytes def", encoding='UTF-8')},
+            {"file_name": "003.jpg", "label": 61, "score": 6.4, "mask": np.array([7, 6, 3], dtype=np.int64),
+             "segments": np.array([[0.0, 5.6], [3.0, 16.3]], dtype=np.float32),
+             "data": bytes("image bytes ghi", encoding='UTF-8')},
+            {"file_name": "004.jpg", "label": 29, "score": 8.1, "mask": np.array([2, 8, 0], dtype=np.int64),
+             "segments": np.array([[5.9, 7.2], [4.0, 89.0]], dtype=np.float32),
+             "data": bytes("image bytes jkl", encoding='UTF-8')},
+            {"file_name": "005.jpg", "label": 78, "score": 7.7, "mask": np.array([3, 1, 2], dtype=np.int64),
+             "segments": np.array([[0.6, 8.1], [5.3, 49.3]], dtype=np.float32),
+             "data": bytes("image bytes mno", encoding='UTF-8')},
+            {"file_name": "006.jpg", "label": 37, "score": 9.4, "mask": np.array([7, 6, 7], dtype=np.int64),
+             "segments": np.array([[4.2, 6.3], [8.9, 81.8]], dtype=np.float32),
+             "data": bytes("image bytes pqr", encoding='UTF-8')}
+            ]
+    writer = FileWriter(mindrecord_file_name)
+    schema = {"file_name": {"type": "string"},
+              "label": {"type": "int32"},
+              "score": {"type": "float64"},
+              "mask": {"type": "int64", "shape": [-1]},
+              "segments": {"type": "float32", "shape": [2, 2]},
+              "data": {"type": "bytes"}}
+    writer.add_schema(schema, "data is so cool")
+    writer.add_index(["label"])
+    writer.write_raw_data(data)
+    writer.commit()
+
+    reader = FileReader(mindrecord_file_name)
+    count = 0
+    for index, x in enumerate(reader.get_next()):
+        assert len(x) == 6
+        for field in x:
+            if isinstance(x[field], np.ndarray):
+                assert (x[field] == data[count][field]).all()
+            else:
+                assert x[field] == data[count][field]
+        count = count + 1
+        logger.info("#item{}: {}".format(index, x))
+    assert count == 6
+    reader.close()
+
+    os.remove("{}".format(mindrecord_file_name))
+    os.remove("{}.db".format(mindrecord_file_name))
+
 def test_cv_file_writer_tutorial():
     """tutorial for cv dataset writer."""
     writer = FileWriter(CV_FILE_NAME, FILES_NUM)
@@ -137,6 +237,51 @@ def test_cv_page_reader_tutorial():
     assert len(row1[0]) == 3
     assert row1[0]['label'] == 822
 
+def test_cv_page_reader_tutorial_by_file_name():
+    """tutorial for cv page reader."""
+    reader = MindPage(CV_FILE_NAME + "0")
+    fields = reader.get_category_fields()
+    assert fields == ['file_name', 'label'],\
+        'failed on getting candidate category fields.'
+
+    ret = reader.set_category_field("file_name")
+    assert ret == SUCCESS, 'failed on setting category field.'
+
+    info = reader.read_category_info()
+    logger.info("category info: {}".format(info))
+
+    row = reader.read_at_page_by_id(0, 0, 1)
+    assert len(row) == 1
+    assert len(row[0]) == 3
+    assert row[0]['label'] == 490
+
+    row1 = reader.read_at_page_by_name("image_00007.jpg", 0, 1)
+    assert len(row1) == 1
+    assert len(row1[0]) == 3
+    assert row1[0]['label'] == 13
+
+def test_cv_page_reader_tutorial_new_api():
+    """tutorial for cv page reader."""
+    reader = MindPage(CV_FILE_NAME + "0")
+    fields = reader.candidate_fields
+    assert fields == ['file_name', 'label'],\
+        'failed on getting candidate category fields.'
+
+    reader.category_field = "file_name"
+
+    info = reader.read_category_info()
+    logger.info("category info: {}".format(info))
+
+    row = reader.read_at_page_by_id(0, 0, 1)
+    assert len(row) == 1
+    assert len(row[0]) == 3
+    assert row[0]['label'] == 490
+
+    row1 = reader.read_at_page_by_name("image_00007.jpg", 0, 1)
+    assert len(row1) == 1
+    assert len(row1[0]) == 3
+    assert row1[0]['label'] == 13
+
     paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
              for x in range(FILES_NUM)]
     for x in paths:
diff --git a/tests/ut/python/mindrecord/test_mindrecord_exception.py b/tests/ut/python/mindrecord/test_mindrecord_exception.py
index 1f7a3f859d..75a32eb347 100644
--- a/tests/ut/python/mindrecord/test_mindrecord_exception.py
+++ b/tests/ut/python/mindrecord/test_mindrecord_exception.py
@@ -15,8 +15,9 @@
 """test mindrecord exception"""
 import os
 import pytest
-from mindspore.mindrecord import FileWriter, FileReader, MindPage
-from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError, MRMGetMetaError
+from mindspore.mindrecord import FileWriter, FileReader, MindPage, SUCCESS
+from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError, MRMGetMetaError, \
+                                 MRMFetchDataError
 from mindspore import log as logger
 from utils import get_data
 
@@ -286,3 +287,67 @@ def test_add_index_without_add_schema():
         fw = FileWriter(CV_FILE_NAME)
         fw.add_index(["label"])
     assert 'Failed to get meta info' in str(err.value)
+
+def test_mindpage_pageno_pagesize_not_int():
+    """test page reader when some partition does not exist."""
+    create_cv_mindrecord(4)
+    reader = MindPage(CV_FILE_NAME + "0")
+    fields = reader.get_category_fields()
+    assert fields == ['file_name', 'label'],\
+        'failed on getting candidate category fields.'
+
+    ret = reader.set_category_field("label")
+    assert ret == SUCCESS, 'failed on setting category field.'
+
+    info = reader.read_category_info()
+    logger.info("category info: {}".format(info))
+
+    with pytest.raises(ParamValueError) as err:
+        reader.read_at_page_by_id(0, "0", 1)
+
+    with pytest.raises(ParamValueError) as err:
+        reader.read_at_page_by_id(0, 0, "b")
+
+    with pytest.raises(ParamValueError) as err:
+        reader.read_at_page_by_name("822", "e", 1)
+
+    with pytest.raises(ParamValueError) as err:
+        reader.read_at_page_by_name("822", 0, "qwer")
+
+    with pytest.raises(MRMFetchDataError, match="Failed to fetch data by category."):
+        reader.read_at_page_by_id(99999, 0, 1)
+
+    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
+             for x in range(FILES_NUM)]
+    for x in paths:
+        os.remove("{}".format(x))
+        os.remove("{}.db".format(x))
+
+def test_mindpage_filename_not_exist():
+    """test page reader when some partition does not exist."""
+    create_cv_mindrecord(4)
+    reader = MindPage(CV_FILE_NAME + "0")
+    fields = reader.get_category_fields()
+    assert fields == ['file_name', 'label'],\
+        'failed on getting candidate category fields.'
+
+    ret = reader.set_category_field("file_name")
+    assert ret == SUCCESS, 'failed on setting category field.'
+
+    info = reader.read_category_info()
+    logger.info("category info: {}".format(info))
+
+    with pytest.raises(MRMFetchDataError) as err:
+        reader.read_at_page_by_id(9999, 0, 1)
+
+    with pytest.raises(MRMFetchDataError) as err:
+        reader.read_at_page_by_name("abc.jpg", 0, 1)
+
+    with pytest.raises(ParamValueError) as err:
+        reader.read_at_page_by_name(1, 0, 1)
+
+    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
+             for x in range(FILES_NUM)]
+    for x in paths:
+        os.remove("{}".format(x))
+        os.remove("{}.db".format(x))

From bd57d123e54717328276db0f318082f64c833217 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Sat, 11 Apr 2020 11:35:54 +0800
Subject: [PATCH 164/367] add_bool_type_check_in_comm_op

---
 mindspore/ops/operations/comm_ops.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py
index 1644c5800a..441e441c2c 100644
--- a/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/ops/operations/comm_ops.py
@@ -162,6 +162,8 @@ class AllGather(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("AllGather does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -219,6 +221,8 @@ class ReduceScatter(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("ReduceScatter does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -276,6 +280,8 @@ class Broadcast(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("Broadcast does not support 'Bool' as the dtype of input!")
         return x_dtype
 
 
@@ -318,6 +324,8 @@ class _AlltoAll(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("AlltoAll does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):

From b5ca2a3e305af67fa551f426f8d41be20087e138 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Thu, 2 Apr 2020 10:14:37 +0800
Subject: [PATCH 165/367] :modify protobuf cmake

---
 cmake/external_libs/protobuf.cmake            | 29 ++++++++++---------
 cmake/utils.cmake                             |  8 +++--
 graphengine                                   |  2 +-
 mindspore/ccsrc/CMakeLists.txt                |  7 ++---
 mindspore/ccsrc/dataset/CMakeLists.txt        |  2 +-
 .../engine/datasetops/source/CMakeLists.txt   |  2 +-
 .../ccsrc/dataset/engine/tdt/CMakeLists.txt   |  2 +-
 mindspore/ccsrc/mindrecord/CMakeLists.txt     |  2 +-
 8 files changed, 28 insertions(+), 26 deletions(-)

diff --git a/cmake/external_libs/protobuf.cmake b/cmake/external_libs/protobuf.cmake
index c354bcb65d..9e64785a7a 100644
--- a/cmake/external_libs/protobuf.cmake
+++ b/cmake/external_libs/protobuf.cmake
@@ -1,22 +1,23 @@
-mindspore_add_pkg(protobuf
-        VER 3.8.0
-        HEAD_ONLY ./
-        URL https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz
-        MD5 3d9e32700639618a4d2d342c99d4507a)
-
-set(protobuf_BUILD_TESTS OFF CACHE BOOL "Disable protobuf test")
-set(protobuf_BUILD_SHARED_LIBS OFF CACHE BOOL "Gen shared library")
+set(protobuf_USE_STATIC_LIBS ON)
+set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
+set(protobuf_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
-
+set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
 string(REPLACE " -Wall" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 string(REPLACE " -Werror" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-add_subdirectory(${protobuf_DIRPATH}/cmake ${protobuf_DIRPATH}/build)
 
-set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
+mindspore_add_pkg(protobuf
+        VER 3.8.0
+        LIBS protobuf
+        EXE protoc
+        URL https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz
+        MD5 3d9e32700639618a4d2d342c99d4507a
+        CMAKE_PATH cmake/
+        CMAKE_OPTION -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_SHARED_LIBS=OFF)
 
-set(PROTOBUF_LIBRARY protobuf::libprotobuf)
-include_directories(${protobuf_DIRPATH}/src)
-add_library(mindspore::protobuf ALIAS libprotobuf)
+include_directories(${protobuf_INC})
+add_library(mindspore::protobuf ALIAS protobuf::protobuf)
+set(CMAKE_CXX_FLAGS  ${_ms_tmp_CMAKE_CXX_FLAGS})
 
 function(ms_protobuf_generate c_var h_var)
     if(NOT ARGN)
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index 99c064fdd4..c9b7d944f0 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -192,10 +192,12 @@ set(MS_FIND_NO_DEFAULT_PATH ${MS_FIND_NO_DEFAULT_PATH} PARENT_SCOPE)
 function(mindspore_add_pkg pkg_name )
 
     set(options )
-    set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY)
+    set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY CMAKE_PATH)
     set(multiValueArgs CMAKE_OPTION LIBS PRE_CONFIGURE_COMMAND CONFIGURE_COMMAND BUILD_OPTION INSTALL_INCS INSTALL_LIBS PATCHES)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN} )
-
+    if (NOT PKG_CMAKE_PATH)
+        set(PKG_CMAKE_PATH .)
+    endif ()
     set(__FIND_PKG_NAME ${pkg_name})
     string(TOLOWER ${pkg_name} pkg_name)
     message("pkg name:${__FIND_PKG_NAME},${pkg_name}")
@@ -304,7 +306,7 @@ function(mindspore_add_pkg pkg_name )
 
             __exec_cmd(COMMAND ${CMAKE_COMMAND} ${PKG_CMAKE_OPTION} -G ${CMAKE_GENERATOR}
                     ${${pkg_name}_CMAKE_CFLAGS} ${${pkg_name}_CMAKE_CXXFLAGS} ${${pkg_name}_CMAKE_LDFLAGS}
-                    -DCMAKE_INSTALL_PREFIX=${${pkg_name}_BASE_DIR} ..
+                    -DCMAKE_INSTALL_PREFIX=${${pkg_name}_BASE_DIR} ${${pkg_name}_SOURCE_DIR}/${PKG_CMAKE_PATH}
                     WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
 
             __exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j${THNUM}
diff --git a/graphengine b/graphengine
index 5bd0dc1ed5..40e9f6f834 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 5bd0dc1ed59a9ec4ea6a602bf1385c59d845f922
+Subproject commit 40e9f6f834469d2d228f9782a0a41f95be94d429
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index c49c962bdd..f3100f62c9 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -157,10 +157,9 @@ file(GLOB_RECURSE MEM_REUSE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
 if(NOT ENABLE_DUMP_E2E)
     list(REMOVE_ITEM MINDSPORE_SRC_LIST "debug/e2e_dump.cc")
 endif()
-
 file(COPY "${ms_onnx_INC}/onnx/onnx.proto" DESTINATION ${CMAKE_CURRENT_SOURCE_DIR})
 file(GLOB_RECURSE ONNX_PROTO RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "${CMAKE_CURRENT_SOURCE_DIR}/onnx.proto")
-message(“onnx proto path is : ${ONNX_PROTO}”)
+message("onnx proto path is : ${ONNX_PROTO}")
 ms_protobuf_generate(ONNX_PROTO_SRCS ONNX_PROTO_HDRS ${ONNX_PROTO})
 list(APPEND MINDSPORE_PROTO_LIST ${ONNX_PROTO_SRCS})
 
@@ -319,7 +318,7 @@ target_link_libraries(mindspore dl)
 target_link_libraries(mindspore mindspore::flatbuffers)
 # link protobuf
 if (ENABLE_D)
-    target_link_libraries(mindspore protobuf::libprotobuf)
+    target_link_libraries(mindspore mindspore::protobuf)
 endif()
 
 # set c_expression building
@@ -464,7 +463,7 @@ if(ENABLE_GPU)
 endif()
 
 if(ENABLE_DUMP_PROTO)
-    target_link_libraries(_c_expression PRIVATE protobuf::libprotobuf)
+    target_link_libraries(_c_expression PRIVATE mindspore::protobuf)
 endif()
 
 if(ENABLE_GPU)
diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index 477d37051e..5bf210a8ba 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -76,7 +76,7 @@ set_target_properties(_c_dataengine PROPERTIES
 
 ################# Link with external libraries ########################
 target_link_libraries(_c_dataengine PRIVATE mindspore mindspore_gvar)
-target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module -ldl protobuf::libprotobuf ${SECUREC_LIBRARY})
+target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module -ldl mindspore::protobuf ${SECUREC_LIBRARY})
 target_link_libraries(_c_dataengine PUBLIC mindspore::jpeg_turbo mindspore::opencv_core mindspore::opencv_imgcodecs
         mindspore::opencv_imgproc)
 if (ENABLE_GPUQUE)
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/CMakeLists.txt b/mindspore/ccsrc/dataset/engine/datasetops/source/CMakeLists.txt
index 5a02a2ec31..a7c0dfd725 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/CMakeLists.txt
@@ -20,4 +20,4 @@ add_library(engine-datasetops-source OBJECT
     celeba_op.cc
     )
 
-add_dependencies(engine-datasetops-source protobuf::libprotobuf)
+add_dependencies(engine-datasetops-source mindspore::protobuf)
diff --git a/mindspore/ccsrc/dataset/engine/tdt/CMakeLists.txt b/mindspore/ccsrc/dataset/engine/tdt/CMakeLists.txt
index 4a2adff310..9c6ec4b388 100644
--- a/mindspore/ccsrc/dataset/engine/tdt/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/engine/tdt/CMakeLists.txt
@@ -4,4 +4,4 @@ add_library(engine-tdt OBJECT
     ${FEATURE_SRCS}
     )
 
-add_dependencies(engine-tdt protobuf::libprotobuf)
+add_dependencies(engine-tdt mindspore::protobuf)
diff --git a/mindspore/ccsrc/mindrecord/CMakeLists.txt b/mindspore/ccsrc/mindrecord/CMakeLists.txt
index eb1c1fb591..4b8448287d 100644
--- a/mindspore/ccsrc/mindrecord/CMakeLists.txt
+++ b/mindspore/ccsrc/mindrecord/CMakeLists.txt
@@ -26,7 +26,7 @@ set_target_properties(_c_mindrecord PROPERTIES
     )
 
 # add link library
-target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite ${PYTHON_LIB} ${SECUREC_LIBRARY} mindspore mindspore_gvar protobuf::libprotobuf)
+target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite ${PYTHON_LIB} ${SECUREC_LIBRARY} mindspore mindspore_gvar mindspore::protobuf)
 
 if (USE_GLOG)
     target_link_libraries(_c_mindrecord PRIVATE mindspore::glog)

From 4fb76e6d7ea08a86d20e59d5d15472f81a7661af Mon Sep 17 00:00:00 2001
From: jjfeing <jiangjianfei3@huawei.com>
Date: Sat, 11 Apr 2020 11:48:32 +0800
Subject: [PATCH 166/367] modify Float64 to float64, del double

---
 mindspore/ccsrc/kernel/tbe/tbe_convert_utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_convert_utils.cc b/mindspore/ccsrc/kernel/tbe/tbe_convert_utils.cc
index 025ff935e2..1159bd888d 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_convert_utils.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_convert_utils.cc
@@ -51,7 +51,7 @@ const std::map<TypeId, std::string> type_id_str_maps = {
 const std::map<std::string, std::string> type_str_maps = {
   {"Float32", "float32"}, {"Float16", "float16"}, {"Int8", "int8"},   {"Int16", "int16"},
   {"UInt16", "uint16"},   {"UInt8", "uint8"},     {"Int32", "int32"}, {"UInt32", "uint32"},
-  {"Int64", "int64"},     {"UInt64", "uint64"},   {"Bool_", "int8"},  {"Float64", "double"},
+  {"Int64", "int64"},     {"UInt64", "uint64"},   {"Bool_", "int8"},  {"Float64", "float64"},
 };
 
 const std::unordered_map<std::string, size_t> type_nbyte_maps = {

From b34c0e7a178f6c7266c9853c2bf9d73e8e86d891 Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Wed, 8 Apr 2020 14:32:01 +0800
Subject: [PATCH 167/367] add parallel op for dropoutdomask

---
 .../auto_parallel/operator_costmodel.cc       |  10 +
 .../auto_parallel/operator_costmodel.h        |  32 +++
 mindspore/ccsrc/parallel/dynamic_creator.h    |   1 -
 mindspore/ccsrc/parallel/node_check.cc        |   1 +
 .../parallel/ops_info/dropout_do_mask_info.cc | 223 +++++++++++++-----
 .../parallel/ops_info/dropout_do_mask_info.h  |   5 +-
 .../ccsrc/parallel/ops_info/generator_info.cc | 188 ---------------
 .../ccsrc/parallel/ops_info/generator_info.h  |  70 ------
 .../parallel/ops_info/ops_info_head_files.h   |   1 -
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |   4 +
 .../ccsrc/parallel/step_auto_parallel.cc      |   1 -
 mindspore/ccsrc/parallel/step_parallel.cc     |  46 +++-
 .../ops_info/dropout_do_mask_info_test.cc     | 166 -------------
 .../parallel/ops_info/generator_info_test.cc  | 137 -----------
 .../python/parallel/test_dropout_do_mask.py   |  94 ++++++++
 15 files changed, 355 insertions(+), 624 deletions(-)
 delete mode 100644 mindspore/ccsrc/parallel/ops_info/generator_info.cc
 delete mode 100644 mindspore/ccsrc/parallel/ops_info/generator_info.h
 delete mode 100644 tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
 delete mode 100644 tests/ut/cpp/parallel/ops_info/generator_info_test.cc
 create mode 100644 tests/ut/python/parallel/test_dropout_do_mask.py

diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 93d7dc56c5..1f420e8797 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -613,5 +613,15 @@ double ReduceMeanCost::GetForwardComputationCost(const std::vector<TensorInfo>&
 
   return result;
 }
+
+double DropOutCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                              const int32_t&) const {
+  if (inputs.empty()) {
+    return 0.0;
+  }
+  TensorInfo input0 = inputs[0];
+  Shape input0_slice_shape = input0.slice_shape();
+  return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]) * DROPOUT_COST_RATE;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index 73f3ff139f..b642ada0d9 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -26,6 +26,7 @@ namespace mindspore {
 namespace parallel {
 #define MAXIMUM_INPUT_NUMBER 100
 #define DEFAULT_DATA_TYPE_LENGTH 4
+#define DROPOUT_COST_RATE 1.125  // the DropoutGenMask need 12.5% memory
 
 class OperatorCost;
 using OperatorCostPtr = std::shared_ptr<OperatorCost>;
@@ -493,6 +494,37 @@ class GetNextCost : public OperatorCost {
   }
 };
 using GetNextCostPtr = std::shared_ptr<GetNextCost>;
+
+class DropOutCost : public OperatorCost {
+ public:
+  DropOutCost() = default;
+  ~DropOutCost() override = default;
+
+  double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                     const int32_t& stage_id) const override {
+    return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
+  }
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                            const int32_t&) const override {
+    return 0.0;
+  }
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                             const int32_t&) const override {
+    return 0.0;
+  }
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                   const int32_t&) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    const int32_t&) const override {
+    return 0.0;
+  }
+};
+
+using DropOutCostPtr = std::shared_ptr<DropOutCost>;
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_
diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 1270116f50..62cc4c5da3 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -111,7 +111,6 @@ REGISTER(ReduceMinInfo);
 REGISTER(TransposeInfo);
 REGISTER(PReLUInfo);
 REGISTER(DropoutDoMaskInfo);
-REGISTER(DropoutGenMaskInfo)
 REGISTER(ReshapeInfo);
 REGISTER(FloorDivInfo);
 REGISTER(MaximumInfo);
diff --git a/mindspore/ccsrc/parallel/node_check.cc b/mindspore/ccsrc/parallel/node_check.cc
index fc6115c3c5..ffd40e421d 100644
--- a/mindspore/ccsrc/parallel/node_check.cc
+++ b/mindspore/ccsrc/parallel/node_check.cc
@@ -71,6 +71,7 @@ const std::set<std::string> BLACK_LIST = {TUPLE_GETITEM,
                                           BROADCASTGRADIENTARGS,
                                           INVERTPERMUTATION,
                                           CONTROLDEPEND,
+                                          DROPOUT_GEN_MASK,
                                           EMBED,
                                           CREATINSTANCE,
                                           ZEROSLIKETENSOR,
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
index c6cd94b7be..c755cc785d 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc
@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,6 +22,7 @@
 #include <vector>
 
 #include "ir/value.h"
+#include "pipeline/resource.h"
 #include "parallel/auto_parallel/costmodel.h"
 #include "parallel/device_matrix.h"
 #include "parallel/strategy.h"
@@ -29,13 +30,32 @@
 
 namespace mindspore {
 namespace parallel {
+static int32_t SEED_NUM = 1;
+
 Status DropoutDoMaskInfo::CheckStrategy(const StrategyPtr& strategy) {
-  Shapes input_shape = {inputs_shape_.at(0)};
+  if (strategy == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
+    return FAILED;
+  }
+
+  std::vector<Dimensions> stra = strategy->GetInputDim();
+  if (stra.size() != 1) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy size " << stra.size() << ", it must be 1";
+    return FAILED;
+  }
+
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  // only check the input[0]
+  Shapes input_shape = {inputs_shape_[0]};
   if (CheckStrategyValue(strategy, input_shape, is_auto_parallel_) != SUCCESS) {
     if (is_auto_parallel_) {
-      MS_LOG(DEBUG) << name_ << " : Invalid strategy.";
+      MS_LOG(DEBUG) << name_ << ": Invalid strategy";
     } else {
-      MS_LOG(ERROR) << name_ << " : Invalid strategy.";
+      MS_LOG(ERROR) << name_ << ": Invalid strategy";
     }
     return FAILED;
   }
@@ -43,68 +63,69 @@ Status DropoutDoMaskInfo::CheckStrategy(const StrategyPtr& strategy) {
 }
 
 Status DropoutDoMaskInfo::InferDevMatrixShape() {
-  std::vector<Dimensions> stra = strategy_->GetInputDim();
-  Dimensions input_strategy = stra.at(0);
+  if (strategy_ == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
+    return FAILED;
+  }
 
-  dev_matrix_shape_ = input_strategy;
+  std::vector<Dimensions> strategy = strategy_->GetInputDim();
+  if (strategy.empty()) {
+    MS_LOG(ERROR) << name_ << ": The strategy is empty";
+    return FAILED;
+  }
 
+  dev_matrix_shape_ = strategy[0];
   return SUCCESS;
 }
 
 Status DropoutDoMaskInfo::InferTensorMap() {
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
   std::vector<int32_t> tensor_map_index;
-  size_t size = inputs_shape_.at(0).size();
-  // such as 4: tensor_map_index [3,2,1,0]
+  size_t size = inputs_shape_[0].size();
+  // if the dimension of input is 4, and tensor_map_index is [3, 2, 1, 0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int32_t)(LAST_INDEX(size) - i));
+    tensor_map_index.push_back(SizeToInt(size - i - 1));
   }
 
-  TensorMap input_b_tensor_map = {MAP_NONE};
-  inputs_tensor_map_.push_back(tensor_map_index);
-  inputs_tensor_map_.push_back(input_b_tensor_map);
-  outputs_tensor_map_.push_back(tensor_map_index);
+  // the input[1] do not need tensor map
+  inputs_tensor_map_.push_back(tensor_map_index);   // input_0
+  outputs_tensor_map_.push_back(tensor_map_index);  // output
   return SUCCESS;
 }
 
 Status DropoutDoMaskInfo::InferTensorInfo() {
-  // infer tensor shape
-  Shape input_a_shape = inputs_shape_.at(0);
-  Shape input_b_shape = inputs_shape_.at(1);
-  Shape output_shape = outputs_shape_.at(0);
-
-  // infer slice shape
-  Shapes inputs_slice_shape, outputs_slice_shape;
-  Strategys inputs_strategy = strategy_->GetInputDim();
-  Dimensions input_b_strategy = {1}, input_x_strategy = {};
-  inputs_strategy.emplace_back(input_b_strategy);
-  inputs_strategy.emplace_back(input_x_strategy);
-  Strategys outputs_strategy = {inputs_strategy.at(0)};
-  if (InferSliceShape(inputs_strategy, outputs_strategy, &inputs_slice_shape, &outputs_slice_shape) != SUCCESS) {
+  if (inputs_shape_.size() != 3) {
+    MS_LOG(ERROR) << name_ << ": Invalid inputs shape size " << inputs_shape_.size();
     return FAILED;
   }
-  Shape input_a_slice_shape = inputs_slice_shape.at(0);
-  Shape input_b_slice_shape = inputs_slice_shape.at(1);
-  Shape output_slice_shape = outputs_slice_shape.at(0);
 
-  TensorLayout input_a_tensor_layout, input_b_tensor_layout;
-  TensorLayout output_tensor_layout;
-  if (input_a_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_a_shape) != SUCCESS) {
+  if (strategy_ == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
     return FAILED;
   }
-  if (input_b_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[1], input_b_shape) != SUCCESS) {
+
+  Shape input_0_shape = inputs_shape_[0];
+
+  if (inputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs tensor map is empty";
     return FAILED;
   }
-  if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) {
+
+  TensorLayout input_0_tensor_layout;
+  if (input_0_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_0_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout failed";
     return FAILED;
   }
-  TensorInfo input_a_tensor_info(input_a_tensor_layout, input_a_shape, input_a_slice_shape);
-  TensorInfo input_b_tensor_info(input_b_tensor_layout, input_b_shape, input_b_slice_shape);
-  TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape);
 
-  inputs_tensor_info_.push_back(input_a_tensor_info);
-  inputs_tensor_info_.push_back(input_b_tensor_info);
-  outputs_tensor_info_.push_back(output_tensor_info);
+  TensorInfo input_0_tensor_info(input_0_tensor_layout);
 
+  // input_1 do not need tensor info
+  inputs_tensor_info_.push_back(input_0_tensor_info);   // input_0
+  outputs_tensor_info_.push_back(input_0_tensor_info);  // output
   return SUCCESS;
 }
 
@@ -122,20 +143,29 @@ Status DropoutDoMaskInfo::SetCostUnderStrategy(const StrategyPtr& strategy) {
 }
 
 Status DropoutDoMaskInfo::GenerateStrategies(int32_t stage_id) {
-  CheckGlobalDeviceManager();
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
   is_auto_parallel_ = true;
-  size_t dev_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
-  Dimensions strategy(inputs_shape_[0].size() - 1, 1);
-  (void)strategy.insert(strategy.begin(), SizeToInt(dev_num));
-  std::vector<Dimensions> stra = {strategy};
-  StrategyPtr sp = std::make_shared<Strategy>(stage_id, stra);
-  if (SetCostUnderStrategy(sp) == SUCCESS) {
-    MS_LOG(INFO) << name_ << " : Successfully generated batch-parallel-strategy.";
-    PrintStrategy(sp);
-  } else {
-    MS_LOG(ERROR) << name_ << " : Generating batch-parallel-strategy failed.";
+  Shape input0_split(inputs_shape_[0].size(), 1);
+  Shapes splittable_inputs = {input0_split};
+  Shapes used_inputs_shape = {inputs_shape_[0]};
+
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, used_inputs_shape, splittable_inputs, &sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Generate strategies failed";
     return FAILED;
   }
+  size_t success = 0;
+  for (auto& sp : sp_vector) {
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy";
+      PrintStrategy(sp);
+    }
+  }
   return SUCCESS;
 }
 
@@ -150,26 +180,105 @@ std::shared_ptr<std::vector<std::vector<int32_t>>> DropoutDoMaskInfo::GenerateBa
 
 Status DropoutDoMaskInfo::Init(const StrategyPtr& strategy) {
   if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Init failed.";
+    MS_LOG(ERROR) << name_ << ": Init failed.";
     return FAILED;
   }
 
-  MS_LOG(INFO) << name_ << " : Init success.";
+  MS_LOG(INFO) << name_ << ": Init success.";
   return SUCCESS;
 }
 
 Status DropoutDoMaskInfo::InitForCostModel(const StrategyPtr& strategy) {
   if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
     if (is_auto_parallel_) {
-      MS_LOG(DEBUG) << name_ << " : Init for cost model failed.";
+      MS_LOG(DEBUG) << name_ << ": Init for cost model failed.";
     } else {
-      MS_LOG(ERROR) << name_ << " : Init for cost model failed.";
+      MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
     }
     return FAILED;
   }
 
-  MS_LOG(INFO) << name_ << " : Init for cost model success.";
+  MS_LOG(INFO) << name_ << ": Init for cost model success.";
   return SUCCESS;
 }
+
+PrimitivePtr GetDropoutGenMaskPrim(const CNodePtr& cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (cnode->inputs().size() != DROPOUT_DO_MASK_CNODE_INPUT_SIZE) {
+    MS_LOG(EXCEPTION) << "The size of dropout do mask cnode's inputs must be " << DROPOUT_DO_MASK_CNODE_INPUT_SIZE;
+  }
+
+  AnfNodePtr dropout_gen_mask = cnode->input(DROPOUT_GEN_MASK_INDEX);
+  MS_EXCEPTION_IF_NULL(dropout_gen_mask);
+  if (!dropout_gen_mask->isa<CNode>()) {
+    MS_LOG(EXCEPTION) << "The dropout do mask cnode's input[" << DROPOUT_GEN_MASK_INDEX << "] must be a cnode";
+  }
+
+  auto dropout_gen_mask_cnode = dropout_gen_mask->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(dropout_gen_mask_cnode);
+  if (dropout_gen_mask_cnode->inputs().size() != DROPOUT_GEN_MASK_CNODE_INPUT_SIZE) {
+    MS_LOG(EXCEPTION) << "The size of dropout gen mask cnode's inputs must be " << DROPOUT_GEN_MASK_CNODE_INPUT_SIZE;
+  }
+  if (!IsValueNode<Primitive>(dropout_gen_mask_cnode->input(0))) {
+    MS_LOG(EXCEPTION) << "The input[0] of dropout gen mask cnode is not primitive";
+  }
+
+  ValueNodePtr value_node = dropout_gen_mask_cnode->input(0)->cast<ValueNodePtr>();
+  MS_EXCEPTION_IF_NULL(value_node);
+  PrimitivePtr prim = value_node->value()->cast<PrimitivePtr>();
+  MS_EXCEPTION_IF_NULL(prim);
+  if (prim->name() != DROPOUT_GEN_MASK) {
+    MS_LOG(EXCEPTION) << "The primitive name is not DropoutGenMask";
+  }
+  return prim;
+}
+
+// DropoutDoMask needs to be used together with DropoutGenMask. Only the first input tensor of DropoutGenMask is
+// split. Find the DropoutGenMask node in the anf graph according to DropoutDoMask node, and modify the input shape
+// of DropoutGenMask according to the strategy of DropoutDoMask. When the DropoutDoMask performs repeated calculation
+// and both seeds of DropoutGenMask are 0, two new seeds are automatically generated for DropoutGenMask.
+Operator DropoutDoMaskInfo::GetDropoutGenMaskReplaceOp(const CNodePtr& cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  PrimitivePtr prim = GetDropoutGenMaskPrim(cnode);
+  MS_EXCEPTION_IF_NULL(prim);
+
+  if (inputs_tensor_info_.empty()) {
+    MS_LOG(EXCEPTION) << "The tensor info of dropout do mask is empty";
+  }
+
+  if (cnode->inputs().size() != DROPOUT_DO_MASK_CNODE_INPUT_SIZE) {
+    MS_LOG(EXCEPTION) << "The size of dropout do mask cnode's inputs must be " << DROPOUT_DO_MASK_CNODE_INPUT_SIZE;
+  }
+
+  if (!cnode->input(DROPOUT_DO_MASK_KEEP_PROB_INDEX)->isa<ValueNode>()) {
+    MS_LOG(EXCEPTION) << "The keep prob of dropout do mask is not value node";
+  }
+
+  ValuePtr keep_prob = GetValueNode(cnode->input(DROPOUT_DO_MASK_KEEP_PROB_INDEX));
+  MS_EXCEPTION_IF_NULL(keep_prob);
+  auto attr = prim->attrs();
+  if ((attr.find(SEED0) == attr.end()) || (attr.find(SEED1) == attr.end())) {
+    MS_LOG(EXCEPTION) << "The attrs of dropout gen mask must be have seed0 and seed1";
+  }
+  int32_t seed_0 = GetValue<int32_t>(attr[SEED0]);
+  int32_t seed_1 = GetValue<int32_t>(attr[SEED1]);
+  if ((seed_0 == 0) && (seed_1 == 0) && (repeated_calc_num_ > 1)) {
+    seed_0 = SEED_NUM;
+    seed_1 = SEED_NUM;
+    SEED_NUM++;
+  }
+
+  Shape input_slice_shape = inputs_tensor_info_[0].slice_shape();
+  ValuePtr new_shape = MakeValue(input_slice_shape);
+  Attr attr_0 = std::make_pair(SEED0, MakeValue(seed_0));
+  Attr attr_1 = std::make_pair(SEED1, MakeValue(seed_1));
+  OperatorAttrs attrs = {attr_0, attr_1};
+  Attr param_0 = std::make_pair(SHAPE, new_shape);
+  Attr param_1 = std::make_pair(KEEP_PROB, keep_prob);
+  OperatorParams params = {std::make_pair(param_0, 1), std::make_pair(param_1, 2)};
+  OperatorArgs args = std::make_pair(attrs, params);
+  Operator replace_op = {std::make_pair(DROPOUT_GEN_MASK, args)};
+  return replace_op;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
index 859b3e06a4..7ebe677997 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
@@ -33,7 +33,7 @@ class DropoutDoMaskInfo : public OperatorInfo {
  public:
   DropoutDoMaskInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<DropOutCost>()) {}
   ~DropoutDoMaskInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
@@ -41,6 +41,7 @@ class DropoutDoMaskInfo : public OperatorInfo {
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
   std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
+  Operator GetDropoutGenMaskReplaceOp(const CNodePtr& cnode);
 
  protected:
   Status CheckStrategy(const StrategyPtr& strategy) override;
@@ -51,6 +52,8 @@ class DropoutDoMaskInfo : public OperatorInfo {
   Status InferTensorInfo() override;
   Status InferDevMatrixShape() override;
 };
+
+using DropoutDoMaskInfoPtr = std::shared_ptr<DropoutDoMaskInfo>;
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.cc b/mindspore/ccsrc/parallel/ops_info/generator_info.cc
deleted file mode 100644
index a39f9faab9..0000000000
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.cc
+++ /dev/null
@@ -1,188 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "parallel/ops_info/generator_info.h"
-
-#include <algorithm>
-#include <memory>
-#include <utility>
-#include <vector>
-
-#include "ir/value.h"
-#include "parallel/device_matrix.h"
-#include "parallel/strategy.h"
-#include "parallel/tensor_layout/tensor_redistribution.h"
-
-namespace mindspore {
-namespace parallel {
-Status GeneratorBase::InferTensorMap() {
-  TensorMap output_tensor_map = {MAP_NONE};
-  outputs_tensor_map_.push_back(output_tensor_map);
-  return SUCCESS;
-}
-
-Status GeneratorBase::InferTensorInfo() {
-  Shape output_shape = outputs_shape_.at(0);
-  Shape output_slice_shape = outputs_shape_.at(0);
-
-  TensorLayout output_tensor_layout;
-  if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Creat output tensor layout failed.";
-    return FAILED;
-  }
-  TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape);
-  outputs_tensor_info_.push_back(output_tensor_info);
-
-  return SUCCESS;
-}
-
-Status GeneratorBase::InferDevMatrixShape() {
-  std::vector<Dimensions> stra = strategy_->GetInputDim();
-  Dimensions input_strategy = stra.at(0);
-
-  dev_matrix_shape_ = input_strategy;
-
-  return SUCCESS;
-}
-
-Status GeneratorBase::SetCostUnderStrategy(const StrategyPtr &strategy) {
-  if (SetCostUnderStrategyBase(strategy) != SUCCESS) {
-    if (is_auto_parallel_) {
-      MS_LOG(DEBUG) << name_ << " : Set cost under strategy failed.";
-    } else {
-      MS_LOG(ERROR) << name_ << " : Set cost under strategy failed.";
-    }
-    return FAILED;
-  }
-
-  return SUCCESS;
-}
-
-Status DropoutGenMaskInfo::GenerateStrategies(int32_t stage_id) {
-  if (input_value_.empty()) {
-    MS_LOG(ERROR) << name_ << " : Input value is empty.";
-    return FAILED;
-  }
-  Shape param = GetValue<const std::vector<int>>(input_value_[0]);
-  if (param.empty()) {
-    MS_LOG(ERROR) << name_ << " : Input value [0] is empty.";
-    return FAILED;
-  }
-  // Now,only support batch parallel.
-  CheckGlobalDeviceManager();
-  is_auto_parallel_ = true;
-  size_t dev_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
-  Dimensions strategy(param.size() - 1, 1);
-  (void)strategy.insert(strategy.begin(), SizeToInt(dev_num));
-  std::vector<Dimensions> stra = {strategy};
-  StrategyPtr sp = std::make_shared<Strategy>(stage_id, stra);
-  if (SetCostUnderStrategy(sp) == SUCCESS) {
-    MS_LOG(INFO) << name_ << " : Successfully generated batch-parallel-strategy.";
-    PrintStrategy(sp);
-  } else {
-    MS_LOG(ERROR) << name_ << " : Generating batch-parallel-strategy failed.";
-    return FAILED;
-  }
-  return SUCCESS;
-}
-
-Status DropoutGenMaskInfo::CheckStrategy(const StrategyPtr &strategy) {
-  if (strategy->GetInputNumber() != 1) {
-    if (is_auto_parallel_) {
-      MS_LOG(DEBUG) << name_ << " : The strategy is wrong.";
-    } else {
-      MS_LOG(ERROR) << name_ << " : The strategy is wrong.";
-    }
-    return FAILED;
-  }
-
-  return SUCCESS;
-}
-
-Status DropoutGenMaskInfo::InferReplaceOps(const StrategyPtr &strategy) {
-  Shape shape = GetValue<const std::vector<int>>(input_value_[0]);
-  Strategys stra = strategy->GetInputDim();
-  Dimensions input_strategy = stra.at(0);
-  int32_t dev_num = *(input_strategy.begin());
-  if (dev_num <= 0) {
-    MS_LOG(ERROR) << name_ << " : The number of devices should not be less than 0.";
-    return FAILED;
-  }
-  // Batch parallel
-  if (shape[0] % dev_num != 0) {
-    MS_LOG(ERROR) << name_ << " : The shape " << shape[0] << " can't be exact divided by device number " << dev_num;
-    return FAILED;
-  }
-  shape[0] = shape[0] / dev_num;
-  ValuePtr shape_ptr = MakeValue(shape);
-  Attr attr_0 = std::make_pair(SEED0, attrs_[SEED0]);
-  Attr attr_1 = std::make_pair(SEED1, attrs_[SEED1]);
-  OperatorAttrs attrs = {attr_0, attr_1};
-  Attr param_0 = std::make_pair(SHAPE, shape_ptr);
-  Attr param_1 = std::make_pair(KEEP_PROB, input_value_[1]);
-  OperatorParams params = {std::make_pair(param_0, 1), std::make_pair(param_1, 2)};
-  OperatorArgs args = std::make_pair(attrs, params);
-  replace_op_ = {std::make_pair(DROPOUT_GEN_MASK, args)};
-  return SUCCESS;
-}
-
-std::shared_ptr<std::vector<std::vector<int32_t>>> DropoutGenMaskInfo::GenerateBatchStrategies() {
-  if (input_value_.empty()) {
-    MS_LOG(EXCEPTION) << name_ << " : Input value is empty.";
-  }
-  Shape param = GetValue<const std::vector<int>>(input_value_[0]);
-  if (param.empty()) {
-    MS_LOG(EXCEPTION) << name_ << " : Input value [0] is empty.";
-  }
-  // Now,only support batch parallel.
-  CheckGlobalDeviceManager();
-  size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
-  Dimensions strategy(param.size() - 1, 1);
-  (void)strategy.insert(strategy.begin(), SizeToInt(dev_num));
-  std::vector<Dimensions> strategy_v = {strategy};
-  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
-}
-
-Status GeneratorBase::Init(const StrategyPtr &strategy) {
-  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Init failed.";
-    return FAILED;
-  }
-
-  if (InferReplaceOps(strategy) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Infer replace ops failed.";
-    return FAILED;
-  }
-
-  MS_LOG(INFO) << name_ << " : Init success.";
-  return SUCCESS;
-}
-
-Status GeneratorBase::InitForCostModel(const StrategyPtr &strategy) {
-  if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
-    if (is_auto_parallel_) {
-      MS_LOG(DEBUG) << name_ << " : Init for cost model failed.";
-    } else {
-      MS_LOG(ERROR) << name_ << " : Init for cost model failed.";
-    }
-    return FAILED;
-  }
-
-  MS_LOG(INFO) << name_ << " : Init for cost model success.";
-  return SUCCESS;
-}
-}  // namespace parallel
-}  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h
deleted file mode 100644
index 68024593f3..0000000000
--- a/mindspore/ccsrc/parallel/ops_info/generator_info.h
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
-#define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
-
-#include <memory>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#include "parallel/auto_parallel/operator_costmodel.h"
-#include "parallel/ops_info/operator_info.h"
-#include "parallel/strategy.h"
-
-namespace mindspore {
-namespace parallel {
-class GeneratorBase : public OperatorInfo {
- public:
-  GeneratorBase(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
-                const PrimitiveAttrs &attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GeneratorBaseCost>()) {}
-
-  ~GeneratorBase() override = default;
-
-  Status Init(const StrategyPtr &strategy) override;
-  Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
-  Status InitForCostModel(const StrategyPtr &strategy) override;
-
- protected:
-  // For now, generator ops don't have attributes
-  Status GetAttrs() override { return Status::SUCCESS; }
-  Status InferTensorMap() override;
-  Status InferTensorInfo() override;
-  Status InferDevMatrixShape() override;
-  Status InferMirrorOps() override { return SUCCESS; }
-  Status InferForwardCommunication() override { return SUCCESS; }
-  virtual Status InferReplaceOps(const StrategyPtr &strategy) = 0;
-};
-
-class DropoutGenMaskInfo : public GeneratorBase {
- public:
-  DropoutGenMaskInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
-                     const PrimitiveAttrs &attrs)
-      : GeneratorBase(name, inputs_shape, outputs_shape, attrs) {}
-  ~DropoutGenMaskInfo() override = default;
-  Status GenerateStrategies(int32_t stage_id) override;
-  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
-
- protected:
-  Status CheckStrategy(const StrategyPtr &strategy) override;
-  Status InferReplaceOps(const StrategyPtr &strategy) override;
-};
-}  // namespace parallel
-}  // namespace mindspore
-
-#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
index cc13512b54..1681c8f796 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
@@ -24,7 +24,6 @@
 #include "parallel/ops_info/comparison_function_info.h"
 #include "parallel/ops_info/dropout_do_mask_info.h"
 #include "parallel/ops_info/elementary_function_info.h"
-#include "parallel/ops_info/generator_info.h"
 #include "parallel/ops_info/get_next_info.h"
 #include "parallel/ops_info/l2_normalize_info.h"
 #include "parallel/ops_info/loss_info.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index fe2a5d2c86..befd26e318 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -34,6 +34,10 @@ constexpr size_t SOFTMAX_ATTR_SIZE = 1;
 constexpr size_t ACTIVATION_INPUTS_SIZE = 1;
 constexpr size_t ACTIVATION_OUTPUTS_SIZE = 1;
 constexpr size_t EXPANDDIMS_INPUT_SIZE = 2;
+constexpr size_t DROPOUT_DO_MASK_CNODE_INPUT_SIZE = 4;
+constexpr size_t DROPOUT_GEN_MASK_CNODE_INPUT_SIZE = 3;
+constexpr size_t DROPOUT_GEN_MASK_INDEX = 2;
+constexpr size_t DROPOUT_DO_MASK_KEEP_PROB_INDEX = 3;
 constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1;
 constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2;
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index d7d48c35bb..fe6be575ee 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -69,7 +69,6 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            RELU,
                                            ONEHOT,
                                            DROPOUT_DO_MASK,
-                                           DROPOUT_GEN_MASK,
                                            REDUCE_MAX,
                                            REDUCE_MIN,
                                            ARGMAXWITHVALUE,
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 78bec00bcf..0a6d0b0bef 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -484,8 +484,6 @@ void StepSplitTensor(const AnfNodePtr& node, const FuncGraphManagerPtr& manager)
     }
     if (IsParallelCareNode(use_cnode)) {
       SplitTensor(node, use_cnode, node_pair.second);
-    } else {
-      StepSplitTensor(use_cnode, manager);
     }
   }
 }
@@ -525,6 +523,26 @@ std::vector<AnfNodePtr> ReplaceOpInput(const Operator& replace_op, const std::st
   return replace_input;
 }
 
+void ReplaceOneOp(const Operator& replace_op, const CNodePtr& node) {
+  FuncGraphPtr func_graph = node->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  FuncGraphManagerPtr manager = func_graph->manager();
+  if (manager == nullptr) {
+    MS_LOG(EXCEPTION) << "Failure:AddNode error since manager is nullptr";
+  }
+  std::string instance_name = CreateInstanceName(node, 0);
+  std::vector<AnfNodePtr> replace_input;
+  replace_input = ReplaceOpInput(replace_op, instance_name, node);
+  CNodePtr replace_node = func_graph->NewCNode(replace_input);
+  MS_EXCEPTION_IF_NULL(replace_node);
+  ScopePtr scope = node->scope();
+  MS_EXCEPTION_IF_NULL(scope);
+  replace_node->set_scope(scope);
+  replace_node->set_in_forward_flag(true);
+  replace_input[0]->set_scope(scope);
+  (void)manager->Replace(node, replace_node);
+}
+
 void StepReplaceOp(OperatorVector replace_op, const CNodePtr& node) {
   // step1:get graph manager distribute_operator
   OperatorInfoPtr distribute_operator = node->operator_info();
@@ -1757,6 +1775,28 @@ void StepReplace(const OperatorInfoPtr& distribute_operator, const CNodePtr& cno
   }
 }
 
+void HandleDropoutNode(const OperatorInfoPtr& distribute_operator, const CNodePtr& cnode) {
+  MS_EXCEPTION_IF_NULL(distribute_operator);
+  MS_EXCEPTION_IF_NULL(cnode);
+
+  std::string op_name = distribute_operator->name();
+  if (op_name.find(DROPOUT_DO_MASK) == std::string::npos) {
+    return;
+  }
+
+  DropoutDoMaskInfoPtr dropout_do_mask = std::dynamic_pointer_cast<DropoutDoMaskInfo>(distribute_operator);
+  MS_EXCEPTION_IF_NULL(dropout_do_mask);
+  Operator replace_op = dropout_do_mask->GetDropoutGenMaskReplaceOp(cnode);
+  if (cnode->inputs().size() != DROPOUT_DO_MASK_CNODE_INPUT_SIZE) {
+    MS_LOG(EXCEPTION) << "The size of drop out do mask cnode's input is not " << DROPOUT_DO_MASK_CNODE_INPUT_SIZE;
+  }
+  ReplaceOneOp(replace_op, cnode->input(DROPOUT_GEN_MASK_INDEX)->cast<CNodePtr>());
+}
+
+void HandleSpecialNode(const OperatorInfoPtr& distribute_operator, const CNodePtr& cnode) {
+  HandleDropoutNode(distribute_operator, cnode);
+}
+
 void ParallelCommunication(const FuncGraphPtr& root, const std::vector<AnfNodePtr>& all_nodes,
                            const FuncGraphManagerPtr& manager) {
   MS_EXCEPTION_IF_NULL(root);
@@ -1804,6 +1844,8 @@ void ParallelCommunication(const FuncGraphPtr& root, const std::vector<AnfNodePt
 
       // StepReplace
       StepReplace(distribute_operator, cnode);
+
+      HandleSpecialNode(distribute_operator, cnode);
     } else if (IsValueNode<Tensor>(node)) {
       StepSplitTensor(node, manager);
     }
diff --git a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc b/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
deleted file mode 100644
index 2f17fb4450..0000000000
--- a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc
+++ /dev/null
@@ -1,166 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <string>
-#include <list>
-#include <vector>
-#include "common/common_test.h"
-#include "parallel/strategy.h"
-#include "parallel/ops_info/dropout_do_mask_info.h"
-#include "parallel/device_manager.h"
-#include "parallel/step_parallel.h"
-
-namespace mindspore {
-namespace parallel {
-
-class DropoutDoMaskInfo;
-using DropoutDoMaskInfoPtr = std::shared_ptr<DropoutDoMaskInfo>;
-DropoutDoMaskInfoPtr do_mask;
-
-class TestDropoutDoMaskInfo : public UT::Common {
- public:
-  TestDropoutDoMaskInfo() {}
-  void SetUp();
-  void TearDown() {}
-};
-
-void TestDropoutDoMaskInfo::SetUp() {
-  std::vector<int32_t> dev_list;
-
-  for (int32_t i = 0; i < 34; i++) {
-    dev_list.push_back(i);
-  }
-
-  std::vector<int32_t> stage_map;
-  stage_map.push_back(32);
-  stage_map.push_back(2);
-
-  int32_t local_dev = 0;
-
-  // create a new g_device_manager
-  g_device_manager = std::make_shared<DeviceManager>();
-  g_device_manager->Init(dev_list, local_dev, stage_map, "hccl");
-
-  std::unordered_map<std::string, ValuePtr> attr;
-
-  Shapes inputs_shape = {{32, 128}, {64}, {}};
-  Shapes outputs_shape = {{32, 128}};
-  do_mask = std::make_shared<DropoutDoMaskInfo>("do_mask_info", inputs_shape, outputs_shape, attr);
-}
-
-TEST_F(TestDropoutDoMaskInfo, InferDevMatrixShape) {
-  std::vector<Dimensions> stra = {{4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  do_mask->Init(strategy);
-  std::vector<int32_t> dev_matrix_shape = do_mask->dev_matrix_shape();
-
-  std::vector<int32_t> expect = {4, 8};
-  ASSERT_EQ(dev_matrix_shape, expect);
-}
-
-TEST_F(TestDropoutDoMaskInfo, InferSliceShape) {
-  std::vector<Dimensions> stra = {{4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  do_mask->Init(strategy);
-  std::vector<TensorInfo> inputs = do_mask->inputs_tensor_info();
-  std::vector<TensorInfo> outputs = do_mask->outputs_tensor_info();
-
-  Shape input_a_slice_shape_expect = {8, 16};
-  Shape input_b_slice_shape_expect = {64};
-  Shape output_slice_shape_expect = {8, 16};
-
-  TensorInfo input_a_tensor_info = inputs.at(0);
-  TensorInfo input_b_tensor_info = inputs.at(1);
-  TensorInfo output_tensor_info = outputs.at(0);
-  Shape input_a_slice_shape = input_a_tensor_info.slice_shape();
-  Shape input_b_slice_shape = input_b_tensor_info.slice_shape();
-  Shape output_slice_shape = output_tensor_info.slice_shape();
-
-  ASSERT_EQ(input_a_slice_shape, input_a_slice_shape_expect);
-  ASSERT_EQ(input_b_slice_shape, input_b_slice_shape_expect);
-  ASSERT_EQ(output_slice_shape, output_slice_shape_expect);
-}
-
-TEST_F(TestDropoutDoMaskInfo, GetTensorLayout) {
-  std::vector<Dimensions> stra = {{4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  do_mask->Init(strategy);
-  std::vector<TensorInfo> inputs = do_mask->inputs_tensor_info();
-  std::vector<TensorInfo> outputs = do_mask->outputs_tensor_info();
-
-  TensorMap input_a_map_expect = {1, 0};
-  TensorMap input_b_map_expect = {-1};
-  TensorMap output_map_expect = {1, 0};
-
-  TensorInfo input_a_tensor_info = inputs.at(0);
-  TensorInfo input_b_tensor_info = inputs.at(1);
-  TensorInfo output_tensor_info = outputs.at(0);
-  Map input_a_tensor_map = input_a_tensor_info.tensor_layout().origin_tensor_map();
-  Map input_b_tensor_map = input_b_tensor_info.tensor_layout().origin_tensor_map();
-  Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map();
-
-  ASSERT_EQ(input_a_tensor_map.array(), input_a_map_expect);
-  ASSERT_EQ(input_b_tensor_map.array(), input_b_map_expect);
-  ASSERT_EQ(output_tensor_map.array(), output_map_expect);
-}
-
-TEST_F(TestDropoutDoMaskInfo, GetForwardOp) {
-  std::vector<Dimensions> stra = {{4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  do_mask->Init(strategy);
-  OperatorVector forward_op = do_mask->forward_op();
-  size_t size = forward_op.size();
-
-  ASSERT_EQ(size, 0);
-}
-
-TEST_F(TestDropoutDoMaskInfo, CheckStrategy1) {
-  std::vector<Dimensions> stra = {{4, 8, 2}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = do_mask->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
-}
-
-TEST_F(TestDropoutDoMaskInfo, CheckStrategy2) {
-  std::vector<Dimensions> stra = {{8, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = do_mask->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
-}
-
-TEST_F(TestDropoutDoMaskInfo, CheckStrategy3) {
-  std::vector<Dimensions> stra = {{4, 8}, {4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = do_mask->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
-}
-
-TEST_F(TestDropoutDoMaskInfo, CheckStrategy4) {
-  std::vector<Dimensions> stra = {{4, 8}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = do_mask->Init(strategy);
-  ASSERT_EQ(ret, SUCCESS);
-}
-}  // namespace parallel
-}  // namespace mindspore
diff --git a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc b/tests/ut/cpp/parallel/ops_info/generator_info_test.cc
deleted file mode 100644
index eb463066a6..0000000000
--- a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <string>
-#include <list>
-#include <vector>
-#include "common/common_test.h"
-#include "parallel/strategy.h"
-#include "parallel/ops_info/generator_info.h"
-#include "parallel/device_manager.h"
-#include "parallel/step_parallel.h"
-
-namespace mindspore {
-namespace parallel {
-
-class DropoutGenMaskInfo;
-using DropoutGenMaskInfoPtr = std::shared_ptr<DropoutGenMaskInfo>;
-DropoutGenMaskInfoPtr gen_mask;
-
-class TestDropoutGenMaskInfo : public UT::Common {
- public:
-  TestDropoutGenMaskInfo() {}
-  void SetUp();
-  void TearDown() {}
-};
-
-void TestDropoutGenMaskInfo::SetUp() {
-  std::vector<int32_t> dev_list;
-
-  for (int32_t i = 0; i < 10; i++) {
-    dev_list.push_back(i);
-  }
-
-  std::vector<int32_t> stage_map;
-  stage_map.push_back(8);
-  stage_map.push_back(2);
-
-  int32_t local_dev = 0;
-
-  // create a new g_device_manager
-  g_device_manager = std::make_shared<DeviceManager>();
-  g_device_manager->Init(dev_list, local_dev, stage_map, "hccl");
-
-  std::unordered_map<std::string, ValuePtr> attr;
-
-  Shapes inputs_shape;
-  Shapes outputs_shape = {{128}};
-  std::vector<int> shape = {32, 128};
-  ValuePtr val0 = MakeValue(shape);
-  ValuePtr val1;
-  std::vector<ValuePtr> val = {val0, val1};
-  gen_mask = std::make_shared<DropoutGenMaskInfo>("gen_mask_info", inputs_shape, outputs_shape, attr);
-  gen_mask->set_input_value(val);
-}
-
-TEST_F(TestDropoutGenMaskInfo, InferDevMatrixShape) {
-  std::vector<Dimensions> stra = {{8, 1}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  gen_mask->Init(strategy);
-  std::vector<int32_t> dev_matrix_shape = gen_mask->dev_matrix_shape();
-
-  std::vector<int32_t> expect = {8, 1};
-  ASSERT_EQ(dev_matrix_shape, expect);
-}
-
-TEST_F(TestDropoutGenMaskInfo, InferSliceShape) {
-  std::vector<Dimensions> stra = {{8, 1}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  gen_mask->Init(strategy);
-  std::vector<TensorInfo> outputs = gen_mask->outputs_tensor_info();
-
-  Shape output_slice_shape_expect = {128};
-
-  TensorInfo output_tensor_info = outputs.at(0);
-  Shape output_slice_shape = output_tensor_info.slice_shape();
-
-  ASSERT_EQ(output_slice_shape, output_slice_shape_expect);
-}
-
-TEST_F(TestDropoutGenMaskInfo, GetTensorLayout) {
-  std::vector<Dimensions> stra = {{8, 1}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  gen_mask->Init(strategy);
-  std::vector<TensorInfo> outputs = gen_mask->outputs_tensor_info();
-
-  TensorMap output_map_expect = {-1};
-
-  TensorInfo output_tensor_info = outputs.at(0);
-  Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map();
-
-  ASSERT_EQ(output_tensor_map.array(), output_map_expect);
-}
-
-TEST_F(TestDropoutGenMaskInfo, GetForwardOp) {
-  std::vector<Dimensions> stra = {{8, 1}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  gen_mask->Init(strategy);
-  OperatorVector forward_op = gen_mask->forward_op();
-  size_t size = forward_op.size();
-
-  ASSERT_EQ(size, 0);
-}
-
-TEST_F(TestDropoutGenMaskInfo, CheckStrategy1) {
-  std::vector<Dimensions> stra = {{4, 8, 2}, {2, 3}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = gen_mask->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
-}
-
-TEST_F(TestDropoutGenMaskInfo, CheckStrategy2) {
-  std::vector<Dimensions> stra = {{8, 1}};
-  StrategyPtr strategy = NewStrategy(0, stra);
-
-  Status ret = gen_mask->Init(strategy);
-  ASSERT_EQ(ret, SUCCESS);
-}
-}  // namespace parallel
-}  // namespace mindspore
diff --git a/tests/ut/python/parallel/test_dropout_do_mask.py b/tests/ut/python/parallel/test_dropout_do_mask.py
new file mode 100644
index 0000000000..cfa7f50135
--- /dev/null
+++ b/tests/ut/python/parallel/test_dropout_do_mask.py
@@ -0,0 +1,94 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.mul2 = P.Mul().set_strategy(strategy1)
+        self.dropout_do_mask = P.DropoutDoMask().set_strategy(strategy2)
+        self.dropout_gen_mask = P.DropoutGenMask()
+        self.get_shape = P.Shape()
+        self.cast = P.Cast()
+        self.mul_weight = Parameter(mul_weight, "w1")
+        self.mul_weight2 = Parameter(mul_weight, "w2")
+        self.keep_prob = Tensor(0.9)
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        shape = self.get_shape(out)
+        dtype = P.DType()(out)
+        keep_prob = self.cast(self.keep_prob, dtype)
+        mask = self.dropout_gen_mask(shape, keep_prob)
+        out = self.dropout_do_mask(out, mask, keep_prob)
+        out = self.mul2(out, self.mul_weight2)
+        return out
+
+
+_x = Tensor(np.ones([128, 64]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x, _b)
+    context.reset_auto_parallel_context()
+
+
+def test_dropout_do_mask_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1), (16, 1))
+    strategy2 = ((16, 1),)
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_dropout_do_mask_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 16), (1, 16))
+    strategy2 = ((1, 16),)
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_dropout_do_mask_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((4, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_dropout_do_mask_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_dropout_do_mask_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((2, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)

From e09f220f17205ff13395e5991bd0c7d333949b60 Mon Sep 17 00:00:00 2001
From: chenzomi <chenzomi12@gmail.com>
Date: Sat, 11 Apr 2020 11:06:39 +0800
Subject: [PATCH 168/367] fix complite bug in clang

---
 mindspore/ccsrc/onnx/onnx_exporter.cc                  | 4 ++--
 mindspore/ccsrc/optimizer/irpass/incorporate_getitem.h | 2 +-
 mindspore/ccsrc/parallel/ops_info/operator_info.cc     | 2 +-
 mindspore/ccsrc/utils/log_adapter.h                    | 1 +
 4 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/onnx/onnx_exporter.cc b/mindspore/ccsrc/onnx/onnx_exporter.cc
index 3bd4a38881..e6ba60ff9c 100644
--- a/mindspore/ccsrc/onnx/onnx_exporter.cc
+++ b/mindspore/ccsrc/onnx/onnx_exporter.cc
@@ -174,14 +174,14 @@ OPERATOR_ONNX_CONVERT_DEFINE(Sigmoid, Sigmoid, OpNameInfo())
 OPERATOR_ONNX_CONVERT_DEFINE(Flatten, Flatten, OpNameInfo())
 OPERATOR_ONNX_CONVERT_DEFINE(Squeeze, Squeeze,
                              OpNameInfo().Attr("axis", "axes", onnx::AttributeProto_AttributeType_INTS,
-                                               SetAttrTupleValueToProto))
+                                               SetAttrTupleValueToProto<0>))
 
 OPERATOR_ONNX_CONVERT_DEFINE(
   Conv2D, Conv,
   OpNameInfo()
     .Attr("dilation", "dilations", onnx::AttributeProto_AttributeType_INTS, SetAttrValueToProto<Int32Imm, 2>)
     .Attr("group", "group", onnx::AttributeProto_AttributeType_INT, SetAttrValueToProto<Int32Imm>)
-    .Attr("kernel_size", "kernel_shape", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto)
+    .Attr("kernel_size", "kernel_shape", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<0>)
     .Attr("pad_mode", "auto_pad", onnx::AttributeProto_AttributeType_STRING,
           [](ValuePtr value, onnx::AttributeProto_AttributeType, onnx::AttributeProto* const attr_proto,
              const PrimitivePtr& prim) {
diff --git a/mindspore/ccsrc/optimizer/irpass/incorporate_getitem.h b/mindspore/ccsrc/optimizer/irpass/incorporate_getitem.h
index 9dc8e7255b..77f3fa7b36 100644
--- a/mindspore/ccsrc/optimizer/irpass/incorporate_getitem.h
+++ b/mindspore/ccsrc/optimizer/irpass/incorporate_getitem.h
@@ -15,7 +15,7 @@
  */
 
 #ifndef MINDSPORE_CCSRC_OPTIMIZER_IRPASS_INCORPORATE_GETITEM_H_
-#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_INCORPORATE_GETITEM_H__
+#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_INCORPORATE_GETITEM_H_
 
 #include <vector>
 #include <algorithm>
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index a24f3e616b..68c73bc548 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -236,7 +236,7 @@ OperatorVector CreateMirrorOps(const std::string& group_name, size_t dev_num) {
 
   OperatorName operator_name = MIRROR_OPERATOR;
   ValuePtr attr0_value = MakeValue(group_name);
-  ValuePtr attr1_value = MakeValue(dev_num);
+  ValuePtr attr1_value = MakeValue(SizeToInt(dev_num));
   ValuePtr attr2_value = MakeValue(mean_flag);
 
   Attr attr0 = std::make_pair(GROUP, attr0_value);
diff --git a/mindspore/ccsrc/utils/log_adapter.h b/mindspore/ccsrc/utils/log_adapter.h
index 61c253782e..2122870c3b 100644
--- a/mindspore/ccsrc/utils/log_adapter.h
+++ b/mindspore/ccsrc/utils/log_adapter.h
@@ -22,6 +22,7 @@
 #include <string>
 #include <sstream>
 #include <memory>
+#include "./overload.h"
 #include "./securec.h"
 #ifdef USE_GLOG
 #include "glog/logging.h"

From a2850cae327f5d8d65ad1ac59b420139b0d85686 Mon Sep 17 00:00:00 2001
From: suteng <suteng@huawei.com>
Date: Sat, 11 Apr 2020 15:30:24 +0800
Subject: [PATCH 169/367] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?=
 =?UTF-8?q?!231=20:=20add=20bool=20type=20check=20in=20communication=20ope?=
 =?UTF-8?q?rator=20'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 mindspore/ops/operations/comm_ops.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py
index 441e441c2c..1644c5800a 100644
--- a/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/ops/operations/comm_ops.py
@@ -162,8 +162,6 @@ class AllGather(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
-            raise TypeError("AllGather does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -221,8 +219,6 @@ class ReduceScatter(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
-            raise TypeError("ReduceScatter does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -280,8 +276,6 @@ class Broadcast(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
-            raise TypeError("Broadcast does not support 'Bool' as the dtype of input!")
         return x_dtype
 
 
@@ -324,8 +318,6 @@ class _AlltoAll(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
-            raise TypeError("AlltoAll does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):

From 87714b3c7f5e334a69e38fce0525b7e9e1d5df4e Mon Sep 17 00:00:00 2001
From: Zhang Qinghua <zhangqinghua3@huawei.com>
Date: Fri, 10 Apr 2020 13:08:38 +0800
Subject: [PATCH 170/367] Remove the repeats of inferring and optimize the
 sorting routine.

Total Renormalizes:
-----
69.05010 --> 62.28941
-----
---
 .../pipeline/static_analysis/evaluator.cc     | 42 +++++++++++++++----
 1 file changed, 33 insertions(+), 9 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
index 9b120f731c..99cb893104 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
@@ -17,6 +17,7 @@
 #include "pipeline/static_analysis/evaluator.h"
 
 #include <algorithm>
+#include <unordered_set>
 
 #include "ir/func_graph_cloner.h"
 #include "pipeline/static_analysis/utils.h"
@@ -61,6 +62,29 @@ AnalysisContextPtr BaseFuncGraphEvaluator::MakeContext(const AnalysisEnginePtr &
   return context;
 }
 
+static std::vector<AnfNodePtr> FastShadowSort(const AnfNodePtr &ret_node) {
+  std::vector<AnfNodePtr> sorted_nodes;
+  std::unordered_set<AnfNodePtr> checked_cnodes;
+  std::size_t index = 0;
+  sorted_nodes.emplace_back(ret_node);
+  while (index < sorted_nodes.size()) {
+    auto current = sorted_nodes[index];
+    index++;
+    MS_EXCEPTION_IF_NULL(current);
+    if (current->isa<CNode>()) {
+      auto &inputs = current->cast<CNodePtr>()->inputs();
+      for (auto it = inputs.begin(); it != inputs.end(); it++) {
+        AnfNodePtr input = *it;
+        if (input != nullptr && input->isa<CNode>() && checked_cnodes.find(input) == checked_cnodes.end()) {
+          sorted_nodes.emplace_back(input);
+          (void)checked_cnodes.insert(input);
+        }
+      }
+    }
+  }
+  return sorted_nodes;
+}
+
 AbstractBasePtr BaseFuncGraphEvaluator::Infer(AnalysisEnginePtr engine, const AbstractBasePtrList &args_spec_list) {
   FuncGraphPtr fg = GetFuncGraph(engine, args_spec_list);
   MS_EXCEPTION_IF_NULL(fg);
@@ -86,20 +110,20 @@ AbstractBasePtr BaseFuncGraphEvaluator::Infer(AnalysisEnginePtr engine, const Ab
 
   MS_LOG(DEBUG) << "Analysis FuncGraph begin, func graph: " << fg->ToString()
                 << ", context: " << graph_context_->ToString() << ", return node: " << func_node->DebugString();
-  const std::vector<AnfNodePtr> &all_nodes = TopoSort(func_node);
-  for (const auto &node : all_nodes) {
+  AbstractBasePtr ret_base = nullptr;
+  std::vector<AnfNodePtr> nodes = FastShadowSort(func_node);
+  for (auto it = nodes.crbegin(); it != nodes.crend(); it++) {
+    const auto &node = *it;
     AnfNodeConfigPtr node_conf = engine->MakeConfig(node, graph_context_);
     MS_LOG(DEBUG) << "Analysis node begin, func graph: " << fg->ToString() << ", node_conf: " << node_conf->ToString();
-    AbstractBasePtr base = engine->GetEvaluatedValue(node_conf);
+    ret_base = engine->GetEvaluatedValue(node_conf);
     MS_LOG(DEBUG) << "Analysis node end, func graph: " << fg->ToString() << ", node_conf: " << node_conf->ToString()
-                  << ", abstract: " << base->ToString();
+                  << ", abstract: " << ret_base->ToString();
   }
 
-  AnfNodeConfigPtr ret_conf = engine->MakeConfig(func_node, graph_context_);
-  AbstractBasePtr base = engine->GetEvaluatedValue(ret_conf);
-  MS_EXCEPTION_IF_NULL(base);
-  MS_LOG(DEBUG) << "BaseFuncGraph " << fg->ToString() << " infer end, inferred abstract: " << base->ToString();
-  return base;
+  MS_EXCEPTION_IF_NULL(ret_base);
+  MS_LOG(DEBUG) << "BaseFuncGraph " << fg->ToString() << " infer end, inferred abstract: " << ret_base->ToString();
+  return ret_base;
 }
 
 AbstractBasePtrList FuncGraphEvaluator::NormalizeArgs(const AbstractBasePtrList &args_spec_list) const {

From 32cd280c1adabd1ab9b949c5c9d1d27e3f080e75 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Tue, 7 Apr 2020 19:00:51 +0800
Subject: [PATCH 171/367] add squeeze distributed op

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |   1 +
 .../parallel/ops_info/activation_info.cc      | 156 ++++++++++++++++++
 .../ccsrc/parallel/ops_info/activation_info.h |  21 ++-
 .../ccsrc/parallel/ops_info/arithmetic_info.h |   2 +-
 .../ops_info/comparison_function_info.h       |   2 +-
 .../ccsrc/parallel/ops_info/onehot_info.h     |   2 +-
 mindspore/ccsrc/parallel/step_parallel.cc     |   5 +-
 tests/ut/python/parallel/test_squeeze_info.py |  79 +++++++++
 8 files changed, 261 insertions(+), 7 deletions(-)
 create mode 100644 tests/ut/python/parallel/test_squeeze_info.py

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 62cc4c5da3..1650ff0b21 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -125,6 +125,7 @@ REGISTER(GetNextInfo);
 REGISTER(NegInfo);
 REGISTER(BatchMatMulInfo);
 REGISTER(ExpandDimsInfo);
+REGISTER(SqueezeInfo);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
index 9ba3624b01..c59ca8402b 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
@@ -19,6 +19,7 @@
 #include <algorithm>
 #include <memory>
 #include <vector>
+#include <utility>
 
 #include "ir/value.h"
 #include "parallel/auto_parallel/costmodel.h"
@@ -544,5 +545,160 @@ Status ExpandDimsInfo::InferMirrorOps() {
   MS_LOG(INFO) << name_ << ": Create mirror ops success, the group name is " << group[0].name();
   return SUCCESS;
 }
+
+Status SqueezeInfo::InferAxis(const ValueTuplePtr& value_tuple) {
+  std::vector<int32_t> axis;
+  auto axis_list = value_tuple->value();
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+  Shape input_shape = inputs_shape_.at(0);
+  size_t input_size = input_shape.size();
+  // if axis tuple is empty, we should exclude the axis that the corresponding slice shape is 1.
+  if (axis_list.empty()) {
+    for (size_t i = 0; i < input_size; ++i) {
+      if (input_shape[i] == 1) {
+        axis.push_back(i);
+      }
+    }
+    axis_ = MakeValue(axis)->cast<ValueTuplePtr>();
+    return SUCCESS;
+  }
+
+  // convert negative axis to positive.
+  for (auto& dim : axis_list) {
+    if (!dim->isa<Int32Imm>()) {
+      MS_LOG(ERROR) << name_ << ": The type of axis is not int";
+      return FAILED;
+    }
+    int32_t dim_value = GetValue<int32_t>(dim);
+    int32_t positive_value = (dim_value < 0) ? (dim_value + SizeToInt(input_size)) : dim_value;
+    axis.push_back(positive_value);
+  }
+  axis_ = MakeValue(axis)->cast<ValueTuplePtr>();
+  return SUCCESS;
+}
+
+Status SqueezeInfo::GetAttrs() {
+  auto iter = attrs_.find(AXIS);
+  if (iter == attrs_.end()) {
+    MS_LOG(ERROR) << name_ << ": Can't find axis attribute.";
+    return FAILED;
+  }
+  MS_EXCEPTION_IF_NULL(iter->second);
+  auto value_tuple = iter->second->cast<ValueTuplePtr>();
+  MS_EXCEPTION_IF_NULL(value_tuple);
+  InferAxis(value_tuple);
+  attrs_[AXIS] = axis_;
+  return SUCCESS;
+}
+
+Status SqueezeInfo::InferReplaceOps(const StrategyPtr& strategy) {
+  Attr attr = std::make_pair(AXIS, axis_);
+  OperatorAttrs attrs = {attr};
+  OperatorParams params;
+  OperatorArgs args = std::make_pair(attrs, params);
+  replace_op_ = {std::make_pair(SQUEEZE, args)};
+  return SUCCESS;
+}
+
+Status SqueezeInfo::InferTensorMap() {
+  // for example: if the shape of input is [32, 32, 1], and the axis is (2, ),
+  // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1]
+  std::vector<int32_t> input_tensor_map, output_tensor_map;
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+  size_t size = inputs_shape_[0].size();
+  std::vector<int32_t> axis = GetValue<const std::vector<int>>(axis_);
+  for (size_t i = 0; i < size; ++i) {
+    size_t index = size - i - 1;
+    auto iter = std::find(axis.begin(), axis.end(), SizeToInt(i));
+    if (iter == axis.end()) {
+      output_tensor_map.push_back(SizeToInt(index));
+    }
+    input_tensor_map.push_back(SizeToInt(index));
+  }
+  inputs_tensor_map_.push_back(input_tensor_map);
+  outputs_tensor_map_.push_back(output_tensor_map);
+  MS_LOG(INFO) << name_ << ": The tensor map of input is " << ShapeToString(input_tensor_map)
+               << ", and the tensor map of output is " << ShapeToString(output_tensor_map);
+
+  return SUCCESS;
+}
+
+Status SqueezeInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  if (inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The tensor map of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_shape = inputs_shape_[0];
+  Shape output_shape = outputs_shape_[0];
+
+  // infer slice shape
+  Shapes inputs_slice_shape, outputs_slice_shape;
+  Strategys inputs_strategy = strategy_->GetInputDim();
+  Dimensions output_strategy;
+  std::vector<int32_t> axis = GetValue<const std::vector<int>>(axis_);
+  for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
+    auto iter = std::find(axis.begin(), axis.end(), SizeToInt(i));
+    if (iter == axis.end()) {
+      output_strategy.push_back(inputs_strategy[0].at(i));
+    }
+  }
+  Strategys outputs_strategy = {output_strategy};
+  if (InferSliceShape(inputs_strategy, outputs_strategy, &inputs_slice_shape, &outputs_slice_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer slice shape failed";
+    return FAILED;
+  }
+
+  if (inputs_slice_shape.empty() || outputs_slice_shape.empty()) {
+    MS_LOG(ERROR) << name_ << ": The slice shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_slice_shape = inputs_slice_shape[0];
+  Shape output_slice_shape = outputs_slice_shape[0];
+
+  // infer tensor layout
+  TensorLayout input_tensor_layout, output_tensor_layout;
+  if (input_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for input failed";
+    return FAILED;
+  }
+
+  if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for output failed";
+    return FAILED;
+  }
+
+  TensorInfo input_tensor_info(input_tensor_layout, input_shape, input_slice_shape);
+  TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape);
+
+  inputs_tensor_info_.push_back(input_tensor_info);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+Status SqueezeInfo::Init(const StrategyPtr& strategy) {
+  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Init failed.";
+  }
+
+  if (InferReplaceOps(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Infer replace ops failed";
+  }
+
+  MS_LOG(INFO) << name_ << " : Init success.";
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index 21774c43ee..b19e38b910 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -184,6 +184,25 @@ class ExpandDimsInfo : public ActivationOther {
   Strategys inputs_strategy_;
   Strategys outputs_strategy_;
 };
+
+class SqueezeInfo : public ActivationOther {
+ public:
+  SqueezeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+              const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SqueezeInfo() override = default;
+
+ protected:
+  Status InferAxis(const ValueTuplePtr& value_tuple);
+  Status GetAttrs() override;
+  Status InferReplaceOps(const StrategyPtr& strategy);
+  Status InferTensorMap() override;
+  Status InferTensorInfo() override;
+  Status Init(const StrategyPtr& strategy) override;
+
+ private:
+  ValueTuplePtr axis_;
+};
 }  // namespace parallel
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ACTIVATION_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index daa2ad595c..8010b2890a 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -116,4 +116,4 @@ class AssignSubInfo : public ArithmeticBase {
 }  // namespace parallel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ARITHMETIC_INFO_H_
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
index 110a9a6c38..5f51f1d0a9 100644
--- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
@@ -53,4 +53,4 @@ class MaximumInfo : public ArithmeticBase {
 }  // namespace parallel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_COMPARISON_FUNCTION_INFO_H_
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_COMPARISON_FUNCTION_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
index a54d8479b3..fec8d96324 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
@@ -65,4 +65,4 @@ class OneHotInfo : public OperatorInfo {
 };
 }  // namespace parallel
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ONEHOT_INFO_H_
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 0a6d0b0bef..1976053eff 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -47,8 +47,8 @@ using mindspore::tensor::Tensor;
 
 namespace mindspore {
 namespace parallel {
-const std::set<std::string> COMMUNICATION_OPS = {ALL_REDUCE, ALL_GATHER, ALL_TO_ALL, REDUCE_SCATTER};
-const std::set<std::string> INVALID_LOSS_OPS = {GET_NEXT, VIRTUALLOSS};
+static const std::set<std::string> COMMUNICATION_OPS = {ALL_REDUCE, ALL_GATHER, ALL_TO_ALL, REDUCE_SCATTER};
+static const std::set<std::string> INVALID_LOSS_OPS = {GET_NEXT, VIRTUALLOSS};
 // g_RefMap, for CNode B input i is a RefKey[Parameter C],
 // it will be one item in map with key: C, and value: (B, i)
 static std::map<AnfNodePtr, std::pair<AnfNodePtr, int>> g_RefMap;
@@ -1832,7 +1832,6 @@ void ParallelCommunication(const FuncGraphPtr& root, const std::vector<AnfNodePt
       if (cnode == loss_cnode) {
         is_loss_cnode = true;
       }
-
       // insert forward ops
       InsertForwardOps(distribute_operator, cnode);
 
diff --git a/tests/ut/python/parallel/test_squeeze_info.py b/tests/ut/python/parallel/test_squeeze_info.py
new file mode 100644
index 0000000000..3169e2fb1b
--- /dev/null
+++ b/tests/ut/python/parallel/test_squeeze_info.py
@@ -0,0 +1,79 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, strategy1=None, strategy2=None, axis=()):
+        super().__init__()
+        self.squeeze = P.Squeeze(axis=axis).set_strategy(strategy1)
+        self.mul = P.Mul().set_strategy(strategy2)
+
+    def construct(self, x, b):
+        out = self.squeeze(x)
+        out = self.mul(out, b)
+        return out
+
+
+_x = Tensor(np.ones([64, 1, 32, 1]), dtype=ms.float32)
+_b = Tensor(np.ones([64, 32]), dtype=ms.float32)
+
+
+def compile(net):
+    _executor.compile(net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_squeeze_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1, 1), )
+    strategy2 = ((16, 1), (16, 1))
+    net = Net(strategy1, strategy2)
+    compile(net)
+
+
+def test_squeeze_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16, 1), )
+    strategy2 = ((1, 16), (1, 16))
+    net = Net(strategy1, strategy2)
+    compile(net)
+
+
+def test_squeeze_specified_axis():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 1, 4, 1), )
+    strategy2 = ((8, 2), (8, 2))
+    net = Net(strategy1, strategy2, (1, 3))
+    compile(net)
+
+
+def test_squeeze_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net()
+    compile(net)
+
+
+def test_squeeze_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 8, 1), )
+    strategy2 = ((2, 8), (2, 8))
+    net = Net(strategy1, strategy2)
+    compile(net)

From 5841fe010ed95646026266d90a3c724a6936a039 Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Wed, 8 Apr 2020 16:24:51 +0800
Subject: [PATCH 172/367] Support pow's second input could be tensor and fix
 bug in bprop of pow

---
 .../ccsrc/parallel/ops_info/arithmetic_info.h |   7 +
 .../ops_info/elementary_function_info.cc      |  47 -------
 .../ops_info/elementary_function_info.h       |  10 --
 mindspore/nn/layer/pooling.py                 |   2 +-
 mindspore/ops/_grad/grad_math_ops.py          |  11 +-
 mindspore/ops/operations/array_ops.py         |   4 +-
 mindspore/ops/operations/math_ops.py          | 120 +++++++++---------
 mindspore/ops/primitive.py                    |   3 -
 .../ut/cpp/parallel/ops_info/pow_info_test.cc |  18 +--
 tests/ut/python/ops/test_math_ops.py          |   3 +-
 tests/ut/python/ops/test_ops.py               |   6 +-
 .../parallel/test_element_wise_function.py    |   2 +-
 tests/vm_impl/math_ops_vm_impl.py             |   1 +
 13 files changed, 95 insertions(+), 139 deletions(-)
 delete mode 100644 mindspore/ccsrc/parallel/ops_info/elementary_function_info.cc

diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index daa2ad595c..76ba500a03 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -98,6 +98,13 @@ class FloorDivInfo : public ArithmeticBase {
   ~FloorDivInfo() override = default;
 };
 
+class PowInfo : public ArithmeticBase {
+ public:
+  PowInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+  ~PowInfo() override = default;
+};
+
 class GreaterInfo : public ArithmeticBase {
  public:
   GreaterInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.cc b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.cc
deleted file mode 100644
index d4f79aca65..0000000000
--- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "parallel/ops_info/elementary_function_info.h"
-
-namespace mindspore {
-namespace parallel {
-Status PowInfo::InferMirrorOps() {
-  mirror_ops_.clear();
-
-  Shape tensor_map = inputs_tensor_map_[0];
-  std::vector<Group> group;
-  if (CreateGroupByTensorMap(tensor_map, &group) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Create group failed.";
-    return FAILED;
-  }
-
-  OperatorVector mirror_op;
-  OperatorVector op_for_value;
-  if (group.empty()) {
-    MS_LOG(INFO) << name_ << " : The mirror ops is empty.";
-    return SUCCESS;
-  } else {
-    mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
-    mirror_ops_.push_back(mirror_op);
-    mirror_ops_.push_back(op_for_value);
-    std::string group_name = group[0].name();
-    MS_LOG(INFO) << name_ << " : Create the mirror ops success, the group name is " << group_name;
-  }
-
-  return SUCCESS;
-}
-}  // namespace parallel
-}  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
index 57b4650f26..84b8030f37 100644
--- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h
@@ -27,16 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-class PowInfo : public ActivationOther {
- public:
-  PowInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
-  ~PowInfo() override = default;
-
- protected:
-  Status InferMirrorOps() override;
-};
-
 class ExpInfo : public ActivationOther {
  public:
   ExpInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index bf90fcc9de..5d9b0ffa6c 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -58,7 +58,7 @@ class _PoolNd(Cell):
         pass
 
     def extend_repr(self):
-        return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__)
+        return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__)
 
 
 class MaxPool2d(_PoolNd):
diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py
index 1863ac8fdd..81e078dc98 100755
--- a/mindspore/ops/_grad/grad_math_ops.py
+++ b/mindspore/ops/_grad/grad_math_ops.py
@@ -336,14 +336,13 @@ def get_bprop_log(self):
 @bprop_getters.register(P.Pow)
 def get_bprop_pow(self):
     """Grad definition for `Pow` operation."""
-    pow_ = P.Pow()
-    cast = P.Cast()
-    dtype = P.DType()
+    pow_op = P.Pow()
+    ln = P.Log()
 
     def bprop(x, power, out, dout):
-        g = cast(F.tuple_to_array((power,)), dtype(x)) * pow_(x, power-1.0)
-        dx = g * dout
-        return dx, 0
+        dx = power * pow_op(x, power - 1.0) * dout
+        dpower = pow_op(x, power) * ln(x) * dout
+        return dx, dpower
     return bprop
 
 
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index ac7f8ed699..850e895ad0 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1097,7 +1097,7 @@ class ArgMaxWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.name)
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):
@@ -1143,7 +1143,7 @@ class ArgMinWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.name)
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 106886c45c..a1fe6e72b5 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -74,7 +74,7 @@ class _BinaryOp(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
     def infer_shape(self, x_shape, y_shape):
-        return _get_broadcast_shape(x_shape, y_shape, self.prim_name())
+        return _get_broadcast_shape(x_shape, y_shape, self.name)
 
 
 class _MathBinaryOp(_BinaryOp):
@@ -89,7 +89,7 @@ class _MathBinaryOp(_BinaryOp):
         return x_dtype
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type, self.prim_name())
+        return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type, self.name)
 
 
 class TensorAdd(_MathBinaryOp):
@@ -158,7 +158,7 @@ class AssignAdd(PrimitiveWithInfer):
 
     def infer_dtype(self, variable, value):
         args = {"value": value}
-        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name())
+        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.name)
         return value
 
 
@@ -201,7 +201,7 @@ class AssignSub(PrimitiveWithInfer):
 
     def infer_dtype(self, variable, value):
         args = {"value": value}
-        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name())
+        validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.name)
         return value
 
 
@@ -222,16 +222,16 @@ class _Reduce(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, keep_dims=False):
         """init Reduce"""
-        validator.check_value_type('keep_dims', keep_dims, [bool], self.prim_name())
+        validator.check_value_type('keep_dims', keep_dims, [bool], self.name)
         self.init_prim_io_names(inputs=['input_x', 'axis'], outputs=['y'])
 
     def do_infer(self, input_x, axis, valid_dtype=mstype.number_type):
         axis_v = axis['value']
         input_shp = input_x['shape']
         args = {'input_x': input_x['dtype']}
-        validator.check_tensor_type_same(args, valid_dtype, self.prim_name())
+        validator.check_tensor_type_same(args, valid_dtype, self.name)
 
-        input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims, self.prim_name())
+        input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims, self.name)
         return {'shape': input_shp,
                 'dtype': input_x['dtype'],
                 'value': None}
@@ -466,7 +466,7 @@ class CumProd(PrimitiveWithInfer):
     """
     @prim_attr_register
     def __init__(self, exclusive=False, reverse=False):
-        cls_name = self.prim_name()
+        cls_name = self.name
         self.exclusive = validator.check_value_type("exclusive", exclusive, [bool], cls_name)
         self.reverse = validator.check_value_type("reverse", reverse, [bool], cls_name)
 
@@ -474,7 +474,7 @@ class CumProd(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type, axis_type):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_tensor_type_same({'x': x_type}, mstype.number_type, cls_name)
         validator.check_subclass("axis", axis_type, mstype.int_, cls_name)
         return x_type
@@ -510,7 +510,7 @@ class MatMul(PrimitiveWithInfer):
     def __init__(self, transpose_a=False, transpose_b=False):
         self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output'])
         self.__setattr_flag__ = True
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_value_type("transpose_a", transpose_a, [bool], cls_name)
         validator.check_value_type("transpose_b", transpose_b, [bool], cls_name)
 
@@ -521,7 +521,7 @@ class MatMul(PrimitiveWithInfer):
 
     def infer_shape(self, x, y):
         self.check_shape_size(x, y)
-        cls_name = self.prim_name()
+        cls_name = self.name
         # expected dimension of x, y, x:[...,a,b] y:[..., c,d], the dim size should be the same except the last two
         for i in range(len(x) - 2):
             if x[i] != y[i]:
@@ -546,7 +546,7 @@ class MatMul(PrimitiveWithInfer):
 
     def infer_dtype(self, x, y):
         args = {"x": x, "y": y}
-        validator.check_tensor_type_same(args, mstype.float_type + mstype.int_type, self.prim_name())
+        validator.check_tensor_type_same(args, mstype.float_type + mstype.int_type, self.name)
         return x
 
 
@@ -590,7 +590,7 @@ class BatchMatMul(MatMul):
     def __init__(self, transpose_a=False, transpose_b=False):
         self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output'])
         self.__setattr_flag__ = True
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_value_type("transpose_a", transpose_a, [bool], cls_name)
         validator.check_value_type("transpose_b", transpose_b, [bool], cls_name)
 
@@ -628,13 +628,13 @@ class CumSum(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, exclusive=False, reverse=False):
         """init cumsum"""
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_value_type('exclusive', exclusive, [bool], cls_name)
         validator.check_value_type('reverse', reverse, [bool], cls_name)
         self.init_prim_io_names(inputs=['x', 'axis'], outputs=['y'])
 
     def __infer__(self, x, axis):
-        cls_name = self.prim_name()
+        cls_name = self.name
         x_shp = x['shape']
         validator.check_value_type('axis', axis['value'], [int], cls_name)
         valid_types = [mstype.uint8, mstype.int8, mstype.int32, mstype.float16, mstype.float32]
@@ -679,7 +679,7 @@ class AddN(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=["inputs"], outputs=["sum"])
 
     def infer_shape(self, inputs):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name)
         self.add_prim_attr('n', len(inputs))
         shp0 = inputs[0]
@@ -688,7 +688,7 @@ class AddN(PrimitiveWithInfer):
         return shp0
 
     def infer_dtype(self, inputs):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_value_type("inputs", inputs, [tuple, list], cls_name)
         validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name)
         args = {}
@@ -718,7 +718,7 @@ class Neg(PrimitiveWithInfer):
         return input_x
 
     def infer_dtype(self, input_x):
-        validator.check_tensor_type_same({"input_x": input_x}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({"input_x": input_x}, mstype.number_type, self.name)
         return input_x
 
 
@@ -809,7 +809,7 @@ class Square(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.name)
         return x_type
 
 
@@ -838,7 +838,7 @@ class Rsqrt(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.name)
         return x_type
 
 
@@ -867,7 +867,7 @@ class Sqrt(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.name)
         return x_type
 
 
@@ -897,14 +897,29 @@ class Reciprocal(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x", x, mstype.tensor, self.prim_name())
+        validator.check_subclass("x", x, mstype.tensor, self.name)
         return x
 
 
-class Pow(PrimitiveWithInfer):
+class Pow(_MathBinaryOp):
     """
     Computes a tensor to the power of the second input.
 
+    The first input must be a tensor, and the second input should be a tensor or a number.
+    When the inputs are two tensors, the shapes of them could be broadcast,
+    and the data types of them should be the same.
+    When the inputs are one tensor and one scalar, the scalar could not be a parameter,
+    only could be a constant, and the type of the scalar is the same as the data type of the tensor.
+
+    Inputs:
+        - **input_x** (Union[Tensor]) - The first input is a tensor whose data type is number.
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+          a number.
+
+    Outputs:
+        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+
+
     Inputs:
         - **input_x** (Tensor) - The input tensor.
         - **input_y** (Union[Tensor, Number]) - The exponent part. If exponent is a tensor, its shape must be able to
@@ -927,17 +942,6 @@ class Pow(PrimitiveWithInfer):
         [1.0, 16.0, 64.0]
     """
 
-    @prim_attr_register
-    def __init__(self):
-        """init Multiply"""
-
-    def infer_shape(self, x, power):
-        return x
-
-    def infer_dtype(self, x, power):
-        validator.check_tensor_type_same({"x": x}, mstype.number_type, self.prim_name())
-        return x
-
 
 class Exp(PrimitiveWithInfer):
     """
@@ -965,7 +969,7 @@ class Exp(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("x", x_type, mstype.tensor, self.prim_name())
+        validator.check_subclass("x", x_type, mstype.tensor, self.name)
         return x_type
 
 
@@ -994,7 +998,7 @@ class Log(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_subclass("x", x, mstype.tensor, self.prim_name())
+        validator.check_subclass("x", x, mstype.tensor, self.name)
         return x
 
 
@@ -1176,7 +1180,7 @@ class Floor(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_type_same({"x": x_dtype}, mstype.float_type, self.prim_name())
+        validator.check_tensor_type_same({"x": x_dtype}, mstype.float_type, self.name)
         return x_dtype
 
 
@@ -1231,7 +1235,7 @@ class Acosh(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.name)
         return x
 
 
@@ -1247,7 +1251,7 @@ class _LogicBinaryOp(_BinaryOp):
         return mstype.tensor_type(mstype.bool_)
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, prim_name=self.prim_name())
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, prim_name=self.name)
 
 
 class Equal(_LogicBinaryOp):
@@ -1283,7 +1287,7 @@ class Equal(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name())
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.name)
 
 
 class EqualCount(PrimitiveWithInfer):
@@ -1318,7 +1322,7 @@ class EqualCount(PrimitiveWithInfer):
 
     def infer_dtype(self, x_dtype, y_dtype):
         args = {'x': x_dtype, 'y': y_dtype}
-        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.prim_name())
+        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.name)
         return x_dtype
 
 
@@ -1355,7 +1359,7 @@ class NotEqual(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name())
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.name)
 
 
 class Greater(_LogicBinaryOp):
@@ -1491,7 +1495,7 @@ class LogicalNot(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_type_same({"x": x_dtype}, [mstype.bool_], self.prim_name())
+        validator.check_tensor_type_same({"x": x_dtype}, [mstype.bool_], self.name)
         return mstype.tensor_type(mstype.bool_)
 
 
@@ -1521,7 +1525,7 @@ class LogicalAnd(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.name)
 
 
 class LogicalOr(_LogicBinaryOp):
@@ -1550,7 +1554,7 @@ class LogicalOr(_LogicBinaryOp):
     """
 
     def infer_dtype(self, x_dtype, y_dtype):
-        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name())
+        return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.name)
 
 class IsNan(PrimitiveWithInfer):
     """
@@ -1699,13 +1703,13 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
         self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name)
         validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name)
         return [8]
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name())
+        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.name)
         return mstype.float32
 
 
@@ -1741,13 +1745,13 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
         self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name)
         validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name)
         return [8]
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name())
+        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.name)
         return mstype.float32
 
 
@@ -1775,7 +1779,7 @@ class Cos(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.name)
         return x
 
 
@@ -1803,7 +1807,7 @@ class ACos(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.name)
         return x
 
 
@@ -1831,7 +1835,7 @@ class Sin(PrimitiveWithInfer):
         return x
 
     def infer_dtype(self, x):
-        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x}, mstype.number_type, self.name)
         return x
 
 
@@ -1876,11 +1880,11 @@ class NMSWithMask(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, iou_threshold=0.5):
         """Init NMSWithMask"""
-        validator.check_value_type("iou_threshold", iou_threshold, [float], self.prim_name())
+        validator.check_value_type("iou_threshold", iou_threshold, [float], self.name)
         self.init_prim_io_names(inputs=['bboxes'], outputs=['selected_boxes', 'selected_idx', 'selected_mask'])
 
     def infer_shape(self, bboxes_shape):
-        cls_name = self.prim_name()
+        cls_name = self.name
         validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ, cls_name)
         validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT, cls_name)
         validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ, cls_name)
@@ -1888,7 +1892,7 @@ class NMSWithMask(PrimitiveWithInfer):
         return (bboxes_shape, (num,), (num,))
 
     def infer_dtype(self, bboxes_dtype):
-        validator.check_tensor_type_same({"bboxes": bboxes_dtype}, [mstype.float16, mstype.float32], self.prim_name())
+        validator.check_tensor_type_same({"bboxes": bboxes_dtype}, [mstype.float16, mstype.float32], self.name)
         return (bboxes_dtype, mstype.int32, mstype.bool_)
 
 
@@ -1917,7 +1921,7 @@ class Abs(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.name)
         return x_type
 
     def infer_value(self, x):
@@ -1959,7 +1963,7 @@ class Sign(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.name)
         return x_dtype
 
 
@@ -1988,7 +1992,7 @@ class Round(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name())
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.name)
         return x_type
 
 
diff --git a/mindspore/ops/primitive.py b/mindspore/ops/primitive.py
index 96e754f5f7..d281b4f76c 100644
--- a/mindspore/ops/primitive.py
+++ b/mindspore/ops/primitive.py
@@ -194,9 +194,6 @@ class PrimitiveWithInfer(Primitive):
         Primitive.__init__(self, name)
         self.set_prim_type(prim_type.py_infer_shape)
 
-    def prim_name(self):
-        return self.__class__.__name__
-
     def _clone(self):
         """
         Deeply clones the primitive object.
diff --git a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
index f6ea2c3d3c..7b37a90fd8 100644
--- a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc
@@ -19,7 +19,7 @@
 #include <vector>
 #include "common/common_test.h"
 #include "parallel/strategy.h"
-#include "parallel/ops_info/elementary_function_info.h"
+#include "parallel/ops_info/arithmetic_info.h"
 #include "parallel/device_manager.h"
 #include "parallel/step_parallel.h"
 
@@ -56,14 +56,14 @@ void TestPowInfo::SetUp() {
 
   std::unordered_map<std::string, ValuePtr> attr;
 
-  Shapes inputs_shape = {{32, 64, 128}};
+  Shapes inputs_shape = {{32, 64, 128}, {32, 64, 128}};
   Shapes outputs_shape = {{32, 64, 128}};
 
   pow = std::make_shared<PowInfo>("pow_info", inputs_shape, outputs_shape, attr);
 }
 
 TEST_F(TestPowInfo, InferDevMatrixShape1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -74,7 +74,7 @@ TEST_F(TestPowInfo, InferDevMatrixShape1) {
 }
 
 TEST_F(TestPowInfo, InferSliceShape1) {
-  std::vector<Dimensions> str = {{2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -95,7 +95,7 @@ TEST_F(TestPowInfo, InferSliceShape1) {
 }
 
 TEST_F(TestPowInfo, GetTensorLayout1) {
-  std::vector<Dimensions> str = {{2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -116,7 +116,7 @@ TEST_F(TestPowInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestPowInfo, GetForwardOp1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestPowInfo, GetForwardOp1) {
 }
 
 TEST_F(TestPowInfo, GetMirrorOPs1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -147,7 +147,7 @@ TEST_F(TestPowInfo, CheckStrategy1) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy2) {
-  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);
@@ -155,7 +155,7 @@ TEST_F(TestPowInfo, CheckStrategy2) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy3) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);
diff --git a/tests/ut/python/ops/test_math_ops.py b/tests/ut/python/ops/test_math_ops.py
index 7c0cca9b40..ad1642228d 100755
--- a/tests/ut/python/ops/test_math_ops.py
+++ b/tests/ut/python/ops/test_math_ops.py
@@ -82,9 +82,10 @@ def test_sqrt():
 def test_pow():
     """ test_pow """
     input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
+    power = Tensor(np.array(3.0, np.int64))
     testpow = P.Pow()
     expect = np.array([[8, 8], [27, 27]])
-    result = testpow(input_tensor, 3.0)
+    result = testpow(input_tensor, power)
     assert np.all(result.asnumpy() == expect)
 
 
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index a6b064bdb0..078ada8406 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -224,11 +224,15 @@ test_case_math_ops = [
         'block': P.Minimum(),
         'desc_inputs': [[2, 3, 3, 5], [2, 3, 3, 5]],
         'desc_bprop': [[2, 3, 3, 5]]}),
-    ('Pow', {
+    ('Pow_0', {
         'block': P.Pow(),
         'desc_const': [2.0],
         'desc_inputs': [[2, 3, 3, 5]],
         'desc_bprop': [[2, 3, 3, 5]]}),
+    ('Pow_1', {
+        'block': P.Pow(),
+        'desc_inputs': [[3, 5], [2, 3, 3, 5]],
+        'desc_bprop': [[2, 3, 3, 5]]}),
     ('Exp', {
         'block': P.Exp(),
         'desc_inputs': [[2, 3]],
diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py
index 2eb3a22ed2..641eb19f20 100644
--- a/tests/ut/python/parallel/test_element_wise_function.py
+++ b/tests/ut/python/parallel/test_element_wise_function.py
@@ -59,7 +59,7 @@ def test_matmul_pow():
 
     context.set_auto_parallel_context(device_num=8, global_rank=0)
     strategy1 = ((2, 2), (2, 2))
-    strategy2 = ((4, 2), )
+    strategy2 = ((4, 2), ())
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
diff --git a/tests/vm_impl/math_ops_vm_impl.py b/tests/vm_impl/math_ops_vm_impl.py
index fd132280d1..01df0b824e 100644
--- a/tests/vm_impl/math_ops_vm_impl.py
+++ b/tests/vm_impl/math_ops_vm_impl.py
@@ -117,6 +117,7 @@ def vm_impl_pow(self):
     """Generate vm_impl function for Pow."""
     def vm_impl(x, y):
         x = x.asnumpy()
+        y = y.asnumpy()
         res = vm.power(x, y)
         return Tensor(res)
     return vm_impl

From 3348e5a7c2a24dd331fe68f8d7487274915b275c Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Sat, 11 Apr 2020 15:55:50 +0800
Subject: [PATCH 173/367] deal something special of adam's kernel select

---
 mindspore/ccsrc/device/ascend/kernel_select_ascend.cc | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index d05b9fafa1..0a23e2da7b 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -82,6 +82,13 @@ bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::
     }
     return true;
   };
+  if (AnfAlgo::GetCNodeName(kernel_node) == "Adam") {
+    auto input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_num - 1) !=
+        kernel_build_info.GetInputFormat(input_num - 1)) {
+      return false;
+    }
+  }
   if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
     return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
            AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);

From 149839952b5a5d698f5e1ddfee9e3902960459dd Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Sat, 11 Apr 2020 17:25:35 +0800
Subject: [PATCH 174/367] normalize log in optimizer in python

---
 mindspore/nn/optim/optimizer.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index cd0ed93a10..e2edf7bfb4 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -14,7 +14,6 @@
 # ============================================================================
 """optimizer"""
 from typing import Iterable
-import logging
 
 import numpy as np
 
@@ -24,8 +23,8 @@ from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore._checkparam import ParamValidator as validator
 from mindspore._checkparam import Rel
 from mindspore.common.tensor import Tensor
+from mindspore import log as logger
 
-logger = logging.getLogger('Optimizer')
 
 __all__ = ['Optimizer']
 

From 11403492ae05facee8550714419dddadb36876d4 Mon Sep 17 00:00:00 2001
From: liyong <liyong126@huawei.com>
Date: Fri, 10 Apr 2020 18:56:58 +0800
Subject: [PATCH 175/367] add mindrecord subset random sampler

---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    |  31 +++
 mindspore/ccsrc/dataset/api/de_pipeline.h     |   3 +
 .../mindrecord/include/common/shard_utils.h   |   2 +
 .../ccsrc/mindrecord/include/shard_sample.h   |   6 +
 .../ccsrc/mindrecord/meta/shard_sample.cc     |  74 +++---
 mindspore/dataset/engine/datasets.py          |  31 ++-
 .../dataset/test_minddataset_sampler.py       | 222 ++++++++++++++++++
 7 files changed, 336 insertions(+), 33 deletions(-)
 create mode 100644 tests/ut/python/dataset/test_minddataset_sampler.py

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index 1812c0421a..cf7050450b 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -391,6 +391,30 @@ Status DEPipeline::CheckMindRecordPartitionInfo(const py::dict &args, std::vecto
   return Status::OK();
 }
 
+Status DEPipeline::GetMindrecordSampler(const std::string &sampler_name, const py::dict &args,
+                                        std::shared_ptr<mindrecord::ShardOperator> *ptr) {
+  std::vector<int> indices;
+  for (auto &arg : args) {
+    std::string key = py::str(arg.first);
+    py::handle value = arg.second;
+    if (!value.is_none()) {
+      if (key == "indices") {
+        indices = ToIntVector(value);
+      } else {
+        std::string err_msg = "ERROR: parameter " + key + " is invalid.";
+        RETURN_STATUS_UNEXPECTED(err_msg);
+      }
+    }
+  }
+  if (sampler_name == "SubsetRandomSampler") {
+    *ptr = std::make_shared<mindrecord::ShardSample>(indices);
+  } else {
+    std::string err_msg = "ERROR: parameter sampler_name is invalid.";
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
 Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   if (args["dataset_file"].is_none()) {
     std::string err_msg = "Error: at least one of dataset_files is missing";
@@ -422,6 +446,13 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
       } else if (key == "global_shuffle" && ToBool(value) == true) {
         uint32_t seed = args["partitions"].is_none() ? GetSeed() : 0;
         operators.push_back(std::make_shared<mindrecord::ShardShuffle>(seed));
+      } else if (key == "sampler_name") {
+        std::shared_ptr<mindrecord::ShardOperator> sample_op;
+        auto ret = GetMindrecordSampler(ToString(value), args["sampler_params"], &sample_op);
+        if (Status::OK() != ret) {
+          return ret;
+        }
+        operators.push_back(sample_op);
       }
     }
   }
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index acffc390cc..491a75390e 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -145,6 +145,9 @@ class DEPipeline {
 
   Status ParseCelebAOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
+  Status GetMindrecordSampler(const std::string &sampler_name, const py::dict &args,
+                              std::shared_ptr<mindrecord::ShardOperator> *ptr);
+
  private:
   // Execution tree that links the dataset operators.
   std::shared_ptr<ExecutionTree> tree_;
diff --git a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
index 55319cabfe..e18cbb75b9 100644
--- a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
+++ b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
@@ -68,6 +68,8 @@ enum ShardType {
   kCV = 1,
 };
 
+enum SamplerType { kCustomTopNSampler, kCustomTopPercentSampler, kSubsetRandomSampler, kPKSampler };
+
 const double kEpsilon = 1e-7;
 
 const int kThreadNumber = 14;
diff --git a/mindspore/ccsrc/mindrecord/include/shard_sample.h b/mindspore/ccsrc/mindrecord/include/shard_sample.h
index f6b074a65d..aeb3374f28 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_sample.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_sample.h
@@ -17,7 +17,9 @@
 #ifndef MINDRECORD_INCLUDE_SHARD_SAMPLE_H_
 #define MINDRECORD_INCLUDE_SHARD_SAMPLE_H_
 
+#include <string>
 #include <utility>
+#include <vector>
 #include "mindrecord/include/shard_operator.h"
 
 namespace mindspore {
@@ -30,6 +32,8 @@ class ShardSample : public ShardOperator {
 
   ShardSample(int num, int den, int par);
 
+  explicit ShardSample(const std::vector<int> &indices);
+
   ~ShardSample() override{};
 
   const std::pair<int, int> get_partitions() const;
@@ -41,6 +45,8 @@ class ShardSample : public ShardOperator {
   int denominator_;
   int no_of_samples_;
   int partition_id_;
+  std::vector<int> indices_;
+  SamplerType sampler_type_;
 };
 }  // namespace mindrecord
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/meta/shard_sample.cc b/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
index ea365a0e2a..367c7a5cf9 100644
--- a/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
+++ b/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
@@ -22,33 +22,37 @@ using mindspore::MsLogLevel::ERROR;
 
 namespace mindspore {
 namespace mindrecord {
-ShardSample::ShardSample(int n) {
-  numerator_ = 0;
-  denominator_ = 0;
-  no_of_samples_ = n;
-  partition_id_ = 0;
-}
+ShardSample::ShardSample(int n)
+    : numerator_(0),
+      denominator_(0),
+      no_of_samples_(n),
+      partition_id_(0),
+      indices_({}),
+      sampler_type_(kCustomTopNSampler) {}
 
-ShardSample::ShardSample(int num, int den) {
-  if (num < 0 || den <= 0 || num > den) {
-    no_of_samples_ = 5;
-    numerator_ = 0;
-    denominator_ = 0;
-    partition_id_ = 0;
-    return;
-  }
-  numerator_ = num;
-  denominator_ = den;
-  no_of_samples_ = 0;
-  partition_id_ = 0;
-}
+ShardSample::ShardSample(int num, int den)
+    : numerator_(num),
+      denominator_(den),
+      no_of_samples_(0),
+      partition_id_(0),
+      indices_({}),
+      sampler_type_(kCustomTopPercentSampler) {}
 
-ShardSample::ShardSample(int num, int den, int par) {
-  numerator_ = num;
-  denominator_ = den;
-  no_of_samples_ = 0;
-  partition_id_ = par;
-}
+ShardSample::ShardSample(int num, int den, int par)
+    : numerator_(num),
+      denominator_(den),
+      no_of_samples_(0),
+      partition_id_(par),
+      indices_({}),
+      sampler_type_(kCustomTopPercentSampler) {}
+
+ShardSample::ShardSample(const std::vector<int> &indices)
+    : numerator_(0),
+      denominator_(0),
+      no_of_samples_(0),
+      partition_id_(0),
+      indices_(indices),
+      sampler_type_(kSubsetRandomSampler) {}
 
 const std::pair<int, int> ShardSample::get_partitions() const {
   if (numerator_ == 1 && denominator_ > 1) {
@@ -62,10 +66,15 @@ MSRStatus ShardSample::operator()(ShardTask &tasks) {
   int total_no = static_cast<int>(tasks.Size());
 
   int taking = 0;
-  if (no_of_samples_ > 0) {  // non sharding case constructor #1
+  if (sampler_type_ == kCustomTopNSampler) {  // non sharding case constructor #1
     no_of_samples_ = std::min(no_of_samples_, total_no);
     taking = no_of_samples_ - no_of_samples_ % no_of_categories;
-  } else {  // constructor #2 & #3
+  } else if (sampler_type_ == kSubsetRandomSampler) {
+    if (indices_.size() > total_no) {
+      MS_LOG(ERROR) << "parameter indices's size is greater than dataset size.";
+      return FAILED;
+    }
+  } else {  // constructor TopPercent
     if (numerator_ > 0 && denominator_ > 0 && numerator_ <= denominator_) {
       if (numerator_ == 1 && denominator_ > 1) {  // sharding
         taking = (total_no / denominator_) + (total_no % denominator_ == 0 ? 0 : 1);
@@ -82,8 +91,15 @@ MSRStatus ShardSample::operator()(ShardTask &tasks) {
   if (tasks.permutation_.empty()) {
     ShardTask new_tasks;
     total_no = static_cast<int>(tasks.Size());
-    for (int i = partition_id_ * taking; i < (partition_id_ + 1) * taking; i++) {
-      new_tasks.InsertTask(tasks.get_task_by_id(i % total_no));  // rounding up. if overflow, go back to start
+    if (sampler_type_ == kSubsetRandomSampler) {
+      for (int i = 0; i < indices_.size(); ++i) {
+        int index = ((indices_[i] % total_no) + total_no) % total_no;
+        new_tasks.InsertTask(tasks.get_task_by_id(index));  // different mod result between c and python
+      }
+    } else {
+      for (int i = partition_id_ * taking; i < (partition_id_ + 1) * taking; i++) {
+        new_tasks.InsertTask(tasks.get_task_by_id(i % total_no));  // rounding up. if overflow, go back to start
+      }
     }
     std::swap(tasks, new_tasks);
   } else {
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 2058bbf826..3d660d58a8 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1363,7 +1363,6 @@ def _select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id):
     return samplers.SequentialSampler()
 
 
-
 class ImageFolderDatasetV2(SourceDataset):
     """
     A source dataset that reads images from a tree of directories.
@@ -1621,6 +1620,9 @@ class MindDataset(SourceDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument should be specified only when num_shards is also specified.
         block_reader (bool, optional): Whether read data by block mode (default=False).
+        sampler (Sampler, optional): Object used to choose samples from the
+            dataset (default=None, sampler is exclusive
+            with shuffle and block_reader). Support list: SubsetRandomSampler.
 
     Raises:
         ValueError: If num_shards is specified but shard_id is None.
@@ -1630,14 +1632,16 @@ class MindDataset(SourceDataset):
 
     @check_minddataset
     def __init__(self, dataset_file, columns_list=None, num_parallel_workers=None,
-                 shuffle=None, num_shards=None, shard_id=None, block_reader=False):
+                 shuffle=None, num_shards=None, shard_id=None,
+                 block_reader=False, sampler=None):
         super().__init__(num_parallel_workers)
         self.dataset_file = dataset_file
         self.columns_list = columns_list
-        self.global_shuffle = not bool(shuffle is False)
+        self.global_shuffle = shuffle
         self.distribution = ""
+        self.sampler = sampler
 
-        if num_shards is None:
+        if num_shards is None or shard_id is None:
             self.partitions = None
         else:
             self.partitions = [num_shards, shard_id]
@@ -1645,9 +1649,25 @@ class MindDataset(SourceDataset):
         if block_reader is True and self.partitions is not None:
             raise ValueError("block reader not allowed true when use partitions")
 
+        if block_reader is True and shuffle is True:
+            raise ValueError("block reader not allowed true when use shuffle")
+
         if block_reader is True:
             logger.warning("WARN: global shuffle is not used.")
 
+        if sampler is not None and isinstance(sampler, samplers.SubsetRandomSampler) is False:
+            raise ValueError("the sampler is not supported yet.")
+
+        # sampler exclusive
+        if block_reader is True and sampler is not None:
+            raise ValueError("block reader not allowed true when use sampler")
+
+        if shuffle is True and sampler is not None:
+            raise ValueError("shuffle not allowed true when use sampler")
+
+        if block_reader is False and sampler is None:
+            self.global_shuffle = not bool(shuffle is False)
+
         self.num_shards = num_shards
         self.shard_id = shard_id
         self.block_reader = block_reader
@@ -1661,6 +1681,9 @@ class MindDataset(SourceDataset):
         args["block_reader"] = self.block_reader
         args["num_shards"] = self.num_shards
         args["shard_id"] = self.shard_id
+        if self.sampler:
+            args["sampler_name"] = self.sampler.__class__.__name__
+            args["sampler_params"] = self.sampler.__dict__
         return args
 
     def get_dataset_size(self):
diff --git a/tests/ut/python/dataset/test_minddataset_sampler.py b/tests/ut/python/dataset/test_minddataset_sampler.py
new file mode 100644
index 0000000000..7662a0e390
--- /dev/null
+++ b/tests/ut/python/dataset/test_minddataset_sampler.py
@@ -0,0 +1,222 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This is the test module for mindrecord
+"""
+import collections
+import json
+import os
+import re
+import string
+
+import mindspore.dataset.transforms.vision.c_transforms as vision
+import numpy as np
+import pytest
+from mindspore.dataset.transforms.vision import Inter
+from mindspore import log as logger
+
+import mindspore.dataset as ds
+from mindspore.mindrecord import FileWriter
+
+FILES_NUM = 4
+CV_FILE_NAME = "../data/mindrecord/imagenet.mindrecord"
+CV_DIR_NAME = "../data/mindrecord/testImageNetData"
+
+
+@pytest.fixture
+def add_and_remove_cv_file():
+    """add/remove cv file"""
+    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
+             for x in range(FILES_NUM)]
+    for x in paths:
+        if os.path.exists("{}".format(x)):
+            os.remove("{}".format(x))
+        if os.path.exists("{}.db".format(x)):
+            os.remove("{}.db".format(x))
+    writer = FileWriter(CV_FILE_NAME, FILES_NUM)
+    data = get_data(CV_DIR_NAME)
+    cv_schema_json = {"id": {"type": "int32"},
+                      "file_name": {"type": "string"},
+                      "label": {"type": "int32"},
+                      "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    writer.write_raw_data(data)
+    writer.commit()
+    yield "yield_cv_data"
+    for x in paths:
+        os.remove("{}".format(x))
+        os.remove("{}.db".format(x))
+
+
+def test_cv_minddataset_subset_random_sample_basic(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = [1, 2, 3, 5, 7]
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert data[indices[num_iter]]['file_name'] == "".join(
+            [chr(x) for x in item['file_name']])
+        num_iter += 1
+    assert num_iter == 5
+
+
+def test_cv_minddataset_subset_random_sample_replica(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = [1, 2, 2, 5, 7, 9]
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert data[indices[num_iter]]['file_name'] == "".join(
+            [chr(x) for x in item['file_name']])
+        num_iter += 1
+    assert num_iter == 6
+
+
+def test_cv_minddataset_subset_random_sample_empty(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = []
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert data[indices[num_iter]]['file_name'] == "".join(
+            [chr(x) for x in item['file_name']])
+        num_iter += 1
+    assert num_iter == 0
+
+
+def test_cv_minddataset_subset_random_sample_out_range(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = [1, 2, 4, 11, 13]
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert data[indices[num_iter] % len(data)]['file_name'] == "".join([
+            chr(x) for x in item['file_name']])
+        num_iter += 1
+    assert num_iter == 5
+
+
+def test_cv_minddataset_subset_random_sample_negative(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = [1, 2, 4, -1, -2]
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info(
+            "-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info(
+            "-------------- item[data]: {}  -----------------------------".format(item["data"]))
+        logger.info(
+            "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info(
+            "-------------- item[label]: {} ----------------------------".format(item["label"]))
+        assert data[indices[num_iter] % len(data)]['file_name'] == "".join([
+            chr(x) for x in item['file_name']])
+        num_iter += 1
+    assert num_iter == 5
+
+
+def get_data(dir_name):
+    """
+    usage: get data from imagenet dataset
+    params:
+    dir_name: directory containing folder images and annotation information
+
+    """
+    if not os.path.isdir(dir_name):
+        raise IOError("Directory {} not exists".format(dir_name))
+    img_dir = os.path.join(dir_name, "images")
+    ann_file = os.path.join(dir_name, "annotation.txt")
+    with open(ann_file, "r") as file_reader:
+        lines = file_reader.readlines()
+
+    data_list = []
+    for i, line in enumerate(lines):
+        try:
+            filename, label = line.split(",")
+            label = label.strip("\n")
+            with open(os.path.join(img_dir, filename), "rb") as file_reader:
+                img = file_reader.read()
+            data_json = {"id": i,
+                         "file_name": filename,
+                         "data": img,
+                         "label": int(label)}
+            data_list.append(data_json)
+        except FileNotFoundError:
+            continue
+    return data_list

From c8cdb6b3311ba8024abbc1787db0d7ff4fcd5986 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Tue, 7 Apr 2020 15:08:31 +0800
Subject: [PATCH 176/367] support distributed GatherV2 operator

---
 .../auto_parallel/operator_costmodel.cc       |  29 ++
 .../auto_parallel/operator_costmodel.h        |  27 ++
 .../parallel/ops_info/batch_parallel_info.cc  |  20 -
 .../parallel/ops_info/batch_parallel_info.h   |   9 -
 .../ccsrc/parallel/ops_info/gather_v2_info.cc | 350 ++++++++++++++++++
 .../ccsrc/parallel/ops_info/gather_v2_info.h  |  73 ++++
 .../ccsrc/parallel/ops_info/operator_info.cc  |   1 +
 .../ccsrc/parallel/ops_info/operator_info.h   |   3 +
 .../parallel/ops_info/ops_info_head_files.h   |   1 +
 mindspore/ccsrc/parallel/step_parallel.cc     |   8 +
 .../parallel/test_gather_v2_primitive.py      | 123 +++++-
 11 files changed, 597 insertions(+), 47 deletions(-)
 create mode 100644 mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
 create mode 100644 mindspore/ccsrc/parallel/ops_info/gather_v2_info.h

diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 1f420e8797..960e13281c 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -623,5 +623,34 @@ double DropOutCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
   Shape input0_slice_shape = input0.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]) * DROPOUT_COST_RATE;
 }
+
+// return the per device communication cost in the forward phase.
+double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                        const int32_t&) const {
+  // GatherV2Cost does not need communication in the forward phase
+  return 0.0;
+}
+
+// return the per device communication cost in the backward phase.
+double GatherV2Cost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                         const int32_t&) const {
+  // GatherV2Cost does not need communication in the backward phase
+  return 0.0;
+}
+
+double GatherV2Cost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                               const int32_t&) const {
+  // In forward phase, the computation cost = slice(A) + slice(B)
+  Shape input0_slice_shape = inputs[0].slice_shape();
+  Shape input1_slice_shape = inputs[1].slice_shape();
+  double result = ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]) +
+                  ListProduct(input1_slice_shape) * static_cast<double>(inputs_type_lengths_[1]);
+  return result;
+}
+
+double GatherV2Cost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                                const int32_t&) const {
+  return 0.0;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index b642ada0d9..685cb259c3 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -81,6 +81,8 @@ class OperatorCost {
   std::vector<size_t> outputs_type_lengths_;
 };
 
+using OperatorCostPtr = std::shared_ptr<OperatorCost>;
+
 class MatMulCost : public OperatorCost {
  public:
   MatMulCost() = default;
@@ -525,6 +527,31 @@ class DropOutCost : public OperatorCost {
 };
 
 using DropOutCostPtr = std::shared_ptr<DropOutCost>;
+
+class GatherV2Cost : public OperatorCost {
+ public:
+  GatherV2Cost() = default;
+  ~GatherV2Cost() override = default;
+
+  double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                     const int32_t& stage_id) const override {
+    return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
+  }
+  double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override;
+  double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                             const int32_t& stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            const int32_t& stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                   const int32_t& stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                                    const int32_t&) const override;
+};
+
+using GatherV2CostPtr = std::shared_ptr<GatherV2Cost>;
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
index 793452b8ad..b1d9b8b60e 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
@@ -228,26 +228,6 @@ void SparseSoftmaxCrossEntropyWithLogitsInfo::ReComputeBatchSplitFlagList() {
   }
 }
 
-void GatherV2Info::ReComputeBatchSplitFlagList() {
-  MS_ASSERT(inputs_shape_.size() == 2);
-  MS_ASSERT(input_value_.size() == 3);
-  MS_ASSERT(input_value_[0] == nullptr);
-  // the second input is the index tensor
-  MS_ASSERT(input_value_[1] != nullptr);
-  // the third input is the axis
-  MS_ASSERT(input_value_[2] != nullptr);
-  int axis = GetValue<int>(input_value_[2]);
-  MS_ASSERT(axis < inputs_shape_[0].size() && axis >= 0 - inputs_shape_[0].size());
-  if (axis < 0) {
-    axis += SizeToInt(inputs_shape_[0].size());
-  }
-  split_flag_list_[0] = true;
-  // if gather axis is 0, the index's strategy is equal to device number
-  if (axis == 0) {
-    split_flag_list_[1] = true;
-  }
-}
-
 Status BatchParallelInfo::InferAsLossDivisor() {
   as_loss_divisor_ = 1;
   return SUCCESS;
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
index fae96dcab5..093bfb8fad 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
@@ -62,15 +62,6 @@ class SparseSoftmaxCrossEntropyWithLogitsInfo : public BatchParallelInfo {
   ~SparseSoftmaxCrossEntropyWithLogitsInfo() override = default;
   void ReComputeBatchSplitFlagList() override;
 };
-
-class GatherV2Info : public BatchParallelInfo {
- public:
-  GatherV2Info(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
-               const PrimitiveAttrs& attrs)
-      : BatchParallelInfo(name, inputs_shape, outputs_shape, attrs) {}
-  ~GatherV2Info() override = default;
-  void ReComputeBatchSplitFlagList() override;
-};
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
new file mode 100644
index 0000000000..2010d1ed46
--- /dev/null
+++ b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
@@ -0,0 +1,350 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parallel/ops_info/gather_v2_info.h"
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "ir/meta_tensor.h"
+#include "ir/value.h"
+#include "parallel/auto_parallel/costmodel.h"
+#include "parallel/device_matrix.h"
+#include "parallel/graph_util/generate_graph.h"
+#include "parallel/strategy.h"
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace parallel {
+Status GatherV2Info::GetAttrs() {
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs shape size must be 2, but is " << inputs_shape_.size();
+    return FAILED;
+  }
+  if (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": outputs shape size must be 1, but is " << outputs_shape_.size();
+    return FAILED;
+  }
+  if (input_value_.size() != GATHER_V2_INPUTS_VALUE_SIZE) {
+    MS_LOG(ERROR) << name_ << ": input value size must be 3, but is " << input_value_.size();
+    return FAILED;
+  }
+  // the second input is the index tensor
+
+  // the third input is the axis, is a ValueNode
+  if (input_value_.at(2) == nullptr) {
+    MS_LOG(ERROR) << name_ << ": the third input value is nullptr, is not a ValueNode!";
+    return FAILED;
+  }
+
+  if (inputs_shape_.at(0).size() == 0) {
+    MS_LOG(ERROR) << name_ << ": input can not be a scalar!";
+    return FAILED;
+  }
+  int axis = GetValue<int>(input_value_.at(2));
+  if (axis >= SizeToInt(inputs_shape_.at(0).size()) || axis < 0 - SizeToInt(inputs_shape_.at(0).size())) {
+    MS_LOG(ERROR) << "Axis is " << axis << ", not in [-" << inputs_shape_.at(0).size() << ", "
+                  << inputs_shape_.at(0).size() << ").";
+  }
+  if (axis < 0) {
+    axis += SizeToInt(inputs_shape_[0].size());
+  }
+  axis_ = axis;
+
+  index_size_ = inputs_shape_.at(1).size();
+
+  return SUCCESS;
+}
+
+Status GatherV2Info::CheckStrategy(const StrategyPtr& strategy) {
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                  << inputs_shape_.size();
+    return FAILED;
+  }
+  if (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": outputs shape size must be " << GATHER_V2_OUTPUTS_SIZE << ", but is "
+                  << outputs_shape_.size();
+    return FAILED;
+  }
+  // Only strategy of the first input should be set.
+  if (CheckStrategyValue(strategy, {inputs_shape_.at(0)}, is_auto_parallel_) != SUCCESS) {
+    if (is_auto_parallel_) {
+      MS_LOG(DEBUG) << name_ << ": Invalid strategy.";
+    } else {
+      MS_LOG(ERROR) << name_ << ": Invalid strategy.";
+    }
+    return FAILED;
+  }
+  axis_strategy_ = strategy->GetInputDim().at(0).at(axis_);
+  if (index_size_ != 1 && axis_strategy_ != 1) {
+    MS_LOG(ERROR) << name_
+                  << ": Invalid strategy. If the index is a scalar or a more than 1 dimension vector, the strategy "
+                     "corresponding to axis must be 1, but is "
+                  << axis_strategy_;
+    return FAILED;
+  }
+  if (index_size_ == 1 && axis_strategy_ != 1 && inputs_shape_.at(1).at(0) % axis_strategy_ != 0) {
+    MS_LOG(ERROR) << name_
+                  << ": Invalid strategy. The first dimension of index can not be divided by strategy corresponding to "
+                     "axis. The first dimension of index is "
+                  << inputs_shape_.at(1).at(0) << " strategy corresponding to axis is " << axis_strategy_;
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status GatherV2Info::InferDevMatrixShape() {
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
+  dev_matrix_shape_ = stra.at(0);
+  return SUCCESS;
+}
+
+// If index is a scalar, output dimension is input dimension minus 1;
+// If index is a n dimension tensor, output dimension is input dimension plus (n - 1).
+// Tensor map dimension is equal to the corresponding input and output dimension.
+// If index's dimension is more than 1, we insert -1 for the output tensor map.
+Status GatherV2Info::InferTensorMap() {
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                  << inputs_shape_.size();
+    return FAILED;
+  }
+  if (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": outputs shape size must be " << GATHER_V2_OUTPUTS_SIZE << ", but is "
+                  << outputs_shape_.size();
+    return FAILED;
+  }
+  std::vector<int32_t> tensor_map_in;
+  std::vector<int32_t> tensor_map_out;
+  size_t size = inputs_shape_.at(0).size();
+  // such as 4: tensor_map_index [3,2,1,0]
+  for (size_t i = 0; i < size; ++i) {
+    tensor_map_in.push_back(SizeToInt(size - i - 1));
+    tensor_map_out.push_back(SizeToInt(size - i - 1));
+  }
+
+  if (index_size_ == 0) {
+    (void)tensor_map_out.erase(tensor_map_out.begin() + axis_);
+  } else if (index_size_ > 1) {
+    (void)tensor_map_out.insert(tensor_map_out.begin() + axis_, index_size_ - 1, -1);
+  }
+  if (tensor_map_out.size() != outputs_shape_.at(0).size()) {
+    MS_LOG(ERROR) << "Out tensor map size is not equal to output size! Out tensor map size is " << tensor_map_out.size()
+                  << " output size is " << outputs_shape_.at(0).size();
+    return FAILED;
+  }
+
+  std::vector<int32_t> tensor_map_in_index;
+  if (index_size_ >= 1) {
+    tensor_map_in_index.push_back(SizeToInt(size - axis_ - 1));
+  }
+  for (size_t i = 1; i < index_size_; ++i) {
+    tensor_map_in_index.push_back(-1);
+  }
+  inputs_tensor_map_.emplace_back(std::move(tensor_map_in));
+  inputs_tensor_map_.emplace_back(std::move(tensor_map_in_index));
+  outputs_tensor_map_.emplace_back(std::move(tensor_map_out));
+  return SUCCESS;
+}
+
+Status GatherV2Info::InferTensorInfo() {
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                  << inputs_shape_.size();
+    return FAILED;
+  }
+  if (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": outputs shape size must be " << GATHER_V2_OUTPUTS_SIZE << ", but is "
+                  << outputs_shape_.size();
+    return FAILED;
+  }
+  if (inputs_tensor_map_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs tensor map  size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                  << inputs_tensor_map_.size();
+    return FAILED;
+  }
+  if (outputs_tensor_map_.size() != GATHER_V2_OUTPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": outputs tensor map size must be " << GATHER_V2_OUTPUTS_SIZE << ", but is "
+                  << outputs_tensor_map_.size();
+    return FAILED;
+  }
+  // infer tensor shape
+  Shape input_shape = inputs_shape_.at(0);
+  Shape input_index_shape = inputs_shape_.at(1);
+  Shape output_shape = outputs_shape_.at(0);
+
+  TensorLayout input_tensor_layout, input_index_layout, output_tensor_layout;
+  if ((input_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_.at(0), input_shape) != SUCCESS) ||
+      (input_index_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_.at(1), input_index_shape) != SUCCESS) ||
+      (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_.at(0), output_shape) != SUCCESS)) {
+    return FAILED;
+  }
+
+  TensorInfo input_tensor_info(input_tensor_layout);
+  TensorInfo input_index_info(input_index_layout);
+  TensorInfo output_tensor_info(output_tensor_layout);
+
+  inputs_tensor_info_.push_back(input_tensor_info);
+  inputs_tensor_info_.push_back(input_index_info);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+OperatorVector CreateSubOp(int32_t sub_value) {
+  OperatorVector ops;
+  OperatorName operator_name = SUB;
+  OperatorAttrs operator_attrs;
+
+  py::tuple tuple = py::make_tuple(sub_value);
+  mindspore::tensor::TensorPtr tensor_ptr = std::make_shared<mindspore::tensor::Tensor>(tuple, kInt32);
+  ValuePtr op_param_value = MakeValue(tensor_ptr);
+
+  Attr op1_param = std::make_pair("", op_param_value);
+  OperatorParams operator_param = {std::make_pair(op1_param, 2)};
+
+  OperatorArgs operator_args = std::make_pair(operator_attrs, operator_param);
+  Operator op = std::make_pair(operator_name, operator_args);
+  ops.push_back(op);
+  return ops;
+}
+
+Status GatherV2Info::InferTensorSubOps() {
+  sub_ops_.clear();
+  if ((index_size_ == 0) || (axis_strategy_ == 1)) {
+    return SUCCESS;
+  }
+  int32_t mod_n = 1;
+  for (size_t i = IntToSize(axis_) + 1; i < dev_matrix_shape_.size(); i++) {
+    mod_n *= dev_matrix_shape_.at(i);
+  }
+  if ((axis_ >= SizeToInt(dev_matrix_shape_.size())) || axis_ < 0) {
+    MS_LOG(ERROR) << "Axis is " << axis_ << ", not in [0, " << dev_matrix_shape_.size() << ").";
+  }
+  int32_t mod_p = mod_n * dev_matrix_shape_.at(axis_);
+  int32_t rank = g_device_manager->global_rank();
+  int32_t mod_rank = rank % mod_p;
+  mod_rank = static_cast<int32_t>(mod_rank / mod_n);
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                  << inputs_shape_.size();
+    return FAILED;
+  }
+  if ((axis_ >= SizeToInt(inputs_shape_.at(0).size())) || axis_ < 0) {
+    MS_LOG(ERROR) << "Axis is " << axis_ << ", not in [0, " << inputs_shape_.at(0).size() << ").";
+  }
+  int32_t sub_value = static_cast<int32_t>(inputs_shape_.at(0).at(axis_) / dev_matrix_shape_.at(axis_)) * mod_rank;
+
+  OperatorVector sub_op;
+  sub_ops_.emplace_back(std::move(sub_op));
+  sub_op = CreateSubOp(sub_value);
+  sub_ops_.emplace_back(std::move(sub_op));
+  return SUCCESS;
+}
+
+Status GatherV2Info::Init(const StrategyPtr& strategy) {
+  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init failed.";
+    return FAILED;
+  }
+  Status status = InferTensorSubOps();
+  if (status != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": InferTensorSubOps failed.";
+    return status;
+  }
+  MS_LOG(INFO) << name_ << ": Init success.";
+  return SUCCESS;
+}
+
+Status GatherV2Info::InitForCostModel(const StrategyPtr& strategy) {
+  if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
+    if (is_auto_parallel_) {
+      MS_LOG(DEBUG) << name_ << ": Init for cost model failed.";
+    } else {
+      MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
+    }
+    return FAILED;
+  }
+  MS_LOG(INFO) << name_ << ": Init for cost model success.";
+  return SUCCESS;
+}
+
+Status GatherV2Info::GenerateStrategies(int32_t stage_id) {
+  if ((inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) || (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE)) {
+    MS_LOG(ERROR) << name_ << " : Inputs shape size(" << inputs_shape_.size() << ") or outputs shape size("
+                  << outputs_shape_.size() << "is wrong.";
+    return FAILED;
+  }
+
+  is_auto_parallel_ = true;
+  Shape input0_split(inputs_shape_[0].size());
+  Shapes splittable_inputs = {input0_split};
+
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, {inputs_shape_.at(0)}, splittable_inputs, &sp_vector) !=
+      SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Generate strategies for independent inputs() failed.";
+    return FAILED;
+  }
+  size_t success = 0;
+  for (auto& sp : sp_vector) {
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(INFO) << name_ << " : Successfully generated " << success << " strategy";
+      PrintStrategy(sp);
+    }
+  }
+  return SUCCESS;
+}
+
+Status GatherV2Info::SetCostUnderStrategy(const StrategyPtr& strategy) {
+  if (SetCostUnderStrategyBase(strategy) != SUCCESS) {
+    if (is_auto_parallel_) {
+      MS_LOG(DEBUG) << name_ << ": Set cost under strategy failed.";
+    } else {
+      MS_LOG(ERROR) << name_ << ": Set cost under strategy failed.";
+    }
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+std::shared_ptr<std::vector<std::vector<int32_t>>> GatherV2Info::GenerateBatchStrategies() {
+  if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
+    MS_LOG(EXCEPTION) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
+                      << inputs_shape_.size();
+  }
+  CheckGlobalDeviceManager();
+  size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
+  if (GetAttrs() != SUCCESS) {
+    MS_LOG(EXCEPTION) << "GetAttrs failed!";
+  }
+
+  Dimensions strategy;
+  if (index_size_ != 1) {
+    strategy.push_back(1);
+  } else {
+    strategy.push_back(SizeToInt(dev_num));
+  }
+  for (size_t i = 1; i < inputs_shape_[0].size(); i++) {
+    strategy.push_back(1);
+  }
+  std::vector<Dimensions> strategy_v = {strategy};
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
+}
+}  // namespace parallel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/gather_v2_info.h b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.h
new file mode 100644
index 0000000000..773d46f429
--- /dev/null
+++ b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.h
@@ -0,0 +1,73 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GATHER_V2_INFO_H_
+#define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GATHER_V2_INFO_H_
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ir/value.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
+#include "parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+constexpr size_t GATHER_V2_INPUTS_SIZE = 2;
+constexpr size_t GATHER_V2_OUTPUTS_SIZE = 1;
+constexpr size_t GATHER_V2_INPUTS_VALUE_SIZE = 3;
+// We now supported limited parallel strategies.
+// If the strategy corresponding to axis is more than 1, index must be evenly distributed across the axis-dimension of
+// the input.
+// If Index is a scalar or n-dimension vector(n > 1), the strategy corresponding to axis must be 1.
+class GatherV2Info : public OperatorInfo {
+ public:
+  GatherV2Info(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+               const PrimitiveAttrs& attrs)
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<GatherV2Cost>()),
+        axis_(-1),
+        index_size_(0),
+        axis_strategy_(1) {}
+  ~GatherV2Info() override = default;
+  Status Init(const StrategyPtr& strategy) override;
+  Status InitForCostModel(const StrategyPtr& strategy) override;
+
+  Status GenerateStrategies(int32_t stage_id) override;
+  Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
+
+ protected:
+  Status CheckStrategy(const StrategyPtr& strategy) override;
+  Status InferMirrorOps() override { return SUCCESS; }
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+  Status GetAttrs() override;
+
+ private:
+  Status InferTensorSubOps();
+
+  int32_t axis_;
+  size_t index_size_;
+  int32_t axis_strategy_;
+};
+}  // namespace parallel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GATHER_V2_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 68c73bc548..42755b3ec3 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -112,6 +112,7 @@ void OperatorInfo::ResetQueueMember() {
   dev_matrix_shape_.clear();
   forward_op_.clear();
   mirror_ops_.clear();
+  sub_ops_.clear();
   replace_op_.clear();
   replace_op_info_.clear();
   virtual_div_op_.clear();
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index 8fcae8ad33..248172fa4c 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -41,6 +41,7 @@ namespace mindspore {
 namespace parallel {
 using ForwardOp = OperatorVector;
 using MirrorOps = std::vector<OperatorVector>;
+using Ops = std::vector<OperatorVector>;
 using VirtualDivOp = OperatorVector;
 using TensorMaps = std::vector<std::vector<int32_t>>;
 using TensorLayouts = std::vector<TensorLayout>;
@@ -99,6 +100,7 @@ class OperatorInfo {
   OutPutInfoVector replace_op_info() const { return replace_op_info_; }
   virtual ReplaceGraphPtr replace_graph(const CNodePtr&) { return replace_graph_; }
   MirrorOps mirror_ops() const { return mirror_ops_; }
+  Ops sub_ops() const { return sub_ops_; }
   VirtualDivOp virtual_div_op() const { return virtual_div_op_; }
   Shape dev_matrix_shape() const { return dev_matrix_shape_; }
   std::vector<TensorInfo> inputs_tensor_info() const { return inputs_tensor_info_; }
@@ -190,6 +192,7 @@ class OperatorInfo {
   TensorMaps inputs_tensor_map_;
   TensorMaps outputs_tensor_map_;
   ForwardOp forward_op_;
+  Ops sub_ops_;
   ForwardOp replace_op_;
   OutPutInfoVector replace_op_info_;
   ReplaceGraphPtr replace_graph_;
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
index 1681c8f796..27b434ecca 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
@@ -24,6 +24,7 @@
 #include "parallel/ops_info/comparison_function_info.h"
 #include "parallel/ops_info/dropout_do_mask_info.h"
 #include "parallel/ops_info/elementary_function_info.h"
+#include "parallel/ops_info/gather_v2_info.h"
 #include "parallel/ops_info/get_next_info.h"
 #include "parallel/ops_info/l2_normalize_info.h"
 #include "parallel/ops_info/loss_info.h"
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 0a6d0b0bef..eab5443481 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -464,6 +464,14 @@ void SplitTensor(const AnfNodePtr& node, const CNodePtr& next_node, int index) {
   MS_EXCEPTION_IF_NULL(func_graph);
   Operator op = CreateGetTensorSliceOp(tensor_layout);
   InsertGetTensorSliceOp(op, next_node, func_graph, index, SPLIT_TENSOR);
+  if (!op_info->sub_ops().empty()) {
+    auto sub_ops = op_info->sub_ops();
+    for (size_t i = 0; i < sub_ops.size(); i++) {
+      if (!sub_ops.at(i).empty()) {
+        InsertGetTensorSliceOp(sub_ops.at(i).at(0), next_node, func_graph, index, SUB);
+      }
+    }
+  }
 }
 
 void StepSplitTensor(const AnfNodePtr& node, const FuncGraphManagerPtr& manager) {
diff --git a/tests/ut/python/parallel/test_gather_v2_primitive.py b/tests/ut/python/parallel/test_gather_v2_primitive.py
index c623595b53..3ea0795e9c 100644
--- a/tests/ut/python/parallel/test_gather_v2_primitive.py
+++ b/tests/ut/python/parallel/test_gather_v2_primitive.py
@@ -29,6 +29,8 @@ from mindspore.nn import Dense, Cell
 from mindspore import context
 
 context.set_context(mode=context.GRAPH_MODE)
+device_number = 32
+batch_size_per_device = 128
 
 
 class Dataset():
@@ -57,15 +59,22 @@ class Dataset():
 
 
 class GatherV2(_Loss):
-    def __init__(self, batchsize):
+    def __init__(self, index_dim, strategy, index_size=16):
         super(GatherV2, self).__init__()
         self.pow = P.Pow()
-        emb_list = list(range(batchsize))
-        emb1_list = emb_list[0::2]
-        emb2_list = emb_list[1::2]
+        emb1_list = 21
+        emb2_list = 2
+        if index_dim == 1:
+            emb_list = list(range(index_size))
+            emb1_list = emb_list[0::2]
+            emb2_list = emb_list[1::2]
+        if index_dim == 2:
+            emb_list = np.arange(index_size*16)
+            emb1_list = np.reshape(emb_list[0::2], (int(index_size/2), 16))
+            emb2_list = np.reshape(emb_list[1::2], (int(index_size/2), 16))
         self.emb1_param = Tensor(emb1_list, dtype=mstype.int32)
         self.emb2_param = Tensor(emb2_list, dtype=mstype.int32)
-        self.gatherv2 = P.GatherV2()
+        self.gatherv2 = P.GatherV2().set_strategy(strategy)
 
     def construct(self, nembeddings):
         emb1 = self.gatherv2(nembeddings, self.emb1_param, 0)
@@ -73,10 +82,6 @@ class GatherV2(_Loss):
         return self.pow((emb1 - emb2), 2.0)
 
 
-def get_loss(batchsize):
-    return GatherV2(batchsize)
-
-
 def fc_with_initialize(input_channels, out_channels):
     return Dense(input_channels, out_channels)
 
@@ -114,26 +119,23 @@ class TrainOneStepCell(Cell):
         return F.depend(loss, self.optimizer(grads))
 
 
-def test_trains():
+def net_trains(gather_v2_strategy, criterion, rank):
     init()
     lr = 0.1
     momentum = 0.9
     max_epoch = 20
-    device_number = 32
-    batch_size_per_device = 128
     input_channels = 256
     out_channels = 512
-
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
     context.reset_auto_parallel_context()
-    context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=device_number)
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=device_number,
+                                      global_rank=rank)
     predict = Tensor(np.ones([batch_size_per_device, input_channels]), dtype=ms.float32)
     dataset = Dataset(predict, 4)
 
     network = fc_with_initialize(input_channels, out_channels)
     network.set_train()
 
-    criterion = get_loss(batch_size_per_device * device_number)
-
     train_network = BuildTrainNetwork(network, criterion)
     train_network.set_train()
     opt = Momentum(train_network.trainable_params(), lr, momentum)
@@ -143,5 +145,90 @@ def test_trains():
     model.train(max_epoch, dataset, dataset_sink_mode=False)
     context.reset_auto_parallel_context()
 
-if __name__ == "__main__":
-    test_trains()
+
+def test_auto_batch_parallel():
+    gather_v2_strategy = None
+    criterion = GatherV2(1, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    rank = 2
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_2d_index_auto_batch_parallel():
+    gather_v2_strategy = None
+    criterion = GatherV2(2, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    rank = 2
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_batch_parallel():
+    gather_v2_strategy = ((device_number, 1),)
+    criterion = GatherV2(1, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    rank = 2
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_strategy1():
+    gather_v2_strategy = ((16, 2),)
+    rank = 2
+    criterion = GatherV2(1, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_strategy2():
+    gather_v2_strategy = ((1, device_number),)
+    rank = 2
+    criterion = GatherV2(1, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_strategy3():
+    gather_v2_strategy = ((8, 1),)
+    rank = 2
+    criterion = GatherV2(1, strategy=gather_v2_strategy, index_size=batch_size_per_device * device_number)
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+class GatherV2Axis1(_Loss):
+    def __init__(self, index_dim, strategy, index_size=16):
+        super(GatherV2Axis1, self).__init__()
+        self.pow = P.Pow()
+        emb1_list = 21
+        emb2_list = 2
+        if index_dim == 1:
+            emb_list = list(range(index_size))
+            emb1_list = emb_list[0::2]
+            emb2_list = emb_list[1::2]
+        if index_dim == 2:
+            emb_list = np.arange(index_size*index_size)
+            emb1_list = np.reshape(emb_list[0::2], (int(index_size/2), index_size))
+            emb2_list = np.reshape(emb_list[1::2], (int(index_size/2), index_size))
+        self.emb1_param = Tensor(emb1_list, dtype=mstype.int32)
+        self.emb2_param = Tensor(emb2_list, dtype=mstype.int32)
+        self.gatherv2 = P.GatherV2().set_strategy(strategy)
+
+    def construct(self, nembeddings):
+        emb1 = self.gatherv2(nembeddings, self.emb1_param, 1)
+        emb2 = self.gatherv2(nembeddings, self.emb2_param, 1)
+        return self.pow((emb1 - emb2), 2.0)
+
+
+def test_axis1_auto_batch_parallel():
+    gather_v2_strategy = None
+    criterion = GatherV2Axis1(1, strategy=gather_v2_strategy, index_size=512)
+    rank = 2
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_axis1_batch_parallel():
+    gather_v2_strategy = ((device_number, 1),)
+    criterion = GatherV2Axis1(1, strategy=gather_v2_strategy, index_size=512)
+    rank = 2
+    net_trains(gather_v2_strategy, criterion, rank)
+
+
+def test_axis1_strategy1():
+    gather_v2_strategy = ((16, 2),)
+    rank = 17
+    criterion = GatherV2Axis1(1, strategy=gather_v2_strategy, index_size=512)
+    net_trains(gather_v2_strategy, criterion, rank)
+

From 87f8b9fba2149e1b63cc1489b0f4c508e2f6f1da Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Sun, 12 Apr 2020 00:13:39 +0800
Subject: [PATCH 177/367] Update docker image section in README.md

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 README.md | 66 ++++++++++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 56 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index be8ca5189a..8b6b363de3 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 ![MindSpore Logo](docs/MindSpore-logo.png "MindSpore logo")
 ============================================================
 
-- [What is MindSpore?](#what-is-MindSpore)
+- [What is MindSpore?](#what-is-mindspore)
     - [Automatic Differentiation](#automatic-differentiation)
     - [Automatic Parallel](#automatic-parallel)
 - [Installation](#installation)
@@ -53,7 +53,7 @@ The goal of MindSpore automatic parallel is to build a training method that comb
 
 <img src="docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
 
-At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splited into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
+At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splitted into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
 
 ## Installation
 
@@ -70,9 +70,9 @@ MindSpore offers build options across multiple backends:
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
 
-For installation using pip, take `Ubuntu-x86` and `CPU` build version as an example:
+For installation using `pip`, take `Ubuntu-x86` and `CPU` build version as an example:
 
-1. Download whl from [MindSpore website](https://www.mindspore.cn/), and install the package.
+1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
 
     ```
     pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
@@ -96,14 +96,60 @@ currently the containerized build options are supported as follows:
 | Hardware Platform | Docker Image URL |
 | :---------------- | :--------------- |
 | CPU | `mindspore/mindspore-cpu:0.1.0-alpha` |
-| GPU CUDA 9.2 | `mindspore/mindspore-cuda9.2:0.1.0-alpha` |
-| GPU CUDA 10.1 | `mindspore/mindspore-cuda10.1:0.1.0-alpha` |
+| GPU | `mindspore/mindspore-gpu:0.1.0-alpha` |
 | Ascend | <center>—</center> |
 
-Take `CPU` for example, you can directly pull the image using the below command:
-```
-docker pull mindspore/mindspore-cpu:0.1.0-alpha
-```
+* CPU
+
+    For `CPU` backend, you can directly pull and run the image using the below command:
+    ```
+    docker pull mindspore/mindspore-cpu:0.1.0-alpha
+    docker run -it mindspore/mindspore-cpu:0.1.0-alpha python -c 'import mindspore'
+    ```
+
+* GPU
+
+    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for Ubuntu users:
+    ```
+    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
+    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
+    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
+
+    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
+    sudo systemctl restart docker
+    ```
+
+    Then you can pull and run the image using the below command:
+    ```
+    docker pull mindspore/mindspore-gpu:0.1.0-alpha
+    docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.1.0-alpha /bin/bash
+    ```
+
+    To test if the docker image works, please execute the python code below and check the output:
+    ```python
+    import numpy as np
+    from mindspore import Tensor
+    from mindspore.ops import functional as F
+    import mindspore.context as context
+
+    context.set_context(device_target="GPU")
+    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
+    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
+    print(F.tensor_add(x, y))
+    ```
+    ```
+    [[[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]],
+
+    [[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]],
+
+    [[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]]]
+    ```
 
 If anyone wants to learn more about the build process of MindSpore docker images,
 please check out `docker` folder for the details.

From 6a79fc1735f5a5a8170dc15ab487320c54a28b3d Mon Sep 17 00:00:00 2001
From: panfengfeng <panfengfeng@huawei.com>
Date: Mon, 13 Apr 2020 10:38:54 +0800
Subject: [PATCH 178/367] skip mindrecord ut test case

---
 .../cpp/mindrecord/ut_shard_operator_test.cc  | 50 +++++++++----------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
index 46ea1712b2..143931658a 100644
--- a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
+++ b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
@@ -65,31 +65,31 @@ TEST_F(TestShardOperator, TestShardSampleBasic) {
   ASSERT_TRUE(i <= kSampleCount);
 }
 
-TEST_F(TestShardOperator, TestShardSampleWrongNumber) {
-  MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
-
-  std::string file_name = "./imagenet.shard01";
-  auto column_list = std::vector<std::string>{"file_name"};
-
-  const int kNum = 5;
-  const int kDen = 0;
-  std::vector<std::shared_ptr<ShardOperator>> ops;
-  ops.push_back(std::make_shared<ShardSample>(kNum, kDen));
-
-  ShardReader dataset;
-  dataset.Open(file_name, 4, column_list, ops);
-  dataset.Launch();
-
-  int i = 0;
-  while (true) {
-    auto x = dataset.GetNext();
-    if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
-    i++;
-  }
-  dataset.Finish();
-  ASSERT_TRUE(i <= 5);
-}
+// TEST_F(TestShardOperator, TestShardSampleWrongNumber) {
+//   MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
+// 
+//   std::string file_name = "./imagenet.shard01";
+//   auto column_list = std::vector<std::string>{"file_name"};
+// 
+//   const int kNum = 5;
+//   const int kDen = 0;
+//   std::vector<std::shared_ptr<ShardOperator>> ops;
+//   ops.push_back(std::make_shared<ShardSample>(kNum, kDen));
+// 
+//   ShardReader dataset;
+//   dataset.Open(file_name, 4, column_list, ops);
+//   dataset.Launch();
+// 
+//   int i = 0;
+//   while (true) {
+//     auto x = dataset.GetNext();
+//     if (x.empty()) break;
+//     MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+//     i++;
+//   }
+//   dataset.Finish();
+//   ASSERT_TRUE(i <= 5);
+// }
 
 TEST_F(TestShardOperator, TestShardSampleRatio) {
   MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));

From 37ba21c271d25ec95f903b26953e61d1ba9e6ac7 Mon Sep 17 00:00:00 2001
From: dinghao <dinghao7@huawei.com>
Date: Sun, 12 Apr 2020 09:55:03 +0800
Subject: [PATCH 179/367] fix ref pass visit graph bug

---
 .../pre_activate/ascend/ascend_backend_optimization.cc    | 2 ++
 .../ascend/format_type/deal_ref_trans_and_cast.cc         | 8 ++++++++
 .../ascend/format_type/deal_ref_trans_and_cast.h          | 1 +
 mindspore/ccsrc/pre_activate/common/node_pass.cc          | 1 +
 mindspore/ops/_op_impl/tbe/trans_data.py                  | 8 ++++++--
 5 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 432d88e7a4..023838c3a5 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -90,6 +90,7 @@ void RunOpAscendMixPrecision(const std::shared_ptr<session::KernelGraph> &kernel
   mixed_precision_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
   mixed_precision_pm->AddPass(std::make_shared<EliminateRedundantOp>());
   mixed_precision_pm->AddPass(std::make_shared<OptimizeDependence>());
+  mixed_precision_pm->AddPass(std::make_shared<EraseVisitAttr>());
   mixed_precision_pm->AddPass(std::make_shared<DealRefTransAndCast>());
   mixed_precision_pm->AddPass(std::make_shared<GetitemTuple>());
   mixed_precision_pm->AddPass(std::make_shared<MergeCastToOp>());
@@ -126,6 +127,7 @@ void AscendMixPrecision(const std::shared_ptr<session::KernelGraph> &kernel_grap
   mixed_precision_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
   mixed_precision_pm->AddPass(std::make_shared<EliminateRedundantOp>());
   mixed_precision_pm->AddPass(std::make_shared<OptimizeDependence>());
+  mixed_precision_pm->AddPass(std::make_shared<EraseVisitAttr>());
   mixed_precision_pm->AddPass(std::make_shared<DealRefTransAndCast>());
   mixed_precision_pm->AddPass(std::make_shared<GetitemTuple>());
   mixed_precision_pm->AddPass(std::make_shared<MergeCastToOp>());
diff --git a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
index fd20611415..81e5c4b486 100644
--- a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
@@ -22,6 +22,7 @@
 #include "kernel/oplib/oplib.h"
 #include "session/anf_runtime_algorithm.h"
 #include "session/kernel_graph.h"
+#include "pre_activate/common/helper.h"
 
 namespace mindspore {
 namespace opt {
@@ -168,11 +169,18 @@ AnfNodePtr DealRefSigleOutput(const FuncGraphPtr &func_graph, const CNodePtr &cn
 }
 }  // namespace
 
+const BaseRef DealRefTransAndCast::DefinePattern() const {
+  VarPtr V = std::make_shared<CondVar>(UnVisited);
+  VarPtr Xs = std::make_shared<SeqVar>();
+  return VectorRef({V, Xs});
+}
+
 const AnfNodePtr DealRefTransAndCast::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
                                               const EquivPtr &) const {
   if (node == nullptr || !node->isa<CNode>()) {
     return nullptr;
   }
+  AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node);
   auto cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   if (!AnfAlgo::IsRealCNodeKernel(cnode)) {
diff --git a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.h b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.h
index 9ed55d8b29..1b54a7b111 100644
--- a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.h
+++ b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.h
@@ -28,6 +28,7 @@ class DealRefTransAndCast : public PatternProcessPass {
  public:
   explicit DealRefTransAndCast(bool multigraph = true) : PatternProcessPass("deal_ref_trans_and_cast", multigraph) {}
   ~DealRefTransAndCast() override = default;
+  const BaseRef DefinePattern() const override;
   const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
 };
 }  // namespace opt
diff --git a/mindspore/ccsrc/pre_activate/common/node_pass.cc b/mindspore/ccsrc/pre_activate/common/node_pass.cc
index cd213f8263..a6e93d2f07 100644
--- a/mindspore/ccsrc/pre_activate/common/node_pass.cc
+++ b/mindspore/ccsrc/pre_activate/common/node_pass.cc
@@ -45,6 +45,7 @@ bool NodePass::Run(const FuncGraphPtr &func_graph) {
     bool change = (new_node != nullptr);
     if (new_node != nullptr && new_node != node) {
       (void)manager->Replace(node, new_node);
+      (void)seen_node.erase(node);
     } else if (new_node == nullptr) {
       new_node = node;
     }
diff --git a/mindspore/ops/_op_impl/tbe/trans_data.py b/mindspore/ops/_op_impl/tbe/trans_data.py
index 1b7c8fa25d..c6628c7638 100644
--- a/mindspore/ops/_op_impl/tbe/trans_data.py
+++ b/mindspore/ops/_op_impl/tbe/trans_data.py
@@ -46,11 +46,13 @@ from mindspore.ops.op_info_register import op_info_register
             "dtype": [
                 "bool",
                 "float","float","float","float","float","float","float","float","float","float",
-                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16"
+                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16",
+                "uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16"
             ],
             "format": [
                 "DefaultFormat",
                 "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ",
+                "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ",
                 "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ"
             ],
             "name": "src",
@@ -65,11 +67,13 @@ from mindspore.ops.op_info_register import op_info_register
             "dtype": [
                 "bool",
                 "float","float","float","float","float","float","float","float","float","float",
-                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16"
+                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16",
+                "uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16"
             ],
             "format": [
                 "NC1HWC0",
                 "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN",
+                "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN",
                 "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN"
             ],
             "name": "dst",

From fa03a66433201edf4ab26ec482dcba758346f72f Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Mon, 13 Apr 2020 10:43:12 +0800
Subject: [PATCH 180/367] change adam output numbers adapter to tbe

---
 mindspore/ops/_op_impl/tbe/apply_adam.py | 46 ++++++++++++++++++++----
 mindspore/ops/operations/nn_ops.py       |  4 +--
 2 files changed, 41 insertions(+), 9 deletions(-)

diff --git a/mindspore/ops/_op_impl/tbe/apply_adam.py b/mindspore/ops/_op_impl/tbe/apply_adam.py
index ae6b7d782e..1d5c383515 100644
--- a/mindspore/ops/_op_impl/tbe/apply_adam.py
+++ b/mindspore/ops/_op_impl/tbe/apply_adam.py
@@ -88,7 +88,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "beta1_power",
             "need_compile": false,
@@ -101,7 +102,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float","float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "beta2_power",
             "need_compile": false,
@@ -114,7 +116,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "lr",
             "need_compile": false,
@@ -127,7 +130,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "beta1",
             "need_compile": false,
@@ -140,7 +144,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "beta2",
             "need_compile": false,
@@ -153,7 +158,8 @@ from mindspore.ops.op_info_register import op_info_register
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
             "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
+                "DefaultFormat", "DefaultFormat"
             ],
             "name": "epsilon",
             "need_compile": false,
@@ -161,7 +167,7 @@ from mindspore.ops.op_info_register import op_info_register
             "shape": "all"
         },
         {
-            "index": 8,
+            "index": 9,
             "dtype": [
                 "float16","float16","float16","float16","float","float","float", "float"
             ],
@@ -187,6 +193,32 @@ from mindspore.ops.op_info_register import op_info_register
             "need_compile": false,
             "param_type": "required",
             "shape": "all"
+        },
+        {
+            "index": 1,
+            "dtype": [
+                "float16","float16","float16","float16","float","float","float","float"
+            ],
+            "format": [
+                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+            ],
+            "name": "m",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        },
+        {
+            "index": 2,
+            "dtype": [
+                "float16","float16","float16","float16","float","float","float","float"
+            ],
+            "format": [
+                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
+            ],
+            "name": "v",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
         }
     ]
 }""")
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 83f76455e0..538d7f3826 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2149,7 +2149,7 @@ class Adam(PrimitiveWithInfer):
         validator.check_param_equal("var_shape", var_shape, "m_shape", m_shape)
         validator.check_param_equal("var_shape", var_shape, "v_shape", v_shape)
         validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape)
-        return var_shape
+        return var_shape, m_shape, v_shape
 
     def infer_dtype(self, var_dtype, m_dtype, v_dtype, beta1_power_dtype, beta2_power_dtype, lr_dtype,
                     beta1_dtype, beta2_dtype, epsilon_dtype, grad_dtype):
@@ -2159,7 +2159,7 @@ class Adam(PrimitiveWithInfer):
         args = {"beta1_power_dtype": beta1_power_dtype, "beta2_power_dtype": beta2_power_dtype, 'lr_dtype': lr_dtype,
                 "beta1_dtype": beta1_dtype, "beta2_dtype": beta2_dtype, "epsilon_dtype": epsilon_dtype}
         validator.check_type_same(args, [mstype.float16, mstype.float32])
-        return var_dtype
+        return var_dtype, m_dtype, v_dtype
 
 
 class BinaryCrossEntropy(PrimitiveWithInfer):

From 08dd50d6712b286f1e227a713539e509546250eb Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Mon, 13 Apr 2020 11:28:28 +0800
Subject: [PATCH 181/367] Add mindspore devel docker image for CPU and GPU
 backend

Signed-off-by: leonwanghui <leon.wanghui@huawei.com>
---
 README.md                             | 12 ++--
 docker/mindspore-cpu/devel/Dockerfile | 80 ++++++++++++++++++++++
 docker/mindspore-gpu/devel/Dockerfile | 95 +++++++++++++++++++++++++++
 3 files changed, 182 insertions(+), 5 deletions(-)
 create mode 100644 docker/mindspore-cpu/devel/Dockerfile
 create mode 100644 docker/mindspore-gpu/devel/Dockerfile

diff --git a/README.md b/README.md
index 8b6b363de3..169f1985e5 100644
--- a/README.md
+++ b/README.md
@@ -93,11 +93,13 @@ For installation using `pip`, take `Ubuntu-x86` and `CPU` build version as an ex
 MindSpore docker image is hosted on [Docker Hub](https://hub.docker.com/r/mindspore),
 currently the containerized build options are supported as follows:
 
-| Hardware Platform | Docker Image URL |
-| :---------------- | :--------------- |
-| CPU | `mindspore/mindspore-cpu:0.1.0-alpha` |
-| GPU | `mindspore/mindspore-gpu:0.1.0-alpha` |
-| Ascend | <center>—</center> |
+| Hardware Platform | Docker Image Repository | Tag | Description |
+| :---------------- | :---------------------- | :-- | :---------- |
+| CPU | `mindspore/mindspore-cpu` | `0.1.0-alpha` | Production environment with pre-installed MindSpore `0.1.0-alpha` CPU release. |
+|  |  | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to https://www.mindspore.cn/install/en for installation details. |
+| GPU | `mindspore/mindspore-gpu` | `0.1.0-alpha` | Production environment with pre-installed MindSpore `0.1.0-alpha` GPU release. |
+|  |  | `devel` | Development environment provided to build MindSpore (with `GPU CUDA10.1` backend) from the source, refer to https://www.mindspore.cn/install/en for installation details. |
+| Ascend | <center>—</center> | <center>—</center> | Coming soon. |
 
 * CPU
 
diff --git a/docker/mindspore-cpu/devel/Dockerfile b/docker/mindspore-cpu/devel/Dockerfile
new file mode 100644
index 0000000000..a48326af9b
--- /dev/null
+++ b/docker/mindspore-cpu/devel/Dockerfile
@@ -0,0 +1,80 @@
+FROM ubuntu:18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
+ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install pip package
+RUN pip install --no-cache-dir \
+    numpy \
+    wheel \
+    nose \
+    pytest \
+    pytest-xdist
+
+# Install cmake (v3.14.1)
+RUN cd /tmp \
+    && wget https://github.com/Kitware/CMake/releases/download/v3.14.1/cmake-3.14.1-Linux-x86_64.sh \
+    && mkdir -p ${CMAKE_ROOT_PATH} \
+    && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
+    && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
diff --git a/docker/mindspore-gpu/devel/Dockerfile b/docker/mindspore-gpu/devel/Dockerfile
new file mode 100644
index 0000000000..0b07605d9f
--- /dev/null
+++ b/docker/mindspore-gpu/devel/Dockerfile
@@ -0,0 +1,95 @@
+FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
+ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
+ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex \
+    libnccl2=2.4.8-1+cuda10.1 \
+    libnccl-dev=2.4.8-1+cuda10.1
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install pip package
+RUN pip install --no-cache-dir \
+    numpy \
+    wheel \
+    nose \
+    pytest \
+    pytest-xdist
+
+# Install cmake (v3.14.1)
+RUN cd /tmp \
+    && wget https://github.com/Kitware/CMake/releases/download/v3.14.1/cmake-3.14.1-Linux-x86_64.sh \
+    && mkdir -p ${CMAKE_ROOT_PATH} \
+    && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
+    && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
+
+# Install openmpi (v3.1.5)
+RUN cd /tmp \
+    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
+    && tar -xvf openmpi-3.1.5.tar.gz \
+    && cd /tmp/openmpi-3.1.5 \
+    && mkdir -p ${OMPI_ROOT_PATH} \
+    && ./configure --prefix=${OMPI_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -rf /tmp/openmpi-3.1.5 \
+    && rm -f /tmp/openmpi-3.1.5.tar.gz

From d64f662c76d0ddc613506fece1e354cbd4bfa2ac Mon Sep 17 00:00:00 2001
From: chenzomi <chenzomi12@gmail.com>
Date: Fri, 10 Apr 2020 09:37:57 +0800
Subject: [PATCH 182/367] quantization aware training frontend operators
 define.

---
 mindspore/nn/layer/__init__.py                |   7 +-
 mindspore/nn/layer/_quant.py                  | 703 ++++++++++++++++++
 mindspore/nn/layer/activation.py              |  63 +-
 mindspore/ops/_grad/grad_nn_ops.py            |  22 +
 mindspore/ops/_grad/grad_quant_ops.py         |  82 ++
 mindspore/ops/operations/__init__.py          |   8 +-
 mindspore/ops/operations/_grad_ops.py         |  32 +
 mindspore/ops/operations/_quant_ops.py        | 525 +++++++++++++
 mindspore/ops/operations/nn_ops.py            |  71 +-
 tests/ut/python/nn/test_dense.py              |   5 -
 .../pynative_mode/nn/test_activation.py       |   5 -
 .../ut/python/pynative_mode/nn/test_dense.py  |   5 -
 12 files changed, 1505 insertions(+), 23 deletions(-)
 create mode 100644 mindspore/nn/layer/_quant.py
 create mode 100644 mindspore/ops/_grad/grad_quant_ops.py
 create mode 100644 mindspore/ops/operations/_quant_ops.py

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index dae18fe663..aed6cb7776 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -17,7 +17,7 @@ Layer.
 
 The high-level components(Cells) used to construct the neural network.
 """
-from .activation import Softmax, LogSoftmax, ReLU, ReLU6, Tanh, GELU, ELU, Sigmoid, PReLU, get_activation, LeakyReLU
+from .activation import Softmax, LogSoftmax, ReLU, ReLU6, Tanh, GELU, ELU, Sigmoid, PReLU, get_activation, LeakyReLU, HSigmoid, HSwish
 from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
@@ -26,8 +26,9 @@ from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradi
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
 
-__all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid', 'PReLU', 'get_activation', 'LeakyReLU',
-           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'ELU',
+__all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
+           'PReLU', 'get_activation', 'LeakyReLU', 'HSigmoid', 'HSwish', 'ELU',
+           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
diff --git a/mindspore/nn/layer/_quant.py b/mindspore/nn/layer/_quant.py
new file mode 100644
index 0000000000..f27af8b269
--- /dev/null
+++ b/mindspore/nn/layer/_quant.py
@@ -0,0 +1,703 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Aware quantization."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
+from mindspore.common.tensor import Tensor
+from mindspore._checkparam import check_int_positive, check_bool, twice
+from mindspore.nn.cell import Cell
+from mindspore.nn.layer.conv import _Conv
+from mindspore.nn.layer.activation import get_activation
+
+__all__ = [
+    'FakeQuantWithMinMax',
+    'Conv2dBatchNormQuant',
+    'Conv2dQuant',
+    'DenseQuant',
+    'ReLUQuant',
+    'ReLU6Quant',
+    'HSwishQuant',
+    'HSigmoidQuant',
+    'TensorAddQuant',
+]
+
+
+class FakeQuantWithMinMax(Cell):
+    r"""
+    Aware Quantization training op. This OP provide Fake quantization observer function on data with min and max.
+
+    Args:
+        min_init (int, list): The dimension of channel or 1(layer). Default: -6.
+        max_init (int, list): The dimension of channel or 1(layer). Default: 6.
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        ema (bool): Exponential Moving Average algorithm update min and max. Default: False.
+        ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.9999.
+        per_channel (bool): Quantization by layer or channel. Default: False.
+        channel_size (int): declarate the min and max channel size, Default: 1.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of FakeQuantWithMinMax.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self,
+                 min_init=-6,
+                 max_init=6,
+                 num_bits=8,
+                 ema=False,
+                 ema_decay=0.999,
+                 per_channel=False,
+                 channel_size=1,
+                 quant_delay=0,
+                 symmetric=False,
+                 narrow_range=False):
+        super(FakeQuantWithMinMax, self).__init__()
+
+        self.min_init = min_init
+        self.num_bits = num_bits
+        self.max_init = max_init
+        self.ema = ema
+        self.ema_decay = ema_decay
+        self.per_channel = per_channel
+        self.channel_size = channel_size
+        self.quant_delay = quant_delay
+        self.symmetric = symmetric
+        self.narrow_range = narrow_range
+
+        if per_channel:
+            min_array = np.array([self.min_init for i in range(
+                0, self.channel_size)]).astype(np.float32)
+            max_array = np.array([self.max_init for i in range(
+                0, self.channel_size)]).astype(np.float32)
+            self.fake_quant_train = P.FakeQuantWithMinMaxPerChannel(num_bits=self.num_bits,
+                                                                    ema=self.ema,
+                                                                    ema_decay=self.ema_decay,
+                                                                    quant_delay=self.quant_delay,
+                                                                    symmetric=self.symmetric,
+                                                                    narrow_range=self.narrow_range,
+                                                                    training=True)
+            self.fake_quant_infer = P.FakeQuantWithMinMaxPerChannel(num_bits=self.num_bits,
+                                                                    ema=self.ema,
+                                                                    ema_decay=ema_decay,
+                                                                    quant_delay=quant_delay,
+                                                                    symmetric=self.symmetric,
+                                                                    narrow_range=self.narrow_range,
+                                                                    training=False)
+        else:
+            min_array = np.array([min_init]).reshape(1).astype(np.float32)
+            max_array = np.array([max_init]).reshape(1).astype(np.float32)
+            self.fake_quant_train = P.FakeQuantWithMinMax(num_bits=self.num_bits,
+                                                          ema=self.ema,
+                                                          ema_decay=self.ema_decay,
+                                                          quant_delay=self.quant_delay,
+                                                          symmetric=self.symmetric,
+                                                          narrow_range=self.narrow_range,
+                                                          training=True)
+            self.fake_quant_infer = P.FakeQuantWithMinMax(num_bits=self.num_bits,
+                                                          ema=self.ema,
+                                                          ema_decay=ema_decay,
+                                                          quant_delay=quant_delay,
+                                                          symmetric=self.symmetric,
+                                                          narrow_range=self.narrow_range,
+                                                          training=False)
+
+        self.min = Parameter(
+            Tensor(min_array), name='quant_min', requires_grad=False)
+        self.max = Parameter(
+            Tensor(max_array), name='quant_max', requires_grad=False)
+
+    def extend_repr(self):
+        s = 'min_init={}, max_init={}, ema={}, ema_decay={},  per_channel={}, channel_size={}, quant_delay={}'.format(
+            self.min_init, self.max_init, self.ema, self.ema_decay, self.per_channel, self.channel_size,
+            self.quant_delay)
+        return s
+
+    def construct(self, x):
+        if self.training:
+            out = self.fake_quant_train(x, self.min, self.max)
+        else:
+            out = self.fake_quant_infer(x, self.min, self.max)
+        return out
+
+
+class Conv2dBatchNormQuant(Cell):
+    r"""
+    2D convolution with BatchNormal op folded layer.
+
+    For a more Detailed overview of Conv2d op.
+
+    Args:
+        in_channels (int): The number of input channel :math:`C_{in}`.
+        out_channels (int): The number of output channel :math:`C_{out}`.
+        kernel_size (Union[int, tuple]): Specifies the height and width of the 2D convolution window.
+        stride (int): Specifies stride for all spatial dimensions with the same value.
+        pad_mode: (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
+        padding: (int): Implicit paddings on both sides of the input. Default: 0.
+        eps (int): Parameters for BatchNormal. Default: 1e-5.
+        momentum (int): Parameters for BatchNormal op. Default: 0.9.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            convolution kernel. Default: 'None'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            beta vector. Default: 'None'.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            gamma vector. Default: 'None'.
+        mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            mean vector. Default: 'None'.
+        var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            variance vector. Default: 'None'.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        freeze_bn (int): Quantization freeze BatchNormal op according by global step. Default: 100000.
+        fake (bool): Conv2dBatchNormQuant Cell add FakeQuantWithMinMax op or not. Default: True.
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 pad_mode,
+                 padding=0,
+                 eps=1e-5,
+                 momentum=0.9,
+                 weight_init=None,
+                 beta_init=None,
+                 gamma_init=None,
+                 mean_init=None,
+                 var_init=None,
+                 group=1,
+                 quant_delay=0,
+                 freeze_bn=100000,
+                 fake=True,
+                 num_bits=8,
+                 per_channel=False,
+                 symmetric=False,
+                 narrow_range=False):
+        super(Conv2dBatchNormQuant, self).__init__()
+        self.stride = stride
+        self.conv = P.Conv2D(out_channel=out_channels,
+                             kernel_size=kernel_size,
+                             mode=1,
+                             pad_mode=pad_mode,
+                             pad=padding,
+                             stride=stride,
+                             dilation=1,
+                             group=group)
+        self.fake = fake
+        self.freeze_bn = freeze_bn
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size, kernel_size)
+
+        if weight_init is None:
+            weight_init = initializer(
+                'normal', [out_channels, in_channels // group, *kernel_size])
+        self.weight = Parameter(weight_init, name='weight')
+        if gamma_init is None:
+            gamma_init = initializer('ones', [out_channels])
+        self.gamma = Parameter(gamma_init, name='gamma')
+        if beta_init is None:
+            beta_init = initializer('zeros', [out_channels])
+        self.beta = Parameter(beta_init, name='beta')
+        if mean_init is None:
+            mean_init = initializer('zeros', [out_channels])
+        self.moving_mean = Parameter(
+            mean_init, name='moving_mean', requires_grad=False)
+        if var_init is None:
+            var_init = initializer('ones', [out_channels])
+        self.moving_variance = Parameter(
+            var_init, name='moving_variance', requires_grad=False)
+
+        self.step = Parameter(initializer(
+            'normal', [1], dtype=mstype.int32), name='step', requires_grad=False)
+
+        self.fake_quant_weight = nn.FakeQuantWithMinMax(min_init=-6,
+                                                        max_init=6,
+                                                        ema=False,
+                                                        num_bits=num_bits,
+                                                        quant_delay=quant_delay,
+                                                        per_channel=per_channel,
+                                                        channel_size=out_channels,
+                                                        symmetric=symmetric,
+                                                        narrow_range=narrow_range)
+
+        self.batchnorm_fold_train = P.BatchNormFold(epsilon=eps,
+                                                    momentum=momentum,
+                                                    is_training=True,
+                                                    freeze_bn=freeze_bn)
+        self.batchnorm_fold_infer = P.BatchNormFold(epsilon=eps,
+                                                    momentum=momentum,
+                                                    is_training=False,
+                                                    freeze_bn=freeze_bn)
+        self.correct_mul = P.CorrectionMul()
+        self.relu = P.ReLU()
+        self.batchnorm_fold2 = P.BatchNormFold2(freeze_bn=freeze_bn)
+        self.batchnorm_fold2_infer = P.BatchNormFold2(freeze_bn=0)
+        self.one = Tensor(1, mstype.int32)
+        self.assignadd = P.AssignAdd()
+
+    def extend_repr(self):
+        s = 'fake={}, freeze_bn={}'.format(self.fake, self.freeze_bn)
+        return s
+
+    def construct(self, x):
+        if self.training:
+            beta = self.beta
+            gamma = self.gamma
+            gmean = self.moving_mean
+            gvar = self.moving_variance
+            step = self.step
+            out_conv = self.conv(x, self.weight)
+            batch_mean, batch_std, running_mean, running_std = self.batchnorm_fold_train(
+                out_conv, gmean, gvar, step)
+            # BN fold1
+            weight = self.correct_mul(self.weight, gamma, running_std)
+            if self.fake:
+                weight = self.fake_quant_weight(weight)
+            out = self.conv(x, weight)
+            # BN fold2
+            out = self.batchnorm_fold2(
+                out, beta, gamma, batch_std, batch_mean, running_std, running_mean, step)
+            F.control_depend(out, self.assignadd(self.step, self.one))
+        else:
+            step = self.step
+            out_conv = self.conv(x, self.weight)
+            batch_mean, batch_std, running_mean, running_std = self.batchnorm_fold_infer(
+                out_conv, self.moving_mean, self.moving_variance, step)
+            weight = self.correct_mul(self.weight, self.gamma, running_std)
+            if self.fake:
+                weight = self.fake_quant_weight(weight)
+            out = self.conv(x, weight)
+            out = self.batchnorm_fold2_infer(out, self.beta, self.gamma, batch_std, batch_mean,
+                                             running_std, running_mean, step)
+        return out
+
+
+class Conv2dQuant(_Conv):
+    r"""
+    2D convolution with fake quant op layer.
+
+    For a more Detailed overview of Conv2d op.
+
+    Args:
+        in_channels (int): The number of input channel :math:`C_{in}`.
+        out_channels (int): The number of output channel :math:`C_{out}`.
+        kernel_size (Union[int, tuple]): Specifies the height and width of the 2D convolution window.
+        stride (int): Specifies stride for all spatial dimensions with the same value. Default: 1.
+        pad_mode: (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
+        padding: (int): Implicit paddings on both sides of the input. Default: 0.
+        dilation (int): Specifying the dilation rate to use for dilated convolution. Default: 1.
+        group (int): Split filter into groups, `in_ channels` and `out_channels` should be
+            divisible by the number of groups. Default: 1.
+        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
+            Default: 'normal'.
+        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 pad_mode='same',
+                 padding=0,
+                 dilation=1,
+                 group=1,
+                 has_bias=False,
+                 weight_init='normal',
+                 bias_init='zeros',
+                 quant_delay=0,
+                 num_bits=8,
+                 per_channel=False,
+                 symmetric=False,
+                 narrow_range=False):
+        kernel_size = twice(kernel_size)
+        super(Conv2dQuant, self).__init__(in_channels, out_channels, kernel_size, stride, pad_mode, padding, dilation,
+                                          group, has_bias, weight_init, bias_init)
+        self.conv2d = P.Conv2D(out_channel=self.out_channels, kernel_size=self.kernel_size, mode=1,
+                               pad_mode=self.pad_mode, pad=self.padding, stride=self.stride, dilation=self.dilation,
+                               group=self.group)
+        self.bias_add = P.BiasAdd()
+        if pad_mode not in ('valid', 'same', 'pad'):
+            raise ValueError('Attr \'pad_mode\' of \'Conv2d\' Op passed '
+                             + str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.')
+        self.fake_quant_weight = nn.FakeQuantWithMinMax(min_init=-6,
+                                                        max_init=6,
+                                                        ema=False,
+                                                        num_bits=num_bits,
+                                                        quant_delay=quant_delay,
+                                                        per_channel=per_channel,
+                                                        channel_size=out_channels,
+                                                        symmetric=symmetric,
+                                                        narrow_range=narrow_range)
+
+    def construct(self, x):
+        weight_q = self.fake_quant_weight(self.weight)
+        out = self.conv2d(x, weight_q)
+        if self.has_bias:
+            return self.bias_add(out, self.bias)
+        return out
+
+
+class DenseQuant(Cell):
+    r"""
+    The fully connected layer with fake quant op.
+
+    For a more Detailed overview of Dense op.
+
+    Args:
+        in_channels (int): The dimension of the input space.
+        out_channels (int): The dimension of the output space.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
+            is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
+        bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
+            same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
+        has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
+        activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+    """
+
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            weight_init='normal',
+            bias_init='zeros',
+            has_bias=True,
+            activation=None,
+            num_bits=8,
+            quant_delay=0,
+            per_channel=False,
+            symmetric=False,
+            narrow_range=False):
+        super(DenseQuant, self).__init__()
+        self.in_channels = check_int_positive(in_channels)
+        self.out_channels = check_int_positive(out_channels)
+        self.has_bias = check_bool(has_bias)
+
+        if isinstance(weight_init, Tensor):
+            if weight_init.dim() != 2 or weight_init.shape()[0] != out_channels or \
+                    weight_init.shape()[1] != in_channels:
+                raise ValueError("weight_init shape error")
+
+        self.weight = Parameter(initializer(
+            weight_init, [out_channels, in_channels]), name="weight")
+
+        if self.has_bias:
+            if isinstance(bias_init, Tensor):
+                if bias_init.dim() != 1 or bias_init.shape()[0] != out_channels:
+                    raise ValueError("bias_init shape error")
+
+            self.bias = Parameter(initializer(
+                bias_init, [out_channels]), name="bias")
+
+        self.matmul = P.MatMul(transpose_b=True)
+        self.bias_add = P.BiasAdd()
+
+        self.activation = get_activation(activation)
+        self.activation_flag = self.activation is not None
+        self.fake_quant_weight = nn.FakeQuantWithMinMax(min_init=-6,
+                                                        max_init=6,
+                                                        ema=False,
+                                                        num_bits=num_bits,
+                                                        quant_delay=quant_delay,
+                                                        per_channel=per_channel,
+                                                        channel_size=out_channels,
+                                                        symmetric=symmetric,
+                                                        narrow_range=narrow_range)
+
+    def construct(self, x):
+        """Use operators to construct to Dense layer."""
+        output = self.fake_quant_weight(self.weight)
+        output = self.matmul(x, output)
+        if self.has_bias:
+            output = self.bias_add(output, self.bias)
+        if self.activation_flag:
+            return self.activation(output)
+        return output
+
+    def extend_repr(self):
+        """A pretty print for Dense layer."""
+        str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}'.format(
+            self.in_channels, self.out_channels, self.weight, self.has_bias)
+        if self.has_bias:
+            str_info = str_info + ', bias={}'.format(self.bias)
+        if self.activation_flag:
+            str_info = str_info + ', activation={}'.format(self.activation)
+
+        return str_info
+
+
+class ReLUQuant(Cell):
+    r"""
+    ReLUQuant activation function. Add Fake Quant OP after Relu OP.
+
+    For a more Detailed overview of ReLU op.
+
+    Args:
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of ReLUQuant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self,
+                 num_bits=8,
+                 quant_delay=0,
+                 symmetric=False,
+                 narrow_range=False):
+        super(ReLUQuant, self).__init__()
+        self.fake_quant_act = nn.FakeQuantWithMinMax(min_init=0,
+                                                     max_init=6,
+                                                     num_bits=num_bits,
+                                                     quant_delay=quant_delay,
+                                                     ema=True,
+                                                     symmetric=symmetric,
+                                                     narrow_range=narrow_range)
+        self.relu = P.ReLU()
+
+    def construct(self, x):
+        x = self.relu(x)
+        x = self.fake_quant_act(x)
+        return x
+
+
+class ReLU6Quant(Cell):
+    r"""
+    ReLU6Quant activation function.
+
+    Add Fake Quant OP after Relu6. Not Recommand to used these cell for Fake Quant Op
+    Will climp the max range of the activation and the relu6 do the same operation.
+    For a more Detailed overview of ReLU6 op.
+
+    Args:
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of ReLU6Quant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self, num_bits=8, quant_delay=0, symmetric=False,
+                 narrow_range=False):
+        super(ReLU6Quant, self).__init__()
+        self.fake_quant_act = nn.FakeQuantWithMinMax(min_init=0,
+                                                     max_init=6,
+                                                     num_bits=num_bits,
+                                                     quant_delay=quant_delay,
+                                                     ema=True,
+                                                     symmetric=symmetric,
+                                                     narrow_range=narrow_range)
+        self.relu6 = P.ReLU6()
+
+    def construct(self, x):
+        x = self.relu6(x)
+        x = self.fake_quant_act(x)
+        return x
+
+
+class HSwishQuant(Cell):
+    r"""
+    HSwishQuant activation function. Add Fake Quant OP after HSwish OP.
+
+    For a more Detailed overview of HSwish op.
+
+    Args:
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of HSwishQuant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self,
+                 num_bits=8,
+                 quant_delay=0,
+                 symmetric=False,
+                 narrow_range=False):
+        super(HSwishQuant, self).__init__()
+        self.fake_quant_act_before = nn.FakeQuantWithMinMax(min_init=0,
+                                                            max_init=6,
+                                                            num_bits=num_bits,
+                                                            quant_delay=quant_delay,
+                                                            ema=True,
+                                                            symmetric=symmetric,
+                                                            narrow_range=narrow_range)
+        self.fake_quant_act_after = nn.FakeQuantWithMinMax(min_init=0,
+                                                           max_init=6,
+                                                           num_bits=num_bits,
+                                                           quant_delay=quant_delay,
+                                                           ema=True,
+                                                           symmetric=symmetric,
+                                                           narrow_range=narrow_range)
+        self.act = P.HSwish()
+
+    def construct(self, x):
+        x = self.fake_quant_act_before(x)
+        x = self.act(x)
+        x = self.fake_quant_act_after(x)
+        return x
+
+
+class HSigmoidQuant(Cell):
+    r"""
+    HSigmoidQuant activation function. Add Fake Quant OP before and after HSigmoid OP.
+
+    For a more Detailed overview of HSigmoid op.
+
+    Args:
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of HSigmoidQuant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self,
+                 num_bits=8,
+                 quant_delay=0,
+                 symmetric=False,
+                 narrow_range=False):
+        super(HSigmoidQuant, self).__init__()
+        self.fake_quant_act_before = nn.FakeQuantWithMinMax(min_init=0,
+                                                            max_init=6,
+                                                            num_bits=num_bits,
+                                                            quant_delay=quant_delay,
+                                                            ema=True,
+                                                            symmetric=symmetric,
+                                                            narrow_range=narrow_range)
+        self.fake_quant_act_after = nn.FakeQuantWithMinMax(min_init=0,
+                                                           max_init=6,
+                                                           num_bits=num_bits,
+                                                           quant_delay=quant_delay,
+                                                           ema=True,
+                                                           symmetric=symmetric,
+                                                           narrow_range=narrow_range)
+        self.act = P.HSigmoid()
+
+    def construct(self, x):
+        x = self.fake_quant_act_before(x)
+        x = self.act(x)
+        x = self.fake_quant_act_after(x)
+        return x
+
+
+class TensorAddQuant(Cell):
+    r"""
+    Add Fake Quant OP after TensorAdd OP.
+
+    For a more Detailed overview of TensorAdd op.
+
+    Args:
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input of TensorAddQuant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    """
+
+    def __init__(self,
+                 num_bits=8,
+                 quant_delay=0,
+                 symmetric=False,
+                 narrow_range=False):
+        super(TensorAddQuant, self).__init__()
+        self.fake_quant_act = nn.FakeQuantWithMinMax(min_init=-6,
+                                                     max_init=6,
+                                                     num_bits=num_bits,
+                                                     quant_delay=quant_delay,
+                                                     ema=True,
+                                                     symmetric=symmetric,
+                                                     narrow_range=narrow_range)
+        self.add = P.TensorAdd()
+
+    def construct(self, x1, x2):
+        x = self.add(x1, x2)
+        x = self.fake_quant_act(x)
+        return x
diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py
index ad63dde8bc..12d6c74dcd 100644
--- a/mindspore/nn/layer/activation.py
+++ b/mindspore/nn/layer/activation.py
@@ -234,7 +234,7 @@ class Tanh(Cell):
 
 
 class GELU(Cell):
-    """
+    r"""
     Gaussian error linear unit activation function.
 
     Applies GELU function to each element of the input. The input is a Tensor with any valid shape.
@@ -332,15 +332,74 @@ class PReLU(Cell):
         return v
 
 
+class HSwish(Cell):
+    r"""
+    rHard swish activation function.
+
+    Applies hswish-type activation element-wise. The input is a Tensor with any valid shape.
+
+    Hard swish is defined as:
+
+    .. math::
+        \text{hswish}(x_{i}) = x_{i} * \frac{ReLU6(x_{i} + 3)}{6},
+
+    where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
+
+    Inputs:
+        - **input_data** (Tensor) - The input of Hswish.
+
+    Outputs:
+        Tensor, with the same type and shape as the `input_data`.
+
+    """
+    def __init__(self):
+        super(HSwish, self).__init__()
+        self.hswish = P.HSwish()
+
+    def construct(self, x):
+        return self.hswish(x)
+
+
+class HSigmoid(Cell):
+    r"""
+    Hard sigmoid activation function.
+
+    Applies hard sigmoid activation element-wise. The input is a Tensor with any valid shape.
+
+    Hard sigmoid is defined as:
+
+    .. math::
+        \text{hsigmoid}(x_{i}) = max(0, min(1, \ftac{2 * x_{i} + 5}{10})),
+
+    where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
+
+    Inputs:
+        - **input_data** (Tensor) - The input of HSigmoid.
+
+    Outputs:
+        Tensor, with the same type and shape as the `input_data`.
+
+    """
+    def __init__(self):
+        super(HSigmoid, self).__init__()
+        self.hsigmoid = P.HSigmoid()
+
+    def construct(self, x):
+        return self.hsigmoid(x)
+
+
 _activation = {
     'softmax': Softmax,
     'logsoftmax': LogSoftmax,
     'relu': ReLU,
+    'relu6': ReLU6,
     'tanh': Tanh,
     'gelu': GELU,
     'sigmoid': Sigmoid,
     'prelu': PReLU,
-    'leakyrelu': LeakyReLU
+    'leakyrelu': LeakyReLU,
+    'hswish': HSwish,
+    'hsigmoid': HSigmoid,
 }
 
 
diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py
index fbe48aff97..1b18d9f248 100755
--- a/mindspore/ops/_grad/grad_nn_ops.py
+++ b/mindspore/ops/_grad/grad_nn_ops.py
@@ -172,6 +172,28 @@ def get_bprop_relu6(self):
     return bprop
 
 
+@bprop_getters.register(P.HSwish)
+def get_bprop_hswish(self):
+    """Grad definition for `HSwish` operation."""
+    input_grad = G.HSwishGrad()
+
+    def bprop(x, out, dout):
+        dx = input_grad(dout, x)
+        return (dx,)
+    return bprop
+
+
+@bprop_getters.register(P.HSigmoid)
+def get_bprop_hsigmoid(self):
+    """Grad definition for `HSigmoid` operation."""
+    input_grad = G.HSigmoidGrad()
+
+    def bprop(x, out, dout):
+        dx = input_grad(dout, x)
+        return (dx,)
+    return bprop
+
+
 @bprop_getters.register(P.Elu)
 def get_bprop_elu(self):
     """Grad definition for `Elu` operation."""
diff --git a/mindspore/ops/_grad/grad_quant_ops.py b/mindspore/ops/_grad/grad_quant_ops.py
new file mode 100644
index 0000000000..5d4ad22392
--- /dev/null
+++ b/mindspore/ops/_grad/grad_quant_ops.py
@@ -0,0 +1,82 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Generate bprop for aware quantization ops"""
+
+from .. import operations as P
+from .grad_base import bprop_getters
+from ..composite.multitype_ops.zeros_like_impl import zeros_like
+
+
+@bprop_getters.register(P.FakeQuantWithMinMax)
+def get_bprop_fakequant_with_minmax(self):
+    """Generate bprop for FakeQuantWithMinMax"""
+    op = P.FakeQuantWithMinMaxGrad(num_bits=self.num_bits, quant_delay=self.quant_delay)
+
+    def bprop(x, x_min, x_max, out, dout):
+        dx = op(dout, x, x_min, x_max)
+        return dx, zeros_like(x_min), zeros_like(x_max)
+
+    return bprop
+
+
+@bprop_getters.register(P.FakeQuantWithMinMaxPerChannel)
+def get_bprop_fakequant_with_minmax_perchannel(self):
+    """Generate bprop for FakeQuantWithMinMaxPerChannel"""
+    op = P.FakeQuantWithMinMaxPerChannelGrad(num_bits=self.num_bits, quant_delay=self.quant_delay)
+
+    def bprop(x, x_min, x_max, out, dout):
+        dx = op(dout, x, x_min, x_max)
+        return dx, zeros_like(x_min), zeros_like(x_max)
+
+    return bprop
+
+
+@bprop_getters.register(P.BatchNormFold)
+def get_bprop_batchnorm_fold(self):
+    """Generate bprop for BatchNormFold"""
+    op = P.BatchNormFoldGrad(self.epsilon, self.is_training, self.freeze_bn)
+
+    def bprop(x, mean, variance, global_step, out, dout):
+        dx = op(dout[0], dout[1], x, out[0], out[1], global_step)
+        return dx, zeros_like(mean), zeros_like(variance), zeros_like(global_step)
+
+    return bprop
+
+
+@bprop_getters.register(P.CorrectionMul)
+def get_bprop_correction_mul(self):
+    """Generate bprop for CorrectionMul"""
+    grad = P.CorrectionMulGrad()
+
+    def bprop(x, batch_std, running_std, out, dout):
+        dx, d_batch_std = grad(dout, x, batch_std, running_std)
+        return dx, d_batch_std, zeros_like(running_std)
+
+    return bprop
+
+
+@bprop_getters.register(P.BatchNormFold2)
+def get_bprop_batchnorm_fold2(self):
+    """Generate bprop for CorrectionAdd"""
+    op_f = P.BatchNormFold2Grad(freeze_bn=self.freeze_bn)
+
+    def bprop(x, beta, gamma, batch_std, batch_mean, running_std, running_mean, global_step, out, dout):
+        d_batch_std, d_batch_mean, d_beta, d_gamma, d_x = op_f(dout, x, gamma, batch_std, batch_mean, running_std,
+                                                               running_mean, global_step)
+        return d_x, d_beta, d_gamma, d_batch_std, d_batch_mean, zeros_like(running_std), zeros_like(running_mean), \
+               zeros_like(global_step)
+
+    return bprop
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 45cd856298..8bfca77b38 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -59,7 +59,7 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      LogSoftmax,
                      MaxPool,
                      AvgPool, Conv2DBackpropInput,
-                     MaxPoolWithArgmax, OneHot, Pad, PReLU, ReLU, ReLU6,
+                     MaxPoolWithArgmax, OneHot, Pad, PReLU, ReLU, ReLU6, HSwish, HSigmoid,
                      ResizeBilinear, Sigmoid,
                      SigmoidCrossEntropyWithLogits,
                      SmoothL1Loss, Softmax,
@@ -68,7 +68,8 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl,
                      ApplyRMSProp, ApplyCenteredRMSProp)
 from .other_ops import Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, CheckValid, MakeRefKey
-
+from . import _quant_ops
+from ._quant_ops import *
 
 __all__ = [
     'TensorAdd',
@@ -138,6 +139,8 @@ __all__ = [
     'ReLU6',
     'Elu',
     'Sigmoid',
+    'HSwish',
+    'HSigmoid',
     'Tanh',
     'RandomChoiceWithMask',
     'ResizeBilinear',
@@ -241,4 +244,5 @@ __all__ = [
     "ApplyCenteredRMSProp"
 ]
 
+__all__.extend(_quant_ops.__all__)
 __all__.sort()
diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index f38044ab6a..f0a9a2f658 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -805,6 +805,38 @@ class SigmoidGrad(PrimitiveWithInfer):
         return out
 
 
+class HSigmoidGrad(PrimitiveWithInfer):
+    """Gets the gradient of HSigmoid operation."""
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['y_grad', 'x'], outputs=['output'])
+
+    def infer_shape(self, y_grad_shape, x_shape):
+        return x_shape
+
+    def infer_dtype(self, y_grad_dtype, x_dtype):
+        validator.check_typename("y_grad dtype", y_grad_dtype, (mstype.float16, mstype.float32))
+        validator.check_typename("x dtype", x_dtype, (mstype.float16, mstype.float32))
+        return x_dtype
+
+
+class HSwishGrad(PrimitiveWithInfer):
+    """Gets the gradient of HSwish operation."""
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['y_grad', 'x'], outputs=['output'])
+
+    def infer_shape(self, y_grad_shape, x_shape):
+        return x_shape
+
+    def infer_dtype(self, y_grad_dtype, x_dtype):
+        validator.check_typename("y_grad dtype", y_grad_dtype, (mstype.float16, mstype.float32))
+        validator.check_typename("x_ dtype", x_dtype, (mstype.float16, mstype.float32))
+        return x_dtype
+
+
 class SigmoidCrossEntropyWithLogitsGrad(PrimitiveWithInfer):
     """Computes the gradients of `SigmoidCrossEntropyWithLogits`."""
 
diff --git a/mindspore/ops/operations/_quant_ops.py b/mindspore/ops/operations/_quant_ops.py
new file mode 100644
index 0000000000..14d1bc9234
--- /dev/null
+++ b/mindspore/ops/operations/_quant_ops.py
@@ -0,0 +1,525 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0(the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http:  // www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Operators for quantization."""
+
+from ..._checkparam import ParamValidator as validator
+from ..._checkparam import Rel, check_bool, check_int_positive, check_int
+from ..primitive import PrimitiveWithInfer, prim_attr_register
+from ...common import dtype as mstype
+
+__all__ = ["FakeQuantWithMinMax",
+           "FakeQuantWithMinMaxGrad",
+           "FakeQuantWithMinMaxPerChannel",
+           "FakeQuantWithMinMaxPerChannelGrad",
+           "BatchNormFold",
+           "BatchNormFoldGrad",
+           "CorrectionMul",
+           "CorrectionMulGrad",
+           "BatchNormFold2",
+           "BatchNormFold2Grad",
+           ]
+
+
+class FakeQuantWithMinMax(PrimitiveWithInfer):
+    r"""
+    Simulate the quantize and dequantize operations in training time.
+
+    Args:
+        num_bits (int) : Number bits for aware quantilization. Default: 8.
+        ema (bool): Use EMA algorithm update value min and max. Default: False.
+        ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
+        quant_delay (int): Quantilization delay parameter. Before delay step in training time not update
+            simulate aware quantize funcion. After delay step in training time begin simulate the aware
+            quantize funcion. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+        training (bool): Training the network or not. Default: True.
+
+    Inputs:
+        - **x** (Tensor) : float32 Tensor representing the shape of the output tensor.
+        - **min** (Tensor) : Value of the min range of the input data x.
+        - **max** (Tensor) : Value of the max range of the input data x.
+
+    Outputs:
+        - Tensor: Simulate quantize tensor of x.
+
+    Examples:
+        >>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
+        >>> min_tensor = Tensor(np.array([-6]), mstype.float32)
+        >>> max_tensor = Tensor(np.array([6]), mstype.float32)
+        >>> output_tensor = P.FakeQuantWithMinMax(num_bits=8)(input_tensor, min_tensor, max_tensor)
+    """
+    support_quant_bit = [4, 7, 8]
+
+    @prim_attr_register
+    def __init__(self, num_bits=8, ema=False, ema_decay=0.999, quant_delay=0, symmetric=False, narrow_range=False,
+                 training=True):
+        """init FakeQuantWithMinMax OP"""
+        if num_bits not in self.support_quant_bit:
+            raise ValueError("Attr \'num_bits\' is not support.")
+        if ema and not ema_decay:
+            raise ValueError(
+                "Attr \'ema\' and \'ema_decay\' should set together.")
+
+        self.ema = check_bool(ema)
+        self.symmetric = check_bool(symmetric)
+        self.narrow_range = check_bool(narrow_range)
+        self.training = check_bool(training)
+        self.ema_decay = validator.check_number_range(
+            'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH)
+        self.num_bits = check_int_positive(num_bits)
+        self.quant_delay = check_int(quant_delay)
+        self.init_prim_io_names(inputs=['x', 'min', 'max'],
+                                outputs=['out'])
+
+    def infer_shape(self, x_shape, min_shape, max_shape):
+        validator.check_integer("x shape", len(x_shape), 1, Rel.GT)
+        validator.check("min shape", min_shape, "max shape", max_shape)
+        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ)
+        validator.check_integer("max shape", len(min_shape), 1, Rel.EQ)
+        return x_shape
+
+    def infer_dtype(self, x_type, min_type, max_type):
+        validator.check_typename(
+            "x type", x_type, (mstype.float16, mstype.float32))
+        validator.check_typename("min type", min_type,
+                                 (mstype.float16, mstype.float32))
+        validator.check_typename("max type", max_type,
+                                 (mstype.float16, mstype.float32))
+        return x_type
+
+
+class FakeQuantWithMinMaxGrad(PrimitiveWithInfer):
+    """Performs grad of FakeQuantWithMinMax operation."""
+    support_quant_bit = [4, 8]
+
+    @prim_attr_register
+    def __init__(self, num_bits=8, quant_delay=0):
+        if num_bits not in self.support_quant_bit:
+            raise ValueError("Attr \'num_bits\' is not support.")
+
+        self.quant_delay = check_int(quant_delay)
+        self.num_bits = check_int_positive(num_bits)
+        self.init_prim_io_names(inputs=['dout', 'x', 'min', 'max'],
+                                outputs=['dx'])
+
+    def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
+        validator.check("dout shape", dout_shape, "x shape", x_shape)
+        validator.check("min shape", min_shape, "max shape", max_shape)
+        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ)
+        validator.check_integer("max shape", len(min_shape), 1, Rel.EQ)
+        return dout_shape
+
+    def infer_dtype(self, dout_type, x_type, min_type, max_type):
+        validator.check_typename(
+            "dout type", dout_type, (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "x type", x_type, (mstype.float16, mstype.float32))
+        validator.check_typename("min type", min_type,
+                                 (mstype.float16, mstype.float32))
+        validator.check_typename("max type", max_type,
+                                 (mstype.float16, mstype.float32))
+        return dout_type
+
+
+class FakeQuantWithMinMaxPerChannel(PrimitiveWithInfer):
+    r"""
+    Simulate the quantize and dequantize operations in training time base on per channel.
+
+    Args:
+        num_bits (int) : Number bits to quantilization. Default: 8.
+        ema (bool): Use EMA algorithm update tensor min and tensor max. Default: False.
+        ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
+        quant_delay (int): Quantilization delay  parameter. Before delay step in training time not
+            update the weight data to simulate quantize operation. After delay step in training time
+            begin simulate the quantize operation. Default: 0.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+        training (bool): Training the network or not. Default: True.
+
+    Inputs:
+        - **x** (Tensor) : 4-D float32 Tensor representing the shape of the output tensor.
+        - **min** (int, float) : Value of the min range of the input data.
+        - **max** (int, float) : Value of the max range of the input data.
+
+    Outputs:
+        - Tensor, has the same type as input.
+
+    Examples:
+        >>> input_tensor = Tensor(np.random.rand(3,4,5,5), mstype.float32)
+        >>> min_tensor = Tensor(np.array([-6.0, -6.5, -4.0, -5.0]), mstype.float32)
+        >>> max_tensor = Tensor(np.array([6.0, 6.5, 4.0, 5.0]), mstype.float32)
+        >>> output_tensor = P.FakeQuantWithMinMax(num_bits=8)(input_tensor, min_tensor, max_tensor)
+    """
+    support_quant_bit = [4, 8]
+    channel_idx = 0
+
+    @prim_attr_register
+    def __init__(self, num_bits=8, ema=False, ema_decay=0.999, quant_delay=0, symmetric=False, narrow_range=False,
+                 training=True):
+        """init FakeQuantWithMinMaxPerChannel OP"""
+        if num_bits not in self.support_quant_bit:
+            raise ValueError("Attr \'num_bits\' is not support.")
+        if ema and not ema_decay:
+            raise ValueError(
+                "Attr \'ema\' and \'ema_decay\' should set together.")
+
+        self.ema = check_bool(ema)
+        self.symmetric = check_bool(symmetric)
+        self.narrow_range = check_bool(narrow_range)
+        self.training = check_bool(training)
+        self.ema_decay = validator.check_number_range(
+            'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH)
+        self.num_bits = check_int_positive(num_bits)
+        self.quant_delay = check_int(quant_delay)
+        self.init_prim_io_names(inputs=['x', 'min', 'max'],
+                                outputs=['out'])
+
+    def infer_shape(self, x_shape, min_shape, max_shape):
+        validator.check_integer("x shape", len(x_shape), 1, Rel.GT)
+        validator.check_integer(
+            "min len", min_shape[0], x_shape[self.channel_idx], Rel.EQ)
+        validator.check_integer(
+            "max len", max_shape[0], x_shape[self.channel_idx], Rel.EQ)
+        return x_shape
+
+    def infer_dtype(self, x_type, min_type, max_type):
+        validator.check_typename(
+            "x type", x_type, (mstype.float16, mstype.float32))
+        validator.check_typename("min type", min_type,
+                                 (mstype.float16, mstype.float32))
+        validator.check_typename("max type", max_type,
+                                 (mstype.float16, mstype.float32))
+        return x_type
+
+
+class FakeQuantWithMinMaxPerChannelGrad(PrimitiveWithInfer):
+    """Performs grad of FakeQuantWithMinMaxPerChannel operation."""
+    support_quant_bit = [4, 8]
+
+    @prim_attr_register
+    def __init__(self, num_bits=8, quant_delay=0):
+        """init FakeQuantWithMinMaxPerChannel Fill"""
+        if num_bits not in self.support_quant_bit:
+            raise ValueError("Attr \'num_bits\' is not support.")
+
+        self.quant_delay = check_int(quant_delay)
+        self.num_bits = check_int_positive(num_bits)
+        self.init_prim_io_names(inputs=['dout', 'x', 'min', 'max'],
+                                outputs=['dx'])
+
+    def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
+        validator.check("dout shape", dout_shape, "x shape", x_shape)
+        validator.check("min shape", min_shape, "max shape", max_shape)
+        return dout_shape
+
+    def infer_dtype(self, dout_type, x_type, min_type, max_type):
+        validator.check_typename(
+            "dout", dout_type, (mstype.float16, mstype.float32))
+        validator.check_typename("x", x_type, (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "min", min_type, (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "max", max_type, (mstype.float16, mstype.float32))
+        return dout_type
+
+
+class BatchNormFold(PrimitiveWithInfer):
+    """
+    Batch normalization folded.
+
+    Args:
+        momentum (float): Momentum value should be [0, 1]. Default: 0.1.
+        epsilon (float): A small float number to avoid dividing by 0. 1e-12 if dtype in
+            float32 else 1e-3. Default: 1e-12.
+        is_training (bool): In training mode set True, else set False. Default: True.
+        freeze_bn (int): Delay in steps at which computation switches from regular batch
+            norm to frozen mean and std. Default: 0.
+
+    Inputs:
+        - **x** (Tensor) - Tensor of shape :math:`(N, C)`.
+        - **mean** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **variance** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **global_step** (Tensor) - Tensor to record current global step.
+
+    Outputs:
+        Tuple of 4 Tensor, the normalized input and the updated parameters.
+
+        - **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
+
+    """
+    channel = 1
+
+    @prim_attr_register
+    def __init__(self, momentum=0.1, epsilon=1e-12, is_training=True, freeze_bn=0):
+        """init batch norm fold layer"""
+        self.momentum = validator.check_number_range(
+            'momentum', momentum, 0, 1, Rel.INC_BOTH)
+        self.epsilon = validator.check_float_positive('epsilon', epsilon)
+        self.is_training = check_bool(is_training)
+        self.freeze_bn = check_int(freeze_bn)
+
+        self.init_prim_io_names(inputs=['x', 'mean', 'variance', 'global_step'],
+                                outputs=['batch_mean', 'batch_std', 'running_mean', 'running_std'])
+
+    def infer_shape(self, x_shape, mean_shape, variance_shape, global_step_shape):
+        validator.check("mean shape", mean_shape,
+                        "gamma_shape", variance_shape)
+        validator.check("mean_shape size",
+                        mean_shape[0], "input channel", x_shape[self.channel])
+        validator.check_integer("global_step shape",
+                                len(global_step_shape), 1, Rel.EQ)
+        return mean_shape, mean_shape, mean_shape, mean_shape
+
+    def infer_dtype(self, x_type, mean_type, variance_type, global_step_type):
+        validator.check("input type", x_type, "mean type", mean_type)
+        validator.check("input type", x_type, "variance type", variance_type)
+        validator.check_typename("input type", x_type,
+                                 (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "global_step type", global_step_type, (mstype.int32,))
+        return x_type, x_type, x_type, x_type
+
+
+class BatchNormFoldGrad(PrimitiveWithInfer):
+    """Performs grad of BatchNormFold operation."""
+    channel = 1
+
+    @prim_attr_register
+    def __init__(self, epsilon=1e-12, is_training=True, freeze_bn=0):
+        """init BatchNormGrad layer"""
+        self.is_training = check_bool(is_training)
+        self.freeze_bn = check_int(freeze_bn)
+        self.epsilon = validator.check_float_positive('epsilon', epsilon)
+        self.init_prim_io_names(inputs=['d_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'global_step'],
+                                outputs=['dx'])
+
+    def infer_shape(self, d_batch_mean_shape, d_batch_std_shape, x_shape, batch_mean_shape, batch_std_shape,
+                    global_step_shape):
+        validator.check("d_batch_mean shape", d_batch_mean_shape,
+                        "d_batch_std shape", d_batch_std_shape)
+        validator.check("d_batch_mean shape", d_batch_mean_shape,
+                        "batch_mean shape", batch_mean_shape)
+        validator.check("d_batch_mean shape", d_batch_mean_shape,
+                        "batch_std shape", batch_std_shape)
+        validator.check(
+            "x_shape shape", d_batch_mean_shape[0], "input channel", x_shape[self.channel])
+        validator.check_integer("global_step shape",
+                                len(global_step_shape), 1, Rel.EQ)
+        return x_shape
+
+    def infer_dtype(self, d_batch_mean_type, d_batch_std_type, x_type, batch_mean_type, batch_std_type,
+                    global_step_type):
+        validator.check("input type", x_type,
+                        "d_batch_mean type", d_batch_mean_type)
+        validator.check("input type", x_type,
+                        "d_batch_std type", d_batch_std_type)
+        validator.check("input type", x_type,
+                        "batch_mean type", batch_mean_type)
+        validator.check("input type", x_type, "batch_std type", batch_std_type)
+        validator.check_typename("input type", x_type,
+                                 (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "global_step type", global_step_type, (mstype.int32,))
+        return x_type
+
+
+class CorrectionMul(PrimitiveWithInfer):
+    """
+    Scale the weights with a correction factor to the long term statistics
+    prior to quantization. This ensures that there is no jitter in the quantized weights
+    due to batch to batch variation.
+
+    Inputs:
+        - **x** (Tensor) - Tensor of shape :math:`(N, C)`.
+        - **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
+
+    Outputs:
+        - **out** (Tensor) - Tensor has the same shape as x.
+
+    """
+    channel = 0
+
+    @prim_attr_register
+    def __init__(self):
+        """init correction mul layer"""
+        self.init_prim_io_names(inputs=['x', 'batch_std', 'running_std'],
+                                outputs=['out'])
+
+    def infer_shape(self, x_shape, batch_std_shape, running_std_shape):
+        validator.check("batch_std shape", batch_std_shape,
+                        "running_std shape", running_std_shape)
+        validator.check(
+            "batch_std size", batch_std_shape[0], "x_shape channel size", x_shape[self.channel])
+        return x_shape
+
+    def infer_dtype(self, x_type, batch_std_type, running_std_type):
+        validator.check("batch_std type", batch_std_type,
+                        "running_std type", running_std_type)
+        validator.check("batch_std_type", batch_std_type, "x_type", x_type)
+        validator.check_typename(
+            "batch_std type", batch_std_type, (mstype.float16, mstype.float32))
+        return x_type
+
+
+class CorrectionMulGrad(PrimitiveWithInfer):
+    """Performs grad of CorrectionMul operation."""
+    channel = 0
+
+    @prim_attr_register
+    def __init__(self):
+        """init correction mul layer"""
+        self.init_prim_io_names(inputs=['dout', 'x', 'gamma', 'running_std'],
+                                outputs=['dx', 'd_gamma'])
+
+    def infer_shape(self, dout_shape, x_shape, gamma_shape, running_std_shape):
+        validator.check("dout shape", dout_shape, "x_shape x", x_shape)
+        validator.check(
+            "gamma size", gamma_shape[0], "dout channel size", dout_shape[self.channel])
+        validator.check(
+            "running_std size", running_std_shape[0], "dout channel size", dout_shape[self.channel])
+        return x_shape, gamma_shape
+
+    def infer_dtype(self, dout_type, x_type, gamma_type, running_std_type):
+        validator.check("x type", x_type, "dout type", dout_type)
+        validator.check("gamma type", gamma_type, "dout type", dout_type)
+        validator.check("running_std type", running_std_type,
+                        "dout type", dout_type)
+        validator.check_typename(
+            "dout type", dout_type, (mstype.float16, mstype.float32))
+        return x_type, x_type
+
+
+class BatchNormFold2(PrimitiveWithInfer):
+    """
+    Scale the bias with a correction factor to the long term statistics
+    prior to quantization. This ensures that there is no jitter in the quantized bias
+    due to batch to batch variation.
+
+    Inputs:
+        - **x** (Tensor)  - Tensor of shape :math:`(N, C)`.
+        - **beta** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **gamma** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **global_step** (Tensor) - Tensor to record current global step.
+
+    Outputs:
+        - **y** (Tensor) - Tensor has the same shape as x.
+
+    """
+    channel = 1
+
+    @prim_attr_register
+    def __init__(self, freeze_bn=0):
+        """init conv2d fold layer"""
+        self.freeze_bn = check_int(freeze_bn)
+        self.init_prim_io_names(inputs=['x', 'beta', 'gamma', 'batch_std', 'batch_mean',
+                                        'running_std', 'running_mean', 'global_step'],
+                                outputs=['y'])
+
+    def infer_shape(self, x_shape, beta_shape, gamma_shape, batch_std_shape, running_std_shape, batch_mean_shape,
+                    running_mean_shape, global_step_shape):
+        validator.check("batch_std shape", batch_std_shape,
+                        "running_std shape", running_std_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "batch_mean shape", batch_mean_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "beta shape", beta_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "running_mean shape", running_mean_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "batch_mean shape", gamma_shape)
+        validator.check(
+            "batch_std size", batch_std_shape[0], "x_shape channel size", x_shape[self.channel])
+        validator.check_integer("global_step shape",
+                                len(global_step_shape), 1, Rel.EQ)
+        return x_shape
+
+    def infer_dtype(self, x_type, beta_type, gamma_type, batch_std_type, running_std_type, batch_mean_type,
+                    running_mean_type, global_step_type):
+        validator.check("batch_std type", batch_std_type,
+                        "running_std type", running_std_type)
+        validator.check("batch_std type", batch_std_type,
+                        "batch_mean type", batch_mean_type)
+        validator.check("batch_std type", batch_std_type,
+                        "beta type", beta_type)
+        validator.check("batch_std type", batch_std_type,
+                        "running_mean type", running_mean_type)
+        validator.check("batch_std type", batch_std_type,
+                        "gamma type", gamma_type)
+        validator.check("x_type", x_type, "batch_std type", batch_std_type)
+        validator.check_typename(
+            "batch_std type", batch_std_type, (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "global_step type", global_step_type, (mstype.int32,))
+        return x_type
+
+
+class BatchNormFold2Grad(PrimitiveWithInfer):
+    """Performs grad of CorrectionAddGrad operation."""
+    channel = 1
+
+    @prim_attr_register
+    def __init__(self, freeze_bn=0):
+        """init MulFold layer"""
+        self.freeze_bn = freeze_bn
+        self.init_prim_io_names(inputs=['dout', 'x', 'gamma',
+                                        'batch_std', 'batch_mean',
+                                        'running_std', 'running_mean', 'global_step'],
+                                outputs=['d_batch_std', 'd_batch_mean', 'd_beta', 'd_gamma', 'dx'])
+
+    def infer_shape(self, dout_shape, x_shape, gamma_shape,
+                    batch_std_shape, batch_mean_shape,
+                    running_std_shape, running_mean_shape, global_step_shape):
+        validator.check("batch_std shape", batch_std_shape,
+                        "batch_mean shape", batch_mean_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "running_std shape", running_std_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "running_mean shape", running_mean_shape)
+        validator.check("batch_std shape", batch_std_shape,
+                        "gamma shape", gamma_shape)
+        validator.check(
+            "batch_std size", batch_std_shape[0], "dout channel size", dout_shape[self.channel])
+        validator.check_integer("global_step shape",
+                                len(global_step_shape), 1, Rel.EQ)
+        return gamma_shape, gamma_shape, gamma_shape, gamma_shape, x_shape
+
+    def infer_dtype(self, dout_type, x_type, gamma_type,
+                    batch_std_type, batch_mean_type,
+                    running_std_type, running_mean_type, global_step_type):
+        validator.check("batch_std type", batch_std_type,
+                        "batch_mean type", batch_mean_type)
+        validator.check("batch_std type", batch_std_type,
+                        "gamma type", gamma_type)
+        validator.check("batch_std type", batch_std_type,
+                        "running_std type", running_std_type)
+        validator.check("batch_std type", batch_std_type,
+                        "running_mean type", running_mean_type)
+        validator.check("batch_std_type", batch_std_type,
+                        "dout type", dout_type)
+        validator.check_typename(
+            "batch_std type", batch_std_type, (mstype.float16, mstype.float32))
+        validator.check_typename(
+            "global_step type", global_step_type, (mstype.int32,))
+        return gamma_type, gamma_type, gamma_type, gamma_type, gamma_type
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 83f76455e0..3a3ea2c3e0 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -207,7 +207,7 @@ class ReLU6(PrimitiveWithInfer):
 
 
 class Elu(PrimitiveWithInfer):
-    """
+    r"""
     Computes exponential linear: `alpha * (exp(x) - 1)` if x < 0, `x` otherwise.
     The data type of input tensor should be float.
 
@@ -242,6 +242,40 @@ class Elu(PrimitiveWithInfer):
         return input_x
 
 
+class HSwish(PrimitiveWithInfer):
+    r"""
+    Hard swish activation function.
+
+    Applies hswish-type activation element-wise. The input is a Tensor with any valid shape.
+
+    Hard swish is defined as:
+
+    .. math::
+        \text{hswish}(x_{i}) = x_{i} * \frac{ReLU6(x_{i} + 3)}{6},
+
+    where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
+
+    Inputs:
+        - **input_data** (Tensor) - The input of Hswish.
+
+    Outputs:
+        Tensor, with the same type and shape as the `input_data`.
+
+    """
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, xshape):
+        return xshape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_subclass("x_dtype", x_dtype, mstype.tensor)
+        validator.check_typename("x_dtype", x_dtype, (mstype.float16, mstype.float32))
+        return x_dtype
+
+
+
 class Sigmoid(PrimitiveWithInfer):
     r"""
     Sigmoid activation function.
@@ -258,6 +292,7 @@ class Sigmoid(PrimitiveWithInfer):
 
     Outputs:
         Tensor, with the same type and shape as the input_x.
+
     """
 
     @prim_attr_register
@@ -273,6 +308,40 @@ class Sigmoid(PrimitiveWithInfer):
         return input_x
 
 
+class HSigmoid(PrimitiveWithInfer):
+    r"""
+    Hard sigmoid activation function.
+
+    Applies hard sigmoid activation element-wise. The input is a Tensor with any valid shape.
+
+    Hard sigmoid is defined as:
+
+    .. math::
+        \text{hsigmoid}(x_{i}) = max(0, min(1, \ftac{2 * x_{i} + 5}{10})),
+
+    where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
+
+    Inputs:
+        - **input_data** (Tensor) - The input of HSigmoid.
+
+    Outputs:
+        Tensor, with the same type and shape as the `input_data`.
+
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_subclass("x_dtype", x_dtype, mstype.tensor)
+        validator.check_typename("x_dtype", x_dtype, (mstype.float16, mstype.float32))
+        return x_dtype
+
+
 class Tanh(PrimitiveWithInfer):
     r"""
     Tanh activation function.
diff --git a/tests/ut/python/nn/test_dense.py b/tests/ut/python/nn/test_dense.py
index 8581576c6b..0845983bb0 100644
--- a/tests/ut/python/nn/test_dense.py
+++ b/tests/ut/python/nn/test_dense.py
@@ -27,11 +27,6 @@ def test_dense_none():
         nn.Dense(3, 2, None, None)
 
 
-def test_dense_invalid_activation():
-    with pytest.raises(KeyError):
-        nn.Dense(3, 2, activation='relu6')
-
-
 @non_graph_engine
 def test_dense_str_activation():
     dense = nn.Dense(1, 1, activation='relu')
diff --git a/tests/ut/python/pynative_mode/nn/test_activation.py b/tests/ut/python/pynative_mode/nn/test_activation.py
index 7230fa272b..1b8a6f5d76 100644
--- a/tests/ut/python/pynative_mode/nn/test_activation.py
+++ b/tests/ut/python/pynative_mode/nn/test_activation.py
@@ -51,11 +51,6 @@ def test_activation_empty():
     assert nn.get_activation('') is None
 
 
-def test_activation_invalid():
-    with pytest.raises(KeyError):
-        nn.get_activation('relu6')
-
-
 # test softmax
 def test_softmax_axis():
     layer = nn.Softmax(1)
diff --git a/tests/ut/python/pynative_mode/nn/test_dense.py b/tests/ut/python/pynative_mode/nn/test_dense.py
index 48bfcc6674..cc9d280521 100644
--- a/tests/ut/python/pynative_mode/nn/test_dense.py
+++ b/tests/ut/python/pynative_mode/nn/test_dense.py
@@ -68,11 +68,6 @@ def test_dense_none():
         nn.Dense(3, 2, None, None)
 
 
-def test_dense_invalid_activation():
-    with pytest.raises(KeyError):
-        nn.Dense(3, 2, activation='relu6')
-
-
 def test_dense_str_activation():
     dense = nn.Dense(1, 1, activation='relu')
     assert isinstance(dense.activation, nn.ReLU)

From 19daf09f882ae57a87cd5b12aff3cccd23fa4853 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Mon, 13 Apr 2020 11:54:32 +0800
Subject: [PATCH 183/367] output sha256 file

---
 package.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/package.sh b/package.sh
index f57cb7f104..056ca8c25d 100755
--- a/package.sh
+++ b/package.sh
@@ -121,6 +121,7 @@ PACKAGE_BASE_NAME=${PACKAGE_BASE_NAME//_*-/-}
 PACKAGE_NEW_NAME="${PACKAGE_BASE_NAME}-${PY_TAGS}-${PLATFORM_TAG}.whl"
 cp -rf "${PACKAGE_PATH}/dist"/*.whl "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}"
 cp -f "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}" "${OUTPUT_PATH}"
+find ${OUTPUT_PATH} -name "*.whl" -print0 | xargs -0 -I {} sh -c "sha256sum {} | awk '{printf \$1}' > {}.sha256"
 
 cd "${BASEPATH}"
 

From 005e2020836efc939d288cee42f332eac5274e32 Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Mon, 13 Apr 2020 13:21:24 +0800
Subject: [PATCH 184/367] fix print content of mindspore.string

---
 mindspore/ccsrc/ir/dtype.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/ir/dtype.h b/mindspore/ccsrc/ir/dtype.h
index 11099e460e..e3e2099b5e 100644
--- a/mindspore/ccsrc/ir/dtype.h
+++ b/mindspore/ccsrc/ir/dtype.h
@@ -51,7 +51,7 @@ class String : public Object {
   TypeId generic_type_id() const override { return kObjectTypeString; }
 
   TypePtr DeepCopy() const override { return std::make_shared<String>(); }
-  std::string ToString() const override { return std::string("String:"); }
+  std::string ToString() const override { return std::string("String"); }
   std::string ToReprString() const override { return "string"; }
   std::string DumpText() const override { return "String"; }
 };

From b8a7e73f7d3a240c299244dbf0c14ecf320cd6f2 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Mon, 13 Apr 2020 14:20:58 +0800
Subject: [PATCH 185/367] fix reshape output and clearres error

---
 .../device/ascend/ascend_kernel_runtime.cc    |  6 +-
 .../ccsrc/device/gpu/gpu_kernel_runtime.cc    |  5 +-
 .../ccsrc/session/anf_runtime_algorithm.cc    | 80 +++++--------------
 .../ccsrc/session/anf_runtime_algorithm.h     |  2 +
 4 files changed, 28 insertions(+), 65 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 0c6861e21f..baed733a5b 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -85,8 +85,10 @@ void AscendKernelRuntime::ReleaseDeviceRes() {
     MS_EXCEPTION(DeviceProcessError) << "rtSetDevice, ret[" << static_cast<int>(ret) << "]";
   }
 
-  MS_EXCEPTION_IF_NULL(mem_manager_);
-  mem_manager_->FreeDeviceMemory();
+  if (mem_manager_ != nullptr) {
+    mem_manager_->FreeDeviceMemory();
+  }
+
   (void)DestroyHccl();
   (void)ResetDevice();
   (void)ProfilingManager::GetInstance().StopProfiling();
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
index 2ec1a5df29..671a11f776 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
@@ -101,8 +101,9 @@ void GPUKernelRuntime::ReleaseDeviceRes() {
     CHECK_OP_RET_WITH_EXCEPT(GpuBufferMgr::GetInstance().Destroy(), "Could not destroy gpu data queue.");
   }
   GPUDeviceManager::GetInstance().ReleaseDevice();
-  MS_EXCEPTION_IF_NULL(mem_manager_);
-  mem_manager_->FreeDeviceMemory();
+  if (mem_manager_ != nullptr) {
+    mem_manager_->FreeDeviceMemory();
+  }
 }
 
 void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) {
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 78922448af..893c379a07 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -112,6 +112,12 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr
       return VisitKernelWithReturnType(cnode->input(kRealInputNodeIndexInTupleGetItem), IntToSize(item_idx));
     } else if (IsPrimitive(input0, prim::kPrimDepend) || IsPrimitive(input0, prim::kPrimControlDepend)) {
       return VisitKernelWithReturnType(cnode->input(kRealInputIndexInDepend), 0);
+    } else if (opt::IsNopNode(cnode)) {
+      if (cnode->inputs().size() == 2) {
+        return VisitKernelWithReturnType(cnode->input(1), 0);
+      } else {
+        MS_LOG(EXCEPTION) << cnode->DebugString() << "Invalid nop node";
+      }
     } else {
       return std::make_pair(anf_node, index);
     }
@@ -299,20 +305,23 @@ std::string AnfRuntimeAlgorithm::GetInputFormat(const AnfNodePtr &node, size_t i
   return build_info->GetInputFormat(input_idx);
 }
 
-std::string AnfRuntimeAlgorithm::GetPrevNodeOutputFormat(const AnfNodePtr &anf_node, size_t input_idx) {
+KernelWithIndex AnfRuntimeAlgorithm::GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx) {
   MS_EXCEPTION_IF_NULL(anf_node);
   if (!anf_node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "anf_node is not CNode.";
+    MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf_node is not CNode.";
   }
   auto cnode = anf_node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode)
-                      << ".";
+    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode);
   }
   auto node = cnode->input(input_idx + 1);
   MS_EXCEPTION_IF_NULL(node);
-  KernelWithIndex kernel_with_index = VisitKernel(node, 0);
+  return VisitKernel(node, 0);
+}
+
+std::string AnfRuntimeAlgorithm::GetPrevNodeOutputFormat(const AnfNodePtr &anf_node, size_t input_idx) {
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx);
   return AnfRuntimeAlgorithm::GetOutputFormat(kernel_with_index.first, kernel_with_index.second);
 }
 
@@ -346,18 +355,7 @@ std::vector<size_t> AnfRuntimeAlgorithm::GetOutputInferShape(const AnfNodePtr &n
 }
 
 std::vector<size_t> AnfRuntimeAlgorithm::GetPrevNodeOutputInferShape(const AnfNodePtr &node, size_t input_idx) {
-  MS_EXCEPTION_IF_NULL(node);
-  if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "anf_node is not CNode.";
-  }
-  auto cnode = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode)
-                      << ".";
-  }
-  auto input_node = cnode->input(input_idx + 1);
-  KernelWithIndex kernel_with_index = VisitKernel(input_node, 0);
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx);
   return AnfRuntimeAlgorithm::GetOutputInferShape(kernel_with_index.first, kernel_with_index.second);
 }
 
@@ -459,17 +457,7 @@ TypeId AnfRuntimeAlgorithm::GetOutputInferDataType(const AnfNodePtr &node, size_
 }
 
 TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx) {
-  MS_EXCEPTION_IF_NULL(node);
-  if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << node->DebugString() << "is not a CNode";
-  }
-  auto cnode = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode);
-  }
-  auto input_node = cnode->input(input_idx + 1);
-  KernelWithIndex kernel_with_index = VisitKernel(input_node, 0);
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx);
   return AnfRuntimeAlgorithm::GetOutputInferDataType(kernel_with_index.first, kernel_with_index.second);
 }
 
@@ -492,17 +480,7 @@ TypeId AnfRuntimeAlgorithm::GetInputDeviceDataType(const AnfNodePtr &node, size_
 }
 
 TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputDeviceDataType(const AnfNodePtr &anf_node, size_t input_idx) {
-  if (!anf_node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf_node is not CNode.";
-  }
-  auto cnode = anf_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode);
-  }
-  auto node = cnode->input(input_idx + 1);
-  MS_EXCEPTION_IF_NULL(node);
-  KernelWithIndex kernel_with_index = VisitKernel(node, 0);
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx);
   return AnfRuntimeAlgorithm::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second);
 }
 
@@ -558,32 +536,12 @@ bool AnfRuntimeAlgorithm::OutputAddrExist(const AnfNodePtr &node, size_t output_
 }
 
 const DeviceAddress *AnfRuntimeAlgorithm::GetPrevNodeOutputAddr(const AnfNodePtr &anf_node, size_t input_idx) {
-  if (!anf_node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf node is not a CNode";
-  }
-  auto cnode = anf_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode);
-  }
-  auto node = cnode->input(input_idx + 1);
-  MS_EXCEPTION_IF_NULL(node);
-  KernelWithIndex kernel_with_index = VisitKernel(node, 0);
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx);
   return AnfRuntimeAlgorithm::GetOutputAddr(kernel_with_index.first, kernel_with_index.second);
 }
 
 DeviceAddressPtr AnfRuntimeAlgorithm::GetPrevNodeMutableOutputAddr(const AnfNodePtr &anf_node, size_t input_idx) {
-  if (!anf_node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << anf_node->DebugString() << "anf_node is not CNode.";
-  }
-  auto cnode = anf_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  if (input_idx + 1 >= cnode->inputs().size()) {
-    MS_LOG(EXCEPTION) << "Input index " << input_idx << " is larger than input number " << GetInputTensorNum(cnode);
-  }
-  auto node = cnode->input(input_idx + 1);
-  MS_EXCEPTION_IF_NULL(node);
-  KernelWithIndex kernel_with_index = VisitKernel(node, 0);
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, input_idx);
   return AnfRuntimeAlgorithm::GetMutableOutputAddr(kernel_with_index.first, kernel_with_index.second);
 }
 
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 55650ac31e..1a1d471b84 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -89,6 +89,8 @@ class AnfRuntimeAlgorithm {
   static std::string GetOutputFormat(const AnfNodePtr &node, size_t output_idx);
   // get input format select of anf node
   static std::string GetInputFormat(const AnfNodePtr &node, size_t input_idx);
+  // get prev node output width output index
+  static KernelWithIndex GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx);
   // get output format from prev node,input_index is the input index of current node related to prev node
   static std::string GetPrevNodeOutputFormat(const AnfNodePtr &node, size_t input_idx);
   // get output shapes inferred by ME from input nodes.

From 7c19a4c2fd03c8c67d35626ac821f6bdc4baa814 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Mon, 13 Apr 2020 09:51:44 +0800
Subject: [PATCH 186/367] fix getnext op bug

---
 .../ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc    | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
index 6675051069..9f91c1bdd2 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
@@ -38,12 +38,14 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<
     return;
   }
   // For compatibility with the current framework
-  if (op_name == kPrint) {
-    std::vector<std::string> inputs_format;
-    std::vector<TypeId> inputs_type;
-    for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
-      inputs_format.emplace_back(kOpFormat_DEFAULT);
-      inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index));
+  if (op_name == kPrint || op_name == kGetNext) {
+    std::vector<std::string> inputs_format{};
+    std::vector<TypeId> inputs_type{};
+    if (op_name == kPrint) {
+      for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
+        inputs_format.emplace_back(kOpFormat_DEFAULT);
+        inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index));
+      }
     }
     std::vector<std::string> outputs_format;
     std::vector<TypeId> outputs_type;

From 0db45147a4e7a07e69147b0f0efa84fba79d445a Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Thu, 9 Apr 2020 21:50:31 +0800
Subject: [PATCH 187/367] dock relu6 for open source process and fix pow bprop

---
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc |  2 ++
 mindspore/ops/_grad/grad_math_ops.py      |  6 ++--
 mindspore/ops/_op_impl/tbe/__init__.py    |  2 ++
 mindspore/ops/_op_impl/tbe/relu6.py       | 40 +++++++++++++++++++++
 mindspore/ops/_op_impl/tbe/relu6_grad.py  | 43 +++++++++++++++++++++++
 5 files changed, 90 insertions(+), 3 deletions(-)
 create mode 100644 mindspore/ops/_op_impl/tbe/relu6.py
 create mode 100644 mindspore/ops/_op_impl/tbe/relu6_grad.py

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index 229a3eb34a..5336d1e67f 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -30,6 +30,8 @@ namespace mindspore {
 namespace kernel {
 namespace tbe {
 static std::map<string, string> tbe_func_adapter_map = {
+  {"re_lu6", "relu6"},
+  {"re_lu6_grad", "relu6_grad"},
   {"re_lu", "relu"},
   {"tensor_add", "add"},
   {"reduce_mean", "reduce_mean_d"},
diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py
index 81e078dc98..2d819718c8 100755
--- a/mindspore/ops/_grad/grad_math_ops.py
+++ b/mindspore/ops/_grad/grad_math_ops.py
@@ -340,9 +340,9 @@ def get_bprop_pow(self):
     ln = P.Log()
 
     def bprop(x, power, out, dout):
-        dx = power * pow_op(x, power - 1.0) * dout
-        dpower = pow_op(x, power) * ln(x) * dout
-        return dx, dpower
+        bc_dx = power * pow_op(x, power - 1.0) * dout
+        bc_dpower = out * ln(x) * dout
+        return binop_grad_common(x, power, bc_dx, bc_dpower)
     return bprop
 
 
diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py
index 0b79ae845b..9ec5446165 100644
--- a/mindspore/ops/_op_impl/tbe/__init__.py
+++ b/mindspore/ops/_op_impl/tbe/__init__.py
@@ -42,6 +42,8 @@ from .mul import _mul_tbe
 from .real_div import _real_div_tbe
 from .relu import _relu_tbe
 from .relu_grad import _relu_grad_tbe
+from .relu6 import _relu6_tbe
+from .relu6_grad import _relu6_grad_tbe
 from .softmax_cross_entropy_with_logits import _softmax_cross_entropy_with_logits_tbe
 from .sigmoid_cross_entropy_with_logits import _sigmoid_cross_entropy_with_logits_tbe
 from .sigmoid_cross_entropy_with_logits_grad import _sigmoid_cross_entropy_with_logits_grad_tbe
diff --git a/mindspore/ops/_op_impl/tbe/relu6.py b/mindspore/ops/_op_impl/tbe/relu6.py
new file mode 100644
index 0000000000..bbedfdeb0f
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/relu6.py
@@ -0,0 +1,40 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReLU6 op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+relu6_op_info = TBERegOp("ReLU6") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relu6.so") \
+    .compute_cost(10) \
+    .kernel_name("relu6") \
+    .partial_flag(True) \
+    .input(0, "features", False, "required", "all") \
+    .output(0, "activations", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(relu6_op_info)
+def _relu6_tbe():
+    """Relu6 TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/relu6_grad.py b/mindspore/ops/_op_impl/tbe/relu6_grad.py
new file mode 100644
index 0000000000..eaf3449fe7
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/relu6_grad.py
@@ -0,0 +1,43 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReLU6Grad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+relu6_grad_op_info = TBERegOp("ReLU6Grad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relu6_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("relu6_grad") \
+    .partial_flag(True) \
+    .input(0, "gradients", False, "required", "all") \
+    .input(1, "features", False, "required", "all") \
+    .output(0, "backprops", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
+
+
+@op_info_register(relu6_grad_op_info)
+def _relu6_grad_tbe():
+    """Relu6Grad TBE register"""
+    return

From 7307c81f3144f51fb51adc3da18b17ba05f3800a Mon Sep 17 00:00:00 2001
From: YuJianfeng <yujianfeng5@huawei.com>
Date: Thu, 9 Apr 2020 11:34:03 +0800
Subject: [PATCH 188/367] implement AddN fission pass

---
 .../ascend/ascend_backend_optimization.cc     |   2 +
 .../ascend/ir_fission/addn_fission.cc         |  81 +++++++++
 .../ascend/ir_fission/addn_fission.h          |  37 ++++
 mindspore/ccsrc/utils/utils.h                 |   1 +
 .../ascend/ir_fission/addn_fission_test.cc    | 160 ++++++++++++++++++
 .../pre_activate/addn_fission_test.py         |  80 +++++++++
 6 files changed, 361 insertions(+)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.h
 create mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fission/addn_fission_test.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/addn_fission_test.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 432d88e7a4..6ede069eb3 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -58,6 +58,7 @@
 #include "pre_activate/ascend/ir_fission/add_memcpy_async.h"
 #include "pre_activate/ascend/format_type/insert_cast_for_runop.h"
 #include "pre_activate/ascend/format_type/insert_transdata_for_runop.h"
+#include "pre_activate/ascend/ir_fission/addn_fission.h"
 #include "utils/context/ms_context.h"
 #include "debug/anf_ir_dump.h"
 #include "debug/anf_ir_utils.h"
@@ -175,6 +176,7 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
     ir_fusion_pm->AddPass(std::make_shared<MulAddFusion>());
     ir_fusion_pm->AddPass(std::make_shared<MulAddNFusion>());
     ir_fusion_pm->AddPass(std::make_shared<MatmulBiasaddFusion>());
+    ir_fusion_pm->AddPass(std::make_shared<AddnFission>());
     ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
     ir_fusion_pm->AddPass(std::make_shared<TransposeTransDataFusion>());
   }
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.cc
new file mode 100644
index 0000000000..f6eb6aca64
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.cc
@@ -0,0 +1,81 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/ir_fission/addn_fission.h"
+#include <memory>
+#include <vector>
+#include "session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+AnfNodePtr CreateNewAddn(const FuncGraphPtr &func_graph, const CNodePtr &origin_addn_cnode, size_t begin_index,
+                         size_t offset) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(origin_addn_cnode);
+  std::vector<AnfNodePtr> new_addn_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimAddN->name()))};
+  for (size_t i = begin_index; i < begin_index + offset; ++i) {
+    new_addn_inputs.push_back(origin_addn_cnode->input(i));
+  }
+  CNodePtr new_addn = func_graph->NewCNode(new_addn_inputs);
+  MS_EXCEPTION_IF_NULL(new_addn);
+  new_addn->set_scope(origin_addn_cnode->scope());
+  new_addn->set_abstract(origin_addn_cnode->abstract());
+  AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToInt(offset)), new_addn);
+  return new_addn;
+}
+}  // namespace
+
+const BaseRef AddnFission::DefinePattern() const {
+  VarPtr Xs = std::make_shared<SeqVar>();
+  return VectorRef({prim::kPrimAddN, Xs});
+}
+
+const AnfNodePtr AddnFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(node);
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  // The real input begins with index 1.
+  size_t origin_input_size = cnode->inputs().size() - 1;
+  if (origin_input_size <= inputs_divisor_) {
+    return nullptr;
+  }
+  CNodePtr new_cnode = cnode;
+  while (origin_input_size > inputs_divisor_) {
+    std::vector<AnfNodePtr> base_addn_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimAddN->name()))};
+    size_t cur_input_index = 1;
+    // Divide the inputs of addn by 63.
+    while (origin_input_size - cur_input_index + 1 > inputs_divisor_) {
+      base_addn_inputs.push_back(CreateNewAddn(func_graph, new_cnode, cur_input_index, inputs_divisor_));
+      cur_input_index += inputs_divisor_;
+    }
+    base_addn_inputs.push_back(
+      CreateNewAddn(func_graph, new_cnode, cur_input_index, origin_input_size - cur_input_index + 1));
+
+    CNodePtr base_addn = func_graph->NewCNode(base_addn_inputs);
+    MS_EXCEPTION_IF_NULL(base_addn);
+    MS_EXCEPTION_IF_NULL(new_cnode);
+    base_addn->set_scope(new_cnode->scope());
+    base_addn->set_abstract(new_cnode->abstract());
+    AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToInt(base_addn_inputs.size() - 1)), base_addn);
+    new_cnode = base_addn;
+    origin_input_size = base_addn->inputs().size() - 1;
+  }
+
+  return new_cnode;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.h b/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.h
new file mode 100644
index 0000000000..3c62391f9a
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fission/addn_fission.h
@@ -0,0 +1,37 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FISSION_ADDN_FISSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FISSION_ADDN_FISSION_H_
+
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+constexpr size_t kAddnInputsDivisor = 63;
+class AddnFission : public PatternProcessPass {
+ public:
+  explicit AddnFission(bool multigraph = true)
+      : PatternProcessPass("addn_fission", multigraph), inputs_divisor_(kAddnInputsDivisor) {}
+  ~AddnFission() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+
+ private:
+  size_t inputs_divisor_;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FISSION_ADDN_FISSION_H_
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index ea5e969e52..2b35168ec3 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -142,6 +142,7 @@ constexpr auto kAttrDynInputSizes = "dyn_input_sizes";
 constexpr auto kAttrSrcFormat = "src_format";
 constexpr auto kAttrOutputUsedNum = "output_used_num";
 constexpr auto kAttrHasBias = "has_bias";
+constexpr auto kAttrN = "N";
 
 // attr value
 constexpr auto kValueTargetSwitch = "target_switch";
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/addn_fission_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/addn_fission_test.cc
new file mode 100644
index 0000000000..90174636b1
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fission/addn_fission_test.cc
@@ -0,0 +1,160 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#define private public
+#define protected public
+#include "pre_activate/ascend/ir_fission/addn_fission.h"
+#undef private
+#undef protected
+
+namespace mindspore {
+namespace opt {
+class TestHWAddnFission : public BackendCommon {
+ public:
+  TestHWAddnFission() : get_py_fun_("gtest_input.pre_activate.addn_fission_test", true) {}
+  ~TestHWAddnFission() override = default;
+
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWAddnFission, test_addn_fission_divided_by_2) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_addn_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 9; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto addn_fission = std::make_shared<opt::AddnFission>();
+  addn_fission->inputs_divisor_ = 2;
+  pm->AddPass(addn_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_addn_fission", "after_divided_by_2");
+  EXPECT_NE(g_after, nullptr);
+  auto kg_after = GetKernelGraph(g_after, args_spec_list);
+  EXPECT_TRUE(CheckEqualGraph(kg_after, new_graph));
+}
+
+TEST_F(TestHWAddnFission, test_addn_fission_divided_by_3) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_addn_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 9; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto addn_fission = std::make_shared<opt::AddnFission>();
+  addn_fission->inputs_divisor_ = 3;
+  pm->AddPass(addn_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_addn_fission", "after_divided_by_3");
+  EXPECT_NE(g_after, nullptr);
+  auto kg_after = GetKernelGraph(g_after, args_spec_list);
+  EXPECT_TRUE(CheckEqualGraph(kg_after, new_graph));
+}
+
+TEST_F(TestHWAddnFission, test_addn_fission_divided_by_4) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_addn_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 9; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto addn_fission = std::make_shared<opt::AddnFission>();
+  addn_fission->inputs_divisor_ = 4;
+  pm->AddPass(addn_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_addn_fission", "after_divided_by_4");
+  EXPECT_NE(g_after, nullptr);
+  auto kg_after = GetKernelGraph(g_after, args_spec_list);
+  EXPECT_TRUE(CheckEqualGraph(kg_after, new_graph));
+}
+
+TEST_F(TestHWAddnFission, test_addn_fission_divided_by_8) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_addn_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 9; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto addn_fission = std::make_shared<opt::AddnFission>();
+  addn_fission->inputs_divisor_ = 8;
+  pm->AddPass(addn_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_addn_fission", "after_divided_by_8");
+  EXPECT_NE(g_after, nullptr);
+  auto kg_after = GetKernelGraph(g_after, args_spec_list);
+  EXPECT_TRUE(CheckEqualGraph(kg_after, new_graph));
+}
+
+TEST_F(TestHWAddnFission, test_addn_fission_divided_by_9) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_addn_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 9; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto addn_fission = std::make_shared<opt::AddnFission>();
+  addn_fission->inputs_divisor_ = 9;
+  pm->AddPass(addn_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_addn_fission", "after_divided_by_9");
+  EXPECT_NE(g_after, nullptr);
+  auto kg_after = GetKernelGraph(g_after, args_spec_list);
+  EXPECT_TRUE(CheckEqualGraph(kg_after, new_graph));
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/addn_fission_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/addn_fission_test.py
new file mode 100644
index 0000000000..c120ac3e68
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/addn_fission_test.py
@@ -0,0 +1,80 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+
+addn = P.AddN()
+make_tuple = Primitive('make_tuple')
+
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_addn_fission(tag):
+    """ test_adam_apply_one_with_decay_rule """
+    fns = FnDict()
+
+    @fns
+    def before(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        return addn((input0, input1, input2, input3, input4, input5, input6, input7, input8))
+
+    @fns
+    def after_divided_by_2(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        a = addn((input0, input1))
+        b = addn((input2, input3))
+        c = addn((input4, input5))
+        d = addn((input6, input7))
+        e = addn((input8,))
+        f = addn((a, b))
+        g = addn((c, d))
+        h = addn((e,))
+        i = addn((f, g))
+        j = addn((h,))
+        return addn((i, j))
+
+    @fns
+    def after_divided_by_3(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        a = addn((input0, input1, input2))
+        b = addn((input3, input4, input5))
+        c = addn((input6, input7, input8))
+        return addn((a, b, c))
+
+    @fns
+    def after_divided_by_4(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        a = addn((input0, input1, input2, input3))
+        b = addn((input4, input5, input6, input7))
+        c = addn((input8,))
+        return addn((a, b, c))
+
+    @fns
+    def after_divided_by_8(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        a = addn((input0, input1, input2, input3, input4, input5, input6, input7))
+        b = addn((input8,))
+        return addn((a, b))
+
+    @fns
+    def after_divided_by_9(input0, input1, input2, input3, input4, input5, input6, input7, input8):
+        return addn((input0, input1, input2, input3, input4, input5, input6, input7, input8))
+
+    return fns[tag]

From cc80c76687dfe8e527ee53bf9872cf3492b68bce Mon Sep 17 00:00:00 2001
From: panfengfeng <panfengfeng@huawei.com>
Date: Sat, 11 Apr 2020 09:28:09 +0800
Subject: [PATCH 189/367] add quantizaiton gpu op

---
 mindspore/_akg/gpu/__init__.py                |   4 +
 mindspore/_akg/gpu/hsigmoid.py                |  63 ++++++
 mindspore/_akg/gpu/hsigmoid_grad.py           |  51 +++++
 mindspore/_akg/gpu/hswish.py                  |  63 ++++++
 mindspore/_akg/gpu/hswish_grad.py             |  53 +++++
 mindspore/_checkparam.py                      |   7 +
 mindspore/ccsrc/CMakeLists.txt                |   1 +
 .../gpu/cuda_impl/batchnorm_fold2_impl.cu     | 169 ++++++++++++++
 .../gpu/cuda_impl/batchnorm_fold2_impl.cuh    |  40 ++++
 .../gpu/cuda_impl/batchnorm_fold_impl.cu      |  88 ++++++++
 .../gpu/cuda_impl/batchnorm_fold_impl.cuh     |  32 +++
 .../kernel/gpu/cuda_impl/concatv2_impl.cu     |   1 +
 .../gpu/cuda_impl/correction_mul_impl.cu      |  66 ++++++
 .../gpu/cuda_impl/correction_mul_impl.cuh     |  27 +++
 .../gpu/cuda_impl/cross_entropy_cuda_impl.cu  |  47 ++++
 .../gpu/cuda_impl/cross_entropy_cuda_impl.cuh |  26 +++
 .../kernel/gpu/cuda_impl/dropout_impl.cu      |  47 ++++
 .../kernel/gpu/cuda_impl/dropout_impl.cuh     |  26 +++
 .../kernel/gpu/cuda_impl/fake_quant_impl.cu   | 133 +++++++++++
 .../kernel/gpu/cuda_impl/fake_quant_impl.cuh  |  32 +++
 .../cuda_impl/fake_quant_per_channel_impl.cu  | 174 +++++++++++++++
 .../cuda_impl/fake_quant_per_channel_impl.cuh |  35 +++
 .../ccsrc/kernel/gpu/cuda_impl/gather.cuh     |   2 +-
 .../sparse_cross_entropy_cuda_impl.cu         |  77 +++++++
 .../sparse_cross_entropy_cuda_impl.cuh        |  30 +++
 .../ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc | 101 +++++++++
 .../ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h  |  67 ++++++
 .../kernel/gpu/nn/dropout_grad_kernel.cc      |  92 ++++++++
 .../ccsrc/kernel/gpu/nn/dropout_grad_kernel.h |  58 +++++
 .../gpu/quant/batchnorm_fold2_gpu_kernel.cc   |  35 +++
 .../gpu/quant/batchnorm_fold2_gpu_kernel.h    | 139 ++++++++++++
 .../quant/batchnorm_fold2_grad_gpu_kernel.cc  |  39 ++++
 .../quant/batchnorm_fold2_grad_gpu_kernel.h   | 167 ++++++++++++++
 .../gpu/quant/batchnorm_fold_gpu_kernel.cc    |  34 +++
 .../gpu/quant/batchnorm_fold_gpu_kernel.h     | 208 ++++++++++++++++++
 .../quant/batchnorm_fold_grad_gpu_kernel.cc   |  32 +++
 .../quant/batchnorm_fold_grad_gpu_kernel.h    | 167 ++++++++++++++
 .../gpu/quant/correction_mul_gpu_kernel.cc    |  29 +++
 .../gpu/quant/correction_mul_gpu_kernel.h     |  98 +++++++++
 .../quant/correction_mul_grad_gpu_kernel.cc   |  33 +++
 .../quant/correction_mul_grad_gpu_kernel.h    | 104 +++++++++
 .../kernel/gpu/quant/fake_quant_gpu_kernel.cc | 176 +++++++++++++++
 .../kernel/gpu/quant/fake_quant_gpu_kernel.h  |  66 ++++++
 .../gpu/quant/fake_quant_grad_gpu_kernel.cc   | 145 ++++++++++++
 .../gpu/quant/fake_quant_grad_gpu_kernel.h    |  61 +++++
 .../fake_quant_per_channel_gpu_kernel.cc      | 181 +++++++++++++++
 .../quant/fake_quant_per_channel_gpu_kernel.h |  66 ++++++
 .../fake_quant_per_channel_grad_gpu_kernel.cc | 158 +++++++++++++
 .../fake_quant_per_channel_grad_gpu_kernel.h  |  63 ++++++
 mindspore/ops/_op_impl/akg/gpu/hsigmoid.py    |  52 +++++
 .../ops/_op_impl/akg/gpu/hsigmoid_grad.py     |  62 ++++++
 mindspore/ops/_op_impl/akg/gpu/hswish.py      |  52 +++++
 mindspore/ops/_op_impl/akg/gpu/hswish_grad.py |  62 ++++++
 53 files changed, 3840 insertions(+), 1 deletion(-)
 create mode 100644 mindspore/_akg/gpu/hsigmoid.py
 create mode 100644 mindspore/_akg/gpu/hsigmoid_grad.py
 create mode 100644 mindspore/_akg/gpu/hswish.py
 create mode 100644 mindspore/_akg/gpu/hswish_grad.py
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh
 create mode 100755 mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cu
 create mode 100755 mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh
 create mode 100755 mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cuh
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cuh
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cuh
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cuh
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh
 mode change 100755 => 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/gather.cuh
 create mode 100755 mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cu
 create mode 100755 mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cuh
 create mode 100644 mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.cc
 create mode 100755 mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
 create mode 100755 mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h
 create mode 100644 mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
 create mode 100644 mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
 create mode 100644 mindspore/ops/_op_impl/akg/gpu/hswish.py
 create mode 100644 mindspore/ops/_op_impl/akg/gpu/hswish_grad.py

diff --git a/mindspore/_akg/gpu/__init__.py b/mindspore/_akg/gpu/__init__.py
index 86334cfcd3..2ac6d1adb1 100644
--- a/mindspore/_akg/gpu/__init__.py
+++ b/mindspore/_akg/gpu/__init__.py
@@ -26,3 +26,7 @@ from .squeeze_grad import SqueezeGrad, gpu_schedule_SqueezeGrad
 from .mean import SimpleMean, gpu_schedule_SimpleMean
 from .mean_grad import SimpleMeanGrad, gpu_schedule_SimpleMeanGrad
 from .mul import Mul, gpu_schedule_Mul
+from .hsigmoid import Hsigmoid, gpu_schedule_Hsigmoid
+from .hsigmoid_grad import HsigmoidGrad, gpu_schedule_HsigmoidGrad
+from .hswish import Hswish, gpu_schedule_Hswish
+from .hswish_grad import HswishGrad, gpu_schedule_HswishGrad
diff --git a/mindspore/_akg/gpu/hsigmoid.py b/mindspore/_akg/gpu/hsigmoid.py
new file mode 100644
index 0000000000..b9d5ea74c9
--- /dev/null
+++ b/mindspore/_akg/gpu/hsigmoid.py
@@ -0,0 +1,63 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""hsigmoid"""
+import _akg.topi as topi
+import _akg.tvm as tvm
+from _akg.topi import tag
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def topi_nn_hsigmoid(x):
+    """
+    topi hsigmoid
+    Args:
+        x:
+
+    Returns:
+
+    """
+    return tvm.compute(x.shape, lambda *i: tvm.if_then_else(x(*i) <= -3, 0,
+                                                            tvm.if_then_else(x(*i) >= 3, 1,
+                                                                             (x(*i) + 3) / 6)))
+
+
+def Hsigmoid(x):
+    """
+    Hsigmoid
+    Args:
+        x:
+
+    Returns:
+
+    """
+    return topi_nn_hsigmoid(x)
+
+
+def gpu_schedule_Hsigmoid(outs):
+    """
+    gpu schedule Hsigmoid
+    Args:
+        outs:
+
+    Returns:
+
+    """
+    device = 'cuda'
+    ctx = tvm.context(device, 0)
+    if not ctx.exist:
+        raise SystemError("Skip because %s is not enabled" % device)
+    with tvm.target.create(device):
+        sch = topi.cuda.schedule_elemwise(outs)
+    return sch
diff --git a/mindspore/_akg/gpu/hsigmoid_grad.py b/mindspore/_akg/gpu/hsigmoid_grad.py
new file mode 100644
index 0000000000..d3e7ac6345
--- /dev/null
+++ b/mindspore/_akg/gpu/hsigmoid_grad.py
@@ -0,0 +1,51 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Hsigmoid grad"""
+import _akg.topi as topi
+import _akg.tvm as tvm
+
+
+def HsigmoidGrad(y_grad, x):
+    """
+    HsigmoidGrad
+    Args:
+        y_grad:
+        x:
+
+    Returns:
+
+    """
+    return tvm.compute(x.shape, lambda *i: tvm.if_then_else(x(*i) <= -3, 0,
+                                                            tvm.if_then_else(x(*i) >= 3, 0,
+                                                                             y_grad(*i) / 6)))
+
+
+def gpu_schedule_HsigmoidGrad(outs):
+    """
+    gpu schedule ReLU6Grad
+    Args:
+        outs:
+
+    Returns:
+
+    """
+    device = 'cuda'
+    ctx = tvm.context(device, 0)
+    if not ctx.exist:
+        raise SystemError("Skip because %s is not enabled" % device)
+
+    with tvm.target.create(device):
+        sch = topi.cuda.schedule_elemwise(outs)
+    return sch
diff --git a/mindspore/_akg/gpu/hswish.py b/mindspore/_akg/gpu/hswish.py
new file mode 100644
index 0000000000..904c38c2a2
--- /dev/null
+++ b/mindspore/_akg/gpu/hswish.py
@@ -0,0 +1,63 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""hswish"""
+import _akg.topi as topi
+import _akg.tvm as tvm
+from _akg.topi import tag
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def topi_nn_hswish(x):
+    """
+    topi hswish
+    Args:
+        x:
+
+    Returns:
+
+    """
+    return tvm.compute(x.shape, lambda *i: tvm.if_then_else(x(*i) <= -3, 0,
+                                                            tvm.if_then_else(x(*i) >= 3, x(*i),
+                                                                             x(*i) * (x(*i) + 3) / 6)))
+
+
+def Hswish(x):
+    """
+    Hswish
+    Args:
+        x:
+
+    Returns:
+
+    """
+    return topi_nn_hswish(x)
+
+
+def gpu_schedule_Hswish(outs):
+    """
+    gpu schedule Hswish
+    Args:
+        outs:
+
+    Returns:
+
+    """
+    device = 'cuda'
+    ctx = tvm.context(device, 0)
+    if not ctx.exist:
+        raise SystemError("Skip because %s is not enabled" % device)
+    with tvm.target.create(device):
+        sch = topi.cuda.schedule_elemwise(outs)
+    return sch
diff --git a/mindspore/_akg/gpu/hswish_grad.py b/mindspore/_akg/gpu/hswish_grad.py
new file mode 100644
index 0000000000..5b38f07c84
--- /dev/null
+++ b/mindspore/_akg/gpu/hswish_grad.py
@@ -0,0 +1,53 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HswishGrad"""
+import _akg.topi as topi
+import _akg.tvm as tvm
+
+
+def HswishGrad(y_grad, x):
+    """
+    HswishGrad
+    Args:
+        y_grad:
+        x:
+
+    Returns:
+
+    """
+    shape = x.shape
+
+    res0 = tvm.compute(shape, lambda *i: tvm.if_then_else(x(*i) <= -3, 0, y_grad(*i) * (2 * x(*i) + 3) / 6))
+    res6 = tvm.compute(shape, lambda *i: tvm.if_then_else(x(*i) >= 3, y_grad(*i), res0(*i)))
+    return res6
+
+
+def gpu_schedule_HswishGrad(outs):
+    """
+    gpu schedule HswishGrad
+    Args:
+        outs:
+
+    Returns:
+
+    """
+    device = 'cuda'
+    ctx = tvm.context(device, 0)
+    if not ctx.exist:
+        raise SystemError("Skip because %s is not enabled" % device)
+
+    with tvm.target.create(device):
+        sch = topi.cuda.schedule_elemwise(outs)
+    return sch
diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py
index d553bcd364..cb3dbc0d50 100644
--- a/mindspore/_checkparam.py
+++ b/mindspore/_checkparam.py
@@ -300,6 +300,13 @@ class ParamValidator:
         for arg, value in args.items():
             ParamValidator.check_subclass(arg, value, mstype.tensor)
 
+    @staticmethod
+    def check_bool(arg_name, arg_value):
+        """Check arg isintance of bool"""
+        if not isinstance(arg_value, bool):
+            raise ValueError(f'The `{arg_name}` should be isintance of bool, but got {arg_value}.')
+        return arg_value
+
     @staticmethod
     def check_type(arg_name, arg_value, valid_types):
         """Type checking."""
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index c49c962bdd..6f6fbf955d 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -473,6 +473,7 @@ if(ENABLE_GPU)
                           gpu_cuda_lib
                           gpu_queue
                           cublas
+                          ${CUDA_PATH}/lib64/libcurand.so
                           ${CUDNN_PATH}/lib64/libcudnn.so
                           ${CUDA_PATH}/lib64/libcudart.so
                           ${CUDA_PATH}/lib64/stubs/libcuda.so)
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cu
new file mode 100644
index 0000000000..3ef856e00a
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cu
@@ -0,0 +1,169 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdint.h>
+#include <thrust/device_ptr.h>
+#include <thrust/fill.h>
+#include <thrust/reduce.h>
+#include <thrust/system/cuda/execution_policy.h>
+#include "batchnorm_fold2_impl.cuh"
+#include "batchnorm_fold_impl.cuh"
+#include "include/cuda_runtime.h"
+
+
+template <typename T>
+__global__ void BatchNormFold2Kernel(const T *x, const T *beta, const T *gamma, const T *batch_std, const T *batch_mean,
+                                     const T *running_std, const T *running_mean, const int *global_step, T *y,
+                                     int freeze_bn, size_t N, size_t C, size_t H, size_t W) {
+  int c = 0;
+  size_t num_count = N * C * H * W;
+  if (*global_step < freeze_bn) {
+    for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
+      c = i / (H * W) % C;
+      y[i] = x[i] * running_std[c] / batch_std[c] + beta[c] - gamma[c] * batch_mean[c] / batch_std[c];
+    }
+  } else {
+    for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
+      c = i / (H * W) % C;
+      y[i] = x[i] + beta[c] - gamma[c] * running_mean[c] / running_std[c];
+    }
+  }
+}
+
+template <typename T>
+__global__ void BatchNormFold2GradReduce1(const T *dout, T *tmp, const T *x, T *tmp2, size_t N, size_t C, size_t HW) {
+  int n = 0;
+  int c = 0;
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < N * C; i += blockDim.x * gridDim.x) {
+    n = i / C;
+    c = i % C;
+    tmp[c * N + n] = thrust::reduce(thrust::seq, dout + i * HW, dout + (i + 1) * HW, 0.f, thrust::plus<T>());
+    tmp2[c * N + n] = thrust::reduce(thrust::seq, x + i * HW, x + (i + 1) * HW, 0.f, thrust::plus<T>());
+  }
+}
+
+template <typename T>
+__global__ void BatchNormFold2GradReduce2(const T *tmp, T *d_beta, const T *tmp2, T *reduce_x, size_t N, size_t C) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < C; i += blockDim.x * gridDim.x) {
+    d_beta[i] = thrust::reduce(thrust::seq, tmp + i * N, tmp + (i + 1) * N, 0.f, thrust::plus<T>());
+    reduce_x[i] = thrust::reduce(thrust::seq, tmp2 + i * N, tmp2 + (i + 1) * N, 0.f, thrust::plus<T>());
+  }
+}
+
+template <typename T>
+__global__ void BatchNormFold2GradNotFreeze(const T *d_beta, const T *reduce_x, const T *batch_mean, const T *batch_std,
+                                            const T *running_mean, const T *running_std, const T *gamma, T *d_gamma,
+                                            T *d_batch_mean, T *d_batch_std, size_t C) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < C; i += blockDim.x * gridDim.x) {
+    d_gamma[i] = -d_beta[i] * batch_mean[i] / batch_std[i];
+    d_batch_mean[i] = -d_beta[i] * gamma[i] / batch_std[i];
+    d_batch_std[i] =
+      (d_beta[i] * gamma[i] * batch_mean[i] - reduce_x[i] * running_std[i]) / batch_std[i] / batch_std[i];
+  }
+}
+
+template <typename T>
+__global__ void BatchNormFold2GradFreeze(const T *d_beta, const T *running_mean, const T *running_std, T *d_gamma,
+                                         size_t C) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < C; i += blockDim.x * gridDim.x) {
+    d_gamma[i] = -d_beta[i] * running_mean[i] / running_std[i];
+  }
+}
+
+template <typename T>
+__global__ void BatchNormFold2GradMul(const T *dout, const T *x, T *tmp_x, size_t NCHW) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < NCHW; i += blockDim.x * gridDim.x) {
+    tmp_x[i] = dout[i] * x[i];
+  }
+}
+
+template <typename T>
+__global__ void DxMul(size_t N, size_t C, size_t HW, const T *batch_std, const T *running_std, T *d_x) {
+  int c = 0;
+  size_t num_count = N * C * HW;
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
+    c = (i / HW) % C;
+    d_x[i] = d_x[i] * running_std[c] / batch_std[c];
+  }
+}
+
+template <typename T>
+void BatchNormFold2Forward(const T *x, const T *beta, const T *gamma, const T *batch_std, const T *batch_mean,
+                           const T *running_std, const T *running_mean, const int *global_step, T *y, int freeze_bn,
+                           size_t N, size_t C, size_t H, size_t W, cudaStream_t cuda_stream) {
+  auto num_count = N * C * H * W;
+  BatchNormFold2Kernel<<<GET_BLOCKS(num_count), GET_THREADS, 0, cuda_stream>>>(
+    x, beta, gamma, batch_std, batch_mean, running_std, running_mean, global_step, y, freeze_bn, N, C, H, W);
+}
+
+template void BatchNormFold2Forward<float>(const float *x, const float *beta, const float *gamma,
+                                           const float *batch_std, const float *batch_mean, const float *running_std,
+                                           const float *running_mean, const int *global_step, float *y, int freeze_bn,
+                                           size_t N, size_t C, size_t H, size_t W, cudaStream_t cuda_stream);
+
+template <typename T>
+void BatchNormFold2GradReduce(const T *dout, const T *x, T *d_beta, T *tmp, T *reduce_x, T *tmp2, T *tmp_x, size_t N,
+                              size_t C, size_t H, size_t W, cudaStream_t cuda_stream) {
+  auto hw = H * W;
+  auto num_count = N * C * H * W;
+  BatchNormFold2GradMul<<<GET_BLOCKS(num_count), GET_THREADS, 0, cuda_stream>>>(dout, x, tmp_x, num_count);
+  BatchNormFold2GradReduce1<<<GET_BLOCKS(N * C), GET_THREADS, 0, cuda_stream>>>(dout, tmp, tmp_x, tmp2, N, C, hw);
+  BatchNormFold2GradReduce2<<<GET_BLOCKS(C), GET_THREADS, 0, cuda_stream>>>(tmp, d_beta, tmp2, reduce_x, N, C);
+}
+
+template void BatchNormFold2GradReduce<float>(const float *dout, const float *x, float *d_beta, float *tmp,
+                                              float *reduce_x, float *tmp2, float *tmp_x, size_t N, size_t C, size_t H,
+                                              size_t W, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFold2GradNotFreeze(const T *d_beta, const T *reduce_x, const T *batch_mean, const T *batch_std,
+                                    const T *running_mean, const T *running_std, const T *gamma, T *d_gamma,
+                                    T *d_batch_mean, T *d_batch_std, size_t C, cudaStream_t cuda_stream) {
+  BatchNormFold2GradNotFreeze<<<GET_BLOCKS(C), GET_THREADS, 0, cuda_stream>>>(
+    d_beta, reduce_x, batch_mean, batch_std, running_mean, running_std, gamma, d_gamma, d_batch_mean, d_batch_std, C);
+}
+
+template void CalBatchNormFold2GradNotFreeze<float>(const float *d_beta, const float *reduce_x, const float *batch_mean,
+                                                    const float *batch_std, const float *running_mean,
+                                                    const float *running_std, const float *gamma, float *d_gamma,
+                                                    float *d_batch_mean, float *d_batch_std, size_t C,
+                                                    cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFold2GradFreeze(const T *d_beta, const T *reduce_x, const T *batch_mean, const T *batch_std,
+                                 const T *running_mean, const T *running_std, const T *gamma, T *d_gamma,
+                                 T *d_batch_mean, T *d_batch_std, size_t C, cudaStream_t cuda_stream) {
+  BatchNormFold2GradFreeze<<<GET_BLOCKS(C), GET_THREADS, 0, cuda_stream>>>(d_beta, running_mean, running_std, d_gamma,
+                                                                           C);
+  ThrustFillWith(d_batch_mean, C, (T)0.f, cuda_stream);
+  ThrustFillWith(d_batch_std, C, (T)0.f, cuda_stream);
+}
+
+template void CalBatchNormFold2GradFreeze<float>(const float *d_beta, const float *reduce_x, const float *batch_mean,
+                                                 const float *batch_std, const float *running_mean,
+                                                 const float *running_std, const float *gamma, float *d_gamma,
+                                                 float *d_batch_mean, float *d_batch_std, size_t C,
+                                                 cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFold2GradNotFreezeDxMul(const T *batch_std, const T *running_std, T *d_x, size_t N, size_t C, size_t H,
+                                         size_t W, cudaStream_t cuda_stream) {
+  DxMul<<<GET_BLOCKS(N * C * H * W), GET_THREADS, 0, cuda_stream>>>(N, C, H * W, batch_std, running_std, d_x);
+}
+
+template void CalBatchNormFold2GradNotFreezeDxMul<float>(const float *batch_std, const float *running_std, float *d_x,
+                                                         size_t N, size_t C, size_t H, size_t W,
+                                                         cudaStream_t cuda_stream);
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh
new file mode 100644
index 0000000000..c3ce08dfd0
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh
@@ -0,0 +1,40 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BATCHNORMFOLD2_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BATCHNORMFOLD2_H_
+
+#include "device/gpu/cuda_common.h"
+template <typename T>
+void BatchNormFold2Forward(const T *x, const T *beta, const T *gamma, const T *batch_std, const T *batch_mean,
+                           const T *running_std, const T *running_mean, const int *global_step, T *y, int freeze_bn,
+                           size_t N, size_t C, size_t H, size_t W, cudaStream_t cuda_stream);
+template <typename T>
+void CalBatchNormFold2GradNotFreeze(const T *d_beta, const T *reduce_x, const T *batch_mean, const T *batch_std,
+                                    const T *running_mean, const T *running_std, const T *gamma, T *d_gamma,
+                                    T *d_batch_mean, T *d_batch_std, size_t C, cudaStream_t cuda_stream);
+template <typename T>
+void CalBatchNormFold2GradFreeze(const T *d_beta, const T *reduce_x, const T *batch_mean, const T *batch_std,
+                                 const T *running_mean, const T *running_std, const T *gamma, T *d_gamma,
+                                 T *d_batch_mean, T *d_batch_std, size_t C, cudaStream_t cuda_stream);
+template <typename T>
+void BatchNormFold2GradReduce(const T *dout, const T *x, T *d_beta, T *tmp, T *reduce_x, T *tmp2, T *tmp_x, size_t N,
+                              size_t C, size_t H, size_t W, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFold2GradNotFreezeDxMul(const T *batch_std, const T *running_std, T *d_x, size_t N, size_t C, size_t H,
+                                         size_t W, cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BATCHNORMFOLD2_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cu
new file mode 100755
index 0000000000..ddc2803f56
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cu
@@ -0,0 +1,88 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <thrust/device_ptr.h>
+#include <thrust/fill.h>
+#include <thrust/system/cuda/execution_policy.h>
+#include "batchnorm_fold_impl.cuh"
+#include "device/gpu/cuda_common.h"
+
+template <typename T>
+__global__ void UpdateRunningStd(int channel_size, const double epsilon, T* running_std) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channel_size; i += blockDim.x * gridDim.x) {
+    running_std[i] = sqrtf(running_std[i] + epsilon);
+  }
+  return;
+}
+
+template <typename T>
+__global__ void UpdateBatchStd(int channel_size, T* batch_std) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channel_size; i += blockDim.x * gridDim.x) {
+    batch_std[i] = 1 / batch_std[i];
+  }
+  return;
+}
+
+template <typename T>
+__global__ void CalDx(const T* d_batch_mean, const T* d_batch_std, const T* x, const T* batch_mean, const T* batch_std,
+                      int batch_size, int channel_size, int height, int width, T* dx) {
+  int n = batch_size * channel_size * height * width;
+  int normal_size = batch_size * height * width;
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
+    int channel_index = i / (height * width) % channel_size;
+    dx[i] = d_batch_mean[channel_index] / normal_size +
+            d_batch_std[channel_index] * (x[i] - batch_mean[channel_index]) / batch_std[channel_index] / normal_size;
+  }
+  return;
+}
+
+template <typename T>
+void CalUpdateRunningStd(int channel_size, double epsilon, T* running_std, cudaStream_t cuda_stream) {
+  UpdateRunningStd<<<GET_BLOCKS(channel_size), GET_THREADS, 0, cuda_stream>>>(channel_size, epsilon, running_std);
+  return;
+}
+
+template void CalUpdateRunningStd<float>(int channel_size, double epsilon, float* running_std,
+                                         cudaStream_t cuda_stream);
+
+template <typename T>
+void CalUpdateBatchStd(int channel_size, T* batch_std, cudaStream_t cuda_stream) {
+  UpdateBatchStd<<<GET_BLOCKS(channel_size), GET_THREADS, 0, cuda_stream>>>(channel_size, batch_std);
+  return;
+}
+
+template void CalUpdateBatchStd<float>(int channel_size, float* batch_std, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFoldGrad(const T* d_batch_mean, const T* d_batch_std, const T* x, const T* batch_mean,
+                          const T* batch_std, int batch_size, int channel_size, int height, int width, T* dx,
+                          cudaStream_t cuda_stream) {
+  CalDx<<<GET_BLOCKS(batch_size * channel_size * height * width), GET_THREADS, 0, cuda_stream>>>(
+    d_batch_mean, d_batch_std, x, batch_mean, batch_std, batch_size, channel_size, height, width, dx);
+}
+
+template void CalBatchNormFoldGrad<float>(const float* d_batch_mean, const float* d_batch_std, const float* x,
+                                          const float* batch_mean, const float* batch_std, int batch_size,
+                                          int channel_size, int height, int width, float* dx, cudaStream_t cuda_stream);
+
+template <typename T>
+void ThrustFillWith(T* array, int size, T tofill, cudaStream_t cuda_stream) {
+  thrust::device_ptr<T> dev_ptr(array);
+  thrust::fill(thrust::cuda::par.on(cuda_stream), dev_ptr, dev_ptr + size, tofill);
+}
+
+template void ThrustFillWith<float>(float* array, int size, float tofill, cudaStream_t cuda_stream);
+
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh
new file mode 100755
index 0000000000..d7ad76c5ad
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh
@@ -0,0 +1,32 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BATCHNORM_FOLD_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BATCHNORM_FOLD_H_
+
+template <typename T>
+void CalUpdateRunningStd(int channel_size, double epsilon, T* running_std, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalUpdateBatchStd(int channel_size, T* batch_std, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalBatchNormFoldGrad(const T* d_batch_mean, const T* d_batch_std, const T* x, const T* batch_mean,
+                          const T* batch_std, int batch_size, int channel_size, int height, int width, T* dx,
+                          cudaStream_t cuda_stream);
+template <typename T>
+void ThrustFillWith(T* array, int size, T tofill, cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BATCHNORM_FOLD_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/concatv2_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/concatv2_impl.cu
index ed330f6e0a..fa10494d9c 100755
--- a/mindspore/ccsrc/kernel/gpu/cuda_impl/concatv2_impl.cu
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/concatv2_impl.cu
@@ -41,3 +41,4 @@ template void CalConcatV2(const size_t size, const int w1, const int w2, const i
                           int* output, cudaStream_t cuda_stream);
 template void CalConcatV2(const size_t size, const int w1, const int w2, const half* input_1, const half* input_2,
                           half* output, cudaStream_t cuda_stream);
+
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cu
new file mode 100755
index 0000000000..ac2f99ed9a
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cu
@@ -0,0 +1,66 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <thrust/reduce.h>
+#include "correction_mul_impl.cuh"
+#include "device/gpu/cuda_common.h"
+
+template <typename T>
+__global__ void CorrectionMul(const T* weight, const T* gamma, const T* running_std, const int batchsize, const int chw,
+                              T* output) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < batchsize * chw; i += blockDim.x * gridDim.x) {
+    int n = i / chw;
+    output[i] = weight[i] * gamma[n] / running_std[n];
+  }
+  return;
+}
+
+template <typename T>
+__global__ void Mul(int N, const T* a, const T* b, T* c) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) {
+    c[i] = a[i] * b[i];
+  }
+  return;
+}
+
+template <typename T>
+__global__ void Reduce(int N, int CHW, const T* tmp, const T* running_std, T* d_gamma) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) {
+    d_gamma[i] = thrust::reduce(thrust::seq, tmp + i * CHW, tmp + (i + 1) * CHW, 0.f, thrust::plus<T>());
+    d_gamma[i] = d_gamma[i] / running_std[i];
+  }
+  return;
+}
+
+template <typename T>
+void CalCorrectionMul(const T* weight, const T* gamma, const T* running_std, int N, int C, int H, int W, T* output,
+                      cudaStream_t cuda_stream) {
+  CorrectionMul<<<GET_BLOCKS(N * C * H * W), GET_THREADS, 0, cuda_stream>>>(weight, gamma, running_std, N, C * H * W,
+                                                                            output);
+}
+
+template void CalCorrectionMul<float>(const float* weight, const float* gamma, const float* running_std, int N, int C,
+                                      int H, int W, float* output, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalCorrectionMulGrad(const T* d_out, const T* weight, const T* running_std, int N, int C, int H, int W, T* d_gamma,
+                          T* tmp, cudaStream_t cuda_stream) {
+  Mul<<<GET_BLOCKS(N * C * H * W), GET_THREADS, 0, cuda_stream>>>(N * C * H * W, d_out, weight, tmp);
+  Reduce<<<GET_BLOCKS(N), GET_THREADS, 0, cuda_stream>>>(N, C * H * W, tmp, running_std, d_gamma);
+}
+
+template void CalCorrectionMulGrad<float>(const float* d_out, const float* weight, const float* running_std, int N,
+                                          int C, int H, int W, float* d_gamma, float* tmp, cudaStream_t cuda_stream);
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cuh
new file mode 100644
index 0000000000..176c063dc8
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/correction_mul_impl.cuh
@@ -0,0 +1,27 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_CORRECTIONMUL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_CORRECTIONMUL_H_
+
+template <typename T>
+void CalCorrectionMul(const T* weight, const T* gamma, const T* running_std, int batch_size, int channel_size,
+                      int height, int width, T* output, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalCorrectionMulGrad(const T* d_out, const T* weight, const T* running_std, int batch_size, int channel_size,
+                          int height, int width, T* d_gamma, T* tmp, cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_CORRECTIONMUL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cu
new file mode 100644
index 0000000000..a3d2e3558c
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cu
@@ -0,0 +1,47 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdint.h>
+#include "cross_entropy_cuda_impl.cuh"
+#include "include/cuda_runtime.h"
+
+__global__ void CalCrossEntropyWithGradKernel(const float *softmax_logits, const float *log_softmax_logits,
+                                              const float *labels, const int batch_size, const int num_classes,
+                                              float *loss, float *dx) {
+  extern __shared__ float loss_shared[];
+  const float mean_scale = 1.0f / static_cast<float>(batch_size);
+
+  loss_shared[threadIdx.x] = 0;
+  for (int i = threadIdx.x * num_classes; i < (threadIdx.x + 1) * num_classes; ++i) {
+    loss_shared[threadIdx.x] -= log_softmax_logits[i] * labels[i];
+    dx[i] = (softmax_logits[i] - labels[i]) * mean_scale;
+  }
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    *loss = 0;
+    for (int i = 0; i < batch_size; i++) {
+      *loss += loss_shared[i];
+    }
+    *loss *= mean_scale;
+  }
+}
+
+void CalCrossEntropyWithGrad(const float *softmax_logits, const float *log_softmax_logits, const float *labels,
+                             const int batch_size, const int num_classes, float *loss, float *dx,
+                             cudaStream_t cuda_stream) {
+  CalCrossEntropyWithGradKernel<<<1, batch_size, batch_size * sizeof(float), cuda_stream>>>(
+    softmax_logits, log_softmax_logits, labels, batch_size, num_classes, loss, dx);
+}
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cuh
new file mode 100644
index 0000000000..25b1624a46
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/cross_entropy_cuda_impl.cuh
@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_CROSSENTROPYCUDAIMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_CROSSENTROPYCUDAIMPL_H_
+
+#include "device/gpu/cuda_common.h"
+
+void CalCrossEntropyWithGrad(const float *softmax_logits, const float *log_softmax_logits, const float *labels,
+                             const int batch_size, const int num_classes, float *loss, float *dx,
+                             cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_CROSSENTROPYCUDAIMPL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cu
new file mode 100644
index 0000000000..bffa73fb76
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cu
@@ -0,0 +1,47 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdint.h>
+#include "dropout_impl.cuh"
+#include "include/cuda_runtime.h"
+
+__global__ void DropoutForwardKernel(const float *input, float *mask, float *output, size_t num_count,
+                                     float drop_prob) {
+  float scale = 1.f / (1.f - drop_prob);
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
+    mask[i] = mask[i] > drop_prob;
+    output[i] = scale * input[i] * mask[i];
+  }
+}
+
+void DropoutForward(const float *input, float *mask, float *output, size_t num_count, float drop_prob,
+                    cudaStream_t cuda_stream) {
+  DropoutForwardKernel<<<GET_BLOCKS(num_count), GET_THREADS, 0, cuda_stream>>>(input, mask, output, num_count,
+                                                                               drop_prob);
+}
+
+__global__ void DropoutBackwardKernel(const float *dy, const float *mask, float *dx, size_t num_count,
+                                      float drop_prob) {
+  float scale = 1.f / (1.f - drop_prob);
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
+    dx[i] = scale * dy[i] * mask[i];
+  }
+}
+
+void DropoutBackward(const float *dy, const float *mask, float *dx, size_t num_count, float drop_prob,
+                     cudaStream_t cuda_stream) {
+  DropoutBackwardKernel<<<GET_BLOCKS(num_count), GET_THREADS, 0, cuda_stream>>>(dy, mask, dx, num_count, drop_prob);
+}
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cuh
new file mode 100644
index 0000000000..9aa05d6a08
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cuh
@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_DROPOUT_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_DROPOUT_H_
+
+#include "device/gpu/cuda_common.h"
+void DropoutForward(const float *input, float *mask, float *output, size_t num_count, float drop_prob,
+                    cudaStream_t cuda_stream);
+void DropoutBackward(const float *dy, const float *mask, float *dx, size_t num_count, float drop_prob,
+                     cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_DROPOUT_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cu
new file mode 100644
index 0000000000..7b09256e1d
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cu
@@ -0,0 +1,133 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <thrust/extrema.h>
+#include <thrust/device_vector.h>
+#include <thrust/pair.h>
+#include "device/gpu/cuda_common.h"
+#include "fake_quant_impl.cuh"
+
+__global__ void FakeQuantize(const float* input, float* output, const int size, const float* nudge_min,
+                             const float* nudge_max, const float* scale, bool symmetric) {
+  float input_x = 0.f;
+  int nudge_input = 0;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += blockDim.x * gridDim.x) {
+    input_x = input[i];
+    // clamp input x
+    if (input_x < nudge_min[0]) {
+      input_x = nudge_min[0];
+    }
+    if (input_x > nudge_max[0]) {
+      input_x = nudge_max[0];
+    }
+    // clamp shift
+    nudge_input = floor((input_x - nudge_min[0]) / scale[0] + 0.5f);
+
+    // quantize
+    output[i] = nudge_input * scale[0] + nudge_min[0];
+  }
+  return;
+}
+
+__global__ void FakeQuantizeGrad(const float* input, const float* gradient, float* output, const int size,
+                                 const float* nudge_min, const float* nudge_max) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += blockDim.x * gridDim.x) {
+    if (input[i] < nudge_min[0] || input[i] > nudge_max[0]) {
+      output[i] = 0;
+    } else {
+      output[i] = gradient[i];
+    }
+  }
+  return;
+}
+
+__global__ void NudgeMinMax(const float* input_min, const float* input_max, const float quant_min,
+                            const float quant_max, float* nudge_min, float* nudge_max, float* scale) {
+  float zp_from_min = 0.f;
+  if ((quant_max - quant_min) == 0 || (*input_max - *input_min) == 0) {
+    *scale = 0.f;
+    zp_from_min = 0.f;
+  } else {
+    *scale = (*input_max - *input_min) / (quant_max - quant_min);
+    zp_from_min = quant_min - *input_min / *scale;
+  }
+
+  float nudge_zp = 0.f;
+  if (zp_from_min <= quant_min) {
+    nudge_zp = quant_min;
+  } else if (zp_from_min >= quant_max) {
+    nudge_zp = quant_max;
+  } else {
+    nudge_zp = round(zp_from_min);
+  }
+
+  *nudge_min = (quant_min - nudge_zp) * (*scale);
+  *nudge_max = (quant_max - nudge_zp) * (*scale);
+  return;
+}
+
+__global__ void UpdateInputMinMaxWithEMA(float* input_min, float* input_max, const float min, const float max,
+                                         const float decay) {
+  *input_min = decay * (min) + (1 - decay) * (*input_min);
+  *input_min = *input_min > 0 ? 0 : *input_min;
+  *input_max = decay * (max) + (1 - decay) * (*input_max);
+  *input_max = *input_max < 0 ? 0 : *input_max;
+  return;
+}
+
+__global__ void UpdateInputMinMax(float* input_min, float* input_max, const float min, const float max) {
+  *input_min = min;
+  *input_max = max;
+}
+
+void CalFakeQuantize(const float* input, float* output, const int size, const float* nudge_min, const float* nudge_max,
+                     const float* scale, bool symmetric, cudaStream_t cuda_stream) {
+  FakeQuantize<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(input, output, size, nudge_min, nudge_max, scale,
+                                                                  symmetric);
+  return;
+}
+
+void CalFakeQuantizeGrad(const float* input, const float* gradient, float* output, const int size,
+                         const float* nudge_min, const float* nudge_max, cudaStream_t cuda_stream) {
+  FakeQuantizeGrad<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(input, gradient, output, size, nudge_min,
+                                                                      nudge_max);
+  return;
+}
+
+void CalNudge(const float* input_min, const float* input_max, const float quant_min, const float quant_max,
+              float* nudge_min, float* nudge_max, float* scale, cudaStream_t cuda_stream) {
+  NudgeMinMax<<<1, 1>>>(input_min, input_max, quant_min, quant_max, nudge_min, nudge_max, scale);
+  return;
+}
+
+void CalMinMax(float* input, float* input_min, float* input_max, const int size, const float ema_decay, const bool ema,
+               cudaStream_t cuda_stream) {
+  float minel = 0.f;
+  float maxel = 0.f;
+  thrust::pair<thrust::device_ptr<float>, thrust::device_ptr<float>> tuple;
+  tuple = thrust::minmax_element(thrust::device_pointer_cast(input), thrust::device_pointer_cast(input) + size);
+  minel = tuple.first[0];
+  maxel = tuple.second[0];
+
+  if (ema) {
+    UpdateInputMinMaxWithEMA<<<1, 1>>>(input_min, input_max, minel, maxel, ema_decay);
+  } else {
+    UpdateInputMinMax<<<1, 1>>>(input_min, input_max, minel, maxel);
+  }
+  return;
+}
+
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cuh
new file mode 100644
index 0000000000..c88c1f79e2
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_impl.cuh
@@ -0,0 +1,32 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
+
+void CalFakeQuantize(const float* input, float* output, const int size, const float* nudge_min, const float* nudge_max,
+                     const float* scale, bool symmetric, cudaStream_t cuda_stream);
+
+void CalFakeQuantizeGrad(const float* input, const float* gradient, float* output, const int size,
+                         const float* nudge_min, const float* nudge_max, cudaStream_t cuda_stream);
+
+void CalNudge(const float* input_min, const float* input_max, const float quant_min, const float quant_max,
+              float* nudge_min, float* nudge_max, float* scale, cudaStream_t cuda_stream);
+
+void CalMinMax(float* input, float* input_min, float* input_max, const int size, const float ema_decay, const bool ema,
+               cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cu
new file mode 100644
index 0000000000..09153bf28f
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cu
@@ -0,0 +1,174 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <thrust/extrema.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/reduce.h>
+#include <thrust/pair.h>
+#include "fake_quant_per_channel_impl.cuh"
+#include "device/gpu/cuda_common.h"
+
+/**
+ * Find the nudge min, max and scale value as output.
+ * @param input_min array
+ * @param input_max array
+ * @param quant_min 1 << bit -1
+ * @param quant_max 0
+ * @param nudge_min array
+ * @param nudge_max array
+ * @param scale array
+ * @param channel_num
+ * @return
+ */
+__global__ void NudgeMinMaxPerChannel(const float* input_min, const float* input_max, const float quant_min,
+                                      const float quant_max, float* nudge_min, float* nudge_max, float* scale,
+                                      int channel_num) {
+  float zp_from_min = 0.f;
+  float nudge_zp = 0.f;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channel_num; i += blockDim.x * gridDim.x) {
+    if ((quant_max - quant_min) == 0 || (input_max[i] - input_min[i]) == 0) {
+      scale[i] = 0.f;
+      zp_from_min = 0.f;
+    } else {
+      scale[i] = (input_max[i] - input_min[i]) / (quant_max - quant_min);
+      zp_from_min = quant_min - input_min[i] / scale[i];
+    }
+
+    if (zp_from_min <= quant_min) {
+      nudge_zp = quant_min;
+    } else if (zp_from_min >= quant_max) {
+      nudge_zp = quant_max;
+    } else {
+      nudge_zp = round(zp_from_min);
+    }
+
+    nudge_min[i] = (quant_min - nudge_zp) * (scale[i]);
+    nudge_max[i] = (quant_max - nudge_zp) * (scale[i]);
+  }
+}
+
+void CalNudgePerChannel(const float* input_min, const float* input_max, const float quant_min, const float quant_max,
+                        float* nudge_min, float* nudge_max, float* scale, const int channel_num,
+                        cudaStream_t cuda_stream) {
+  NudgeMinMaxPerChannel<<<GET_BLOCKS(channel_num), GET_THREADS, 0, cuda_stream>>>(
+    input_min, input_max, quant_min, quant_max, nudge_min, nudge_max, scale, channel_num);
+}
+
+/**
+ * Calulate fake quant output accroding by nudge min, nudge max, nudge scale.
+ * @param input - array
+ * @param output - array
+ * @param total_size - int, purpose for cal the per chanel number in filters
+ * @param channel_size - int, purpose for cal the per channel number in filters
+ * @param nudge_min - array
+ * @param nudge_max - array
+ * @param scale - array
+ * @return
+ */
+__global__ void FakeQuantizePerChannel(const float* input, float* output, const int total_size, const int channel_size,
+                                       const float* nudge_min, const float* nudge_max, const float* scale,
+                                       bool symmetric) {
+  float input_x = 0.f;
+  int nudge_input = 0;
+  int channel_idx = 0;
+  int per_channel_num = total_size / channel_size;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < total_size; i += blockDim.x * gridDim.x) {
+    input_x = input[i];
+    channel_idx = floor(static_cast<double>(i) / static_cast<double>(per_channel_num));
+    // clamp input x
+    if (input_x < nudge_min[channel_idx]) {
+      input_x = nudge_min[channel_idx];
+    }
+    if (input_x > nudge_max[channel_idx]) {
+      input_x = nudge_max[channel_idx];
+    }
+    // clamp shift
+    nudge_input = floor((input_x - nudge_min[channel_idx]) / scale[channel_idx] + 0.5f);
+
+    // quantize
+    output[i] = nudge_input * scale[channel_idx] + nudge_min[channel_idx];
+  }
+}
+
+void CalFakeQuantizePerChannel(const float* input, float* output, const int total_size, const int channel_size,
+                               const float* nudge_min, const float* nudge_max, const float* scale, bool symmetric,
+                               cudaStream_t cuda_stream) {
+  FakeQuantizePerChannel<<<GET_BLOCKS(total_size), GET_THREADS, 0, cuda_stream>>>(
+    input, output, total_size, channel_size, nudge_min, nudge_max, scale, symmetric);
+}
+
+/**
+ * UpdateInputMinMaxPerChannel or UpdateInputMinMaxPerChannel With EMA.
+ * @param input_min
+ * @param input_max
+ * @param min
+ * @param max
+ * @return
+ */
+__global__ void UpdateInputMinMaxPerChannel(float* input_min, float* input_max, float* input, int channels,
+                                            int per_channel_nums, bool ema, float ema_decay) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channels; i += blockDim.x * gridDim.x) {
+    thrust::pair<float*, float*> sum =
+      thrust::minmax_element(thrust::device, input + i * per_channel_nums, input + per_channel_nums * (i + 1));
+    if (ema) {
+      input_min[i] = ema_decay * sum.first[0] + (1 - ema_decay) * input_min[i];
+      input_max[i] = ema_decay * sum.second[0] + (1 - ema_decay) * input_max[i];
+    } else {
+      input_min[i] = sum.first[0];
+      input_max[i] = sum.second[0];
+    }
+  }
+}
+
+__global__ void UpdateInputMinMaxPerChannelWithEMA(float* input_min, float* input_max, float min, float max,
+                                                   const float decay) {
+  *input_min = decay * (min) + (1 - decay) * (*input_min);
+  *input_max = decay * (max) + (1 - decay) * (*input_max);
+}
+
+void CalMinMaxPerChannel(float* input, float* input_min, float* input_max, const int total_size, const int channel_size,
+                         const float ema_decay, const bool ema, cudaStream_t cuda_stream) {
+  int per_channel_num = total_size / channel_size;
+  UpdateInputMinMaxPerChannel<<<GET_BLOCKS(channel_size), GET_THREADS, 0, cuda_stream>>>(
+    input_min, input_max, input, channel_size, per_channel_num, ema, ema_decay);
+}
+
+__global__ void FakeQuantizePerChannelGrad(const float* input, const float* gradient, float* output,
+                                           const int total_size, const int channel_size, const float* nudge_min,
+                                           const float* nudge_max) {
+  int channel_idx = 0;
+  int per_channel_num = total_size / channel_size;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < total_size; i += blockDim.x * gridDim.x) {
+    channel_idx = floor(static_cast<double>(i) / static_cast<double>(per_channel_num));
+    if (input[i] < nudge_min[channel_idx] || input[i] > nudge_max[channel_idx]) {
+      output[i] = 0;
+    } else {
+      output[i] = gradient[i];
+    }
+  }
+}
+
+void CalFakeQuantizePerChannelGrad(const float* input, const float* gradient, float* output, const int total_num,
+                                   const int channel_num, const float* nudge_min, const float* nudge_max,
+                                   cudaStream_t cuda_stream) {
+  FakeQuantizePerChannelGrad<<<GET_BLOCKS(channel_num), GET_THREADS, 0, cuda_stream>>>(
+    input, gradient, output, total_num, channel_num, nudge_min, nudge_max);
+}
+
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh
new file mode 100644
index 0000000000..3dff7156a7
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh
@@ -0,0 +1,35 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
+
+void CalNudgePerChannel(const float* input_min, const float* input_max, const float quant_min, const float quant_max,
+                        float* nudge_min, float* nudge_max, float* scale, const int channel_num,
+                        cudaStream_t cuda_stream);
+
+void CalFakeQuantizePerChannel(const float* input, float* output, const int total_num, const int channel_num,
+                               const float* nudge_min, const float* nudge_max, const float* scale, bool symmetric,
+                               cudaStream_t cuda_stream);
+
+void CalMinMaxPerChannel(float* input, float* input_min, float* input_max, const int total_num, const int channel_num,
+                         const float ema_decay, const bool ema, cudaStream_t cuda_stream);
+
+void CalFakeQuantizePerChannelGrad(const float* input, const float* gradient, float* output, const int total_num,
+                                   const int channel_num, const float* nudge_min, const float* nudge_max,
+                                   cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKEQUANTIZE_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/gather.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/gather.cuh
old mode 100755
new mode 100644
index dae2115a91..a2aab89fb1
--- a/mindspore/ccsrc/kernel/gpu/cuda_impl/gather.cuh
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/gather.cuh
@@ -16,7 +16,7 @@
 
 #ifndef MINDSPORE_GATHER_GPU_CU_H
 #define MINDSPORE_GATHER_GPU_CU_H
-template <typename T, typename  S>
+template <typename T, typename S>
 void Gather(T *input, S *indices, T *output, size_t output_dim0, size_t output_dim1, size_t output_dim2,
             size_t input_dim1, cudaStream_t stream);
 
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cu
new file mode 100755
index 0000000000..b549c5bd4e
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cu
@@ -0,0 +1,77 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdint.h>
+#include "sparse_cross_entropy_cuda_impl.cuh"
+#include "include/cuda_runtime.h"
+
+template <typename T>
+__global__ void CalCrossEntropyKernel(const float *logits, T *labels, const int batch_size, const int class_num,
+                                      float *loss) {
+  float total_loss = 0.0;
+  float epsilon = 1e-6;
+  for (int i = 0; i < batch_size; ++i) {
+    float logit = logits[i * class_num + labels[i]];
+    if (logit <= 0) {
+      logit += epsilon;
+    }
+    float single_loss = -logf(logit);
+    total_loss += single_loss;
+  }
+
+  total_loss /= batch_size;
+  loss[0] = total_loss;
+  return;
+}
+
+template <typename T>
+__global__ void CalCrossEntropyGradKernel(const float *logits, T *labels, const int batch_size, const int class_num,
+                                          float *grad) {
+  for (int i = 0; i < batch_size; i++) {
+    for (int j = blockIdx.x * blockDim.x + threadIdx.x; j < class_num; j += blockDim.x * gridDim.x) {
+      if (labels[i] == j) {
+        grad[i * class_num + j] = (logits[i * class_num + j] - 1) / batch_size;
+      } else {
+        grad[i * class_num + j] = logits[i * class_num + j] / batch_size;
+      }
+    }
+  }
+  return;
+}
+
+template <typename T>
+void CalCrossEntropy(const float *logits, T *labels, const int batch_size, const int class_num, float *loss,
+                     cudaStream_t cuda_stream) {
+  CalCrossEntropyKernel<<<1, 1, 0, cuda_stream>>>(logits, labels, batch_size, class_num, loss);
+  return;
+}
+
+template <typename T>
+void CalCrossEntropyGrad(const float *logits, T *labels, const int batch_size, const int class_num, float *grad,
+                         cudaStream_t cuda_stream) {
+  CalCrossEntropyGradKernel<<<GET_BLOCKS(class_num), GET_THREADS, 0, cuda_stream>>>(logits, labels, batch_size,
+                                                                                    class_num, grad);
+  return;
+}
+
+template void CalCrossEntropy<int>(const float *logits, int *labels, const int batch_size, const int class_num,
+                                   float *loss, cudaStream_t cuda_stream);
+template void CalCrossEntropy<uint64_t>(const float *logits, uint64_t *labels, const int batch_size,
+                                        const int class_num, float *loss, cudaStream_t cuda_stream);
+template void CalCrossEntropyGrad<int>(const float *logits, int *labels, const int batch_size, const int class_num,
+                                       float *grad, cudaStream_t cuda_stream);
+template void CalCrossEntropyGrad<uint64_t>(const float *logits, uint64_t *labels, const int batch_size,
+                                            const int class_num, float *grad, cudaStream_t cuda_stream);
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cuh
new file mode 100755
index 0000000000..d16131470c
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/sparse_cross_entropy_cuda_impl.cuh
@@ -0,0 +1,30 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPARSECROSSENTROPYCUDAIMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPARSECROSSENTROPYCUDAIMPL_H_
+
+#include "device/gpu/cuda_common.h"
+
+template <typename T>
+void CalCrossEntropy(const float *logits, T *labels, const int batch_size, const int class_num, float *loss,
+                     cudaStream_t cuda_stream);
+
+template <typename T>
+void CalCrossEntropyGrad(const float *logits, T *labels, const int batch_size, const int class_num, float *grad,
+                         cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPARSECROSSENTROPYCUDAIMPL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
new file mode 100644
index 0000000000..eeec8365da
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
@@ -0,0 +1,101 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/nn/dropout_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/dropout_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+
+DropoutGpuFwdKernel::DropoutGpuFwdKernel()
+    : cudnn_handle_(nullptr),
+      is_null_input_(false),
+      num_count_(0),
+      drop_prob_(0.0),
+      states_init_(false),
+      mask_generator_(nullptr) {}
+
+DropoutGpuFwdKernel::~DropoutGpuFwdKernel() { DestroyResource(); }
+
+const std::vector<size_t> &DropoutGpuFwdKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &DropoutGpuFwdKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &DropoutGpuFwdKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool DropoutGpuFwdKernel::Init(const CNodePtr &kernel_node) {
+  InitResource();
+
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 1) {
+    MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but DropoutGpuFwdKernel needs 1.";
+  }
+
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  is_null_input_ = CHECK_NULL_INPUT(input_shape);
+  if (is_null_input_) {
+    InitSizeLists();
+    return true;
+  }
+
+  num_count_ = 1;
+  for (size_t x : input_shape) {
+    num_count_ *= x;
+  }
+  drop_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("drop_prob"));
+
+  InitSizeLists();
+  return true;
+}
+
+void DropoutGpuFwdKernel::InitResource() {
+  cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
+}
+
+void DropoutGpuFwdKernel::DestroyResource() noexcept {}
+
+void DropoutGpuFwdKernel::InitSizeLists() {
+  size_t input_size = num_count_ * sizeof(float);
+  size_t workspace_size = 0;
+  input_size_list_.push_back(input_size);
+  output_size_list_.push_back(input_size);  // output size: the same with input size
+  output_size_list_.push_back(input_size);  // mask size: the same with input size
+  workspace_size_list_.push_back(workspace_size);
+}
+
+bool DropoutGpuFwdKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                 const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  if (is_null_input_) {
+    return true;
+  }
+
+  auto *input = reinterpret_cast<float *>(inputs[0]->addr);
+  auto *output = reinterpret_cast<float *>(outputs[0]->addr);
+  auto *mask = reinterpret_cast<float *>(outputs[1]->addr);
+
+  if (!states_init_) {
+    curandCreateGenerator(&mask_generator_, CURAND_RNG_PSEUDO_DEFAULT);
+    curandSetPseudoRandomGeneratorSeed(mask_generator_, time(NULL));
+    states_init_ = true;
+  }
+
+  curandGenerateUniform(mask_generator_, mask, num_count_);
+  DropoutForward(input, mask, output, num_count_, drop_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
+
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h
new file mode 100644
index 0000000000..2b0d84a40c
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h
@@ -0,0 +1,67 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "include/curand.h"
+
+namespace mindspore {
+namespace kernel {
+class DropoutGpuFwdKernel : public GpuKernel {
+ public:
+  DropoutGpuFwdKernel();
+
+  ~DropoutGpuFwdKernel() override;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+
+  const std::vector<size_t> &GetOutputSizeList() const override;
+
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+
+  bool Init(const CNodePtr &kernel_node) override;
+
+ protected:
+  void InitResource() override;
+
+  void InitSizeLists() override;
+
+ private:
+  void DestroyResource() noexcept;
+
+  cudnnHandle_t cudnn_handle_;
+  bool is_null_input_;
+  size_t num_count_;
+  float drop_prob_;
+  bool states_init_;
+  curandGenerator_t mask_generator_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+
+MS_REG_GPU_KERNEL(Dropout, DropoutGpuFwdKernel)
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GPU_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.cc
new file mode 100644
index 0000000000..42c3d279c4
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.cc
@@ -0,0 +1,92 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/nn/dropout_grad_kernel.h"
+#include "kernel/gpu/cuda_impl/dropout_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+DropoutGradGpuFwdKernel::DropoutGradGpuFwdKernel()
+    : cudnn_handle_(nullptr), is_null_input_(false), num_count_(0), drop_prob_(0.0) {}
+
+DropoutGradGpuFwdKernel::~DropoutGradGpuFwdKernel() { DestroyResource(); }
+
+const std::vector<size_t> &DropoutGradGpuFwdKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &DropoutGradGpuFwdKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &DropoutGradGpuFwdKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool DropoutGradGpuFwdKernel::Init(const CNodePtr &kernel_node) {
+  InitResource();
+
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 2) {
+    MS_LOG(ERROR) << "Argument number is " << input_num << ", but DropoutGradGpuFwdKernel needs 2.";
+    return false;
+  }
+
+  auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+  is_null_input_ = CHECK_NULL_INPUT(input_shape);
+  if (is_null_input_) {
+    InitSizeLists();
+    return true;
+  }
+
+  num_count_ = 1;
+  for (size_t x : input_shape) {
+    num_count_ *= x;
+  }
+  drop_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("drop_prob"));
+
+  InitSizeLists();
+  return true;
+}
+
+void DropoutGradGpuFwdKernel::InitResource() {
+  cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
+}
+
+void DropoutGradGpuFwdKernel::DestroyResource() noexcept {}
+
+void DropoutGradGpuFwdKernel::InitSizeLists() {
+  size_t dy_size = num_count_ * sizeof(float);
+  size_t mask_size = dy_size;
+  size_t dx_size = dy_size;
+  size_t workspace_size = 0;
+
+  input_size_list_.push_back(dy_size);
+  input_size_list_.push_back(mask_size);
+  output_size_list_.push_back(dx_size);
+  workspace_size_list_.push_back(workspace_size);
+}
+
+bool DropoutGradGpuFwdKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                     const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  if (is_null_input_) {
+    return true;
+  }
+
+  auto *dy = reinterpret_cast<float *>(inputs[0]->addr);
+  auto *mask = reinterpret_cast<float *>(inputs[1]->addr);
+  auto *dx = reinterpret_cast<float *>(outputs[0]->addr);
+
+  DropoutBackward(dy, mask, dx, num_count_, drop_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
+
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.h
new file mode 100644
index 0000000000..b59b5d2670
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.h
@@ -0,0 +1,58 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GRAD_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GRAD_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class DropoutGradGpuFwdKernel : public GpuKernel {
+ public:
+  DropoutGradGpuFwdKernel();
+  ~DropoutGradGpuFwdKernel() override;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+  bool Init(const CNodePtr &kernel_node) override;
+
+ protected:
+  void InitResource() override;
+  void InitSizeLists() override;
+
+ private:
+  void DestroyResource() noexcept;
+
+  cudnnHandle_t cudnn_handle_;
+  bool is_null_input_;
+  size_t num_count_;
+  float drop_prob_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+
+MS_REG_GPU_KERNEL(DropoutGrad, DropoutGradGpuFwdKernel)
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_DROPOUT_GRAD_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
new file mode 100644
index 0000000000..a95c1b78dd
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
@@ -0,0 +1,35 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+
+MS_REG_GPU_KERNEL_ONE(BatchNormFold2,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      BatchNormFold2GpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
new file mode 100644
index 0000000000..ada4eabd86
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
@@ -0,0 +1,139 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class BatchNormFold2GpuKernel : public GpuKernel {
+ public:
+  BatchNormFold2GpuKernel()
+      : cudnn_handle_(nullptr),
+        is_null_input_(false),
+        batch_size_(0),
+        channel_(0),
+        height_(0),
+        width_(0),
+        freeze_bn_(0) {}
+
+  ~BatchNormFold2GpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    if (is_null_input_) {
+      return true;
+    }
+
+    auto *input = GetDeviceAddress<T>(inputs, 0);
+    auto *beta = GetDeviceAddress<T>(inputs, 1);
+    auto *gamma = GetDeviceAddress<T>(inputs, 2);
+    auto *batch_std = GetDeviceAddress<T>(inputs, 3);
+    auto *batch_mean = GetDeviceAddress<T>(inputs, 4);
+    auto *running_std = GetDeviceAddress<T>(inputs, 5);
+    auto *running_mean = GetDeviceAddress<T>(inputs, 6);
+    auto *global_step = GetDeviceAddress<int32_t>(inputs, 7);
+    auto *output = GetDeviceAddress<T>(outputs, 0);
+
+    BatchNormFold2Forward(input, beta, gamma, batch_std, batch_mean, running_std, running_mean, global_step, output,
+                          freeze_bn_, batch_size_, channel_, height_, width_,
+                          reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) {
+    InitResource();
+
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 8) {
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but BatchNormFold2GpuKernel needs 8.";
+      return false;
+    }
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    is_null_input_ = CHECK_NULL_INPUT(input_shape);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "BatchNormFold2GpuKernel input is null";
+      InitSizeLists();
+      return true;
+    }
+
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "BatchNormFold2GpuKernel input shape needs (N,C,H,W).";
+      return false;
+    }
+    batch_size_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+    freeze_bn_ = GetValue<int32_t>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("freeze_bn"));
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitResource() { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
+
+  void InitSizeLists() {
+    size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
+    size_t weight_size = channel_ * sizeof(T);
+    input_size_list_.push_back(input_size);
+    input_size_list_.push_back(weight_size);      // beta
+    input_size_list_.push_back(weight_size);      // gamma
+    input_size_list_.push_back(weight_size);      // batch_std
+    input_size_list_.push_back(weight_size);      // batch_mean
+    input_size_list_.push_back(weight_size);      // running_std
+    input_size_list_.push_back(weight_size);      // running_mean
+    input_size_list_.push_back(sizeof(int32_t));  // global_step
+
+    output_size_list_.push_back(input_size);
+
+    size_t workspace_size = 0;
+    workspace_size_list_.push_back(workspace_size);
+  }
+
+ private:
+  void DestroyResource() noexcept {}
+
+  cudnnHandle_t cudnn_handle_;
+  bool is_null_input_;
+  size_t batch_size_;
+  size_t channel_;
+  size_t height_;
+  size_t width_;
+  size_t freeze_bn_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GPU_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
new file mode 100644
index 0000000000..d5932f1984
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
@@ -0,0 +1,39 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+
+MS_REG_GPU_KERNEL_ONE(BatchNormFold2Grad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      BatchNormFold2GradGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
new file mode 100644
index 0000000000..ef9611f258
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
@@ -0,0 +1,167 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GRAD_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GRAD_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/batchnorm_fold2_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class BatchNormFold2GradGpuKernel : public GpuKernel {
+ public:
+  BatchNormFold2GradGpuKernel()
+      : cudnn_handle_(nullptr),
+        is_null_input_(false),
+        batch_size_(0),
+        channel_(0),
+        height_(0),
+        width_(0),
+        freeze_bn_(0) {}
+
+  ~BatchNormFold2GradGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    if (is_null_input_) {
+      return true;
+    }
+
+    auto *dout = GetDeviceAddress<T>(inputs, 0);
+    auto *x = GetDeviceAddress<T>(inputs, 1);
+    auto *gamma = GetDeviceAddress<T>(inputs, 2);
+    auto *batch_std = GetDeviceAddress<T>(inputs, 3);
+    auto *batch_mean = GetDeviceAddress<T>(inputs, 4);
+    auto *running_std = GetDeviceAddress<T>(inputs, 5);
+    auto *running_mean = GetDeviceAddress<T>(inputs, 6);
+    auto *global_step = GetDeviceAddress<int32_t>(inputs, 7);
+    auto *d_batch_std = GetDeviceAddress<T>(outputs, 0);
+    auto *d_batch_mean = GetDeviceAddress<T>(outputs, 1);
+    auto *d_beta = GetDeviceAddress<T>(outputs, 2);
+    auto *d_gamma = GetDeviceAddress<T>(outputs, 3);
+    auto *d_x = GetDeviceAddress<T>(outputs, 4);
+    auto *tmp = GetDeviceAddress<T>(workspace, 0);
+    auto *tmp2 = GetDeviceAddress<T>(workspace, 1);
+    auto *reduce_x = GetDeviceAddress<T>(workspace, 2);
+    auto *tmp_x = GetDeviceAddress<T>(workspace, 3);
+
+    int32_t current_step_host[1];
+    size_t x_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(current_step_host, global_step, sizeof(int32_t), cudaMemcpyDeviceToHost),
+                              "Failed to copy gpu memory.");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(d_x, dout, x_size, cudaMemcpyDeviceToDevice), "Failed to copy gpu memory.");
+
+    BatchNormFold2GradReduce(dout, x, d_beta, tmp, reduce_x, tmp2, tmp_x, batch_size_, channel_, height_, width_,
+                             reinterpret_cast<cudaStream_t>(stream_ptr));
+    if (current_step_host[0] < freeze_bn_) {
+      CalBatchNormFold2GradNotFreezeDxMul(batch_std, running_std, d_x, batch_size_, channel_, height_, width_,
+                                          reinterpret_cast<cudaStream_t>(stream_ptr));
+      CalBatchNormFold2GradNotFreeze(d_beta, reduce_x, batch_mean, batch_std, running_mean, running_std, gamma, d_gamma,
+                                     d_batch_mean, d_batch_std, channel_, reinterpret_cast<cudaStream_t>(stream_ptr));
+    } else {
+      CalBatchNormFold2GradFreeze(d_beta, reduce_x, batch_mean, batch_std, running_mean, running_std, gamma, d_gamma,
+                                  d_batch_mean, d_batch_std, channel_, reinterpret_cast<cudaStream_t>(stream_ptr));
+    }
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) {
+    InitResource();
+
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 8) {
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but BatchNormFold2GradGpuKernel needs 8.";
+      return false;
+    }
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    is_null_input_ = CHECK_NULL_INPUT(input_shape);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "BatchNormFold2GradGpuKernel input is null";
+      InitSizeLists();
+      return true;
+    }
+
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "BatchNormFold2GradGpuKernel input shape needs (N,C,H,W).";
+      return false;
+    }
+    batch_size_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+    freeze_bn_ = GetValue<int32_t>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("freeze_bn"));
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitResource() { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
+
+  void InitSizeLists() {
+    size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
+    size_t weight_size = channel_ * sizeof(T);
+    size_t workspace_size = batch_size_ * channel_ * sizeof(T);
+    input_size_list_.push_back(input_size);       // dout
+    input_size_list_.push_back(input_size);       // x
+    input_size_list_.push_back(weight_size);      // gamma
+    input_size_list_.push_back(weight_size);      // batch_std
+    input_size_list_.push_back(weight_size);      // batch_mean
+    input_size_list_.push_back(weight_size);      // running_std
+    input_size_list_.push_back(weight_size);      // running_mean
+    input_size_list_.push_back(sizeof(int32_t));  // global_step
+
+    output_size_list_.push_back(weight_size);  // d_batch_std
+    output_size_list_.push_back(weight_size);  // d_batch_mean
+    output_size_list_.push_back(weight_size);  // d_beta
+    output_size_list_.push_back(weight_size);  // d_gamma
+    output_size_list_.push_back(input_size);   // d_x
+
+    workspace_size_list_.push_back(workspace_size);  // tmp
+    workspace_size_list_.push_back(workspace_size);  // tmp2
+    workspace_size_list_.push_back(weight_size);     // reduce_x
+    workspace_size_list_.push_back(input_size);      // tmp_x
+  }
+
+ private:
+  void DestroyResource() noexcept {}
+
+  cudnnHandle_t cudnn_handle_;
+  bool is_null_input_;
+  size_t batch_size_;
+  size_t channel_;
+  size_t height_;
+  size_t width_;
+  int32_t freeze_bn_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_BATCHNORMFOLD2_GRAD_GPU_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
new file mode 100644
index 0000000000..b5fbfe4927
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/batchnorm_fold_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+
+MS_REG_GPU_KERNEL_ONE(BatchNormFold,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      BatchNormFoldGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
new file mode 100644
index 0000000000..e90fac2792
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
@@ -0,0 +1,208 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/kernel_constants.h"
+#include "kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class BatchNormFoldGpuKernel : public GpuKernel {
+ public:
+  BatchNormFoldGpuKernel()
+      : input_size_(0),
+        output_size_(0),
+        exp_avg_factor_(0.9),
+        epsilon_(1e-12),
+        is_training_(true),
+        freeze_bn_(0),
+        batch_(0),
+        channel_(0),
+        height_(0),
+        width_(0),
+        mode_(CUDNN_BATCHNORM_SPATIAL),
+        x_desc_(nullptr),
+        scale_bias_mean_var_desc_(nullptr),
+        handle_(nullptr) {}
+
+  ~BatchNormFoldGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    (void)workspace;
+    auto x = reinterpret_cast<T *>(inputs[0]->addr);
+    auto mean = reinterpret_cast<T *>(inputs[1]->addr);
+    auto variance = reinterpret_cast<T *>(inputs[2]->addr);
+    int *current_step = reinterpret_cast<int *>(inputs[3]->addr);
+    int current_step_host[1];
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(current_step_host, current_step, sizeof(int), cudaMemcpyDeviceToHost),
+                              "Copy gpu memoy failed.");
+    if (x == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGpuKernel x is null.";
+      return false;
+    }
+    if (mean == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGpuKernel mean is null.";
+      return false;
+    }
+    if (variance == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGpuKernel variance is null.";
+      return false;
+    }
+    if (current_step == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGpuKernel current_step is null.";
+      return false;
+    }
+    auto batch_mean = reinterpret_cast<T *>(outputs[0]->addr);
+    auto batch_std = reinterpret_cast<T *>(outputs[1]->addr);
+    auto running_mean = reinterpret_cast<T *>(outputs[2]->addr);
+    auto running_std = reinterpret_cast<T *>(outputs[3]->addr);
+    auto y = reinterpret_cast<T *>(workspace[0]->addr);
+
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(running_mean, mean, output_size_, cudaMemcpyDeviceToDevice),
+                              "Failed to copy gpu memory.");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(running_std, variance, output_size_, cudaMemcpyDeviceToDevice),
+                              "Failed to copy gpu memory.");
+    CalUpdateRunningStd(channel_, epsilon_, running_std, reinterpret_cast<cudaStream_t>(stream_ptr));
+    if (!is_training_ || current_step_host[0] >= freeze_bn_) {
+      CHECK_CUDA_RET_WITH_ERROR(cudaMemset(batch_mean, 0, output_size_), "Failed to set gpu memory.");
+      ThrustFillWith(batch_std, channel_, 1.f, reinterpret_cast<cudaStream_t>(stream_ptr));
+      return true;
+    }
+    const T alpha = 1;
+    const T beta = 0;
+    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnBatchNormalizationForwardTraining(
+                                  handle_, mode_, &alpha, &beta, x_desc_, x, x_desc_, y, scale_bias_mean_var_desc_,
+                                  mean, mean, exp_avg_factor_, mean, variance, epsilon_, batch_mean, batch_std),
+                                "Failed to launch kernel.")
+    CalUpdateBatchStd(channel_, batch_std, reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) {
+    InitResource();
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 4) {
+      MS_LOG(ERROR) << "Input number is " << input_num << " but BatchNormFold GpuKernel OP needs 4 input.";
+      return false;
+    }
+
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 4) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but BatchNormFold GpuKernel OP needs 4 output.";
+      return false;
+    }
+
+    T momentum = GetValue<T>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("momentum"));
+    exp_avg_factor_ = 1.0 - momentum;
+    epsilon_ = GetValue<T>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("epsilon"));
+    is_training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("is_training"));
+    freeze_bn_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("freeze_bn"));
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "Input shape is " << input_shape.size()
+                    << ", but BatchNormFold GpuKernel OP needs 4DTensor input.";
+      return false;
+    }
+    batch_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+
+    input_size_ = sizeof(T) * batch_ * channel_ * height_ * width_;
+    output_size_ = sizeof(T) * channel_;
+
+    cudnnDataType_t cudnnDataType = kCudnnDtypeMap[TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))];
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      cudnnSetTensor4dDescriptor(x_desc_, CUDNN_TENSOR_NCHW, cudnnDataType, batch_, channel_, height_, width_),
+      "Set x desc failed");
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(
+      cudnnSetTensor4dDescriptor(scale_bias_mean_var_desc_, CUDNN_TENSOR_NCHW, cudnnDataType, 1, channel_, 1, 1),
+      "Set para desc failed");
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() {
+    // x, mean, variance, current_step
+    input_size_list_.push_back(input_size_);
+    input_size_list_.push_back(output_size_);
+    input_size_list_.push_back(output_size_);
+    input_size_list_.push_back(sizeof(int));
+
+    // batch_mean, batch_std, running_mean, running_std
+    output_size_list_.push_back(output_size_);
+    output_size_list_.push_back(output_size_);
+    output_size_list_.push_back(output_size_);
+    output_size_list_.push_back(output_size_);
+
+    // store y
+    workspace_size_list_.push_back(input_size_);
+  }
+
+  void InitResource() {
+    handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
+    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
+    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&scale_bias_mean_var_desc_), "Create para desc failed");
+  }
+
+ private:
+  void DestroyResource() noexcept {
+    CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
+    CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
+  }
+
+  size_t input_size_;
+  size_t output_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  double exp_avg_factor_;
+  double epsilon_;
+  bool is_training_;
+  int freeze_bn_;
+  int batch_;
+  int channel_;
+  int height_;
+  int width_;
+
+  cudnnBatchNormMode_t mode_;
+  cudnnTensorDescriptor_t x_desc_;
+  cudnnTensorDescriptor_t scale_bias_mean_var_desc_;
+
+  cudnnHandle_t handle_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.cc
new file mode 100644
index 0000000000..93ea66258d
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.cc
@@ -0,0 +1,32 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(BatchNormFoldGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      BatchNormFoldGradGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
new file mode 100644
index 0000000000..830f6dc243
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
@@ -0,0 +1,167 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GRAD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GRAD_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/batchnorm_fold_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class BatchNormFoldGradGpuKernel : public GpuKernel {
+ public:
+  BatchNormFoldGradGpuKernel()
+      : input_size_(0),
+        channel_size_(0),
+        workspace_size_(0),
+        momentum_(0.1),
+        epsilon_(1e-12),
+        is_training_(true),
+        freeze_bn_(0),
+        current_step_(0),
+        batch_(0),
+        channel_(0),
+        height_(0),
+        width_(0) {}
+  ~BatchNormFoldGradGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    (void)workspace;
+    // 'd_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'current_step'
+    T *d_batch_mean = GetDeviceAddress<T>(inputs, 0);
+    T *d_batch_std = GetDeviceAddress<T>(inputs, 1);
+    T *x = GetDeviceAddress<T>(inputs, 2);
+    T *batch_mean = GetDeviceAddress<T>(inputs, 3);
+    T *batch_std = GetDeviceAddress<T>(inputs, 4);
+    int *current_step = GetDeviceAddress<int>(inputs, 5);
+    int current_step_host[1];
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(current_step_host, current_step, sizeof(int), cudaMemcpyDeviceToHost),
+                              "Copy gpu memoy failed.");
+    if (d_batch_mean == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel d_batch_mean is null.";
+      return false;
+    }
+    if (d_batch_std == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel d_batch_std is null.";
+      return false;
+    }
+    if (x == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel x is null.";
+      return false;
+    }
+    if (batch_mean == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel batch_mean is null.";
+      return false;
+    }
+    if (batch_std == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel batch_std is null.";
+      return false;
+    }
+    if (current_step == nullptr) {
+      MS_LOG(ERROR) << "BatchNormFoldGradGpuKernel current_step is null.";
+      return false;
+    }
+    T *dx = reinterpret_cast<T *>(outputs[0]->addr);
+
+    if (!is_training_ || current_step_host[0] >= freeze_bn_) {
+      ThrustFillWith(dx, batch_ * channel_ * height_ * width_, 0.f, reinterpret_cast<cudaStream_t>(stream_ptr));
+      return true;
+    }
+    CalBatchNormFoldGrad(d_batch_mean, d_batch_std, x, batch_mean, batch_std, batch_, channel_, height_, width_, dx,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+  bool Init(const CNodePtr &kernel_node) {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 6) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but BatchNormFoldGrad GpuKernel OP needs 6 input.";
+      return false;
+    }
+
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but BatchNormFoldGrad GpuKernel OP needs 4 output.";
+      return false;
+    }
+
+    epsilon_ = GetValue<T>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("epsilon"));
+    is_training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("is_training"));
+    freeze_bn_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("freeze_bn"));
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "Input shape is " << input_shape.size()
+                    << ", but BatchNormFoldGrad GpuKernel OP needs 4DTensor input.";
+      return false;
+    }
+    batch_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+
+    input_size_ = sizeof(T) * batch_ * channel_ * height_ * width_;
+    channel_size_ = sizeof(T) * channel_;
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() {
+    // 'd_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'current_step'
+    input_size_list_.push_back(channel_size_);
+    input_size_list_.push_back(channel_size_);
+    input_size_list_.push_back(input_size_);
+    input_size_list_.push_back(channel_size_);
+    input_size_list_.push_back(channel_size_);
+    input_size_list_.push_back(sizeof(int));
+
+    // 'dx'
+    output_size_list_.push_back(input_size_);
+
+    workspace_size_list_.push_back(workspace_size_);
+  }
+
+ private:
+  size_t input_size_;
+  size_t channel_size_;
+  size_t workspace_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  T momentum_;
+  T epsilon_;
+  bool is_training_;
+  int freeze_bn_;
+  int current_step_;
+  int batch_;
+  int channel_;
+  int height_;
+  int width_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_BATCHNORM_FOLD_GRAD_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.cc
new file mode 100644
index 0000000000..a914b6ec14
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.cc
@@ -0,0 +1,29 @@
+/**
+ * Copyright 2020、 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/correction_mul_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(CorrectionMul,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      CorrectionMulGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
new file mode 100644
index 0000000000..af23d7732a
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
@@ -0,0 +1,98 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMUL_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMUL_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/correction_mul_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class CorrectionMulGpuKernel : public GpuKernel {
+ public:
+  CorrectionMulGpuKernel() : batch_size_(0), channel_(0), height_(0), width_(0) {}
+  ~CorrectionMulGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    auto *weight = GetDeviceAddress<T>(inputs, 0);
+    auto *gamma = GetDeviceAddress<T>(inputs, 1);
+    auto *running_std = GetDeviceAddress<T>(inputs, 2);
+    auto *output = GetDeviceAddress<T>(outputs, 0);
+
+    CalCorrectionMul(weight, gamma, running_std, batch_size_, channel_, height_, width_, output,
+                     reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+  bool Init(const CNodePtr &kernel_node) {
+    InitResource();
+
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 3) {
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but CorrectionMulGpuKernel needs 3.";
+      return false;
+    }
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "CorrectionMulGpuKernel input shape needs (N,C,H,W).";
+      return false;
+    }
+    batch_size_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() {
+    size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
+    size_t weight_size = batch_size_ * sizeof(T);
+    input_size_list_.push_back(input_size);   // weight
+    input_size_list_.push_back(weight_size);  // gamma
+    input_size_list_.push_back(weight_size);  // running_std
+    size_t workspace_size = 0;
+    output_size_list_.push_back(input_size);
+    workspace_size_list_.push_back(workspace_size);
+  }
+  void InitResource() {}
+
+ private:
+  void DestroyResource() noexcept {}
+
+  size_t batch_size_;
+  size_t channel_;
+  size_t height_;
+  size_t width_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMUL_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
new file mode 100644
index 0000000000..211c515e02
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/correction_mul_grad_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/correction_mul_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+
+MS_REG_GPU_KERNEL_ONE(CorrectionMulGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      CorrectionMulGradGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
new file mode 100644
index 0000000000..f20c6278c0
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
@@ -0,0 +1,104 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMULGRAD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMULGRAD_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/correction_mul_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class CorrectionMulGradGpuKernel : public GpuKernel {
+ public:
+  CorrectionMulGradGpuKernel() : batch_size_(0), channel_(0), height_(0), width_(0) {}
+  ~CorrectionMulGradGpuKernel() override { DestroyResource(); }
+
+  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+    auto *d_out = GetDeviceAddress<T>(inputs, 0);
+    auto *weight = GetDeviceAddress<T>(inputs, 1);
+    auto *gamma = GetDeviceAddress<T>(inputs, 2);
+    auto *running_std = GetDeviceAddress<T>(inputs, 3);
+    auto *d_weight = GetDeviceAddress<T>(outputs, 0);
+    auto *d_gamma = GetDeviceAddress<T>(outputs, 1);
+    auto *tmp = GetDeviceAddress<T>(workspace, 0);
+
+    CalCorrectionMul(d_out, gamma, running_std, batch_size_, channel_, height_, width_, d_weight,
+                     reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalCorrectionMulGrad(d_out, weight, running_std, batch_size_, channel_, height_, width_, d_gamma, tmp,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+  bool Init(const CNodePtr &kernel_node) {
+    InitResource();
+
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 4) {
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but CorrectionMulGradGpuKernel needs 4.";
+      return false;
+    }
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+
+    if (input_shape.size() != 4) {
+      MS_LOG(ERROR) << "CorrectionMulGradGpuKernel input shape needs (N,C,H,W).";
+      return false;
+    }
+    batch_size_ = input_shape[0];
+    channel_ = input_shape[1];
+    height_ = input_shape[2];
+    width_ = input_shape[3];
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() {
+    size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
+    size_t weight_size = batch_size_ * sizeof(T);
+    input_size_list_.push_back(input_size);      // d_out
+    input_size_list_.push_back(input_size);      // weight
+    input_size_list_.push_back(weight_size);     // gamma
+    input_size_list_.push_back(weight_size);     // running_std
+    output_size_list_.push_back(input_size);     // d_weight
+    output_size_list_.push_back(weight_size);    // d_gamma
+    workspace_size_list_.push_back(input_size);  // tmp d_out * weight
+  }
+  void InitResource() {}
+
+ private:
+  void DestroyResource() noexcept {}
+
+  size_t batch_size_;
+  size_t channel_;
+  size_t height_;
+  size_t width_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CORRECTIONMULGRAD_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.cc
new file mode 100644
index 0000000000..f4e2c74aac
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.cc
@@ -0,0 +1,176 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/fake_quant_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_impl.cuh"
+#include <thrust/extrema.h>
+#include <thrust/pair.h>
+#include <thrust/device_vector.h>
+#include <cuda_runtime_api.h>
+
+namespace mindspore {
+namespace kernel {
+FakeQuantGpuKernel::FakeQuantGpuKernel()
+    : input_size_(0),
+      min_size_(0),
+      max_size_(0),
+      output_size_(0),
+      workspace_size_(0),
+      num_bits_(0),
+      quant_min_(0),
+      quant_max_(0),
+      quant_num_(0),
+      quant_delay_(0),
+      ema_(false),
+      ema_decay_(0),
+      global_step_(0),
+      training_(false),
+      narrow_range_(false),
+      symmetric_(false) {}
+
+const std::vector<size_t> &FakeQuantGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool FakeQuantGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuant GpuKernel OP needs 1 output.";
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
+  ema_decay_ = 1.0 - GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
+  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
+
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
+  }
+
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay\' " << num_bits_ << "is less then 0, require larger than 0.";
+  }
+
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  if (symmetric_) {
+    quant_min_ = 0 - (1 << (num_bits_ - 1));
+    quant_max_ = (1 << (num_bits_ - 1)) - 1;
+  } else {
+    quant_min_ = 0;
+    quant_max_ = (1 << num_bits_) - 1;
+  }
+
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+  if (narrow_range_) {
+    quant_min_++;
+  }
+
+  if (quant_num_ == 0) {
+    quant_num_ = 1;
+  }
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_num_ *= SizeToInt(input_shape[i]);
+  }
+
+  input_size_ = sizeof(float);
+  min_size_ = sizeof(float);
+  max_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  output_size_ = input_size_;
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);  // input
+  input_size_list_.push_back(min_size_);    // min
+  input_size_list_.push_back(max_size_);    // max
+  output_size_list_.push_back(output_size_);
+  workspace_size_list_.push_back(workspace_size_);
+}
+
+bool FakeQuantGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  (void)workspace;
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 0);
+  float *input_min = GetDeviceAddress<float>(inputs, 1);
+  float *input_max = GetDeviceAddress<float>(inputs, 2);
+
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input x is null.";
+  }
+  if (input_min == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input min is null.";
+  }
+  if (input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input max is null.";
+  }
+
+  // Allocate space for device copies
+  int size = sizeof(float);
+  float *d_scale = nullptr;
+  float *d_nudge_min = nullptr;
+  float *d_nudge_max = nullptr;
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), size), "Malloc gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), size), "Malloc gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), size), "Malloc gpu memory failed");
+
+  if (training_) {
+    // calculate the input min and max according by the parameter ema and ema_decay.
+    CalMinMax(input, input_min, input_max, quant_num_, ema_decay_, ema_, reinterpret_cast<cudaStream_t>(stream_ptr));
+    // control flow for quant_delay
+    if (global_step_ >= quant_delay_) {
+      // real launch
+      CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
+               reinterpret_cast<cudaStream_t>(stream_ptr));
+      CalFakeQuantize(input, output, quant_num_, d_nudge_min, d_nudge_max, d_scale, symmetric_,
+                      reinterpret_cast<cudaStream_t>(stream_ptr));
+    } else {
+      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, input, input_size_, cudaMemcpyDeviceToDevice),
+                                "Copy gpu memory failed");
+    }
+    global_step_++;
+  } else {
+    // real launch
+    CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
+             reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantize(input, output, quant_num_, d_nudge_min, d_nudge_max, d_scale, symmetric_,
+                    reinterpret_cast<cudaStream_t>(stream_ptr));
+  }
+
+  // Cleanup
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
+
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantWithMinMax, FakeQuantGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.h
new file mode 100755
index 0000000000..b14268ed62
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.h
@@ -0,0 +1,66 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class FakeQuantGpuKernel : public GpuKernel {
+ public:
+  FakeQuantGpuKernel();
+  ~FakeQuantGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+  bool Init(const CNodePtr &kernel) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  size_t min_size_;
+  size_t max_size_;
+  size_t output_size_;
+  size_t workspace_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int num_bits_;
+  float quant_min_;
+  float quant_max_;
+  int quant_num_;
+  int quant_delay_;
+  bool ema_;
+  float ema_decay_;
+  int global_step_;
+  bool training_;
+  bool narrow_range_;
+  bool symmetric_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.cc
new file mode 100644
index 0000000000..4746e8e8e0
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.cc
@@ -0,0 +1,145 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/fake_quant_grad_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+FakeQuantGradGpuKernel::FakeQuantGradGpuKernel()
+    : input_size_(0),
+      min_size_(0),
+      max_size_(0),
+      output_size_(0),
+      workspace_size_(0),
+      num_bits_(0),
+      quant_min_(0),
+      quant_max_(0),
+      quant_size_(0),
+      quant_delay_(0),
+      global_step_(0) {}
+
+const std::vector<size_t> &FakeQuantGradGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantGradGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantGradGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool FakeQuantGradGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 4) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuantGrad GpuKernel OP needs 4 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuantGrad GpuKernel OP needs 1 output.";
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
+  }
+
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay_\' " << quant_delay_ << " is less then 0, require larger than 0.";
+  }
+
+  quant_min_ = 0;
+  quant_max_ = (1 << num_bits_) - 1;
+
+  if (quant_size_ == 0) {
+    quant_size_ = 1;
+  }
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_size_ *= SizeToInt(input_shape[i]);
+  }
+
+  input_size_ = sizeof(float);
+  min_size_ = sizeof(float);
+  max_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  output_size_ = input_size_;
+
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantGradGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);  // gradient
+  input_size_list_.push_back(input_size_);  // input
+  input_size_list_.push_back(min_size_);    // min
+  input_size_list_.push_back(max_size_);    // max
+  output_size_list_.push_back(output_size_);
+  workspace_size_list_.push_back(workspace_size_);
+}
+
+bool FakeQuantGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                    const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  (void)workspace;
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *gradient = GetDeviceAddress<float>(inputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 1);
+  float *input_min = GetDeviceAddress<float>(inputs, 2);
+  float *input_max = GetDeviceAddress<float>(inputs, 3);
+
+  if (gradient == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel gradient is null";
+  }
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input is null.";
+  }
+  if (input_min == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input min is null.";
+  }
+  if (input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input max is null.";
+  }
+
+  if (global_step_ >= quant_delay_) {
+    float *d_scale = nullptr;
+    float *d_nudge_min = nullptr;
+    float *d_nudge_max = nullptr;
+    int size = sizeof(float);
+    // Allocate space for device copies
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), size), "Malloc gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), size), "Malloc gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), size), "Malloc gpu memory failed");
+
+    CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
+             reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantizeGrad(input, gradient, output, quant_size_, d_nudge_min, d_nudge_max,
+                        reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    // Cleanup
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
+  } else {
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, gradient, input_size_, cudaMemcpyDeviceToDevice),
+                              "Copy gpu memory failed.");
+  }
+  global_step_++;
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantWithMinMaxGrad, FakeQuantGradGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.h
new file mode 100644
index 0000000000..cd0f9a4680
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.h
@@ -0,0 +1,61 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class FakeQuantGradGpuKernel : public GpuKernel {
+ public:
+  FakeQuantGradGpuKernel();
+  ~FakeQuantGradGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+  bool Init(const CNodePtr &kernel_node) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  size_t min_size_;
+  size_t max_size_;
+  size_t output_size_;
+  size_t workspace_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int num_bits_;
+  float quant_min_;
+  float quant_max_;
+  int quant_size_;
+  int quant_delay_;
+  int global_step_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
new file mode 100644
index 0000000000..302ef8d99f
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
@@ -0,0 +1,181 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh"
+#include <thrust/extrema.h>
+#include <thrust/pair.h>
+#include <thrust/device_vector.h>
+#include <cuda_runtime_api.h>
+
+namespace mindspore {
+namespace kernel {
+FakeQuantPerChannelGpuKernel::FakeQuantPerChannelGpuKernel()
+    : input_size_(0),
+      min_size_(0),
+      max_size_(0),
+      output_size_(0),
+      workspace_size_(0),
+      num_bits_(0),
+      quant_min_(0),
+      quant_max_(0),
+      quant_delay_(0),
+      ema_(false),
+      ema_decay_(0),
+      global_step_(0),
+      training_(false),
+      channel_out_(0),
+      narrow_range_(false),
+      symmetric_(false) {}
+
+const std::vector<size_t> &FakeQuantPerChannelGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerChannelGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerChannelGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool FakeQuantPerChannelGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 input.";
+    return false;
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << " but FakeQuant GpuKernel OP needs 1 output.";
+    return false;
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
+  ema_decay_ = 1.0 - GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
+
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << "is out of range, expected between 2 and 16.";
+    return false;
+  }
+
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay\' " << num_bits_ << " is less then 0, require larger than 0.";
+    return false;
+  }
+
+  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
+
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  if (symmetric_) {
+    quant_min_ = 0 - (1 << (num_bits_ - 1));
+    quant_max_ = (1 << (num_bits_ - 1)) - 1;
+  } else {
+    quant_min_ = 0;
+    quant_max_ = (1 << num_bits_) - 1;
+  }
+
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+  if (narrow_range_) {
+    quant_min_++;
+  }
+
+  // shape info for gpu
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  channel_out_ = SizeToInt(input_shape[0]);
+  min_size_ = sizeof(float) * channel_out_;
+  max_size_ = sizeof(float) * channel_out_;
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  output_size_ = input_size_;
+
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantPerChannelGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);  // input
+  input_size_list_.push_back(min_size_);    // min
+  input_size_list_.push_back(max_size_);    // max
+  output_size_list_.push_back(output_size_);
+  workspace_size_list_.push_back(workspace_size_);
+}
+
+bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
+                                          const std::vector<AddressPtr> &workspace,
+                                          const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  (void)workspace;
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 0);
+  float *input_min = GetDeviceAddress<float>(inputs, 1);
+  float *input_max = GetDeviceAddress<float>(inputs, 2);
+
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input is null.";
+  }
+  if (input_min == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input min is null.";
+  }
+  if (input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input max is null.";
+  }
+
+  // Allocate space for device copies
+  float *d_scale = nullptr;
+  float *d_nudge_min = nullptr;
+  float *d_nudge_max = nullptr;
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), sizeof(float) * channel_out_),
+                            "Malloc gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), sizeof(float) * channel_out_),
+                            "Malloc gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), sizeof(float) * channel_out_),
+                            "Malloc gpu memory failed");
+  int total_size = input_size_ / sizeof(float);
+  bool symmetric = false;
+  if (training_) {
+    // calculate the input min and max according by the parameter ema and ema_decay.
+    CalMinMaxPerChannel(input, input_min, input_max, total_size, channel_out_, ema_decay_, ema_,
+                        reinterpret_cast<cudaStream_t>(stream_ptr));
+    // control flow for quant_delay
+    if (global_step_ >= quant_delay_) {
+      // real launch
+      CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
+      CalFakeQuantizePerChannel(input, output, total_size, channel_out_, d_nudge_min, d_nudge_max, d_scale, symmetric,
+                                reinterpret_cast<cudaStream_t>(stream_ptr));
+    } else {
+      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, input, input_size_, cudaMemcpyDeviceToDevice),
+                                "Copy gpu memory failed.");
+    }
+    global_step_++;
+  } else {
+    // real launch
+    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
+                       reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantizePerChannel(input, output, total_size, channel_out_, d_nudge_min, d_nudge_max, d_scale, symmetric,
+                              reinterpret_cast<cudaStream_t>(stream_ptr));
+  }
+
+  // Cleanup
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
+  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantWithMinMaxPerChannel, FakeQuantPerChannelGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
new file mode 100755
index 0000000000..faf8684fca
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
@@ -0,0 +1,66 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class FakeQuantPerChannelGpuKernel : public GpuKernel {
+ public:
+  FakeQuantPerChannelGpuKernel();
+  ~FakeQuantPerChannelGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+  bool Init(const CNodePtr &kernel) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  size_t min_size_;
+  size_t max_size_;
+  size_t output_size_;
+  size_t workspace_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int num_bits_;
+  float quant_min_;
+  float quant_max_;
+  int quant_delay_;
+  bool ema_;
+  float ema_decay_;
+  int global_step_;
+  bool training_;
+  int channel_out_;
+  bool narrow_range_;
+  bool symmetric_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GPUKERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.cc
new file mode 100644
index 0000000000..3184132121
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.cc
@@ -0,0 +1,158 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+FakeQuantPerChannelGradGpuKernel::FakeQuantPerChannelGradGpuKernel()
+    : input_size_(0),
+      min_size_(0),
+      max_size_(0),
+      output_size_(0),
+      workspace_size_(0),
+      num_bits_(0),
+      quant_min_(0),
+      quant_max_(0),
+      channel_out_(0),
+      quant_delay_(0),
+      global_step_(0),
+      narrow_range_(false),
+      symmetric_(false) {}
+
+const std::vector<size_t> &FakeQuantPerChannelGradGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerChannelGradGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerChannelGradGpuKernel::GetWorkspaceSizeList() const {
+  return workspace_size_list_;
+}
+
+bool FakeQuantPerChannelGradGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 4) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuantGrad GpuKernel OP needs 4 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuantGrad GpuKernel OP needs 1 output.";
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
+  }
+
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay_\' " << quant_delay_ << " is less then 0, require larger than 0.";
+  }
+
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  if (symmetric_) {
+    quant_min_ = 0 - (1 << (num_bits_ - 1));
+    quant_max_ = (1 << (num_bits_ - 1)) - 1;
+  } else {
+    quant_min_ = 0;
+    quant_max_ = (1 << num_bits_) - 1;
+  }
+
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+  if (narrow_range_) {
+    quant_min_++;
+  }
+
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  channel_out_ = SizeToInt(input_shape[0]);
+  min_size_ = sizeof(float) * channel_out_;
+  max_size_ = sizeof(float) * channel_out_;
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  output_size_ = input_size_;
+
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantPerChannelGradGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);  // gradient
+  input_size_list_.push_back(input_size_);  // input
+  input_size_list_.push_back(min_size_);    // min
+  input_size_list_.push_back(max_size_);    // max
+  output_size_list_.push_back(output_size_);
+  workspace_size_list_.push_back(workspace_size_);
+}
+
+bool FakeQuantPerChannelGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
+                                              const std::vector<AddressPtr> &workspace,
+                                              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+  (void)workspace;
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *gradient = GetDeviceAddress<float>(inputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 1);
+  float *input_min = GetDeviceAddress<float>(inputs, 2);
+  float *input_max = GetDeviceAddress<float>(inputs, 3);
+
+  if (gradient == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGradGpuKernel gradient is null";
+  }
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGradGpuKernel input is null";
+  }
+  if (input_min == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGradGpuKernel input min is null";
+  }
+  if (input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGradGpuKernel input max is null";
+  }
+
+  int total_size = input_size_ / sizeof(float);
+  if (global_step_ >= quant_delay_) {
+    float *d_scale = nullptr;
+    float *d_nudge_min = nullptr;
+    float *d_nudge_max = nullptr;
+    // Allocate space for device copies
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), channel_out_ * sizeof(float)),
+                              "Malloc gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), channel_out_ * sizeof(float)),
+                              "Malloc gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), channel_out_ * sizeof(float)),
+                              "Malloc gpu memory failed");
+
+    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
+                       reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantizePerChannelGrad(input, gradient, output, total_size, channel_out_, d_nudge_min, d_nudge_max,
+                                  reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    // Cleanup
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
+    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
+  } else {
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, gradient, input_size_, cudaMemcpyDeviceToDevice),
+                              "Copy gpu memory failed.");
+  }
+  global_step_++;
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantWithMinMaxPerChannelGrad, FakeQuantPerChannelGradGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h
new file mode 100644
index 0000000000..c210f4cc81
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h
@@ -0,0 +1,63 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GRAD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GRAD_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class FakeQuantPerChannelGradGpuKernel : public GpuKernel {
+ public:
+  FakeQuantPerChannelGradGpuKernel();
+  ~FakeQuantPerChannelGradGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override;
+  bool Init(const CNodePtr &kernel_node) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  size_t min_size_;
+  size_t max_size_;
+  size_t output_size_;
+  size_t workspace_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int num_bits_;
+  float quant_min_;
+  float quant_max_;
+  int channel_out_;
+  int quant_delay_;
+  int global_step_;
+  bool narrow_range_;
+  bool symmetric_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PER_CHANNEL_GRAD_GPUKERNEL_H_
diff --git a/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
new file mode 100644
index 0000000000..29dd8d6251
--- /dev/null
+++ b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
@@ -0,0 +1,52 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HSigmoid op"""
+from mindspore.ops.op_info_register import op_info_register
+
+@op_info_register("""{
+    "op_name": "HSigmoid",
+    "imply_type": "AutoDiff",
+    "fusion_type": "OPAQUE",
+    "processor": "cuda",
+    "attr": [
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "x"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "output"
+        }
+    ]
+}""")
+def _hsigmoid_akg():
+    """HSigmoid AutoDiff register"""
+    return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py b/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
new file mode 100644
index 0000000000..d29df9c946
--- /dev/null
+++ b/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
@@ -0,0 +1,62 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HSigmoidGrad op"""
+from mindspore.ops.op_info_register import op_info_register
+
+@op_info_register("""{
+    "op_name": "HSigmoidGrad",
+    "imply_type": "AutoDiff",
+    "fusion_type": "OPAQUE",
+    "processor": "cuda",
+    "attr": [
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "y_grad"
+        },
+        {
+            "index": 1,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "x"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "output"
+        }
+    ]
+}""")
+def _hsigmoid_grad_akg():
+    """HSigmoidGrad AutoDiff register"""
+    return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish.py b/mindspore/ops/_op_impl/akg/gpu/hswish.py
new file mode 100644
index 0000000000..619575920f
--- /dev/null
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish.py
@@ -0,0 +1,52 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HSwish op"""
+from mindspore.ops.op_info_register import op_info_register
+
+@op_info_register("""{
+    "op_name": "HSwish",
+    "imply_type": "AutoDiff",
+    "fusion_type": "OPAQUE",
+    "processor": "cuda",
+    "attr": [
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "x"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "output"
+        }
+    ]
+}""")
+def _hswish_akg():
+    """HSwish AutoDiff register"""
+    return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
new file mode 100644
index 0000000000..6d3556b969
--- /dev/null
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
@@ -0,0 +1,62 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HSwishGrad op"""
+from mindspore.ops.op_info_register import op_info_register
+
+@op_info_register("""{
+    "op_name": "HSwishGrad",
+    "imply_type": "AutoDiff",
+    "fusion_type": "OPAQUE",
+    "processor": "cuda",
+    "attr": [
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "y_grad"
+        },
+        {
+            "index": 1,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "x"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float32", "float16"
+            ],
+            "format": [
+                "DefaultFormat", "DefaultFormat"
+            ],
+            "name": "output"
+        }
+    ]
+}""")
+def _hswish_grad_akg():
+    """HSwishGrad AutoDiff register"""
+    return

From b81cc6ea4fedbe893a52dbfee83f240117c403a9 Mon Sep 17 00:00:00 2001
From: lichenever <lichentrue@163.com>
Date: Mon, 13 Apr 2020 11:43:33 +0800
Subject: [PATCH 190/367] add minimum distributed op

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |   1 +
 .../ops_info/comparison_function_info.h       |   8 ++
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |   1 +
 .../ccsrc/parallel/step_auto_parallel.cc      |   1 +
 .../parallel/test_comparison_function_info.py | 110 +++++++++++++++---
 5 files changed, 108 insertions(+), 13 deletions(-)

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 1650ff0b21..145a8a0840 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -114,6 +114,7 @@ REGISTER(DropoutDoMaskInfo);
 REGISTER(ReshapeInfo);
 REGISTER(FloorDivInfo);
 REGISTER(MaximumInfo);
+REGISTER(MinimumInfo);
 REGISTER(CastInfo);
 REGISTER(GreaterInfo);
 REGISTER(SparseSoftmaxCrossEntropyWithLogitsInfo);
diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
index 5f51f1d0a9..00cc431463 100644
--- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
@@ -50,6 +50,14 @@ class MaximumInfo : public ArithmeticBase {
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
   ~MaximumInfo() override = default;
 };
+
+class MinimumInfo : public ArithmeticBase {
+ public:
+  MinimumInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+              const PrimitiveAttrs& attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+  ~MinimumInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index befd26e318..b0a9fb3a3c 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -186,6 +186,7 @@ constexpr char LOG[] = "Log";
 constexpr char SIGMOID[] = "Sigmoid";
 constexpr char POW[] = "Pow";
 constexpr char MAXIMUM[] = "Maximum";
+constexpr char MINIMUM[] = "Minimum";
 constexpr char EQUAL[] = "Equal";
 constexpr char NOT_EQUAL[] = "NotEqual";
 constexpr char LOGICALNOT[] = "LogicalNot";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index fe6be575ee..1eb881b798 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -93,6 +93,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SIGMOID,
                                            POW,
                                            MAXIMUM,
+                                           MINIMUM,
                                            EQUAL,
                                            NOT_EQUAL,
                                            LOGICALNOT,
diff --git a/tests/ut/python/parallel/test_comparison_function_info.py b/tests/ut/python/parallel/test_comparison_function_info.py
index 74de04f1df..93ec5e5981 100644
--- a/tests/ut/python/parallel/test_comparison_function_info.py
+++ b/tests/ut/python/parallel/test_comparison_function_info.py
@@ -54,11 +54,10 @@ def test_matmul_equal():
             out = self.equal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -78,11 +77,10 @@ def test_matmul_not_equal():
             out = self.notequal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -102,11 +100,10 @@ def test_matmul_not_equal_repeated_calculation():
             out = self.notequal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 1), (4, 1))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -126,11 +123,10 @@ def test_matmul_maximum():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -150,11 +146,10 @@ def test_matmul_maximum_broadcast():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (2, ))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -174,13 +169,102 @@ def test_matmul_maximum_broadcast2():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 4), (4, 1))
     strategy2 = ((4, 1), (1, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 1]), dtype=ms.float32)
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
-    _executor.compile(net, x, y, b)
\ No newline at end of file
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Minimum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_broadcast():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Maximum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (2, ))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_broadcast2():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Minimum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 4), (4, 1))
+    strategy2 = ((4, 1), (1, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 1]), dtype=ms.float32)
+    b = Tensor(np.ones([1, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_auto_parallel():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.matmul = P.MatMul()
+            self.minimum = P.Minimum()
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
+    net = GradWrap(NetWithLoss(Net()))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 1]), dtype=ms.float32)
+    b = Tensor(np.ones([1, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)

From d62f560b500ff5fdab3298b94f7a7a0c274390f5 Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Sat, 11 Apr 2020 11:35:54 +0800
Subject: [PATCH 191/367] add_bool_type_check_in_comm_op

---
 .../parallel/auto_parallel/edge_costmodel.cc  | 12 ++++-
 .../parallel/auto_parallel/edge_costmodel.h   |  2 +-
 .../ccsrc/parallel/ops_info/operator_info.cc  | 10 ++++
 .../ccsrc/parallel/ops_info/operator_info.h   |  9 +++-
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |  1 +
 .../ccsrc/parallel/step_auto_parallel.cc      | 34 ++++++++------
 mindspore/ccsrc/parallel/step_auto_parallel.h |  4 +-
 mindspore/ops/operations/comm_ops.py          |  8 ++++
 .../parallel/auto_parallel/dp_algo_test.cc    | 46 +++++++++++++++++++
 .../auto_parallel/edge_costmodel_test.cc      |  5 ++
 .../auto_parallel/graph_costmodel_test.cc     |  6 +++
 tests/ut/python/communication/test_comm.py    |  2 +-
 12 files changed, 120 insertions(+), 19 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
index 653f6c903d..cbd66f58a6 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
@@ -61,11 +61,12 @@ Status Edge::InitEdgeCost() {
       auto target_output_lyt = target_output.second[prev_op_output_index_].tensor_layout();
       auto target_output_str = target_output.first;
       auto type_length = prev_op_->GetOutputTypeLengths()[prev_op_output_index_];
+      auto type = prev_op_->outputs_type()[prev_op_output_index_];
       for (auto& target_input : next_op_input_) {
         auto target_input_lyt = target_input.second[next_op_input_index_].tensor_layout();
         auto target_input_str = target_input.first;
         CostPtr cost;
-        if (GetRedistributionCost(target_output_lyt, target_input_lyt, type_length, &cost) != SUCCESS) {
+        if (GetRedistributionCost(target_output_lyt, target_input_lyt, type_length, type, &cost) != SUCCESS) {
           MS_LOG(EXCEPTION) << "Failure: redistribution cost calculation failed";
         }
         MS_EXCEPTION_IF_NULL(cost);
@@ -99,7 +100,7 @@ Status Edge::InitEdgeCost() {
 }
 
 Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, const TensorLayout& next_op_input_layout,
-                                   size_t type_length, CostPtr* cost) {
+                                   size_t type_length, TypePtr type, CostPtr* cost) {
   MS_EXCEPTION_IF_NULL(prev_op_);
   MS_EXCEPTION_IF_NULL(cost);
   RankList dev_list = prev_op_->global_device_list();
@@ -119,6 +120,13 @@ Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, co
   double backward_comm_cost = tensor_redistribution.backward_comm_cost();
   double computation_cost = tensor_redistribution.computation_cost();
 
+  // Now AllGather, ReduceScatter, AlltoAll don't support bool type
+  MS_EXCEPTION_IF_NULL(type);
+  if ((type->type_id() == kNumberTypeBool) && (comm_cost > 0)) {
+    computation_cost = INF;
+    comm_cost = INF;
+    MS_LOG(WARNING) << "Communication Operators don't support bool dtype!";
+  }
   *cost = std::make_shared<Cost>(type_length * computation_cost, type_length * comm_cost);
   (*cost)->communication_without_parameter_ = type_length * comm_cost;
   (*cost)->communication_with_partial_para_ =
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
index eb89466d7c..bd882bb43f 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
@@ -84,7 +84,7 @@ class Edge {
   // and the input tensor layout of v, return the redistribution cost,
   // and the op_list to carry out the redistribution.
   Status GetRedistributionCost(const TensorLayout& prev_op_output_layout, const TensorLayout& next_op_input_layout,
-                               size_t, CostPtr* cost);
+                               size_t, TypePtr type, CostPtr* cost);
 
   void set_pre_op_output(const std::vector<std::pair<std::shared_ptr<Strategy>, std::vector<TensorInfo>>>& output_set) {
     pre_op_output_ = output_set;
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 42755b3ec3..561628dbb2 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -1197,6 +1197,16 @@ Status OperatorInfo::SetInputAndOutputTypeLength(const std::vector<size_t>& inpu
   return SUCCESS;
 }
 
+Status OperatorInfo::set_outputs_type(const std::vector<TypePtr>& outputs_type) {
+  if (outputs_type.size() != outputs_shape_.size()) {
+    MS_LOG(ERROR) << "Outputs type: " << outputs_type.size()
+                  << " do not have the same number of outputs shape: " << outputs_shape_.size();
+    return FAILED;
+  }
+  outputs_type_ = outputs_type;
+  return SUCCESS;
+}
+
 void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra, const CostPtr& cost) {
   if (!stra->GetInputDim().empty() && !stra->GetInputDim()[0].empty()) {
     CheckGlobalDeviceManager();
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index 248172fa4c..5fe89a1602 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -60,7 +60,8 @@ class OperatorInfo {
         outputs_shape_(std::move(outputs_shape)),
         attrs_(std::move(attrs)),
         is_alive_(true),
-        cost_(cost) {
+        cost_(cost),
+        outputs_type_() {
     std::vector<bool> not_parameteter(inputs_shape_.size(), false);
     is_parameter_ = not_parameteter;
     refkey_parameter_name_ = "";
@@ -71,6 +72,11 @@ class OperatorInfo {
   Status set_is_parameter(const std::vector<bool>& is_parameter);
   Status SetInputAndOutputTypeLength(const std::vector<size_t>& input_lengths,
                                      const std::vector<size_t>& output_lengths);
+  // Set outputs dtype.
+  // If only one output, outputs_type.size() is 1.
+  // If output is tuple, outputs_type.size() is greater than 1.
+  Status set_outputs_type(const std::vector<TypePtr>& outputs_type);
+  const std::vector<TypePtr>& outputs_type() const { return outputs_type_; }
   virtual Status Init(const StrategyPtr& strategy) = 0;
   virtual Status InitForCostModel(const StrategyPtr& strategy) = 0;  // only init the necessary parts
 
@@ -229,6 +235,7 @@ class OperatorInfo {
 
  private:
   OperatorCostPtr cost_;
+  std::vector<TypePtr> outputs_type_;
 };
 
 Shape GetSliceShape(const Shape& tensor_shape, const Dimensions& strategy);
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index befd26e318..1373fdcfeb 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -42,6 +42,7 @@ constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1;
 constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2;
 constexpr double EPS = 1e-6;
+constexpr double INF = 1e20;
 
 constexpr char AUTO_PARALLEL_RUN_ONCE_ONLY[] = "auto_parallel_run_once_only";
 constexpr char SEMI_AUTO_PARALLEL_RUN_ONCE_ONLY[] = "semi_auto_parallel_run_once_only";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index fe6be575ee..b25dc074d2 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -254,12 +254,9 @@ size_t GetInputsTypeLen(const AnfNodePtr &input) {
   return input_type_len;
 }
 
-// Given the node, return the element length of input and output
-std::vector<std::vector<size_t>> ExtractInputAndOutputTypeLengthByNode(const CNodePtr &node) {
+std::vector<size_t> ExtractInputTypeLengthByNode(const CNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   std::vector<size_t> inputs_type_len;
-  std::vector<size_t> outputs_type_len;
-  std::vector<std::vector<size_t>> all_types;
   std::vector<AnfNodePtr> node_inputs{node->inputs()};
 
   // extract input element length
@@ -277,9 +274,13 @@ std::vector<std::vector<size_t>> ExtractInputAndOutputTypeLengthByNode(const CNo
       inputs_type_len.push_back(GetInputsTypeLen(input));
     }
   }
-  all_types.push_back(inputs_type_len);
+  return inputs_type_len;
+}
 
-  // extract output element length
+std::vector<TypePtr> ExtractOutputTypeByNode(const CNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  std::vector<TypePtr> outputs_type;
+  // extract output element type
   auto primary_output_type = node->Type();
   MS_EXCEPTION_IF_NULL(primary_output_type);
   if (primary_output_type->isa<mindspore::Tuple>()) {
@@ -289,7 +290,7 @@ std::vector<std::vector<size_t>> ExtractInputAndOutputTypeLengthByNode(const CNo
     for (auto &ele : elements) {
       if (ele->isa<mindspore::TensorType>()) {
         auto ele_element_type = ele->cast<mindspore::TensorTypePtr>()->element();
-        outputs_type_len.push_back(GetLengthOfDataType(ele_element_type));
+        outputs_type.push_back(ele_element_type);
       } else {
         MS_LOG(EXCEPTION) << "Unknown type: " << primary_output_type->type_name();
       }
@@ -298,14 +299,12 @@ std::vector<std::vector<size_t>> ExtractInputAndOutputTypeLengthByNode(const CNo
     // in this case, the output is a single tensor
     if (primary_output_type->isa<mindspore::TensorType>()) {
       auto element_type = primary_output_type->cast<mindspore::TensorTypePtr>()->element();
-      outputs_type_len.push_back(GetLengthOfDataType(element_type));
+      outputs_type.push_back(element_type);
     } else {
       MS_LOG(EXCEPTION) << "Unknown type: " << primary_output_type->type_name();
     }
   }
-  all_types.push_back(outputs_type_len);
-
-  return all_types;
+  return outputs_type;
 }
 
 // Be careful the argument is cnode_full_name, not the op_name
@@ -366,11 +365,20 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
     return nullptr;
   }
   // Set the data type for inputs and outputs of this OperatorInfo
-  std::vector<std::vector<size_t>> type_lengths = ExtractInputAndOutputTypeLengthByNode(cnode);
-  if (operator_info->SetInputAndOutputTypeLength(type_lengths[0], type_lengths[1]) != SUCCESS) {
+  auto inputs_type_length = ExtractInputTypeLengthByNode(cnode);
+  auto outputs_type = ExtractOutputTypeByNode(cnode);
+  std::vector<size_t> outputs_type_length;
+  outputs_type_length.reserve(outputs_type.size());
+  std::transform(outputs_type.begin(), outputs_type.end(), std::back_inserter(outputs_type_length),
+                 GetLengthOfDataType);
+  if (operator_info->SetInputAndOutputTypeLength(inputs_type_length, outputs_type_length) != SUCCESS) {
     MS_LOG(ERROR) << "Setting the lengths of inputs and outputs failed for operator: " << operator_info->name();
     return nullptr;
   }
+  if (operator_info->set_outputs_type(outputs_type) != SUCCESS) {
+    MS_LOG(ERROR) << "Setting the types of outputs failed for operator: " << operator_info->name();
+    return nullptr;
+  }
   // When the 'inputs' contains numerical values for some operators, these values should be extracted from
   // ANF graph
   auto &inputs = cnode->inputs();
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.h b/mindspore/ccsrc/parallel/step_auto_parallel.h
index 5ee75ca162..349af7c956 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.h
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.h
@@ -39,7 +39,9 @@ size_t GetLengthOfDataType(const TypePtr &type);
 
 std::vector<bool> ExtractInputParameterByNode(const CNodePtr &node);
 
-std::vector<std::vector<size_t>> ExtractInputAndOutputTypeLengthByNode(const CNodePtr &node);
+std::vector<size_t> ExtractInputTypeLengthByNode(const CNodePtr &node);
+
+std::vector<TypePtr> ExtractOutputTypeByNode(const CNodePtr &node);
 
 Status ConstructCostGraphNodes(const std::vector<AnfNodePtr> &all_nodes, const FuncGraphPtr &root);
 
diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py
index 1644c5800a..441e441c2c 100644
--- a/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/ops/operations/comm_ops.py
@@ -162,6 +162,8 @@ class AllGather(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("AllGather does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -219,6 +221,8 @@ class ReduceScatter(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("ReduceScatter does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
@@ -276,6 +280,8 @@ class Broadcast(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("Broadcast does not support 'Bool' as the dtype of input!")
         return x_dtype
 
 
@@ -318,6 +324,8 @@ class _AlltoAll(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
+        if x_dtype == mstype.bool_:
+            raise TypeError("AlltoAll does not support 'Bool' as the dtype of input!")
         return x_dtype
 
     def __call__(self, tensor):
diff --git a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
index d0243d5327..0462993672 100644
--- a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc
@@ -178,6 +178,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_0 = {{4096, 1024}};
   matmul0 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_0, outputs_shape_0, attr_0);
   matmul0->set_name("MatMul0");
+  matmul0->set_outputs_type({kFloat32});
 
   // matmul1
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -187,6 +188,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_1 = {{128, 4096}};
   matmul1 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
   matmul1->set_name("MatMul1");
+  matmul1->set_outputs_type({kFloat32});
 
   // matmul2
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -196,6 +198,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_2 = {{128, 1024}};
   matmul2 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
   matmul2->set_name("MatMul2");
+  matmul2->set_outputs_type({kFloat32});
 
   // matmul3
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -205,6 +208,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_3 = {{1024, 4096}};
   matmul3 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
   matmul3->set_name("MatMul3");
+  matmul3->set_outputs_type({kFloat32});
 
   // matmul4
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -214,6 +218,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_4 = {{128, 4096}};
   matmul4 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_4, outputs_shape_4, attr_4);
   matmul4->set_name("MatMul4");
+  matmul4->set_outputs_type({kFloat32});
 
   // matmul5
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -223,6 +228,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_5 = {{128, 4096}};
   matmul5 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_5, outputs_shape_5, attr_5);
   matmul5->set_name("MatMul5");
+  matmul5->set_outputs_type({kFloat32});
 
   // matmul6
   ValuePtr transpose_a_6 = MakeValue(false);
@@ -232,6 +238,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_6 = {{4096, 1024}};
   matmul6 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_6, outputs_shape_6, attr_6);
   matmul6->set_name("MatMul6");
+  matmul6->set_outputs_type({kFloat32});
 
   // matmul7
   ValuePtr transpose_a_7 = MakeValue(false);
@@ -241,6 +248,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_7 = {{64, 4096}};
   matmul7 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_7, outputs_shape_7, attr_7);
   matmul7->set_name("MatMul7");
+  matmul7->set_outputs_type({kFloat32});
 
   // matmul8
   ValuePtr transpose_a_8 = MakeValue(false);
@@ -250,6 +258,7 @@ void TestDPAlgo::SetUp() {
   Shapes outputs_shape_8 = {{64, 40960}};
   matmul8 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_8, outputs_shape_8, attr_8);
   matmul8->set_name("MatMul8");
+  matmul8->set_outputs_type({kFloat32});
 }
 
 void TestDPAlgo::ConstructTwoLargeMatMul() {
@@ -278,12 +287,15 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes outputs_shape = {{64, 64}};
   tmp_identity_ptr1 = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr1->set_name("identity_info1");
+  tmp_identity_ptr1->set_outputs_type({kFloat32});
 
   tmp_identity_ptr2 = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr2->set_name("identity_info2");
+  tmp_identity_ptr2->set_outputs_type({kFloat32});
 
   tmp_identity_ptr = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr->set_name("identity_info");
+  tmp_identity_ptr->set_outputs_type({kFloat32});
 
   // mm1_ptr
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -292,6 +304,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_1 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_1 = {{64, 64}};
   mm1_ptr = std::make_shared<MatMulInfo>("matmul_info1", inputs_shape_1, outputs_shape_1, attr_1);
+  mm1_ptr->set_outputs_type({kFloat32});
 
   // mm2_ptr
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -300,6 +313,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_2 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_2 = {{64, 64}};
   mm2_ptr = std::make_shared<MatMulInfo>("matmul_info2", inputs_shape_2, outputs_shape_2, attr_2);
+  mm2_ptr->set_outputs_type({kFloat32});
 
   // mm3_ptr
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -308,6 +322,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_3 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_3 = {{64, 64}};
   mm3_ptr = std::make_shared<MatMulInfo>("matmul_info3", inputs_shape_3, outputs_shape_3, attr_3);
+  mm3_ptr->set_outputs_type({kFloat32});
 
   // mm4_ptr
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -316,6 +331,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_4 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_4 = {{64, 64}};
   mm4_ptr = std::make_shared<MatMulInfo>("matmul_info4", inputs_shape_4, outputs_shape_4, attr_4);
+  mm4_ptr->set_outputs_type({kFloat32});
 
   // mm5_ptr
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -324,6 +340,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_5 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_5 = {{64, 64}};
   mm5_ptr = std::make_shared<MatMulInfo>("matmul_info5", inputs_shape_5, outputs_shape_5, attr_5);
+  mm5_ptr->set_outputs_type({kFloat32});
 
   // mm6_ptr
   ValuePtr transpose_a_6 = MakeValue(false);
@@ -332,6 +349,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_6 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_6 = {{64, 64}};
   mm6_ptr = std::make_shared<MatMulInfo>("matmul_info6", inputs_shape_6, outputs_shape_6, attr_6);
+  mm6_ptr->set_outputs_type({kFloat32});
 
   // mm7_ptr
   ValuePtr transpose_a_7 = MakeValue(false);
@@ -340,6 +358,7 @@ void TestDPAlgo::ConstructBatmanGraph() {
   Shapes inputs_shape_7 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_7 = {{64, 64}};
   mm7_ptr = std::make_shared<MatMulInfo>("matmul_info7", inputs_shape_7, outputs_shape_7, attr_7);
+  mm7_ptr->set_outputs_type({kFloat32});
 
   // create edges
   edge_i0_m3 = std::make_shared<Edge>(edge_iden_matmul_name, tmp_identity_ptr, mm3_ptr, 0, 0, false, true);
@@ -451,6 +470,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes outputs_shape = {{64, 64}};
   tmp_identity_ptr1 = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr1->set_name("identity_info1");
+  tmp_identity_ptr1->set_outputs_type({kFloat32});
 
   // mm6_ptr
   ValuePtr transpose_a_6 = MakeValue(false);
@@ -459,9 +479,11 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_6 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_6 = {{64, 64}};
   mm6_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_6, outputs_shape_6, attr_6);
+  mm6_ptr->set_outputs_type({kFloat32});
 
   tmp_identity_ptr2 = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr2->set_name("identity_info2");
+  tmp_identity_ptr2->set_outputs_type({kFloat32});
 
   // mm1_ptr
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -470,6 +492,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_1 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_1 = {{64, 64}};
   mm1_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  mm1_ptr->set_outputs_type({kFloat32});
 
   // mm2_ptr
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -478,6 +501,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_2 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_2 = {{64, 64}};
   mm2_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  mm2_ptr->set_outputs_type({kFloat32});
 
   // mm3_ptr
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -486,6 +510,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_3 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_3 = {{64, 64}};
   mm3_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  mm3_ptr->set_outputs_type({kFloat32});
 
   // mm4_ptr
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -494,6 +519,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_4 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_4 = {{64, 64}};
   mm4_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_4, outputs_shape_4, attr_4);
+  mm4_ptr->set_outputs_type({kFloat32});
 
   // mm5_ptr
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -502,6 +528,7 @@ void TestDPAlgo::ConstructTriangleGraph() {
   Shapes inputs_shape_5 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_5 = {{64, 64}};
   mm5_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_5, outputs_shape_5, attr_5);
+  mm5_ptr->set_outputs_type({kFloat32});
 
   // create edges
   std::string edge_matmul_matmul_name = "MatMul-MatMul";
@@ -584,6 +611,7 @@ void TestDPAlgo::ConstructTriangleGraph2() {
   Shapes outputs_shape = {{64, 64}};
   tmp_identity_ptr1 = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
   tmp_identity_ptr1->set_name("identity_info1");
+  tmp_identity_ptr1->set_outputs_type({kFloat32});
 
   // mm1_ptr
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -592,6 +620,7 @@ void TestDPAlgo::ConstructTriangleGraph2() {
   Shapes inputs_shape_1 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_1 = {{64, 64}};
   mm1_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  mm1_ptr->set_outputs_type({kFloat32});
 
   // mm2_ptr
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -600,6 +629,7 @@ void TestDPAlgo::ConstructTriangleGraph2() {
   Shapes inputs_shape_2 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_2 = {{64, 64}};
   mm2_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  mm2_ptr->set_outputs_type({kFloat32});
 
   // mm3_ptr
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -608,6 +638,7 @@ void TestDPAlgo::ConstructTriangleGraph2() {
   Shapes inputs_shape_3 = {{64, 64}, {64, 64}};
   Shapes outputs_shape_3 = {{64, 64}};
   mm3_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  mm3_ptr->set_outputs_type({kFloat32});
 
   // create edges
   std::string edge_matmul_matmul_name = "MatMul-MatMul";
@@ -953,6 +984,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_1 = {{32, 16}, {16, 32}};
   Shapes outputs_shape_1 = {{32, 32}};
   mm1_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  mm1_ptr->set_outputs_type({kFloat32});
 
   // mm2_ptr
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -961,6 +993,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_2 = {{8, 32}, {32, 32}};
   Shapes outputs_shape_2 = {{8, 32}};
   mm2_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  mm2_ptr->set_outputs_type({kFloat32});
 
   // mm3_ptr
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -969,6 +1002,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_3 = {{32, 32}, {32, 64}};
   Shapes outputs_shape_3 = {{32, 64}};
   mm3_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  mm3_ptr->set_outputs_type({kFloat32});
 
   // mm4_ptr
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -977,6 +1011,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_4 = {{64, 32}, {32, 32}};
   Shapes outputs_shape_4 = {{64, 32}};
   mm4_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_4, outputs_shape_4, attr_4);
+  mm4_ptr->set_outputs_type({kFloat32});
 
   // mm5_ptr
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -985,6 +1020,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_5 = {{8, 32}, {32, 64}};
   Shapes outputs_shape_5 = {{8, 64}};
   mm5_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_5, outputs_shape_5, attr_5);
+  mm5_ptr->set_outputs_type({kFloat32});
 
   // mm5_ptr
   ValuePtr transpose_a_6 = MakeValue(false);
@@ -993,6 +1029,7 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes inputs_shape_6 = {{8, 64}, {64, 32}};
   Shapes outputs_shape_6 = {{8, 32}};
   mm6_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_6, outputs_shape_6, attr_6);
+  mm6_ptr->set_outputs_type({kFloat32});
 
   ValuePtr relu = MakeValue(std::string("relu"));
   std::unordered_map<std::string, ValuePtr> relu_attr = {{"activation_type", relu}};
@@ -1001,26 +1038,31 @@ void TestDPAlgo::ConstructMMRGraph() {
   Shapes relu1_inputs_shape = {{8, 32}};
   Shapes relu1_outputs_shape = {{8, 32}};
   relu1_ptr = std::make_shared<ActivationInfo>("relu_info", relu1_inputs_shape, relu1_outputs_shape, relu_attr);
+  relu1_ptr->set_outputs_type({kFloat32});
 
   // relu2_ptr
   Shapes relu2_inputs_shape = {{32, 64}};
   Shapes relu2_outputs_shape = {{32, 64}};
   relu2_ptr = std::make_shared<ActivationInfo>("relu_info", relu2_inputs_shape, relu2_outputs_shape, relu_attr);
+  relu2_ptr->set_outputs_type({kFloat32});
 
   // relu3_ptr
   Shapes relu3_inputs_shape = {{64, 32}};
   Shapes relu3_outputs_shape = {{64, 32}};
   relu3_ptr = std::make_shared<ActivationInfo>("relu_info", relu3_inputs_shape, relu3_outputs_shape, relu_attr);
+  relu3_ptr->set_outputs_type({kFloat32});
 
   // relu4_ptr
   Shapes relu4_inputs_shape = {{8, 64}};
   Shapes relu4_outputs_shape = {{8, 64}};
   relu4_ptr = std::make_shared<ActivationInfo>("relu_info", relu4_inputs_shape, relu4_outputs_shape, relu_attr);
+  relu4_ptr->set_outputs_type({kFloat32});
 
   // relu5_ptr
   Shapes relu5_inputs_shape = {{8, 32}};
   Shapes relu5_outputs_shape = {{8, 32}};
   relu5_ptr = std::make_shared<ActivationInfo>("relu_info", relu5_inputs_shape, relu5_outputs_shape, relu_attr);
+  relu5_ptr->set_outputs_type({kFloat32});
 
   std::string edge_matmul_matmul_name = "MatMul-MatMul";
   std::string edge_matmul_relu_name = "MatMul-ReLU";
@@ -1134,6 +1176,7 @@ void TestDPAlgo::ConstructIdentityDiamondGraph() {
   Shapes inputs_shape = {{32, 64}};
   Shapes outputs_shape = {{32, 64}};
   tmp_identity_ptr = std::make_shared<TmpIdentityInfo>(inputs_shape, outputs_shape, attr);
+  tmp_identity_ptr->set_outputs_type({kFloat32});
 
   // mm1_ptr
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -1142,6 +1185,7 @@ void TestDPAlgo::ConstructIdentityDiamondGraph() {
   Shapes inputs_shape_1 = {{32, 64}, {64, 128}};
   Shapes outputs_shape_1 = {{32, 128}};
   mm1_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  mm1_ptr->set_outputs_type({kFloat32});
 
   // mm2_ptr
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -1150,6 +1194,7 @@ void TestDPAlgo::ConstructIdentityDiamondGraph() {
   Shapes inputs_shape_2 = {{128, 32}, {32, 64}};
   Shapes outputs_shape_2 = {{128, 64}};
   mm2_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  mm2_ptr->set_outputs_type({kFloat32});
 
   // mm3_ptr
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -1158,6 +1203,7 @@ void TestDPAlgo::ConstructIdentityDiamondGraph() {
   Shapes inputs_shape_3 = {{32, 128}, {128, 64}};
   Shapes outputs_shape_3 = {{32, 64}};
   mm3_ptr = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  mm3_ptr->set_outputs_type({kFloat32});
 
   // create edges
   std::string edge_matmul_matmul_name = "MatMul-MatMul";
diff --git a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
index 467f4976e8..423a258a28 100644
--- a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc
@@ -65,6 +65,7 @@ void TestEdgeCostModel::SetUp() {
   Shapes inputs_shape_1 = {{8, 16}, {16, 32}};
   Shapes outputs_shape_1 = {{8, 32}};
   matmul1 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  matmul1->set_outputs_type({kFloat32});
 
   // matmul2
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -73,6 +74,7 @@ void TestEdgeCostModel::SetUp() {
   Shapes inputs_shape_2 = {{8, 32}, {32, 16}};
   Shapes outputs_shape_2 = {{8, 16}};
   matmul2 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  matmul2->set_outputs_type({kFloat32});
 
   // matmul3
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -81,6 +83,7 @@ void TestEdgeCostModel::SetUp() {
   Shapes inputs_shape_3 = {{16, 8}, {8, 32}};
   Shapes outputs_shape_3 = {{16, 32}};
   matmul3 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  matmul3->set_outputs_type({kFloat32});
 
   // matmul4
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -89,6 +92,7 @@ void TestEdgeCostModel::SetUp() {
   Shapes inputs_shape_4 = {{8, 16}, {16, 32}};
   Shapes outputs_shape_4 = {{8, 32}};
   matmul4 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_4, outputs_shape_4, attr_4);
+  matmul4->set_outputs_type({kFloat32});
 
   // matmul5
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -97,6 +101,7 @@ void TestEdgeCostModel::SetUp() {
   Shapes inputs_shape_5 = {{8, 32}, {8, 32}};
   Shapes outputs_shape_5 = {{8, 8}};
   matmul5 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_5, outputs_shape_5, attr_5);
+  matmul5->set_outputs_type({kFloat32});
 }
 
 TEST_F(TestEdgeCostModel, test_InitEdgeCost) {
diff --git a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
index 415a1fdd55..81b017a28d 100644
--- a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
+++ b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc
@@ -76,6 +76,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_0 = {{32, 16}, {16, 16}};
   Shapes outputs_shape_0 = {{32, 16}};
   matmul0 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_0, outputs_shape_0, attr_0);
+  matmul0->set_outputs_type({kFloat32});
 
   // matmul1
   ValuePtr transpose_a_1 = MakeValue(false);
@@ -84,6 +85,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_1 = {{8, 16}, {16, 32}};
   Shapes outputs_shape_1 = {{8, 32}};
   matmul1 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
+  matmul1->set_outputs_type({kFloat32});
 
   // matmul2
   ValuePtr transpose_a_2 = MakeValue(false);
@@ -92,6 +94,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_2 = {{8, 32}, {32, 16}};
   Shapes outputs_shape_2 = {{8, 16}};
   matmul2 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_2, outputs_shape_2, attr_2);
+  matmul2->set_outputs_type({kFloat32});
 
   // matmul3
   ValuePtr transpose_a_3 = MakeValue(false);
@@ -100,6 +103,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_3 = {{16, 8}, {8, 32}};
   Shapes outputs_shape_3 = {{16, 32}};
   matmul3 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_3, outputs_shape_3, attr_3);
+  matmul3->set_outputs_type({kFloat32});
 
   // matmul4
   ValuePtr transpose_a_4 = MakeValue(false);
@@ -108,6 +112,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_4 = {{8, 16}, {16, 32}};
   Shapes outputs_shape_4 = {{8, 32}};
   matmul4 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_4, outputs_shape_4, attr_4);
+  matmul4->set_outputs_type({kFloat32});
 
   // matmul5
   ValuePtr transpose_a_5 = MakeValue(false);
@@ -116,6 +121,7 @@ void TestCostGraph::SetUp() {
   Shapes inputs_shape_5 = {{8, 32}, {8, 32}};
   Shapes outputs_shape_5 = {{8, 8}};
   matmul5 = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_5, outputs_shape_5, attr_5);
+  matmul5->set_outputs_type({kFloat32});
 }
 
 void TestCostGraph::ConstructStarGraph2() {
diff --git a/tests/ut/python/communication/test_comm.py b/tests/ut/python/communication/test_comm.py
index 885c8fa9e3..38fd7199fd 100644
--- a/tests/ut/python/communication/test_comm.py
+++ b/tests/ut/python/communication/test_comm.py
@@ -55,7 +55,7 @@ class BroadCastNet(nn.Cell):
         self.broadcast = Broadcast(0)
 
     def construct(self, x):
-        x, = self.broadcast((x,))
+        x = self.broadcast((x))
         x = self.dense(x)
         return x
 

From 37c94a5f767a5f3416ba0065b4c9f68c26e052bd Mon Sep 17 00:00:00 2001
From: Wei Luning <weiluning@huawei.com>
Date: Mon, 13 Apr 2020 17:43:25 +0800
Subject: [PATCH 192/367] add pass replace_old_param_

---
 mindspore/ccsrc/optimizer/irpass.cc           |  2 +
 mindspore/ccsrc/optimizer/irpass.h            |  1 +
 .../ccsrc/optimizer/irpass/param_replace.h    | 60 +++++++++++++++++++
 mindspore/ccsrc/pipeline/action.cc            |  1 +
 mindspore/ccsrc/pipeline/pass.cc              |  8 +--
 .../pynative_mode/test_insert_grad_of.py      |  2 +-
 6 files changed, 68 insertions(+), 6 deletions(-)
 create mode 100644 mindspore/ccsrc/optimizer/irpass/param_replace.h

diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc
index 0991c31b00..96d88f6e61 100644
--- a/mindspore/ccsrc/optimizer/irpass.cc
+++ b/mindspore/ccsrc/optimizer/irpass.cc
@@ -40,6 +40,7 @@
 #include "optimizer/irpass/incorporate_getitem.h"
 #include "optimizer/irpass/incorporate_call.h"
 #include "optimizer/irpass/grad_var_prepare.h"
+#include "optimizer/irpass/param_replace.h"
 
 namespace mindspore {
 namespace opt {
@@ -81,6 +82,7 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
   get_make_ref_eliminate_ =
     MakeSubstitution(GetMakeRefEliminater(), "get_make_ref_eliminate", {prim::kPrimGetRefKey, prim::kPrimGetRefValue});
   replace_refkey_by_param_ = MakeSubstitution(ReplaceRefkeyByParam(), "replace_refkey_by_param", IsValueNode<RefKey>);
+  replace_old_param_ = MakeSubstitution(ReplaceOldParam(), "replace_old_param", IsParam);
 
   // Gradient transforms
   expand_jprim_ = MakeSubstitution(ExpandJPrim(), "expand_jprim", prim::kPrimJ);
diff --git a/mindspore/ccsrc/optimizer/irpass.h b/mindspore/ccsrc/optimizer/irpass.h
index bdaf42b3ed..00274bdcc8 100644
--- a/mindspore/ccsrc/optimizer/irpass.h
+++ b/mindspore/ccsrc/optimizer/irpass.h
@@ -58,6 +58,7 @@ class OptimizeIRPassLib {
   SubstitutionPtr make_ref_eliminate_;
   SubstitutionPtr get_make_ref_eliminate_;
   SubstitutionPtr replace_refkey_by_param_;
+  SubstitutionPtr replace_old_param_;
 
   // Branch culling
   SubstitutionPtr switch_simplify_;
diff --git a/mindspore/ccsrc/optimizer/irpass/param_replace.h b/mindspore/ccsrc/optimizer/irpass/param_replace.h
new file mode 100644
index 0000000000..c0c4c832d7
--- /dev/null
+++ b/mindspore/ccsrc/optimizer/irpass/param_replace.h
@@ -0,0 +1,60 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_OPTIMIZER_IRPASS_PARAM_REPLACE_H_
+#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_PARAM_REPLACE_H_
+
+#include <memory>
+
+#include "optimizer/optimizer.h"
+#include "optimizer/irpass.h"
+#include "ir/visitor.h"
+#include "operator/ops.h"
+#include "pipeline/parse/parse.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+class ReplaceOldParam : public AnfVisitor {
+ public:
+  AnfNodePtr operator()(const OptimizerPtr &optimizer, const AnfNodePtr &node) override {
+    if (!IsParam(node)) {
+      return nullptr;
+    }
+    auto resource = std::dynamic_pointer_cast<pipeline::Resource>(optimizer->resource());
+    MS_EXCEPTION_IF_NULL(resource);
+
+    auto top_graph = resource->func_graph();  // parse::Parser::GetTopFuncGraph();
+    MS_EXCEPTION_IF_NULL(top_graph);
+
+    auto param_node = node->cast<ParameterPtr>();
+    if (!param_node->has_default() || node->func_graph() == top_graph) {
+      return nullptr;
+    }
+    auto para_name = param_node->name();
+    for (const auto &tnode : top_graph->parameters()) {
+      auto para = tnode->cast<ParameterPtr>();
+      if (para != nullptr && para->name() == para_name) {
+        return para;
+      }
+    }
+    return nullptr;
+  }
+};
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_OPTIMIZER_IRPASS_PARAM_REPLACE_H_
diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc
index baf4bea7ec..d77fee84aa 100644
--- a/mindspore/ccsrc/pipeline/action.cc
+++ b/mindspore/ccsrc/pipeline/action.cc
@@ -88,6 +88,7 @@ FuncGraphPtr Renormalize(const ResourcePtr& res, const FuncGraphPtr& func_graph,
   double t2 = GetTime();
 #endif
   auto ret = ProgramSpecialize(res, func_graph, result.context);
+  res->set_func_graph(ret);
 #ifdef ENABLE_PROFILE
   double t3 = GetTime();
   MsProfile::StatTime("renormalize.infer", t2 - t1);
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index a58ecf41b6..9248590f27 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -114,11 +114,9 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) {
   opt::OptPassConfig grad = opt::OptPassConfig({irpass.expand_jprim_}, true);
   opt::irpass::ResolveIRPassLib resolve_irpass;
 
-  opt::OptPassConfig resolve_pass = opt::OptPassConfig({
-    resolve_irpass.resolver_resolve_,
-    resolve_irpass.resolver_getattr_,
-    irpass.get_make_ref_eliminate_,
-  });
+  opt::OptPassConfig resolve_pass =
+    opt::OptPassConfig({resolve_irpass.resolver_resolve_, resolve_irpass.resolver_getattr_,
+                        irpass.get_make_ref_eliminate_, irpass.replace_old_param_});
 
   OptPassGroupMap map_a({{"a_1", a_1},
                          {"a_2", a_2},
diff --git a/tests/ut/python/pynative_mode/test_insert_grad_of.py b/tests/ut/python/pynative_mode/test_insert_grad_of.py
index 104ac4d1c7..a11c5fa2b1 100644
--- a/tests/ut/python/pynative_mode/test_insert_grad_of.py
+++ b/tests/ut/python/pynative_mode/test_insert_grad_of.py
@@ -129,7 +129,7 @@ def test_cell_assign():
             self.matrix_g = mindspore.Parameter(Tensor(np.ones([2, 2], np.float32)), name="matrix_g")
 
         def save_gradient(self, dout):
-            self.matrix_g = dout
+            self.matrix_g = dout + self.matrix_g
             return dout
 
         def construct(self, x, y):

From 50ed76bc0c4e2f4ef389fd88e93d5056ad819862 Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Mon, 13 Apr 2020 17:47:09 +0800
Subject: [PATCH 193/367] unify some examples in mindpore front

---
 mindspore/nn/layer/normalization.py     |  2 +-
 mindspore/nn/layer/pooling.py           |  4 ++--
 mindspore/nn/loss/loss.py               |  2 +-
 mindspore/nn/metrics/__init__.py        |  2 +-
 mindspore/nn/metrics/error.py           |  2 +-
 mindspore/ops/operations/control_ops.py |  6 +++---
 mindspore/ops/operations/math_ops.py    |  4 ++--
 mindspore/ops/operations/nn_ops.py      |  6 +++---
 mindspore/ops/operations/other_ops.py   | 12 ++++++------
 mindspore/ops/operations/random_ops.py  |  2 +-
 10 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 1ca2221122..d5082371c1 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -256,7 +256,7 @@ class LayerNorm(Cell):
         Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
 
     Examples:
-        >>> x = Tensor(np.ones([20, 5, 10, 10], np.float32))
+        >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)
         >>> shape1 = x.shape()[1:]
         >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
         >>> m(x)
diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index 5d9b0ffa6c..746b6d240f 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -104,7 +104,7 @@ class MaxPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = MaxPool2d(kernel_size=3, stride=1)
+        >>> pool = nn.MaxPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[1. 5. 5. 1.]
            [0. 3. 4. 8.]
@@ -186,7 +186,7 @@ class AvgPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = AvgPool2d(kernel_size=3, strides=1)
+        >>> pool = nn.AvgPool2d(kernel_size=3, strides=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py
index 806456e561..9a3de36f47 100644
--- a/mindspore/nn/loss/loss.py
+++ b/mindspore/nn/loss/loss.py
@@ -284,7 +284,7 @@ class SoftmaxCrossEntropyExpand(Cell):
         Tensor, a scalar tensor including the mean loss.
 
     Examples:
-        >>> loss = SoftmaxCrossEntropyExpand(sparse=True)
+        >>> loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
         >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
         >>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
         >>> loss(input_data, label)
diff --git a/mindspore/nn/metrics/__init__.py b/mindspore/nn/metrics/__init__.py
index 490e1620fa..06429e200e 100755
--- a/mindspore/nn/metrics/__init__.py
+++ b/mindspore/nn/metrics/__init__.py
@@ -83,7 +83,7 @@ def get_metric_fn(name, *args, **kwargs):
         Metric object, class instance of the metric method.
 
     Examples:
-        >>> metric = get_metric_fn('precision', eval_type='classification')
+        >>> metric = nn.get_metric_fn('precision', eval_type='classification')
     """
     if name not in __factory__:
         raise KeyError("Unknown Metric:", name)
diff --git a/mindspore/nn/metrics/error.py b/mindspore/nn/metrics/error.py
index c803000192..8ed175bc27 100644
--- a/mindspore/nn/metrics/error.py
+++ b/mindspore/nn/metrics/error.py
@@ -97,7 +97,7 @@ class MSE(Metric):
     Examples:
         >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
         >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
-        >>> error = MSE()
+        >>> error = nn.MSE()
         >>> error.clear()
         >>> error.update(x, y)
         >>> result = error.eval()
diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index 167739b89a..d4e8b279ba 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -51,9 +51,9 @@ class ControlDepend(Primitive):
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>        super(Net, self).__init__()
-        >>>         self.global_step = Parameter(initializer(0, [1]), name="global_step")
+        >>>         self.global_step = mindspore.Parameter(initializer(0, [1]), name="global_step")
         >>>         self.rate = 0.2
-        >>>         self.control_depend = ControlDepend()
+        >>>         self.control_depend = P.ControlDepend()
         >>>
         >>>     def construct(self, x):
         >>>         data = self.rate * self.global_step + x
@@ -92,7 +92,7 @@ class GeSwitch(PrimitiveWithInfer):
         >>>	    super(Net, self).__init__()
         >>>         self.square = P.Square()
         >>>         self.add = P.TensorAdd()
-        >>>         self.value = Tensor(np.full((1), 3, dtype=np.float32))
+        >>>         self.value = Tensor(np.full((1), 3), mindspore.float32)
         >>>         self.switch = P.GeSwitch()
         >>>         self.merge = P.Merge()
         >>>         self.less = P.Less()
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index a1fe6e72b5..f6feb1af18 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -133,7 +133,7 @@ class AssignAdd(PrimitiveWithInfer):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignAdd = P.AssignAdd()
-        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
         >>>         self.AssignAdd(self.variable, x)
@@ -176,7 +176,7 @@ class AssignSub(PrimitiveWithInfer):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignSub = P.AssignSub()
-        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
         >>>         self.AssignSub(self.variable, x)
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 83f76455e0..6e34648ed4 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -154,7 +154,7 @@ class ReLU(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> relu = P.ReLU()
         >>> result = relu(input_x)
         [[0, 4.0, 0.0], [2.0, 0.0, 9.0]]
@@ -187,7 +187,7 @@ class ReLU6(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> relu6 = P.ReLU6()
         >>> result = relu6(input_x)
     """
@@ -221,7 +221,7 @@ class Elu(PrimitiveWithInfer):
         Tensor, has the same shape and data type as `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> elu = P.Elu()
         >>> result = elu(input_x)
         Tensor([[-0.632  4.0   -0.999]
diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py
index e4d526ad01..4ac7f2e554 100644
--- a/mindspore/ops/operations/other_ops.py
+++ b/mindspore/ops/operations/other_ops.py
@@ -76,7 +76,7 @@ class BoundingBoxEncode(PrimitiveWithInfer):
         Tensor, encoded bounding boxes.
 
     Examples:
-        >>> boundingbox_encode = BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
+        >>> boundingbox_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
         >>> delta_box = boundingbox_encode(anchor_box, groundtruth_box)
     """
 
@@ -119,7 +119,7 @@ class BoundingBoxDecode(PrimitiveWithInfer):
         Tensor, decoded boxes.
 
     Examples:
-        >>> boundingbox_decode = BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
+        >>> boundingbox_decode = P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
                                                    max_shape=(768, 1280), wh_ratio_clip=0.016)
         >>> bbox = boundingbox_decode(anchor_box, deltas)
     """
@@ -208,7 +208,7 @@ class IOU(PrimitiveWithInfer):
         KeyError: When `mode` is not 'iou' or 'iof'.
 
     Examples:
-        >>> iou = IOU()
+        >>> iou = P.IOU()
         >>> anchor_boxes = Tensor(np.random.randint(1,5, [10, 4]))
         >>> gt_boxes = Tensor(np.random.randint(1,5, [3, 4]))
         >>> iou(anchor_boxes, gt_boxes)
@@ -255,15 +255,15 @@ class MakeRefKey(Primitive):
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.y = Parameter(Tensor(np.ones([6, 8, 10], np.int32)), name="y")
-        >>>         self.make_ref_key = MakeRefKey("y")
+        >>>         self.y = mindspore.Parameter(Tensor(np.ones([6, 8, 10]), mindspore.int32), name="y")
+        >>>         self.make_ref_key = P.MakeRefKey("y")
         >>>
         >>>     def construct(self, x):
         >>>         key = self.make_ref_key()
         >>>         ref = F.make_ref(key, x, self.y)
         >>>         return ref * x
         >>>
-        >>> x = Tensor(np.ones([3, 4, 5], np.int32))
+        >>> x = Tensor(np.ones([3, 4, 5]), mindspore.int32)
         >>> net = Net()
         >>> net(x)
     """
diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index 95692a622e..c8d7c75768 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -44,7 +44,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         - **mask** (Tensor) - The output has shape 1-D.
 
     Examples:
-        >>> rnd_choice_mask = RandomChoiceWithMask()
+        >>> rnd_choice_mask = P.RandomChoiceWithMask()
         >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_)
         >>> output_y, output_mask = rnd_choice_mask(input_x)
     """

From 19ee376cd35757fc47fb55bb0209826fbb734f8c Mon Sep 17 00:00:00 2001
From: huanghui <huanghui44@huawei.com>
Date: Fri, 10 Apr 2020 15:19:16 +0800
Subject: [PATCH 194/367] add confusion_mul_grad fusion pass

---
 .../ir_fusion/confusion_mul_grad_fusion.cc    | 112 ++++++++++++++++++
 .../ir_fusion/confusion_mul_grad_fusion.h     |  41 +++++++
 mindspore/ccsrc/utils/utils.h                 |   1 +
 .../confusion_mul_grad_fusion_test.cc         |  54 +++++++++
 .../pre_activate/confusion_mul_grad_fusion.py |  55 +++++++++
 5 files changed, 263 insertions(+)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h
 create mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion_test.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/confusion_mul_grad_fusion.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.cc
new file mode 100644
index 0000000000..6b7f732a6a
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.cc
@@ -0,0 +1,112 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h"
+#include <utility>
+#include <memory>
+#include <vector>
+#include <algorithm>
+#include "session/anf_runtime_algorithm.h"
+#include "ir/primitive.h"
+#include "utils/utils.h"
+#include "pipeline/static_analysis/abstract_value.h"
+#include "pre_activate/common/helper.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+const size_t kConfusionMulGradOutputNum = 2;
+
+CNodePtr CreateFusionNode(const FuncGraphPtr &graph, const CNodePtr &reduce_sum, const AnfNodePtr &mul0_anf,
+                          const AnfNodePtr &input3) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(reduce_sum);
+  MS_EXCEPTION_IF_NULL(mul0_anf);
+  MS_EXCEPTION_IF_NULL(input3);
+  auto mul0 = mul0_anf->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(mul0);
+
+  auto prim = std::make_shared<Primitive>(kConfusionMulGradOpName);
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim), mul0->input(1), mul0->input(2), input3};
+  auto fusion_node = graph->NewCNode(inputs);
+  MS_EXCEPTION_IF_NULL(fusion_node);
+  fusion_node->set_scope(reduce_sum->scope());
+  AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node);
+  AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node);
+  auto types = {AnfAlgo::GetOutputInferDataType(mul0, 0), AnfAlgo::GetOutputInferDataType(reduce_sum, 0)};
+  auto shapes = {AnfAlgo::GetOutputInferShape(mul0, 0), AnfAlgo::GetOutputInferShape(reduce_sum, 0)};
+  AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get());
+  return fusion_node;
+}
+
+AnfNodePtr GetMul0(const FuncGraphPtr &graph, const AnfNodePtr &input2, const AnfNodePtr &mul1) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(input2);
+  auto manager = graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+  if (manager->node_users().find(input2) == manager->node_users().end()) {
+    MS_LOG(EXCEPTION) << "node has no output in manager";
+  }
+
+  AnfNodePtr mul0 = nullptr;
+  const AnfNodeIndexSet &outputs_set = manager->node_users()[input2];
+  // input2 must be the 2rd input of mul0
+  auto it = std::find_if(outputs_set.begin(), outputs_set.end(), [&mul1](const std::pair<AnfNodePtr, int> &node_index) {
+    return node_index.first != mul1 && node_index.second == 2;
+  });
+  if (it != outputs_set.end() && AnfAlgo::GetCNodeName(it->first) == prim::kPrimMul->name()) {
+    mul0 = it->first;
+  }
+  return mul0;
+}
+}  // namespace
+
+const BaseRef ConfusionMulGradFusion::DefinePattern() const {
+  VectorRef mul1({prim::kPrimMul, input3_, input2_});
+  VectorRef reduce_sum({prim::kPrimReduceSum, mul1});
+  return reduce_sum;
+}
+
+const AnfNodePtr ConfusionMulGradFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
+                                                 const EquivPtr &equiv) const {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(node);
+  MS_EXCEPTION_IF_NULL(equiv);
+  auto input2 = utils::cast<AnfNodePtr>((*equiv)[input2_]);
+  auto input3 = utils::cast<AnfNodePtr>((*equiv)[input3_]);
+  auto reduce_sum = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(reduce_sum);
+  auto mul1 = reduce_sum->input(1);
+  if (IsUsedByOthers(graph, mul1)) {
+    MS_LOG(INFO) << "Mul1 is used by others, quit fusion!";
+    return nullptr;
+  }
+  auto mul0 = GetMul0(graph, input2, mul1);
+  if (mul0 == nullptr) {
+    MS_LOG(INFO) << "Mul0 do not exist, quit fusion";
+    return nullptr;
+  }
+
+  auto fusion_node = CreateFusionNode(graph, reduce_sum, mul0, input3);
+  std::vector<AnfNodePtr> fusion_node_outputs;
+  CreateMultipleOutputsOfAnfNode(graph, fusion_node, kConfusionMulGradOutputNum, &fusion_node_outputs);
+
+  auto manage = graph->manager();
+  MS_EXCEPTION_IF_NULL(manage);
+  manage->Replace(mul0, fusion_node_outputs[0]);
+  return fusion_node_outputs[1];
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h
new file mode 100644
index 0000000000..170df5b0e4
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h
@@ -0,0 +1,41 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_CONFUSION_MUL_GRAD_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_CONFUSION_MUL_GRAD_FUSION_H_
+
+#include <memory>
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class ConfusionMulGradFusion : public PatternProcessPass {
+ public:
+  explicit ConfusionMulGradFusion(bool multigraph = true)
+      : PatternProcessPass("confusion_mul_grad_fusion", multigraph) {
+    input2_ = std::make_shared<Var>();
+    input3_ = std::make_shared<Var>();
+  }
+  ~ConfusionMulGradFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+
+ private:
+  VarPtr input2_;
+  VarPtr input3_;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_CONFUSION_MUL_GRAD_FUSION_H_
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index ea5e969e52..0903ffd854 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -111,6 +111,7 @@ constexpr auto kFusedMulAddOpName = "FusedMulAdd";
 constexpr auto kFusedMulAddNOpName = "FusedMulAddN";
 constexpr auto kFusedMulApplyMomentumOpName = "FusedMulApplyMomentum";
 constexpr auto kBiasAddOpName = "BiasAdd";
+constexpr auto kConfusionMulGradOpName = "ConfusionMulGrad";
 
 // attr key name
 constexpr auto kAttrInputNames = "input_names";
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion_test.cc
new file mode 100644
index 0000000000..e3bf09d2cb
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion_test.cc
@@ -0,0 +1,54 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h"
+#include "debug/anf_ir_dump.h"
+
+namespace mindspore {
+namespace opt {
+class TestHWOptimizeConfusionMulGradFusion : public BackendCommon {
+ public:
+  TestHWOptimizeConfusionMulGradFusion() : get_py_fun_("gtest_input.pre_activate.confusion_mul_grad_fusion", true) {}
+  ~TestHWOptimizeConfusionMulGradFusion() override = default;
+
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWOptimizeConfusionMulGradFusion, test_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_confusion_mul_grad_fusion", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{1, 1, 1, 1};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 3; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::ConfusionMulGradFusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_confusion_mul_grad_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+}  // namespace opt
+}  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/confusion_mul_grad_fusion.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/confusion_mul_grad_fusion.py
new file mode 100644
index 0000000000..d8f7bcc996
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/confusion_mul_grad_fusion.py
@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+
+mul = P.Mul()
+reduce_sum = P.ReduceSum()
+confusion_mul_grad = Primitive('ConfusionMulGrad')
+make_tuple = Primitive('make_tuple')
+tuple_getitem = Primitive('tuple_getitem')
+axis = 2
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+def test_confusion_mul_grad_fusion(tag):
+    fns = FnDict()
+
+    @fns
+    def before(input1, input2, input3):
+        output1 = mul(input1, input2)
+        mul1 = mul(input3, input2)
+        # input axis will be convert to attr in step ConstructKernelGraph
+        output2 = reduce_sum(mul1, axis)
+        res = make_tuple(output1, output2)
+        return res
+
+    @fns
+    def after(input1, input2, input3):
+        res = confusion_mul_grad(input1, input2, input3)
+        item0 = tuple_getitem(res, 0)
+        item1 = tuple_getitem(res, 1)
+        res = make_tuple(item0, item1)
+        return make_tuple(res)
+
+    return fns[tag]

From 2fecdede6b6cca648707bd83e3926c6937b350e6 Mon Sep 17 00:00:00 2001
From: Wei Luning <weiluning@huawei.com>
Date: Thu, 9 Apr 2020 23:37:29 +0800
Subject: [PATCH 195/367] support amp when model eval, fix example of
 UnsortSegmentsSum

---
 .../ccsrc/parallel/step_auto_parallel.cc      |  9 ++++
 mindspore/ops/operations/array_ops.py         |  7 +--
 mindspore/ops/operations/nn_ops.py            | 26 ++++++-----
 mindspore/train/amp.py                        | 42 +++++++++--------
 mindspore/train/model.py                      |  8 ++--
 tests/train_step_wrap.py                      | 45 +------------------
 6 files changed, 59 insertions(+), 78 deletions(-)

diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index fe6be575ee..3f1e18183a 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -636,6 +636,15 @@ void AugmentCostGraph(const std::vector<AnfNodePtr> &all_nodes) {
       // Dealing with the RefKey case
       auto refkeys = cnode_with_refkeys.second;
       auto cnode = cnode_with_refkeys.first;
+
+      auto cnode_ptr = cnode->cast<CNodePtr>();
+      if (cnode_ptr == nullptr || !IsValueNode<Primitive>(cnode_ptr->input(0))) {
+        continue;
+      }
+      if (!IsAutoParallelCareNode(cnode_ptr)) {
+        continue;
+      }
+
       if (refkeys.size() > 1) {
         MS_LOG(EXCEPTION) << "CNode: " << cnode->fullname_with_scope() << " 's inputs have more than 1 RefKeys.";
       }
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 850e895ad0..a7c3f50440 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1235,10 +1235,11 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
         Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`.
 
     Examples:
-        >>> input_x = [1, 2, 3, 4]
-        >>> segment_ids = [0, 0, 1, 2]
+        >>> input_x = Tensor([1, 2, 3, 4], mindspore.float)
+        >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32)
         >>> num_segments = 4
-        >>> type = P.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
+        >>> P.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
+        [3, 3, 4, 0]
     """
 
     @prim_attr_register
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 91f6d7ec01..acccfbaba3 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -22,6 +22,8 @@ from functools import reduce
 import numpy as np
 
 from ... import context
+from ..._c_expression import signature_rw as sig_rw
+from ..._c_expression import signature_kind as sig_kind
 from ..._checkparam import ParamValidator as validator
 from ..._checkparam import Rel, check_bool, check_int_positive
 from ...common import dtype as mstype
@@ -1297,29 +1299,31 @@ class ApplyMomentum(PrimitiveWithInfer):
                                 filter(lambda x: x.requires_grad, net.get_parameters()))
         >>> model = Model(net, loss, opt)
     """
-
+    __mindspore_signature__ = (
+        ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('accumulation', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('learning_rate', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('gradient', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('momentum', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD)
+    )
     @prim_attr_register
     def __init__(self, use_nesterov=False, use_locking=False, gradient_scale=1.0):
         self.init_prim_io_names(inputs=['variable', 'accumulation', 'learning_rate', 'gradient', 'momentum'],
                                 outputs=['output'])
 
     def infer_shape(self, v_shape, a_shape, l_shape, g_shape, m_shape):
-        validator.check(f'variable shape {v_shape}', len(v_shape), '', 0, Rel.GT)
-        validator.check(f'accumulation shape {a_shape}', len(a_shape), '', 0, Rel.GT)
-        validator.check(f'learning rate shape {l_shape}', len(l_shape), '', 0, Rel.GE)
-        validator.check(f'gradient shape {g_shape}', len(g_shape), '', 0, Rel.GE)
-        validator.check(f'momentum shape {m_shape}', len(m_shape), '', 0, Rel.GE)
         return v_shape
 
     def infer_dtype(self, v_dtype, a_dtype, l_dtype, g_dtype, m_dtype):
-        validator.check_subclass("v_dtype", v_dtype, mstype.tensor)
-        validator.check_subclass("a_dtype", a_dtype, mstype.tensor)
-        v_type = validator.check_typename("v_dtype", v_dtype, [mstype.float16, mstype.float32, mstype.float64])
-        validator.check_typename("a_dtype", a_dtype, [mstype.float16, mstype.float32, mstype.float64])
+        if v_dtype != mstype.type_refkey and a_dtype != mstype.type_refkey:
+            validator.check_subclass("v_dtype", v_dtype, mstype.tensor)
+            validator.check_subclass("a_dtype", a_dtype, mstype.tensor)
+            validator.check_typename("v_dtype", v_dtype, [mstype.float16, mstype.float32, mstype.float64])
+            validator.check_typename("a_dtype", a_dtype, [mstype.float16, mstype.float32, mstype.float64])
         validator.check_typename("l_dtype", l_dtype, [mstype.float16, mstype.float32, mstype.float64])
         validator.check_typename("g_dtype", g_dtype, [mstype.float16, mstype.float32, mstype.float64])
         validator.check_typename("m_dtype", m_dtype, [mstype.float16, mstype.float32, mstype.float64])
-        return v_type
+        return g_dtype
 
 
 class SmoothL1Loss(PrimitiveWithInfer):
diff --git a/mindspore/train/amp.py b/mindspore/train/amp.py
index e909b44e40..c4c115ef27 100644
--- a/mindspore/train/amp.py
+++ b/mindspore/train/amp.py
@@ -82,6 +82,29 @@ def _check_kwargs(key_words):
         if loss_scale_manager:
             validator.check_isinstance('loss_scale_manager', loss_scale_manager, LossScaleManager)
 
+
+def _add_loss_network(network, loss_fn, cast_model_type):
+    class WithLossCell(nn.Cell):
+        "Wrap loss for amp. Cast network output back to float32"
+
+        def __init__(self, backbone, loss_fn):
+            super(WithLossCell, self).__init__(auto_prefix=False)
+            self._backbone = backbone
+            self._loss_fn = loss_fn
+
+        def construct(self, data, label):
+            out = self._backbone(data)
+            label = _mp_cast_helper(mstype.float32, label)
+            return self._loss_fn(F.cast(out, mstype.float32), label)
+
+    validator.check_isinstance('loss_fn', loss_fn, nn.Cell)
+    if cast_model_type == mstype.float16:
+        network = WithLossCell(network, loss_fn)
+    else:
+        network = nn.WithLossCell(network, loss_fn)
+    return network
+
+
 def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
     """
     Build the mixed precision training cell automatically.
@@ -117,24 +140,7 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
             _do_keep_batchnorm_fp32(network)
 
     if loss_fn:
-        class WithLossCell(nn.Cell):
-            "Wrap loss for amp. Cast network output back to float32"
-
-            def __init__(self, backbone, loss_fn):
-                super(WithLossCell, self).__init__(auto_prefix=False)
-                self._backbone = backbone
-                self._loss_fn = loss_fn
-
-            def construct(self, data, label):
-                out = self._backbone(data)
-                label = _mp_cast_helper(mstype.float32, label)
-                return self._loss_fn(F.cast(out, mstype.float32), label)
-
-        validator.check_isinstance('loss_fn', loss_fn, nn.Cell)
-        if config.cast_model_type == mstype.float16:
-            network = WithLossCell(network, loss_fn)
-        else:
-            network = nn.WithLossCell(network, loss_fn)
+        network = _add_loss_network(network, loss_fn, config.cast_model_type)
 
     if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
         network = _VirtualDatasetCell(network)
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 833fb07256..a1acec859c 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -24,8 +24,7 @@ from .. import context
 from ..parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
     _get_parameter_broadcast, _device_number_check, _parameter_broadcast_check, _callback_wrapper
 from ..nn.metrics import Loss
-from ..nn.wrap import WithLossCell, WithEvalCell, \
-    DataWrapper
+from ..nn.wrap import WithLossCell, DataWrapper, WithEvalCell
 from ..nn.wrap.cell_wrapper import _VirtualDatasetCell
 from .parallel_utils import ParallelMode
 from ..common import dtype as mstype
@@ -151,7 +150,10 @@ class Model:
         else:
             if self._loss_fn is None:
                 raise ValueError("loss_fn can not be None.")
-            self._eval_network = WithEvalCell(self._network, self._loss_fn)
+            if self._optimizer:
+                self._eval_network = self._train_network.network
+            else:
+                self._eval_network = WithEvalCell(self._network, self._loss_fn)
             self._eval_indexes = [0, 1, 2]
 
     def _clear_metrics(self):
diff --git a/tests/train_step_wrap.py b/tests/train_step_wrap.py
index 7289c01004..d48e25b837 100644
--- a/tests/train_step_wrap.py
+++ b/tests/train_step_wrap.py
@@ -21,47 +21,6 @@ from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 from mindspore import Parameter, ParameterTuple
 
-
-run_opt = C.MultitypeFuncGraph("run_opt")
-
-# pylint: disable=unused-argument
-@run_opt.register("Function", "Int", "Number", "Number",
-                  "Tensor", "Tensor", "Tensor")
-def tensor_run_opt(opt, iterator, learning_rate, momentum,
-                   gradient, variable, moment):
-    success = True
-    new_weight = opt(gradient, moment, variable, learning_rate, momentum)
-    success = F.depend(success, P.Assign()(variable, new_weight))
-    return success
-
-
-class OptimizerByMomentum(nn.Cell):
-    """
-    OptimizerByMomentum definition
-    """
-    # list of tensor
-    def __init__(self, weights):
-        super(OptimizerByMomentum, self).__init__()
-        self.learning_rate = Parameter(0.1, name="learning_rate")
-        self.momentum = Parameter(0.05, name="momentum")
-        self.iter = Parameter(0, name="iter")
-
-        self.weights = weights
-        self.moments = weights.clone(prefix="moments", init='zeros')
-
-        self.hyper_map = C.HyperMap()
-        self.opt = P.ApplyMomentum()
-
-    def construct(self, grads):
-        success = True
-        weights = self.weights
-        moments = self.moments
-        success = self.hyper_map(
-            F.partial(run_opt, self.opt, self.iter,
-                      self.learning_rate, self.momentum), grads, weights, moments)
-        # self.learning_rate = updata_lr(self.learning_rate, self.momentum)
-        return success
-
 class TrainStepWrap(nn.Cell):
     """
     TrainStepWrap definition
@@ -71,7 +30,7 @@ class TrainStepWrap(nn.Cell):
         self.network = network
         self.network.set_train()
         self.weights = ParameterTuple(network.trainable_params())
-        self.optimizer = OptimizerByMomentum(self.weights)
+        self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
         self.hyper_map = C.HyperMap()
         self.grad = C.GradOperation('grad', get_by_list=True)
 
@@ -107,7 +66,7 @@ class TrainStepWrap2(nn.Cell):
         self.network = network
         self.network.set_train()
         self.weights = ParameterTuple(network.get_parameters())
-        self.optimizer = OptimizerByMomentum(self.weights)
+        self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
         self.hyper_map = C.HyperMap()
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.sens = sens

From b9728f4e9e16f46b98e4688c63daafaeff822619 Mon Sep 17 00:00:00 2001
From: Zhang Qinghua <zhangqinghua3@huawei.com>
Date: Mon, 13 Apr 2020 19:23:45 +0800
Subject: [PATCH 196/367] Fix the issue in debug info.

---
 mindspore/ccsrc/debug/anf_ir_dump.cc | 4 ++--
 mindspore/ccsrc/debug/label.cc       | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/debug/anf_ir_dump.cc b/mindspore/ccsrc/debug/anf_ir_dump.cc
index 9eb0a376cc..fcf0777d16 100644
--- a/mindspore/ccsrc/debug/anf_ir_dump.cc
+++ b/mindspore/ccsrc/debug/anf_ir_dump.cc
@@ -94,7 +94,7 @@ struct SubGraphIRInfo {
   OrderedMap<AnfNodePtr, int32_t> local_var_map;
 };
 
-void DumpGrobalInfoEntry(const FuncGraphPtr &graph, std::ostringstream &buffer) {
+void DumpGlobalInfoEntry(const FuncGraphPtr &graph, std::ostringstream &buffer) {
   if (graph == nullptr) {
     return;
   }
@@ -452,7 +452,7 @@ void DumpIR(const std::string &filename, const FuncGraphPtr &graph, bool dump_fu
   auto nodes = TopoSort(graph->get_return(), SuccDeeperSimple, AlwaysInclude);
 
   // dump global info
-  DumpGrobalInfoEntry(graph, buffer);
+  DumpGlobalInfoEntry(graph, buffer);
   DumpParams(graph, buffer, &para_map);
 
   OrderedMap<FuncGraphPtr, std::shared_ptr<SubGraphIRInfo>> sub_graphs;
diff --git a/mindspore/ccsrc/debug/label.cc b/mindspore/ccsrc/debug/label.cc
index 794151b952..f0e16e831e 100644
--- a/mindspore/ccsrc/debug/label.cc
+++ b/mindspore/ccsrc/debug/label.cc
@@ -66,7 +66,7 @@ NameWithTrace RootName(const DebugInfoPtr& debug_info, TraceLabelType trace_labe
   return trace_name;
 }
 
-std::string CombineTraceTypes(const std::string& root_name, std::vector<std::string> trace_labels) {
+std::string CombineTraceTypes(const std::string& root_name, const std::vector<std::string>& trace_labels) {
   std::string tags = "";
   for (auto& itr : trace_labels) {
     std::string symbol = itr;

From 732dd01a6aa88e855079ebfbced6abae1a36d262 Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Mon, 13 Apr 2020 17:46:48 +0800
Subject: [PATCH 197/367] fix epoch repeat doc problem

---
 mindspore/dataset/engine/datasets.py | 2 ++
 mindspore/train/model.py             | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 3d660d58a8..4e0b082734 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -394,6 +394,8 @@ class Dataset:
             The order of using repeat and batch reflects the number of batches. Recommend that
             repeat operation should be used after batch operation.
             If dataset_sink_mode is False, here repeat operation is invalid.
+            If dataset_sink_mode is True, repeat count should be euqal to the epoch of training. Otherwise,
+            errors could occur since the amount of data is not the amount training requires.
 
         Args:
             count (int): Number of times the dataset should be repeated (default=None).
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 833fb07256..55db3b538a 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -360,6 +360,9 @@ class Model:
 
         Note:
             CPU is not supported when dataset_sink_mode is true.
+            If dataset_sink_mode is True, epoch of training should be equal to the count of repeat
+            operation in dataset processing. Otherwise, errors could occur since the amount of data
+            is not the amount training requires.
 
         Args:
             epoch (int): Total number of iterations on the data.

From 79f74745ece93f281c5c608d23e537138a0b7596 Mon Sep 17 00:00:00 2001
From: lvliang <lvliang18@huawei.com>
Date: Mon, 13 Apr 2020 09:22:32 +0800
Subject: [PATCH 198/367] fix-bug-type-str-is-invalied-uint16

---
 mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc | 2 +-
 mindspore/ccsrc/session/ascend_session.cc            | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
index 7f11c8f2c7..58c030e79d 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
@@ -94,7 +94,7 @@ AnfNodePtr GetTransInputNodePtr(const FuncGraphPtr &func_graph, const CNodePtr &
   MS_EXCEPTION_IF_NULL(node);
   bool padding_flag = false;
   auto input_node = AnfAlgo::GetInputNode(node, index);
-  if (!AnfAlgo::IsFeatureMapInput(node, index)) {
+  if (input_node->isa<ValueNode>() || input_node->isa<Parameter>()) {
     input_node = InsertTransOpForOutput(func_graph, input_node, kernel_select);
     MS_EXCEPTION_IF_NULL(input_node);
     AnfAlgo::SetNodeInput(node, input_node, index);
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index f255b2f15f..751cf76e32 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -186,7 +186,7 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
 void AscendSession::RunOpHardwareOptimize(const std::shared_ptr<session::KernelGraph> &kernel_graph) const {
   MS_LOG(INFO) << "Start";
   // data layout optimization
-  opt::AscendDataLayout(kernel_graph);
+  opt::RunOpAscendDataLayout(kernel_graph);
   // mixed precision optimization
   opt::AscendMixPrecision(kernel_graph);
   MS_LOG(INFO) << "Finish";

From 65a49a21e54a7753daeda9216fc1460cfd705686 Mon Sep 17 00:00:00 2001
From: chenzomi <chenzomi12@gmail.com>
Date: Mon, 13 Apr 2020 19:42:12 +0800
Subject: [PATCH 199/367] fix complite error while using .so with clang on
 macos

---
 CMakeLists.txt                         |  7 ++++++-
 build.sh                               |  2 +-
 cmake/external_libs/gtest.cmake        |  4 ++--
 cmake/external_libs/jpeg_turbo.cmake   |  8 ++++++--
 cmake/external_libs/libtiff.cmake      | 13 ++++++++++---
 cmake/external_libs/opencv.cmake       | 13 +++++++++----
 cmake/external_libs/protobuf.cmake     |  6 +++++-
 cmake/external_libs/sqlite.cmake       |  6 +++++-
 mindspore/ccsrc/CMakeLists.txt         | 21 ++++++++++++---------
 mindspore/ccsrc/dataset/CMakeLists.txt |  7 ++++++-
 mindspore/ccsrc/kernel/common_utils.cc |  4 ++--
 11 files changed, 64 insertions(+), 27 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d11314408e..6fe159590f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -5,8 +5,13 @@ include(${CMAKE_SOURCE_DIR}/cmake/options.cmake)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake/modules/")
 
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Werror -Wno-return-std-move -Wno-unused-private-field -Wno-unused-lambda-capture -Wno-sign-compare -Wno-overloaded-virtual -Wno-unneeded-internal-declaration -Wno-unused-variable -Wno-pessimizing-move -Wno-inconsistent-missing-override -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")    
+else()
+    set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Wl,--allow-shlib-undefined -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")
+endif()
+
 set(CMAKE_CXX_FLAGS_DEBUG "$ENV{CXXFLAGS} -O0 -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -Wl,--allow-shlib-undefined -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)=' -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2 -Wno-cpp")
-set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Wl,--allow-shlib-undefined -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -I/usr/local/include -std=c++17 -Werror -Wall -Wno-deprecated-declarations -fPIC")
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
diff --git a/build.sh b/build.sh
index 6dc699000a..3c6de1cf77 100755
--- a/build.sh
+++ b/build.sh
@@ -297,7 +297,7 @@ build_mindspore()
     if [[ "X$ENABLE_DUMPE2E" = "Xon" ]]; then
         CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_E2E=ON"
     fi
-    CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_IR=${ENABLE_DUMP_IR^^}"
+    CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_IR=${ENABLE_DUMP_IR}"
     if [[ "X$ENABLE_MPI" = "Xon" ]]; then
         CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_MPI=ON"
     fi
diff --git a/cmake/external_libs/gtest.cmake b/cmake/external_libs/gtest.cmake
index 5384b48825..df2eaec2cc 100644
--- a/cmake/external_libs/gtest.cmake
+++ b/cmake/external_libs/gtest.cmake
@@ -9,5 +9,5 @@ mindspore_add_pkg(gtest
         -DCMAKE_MACOSX_RPATH=TRUE -Dgtest_disable_pthreads=ON)
 include_directories(${gtest_INC})
 add_library(mindspore::gtest ALIAS gtest::gtest)
-file(COPY ${gtest_LIBPATH}/libgtest.so DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
-file(COPY ${gtest_LIBPATH}/libgtest_main.so DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
+file(COPY ${gtest_LIBPATH}/libgtest${CMAKE_SHARED_LIBRARY_SUFFIX} DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
+file(COPY ${gtest_LIBPATH}/libgtest_main${CMAKE_SHARED_LIBRARY_SUFFIX} DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
diff --git a/cmake/external_libs/jpeg_turbo.cmake b/cmake/external_libs/jpeg_turbo.cmake
index 84d6e3006c..6c2c70c709 100644
--- a/cmake/external_libs/jpeg_turbo.cmake
+++ b/cmake/external_libs/jpeg_turbo.cmake
@@ -1,6 +1,10 @@
-
 set(jpeg_turbo_USE_STATIC_LIBS ON)
-set(jpeg_turbo_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(jpeg_turbo_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+else()
+    set(jpeg_turbo_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+endif()
+
 set(jpeg_turbo_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 mindspore_add_pkg(jpeg_turbo
         VER 2.0.4
diff --git a/cmake/external_libs/libtiff.cmake b/cmake/external_libs/libtiff.cmake
index 461b9c4481..c9934bfaa1 100644
--- a/cmake/external_libs/libtiff.cmake
+++ b/cmake/external_libs/libtiff.cmake
@@ -1,8 +1,15 @@
-
-set(tiff_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(tiff_CXXFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -Wno-unused-result \
     -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
-set(tiff_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
+    set(tiff_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -Wno-unused-result \
     -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
+else()
+    set(tiff_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
+        -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
+    set(tiff_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
+        -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
+endif()
+
 set(tiff_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 
 mindspore_add_pkg(tiff
diff --git a/cmake/external_libs/opencv.cmake b/cmake/external_libs/opencv.cmake
index e67c3f232f..5c60a2fa61 100644
--- a/cmake/external_libs/opencv.cmake
+++ b/cmake/external_libs/opencv.cmake
@@ -1,7 +1,12 @@
-
-set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
-set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
-set(opencv_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(opencv_CXXFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_LDFLAGS "-Wl")
+else()
+    set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
+endif()
 
 mindspore_add_pkg(opencv
         VER 4.2.0
diff --git a/cmake/external_libs/protobuf.cmake b/cmake/external_libs/protobuf.cmake
index 9e64785a7a..987d5c6a8b 100644
--- a/cmake/external_libs/protobuf.cmake
+++ b/cmake/external_libs/protobuf.cmake
@@ -1,5 +1,9 @@
 set(protobuf_USE_STATIC_LIBS ON)
-set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
+else()
+    set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
+endif()
 set(protobuf_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
 set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
diff --git a/cmake/external_libs/sqlite.cmake b/cmake/external_libs/sqlite.cmake
index 35e48b2d0e..e3fe77d96b 100644
--- a/cmake/external_libs/sqlite.cmake
+++ b/cmake/external_libs/sqlite.cmake
@@ -1,7 +1,11 @@
 
 set(sqlite_USE_STATIC_LIBS ON)
 set(sqlite_CXXFLAGS)
-set(sqlite_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(sqlite_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+else()
+    set(sqlite_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+endif()
 set(sqlite_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 
 mindspore_add_pkg(sqlite
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index f3100f62c9..1b35856431 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -330,10 +330,10 @@ set(PYTHON_MODULE_SOURCE
 
 set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE)
 pybind11_add_module(_c_expression ${PYTHON_MODULE_SOURCE})
-target_link_options(_c_expression PRIVATE -Wl,-init,mindspore_log_init)
 
 MESSAGE(STATUS "operation system is ${CMAKE_SYSTEM}")
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+    target_link_options(_c_expression PRIVATE -Wl,-init,mindspore_log_init)
     set(ORIGIN_PATH $ORIGIN)
 elseif (CMAKE_SYSTEM_NAME MATCHES "Darwin")
     set_target_properties(_c_expression PROPERTIES MACOSX_RPATH ON)
@@ -497,7 +497,7 @@ set(MS_LIB_PATH ${CMAKE_SOURCE_DIR}/build/package/mindspore/lib/)
 add_custom_target(add_ms_lib ALL
     COMMAND mkdir -pv ${MS_LIB_PATH}
     COMMAND cp ${MS_CCSRC_BUILD_PATH}/_c_expression* ${MS_PACK_PATH}
-    COMMAND cp ${MS_CCSRC_BUILD_PATH}/libmindspore_gvar.so ${MS_LIB_PATH}
+    COMMAND cp ${MS_CCSRC_BUILD_PATH}/libmindspore_gvar${CMAKE_SHARED_LIBRARY_SUFFIX} ${MS_LIB_PATH}
 )
 add_dependencies(add_ms_lib _c_expression)
 
@@ -549,9 +549,11 @@ if (ENABLE_GPU)
 endif()
 
 if (ENABLE_CPU)
-    add_custom_target(add_cpu_lib ALL
-            COMMAND cp ${onednn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1
-            )
+    if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
+        add_custom_target(add_cpu_lib ALL COMMAND cp ${onednn_LIBPATH}/libdnnl.1.1.dylib ${MS_LIB_PATH}/libdnnl.1.1.dylib)
+    else ()
+        add_custom_target(add_cpu_lib ALL COMMAND cp ${onednn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1)
+    endif ()
     add_dependencies(add_cpu_lib add_ms_lib)
 endif()
 
@@ -573,8 +575,9 @@ endif()
 
 if (USE_GLOG)
     target_link_libraries(_c_expression PRIVATE mindspore::glog)
-    add_custom_target(add_glog_lib ALL
-        COMMAND cp ${glog_LIBPATH}/libglog*.so.0 ${MS_LIB_PATH}
-            )
-    add_dependencies(add_glog_lib add_ms_lib)
+    if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
+        add_custom_target(add_glog_lib ALL COMMAND cp ${glog_LIBPATH}/libglog*.dylib ${MS_LIB_PATH})
+    else ()
+        add_custom_target(add_glog_lib ALL COMMAND cp ${glog_LIBPATH}/libglog*.so.0 ${MS_LIB_PATH})
+    endif ()
 endif()
diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index 5bf210a8ba..52ba328828 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -3,7 +3,12 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-reorder")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-switch")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-sequence-point")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-variable")
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-maybe-uninitialized")
+
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-uninitialized")
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-maybe-uninitialized")
+endif()
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-format")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-attributes")
 
diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc
index 137ae65414..065cfaddc8 100644
--- a/mindspore/ccsrc/kernel/common_utils.cc
+++ b/mindspore/ccsrc/kernel/common_utils.cc
@@ -89,13 +89,13 @@ bool IsAtomicNode(const CNodePtr &kernel_node) {
       parameters_indexs.push_back(0);
     }
   }
-  std::vector<size_t> clean_output_indexs;
+  std::vector<int> clean_output_indexs;
   // in parameters data sort as input->workspace->output
   size_t index = 0;
   while (index < output_num) {
     if (parameters_indexs[input_num + workspace_num + index] == 1) {
       atomic_flag = true;
-      clean_output_indexs.push_back(index);
+      clean_output_indexs.push_back(SizeToInt(index));
     }
     index++;
   }

From 0ce83e39e16d6b0c1996ad2918548afc5f29d0f2 Mon Sep 17 00:00:00 2001
From: liyong <liyong126@huawei.com>
Date: Mon, 13 Apr 2020 16:42:13 +0800
Subject: [PATCH 200/367] fix TestShardSampleWrongNumber

adjust internal api
---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    | 34 +------
 mindspore/ccsrc/dataset/api/de_pipeline.h     |  3 -
 .../ccsrc/dataset/api/python_bindings.cc      |  8 ++
 .../ccsrc/mindrecord/include/shard_category.h |  2 +-
 .../ccsrc/mindrecord/include/shard_operator.h | 20 +++-
 .../ccsrc/mindrecord/include/shard_sample.h   | 10 +-
 .../ccsrc/mindrecord/include/shard_shuffle.h  |  2 +-
 mindspore/ccsrc/mindrecord/io/shard_reader.cc | 11 ++-
 .../ccsrc/mindrecord/meta/shard_category.cc   |  2 +-
 .../ccsrc/mindrecord/meta/shard_sample.cc     | 17 +++-
 .../ccsrc/mindrecord/meta/shard_shuffle.cc    |  2 +-
 mindspore/dataset/engine/datasets.py          |  4 +-
 mindspore/dataset/engine/samplers.py          |  2 +
 .../cpp/mindrecord/ut_shard_operator_test.cc  | 95 +++++++++----------
 .../dataset/test_minddataset_sampler.py       | 10 --
 15 files changed, 115 insertions(+), 107 deletions(-)

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index cf7050450b..f572db0cdf 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -391,30 +391,6 @@ Status DEPipeline::CheckMindRecordPartitionInfo(const py::dict &args, std::vecto
   return Status::OK();
 }
 
-Status DEPipeline::GetMindrecordSampler(const std::string &sampler_name, const py::dict &args,
-                                        std::shared_ptr<mindrecord::ShardOperator> *ptr) {
-  std::vector<int> indices;
-  for (auto &arg : args) {
-    std::string key = py::str(arg.first);
-    py::handle value = arg.second;
-    if (!value.is_none()) {
-      if (key == "indices") {
-        indices = ToIntVector(value);
-      } else {
-        std::string err_msg = "ERROR: parameter " + key + " is invalid.";
-        RETURN_STATUS_UNEXPECTED(err_msg);
-      }
-    }
-  }
-  if (sampler_name == "SubsetRandomSampler") {
-    *ptr = std::make_shared<mindrecord::ShardSample>(indices);
-  } else {
-    std::string err_msg = "ERROR: parameter sampler_name is invalid.";
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
-  return Status::OK();
-}
-
 Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   if (args["dataset_file"].is_none()) {
     std::string err_msg = "Error: at least one of dataset_files is missing";
@@ -446,12 +422,10 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
       } else if (key == "global_shuffle" && ToBool(value) == true) {
         uint32_t seed = args["partitions"].is_none() ? GetSeed() : 0;
         operators.push_back(std::make_shared<mindrecord::ShardShuffle>(seed));
-      } else if (key == "sampler_name") {
-        std::shared_ptr<mindrecord::ShardOperator> sample_op;
-        auto ret = GetMindrecordSampler(ToString(value), args["sampler_params"], &sample_op);
-        if (Status::OK() != ret) {
-          return ret;
-        }
+      } else if (key == "sampler") {
+        auto create = py::reinterpret_borrow<py::object>(value).attr("_create_for_minddataset");
+        std::shared_ptr<mindrecord::ShardOperator> sample_op =
+          create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
         operators.push_back(sample_op);
       }
     }
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index 491a75390e..acffc390cc 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -145,9 +145,6 @@ class DEPipeline {
 
   Status ParseCelebAOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
-  Status GetMindrecordSampler(const std::string &sampler_name, const py::dict &args,
-                              std::shared_ptr<mindrecord::ShardOperator> *ptr);
-
  private:
   // Execution tree that links the dataset operators.
   std::shared_ptr<ExecutionTree> tree_;
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index e6c2691281..b7cf74d435 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -54,6 +54,9 @@
 #include "dataset/engine/datasetops/source/tf_reader_op.h"
 #include "dataset/engine/jagged_connector.h"
 #include "dataset/kernels/data/to_float16_op.h"
+#include "dataset/util/random.h"
+#include "mindrecord/include/shard_operator.h"
+#include "mindrecord/include/shard_sample.h"
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
 #include "pybind11/stl_bind.h"
@@ -382,6 +385,7 @@ void bindTensorOps4(py::module *m) {
 
 void bindSamplerOps(py::module *m) {
   (void)py::class_<Sampler, std::shared_ptr<Sampler>>(*m, "Sampler");
+  (void)py::class_<mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardOperator>>(*m, "ShardOperator");
 
   (void)py::class_<DistributedSampler, Sampler, std::shared_ptr<DistributedSampler>>(*m, "DistributedSampler")
     .def(py::init<int64_t, int64_t, bool, uint32_t>(), py::arg("numDev"), py::arg("devId"), py::arg("shuffle"),
@@ -399,6 +403,10 @@ void bindSamplerOps(py::module *m) {
   (void)py::class_<SubsetRandomSampler, Sampler, std::shared_ptr<SubsetRandomSampler>>(*m, "SubsetRandomSampler")
     .def(py::init<std::vector<int64_t>>(), py::arg("indices"));
 
+  (void)py::class_<mindrecord::ShardSample, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardSample>>(
+    *m, "MindrecordSubsetRandomSampler")
+    .def(py::init<std::vector<int64_t>, uint32_t>(), py::arg("indices"), py::arg("seed") = GetSeed());
+
   (void)py::class_<WeightedRandomSampler, Sampler, std::shared_ptr<WeightedRandomSampler>>(*m, "WeightedRandomSampler")
     .def(py::init<std::vector<double>, int64_t, bool>(), py::arg("weights"), py::arg("numSamples"),
          py::arg("replacement"));
diff --git a/mindspore/ccsrc/mindrecord/include/shard_category.h b/mindspore/ccsrc/mindrecord/include/shard_category.h
index 08e5ac9c2e..b8a7611540 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_category.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_category.h
@@ -32,7 +32,7 @@ class ShardCategory : public ShardOperator {
 
   const std::vector<std::pair<std::string, std::string>> &get_categories() const;
 
-  MSRStatus operator()(ShardTask &tasks) override;
+  MSRStatus execute(ShardTask &tasks) override;
 
  private:
   std::vector<std::pair<std::string, std::string>> categories_;
diff --git a/mindspore/ccsrc/mindrecord/include/shard_operator.h b/mindspore/ccsrc/mindrecord/include/shard_operator.h
index 9d00fb7628..9f302e5321 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_operator.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_operator.h
@@ -24,7 +24,25 @@ namespace mindrecord {
 class ShardOperator {
  public:
   virtual ~ShardOperator() = default;
-  virtual MSRStatus operator()(ShardTask &tasks) = 0;
+
+  MSRStatus operator()(ShardTask &tasks) {
+    if (SUCCESS != this->pre_execute(tasks)) {
+      return FAILED;
+    }
+    if (SUCCESS != this->execute(tasks)) {
+      return FAILED;
+    }
+    if (SUCCESS != this->suf_execute(tasks)) {
+      return FAILED;
+    }
+    return SUCCESS;
+  }
+
+  virtual MSRStatus pre_execute(ShardTask &tasks) { return SUCCESS; }
+
+  virtual MSRStatus execute(ShardTask &tasks) = 0;
+
+  virtual MSRStatus suf_execute(ShardTask &tasks) { return SUCCESS; }
 };
 }  // namespace mindrecord
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/include/shard_sample.h b/mindspore/ccsrc/mindrecord/include/shard_sample.h
index aeb3374f28..15353fd0ff 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_sample.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_sample.h
@@ -17,10 +17,12 @@
 #ifndef MINDRECORD_INCLUDE_SHARD_SAMPLE_H_
 #define MINDRECORD_INCLUDE_SHARD_SAMPLE_H_
 
+#include <memory>
 #include <string>
 #include <utility>
 #include <vector>
 #include "mindrecord/include/shard_operator.h"
+#include "mindrecord/include/shard_shuffle.h"
 
 namespace mindspore {
 namespace mindrecord {
@@ -32,21 +34,23 @@ class ShardSample : public ShardOperator {
 
   ShardSample(int num, int den, int par);
 
-  explicit ShardSample(const std::vector<int> &indices);
+  ShardSample(const std::vector<int64_t> &indices, uint32_t seed);
 
   ~ShardSample() override{};
 
   const std::pair<int, int> get_partitions() const;
 
-  MSRStatus operator()(ShardTask &tasks) override;
+  MSRStatus execute(ShardTask &tasks) override;
+  MSRStatus suf_execute(ShardTask &tasks) override;
 
  private:
   int numerator_;
   int denominator_;
   int no_of_samples_;
   int partition_id_;
-  std::vector<int> indices_;
+  std::vector<int64_t> indices_;
   SamplerType sampler_type_;
+  std::shared_ptr<ShardShuffle> shuffle_op_;
 };
 }  // namespace mindrecord
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/include/shard_shuffle.h b/mindspore/ccsrc/mindrecord/include/shard_shuffle.h
index a9992ab4bc..464881aa7a 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_shuffle.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_shuffle.h
@@ -28,7 +28,7 @@ class ShardShuffle : public ShardOperator {
 
   ~ShardShuffle() override{};
 
-  MSRStatus operator()(ShardTask &tasks) override;
+  MSRStatus execute(ShardTask &tasks) override;
 
  private:
   uint32_t shuffle_seed_;
diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index 12aecea21f..2413da3737 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -779,8 +779,12 @@ MSRStatus ShardReader::Launch(bool isSimpleReader) {
 
   // Sort row group by (group_id, shard_id), prepare for parallel reading
   std::sort(row_group_summary.begin(), row_group_summary.end(), ResortRowGroups);
-  CreateTasks(row_group_summary, operators_);
-  MS_LOG(INFO) << "Launching read threads";
+  if (CreateTasks(row_group_summary, operators_) != SUCCESS) {
+    MS_LOG(ERROR) << "Failed to launch read threads.";
+    interrupt_ = true;
+    return FAILED;
+  }
+  MS_LOG(INFO) << "Launching read threads.";
 
   if (isSimpleReader) return SUCCESS;
 
@@ -1152,6 +1156,9 @@ std::vector<std::tuple<std::vector<uint8_t>, json>> ShardReader::GetBlockNext()
 }
 
 std::vector<std::tuple<std::vector<uint8_t>, json>> ShardReader::GetNext() {
+  if (interrupt_) {
+    return std::vector<std::tuple<std::vector<uint8_t>, json>>();
+  }
   if (block_reader_) return GetBlockNext();
   if (deliver_id_ >= static_cast<int>(tasks_.Size())) {
     return std::vector<std::tuple<std::vector<uint8_t>, json>>();
diff --git a/mindspore/ccsrc/mindrecord/meta/shard_category.cc b/mindspore/ccsrc/mindrecord/meta/shard_category.cc
index c64a7bfc70..859a3b343f 100644
--- a/mindspore/ccsrc/mindrecord/meta/shard_category.cc
+++ b/mindspore/ccsrc/mindrecord/meta/shard_category.cc
@@ -23,6 +23,6 @@ ShardCategory::ShardCategory(const std::vector<std::pair<std::string, std::strin
 
 const std::vector<std::pair<std::string, std::string>> &ShardCategory::get_categories() const { return categories_; }
 
-MSRStatus ShardCategory::operator()(ShardTask &tasks) { return SUCCESS; }
+MSRStatus ShardCategory::execute(ShardTask &tasks) { return SUCCESS; }
 }  // namespace mindrecord
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/meta/shard_sample.cc b/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
index 367c7a5cf9..ef627b0c09 100644
--- a/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
+++ b/mindspore/ccsrc/mindrecord/meta/shard_sample.cc
@@ -46,13 +46,15 @@ ShardSample::ShardSample(int num, int den, int par)
       indices_({}),
       sampler_type_(kCustomTopPercentSampler) {}
 
-ShardSample::ShardSample(const std::vector<int> &indices)
+ShardSample::ShardSample(const std::vector<int64_t> &indices, uint32_t seed)
     : numerator_(0),
       denominator_(0),
       no_of_samples_(0),
       partition_id_(0),
       indices_(indices),
-      sampler_type_(kSubsetRandomSampler) {}
+      sampler_type_(kSubsetRandomSampler) {
+  shuffle_op_ = std::make_shared<ShardShuffle>(seed);
+}
 
 const std::pair<int, int> ShardSample::get_partitions() const {
   if (numerator_ == 1 && denominator_ > 1) {
@@ -61,7 +63,7 @@ const std::pair<int, int> ShardSample::get_partitions() const {
   return std::pair<int, int>(-1, -1);
 }
 
-MSRStatus ShardSample::operator()(ShardTask &tasks) {
+MSRStatus ShardSample::execute(ShardTask &tasks) {
   int no_of_categories = static_cast<int>(tasks.categories);
   int total_no = static_cast<int>(tasks.Size());
 
@@ -115,5 +117,14 @@ MSRStatus ShardSample::operator()(ShardTask &tasks) {
   }
   return SUCCESS;
 }
+
+MSRStatus ShardSample::suf_execute(ShardTask &tasks) {
+  if (sampler_type_ == kSubsetRandomSampler) {
+    if (SUCCESS != (*shuffle_op_)(tasks)) {
+      return FAILED;
+    }
+  }
+  return SUCCESS;
+}
 }  // namespace mindrecord
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/mindrecord/meta/shard_shuffle.cc b/mindspore/ccsrc/mindrecord/meta/shard_shuffle.cc
index 14816e9e9f..f8ad2c341d 100644
--- a/mindspore/ccsrc/mindrecord/meta/shard_shuffle.cc
+++ b/mindspore/ccsrc/mindrecord/meta/shard_shuffle.cc
@@ -22,7 +22,7 @@ namespace mindspore {
 namespace mindrecord {
 ShardShuffle::ShardShuffle(uint32_t seed) : shuffle_seed_(seed) {}
 
-MSRStatus ShardShuffle::operator()(ShardTask &tasks) {
+MSRStatus ShardShuffle::execute(ShardTask &tasks) {
   if (tasks.categories < 1) {
     return FAILED;
   }
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 3d660d58a8..6052490928 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1681,9 +1681,7 @@ class MindDataset(SourceDataset):
         args["block_reader"] = self.block_reader
         args["num_shards"] = self.num_shards
         args["shard_id"] = self.shard_id
-        if self.sampler:
-            args["sampler_name"] = self.sampler.__class__.__name__
-            args["sampler_params"] = self.sampler.__dict__
+        args["sampler"] = self.sampler
         return args
 
     def get_dataset_size(self):
diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py
index 62a3dbed18..fd9c50e951 100644
--- a/mindspore/dataset/engine/samplers.py
+++ b/mindspore/dataset/engine/samplers.py
@@ -195,6 +195,8 @@ class SubsetRandomSampler():
     def create(self):
         return cde.SubsetRandomSampler(self.indices)
 
+    def _create_for_minddataset(self):
+        return cde.MindrecordSubsetRandomSampler(self.indices)
 
 class WeightedRandomSampler():
     """
diff --git a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
index 143931658a..549e2140f4 100644
--- a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
+++ b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc
@@ -30,9 +30,9 @@
 #include "mindrecord/include/shard_shuffle.h"
 #include "ut_common.h"
 
-using mindspore::MsLogLevel::INFO;
-using mindspore::ExceptionType::NoExceptionType;
 using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
 
 namespace mindspore {
 namespace mindrecord {
@@ -65,31 +65,31 @@ TEST_F(TestShardOperator, TestShardSampleBasic) {
   ASSERT_TRUE(i <= kSampleCount);
 }
 
-// TEST_F(TestShardOperator, TestShardSampleWrongNumber) {
-//   MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
-// 
-//   std::string file_name = "./imagenet.shard01";
-//   auto column_list = std::vector<std::string>{"file_name"};
-// 
-//   const int kNum = 5;
-//   const int kDen = 0;
-//   std::vector<std::shared_ptr<ShardOperator>> ops;
-//   ops.push_back(std::make_shared<ShardSample>(kNum, kDen));
-// 
-//   ShardReader dataset;
-//   dataset.Open(file_name, 4, column_list, ops);
-//   dataset.Launch();
-// 
-//   int i = 0;
-//   while (true) {
-//     auto x = dataset.GetNext();
-//     if (x.empty()) break;
-//     MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
-//     i++;
-//   }
-//   dataset.Finish();
-//   ASSERT_TRUE(i <= 5);
-// }
+TEST_F(TestShardOperator, TestShardSampleWrongNumber) {
+  MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
+
+  std::string file_name = "./imagenet.shard01";
+  auto column_list = std::vector<std::string>{"file_name"};
+
+  const int kNum = 5;
+  const int kDen = 0;
+  std::vector<std::shared_ptr<ShardOperator>> ops;
+  ops.push_back(std::make_shared<ShardSample>(kNum, kDen));
+
+  ShardReader dataset;
+  dataset.Open(file_name, 4, column_list, ops);
+  dataset.Launch();
+
+  int i = 0;
+  while (true) {
+    auto x = dataset.GetNext();
+    if (x.empty()) break;
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]);
+    i++;
+  }
+  dataset.Finish();
+  ASSERT_TRUE(i <= 5);
+}
 
 TEST_F(TestShardOperator, TestShardSampleRatio) {
   MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
@@ -117,7 +117,6 @@ TEST_F(TestShardOperator, TestShardSampleRatio) {
   ASSERT_TRUE(i <= 10);
 }
 
-
 TEST_F(TestShardOperator, TestShardSamplePartition) {
   MS_LOG(INFO) << common::SafeCStr(FormatInfo("Test read imageNet"));
   std::string file_name = "./imagenet.shard01";
@@ -170,8 +169,8 @@ TEST_F(TestShardOperator, TestShardCategory) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
 
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
@@ -199,8 +198,8 @@ TEST_F(TestShardOperator, TestShardShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
   }
   dataset.Finish();
@@ -224,8 +223,8 @@ TEST_F(TestShardOperator, TestShardSampleShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
   }
   dataset.Finish();
@@ -251,8 +250,8 @@ TEST_F(TestShardOperator, TestShardShuffleSample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
   }
   dataset.Finish();
@@ -278,8 +277,8 @@ TEST_F(TestShardOperator, TestShardSampleShuffleSample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
   }
   dataset.Finish();
@@ -307,8 +306,8 @@ TEST_F(TestShardOperator, TestShardShuffleCompare) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
 
     auto y = compare_dataset.GetNext();
@@ -342,8 +341,8 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle1) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
@@ -376,8 +375,8 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle2) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
     category_no++;
@@ -410,8 +409,8 @@ TEST_F(TestShardOperator, TestShardCategorySample) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
@@ -448,8 +447,8 @@ TEST_F(TestShardOperator, TestShardCategorySampleShuffle) {
   while (true) {
     auto x = dataset.GetNext();
     if (x.empty()) break;
-    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) <<
-        ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
+    MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"])
+                 << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump());
     i++;
 
     ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second);
diff --git a/tests/ut/python/dataset/test_minddataset_sampler.py b/tests/ut/python/dataset/test_minddataset_sampler.py
index 7662a0e390..3cad3877ef 100644
--- a/tests/ut/python/dataset/test_minddataset_sampler.py
+++ b/tests/ut/python/dataset/test_minddataset_sampler.py
@@ -81,8 +81,6 @@ def test_cv_minddataset_subset_random_sample_basic(add_and_remove_cv_file):
             "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info(
             "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        assert data[indices[num_iter]]['file_name'] == "".join(
-            [chr(x) for x in item['file_name']])
         num_iter += 1
     assert num_iter == 5
 
@@ -107,8 +105,6 @@ def test_cv_minddataset_subset_random_sample_replica(add_and_remove_cv_file):
             "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info(
             "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        assert data[indices[num_iter]]['file_name'] == "".join(
-            [chr(x) for x in item['file_name']])
         num_iter += 1
     assert num_iter == 6
 
@@ -133,8 +129,6 @@ def test_cv_minddataset_subset_random_sample_empty(add_and_remove_cv_file):
             "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info(
             "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        assert data[indices[num_iter]]['file_name'] == "".join(
-            [chr(x) for x in item['file_name']])
         num_iter += 1
     assert num_iter == 0
 
@@ -159,8 +153,6 @@ def test_cv_minddataset_subset_random_sample_out_range(add_and_remove_cv_file):
             "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info(
             "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        assert data[indices[num_iter] % len(data)]['file_name'] == "".join([
-            chr(x) for x in item['file_name']])
         num_iter += 1
     assert num_iter == 5
 
@@ -185,8 +177,6 @@ def test_cv_minddataset_subset_random_sample_negative(add_and_remove_cv_file):
             "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
         logger.info(
             "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        assert data[indices[num_iter] % len(data)]['file_name'] == "".join([
-            chr(x) for x in item['file_name']])
         num_iter += 1
     assert num_iter == 5
 

From 7d700295f8ef8ea112c0bd93e34cfe1505ebcb7a Mon Sep 17 00:00:00 2001
From: root <leilei_snow>
Date: Sat, 11 Apr 2020 09:19:11 +0000
Subject: [PATCH 201/367] add dynamic lr and enhance optim

---
 mindspore/nn/dynamic_lr.py                 | 300 +++++++++++++++++++++
 mindspore/nn/optim/adam.py                 |  28 +-
 mindspore/nn/optim/momentum.py             |  38 +--
 mindspore/nn/optim/optimizer.py            | 102 ++++++-
 mindspore/nn/optim/rmsprop.py              |  32 +--
 mindspore/nn/optim/sgd.py                  |  40 +--
 tests/ut/python/nn/optim/test_optimizer.py |  10 +-
 tests/ut/python/nn/test_dynamic_lr.py      | 234 ++++++++++++++++
 8 files changed, 650 insertions(+), 134 deletions(-)
 create mode 100644 mindspore/nn/dynamic_lr.py
 create mode 100644 tests/ut/python/nn/test_dynamic_lr.py

diff --git a/mindspore/nn/dynamic_lr.py b/mindspore/nn/dynamic_lr.py
new file mode 100644
index 0000000000..cf25f1f50e
--- /dev/null
+++ b/mindspore/nn/dynamic_lr.py
@@ -0,0 +1,300 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""dynamic learning rate"""
+import math
+
+from mindspore._checkparam import ParamValidator as validator
+from mindspore._checkparam import Rel
+
+
+def piecewise_constant_lr(milestone, learning_rates):
+    r"""
+    Get piecewise constant learning rate.
+
+    Calculate learning rate by given `milestone` and `learning_rates`. Let the value of `milestone` be
+    :math:`(M_1, M_2, ..., M_N)` and the value of `learning_rates` be :math:`(x_1, x_2, ..., x_N)`. N is the length of
+    `milestone`. Let the output learning rate be `y`.
+
+    .. math::
+        y[i] = x_t for i \in [M_{t-1}, M_t)
+
+    Args:
+        milestone (list[int]): A list of milestone. This list is a monotone increasing list.
+        learning_rates (list[float]): A list of learning rates.
+
+    Returns:
+        list[float]. The size of list is :math:`M_N`.
+
+    Examples:
+        >>> milestone = [2, 5, 10]
+        >>> learning_rates = [0.1, 0.05, 0.01]
+        >>> lr = piecewise_constant_lr(milestone, learning_rates)
+        [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
+    """
+    validator.check_type('milestone', milestone, (tuple, list))
+    validator.check_type('learning_rates', learning_rates, (tuple, list))
+    if len(milestone) != len(learning_rates):
+        raise ValueError('The size of `milestone` must be same with the size of `learning_rates`.')
+
+    lr = []
+    last_item = 0
+    for i, item in enumerate(milestone):
+        validator.check_integer(f'milestone[{i}]', item, 0, Rel.GT)
+        validator.check_type(f'learning_rates[{i}]', learning_rates[i], [float])
+        if item < last_item:
+            raise ValueError(f'The value of milestone[{i}] must be greater than milestone[{i - 1}]')
+        lr += [learning_rates[i]] * (item - last_item)
+        last_item = item
+
+    return lr
+
+
+def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair):
+    validator.check_integer('total_step', total_step, 0, Rel.GT)
+    validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT)
+    validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT)
+    validator.check_float_positive('learning_rate', learning_rate)
+    validator.check_float_positive('decay_rate', decay_rate)
+    validator.check_type('is_stair', is_stair, [bool])
+
+
+def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
+    r"""
+    Calculate learning rate base on exponential decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{\frac{current\_epoch}{decay\_epoch}}
+
+    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        total_step (int): The total number of steps.
+        step_per_epoch (int): The number of steps in per epoch.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
+
+    Returns:
+        list[float]. The size of list is `total_step`.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.9
+        >>> total_step = 6
+        >>> step_per_epoch = 2
+        >>> decay_epoch = 1
+        >>> lr = exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+        [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]
+    """
+    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
+
+    lr = []
+    for i in range(total_step):
+        if is_stair:
+            lr.append(learning_rate * decay_rate ** math.floor(math.floor(i / step_per_epoch) / decay_epoch))
+        else:
+            lr.append(learning_rate * decay_rate ** (math.floor(i / step_per_epoch) / decay_epoch))
+    return lr
+
+
+def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
+    r"""
+    Calculate learning rate base on natural exponential decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch}
+
+    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        total_step (int): The total number of steps.
+        step_per_epoch (int): The number of steps in per epoch.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
+
+    Returns:
+        list[float]. The size of list is `total_step`.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.9
+        >>> total_step = 6
+        >>> step_per_epoch = 2
+        >>> decay_epoch = 2
+        >>> lr = natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657]
+    """
+    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
+
+    function = lambda x, y: x
+    if is_stair:
+        function = lambda x, y: math.floor(x / y) * y
+
+    lr = []
+    for i in range(total_step):
+        lr.append(learning_rate * math.e ** (-decay_rate * function(math.floor(i / step_per_epoch), decay_epoch)))
+    return lr
+
+
+def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
+    r"""
+    Calculate learning rate base on inverse-time decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * current\_epoch / decay\_epoch)
+
+    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        total_step (int): The total number of steps.
+        step_per_epoch (int): The number of steps in per epoch.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
+
+    Returns:
+        list[float]. The size of list is `total_step`.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.5
+        >>> total_step = 6
+        >>> step_per_epoch = 1
+        >>> decay_epoch = 1
+        >>> lr = inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574]
+    """
+    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
+
+    lr = []
+    for i in range(total_step):
+        if is_stair:
+            lr.append(learning_rate / (1 + decay_rate * math.floor(math.floor(i / step_per_epoch) / decay_epoch)))
+        else:
+            lr.append(learning_rate / (1 + decay_rate * math.floor(i / step_per_epoch) / decay_epoch))
+    return lr
+
+
+def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
+    r"""
+    Calculate learning rate base on cosine decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
+        (1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
+
+    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+
+    Args:
+        min_lr (float): The minimum value of learning rate.
+        max_lr (float): The maximum value of learning rate.
+        total_step (int): The total number of steps.
+        step_per_epoch (int): The number of steps in per epoch.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+
+    Returns:
+        list[float]. The size of list is `total_step`.
+
+    Examples:
+        >>> min_lr = 0.01
+        >>> max_lr = 0.1
+        >>> total_step = 6
+        >>> step_per_epoch = 2
+        >>> decay_epoch = 2
+        >>> lr = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
+        [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
+    """
+    validator.check_float_positive('min_lr', min_lr)
+    validator.check_float_positive('max_lr', max_lr)
+    validator.check_integer('total_step', total_step, 0, Rel.GT)
+    validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT)
+    validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT)
+
+    delta = 0.5 * (max_lr - min_lr)
+    lr = []
+    for i in range(total_step):
+        tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
+        lr.append(min_lr + delta * (1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
+    return lr
+
+
+def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
+                        update_decay_epoch=False):
+    r"""
+    Calculate learning rate base on polynomial decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
+        (1 - tmp\_epoch / decay\_epoch)^{power} + end\_learning\_rate
+
+    Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch), current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+    If `update_decay_epoch` is true, update the value of `decay_epoch` every epoch. The formula is
+    :math:`decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        end_learning_rate (float): The end value of learning rate.
+        total_step (int): The total number of steps.
+        step_per_epoch (int): The number of steps in per epoch.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+        power (float): A value used to calculate decayed learning rate.
+        update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
+
+    Returns:
+        list[float]. The size of list is `total_step`.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> end_learning_rate = 0.01
+        >>> total_step = 6
+        >>> step_per_epoch = 2
+        >>> decay_epoch = 2
+        >>> power = 0.5
+        >>> lr = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+        [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
+    """
+    validator.check_float_positive('learning_rate', learning_rate)
+    validator.check_float_positive('end_learning_rate', end_learning_rate)
+    validator.check_integer('total_step', total_step, 0, Rel.GT)
+    validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT)
+    validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT)
+    validator.check_type('power', power, [float])
+    validator.check_type('update_decay_epoch', update_decay_epoch, [bool])
+
+    function = lambda x, y: (x, min(x, y))
+    if update_decay_epoch:
+        function = lambda x, y: (x * max(math.ceil(y / x), 1), y)
+
+    lr = []
+    delta = learning_rate - end_learning_rate
+    for i in range(total_step):
+        current_epoch = math.floor(i / step_per_epoch)
+        decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
+        lr.append(delta * (1 - tmp_epoch / decay_epoch) ** power + end_learning_rate)
+    return lr
diff --git a/mindspore/nn/optim/adam.py b/mindspore/nn/optim/adam.py
index 521510fa58..eb4e33751f 100755
--- a/mindspore/nn/optim/adam.py
+++ b/mindspore/nn/optim/adam.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """adam"""
-from typing import Iterable
 import numpy as np
 
 from mindspore.common import dtype as mstype
@@ -25,7 +24,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.common.tensor import Tensor
 from mindspore._checkparam import ParamValidator as validator
 from mindspore._checkparam import Rel
-from .optimizer import Optimizer, apply_decay, grad_scale
+from .optimizer import Optimizer
 
 _learning_rate_update_func = ['linear', 'cos', 'sin']
 
@@ -168,22 +167,13 @@ class Adam(Optimizer):
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                  use_nesterov=False, weight_decay=0.0, loss_scale=1.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(Adam, self).__init__(learning_rate, params)
+        super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale, decay_filter)
         _check_param_value(beta1, beta2, eps, weight_decay)
         validator.check_type("use_locking", use_locking, [bool])
         validator.check_type("use_nesterov", use_nesterov, [bool])
         validator.check_type("loss_scale", loss_scale, [float])
         validator.check_number_range("loss_scale", loss_scale, 1.0, float("inf"), Rel.INC_LEFT)
 
-        self.dynamic_lr = False
-        if isinstance(learning_rate, Iterable) or \
-                (isinstance(learning_rate, Tensor) and learning_rate.dim() == 1):
-            self.dynamic_lr = True
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
-            self.axis = 0
-
         self.beta1 = Tensor(beta1, mstype.float32)
         self.beta2 = Tensor(beta2, mstype.float32)
         self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power")
@@ -196,8 +186,6 @@ class Adam(Optimizer):
         self.decay_tf = tuple(decay_filter(x) for x in self.parameters)
         self.hyper_map = C.HyperMap()
         self.opt = P.Adam(use_locking, use_nesterov)
-        self.weight_decay = weight_decay * loss_scale
-        self.reciprocal_scale = 1.0 / loss_scale
 
         self.pow = P.Pow()
         self.sqrt = P.Sqrt()
@@ -208,15 +196,9 @@ class Adam(Optimizer):
         params = self.parameters
         moment1 = self.moment1
         moment2 = self.moment2
-        if self.weight_decay > 0:
-            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients)
-        if self.reciprocal_scale != 1.0:
-            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
-
-        lr = self.learning_rate
-        if self.dynamic_lr:
-            lr = self.gather(self.learning_rate, self.global_step, self.axis)
-            F.control_depend(lr, self.assignadd(self.global_step, self.one))
+        gradients = self.decay_weight(gradients)
+        gradients = self.scale_grad(gradients)
+        lr = self.get_lr()
 
         beta1_power = self.beta1_power * self.beta1
         self.beta1_power = beta1_power
diff --git a/mindspore/nn/optim/momentum.py b/mindspore/nn/optim/momentum.py
index 21d3cc864e..bac8e74a42 100755
--- a/mindspore/nn/optim/momentum.py
+++ b/mindspore/nn/optim/momentum.py
@@ -13,14 +13,9 @@
 # limitations under the License.
 # ============================================================================
 """momentum"""
-from typing import Iterable
-
 from mindspore.ops import functional as F, composite as C, operations as P
-from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
-import mindspore.common.dtype as mstype
-from mindspore.common import Tensor
-from .optimizer import Optimizer, apply_decay, grad_scale
+from .optimizer import Optimizer
 
 momentum_opt = C.MultitypeFuncGraph("momentum_opt")
 
@@ -88,43 +83,20 @@ class Momentum(Optimizer):
     """
     def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(Momentum, self).__init__(learning_rate, params)
+        super(Momentum, self).__init__(learning_rate, params, weight_decay, loss_scale, decay_filter)
         if isinstance(momentum, float) and momentum < 0.0:
             raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
-        if isinstance(learning_rate, Iterable) or \
-                (isinstance(learning_rate, Tensor) and learning_rate.dim() == 1):
-            self.dynamic_lr = True
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
-            self.axis = 0
-        else:
-            self.dynamic_lr = False
-            self.gather = None
-            self.assignadd = None
-            self.global_step = None
-            self.axis = None
         self.momentum = Parameter(momentum, name="momentum")
         self.params = self.parameters
         self.moments = self.params.clone(prefix="moments", init='zeros')
-        self.decay_tf = tuple(decay_filter(x) for x in self.parameters)
         self.hyper_map = C.HyperMap()
         self.opt = P.ApplyMomentum()
-        self.weight_decay = weight_decay * loss_scale
-        self.reciprocal_scale = 1.0 / loss_scale
-        self.one = Tensor(1, mstype.int32)
 
     def construct(self, gradients):
         params = self.params
         moments = self.moments
-        if self.weight_decay > 0:
-            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients)
-        if self.reciprocal_scale != 1.0:
-            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
-        if self.dynamic_lr:
-            lr = self.gather(self.learning_rate, self.global_step, self.axis)
-            F.control_depend(lr, self.assignadd(self.global_step, self.one))
-        else:
-            lr = self.learning_rate
+        gradients = self.decay_weight(gradients)
+        gradients = self.scale_grad(gradients)
+        lr = self.get_lr()
         success = self.hyper_map(F.partial(momentum_opt, self.opt, lr, self.momentum), gradients, params, moments)
         return success
diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index e2edf7bfb4..c2a419c565 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -17,9 +17,11 @@ from typing import Iterable
 
 import numpy as np
 
+import mindspore
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.nn.cell import Cell
 from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common.initializer import initializer
 from mindspore._checkparam import ParamValidator as validator
 from mindspore._checkparam import Rel
 from mindspore.common.tensor import Tensor
@@ -42,34 +44,110 @@ class Optimizer(Cell):
     Args:
         learning_rate (float): A floating point value for the learning rate. Should be greater than 0.
         parameters (list): A list of parameter, which will be updated. The element in `parameters`
-                           should be class mindspore.Parameter.
+            should be class mindspore.Parameter.
+        weight_decay (float): A floating point value for the weight decay. Default: 0.0.
+        loss_scale (float): A floating point value for the loss scale. Default: 1.0. Should be greater than 0.
+        decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda
+            x: 'beta' not in x.name and 'gamma' not in x.name.
 
     Raises:
         ValueError: If the learning_rate is a Tensor, but the dims of tensor is greater than 1.
         TypeError: If the learning_rate is not any of the three types: float, Tensor, Iterable.
     """
 
-    def __init__(self, learning_rate, parameters):
+    def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0,
+                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
         super(Optimizer, self).__init__()
         if isinstance(learning_rate, float):
+            self.dynamic_lr = False
+            self.gather = None
+            self.assignadd = None
+            self.global_step = None
             validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT)
-        elif isinstance(learning_rate, Iterable):
-            learning_rate = Tensor(np.array(list(learning_rate)).astype(np.float32))
-        elif isinstance(learning_rate, Tensor):
-            if learning_rate.dim() > 1:
-                raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`,"
-                                 f"but got {learning_rate.dim()}.")
         else:
-            raise TypeError("Learning rate should be float, Tensor or Iterable.")
+            self.dynamic_lr = True
+            self.gather = P.GatherV2()
+            self.assignadd = P.AssignAdd()
+            self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
+            if isinstance(learning_rate, Iterable):
+                learning_rate = Tensor(np.array(list(learning_rate)).astype(np.float32))
+            elif isinstance(learning_rate, Tensor):
+                if learning_rate.dim() > 1:
+                    raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`,"
+                                     f"but got {learning_rate.dim()}.")
+                if learning_rate.dim() == 1 and learning_rate.size() < 2:
+                    logger.warning("If want to use the dynamic learning rate, please make sure that the number "
+                                   "of elements in the list, tuple or tensor passed is greater than 1.")
+            else:
+                raise TypeError("Learning rate should be float, Tensor or Iterable.")
+
+        if loss_scale <= 0.0:
+            raise ValueError("Loss scale should be greater than 0, but got {}".format(loss_scale))
+        if weight_decay < 0.0:
+            raise ValueError("Weight decay should be equal or greater than 0, but got {}".format(weight_decay))
 
-        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1 and learning_rate.size() < 2:
-            logger.warning("If want to use the dynamic learning rate, please make sure that "
-                           "the number of elements in the list, tuple or tensor passed is greater than 1.")
         self.learning_rate = Parameter(learning_rate, name="learning_rate")
         self.parameters = ParameterTuple(parameters)
+        self.reciprocal_scale = 1.0 / loss_scale
+        self.weight_decay = weight_decay * loss_scale
+        self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
+
         if not self.parameters:
             raise ValueError("optimizer got an empty parameter list.")
 
+    def decay_weight(self, gradients):
+        """
+        Weight decay.
+
+        An approach to reduce the overfitting of a deep learning neural network model.
+
+        Args:
+            gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape with
+                `self.parameters`.
+
+        Returns:
+            tuple[Tensor], The gradients after weight decay.
+        """
+        if self.weight_decay > 0:
+            params = self.params
+            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_flags, params, gradients)
+
+        return gradients
+
+    def scale_grad(self, gradients):
+        """
+        Loss scale for mixed precision.
+
+        An approach of mixed precision training to improve the speed and energy efficiency of training deep neural
+        network.
+
+        Args:
+            gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape with
+                `self.parameters`.
+
+        Returns:
+            tuple[Tensor], The gradients after loss scale.
+
+        """
+        if self.reciprocal_scale != 1.0:
+            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
+
+        return gradients
+
+    def get_lr(self):
+        """
+        Get the learning rate of current step.
+
+        Returns:
+            float, the learning rate of current step.
+        """
+        lr = self.learning_rate
+        if self.dynamic_lr:
+            lr = self.gather(self.learning_rate, self.global_step, 0)
+            F.control_depend(lr, self.assignadd(self.global_step, 1))
+
+        return lr
+
     def construct(self, *hyper_params):
         raise NotImplementedError
 
diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py
index b17a101708..a68dc6f7c4 100644
--- a/mindspore/nn/optim/rmsprop.py
+++ b/mindspore/nn/optim/rmsprop.py
@@ -14,12 +14,8 @@
 # ============================================================================
 """rmsprop"""
 from mindspore.ops import functional as F, composite as C, operations as P
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
 from mindspore._checkparam import ParamValidator as validator
-import mindspore.common.dtype as mstype
-from mindspore.common import Tensor
-from .optimizer import Optimizer, grad_scale, apply_decay
+from .optimizer import Optimizer
 
 rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
 centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
@@ -138,7 +134,7 @@ class RMSProp(Optimizer):
     def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
                  use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(RMSProp, self).__init__(learning_rate, params)
+        super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale, decay_filter)
 
         if isinstance(momentum, float) and momentum < 0.0:
             raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
@@ -157,15 +153,6 @@ class RMSProp(Optimizer):
         else:
             self.opt = P.ApplyRMSProp(use_locking)
 
-        self.dynamic_lr = False
-        if not isinstance(learning_rate, float):
-            self.dynamic_lr = True
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
-            self.axis = 0
-            self.one = Tensor(1, mstype.int32)
-
         self.momentum = momentum
 
         self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
@@ -173,21 +160,12 @@ class RMSProp(Optimizer):
         self.hyper_map = C.HyperMap()
 
         self.decay = decay
-        self.decay_tf = tuple(decay_filter(x) for x in self.parameters)
-        self.reciprocal_scale = 1.0 / loss_scale
-        self.weight_decay = weight_decay * loss_scale
 
     def construct(self, gradients):
         params = self.parameters
-        if self.weight_decay > 0:
-            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients)
-        if self.reciprocal_scale != 1.0:
-            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
-        if self.dynamic_lr:
-            lr = self.gather(self.learning_rate, self.global_step, self.axis)
-            F.control_depend(lr, self.assignadd(self.global_step, self.one))
-        else:
-            lr = self.learning_rate
+        gradients = self.decay_weight(gradients)
+        gradients = self.scale_grad(gradients)
+        lr = self.get_lr()
         if self.centered:
             success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
                                                self.momentum), params, self.mg, self.ms, self.moment, gradients)
diff --git a/mindspore/nn/optim/sgd.py b/mindspore/nn/optim/sgd.py
index dbc81ecdd6..a18adb8184 100755
--- a/mindspore/nn/optim/sgd.py
+++ b/mindspore/nn/optim/sgd.py
@@ -14,11 +14,9 @@
 # ============================================================================
 """sgd"""
 from mindspore.ops import functional as F, composite as C, operations as P
-from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore._checkparam import ParamValidator as validator
-import mindspore.common.dtype as mstype
-from .optimizer import Optimizer, grad_scale
+from .optimizer import Optimizer
 
 sgd_opt = C.MultitypeFuncGraph("sgd_opt")
 
@@ -83,7 +81,7 @@ class SGD(Optimizer):
     def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,
                  loss_scale=1.0):
 
-        super(SGD, self).__init__(learning_rate, params)
+        super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale)
 
         if isinstance(momentum, float) and momentum < 0.0:
             raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
@@ -92,44 +90,22 @@ class SGD(Optimizer):
             raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening))
         self.dampening = dampening
 
-        if weight_decay < 0.0:
-            raise ValueError("weight_decay should be at least 0.0, but got weight_decay {}".format(weight_decay))
-        self.weight_decay = weight_decay
-
         validator.check_type("nesterov", nesterov, [bool])
         self.nesterov = nesterov
 
         self.opt = P.SGD(dampening, weight_decay, nesterov)
 
-        self.dynamic_lr = False
-        self.gather = None
-        self.global_step = None
-        self.axis = None
-        if not isinstance(learning_rate, float):
-            self.dynamic_lr = True
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
-            self.axis = 0
         self.momentum = Parameter(momentum, name="momentum")
-        self.params = self.parameters
-        self.accum = self.params.clone(prefix="accum", init='zeros')
-        self.stat = self.params.clone(prefix="stat", init='ones')
+        self.accum = self.parameters.clone(prefix="accum", init='zeros')
+        self.stat = self.parameters.clone(prefix="stat", init='ones')
         self.hyper_map = C.HyperMap()
 
-        self.weight_decay = weight_decay * loss_scale
-        self.reciprocal_scale = 1.0 / loss_scale
-
     def construct(self, gradients):
-        params = self.params
+        params = self.parameters
         accum = self.accum
         stat = self.stat
-        if self.reciprocal_scale != 1.0:
-            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
-        if self.dynamic_lr:
-            lr = self.gather(self.learning_rate, self.global_step, self.axis)
-            F.control_depend(lr, self.assignadd(self.global_step, 1))
-        else:
-            lr = self.learning_rate
+        gradients = self.decay_weight(gradients)
+        gradients = self.scale_grad(gradients)
+        lr = self.get_lr()
         success = self.hyper_map(F.partial(sgd_opt, self.opt, lr, self.momentum), gradients, params, accum, stat)
         return success
diff --git a/tests/ut/python/nn/optim/test_optimizer.py b/tests/ut/python/nn/optim/test_optimizer.py
index 860d751fd5..89fb1d812b 100644
--- a/tests/ut/python/nn/optim/test_optimizer.py
+++ b/tests/ut/python/nn/optim/test_optimizer.py
@@ -15,17 +15,11 @@
 """ test optimizer """
 import numpy as np
 import pytest
-from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
 from mindspore import Tensor
+from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
 from mindspore.common.parameter import Parameter
 
 
-gradient = Tensor(np.zeros([1, 2, 3]))
-accumulation = gradient
-variable = accumulation
-
-
-paramsTensor = Tensor(np.zeros([1, 2, 3]))
 class IterableObjc:
     def __iter__(self):
         cont = 0
@@ -56,6 +50,7 @@ class TestAdam():
 
     def test_construct(self):
         with pytest.raises(TypeError):
+            gradient = Tensor(np.zeros([1, 2, 3]))
             adam = Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                         use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
             adam.construct(gradient)
@@ -105,4 +100,5 @@ class TestUnsupportParam():
 
     def test_Sgd_init(self):
         with pytest.raises(TypeError):
+            paramsTensor = Tensor(np.zeros([1, 2, 3]))
             SGD(paramsTensor)
diff --git a/tests/ut/python/nn/test_dynamic_lr.py b/tests/ut/python/nn/test_dynamic_lr.py
new file mode 100644
index 0000000000..cb959956d6
--- /dev/null
+++ b/tests/ut/python/nn/test_dynamic_lr.py
@@ -0,0 +1,234 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" Test Dynamic Learning Rate """
+import pytest
+import mindspore
+from mindspore.nn import dynamic_lr as dr
+
+milestone = [10, 20, 30]
+learning_rates = [0.1, 0.05, 0.01]
+learning_rate = 0.1
+end_learning_rate = 0.01
+decay_rate = 0.9
+total_step = 30
+step_per_epoch = 3
+decay_epoch = 2
+min_lr = 0.01
+max_lr = 0.1
+power = 0.5
+
+class TestInputs:
+    def test_milestone1(self):        
+        milestone1 = 1
+        with pytest.raises(ValueError):
+            dr.piecewise_constant_lr(milestone1, learning_rates)
+
+    def test_milestone2(self):
+        milestone1 = [20, 10, 1]
+        with pytest.raises(ValueError):
+            dr.piecewise_constant_lr(milestone1, learning_rates)
+
+        milestone2 = [1.0, 2.0, True]
+        with pytest.raises(ValueError):
+            dr.piecewise_constant_lr(milestone2, learning_rates)
+
+    def test_learning_rates1(self):
+        lr = True
+        with pytest.raises(ValueError):
+            dr.piecewise_constant_lr(milestone, lr)
+
+    def test_learning_rates2(self):
+        lr = [1, 2, 1]
+        with pytest.raises(ValueError):
+            dr.piecewise_constant_lr(milestone, lr)
+
+    def test_learning_rate_type(self):
+        lr = True
+        with pytest.raises(TypeError):
+            dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch)
+        
+        with pytest.raises(TypeError):
+            dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+
+    def test_learning_rate_value(self):
+        lr = -1.0
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+
+    def test_end_learning_rate_type(self):
+        lr = True        
+        with pytest.raises(TypeError):
+            dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
+
+    def test_end_learning_rate_value(self):
+        lr = -1.0
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
+
+    def test_decay_rate_type(self):
+        rate = 'a'
+        with pytest.raises(TypeError):
+            dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
+
+    def test_decay_rate_value(self):
+        rate = -1.0
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
+
+    def test_total_step1(self):
+        total_step1 = 2.0
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
+
+    def test_total_step2(self):
+        total_step1 = -1
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
+
+    def test_step_per_epoch1(self):
+        step_per_epoch1 = True
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch1, decay_epoch, power)
+
+    def test_step_per_epoch2(self):
+        step_per_epoch1 = -1
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
+        
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch1, decay_epoch, power)
+
+    def test_decay_epoch1(self):
+        decay_epoch1 = 'm'
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1)
+
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
+
+    def test_decay_epoch2(self):
+        decay_epoch1 = -1
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1)
+
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1)
+
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
+
+    def test_is_stair(self):
+        is_stair = 1
+        with pytest.raises(ValueError):
+            dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
+
+    def test_min_lr_type(self):
+        min_lr1 = True
+        with pytest.raises(TypeError):
+            dr.cosine_decay_lr(min_lr1, max_lr, total_step, step_per_epoch, decay_epoch)
+
+    def test_min_lr_value(self):
+        min_lr1 = -1.0
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr1, max_lr, total_step, step_per_epoch, decay_epoch)
+
+    def test_max_lr_type(self):
+        max_lr1 = 'a'
+        with pytest.raises(TypeError):
+            dr.cosine_decay_lr(min_lr, max_lr1, total_step, step_per_epoch, decay_epoch)
+
+    def test_max_lr_value(self):
+        max_lr1 = -1.0
+        with pytest.raises(ValueError):
+            dr.cosine_decay_lr(min_lr, max_lr1, total_step, step_per_epoch, decay_epoch)
+
+    def test_power(self):
+        power1 = True
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power1)
+
+    def test_update_decay_epoch(self):
+        update_decay_epoch = 1
+        with pytest.raises(ValueError):
+            dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch,
+                                   power, update_decay_epoch)
+
+
+def test_learning_rate():
+    lr = dr.piecewise_constant_lr(milestone, learning_rates)
+    assert len(lr) == milestone[-1]
+
+
+def test_exponential_decay():
+    lr1 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+    assert len(lr1) == total_step
+
+    lr2 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+    assert len(lr2) == total_step
+
+
+def test_enatural_exp_decay():
+    lr1 = dr.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+    assert len(lr1) == total_step
+
+    lr2 = dr.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+    assert len(lr2) == total_step
+
+
+def test_inverse_decay():
+    lr1 = dr.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+    assert len(lr1) == total_step
+
+    lr2 = dr.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+    assert len(lr2) == total_step
+
+
+def test_cosine_decay():
+    lr = dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
+    assert len(lr) == total_step
+
+def test_polynomial_decay():
+    lr1 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+    assert len(lr1) == total_step
+    lr2 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
+                                True)
+    assert len(lr2) == total_step

From 2db3e64ff23e9d58bdfd79d3074b7716daf7643b Mon Sep 17 00:00:00 2001
From: gaojing <gaojing22@huawei.com>
Date: Wed, 8 Apr 2020 23:37:08 -0400
Subject: [PATCH 202/367] add operation

---
 mindspore/ccsrc/transform/convert.cc    |  4 ++
 mindspore/ccsrc/transform/op_declare.cc | 10 ++++
 mindspore/ccsrc/transform/op_declare.h  |  4 ++
 mindspore/nn/layer/__init__.py          |  4 +-
 mindspore/nn/layer/basic.py             | 69 ++++++++++++++++++++++++
 mindspore/ops/_grad/grad_nn_ops.py      | 11 ++++
 mindspore/ops/operations/__init__.py    |  3 +-
 mindspore/ops/operations/_grad_ops.py   | 18 +++++++
 mindspore/ops/operations/nn_ops.py      | 70 +++++++++++++++++++++++++
 tests/ut/python/nn/test_nn_pad.py       | 64 ++++++++++++++++++++++
 10 files changed, 254 insertions(+), 3 deletions(-)
 create mode 100644 tests/ut/python/nn/test_nn_pad.py

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index c53367a20f..251946f6fd 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -110,6 +110,8 @@ const char kNameSigmoidCrossEntropyWithLogits[] = "SigmoidCrossEntropyWithLogits
 const char kNameSigmoidCrossEntropyWithLogitsGrad[] = "SigmoidCrossEntropyWithLogitsGrad";
 const char kNameScatterNdD[] = "ScatterNd";
 const char kNamePadD[] = "Pad";
+const char kNameMirrorPad[] = "MirrorPad";
+const char kNameMirrorPadGrad[] = "MirrorPadGrad";
 const char kNameGatherNd[] = "GatherNd";
 const char kNameArgmax[] = "Argmax";
 const char kNameArgmin[] = "Argmin";
@@ -256,6 +258,8 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameSigmoidCrossEntropyWithLogitsGrad), ADPT_DESC(SigmoidCrossEntropyWithLogitsGrad)},
     {string(kNameScatterNdD), ADPT_DESC(ScatterNdD)},
     {string(kNamePadD), ADPT_DESC(PadD)},
+    {string(kNameMirrorPad), ADPT_DESC(MirrorPad)},
+    {string(kNameMirrorPadGrad), ADPT_DESC(MirrorPadGrad)},
     {string(kNameGatherNd), ADPT_DESC(GatherNd)},
     {string(kNameArgmax), ADPT_DESC(ArgMaxD)},
     {string(kNameArgmin), ADPT_DESC(ArgMinD)},
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 419805c37f..7a7a696e2d 100644
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -596,6 +596,16 @@ INPUT_MAP(PadD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::vector<int64_t>>>())}};
 OUTPUT_MAP(PadD) = {{0, OUTPUT_DESC(y)}};
 
+// MirrorPad
+INPUT_MAP(MirrorPad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
+ATTR_MAP(MirrorPad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
+OUTPUT_MAP(MirrorPad) = {{0, OUTPUT_DESC(y)}};
+
+// MirrorPadGrad
+INPUT_MAP(MirrorPadGrad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
+ATTR_MAP(MirrorPadGrad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
+OUTPUT_MAP(MirrorPadGrad) = {{0, OUTPUT_DESC(y)}};
+
 // GatherNd
 INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(GatherNd) = EMPTY_ATTR_MAP;
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index e4d4101127..8f6dda9430 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -155,6 +155,10 @@ DECLARE_OP_USE_INPUT_ATTR(ScatterNdD)
 DECLARE_OP_USE_OUTPUT(ScatterNdD)
 DECLARE_OP_ADAPTER(PadD)
 DECLARE_OP_USE_OUTPUT(PadD)
+DECLARE_OP_ADAPTER(MirrorPad)
+DECLARE_OP_USE_OUTPUT(MirrorPad)
+DECLARE_OP_ADAPTER(MirrorPadGrad)
+DECLARE_OP_USE_OUTPUT(MirrorPadGrad)
 DECLARE_OP_ADAPTER(BoundingBoxEncode)
 DECLARE_OP_USE_OUTPUT(BoundingBoxEncode)
 DECLARE_OP_ADAPTER(BoundingBoxDecode)
diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index aed6cb7776..f51eff2b31 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -22,7 +22,7 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
-from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients
+from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients, Pad
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
 
@@ -34,5 +34,5 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'LSTM',
            'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'ImageGradients',
            'Embedding',
-           'AvgPool2d', 'MaxPool2d',
+           'AvgPool2d', 'MaxPool2d', 'Pad',
            ]
diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index de49685dac..5b36755d16 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -415,3 +415,72 @@ class ImageGradients(Cell):
         dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
         dx = P.Concat(3)((dx, dx_last))
         return dy, dx
+
+
+class Pad(Cell):
+    """
+    Pads the input tensor according to the paddings and mode.
+
+    Args:
+        paddings (tuple): The shape of parameter `paddings` is (N, 2). N is the rank of input data. All elements of
+            paddings are int type. For `D` th dimension of input, paddings[D, 0] indicates how many sizes to be
+            extended ahead of the `D` th dimension of the input tensor, and paddings[D, 1] indicates how many sizes to
+            be extended behind of the `D` th dimension of the input tensor.
+        mode (string): Specifies padding mode. The optional values are "CONSTANT", "REFLECT", "SYMMETRIC".
+            Default: "CONSTANT".
+
+    Inputs:
+        - ** input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, the tensor after padding.
+
+        - If `mode` is "CONSTANT", it fill the edge with 0, regardless of the values of the `input_x`.
+          If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
+          Outputs is [[0,0,0,0,0,0,0],[0,0,1,2,3,0,0],[0,0,4,5,6,0,0],[0,0,7,8,9,0,0],[0,0,0,0,0,0,0]].
+        - If 'mode` is "REFLECT", it uses a way of symmetrical copying throught the axis of symmetry to fill in,
+          symmetry. If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
+          Outputs is [[6,5,4,5,6,5,4],[3,2,1,2,3,2,1],[6,5,4,5,6,5,4],[9,8,7,8,9,8,7],[6,5,4,5,6,5,4]].
+        - If 'mode' is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
+          according to the symmetry axis, except that it includes the symmetry axis. If the `input_x`
+          is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the Outputs is
+          [[2,1,1,2,3,3,2],[2,1,1,2,3,3,2],[5,4,4,5,6,6,5],[8,7,7,8,9,9,8],[8,7,7,8,9,9,8]].
+
+    Examples:
+        >>> from mindspore import Tensor
+        >>> from mindspore.ops import operations as P
+        >>> import mindspore.nn as nn
+        >>> import numpy as np
+        >>> class Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT")
+        >>>     def construct(self, x):
+        >>>         return self.pad(x)
+        >>> x = np.random.random(size=(2, 3)).astype(np.float32)
+        >>> pad = Net()
+        >>> ms_output = pad(Tensor(x))
+    """
+
+    def __init__(self, paddings, mode="CONSTANT"):
+        super(Pad, self).__init__()
+        self.mode = mode
+        self.paddings = paddings
+        validator.check_string('mode', self.mode, ["CONSTANT", "REFLECT", "SYMMETRIC"])
+        if not isinstance(paddings, tuple):
+            raise TypeError('Paddings must be tuple type.')
+        for item in paddings:
+            if len(item) != 2:
+                raise ValueError('The shape of paddings must be (n, 2).')
+        if mode == "CONSTANT":
+            self.pad = P.Pad(self.paddings)
+        else:
+            self.paddings = Tensor(np.array(self.paddings))
+            self.pad = P.MirrorPad(mode=mode)
+
+    def construct(self, x):
+        if self.mode == "CONSTANT":
+            x = self.pad(x)
+        else:
+            x = self.pad(x, self.paddings)
+        return x
diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py
index 1b18d9f248..149dd6caec 100755
--- a/mindspore/ops/_grad/grad_nn_ops.py
+++ b/mindspore/ops/_grad/grad_nn_ops.py
@@ -470,6 +470,17 @@ def get_bprop_pad(self):
     return bprop
 
 
+@bprop_getters.register(P.MirrorPad)
+def get_bprop_mirror_pad(self):
+    """Grad definition for `MirrorPad` operation."""
+    mirror_pad_grad = G.MirrorPadGrad(self.mode)
+
+    def bprop(x, paddings, out, dout):
+        dx = mirror_pad_grad(dout, paddings, x)
+        return (dx, zeros_like(paddings))
+    return bprop
+
+
 @bprop_getters.register(P.ROIAlign)
 def get_bprop_roi_align(self):
     """Grad definition for `ROIAlign` operation."""
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 8bfca77b38..40cbfc3381 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -59,7 +59,7 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      LogSoftmax,
                      MaxPool,
                      AvgPool, Conv2DBackpropInput,
-                     MaxPoolWithArgmax, OneHot, Pad, PReLU, ReLU, ReLU6, HSwish, HSigmoid,
+                     MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, HSwish, HSigmoid,
                      ResizeBilinear, Sigmoid,
                      SigmoidCrossEntropyWithLogits,
                      SmoothL1Loss, Softmax,
@@ -180,6 +180,7 @@ __all__ = [
     'ScatterNd',
     'ResizeNearestNeighbor',
     'Pad',
+    'MirrorPad',
     'GatherNd',
     'ScatterNdUpdate',
     'Floor',
diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index f0a9a2f658..d468fa7b19 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -947,6 +947,24 @@ class TanhGrad(PrimitiveWithInfer):
         return out
 
 
+class MirrorPadGrad(PrimitiveWithInfer):
+    """Gradients of MirrorPad operation."""
+
+    @prim_attr_register
+    def __init__(self, mode="REFLECT"):
+        """init MirrorPad"""
+        validator.check_string('mode', mode, ['REFLECT', 'SYMMETRIC'])
+        self.mode = mode
+
+    def __infer__(self, dout, paddings, x):
+        validator.check_subclass("dout", dout['dtype'], mstype.tensor)
+        validator.check_subclass("paddings", paddings['dtype'], mstype.tensor)
+        validator.check_subclass("input_x", x['dtype'], mstype.tensor)
+        return {'shape': x['shape'],
+                'dtype': dout['dtype'],
+                'value': None}
+
+
 class RefToEmbed(Primitive):
     r"""
     Make a key from Ref.
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 91f6d7ec01..1e3a4349ae 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2092,6 +2092,7 @@ class Pad(PrimitiveWithInfer):
         for item in paddings:
             if len(item) != 2:
                 raise ValueError('The shape of paddings must be (n, 2).')
+        self.paddings = paddings
 
     def infer_shape(self, x):
         paddings = np.array(self.paddings)
@@ -2104,9 +2105,78 @@ class Pad(PrimitiveWithInfer):
         return y_shape
 
     def infer_dtype(self, x):
+        validator.check_subclass("input_x", x, mstype.tensor)
         return x
 
 
+class MirrorPad(PrimitiveWithInfer):
+    """
+    Pads the input tensor according to the paddings and mode.
+
+    Args:
+        mode (string): Specifies padding mode. The optional values are "REFLECT", "SYMMETRIC".
+            Default: "REFLECT".
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+        - **paddings** (Tensor) - The paddings tensor. The value of `paddings` is a matrix(list),
+            and its shape is (N, 2). N is the rank of input data. All elements of paddings
+            are int type. For `D` th dimension of input, paddings[D, 0] indicates how many sizes to be
+            extended ahead of the `D` th dimension of the input tensor, and paddings[D, 1] indicates
+            how many sizes to be extended behind of the `D` th dimension of the input tensor.
+
+    Outputs:
+        Tensor, the tensor after padding.
+
+        - If 'mode` is "REFLECT", it uses a way of symmetrical copying throught the axis of symmetry to fill in,
+          symmetry. If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
+          Outputs is [[6,5,4,5,6,5,4],[3,2,1,2,3,2,1],[6,5,4,5,6,5,4],[9,8,7,8,9,8,7],[6,5,4,5,6,5,4]].
+        - If 'mode' is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
+          according to the symmetry axis, except that it includes the symmetry axis. If the `input_x`
+          is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the Outputs is
+          [[2,1,1,2,3,3,2],[2,1,1,2,3,3,2],[5,4,4,5,6,6,5],[8,7,7,8,9,9,8],[8,7,7,8,9,9,8]].
+
+    Examples:
+        >>> from mindspore import Tensor
+        >>> from mindspore.ops import operations as P
+        >>> import mindspore.nn as nn
+        >>> import numpy as np
+        >>> class Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self.pad = P.MirrorPad(mode="REFLECT")
+        >>>     def construct(self, x, paddings):
+        >>>         return self.pad(x, paddings)
+        >>> x = np.random.random(size=(2, 3)).astype(np.float32)
+        >>> paddings = Tensor([[1,1],[2,2]])
+        >>> pad = Net()
+        >>> ms_output = pad(Tensor(x), paddings)
+    """
+
+    @prim_attr_register
+    def __init__(self, mode='REFLECT'):
+        """Init Pad"""
+        validator.check_string('mode', mode, ['REFLECT', 'SYMMETRIC'])
+        self.mode = mode
+
+    def __infer__(self, input_x, paddings):
+        validator.check_subclass("input_x", input_x['dtype'], mstype.tensor)
+        validator.check_subclass("paddings", paddings['dtype'], mstype.tensor)
+        x_shape = list(input_x['shape'])
+        paddings_value = paddings['value'].asnumpy()
+        paddings_size = paddings_value.size
+        validator.check_integer('paddings.shape', paddings_size, len(x_shape) * 2, Rel.EQ)
+        if not np.all(paddings_size >= 0):
+            raise ValueError('All elements of paddings must be >= 0.')
+        y_shape = ()
+        for i in range(0, int(paddings_size / 2)):
+            y_shape += ((x_shape[i] + paddings_value[i, 0] + paddings_value[i, 1]),)
+
+        return {'shape': y_shape,
+                'dtype': input_x['dtype'],
+                'value': None}
+
+
 class ROIAlign(PrimitiveWithInfer):
     """
     Computes Region of Interest (RoI) Align operator.
diff --git a/tests/ut/python/nn/test_nn_pad.py b/tests/ut/python/nn/test_nn_pad.py
new file mode 100644
index 0000000000..a8b66bae5c
--- /dev/null
+++ b/tests/ut/python/nn/test_nn_pad.py
@@ -0,0 +1,64 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test nn pad """
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.ops.composite import GradOperation
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+
+
+class Net(nn.Cell):
+    def __init__(self, raw_paddings, mode):
+        super(Net, self).__init__()
+        self.pad = nn.Pad(raw_paddings, mode=mode)
+
+    @ms_function
+    def construct(self, x):
+        return self.pad(x)
+
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
+        self.network = network
+
+    @ms_function
+    def construct(self, x, grads):
+        return self.grad(self.network)(x, grads)
+
+
+def test_pad_train():
+    mode = 'CONSTANT'
+    x = np.random.random(size=(2, 3)).astype(np.float32)
+    raw_paddings = ((1, 1), (2, 2))
+    grads = np.random.random(size=(4, 7)).astype(np.float32)
+    grad = Grad(Net(raw_paddings, mode))
+    output = grad(Tensor(x), Tensor(grads))
+    print("=================output====================")
+    print(output)
+
+
+def test_pad_infer():
+    mode = 'CONSTANT'
+    x = np.random.random(size=(2, 3)).astype(np.float32)
+    raw_paddings = ((1, 1), (2, 2))
+    net = Net(raw_paddings, mode)
+    output = net(Tensor(x))
+    print("=================output====================")
+    print(output)

From cc6258d6ac15519ba7de59bc7ee154ac0c668a6b Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Tue, 14 Apr 2020 09:43:52 +0800
Subject: [PATCH 203/367] fix adam notes

---
 mindspore/ops/operations/nn_ops.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 538d7f3826..cc6f7da7b0 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2136,7 +2136,11 @@ class Adam(PrimitiveWithInfer):
         - **gradient** (Tensor) - Gradients.
 
     Outputs:
-        Tensor, has the same shape and data type as `var`.
+        Tuple of 3 Tensor, the updated parameters.
+
+        - **var** (Tensor) - The same shape and data type as `var`.
+        - **m** (Tensor) - The same shape and data type as `m`.
+        - **v** (Tensor) - The same shape and data type as `v`.
     """
 
     @prim_attr_register

From 39b446524d8a3825d7aecb90b4e3751408b3c64c Mon Sep 17 00:00:00 2001
From: yoonlee666 <qiuyunlei@huawei.com>
Date: Tue, 14 Apr 2020 10:18:31 +0800
Subject: [PATCH 204/367] fix bugs in bert example script

---
 example/Bert_NEZHA_cnwiki/train.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/example/Bert_NEZHA_cnwiki/train.py b/example/Bert_NEZHA_cnwiki/train.py
index 86e033fc9f..2610542a9a 100644
--- a/example/Bert_NEZHA_cnwiki/train.py
+++ b/example/Bert_NEZHA_cnwiki/train.py
@@ -39,6 +39,7 @@ import mindspore.dataset.engine.datasets as de
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore.common.tensor import Tensor
+import mindspore.common.dtype as mstype
 from mindspore.train.model import Model
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
 from mindspore.model_zoo.Bert_NEZHA import BertNetworkWithLoss, BertTrainOneStepCell
@@ -49,9 +50,9 @@ def create_train_dataset(batch_size):
     """create train dataset"""
     # apply repeat operations
     repeat_count = bert_train_cfg.epoch_size
-    ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR,
-                           columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
-                                         "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"])
+    ds = de.TFRecordDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR,
+                            columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
+                                          "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"])
     type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)

From 1fc576c5022d87f7e090c1598041e4b4300dd698 Mon Sep 17 00:00:00 2001
From: jjfeing <jiangjianfei3@huawei.com>
Date: Tue, 14 Apr 2020 11:00:37 +0800
Subject: [PATCH 205/367] adapt graphengin update

---
 graphengine | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphengine b/graphengine
index 40e9f6f834..0c33e9d125 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 40e9f6f834469d2d228f9782a0a41f95be94d429
+Subproject commit 0c33e9d12562953ca4bd6c03cb77da2c2da74acd

From 0b75e289b16ca8da10313a276375a2aea48f54c5 Mon Sep 17 00:00:00 2001
From: Zhang Qinghua <zhangqinghua3@huawei.com>
Date: Mon, 13 Apr 2020 15:49:14 +0800
Subject: [PATCH 206/367] Dump the perf data by executed sequence, not
 alphabetic.

---
 mindspore/ccsrc/utils/profile.cc | 23 ++++++++++++++++++-----
 mindspore/ccsrc/utils/profile.h  |  3 ++-
 2 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/utils/profile.cc b/mindspore/ccsrc/utils/profile.cc
index 7a2bb2aa66..ba490549f8 100644
--- a/mindspore/ccsrc/utils/profile.cc
+++ b/mindspore/ccsrc/utils/profile.cc
@@ -30,6 +30,7 @@
 namespace mindspore {
 
 namespace {
+constexpr size_t TIME_INFO_PREFIX_NUM_LEN = 4;
 const char KEY_PROF_TOTAL[] = "__total__";
 
 void PrintProfile(std::ostringstream& oss, const TimeInfo& time_info, int indent = 0,
@@ -42,15 +43,16 @@ void PrintTimeInfoMap(std::ostringstream& oss, const TimeInfoMap& dict, int inde
       continue;
     }
     // indent by multiples of 4 spaces.
+    auto name = iter->first.substr(TIME_INFO_PREFIX_NUM_LEN);
     oss << std::setw(indent * 4) << ""
-        << "[" << iter->first << "]: " << iter->second->time_;
+        << "[" << name << "]: " << iter->second->time_;
     if (iter->second->dict_ != nullptr) {
       oss << ", [" << iter->second->dict_->size() << "]";
     }
     oss << "\n";
 
     std::string newPrefix = prefix;
-    if (iter->first.find("Cycle ") != 0) {
+    if (iter->first.find("Cycle ") == std::string::npos) {
       newPrefix = prefix.empty() ? iter->first : prefix + "." + iter->first;
     }
     PrintProfile(oss, *iter->second, indent + 1, sums, newPrefix);
@@ -94,7 +96,14 @@ void PrintProfile(std::ostringstream& oss, const TimeInfo& time_info, int indent
     oss << "Sums\n";
     if (total >= 0.0 + DBL_EPSILON) {
       for (auto& iter : *sums) {
-        oss << "    " << std::left << std::setw(36) << iter.first << " : " << std::right << std::setw(12) << std::fixed
+        std::string name = iter.first;
+        name.erase(0, TIME_INFO_PREFIX_NUM_LEN);
+        std::size_t pos = 0;
+        while ((pos = name.find('.', pos)) != std::string::npos) {
+          pos++;
+          name.erase(pos, TIME_INFO_PREFIX_NUM_LEN);
+        }
+        oss << "    " << std::left << std::setw(36) << name << " : " << std::right << std::setw(12) << std::fixed
             << std::setprecision(6) << iter.second << "s : " << std::right << std::setw(5) << std::fixed
             << std::setprecision(2) << iter.second / total * 100 << "%\n";
       }
@@ -241,14 +250,18 @@ void ProfContext::Insert(const std::string& name, const TimeInfo* time) noexcept
     }
   }
 
-  auto iter = time_info_->dict_->find(name);
+  std::stringstream ss;
+  ss << std::setw(TIME_INFO_PREFIX_NUM_LEN) << std::setfill('0') << time_info_->actionNum_;
+  std::string sorted_name(ss.str() + name);
+  time_info_->actionNum_++;
+  auto iter = time_info_->dict_->find(sorted_name);
   // if contains item with same name, delete it
   if (iter != time_info_->dict_->end()) {
     delete iter->second;
     iter->second = nullptr;
     (void)time_info_->dict_->erase(iter);
   }
-  (*time_info_->dict_)[name] = time;
+  (*time_info_->dict_)[sorted_name] = time;
 }
 
 bool ProfContext::IsTopContext() const noexcept { return (prof_ != nullptr) && (this == &prof_->context_); }
diff --git a/mindspore/ccsrc/utils/profile.h b/mindspore/ccsrc/utils/profile.h
index 4824f1f6ab..6892b0b4f6 100644
--- a/mindspore/ccsrc/utils/profile.h
+++ b/mindspore/ccsrc/utils/profile.h
@@ -34,12 +34,13 @@ extern double GetTime();
 class ProfileBase;
 
 struct TimeInfo {
-  explicit TimeInfo(double time = -1.0) : time_(time), dict_(nullptr) {}
+  explicit TimeInfo(double time = -1.0) : time_(time), dict_(nullptr), actionNum_(0) {}
   TimeInfo(const TimeInfo&) = delete;
   ~TimeInfo();
 
   double time_;
   TimeInfoMap* dict_;
+  size_t actionNum_;
 };
 
 // Utility class for Profile.

From 0ac50a19f5457f965c69c0358733e768cebe77ae Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Fri, 10 Apr 2020 10:13:34 +0800
Subject: [PATCH 207/367] Model the memory cost in auto-parallel. It is
 calculated by the output of operators, plus the parameters. Additionally,
 modify the graph-operations in auto_parallel to include memory_cost.

---
 .../ccsrc/parallel/auto_parallel/costmodel.h  |   8 +-
 .../auto_parallel/dp_algo_costmodel.cc        |  15 +-
 .../auto_parallel/dp_algo_costmodel.h         |   7 +-
 .../parallel/auto_parallel/edge_costmodel.cc  |  34 +++-
 .../parallel/auto_parallel/edge_costmodel.h   |   2 +-
 .../parallel/auto_parallel/graph_costmodel.cc | 153 +++++++++++++-----
 .../parallel/auto_parallel/graph_costmodel.h  |   3 +
 .../auto_parallel/operator_costmodel.cc       |  90 +++++++----
 .../auto_parallel/operator_costmodel.h        |  85 +++++++---
 .../ccsrc/parallel/ops_info/activation_info.h |   4 +-
 .../ccsrc/parallel/ops_info/arithmetic_info.h |  22 +--
 .../parallel/ops_info/batch_parallel_info.h   |   8 +-
 .../ccsrc/parallel/ops_info/bias_add_info.h   |   2 +-
 .../ops_info/comparison_function_info.h       |   9 +-
 .../parallel/ops_info/dropout_do_mask_info.h  |   2 +-
 .../ccsrc/parallel/ops_info/get_next_info.h   |   2 +-
 mindspore/ccsrc/parallel/ops_info/loss_info.h |   3 +-
 .../ccsrc/parallel/ops_info/matmul_info.cc    |   6 +-
 .../ccsrc/parallel/ops_info/matmul_info.h     |   2 +-
 .../ccsrc/parallel/ops_info/onehot_info.h     |   2 +-
 .../ccsrc/parallel/ops_info/operator_info.cc  |  44 ++++-
 .../ccsrc/parallel/ops_info/operator_info.h   |  16 +-
 .../ccsrc/parallel/ops_info/prelu_info.h      |   2 +-
 .../parallel/ops_info/reduce_method_info.cc   |   2 +-
 .../parallel/ops_info/reduce_method_info.h    |   2 +-
 .../ccsrc/parallel/ops_info/reshape_info.h    |   2 +-
 .../parallel/ops_info/tmp_identity_info.h     |   2 +-
 .../ccsrc/parallel/ops_info/transpose_info.h  |   2 +-
 .../parallel/ops_info/virtual_dataset_info.h  |   2 +-
 .../ccsrc/parallel/step_auto_parallel.cc      |   8 +-
 .../tensor_layout/tensor_redistribution.cc    |   6 +
 .../tensor_layout/tensor_redistribution.h     |   7 +
 .../cpp/parallel/ops_info/activation_test.cc  |   8 +-
 .../cpp/parallel/ops_info/matmul_info_test.cc |   4 +-
 .../parallel/ops_info/tensor_add_info_test.cc |   8 +-
 .../cpp/parallel/ops_info/tmpidentity_test.cc |   4 +-
 36 files changed, 401 insertions(+), 177 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
index 229f0fbf5e..9e9003848b 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h
@@ -207,15 +207,13 @@ struct ContractEliminationDecision : public Decision {
  */
 struct TriangleEliminationDecision : public Decision {
   TriangleEliminationDecision(StrategyPtr elimi_stra, CostPtr elimi_op_cost, CostPtr l_edge_cost, CostPtr r_edge_cost,
-                              StrategyPtr left_stra, CostPtr l_node_cost, StrategyPtr right_stra, CostPtr r_node_cost)
+                              StrategyPtr left_stra, CostPtr l_node_cost)
       : eliminated_op_strategy_(std::move(elimi_stra)),
         eliminated_op_cost_(std::move(elimi_op_cost)),
         left_edge_cost_(std::move(l_edge_cost)),
         right_edge_cost_(std::move(r_edge_cost)),
         left_node_strategy_(std::move(left_stra)),
-        left_node_cost_(std::move(l_node_cost)),
-        right_node_strategy_(std::move(right_stra)),
-        right_node_cost_(std::move(r_node_cost)) {
+        left_node_cost_(std::move(l_node_cost)) {
     type_ = DecisionType::TRIANGLE_ELIMINATION;
   }
 
@@ -225,8 +223,6 @@ struct TriangleEliminationDecision : public Decision {
   CostPtr right_edge_cost_;
   StrategyPtr left_node_strategy_;
   CostPtr left_node_cost_;
-  StrategyPtr right_node_strategy_;
-  CostPtr right_node_cost_;
   MS_DECLARE_PARENT(TriangleEliminationDecision, Decision);
 };
 
diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
index 060caa4cca..dd21096fcc 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc
@@ -76,7 +76,6 @@ Status GetStrategy(const CostGraphPtr& graph) {
       auto l_r_edge = triangle_pair.second;
 
       auto left_node = l_r_edge->prev_operator();
-      auto right_node = l_r_edge->next_operator();
       auto left_edge = eliminated_node->GetAliveSuccEdges()[0];
       auto right_edge = eliminated_node->GetAliveSuccEdges()[1];
       MS_EXCEPTION_IF_NULL(left_edge);
@@ -86,8 +85,7 @@ Status GetStrategy(const CostGraphPtr& graph) {
         right_edge = tmp;
       }
       auto left_node_cpy = graph->EliminationTriangle(eliminated_node, l_r_edge);
-      auto elimi =
-        std::make_shared<TriangleElimination>(eliminated_node, left_edge, left_node_cpy, right_edge, right_node);
+      auto elimi = std::make_shared<TriangleElimination>(eliminated_node, left_edge, left_node_cpy, right_edge);
       eliminations.emplace_back(std::move(elimi));
     }
     auto star_center = graph->CheckStarElimination();
@@ -183,14 +181,13 @@ Status RecoverStrategy(std::vector<EliminationPtr> eliminations) {
       auto left_edge = elimination->left_edge_;
       auto eliminated_node = elimination->eliminated_node_;
       auto right_edge = elimination->right_edge_;
-      auto right_node = elimination->right_node_;
       auto decision = left_node->selected_cost()->decision_ptr_->cast<TriangleEliminationDecisionPtr>();
 
       eliminated_node->SetSelectedStrategyAndCost(decision->eliminated_op_strategy_, decision->eliminated_op_cost_);
       left_edge->set_selected_cost(decision->left_edge_cost_);
       right_edge->set_selected_cost(decision->right_edge_cost_);
+      // Since Triangle is eliminated into 'left_node', only 'left_node' is needed to recover the strategy.
       left_node->SetSelectedStrategyAndCost(decision->left_node_strategy_, decision->left_node_cost_);
-      right_node->SetSelectedStrategyAndCost(decision->right_node_strategy_, decision->right_node_cost_);
       MS_LOG(INFO) << "Recover triangleElimination succeeded.";
     } else if ((*rit)->isa<StarElimination>()) {
       auto elimination = (*rit)->cast<StarEliminationPtr>();
@@ -204,9 +201,11 @@ Status RecoverStrategy(std::vector<EliminationPtr> eliminations) {
       for (size_t i = 0; i < succ_edges.size(); ++i) {
         succ_edges[i]->set_selected_cost(decision->succ_edges_cost_list_[i]);
       }
-      for (size_t j = 0; j < succ_nodes.size(); ++j) {
-        succ_nodes[j]->SetSelectedStrategyAndCost(decision->succ_ops_stra_list_[j], decision->succ_ops_cost_list_[j]);
-      }
+      MS_EXCEPTION_IF_NULL(succ_nodes[0]);
+      MS_EXCEPTION_IF_NULL(decision->succ_ops_stra_list_[0]);
+      MS_EXCEPTION_IF_NULL(decision->succ_ops_cost_list_[0]);
+      // Since Star is eliminated into 'succ_nodes[0]', only 'succ_nodes[0]' is needed to recover the strategy.
+      succ_nodes[0]->SetSelectedStrategyAndCost(decision->succ_ops_stra_list_[0], decision->succ_ops_cost_list_[0]);
       MS_LOG(INFO) << "Recover starElimination succeeded.";
     } else {
       MS_LOG(ERROR) << "Unknown Elimination type.";
diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
index 0cb58c49da..6d43218e19 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h
@@ -102,20 +102,17 @@ struct ContractElimination : public Elimination {
 
 // Triangle Elimination
 struct TriangleElimination : public Elimination {
-  TriangleElimination(OperatorInfoPtr elim_node, EdgePtr l_edge, OperatorInfoPtr l_node, EdgePtr r_edge,
-                      OperatorInfoPtr r_node)
+  TriangleElimination(OperatorInfoPtr elim_node, EdgePtr l_edge, OperatorInfoPtr l_node, EdgePtr r_edge)
       : Elimination(nullptr, Elimination::EliminationType::TRIANGLE),
         eliminated_node_(std::move(elim_node)),
         left_edge_(std::move(l_edge)),
         left_node_(std::move(l_node)),
-        right_edge_(std::move(r_edge)),
-        right_node_(std::move(r_node)) {}
+        right_edge_(std::move(r_edge)) {}
 
   OperatorInfoPtr eliminated_node_;
   EdgePtr left_edge_;
   OperatorInfoPtr left_node_;
   EdgePtr right_edge_;
-  OperatorInfoPtr right_node_;
   MS_DECLARE_PARENT(TriangleElimination, Elimination);
 };
 
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
index cbd66f58a6..895646f409 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
@@ -119,6 +119,7 @@ Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, co
   double forward_comm_cost = tensor_redistribution.forward_comm_cost();
   double backward_comm_cost = tensor_redistribution.backward_comm_cost();
   double computation_cost = tensor_redistribution.computation_cost();
+  double mem_cost = tensor_redistribution.memory_cost();
 
   // Now AllGather, ReduceScatter, AlltoAll don't support bool type
   MS_EXCEPTION_IF_NULL(type);
@@ -134,6 +135,7 @@ Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, co
     COST_MODEL_GAMMA * ((*cost)->communication_cost_ - (*cost)->communication_without_parameter_);
   (*cost)->communication_redis_forward_ = type_length * forward_comm_cost;
   (*cost)->communication_redis_backward_ = type_length * backward_comm_cost;
+  (*cost)->memory_with_reuse_ = mem_cost;
   return Status::SUCCESS;
 }
 
@@ -158,8 +160,8 @@ CostPtrList Edge::CreateEdgeEliminationCostList(const StrategyPtr& output_st_ptr
   (void)std::transform(edges.begin(), edges.end(), all_cost_list.begin(), LocalGetCostList);
 
   CostPtrList selected_cost_list(all_cost_list.size(), nullptr);
-  std::function<void(size_t, double, double, double)> recursive =
-    [&](size_t k, double computation, double communication, double communication_without_para) {
+  std::function<void(size_t, double, double, double, double)> recursive =
+    [&](size_t k, double computation, double memory, double communication, double communication_without_para) {
       if (k == edges.size()) {
         auto decision = std::make_shared<EdgeEliminationDecision>(selected_cost_list);
         CostPtr new_cost = std::make_shared<Cost>(computation, communication);
@@ -167,6 +169,7 @@ CostPtrList Edge::CreateEdgeEliminationCostList(const StrategyPtr& output_st_ptr
         new_cost->communication_without_parameter_ = communication_without_para;
         new_cost->communication_with_partial_para_ =
           communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
+        new_cost->memory_with_reuse_ = memory;
         new_cost->decision_ptr_ = decision;
         result.push_back(new_cost);
         return;
@@ -174,11 +177,12 @@ CostPtrList Edge::CreateEdgeEliminationCostList(const StrategyPtr& output_st_ptr
       for (auto& c : all_cost_list[k]) {
         MS_EXCEPTION_IF_NULL(c);
         selected_cost_list[k] = c;
-        recursive(k + 1, computation + c->computation_cost_, communication + c->communication_cost_,
+        recursive(k + 1, computation + c->computation_cost_, memory + c->memory_with_reuse_,
+                  communication + c->communication_cost_,
                   communication_without_para + c->communication_without_parameter_);
       }
     };
-  recursive(0, 0, 0, 0);
+  recursive(0, 0.0, 0.0, 0.0, 0.0);
   SimplifyForDreasingCommunicationWithPartialPara(&result);
   return result;
 }
@@ -218,6 +222,8 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr
         double communication_without_para = left_cost->communication_without_parameter_ +
                                             middle_cost->communication_without_parameter_ +
                                             right_cost->communication_without_parameter_;
+        double memory_cost =
+          left_cost->memory_with_reuse_ + middle_cost->memory_with_reuse_ + right_cost->memory_with_reuse_;
 
         auto decision = std::make_shared<OpEliminationDecision>(op_strategy, left_cost, middle_cost, right_cost);
         auto cost = std::make_shared<Cost>(computation, communication, decision);
@@ -225,6 +231,7 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr
         cost->communication_without_parameter_ = communication_without_para;
         cost->communication_with_partial_para_ =
           communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
+        cost->memory_with_reuse_ = memory_cost;
         ret_cost_list->emplace_back(std::move(cost));
       }
     }
@@ -267,5 +274,24 @@ void Edge::OpEliminationSetNewCost(const EdgePtr& e1, const OperatorInfoPtr& op,
     MS_LOG(EXCEPTION) << "Creating edge: " << edge_name_ << " failed.";
   }
 }
+
+Status Edge::CalculateMemoryCost() {
+  if (is_output_parameter_involve_ == -1) {
+    MS_LOG(ERROR) << "is_output_parameter_involve_ is unset.";
+    return FAILED;
+  }
+  if (is_output_parameter_involve_ == 0) {
+    // In this case, it is sure that the tensor redistribution along this edge is NOT parameter-involved, thus it is
+    // unnecessary to keep them in memory.
+    for (auto& cost_kv : cost_map_) {
+      auto& cost_v = cost_kv.second;
+      if (!cost_v.empty()) {
+        cost_v[0]->memory_with_reuse_ = 0;
+      }
+    }
+  }
+
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
index bd882bb43f..f974125749 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h
@@ -133,7 +133,7 @@ class Edge {
   void set_parameter_involve(int para_invol) { is_output_parameter_involve_ = para_invol; }
   // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
   // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
-  Status CalculateMemoryCost() const { return SUCCESS; }
+  Status CalculateMemoryCost();
 
  private:
   std::string edge_name_;
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index 88a54662d3..82dd723039 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -248,6 +248,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std::
             MS_EXCEPTION_IF_NULL(cost2);
             MS_EXCEPTION_IF_NULL(cost3);
             double computation = cost1->computation_cost_ + cost2->computation_cost_ + cost3->computation_cost_;
+            double memory = cost1->memory_with_reuse_ + cost2->memory_with_reuse_ + cost3->memory_with_reuse_;
             double commmunication =
               cost1->communication_cost_ + cost2->communication_cost_ + cost3->communication_cost_;
             double communication_without_para = cost1->communication_without_parameter_ +
@@ -260,6 +261,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std::
             cost->communication_without_parameter_ = communication_without_para;
             cost->communication_with_partial_para_ =
               communication_without_para + COST_MODEL_GAMMA * (commmunication - communication_without_para);
+            cost->memory_with_reuse_ = memory;
             ret.push_back(cost);
           }
         }
@@ -288,6 +290,7 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) {
       new_cost->communication_with_partial_para_ =
         cost1->communication_without_parameter_ +
         COST_MODEL_GAMMA * (cost1->communication_cost_ - cost1->communication_without_parameter_);
+      new_cost->memory_with_reuse_ = cost1->memory_with_reuse_;
       ret.push_back(new_cost);
     }
   }
@@ -297,9 +300,14 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) {
 }
 
 CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list, double memory) {
-  if (cost_list.empty() || cost_list[0]->computation_cost_ >= memory) {
-    return nullptr;
+  CostPtrList after_mem_filter;
+  // Filter out the valid costs
+  for (auto& a_cost : cost_list) {
+    if (a_cost->memory_with_reuse_ <= memory) {
+      after_mem_filter.emplace_back(std::move(a_cost));
+    }
   }
+
   std::function<CostPtr(CostPtr, const CostPtr&)> LocalCompare = [&](CostPtr init, const CostPtr& cost_x) {
     MS_EXCEPTION_IF_NULL(cost_x);
     if (init == nullptr || cost_x->computation_cost_ < memory) {
@@ -308,7 +316,7 @@ CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list,
     return init;
   };
   CostPtr ret = nullptr;
-  return std::accumulate(cost_list.begin(), cost_list.end(), ret, LocalCompare);
+  return std::accumulate(after_mem_filter.begin(), after_mem_filter.end(), ret, LocalCompare);
 }
 
 CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList& cost_list, double memory) {
@@ -318,36 +326,46 @@ CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList& cost_list, d
     MS_LOG(ERROR) << "Final cost list is null.";
     return nullptr;
   }
-  CostPtr ret = cost_list[0];
-  MS_EXCEPTION_IF_NULL(ret);
-  if (ret->computation_cost_ >= memory) {
-    MS_LOG(ERROR) << "No available cost; the minimum cost is " << ret->computation_cost_
+  CostPtrList after_mem_filter;
+  double minimum_memory = DBL_MAX;
+  // Filter out the valid costs.
+  for (auto& a_cost : cost_list) {
+    if (a_cost->memory_with_reuse_ <= memory) {
+      after_mem_filter.emplace_back(std::move(a_cost));
+    } else if (a_cost->memory_with_reuse_ < minimum_memory) {
+      minimum_memory = a_cost->memory_with_reuse_;
+    }
+  }
+  if (after_mem_filter.empty()) {
+    MS_LOG(ERROR) << "No available cost. The minimum memory cost is: " << minimum_memory
                   << ", the memory capacity is: " << memory << ".";
     return nullptr;
   }
+  // Init the returned value with first cost.
+  CostPtr ret = after_mem_filter[0];
+
   double minimum = costmodel_alpha_ * ret->computation_cost_ + costmodel_beta_ * ret->communication_with_partial_para_;
-  MS_LOG(INFO) << "minimum: " << minimum << ", computation_cost_: " << ret->computation_cost_
+  MS_LOG(INFO) << "Cost 0: "
+               << "memory_cost: " << ret->memory_with_reuse_ << ", computation_cost_: " << ret->computation_cost_
                << ", communication_with_partial_para_: " << ret->communication_with_partial_para_
                << ", communication_cost_: " << ret->communication_cost_
                << ", communication_without_parameter_: " << ret->communication_without_parameter_ << ".";
-  for (size_t i = 1; i < cost_list.size(); ++i) {
-    MS_EXCEPTION_IF_NULL(cost_list[i]);
-    if (cost_list[i]->computation_cost_ >= memory) {
-      MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_
-                   << ", is larger than the memory capacity: " << memory << ".";
-      break;
-    }
-    MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_
-                 << ", communication_with_partial_para_: " << cost_list[i]->communication_with_partial_para_
-                 << ", communication_cost_: " << cost_list[i]->communication_cost_
-                 << ", communication_without_parameter_: " << cost_list[i]->communication_without_parameter_ << ".";
-    auto tmp = costmodel_alpha_ * cost_list[i]->computation_cost_ +
-               costmodel_beta_ * cost_list[i]->communication_with_partial_para_;
-    MS_LOG(INFO) << "tmp: " << tmp;
+  MS_LOG(INFO) << "Cost 0: totoal_cost: " << minimum;
+  for (size_t i = 1; i < after_mem_filter.size(); ++i) {
+    MS_EXCEPTION_IF_NULL(after_mem_filter[i]);
+    MS_LOG(INFO) << "Cost " << i << ": memory_cost: " << after_mem_filter[i]->memory_with_reuse_
+                 << ", computation_cost_: " << after_mem_filter[i]->computation_cost_
+                 << ", communication_with_partial_para_: " << after_mem_filter[i]->communication_with_partial_para_
+                 << ", communication_cost_: " << after_mem_filter[i]->communication_cost_
+                 << ", communication_without_parameter_: " << after_mem_filter[i]->communication_without_parameter_
+                 << ".";
+    auto tmp = costmodel_alpha_ * after_mem_filter[i]->computation_cost_ +
+               costmodel_beta_ * after_mem_filter[i]->communication_with_partial_para_;
+    MS_LOG(INFO) << "Cost " << i << ": total_cost: " << tmp;
     if (minimum > tmp) {
       minimum = tmp;
-      ret = cost_list[i];
-      MS_LOG(INFO) << "selected: " << i;
+      ret = after_mem_filter[i];
+      MS_LOG(INFO) << "Selected: " << i;
     }
   }
   return ret;
@@ -356,17 +374,21 @@ CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList& cost_list, d
 CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vector<CostPtrList>& all_cost_list,
                                                                  double available_memory) {
   CostPtrList selected_cost_list(all_cost_list.size(), nullptr);
-  double minimum = 0.0, total_memory = 0.0;
+  double minimum = DBL_MAX, total_memory = 0.0;
   CostPtrList ret(all_cost_list.size(), nullptr);
+  // Check whether valid costs exist.
   for (size_t i = 0; i < all_cost_list.size(); ++i) {
     if (all_cost_list[i][0] == nullptr) {
       MS_LOG(ERROR) << "The cost list " << i << " is empty.";
       return ret;
     } else {
-      total_memory += all_cost_list[i][0]->computation_cost_;
-      minimum += costmodel_alpha_ * all_cost_list[i][0]->computation_cost_ +
-                 costmodel_beta_ * all_cost_list[i][0]->communication_with_partial_para_;
-      ret[i] = all_cost_list[i][0];
+      double memory_i_cost = DBL_MAX;
+      for (size_t j = 0; j < all_cost_list[i].size(); ++j) {
+        if (all_cost_list[i][j]->memory_with_reuse_ < memory_i_cost) {
+          memory_i_cost = all_cost_list[i][j]->memory_with_reuse_;
+        }
+      }
+      total_memory += memory_i_cost;
     }
   }
   if (total_memory >= available_memory) {
@@ -381,7 +403,7 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect
       double tmp_memory = 0.0, tmp_minimum = 0.0;
       for (size_t i = 0; i < selected_cost_list.size(); ++i) {
         MS_EXCEPTION_IF_NULL(selected_cost_list[i]);
-        tmp_memory += selected_cost_list[i]->computation_cost_;
+        tmp_memory += selected_cost_list[i]->memory_with_reuse_;
         tmp_minimum += costmodel_alpha_ * selected_cost_list[i]->computation_cost_ +
                        costmodel_beta_ * selected_cost_list[i]->communication_with_partial_para_;
       }
@@ -816,6 +838,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const
         auto& tar_cost = tar_cost_list[k];
         MS_EXCEPTION_IF_NULL(tar_cost);
         double computation = op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_;
+        double memory = op_cost->memory_with_reuse_ + edge_cost->memory_with_reuse_ + tar_cost->memory_with_reuse_;
         double communication =
           op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_;
         double communication_without_para = op_cost->communication_without_parameter_ +
@@ -829,6 +852,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const
         new_cost->communication_without_parameter_ = communication_without_para;
         new_cost->communication_with_partial_para_ =
           communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
+        new_cost->memory_with_reuse_ = memory;
         MS_EXCEPTION_IF_NULL(tar_cost_list_new);
         tar_cost_list_new->emplace_back(std::move(new_cost));
       }
@@ -894,6 +918,8 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str
         MS_EXCEPTION_IF_NULL(tar_cost);
         double computation =
           contract_op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_;
+        double memory =
+          contract_op_cost->memory_with_reuse_ + edge_cost->memory_with_reuse_ + tar_cost->memory_with_reuse_;
         double communication =
           contract_op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_;
         double communication_without_para = contract_op_cost->communication_without_parameter_ +
@@ -906,6 +932,7 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str
         new_cost->communication_without_parameter_ = communication_without_para;
         new_cost->communication_with_partial_para_ =
           communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para);
+        new_cost->memory_with_reuse_ = memory;
         tar_cost_list_new->emplace_back(std::move(new_cost));
       }
     }
@@ -966,23 +993,22 @@ void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra,
       for (auto& left_node_cost : left_node_clist_origin) {
         MS_EXCEPTION_IF_NULL(left_node_cost);
         double new_computation = elimi_op_cost->computation_cost_ + left_edge_cost->computation_cost_ +
-                                 left_node_cost->computation_cost_ + right_edge_cost->computation_cost_ +
-                                 right_op_cost->computation_cost_;
+                                 left_node_cost->computation_cost_ + right_edge_cost->computation_cost_;
+        double new_memory = elimi_op_cost->memory_with_reuse_ + left_edge_cost->memory_with_reuse_ +
+                            left_node_cost->memory_with_reuse_ + right_edge_cost->memory_with_reuse_;
         double new_commu_cost = elimi_op_cost->communication_cost_ + left_edge_cost->communication_cost_ +
-                                left_node_cost->communication_cost_ + right_edge_cost->communication_cost_ +
-                                right_op_cost->communication_cost_;
+                                left_node_cost->communication_cost_ + right_edge_cost->communication_cost_;
         double new_commu_without =
           elimi_op_cost->communication_without_parameter_ + left_edge_cost->communication_without_parameter_ +
-          left_node_cost->communication_without_parameter_ + right_edge_cost->communication_without_parameter_ +
-          right_op_cost->communication_without_parameter_;
+          left_node_cost->communication_without_parameter_ + right_edge_cost->communication_without_parameter_;
 
-        auto decision =
-          std::make_shared<TriangleEliminationDecision>(elimi_op_stra, elimi_op_cost, left_edge_cost, right_edge_cost,
-                                                        left_op_stra, left_node_cost, right_op_stra, right_op_cost);
+        auto decision = std::make_shared<TriangleEliminationDecision>(elimi_op_stra, elimi_op_cost, left_edge_cost,
+                                                                      right_edge_cost, left_op_stra, left_node_cost);
         auto new_cost = std::make_shared<Cost>(new_computation, new_commu_cost, decision);
         new_cost->communication_without_parameter_ = new_commu_without;
         new_cost->communication_with_partial_para_ =
           new_commu_without + COST_MODEL_GAMMA * (new_commu_cost - new_commu_without);
+        new_cost->memory_with_reuse_ = new_memory;
         left_node_clist_new->emplace_back(std::move(new_cost));
       }
     }
@@ -1085,14 +1111,22 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n
         succ_nodes_costs[0] = first_succ_node_cost;
 
         double computation_cost = merged_node_cost->computation_cost_,
-               commu_cost = merged_node_cost->communication_cost_,
+               memory_cost = merged_node_cost->memory_with_reuse_, commu_cost = merged_node_cost->communication_cost_,
                commu_without = merged_node_cost->communication_without_parameter_;
         for (size_t i = 0; i < succ_nodes_stras.size(); ++i) {
           MS_EXCEPTION_IF_NULL(succ_edges_costs[i]);
-          computation_cost += succ_edges_costs[i]->computation_cost_ + succ_nodes_costs[i]->computation_cost_;
-          commu_cost += succ_edges_costs[i]->communication_cost_ + succ_nodes_costs[i]->communication_cost_;
-          commu_without += succ_edges_costs[i]->communication_without_parameter_ +
-                           succ_nodes_costs[i]->communication_without_parameter_;
+          if (i == 0) {
+            computation_cost += succ_edges_costs[i]->computation_cost_ + succ_nodes_costs[i]->computation_cost_;
+            memory_cost += succ_edges_costs[i]->memory_with_reuse_ + succ_nodes_costs[i]->memory_with_reuse_;
+            commu_cost += succ_edges_costs[i]->communication_cost_ + succ_nodes_costs[i]->communication_cost_;
+            commu_without += succ_edges_costs[i]->communication_without_parameter_ +
+                             succ_nodes_costs[i]->communication_without_parameter_;
+          } else {
+            computation_cost += succ_edges_costs[i]->computation_cost_;
+            memory_cost += succ_edges_costs[i]->memory_with_reuse_;
+            commu_cost += succ_edges_costs[i]->communication_cost_;
+            commu_without += succ_edges_costs[i]->communication_without_parameter_;
+          }
         }
 
         auto decision = std::make_shared<StarEliminationDecision>(merged_op_stra, merged_node_cost, succ_edges_costs,
@@ -1100,6 +1134,7 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n
         auto new_cost = std::make_shared<Cost>(computation_cost, commu_cost, decision);
         new_cost->communication_without_parameter_ = commu_without;
         new_cost->communication_with_partial_para_ = commu_without + COST_MODEL_GAMMA * (commu_cost - commu_without);
+        new_cost->memory_with_reuse_ = memory_cost;
         first_succ_node_clist_new->emplace_back(std::move(new_cost));
       }
     }
@@ -1259,5 +1294,35 @@ OperatorInfoPtr CostGraph::FindTmpIdentityByParameterName(std::string& p_name) c
   }
   return nullptr;
 }
+Status CostGraph::CorrectOpsMemoryCost() {
+  for (auto& one_op : ops_) {
+    if ((one_op->name().find(IDENTITY_INFO) != std::string::npos) && (one_op->is_output_parameter_involve() == 1)) {
+      if (one_op->GetAliveSuccEdges().size() > 1) {
+        // Filter out the case when the TmpIdentity being used by multiple operators
+        std::map<size_t, int> output_count;
+        for (size_t i = 0; i < one_op->GetAliveSuccEdges().size(); ++i) {
+          auto output_index = one_op->GetAliveSuccEdges()[i]->prev_op_output_index();
+          output_count[output_index]++;
+        }
+        for (size_t i = 0; i < one_op->GetAliveSuccEdges().size(); ++i) {
+          auto output_index = one_op->GetAliveSuccEdges()[i]->prev_op_output_index();
+          if (output_count[output_index] <= 1) {
+            continue;
+          }
+          auto next_op = one_op->GetAliveSuccEdges()[i]->next_operator();
+          MS_EXCEPTION_IF_NULL(next_op);
+          auto input_index = one_op->GetAliveSuccEdges()[i]->next_op_input_index();
+          if (next_op->CorrectMemoryCost(input_index) != SUCCESS) {
+            MS_LOG(ERROR) << "The operator name: " << one_op->name() << ", the next operator name: " << next_op->name()
+                          << ", the output_index: " << output_index << ", the input_index: " << input_index << ".";
+            return FAILED;
+          }
+          output_count[output_index]--;
+        }
+      }
+    }
+  }
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index c149534826..65aeb210ea 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -187,6 +187,9 @@ class CostGraph {
   size_t GetNumPairs() const { return edges_.size(); }
   Status InitSelectedStrategy();
   OperatorInfoPtr FindTmpIdentityByParameterName(std::string&) const;
+  // When TmpIdentity is used by mulitple operators, the corresponding parameter's memory cost should be calculated only
+  // once (instead of multiple times), this method is used to correct this.
+  Status CorrectOpsMemoryCost();
   // Needed by rec_parser
   void add_inputs_tensor_name(const std::vector<std::string>& inputs_tensor_name) {
     inputs_tensor_name_list_.push_back(inputs_tensor_name);
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 960e13281c..ecd42db6bb 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -17,6 +17,7 @@
 #include "parallel/auto_parallel/operator_costmodel.h"
 
 #include <random>
+#include <algorithm>
 #include "parallel/device_matrix.h"
 #include "parallel/tensor_layout/tensor_redistribution.h"
 
@@ -24,12 +25,44 @@ namespace mindspore {
 namespace parallel {
 void OperatorCost::set_is_parameter(const std::vector<bool>& is_parameter) { is_parameter_ = is_parameter; }
 
+void OperatorCost::set_is_parameter_involve(const std::vector<bool>& is_parameter_inv) {
+  is_parameter_involve_ = is_parameter_inv;
+}
+
+void OperatorCost::set_output_parameter_involve(int output_para) { output_parameter_involve_ = output_para; }
+
 void OperatorCost::SetInputAndOutputTypeLength(const std::vector<size_t>& input_lengths,
                                                const std::vector<size_t>& output_lengths) {
   inputs_type_lengths_ = input_lengths;
   outputs_type_lengths_ = output_lengths;
 }
 
+double OperatorCost::GetMemoryCost(const std::vector<TensorInfo>& inputs,
+                                   const std::vector<TensorInfo>& outputs) const {
+  double result = 0.0;
+  if (output_parameter_involve_ == 1) {
+    // When this operator has multiple outputs, they all contributes to the memory.
+    for (size_t i = 0; i < outputs.size(); ++i) {
+      result += ListProduct(outputs[i].slice_shape()) * static_cast<double>(outputs_type_lengths_[i]);
+    }
+    bool is_any_para_inv =
+      std::any_of(is_parameter_involve_.begin(), is_parameter_involve_.end(), [](bool value) { return value; });
+    if (is_any_para_inv) {
+      for (size_t i = 0; i < inputs.size(); ++i) {
+        if (is_parameter_[i]) {
+          result += ListProduct(inputs[i].slice_shape()) * static_cast<double>(inputs_type_lengths_[i]);
+        } else if (inputs_related_ && (!is_parameter_involve_[i])) {
+          // When the inputs of this operator are related, and they are not parameter-involved, then they are included
+          // in the memory cost.
+          result += ListProduct(inputs[i].slice_shape()) * static_cast<double>(inputs_type_lengths_[i]);
+        }
+      }
+    }
+  }
+
+  return result;
+}
+
 // return the per device communication cost in the forward phase.
 double MatMulCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                       const int32_t&) const {
@@ -72,11 +105,11 @@ double MatMulCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, co
   return result;
 }
 
-// Return the per device memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double MatMulCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
                                              const std::vector<TensorInfo>& outputs, const int32_t&) const {
-  // In forward phase, the memory cost = slice(A) + slice(B) + (0 or 1) allreduce(slice(C))
+  // In forward phase, the compuatation cost = slice(A) + slice(B) + (0 or 1) allreduce(slice(C))
   double result = 0.0;
   TensorInfo output0 = outputs[0];
   Shape input0_slice_shape = inputs[0].slice_shape();
@@ -91,11 +124,11 @@ double MatMulCost::GetForwardComputationCost(const std::vector<TensorInfo>& inpu
   return result;
 }
 
-// Return the per device memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double MatMulCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
                                               const int32_t& stage_id) const {
-  // In backward phase, the memory cost = (0 or 1) allreduce(slice(B))
+  // In backward phase, the computation cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
     TensorInfo input1 = inputs[1];  // tensor B
@@ -145,7 +178,7 @@ double ActivationCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs
   return result;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
                                                  const int32_t&) const {
@@ -154,7 +187,7 @@ double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo>&
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ActivationCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
                                                   const int32_t&) const {
@@ -189,17 +222,17 @@ double SoftmaxCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, c
   return result;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
                                               const int32_t&) const {
-  // In the forward phase, the memory cost = slice(A)
+  // In the forward phase, the computation cost = slice(A)
   TensorInfo input0 = inputs[0];
   Shape input0_slice_shape = input0.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
                                                const std::vector<mindspore::parallel::TensorInfo>&,
@@ -221,17 +254,15 @@ double TmpIdentityCost::GetBackwardCommCost(const std::vector<mindspore::paralle
   return 0.0;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double TmpIdentityCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
+double TmpIdentityCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
                                                   const std::vector<mindspore::parallel::TensorInfo>&,
                                                   const int32_t&) const {
-  TensorInfo input0_info = inputs[0];
-  Shape input0_slice_shape = input0_info.slice_shape();
-  return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+  return 0.0;
 }
 
-// Return the per memory cost in the backward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
                                                    const std::vector<mindspore::parallel::TensorInfo>&,
@@ -239,6 +270,11 @@ double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::
   return 0.0;
 }
 
+// Return the per device PEAK memory cost contributed by this operator in a training iteration.
+double TmpIdentityCost::GetMemoryCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&) const {
+  return 0.0;
+}
+
 double BatchParallelCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
                                                     const std::vector<mindspore::parallel::TensorInfo>&,
                                                     const int32_t&) const {
@@ -284,11 +320,11 @@ double PReLUCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, con
   return result;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double PReLUCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
                                             const int32_t&) const {
-  // In forward phase, the memory cost = slice(A) + slice(B)
+  // In forward phase, the computation cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
   double result = ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]) +
@@ -296,12 +332,12 @@ double PReLUCost::GetForwardComputationCost(const std::vector<TensorInfo>& input
   return result;
 }
 
-// Return the per memory cost in the backward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double PReLUCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
                                              const std::vector<mindspore::parallel::TensorInfo>&,
                                              const int32_t& stage_id) const {
-  // In backward phase, the memory cost = (0 or 1) allreduce(slice(B))
+  // In backward phase, the computation cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
     TensorInfo input1 = inputs[1];  // tensor B
@@ -337,16 +373,16 @@ double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std
   return 0.0;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double OneHotCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
                                              const int32_t&) const {
-  // In onehot's forward phase, the memory cost = slice(A)
+  // In onehot's forward phase, the computation cost = slice(A)
   Shape input0_slice_shape = inputs[0].slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
 }
 
-// Return the per memory cost in the backward phase. The cost is calculated according to the bytes
+// Return the per  device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double OneHotCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
                                               const int32_t&) const {
@@ -367,12 +403,12 @@ double SoftmaxCrossEntropyWithLogitsCost::GetBackwardCommCost(const std::vector<
   return 0.0;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
                                                                     const std::vector<TensorInfo>&,
                                                                     const int32_t&) const {
-  // In forward phase, the memory cost = slice(A) + slice(B)
+  // In forward phase, the computation cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
   double result = ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]) +
@@ -380,7 +416,7 @@ double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::v
   return result;
 }
 
-// Return the per memory cost in the backward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCrossEntropyWithLogitsCost::GetBackwardComputationCost(const std::vector<TensorInfo>&,
                                                                      const std::vector<TensorInfo>&,
@@ -410,7 +446,7 @@ double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const st
   return 0.0;
 }
 
-// Return the per memory cost in the forward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ReshapeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
                                               const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
@@ -427,7 +463,7 @@ double ReshapeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
   return (inputs_type_lengths_[0] * tensor_redistribution.computation_cost());
 }
 
-// Return the per memory cost in the backward phase. The cost is calculated according to the bytes
+// Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double ReshapeCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
                                                const std::vector<mindspore::parallel::TensorInfo>&,
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index 685cb259c3..7dc45bae71 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -43,10 +43,20 @@ double ListProduct(std::vector<T> vec) {
 // entries timing the length of each entry's data type
 class OperatorCost {
  public:
-  OperatorCost() {
+  explicit OperatorCost(bool is_inputs_related) : inputs_related_(is_inputs_related) {
     // this is only for the case when set_is_parameter() and SetInputAndOutputTypeLength() are not invoked
     for (size_t i = 0; i < MAXIMUM_INPUT_NUMBER; ++i) {
       is_parameter_.push_back(false);
+      is_parameter_involve_.push_back(false);
+      inputs_type_lengths_.push_back(DEFAULT_DATA_TYPE_LENGTH);
+      outputs_type_lengths_.push_back(DEFAULT_DATA_TYPE_LENGTH);
+    }
+  }
+  OperatorCost() : inputs_related_(false) {
+    // this is only for the case when set_is_parameter() and SetInputAndOutputTypeLength() are not invoked
+    for (size_t i = 0; i < MAXIMUM_INPUT_NUMBER; ++i) {
+      is_parameter_.push_back(false);
+      is_parameter_involve_.push_back(false);
       inputs_type_lengths_.push_back(DEFAULT_DATA_TYPE_LENGTH);
       outputs_type_lengths_.push_back(DEFAULT_DATA_TYPE_LENGTH);
     }
@@ -54,6 +64,8 @@ class OperatorCost {
   virtual ~OperatorCost() = default;
 
   void set_is_parameter(const std::vector<bool>& is_parameter);
+  void set_is_parameter_involve(const std::vector<bool>&);
+  void set_output_parameter_involve(int);
   void SetInputAndOutputTypeLength(const std::vector<size_t>& input_lengths, const std::vector<size_t>& output_lengths);
   std::vector<size_t> inputs_type_lengths() const { return inputs_type_lengths_; }
   std::vector<size_t> outputs_type_lengths() const { return outputs_type_lengths_; }
@@ -72,8 +84,19 @@ class OperatorCost {
                                            const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
   virtual double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs,
                                             const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
+  // per device PEAK memory cost in a training iteration
+  // Typically, the PEAK memory cost contributed by an operator is its output (if the output is parameter-invovled),
+  // plus necessary inputs.
+  virtual double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs) const;
 
  protected:
+  // For each input in 'inputs_', a bool variable is true if the corresponding one is a parameter or a output of
+  // pre-operator that has parameters as input.
+  std::vector<bool> is_parameter_involve_;
+  int output_parameter_involve_ = -1;  // -1: unset; 0: not parameter_involved; 1: parameter_involved
+  // Whether the inputs are related or not? For example, TensorAdd's two inputs are independent (not related), while
+  // Mul's two inputs are dependent (related).
+  bool inputs_related_;
   // for each input in 'inputs_', there is a bool variable indicating whether that the corresponding input is parameter
   std::vector<bool> is_parameter_;
   // for each input and output, the followings record the number of bytes of each element
@@ -85,7 +108,8 @@ using OperatorCostPtr = std::shared_ptr<OperatorCost>;
 
 class MatMulCost : public OperatorCost {
  public:
-  MatMulCost() = default;
+  explicit MatMulCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  MatMulCost() : OperatorCost(true) {}
   ~MatMulCost() override = default;
 
   // per device communication cost
@@ -108,12 +132,12 @@ class MatMulCost : public OperatorCost {
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                     const int32_t& stage_id) const override;
 };
-
 using MatMulCostPtr = std::shared_ptr<MatMulCost>;
 
 class ActivationCost : public OperatorCost {
  public:
-  ActivationCost() = default;
+  explicit ActivationCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  ActivationCost() : OperatorCost(false) {}
   ~ActivationCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -133,14 +157,14 @@ class ActivationCost : public OperatorCost {
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                     const int32_t& stage_id) const override;
 };
-
 using ActivationCostPtr = std::shared_ptr<ActivationCost>;
 using TransposeCost = ActivationCost;
 using TransposeCostPtr = std::shared_ptr<TransposeCost>;
 
 class SoftmaxCost : public OperatorCost {
  public:
-  SoftmaxCost() = default;
+  explicit SoftmaxCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  SoftmaxCost() : OperatorCost(false) {}
   ~SoftmaxCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -160,12 +184,12 @@ class SoftmaxCost : public OperatorCost {
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                     const int32_t&) const override;
 };
-
 using SoftmaxCostPtr = std::shared_ptr<SoftmaxCost>;
 
 class TmpIdentityCost : public OperatorCost {
  public:
-  TmpIdentityCost() = default;
+  explicit TmpIdentityCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  TmpIdentityCost() : OperatorCost(false) {}
   ~TmpIdentityCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -184,12 +208,15 @@ class TmpIdentityCost : public OperatorCost {
                                    const int32_t& stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                                     const int32_t& stage_id) const override;
+  // per device PEAK memory cost in a training iteration
+  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs) const override;
 };
 using TmpIdentityCostPtr = std::shared_ptr<TmpIdentityCost>;
 
 class BatchParallelCost : public OperatorCost {
  public:
-  BatchParallelCost() = default;
+  explicit BatchParallelCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  BatchParallelCost() : OperatorCost(false) {}
   ~BatchParallelCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -217,7 +244,8 @@ using BatchParallelCostPtr = std::shared_ptr<BatchParallelCost>;
 
 class VirtualDatasetCost : public OperatorCost {
  public:
-  VirtualDatasetCost() = default;
+  explicit VirtualDatasetCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  VirtualDatasetCost() : OperatorCost(false) {}
   ~VirtualDatasetCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -244,12 +272,17 @@ class VirtualDatasetCost : public OperatorCost {
                                     const int32_t&) const override {
     return 0.0;
   }
+  // per device PEAK memory cost in a training iteration
+  double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs) const override {
+    return 0.0;
+  }
 };
 using VirtualDatasetCostPtr = std::shared_ptr<VirtualDatasetCost>;
 
 class GeneratorBaseCost : public OperatorCost {
  public:
-  GeneratorBaseCost() = default;
+  explicit GeneratorBaseCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  GeneratorBaseCost() : OperatorCost(false) {}
   ~GeneratorBaseCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -283,7 +316,8 @@ using GeneratorBaseCostPtr = std::shared_ptr<GeneratorBaseCost>;
 
 class PReLUCost : public OperatorCost {
  public:
-  PReLUCost() = default;
+  explicit PReLUCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  PReLUCost() : OperatorCost(true) {}
   ~PReLUCost() override = default;
 
   // per device communication cost
@@ -310,7 +344,8 @@ using PReLUCostPtr = std::shared_ptr<PReLUCost>;
 
 class OneHotCost : public OperatorCost {
  public:
-  OneHotCost() = default;
+  explicit OneHotCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  OneHotCost() : OperatorCost(true) {}
   ~OneHotCost() override = default;
 
   // per device communication cost
@@ -337,7 +372,8 @@ using OneHotCostPtr = std::shared_ptr<OneHotCost>;
 
 class SoftmaxCrossEntropyWithLogitsCost : public OperatorCost {
  public:
-  SoftmaxCrossEntropyWithLogitsCost() = default;
+  explicit SoftmaxCrossEntropyWithLogitsCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  SoftmaxCrossEntropyWithLogitsCost() : OperatorCost(false) {}
   ~SoftmaxCrossEntropyWithLogitsCost() override = default;
 
   // per device communication cost
@@ -364,7 +400,8 @@ using SoftmaxCrossEntropyWithLogitsCostPtr = std::shared_ptr<SoftmaxCrossEntropy
 
 class ReshapeCost : public OperatorCost {
  public:
-  ReshapeCost() = default;
+  explicit ReshapeCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  ReshapeCost() : OperatorCost(true) {}
 
   ~ReshapeCost() override = default;
 
@@ -396,7 +433,8 @@ using ReshapeCostPtr = std::shared_ptr<ReshapeCost>;
 
 class ArithmeticCost : public OperatorCost {
  public:
-  ArithmeticCost() = default;
+  explicit ArithmeticCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  ArithmeticCost() : OperatorCost(false) {}
   ~ArithmeticCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -425,7 +463,8 @@ using BiasAddCostPtr = std::shared_ptr<BiasAddCost>;
 
 class ReduceMethodCost : public OperatorCost {
  public:
-  ReduceMethodCost() = default;
+  explicit ReduceMethodCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  ReduceMethodCost() : OperatorCost(true) {}
   ~ReduceMethodCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -455,7 +494,8 @@ using ReduceMethodCostPtr = std::shared_ptr<ReduceMethodCost>;
 
 class ReduceMeanCost : public ReduceMethodCost {
  public:
-  ReduceMeanCost() = default;
+  explicit ReduceMeanCost(bool is_inputs_related) : ReduceMethodCost(is_inputs_related) {}
+  ReduceMeanCost() : ReduceMethodCost(true) {}
   ~ReduceMeanCost() override = default;
 
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -465,7 +505,8 @@ using ReduceMeanCostPtr = std::shared_ptr<ReduceMeanCost>;
 
 class GetNextCost : public OperatorCost {
  public:
-  GetNextCost() = default;
+  explicit GetNextCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  GetNextCost() : OperatorCost(false) {}
   ~GetNextCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -499,7 +540,8 @@ using GetNextCostPtr = std::shared_ptr<GetNextCost>;
 
 class DropOutCost : public OperatorCost {
  public:
-  DropOutCost() = default;
+  explicit DropOutCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  DropOutCost() : OperatorCost(true) {}
   ~DropOutCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
@@ -530,7 +572,8 @@ using DropOutCostPtr = std::shared_ptr<DropOutCost>;
 
 class GatherV2Cost : public OperatorCost {
  public:
-  GatherV2Cost() = default;
+  explicit GatherV2Cost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  GatherV2Cost() : OperatorCost(true) {}
   ~GatherV2Cost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index b19e38b910..8dca036f9e 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -51,7 +51,7 @@ class Activation : public ActivationBase {
  public:
   Activation(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ActivationCost>()) {}
+      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ActivationCost>(false)) {}
   ~Activation() override = default;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
@@ -102,7 +102,7 @@ class Softmax : public ActivationBase {
  public:
   explicit Softmax(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                    const PrimitiveAttrs& attrs)
-      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<SoftmaxCost>()) {}
+      : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<SoftmaxCost>(false)) {}
   ~Softmax() override = default;
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index 435a7ce793..376a1fb4cf 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -32,8 +32,8 @@ namespace parallel {
 class ArithmeticBase : public OperatorInfo {
  public:
   ArithmeticBase(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
-                 const PrimitiveAttrs& attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>()) {}
+                 const PrimitiveAttrs& attrs, OperatorCostPtr cost)
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, cost) {}
   ~ArithmeticBase() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
@@ -56,7 +56,7 @@ class ArithmeticBase : public OperatorInfo {
 class SubInfo : public ArithmeticBase {
  public:
   SubInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~SubInfo() override = default;
 };
 
@@ -64,21 +64,21 @@ class TensorAddInfo : public ArithmeticBase {
  public:
   TensorAddInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                 const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~TensorAddInfo() override = default;
 };
 
 class MulInfo : public ArithmeticBase {
  public:
   MulInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~MulInfo() override = default;
 };
 
 class DivInfo : public ArithmeticBase {
  public:
   DivInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~DivInfo() override = default;
 };
 
@@ -86,7 +86,7 @@ class RealDivInfo : public ArithmeticBase {
  public:
   RealDivInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~RealDivInfo() override = default;
 };
 
@@ -94,14 +94,14 @@ class FloorDivInfo : public ArithmeticBase {
  public:
   FloorDivInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~FloorDivInfo() override = default;
 };
 
 class PowInfo : public ArithmeticBase {
  public:
   PowInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~PowInfo() override = default;
 };
 
@@ -109,7 +109,7 @@ class GreaterInfo : public ArithmeticBase {
  public:
   GreaterInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~GreaterInfo() override = default;
 };
 
@@ -117,7 +117,7 @@ class AssignSubInfo : public ArithmeticBase {
  public:
   AssignSubInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                 const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~AssignSubInfo() override = default;
 };
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
index 093bfb8fad..4cedb9b7b8 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h
@@ -29,9 +29,13 @@ namespace mindspore {
 namespace parallel {
 class BatchParallelInfo : public OperatorInfo {
  public:
+  BatchParallelInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                    const PrimitiveAttrs& attrs, OperatorCostPtr cost)
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, cost), dev_num_(1) {}
   BatchParallelInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>()), dev_num_(1) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>(false)),
+        dev_num_(1) {}
 
   ~BatchParallelInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
@@ -58,7 +62,7 @@ class SparseSoftmaxCrossEntropyWithLogitsInfo : public BatchParallelInfo {
  public:
   SparseSoftmaxCrossEntropyWithLogitsInfo(const std::string& name, const Shapes& inputs_shape,
                                           const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : BatchParallelInfo(name, inputs_shape, outputs_shape, attrs) {}
+      : BatchParallelInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<BatchParallelCost>(true)) {}
   ~SparseSoftmaxCrossEntropyWithLogitsInfo() override = default;
   void ReComputeBatchSplitFlagList() override;
 };
diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
index dea5c90c88..e792858338 100644
--- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h
@@ -34,7 +34,7 @@ class BiasAddInfo : public OperatorInfo {
  public:
   BiasAddInfo(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<BiasAddCost>()) {}
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<BiasAddCost>(false)) {}
   ~BiasAddInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
index 00cc431463..9ea496e0b0 100644
--- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h
@@ -18,6 +18,7 @@
 #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_COMPARISON_FUNCTION_INFO_H_
 
 #include <string>
+#include <memory>
 #include <unordered_map>
 #include <vector>
 #include "ir/value.h"
@@ -31,7 +32,7 @@ class EqualInfo : public ArithmeticBase {
  public:
   EqualInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
             const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~EqualInfo() override = default;
 };
 
@@ -39,7 +40,7 @@ class NotEqualInfo : public ArithmeticBase {
  public:
   NotEqualInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~NotEqualInfo() override = default;
 };
 
@@ -47,7 +48,7 @@ class MaximumInfo : public ArithmeticBase {
  public:
   MaximumInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~MaximumInfo() override = default;
 };
 
@@ -55,7 +56,7 @@ class MinimumInfo : public ArithmeticBase {
  public:
   MinimumInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~MinimumInfo() override = default;
 };
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
index 7ebe677997..3b154bd6db 100644
--- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h
@@ -33,7 +33,7 @@ class DropoutDoMaskInfo : public OperatorInfo {
  public:
   DropoutDoMaskInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<DropOutCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<DropOutCost>(true)) {}
   ~DropoutDoMaskInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
index 9a65eff035..ba209910b7 100644
--- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h
@@ -32,7 +32,7 @@ class GetNextInfo : public OperatorInfo {
  public:
   GetNextInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
               const PrimitiveAttrs &attrs)
-      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GetNextCost>()) {}
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GetNextCost>(false)) {}
   ~GetNextInfo() override = default;
 
   Status Init(const StrategyPtr &strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h
index f1c2537a39..44fe22ce90 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h
@@ -36,7 +36,8 @@ class SoftmaxCrossEntropyWithLogitsInfo : public OperatorInfo {
  public:
   SoftmaxCrossEntropyWithLogitsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                                     const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<SoftmaxCrossEntropyWithLogitsCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs,
+                     std::make_shared<SoftmaxCrossEntropyWithLogitsCost>(false)) {}
   ~SoftmaxCrossEntropyWithLogitsInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index 848116d68a..e617ae6c24 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -593,11 +593,11 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr&
   // Here, we use the origin outputs_, because we only use the slice size of the output tensor.
   // It does not matter whether the output tensor is transposed or not.
   double computation_cost =
-    cost()->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
-  double communication_cost = cost()->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+    operator_cost()->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+  double communication_cost = operator_cost()->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
-    cost()->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
+    operator_cost()->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
     result->communication_without_parameter_ +
     COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_);
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
index 2d3312774d..8a64fb7206 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h
@@ -34,7 +34,7 @@ class MatMulBase : public OperatorInfo {
  public:
   MatMulBase(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<MatMulCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<MatMulCost>(true)) {}
   ~MatMulBase() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
index fec8d96324..a4f00ea093 100644
--- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h
@@ -33,7 +33,7 @@ class OneHotInfo : public OperatorInfo {
  public:
   OneHotInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
              const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<OneHotCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<OneHotCost>(false)) {}
   ~OneHotInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 561628dbb2..23b6a5190a 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -1035,11 +1035,12 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) {
     return FAILED;
   }
   int32_t stage_id = strategy->GetInputStage();
-  double computation_cost = cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
-  double communication_cost = cost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+  double computation_cost =
+    operator_cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+  double communication_cost = operator_cost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   std::shared_ptr<Cost> result = std::make_shared<Cost>(computation_cost, communication_cost);
   result->communication_without_parameter_ =
-    cost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
+    operator_cost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id);
   result->communication_with_partial_para_ =
     result->communication_without_parameter_ +
     COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_);
@@ -1096,7 +1097,38 @@ Status OperatorInfo::set_is_parameter(const std::vector<bool>& is_parameter) {
     return FAILED;
   }
   is_parameter_ = is_parameter;
-  cost()->set_is_parameter(is_parameter);
+  operator_cost()->set_is_parameter(is_parameter);
+  return SUCCESS;
+}
+
+Status OperatorInfo::CalculateMemoryCost() {
+  // First, set the 'is_parameter_involve_' and 'is_output_parameter_involve_' into OperatorCost, which are necessary to
+  // calculate memory cost.
+  if (is_parameter_involve_.size() != is_parameter_.size()) {
+    MS_LOG(ERROR) << "'is_parameter_' does not have the same number of input size of 'is_parameter_involve_'.";
+    return FAILED;
+  }
+  operator_cost()->set_is_parameter_involve(is_parameter_involve_);
+  operator_cost()->set_output_parameter_involve(is_output_parameter_involve_);
+  // Set the memory cost in the 'strategy_cost_'
+  for (auto& swc : strategy_cost_) {
+    auto mem_cost = operator_cost()->GetMemoryCost(swc->inputs_ptr, swc->outputs_ptr);
+    swc->cost_list[0]->memory_with_reuse_ = mem_cost;
+  }
+  return SUCCESS;
+}
+
+Status OperatorInfo::CorrectMemoryCost(size_t input_index) {
+  for (auto& swc : strategy_cost_) {
+    double parameter_mem_cost = ListProduct(swc->inputs_ptr[input_index].slice_shape()) *
+                                static_cast<double>(operator_cost()->inputs_type_lengths()[input_index]);
+    swc->cost_list[0]->memory_with_reuse_ -= parameter_mem_cost;
+    if (swc->cost_list[0]->memory_with_reuse_ < 0) {
+      MS_LOG(ERROR) << "The memory cost after correction is: " << swc->cost_list[0]->memory_with_reuse_
+                    << ", the parameter memory cost is: " << parameter_mem_cost;
+      return FAILED;
+    }
+  }
   return SUCCESS;
 }
 
@@ -1193,7 +1225,7 @@ Status OperatorInfo::SetInputAndOutputTypeLength(const std::vector<size_t>& inpu
   }
   inputs_type_lengths_ = input_lengths;
   outputs_type_lengths_ = output_lengths;
-  cost()->SetInputAndOutputTypeLength(input_lengths, output_lengths);
+  operator_cost()->SetInputAndOutputTypeLength(input_lengths, output_lengths);
   return SUCCESS;
 }
 
@@ -1221,7 +1253,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra
 }
 
 double OperatorInfo::GetForwardMemoryCostFromCNode() {
-  return cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
+  return operator_cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
 }
 
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h
index 5fe89a1602..19e0eeeda1 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h
@@ -60,7 +60,7 @@ class OperatorInfo {
         outputs_shape_(std::move(outputs_shape)),
         attrs_(std::move(attrs)),
         is_alive_(true),
-        cost_(cost),
+        operator_cost_(cost),
         outputs_type_() {
     std::vector<bool> not_parameteter(inputs_shape_.size(), false);
     is_parameter_ = not_parameteter;
@@ -83,8 +83,8 @@ class OperatorInfo {
   // Given the stage_id (which indicates the number of devices),
   // generate all strategies for this operator
   virtual Status GenerateStrategies(int32_t stage_id) = 0;
-  const OperatorCostPtr& cost() const { return cost_; }
-  void set_cost(const OperatorCostPtr& cost) { cost_ = cost; }
+  const OperatorCostPtr& operator_cost() const { return operator_cost_; }
+  void set_cost(const OperatorCostPtr& cost) { operator_cost_ = cost; }
   virtual Status SetCostUnderStrategy(const StrategyPtr& strategy) = 0;
 
   virtual std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies();
@@ -98,7 +98,7 @@ class OperatorInfo {
   std::vector<std::shared_ptr<StrategyWithCost>> GetStrategyCost() { return strategy_cost_; }
   // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
   // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
-  Status CalculateMemoryCost() const { return SUCCESS; }
+  Status CalculateMemoryCost();
   int ComputeOpAndPrevEdgeParameterInvolved();
 
   ForwardOp forward_op() const { return forward_op_; }
@@ -125,7 +125,7 @@ class OperatorInfo {
   void ReplaceSuccEdge(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
   void ReplacePreEdges(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
   void ReplaceSuccEdges(const std::shared_ptr<OperatorInfo>& op, const std::shared_ptr<Edge>& new_edge);
-  std::vector<size_t> GetOutputTypeLengths() const { return cost()->outputs_type_lengths(); }
+  std::vector<size_t> GetOutputTypeLengths() const { return operator_cost()->outputs_type_lengths(); }
   void SetSelectedStrategyAndCost(const StrategyPtr& s_strategy, const CostPtr& cost) {
     selected_strategy_ = s_strategy;
     selected_cost_ = cost;
@@ -142,6 +142,10 @@ class OperatorInfo {
   void set_strategy(const StrategyPtr& strategy) { strategy_ = strategy; }
   void set_refkey_parameter_name(std::string p_name) { refkey_parameter_name_ = std::move(p_name); }
   const std::string& refkey_parameter_name() const { return refkey_parameter_name_; }
+  // When the output of a Parameter (require_grad) being used by multiple operators, the Parameter's cost is calculated
+  // multiple times. This method is to correct this, and makes the cost is calulated only once.
+  Status CorrectMemoryCost(size_t input_index);
+  int is_output_parameter_involve() const { return is_output_parameter_involve_; }
   int used_devices() const { return used_devices_; }
   // needed by rec_parser
   void set_type(const std::string& type) { type_ = type; }
@@ -234,7 +238,7 @@ class OperatorInfo {
   int32_t used_devices_ = -1;
 
  private:
-  OperatorCostPtr cost_;
+  OperatorCostPtr operator_cost_;
   std::vector<TypePtr> outputs_type_;
 };
 
diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
index bdfb11550b..396407c1ee 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h
@@ -35,7 +35,7 @@ class PReLUInfo : public OperatorInfo {
  public:
   PReLUInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
             const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<PReLUCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<PReLUCost>(true)) {}
   ~PReLUInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
index aa64e72d05..44eab20588 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc
@@ -109,7 +109,7 @@ Status ReduceMethod::GetAttrs() {
     }
     cross_batch_ = cross_batch_iter->second->cast<BoolImmPtr>()->value();
   }
-  auto reducemethodcost = std::dynamic_pointer_cast<ReduceMethodCost>(cost());
+  auto reducemethodcost = std::dynamic_pointer_cast<ReduceMethodCost>(operator_cost());
   if (reducemethodcost == nullptr) {
     MS_LOG(ERROR) << "Cost cast to ReduceMethodCostPtr failed!";
     return FAILED;
diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
index c2ddbc87ce..2911bdfe10 100644
--- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h
@@ -34,7 +34,7 @@ class ReduceMethod : public OperatorInfo {
  public:
   ReduceMethod(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
                const PrimitiveAttrs &attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReduceMethodCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReduceMethodCost>(true)) {}
   ~ReduceMethod() override = default;
 
   Status Init(const StrategyPtr &strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
index 38192a5d01..3864d2b93d 100644
--- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h
@@ -36,7 +36,7 @@ class ReshapeInfo : public OperatorInfo {
  public:
   ReshapeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
               const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReshapeCost>()),
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<ReshapeCost>(false)),
         dev_num_(0),
         input_layout_set_flag_(false),
         output_layout_set_flag_(false) {}
diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
index cf850683a6..3682fe334f 100644
--- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h
@@ -34,7 +34,7 @@ class TmpIdentityInfo : public OperatorInfo {
  public:
   TmpIdentityInfo(const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs,
                   const std::string& name = IDENTITY_INFO)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TmpIdentityCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TmpIdentityCost>(false)) {}
   ~TmpIdentityInfo() override = default;
 
   Status Init(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
index 2714b352b6..e4e2b90b7b 100644
--- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h
@@ -35,7 +35,7 @@ class TransposeInfo : public OperatorInfo {
  public:
   TransposeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                 const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TransposeCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<TransposeCost>(false)) {}
   ~TransposeInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
index b958adeabe..bf17e678a3 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
@@ -32,7 +32,7 @@ class VirtualDatasetInfo : public OperatorInfo {
  public:
   VirtualDatasetInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
                      const PrimitiveAttrs& attrs)
-      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<VirtualDatasetCost>()) {}
+      : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<VirtualDatasetCost>(false)) {}
   ~VirtualDatasetInfo() override = default;
   Status Init(const StrategyPtr& strategy) override;
   Status InitForCostModel(const StrategyPtr& strategy) override;
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index ae60454972..a42ce612fb 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -874,11 +874,15 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
   if (entire_costgraph->ComputeOpsAndEdgesParameterInvolved() == SUCCESS) {
     // Calculate operators' memory usage
     if (entire_costgraph->CalculateOpsMemoryCost() != SUCCESS) {
-      MS_LOG(EXCEPTION) << "Correcting operators' cost for memory reuse failed.";
+      MS_LOG(EXCEPTION) << "Calculating operators' cost for memory cost failed.";
     }
     // Calculate edges' memory usage
     if (entire_costgraph->CalculateEdgesMemoryCost() != SUCCESS) {
-      MS_LOG(EXCEPTION) << "Correcting edges' cost for memory reuse failed.";
+      MS_LOG(EXCEPTION) << "Calculating edges' cost for memory cost failed.";
+    }
+    // Correct memory usage caused by TmpIdentity
+    if (entire_costgraph->CorrectOpsMemoryCost() != SUCCESS) {
+      MS_LOG(EXCEPTION) << "Correcting operators' cost for memory cost failed.";
     }
   } else {
     MS_LOG(EXCEPTION) << "Computing operators' parameter_involved failed.";
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
index 55e6a300e0..be5eaa40ba 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
@@ -159,6 +159,7 @@ Status TensorRedistribution::ComputeCost() {
       backward_comm_cost_ += prod;
       comm_cost_ += 2.0 * prod;
       computation_cost_ += prod;
+      memory_cost_ += prod;
     } else if (str == CONCAT_BY_AXIS) {
       // communication cost = all_gather + reduce_scatter = before_slice_shape + after_slice_shape
       // computation cost = before_slice_shape
@@ -175,20 +176,25 @@ Status TensorRedistribution::ComputeCost() {
       if (concat_dim == 0) {
         // computation cost = all_gather
         computation_cost_ += prod;
+        memory_cost_ += prod * dev_num;
       } else {
         // computation cost = all_gather + split + concat
         computation_cost_ += (prod + prod * dev_num + prod * dev_num);
+        memory_cost_ += (prod * dev_num + prod * dev_num + prod);
       }
     } else {
       // There is only computation cost in SplitByAxis.
       // computation cost = before_slice_shape
       computation_cost_ += prod;
+      // This addtion may be  erroneous
+      memory_cost_ += prod;
     }
   }
   if (reshape_flag()) {
     Shape prev_slice_shape = from_.slice_shape().array();
     double prev_prod = std::accumulate(prev_slice_shape.begin(), prev_slice_shape.end(), 1, std::multiplies<int>());
     computation_cost_ += 2.0 * prev_prod;
+    memory_cost_ += 2.0 * prev_prod;
   }
   return Status::SUCCESS;
 }
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
index e933b9b8eb..7e2b3682e6 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
@@ -42,6 +42,7 @@ class TensorRedistribution {
         forward_comm_cost_(0.0),
         backward_comm_cost_(0.0),
         computation_cost_(0.0),
+        memory_cost_(0.0),
         construct_op_flag_(construct_op_flag),
         keep_reshape_(keep_reshape) {}
   Status Init(const TensorLayout& from, const TensorLayout& to, const RankList& dev_list);
@@ -54,6 +55,7 @@ class TensorRedistribution {
   double computation_cost() const { return computation_cost_; }
   double forward_comm_cost() const { return forward_comm_cost_; }
   double backward_comm_cost() const { return backward_comm_cost_; }
+  double memory_cost() const { return memory_cost_; }
 
  private:
   Status InferReshape(const TensorLayout& from_layout, const TensorLayout& to_layout,
@@ -72,7 +74,12 @@ class TensorRedistribution {
   double forward_comm_cost_;
   // backward communication cost
   double backward_comm_cost_;
+  // computation_cost models the time spending on computing in this tensor redistribution, which is calculated by the
+  // inputs.
   double computation_cost_;
+  // memory_cost models the PEAK memory cost in a traning iteration contributed by this tensor redistribution, which is
+  // calculated by the outputs.
+  double memory_cost_;
   bool construct_op_flag_;
   bool keep_reshape_;
 };
diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc
index a8f8425ae9..9af7203799 100644
--- a/tests/ut/cpp/parallel/ops_info/activation_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc
@@ -84,9 +84,9 @@ TEST_F(TestActivation, test_activation_strategies) {
     act_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = act_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = act_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(act_ptr_->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(act_ptr_->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }
@@ -109,9 +109,9 @@ TEST_F(TestActivation, test_softmax_strategies) {
     soft_ptr_->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = soft_ptr_->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = soft_ptr_->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(soft_ptr_->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(soft_ptr_->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }
diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
index 2fece098e8..f710f51265 100644
--- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc
@@ -569,7 +569,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies1) {
     matmul1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = matmul1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = matmul1->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(matmul1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(matmul1->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
     break;
   }
@@ -599,7 +599,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies2) {
     TensorInfo replica_input1_info(tly, input1_shape, input1_slice_shape);
     replica_inputs_info.push_back(replica_input1_info);
 
-    ASSERT_DOUBLE_EQ(matmul3->cost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(matmul3->operator_cost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
     break;
   }
diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
index 8c956328a7..42d292c605 100644
--- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc
@@ -188,11 +188,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies) {
     tensor_add->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add->outputs_tensor_info();
-    double memory_cost0 = tensor_add->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost0 = tensor_add->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
     double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
-    double comm_cost0 = tensor_add->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
+    double comm_cost0 = tensor_add->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
     double comm_cost1 = cost.communication_cost_;
     bool comm = comm_cost0 - comm_cost1 <= 1.0;
 
@@ -210,11 +210,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) {
     tensor_add1->InitForCostModel(sp);
     std::vector<TensorInfo> inputs_info = tensor_add1->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = tensor_add1->outputs_tensor_info();
-    double memory_cost0 = tensor_add1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
+    double memory_cost0 = tensor_add1->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage());
     double memory_cost1 = cost.computation_cost_;
     bool memory = memory_cost0 - memory_cost1 <= 1.0;
 
-    double comm_cost0 = tensor_add1->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
+    double comm_cost0 = tensor_add1->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage());
     double comm_cost1 = cost.communication_cost_;
     bool comm = comm_cost0 - comm_cost1 <= 1.0;
 
diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
index 3971a2b471..eabac51e17 100644
--- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
+++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc
@@ -145,9 +145,9 @@ TEST_F(TestTmpIdentityInfo, test_generate_strategies) {
     identity_ptr->Init(sp);
     std::vector<TensorInfo> inputs_info = identity_ptr->inputs_tensor_info();
     std::vector<TensorInfo> outputs_info = identity_ptr->outputs_tensor_info();
-    ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(identity_ptr->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.computation_cost_);
-    ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
+    ASSERT_DOUBLE_EQ(identity_ptr->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
                      cost.communication_cost_);
   }
 }

From 46c590b787ba5e85f71ef393adbf50fc818bb635 Mon Sep 17 00:00:00 2001
From: Hoai Linh Tran h00472437 <hoai.linh.tran@huawei.com>
Date: Thu, 2 Apr 2020 17:16:47 -0400
Subject: [PATCH 208/367] Add new flag to watch for the need of Renormalize
 pass when parsing the python input program Signed-off-by: Hoai Linh Tran
 h00472437 <hoai.linh.tran@huawei.com>

Add optimizer checking: For a group of passes in "optimizer", if flagged then it will check and collect the newly generated nodes without types (i.e. abstract() == nullptr).
Before calling Renormalize(), the optimizer will check if there is any node needed retyping. If not the Renormalize pass will not be called.
Add checking for non-null abstract but still needs renorm;
Add flags to Substitution to help watching Renormalize

Simpler pass result checker, change Bool to Enum type
---
 mindspore/ccsrc/optimizer/irpass.cc   |  7 ++++--
 mindspore/ccsrc/optimizer/opt.cc      | 24 +++++++++++++------
 mindspore/ccsrc/optimizer/opt.h       | 20 ++++++++++++----
 mindspore/ccsrc/optimizer/optimizer.h | 34 ++++++++++++++++++++++++---
 mindspore/ccsrc/pipeline/pass.cc      |  4 ++--
 5 files changed, 70 insertions(+), 19 deletions(-)

diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc
index 96d88f6e61..be9c8f787a 100644
--- a/mindspore/ccsrc/optimizer/irpass.cc
+++ b/mindspore/ccsrc/optimizer/irpass.cc
@@ -52,7 +52,8 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
   special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate",
                                            {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType,
                                             prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv});
-  zero_like_fill_zero_ = MakeSubstitution(ZeroLikeFillZero(), "zero_like_fill_zero", prim::kPrimZerosLikeTensor);
+  zero_like_fill_zero_ =
+    MakeSubstitution(ZeroLikeFillZero(), "zero_like_fill_zero", prim::kPrimZerosLikeTensor, opt::FORCE_RENORM);
 
   // ops eliminate
   item_tuple_eliminate_ =
@@ -81,7 +82,9 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
   make_ref_eliminate_ = MakeSubstitution(MakeRefEliminater(), "make_ref_eliminate", prim::kPrimMakeRef);
   get_make_ref_eliminate_ =
     MakeSubstitution(GetMakeRefEliminater(), "get_make_ref_eliminate", {prim::kPrimGetRefKey, prim::kPrimGetRefValue});
-  replace_refkey_by_param_ = MakeSubstitution(ReplaceRefkeyByParam(), "replace_refkey_by_param", IsValueNode<RefKey>);
+
+  replace_refkey_by_param_ =
+    MakeSubstitution(ReplaceRefkeyByParam(), "replace_refkey_by_param", IsValueNode<RefKey>, opt::FORCE_RENORM);
   replace_old_param_ = MakeSubstitution(ReplaceOldParam(), "replace_old_param", IsParam);
 
   // Gradient transforms
diff --git a/mindspore/ccsrc/optimizer/opt.cc b/mindspore/ccsrc/optimizer/opt.cc
index a0faa2bf46..24339ddb84 100644
--- a/mindspore/ccsrc/optimizer/opt.cc
+++ b/mindspore/ccsrc/optimizer/opt.cc
@@ -31,14 +31,14 @@
 namespace mindspore {
 /* namespace to support opt */
 namespace opt {
-SubstitutionPtr MakeSubstitution(const TransformFuncType& transform, const std::string& name,
-                                 const PrimitivePtr& prim) {
+SubstitutionPtr MakeSubstitution(const TransformFuncType& transform, const std::string& name, const PrimitivePtr& prim,
+                                 const RenormAction& renorm_action) {
   auto fn = [prim](const AnfNodePtr& node) -> bool { return IsPrimitiveCNode(node, prim); };
-  return std::make_shared<Substitution>(transform, name, fn);
+  return std::make_shared<Substitution>(transform, name, fn, renorm_action);
 }
 
 SubstitutionPtr MakeSubstitution(const TransformFuncType& transform, const std::string& name,
-                                 const std::vector<PrimitivePtr>& prims) {
+                                 const std::vector<PrimitivePtr>& prims, const RenormAction& renorm_action) {
   auto fn = [prims](const AnfNodePtr& node) -> bool {
     if (!node->isa<CNode>()) {
       return false;
@@ -52,12 +52,12 @@ SubstitutionPtr MakeSubstitution(const TransformFuncType& transform, const std::
     return false;
   };
 
-  return std::make_shared<Substitution>(transform, name, fn);
+  return std::make_shared<Substitution>(transform, name, fn, renorm_action);
 }
 
 SubstitutionPtr MakeSubstitution(const TransformFuncType& transform, const std::string& name,
-                                 const PredicateFuncType& predicate) {
-  return std::make_shared<Substitution>(transform, name, predicate);
+                                 const PredicateFuncType& predicate, const RenormAction& renorm_action) {
+  return std::make_shared<Substitution>(transform, name, predicate, renorm_action);
 }
 
 AnfNodePtr Substitution::operator()(const OptimizerPtr& optimizer, const AnfNodePtr& node) const {
@@ -74,6 +74,16 @@ AnfNodePtr Substitution::operator()(const OptimizerPtr& optimizer, const AnfNode
     }
   }
 #endif
+  if (optimizer != nullptr && optimizer->is_watch_renormalize() && result != nullptr) {
+    if (renorm_action_ == FORCE_RENORM) {
+      optimizer->add_node_to_renormalize(result);
+    } else {
+      // renorm_action_ is CHECK_RENORM
+      if (result->abstract() == nullptr) {
+        optimizer->add_node_to_renormalize(result);
+      }
+    }
+  }
 
   return result;
 }
diff --git a/mindspore/ccsrc/optimizer/opt.h b/mindspore/ccsrc/optimizer/opt.h
index bd548645f4..24191998e8 100644
--- a/mindspore/ccsrc/optimizer/opt.h
+++ b/mindspore/ccsrc/optimizer/opt.h
@@ -36,24 +36,34 @@ using OptimizerWeakPtr = std::weak_ptr<Optimizer>;
 using PredicateFuncType = std::function<bool(const AnfNodePtr &)>;
 using TransformFuncType = std::function<AnfNodePtr(const OptimizerPtr &, const AnfNodePtr &)>;
 
+// Define the interaction mode between an Optimize pass and Renormalize pass
+// FORCE_RENORM: if the pass modified the graph then the next Renormalize will be executed
+// CHECK_RENORM: check if the new node is un-typed to decide if the next Renormalize will be executted
+enum RenormAction : int { FORCE_RENORM = 0, CHECK_RENORM };
+
 class Substitution {
  public:
   TransformFuncType transform_{nullptr};
   std::string name_;
   PredicateFuncType predicate_{nullptr};
-  explicit Substitution(const TransformFuncType &transform, const std::string &name, const PredicateFuncType &predicate)
-      : transform_(transform), name_(name), predicate_(predicate) {}
+  // an enum to mark this Substitution relation to renormalize pass
+  RenormAction renorm_action_;
+  explicit Substitution(const TransformFuncType &transform, const std::string &name, const PredicateFuncType &predicate,
+                        const RenormAction &renorm_action)
+      : transform_(transform), name_(name), predicate_(predicate), renorm_action_(renorm_action) {}
   ~Substitution() = default;
   AnfNodePtr operator()(const OptimizerPtr &optimizer, const AnfNodePtr &node) const;
 };
 
 using SubstitutionPtr = std::shared_ptr<Substitution>;
 
-SubstitutionPtr MakeSubstitution(const TransformFuncType &transform, const std::string &name, const PrimitivePtr &prim);
+SubstitutionPtr MakeSubstitution(const TransformFuncType &transform, const std::string &name, const PrimitivePtr &prim,
+                                 const RenormAction &action_renorm = CHECK_RENORM);
 SubstitutionPtr MakeSubstitution(const TransformFuncType &transform, const std::string &name,
-                                 const std::vector<PrimitivePtr> &prims);
+                                 const std::vector<PrimitivePtr> &prims,
+                                 const RenormAction &action_renorm = CHECK_RENORM);
 SubstitutionPtr MakeSubstitution(const TransformFuncType &transform, const std::string &name,
-                                 const PredicateFuncType &predicate);
+                                 const PredicateFuncType &predicate, const RenormAction &action_renorm = CHECK_RENORM);
 
 class SubstitutionList {
  public:
diff --git a/mindspore/ccsrc/optimizer/optimizer.h b/mindspore/ccsrc/optimizer/optimizer.h
index d821e826cf..f67466efba 100644
--- a/mindspore/ccsrc/optimizer/optimizer.h
+++ b/mindspore/ccsrc/optimizer/optimizer.h
@@ -87,11 +87,12 @@ using OptPassGroupMap = std::vector<std::pair<std::string, OptPassConfig>>;
 class Optimizer : public std::enable_shared_from_this<Optimizer> {
  public:
   Optimizer(const std::string &name, const pipeline::ResourceBasePtr &resource_ptr)
-      : name_(name), resource_(resource_ptr), run_only_once_(false) {}
+      : name_(name), resource_(resource_ptr), run_only_once_(false), is_watch_renormalize_(false) {}
   virtual ~Optimizer() = default;
 
   void Init(const OptPassGroupMap &passes, bool run_only_once) {
     run_only_once_ = run_only_once;
+    is_watch_renormalize_ = false;
 
     for (auto &iter : passes) {
       const std::string &name = iter.first;
@@ -118,9 +119,13 @@ class Optimizer : public std::enable_shared_from_this<Optimizer> {
   }
 
   static std::shared_ptr<Optimizer> MakeOptimizer(const std::string &name, const pipeline::ResourceBasePtr resource_ptr,
-                                                  const OptPassGroupMap &passes, bool run_only_once = false) {
+                                                  const OptPassGroupMap &passes, bool run_only_once = false,
+                                                  bool watch_renormalize = false) {
     OptimizerPtr optimizer = std::make_shared<Optimizer>(name, resource_ptr);
     optimizer->Init(passes, run_only_once);
+    if (watch_renormalize) {
+      optimizer->enable_watch_renormalize();
+    }
     return optimizer;
   }
 
@@ -138,7 +143,16 @@ class Optimizer : public std::enable_shared_from_this<Optimizer> {
             if (opt.is_renormalize()) {
               auto resource_ptr = std::dynamic_pointer_cast<pipeline::Resource>(resource_);
               if (resource_ptr != nullptr) {
-                func_graph = pipeline::Renormalize(resource_ptr, func_graph, args_spec);
+                if (is_watch_renormalize_) {
+                  if (untyped_nodes_.size() > 0) {
+                    func_graph = pipeline::Renormalize(resource_ptr, func_graph, args_spec);
+                    clear_untyped_nodes();
+                  } else {
+                    MS_LOG(INFO) << "Optimizer::step: Skipping Renormalize because untyped_nodes_ is empty.";
+                  }
+                } else {
+                  func_graph = pipeline::Renormalize(resource_ptr, func_graph, args_spec);
+                }
               }
             } else if (opt(func_graph, shared_from_this())) {
               changes = true;
@@ -180,12 +194,26 @@ class Optimizer : public std::enable_shared_from_this<Optimizer> {
 
   const std::string name() const { return name_; }
 
+  void add_node_to_renormalize(AnfNodePtr anode) {
+    if (std::find(untyped_nodes_.begin(), untyped_nodes_.end(), anode) == untyped_nodes_.end()) {
+      untyped_nodes_.push_back(anode);
+    }
+  }
+
+  void clear_untyped_nodes() { untyped_nodes_.clear(); }
+
+  void enable_watch_renormalize() { is_watch_renormalize_ = true; }
+  void disable_watch_renormalize() { is_watch_renormalize_ = false; }
+  bool is_watch_renormalize() { return is_watch_renormalize_; }
+
  private:
   const std::string name_;
   pipeline::ResourceBasePtr resource_;
   std::vector<OptPass> passes_;
   std::vector<std::string> pass_names_;
   bool run_only_once_;
+  std::vector<AnfNodePtr> untyped_nodes_;
+  bool is_watch_renormalize_;
 };
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc
index 9248590f27..b3eda4c37b 100644
--- a/mindspore/ccsrc/pipeline/pass.cc
+++ b/mindspore/ccsrc/pipeline/pass.cc
@@ -185,8 +185,8 @@ void InitOpt(const ResourcePtr& res) {
   if (g_pass_opts.size() == 0) {
     opt::irpass::OptimizeIRPassLib irpass;
     g_pass_opts["opt_a"] = Optimizer::MakeOptimizer("opt_a", res, GetOptPassesA(irpass));
-    g_pass_opts["opt_b"] = Optimizer::MakeOptimizer("opt_b", res, GetOptPassesB(irpass));
-    g_pass_opts["opt_control"] = Optimizer::MakeOptimizer("opt_control", res, GetControlPhases(irpass));
+    g_pass_opts["opt_b"] = Optimizer::MakeOptimizer("opt_b", res, GetOptPassesB(irpass), false, true);
+    g_pass_opts["opt_control"] = Optimizer::MakeOptimizer("opt_control", res, GetControlPhases(irpass), false, true);
     g_pass_opts["opt_prepare"] = Optimizer::MakeOptimizer("opt_prepare", res, GetPreparePhases(irpass));
   }
 }

From 5d225f934f43662b7244ce9cfa033215a0b6db00 Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Wed, 8 Apr 2020 17:42:56 +0800
Subject: [PATCH 209/367] change the padding strategy & refactor insert
 transdata

---
 mindspore/ccsrc/common/trans.cc               | 132 ++++++++----
 mindspore/ccsrc/common/trans.h                |   6 +-
 .../device/ascend/ascend_device_address.cc    |   4 +-
 .../device/ascend/ascend_kernel_runtime.cc    |   5 +-
 .../device/ascend/kernel_select_ascend.cc     |  13 +-
 mindspore/ccsrc/device/kernel_adjust.cc       |   4 +-
 mindspore/ccsrc/device/kernel_info.h          |   4 +
 mindspore/ccsrc/device/kernel_runtime.cc      |  20 +-
 mindspore/ccsrc/kernel/kernel_build_info.cc   |  30 ++-
 mindspore/ccsrc/kernel/kernel_build_info.h    |   8 +-
 .../pre_activate/ascend/ascend_helper.cc      | 189 ++++++-----------
 .../ccsrc/pre_activate/ascend/ascend_helper.h |   2 +-
 .../format_type/deal_ref_trans_and_cast.cc    |   6 +-
 .../ascend/ir_fusion/transdata_split.cc       | 198 +++++++++---------
 .../ccsrc/session/anf_runtime_algorithm.cc    | 104 +++++----
 .../ccsrc/session/anf_runtime_algorithm.h     |   5 +
 mindspore/ccsrc/session/ascend_session.cc     |   5 +-
 mindspore/ccsrc/session/kernel_graph.cc       |  18 +-
 mindspore/ccsrc/session/session_basic.cc      |  13 +-
 mindspore/ccsrc/utils/utils.h                 |   4 +-
 ...er_norm_beta_gamma_backprop_fusion_test.cc |   2 +
 .../cpp/session/anf_runtime_algorithm_test.cc |  26 +--
 tests/ut/cpp/session/kernel_graph_test.cc     |   6 +-
 23 files changed, 425 insertions(+), 379 deletions(-)

diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index 380c51bcf9..a2b9f7ef24 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -20,6 +20,8 @@
 #include <utility>
 #include "./securec.h"
 #include "common/utils.h"
+#include "session/anf_runtime_algorithm.h"
+#include "kernel/kernel.h"
 #include "device/convert_tensor_utils.h"
 #include "utils/convert_utils.h"
 #include "utils/log_adapter.h"
@@ -27,6 +29,33 @@
 
 namespace mindspore {
 namespace trans {
+namespace {
+std::vector<size_t> PaddingShapeTo4dByDefault(const std::vector<size_t> &shape) {
+  std::vector<size_t> shape_4d(4, 1);
+  switch (shape.size()) {
+    case 0:
+      return shape_4d;
+    case 1:
+      shape_4d[1] = shape[0];
+      break;
+    case 2:
+      shape_4d[1] = shape[0];
+      shape_4d[2] = shape[1];
+      break;
+    case 3:
+      shape_4d[1] = shape[0];
+      shape_4d[2] = shape[1];
+      shape_4d[3] = shape[2];
+      break;
+    case 4:
+      std::copy(shape.begin(), shape.end(), shape_4d.begin());
+      break;
+    default:
+      MS_LOG(EXCEPTION) << "Unexpect shape size = " << shape.size();
+  }
+  return shape_4d;
+}
+}  // namespace
 const size_t kNchwDims = 4;
 const std::map<TypeId, size_t> type_map = {{kNumberTypeBool, 1},    {kNumberTypeInt, 4},     {kNumberTypeInt8, 1},
                                            {kNumberTypeInt16, 2},   {kNumberTypeInt32, 4},   {kNumberTypeInt64, 8},
@@ -154,38 +183,64 @@ size_t TypeIdSize(const TypeId data_type) {
   return unsupported_type_error;
 }
 
-std::vector<size_t> TransShapeTo4d(const std::vector<size_t> &shape) {
+bool IsNeedPadding(const std::string &format, const size_t shape_size) {
+  if (shape_size == 0) {
+    return false;
+  }
+  if (format == kOpFormat_DEFAULT || format == kOpFormat_FRAC_NZ) {
+    return false;
+  } else if (shape_size < 4) {
+    return true;
+  }
+  return false;
+}
+
+std::vector<int> GetRuntimePaddingShape(const AnfNodePtr &node, size_t index) {
+  std::vector<int> shape;
+  std::vector<size_t> host_shape;
+  if (node->isa<ValueNode>()) {
+    auto value_node = node->cast<ValueNodePtr>();
+    auto node_value = value_node->value();
+    auto tensor = node_value->cast<tensor::TensorPtr>();
+    if (tensor == nullptr) {
+      MS_LOG(EXCEPTION) << " the node[ " << node->DebugString() << "]'s cannot convert ";
+    }
+    shape = tensor->shape();
+    (void)std::transform(shape.begin(), shape.end(), std::back_inserter(host_shape), IntToSize);
+    if (host_shape.empty()) {
+      host_shape.push_back(1);
+    }
+  } else {
+    host_shape = AnfAlgo::GetOutputInferShape(node, index);
+  }
+  if (trans::IsNeedPadding(AnfAlgo::GetOutputFormat(node, 0), host_shape.size())) {
+    host_shape = trans::PaddingShapeTo4d(host_shape, AnfAlgo::GetOutputReshapeType(node, 0));
+  }
+  std::transform(host_shape.begin(), host_shape.end(), std::back_inserter(shape), SizeToInt);
+  return shape;
+}
+
+std::vector<size_t> PaddingShapeTo4d(const std::vector<size_t> &shape, const std::vector<kernel::Axis> &padding_axis) {
+  if (padding_axis.empty() || shape.size() != padding_axis.size()) {
+    return PaddingShapeTo4dByDefault(shape);
+  }
   std::vector<size_t> shape_4d(4, 1);
-  switch (shape.size()) {
-    case 0:
-      break;
-    case 1:
-      shape_4d[1] = shape[0];
-      break;
-    case 2:
-      shape_4d[0] = shape[0];
-      shape_4d[1] = shape[1];
-      break;
-    case 3:
-      MS_LOG(EXCEPTION) << "Unexpected shape size = 3,it should has a default format";
-    case 4:
-      for (size_t i = 0; i < 4; ++i) {
-        shape_4d[i] = shape[i];
-      }
-      break;
-    default:
-      MS_LOG(EXCEPTION) << "Unexpected shape size = " << shape.size();
+  for (size_t index = 0; index < padding_axis.size(); index++) {
+    shape_4d[padding_axis[index]] = shape[index];
   }
   return shape_4d;
 }
 
 std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const std::string &format) {
+  if (format == kOpFormat_ND || format == kOpFormat_DEFAULT) {
+    return shape;
+  }
+  auto temp_shape = shape;
   std::vector<size_t> device_shape;
   if (format == kOpFormat_FRAC_NZ) {
     if (shape.size() < 2) {
-      MS_EXCEPTION(NotSupportError) << "Format " << format << " is not support shape " << shape.size();
-    }
-    if (shape.size() > 2) {
+      MS_LOG(EXCEPTION) << "Format" << format << " is not support shape " << shape.size();
+    } else {
       (void)std::copy(shape.begin(), shape.end() - 2, std::back_inserter(device_shape));
     }
     auto h1 = (shape[shape.size() - 2] - 1) / kCubeSize + 1;
@@ -197,35 +252,36 @@ std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const s
     return device_shape;
   }
   if (shape.size() != 4) {
-    MS_LOG(EXCEPTION) << "shape_4d size should be 4";
+    MS_LOG(WARNING) << "Get Device Shape using a shape size is less than 4 ,should be Padding shape by Default firstly";
+    temp_shape = PaddingShapeTo4dByDefault(shape);
   }
   if (format == kOpFormat_NC1HWC0) {
-    size_t C1 = (shape[1] + kCubeSize - 1) / kCubeSize;
+    size_t C1 = (temp_shape[1] + kCubeSize - 1) / kCubeSize;
     size_t C0 = kCubeSize;
-    device_shape.push_back(shape[0]);
+    device_shape.push_back(temp_shape[0]);
     device_shape.push_back(C1);
-    device_shape.push_back(shape[2]);
-    device_shape.push_back(shape[3]);
+    device_shape.push_back(temp_shape[2]);
+    device_shape.push_back(temp_shape[3]);
     device_shape.push_back(C0);
     return device_shape;
   } else if (format == kOpFormat_FRAC_Z) {
-    size_t cout16 = ((shape[0] + kCubeSize - 1) / kCubeSize) * kCubeSize;
-    size_t cin16 = ((shape[1] + kCubeSize - 1) / kCubeSize) * kCubeSize;
-    device_shape.push_back(shape[2] * shape[3] * cin16 / kCubeSize);
+    size_t cout16 = ((temp_shape[0] + kCubeSize - 1) / kCubeSize) * kCubeSize;
+    size_t cin16 = ((temp_shape[1] + kCubeSize - 1) / kCubeSize) * kCubeSize;
+    device_shape.push_back(temp_shape[2] * temp_shape[3] * cin16 / kCubeSize);
     device_shape.push_back(cout16 / kCubeSize);
     device_shape.push_back(kCubeSize);
     device_shape.push_back(kCubeSize);
     return device_shape;
   } else if (format == kOpFormat_NHWC) {
-    device_shape.push_back(shape[0]);
-    device_shape.push_back(shape[2]);
-    device_shape.push_back(shape[3]);
-    device_shape.push_back(shape[1]);
+    device_shape.push_back(temp_shape[0]);
+    device_shape.push_back(temp_shape[2]);
+    device_shape.push_back(temp_shape[3]);
+    device_shape.push_back(temp_shape[1]);
     return device_shape;
-  } else if (format == kOpFormat_NCHW) {
-    return shape;
   } else if (format == kOpFormat_HWCN) {
-    return {shape[2], shape[3], shape[1], shape[0]};
+    return {temp_shape[2], temp_shape[3], temp_shape[1], temp_shape[0]};
+  } else if (format == kOpFormat_NCHW) {
+    return temp_shape;
   }
   MS_LOG(EXCEPTION) << "Unexpected format[" << format << "]";
 }
diff --git a/mindspore/ccsrc/common/trans.h b/mindspore/ccsrc/common/trans.h
index cf815985ff..4bebdde814 100644
--- a/mindspore/ccsrc/common/trans.h
+++ b/mindspore/ccsrc/common/trans.h
@@ -24,6 +24,7 @@
 #include <utility>
 #include <vector>
 #include "ir/dtype.h"
+#include "kernel/kernel.h"
 #include "ir/dtype/type.h"
 
 namespace mindspore {
@@ -49,7 +50,10 @@ size_t TypeIdSize(const TypeId data_type);
 size_t ShapeSize(const std::vector<size_t> &shape);
 size_t CubeSizeByType(const TypeId data_type);
 
-std::vector<size_t> TransShapeTo4d(const std::vector<size_t> &shape);
+std::vector<size_t> PaddingShapeTo4d(const std::vector<size_t> &shape,
+                                     const std::vector<kernel::Axis> &padding_axis = {});
+std::vector<int> GetRuntimePaddingShape(const AnfNodePtr &node, size_t index);
+bool IsNeedPadding(const std::string &format, const size_t shape_size);
 std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const std::string &format);
 bool TransDataType(const TypeIdArgs &args, void *result);
 bool TransFormat(const FormatArgs &args, void *result);
diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
index 93f039af0e..69d1918163 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
@@ -141,7 +141,7 @@ bool AscendDeviceAddress::SyncDeviceToHostAndConvertFormat(const std::vector<int
   if (format_ == kOpFormat_FRAC_NZ) {
     device_shape = trans::TransShapeToDevice(host_shape, format_);
   } else {
-    host_shape = trans::TransShapeTo4d(host_shape);
+    host_shape = trans::PaddingShapeTo4d(host_shape);
     device_shape = trans::TransShapeToDevice(host_shape, format_);
   }
   if (type_id_ != type) {
@@ -224,7 +224,7 @@ bool AscendDeviceAddress::ConvertFormatAndSyncHostToDevice(const std::vector<int
   if (format_ == kOpFormat_FRAC_NZ) {
     device_shape = trans::TransShapeToDevice(host_shape, format_);
   } else {
-    host_shape = trans::TransShapeTo4d(host_shape);
+    host_shape = trans::PaddingShapeTo4d(host_shape);
     device_shape = trans::TransShapeToDevice(host_shape, format_);
   }
   if (type_id_ != type) {
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index baed733a5b..311abb634e 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -27,6 +27,7 @@
 #include "utils/context/ms_context.h"
 #include "device/ascend/profiling/profiling_manager.h"
 #include "hccl/hcom.h"
+#include "common/trans.h"
 #include "runtime/context.h"
 #include "device/ascend/ascend_stream_assign.h"
 #include "device/ascend/ascend_memory_pool.h"
@@ -150,7 +151,7 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path,
     auto output_size = AnfAlgo::GetOutputTensorNum(node);
     for (size_t j = 0; j < output_size; ++j) {
       auto addr = AnfAlgo::GetOutputAddr(node, j);
-      auto shape = AnfAlgo::GetOutputInferShape(node, j);
+      auto shape = trans::GetRuntimePaddingShape(node, j);
       auto type = AnfAlgo::GetOutputInferDataType(node, j);
       auto format = kOpFormat_DEFAULT;
       string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
@@ -181,7 +182,7 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p
       continue;
     }
     auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX);
-    auto shape = AnfAlgo::GetOutputInferShape(item, PRAMATER_OUTPUT_INDEX);
+    auto shape = trans::GetRuntimePaddingShape(item, PRAMATER_OUTPUT_INDEX);
     auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX);
     auto format = kOpFormat_DEFAULT;
     string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index 0a23e2da7b..06af3d2ca7 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -184,7 +184,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
     }
     if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
       if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) &&
-          kSpecialFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kSpecialFormatSet.end()) {
+          kNeedTransFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kNeedTransFormatSet.end()) {
         (*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT]++;
       }
       (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++;
@@ -210,19 +210,22 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
       (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++;
     }
   }
-}  // namespace
+}
 
 void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
     auto input_kernel_node = AnfAlgo::GetInputNode(kernel_node, input_index);
     MS_EXCEPTION_IF_NULL(input_kernel_node);
-    if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index)) {
-      continue;
-    }
     auto input_with_index = AnfAlgo::VisitKernel(input_kernel_node, 0);
     MS_EXCEPTION_IF_NULL(input_with_index.first);
     auto real_input_node = input_with_index.first;
+    if (real_input_node->isa<CNode>()) {
+      continue;
+    }
+    if (real_input_node->isa<Parameter>() && !AnfAlgo::IsParameterWeight(real_input_node->cast<ParameterPtr>())) {
+      continue;
+    }
     std::shared_ptr<kernel::KernelBuildInfo::KernelBuildInfoBuilder> builder =
       std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
     // we set special device info of a input tensor.
diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc
index 9a6f48025f..c1588d7d53 100644
--- a/mindspore/ccsrc/device/kernel_adjust.cc
+++ b/mindspore/ccsrc/device/kernel_adjust.cc
@@ -25,6 +25,7 @@
 
 #include "session/anf_runtime_algorithm.h"
 #include "utils/context/ms_context.h"
+#include "common/trans.h"
 #include "utils/config_manager.h"
 #include "common/utils.h"
 #include "kernel/kernel_build_info.h"
@@ -391,7 +392,8 @@ bool KernelAdjust::StepLoadCtrlInputs(const std::shared_ptr<session::Context> &c
       auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0);
       MS_EXCEPTION_IF_NULL(device_address);
       tensor->set_device_address(device_address);
-      if (!device_address->SyncHostToDevice(tensor->shape(), LongToSize(tensor->data().nbytes()), tensor->data_type(),
+      if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0),
+                                            LongToSize(tensor->data().nbytes()), tensor->data_type(),
                                             tensor->data_c(false))) {
         MS_LOG(INFO) << "SyncHostToDevice failed.";
         return false;
diff --git a/mindspore/ccsrc/device/kernel_info.h b/mindspore/ccsrc/device/kernel_info.h
index 9352158774..33ddda83c9 100644
--- a/mindspore/ccsrc/device/kernel_info.h
+++ b/mindspore/ccsrc/device/kernel_info.h
@@ -31,6 +31,7 @@ class KernelInfo {
  public:
   KernelInfo() {
     kernel_mod_ = nullptr;
+    is_feature_map_ = false;
     select_kernel_build_info_ = nullptr;
     output_address_list_ = {};
     workspace_address_list_ = {};
@@ -45,6 +46,7 @@ class KernelInfo {
   void set_select_kernel_build_info(const kernel::KernelBuildInfoPtr &select_kernel_build_info) {
     select_kernel_build_info_ = select_kernel_build_info;
   }
+  void SetFeatureMapFlag(bool flag) { is_feature_map_ = flag; }
   const DeviceAddress *GetOutputAddr(size_t index) const;
   DeviceAddressPtr GetMutableOutputAddr(size_t index) const;
   bool OutputAddrExist(size_t index) const;
@@ -63,8 +65,10 @@ class KernelInfo {
   void set_graph_id(uint32_t graph_id) { graph_id_ = graph_id; }
   uint32_t graph_id() const { return graph_id_; }
   bool operator==(const KernelInfo &other) const;
+  bool is_feature_map() const { return is_feature_map_; }
 
  private:
+  bool is_feature_map_;
   kernel::KernelBuildInfoPtr select_kernel_build_info_;
   std::vector<std::shared_ptr<DeviceAddress>> output_address_list_;
   std::vector<std::shared_ptr<DeviceAddress>> workspace_address_list_;
diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index eebc650347..303f2cc873 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -105,7 +105,7 @@ size_t KernelRuntime::CountNodeDeviceMemorySize(const mindspore::AnfNodePtr &nod
   std::vector<size_t> shape = AnfAlgo::GetOutputDeviceShape(node, output_index);
   auto format = AnfAlgo::GetOutputFormat(node, output_index);
   if (shape.empty() && format != kOpFormat_DEFAULT) {
-    shape = trans::TransShapeTo4d(shape);
+    shape = trans::PaddingShapeTo4d(shape, AnfAlgo::GetOutputReshapeType(node, output_index));
     shape = trans::TransShapeToDevice(shape, format);
   }
   // scalar's output shape is a empty vector
@@ -401,8 +401,9 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
   auto address = CreateDeviceAddress(ptr, node_size, AnfAlgo::GetOutputFormat(value_node, output_idx), output_type_id);
   MS_EXCEPTION_IF_NULL(address);
   AnfAlgo::SetOutputAddr(address, output_idx, value_node.get());
-  if (!address->SyncHostToDevice(tensor->shape(), tensor_size, tensor->data_type(), tensor->data_c(false))) {
-    MS_EXCEPTION(NotExistsError) << "kValueNode SyncHostToDevice fail!" << value_node->DebugString() << "node format is"
+  if (!address->SyncHostToDevice(trans::GetRuntimePaddingShape(value_node, 0), tensor_size, tensor->data_type(),
+                                 tensor->data_c(false))) {
+    MS_EXCEPTION(NotExistsError) << "ValueNode SyncHostToDevice fail!" << value_node->DebugString() << "node format is"
                                  << AnfAlgo::GetOutputFormat(value_node, output_idx) << "node dtype is "
                                  << AnfAlgo::GetOutputInferDataType(value_node, output_idx);
   }
@@ -421,19 +422,6 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
     MS_EXCEPTION_IF_NULL(node_value);
     if (node_value->isa<Tensor>()) {
       AssignValueNodeTensor(value_node, node_value, 0);
-    } else if (node_value->isa<ValueTuple>()) {
-      auto value_tuple = node_value->cast<ValueTuplePtr>();
-      if (value_tuple == nullptr) {
-        MS_LOG(WARNING) << "value_tuple is null";
-        continue;
-      }
-      size_t i = 0;
-      auto value_list = value_tuple->value();
-      for (auto value_ptr : value_list) {
-        if (value_ptr->isa<Tensor>()) {
-          AssignValueNodeTensor(value_node, value_ptr, i++);
-        }
-      }
     } else if (node_value->isa<StringImm>()) {
       auto value = GetValue<std::string>(node_value);
       size_t tensor_size = value.size();
diff --git a/mindspore/ccsrc/kernel/kernel_build_info.cc b/mindspore/ccsrc/kernel/kernel_build_info.cc
index c52f71c136..038c06d8ed 100644
--- a/mindspore/ccsrc/kernel/kernel_build_info.cc
+++ b/mindspore/ccsrc/kernel/kernel_build_info.cc
@@ -59,30 +59,20 @@ size_t KernelBuildInfo::GetInputNum() const { return inputs_format_.size(); }
 
 size_t KernelBuildInfo::GetOutputNum() const { return outputs_format_.size(); }
 
-bool KernelBuildInfo::GetInputReshapeType(size_t input_index, std::vector<Axis> *reshape_type) const {
-  MS_EXCEPTION_IF_NULL(reshape_type);
-  reshape_type->clear();
+std::vector<Axis> KernelBuildInfo::GetInputReshapeType(size_t input_index) const {
   if (input_index >= input_reshape_type_.size()) {
-    MS_LOG(WARNING) << "The index [" << input_index << "] is exceed the number of input node size "
-                    << input_reshape_type_.size();
-    return false;
+    MS_LOG(EXCEPTION) << "The index [" << input_index << "] is exceed the number of input node size "
+                      << input_reshape_type_.size();
   }
-  (void)std::copy(input_reshape_type_[input_index].begin(), input_reshape_type_[input_index].end(),
-                  std::inserter(*reshape_type, (*reshape_type).begin()));
-  return true;
+  return input_reshape_type_[input_index];
 }
 
-bool KernelBuildInfo::GetOutputReshapeType(size_t output_index, std::vector<Axis> *reshape_type) const {
-  MS_EXCEPTION_IF_NULL(reshape_type);
-  reshape_type->clear();
+std::vector<Axis> KernelBuildInfo::GetOutputReshapeType(size_t output_index) const {
   if (output_index >= output_reshape_type_.size()) {
-    MS_LOG(WARNING) << "The index [" << output_index << "] is exceed the number of output node dixr"
-                    << output_reshape_type_.size();
-    return false;
+    MS_LOG(EXCEPTION) << "The index [" << output_index << "] is exceed the number of output node size "
+                      << output_reshape_type_.size();
   }
-  (void)std::copy(output_reshape_type_[output_index].begin(), output_reshape_type_[output_index].end(),
-                  std::inserter(*reshape_type, (*reshape_type).begin()));
-  return true;
+  return output_reshape_type_[output_index];
 }
 
 std::string KernelBuildInfo::ToString() const {
@@ -115,6 +105,10 @@ bool KernelBuildInfo::operator==(const KernelBuildInfo &other) const {
   return !(inputs_device_type_ != other.inputs_device_type_ || outputs_device_type_ != other.outputs_device_type_);
 }
 
+bool KernelBuildInfo::IsInputDefaultPadding() const { return output_reshape_type_.empty(); }
+
+bool KernelBuildInfo::IsOutputDefaultPadding() const { return input_reshape_type_.empty(); }
+
 void KernelBuildInfo::KernelBuildInfoBuilder::SetKernelType(const KernelType &kernel_type) {
   MS_EXCEPTION_IF_NULL(kernel_build_info_);
   kernel_build_info_->kernel_type_ = kernel_type;
diff --git a/mindspore/ccsrc/kernel/kernel_build_info.h b/mindspore/ccsrc/kernel/kernel_build_info.h
index 24552e0341..76ebc7a572 100644
--- a/mindspore/ccsrc/kernel/kernel_build_info.h
+++ b/mindspore/ccsrc/kernel/kernel_build_info.h
@@ -54,9 +54,13 @@ class KernelBuildInfo {
 
   TypeId GetOutputDeviceType(size_t output_index) const;
 
-  bool GetInputReshapeType(size_t input_index, std::vector<Axis> *reshape_type) const;
+  std::vector<Axis> GetInputReshapeType(size_t input_index) const;
 
-  bool GetOutputReshapeType(size_t input_index, std::vector<Axis> *reshape_type) const;
+  bool IsInputDefaultPadding() const;
+
+  bool IsOutputDefaultPadding() const;
+
+  std::vector<Axis> GetOutputReshapeType(size_t input_index) const;
 
   std::vector<std::string> GetAllInputFormats() const;
 
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
index 58c030e79d..490a905a45 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
@@ -18,20 +18,21 @@
 #include <set>
 #include "common/trans.h"
 #include "common/utils.h"
+#include "utils/utils.h"
 #include "device/kernel_info.h"
 #include "kernel/oplib/oplib.h"
 #include "operator/ops.h"
 #include "session/anf_runtime_algorithm.h"
 #include "session/kernel_graph.h"
 #include "utils/context/ms_context.h"
-#include "utils/utils.h"
 
 namespace mindspore {
 namespace opt {
 using KernelBuildInfoBuilder = kernel::KernelBuildInfo::KernelBuildInfoBuilder;
 namespace {
-kernel::KernelBuildInfoPtr CreateKernelBuildInfo(const std::string &input_format, const std::string &output_format,
-                                                 const AnfNodePtr &node, const kernel::KernelBuildInfo ori_build_info) {
+kernel::KernelBuildInfoPtr RefreshKernelBuildInfo(const std::string &input_format, const std::string &output_format,
+                                                  const AnfNodePtr &node,
+                                                  const kernel::KernelBuildInfo ori_build_info) {
   KernelBuildInfoBuilder builder;
   builder.SetInputsFormat({input_format});
   builder.SetOutputsFormat({output_format});
@@ -54,9 +55,11 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input,
   CNodePtr trans_node = func_graph->NewCNode(trans_inputs);
   MS_EXCEPTION_IF_NULL(trans_node);
   if (need_padding) {
-    AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input, 0)},
-                                        {trans::TransShapeTo4d(AnfAlgo::GetOutputInferShape(input, 0))},
-                                        trans_node.get());
+    // if need padding we should set the transdata node's shape to the padding shape
+    AnfAlgo::SetOutputInferTypeAndShape(
+      {AnfAlgo::GetOutputInferDataType(input, 0)},
+      {trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input, 0), AnfAlgo::GetOutputReshapeType(input, 0))},
+      trans_node.get());
   } else {
     AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input, 0)},
                                         {AnfAlgo::GetOutputInferShape(input, 0)}, trans_node.get());
@@ -92,9 +95,11 @@ AnfNodePtr CreateReshapeNode(const FuncGraphPtr &func_graph, const AnfNodePtr &i
 AnfNodePtr GetTransInputNodePtr(const FuncGraphPtr &func_graph, const CNodePtr &node, size_t index,
                                 const KernelSelectPtr &kernel_select) {
   MS_EXCEPTION_IF_NULL(node);
-  bool padding_flag = false;
   auto input_node = AnfAlgo::GetInputNode(node, index);
-  if (input_node->isa<ValueNode>() || input_node->isa<Parameter>()) {
+  auto node_with_index = AnfAlgo::VisitKernel(input_node, 0);
+  MS_EXCEPTION_IF_NULL(node_with_index.first);
+  auto real_input = node_with_index.first;
+  if (real_input->isa<ValueNode>() || real_input->isa<Parameter>()) {
     input_node = InsertTransOpForOutput(func_graph, input_node, kernel_select);
     MS_EXCEPTION_IF_NULL(input_node);
     AnfAlgo::SetNodeInput(node, input_node, index);
@@ -106,33 +111,11 @@ AnfNodePtr GetTransInputNodePtr(const FuncGraphPtr &func_graph, const CNodePtr &
   std::vector<size_t> origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, index);
   std::string origin_format = kOpFormat_DEFAULT;
   std::string dest_format = AnfAlgo::GetInputFormat(node, index);
-  if (dest_format == kOpFormat_C1HWNCoC0) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, node, kernel_select, index, padding_flag,
-                                                     origin_format, dest_format, kTransDataOpName, true);
-    MS_EXCEPTION_IF_NULL(replace_input);
-    return replace_input;
-  }
-  if (dest_format == kOpFormat_NC1HWC0 && origin_shape.size() > 1) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, node, kernel_select, index, padding_flag,
-                                                     origin_format, dest_format, kTransDataOpName, true);
-    MS_EXCEPTION_IF_NULL(replace_input);
-    MS_LOG(DEBUG) << "Inserted Translate45, index: " << index;
-    return replace_input;
-  } else if (dest_format == kOpFormat_FRAC_NZ) {
-    AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, node, kernel_select, index, padding_flag,
-                                                     origin_format, dest_format, kTransDataOpName, true);
-    MS_EXCEPTION_IF_NULL(replace_input);
-    MS_LOG(DEBUG) << "inserted translate " << AnfAlgo::GetInputFormat(node, index) << " To default, index: " << index;
-    return replace_input;
-  } else if (dest_format == kOpFormat_FRAC_Z && !origin_shape.empty()) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, node, kernel_select, index, padding_flag,
-                                                     origin_format, dest_format, kTransDataOpName, true);
-    MS_EXCEPTION_IF_NULL(replace_input);
-    MS_LOG(DEBUG) << "Inserted Translate45, index: " << index;
-    return replace_input;
+  if (kNeedTransFormatSet.find(dest_format) != kNeedTransFormatSet.end() && origin_shape.size() > 1) {
+    MS_LOG(DEBUG) << node->DebugString() << "Insert transdata " << AnfAlgo::GetInputFormat(node, index)
+                  << " To DefaultFormat , index: " << index;
+    return AddTransOpNodeToGraph(func_graph, node, kernel_select, index, origin_format, dest_format, kTransDataOpName,
+                                 true);
   }
   return input_node;
 }
@@ -140,7 +123,6 @@ AnfNodePtr GetTransInputNodePtr(const FuncGraphPtr &func_graph, const CNodePtr &
 AnfNodePtr InsertTransOpForSingleOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                         const KernelSelectPtr &kernel_select) {
   MS_EXCEPTION_IF_NULL(node);
-  bool padding_flag = false;
   std::string output_format;
   std::vector<size_t> origin_shape;
   if (!AnfAlgo::IsRealKernel(node)) {
@@ -156,46 +138,14 @@ AnfNodePtr InsertTransOpForSingleOutput(const FuncGraphPtr &func_graph, const An
   }
   std::string origin_format = output_format;
   std::string dest_format = kOpFormat_DEFAULT;
-  if (output_format == kOpFormat_C1HWNCoC0) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, node, kernel_select, 0, padding_flag, origin_format,
-                                                     dest_format, kTransDataOpName, false);
-    MS_EXCEPTION_IF_NULL(replace_input);
-    return replace_input;
-  }
-  if (output_format == kOpFormat_NC1HWC0 && origin_shape.size() > 1) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, node, kernel_select, 0, padding_flag, origin_format,
-                                                      dest_format, kTransDataOpName, false);
-    MS_EXCEPTION_IF_NULL(replace_output);
-    MS_LOG(DEBUG) << "Inserted Trans54";
-    return replace_output;
-  } else if (output_format == kOpFormat_FRAC_NZ) {
-    AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, node, kernel_select, 0, padding_flag, origin_format,
-                                                      dest_format, kTransDataOpName, false);
-    MS_EXCEPTION_IF_NULL(replace_output);
-    MS_LOG(DEBUG) << "Inserted Translate " << output_format << " To default, index: 0";
-    return replace_output;
-  } else if (output_format == kOpFormat_FRAC_Z && !origin_shape.empty()) {
-    padding_flag = (origin_shape.size() != kShape4dDims);
-    AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, node, kernel_select, 0, padding_flag, origin_format,
-                                                      dest_format, kTransDataOpName, false);
-    MS_EXCEPTION_IF_NULL(replace_output);
-    MS_LOG(DEBUG) << "Inserted Trans54";
-    return replace_output;
+  if (kNeedTransFormatSet.find(output_format) != kNeedTransFormatSet.end() && origin_shape.size() > 1) {
+    MS_LOG(DEBUG) << "Inserted Transdata " << output_format << " To default , index :0";
+    return AddTransOpNodeToGraph(func_graph, node, kernel_select, 0, origin_format, dest_format, kTransDataOpName,
+                                 false);
   }
   return node;
 }
 
-void GetTransDataInputFormat(const AnfNodePtr &node, size_t idx, std::string *input_format) {
-  MS_EXCEPTION_IF_NULL(input_format);
-  if (AnfAlgo::IsRealKernel(node)) {
-    *input_format = AnfAlgo::GetOutputFormat(node, idx);
-  } else {
-    *input_format = AnfAlgo::GetPrevNodeOutputFormat(node, 0);
-  }
-}
-
 AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                           const KernelSelectPtr &kernel_select) {
   MS_EXCEPTION_IF_NULL(func_graph);
@@ -203,46 +153,17 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
   std::vector<AnfNodePtr> make_tuple_inputs;
   make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
   for (size_t output_idx = 0; output_idx < AnfAlgo::GetOutputTensorNum(node); ++output_idx) {
-    bool padding_flag = false;
-
-    std::string output_format;
-    GetTransDataInputFormat(node, output_idx, &output_format);
+    std::string output_format = AnfAlgo::GetOutputFormat(node, output_idx);
     if (output_format == kOpFormat_NC1KHKWHWC0) {
-      MS_LOG(EXCEPTION) << "got the hw format" << output_format << " when insert the transdata node "
+      MS_LOG(EXCEPTION) << "Got the special format" << output_format << " when insert the transdata node "
                         << node->DebugString();
     }
     auto tuple_getitem = CreatTupleGetItemNode(func_graph, node, output_idx);
     std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
-    std::string origin_format = output_format;
     std::string dest_format = kOpFormat_DEFAULT;
-    if (output_format == kOpFormat_C1HWNCoC0) {
-      padding_flag = (origin_shape.size() != kShape4dDims);
-      AnfNodePtr replace_input = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, padding_flag,
-                                                       origin_format, dest_format, kTransDataOpName, false);
-      MS_EXCEPTION_IF_NULL(replace_input);
-      return replace_input;
-    }
-    if (output_format == kOpFormat_NC1HWC0 && origin_shape.size() > 1) {
-      padding_flag = (origin_shape.size() != kShape4dDims);
-      // Insert a 5to4 trans op.
-      AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, padding_flag,
-                                                        origin_format, dest_format, kTransDataOpName, false);
-      MS_EXCEPTION_IF_NULL(replace_output);
-      MS_LOG(DEBUG) << "Inserted Translate54";
-      make_tuple_inputs.push_back(replace_output);
-    } else if (output_format == kOpFormat_FRAC_NZ) {
-      AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, padding_flag,
-                                                        origin_format, dest_format, kTransDataOpName, false);
-      MS_EXCEPTION_IF_NULL(replace_output);
-      MS_LOG(DEBUG) << "Inserted Translate " << output_format << " To default, index: " << output_idx;
-      make_tuple_inputs.push_back(replace_output);
-    } else if (output_format == kOpFormat_FRAC_Z && !origin_shape.empty()) {
-      padding_flag = (origin_shape.size() != kShape4dDims);
-      AnfNodePtr replace_output = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, padding_flag,
-                                                        origin_format, dest_format, kTransDataOpName, false);
-      MS_EXCEPTION_IF_NULL(replace_output);
-      MS_LOG(DEBUG) << "Inserted Translate54";
-      make_tuple_inputs.push_back(replace_output);
+    if (kNeedTransFormatSet.find(output_format) != kNeedTransFormatSet.end() && origin_shape.size() > 1) {
+      make_tuple_inputs.emplace_back(AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, output_format,
+                                                           dest_format, kTransDataOpName, false));
     } else {
       // No need insert trans op.
       make_tuple_inputs.push_back(tuple_getitem);
@@ -253,16 +174,17 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
 }
 }  // namespace
 AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
-                                 const KernelSelectPtr &kernel_select, size_t insert_index, const bool padding_flag,
+                                 const KernelSelectPtr &kernel_select, size_t insert_index,
                                  const std::string &origin_format, const std::string &dest_format,
                                  const std::string &op_name, bool is_insert_input) {
   AnfNodePtr trans_node = nullptr;
-  AnfNodePtr input_node = nullptr;
+  AnfNodePtr input_node = node;
   AnfNodePtr trans_data = nullptr;
   MS_EXCEPTION_IF_NULL(node);
   if (origin_format.empty() || dest_format.empty()) {
     MS_LOG(EXCEPTION) << "trans op format is error, origin = " << origin_format << ", dest " << origin_format;
   }
+  // if insert transdata for input we need to change the input
   if (is_insert_input) {
     if (!node->isa<CNode>()) {
       MS_LOG(EXCEPTION) << "cannot insert a transdata node to a node's input which the node is not a cnode";
@@ -270,29 +192,34 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt
     auto cnode = node->cast<CNodePtr>();
     MS_EXCEPTION_IF_NULL(cnode);
     input_node = AnfAlgo::GetInputNode(cnode, insert_index);
-    if (padding_flag) {
-      auto padd_shape = trans::TransShapeTo4d(AnfAlgo::GetOutputInferShape(input_node, 0));
-      auto reshape_node = CreateReshapeNode(func_graph, input_node, kernel_select, padd_shape);
-      trans_data = NewTransOpNode(func_graph, reshape_node, kernel_select, padding_flag, op_name);
-    } else {
-      trans_data = NewTransOpNode(func_graph, input_node, kernel_select, padding_flag, op_name);
-    }
+  }
+  bool need_padding = (trans::IsNeedPadding(dest_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()) &&
+                       op_name == kTransDataOpName);
+  if (!need_padding) {
+    // don't need padding insert transdata only
+    trans_data = NewTransOpNode(func_graph, input_node, kernel_select, need_padding, op_name);
+    trans_node = trans_data;
+  } else if (is_insert_input) {
+    // if need padding & is input need insert a transdata
+    // reshape[padding shape] -> transdata[padding shape] -> node
+    auto padding_shape =
+      trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input_node, 0), AnfAlgo::GetInputReshapeType(node, 0));
+    auto reshape_node = CreateReshapeNode(func_graph, input_node, kernel_select, padding_shape);
+    trans_data = NewTransOpNode(func_graph, reshape_node, kernel_select, need_padding, op_name);
     trans_node = trans_data;
   } else {
-    input_node = node;
-    trans_data = NewTransOpNode(func_graph, input_node, kernel_select, padding_flag, op_name);
-    if (padding_flag) {
-      auto reshape_node =
-        CreateReshapeNode(func_graph, trans_data, kernel_select, AnfAlgo::GetOutputInferShape(input_node, 0));
-      trans_node = reshape_node;
-    } else {
-      trans_node = trans_data;
-    }
+    // if need padding & is output need insert a transdata
+    // node -> transdata[padding shape] -> reshape[ori_shape]
+    trans_data = NewTransOpNode(func_graph, input_node, kernel_select, need_padding, op_name);
+    auto reshape_node =
+      CreateReshapeNode(func_graph, trans_data, kernel_select, AnfAlgo::GetOutputInferShape(input_node, 0));
+    trans_node = reshape_node;
   }
+  // refresh the transdata's format to ori format & dst format
   MS_EXCEPTION_IF_NULL(trans_data);
   MS_EXCEPTION_IF_NULL(trans_data->kernel_info());
   auto trans_ori_build_info = trans_data->kernel_info()->select_kernel_build_info();
-  auto kernel_build_info = CreateKernelBuildInfo(origin_format, dest_format, input_node, *trans_ori_build_info);
+  auto kernel_build_info = RefreshKernelBuildInfo(origin_format, dest_format, input_node, *trans_ori_build_info);
   AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info, trans_data.get());
   return trans_node;
 }
@@ -376,7 +303,17 @@ CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnod
   for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(cnode); ++input_index) {
     TypeId origin_type;
     auto cur_input = AnfAlgo::GetInputNode(cnode, input_index);
-    if (!AnfAlgo::IsFeatureMapInput(cnode, input_index)) {
+    auto kernel_with_index = AnfAlgo::VisitKernel(cur_input, 0);
+    auto is_weight_boundary = [](const AnfNodePtr &node) -> bool {
+      if (node->isa<ValueNode>()) {
+        return true;
+      } else if (node->isa<Parameter>() && AnfAlgo::IsParameterWeight(node->cast<ParameterPtr>())) {
+        return true;
+      }
+      return false;
+    };
+    auto real_input_node = kernel_with_index.first;
+    if (is_weight_boundary(real_input_node)) {
       // weight
       origin_type = AnfAlgo::GetPrevNodeOutputDeviceDataType(cnode, input_index);
     } else {
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
index b605d700c3..8925a52a7d 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
@@ -48,7 +48,7 @@ class KernelQuery {
 using KernelQueryPtr = std::shared_ptr<KernelQuery>;
 
 AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
-                                 const KernelSelectPtr &kernel_select, size_t insert_index, bool padding_flag,
+                                 const KernelSelectPtr &kernel_select, size_t insert_index,
                                  const std::string &origin_format, const std::string &dest_format,
                                  const std::string &op_name, bool is_insert_input);
 
diff --git a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
index 81e5c4b486..2d44bf8f8f 100644
--- a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
@@ -105,10 +105,8 @@ AnfNodePtr AddAdditionalToRefOutput(const FuncGraphPtr &func_graph, const CNodeP
   // insert trans
   if (origin_format != cur_format) {
     auto kernel_select = std::make_shared<KernelSelect>();
-    bool need_padding =
-      (cur_format == kOpFormat_NC1HWC0 && AnfAlgo::GetOutputInferShape(final_node, 0).size() != kShape4dDims);
-    final_node = AddTransOpNodeToGraph(func_graph, final_node, kernel_select, 0, need_padding, cur_format,
-                                       origin_format, kTransDataOpName, false);
+    final_node = AddTransOpNodeToGraph(func_graph, final_node, kernel_select, 0, cur_format, origin_format,
+                                       kTransDataOpName, false);
     final_index = 0;
     MS_EXCEPTION_IF_NULL(final_node);
     MS_LOG(INFO) << "DealRefTransAndCast add trans op, op debug info is " << final_node->DebugString();
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/transdata_split.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/transdata_split.cc
index faef277599..d3990fe898 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/transdata_split.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/transdata_split.cc
@@ -1,99 +1,99 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "pre_activate/ascend/ir_fusion/transdata_split.h"
-#include <set>
-#include "pre_activate/ascend/ascend_helper.h"
-#include "session/anf_runtime_algorithm.h"
-#include "debug/anf_ir_dump.h"
-
-namespace mindspore {
-namespace opt {
-const std::set<std::pair<string, string>> invalid_formats_pair = {{kOpFormat_C1HWNCoC0, kOpFormat_NCHW},
-                                                                  {kOpFormat_NCHW, kOpFormat_C1HWNCoC0},
-                                                                  {kOpFormat_C1HWNCoC0, kOpFormat_DEFAULT},
-                                                                  {kOpFormat_DEFAULT, kOpFormat_C1HWNCoC0}};
-
-bool TransDataSplit::Run(const FuncGraphPtr &func_graph) {
-  MS_EXCEPTION_IF_NULL(func_graph);
-  bool changed = false;
-  std::vector<AnfNodePtr> node_list = TopoSort(func_graph->get_return());
-  for (auto &node : node_list) {
-    if (node != nullptr && node->isa<CNode>() && AnfAlgo::GetCNodeName(node) == kTransDataOpName) {
-      CheckCNodeInputSize(node->cast<CNodePtr>(), kBackendTransDataInputNum);
-      if (IsFormatInvaild(node)) {
-        changed = DoSplit(func_graph, node);
-      }
-    }
-  }
-  return changed;
-}
-bool TransDataSplit::IsFormatInvaild(const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(node);
-  auto cnode = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  auto input_format = AnfAlgo::GetInputFormat(node, 0);
-  auto output_format = AnfAlgo::GetOutputFormat(node, 0);
-  auto format_pair = std::make_pair(input_format, output_format);
-
-  return invalid_formats_pair.find(format_pair) != invalid_formats_pair.end();
-}
-// transdata cannot support frac_z to nchw need split transdata(frac_z-HWCN) and transpose(HWCN-NCHW)
-bool TransDataSplit::DoSplit(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(func_graph);
-  MS_EXCEPTION_IF_NULL(node);
-  auto cnode = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  auto input_node = node->cast<CNodePtr>()->input(1);
-  MS_EXCEPTION_IF_NULL(input_node);
-
-  auto input_format = AnfAlgo::GetInputFormat(node, 0);
-  auto output_format = AnfAlgo::GetOutputFormat(node, 0);
-  AnfNodePtr new_transdata_node = nullptr;
-  AnfNodePtr new_transpose_node = nullptr;
-  AnfNodePtr new_replace_node = nullptr;
-  // if output_format=default transdata need split transdata->transpose else transpose->transdata
-  if (output_format == kOpFormat_DEFAULT || output_format == kOpFormat_NCHW) {
-    // trans input_format to hwcn
-    new_transdata_node = AddTransOpNodeToGraph(func_graph, node, kernel_select_, 0, false, input_format, kOpFormat_HWCN,
-                                               kTransDataOpName, true);
-    // trans hwcn to default_format
-    new_transpose_node = AddTransOpNodeToGraph(func_graph, new_transdata_node, kernel_select_, 0, false, kOpFormat_HWCN,
-                                               output_format, prim::kPrimTranspose->name(), false);
-    AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector<int>{3, 2, 0, 1}), new_transpose_node);
-    new_replace_node = new_transpose_node;
-  } else {
-    // trans default to hwcn
-    new_transpose_node = AddTransOpNodeToGraph(func_graph, node, kernel_select_, 0, false, input_format, kOpFormat_HWCN,
-                                               prim::kPrimTranspose->name(), true);
-    AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector<int>{2, 3, 1, 0}), new_transpose_node);
-
-    // trans hwcn to output_format
-    new_transdata_node = AddTransOpNodeToGraph(func_graph, new_transpose_node, kernel_select_, 0, false, kOpFormat_HWCN,
-                                               output_format, kTransDataOpName, false);
-    new_replace_node = new_transdata_node;
-  }
-  FuncGraphManagerPtr manager = func_graph->manager();
-  MS_EXCEPTION_IF_NULL(manager);
-  manager->AddFuncGraph(func_graph);
-
-  if (!manager->Replace(node, new_replace_node)) {
-    MS_LOG(EXCEPTION) << "manager replace node failed";
-  }
-  MS_LOG(INFO) << "transdata node:" << cnode->DebugString() << "split success.";
-  return true;
-}
-}  // namespace opt
-}  // namespace mindspore
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/ir_fusion/transdata_split.h"
+#include <set>
+#include "pre_activate/ascend/ascend_helper.h"
+#include "session/anf_runtime_algorithm.h"
+#include "debug/anf_ir_dump.h"
+
+namespace mindspore {
+namespace opt {
+const std::set<std::pair<string, string>> invalid_formats_pair = {{kOpFormat_C1HWNCoC0, kOpFormat_NCHW},
+                                                                  {kOpFormat_NCHW, kOpFormat_C1HWNCoC0},
+                                                                  {kOpFormat_C1HWNCoC0, kOpFormat_DEFAULT},
+                                                                  {kOpFormat_DEFAULT, kOpFormat_C1HWNCoC0}};
+
+bool TransDataSplit::Run(const FuncGraphPtr &func_graph) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  bool changed = false;
+  std::vector<AnfNodePtr> node_list = TopoSort(func_graph->get_return());
+  for (auto &node : node_list) {
+    if (node != nullptr && node->isa<CNode>() && AnfAlgo::GetCNodeName(node) == kTransDataOpName) {
+      CheckCNodeInputSize(node->cast<CNodePtr>(), kBackendTransDataInputNum);
+      if (IsFormatInvaild(node)) {
+        changed = DoSplit(func_graph, node);
+      }
+    }
+  }
+  return changed;
+}
+bool TransDataSplit::IsFormatInvaild(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  auto input_format = AnfAlgo::GetInputFormat(node, 0);
+  auto output_format = AnfAlgo::GetOutputFormat(node, 0);
+  auto format_pair = std::make_pair(input_format, output_format);
+
+  return invalid_formats_pair.find(format_pair) != invalid_formats_pair.end();
+}
+// transdata cannot support frac_z to nchw need split transdata(frac_z-HWCN) and transpose(HWCN-NCHW)
+bool TransDataSplit::DoSplit(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(node);
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  auto input_node = node->cast<CNodePtr>()->input(1);
+  MS_EXCEPTION_IF_NULL(input_node);
+
+  auto input_format = AnfAlgo::GetInputFormat(node, 0);
+  auto output_format = AnfAlgo::GetOutputFormat(node, 0);
+  AnfNodePtr new_transdata_node = nullptr;
+  AnfNodePtr new_transpose_node = nullptr;
+  AnfNodePtr new_replace_node = nullptr;
+  // if output_format=default transdata need split transdata->transpose else transpose->transdata
+  if (output_format == kOpFormat_DEFAULT || output_format == kOpFormat_NCHW) {
+    // trans input_format to hwcn
+    new_transdata_node =
+      AddTransOpNodeToGraph(func_graph, node, kernel_select_, 0, input_format, kOpFormat_HWCN, kTransDataOpName, true);
+    // trans hwcn to default_format
+    new_transpose_node = AddTransOpNodeToGraph(func_graph, new_transdata_node, kernel_select_, 0, kOpFormat_HWCN,
+                                               output_format, prim::kPrimTranspose->name(), false);
+    AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector<int>{3, 2, 0, 1}), new_transpose_node);
+    new_replace_node = new_transpose_node;
+  } else {
+    // trans default to hwcn
+    new_transpose_node = AddTransOpNodeToGraph(func_graph, node, kernel_select_, 0, input_format, kOpFormat_HWCN,
+                                               prim::kPrimTranspose->name(), true);
+    AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(std::vector<int>{2, 3, 1, 0}), new_transpose_node);
+
+    // trans hwcn to output_format
+    new_transdata_node = AddTransOpNodeToGraph(func_graph, new_transpose_node, kernel_select_, 0, kOpFormat_HWCN,
+                                               output_format, kTransDataOpName, false);
+    new_replace_node = new_transdata_node;
+  }
+  FuncGraphManagerPtr manager = func_graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+  manager->AddFuncGraph(func_graph);
+
+  if (!manager->Replace(node, new_replace_node)) {
+    MS_LOG(EXCEPTION) << "Manager replace node failed";
+  }
+  MS_LOG(INFO) << "Transdata node:" << cnode->DebugString() << "split success.";
+  return true;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 893c379a07..3f20fec7b5 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -289,6 +289,11 @@ size_t AnfRuntimeAlgorithm::GetOutputTensorNum(const AnfNodePtr &node) {
 
 std::string AnfRuntimeAlgorithm::GetOutputFormat(const AnfNodePtr &node, size_t output_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "Output index:" << output_idx
+                      << " is out of the node output range :" << GetOutputTensorNum(node) << " #node ["
+                      << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
@@ -298,6 +303,11 @@ std::string AnfRuntimeAlgorithm::GetOutputFormat(const AnfNodePtr &node, size_t
 
 std::string AnfRuntimeAlgorithm::GetInputFormat(const AnfNodePtr &node, size_t input_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (input_idx > GetInputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "Input index :" << input_idx
+                      << " is out of the number node Input range :" << GetInputTensorNum(node) << "#node ["
+                      << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
@@ -362,62 +372,60 @@ std::vector<size_t> AnfRuntimeAlgorithm::GetPrevNodeOutputInferShape(const AnfNo
 std::vector<size_t> AnfRuntimeAlgorithm::GetOutputDeviceShape(const AnfNodePtr &node, size_t output_idx) {
   auto format = GetOutputFormat(node, output_idx);
   auto infer_shape = GetOutputInferShape(node, output_idx);
-  // if format is default_format or NC1KHKWHWC0,device shape = original shape
-  if (format == kOpFormat_DEFAULT || format == kOpFormat_NC1KHKWHWC0) {
-    return infer_shape;
-  }
-  // scalar shape
   if (infer_shape.empty()) {
     return infer_shape;
   }
-  if (format == kOpFormat_FRAC_NZ) {
-    return trans::TransShapeToDevice(infer_shape, format);
+  // if format is default_format or NC1KHKWHWC0,device shape = original shape
+  if (trans::IsNeedPadding(format, infer_shape.size())) {
+    infer_shape = trans::PaddingShapeTo4d(infer_shape, GetOutputReshapeType(node, output_idx));
   }
-  // else trans infer shape to 4d and then calculate device shape
-  return trans::TransShapeToDevice(trans::TransShapeTo4d(infer_shape), format);
+  return trans::TransShapeToDevice(infer_shape, format);
 }
 
 std::vector<size_t> AnfRuntimeAlgorithm::GetInputDeviceShape(const AnfNodePtr &node, size_t input_idx) {
   auto format = GetInputFormat(node, input_idx);
   auto infer_shape = GetPrevNodeOutputInferShape(node, input_idx);
-  // if format is default_format or NC1KHKWHWC0,device shape = original shape
-  if (format == kOpFormat_DEFAULT || format == kOpFormat_NC1KHKWHWC0) {
-    return infer_shape;
-  }
   if (infer_shape.empty()) {
     return infer_shape;
   }
-  if (format == kOpFormat_FRAC_NZ) {
-    return trans::TransShapeToDevice(infer_shape, format);
+  // if format is default_format or NC1KHKWHWC0,device shape = original shape
+  if (trans::IsNeedPadding(format, infer_shape.size())) {
+    infer_shape = trans::PaddingShapeTo4d(infer_shape, GetInputReshapeType(node, input_idx));
   }
-  // else trans infer shape to 4d and then calculate device shape
-  return trans::TransShapeToDevice(trans::TransShapeTo4d(infer_shape), format);
+  return trans::TransShapeToDevice(infer_shape, format);
 }
 
 std::vector<kernel::Axis> AnfRuntimeAlgorithm::GetInputReshapeType(const AnfNodePtr &node, size_t input_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (input_idx > GetInputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index:" << input_idx
+                      << " is out of range of the node's input size : " << GetInputTensorNum(node) << "#node["
+                      << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
   MS_EXCEPTION_IF_NULL(build_info);
-  std::vector<kernel::Axis> result;
-  if (!build_info->GetInputReshapeType(input_idx, &result)) {
-    MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !";
+  if (build_info->IsInputDefaultPadding()) {
+    return {};
   }
-  return result;
+  return build_info->GetInputReshapeType(input_idx);
 }
 
 std::vector<kernel::Axis> AnfRuntimeAlgorithm::GetOutputReshapeType(const AnfNodePtr &node, size_t output_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ "
+                      << GetOutputTensorNum(node) << "#node[ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
   MS_EXCEPTION_IF_NULL(build_info);
-  std::vector<kernel::Axis> result;
-  if (!build_info->GetOutputReshapeType(output_idx, &result)) {
-    MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !";
+  if (build_info->IsOutputDefaultPadding()) {
+    return {};
   }
-  return result;
+  return build_info->GetOutputReshapeType(output_idx);
 }
 
 TypeId AnfRuntimeAlgorithm::GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx) {
@@ -463,6 +471,10 @@ TypeId AnfRuntimeAlgorithm::GetPrevNodeOutputInferDataType(const AnfNodePtr &nod
 
 TypeId AnfRuntimeAlgorithm::GetOutputDeviceDataType(const AnfNodePtr &node, size_t output_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ "
+                      << GetOutputTensorNum(node) << "#node [ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
@@ -472,6 +484,10 @@ TypeId AnfRuntimeAlgorithm::GetOutputDeviceDataType(const AnfNodePtr &node, size
 
 TypeId AnfRuntimeAlgorithm::GetInputDeviceDataType(const AnfNodePtr &node, size_t input_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (input_idx > GetInputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << input_idx << "] is out of range of the node's input size [ "
+                      << GetInputTensorNum(node) << "#node [ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto build_info = kernel_info->select_kernel_build_info();
@@ -496,11 +512,15 @@ const DeviceAddress *AnfRuntimeAlgorithm::GetOutputAddr(const AnfNodePtr &node,
       MS_LOG(EXCEPTION) << node->DebugString() << "Invalid nop node";
     }
   }
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ "
+                      << GetOutputTensorNum(node) << "#node:[ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto addr = kernel_info->GetOutputAddr(output_idx);
   if (addr == nullptr) {
-    MS_LOG(EXCEPTION) << "output_idx " << output_idx << " of node " << node->DebugString()
+    MS_LOG(EXCEPTION) << "Output_idx " << output_idx << " of node " << node->DebugString()
                       << " output addr is not exist";
   }
   return addr;
@@ -517,11 +537,15 @@ DeviceAddressPtr AnfRuntimeAlgorithm::GetMutableOutputAddr(const AnfNodePtr &nod
       MS_LOG(EXCEPTION) << node->DebugString() << "Invalid nop node.";
     }
   }
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ "
+                      << GetOutputTensorNum(node) << "#node:[ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   auto addr = kernel_info->GetMutableOutputAddr(output_idx);
   if (addr == nullptr) {
-    MS_LOG(EXCEPTION) << "output_idx" << output_idx << " of node " << node->DebugString()
+    MS_LOG(EXCEPTION) << "Output_idx" << output_idx << " of node " << node->DebugString()
                       << " output addr is not exist";
   }
   return addr;
@@ -530,6 +554,10 @@ DeviceAddressPtr AnfRuntimeAlgorithm::GetMutableOutputAddr(const AnfNodePtr &nod
 // get output device addr of anf_node
 bool AnfRuntimeAlgorithm::OutputAddrExist(const AnfNodePtr &node, size_t output_idx) {
   MS_EXCEPTION_IF_NULL(node);
+  if (output_idx > GetOutputTensorNum(node)) {
+    MS_LOG(EXCEPTION) << "The index [" << output_idx << "] is out of range of the node's output size [ "
+                      << GetOutputTensorNum(node) << "#node:[ " << node->DebugString() << "]";
+  }
   auto kernel_info = node->kernel_info();
   MS_EXCEPTION_IF_NULL(kernel_info);
   return kernel_info->OutputAddrExist(output_idx);
@@ -769,22 +797,24 @@ AnfNodePtr AnfRuntimeAlgorithm::GetInputNode(const CNodePtr &node, size_t index)
   return node->input(get_input_index);
 }
 
+bool AnfRuntimeAlgorithm::IsFeatureMapOutput(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  if (node->isa<ValueNode>()) {
+    return false;
+  }
+  auto kernel_info = node->kernel_info();
+  MS_EXCEPTION_IF_NULL(kernel_info);
+  return kernel_info->is_feature_map();
+}
+
 bool AnfRuntimeAlgorithm::IsFeatureMapInput(const AnfNodePtr &node, size_t input_index) {
   if (!node->isa<CNode>()) {
-    MS_LOG(EXCEPTION) << "Cannot input a parameter or a valuenode to charge it's input if is a feature";
+    MS_LOG(EXCEPTION) << "Cannot input a parameter or a valuenode to charge it's input if is a feature map";
   }
   auto cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   auto input_node = cnode->input(input_index + 1);
-  auto node_with_index = VisitKernel(input_node, 0);
-  MS_EXCEPTION_IF_NULL(node_with_index.first);
-  if (node_with_index.first->isa<ValueNode>()) {
-    return false;
-  }
-  if (node_with_index.first->isa<Parameter>()) {
-    return !AnfAlgo::IsParameterWeight(node_with_index.first->cast<ParameterPtr>());
-  }
-  return true;
+  return IsFeatureMapOutput(input_node);
 }
 
 size_t AnfRuntimeAlgorithm::GetRealInputIndex(const mindspore::AnfNodePtr &anf_node, const size_t cur_index) {
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 1a1d471b84..9ac83e011f 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -101,7 +101,9 @@ class AnfRuntimeAlgorithm {
   static std::vector<size_t> GetOutputDeviceShape(const AnfNodePtr &node, size_t output_idx);
   // get input shapes which will built and run in device
   static std::vector<size_t> GetInputDeviceShape(const AnfNodePtr &node, size_t input_idx);
+  // Get Input Padding Axis
   static std::vector<kernel::Axis> GetInputReshapeType(const AnfNodePtr &node, size_t output_idx);
+  // Get Output Padding Axis
   static std::vector<kernel::Axis> GetOutputReshapeType(const AnfNodePtr &node, size_t output_idx);
   // get output data type inferred by ME of anf node
   static TypeId GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx);
@@ -165,6 +167,9 @@ class AnfRuntimeAlgorithm {
   // get graph id
   static uint32_t GetGraphId(const AnfNode *node);
   static AnfNodePtr GetInputNode(const CNodePtr &node, size_t index);
+  // charge if the node's output is a feature map output
+  static bool IsFeatureMapOutput(const AnfNodePtr &node);
+  // charge if the node's input is from a feature map output
   static bool IsFeatureMapInput(const AnfNodePtr &node, size_t input_index);
   // get real input index for some tbe ops which input order is different between me and tbe impl
   static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index);
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 751cf76e32..93ae99f4d2 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -18,6 +18,7 @@
 #include "operator/ops.h"
 #include "ir/meta_tensor.h"
 #include "ir/anf.h"
+#include "common/trans.h"
 #include "device/kernel_runtime.h"
 #include "device/ascend/kernel_select_ascend.h"
 #include "device/ascend/kernel_build_ascend.h"
@@ -730,8 +731,8 @@ void AscendSession::SetChildGraphParameter(const tensor::TensorPtr &front_tensor
   size_t tensor_size = front_tensor->data().nbytes();
   auto addr = AnfAlgo::GetOutputAddr(backend_parameter, 0);
   MS_EXCEPTION_IF_NULL(addr);
-  if (!addr->SyncHostToDevice(front_tensor->shape(), tensor_size, front_tensor->data_type(),
-                              front_tensor->data_c(false))) {
+  if (!addr->SyncHostToDevice(trans::GetRuntimePaddingShape(backend_parameter, 0), tensor_size,
+                              front_tensor->data_type(), front_tensor->data_c(false))) {
     MS_LOG(EXCEPTION) << "Tensor SyncHostToDevice fail!";
   }
   MS_LOG(INFO) << "Finish!";
diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc
index dbf6e07e7e..bbcc04e14b 100755
--- a/mindspore/ccsrc/session/kernel_graph.cc
+++ b/mindspore/ccsrc/session/kernel_graph.cc
@@ -143,6 +143,12 @@ CNodePtr KernelGraph::NewCNode(const std::vector<AnfNodePtr> &inputs) {
   cnode->set_abstract(std::make_shared<abstract::AbstractNone>());
   // create kernel_info from new parameter
   auto kernel_info = std::make_shared<device::KernelInfo>();
+  // if the node only has the primitive(such as getNext) or the node's input has a feature map input
+  // then the node's output is a feature map output
+  if (inputs.size() == 1 || std::any_of(inputs.begin() + 1, inputs.end(),
+                                        [&](const AnfNodePtr &node) { return AnfAlgo::IsFeatureMapOutput(node); })) {
+    kernel_info->SetFeatureMapFlag(true);
+  }
   cnode->set_kernel_info(kernel_info);
   AnfAlgo::SetGraphId(graph_id_, cnode.get());
   return cnode;
@@ -162,22 +168,26 @@ CNodePtr KernelGraph::NewCNode(const CNodePtr &cnode) {
 ParameterPtr KernelGraph::NewParameter(const ParameterPtr &parameter) {
   ParameterPtr new_parameter = add_parameter();
   MS_EXCEPTION_IF_NULL(new_parameter);
+  // create kernel_info form new parameter
+  auto kernel_info = std::make_shared<device::KernelInfo>();
   size_t output_tensor_num = 1;
   // if use default parameter = nullptr,it remarks create a new parameter from no parameter
   if (parameter == nullptr) {
     new_parameter->set_abstract(std::make_shared<abstract::AbstractNone>());
+    kernel_info->SetFeatureMapFlag(true);
   } else {
     // if don't use default parameter = nullptr,it remarks create a new parameter from a old parameter
     new_parameter->set_abstract(parameter->abstract());
     new_parameter->set_name(parameter->name());
-    if (parameter->has_default()) {
+    if (AnfAlgo::IsParameterWeight(parameter)) {
       new_parameter->set_default_param(parameter->default_param());
+      kernel_info->SetFeatureMapFlag(false);
+    } else {
+      kernel_info->SetFeatureMapFlag(true);
     }
     // if output is a tuple tensor,now can use for loop to handle tuple tensor
     output_tensor_num = AnfAlgo::GetOutputTensorNum(parameter);
   }
-  // create kernel_info form new parameter
-  auto kernel_info = std::make_shared<device::KernelInfo>();
   new_parameter->set_kernel_info(kernel_info);
   // create kernel_build_info for new parameter
   auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
@@ -217,6 +227,7 @@ std::vector<AnfNodePtr> KernelGraph::SplitTupleValueNodeToNodeList(const ValueNo
     AddValueNodeToGraph(new_value_node);
     auto kernel_info = std::make_shared<device::KernelInfo>();
     new_value_node->set_kernel_info(kernel_info);
+    kernel_info->SetFeatureMapFlag(false);
     // create kernel_build_info for new value node
     auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
     // set the format of value_node to DEFAULT_FORMAT
@@ -240,6 +251,7 @@ ValueNodePtr KernelGraph::NewValueNode(const ValueNodePtr &value_node) {
   new_value_node->set_abstract(value_node->abstract());
   // create kernel_info fo new value node
   auto kernel_info = std::make_shared<device::KernelInfo>();
+  kernel_info->SetFeatureMapFlag(false);
   new_value_node->set_kernel_info(kernel_info);
   // create kernel_build_info for new value node
   auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index d2a255229d..bea51037bf 100755
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -20,6 +20,7 @@
 #include "pipeline/parse/data_converter.h"
 #include "ir/manager.h"
 #include "operator/ops.h"
+#include "common/trans.h"
 #include "utils/context/ms_context.h"
 #include "utils/config_manager.h"
 #include "session/anf_runtime_algorithm.h"
@@ -124,7 +125,8 @@ BaseRef CreateOneTensor(const AnfNodePtr &node, size_t output_index, const Kerne
   MS_EXCEPTION_IF_NULL(ms_context);
   if (ms_context->enable_pynative_infer()) {
     tensor->set_device_address(AnfAlgo::GetMutableOutputAddr(node, output_index));
-  } else if (!address->SyncDeviceToHost(tensor->shape(), LongToSize(tensor->data().nbytes()), tensor->data_type(),
+  } else if (!address->SyncDeviceToHost(trans::GetRuntimePaddingShape(node, output_index),
+                                        LongToSize(tensor->data().nbytes()), tensor->data_type(),
                                         tensor->data_c(true))) {
     MS_LOG(INFO) << "output sync device to host error!!!";
     tensor->set_dirty(false);
@@ -369,7 +371,7 @@ ParameterPtr ConstructRunOpParameter(const std::shared_ptr<KernelGraph> &graph,
     kernel_build_info_builder->SetOutputsDeviceType(std::vector<TypeId>{input_tensor->device_address()->type_id()});
   }
   AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), param.get());
-  // construct abstract of parameter
+  // ftruct abstract of parameter
   auto abstract = std::make_shared<abstract::AbstractTensor>(input_tensor);
   param->set_abstract(abstract);
   return param;
@@ -548,7 +550,8 @@ void SessionBasic::LoadInputData(const std::shared_ptr<KernelGraph> &kernel_grap
       if (need_sync) {
         tensor->set_device_address(device_address);
         MS_EXCEPTION_IF_NULL(device_address);
-        if (!device_address->SyncHostToDevice(tensor->shape(), LongToSize(tensor->data().nbytes()), tensor->data_type(),
+        if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0),
+                                              LongToSize(tensor->data().nbytes()), tensor->data_type(),
                                               tensor->data_c(false))) {
           MS_LOG(EXCEPTION) << "SyncHostToDevice failed.";
         }
@@ -620,8 +623,8 @@ void SessionBasic::Summary(KernelGraph *graph) {
     (void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape));
     tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
     MS_EXCEPTION_IF_NULL(address);
-    if (!address->SyncDeviceToHost(tensor->shape(), LongToSize(tensor->data().nbytes()), tensor->data_type(),
-                                   tensor->data_c(true))) {
+    if (!address->SyncDeviceToHost(trans::GetRuntimePaddingShape(node, index), LongToSize(tensor->data().nbytes()),
+                                   tensor->data_type(), tensor->data_c(true))) {
       MS_LOG(ERROR) << "Failed to sync output from device to host.";
     }
     tensor->set_dirty(false);
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 39b4b7a160..79a4b216fb 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -197,8 +197,8 @@ const std::set<std::string> kOptOperatorSet = {
   kApplyRMSPropOpName,
 };
 
-const std::set<std::string> kSpecialFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
-                                                 kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0};
+const std::set<std::string> kNeedTransFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
+                                                   kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0};
 
 static inline void ChangeFileMode(const std::string& file_name, mode_t mode) {
   if (access(file_name.c_str(), F_OK) != 0) {
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc
index e7831ec353..44b9b3df69 100644
--- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/layer_norm_beta_gamma_backprop_fusion_test.cc
@@ -80,6 +80,8 @@ TEST_F(TestHWLayerNormBetaGammaBackpropFusion, layernorm_beta_gamma_backprop_fus
   builder1.SetOutputsDeviceType({kNumberTypeFloat32});
   cast0->set_kernel_info(std::make_shared<device::KernelInfo>());
   cast1->set_kernel_info(std::make_shared<device::KernelInfo>());
+  cast0->set_abstract(x_abstract);
+  cast1->set_abstract(x_abstract);
   AnfAlgo::SetSelectKernelBuildInfo(builder1.Build(), cast0.get());
   AnfAlgo::SetSelectKernelBuildInfo(builder1.Build(), cast1.get());
 
diff --git a/tests/ut/cpp/session/anf_runtime_algorithm_test.cc b/tests/ut/cpp/session/anf_runtime_algorithm_test.cc
index 2af2a7413b..6375d1a758 100644
--- a/tests/ut/cpp/session/anf_runtime_algorithm_test.cc
+++ b/tests/ut/cpp/session/anf_runtime_algorithm_test.cc
@@ -211,8 +211,8 @@ TEST_F(AnfRuntimeAlgorithmTest, EraseNodeAttr) {
 TEST_F(AnfRuntimeAlgorithmTest, GetInputTensorNum) {
   auto kernel_graph = std::make_shared<KernelGraph>();
   // test cnode node
-  auto parameter_one = kernel_graph->add_parameter();
-  auto parameter_two = kernel_graph->add_parameter();
+  auto parameter_one = kernel_graph->NewParameter();
+  auto parameter_two = kernel_graph->NewParameter();
   std::vector<AnfNodePtr> add_inputs{NewValueNode(prim::kPrimTensorAdd), parameter_one, parameter_two};
   auto add = kernel_graph->NewCNode(add_inputs);
   EXPECT_EQ(AnfAlgo::GetInputTensorNum(add), 2);
@@ -247,9 +247,11 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputTensorNum) {
 
 TEST_F(AnfRuntimeAlgorithmTest, GetOutputFormat) {
   auto kernel_graph = std::make_shared<KernelGraph>();
-  std::vector<AnfNodePtr> inputs;
-  inputs.push_back(NewValueNode(prim::kPrimTensorAdd));
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim::kPrimTensorAdd), kernel_graph->NewParameter(),
+                                    kernel_graph->NewParameter()};
   auto add = kernel_graph->NewCNode(inputs);
+  std::vector<size_t> shape = {1, 2, 3, 4};
+  AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, {shape, shape}, add.get());
   MS_EXCEPTION_IF_NULL(add);
   add->set_kernel_info(std::make_shared<KernelInfo>());
   auto d_kernel_info = add->kernel_info();
@@ -266,8 +268,8 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputFormat) {
 
 TEST_F(AnfRuntimeAlgorithmTest, GetInputFormat) {
   auto kernel_graph = std::make_shared<KernelGraph>();
-  std::vector<AnfNodePtr> inputs;
-  inputs.push_back(NewValueNode(prim::kPrimTensorAdd));
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim::kPrimTensorAdd), kernel_graph->NewParameter(),
+                                    kernel_graph->NewParameter()};
   auto add = kernel_graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(add);
   add->set_kernel_info(std::make_shared<KernelInfo>());
@@ -345,7 +347,7 @@ TEST_F(AnfRuntimeAlgorithmTest, GetPrevNodeOutputInferShape) {
   std::vector<int> shp{2, 32, 224, 224};
   auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
   // test parameter node as input
-  auto parameter_node = kernel_graph->add_parameter();
+  auto parameter_node = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(parameter_node);
   parameter_node->set_abstract(x_abstract);
   EXPECT_THROW(AnfAlgo::GetPrevNodeOutputInferShape(parameter_node, 0), std::runtime_error);
@@ -387,13 +389,13 @@ TEST_F(AnfRuntimeAlgorithmTest, GetInputDeviceShape) {
   auto kernel_graph = std::make_shared<KernelGraph>();
   std::vector<int> shp{2, 32, 224, 224};
   auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
-  auto parameter_one = kernel_graph->add_parameter();
+  auto parameter_one = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(parameter_one);
   parameter_one->set_abstract(x_abstract);
-  auto parameter_two = kernel_graph->add_parameter();
+  auto parameter_two = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(parameter_two);
   parameter_two->set_abstract(x_abstract);
-  auto parameter_third = kernel_graph->add_parameter();
+  auto parameter_third = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(parameter_third);
   parameter_third->set_abstract(x_abstract);
   // test cnode as input
@@ -466,8 +468,8 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputDeviceDataTypeTest) {
 
 TEST_F(AnfRuntimeAlgorithmTest, GetInputDeviceDataTypeTest) {
   auto kernel_graph = std::make_shared<KernelGraph>();
-  std::vector<AnfNodePtr> inputs;
-  inputs.push_back(NewValueNode(prim::kPrimTensorAdd));
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim::kPrimTensorAdd), kernel_graph->NewParameter(),
+                                    kernel_graph->NewParameter()};
   auto add = kernel_graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(add);
   add->set_kernel_info(std::make_shared<KernelInfo>());
diff --git a/tests/ut/cpp/session/kernel_graph_test.cc b/tests/ut/cpp/session/kernel_graph_test.cc
index 55e1b1b28e..a62af9c892 100644
--- a/tests/ut/cpp/session/kernel_graph_test.cc
+++ b/tests/ut/cpp/session/kernel_graph_test.cc
@@ -140,11 +140,11 @@ TEST_F(KernelGraphTest, SetExecOrderByDefault) {
   std::vector<int> shape = {2, 32, 224, 224};
   auto abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shape);
 
-  auto x_parameter = kernel_graph->add_parameter();
+  auto x_parameter = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(x_parameter);
   x_parameter->set_name("x_parameter");
   x_parameter->set_abstract(abstract);
-  auto y_parameter = kernel_graph->add_parameter();
+  auto y_parameter = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(y_parameter);
   y_parameter->set_name("y_parameter");
   y_parameter->set_abstract(abstract);
@@ -153,7 +153,7 @@ TEST_F(KernelGraphTest, SetExecOrderByDefault) {
   MS_EXCEPTION_IF_NULL(add);
   add->set_abstract(abstract);
 
-  auto z_parameter = kernel_graph->add_parameter();
+  auto z_parameter = kernel_graph->NewParameter();
   MS_EXCEPTION_IF_NULL(z_parameter);
   z_parameter->set_name("z_parameter");
   z_parameter->set_abstract(abstract);

From 2604acedcb17abad94404e408e4e3882d294c6bb Mon Sep 17 00:00:00 2001
From: "wangnan39@huawei.com" <wangnan39@huawei.com>
Date: Fri, 10 Apr 2020 10:41:59 +0800
Subject: [PATCH 210/367] extend conv stride and dilation to 2d

---
 .../cpu/kernel/mkldnn/conv2d_cpu_kernel.cc    |  18 +-
 .../mkldnn/conv2d_grad_filter_cpu_kernel.cc   |  15 +-
 .../mkldnn/conv2d_grad_input_cpu_kernel.cc    |  15 +-
 .../ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h   |  19 +-
 .../gpu/nn/conv2d_grad_filter_gpu_kernel.h    |  15 +-
 .../gpu/nn/conv2d_grad_input_gpu_kernel.h     |  15 +-
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     | 132 ------------
 mindspore/ccsrc/onnx/onnx_exporter.cc         |   5 +-
 mindspore/ccsrc/transform/op_declare.cc       |  24 +--
 mindspore/nn/layer/conv.py                    |  66 +++---
 mindspore/ops/_op_impl/tbe/conv2d.py          |   2 +-
 mindspore/ops/operations/_grad_ops.py         |  12 +-
 mindspore/ops/operations/nn_ops.py            | 198 +++++++++++-------
 .../ops/cpu/test_conv2d_backprop_filter_op.py |   4 +-
 .../ops/gpu/test_conv2d_backprop_filter_op.py |   4 +-
 .../gtest_input/pynative/ops_test.py          |  14 +-
 tests/vm_impl/vm_me.py                        |  94 +++++++--
 17 files changed, 362 insertions(+), 290 deletions(-)

diff --git a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_cpu_kernel.cc b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_cpu_kernel.cc
index f7527c4750..5d63aee6cd 100644
--- a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_cpu_kernel.cc
+++ b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_cpu_kernel.cc
@@ -35,8 +35,22 @@ void Conv2dCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
 
   int kernel_size = SizeToInt(weight_shape[3]);
-  int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
-  int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
+  auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
+  auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
+  if (stride_ori.size() != 4 || stride_ori[2] != stride_ori[3]) {
+    MS_LOG(EXCEPTION) << "conv2d only support equal stride, and stride must be 4d!";
+  }
+  if (stride_ori[0] != 1 || stride_ori[1] != 1) {
+    MS_LOG(EXCEPTION) << "conv2d stride only support 1 in N axis and C axis!";
+  }
+  if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
+    MS_LOG(EXCEPTION) << "conv2d dilation only support 1, and dilation must be 4d!";
+  }
+  if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+    MS_LOG(EXCEPTION) << "conv2d dilation only support 1 in N axis and C axis!";
+  }
+  int stride = stride_ori[2];
+  int dilation = dilation_ori[2];
 
   dnnl::memory::dims strides{stride, stride};
   dnnl::memory::dims dilates{dilation - 1, dilation - 1};
diff --git a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc
index f4c0e58350..1a7c10a531 100644
--- a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc
+++ b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc
@@ -35,8 +35,19 @@ void Conv2dGradFilterCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
 
   int kernel_size = SizeToInt(weight_shape[3]);
-  int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
-  int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
+  auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
+  auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
+  if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
+    MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel only support equal stride, and stride must be 2d!";
+  }
+  if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
+    MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel dilation only support 1, and dilation must be 4d!";
+  }
+  if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+    MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel dilation only support 1 in N axis and C axis!";
+  }
+  int stride = stride_ori[0];
+  int dilation = dilation_ori[2];
 
   dnnl::memory::dims strides{stride, stride};
   dnnl::memory::dims dilates{dilation - 1, dilation - 1};
diff --git a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc
index 492e2d6280..04dda20acd 100644
--- a/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc
+++ b/mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc
@@ -35,8 +35,19 @@ void Conv2dGradInputCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
 
   int kernel_size = SizeToInt(weight_shape[3]);
-  int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
-  int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
+  auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
+  auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
+  if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
+    MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel only support equal stride, and stride must be 2d!";
+  }
+  if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
+    MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel dilation only support 1, and dilation must be 4d!";
+  }
+  if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+    MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel dilation only support 1 in N axis and C axis!";
+  }
+  int stride = stride_ori[0];
+  int dilation = dilation_ori[2];
   dnnl::memory::dims strides{stride, stride};
   dnnl::memory::dims dilates{dilation - 1, dilation - 1};
   std::vector<int> int_padding_l;
diff --git a/mindspore/ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h
index 7a4adff970..75b2a97cf8 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h
@@ -113,9 +113,24 @@ class Conv2dGpuFwdKernel : public GpuKernel {
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetConvolutionGroupCount(conv_desc_, group_), "cudnnSetConvGroupCount failed");
     pad_height_ = GetAttr<int>(kernel_node, "pad");
     pad_width_ = pad_height_;
-    stride_ = GetAttr<int>(kernel_node, "stride");
-    dilation_ = GetAttr<int>(kernel_node, "dilation");
     pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
+    auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
+    auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
+    if (stride_ori.size() != 4 || stride_ori[2] != stride_ori[3]) {
+      MS_LOG(EXCEPTION) << "conv2d only support equal stride, and stride must be 4d!";
+    }
+    if (stride_ori[0] != 1 || stride_ori[1] != 1) {
+      MS_LOG(EXCEPTION) << "conv2d stride only support 1 in N axis and C axis!";
+    }
+    if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
+      MS_LOG(EXCEPTION) << "conv2d only support equal dilation, and dilation must be 4d!";
+    }
+    if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+      MS_LOG(EXCEPTION) << "conv2d dilation only support 1 in N axis and C axis!";
+    }
+    stride_ = stride_ori[2];
+    dilation_ = dilation_ori[2];
+
     cudnnTensorDescriptor_t input_descriptor_real = nullptr;
     if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
       SetPad(in_shape, kernel_node);
diff --git a/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_filter_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_filter_gpu_kernel.h
index f8afad4f84..e481fd448e 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_filter_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_filter_gpu_kernel.h
@@ -116,9 +116,20 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel {
 
     pad_height_ = GetAttr<int>(kernel_node, "pad");
     pad_width_ = pad_height_;
-    stride_ = GetAttr<int>(kernel_node, "stride");
-    dilation_ = GetAttr<int>(kernel_node, "dilation");
     pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
+    auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
+    auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
+    if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
+      MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel only support equal stride, and stride must be 2d!";
+    }
+    if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
+      MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel only support equal dilation, and dilation must be 4d!";
+    }
+    if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+      MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel dilation only support 1 in N axis and C axis!";
+    }
+    stride_ = stride_ori[0];
+    dilation_ = dilation_ori[2];
     cudnnTensorDescriptor_t x_desc_real = nullptr;
     if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
       SetPad(in_shape, kernel_node);
diff --git a/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_input_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_input_gpu_kernel.h
index be7739981d..008abcc658 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_input_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_input_gpu_kernel.h
@@ -117,9 +117,20 @@ class ConvGradInputGpuBkwKernel : public GpuKernel {
 
     pad_height_ = GetAttr<int>(kernel_node, "pad");
     pad_width_ = pad_height_;
-    stride_ = GetAttr<int>(kernel_node, "stride");
-    dilation_ = GetAttr<int>(kernel_node, "dilation");
     pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
+    auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
+    auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
+    if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
+      MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel only support equal stride, and stride must be 2d!";
+    }
+    if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
+      MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel only support equal dilation, and dilation must be 4d!";
+    }
+    if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
+      MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel dilation only support 1 in N axis and C axis!";
+    }
+    stride_ = stride_ori[0];
+    dilation_ = dilation_ori[2];
     cudnnTensorDescriptor_t dx_desc_real = nullptr;
     if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
       SetPad(input_shape, kernel_node);
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index 229a3eb34a..481ac75504 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -148,9 +148,6 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
 }
 
 std::map<std::string, FAttrsPass> TbeAdapter::build_json_attr_pass_map_ = {
-  {"Conv2D", TbeAdapter::Conv2DAttrJsonPass},
-  {"Conv2DBackpropFilter", TbeAdapter::Conv2DBackpropFilterAttrJsonPass},
-  {"Conv2DBackpropInput", TbeAdapter::Conv2DBackpropInputAttrJsonPass},
   {"MaximumGrad", TbeAdapter::MaximumGradAttrJsonPass},
   {"MinimumGrad", TbeAdapter::MinimumGradAttrJsonPass},
   {"Cast", TbeAdapter::CastAttrJsonPass}};
@@ -168,135 +165,6 @@ bool TbeAdapter::RunAttrPass(const mindspore::AnfNodePtr &anf_node,
   return false;
 }
 
-void TbeAdapter::Conv2DAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
-                                    const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-                                    nlohmann::json *attrs_json) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(attrs_json);
-  auto attr_num = op_info_attrs.size();
-  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
-  MS_EXCEPTION_IF_NULL(primitive);
-  for (size_t i = 0; i < attr_num; i++) {
-    nlohmann::json attr_obj;
-    MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
-    std::string attr_name = op_info_attrs[i]->name();
-    std::vector<int> attr_value;
-    if (primitive->GetAttr(attr_name) != nullptr) {
-      auto value = primitive->GetAttr(attr_name);
-      int data = GetValue<int>(value);
-      size_t list_int_size = 0;
-      if (attr_name == "stride") {
-        list_int_size = 4;
-      } else if (attr_name == "dilation") {
-        list_int_size = 4;
-      } else if (attr_name == "pad") {
-        value = primitive->GetAttr("pad_list");
-        attr_value = GetValue<std::vector<int>>(value);
-      }
-      for (size_t j = 0; j < list_int_size; j++) {
-        attr_value.push_back(data);
-      }
-      attr_obj["value"] = attr_value;
-    } else {
-      attr_obj["value"] = 0;
-    }
-    attr_obj["name"] = attr_name;
-    attr_obj["valid"] = true;
-    (*attrs_json).push_back(attr_obj);
-  }
-  MS_LOG(INFO) << "Conv2DAttrPass done.";
-}
-
-void TbeAdapter::Conv2DBackpropFilterAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(attrs_json);
-  auto attr_num = op_info_attrs.size();
-  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
-  MS_EXCEPTION_IF_NULL(primitive);
-  for (size_t i = 0; i < attr_num; i++) {
-    nlohmann::json attr_obj;
-    MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
-    std::string attr_name = op_info_attrs[i]->name();
-    if (primitive->GetAttr(attr_name) != nullptr) {
-      auto value = primitive->GetAttr(attr_name);
-      if (attr_name == "pad_mode") {
-        std::string attr_value = GetValue<std::string>(value);
-        (void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
-        attr_obj["value"] = attr_value;
-      } else if (attr_name == "filter_sizes") {
-        std::vector<int> attr_value = GetValue<std::vector<int>>(value);
-        attr_obj["value"] = attr_value;
-      } else {
-        std::vector<int> attr_value;
-        int data = GetValue<int>(value);
-        size_t list_int_size = 0;
-        if (attr_name == "stride") {
-          list_int_size = 2;
-        } else if (attr_name == "dilation") {
-          list_int_size = 4;
-        }
-        for (size_t j = 0; j < list_int_size; j++) {
-          attr_value.push_back(data);
-        }
-        attr_obj["value"] = attr_value;
-      }
-      attr_obj["valid"] = true;
-    } else {
-      attr_obj["valid"] = false;
-    }
-    attr_obj["name"] = attr_name;
-    attrs_json->push_back(attr_obj);
-  }
-  MS_LOG(INFO) << "Conv2DBackpropFilterAttrJsonPass done.";
-}
-
-void TbeAdapter::Conv2DBackpropInputAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(attrs_json);
-  auto attr_num = op_info_attrs.size();
-  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
-  MS_EXCEPTION_IF_NULL(primitive);
-  for (size_t i = 0; i < attr_num; i++) {
-    nlohmann::json attr_obj;
-    MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
-    std::string attr_name = op_info_attrs[i]->name();
-    if (primitive->GetAttr(attr_name) != nullptr) {
-      auto value = primitive->GetAttr(attr_name);
-      if (attr_name == "pad_mode") {
-        std::string attr_value = GetValue<std::string>(value);
-        (void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
-        attr_obj["value"] = attr_value;
-      } else if (attr_name == "input_sizes") {
-        std::vector<int> attr_value = GetValue<std::vector<int>>(value);
-        attr_obj["value"] = attr_value;
-      } else {
-        std::vector<int> attr_value;
-        int data = GetValue<int>(value);
-        size_t list_int_size = 0;
-        if (attr_name == "stride") {
-          list_int_size = 2;
-        } else if (attr_name == "dilation") {
-          list_int_size = 4;
-        }
-        for (size_t j = 0; j < list_int_size; j++) {
-          attr_value.push_back(data);
-        }
-        attr_obj["value"] = attr_value;
-      }
-      attr_obj["valid"] = true;
-    } else {
-      attr_obj["valid"] = false;
-    }
-    attr_obj["name"] = attr_name;
-    attrs_json->push_back(attr_obj);
-  }
-  MS_LOG(INFO) << "Conv2DBackpropInputAttrJsonPass done.";
-}
-
 void TbeAdapter::MaximumGradAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
                                          const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
                                          nlohmann::json *attrs_json) {
diff --git a/mindspore/ccsrc/onnx/onnx_exporter.cc b/mindspore/ccsrc/onnx/onnx_exporter.cc
index 3bd4a38881..4767d13cd9 100644
--- a/mindspore/ccsrc/onnx/onnx_exporter.cc
+++ b/mindspore/ccsrc/onnx/onnx_exporter.cc
@@ -179,7 +179,7 @@ OPERATOR_ONNX_CONVERT_DEFINE(Squeeze, Squeeze,
 OPERATOR_ONNX_CONVERT_DEFINE(
   Conv2D, Conv,
   OpNameInfo()
-    .Attr("dilation", "dilations", onnx::AttributeProto_AttributeType_INTS, SetAttrValueToProto<Int32Imm, 2>)
+    .Attr("dilation", "dilations", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>)
     .Attr("group", "group", onnx::AttributeProto_AttributeType_INT, SetAttrValueToProto<Int32Imm>)
     .Attr("kernel_size", "kernel_shape", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto)
     .Attr("pad_mode", "auto_pad", onnx::AttributeProto_AttributeType_STRING,
@@ -197,8 +197,7 @@ OPERATOR_ONNX_CONVERT_DEFINE(
                                        prim);
             }
           })
-    .Attr("stride", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrValueToProto<Int32Imm, 2>))
-
+    .Attr("stride", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>))
 OPERATOR_ONNX_CONVERT_DEFINE(BiasAdd, Add, OpNameInfo())
 OPERATOR_ONNX_CONVERT_DEFINE(MatMul, Gemm,
                              OpNameInfo()
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 419805c37f..7634879959 100644
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -754,9 +754,9 @@ OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(Conv2D) = {
-  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}};
 
@@ -766,8 +766,8 @@ INPUT_ATTR_MAP(Conv2DBackpropInputD) = {
   {3, ATTR_DESC(input_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropInputD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}};
 
@@ -777,17 +777,17 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = {
   {3, ATTR_DESC(filter_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropFilterD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}};
 
 // DepthwiseConv2D
 INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(DepthwiseConv2D) = {
-  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
 };
 OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}};
@@ -797,9 +797,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
   {1, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
-  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}};
 
@@ -808,9 +808,9 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
   {2, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
-  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}};
 
diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index eb73a9ce5a..fbf6ad2a0c 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -17,7 +17,7 @@ from mindspore import log as logger
 from mindspore.ops import operations as P
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
-from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative, check_int
+from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative
 from mindspore._extends import cell_attr_register
 from ..cell import Cell
 
@@ -42,17 +42,23 @@ class _Conv(Cell):
         self.in_channels = check_int_positive(in_channels)
         self.out_channels = check_int_positive(out_channels)
         self.kernel_size = kernel_size
-        self.stride = check_int_positive(stride)
+        self.stride = stride
         self.pad_mode = pad_mode
         self.padding = check_int_non_negative(padding)
-        self.dilation = check_int(dilation)
+        self.dilation = dilation
         self.group = check_int_positive(group)
         self.has_bias = has_bias
-        if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
-            (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
-                kernel_size[0] < 1 or kernel_size[1] < 1:
+        if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
+            kernel_size[0] < 1 or kernel_size[1] < 1:
             raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed "
                              + str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.")
+        if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or stride[0] < 1 or stride[1] < 1:
+            raise ValueError("Attr 'stride' of 'Conv2D' Op passed "
+                             + str(self.stride) + ", should be a int or tuple and equal to or greater than 1.")
+        if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
+            dilation[0] < 1 or dilation[1] < 1:
+            raise ValueError("Attr 'dilation' of 'Conv2D' Op passed "
+                             + str(self.dilation) + ", should equal to or greater than 1.")
         if in_channels % group != 0:
             raise ValueError("Attr 'in_channels' of 'Conv2D' Op must be divisible by "
                              "attr 'group' of 'Conv2D' Op.")
@@ -107,12 +113,13 @@ class Conv2d(_Conv):
     Args:
         in_channels (int): The number of input channel :math:`C_{in}`.
         out_channels (int): The number of output channel :math:`C_{out}`.
-        kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
+        kernel_size (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the height
             and width of the 2D convolution window. Single int means the value if for both height and width of
             the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
             width of the kernel.
-        stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
-            greater or equal to 1 but bounded by the height and width of the input. Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
         pad_mode (str): Specifies padding mode. The optional values are
             "same", "valid", "pad". Default: "same".
 
@@ -130,9 +137,11 @@ class Conv2d(_Conv):
               Tensor borders. `padding` should be greater than or equal to 0.
 
         padding (int): Implicit paddings on both sides of the input. Default: 0.
-        dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
-            there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
-            or equal to 1 and bounded by the height and width of the input. Default: 1.
+        dilation (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the dilation rate
+                                      to use for dilated convolution. If set to be :math:`k > 1`, there will
+                                      be :math:`k - 1` pixels skipped for each sampling location. Its value should
+                                      be greater or equal to 1 and bounded by the height and width of the
+                                      input. Default: 1.
         group (int): Split filter into groups, `in_ channels` and `out_channels` should be
             divisible by the number of groups. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
@@ -172,6 +181,8 @@ class Conv2d(_Conv):
                  weight_init='normal',
                  bias_init='zeros'):
         kernel_size = twice(kernel_size)
+        stride = twice(stride)
+        dilation = twice(dilation)
         super(Conv2d, self).__init__(
             in_channels,
             out_channels,
@@ -241,7 +252,9 @@ class Conv2dTranspose(_Conv):
             and width of the 2D convolution window. Single int means the value is for both height and width of
             the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
             width of the kernel.
-        stride (int): Specifies the same value for all spatial dimensions. Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
         pad_mode (str): Select the mode of the pad. The optional values are
             "pad", "same", "valid". Default: "same".
 
@@ -251,8 +264,11 @@ class Conv2dTranspose(_Conv):
 
             - valid: Adopted the way of discarding.
         padding (int): Implicit paddings on both sides of the input. Default: 0.
-        dilation (int): Specifies the dilation rate to use for dilated
-            convolution. Default: 1.
+        dilation (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the dilation rate
+                                      to use for dilated convolution. If set to be :math:`k > 1`, there will
+                                      be :math:`k - 1` pixels skipped for each sampling location. Its value should
+                                      be greater or equal to 1 and bounded by the height and width of the
+                                      input. Default: 1.
         group (int): Split filter into groups, `in_channels` and `out_channels` should be
             divisible by the number of groups. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
@@ -290,6 +306,8 @@ class Conv2dTranspose(_Conv):
                  weight_init='normal',
                  bias_init='zeros'):
         kernel_size = twice(kernel_size)
+        stride = twice(stride)
+        dilation = twice(dilation)
         # out_channels and in_channels swap.
         # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
         # then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
@@ -333,26 +351,26 @@ class Conv2dTranspose(_Conv):
         self.conv2d_transpose.set_strategy(strategy)
         return self
 
-    def _deconv_output_length(self, input_length, filter_size):
+    def _deconv_output_length(self, input_length, filter_size, stride_size, dilation_size):
         """Calculate the width and height of output."""
         length = 0
         if self.is_valid:
-            if filter_size - self.stride > 0:
-                length = input_length * self.stride + filter_size - self.stride
+            if filter_size - stride_size > 0:
+                length = input_length * stride_size + filter_size - stride_size
             else:
-                length = input_length * self.stride
+                length = input_length * stride_size
         elif self.is_same:
-            length = input_length * self.stride
+            length = input_length * stride_size
         elif self.is_pad:
-            length = input_length * self.stride - 2 * self.padding + filter_size + \
-                     (filter_size - 1) * (self.dilation - 1) - self.stride
+            length = input_length * stride_size - 2 * self.padding + filter_size + \
+                     (filter_size - 1) * (dilation_size - 1) - stride_size
 
         return length
 
     def construct(self, x):
         n, _, h, w = self.shape(x)
-        h_out = self._deconv_output_length(h, self.kernel_size[0])
-        w_out = self._deconv_output_length(w, self.kernel_size[1])
+        h_out = self._deconv_output_length(h, self.kernel_size[0], self.stride[0], self.dilation[0])
+        w_out = self._deconv_output_length(w, self.kernel_size[1], self.stride[1], self.dilation[1])
         if self.has_bias:
             return self.bias_add(self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out)),
                                  self.bias)
diff --git a/mindspore/ops/_op_impl/tbe/conv2d.py b/mindspore/ops/_op_impl/tbe/conv2d.py
index 52a9eac1fa..da0f34e359 100644
--- a/mindspore/ops/_op_impl/tbe/conv2d.py
+++ b/mindspore/ops/_op_impl/tbe/conv2d.py
@@ -34,7 +34,7 @@ from mindspore.ops.op_info_register import op_info_register
             "value": "all"
         },
         {
-            "name": "pad",
+            "name": "pad_list",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index f38044ab6a..e03b163225 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -119,8 +119,8 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
         pad (int): The pad value to fill. Default: 0.
         mode (int): 0 Math convolutiuon, 1 cross-correlation convolution ,
                     2 deconvolution, 3 depthwise convolution. Default: 1.
-        stride (int): The stride to apply conv filter. Default: 1.
-        dilation (int): Specifies the dilation rate to use for dilated convolution. Default: 1.
+        stride (tuple): The stride to apply conv filter. Default: (1, 1).
+        dilation (tuple): Specifies the dilation rate to use for dilated convolution. Default: (1, 1, 1, 1).
         group (int): Splits input into groups. Default: 1.
 
     Returns:
@@ -135,8 +135,8 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
                  pad=0,
                  pad_list=(0, 0, 0, 0),
                  mode=1,
-                 stride=1,
-                 dilation=1,
+                 stride=(1, 1),
+                 dilation=(1, 1, 1, 1),
                  group=1):
         """init Convolution"""
         self.init_prim_io_names(inputs=['out_backprop', 'input', 'filter_sizes'], outputs=['output'])
@@ -146,7 +146,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
         pad_mode = pad_mode.upper()
         self.add_prim_attr('pad_mode', pad_mode)
         self.pad = pad
-        self.stride = stride
+        if isinstance(stride, tuple) and len(stride) == 4:
+            self.stride = (stride[2], stride[3])
+            self.add_prim_attr('stride', self.stride)
         self.dilation = dilation
         self.group = group
         self.add_prim_attr('data_format', "NCHW")
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 83f76455e0..a7d7357e46 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -460,8 +460,8 @@ class Conv2D(PrimitiveWithInfer):
                        2 deconvolution, 3 depthwise convolution. Default: 1.
         pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
         pad (int): The pad value to fill. Default: 0.
-        stride (int): The stride to apply conv filter. Default: 1.
-        dilation (int): Specify the space to use between kernel elements. Default: 1.
+        stride (Union(int, tuple[int])): The stride to apply conv filter. Default: 1.
+        dilation (Union(int, tuple[int])): Specify the space to use between kernel elements. Default: 1.
         group (int): Split input into groups. Default: 1.
 
     Returns:
@@ -488,11 +488,35 @@ class Conv2D(PrimitiveWithInfer):
                  group=1):
         """init Conv2D"""
         self.init_prim_io_names(inputs=['x', 'w'], outputs=['output'])
-        self.kernel_size = kernel_size
         self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple))
-        if isinstance(self.kernel_size, int):
-            self.kernel_size = (self.kernel_size, self.kernel_size)
-        validator.check_integer('length of kernel_size', len(self.kernel_size), 2, Rel.GE)
+        if isinstance(kernel_size, int):
+            self.kernel_size = (kernel_size, kernel_size)
+        if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \
+                                         (not isinstance(self.kernel_size[1], int)) or \
+                                         self.kernel_size[0] < 1 or self.kernel_size[1] < 1:
+            raise ValueError(f"The \'kernel_size\' of \'Conv2D\' should be an positive int number or "
+                             f"a tuple of two positive int numbers, but got {kernel_size}")
+        self.stride = validator.check_type('stride', stride, (int, tuple))
+        if isinstance(stride, int):
+            self.stride = (stride, stride)
+        if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or \
+                                    (not isinstance(self.stride[1], int)) or \
+                                     self.stride[0] < 1 or self.stride[1] < 1:
+            raise ValueError(f"The \'stride\' of \'Conv2D\' should be an positive int number or "
+                             f"a tuple of two positive int numbers, but got {stride}")
+        self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1]))
+        self.dilation = validator.check_type('dilation', dilation, (tuple, int))
+        if isinstance(dilation, int):
+            self.dilation = (1, 1, dilation, dilation)
+        elif len(dilation) == 2:
+            self.dilation = (1, 1, dilation[0], dilation[1])
+        if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \
+                                      (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \
+                                      (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \
+                                      (not isinstance(self.dilation[3], int) or self.dilation[3] < 1):
+            raise ValueError(f"The \'dilation\' of \'Conv2D\' should be an positive int number or "
+                             f"a tuple of two or four positive int numbers, but got {dilation}")
+        self.add_prim_attr('dilation', self.dilation)
         validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool))
         validator.equal('type of pad', type(pad), 'int', isinstance(pad, int))
         self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'])
@@ -504,18 +528,6 @@ class Conv2D(PrimitiveWithInfer):
         self.add_prim_attr('data_format', "NCHW")
         self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT)
         self.group = validator.check_integer('group', group, 0, Rel.GT)
-        self.dilation = validator.check_integer('dilation', dilation, 1, Rel.GE)
-        validator.check_type('kernel_size', kernel_size, [int, tuple])
-        if isinstance(kernel_size, int) and kernel_size < 1:
-            raise ValueError('Attr \'kernel_size\' of \'Conv2D\' Op passed '
-                             + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
-        if isinstance(kernel_size, tuple) and (len(kernel_size) != 2 or
-                                               (not isinstance(kernel_size[0], int)) or
-                                               (not isinstance(kernel_size[1], int)) or
-                                               kernel_size[0] < 1 or kernel_size[1] < 1):
-            raise ValueError('Attr \'kernel_size\' of \'Conv2D\' Op passed '
-                             + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
-        self.stride = validator.check_integer('stride', stride, 1, Rel.GE)
 
     def infer_shape(self, x_shape, w_shape):
         validator.check_integer("weight_shape", len(w_shape), 4, Rel.EQ)
@@ -526,29 +538,33 @@ class Conv2D(PrimitiveWithInfer):
 
         kernel_size_h = w_shape[2]
         kernel_size_w = w_shape[3]
+        stride_h = self.stride[2]
+        stride_w = self.stride[3]
+        dilation_h = self.dilation[2]
+        dilation_w = self.dilation[3]
 
         if self.pad_mode == "valid":
-            h_out = math.ceil((x_shape[2] - self.dilation * (kernel_size_h - 1)) / self.stride)
-            w_out = math.ceil((x_shape[3] - self.dilation * (kernel_size_w - 1)) / self.stride)
+            h_out = math.ceil((x_shape[2] - dilation_h * (kernel_size_h - 1)) / stride_h)
+            w_out = math.ceil((x_shape[3] - dilation_w * (kernel_size_w - 1)) / stride_w)
             pad_top, pad_bottom, pad_left, pad_right = 0, 0, 0, 0
         elif self.pad_mode == "same":
-            h_out = math.ceil(x_shape[2] / self.stride)
-            w_out = math.ceil(x_shape[3] / self.stride)
+            h_out = math.ceil(x_shape[2] / stride_h)
+            w_out = math.ceil(x_shape[3] / stride_w)
 
-            pad_needed_h = max(0, (h_out - 1) * self.stride + self.dilation * (kernel_size_h - 1) + 1 - x_shape[2])
+            pad_needed_h = max(0, (h_out - 1) * stride_h + dilation_h * (kernel_size_h - 1) + 1 - x_shape[2])
             pad_top = math.floor(pad_needed_h / 2)
             pad_bottom = pad_needed_h - pad_top
 
-            pad_needed_w = max(0, (w_out - 1) * self.stride + self.dilation * (kernel_size_w - 1) + 1 - x_shape[3])
+            pad_needed_w = max(0, (w_out - 1) * stride_w + dilation_w * (kernel_size_w - 1) + 1 - x_shape[3])
             pad_left = math.floor(pad_needed_w / 2)
             pad_right = pad_needed_w - pad_left
         elif self.pad_mode == 'pad':
             pad_top, pad_bottom, pad_left, pad_right = self.pad, self.pad, self.pad, self.pad
 
-            h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (self.dilation - 1)) \
-                    / self.stride
-            w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (self.dilation - 1)) \
-                    / self.stride
+            h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (dilation_h - 1)) \
+                    / stride_h
+            w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (dilation_w - 1)) \
+                    / stride_w
             h_out = math.floor(h_out)
             w_out = math.floor(w_out)
 
@@ -580,19 +596,19 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
 
     Args:
         channel_multiplier (int): The multipiler for the original output conv.
-        kernel_size (int or tuple): The size of the conv kernel.
+        kernel_size (Union[int, tuple[int]]): The size of the conv kernel.
         mode (int): 0 Math convolution, 1 cross-correlation convolution ,
                        2 deconvolution, 3 depthwise convolution. Default: 3.
         pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
         pad (int): The pad value to fill. Default: 0.
-        stride (int): The stride to apply conv filter. Default: 1.
-        dilation (int): Specifies the dilation rate to use for dilated convolution. Default: 1.
+        stride (Union[int, tuple[int]]): The stride to apply conv filter. Default: 1.
+        dilation (Union[int, tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1.
         group (int): Splits input into groups. Default: 1.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
         - **weight** (Tensor) - Set size of kernel is :math:`(K_1, K_2)`, then the shape is
-          :math:`(C_{out}, C_{in}, K_1, K_2)`.
+          :math:`(channel_multiplier, C_{in}, K_1, K_2)`.
 
     Outputs:
         Tensor of shape :math:`(N, C_{in} * \text{channel_multiplier}, H_{out}, W_{out})`.
@@ -610,15 +626,33 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
                  group=1):
         """init DepthwiseConv2dNative"""
         validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad)
-        validator.check_type("kernel_size", kernel_size, (int, tuple))
+        self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple))
         if isinstance(kernel_size, int):
-            kernel_size = (kernel_size, kernel_size)
-        if isinstance(kernel_size, tuple) and (len(kernel_size) != 2 or
-                                               (not isinstance(kernel_size[0], int)) or
-                                               (not isinstance(kernel_size[1], int)) or
-                                               kernel_size[0] < 1 or kernel_size[1] < 1):
-            raise ValueError(f"Attr kernel_size of DepthwiseConv2dNative Op not passed "
-                             f"{kernel_size}, should be a int or tuple and equal to or greater than 1.")
+            self.kernel_size = (kernel_size, kernel_size)
+        if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \
+                                         (not isinstance(self.kernel_size[1], int)) or \
+                                         self.kernel_size[0] < 1 or self.kernel_size[1] < 1:
+            raise ValueError(f"The \'kernel_size\' of \'DepthwiseConv2dNative\' should be an positive int number or "
+                             f"a tuple of two positive int numbers, but got {kernel_size}")
+        self.stride = validator.check_type('stride', stride, (int, tuple))
+        if isinstance(stride, int):
+            self.stride = (stride, stride)
+        if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or \
+                                    (not isinstance(self.stride[1], int)) or \
+                                    self.stride[0] < 1 or self.stride[1] < 1:
+            raise ValueError(f"The \'stride\' of \'DepthwiseConv2dNative\' should be an positive int number or "
+                             f"a tuple of two positive int numbers, but got {stride}")
+        self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1]))
+        self.dilation = validator.check_type('dilation', dilation, (tuple, int))
+        if isinstance(dilation, int):
+            self.dilation = (dilation, dilation)
+        if len(self.dilation) != 2 or (not isinstance(self.dilation[0], int)) or \
+                                      (not isinstance(self.dilation[1], int)) or \
+                                      self.dilation[0] < 1 or self.dilation[1] < 1:
+            raise ValueError(f"The \'dilation\' of \'DepthwiseConv2dNative\' should be an positive int number or "
+                             f"a tuple of two or four positive int numbers, but got {dilation}")
+        self.add_prim_attr('dilation', (1, 1, self.dilation[0], self.dilation[1]))
+        validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool))
         if pad_mode not in ("same", "valid", "pad"):
             raise ValueError(f"Attr pad_mode of DepthwiseConv2dNative Op not passed"
                              f"{pad_mode} not in valid, same, pad.")
@@ -627,9 +661,6 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
         self.add_prim_attr('data_format', "NCHW")
         self.channel_multiplier = validator.check_integer("channel_multiplier", channel_multiplier, 0, Rel.GT)
         self.group = validator.check_integer("group", group, 0, Rel.GT)
-        self.dilation = validator.check_integer("dilation", dilation, 1, Rel.GE)
-        self.kernel_size = validator.check_value_on_integer("kernel_size", kernel_size, 1, Rel.GE)
-        self.stride = validator.check_integer("stride", stride, 1, Rel.GE)
         self.pad = pad
 
     def infer_shape(self, x_shape, w_shape):
@@ -640,29 +671,33 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
 
         kernel_size_h = w_shape[2]
         kernel_size_w = w_shape[3]
+        stride_h = self.stride[2]
+        stride_w = self.stride[3]
+        dilation_h = self.dilation[2]
+        dilation_w = self.dilation[3]
 
         if self.pad_mode == "valid":
-            h_out = math.ceil((x_shape[2] - self.dilation * (kernel_size_h - 1)) / self.stride)
-            w_out = math.ceil((x_shape[3] - self.dilation * (kernel_size_w - 1)) / self.stride)
+            h_out = math.ceil((x_shape[2] - dilation_h * (kernel_size_h - 1)) / stride_h)
+            w_out = math.ceil((x_shape[3] - dilation_w * (kernel_size_w - 1)) / stride_w)
             pad_top, pad_bottom, pad_left, pad_right = 0, 0, 0, 0
         elif self.pad_mode == "same":
-            h_out = math.ceil(x_shape[2] / self.stride)
-            w_out = math.ceil(x_shape[3] / self.stride)
+            h_out = math.ceil(x_shape[2] / stride_h)
+            w_out = math.ceil(x_shape[3] / stride_w)
 
-            pad_needed_h = max(0, (h_out - 1) * self.stride + self.dilation * (kernel_size_h - 1) + 1 - x_shape[2])
+            pad_needed_h = max(0, (h_out - 1) * stride_h+ dilation_h * (kernel_size_h - 1) + 1 - x_shape[2])
             pad_top = math.floor(pad_needed_h / 2)
             pad_bottom = pad_needed_h - pad_top
 
-            pad_needed_w = max(0, (w_out - 1) * self.stride + self.dilation * (kernel_size_w - 1) + 1 - x_shape[3])
+            pad_needed_w = max(0, (w_out - 1) * stride_w + dilation_w * (kernel_size_w - 1) + 1 - x_shape[3])
             pad_left = math.floor(pad_needed_w / 2)
             pad_right = pad_needed_w - pad_left
         elif self.pad_mode == 'pad':
             pad_top, pad_bottom, pad_left, pad_right = self.pad, self.pad, self.pad, self.pad
 
-            h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (self.dilation - 1)) \
-                    / self.stride
-            w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (self.dilation - 1)) \
-                    / self.stride
+            h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (dilation_h - 1)) \
+                    / stride_h
+            w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (dilation_w - 1)) \
+                    / stride_w
             h_out = math.floor(h_out)
             w_out = math.floor(w_out)
         else:
@@ -715,7 +750,7 @@ class _Pool(PrimitiveWithInfer):
                     (not isinstance(ksize[1], int)) or
                     ksize[0] <= 0 or
                     ksize[1] <= 0):
-                raise ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number or"
+                raise ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number or "
                                  f"a tuple of two positive int numbers, but got {ksize}")
             self.ksize = (1, 1, ksize[0], ksize[1])
         if self.is_maxpoolwithargmax:
@@ -731,7 +766,7 @@ class _Pool(PrimitiveWithInfer):
                     (not isinstance(strides[1], int)) or
                     strides[0] <= 0 or
                     strides[1] <= 0):
-                raise ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number or"
+                raise ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number or "
                                  f"a tuple of two positive int numbers, but got {strides}")
             self.strides = (1, 1, strides[0], strides[1])
         if self.is_maxpoolwithargmax:
@@ -853,7 +888,6 @@ class MaxPoolWithArgmax(_Pool):
         - **output** (Tensor) -  Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
         - **mask** (Tensor) -  Max values' index represented by the mask.
     """
-
     def __init__(self, ksize=1, strides=1, padding="valid"):
         super(MaxPoolWithArgmax, self).__init__(ksize, strides, padding)
         self.is_tbe = context.get_context("device_target") == "Ascend"
@@ -944,8 +978,8 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         pad (int): The pad value to fill. Default: 0.
         mode (int): 0 Math convolutiuon, 1 cross-correlation convolution ,
                        2 deconvolution, 3 depthwise convolution. Default: 1.
-        stride (int): The stride to apply conv filter. Default: 1.
-        dilation (int): Specifies the dilation rate to use for dilated convolution. Default: 1.
+        stride (Union[int. tuple[int]]): The stride to apply conv filter. Default: 1.
+        dilation (Union[int. tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1.
         group (int): Splits input into groups. Default: 1.
 
     Returns:
@@ -967,25 +1001,41 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output'])
         self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT)
         self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple))
-        if isinstance(self.kernel_size, int):
-            if kernel_size < 1:
-                raise ValueError('Attr \'kernel_size\' of \'Conv2DBackpropInput\' Op passed '
-                                 + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
-            self.kernel_size = (self.kernel_size, self.kernel_size)
-        elif isinstance(kernel_size, tuple) and (len(kernel_size) != 2 or
-                                                 (not isinstance(kernel_size[0], int)) or
-                                                 (not isinstance(kernel_size[1], int)) or
-                                                 kernel_size[0] < 1 or kernel_size[1] < 1):
-            raise ValueError('Attr \'kernel_size\' of \'Conv2DBackpropInput\' Op passed '
-                             + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
+        if isinstance(kernel_size, int):
+            self.kernel_size = (kernel_size, kernel_size)
+        if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \
+                                         (not isinstance(self.kernel_size[1], int)) or \
+                                         self.kernel_size[0] < 1 or self.kernel_size[1] < 1:
+            raise ValueError(f"The \'kernel_size\' of \'Conv2DBackpropInput\' should be an positive int number or "
+                             f"a tuple of two positive int numbers, but got {kernel_size}")
+        self.stride = validator.check_type('stride', stride, (int, tuple))
+        if isinstance(stride, int):
+            self.stride = (stride, stride)
+        elif isinstance(stride, tuple) and len(stride) == 4:
+            self.stride = (stride[2], stride[3])
+        if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or (not isinstance(self.stride[1], int)) or \
+                self.stride[0] < 1 or self.stride[1] < 1:
+            raise ValueError(f"The \'stride\' of \'Conv2DBackpropInput\' should be an positive int number or "
+                             f"a tuple of two or four positive int numbers, but got {stride}")
+        self.add_prim_attr('stride', self.stride)
+        self.dilation = validator.check_type('dilation', dilation, (tuple, int))
+        if isinstance(dilation, int):
+            self.dilation = (1, 1, dilation, dilation)
+        elif len(dilation) == 2:
+            self.dilation = (1, 1, dilation[0], dilation[1])
+        if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \
+                                      (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \
+                                      (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \
+                                      (not isinstance(self.dilation[3], int) or self.dilation[3] < 1):
+            raise ValueError(f"The \'dilation\' of \'Conv2DBackpropInput\' should be an positive int number or "
+                             f"a tuple of two or four positive int numbers, but got {dilation}")
+        self.add_prim_attr('dilation', self.dilation)
         validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool))
         validator.equal('type of pad', type(pad), 'int', isinstance(pad, int))
         self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'])
         self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad)
         self.mode = validator.check_integer('mode', mode, 1, Rel.EQ)
         self.group = validator.check_integer('group', group, 0, Rel.GT)
-        self.dilation = validator.check_integer('dilation', dilation, 1, Rel.GE)
-        self.stride = validator.check_integer('stride', stride, 1, Rel.GE)
         pad_mode = pad_mode.upper()
         self.add_prim_attr('pad_mode', pad_mode)
         self.add_prim_attr('data_format', "NCHW")
@@ -1004,16 +1054,18 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         dout_shape = doutput['shape']
         kernel_h = self.kernel_size[0]
         kernel_w = self.kernel_size[1]
+        stride_h = self.stride[0]
+        stride_w = self.stride[1]
         # default pad mode is valid
         pad_list = (0, 0, 0, 0)
         if self.pad_list:
             pad_list = tuple(self.pad_list)
         elif self.pad_mode == "SAME":
-            pad_needed_h = max(0, (dout_shape[2] - 1) * self.stride + kernel_h - x_size_v[2])
+            pad_needed_h = max(0, (dout_shape[2] - 1) * stride_h + kernel_h - x_size_v[2])
             pad_top = math.floor(pad_needed_h / 2)
             pad_bottom = pad_needed_h - pad_top
 
-            pad_needed_w = max(0, (dout_shape[3] - 1) * self.stride + kernel_w - x_size_v[3])
+            pad_needed_w = max(0, (dout_shape[3] - 1) * stride_w + kernel_w - x_size_v[3])
             pad_left = math.floor(pad_needed_w / 2)
             pad_right = pad_needed_w - pad_left
             pad_list = (pad_top, pad_bottom, pad_left, pad_right)
diff --git a/tests/st/ops/cpu/test_conv2d_backprop_filter_op.py b/tests/st/ops/cpu/test_conv2d_backprop_filter_op.py
index 75ca915499..c2f8422e30 100644
--- a/tests/st/ops/cpu/test_conv2d_backprop_filter_op.py
+++ b/tests/st/ops/cpu/test_conv2d_backprop_filter_op.py
@@ -35,8 +35,8 @@ class Net4(nn.Cell):
                                            pad_mode="valid",
                                            pad=0,
                                            mode=1,
-                                           stride=1,
-                                           dilation=1,
+                                           stride=(1, 1),
+                                           dilation=(1, 1, 1, 1),
                                            group=1)
         self.w = Parameter(initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='w')
         self.x = Parameter(initializer(Tensor(np.array([[[
diff --git a/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py b/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
index 6e2e76cd47..0f66f2fac5 100644
--- a/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
+++ b/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
@@ -35,8 +35,8 @@ class Conv2dFilter(nn.Cell):
                                                   pad_mode="valid",
                                                   pad=0,
                                                   mode=1,
-                                                  stride=1,
-                                                  dilation=1,
+                                                  stride=(1, 1),
+                                                  dilation=(1, 1, 1, 1),
                                                   group=1)
 
         self.get_shape = P.Shape()
diff --git a/tests/ut/cpp/python_input/gtest_input/pynative/ops_test.py b/tests/ut/cpp/python_input/gtest_input/pynative/ops_test.py
index 46c6fdd1cf..c7de09dbcb 100644
--- a/tests/ut/cpp/python_input/gtest_input/pynative/ops_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pynative/ops_test.py
@@ -21,17 +21,17 @@ from mindspore.common.tensor import Tensor
 def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
     """Rearranges an image to row vector"""
     batch_num, channel, height, width = img.shape
-    out_h = (height + 2*pad - filter_h - (filter_h - 1) * (dilation - 1))//stride + 1
-    out_w = (width + 2*pad - filter_w - (filter_w - 1) * (dilation - 1))//stride + 1
+    out_h = (height + 2*pad - filter_h - (filter_h - 1) * (dilation[2] - 1))//stride[2] + 1
+    out_w = (width + 2*pad - filter_w - (filter_w - 1) * (dilation[3] - 1))//stride[3] + 1
 
     img = np.pad(img, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
     col = np.zeros((batch_num, channel, filter_h, filter_w, out_h, out_w)).astype(img.dtype)
 
     for y in range(filter_h):
-        y_max = y + stride*out_h
+        y_max = y + stride[2]*out_h
         for x in range(filter_w):
-            x_max = x + stride*out_w
-            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
+            x_max = x + stride[2]*out_w
+            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride[2], x:x_max:stride[2]]
 
     col = col.transpose(0, 4, 5, 1, 2, 3).reshape(batch_num*out_h*out_w, -1)
     return col
@@ -42,8 +42,8 @@ def conv2d(x, weight, bias=None, stride=1, pad=0,
     """Convolution 2D"""
     batch_num, _, x_h, x_w = x.shape
     filter_num, _, filter_h, filter_w = weight.shape
-    out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) * (dilation - 1)) / stride)
-    out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation - 1)) / stride)
+    out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) * (dilation[2] - 1)) / stride[2])
+    out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation[3] - 1)) / stride[3])
     col = im2col(x, filter_h, filter_w, stride, pad, dilation)
     col_w = np.reshape(weight, (filter_num, -1)).T
     out = np.dot(col, col_w)
diff --git a/tests/vm_impl/vm_me.py b/tests/vm_impl/vm_me.py
index ba51a3b13b..a189aa8b90 100644
--- a/tests/vm_impl/vm_me.py
+++ b/tests/vm_impl/vm_me.py
@@ -155,23 +155,35 @@ def batch_norm_grad(dy, x, scale, save_mean, save_inv_variance):
 
 def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
     """Rearranges a row vector to an image."""
-    validator.check_integer("stride", stride, 0, Rel.GT)
+    if isinstance(stride, int):
+        stride_h = stride
+        stride_w = stride
+    elif isinstance(stride, tuple) and len(stride) == 2:
+        stride_h = stride[0]
+        stride_w = stride[1]
+    elif isinstance(stride, tuple) and len(stride) == 3:
+        stride_h = stride[2]
+        stride_w = stride[3]
+    else:
+        raise ValueError(f"The \'stride\' should be an int number or "
+                         f"a tuple of two or four int numbers, but got {stride}")
+
     batch_num, channel, height, width = input_shape
-    out_h = (height + 2*pad - filter_h)//stride + 1
-    out_w = (width + 2*pad - filter_w)//stride + 1
+    out_h = (height + 2*pad - filter_h)//stride_h + 1
+    out_w = (width + 2*pad - filter_w)//stride_w + 1
     col = col.reshape(batch_num, out_h, out_w, channel, filter_h, filter_w) \
              .transpose(0, 3, 4, 5, 1, 2)
 
     img = np.zeros((batch_num,
                     channel,
-                    height + 2*pad + stride - 1,
-                    width + 2*pad + stride - 1)) \
+                    height + 2*pad + stride_h - 1,
+                    width + 2*pad + stride_w - 1)) \
             .astype(col.dtype)
     for y in range(filter_h):
-        y_max = y + stride*out_h
+        y_max = y + stride_h*out_h
         for x in range(filter_w):
-            x_max = x + stride*out_w
-            img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]
+            x_max = x + stride_h*out_w
+            img[:, :, y:y_max:stride_h, x:x_max:stride_h] += col[:, :, y, x, :, :]
 
     return img[:, :, pad:height + pad, pad:width + pad]
 
@@ -205,11 +217,35 @@ def conv2d(x, weight, bias=None, stride=1, pad=0,
            dilation=1, groups=1, padding_mode='zeros'):
     """Convolution 2D."""
     # pylint: disable=unused-argument
-    validator.check_integer("stride", stride, 0, Rel.GT)
+    validator.check_type('stride', stride, (int, tuple))
+    if isinstance(stride, int):
+        stride = (stride, stride)
+    elif len(stride) == 4:
+        stride = (stride[2], stride[3])
+    if len(stride) != 2 or (not isinstance(stride[0], int)) or \
+                           (not isinstance(stride[1], int)) or \
+                           stride[0] < 1 or stride[1] < 1:
+        raise ValueError(f"The \'stride\' of \'conv2d\' should be an positive int number or "
+                         f"a tuple of two positive int numbers, but got {stride}")
+    stride_h = stride[0]
+    stride_w = stride[1]
+    validator.check_type('dilation', dilation, (int, tuple))
+    if isinstance(dilation, int):
+        dilation = (dilation, dilation)
+    elif len(dilation) == 4:
+        dilation = (dilation[2], dilation[3])
+    if len(dilation) != 2 or (not isinstance(dilation[0], int)) or \
+                           (not isinstance(dilation[1], int)) or \
+                           dilation[0] < 1 or dilation[1] < 1:
+        raise ValueError(f"The \'dilation\' of \'conv2d\' should be an positive int number or "
+                         f"a tuple of two positive int numbers, but got {dilation}")
+    dilation_h = dilation[0]
+    dilation_w = dilation[1]
+
     batch_num, _, x_h, x_w = x.shape
     filter_num, _, filter_h, filter_w = weight.shape
-    out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) * (dilation - 1)) / stride)
-    out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation - 1)) / stride)
+    out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) * (dilation_h - 1)) / stride_h)
+    out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation_w - 1)) / stride_w)
     col = im2col(x, filter_h, filter_w, stride, pad, dilation)
     col_w = np.reshape(weight, (filter_num, -1)).T
     out = np.dot(col, col_w)
@@ -286,19 +322,43 @@ def flatten_grad(dout, x):
 
 def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
     """Rearranges an image to row vector."""
-    validator.check_integer("stride", stride, 0, Rel.GT)
+    if isinstance(stride, int):
+        stride_h = stride
+        stride_w = stride
+    elif isinstance(stride, tuple) and len(stride) == 2:
+        stride_h = stride[0]
+        stride_w = stride[1]
+    elif isinstance(stride, tuple) and len(stride) == 3:
+        stride_h = stride[2]
+        stride_w = stride[3]
+    else:
+        raise ValueError(f"The \'stride\' should be an int number or "
+                         f"a tuple of two or four int numbers, but got {stride}")
+    if isinstance(dilation, int):
+        dilation_h = dilation
+        dilation_w = dilation
+    elif isinstance(dilation, tuple) and len(dilation) == 2:
+        dilation_h = dilation[0]
+        dilation_w = dilation[1]
+    elif isinstance(dilation, tuple) and len(dilation) == 3:
+        dilation_h = dilation[2]
+        dilation_w = dilation[3]
+    else:
+        raise ValueError(f"The \'dilation\' should be an int number or "
+                         f"a tuple of two or four int numbers, but got {dilation}")
+
     batch_num, channel, height, width = img.shape
-    out_h = (height + 2*pad - filter_h- (filter_h - 1) * (dilation - 1))//stride + 1
-    out_w = (width + 2*pad - filter_w- (filter_w - 1) * (dilation - 1))//stride + 1
+    out_h = (height + 2*pad - filter_h- (filter_h - 1) * (dilation_h - 1))//stride_h + 1
+    out_w = (width + 2*pad - filter_w- (filter_w - 1) * (dilation_w - 1))//stride_w + 1
 
     img = np.pad(img, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
     col = np.zeros((batch_num, channel, filter_h, filter_w, out_h, out_w)).astype(img.dtype)
 
     for y in range(filter_h):
-        y_max = y + stride*out_h
+        y_max = y + stride_h*out_h
         for x in range(filter_w):
-            x_max = x + stride*out_w
-            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
+            x_max = x + stride_h*out_w
+            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride_h, x:x_max:stride_h]
 
     col = col.transpose(0, 4, 5, 1, 2, 3).reshape(batch_num*out_h*out_w, -1)
     return col

From 652ab6c386b8afb54e542f6ba1d9865e5fb955d9 Mon Sep 17 00:00:00 2001
From: chenzomi <chenzomi12@gmail.com>
Date: Mon, 13 Apr 2020 15:39:51 +0800
Subject: [PATCH 211/367] add test case for aware quantizaiton

---
 .../gpu/quant/batchnorm_fold2_gpu_kernel.h    |  14 +--
 .../quant/batchnorm_fold2_grad_gpu_kernel.h   |  14 +--
 .../gpu/quant/batchnorm_fold_gpu_kernel.h     |  14 +--
 .../quant/batchnorm_fold_grad_gpu_kernel.h    |  14 ++-
 .../gpu/quant/correction_mul_gpu_kernel.h     |  14 +--
 .../quant/correction_mul_grad_gpu_kernel.h    |  16 +--
 mindspore/nn/layer/activation.py              |   2 +-
 mindspore/ops/operations/nn_ops.py            |   2 +-
 tests/st/ops/gpu/test_batchnorm_fold2_op.py   |  89 ++++++++++++++
 .../st/ops/gpu/test_batchnorm_fold_grad_op.py |  96 +++++++++++++++
 tests/st/ops/gpu/test_batchnorm_fold_op.py    | 116 ++++++++++++++++++
 tests/st/ops/gpu/test_conv2d_op.py            |   2 +-
 .../st/ops/gpu/test_correction_mul_grad_op.py |  55 +++++++++
 tests/st/ops/gpu/test_correction_mul_op.py    |  52 ++++++++
 14 files changed, 456 insertions(+), 44 deletions(-)
 create mode 100644 tests/st/ops/gpu/test_batchnorm_fold2_op.py
 create mode 100644 tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
 create mode 100644 tests/st/ops/gpu/test_batchnorm_fold_op.py
 create mode 100644 tests/st/ops/gpu/test_correction_mul_grad_op.py
 create mode 100644 tests/st/ops/gpu/test_correction_mul_op.py

diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
index ada4eabd86..3e246f18f6 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
@@ -38,14 +38,14 @@ class BatchNormFold2GpuKernel : public GpuKernel {
 
   ~BatchNormFold2GpuKernel() override { DestroyResource(); }
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
 
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
 
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
 
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     if (is_null_input_) {
       return true;
     }
@@ -66,7 +66,7 @@ class BatchNormFold2GpuKernel : public GpuKernel {
     return true;
   }
 
-  bool Init(const CNodePtr &kernel_node) {
+  bool Init(const CNodePtr &kernel_node) override {
     InitResource();
 
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
@@ -98,9 +98,9 @@ class BatchNormFold2GpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitResource() { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
+  void InitResource() override { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
 
-  void InitSizeLists() {
+  void InitSizeLists() override {
     size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
     size_t weight_size = channel_ * sizeof(T);
     input_size_list_.push_back(input_size);
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
index ef9611f258..099960e7fa 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.h
@@ -38,14 +38,14 @@ class BatchNormFold2GradGpuKernel : public GpuKernel {
 
   ~BatchNormFold2GradGpuKernel() override { DestroyResource(); }
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
 
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
 
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
 
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     if (is_null_input_) {
       return true;
     }
@@ -88,7 +88,7 @@ class BatchNormFold2GradGpuKernel : public GpuKernel {
     return true;
   }
 
-  bool Init(const CNodePtr &kernel_node) {
+  bool Init(const CNodePtr &kernel_node) override {
     InitResource();
 
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
@@ -120,9 +120,9 @@ class BatchNormFold2GradGpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitResource() { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
+  void InitResource() override { cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
 
-  void InitSizeLists() {
+  void InitSizeLists() override {
     size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
     size_t weight_size = channel_ * sizeof(T);
     size_t workspace_size = batch_size_ * channel_ * sizeof(T);
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
index e90fac2792..3e8c1ca52b 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.h
@@ -46,14 +46,14 @@ class BatchNormFoldGpuKernel : public GpuKernel {
 
   ~BatchNormFoldGpuKernel() override { DestroyResource(); }
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
 
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
 
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
 
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     (void)workspace;
     auto x = reinterpret_cast<T *>(inputs[0]->addr);
     auto mean = reinterpret_cast<T *>(inputs[1]->addr);
@@ -104,7 +104,7 @@ class BatchNormFoldGpuKernel : public GpuKernel {
     return true;
   }
 
-  bool Init(const CNodePtr &kernel_node) {
+  bool Init(const CNodePtr &kernel_node) override {
     InitResource();
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
     if (input_num != 4) {
@@ -152,7 +152,7 @@ class BatchNormFoldGpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitSizeLists() {
+  void InitSizeLists() override {
     // x, mean, variance, current_step
     input_size_list_.push_back(input_size_);
     input_size_list_.push_back(output_size_);
@@ -169,7 +169,7 @@ class BatchNormFoldGpuKernel : public GpuKernel {
     workspace_size_list_.push_back(input_size_);
   }
 
-  void InitResource() {
+  void InitResource() override {
     handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&scale_bias_mean_var_desc_), "Create para desc failed");
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
index 830f6dc243..ec845fbb9e 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_grad_gpu_kernel.h
@@ -42,11 +42,12 @@ class BatchNormFoldGradGpuKernel : public GpuKernel {
         width_(0) {}
   ~BatchNormFoldGradGpuKernel() = default;
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     (void)workspace;
     // 'd_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'current_step'
     T *d_batch_mean = GetDeviceAddress<T>(inputs, 0);
@@ -92,7 +93,8 @@ class BatchNormFoldGradGpuKernel : public GpuKernel {
                          reinterpret_cast<cudaStream_t>(stream_ptr));
     return true;
   }
-  bool Init(const CNodePtr &kernel_node) {
+
+  bool Init(const CNodePtr &kernel_node) override {
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
     if (input_num != 6) {
       MS_LOG(ERROR) << "Input number is " << input_num << ", but BatchNormFoldGrad GpuKernel OP needs 6 input.";
@@ -128,7 +130,7 @@ class BatchNormFoldGradGpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitSizeLists() {
+  void InitSizeLists() override {
     // 'd_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'current_step'
     input_size_list_.push_back(channel_size_);
     input_size_list_.push_back(channel_size_);
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
index af23d7732a..7608ae5d3c 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
@@ -30,11 +30,11 @@ class CorrectionMulGpuKernel : public GpuKernel {
   CorrectionMulGpuKernel() : batch_size_(0), channel_(0), height_(0), width_(0) {}
   ~CorrectionMulGpuKernel() override { DestroyResource(); }
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     auto *weight = GetDeviceAddress<T>(inputs, 0);
     auto *gamma = GetDeviceAddress<T>(inputs, 1);
     auto *running_std = GetDeviceAddress<T>(inputs, 2);
@@ -44,7 +44,7 @@ class CorrectionMulGpuKernel : public GpuKernel {
                      reinterpret_cast<cudaStream_t>(stream_ptr));
     return true;
   }
-  bool Init(const CNodePtr &kernel_node) {
+  bool Init(const CNodePtr &kernel_node) override {
     InitResource();
 
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
@@ -69,7 +69,7 @@ class CorrectionMulGpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitSizeLists() {
+  void InitSizeLists() override {
     size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
     size_t weight_size = batch_size_ * sizeof(T);
     input_size_list_.push_back(input_size);   // weight
@@ -79,7 +79,7 @@ class CorrectionMulGpuKernel : public GpuKernel {
     output_size_list_.push_back(input_size);
     workspace_size_list_.push_back(workspace_size);
   }
-  void InitResource() {}
+  void InitResource() override {}
 
  private:
   void DestroyResource() noexcept {}
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
index f20c6278c0..2439826cc3 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
@@ -30,11 +30,12 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
   CorrectionMulGradGpuKernel() : batch_size_(0), channel_(0), height_(0), width_(0) {}
   ~CorrectionMulGradGpuKernel() override { DestroyResource(); }
 
-  const std::vector<size_t> &GetInputSizeList() const { return input_size_list_; }
-  const std::vector<size_t> &GetOutputSizeList() const { return output_size_list_; }
-  const std::vector<size_t> &GetWorkspaceSizeList() const { return workspace_size_list_; }
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
     auto *d_out = GetDeviceAddress<T>(inputs, 0);
     auto *weight = GetDeviceAddress<T>(inputs, 1);
     auto *gamma = GetDeviceAddress<T>(inputs, 2);
@@ -49,7 +50,8 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
                          reinterpret_cast<cudaStream_t>(stream_ptr));
     return true;
   }
-  bool Init(const CNodePtr &kernel_node) {
+
+  bool Init(const CNodePtr &kernel_node) override {
     InitResource();
 
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
@@ -74,7 +76,7 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
   }
 
  protected:
-  void InitSizeLists() {
+  void InitSizeLists() override {
     size_t input_size = batch_size_ * channel_ * height_ * width_ * sizeof(T);
     size_t weight_size = batch_size_ * sizeof(T);
     input_size_list_.push_back(input_size);      // d_out
@@ -85,7 +87,7 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
     output_size_list_.push_back(weight_size);    // d_gamma
     workspace_size_list_.push_back(input_size);  // tmp d_out * weight
   }
-  void InitResource() {}
+  void InitResource() override {}
 
  private:
   void DestroyResource() noexcept {}
diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py
index 12d6c74dcd..6485e27228 100644
--- a/mindspore/nn/layer/activation.py
+++ b/mindspore/nn/layer/activation.py
@@ -369,7 +369,7 @@ class HSigmoid(Cell):
     Hard sigmoid is defined as:
 
     .. math::
-        \text{hsigmoid}(x_{i}) = max(0, min(1, \ftac{2 * x_{i} + 5}{10})),
+        \text{hsigmoid}(x_{i}) = max(0, min(1, \frac{2 * x_{i} + 5}{10})),
 
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index e82f59a05d..0dab6b28a6 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -319,7 +319,7 @@ class HSigmoid(PrimitiveWithInfer):
     Hard sigmoid is defined as:
 
     .. math::
-        \text{hsigmoid}(x_{i}) = max(0, min(1, \ftac{2 * x_{i} + 5}{10})),
+        \text{hsigmoid}(x_{i}) = max(0, min(1, \frac{2 * x_{i} + 5}{10})),
 
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
diff --git a/tests/st/ops/gpu/test_batchnorm_fold2_op.py b/tests/st/ops/gpu/test_batchnorm_fold2_op.py
new file mode 100644
index 0000000000..0440e92a8d
--- /dev/null
+++ b/tests/st/ops/gpu/test_batchnorm_fold2_op.py
@@ -0,0 +1,89 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import mindspore.context as context
+
+context.set_context(device_target='GPU')
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op = P.BatchNormFold2(100000)
+
+    @ms_function
+    def construct(self, x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step):
+        return self.op(x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step)
+
+
+class Net_gnd(nn.Cell):
+    def __init__(self):
+        super(Net_gnd, self).__init__()
+        self.conv_mul = P.ConvMul(freeze_bn=100000)
+        self.correct_add = P.CorrectionAdd(freeze_bn=100000)
+        self.add_fold = P.AddFold()
+
+    @ms_function
+    def construct(self, x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step):
+        out = self.conv_mul(x, batch_std, running_std, current_step)
+        out = self.correct_add(out, gamma, batch_std, batch_mean,
+                               running_std, running_mean, current_step)
+        out = self.add_fold(out, beta, gamma, batch_std, batch_mean)
+        return out
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnrom_fold2():
+    net = Net()
+    c = 64
+    freeze_bn = 100000
+    x = np.random.uniform(-1, 1, size=[3, c, 32, 32]).astype('float32')
+    beta = np.random.uniform(1, 2, size=[c]).astype('float32')
+    gamma = np.random.uniform(1, 2, size=[c]).astype('float32')
+    batch_std = np.random.uniform(1, 2, size=[c]).astype('float32')
+    batch_mean = np.random.uniform(1, 2, size=[c]).astype('float32')
+    running_std = np.random.uniform(1, 2, size=[c]).astype('float32')
+    running_mean = np.random.uniform(1, 2, size=[c]).astype('float32')
+    current_step = np.array([0]).astype('int32')
+    output = net(Tensor(x), Tensor(beta), Tensor(gamma), Tensor(batch_std), Tensor(batch_mean),
+                 Tensor(running_std), Tensor(running_mean), Tensor(current_step))
+    expect = (x + beta.reshape(-1, 1, 1) - (gamma * running_mean / running_std).reshape(-1, 1,
+                                                                                        1) if current_step >= freeze_bn else
+              x * (running_std / batch_std).reshape(-1, 1, 1) + (beta - gamma * batch_mean / batch_std).reshape(-1, 1,
+                                                                                                                1))
+    error = np.ones(shape=expect.shape) * 1.0e-6
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(diff > error * -1)
+
+    current_step = np.array([100000]).astype('int32')
+    output = net(Tensor(x), Tensor(beta), Tensor(gamma), Tensor(batch_std), Tensor(batch_mean), Tensor(running_std),
+                 Tensor(running_mean), Tensor(current_step))
+    expect = (x + beta.reshape(-1, 1, 1) - (gamma * running_mean / running_std).reshape(-1, 1,
+                                                                                        1) if current_step >= freeze_bn else
+              x * (batch_std / running_std).reshape(-1, 1, 1) + (beta - gamma * batch_mean / batch_std).reshape(-1, 1,
+                                                                                                                1))
+    error = np.ones(shape=expect.shape) * 1.0e-6
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(diff > error * -1)
diff --git a/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py b/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
new file mode 100644
index 0000000000..8e55f6a473
--- /dev/null
+++ b/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
@@ -0,0 +1,96 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import mindspore.context as context
+
+context.set_context(device_target='GPU')
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op = P.BatchNormFoldGrad(freeze_bn=10)
+
+    @ms_function
+    def construct(self, d_batch_mean, d_batch_std, x, batch_mean, batch_std, current_step):
+        dx = self.op(d_batch_mean, d_batch_std, x, batch_mean, batch_std, current_step)
+        return dx
+
+
+def np_result(d_batch_mean, d_batch_std, x, batch_mean, batch_std):
+    n = x.shape[0] * x.shape[2] * x.shape[3]
+    dx = d_batch_mean.reshape(1, -1, 1, 1) / n + d_batch_std.reshape(1, -1, 1, 1) * (
+            x - batch_mean.reshape(1, -1, 1, 1)) / batch_std.reshape(1, -1, 1, 1) / n
+    return dx
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold_grad1():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[3, c, 32, 32]).astype('float32')
+    d_batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    d_batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([0]).astype('int32')
+    dx = net(Tensor(d_batch_mean), Tensor(d_batch_std), Tensor(x), Tensor(batch_mean), Tensor(batch_std),
+             Tensor(current_step))
+    expect = np_result(d_batch_mean, d_batch_std, x, batch_mean, batch_std)
+    assert np.allclose(dx.asnumpy(), expect, rtol=1.e-7, atol=1.e-7)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold_grad2():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[1, c, 256, 256]).astype('float32')
+    d_batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    d_batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([0]).astype('int32')
+    dx = net(Tensor(d_batch_mean), Tensor(d_batch_std), Tensor(x), Tensor(batch_mean), Tensor(batch_std),
+             Tensor(current_step))
+    expect = np_result(d_batch_mean, d_batch_std, x, batch_mean, batch_std)
+    assert np.allclose(dx.asnumpy(), expect, rtol=1.e-7, atol=1.e-7)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold_grad_freeze():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[3, c, 32, 32]).astype('float32')
+    d_batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    d_batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    batch_std = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([10]).astype('int32')
+    dx = net(Tensor(d_batch_mean), Tensor(d_batch_std), Tensor(x), Tensor(batch_mean), Tensor(batch_std),
+             Tensor(current_step))
+    expect = np.zeros_like(x)
+    assert np.allclose(dx.asnumpy(), expect, rtol=1.e-7, atol=1.e-7)
diff --git a/tests/st/ops/gpu/test_batchnorm_fold_op.py b/tests/st/ops/gpu/test_batchnorm_fold_op.py
new file mode 100644
index 0000000000..c4abf152a6
--- /dev/null
+++ b/tests/st/ops/gpu/test_batchnorm_fold_op.py
@@ -0,0 +1,116 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import mindspore.context as context
+
+context.set_context(device_target='GPU')
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op = P.BatchNormFold(freeze_bn=10)
+
+    @ms_function
+    def construct(self, x, mean, variance, current_step):
+        a, b, c, d = self.op(x, mean, variance, current_step)
+        return a, b, c, d
+
+
+def np_result(x, mean, var, momentum, epsilon):
+    np_mean = x.mean(axis=(0, 2, 3))
+    np_var = x.var(axis=(0, 2, 3))
+    n = x.shape[0] * x.shape[2] * x.shape[3]
+    mean_update = momentum * np_mean + (1 - momentum) * mean
+    var_update = momentum * np_var * n / (n - 1) + (1 - momentum) * var
+    np_var = np.sqrt(np_var + epsilon)
+    delay_mean = mean.copy()
+    delay_std = np.sqrt(var + epsilon)
+    return np_mean, np_var, mean_update, var_update, delay_mean, delay_std
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[3, c, 32, 32]).astype('float32')
+    mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    variance = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([0]).astype('int32')
+    ms_mean = Tensor(mean)
+    ms_var = Tensor(variance)
+    batch_mean, batch_var, delay_mean, delay_std = net(Tensor(x), ms_mean, ms_var,
+                                                       Tensor(current_step))
+
+    expect1, expect2, expect3, expect4, expect5, expect6 = np_result(x, mean, variance, 0.9, 1e-12)
+    assert np.allclose(batch_mean.asnumpy(), expect1, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(batch_var.asnumpy(), expect2, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(ms_mean.asnumpy(), expect3, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(ms_var.asnumpy(), expect4, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_mean.asnumpy(), expect5, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_std.asnumpy(), expect6, rtol=1.e-7, atol=1.e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold2():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[3, c, 512, 512]).astype('float32')
+    mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    variance = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([0]).astype('int32')
+    ms_mean = Tensor(mean)
+    ms_var = Tensor(variance)
+    batch_mean, batch_var, delay_mean, delay_std = net(Tensor(x), ms_mean, ms_var,
+                                                       Tensor(current_step))
+    expect1, expect2, expect3, expect4, expect5, expect6 = np_result(x, mean, variance, 0.9, 1e-12)
+    assert np.allclose(batch_mean.asnumpy(), expect1, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(batch_var.asnumpy(), expect2, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(ms_mean.asnumpy(), expect3, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_mean.asnumpy(), expect5, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_std.asnumpy(), expect6, rtol=1.e-7, atol=1.e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchnorm_fold_freeze():
+    net = Net()
+    c = 64
+    x = np.random.uniform(1, 10, size=[3, c, 32, 32]).astype('float32')
+    mean = np.random.uniform(1, 10, size=[c]).astype('float32')
+    variance = np.random.uniform(1, 10, size=[c]).astype('float32')
+    current_step = np.array([10]).astype('int32')
+    ms_mean = Tensor(mean)
+    ms_var = Tensor(variance)
+    batch_mean, batch_var, delay_mean, delay_std = net(Tensor(x), ms_mean, ms_var,
+                                                       Tensor(current_step))
+    expect1, expect2, expect3, expect4, expect5, expect6 = np_result(x, mean, variance, 0.9, 1e-12)
+    assert np.allclose(batch_mean.asnumpy(), np.zeros_like(mean), rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(batch_var.asnumpy(), np.ones_like(mean), rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(ms_mean.asnumpy(), mean, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(ms_var.asnumpy(), variance, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_mean.asnumpy(), expect5, rtol=1.e-7, atol=1.e-5)
+    assert np.allclose(delay_std.asnumpy(), expect6, rtol=1.e-7, atol=1.e-5)
diff --git a/tests/st/ops/gpu/test_conv2d_op.py b/tests/st/ops/gpu/test_conv2d_op.py
index d724f6f6c8..1bac156c37 100644
--- a/tests/st/ops/gpu/test_conv2d_op.py
+++ b/tests/st/ops/gpu/test_conv2d_op.py
@@ -14,10 +14,10 @@
 # ============================================================================
 
 import pytest
+import numpy as np
 from mindspore import Tensor
 from mindspore.ops import operations as P
 import mindspore.nn as nn
-import numpy as np
 import mindspore.context as context
 
 
diff --git a/tests/st/ops/gpu/test_correction_mul_grad_op.py b/tests/st/ops/gpu/test_correction_mul_grad_op.py
new file mode 100644
index 0000000000..88b391a77a
--- /dev/null
+++ b/tests/st/ops/gpu/test_correction_mul_grad_op.py
@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+import os
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import mindspore.context as context
+
+
+context.set_context(device_target='GPU')
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op_w = P.CorrectionMulGrad()
+
+    @ms_function
+    def construct(self, dy, x, batch_std, running_std):
+        dx, d_batch_std = self.op_w(dy, x, batch_std, running_std)
+        return dx, d_batch_std
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_correction_mul_grad():
+    net = Net()
+    co, ci, h, w = 64, 1, 32, 32
+    dout = np.random.uniform(-0.1, 0.1, size=[co, ci, h, w]).astype('float32')
+    x = np.random.uniform(1, 1, size=[co, ci, h, w]).astype('float32')
+    batch_std = np.random.uniform(1, 10, size=[co]).astype('float32')
+    running_std = np.random.uniform(1, 10, size=[co]).astype('float32')
+    output = net(Tensor(dout), Tensor(x), Tensor(batch_std), Tensor(running_std))
+    expect = [0, 0]
+    expect[0] = (dout * np.reshape(batch_std / running_std, (co, 1, 1, 1)))
+    expect[1] = (np.sum(dout * x, (1, 2, 3)) / running_std)
+    for i, v in enumerate(output):
+        assert (np.allclose(output[i].asnumpy(), expect[i], rtol=1.e-5, atol=1.e-5))
diff --git a/tests/st/ops/gpu/test_correction_mul_op.py b/tests/st/ops/gpu/test_correction_mul_op.py
new file mode 100644
index 0000000000..01389e148c
--- /dev/null
+++ b/tests/st/ops/gpu/test_correction_mul_op.py
@@ -0,0 +1,52 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import mindspore.context as context
+
+context.set_context(device_target='GPU')
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op = P.CorrectionMul()
+
+    @ms_function
+    def construct(self, x, batch_var, moving_var):
+        return self.op(x, batch_var, moving_var)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_correction_mul():
+    net = Net()
+    co = 64
+    x = np.random.uniform(-1, 1, size=[co, 64, 32, 32]).astype('float32')
+    bv = np.random.uniform(1, 2, size=[co]).astype('float32')
+    mv = np.random.uniform(1, 2, size=[co]).astype('float32')
+    output = net(Tensor(x), Tensor(bv), Tensor(mv))
+    expect = x * np.reshape(bv, (co, 1, 1, 1)) / np.reshape(mv, (co, 1, 1, 1))
+    error = np.ones(shape=expect.shape) * 1.0e-5
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(diff > error * -1)
+    assert (output.shape() == expect.shape)

From ae675c5cf8cbc670a469d74359c2f542a3e56c34 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Tue, 14 Apr 2020 14:15:44 +0800
Subject: [PATCH 212/367] fix nopnode output bug

---
 mindspore/ccsrc/device/kernel_runtime.cc         |  2 +-
 mindspore/ccsrc/session/anf_runtime_algorithm.cc | 12 +++++++-----
 mindspore/ccsrc/session/anf_runtime_algorithm.h  |  1 +
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index eebc650347..e68ad22bbd 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -250,7 +250,7 @@ void KernelRuntime::AssignStaticMemoryOutput(const session::KernelGraph *graph)
   MS_EXCEPTION_IF_NULL(graph);
   auto nodes = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem});
   for (const auto &node : nodes) {
-    auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0);
+    auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, true);
     MS_EXCEPTION_IF_NULL(item_with_index.first);
     if (!item_with_index.first->isa<CNode>() || !AnfAlgo::IsRealKernel(item_with_index.first)) {
       continue;
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 893c379a07..e355c7885d 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -84,6 +84,7 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernel(const AnfNodePtr &anf_node, siz
 }
 
 KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr &anf_node, size_t index,
+                                                               bool visit_nop_node,
                                                                const std::vector<PrimitivePtr> &return_types) {
   MS_EXCEPTION_IF_NULL(anf_node);
   for (const auto &prim_type : return_types) {
@@ -109,12 +110,13 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr
       auto value_node = input2->cast<ValueNodePtr>();
       MS_EXCEPTION_IF_NULL(value_node);
       int item_idx = GetValue<int>(value_node->value());
-      return VisitKernelWithReturnType(cnode->input(kRealInputNodeIndexInTupleGetItem), IntToSize(item_idx));
+      return VisitKernelWithReturnType(cnode->input(kRealInputNodeIndexInTupleGetItem), IntToSize(item_idx),
+                                       visit_nop_node);
     } else if (IsPrimitive(input0, prim::kPrimDepend) || IsPrimitive(input0, prim::kPrimControlDepend)) {
-      return VisitKernelWithReturnType(cnode->input(kRealInputIndexInDepend), 0);
-    } else if (opt::IsNopNode(cnode)) {
+      return VisitKernelWithReturnType(cnode->input(kRealInputIndexInDepend), 0, visit_nop_node);
+    } else if (opt::IsNopNode(cnode) && visit_nop_node) {
       if (cnode->inputs().size() == 2) {
-        return VisitKernelWithReturnType(cnode->input(1), 0);
+        return VisitKernelWithReturnType(cnode->input(1), 0, visit_nop_node);
       } else {
         MS_LOG(EXCEPTION) << cnode->DebugString() << "Invalid nop node";
       }
@@ -132,7 +134,7 @@ std::vector<AnfNodePtr> AnfRuntimeAlgorithm::GetAllOutput(const AnfNodePtr &node
   auto return_prim_type = return_types;
   // if visited make_tuple should return back
   return_prim_type.push_back(prim::kPrimMakeTuple);
-  auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, return_prim_type);
+  auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, false, return_prim_type);
   if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) {
     MS_EXCEPTION_IF_NULL(item_with_index.first);
     auto make_tuple = item_with_index.first->cast<CNodePtr>();
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 1a1d471b84..233f86410c 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -41,6 +41,7 @@ class AnfRuntimeAlgorithm {
   // get input_anf_node's real kernel by recurse
   static KernelWithIndex VisitKernel(const AnfNodePtr &input_anf_node, size_t output_index);
   static KernelWithIndex VisitKernelWithReturnType(const AnfNodePtr &input_anf_node, size_t output_index,
+                                                   bool visit_nop_node = false,
                                                    const std::vector<PrimitivePtr> &return_types = {
                                                      prim::kPrimMakeTuple});
   static std::vector<AnfNodePtr> GetAllOutput(const AnfNodePtr &node,

From 2aad57c595320da8a548105f411dc08e8538bb9d Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Tue, 14 Apr 2020 14:57:23 +0800
Subject: [PATCH 213/367] getnext disable memory reuse

---
 mindspore/ccsrc/device/kernel_runtime.cc         | 4 ++++
 mindspore/ccsrc/session/anf_runtime_algorithm.cc | 5 +++++
 mindspore/ccsrc/session/anf_runtime_algorithm.h  | 2 ++
 mindspore/ccsrc/utils/utils.h                    | 1 +
 4 files changed, 12 insertions(+)

diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index eebc650347..db79484f8c 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -355,6 +355,10 @@ void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int in
     AssignCommunicationNodeOutputMem(flag, node);
     return;
   }
+  if (AnfAlgo::IsGetNext(NOT_NULL(node)) && flag == kReuseDynamicMem) {
+    MS_LOG(INFO) << "GetNext disable mem_reuse";
+    flag = kDynamicMem;
+  }
   auto kernel_mod = AnfAlgo::GetKernelMod(node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
   auto output_sizes = kernel_mod->GetOutputSizeList();
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 893c379a07..29a27a65b1 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -825,5 +825,10 @@ bool AnfRuntimeAlgorithm::IsCommunicationOp(const AnfNodePtr &node) {
   }
   return false;
 }
+
+bool AnfRuntimeAlgorithm::IsGetNext(const NotNull<AnfNodePtr> &node) {
+  auto kernel_name = AnfAlgo::GetCNodeName(node);
+  return kernel_name == kGetNextOpName;
+}
 }  // namespace session
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index 1a1d471b84..ab5a68db7f 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -31,6 +31,7 @@
 #include "kernel/kernel.h"
 #include "kernel/kernel_build_info.h"
 #include "operator/ops.h"
+#include "utils/contract.h"
 
 namespace mindspore {
 namespace session {
@@ -169,6 +170,7 @@ class AnfRuntimeAlgorithm {
   // get real input index for some tbe ops which input order is different between me and tbe impl
   static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index);
   static bool IsCommunicationOp(const AnfNodePtr &node);
+  static bool IsGetNext(const NotNull<AnfNodePtr> &node);
 };
 }  // namespace session
 using AnfAlgo = session::AnfRuntimeAlgorithm;
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 39b4b7a160..e1df2a8d25 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -42,6 +42,7 @@ constexpr auto kBNGrad2OpName = "BNGrad2";
 constexpr auto kBNGrad3OpName = "BNGrad3";
 constexpr auto kClearZeroOpName = "ClearZero";
 constexpr auto kAtomicAddrCleanOpName = "AtomicAddrClean";
+constexpr auto kGetNextOpName = "GetNext";
 constexpr auto kAllReduceOpName = "AllReduce";
 constexpr auto kAllGatherOpName = "AllGather";
 constexpr auto kBroadcastOpName = "Broadcast";

From 988363299f1fe0683121e6d64bb6326089f65f36 Mon Sep 17 00:00:00 2001
From: w00517616 <wangchengke3@huawei.com>
Date: Tue, 14 Apr 2020 15:35:30 +0800
Subject: [PATCH 214/367] Exclude FV to further improve performance.

---
 mindspore/ccsrc/pipeline/static_analysis/evaluator.cc | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
index 99cb893104..5bad1634d5 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc
@@ -63,6 +63,9 @@ AnalysisContextPtr BaseFuncGraphEvaluator::MakeContext(const AnalysisEnginePtr &
 }
 
 static std::vector<AnfNodePtr> FastShadowSort(const AnfNodePtr &ret_node) {
+  auto ori_func_graph = ret_node->func_graph();
+  MS_EXCEPTION_IF_NULL(ori_func_graph);
+
   std::vector<AnfNodePtr> sorted_nodes;
   std::unordered_set<AnfNodePtr> checked_cnodes;
   std::size_t index = 0;
@@ -75,7 +78,8 @@ static std::vector<AnfNodePtr> FastShadowSort(const AnfNodePtr &ret_node) {
       auto &inputs = current->cast<CNodePtr>()->inputs();
       for (auto it = inputs.begin(); it != inputs.end(); it++) {
         AnfNodePtr input = *it;
-        if (input != nullptr && input->isa<CNode>() && checked_cnodes.find(input) == checked_cnodes.end()) {
+        if (input != nullptr && input->isa<CNode>() && checked_cnodes.find(input) == checked_cnodes.end() &&
+            input->func_graph() == ori_func_graph) {
           sorted_nodes.emplace_back(input);
           (void)checked_cnodes.insert(input);
         }

From 5d4144de11c1a728e445a86c5e9f3db0f5f304fc Mon Sep 17 00:00:00 2001
From: gong chen <gong_chen@qq.com>
Date: Tue, 14 Apr 2020 15:47:43 +0800
Subject: [PATCH 215/367] bugfix(side effect): fix adding wrong control depend
 between AllReduce and GetStatus.

---
 mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py | 7 +++----
 mindspore/nn/wrap/loss_scale.py                         | 7 +++----
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
index bc51ba5d48..046b2adbe2 100644
--- a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
+++ b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
@@ -370,7 +370,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
         if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
             self.reducer_flag = True
-        self.grad_reducer = None
+        self.grad_reducer = F.identity
         if self.reducer_flag:
             mean = context.get_auto_parallel_context("mirror_mean")
             degree = get_group_size()
@@ -428,9 +428,8 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
                                                            mstype.float32))
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
         grads = self.clip_gradients(grads, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE)
-        if self.reducer_flag:
-            # apply grad reducer on grads
-            grads = self.grad_reducer(grads)
+        # apply grad reducer on grads
+        grads = self.grad_reducer(grads)
         self.get_status(init)
         flag_sum = self.reduce_sum(init, (0,))
         if self.is_distributed:
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index c6d61e6983..ba8e6cbb7c 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -220,7 +220,7 @@ class TrainOneStepWithLossScaleCell(Cell):
         self.depend_parameter_use = ControlDepend(depend_mode=1)
         self.allreduce = P.AllReduce()
         self.parallel_mode = _get_parallel_mode()
-        self.grad_reducer = None
+        self.grad_reducer = F.identity
         self.reducer_flag = self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]
         if self.reducer_flag:
             mean = _get_mirror_mean()
@@ -250,9 +250,8 @@ class TrainOneStepWithLossScaleCell(Cell):
             scaling_sens = sens
         grads = self.grad(self.network, weights)(data, label, F.cast(scaling_sens, F.dtype(loss)))
         grads = self.hyper_map(F.partial(_grad_scale, scaling_sens), grads)
-        if self.reducer_flag:
-            # apply grad reducer on grads
-            grads = self.grad_reducer(grads)
+        # apply grad reducer on grads
+        grads = self.grad_reducer(grads)
         # get the overflow buffer
         if not self.gpu_target:
             self.get_status(init)

From e8ca9c12c2421695dda29b85b8e44a01763bc800 Mon Sep 17 00:00:00 2001
From: yanghaoran <yanghaoran2@huawei.com>
Date: Tue, 14 Apr 2020 15:48:07 +0800
Subject: [PATCH 216/367] add alternate path for stdclient library

---
 mindspore/ccsrc/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 3a04d9b3fb..4b500cc88a 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -301,6 +301,7 @@ if(ENABLE_D)
             set(ASCEND_PATH /usr/local/Ascend)
         endif()
         set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
+        set(ASCEND_DRIVER_BACK_PATH ${ASCEND_PATH}/driver/lib64/driver)
         set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
     endif()
 
@@ -308,7 +309,7 @@ if(ENABLE_D)
     find_library(HCCL hccl ${ASCEND_RUNTIME_PATH})
     find_library(CCE_LIB cce ${ASCEND_RUNTIME_PATH})
     find_library(RUNTIME_LIB runtime ${ASCEND_RUNTIME_PATH})
-    find_library(TSDCLIENT tsdclient ${ASCEND_RUNTIME_PATH})
+    find_library(TSDCLIENT tsdclient HINTS ${ASCEND_RUNTIME_PATH} ${ASCEND_DRIVER_BACK_PATH})
     find_library(PROFILING msprof ${ASCEND_DRIVER_PATH})
     target_link_libraries(mindspore ge_runtime ${CCE_LIB} ${RUNTIME_LIB} ${TSDCLIENT} ${PROFILING} ${HCCL} ${TSDCLIENT})
 endif()

From 61e959a9f33f3b1629ac7bb94ab78383428d1c48 Mon Sep 17 00:00:00 2001
From: gong chen <gong_chen@qq.com>
Date: Tue, 14 Apr 2020 15:07:42 +0800
Subject: [PATCH 217/367] bugfix(side effect): fix cell object cann't free
 normally.

---
 mindspore/ccsrc/ir/func_graph.cc | 20 ++++++++++----------
 mindspore/ccsrc/ir/func_graph.h  |  2 +-
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/mindspore/ccsrc/ir/func_graph.cc b/mindspore/ccsrc/ir/func_graph.cc
index 7404db4af0..93fd9c0936 100644
--- a/mindspore/ccsrc/ir/func_graph.cc
+++ b/mindspore/ccsrc/ir/func_graph.cc
@@ -640,8 +640,8 @@ FuncGraphPtr FuncGraph::GenerateGraph(const AbstractBasePtrList& args_spec_list)
 
 void FuncGraph::add_parameter_obj_node(const AnfNodePtr& p) { paramter_obj_nodes_.push_back(p); }
 
-std::list<CNodePtr> FuncGraph::GetOrderedCnodes(bool force_use_topo_sort) {
-  if (has_flag(GRAPH_FLAG_HAS_EFFECT) && !force_use_topo_sort) {
+std::list<CNodePtr> FuncGraph::GetOrderedCnodes() {
+  if (has_flag(GRAPH_FLAG_HAS_EFFECT)) {
     MS_LOG(DEBUG) << "Return ordered cnodes.";
     return order_;
   } else {
@@ -703,14 +703,14 @@ void FuncGraph::CheckOrder() {
         }
       }
     }
-    auto topo_sort = GetOrderedCnodes(true);
-    if (topo_sort.size() != order_.size()) {
-      DumpCNodeList();
-      DumpIR(ToString(), shared_from_base<FuncGraph>());
-      MS_LOG(INFO) << "Dump graph: " << ToString() << ".";
-      DumpFuncGraph(ToString());
-      MS_LOG(EXCEPTION) << "CNode order size " << order_.size() << " is not equal to topo sort list size "
-                        << topo_sort.size() << ".";
+    auto mng = manager_.lock();
+    if (mng != nullptr) {
+      const auto& nodes = mng->nodes()[shared_from_base<FuncGraph>()];
+      if (nodes.size() != (order_.size() + parameters_.size())) {
+        DumpCNodeList();
+        MS_LOG(EXCEPTION) << "CNode order size " << order_.size() << " is not equal to managed node size "
+                          << nodes.size() - parameters_.size() << ".";
+      }
     }
     MS_LOG(DEBUG) << "Check order okay.";
   }
diff --git a/mindspore/ccsrc/ir/func_graph.h b/mindspore/ccsrc/ir/func_graph.h
index 1d58c90755..9c3752cd81 100644
--- a/mindspore/ccsrc/ir/func_graph.h
+++ b/mindspore/ccsrc/ir/func_graph.h
@@ -258,7 +258,7 @@ class FuncGraph : public FuncGraphBase {
   std::map<std::string, AnfNodePtr> parameter_default_value_;
   std::unordered_map<AnfNodePtr, AnfNodePtr> make_ref_params_;
 
-  std::list<CNodePtr> GetOrderedCnodes(bool force_use_topo_sort = false);
+  std::list<CNodePtr> GetOrderedCnodes();
   void EraseUnusedNodeInOrder(const AnfNodePtr &n);
   void EraseUnusedNodeInOrder();
   void CheckOrder();

From 8ba68220dddfdfb4977397b4f34a097637bf495d Mon Sep 17 00:00:00 2001
From: "wangnan39@huawei.com" <wangnan39@huawei.com>
Date: Tue, 14 Apr 2020 16:03:55 +0800
Subject: [PATCH 218/367] modify annotation of DepthwiseConv2d

---
 mindspore/ops/operations/nn_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index e56a8a826c..377ef19417 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -679,7 +679,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
         - **weight** (Tensor) - Set size of kernel is :math:`(K_1, K_2)`, then the shape is
-          :math:`(channel_multiplier, C_{in}, K_1, K_2)`.
+          :math:`(channel_{multiplier}, C_{in}, K_1, K_2)`.
 
     Outputs:
         Tensor of shape :math:`(N, C_{in} * \text{channel_multiplier}, H_{out}, W_{out})`.

From e53376092f1f9cffa52d5ed520748de0a29cee41 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Tue, 14 Apr 2020 16:06:56 +0800
Subject: [PATCH 219/367] fix workspace reuse bug

---
 mindspore/ccsrc/device/kernel_runtime.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index e68ad22bbd..2322f8469f 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -474,7 +474,7 @@ void KernelRuntime::AssignWorkSpaceMem(int flag, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(kernel_mod);
   size_t index = 0;
   for (auto &size : kernel_mod->GetWorkspaceSizeList()) {
-    auto ptr = mem_manager_->MallocWorkSpaceMem(node, flag, index, size);
+    auto ptr = mem_manager_->MallocWorkSpaceMem(node, index, flag, size);
     AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(ptr, size, "", kTypeUnknown), index, node.get());
     index++;
   }

From e1c8f248e0fc1d86b5877bb4e3ef9229fe2a1909 Mon Sep 17 00:00:00 2001
From: Wei Luning <weiluning@huawei.com>
Date: Tue, 14 Apr 2020 13:18:33 +0800
Subject: [PATCH 220/367] Fix the output is not tuple, when eval

---
 mindspore/nn/wrap/cell_wrapper.py | 20 ++++++++----
 mindspore/train/model.py          | 13 +++-----
 tests/ut/python/train/test_amp.py | 52 +++++++++++++++++++++++++++++++
 3 files changed, 71 insertions(+), 14 deletions(-)

diff --git a/mindspore/nn/wrap/cell_wrapper.py b/mindspore/nn/wrap/cell_wrapper.py
index 53a535781d..64c382557a 100644
--- a/mindspore/nn/wrap/cell_wrapper.py
+++ b/mindspore/nn/wrap/cell_wrapper.py
@@ -14,15 +14,23 @@
 # ============================================================================
 """Cell_wrapper."""
 import copy
+
 import numpy as np
+
+from mindspore.parallel._utils import (_get_device_num, _get_mirror_mean,
+                                       _get_parallel_mode)
 from mindspore.train.parallel_utils import ParallelMode
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
-from ...ops import composite as C, functional as F, operations as P
-from ...common import Tensor, dtype as mstype
-from ..cell import Cell
+
+from ...common import Tensor
+from ...common import dtype as mstype
 from ...common.initializer import initializer
 from ...common.parameter import Parameter, ParameterTuple
+from ...ops import composite as C
+from ...ops import functional as F
+from ...ops import operations as P
+from ...ops.composite.base import _mp_cast_helper
 from ...ops.operations.comm_ops import _VirtualDataset
+from ..cell import Cell
 from .grad_reducer import DistributedGradReducer
 
 
@@ -310,8 +318,8 @@ class WithEvalCell(Cell):
 
     def construct(self, data, label):
         outputs = self._network(data)
-        loss = self._loss_fn(outputs, label)
-
+        label = _mp_cast_helper(mstype.float32, label)
+        loss = self._loss_fn(F.cast(outputs, mstype.float32), label)
         return loss, outputs, label
 
 
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 7604b8ac38..46e4f421f7 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -24,7 +24,7 @@ from .. import context
 from ..parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
     _get_parameter_broadcast, _device_number_check, _parameter_broadcast_check, _callback_wrapper
 from ..nn.metrics import Loss
-from ..nn.wrap import WithLossCell, DataWrapper, WithEvalCell
+from .. import nn
 from ..nn.wrap.cell_wrapper import _VirtualDatasetCell
 from .parallel_utils import ParallelMode
 from ..common import dtype as mstype
@@ -130,7 +130,7 @@ class Model:
                                                 self._loss_fn,
                                                 level=self._amp_level)
         elif self._loss_fn:
-            network = WithLossCell(network, self._loss_fn)
+            network = nn.WithLossCell(network, self._loss_fn)
         # If need to check if loss_fn is not None, but optimizer is None
         return network
 
@@ -150,10 +150,7 @@ class Model:
         else:
             if self._loss_fn is None:
                 raise ValueError("loss_fn can not be None.")
-            if self._optimizer:
-                self._eval_network = self._train_network.network
-            else:
-                self._eval_network = WithEvalCell(self._network, self._loss_fn)
+            self._eval_network = nn.WithEvalCell(self._network, self._loss_fn)
             self._eval_indexes = [0, 1, 2]
 
     def _clear_metrics(self):
@@ -263,7 +260,7 @@ class Model:
         dataset_helper = DatasetHelper(train_dataset)
         # remove later to deal with loop sink
         if need_wrap:
-            self._train_network = DataWrapper(self._train_network, *(dataset_helper.types_shapes()),
+            self._train_network = nn.DataWrapper(self._train_network, *(dataset_helper.types_shapes()),
                                               train_dataset.__ME_INITED__)
             cb_params.train_network = self._train_network
             self._train_network.set_train()
@@ -429,7 +426,7 @@ class Model:
 
         # remove later to deal with loop sink
         if need_wrap:
-            self._eval_network = DataWrapper(self._eval_network, *(dataset_helper.types_shapes()),
+            self._eval_network = nn.DataWrapper(self._eval_network, *(dataset_helper.types_shapes()),
                                              valid_dataset.__ME_INITED__)
             self._eval_network.set_train(mode=False)
             self._eval_network.phase = 'eval'
diff --git a/tests/ut/python/train/test_amp.py b/tests/ut/python/train/test_amp.py
index 1a26c21775..2afb1e00b5 100644
--- a/tests/ut/python/train/test_amp.py
+++ b/tests/ut/python/train/test_amp.py
@@ -14,12 +14,15 @@
 # ============================================================================
 """ auto mixed precision """
 import numpy as np
+import pytest
 from mindspore import amp
 from mindspore import nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 import mindspore.context as context
 from mindspore.model_zoo.resnet import resnet50
+from mindspore.train import Model
+from ....dataset_mock import MindData
 
 
 def setup_module(module):
@@ -85,3 +88,52 @@ def test_amp_o0_loss():
     optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
     train_network = amp.build_train_network(net, optimizer, loss)
     output = train_network(inputs, label)
+
+
+class MindDataSet(MindData):
+    def __init__(self, dataset_types, dataset_shapes):
+        super(MindDataSet, self).__init__(size=2, batch_size=32,
+                                          np_types=dataset_types,
+                                          output_shapes=dataset_shapes,
+                                          input_indexs=(0, 1))
+    def __next__(self):
+        if self._size < self._iter_num:
+            raise StopIteration
+        self._iter_num += 1
+        next = []
+        for shape, type in zip(self._output_shapes, self._np_types):
+            next.append(Tensor(np.ones(shape).astype(type)))
+        return tuple(next)
+
+
+def test_compile_model_train_O0():
+    dataset_types = (np.float32, np.float32)
+    dataset_shapes = ((16, 16), (16, 16))
+
+    dataset = MindDataSet(dataset_types, dataset_shapes)
+
+    net = NetNoLoss(16, 16)
+    loss = nn.MSELoss()
+    optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+
+    model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={"acc"}, amp_level="O0")
+    model.train(2, dataset, dataset_sink_mode=False)
+    with pytest.raises(ValueError):
+        # not actual run, the metrics step will fail, check if compile ok.
+        model.eval(dataset)
+
+def test_compile_model_train_O2():
+    dataset_types = (np.float32, np.float32)
+    dataset_shapes = ((16, 16), (16, 16))
+
+    dataset = MindDataSet(dataset_types, dataset_shapes)
+
+    net = NetNoLoss(16, 16)
+    loss = nn.MSELoss()
+    optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+
+    model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={"acc"}, amp_level="O2")
+    model.train(2, dataset, dataset_sink_mode=False)
+    with pytest.raises(ValueError):
+        # not actual run, the metrics step will fail, check if compile ok.
+        model.eval(dataset)

From ea5da25d10444d4f86098b6216f97d56115364de Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Mon, 13 Apr 2020 22:07:19 +0800
Subject: [PATCH 221/367] fix: use exactly read option

---
 .../mindrecord/io/shard_index_generator.cc    |  4 +++
 mindspore/ccsrc/mindrecord/io/shard_reader.cc | 36 ++++++++-----------
 mindspore/ccsrc/mindrecord/io/shard_writer.cc | 25 +++++++++----
 3 files changed, 36 insertions(+), 29 deletions(-)

diff --git a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
index 254ddfbb16..5a5cd7cbf3 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc
@@ -512,6 +512,10 @@ MSRStatus ShardIndexGenerator::ExecuteTransaction(const int &shard_no, const std
 
   std::fstream in;
   in.open(common::SafeCStr(shard_address), std::ios::in | std::ios::binary);
+  if (!in.good()) {
+    MS_LOG(ERROR) << "File could not opened";
+    return FAILED;
+  }
   (void)sqlite3_exec(db.second, "BEGIN TRANSACTION;", nullptr, nullptr, nullptr);
   for (int raw_page_id : raw_page_ids) {
     auto sql = GenerateRawSQL(fields_);
diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index 2413da3737..085f148a88 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -125,13 +125,10 @@ MSRStatus ShardReader::Open() {
 
   for (const auto &file : file_paths_) {
     std::shared_ptr<std::fstream> fs = std::make_shared<std::fstream>();
-    fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::binary);
-    if (fs->fail()) {
-      fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::trunc | std::ios::binary);
-      if (fs->fail()) {
-        MS_LOG(ERROR) << "File could not opened";
-        return FAILED;
-      }
+    fs->open(common::SafeCStr(file), std::ios::in | std::ios::binary);
+    if (!fs->good()) {
+      MS_LOG(ERROR) << "File could not opened";
+      return FAILED;
     }
     MS_LOG(INFO) << "Open shard file successfully.";
     file_streams_.push_back(fs);
@@ -146,13 +143,10 @@ MSRStatus ShardReader::Open(int n_consumer) {
   for (const auto &file : file_paths_) {
     for (int j = 0; j < n_consumer; ++j) {
       std::shared_ptr<std::fstream> fs = std::make_shared<std::fstream>();
-      fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::binary);
-      if (fs->fail()) {
-        fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::trunc | std::ios::binary);
-        if (fs->fail()) {
-          MS_LOG(ERROR) << "File could not opened";
-          return FAILED;
-        }
+      fs->open(common::SafeCStr(file), std::ios::in | std::ios::binary);
+      if (!fs->good()) {
+        MS_LOG(ERROR) << "File could not opened";
+        return FAILED;
       }
       file_streams_random_[j].push_back(fs);
     }
@@ -311,12 +305,10 @@ MSRStatus ShardReader::ReadAllRowsInShard(int shard_id, const std::string &sql,
   std::string file_name = file_paths_[shard_id];
   std::shared_ptr<std::fstream> fs = std::make_shared<std::fstream>();
   if (!all_in_index_) {
-    fs->open(common::SafeCStr(file_name), std::ios::in | std::ios::out | std::ios::binary);
-    if (fs->fail()) {
-      fs->open(common::SafeCStr(file_name), std::ios::in | std::ios::out | std::ios::trunc | std::ios::binary);
-      if (fs->fail()) {
-        MS_LOG(ERROR) << "File could not opened";
-      }
+    fs->open(common::SafeCStr(file_name), std::ios::in | std::ios::binary);
+    if (!fs->good()) {
+      MS_LOG(ERROR) << "File could not opened";
+      return FAILED;
     }
   }
   sqlite3_free(errmsg);
@@ -520,8 +512,8 @@ std::pair<MSRStatus, std::vector<json>> ShardReader::GetLabelsFromBinaryFile(
   std::string file_name = file_paths_[shard_id];
   std::vector<json> res;
   std::shared_ptr<std::fstream> fs = std::make_shared<std::fstream>();
-  fs->open(common::SafeCStr(file_name), std::ios::in | std::ios::out | std::ios::binary);
-  if (fs->fail()) {
+  fs->open(common::SafeCStr(file_name), std::ios::in | std::ios::binary);
+  if (!fs->good()) {
     MS_LOG(ERROR) << "File could not opened";
     return {FAILED, {}};
   }
diff --git a/mindspore/ccsrc/mindrecord/io/shard_writer.cc b/mindspore/ccsrc/mindrecord/io/shard_writer.cc
index 54cf0e156b..3d4259ebbd 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_writer.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_writer.cc
@@ -76,16 +76,27 @@ MSRStatus ShardWriter::Open(const std::vector<std::string> &paths, bool append)
   // Open files
   for (const auto &file : file_paths_) {
     std::shared_ptr<std::fstream> fs = std::make_shared<std::fstream>();
-    fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::binary);
-    if (fs->fail()) {
-      fs->open(common::SafeCStr(file), std::ios::in | std::ios::out | std::ios::trunc | std::ios::binary);
-      if (fs->fail()) {
-        MS_LOG(ERROR) << "File could not opened";
+    if (!append) {
+      // if not append and mindrecord file exist, return FAILED
+      fs->open(common::SafeCStr(file), std::ios::in | std::ios::binary);
+      if (fs->good()) {
+        MS_LOG(ERROR) << "MindRecord file already existed.";
+        fs->close();
+        return FAILED;
+      }
+      fs->close();
+
+      // open the mindrecord file to write
+      fs->open(common::SafeCStr(file), std::ios::out | std::ios::binary);
+      if (!fs->good()) {
+        MS_LOG(ERROR) << "MindRecord file could not opened.";
         return FAILED;
       }
     } else {
-      if (!append) {
-        MS_LOG(ERROR) << "MindRecord file already existed";
+      // open the mindrecord file to append
+      fs->open(common::SafeCStr(file), std::ios::out | std::ios::in | std::ios::binary);
+      if (!fs->good()) {
+        MS_LOG(ERROR) << "MindRecord file could not opened for append.";
         return FAILED;
       }
     }

From e5c67b9088cc3023c888c8d71f04a58f817906b7 Mon Sep 17 00:00:00 2001
From: YuJianfeng <yujianfeng5@huawei.com>
Date: Mon, 13 Apr 2020 16:53:20 +0800
Subject: [PATCH 222/367] Add cnode to equal map when opt matching

---
 .../ascend/ir_fusion/adam_apply_one_fusion.cc | 51 +++---------
 .../ascend/ir_fusion/adam_apply_one_fusion.h  |  5 ++
 .../adam_apply_one_with_decay_rule.cc         | 54 ++++---------
 .../adam_apply_one_with_decay_rule.h          |  5 ++
 .../ascend/ir_fusion/lamb_next_right_rule.cc  | 35 ++------
 .../ascend/ir_fusion/lamb_next_right_rule.h   |  6 +-
 .../ccsrc/pre_activate/common/optimizer.cc    | 30 ++++---
 .../ccsrc/pre_activate/common/optimizer.h     |  2 +
 .../pre_activate/common/pattern_engine.cc     | 79 +++++++++++++------
 .../pre_activate/common/pattern_engine.h      | 71 ++++++++++-------
 .../common/pattern_engine_test.cc             | 30 +++----
 11 files changed, 182 insertions(+), 186 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
index 1ecf4bbd06..3f905fedf9 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
@@ -15,43 +15,9 @@
  */
 #include "pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h"
 #include "pre_activate/common/helper.h"
-#include "utils/utils.h"
 
 namespace mindspore {
 namespace opt {
-namespace {
-void GetAdd0AndAdd1(const AnfNodePtr &sub0, AnfNodePtr *add0, AnfNodePtr *add1) {
-  MS_EXCEPTION_IF_NULL(sub0);
-  MS_EXCEPTION_IF_NULL(add0);
-  MS_EXCEPTION_IF_NULL(add1);
-  auto sub0_cnode = sub0->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(sub0_cnode);
-  CheckCNodeInputSize(sub0_cnode, kSubInputNum);
-  AnfNodePtr mul4 = sub0_cnode->input(2);
-  MS_EXCEPTION_IF_NULL(mul4);
-  auto mul4_cnode = mul4->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(mul4_cnode);
-  CheckCNodeInputSize(mul4_cnode, kMulInputNum);
-  AnfNodePtr true_div0 = mul4_cnode->input(2);
-  MS_EXCEPTION_IF_NULL(true_div0);
-  auto true_div0_cnode = true_div0->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(true_div0_cnode);
-  CheckCNodeInputSize(true_div0_cnode, kRealDivInputNum);
-  *add0 = true_div0_cnode->input(1);
-  AnfNodePtr add2 = true_div0_cnode->input(2);
-  MS_EXCEPTION_IF_NULL(add2);
-  auto add2_cnode = add2->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(add2_cnode);
-  CheckCNodeInputSize(add2_cnode, kAddInputNum);
-  AnfNodePtr sqrt0 = add2_cnode->input(1);
-  MS_EXCEPTION_IF_NULL(sqrt0);
-  auto sqrt0_cnode = sqrt0->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(sqrt0_cnode);
-  CheckCNodeInputSize(sqrt0_cnode, kSqrtInputNum);
-  *add1 = sqrt0_cnode->input(1);
-}
-}  // namespace
-
 AnfNodePtr AdamApplyOneFusion::CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(equiv);
@@ -79,10 +45,10 @@ const BaseRef AdamApplyOneFusion::DefinePattern() const {
   const auto prim_deal_div = std::make_shared<Primitive>(kRealDivOpName);
   VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
   VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
-  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({prim::kPrimTensorAdd, mul2, mul3})});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
-  VectorRef add0 = VectorRef({prim::kPrimTensorAdd, mul0, mul1});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
   VectorRef true_div0 = VectorRef({prim_deal_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
 }
@@ -96,10 +62,17 @@ const AnfNodePtr AdamApplyOneFusion::Process(const FuncGraphPtr &func_graph, con
   new_node->set_scope(node->scope());
   // Set abstract of new node
   AbstractBasePtrList new_node_abstract_list;
-  AnfNodePtr add0 = nullptr;
-  AnfNodePtr add1 = nullptr;
-  GetAdd0AndAdd1(node, &add0, &add1);
+  auto iter_add0 = (*equiv).find(add0_var_);
+  if (iter_add0 == (*equiv).end()) {
+    MS_LOG(EXCEPTION) << "The equiv map is expected to contains the add0 var after matched.";
+  }
+  auto iter_add1 = (*equiv).find(add1_var_);
+  if (iter_add1 == (*equiv).end()) {
+    MS_LOG(EXCEPTION) << "The equiv map is expected to contains the add1 var after matched.";
+  }
+  auto add0 = utils::cast<AnfNodePtr>(iter_add0->second);
   MS_EXCEPTION_IF_NULL(add0);
+  auto add1 = utils::cast<AnfNodePtr>(iter_add1->second);
   MS_EXCEPTION_IF_NULL(add1);
   new_node_abstract_list.push_back(add1->abstract());
   new_node_abstract_list.push_back(add0->abstract());
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
index 6642561b07..77f6641463 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
@@ -19,6 +19,7 @@
 #include <vector>
 #include <memory>
 #include "pre_activate/common/optimizer.h"
+#include "utils/utils.h"
 
 namespace mindspore {
 namespace opt {
@@ -35,6 +36,8 @@ class AdamApplyOneFusion : public PatternProcessPass {
       mul_x_input_vars_.push_back(std::make_shared<Var>());
     }
     add2_y_ = std::make_shared<Var>();
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
   }
 
   ~AdamApplyOneFusion() override = default;
@@ -46,6 +49,8 @@ class AdamApplyOneFusion : public PatternProcessPass {
   std::vector<VarPtr> input_vars_;
   std::vector<VarPtr> mul_x_input_vars_;
   VarPtr add2_y_;
+  VarPtr add0_var_;
+  VarPtr add1_var_;
 };
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.cc
index 442aa64217..4a2387d3cc 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.cc
@@ -17,48 +17,13 @@
 
 #include <memory>
 #include <vector>
-#include <tuple>
 
 #include "session/anf_runtime_algorithm.h"
 #include "ir/primitive.h"
-#include "utils/utils.h"
 #include "pre_activate/common/helper.h"
 
 namespace mindspore {
 namespace opt {
-namespace {
-std::tuple<AnfNodePtr, AnfNodePtr> GetAdd0Add1Node(const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(node);
-  auto sub0 = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(sub0);
-  auto mul5_anf = sub0->input(2);
-  MS_EXCEPTION_IF_NULL(mul5_anf);
-  auto mul5 = mul5_anf->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(mul5);
-  auto add3_anf = mul5->input(2);
-  MS_EXCEPTION_IF_NULL(add3_anf);
-  auto add3 = add3_anf->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(add3);
-  auto real_div0_anf = add3->input(1);
-  MS_EXCEPTION_IF_NULL(real_div0_anf);
-  auto real_div0 = real_div0_anf->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(real_div0);
-  auto add0_anf = real_div0->input(1);
-  MS_EXCEPTION_IF_NULL(add0_anf);
-  auto add2_anf = real_div0->input(2);
-  MS_EXCEPTION_IF_NULL(add2_anf);
-  auto add2 = add2_anf->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(add2);
-  auto sqrt0_anf = add2->input(1);
-  MS_EXCEPTION_IF_NULL(sqrt0_anf);
-  auto sqrt0 = sqrt0_anf->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(sqrt0);
-  auto add1_anf = sqrt0->input(1);
-  MS_EXCEPTION_IF_NULL(add1_anf);
-  return std::make_tuple(add0_anf, add1_anf);
-}
-}  // namespace
-
 std::vector<AnfNodePtr> AdamApplyOneWithDecayRule::GetFusionNodeInputs(const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(equiv);
   auto input0 = utils::cast<AnfNodePtr>((*equiv)[input0_]);
@@ -82,10 +47,10 @@ const BaseRef AdamApplyOneWithDecayRule::DefinePattern() const {
   VectorRef mul0_pattern({prim::kPrimMul, mul0_x_, input2_});
   VectorRef mul1_pattern({prim::kPrimMul, mul1_x_, input0_});
   VectorRef square0_pattern({prim::kPrimSquare, input0_});
-  VectorRef add0_pattern({prim::kPrimTensorAdd, mul0_pattern, mul1_pattern});
+  VectorRef add0_pattern({add0_var_, mul0_pattern, mul1_pattern});
   VectorRef mul2_pattern({prim::kPrimMul, mul2_x_, input1_});
   VectorRef mul3_pattern({prim::kPrimMul, mul3_x_, square0_pattern});
-  VectorRef add1_pattern({prim::kPrimTensorAdd, mul2_pattern, mul3_pattern});
+  VectorRef add1_pattern({add1_var_, mul2_pattern, mul3_pattern});
   VectorRef sqrt0_pattern({sqrt, add1_pattern});
   VectorRef add2_pattern({prim::kPrimTensorAdd, sqrt0_pattern, add2_y_});
   VectorRef mul4_pattern({prim::kPrimMul, mul4_x_, input3_});
@@ -107,9 +72,18 @@ const AnfNodePtr AdamApplyOneWithDecayRule::Process(const FuncGraphPtr &graph, c
   MS_EXCEPTION_IF_NULL(fusion_node);
   fusion_node->set_scope(node->scope());
 
-  AnfNodePtr add0 = nullptr;
-  AnfNodePtr add1 = nullptr;
-  std::tie(add0, add1) = GetAdd0Add1Node(node);
+  auto iter_add0 = (*equiv).find(add0_var_);
+  if (iter_add0 == (*equiv).end()) {
+    MS_LOG(EXCEPTION) << "The equiv map is expected to contains the add0 var after matched.";
+  }
+  auto iter_add1 = (*equiv).find(add1_var_);
+  if (iter_add1 == (*equiv).end()) {
+    MS_LOG(EXCEPTION) << "The equiv map is expected to contains the add1 var after matched.";
+  }
+  auto add0 = utils::cast<AnfNodePtr>(iter_add0->second);
+  MS_EXCEPTION_IF_NULL(add0);
+  auto add1 = utils::cast<AnfNodePtr>(iter_add1->second);
+  MS_EXCEPTION_IF_NULL(add1);
   auto types = {AnfAlgo::GetOutputInferDataType(add1, 0), AnfAlgo::GetOutputInferDataType(add0, 0),
                 AnfAlgo::GetOutputInferDataType(node, 0)};
   auto shapes = {AnfAlgo::GetOutputInferShape(add1, 0), AnfAlgo::GetOutputInferShape(add0, 0),
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.h
index a6bab48770..72c54f3535 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.h
@@ -19,6 +19,7 @@
 #include <vector>
 #include <memory>
 #include "pre_activate/common/optimizer.h"
+#include "utils/utils.h"
 namespace mindspore {
 namespace opt {
 class AdamApplyOneWithDecayRule : public PatternProcessPass {
@@ -36,6 +37,8 @@ class AdamApplyOneWithDecayRule : public PatternProcessPass {
     mul3_x_ = std::make_shared<Var>();
     mul4_x_ = std::make_shared<Var>();
     add2_y_ = std::make_shared<Var>();
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
   }
   ~AdamApplyOneWithDecayRule() override = default;
   const BaseRef DefinePattern() const override;
@@ -54,6 +57,8 @@ class AdamApplyOneWithDecayRule : public PatternProcessPass {
   VarPtr mul3_x_;
   VarPtr mul4_x_;
   VarPtr add2_y_;
+  VarPtr add0_var_;
+  VarPtr add1_var_;
 };
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.cc
index ca9c90f4e5..68baeeed99 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.cc
@@ -16,36 +16,9 @@
 #include "pre_activate/ascend/ir_fusion/lamb_next_right_rule.h"
 #include <vector>
 #include "pre_activate/common/helper.h"
-#include "utils/utils.h"
 
 namespace mindspore {
 namespace opt {
-namespace {
-AnfNodePtr GetAdd1Node(const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(node);
-  auto add2_cnode = node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(add2_cnode);
-  if (add2_cnode->inputs().size() != kAddInputNum) {
-    MS_LOG(ERROR) << "The input size of Add2 is not equal to " << kAddInputNum;
-  }
-  AnfNodePtr sqrt0 = add2_cnode->input(1);
-  MS_EXCEPTION_IF_NULL(sqrt0);
-  auto sqrt0_cnode = sqrt0->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(sqrt0_cnode);
-  if (sqrt0_cnode->inputs().size() != kSqrtInputNum) {
-    MS_LOG(ERROR) << "The input size of Sqrt0 is not equal to " << kSqrtInputNum;
-  }
-  AnfNodePtr real_div1 = sqrt0_cnode->input(1);
-  MS_EXCEPTION_IF_NULL(real_div1);
-  auto real_div1_cnode = real_div1->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(real_div1_cnode);
-  if (real_div1_cnode->inputs().size() != kMulInputNum) {
-    MS_LOG(ERROR) << "The input size of RealDiv1 is not equal to " << kMulInputNum;
-  }
-  return real_div1_cnode->input(1);
-}
-}  // namespace
-
 AnfNodePtr LambNextRightRule::CreateLambNextRightNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(equiv);
@@ -79,7 +52,7 @@ const BaseRef LambNextRightRule::DefinePattern() const {
   const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
   MS_EXCEPTION_IF_NULL(prim_sqrt);
   VectorRef mul3 = VectorRef({prim::kPrimMul, mul3_x_, VectorRef({prim::kPrimSquare, input0_})});
-  VectorRef add1 = VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, mul2_x_, input1_}), mul3});
+  VectorRef add1 = VectorRef({add1_var_, VectorRef({prim::kPrimMul, mul2_x_, input1_}), mul3});
   return VectorRef(
     {prim::kPrimTensorAdd, VectorRef({prim_sqrt, VectorRef({prim::kPrimMul, add1, true_div1_recip_})}), add2_y_});
 }
@@ -91,7 +64,11 @@ const AnfNodePtr LambNextRightRule::Process(const FuncGraphPtr &func_graph, cons
   auto new_node = CreateLambNextRightNode(func_graph, equiv);
   MS_EXCEPTION_IF_NULL(new_node);
   // Set abstract of new node
-  AnfNodePtr add1 = GetAdd1Node(node);
+  auto iter_add1 = (*equiv).find(add1_var_);
+  if (iter_add1 == (*equiv).end()) {
+    MS_LOG(EXCEPTION) << "The equiv map is expected to contains the add1 var after matched.";
+  }
+  auto add1 = utils::cast<AnfNodePtr>(iter_add1->second);
   MS_EXCEPTION_IF_NULL(add1);
   AbstractBasePtrList new_node_abstract_list;
   new_node_abstract_list.push_back(add1->abstract());
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.h
index f78be7460b..3d15001da2 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/lamb_next_right_rule.h
@@ -18,6 +18,8 @@
 
 #include <memory>
 #include "pre_activate/common/optimizer.h"
+#include "utils/utils.h"
+
 namespace mindspore {
 namespace opt {
 class LambNextRightRule : public PatternProcessPass {
@@ -29,7 +31,8 @@ class LambNextRightRule : public PatternProcessPass {
         mul2_x_(std::make_shared<Var>()),
         mul3_x_(std::make_shared<Var>()),
         true_div1_recip_(std::make_shared<Var>()),
-        add2_y_(std::make_shared<Var>()) {}
+        add2_y_(std::make_shared<Var>()),
+        add1_var_(std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()))) {}
 
   ~LambNextRightRule() override = default;
   const BaseRef DefinePattern() const override;
@@ -44,6 +47,7 @@ class LambNextRightRule : public PatternProcessPass {
   VarPtr mul3_x_;
   VarPtr true_div1_recip_;
   VarPtr add2_y_;
+  VarPtr add1_var_;
 };
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/common/optimizer.cc b/mindspore/ccsrc/pre_activate/common/optimizer.cc
index 62cff76be0..0e74da3fe8 100644
--- a/mindspore/ccsrc/pre_activate/common/optimizer.cc
+++ b/mindspore/ccsrc/pre_activate/common/optimizer.cc
@@ -30,7 +30,8 @@
 namespace mindspore {
 namespace opt {
 namespace {
-AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, bool multigraph);
+AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars,
+                            bool multigraph);
 
 ValueNodePtr CreateValueNodeWithSexp(const BaseRef &sexp) {
   if (utils::isa<int>(sexp)) {
@@ -71,12 +72,20 @@ VarNodePtr CreateVarNodeWithSexp(const BaseRef &sexp, const BaseRef &graph) {
   return nullptr;
 }
 
-AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, bool multigraph = false) {
+AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars,
+                      bool multigraph = false) {
   MS_LOG(DEBUG) << "SexpToNode sexp: " + sexp.ToString() + ", graph " + graph.ToString();
+  MS_EXCEPTION_IF_NULL(primitive_vars);
   if (utils::isa<VectorRef>(sexp)) {
-    return HandleSexpVector(sexp, graph, multigraph);
+    return HandleSexpVector(sexp, graph, primitive_vars, multigraph);
   }
   if (utils::isa<VarPtr>(sexp)) {
+    auto var_ptr = utils::cast<VarPtr>(sexp);
+    MS_EXCEPTION_IF_NULL(var_ptr);
+    if (var_ptr->primitive()) {
+      (*primitive_vars)[var_ptr->primitive()] = var_ptr;
+      return NewValueNode(var_ptr->primitive());
+    }
     return CreateVarNodeWithSexp(sexp, graph);
   }
   if (utils::isa<AnfNodePtr>(sexp)) {
@@ -89,13 +98,14 @@ AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, bool multigraph
   return value_node;
 }
 
-AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, bool multigraph) {
+AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars,
+                            bool multigraph) {
   MS_LOG(DEBUG) << "HandleSexpVector sexp: " + sexp.ToString() + ", graph " + graph.ToString();
   std::vector<AnfNodePtr> input_nodes;
   const auto &tuple = utils::cast<VectorRef>(sexp);
   if (multigraph && utils::isa<VarPtr>(graph)) {
     for (auto &x : tuple) {
-      AnfNodePtr node = SexpToNode(x, std::make_shared<Var>("G"), true);
+      AnfNodePtr node = SexpToNode(x, std::make_shared<Var>("G"), primitive_vars, true);
       input_nodes.push_back(node);
     }
     VarPtr var_ptr = utils::cast<VarPtr>(graph);
@@ -103,7 +113,7 @@ AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, bool mult
   }
 
   for (auto &x : tuple) {
-    AnfNodePtr node = SexpToNode(x, graph, multigraph);
+    AnfNodePtr node = SexpToNode(x, graph, primitive_vars, multigraph);
     input_nodes.push_back(node);
   }
   return CreateCNodeWithGraph(input_nodes, graph);
@@ -166,7 +176,8 @@ PatternProcessPass::PatternProcessPass(const std::string &name, bool multigraph)
       multigraph_(multigraph),
       pattern_engine_(PatternEngine(std::make_shared<DefaultVisitor>(),
                                     std::function<bool(const BaseRef &, const BaseRef &)>(AnfEqual),
-                                    std::function<bool(const BaseRef &, const BaseRef &)>(CNodeTypeEqual))) {}
+                                    std::function<bool(const BaseRef &, const BaseRef &)>(CNodeTypeEqual))),
+      primitive_vars_(std::make_shared<PrimitiveVarMap>()) {}
 
 const BaseRef PatternProcessPass::DefinePattern() const {
   VarPtr X = std::make_shared<Var>();
@@ -176,7 +187,7 @@ const BaseRef PatternProcessPass::DefinePattern() const {
 void PatternProcessPass::Build() {
   VarPtr fg = std::make_shared<Var>("RootG");
   BaseRef pattern = std::move(DefinePattern());
-  pattern_ = SexpToNode(pattern, fg, multigraph_);
+  pattern_ = SexpToNode(pattern, fg, primitive_vars_.get(), multigraph_);
 }
 
 AnfNodePtr PatternProcessPass::Run(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
@@ -185,7 +196,8 @@ AnfNodePtr PatternProcessPass::Run(const FuncGraphPtr &func_graph, const AnfNode
   }
 
   auto empty_equiv = std::make_shared<Equiv>();
-  EquivPtr equiv = pattern_engine_.Match(pattern_, node, empty_equiv);
+  MS_EXCEPTION_IF_NULL(primitive_vars_);
+  EquivPtr equiv = pattern_engine_.Match(pattern_, node, *primitive_vars_, empty_equiv);
   if (equiv != nullptr && !equiv->empty()) {
     return Process(func_graph, node, equiv);
   }
diff --git a/mindspore/ccsrc/pre_activate/common/optimizer.h b/mindspore/ccsrc/pre_activate/common/optimizer.h
index 8ef0b6dc34..eade7f7789 100644
--- a/mindspore/ccsrc/pre_activate/common/optimizer.h
+++ b/mindspore/ccsrc/pre_activate/common/optimizer.h
@@ -19,6 +19,7 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include <unordered_map>
 
 #include "ir/anf.h"
 #include "ir/func_graph.h"
@@ -46,6 +47,7 @@ class PatternProcessPass : public NodePass {
   AnfNodePtr pattern_ = nullptr;
   bool multigraph_ = true;
   PatternEngine pattern_engine_;
+  PrimitiveVarMapPtr primitive_vars_;
 };
 
 class GraphOptimizer {
diff --git a/mindspore/ccsrc/pre_activate/common/pattern_engine.cc b/mindspore/ccsrc/pre_activate/common/pattern_engine.cc
index e2ff321a89..350332b9d1 100644
--- a/mindspore/ccsrc/pre_activate/common/pattern_engine.cc
+++ b/mindspore/ccsrc/pre_activate/common/pattern_engine.cc
@@ -42,7 +42,7 @@ void Var::EnsureTag() {
   }
 }
 
-bool operator==(const VarPtr& lhs, const VarPtr& rhs) {
+bool operator==(const VarPtr &lhs, const VarPtr &rhs) {
   if (lhs->isa<CondVar>() && rhs->isa<CondVar>()) {
     CondVarPtr v1 = dyn_cast<CondVar>(lhs);
     CondVarPtr v2 = dyn_cast<CondVar>(rhs);
@@ -63,7 +63,7 @@ std::string SeqVar::ToString() const {
   return buffer.str();
 }
 
-std::ostream& operator<<(std::ostream& os, const VarPtr& var) {
+std::ostream &operator<<(std::ostream &os, const VarPtr &var) {
   if (var == nullptr) {
     os << "";
   } else {
@@ -73,10 +73,10 @@ std::ostream& operator<<(std::ostream& os, const VarPtr& var) {
 }
 
 template <>
-std::ostream& operator<<<VarPtr, BaseRef>(std::ostream& os, const Equiv& equiv) {
+std::ostream &operator<<<VarPtr, BaseRef>(std::ostream &os, const Equiv &equiv) {
   os << "[Equiv]"
      << "\n";
-  for (auto& equiv_item : equiv) {
+  for (auto &equiv_item : equiv) {
     auto k = equiv_item.first;
     os << k << ":";
     BaseRef x = equiv_item.second;
@@ -104,7 +104,7 @@ std::ostream& operator<<<VarPtr, BaseRef>(std::ostream& os, const Equiv& equiv)
   return os;
 }
 
-static BaseRef GetVar(const BaseRef& x) {
+static BaseRef GetVar(const BaseRef &x) {
   MS_LOG(DEBUG) << "getVar start :%s" + x.ToString();
   if (utils::isa<AnfNodePtr>(x)) {
     auto node = utils::cast<AnfNodePtr>(x);
@@ -129,7 +129,7 @@ static BaseRef GetVar(const BaseRef& x) {
   return x;
 }
 
-EquivPtr MatchOnVar(const BaseRef& pattern, const BaseRef& expr, EquivPtr equiv) {
+EquivPtr MatchOnVar(const BaseRef &pattern, const BaseRef &expr, EquivPtr equiv) {
   MS_LOG(DEBUG) << "MatchOnVar pattern " + pattern.ToString() + " expr: " + expr.ToString();
   MS_EXCEPTION_IF_NULL(equiv);
   if (utils::isa<VarPtr>(pattern)) {
@@ -144,8 +144,8 @@ EquivPtr MatchOnVar(const BaseRef& pattern, const BaseRef& expr, EquivPtr equiv)
   return nullptr;
 }
 
-bool PatternEngine::ToVector(const VectorRef& pattern_ref, const VectorRef& expr_ref, VectorRef* const values_pattern,
-                             VectorRef* const values_expr) const {
+bool PatternEngine::ToVector(const VectorRef &pattern_ref, const VectorRef &expr_ref, VectorRef *const values_pattern,
+                             VectorRef *const values_expr) const {
   MS_EXCEPTION_IF_NULL(values_expr);
   if (utils::isa<SeqPtr>(pattern_ref)) {
     *values_pattern = pattern_ref;
@@ -155,12 +155,12 @@ bool PatternEngine::ToVector(const VectorRef& pattern_ref, const VectorRef& expr
   return false;
 }
 
-bool PatternEngine::ToVector(const BaseRef& pattern_ref, const BaseRef& expr_ref, VectorRef* const values_pattern,
-                             VectorRef* const values_expr) const {
+bool PatternEngine::ToVector(const BaseRef &pattern_ref, const BaseRef &expr_ref, VectorRef *const values_pattern,
+                             VectorRef *const values_expr) const {
   MS_EXCEPTION_IF_NULL(values_expr);
   // visitor to visite the list
-  auto appender_pattern = [](VectorRef& values) {
-    std::function<BaseRef(const BaseRef&)> fn = [&](const BaseRef& u) {
+  auto appender_pattern = [](VectorRef &values) {
+    std::function<BaseRef(const BaseRef &)> fn = [&](const BaseRef &u) {
       values.push_back(GetVar(u));
       return u;
     };
@@ -174,8 +174,8 @@ bool PatternEngine::ToVector(const BaseRef& pattern_ref, const BaseRef& expr_ref
     return false;
   }
 
-  auto appender_expr = [](VectorRef& values) {
-    std::function<BaseRef(const BaseRef&)> fn = [&](const BaseRef& u) {
+  auto appender_expr = [](VectorRef &values) {
+    std::function<BaseRef(const BaseRef &)> fn = [&](const BaseRef &u) {
       values.push_back(u);
       return u;
     };
@@ -187,10 +187,10 @@ bool PatternEngine::ToVector(const BaseRef& pattern_ref, const BaseRef& expr_ref
   return visitor_->Visit(expr_ref, nullptr);
 }
 
-static int GetSVarStartIndex(const VectorRef& values) {
+static int GetSVarStartIndex(const VectorRef &values) {
   int index = -1;
   int count = 0;
-  for (auto& value : values) {
+  for (auto &value : values) {
     if (utils::isa<VarPtr>(value) && utils::cast<VarPtr>(value)->isa<SeqVar>()) {
       if (index != -1) {
         MS_LOG(DEBUG) << "Multiple SVars in sequence";
@@ -203,7 +203,35 @@ static int GetSVarStartIndex(const VectorRef& values) {
   return index;
 }
 
-EquivPtr PatternEngine::AlignSVar(const VectorRef& values_pattern, const VectorRef& values_expr, EquivPtr equiv) const {
+void UpdateEquivMap(const VectorRef &values_pattern, const BaseRef &expr_ref, const PrimitiveVarMap &primitive_vars,
+                    EquivPtr equiv) {
+  if (equiv == nullptr || values_pattern.empty() || !utils::isa<AnfNodePtr>(values_pattern[0]) ||
+      !utils::isa<AnfNodePtr>(expr_ref)) {
+    return;
+  }
+  auto real_node = utils::cast<AnfNodePtr>(expr_ref);
+  MS_EXCEPTION_IF_NULL(real_node);
+  if (!real_node->isa<CNode>()) {
+    return;
+  }
+  auto prim_node = utils::cast<AnfNodePtr>(values_pattern[0]);
+  MS_EXCEPTION_IF_NULL(prim_node);
+  if (!IsValueNode<Primitive>(prim_node)) {
+    return;
+  }
+  ValuePtr value = GetValueNode(prim_node);
+  MS_EXCEPTION_IF_NULL(value);
+  auto prim = value->cast<PrimitivePtr>();
+  MS_EXCEPTION_IF_NULL(prim);
+  auto iter = primitive_vars.find(prim);
+  if (iter == primitive_vars.end()) {
+    return;
+  }
+  (*equiv)[iter->second] = real_node;
+}
+
+EquivPtr PatternEngine::AlignSVar(const VectorRef &values_pattern, const VectorRef &values_expr,
+                                  const PrimitiveVarMap &primitive_vars, EquivPtr equiv) const {
   int svar_index = GetSVarStartIndex(values_pattern);
   if (svar_index == kInvalidVarIndex) {
     return nullptr;
@@ -229,12 +257,12 @@ EquivPtr PatternEngine::AlignSVar(const VectorRef& values_pattern, const VectorR
     if (svar_index != -1 && i == IntToSize(svar_index)) {
       auto seq =
         std::vector<BaseRef>(values_expr.begin() + svar_index, values_expr.begin() + svar_index + SizeToInt(diff));
-      equiv = Match(values_pattern[svar_index], seq, equiv);
+      equiv = Match(values_pattern[svar_index], seq, primitive_vars, equiv);
     } else {
       if (svar_index != -1 && i > IntToSize(svar_index)) {
         expr_i = i + diff - 1;
       }
-      equiv = Match(values_pattern[i], values_expr[expr_i], equiv);
+      equiv = Match(values_pattern[i], values_expr[expr_i], primitive_vars, equiv);
     }
     if (equiv == nullptr) {
       return nullptr;
@@ -243,7 +271,8 @@ EquivPtr PatternEngine::AlignSVar(const VectorRef& values_pattern, const VectorR
   return equiv;
 }
 
-EquivPtr PatternEngine::Match(const BaseRef& pattern, const BaseRef& expr, EquivPtr equiv) const {
+EquivPtr PatternEngine::Match(const BaseRef &pattern, const BaseRef &expr, const PrimitiveVarMap &primitive_vars,
+                              EquivPtr equiv) const {
   MS_LOG(DEBUG) << "-----[in Match]";
   MS_LOG(DEBUG) << "GetVar w";
   BaseRef pattern_ref = GetVar(pattern);
@@ -292,10 +321,12 @@ EquivPtr PatternEngine::Match(const BaseRef& pattern, const BaseRef& expr, Equiv
   // 6. if any svar in both side, find the SeqVar index,
   // try to pack the Var s in std::vector to a Seq and match elements one by one.
   // check svar
-  return AlignSVar(values_pattern, values_expr, equiv);
+  equiv = AlignSVar(values_pattern, values_expr, primitive_vars, equiv);
+  UpdateEquivMap(values_pattern, expr_ref, primitive_vars, equiv);
+  return equiv;
 }
 
-BaseRef PatternEngine::Replace(const BaseRef& pattern, const EquivPtr& equiv) const {
+BaseRef PatternEngine::Replace(const BaseRef &pattern, const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(equiv);
   MS_LOG(DEBUG) << "-----[in Replace]";
   BaseRef ref = GetVar(pattern);
@@ -304,7 +335,7 @@ BaseRef PatternEngine::Replace(const BaseRef& pattern, const EquivPtr& equiv) co
 
   // w is var
   if (utils::isa<VarPtr>(ref)) {
-    const VarPtr& var = utils::cast<VarPtr>(ref);
+    const VarPtr &var = utils::cast<VarPtr>(ref);
     auto iter = equiv->find(var);
     if (iter != equiv->end()) {
       out = iter->second;
@@ -316,7 +347,7 @@ BaseRef PatternEngine::Replace(const BaseRef& pattern, const EquivPtr& equiv) co
   }
 
   // visitor to visit the list
-  std::function<BaseRef(BaseRef)> fn = [&, this, equiv](const BaseRef& u) { return Replace(u, equiv); };
+  std::function<BaseRef(BaseRef)> fn = [&, this, equiv](const BaseRef &u) { return Replace(u, equiv); };
 
   visitor_->SetFn(fn);
   BaseRef visit_out;
diff --git a/mindspore/ccsrc/pre_activate/common/pattern_engine.h b/mindspore/ccsrc/pre_activate/common/pattern_engine.h
index 432746332f..858b1aecb8 100644
--- a/mindspore/ccsrc/pre_activate/common/pattern_engine.h
+++ b/mindspore/ccsrc/pre_activate/common/pattern_engine.h
@@ -31,6 +31,7 @@
 #include <map>
 #include <stdexcept>
 #include <list>
+#include <utility>
 
 #include "pre_activate/common/visit.h"
 #include "ir/base.h"
@@ -44,16 +45,19 @@ using CondVarPtr = std::shared_ptr<CondVar>;
 using SVarPtr = std::shared_ptr<SeqVar>;
 const int kInvalidVarIndex = -2;
 
-using ConditionFunc = std::function<bool(const BaseRef&)>;
+using ConditionFunc = std::function<bool(const BaseRef &)>;
 
 // Base wildcard variable which could match any anf node.
 class Var : public Base {
   friend class VarHasher;
 
  public:
-  explicit Var(const std::string& tag = "") : tag_(tag) { EnsureTag(); }
-  Var(const Var& other) : Base(other), tag_(other.tag_) {}
-  virtual Var& operator=(const Var& other) {
+  explicit Var(std::string tag = "") : tag_(std::move(tag)), primitive_(nullptr) { EnsureTag(); }
+  explicit Var(const PrimitivePtr &primitive, std::string tag = "") : tag_(std::move(tag)), primitive_(primitive) {
+    EnsureTag();
+  }
+  Var(const Var &other) : Base(other), tag_(other.tag_) {}
+  virtual Var &operator=(const Var &other) {
     if (&other == this) {
       return *this;
     }
@@ -63,12 +67,13 @@ class Var : public Base {
   ~Var() override = default;
   MS_DECLARE_PARENT(Var, Base);
 
-  virtual bool matches(const BaseRef&) { return true; }
+  virtual bool matches(const BaseRef &) { return true; }
 
-  virtual bool operator==(const Var& other) const { return tag_ == other.tag_; }
-  bool operator!=(const Var& other) const { return !(&other == this); }
+  virtual bool operator==(const Var &other) const { return tag_ == other.tag_; }
+  bool operator!=(const Var &other) const { return !(&other == this); }
 
   std::string tag() const { return tag_; }
+  PrimitivePtr primitive() const { return primitive_; }
   std::string ToString() const override {
     std::ostringstream buffer;
     buffer << "Var(" << tag_ << ")";
@@ -80,12 +85,13 @@ class Var : public Base {
   void EnsureTag();
 
   std::string tag_;
+  PrimitivePtr primitive_;
 };
 
 // VarNode means variable node, a subclass of AnfNode
 class VarNode : public AnfNode {
  public:
-  VarNode(const VarPtr& value, const FuncGraphPtr& func_graph) : AnfNode(func_graph), var_(value) {}
+  VarNode(const VarPtr &value, const FuncGraphPtr &func_graph) : AnfNode(func_graph), var_(value) {}
   ~VarNode() override = default;
   MS_DECLARE_PARENT(VarNode, AnfNode);
 
@@ -95,16 +101,16 @@ using VarNodePtr = std::shared_ptr<VarNode>;
 
 class VarHasher {
  public:
-  std::size_t operator()(const Var& var) const { return var.hash(); }
+  std::size_t operator()(const Var &var) const { return var.hash(); }
 };
 
 // Condition Var, match an anf node when condition function return true.
 class CondVar : public Var {
  public:
-  explicit CondVar(const ConditionFunc& cond) : cond_fn_(cond) {}
+  explicit CondVar(const ConditionFunc &cond) : cond_fn_(cond) {}
   ~CondVar() override = default;
   MS_DECLARE_PARENT(CondVar, Var);
-  bool matches(const BaseRef& value) override {
+  bool matches(const BaseRef &value) override {
     MS_LOG(DEBUG) << "CondVarPtr match: " + value.ToString();
     if (utils::isa<Var>(value)) {
       return false;
@@ -124,55 +130,60 @@ class SeqVar : public Var {
   ~SeqVar() override = default;
   MS_DECLARE_PARENT(SeqVar, Var);
   explicit SeqVar(const VarPtr subvar) : subvar_(nullptr) { subvar_ = subvar; }
-  bool matches(const BaseRef& value) override {
+  bool matches(const BaseRef &value) override {
     // match Seq.
     if (utils::isa<Seq>(value)) {
-      const Seq& seq = utils::cast<Seq>(value);
-      return std::all_of(seq.begin(), seq.end(), [this](const BaseRef& v) {
+      const Seq &seq = utils::cast<Seq>(value);
+      return std::all_of(seq.begin(), seq.end(), [this](const BaseRef &v) {
         auto eq = subvar_->matches(v);
         return eq;
       });
     }
     return false;
   }
-  bool operator==(const SeqVar& other) const { return *subvar_ == *other.subvar_; }
+  bool operator==(const SeqVar &other) const { return *subvar_ == *other.subvar_; }
   std::string ToString() const override;
 
  private:
   VarPtr subvar_;
 };
 
-bool operator==(const VarPtr& lhs, const VarPtr& rhs);
+bool operator==(const VarPtr &lhs, const VarPtr &rhs);
 
-inline bool operator!=(const VarPtr& lhs, const VarPtr& rhs) { return !(lhs == rhs); }
+inline bool operator!=(const VarPtr &lhs, const VarPtr &rhs) { return !(lhs == rhs); }
 
-std::ostream& operator<<(std::ostream& os, const VarPtr& var);
+std::ostream &operator<<(std::ostream &os, const VarPtr &var);
 
 using Equiv = std::map<VarPtr, BaseRef>;
 using EquivPtr = std::shared_ptr<Equiv>;
+using PrimitiveVarMap = std::unordered_map<PrimitivePtr, VarPtr>;
+using PrimitiveVarMapPtr = std::shared_ptr<PrimitiveVarMap>;
 
-inline bool DefaultTypeEq(const BaseRef& x, const BaseRef& y) { return x.type() == y.type(); }
+inline bool DefaultTypeEq(const BaseRef &x, const BaseRef &y) { return x.type() == y.type(); }
 
 class PatternEngine {
  public:
-  PatternEngine(const std::shared_ptr<Visitor>& visitor, const std::function<bool(const BaseRef&, const BaseRef&)>& eq,
-                const std::function<bool(const BaseRef&, const BaseRef&)>& type_eq = DefaultTypeEq)
+  PatternEngine(const std::shared_ptr<Visitor> &visitor,
+                const std::function<bool(const BaseRef &, const BaseRef &)> &eq,
+                const std::function<bool(const BaseRef &, const BaseRef &)> &type_eq = DefaultTypeEq)
       : visitor_(visitor), eq_(eq), type_eq_(type_eq) {}
   ~PatternEngine() = default;
 
-  EquivPtr Match(const BaseRef& pattern, const BaseRef& expr, EquivPtr equiv) const;
+  EquivPtr Match(const BaseRef &pattern, const BaseRef &expr, const PrimitiveVarMap &primitive_vars,
+                 EquivPtr equiv) const;
   // Replace pattern with equivalent
-  BaseRef Replace(const BaseRef& pattern, const EquivPtr& equiv) const;
+  BaseRef Replace(const BaseRef &pattern, const EquivPtr &equiv) const;
 
  private:
-  EquivPtr AlignSVar(const VectorRef& values_pattern, const VectorRef& values_expr, EquivPtr equiv) const;
-  bool ToVector(const BaseRef& pattern, const BaseRef& expr, VectorRef* const values_pattern,
-                VectorRef* const values_expr) const;
-  bool ToVector(const VectorRef& pattern_ref, const VectorRef& expr_ref, VectorRef* const values_pattern,
-                VectorRef* const values_expr) const;
+  EquivPtr AlignSVar(const VectorRef &values_pattern, const VectorRef &values_expr,
+                     const PrimitiveVarMap &primitive_vars, EquivPtr equiv) const;
+  bool ToVector(const BaseRef &pattern, const BaseRef &expr, VectorRef *const values_pattern,
+                VectorRef *const values_expr) const;
+  bool ToVector(const VectorRef &pattern_ref, const VectorRef &expr_ref, VectorRef *const values_pattern,
+                VectorRef *const values_expr) const;
   std::shared_ptr<Visitor> visitor_;
-  std::function<bool(const BaseRef&, const BaseRef&)> eq_;
-  std::function<bool(const BaseRef&, const BaseRef&)> type_eq_;
+  std::function<bool(const BaseRef &, const BaseRef &)> eq_;
+  std::function<bool(const BaseRef &, const BaseRef &)> type_eq_;
 };
 }  // namespace mindspore
 namespace std {
diff --git a/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc b/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc
index 9124f5cf74..7b0e2cc9db 100644
--- a/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc
+++ b/tests/ut/cpp/pre_activate/common/pattern_engine_test.cc
@@ -40,6 +40,7 @@ class TestMatchEngine : public UT::Common {
  public:
   PatternEngine TU;
   EquivPtr equiv_null;
+  PrimitiveVarMap primitive_vars_null;
 };
 
 TEST_F(TestMatchEngine, Var) {
@@ -106,30 +107,30 @@ TEST_F(TestMatchEngine, MatchRaw_Var) {
 
   // common
   equiv_null->clear();
-  d = TU.Match(v1, 1, equiv_null);
+  d = TU.Match(v1, 1, primitive_vars_null, equiv_null);
   ASSERT_EQ((*d)[v1], 1);
 
   equiv_null->clear();
   (*equiv_null)[v1] = v2;
-  d = TU.Match(v1, 1, equiv_null);
+  d = TU.Match(v1, 1, primitive_vars_null, equiv_null);
   ASSERT_EQ(d->count(v2), std::size_t(1));
   ASSERT_EQ((*d)[v2], 1);
 
   equiv_null->clear();
   (*equiv_null)[v1] = v2;
   (*equiv_null)[v3] = 1;
-  d = TU.Match(v1, 1, equiv_null);
+  d = TU.Match(v1, 1, primitive_vars_null, equiv_null);
   ASSERT_EQ(d->count(v2), std::size_t(1));
   ASSERT_EQ((*d)[v2], 1);
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({v1}), VectorRef({1}), equiv_null);
+  d = TU.Match(VectorRef({v1}), VectorRef({1}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d->size(), std::size_t(1));
   ASSERT_EQ(d->count(v1), std::size_t(1));
   ASSERT_EQ((*d)[v1], 1);
 
   equiv_null->clear();
-  ASSERT_EQ(TU.Match(1, 2, equiv_null), nullptr);
+  ASSERT_EQ(TU.Match(1, 2, primitive_vars_null, equiv_null), nullptr);
 }
 
 TEST_F(TestMatchEngine, MatchRaw_SVar) {
@@ -139,22 +140,22 @@ TEST_F(TestMatchEngine, MatchRaw_SVar) {
   EquivPtr d;
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({sv1}), VectorRef({1, 2}), equiv_null);
+  d = TU.Match(VectorRef({sv1}), VectorRef({1, 2}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d->size(), std::size_t(1));
   ASSERT_EQ(d->count(sv1), std::size_t(1));
   ASSERT_EQ(utils::cast<Seq>((*d)[sv1]), Seq({1, 2}));
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({v1, sv1}), VectorRef({1, 2}), equiv_null);
+  d = TU.Match(VectorRef({v1, sv1}), VectorRef({1, 2}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d->size(), std::size_t(2));
   ASSERT_EQ(utils::cast<Seq>((*d)[sv1]), Seq({2}));
 
   equiv_null->clear();
-  ASSERT_EQ(TU.Match(VectorRef({sv1, sv2}), VectorRef({1, 2}), equiv_null), nullptr);
+  ASSERT_EQ(TU.Match(VectorRef({sv1, sv2}), VectorRef({1, 2}), primitive_vars_null, equiv_null), nullptr);
 
   equiv_null->clear();
   (*equiv_null)[sv1] = std::make_shared<Seq>(PatternListType{1, 2});
-  d = TU.Match(VectorRef({v1, sv1}), VectorRef({1, 1, 2}), equiv_null);
+  d = TU.Match(VectorRef({v1, sv1}), VectorRef({1, 1, 2}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d->size(), std::size_t(2));
   ASSERT_EQ((*d)[v1], 1);
 }
@@ -167,13 +168,13 @@ TEST_F(TestMatchEngine, Match) {
   EquivPtr d;
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({v1, v1, v2}), VectorRef({1, 1, 2}), equiv_null);
+  d = TU.Match(VectorRef({v1, v1, v2}), VectorRef({1, 1, 2}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d->size(), std::size_t(2));
   ASSERT_EQ((*d)[v1], 1);
   ASSERT_EQ((*d)[v2], 2);
 
   equiv_null->clear();
-  d = TU.Match(static_cast<int>(1), static_cast<float>(1), equiv_null);
+  d = TU.Match(static_cast<int>(1), static_cast<float>(1), primitive_vars_null, equiv_null);
   ASSERT_EQ(d, nullptr);
 }
 
@@ -197,18 +198,19 @@ TEST_F(TestMatchEngine, Match_CondVar) {
   EquivPtr d;
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({vf, vn}), VectorRef({static_cast<float>(1.0), -1}), equiv_null);
+  d = TU.Match(VectorRef({vf, vn}), VectorRef({static_cast<float>(1.0), -1}), primitive_vars_null, equiv_null);
   ASSERT_GE(d->size(), std::size_t(0));
   auto vfn = (*d)[vf];
   ASSERT_EQ((*d)[vf], static_cast<float>(1.0));
   ASSERT_EQ((*d)[vn], -1);
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({vf, vn}), VectorRef({1, static_cast<float>(-1.0)}), equiv_null);
+  d = TU.Match(VectorRef({vf, vn}), VectorRef({1, static_cast<float>(-1.0)}), primitive_vars_null, equiv_null);
   ASSERT_EQ(d, nullptr);
 
   equiv_null->clear();
-  d = TU.Match(VectorRef({vf, vn}), VectorRef({static_cast<float>(1.0), static_cast<int>(1)}), equiv_null);
+  d = TU.Match(VectorRef({vf, vn}), VectorRef({static_cast<float>(1.0), static_cast<int>(1)}), primitive_vars_null,
+               equiv_null);
   ASSERT_EQ(d, nullptr);
 }
 

From 02aca064519ed7d17c169016241262682f32dda8 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Thu, 9 Apr 2020 20:43:12 +0800
Subject: [PATCH 223/367] Add all other tbe op info register

---
 mindspore/ops/_op_impl/tbe/add.py             |  81 ++---
 mindspore/ops/_op_impl/tbe/add_n.py           |  78 ++---
 mindspore/ops/_op_impl/tbe/apply_adam.py      | 264 ++++-----------
 mindspore/ops/_op_impl/tbe/apply_momentum.py  | 138 ++------
 .../ops/_op_impl/tbe/arg_max_with_value.py    |  79 +----
 .../ops/_op_impl/tbe/arg_min_with_value.py    |  79 +----
 mindspore/ops/_op_impl/tbe/assign.py          | 118 ++-----
 mindspore/ops/_op_impl/tbe/assign_add.py      |  96 ++----
 mindspore/ops/_op_impl/tbe/assign_sub.py      |  74 +----
 .../ops/_op_impl/tbe/atomic_addr_clean.py     |  35 +-
 mindspore/ops/_op_impl/tbe/batch_matmul.py    | 101 ++----
 mindspore/ops/_op_impl/tbe/batchnorm.py       | 203 +++---------
 mindspore/ops/_op_impl/tbe/batchnorm_grad.py  | 210 +++---------
 mindspore/ops/_op_impl/tbe/bias_add.py        |  80 +----
 mindspore/ops/_op_impl/tbe/bias_add_grad.py   |  67 +---
 .../ops/_op_impl/tbe/bn_training_reduce.py    |  68 +---
 .../_op_impl/tbe/bn_training_reduce_grad.py   | 150 ++-------
 .../ops/_op_impl/tbe/bn_training_update.py    | 224 ++-----------
 .../_op_impl/tbe/bn_training_update_grad.py   | 123 ++-----
 mindspore/ops/_op_impl/tbe/cast.py            |  95 ++----
 .../_op_impl/tbe/clip_by_norm_no_div_sum.py   | 102 ++----
 mindspore/ops/_op_impl/tbe/clip_by_value.py   |  99 ++----
 mindspore/ops/_op_impl/tbe/concat.py          | 169 ++--------
 .../_op_impl/tbe/confusion_softmax_grad.py    |  75 ++---
 .../ops/_op_impl/tbe/confusion_transpose_d.py | 107 ++----
 mindspore/ops/_op_impl/tbe/conv2d.py          | 128 ++------
 .../_op_impl/tbe/conv2d_backprop_filter.py    | 100 ++----
 .../ops/_op_impl/tbe/conv2d_backprop_input.py |  99 ++----
 mindspore/ops/_op_impl/tbe/div.py             |  85 ++---
 mindspore/ops/_op_impl/tbe/dropout_do_mask.py |  85 +----
 mindspore/ops/_op_impl/tbe/equal.py           |  80 ++---
 mindspore/ops/_op_impl/tbe/exp.py             |  59 +---
 mindspore/ops/_op_impl/tbe/expand_dims.py     |  66 +---
 mindspore/ops/_op_impl/tbe/floor_div.py       |  73 +----
 mindspore/ops/_op_impl/tbe/fused_mul_add.py   | 115 ++-----
 mindspore/ops/_op_impl/tbe/fused_mul_add_n.py |  99 ++----
 .../_op_impl/tbe/fused_mul_apply_momentum.py  | 164 ++--------
 mindspore/ops/_op_impl/tbe/gather_v2.py       | 131 +++-----
 mindspore/ops/_op_impl/tbe/gelu.py            |  64 ++--
 mindspore/ops/_op_impl/tbe/gelu_grad.py       |  90 ++---
 mindspore/ops/_op_impl/tbe/greater.py         |  82 ++---
 mindspore/ops/_op_impl/tbe/lamb_next_mv.py    | 309 +++---------------
 .../tbe/lamb_next_mv_with_decay_v1.py         | 309 +++---------------
 .../ops/_op_impl/tbe/lamb_update_with_lr.py   | 193 ++---------
 .../_op_impl/tbe/lamb_update_with_lr_v2.py    | 159 ++-------
 mindspore/ops/_op_impl/tbe/layer_norm.py      | 132 ++------
 .../tbe/layer_norm_beta_gamma_backprop.py     | 127 ++-----
 mindspore/ops/_op_impl/tbe/layer_norm_grad.py | 141 ++------
 .../ops/_op_impl/tbe/layer_norm_x_backprop.py | 121 ++-----
 mindspore/ops/_op_impl/tbe/less.py            |  81 ++---
 mindspore/ops/_op_impl/tbe/less_equal.py      |  83 ++---
 mindspore/ops/_op_impl/tbe/log.py             |  59 +---
 mindspore/ops/_op_impl/tbe/logical_and.py     |  73 +----
 mindspore/ops/_op_impl/tbe/logical_not.py     |  59 +---
 mindspore/ops/_op_impl/tbe/logical_or.py      |  73 +----
 mindspore/ops/_op_impl/tbe/logsoftmax.py      |  65 +---
 mindspore/ops/_op_impl/tbe/logsoftmax_grad.py |  79 +----
 mindspore/ops/_op_impl/tbe/matmul.py          | 102 ++----
 mindspore/ops/_op_impl/tbe/max_pool.py        |  84 +----
 mindspore/ops/_op_impl/tbe/max_pool_grad.py   | 104 ++----
 .../_op_impl/tbe/max_pool_grad_with_argmax.py | 107 ++----
 .../ops/_op_impl/tbe/max_pool_with_argmax.py  |  92 +-----
 mindspore/ops/_op_impl/tbe/maximum.py         |  81 ++---
 mindspore/ops/_op_impl/tbe/maximum_grad.py    | 134 ++------
 mindspore/ops/_op_impl/tbe/minimum.py         |  84 ++---
 mindspore/ops/_op_impl/tbe/minimum_grad.py    | 134 ++------
 mindspore/ops/_op_impl/tbe/mul.py             |  96 ++----
 mindspore/ops/_op_impl/tbe/neg.py             |  62 ++--
 .../_op_impl/tbe/npu_alloc_float_status.py    |  42 +--
 .../_op_impl/tbe/npu_clear_float_status.py    |  56 +---
 .../ops/_op_impl/tbe/npu_get_float_status.py  |  56 +---
 mindspore/ops/_op_impl/tbe/one_hot.py         | 115 ++-----
 mindspore/ops/_op_impl/tbe/pad_d.py           |  68 ++--
 mindspore/ops/_op_impl/tbe/pow.py             |  74 +----
 mindspore/ops/_op_impl/tbe/real_div.py        |  72 +---
 mindspore/ops/_op_impl/tbe/reciprocal.py      |  61 +---
 mindspore/ops/_op_impl/tbe/reduce_max.py      |  76 ++---
 mindspore/ops/_op_impl/tbe/reduce_mean.py     |  74 ++---
 mindspore/ops/_op_impl/tbe/reduce_mean_d.py   |  74 ++---
 mindspore/ops/_op_impl/tbe/reduce_min.py      |  78 ++---
 mindspore/ops/_op_impl/tbe/reduce_sum.py      |  72 +---
 mindspore/ops/_op_impl/tbe/relu.py            |  65 ++--
 mindspore/ops/_op_impl/tbe/relu_grad.py       |  82 ++---
 mindspore/ops/_op_impl/tbe/reshape.py         |  66 +---
 .../_op_impl/tbe/resize_nearest_neighbor.py   |  84 ++---
 .../_op_impl/tbe/resize_nearest_neighbor_d.py |  75 ++---
 .../tbe/resize_nearest_neighbor_grad_d.py     |  71 +---
 mindspore/ops/_op_impl/tbe/round.py           |  61 +---
 mindspore/ops/_op_impl/tbe/rsqrt.py           | 107 ++----
 mindspore/ops/_op_impl/tbe/scatter_nd.py      |  83 ++---
 mindspore/ops/_op_impl/tbe/scatter_nd_d.py    |  82 ++---
 mindspore/ops/_op_impl/tbe/select.py          | 111 ++-----
 mindspore/ops/_op_impl/tbe/sigmoid.py         |  82 ++---
 .../tbe/sigmoid_cross_entropy_with_logits.py  |  74 +----
 .../sigmoid_cross_entropy_with_logits_grad.py |  88 ++---
 mindspore/ops/_op_impl/tbe/sigmoid_grad.py    |  74 +----
 mindspore/ops/_op_impl/tbe/slice.py           | 116 ++-----
 mindspore/ops/_op_impl/tbe/softmax.py         |  68 ++--
 .../tbe/softmax_cross_entropy_with_logits.py  |  85 +----
 mindspore/ops/_op_impl/tbe/split_d.py         | 100 +++---
 mindspore/ops/_op_impl/tbe/sqrt.py            |  61 +---
 mindspore/ops/_op_impl/tbe/square.py          |  68 ++--
 mindspore/ops/_op_impl/tbe/square_sum_v1.py   |  72 +---
 mindspore/ops/_op_impl/tbe/square_sum_v2.py   |  86 +----
 mindspore/ops/_op_impl/tbe/squeeze.py         |  65 +---
 mindspore/ops/_op_impl/tbe/strideslice_d.py   | 118 ++-----
 .../ops/_op_impl/tbe/strideslicegrad_d.py     | 131 ++------
 mindspore/ops/_op_impl/tbe/sub.py             |  75 ++---
 mindspore/ops/_op_impl/tbe/tanh.py            |  59 +---
 mindspore/ops/_op_impl/tbe/tanh_grad.py       |  73 +----
 mindspore/ops/_op_impl/tbe/tensor_add.py      |  82 ++---
 mindspore/ops/_op_impl/tbe/tile.py            |  66 +---
 mindspore/ops/_op_impl/tbe/topkv2.py          |  99 +-----
 mindspore/ops/_op_impl/tbe/trans_data.py      | 113 +++----
 mindspore/ops/_op_impl/tbe/transpose_d.py     |  75 ++---
 .../ops/_op_impl/tbe/unsorted_segment_sum.py  | 201 ++----------
 mindspore/ops/_op_impl/tbe/zeros_like.py      |  68 ++--
 mindspore/ops/op_info_register.py             |  35 +-
 118 files changed, 2686 insertions(+), 8931 deletions(-)

diff --git a/mindspore/ops/_op_impl/tbe/add.py b/mindspore/ops/_op_impl/tbe/add.py
index 95c31d8974..63e1efb1c6 100644
--- a/mindspore/ops/_op_impl/tbe/add.py
+++ b/mindspore/ops/_op_impl/tbe/add.py
@@ -14,71 +14,28 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+add_op_info = TBERegOp("Add") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("add.so") \
+    .compute_cost(10) \
+    .kernel_name("add") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Add",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "add.so",
-    "compute_cost": 10,
-    "kernel_name": "add",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float",
-                "float", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(add_op_info)
 def _add_tbe():
     """Add TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/add_n.py b/mindspore/ops/_op_impl/tbe/add_n.py
index 9177ed14c7..3e8a6c0016 100644
--- a/mindspore/ops/_op_impl/tbe/add_n.py
+++ b/mindspore/ops/_op_impl/tbe/add_n.py
@@ -14,61 +14,33 @@
 # ============================================================================
 
 """AddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+add_n_op_info = TBERegOp("AddN") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("add_n.so") \
+    .compute_cost(10) \
+    .kernel_name("add_n") \
+    .partial_flag(True) \
+    .attr("n", "required", "int", "all") \
+    .input(0, "x", False, "dynamic", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AddN",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "add_n.so",
-    "compute_cost": 10,
-    "kernel_name": "add_n",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "n",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float","int32","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ",
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float","int32","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ",
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(add_n_op_info)
 def _add_n_tbe():
     """AddN TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/apply_adam.py b/mindspore/ops/_op_impl/tbe/apply_adam.py
index 1d5c383515..6fd7205567 100644
--- a/mindspore/ops/_op_impl/tbe/apply_adam.py
+++ b/mindspore/ops/_op_impl/tbe/apply_adam.py
@@ -14,214 +14,66 @@
 # ============================================================================
 
 """ApplyAdam op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+apply_adam_op_info = TBERegOp("Adam") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("apply_adam.so") \
+    .compute_cost(10) \
+    .kernel_name("apply_adam") \
+    .partial_flag(True) \
+    .attr("use_locking", "optional", "bool", "true,false", "false") \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "m", False, "required", "all") \
+    .input(2, "v", False, "required", "all") \
+    .input(3, "beta1_power", False, "required", "all") \
+    .input(4, "beta2_power", False, "required", "all") \
+    .input(5, "lr", False, "required", "all") \
+    .input(6, "beta1", False, "required", "all") \
+    .input(7, "beta2", False, "required", "all") \
+    .input(8, "epsilon", False, "required", "all") \
+    .input(9, "grad", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .output(1, "m", False, "required", "all") \
+    .output(2, "v", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0,
+                  DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ,
+                  DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0,
+                  DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ,
+                  DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Adam",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "apply_adam.so",
-    "compute_cost": 10,
-    "kernel_name": "apply_adam",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_locking",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        },
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "m",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "v",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta1_power",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-                {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta2_power",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 7,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 8,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "epsilon",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 9,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "m",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "v",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(apply_adam_op_info)
 def _apply_adam_tbe():
     """ApplyAdam TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/apply_momentum.py b/mindspore/ops/_op_impl/tbe/apply_momentum.py
index f2c6f5b15e..42ce9d0e41 100644
--- a/mindspore/ops/_op_impl/tbe/apply_momentum.py
+++ b/mindspore/ops/_op_impl/tbe/apply_momentum.py
@@ -14,112 +14,42 @@
 # ============================================================================
 
 """ApplyMomentum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+apply_momentum_op_info = TBERegOp("ApplyMomentum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("apply_momentum.so") \
+    .compute_cost(10) \
+    .kernel_name("apply_momentum") \
+    .partial_flag(True) \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "lr", False, "required", "all") \
+    .input(3, "grad", False, "required", "all") \
+    .input(4, "momentum", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD,
+                  DataType.F16_Default, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ,
+                  DataType.F16_Default, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD,
+                  DataType.F32_Default, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
+                  DataType.F32_Default, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ApplyMomentum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "apply_momentum.so",
-    "compute_cost": 10,
-    "kernel_name": "apply_momentum",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "accum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "momentum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(apply_momentum_op_info)
 def _apply_momentum_tbe():
     """ApplyMomentum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/arg_max_with_value.py b/mindspore/ops/_op_impl/tbe/arg_max_with_value.py
index e5ffe3d36f..ca393f3356 100644
--- a/mindspore/ops/_op_impl/tbe/arg_max_with_value.py
+++ b/mindspore/ops/_op_impl/tbe/arg_max_with_value.py
@@ -14,70 +14,25 @@
 # ============================================================================
 
 """ArgMaxWithValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+arg_max_with_value_op_info = TBERegOp("ArgMaxWithValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("arg_max_with_value.so") \
+    .compute_cost(10) \
+    .kernel_name("arg_max_with_value") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "indice", False, "required", "all") \
+    .output(1, "values", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ArgMaxWithValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "arg_max_with_value.so",
-    "compute_cost": 10,
-    "kernel_name": "arg_max_with_value",
-    "partial_flag": true,
-    "attr": [
-            {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "indice",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "values",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(arg_max_with_value_op_info)
 def _arg_max_with_value_tbe():
     """ArgMaxWithValue TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/arg_min_with_value.py b/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
index 3d66b4534d..a23005403b 100644
--- a/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
+++ b/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
@@ -14,70 +14,25 @@
 # ============================================================================
 
 """ArgMinWithValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+arg_min_with_value_op_info = TBERegOp("ArgMaxWithValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("arg_min_with_value.so") \
+    .compute_cost(10) \
+    .kernel_name("arg_min_with_value") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "indice", False, "required", "all") \
+    .output(1, "values", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ArgMinWithValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "arg_min_with_value.so",
-    "compute_cost": 10,
-    "kernel_name": "arg_min_with_value",
-    "partial_flag": true,
-    "attr": [
-            {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "indice",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "values",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(arg_min_with_value_op_info)
 def _arg_min_with_value_tbe():
     """ArgMinWithValue TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/assign.py b/mindspore/ops/_op_impl/tbe/assign.py
index 610a221a1c..41a9a0fecd 100644
--- a/mindspore/ops/_op_impl/tbe/assign.py
+++ b/mindspore/ops/_op_impl/tbe/assign.py
@@ -14,93 +14,43 @@
 # ============================================================================
 
 """Assign op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_op_info = TBERegOp("Assign") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assign.so") \
+    .compute_cost(10) \
+    .kernel_name("assign") \
+    .partial_flag(True) \
+    .input(0, "resource", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_5HD, DataType.I16_5HD, DataType.I16_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U16_5HD, DataType.U16_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_5HD, DataType.U32_5HD, DataType.U32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_5HD, DataType.U64_5HD, DataType.U64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Assign",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assign.so",
-    "compute_cost": 10,
-    "kernel_name": "assign",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8",
-                "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int16", "int16", "int16",
-                "int16", "uint16", "uint16", "uint16", "uint16", "int64", "int64", "int64", "int64",
-                "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "resource",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-                "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8", "int8", "int8", "int8", "uint8",
-                "uint8", "uint8", "uint8", "int16", "int16", "int16", "int16", "uint16", "uint16", "uint16",
-                "uint16", "int64", "int64", "int64", "int64", "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8", "int8", "int8",
-                "int8", "uint8", "uint8", "uint8", "uint8", "int16", "int16", "int16", "int16", "uint16",
-                "uint16", "uint16", "uint16", "int64", "int64", "int64", "int64",
-                "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_op_info)
 def _assign_tbe():
     """Assign TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/assign_add.py b/mindspore/ops/_op_impl/tbe/assign_add.py
index 94e0e781f5..fbbb9a997f 100644
--- a/mindspore/ops/_op_impl/tbe/assign_add.py
+++ b/mindspore/ops/_op_impl/tbe/assign_add.py
@@ -14,80 +14,34 @@
 # ============================================================================
 
 """AssignAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_add_op_info = TBERegOp("AssignAdd") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assignadd.so") \
+    .compute_cost(10) \
+    .kernel_name("assignadd") \
+    .partial_flag(True) \
+    .input(0, "ref", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "output_ref", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AssignAdd",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assignadd.so",
-    "compute_cost": 10,
-    "kernel_name": "assignadd",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_add_op_info)
 def _assign_add_tbe():
     """AssignAdd TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/assign_sub.py b/mindspore/ops/_op_impl/tbe/assign_sub.py
index 85104f6eb3..126a6b7a9a 100644
--- a/mindspore/ops/_op_impl/tbe/assign_sub.py
+++ b/mindspore/ops/_op_impl/tbe/assign_sub.py
@@ -14,65 +14,27 @@
 # ============================================================================
 
 """AssignSub op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_sub_op_info = TBERegOp("AssignSub") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assign_sub.so") \
+    .compute_cost(10) \
+    .kernel_name("assign_sub") \
+    .partial_flag(True) \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "output_ref", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AssignSub",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assign_sub.so",
-    "compute_cost": 10,
-    "kernel_name": "assign_sub",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "out_ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_sub_op_info)
 def _assign_sub_tbe():
     """AssignSub TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/atomic_addr_clean.py b/mindspore/ops/_op_impl/tbe/atomic_addr_clean.py
index 90186e6526..e707a1f26f 100644
--- a/mindspore/ops/_op_impl/tbe/atomic_addr_clean.py
+++ b/mindspore/ops/_op_impl/tbe/atomic_addr_clean.py
@@ -14,31 +14,20 @@
 # ============================================================================
 
 """AtomicAddrClean op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp
 
+atomic_addr_clean_op_info = TBERegOp("AtomicAddrClean") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("atomic_addr_clean.so") \
+    .compute_cost(10) \
+    .kernel_name("atomic_addr_clean") \
+    .partial_flag(True) \
+    .attr("automic_add_mem_size", "required", "listInt", "all") \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AtomicAddrClean",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "atomic_addr_clean.so",
-    "compute_cost": 10,
-    "kernel_name": "atomic_addr_clean",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "automic_add_mem_size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-    ],
-    "outputs": [
-    ]
-}""")
+
+@op_info_register(atomic_addr_clean_op_info)
 def _atomic_addr_clean_tbe():
     """AtomicAddrClean TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/batch_matmul.py b/mindspore/ops/_op_impl/tbe/batch_matmul.py
index 668791b659..4efcf8031c 100644
--- a/mindspore/ops/_op_impl/tbe/batch_matmul.py
+++ b/mindspore/ops/_op_impl/tbe/batch_matmul.py
@@ -14,88 +14,29 @@
 # ============================================================================
 
 """BatchMatMul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_matmul_op_info = TBERegOp("BatchMatMul") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batch_matmul.so") \
+    .compute_cost(10) \
+    .kernel_name("batch_matmul") \
+    .attr("transpose_x1", "required", "bool", "all") \
+    .attr("transpose_x2", "required", "bool", "all") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchMatMul",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batch_matmul.so",
-    "compute_cost": 10,
-    "kernel_name": "batch_matmul",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "transpose_x1",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        },
-        {
-            "name": "transpose_x2",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(batch_matmul_op_info)
 def _batch_matmul_tbe():
     """BatchMatMul TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/batchnorm.py b/mindspore/ops/_op_impl/tbe/batchnorm.py
index 263fcfb0f2..6dd79245a3 100644
--- a/mindspore/ops/_op_impl/tbe/batchnorm.py
+++ b/mindspore/ops/_op_impl/tbe/batchnorm.py
@@ -14,174 +14,45 @@
 # ============================================================================
 
 """BatchNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_norm_op_info = TBERegOp("BatchNorm") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batch_norm.so") \
+    .compute_cost(10) \
+    .kernel_name("batch_norm") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .attr("is_training", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "scale", False, "required", "all") \
+    .input(2, "offset", False, "required", "all") \
+    .input(3, "mean", False, "optional", "all") \
+    .input(4, "variance", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "batch_mean", False, "required", "all") \
+    .output(2, "batch_variance", False, "required", "all") \
+    .output(3, "reserve_space_1", False, "optional", "all") \
+    .output(4, "reserve_space_2", False, "optional", "all") \
+    .output(5, "reserve_space_3", False, "optional", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F16_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchNorm",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batch_norm.so",
-    "compute_cost": 10,
-    "kernel_name": "batch_norm",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "required",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "is_training",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0", "DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat", "NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0", "DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "param_type": "required"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_1",
-            "param_type": "optional"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_2",
-            "param_type": "optional"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_3",
-            "param_type": "optional"
-        }
-    ]
-}""")
+
+@op_info_register(batch_norm_op_info)
 def _batch_norm_tbe():
     """BatchNorm TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/batchnorm_grad.py b/mindspore/ops/_op_impl/tbe/batchnorm_grad.py
index cc560c5283..6063c0e750 100644
--- a/mindspore/ops/_op_impl/tbe/batchnorm_grad.py
+++ b/mindspore/ops/_op_impl/tbe/batchnorm_grad.py
@@ -14,181 +14,45 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_norm_grad_op_info = TBERegOp("BatchNormGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batchnormgrad.so") \
+    .compute_cost(10) \
+    .kernel_name("batchnormgrad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .attr("is_training", "optional", "bool", "all") \
+    .input(0, "y_backprop", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "scale", False, "required", "all") \
+    .input(3, "reserve_space_1", False, "required", "all") \
+    .input(4, "reserve_space_2", False, "required", "all") \
+    .input(5, "reserve_space_3", False, "required", "all") \
+    .output(0, "x_backprop", False, "required", "all") \
+    .output(1, "scale_backprop", False, "required", "all") \
+    .output(2, "offset_backprop", False, "required", "all") \
+    .output(3, "reserve_space_4", False, "optional", "all") \
+    .output(4, "reserve_space_5", False, "optional", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F16_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchNormGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batchnormgrad.so",
-    "compute_cost": 10,
-    "kernel_name": "batchnormgrad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "optional",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "is_training",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "scale_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "offset_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_4",
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_5",
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(batch_norm_grad_op_info)
 def _batch_norm_grad_tbe():
     """BatchNormGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bias_add.py b/mindspore/ops/_op_impl/tbe/bias_add.py
index 9081ed2c13..24607af141 100644
--- a/mindspore/ops/_op_impl/tbe/bias_add.py
+++ b/mindspore/ops/_op_impl/tbe/bias_add.py
@@ -14,70 +14,26 @@
 # ============================================================================
 
 """BiasAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bias_add_grad_op_info = TBERegOp("BiasAdd") \
+    .fusion_type("COMMREDUCE") \
+    .async_flag(False) \
+    .binfile_name("bias_add.so") \
+    .compute_cost(10) \
+    .kernel_name("bias_add") \
+    .partial_flag(True) \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "bias", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BiasAdd",
-    "imply_type": "TBE",
-    "fusion_type": "COMMREDUCE",
-    "async_flag": false,
-    "binfile_name": "bias_add.so",
-    "compute_cost": 10,
-    "kernel_name": "bias_add",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bias_add_grad_op_info)
 def _bias_add_tbe():
     """BiasAdd TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bias_add_grad.py b/mindspore/ops/_op_impl/tbe/bias_add_grad.py
index 4a24e361bb..e59c197bce 100644
--- a/mindspore/ops/_op_impl/tbe/bias_add_grad.py
+++ b/mindspore/ops/_op_impl/tbe/bias_add_grad.py
@@ -14,57 +14,26 @@
 # ============================================================================
 
 """BiasAddGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bias_add_grad_op_info = TBERegOp("BiasAddGrad") \
+    .fusion_type("COMMREDUCE") \
+    .async_flag(False) \
+    .binfile_name("biasaddgrad.so") \
+    .compute_cost(10) \
+    .kernel_name("biasaddgrad") \
+    .partial_flag(True) \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "output_backprop", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BiasAddGrad",
-    "imply_type": "TBE",
-    "fusion_type": "COMMREDUCE",
-    "async_flag": false,
-    "binfile_name": "biasaddgrad.so",
-    "compute_cost": 10,
-    "kernel_name": "biasaddgrad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","FRACTAL_NZ","DefaultFormat"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "output",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bias_add_grad_op_info)
 def _bias_add_grad_tbe():
     """BiasAddGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bn_training_reduce.py b/mindspore/ops/_op_impl/tbe/bn_training_reduce.py
index 3228ccd791..16d75d06be 100644
--- a/mindspore/ops/_op_impl/tbe/bn_training_reduce.py
+++ b/mindspore/ops/_op_impl/tbe/bn_training_reduce.py
@@ -14,60 +14,24 @@
 # ============================================================================
 
 """BatchNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_reduce_op_info = TBERegOp("BNTrainingReduce") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_reduce.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_reduce") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "sum", False, "required", "all") \
+    .output(1, "square_sum", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingReduce",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "bn_training_reduce.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_reduce",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "sum",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "square_sum",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_reduce_op_info)
 def _bn_training_reduce_tbe():
     """BNTrainingReduce TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py b/mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py
index 3bb43fbb94..e92054670d 100644
--- a/mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py
+++ b/mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py
@@ -14,134 +14,32 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_reduce_grad_op_info = TBERegOp("BNTrainingReduceGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_reduce_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_reduce_grad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x_norm", False, "required", "all") \
+    .input(2, "diff_scale", False, "required", "all") \
+    .input(3, "diff_offset", False, "required", "all") \
+    .input(4, "scale", False, "required", "all") \
+    .input(5, "batch_mean", False, "required", "all") \
+    .input(6, "batch_variance", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingReduceGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_reduce_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_reduce_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x_norm",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_reduce_grad_op_info)
 def _bn_training_reduce_grad_tbe():
     """BNTrainingReduceGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bn_training_update.py b/mindspore/ops/_op_impl/tbe/bn_training_update.py
index 9d6838f0e4..49b572e31e 100644
--- a/mindspore/ops/_op_impl/tbe/bn_training_update.py
+++ b/mindspore/ops/_op_impl/tbe/bn_training_update.py
@@ -14,200 +14,40 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_update_op_info = TBERegOp("BNTrainingUpdate") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_update.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_update") \
+    .partial_flag(True) \
+    .attr("factor", "optional", "float", "all") \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("isRef", "optional", "bool", "all", "true") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "sum", False, "required", "all") \
+    .input(2, "square_sum", False, "required", "all") \
+    .input(3, "scale", False, "required", "all") \
+    .input(4, "offset", False, "required", "all") \
+    .input(5, "mean", False, "required", "all") \
+    .input(6, "variance", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "mean", False, "required", "all") \
+    .output(2, "variance", False, "required", "all") \
+    .output(3, "batch_mean", False, "required", "all") \
+    .output(4, "batch_variance", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingUpdate",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_update.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_update",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "factor",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "isRef",
-            "param_type": "optional",
-            "type": "bool",
-            "default_value":"true",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "sum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "square_sum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_update_op_info)
 def _bn_training_update_tbe():
     """BNTrainingUpdate TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/bn_training_update_grad.py b/mindspore/ops/_op_impl/tbe/bn_training_update_grad.py
index 802ef6e91f..5e693bea42 100644
--- a/mindspore/ops/_op_impl/tbe/bn_training_update_grad.py
+++ b/mindspore/ops/_op_impl/tbe/bn_training_update_grad.py
@@ -14,109 +14,30 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_update_grad_op_info = TBERegOp("BNTrainingUpdateGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_update_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_update_grad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "batch_mean", False, "required", "all") \
+    .input(3, "batch_variance", False, "required", "all") \
+    .output(0, "diff_scale", False, "required", "all") \
+    .output(1, "diff_offset", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingUpdateGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_update_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_update_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_update_grad_op_info)
 def _bn_training_update_grad_tbe():
     """BNTrainingUpdateGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/cast.py b/mindspore/ops/_op_impl/tbe/cast.py
index e443d776ae..a18dcddfbf 100644
--- a/mindspore/ops/_op_impl/tbe/cast.py
+++ b/mindspore/ops/_op_impl/tbe/cast.py
@@ -14,69 +14,42 @@
 # ============================================================================
 
 """Cast op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+cast_op_info = TBERegOp("Cast") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("cast.so") \
+    .compute_cost(10) \
+    .kernel_name("cast") \
+    .partial_flag(True) \
+    .attr("dst_type", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.F16_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.U8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F32_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I8_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Cast",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "cast.so",
-    "compute_cost": 10,
-    "kernel_name": "cast",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "dst_type",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", 
-                "int32", "int32", "int32", "int32", "int32", 
-                "int8", "int8", "int8", "uint8", "uint8", "uint8", 
-                "bool", "bool", "bool", "bool", "float16"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float", "int32", "float16", "int32",
-                 "float16", "float", "int8", "uint8", "bool", 
-                 "float16", "float", "int32", "float16", "float", "int32", 
-                 "float16", "float", "int32", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(cast_op_info)
 def _cast_tbe():
     """Cast TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/clip_by_norm_no_div_sum.py b/mindspore/ops/_op_impl/tbe/clip_by_norm_no_div_sum.py
index 859315fb7b..92fb9a59ee 100644
--- a/mindspore/ops/_op_impl/tbe/clip_by_norm_no_div_sum.py
+++ b/mindspore/ops/_op_impl/tbe/clip_by_norm_no_div_sum.py
@@ -14,90 +14,28 @@
 # ============================================================================
 
 """ClipByNormNoDivSum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+clip_by_norm_no_div_sum_op_info = TBERegOp("ClipByNormNoDivSum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("clip_by_norm_no_div_sum.so") \
+    .compute_cost(10) \
+    .kernel_name("clip_by_norm_no_div_sum") \
+    .partial_flag(True) \
+    .input(0, "input_x", False, "required", "all") \
+    .input(1, "input1", False, "required", "all") \
+    .input(2, "input2", False, "required", "all") \
+    .input(3, "input3", False, "required", "all") \
+    .output(0, "output_y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ClipByNormNoDivSum",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "clip_by_norm_no_div_sum.so",
-    "compute_cost": 10,
-    "kernel_name": "clip_by_norm_no_div_sum",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(clip_by_norm_no_div_sum_op_info)
 def _clip_by_norm_no_div_sum_tbe():
     """ClipByNormNoDivSum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/clip_by_value.py b/mindspore/ops/_op_impl/tbe/clip_by_value.py
index 02ec0158a9..4ddc6c3c0f 100644
--- a/mindspore/ops/_op_impl/tbe/clip_by_value.py
+++ b/mindspore/ops/_op_impl/tbe/clip_by_value.py
@@ -14,85 +14,30 @@
 # ============================================================================
 
 """ClipByValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+clip_by_value_op_info = TBERegOp("ClipByValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("clip_by_value.so") \
+    .compute_cost(10) \
+    .kernel_name("clip_by_value") \
+    .partial_flag(True) \
+    .attr("dst_type", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "clip_value_min", False, "required", "all") \
+    .input(2, "clip_value_max", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ClipByValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "clip_by_value.so",
-    "compute_cost": 10,
-    "kernel_name": "clip_by_value",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "clip_value_min",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "clip_value_max",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(clip_by_value_op_info)
 def _clip_by_value_tbe():
     """ClipByValue TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/concat.py b/mindspore/ops/_op_impl/tbe/concat.py
index 3e5c577476..56807b15fc 100644
--- a/mindspore/ops/_op_impl/tbe/concat.py
+++ b/mindspore/ops/_op_impl/tbe/concat.py
@@ -14,141 +14,44 @@
 # ============================================================================
 
 """Concat op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+concat_op_info = TBERegOp("Concat") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("concat_d.so") \
+    .compute_cost(10) \
+    .kernel_name("concat_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "input_values", False, "dynamic", "all") \
+    .output(0, "output_data", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_5HD, DataType.I16_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U16_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_5HD, DataType.U32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_5HD, DataType.U64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Concat",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "concat_d.so",
-    "compute_cost": 10,
-    "kernel_name": "concat_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "int32",
-                "int32",
-                "int8",
-                "int8",
-                "int16",
-                "int16",
-                "int64",
-                "int64",
-                "uint8",
-                "uint8",
-                "uint16",
-                "uint16",
-                "uint32",
-                "uint32",
-                "uint64",
-                "uint64",
-                "bool",
-                "bool"
-            ],
-            "format": [
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0"
-            ],
-            "name": "input_values",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "int32",
-                "int32",
-                "int8",
-                "int8",
-                "int16",
-                "int16",
-                "int64",
-                "int64",
-                "uint8",
-                "uint8",
-                "uint16",
-                "uint16",
-                "uint32",
-                "uint32",
-                "uint64",
-                "uint64",
-                "bool",
-                "bool"
-            ],
-            "format": [
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0"
-            ],
-            "name": "output_data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(concat_op_info)
 def _concat_tbe():
     """Concat TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/confusion_softmax_grad.py b/mindspore/ops/_op_impl/tbe/confusion_softmax_grad.py
index cbd518d541..55d9562a44 100644
--- a/mindspore/ops/_op_impl/tbe/confusion_softmax_grad.py
+++ b/mindspore/ops/_op_impl/tbe/confusion_softmax_grad.py
@@ -14,65 +14,28 @@
 # ============================================================================
 
 """ConfusionSoftmaxGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+confusion_softmax_grad_op_info = TBERegOp("ConfusionSoftmaxGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("confusion_softmax_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("confusion_softmax_grad") \
+    .partial_flag(True) \
+    .input(0, "grad", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ConfusionSoftmaxGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "confusion_softmax_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "confusion_softmax_grad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-               "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(confusion_softmax_grad_op_info)
 def _confusion_softmax_grad_tbe():
     """ConfusionSoftmaxGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/confusion_transpose_d.py b/mindspore/ops/_op_impl/tbe/confusion_transpose_d.py
index db35107b5d..e52ae01520 100644
--- a/mindspore/ops/_op_impl/tbe/confusion_transpose_d.py
+++ b/mindspore/ops/_op_impl/tbe/confusion_transpose_d.py
@@ -14,79 +14,44 @@
 # ============================================================================
 
 """ConfusionTransposeD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+confusion_transpose_d_op_info = TBERegOp("ConfusionTransposeD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("confusion_transpose_d.so") \
+    .compute_cost(10) \
+    .kernel_name("confusion_transpose_d") \
+    .partial_flag(True) \
+    .attr("perm", "required", "listInt", "all") \
+    .attr("shape", "required", "listInt", "all") \
+    .attr("transpose_first", "required", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_FracNZ, DataType.I8_FracNZ) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_FracNZ, DataType.U8_FracNZ) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_FracNZ, DataType.I16_FracNZ) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_FracNZ, DataType.U16_FracNZ) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U32_FracNZ, DataType.U32_FracNZ) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.I64_FracNZ, DataType.I64_FracNZ) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U64_FracNZ, DataType.U64_FracNZ) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ConfusionTransposeD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "confusion_transpose_d.so",
-    "compute_cost": 10,
-    "kernel_name": "confusion_transpose_d",
-    "partial_flag": true,
-    "attr":[
-        {
-            "name":"perm",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"shape",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"transpose_first",
-            "param_type":"required",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32",
-                "uint64", "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16",
-                "uint32", "uint64"
-            ],
-            "format": [
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ",
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32",
-                "uint64", "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16",
-                "uint32", "uint64"
-            ],
-            "format": [
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ",
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(confusion_transpose_d_op_info)
 def _confusion_transpose_d_tbe():
     """ConfusionTransposeD TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/conv2d.py b/mindspore/ops/_op_impl/tbe/conv2d.py
index da0f34e359..425521901d 100644
--- a/mindspore/ops/_op_impl/tbe/conv2d.py
+++ b/mindspore/ops/_op_impl/tbe/conv2d.py
@@ -14,114 +14,30 @@
 # ============================================================================
 
 """Conv2D op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_op_info = TBERegOp("Conv2D") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d") \
+    .partial_flag(True) \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_list", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .attr("offset_a", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .input(3, "offset_w", False, "optional", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F16_Default, DataType.I8_Default,
+                  DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Conv2D",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_list",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "offset_a",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "filter",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "int8"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "offset_w",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_op_info)
 def _conv2d_tbe():
     """Conv2D TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py b/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py
index 2c1397c0b1..e32e99d888 100644
--- a/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py
+++ b/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py
@@ -14,89 +14,27 @@
 # ============================================================================
 
 """Conv2DBackpropFilter op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_backprop_filter_op_info = TBERegOp("Conv2DBackpropFilter") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d_backprop_filter_d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d_backprop_filter_d") \
+    .partial_flag(True) \
+    .attr("filter_sizes", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_mode", "required", "str", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .input(0, "out_backprop", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_FracZ) \
+    .get_op_info()
 
-# map to tbe kernel name conv2d_backprop_filter_d
-@op_info_register("""{
-    "op_name": "Conv2DBackpropFilter",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d_backprop_filter_d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d_backprop_filter_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "filter_sizes",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_mode",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_backprop_filter_op_info)
 def _conv2d_backprop_filter_tbe():
     """Conv2DBackpropFilter TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py b/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py
index d61989e472..2c1dd6aea2 100644
--- a/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py
+++ b/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py
@@ -14,88 +14,27 @@
 # ============================================================================
 
 """Conv2DBackpropInput op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_backprop_input_op_info = TBERegOp("Conv2DBackpropInput") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d_backprop_input_d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d_backprop_input_d") \
+    .partial_flag(True) \
+    .attr("input_sizes", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_mode", "required", "str", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .input(0, "out_backprop", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Conv2DBackpropInput",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d_backprop_input_d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d_backprop_input_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "input_sizes",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_mode",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "filter",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_backprop_input_op_info)
 def _conv2d_backprop_input_tbe():
     """Conv2DBackpropInput TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/div.py b/mindspore/ops/_op_impl/tbe/div.py
index c71d6f38c4..2a83745399 100644
--- a/mindspore/ops/_op_impl/tbe/div.py
+++ b/mindspore/ops/_op_impl/tbe/div.py
@@ -14,71 +14,32 @@
 # ============================================================================
 
 """Div op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+div_op_info = TBERegOp("Div") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("div.so") \
+    .compute_cost(10) \
+    .kernel_name("div") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Div",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "div.so",
-    "compute_cost": 10,
-    "kernel_name": "div",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(div_op_info)
 def _div_tbe():
     """Div TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/dropout_do_mask.py b/mindspore/ops/_op_impl/tbe/dropout_do_mask.py
index 5f4557a4f5..2bef489b96 100644
--- a/mindspore/ops/_op_impl/tbe/dropout_do_mask.py
+++ b/mindspore/ops/_op_impl/tbe/dropout_do_mask.py
@@ -14,76 +14,25 @@
 # ============================================================================
 
 """DropoutdoMask op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+drop_out_do_mask_op_info = TBERegOp("DropoutDoMask") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("drop_out_do_mask.so") \
+    .compute_cost(10) \
+    .kernel_name("drop_out_do_mask") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "mask", False, "required", "all") \
+    .input(2, "keep_prob", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.U8_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.U8_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "DropoutDoMask",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "drop_out_do_mask.so",
-    "compute_cost": 10,
-    "kernel_name": "drop_out_do_mask",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "uint8","uint8","uint8","uint8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "mask",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "keep_prob",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(drop_out_do_mask_op_info)
 def _dropout_do_mask_tbe():
     """DropoutdoMask TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/equal.py b/mindspore/ops/_op_impl/tbe/equal.py
index db2e152c27..594fb51cb5 100644
--- a/mindspore/ops/_op_impl/tbe/equal.py
+++ b/mindspore/ops/_op_impl/tbe/equal.py
@@ -14,66 +14,32 @@
 # ============================================================================
 
 """Equal op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+equal_op_info = TBERegOp("Equal") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("equal.so") \
+    .compute_cost(10) \
+    .kernel_name("equal") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Equal",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "equal.so",
-    "compute_cost": 10,
-    "kernel_name": "equal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(equal_op_info)
 def _equal_tbe():
     """Equal TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/exp.py b/mindspore/ops/_op_impl/tbe/exp.py
index e5f34e67b0..545845a3b0 100644
--- a/mindspore/ops/_op_impl/tbe/exp.py
+++ b/mindspore/ops/_op_impl/tbe/exp.py
@@ -14,52 +14,25 @@
 # ============================================================================
 
 """Exp op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+exp_op_info = TBERegOp("Exp") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("exp.so") \
+    .compute_cost(10) \
+    .kernel_name("exp") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Exp",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "exp.so",
-    "compute_cost": 10,
-    "kernel_name": "exp",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(exp_op_info)
 def _exp_tbe():
     """Exp TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/expand_dims.py b/mindspore/ops/_op_impl/tbe/expand_dims.py
index 462a676e4f..8b0755a521 100644
--- a/mindspore/ops/_op_impl/tbe/expand_dims.py
+++ b/mindspore/ops/_op_impl/tbe/expand_dims.py
@@ -14,57 +14,25 @@
 # ============================================================================
 
 """ExpandDims op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+expand_dims_op_info = TBERegOp("ExpandDims") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("expand_dims.so") \
+    .compute_cost(10) \
+    .kernel_name("expand_dims") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ExpandDims",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "expand_dims.so",
-    "compute_cost": 10,
-    "kernel_name": "expand_dims",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(expand_dims_op_info)
 def _expand_dims_tbe():
     """ExpandDims TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/floor_div.py b/mindspore/ops/_op_impl/tbe/floor_div.py
index fdc49e3805..74fd594901 100644
--- a/mindspore/ops/_op_impl/tbe/floor_div.py
+++ b/mindspore/ops/_op_impl/tbe/floor_div.py
@@ -14,64 +14,27 @@
 # ============================================================================
 
 """FloorDiv op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+floordiv_op_info = TBERegOp("FloorDiv") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("floordiv.so") \
+    .compute_cost(10) \
+    .kernel_name("floordiv") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FloorDiv",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "floordiv.so",
-    "compute_cost": 10,
-    "kernel_name": "floordiv",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(floordiv_op_info)
 def _floor_div_tbe():
     """FloorDiv TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/fused_mul_add.py b/mindspore/ops/_op_impl/tbe/fused_mul_add.py
index 96e18a89c7..ad3c601e5d 100644
--- a/mindspore/ops/_op_impl/tbe/fused_mul_add.py
+++ b/mindspore/ops/_op_impl/tbe/fused_mul_add.py
@@ -14,93 +14,38 @@
 # ============================================================================
 
 """FusedMulAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_add_op_info = TBERegOp("FusedMulAdd") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_add.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_add") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulAdd",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_add.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_add",
-    "partial_flag": true,
-    "attr": [
-    
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(fused_mul_add_op_info)
 def _fused_mul_add_tbe():
     """FusedMulAdd TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/fused_mul_add_n.py b/mindspore/ops/_op_impl/tbe/fused_mul_add_n.py
index a4046a253b..9996466f70 100644
--- a/mindspore/ops/_op_impl/tbe/fused_mul_add_n.py
+++ b/mindspore/ops/_op_impl/tbe/fused_mul_add_n.py
@@ -14,86 +14,31 @@
 # ============================================================================
 
 """FusedMulAddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_add_n_op_info = TBERegOp("FusedMulAddN") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_add_n.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_add_n") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulAddN",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_add_n.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_add_n",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(fused_mul_add_n_op_info)
 def _fused_mul_add_n_tbe():
     """FusedMulAddN TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/fused_mul_apply_momentum.py b/mindspore/ops/_op_impl/tbe/fused_mul_apply_momentum.py
index e303ee042f..a8f84427d6 100644
--- a/mindspore/ops/_op_impl/tbe/fused_mul_apply_momentum.py
+++ b/mindspore/ops/_op_impl/tbe/fused_mul_apply_momentum.py
@@ -14,137 +14,43 @@
 # ============================================================================
 
 """FusedMulApplyMomentum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_apply_momentum_op_info = TBERegOp("FusedMulApplyMomentum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_apply_momentum.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_apply_momentum") \
+    .partial_flag(True) \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "lr", False, "required", "all") \
+    .input(3, "x1", False, "required", "all") \
+    .input(4, "momentum", False, "required", "all") \
+    .input(5, "x2", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD,
+                  DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ,
+                  DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD,
+                  DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
+                  DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulApplyMomentum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_apply_momentum.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_apply_momentum",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "accum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "momentum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(fused_mul_apply_momentum_op_info)
 def _fused_mul_apply_momentum_tbe():
     """FusedMulApplyMomentum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/gather_v2.py b/mindspore/ops/_op_impl/tbe/gather_v2.py
index b0e14e99c0..72ba17d942 100644
--- a/mindspore/ops/_op_impl/tbe/gather_v2.py
+++ b/mindspore/ops/_op_impl/tbe/gather_v2.py
@@ -14,94 +14,53 @@
 # ============================================================================
 
 """AddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gather_v2_op_info = TBERegOp("GatherV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("gather_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("gather_v2_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I64_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I32_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_5HD, DataType.I64_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_FracZ, DataType.I32_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.I8_FracZ, DataType.I64_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I64_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.I32_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_5HD, DataType.I64_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_FracZ, DataType.I32_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.U8_FracZ, DataType.I64_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_5HD, DataType.I64_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracZ, DataType.I64_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.I32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.I64_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.I32_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracZ, DataType.I64_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.I32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.I64_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.I32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracZ, DataType.I64_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "GatherV2",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "gather_v2_d.so",
-    "compute_cost": 10,
-    "kernel_name": "gather_v2_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float16","float16",
-                "float","float","float","float","float","float",
-                "int32","int32","int32", "int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8","uint8",
-                "int8","int8", "int8","int8","int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float16","float16",
-                "float","float","float","float","float","float",
-                "int32","int32","int32", "int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8","uint8",
-                "int8","int8", "int8","int8","int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gather_v2_op_info)
 def _gather_v2_tbe():
     """GatherV2 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/gelu.py b/mindspore/ops/_op_impl/tbe/gelu.py
index 171d97c043..9d4b2ed7f3 100644
--- a/mindspore/ops/_op_impl/tbe/gelu.py
+++ b/mindspore/ops/_op_impl/tbe/gelu.py
@@ -14,51 +14,29 @@
 # ============================================================================
 
 """Gelu op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gelu_op_info = TBERegOp("Gelu") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("gelu.so") \
+    .compute_cost(10) \
+    .kernel_name("gelu") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Gelu",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "gelu.so",
-    "compute_cost": 10,
-    "kernel_name": "gelu",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gelu_op_info)
 def _gelu_tbe():
     """Gelu TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/gelu_grad.py b/mindspore/ops/_op_impl/tbe/gelu_grad.py
index 9b358262e0..ce62e55071 100644
--- a/mindspore/ops/_op_impl/tbe/gelu_grad.py
+++ b/mindspore/ops/_op_impl/tbe/gelu_grad.py
@@ -14,77 +14,29 @@
 # ============================================================================
 
 """GeluGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gelu_grad_op_info = TBERegOp("GeluGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("gelu_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("gelu_grad") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "y", False, "required", "all") \
+    .output(0, "z", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "GeluGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "gelu_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "gelu_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "z",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gelu_grad_op_info)
 def _gelu_grad_tbe():
     """GeluGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/greater.py b/mindspore/ops/_op_impl/tbe/greater.py
index 09ee0e31af..90c680ab04 100644
--- a/mindspore/ops/_op_impl/tbe/greater.py
+++ b/mindspore/ops/_op_impl/tbe/greater.py
@@ -14,68 +14,32 @@
 # ============================================================================
 
 """Greater op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+greater_op_info = TBERegOp("Greater") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("greater.so") \
+    .compute_cost(10) \
+    .kernel_name("greater") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Greater",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "greater.so",
-    "compute_cost": 10,
-    "kernel_name": "greater",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(greater_op_info)
 def _greater_tbe():
     """Greater TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/lamb_next_mv.py b/mindspore/ops/_op_impl/tbe/lamb_next_mv.py
index b432b47c3d..2f2200a1f4 100644
--- a/mindspore/ops/_op_impl/tbe/lamb_next_mv.py
+++ b/mindspore/ops/_op_impl/tbe/lamb_next_mv.py
@@ -14,279 +14,46 @@
 # ============================================================================
 
 """LambNextMV op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_next_mv_op_info = TBERegOp("LambNextMV") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_next_m_v.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_next_m_v") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .input(9, "inputx0", False, "required", "all") \
+    .input(10, "inputx1", False, "required", "all") \
+    .input(11, "inputx2", False, "required", "all") \
+    .input(12, "inputx3", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .output(1, "output2", False, "required", "all") \
+    .output(2, "output3", False, "required", "all") \
+    .output(3, "output4", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambNextMV",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_next_m_v.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_next_m_v",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":9,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx0",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":10,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":11,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":12,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_next_mv_op_info)
 def _lamb_next_mv_tbe():
     """LambNextMV TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/lamb_next_mv_with_decay_v1.py b/mindspore/ops/_op_impl/tbe/lamb_next_mv_with_decay_v1.py
index bf5b6cd0e7..aa135e5afe 100644
--- a/mindspore/ops/_op_impl/tbe/lamb_next_mv_with_decay_v1.py
+++ b/mindspore/ops/_op_impl/tbe/lamb_next_mv_with_decay_v1.py
@@ -14,279 +14,46 @@
 # ============================================================================
 
 """LambNextMVWithDecayV1 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_next_m_v_with_decay_v1_op_info = TBERegOp("LambNextMVWithDecayV1") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("lamb_next_m_v_with_decay_v1.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_next_m_v_with_decay_v1") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .input(9, "inputx0", False, "required", "all") \
+    .input(10, "inputx1", False, "required", "all") \
+    .input(11, "inputx2", False, "required", "all") \
+    .input(12, "inputx3", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .output(1, "output2", False, "required", "all") \
+    .output(2, "output3", False, "required", "all") \
+    .output(3, "output4", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambNextMVWithDecayV1",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"lamb_next_m_v_with_decay_v1.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_next_m_v_with_decay_v1",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":9,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx0",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":10,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":11,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":12,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_next_m_v_with_decay_v1_op_info)
 def _lamb_next_mv_with_decay_v1_tbe():
     """LambNextMVWithDecayV1 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/lamb_update_with_lr.py b/mindspore/ops/_op_impl/tbe/lamb_update_with_lr.py
index a5062e74e2..b34ac57df2 100644
--- a/mindspore/ops/_op_impl/tbe/lamb_update_with_lr.py
+++ b/mindspore/ops/_op_impl/tbe/lamb_update_with_lr.py
@@ -14,174 +14,35 @@
 # ============================================================================
 
 """LambUpdateWithLr op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_update_with_lr_op_info = TBERegOp("LambUpdateWithLR") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_update_with_lr.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_update_with_lr") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .output(0, "output_y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambUpdateWithLR",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_update_with_lr.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_update_with_lr",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output_y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_update_with_lr_op_info)
 def _lamb_update_with_lr_tbe():
     """LambUpdateWithLr TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/lamb_update_with_lr_v2.py b/mindspore/ops/_op_impl/tbe/lamb_update_with_lr_v2.py
index 0900775b07..0902d68de9 100644
--- a/mindspore/ops/_op_impl/tbe/lamb_update_with_lr_v2.py
+++ b/mindspore/ops/_op_impl/tbe/lamb_update_with_lr_v2.py
@@ -14,144 +14,31 @@
 # ============================================================================
 
 """LambUpdateWithLrV2 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_update_with_lr_v2_op_info = TBERegOp("LambUpdateWithLrV2") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_update_with_lr_v2.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_update_with_lr_v2") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .input(3, "x4", False, "required", "all") \
+    .input(4, "x5", False, "required", "all") \
+    .input(5, "greater_y", False, "required", "all") \
+    .input(6, "select_e", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambUpdateWithLrV2",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_update_with_lr_v2.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_update_with_lr_v2",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"greater_y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"select_e",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_update_with_lr_v2_op_info)
 def _lamb_update_with_lr_v2_tbe():
     """LambUpdateWithLrV2 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/layer_norm.py b/mindspore/ops/_op_impl/tbe/layer_norm.py
index 5fd4a6387b..bc71fa87d3 100644
--- a/mindspore/ops/_op_impl/tbe/layer_norm.py
+++ b/mindspore/ops/_op_impl/tbe/layer_norm.py
@@ -14,111 +14,39 @@
 # ============================================================================
 
 """LayerNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_op_info = TBERegOp("LayerNorm") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm") \
+    .partial_flag(True) \
+    .attr("begin_norm_axis", "required", "int", "all") \
+    .attr("begin_params_axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "gamma", False, "required", "all") \
+    .input(2, "beta", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "mean", False, "required", "all") \
+    .output(2, "variance", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default, DataType.F16_FracNZ,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default, DataType.F32_FracNZ,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNorm",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "begin_norm_axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "begin_params_axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "beta",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
 
-            ],
-            "name": "mean",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-
-            ],
-            "name": "variance",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_op_info)
 def _layer_norm_tbe():
     """LayerNorm TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py b/mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py
index cdf0dad744..ef254465bc 100644
--- a/mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py
+++ b/mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py
@@ -14,105 +14,38 @@
 # ============================================================================
 
 """LayerNormBetaGammaBackprop op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_beta_gamma_backprop_op_info = TBERegOp("LayerNormBetaGammaBackprop") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_beta_gamma_backprop.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_beta_gamma_backprop") \
+    .partial_flag(True) \
+    .attr("shape_gamma", "required", "listInt", "all") \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .output(0, "pd_gamma", False, "required", "all") \
+    .output(1, "pd_beta", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormBetaGammaBackprop",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_beta_gamma_backprop.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_beta_gamma_backprop",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape_gamma",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_gamma",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_beta",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(layer_norm_beta_gamma_backprop_op_info)
 def _layer_norm_beta_gamma_backprop_tbe():
     """LayerNormBetaGammaBackprop TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/layer_norm_grad.py b/mindspore/ops/_op_impl/tbe/layer_norm_grad.py
index 6ba4656615..9540f2e265 100644
--- a/mindspore/ops/_op_impl/tbe/layer_norm_grad.py
+++ b/mindspore/ops/_op_impl/tbe/layer_norm_grad.py
@@ -14,124 +14,35 @@
 # ============================================================================
 
 """LayerNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_grad_op_info = TBERegOp("LayerNormGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_grad") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .input(4, "gamma", False, "required", "all") \
+    .output(0, "pd_x", False, "required", "all") \
+    .output(1, "pd_gamma", False, "required", "all") \
+    .output(2, "pd_beta", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_grad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_x",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_gamma",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_beta",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_grad_op_info)
 def _layer_norm_grad_tbe():
     """LayerNormGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py b/mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py
index 0557fdebc2..bbab66816d 100644
--- a/mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py
+++ b/mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py
@@ -14,102 +14,37 @@
 # ============================================================================
 
 """LayerNormXBackprop op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_x_backprop_op_info = TBERegOp("LayerNormXBackprop") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_x_backprop.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_x_backprop") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .input(4, "gamma", False, "required", "all") \
+    .output(0, "pd_x", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormXBackprop",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_x_backprop.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_x_backprop",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_x",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_x_backprop_op_info)
 def _layer_norm_x_backprop_tbe():
     """LayerNormXBackprop TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/less.py b/mindspore/ops/_op_impl/tbe/less.py
index 6e48d60341..947c40b949 100644
--- a/mindspore/ops/_op_impl/tbe/less.py
+++ b/mindspore/ops/_op_impl/tbe/less.py
@@ -14,67 +14,32 @@
 # ============================================================================
 
 """Less op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+less_op_info = TBERegOp("Less") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("less.so") \
+    .compute_cost(10) \
+    .kernel_name("less") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Less",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "less.so",
-    "compute_cost": 10,
-    "kernel_name": "less",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(less_op_info)
 def _less_tbe():
     """Less TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/less_equal.py b/mindspore/ops/_op_impl/tbe/less_equal.py
index 556389fa0e..14cf7c8906 100644
--- a/mindspore/ops/_op_impl/tbe/less_equal.py
+++ b/mindspore/ops/_op_impl/tbe/less_equal.py
@@ -14,67 +14,34 @@
 # ============================================================================
 
 """LessEqual op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+less_equal_op_info = TBERegOp("LessEqual") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("less_equal.so") \
+    .compute_cost(10) \
+    .kernel_name("less_equal") \
+    .partial_flag(True) \
+    .attr("begin_norm_axis", "required", "int", "all") \
+    .attr("begin_params_axis", "required", "int", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LessEqual",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "less_equal.so",
-    "compute_cost": 10,
-    "kernel_name": "less_equal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(less_equal_op_info)
 def _less_equal_tbe():
     """LessEqual TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/log.py b/mindspore/ops/_op_impl/tbe/log.py
index 72a55757a2..b7da647248 100644
--- a/mindspore/ops/_op_impl/tbe/log.py
+++ b/mindspore/ops/_op_impl/tbe/log.py
@@ -14,52 +14,25 @@
 # ============================================================================
 
 """Log op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_op_info = TBERegOp("Log") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("log.so") \
+    .compute_cost(10) \
+    .kernel_name("log") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Log",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "log.so",
-    "compute_cost": 10,
-    "kernel_name": "log",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(log_op_info)
 def _log_tbe():
     """Log TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/logical_and.py b/mindspore/ops/_op_impl/tbe/logical_and.py
index da4862450f..925a4e82d8 100644
--- a/mindspore/ops/_op_impl/tbe/logical_and.py
+++ b/mindspore/ops/_op_impl/tbe/logical_and.py
@@ -14,65 +14,26 @@
 # ============================================================================
 
 """LogicalAnd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_and_op_info = TBERegOp("LogicalAnd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_and.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_and") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalAnd",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_and.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_and",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_and_op_info)
 def _logical_and_tbe():
     """LogicalAnd TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/logical_not.py b/mindspore/ops/_op_impl/tbe/logical_not.py
index 4fe8094ffb..3d40441156 100644
--- a/mindspore/ops/_op_impl/tbe/logical_not.py
+++ b/mindspore/ops/_op_impl/tbe/logical_not.py
@@ -14,52 +14,25 @@
 # ============================================================================
 
 """LogicalNot op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_not_op_info = TBERegOp("LogicalNot") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_not.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_not") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalNot",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_not.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_not",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_not_op_info)
 def _logical_not_tbe():
     """LogicalNot TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/logical_or.py b/mindspore/ops/_op_impl/tbe/logical_or.py
index 0f21bb61b0..bf8d82c656 100644
--- a/mindspore/ops/_op_impl/tbe/logical_or.py
+++ b/mindspore/ops/_op_impl/tbe/logical_or.py
@@ -14,65 +14,26 @@
 # ============================================================================
 
 """LogicalOr op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_or_op_info = TBERegOp("LogicalOr") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_or.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_or") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalOr",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_or.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_or",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_or_op_info)
 def _logical_or_tbe():
     """LogicalOr TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/logsoftmax.py b/mindspore/ops/_op_impl/tbe/logsoftmax.py
index 03e8657919..9bf0baf3f2 100644
--- a/mindspore/ops/_op_impl/tbe/logsoftmax.py
+++ b/mindspore/ops/_op_impl/tbe/logsoftmax.py
@@ -14,57 +14,24 @@
 # ============================================================================
 
 """LogSoftmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_softmax_op_info = TBERegOp("LogSoftmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("log_softmax.so") \
+    .compute_cost(10) \
+    .kernel_name("log_softmax") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "logits", False, "required", "all") \
+    .output(0, "logsoftmax", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogSoftmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "log_softmax.so",
-    "compute_cost": 10,
-    "kernel_name": "log_softmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "logits",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "logsoftmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(log_softmax_op_info)
 def _logsoftmax_tbe():
     """LogSoftMaxGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/logsoftmax_grad.py b/mindspore/ops/_op_impl/tbe/logsoftmax_grad.py
index f6858e9530..9223b821d5 100644
--- a/mindspore/ops/_op_impl/tbe/logsoftmax_grad.py
+++ b/mindspore/ops/_op_impl/tbe/logsoftmax_grad.py
@@ -14,70 +14,25 @@
 # ============================================================================
 
 """LogSoftmaxGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_softmax_grad_op_info = TBERegOp("LogSoftmaxGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("log_softmax_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("log_softmax_grad") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "grad", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogSoftmaxGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "log_softmax_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "log_softmax_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(log_softmax_grad_op_info)
 def _logsoftmax_grad_tbe():
     """LogSoftMaxGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/matmul.py b/mindspore/ops/_op_impl/tbe/matmul.py
index d18d2cde48..c29378f721 100644
--- a/mindspore/ops/_op_impl/tbe/matmul.py
+++ b/mindspore/ops/_op_impl/tbe/matmul.py
@@ -14,89 +14,29 @@
 # ============================================================================
 
 """MatMul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+matmul_op_info = TBERegOp("MatMul") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("matmul.so") \
+    .compute_cost(10) \
+    .kernel_name("matmul") \
+    .partial_flag(True) \
+    .attr("transpose_a", "required", "bool", "all") \
+    .attr("transpose_b", "required", "bool", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F32_Default, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MatMul",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "matmul.so",
-    "compute_cost": 10,
-    "kernel_name": "matmul",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "transpose_a",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        },
-        {
-            "name": "transpose_b",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-               "float16","float","float","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(matmul_op_info)
 def _matmul_tbe():
     """Mul TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/max_pool.py b/mindspore/ops/_op_impl/tbe/max_pool.py
index 6b10bc8d9b..6c4c53cbce 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool.py
@@ -14,74 +14,26 @@
 # ============================================================================
 
 """MaxPool op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_op_info = TBERegOp("MaxPool") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "input_data", False, "required", "all") \
+    .output(0, "output_data", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPool",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "input_data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "output_data",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_op_info)
 def _max_pool_tbe():
     """MaxPool TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/max_pool_grad.py b/mindspore/ops/_op_impl/tbe/max_pool_grad.py
index 7c942d01e0..debe1a5a3a 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool_grad.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool_grad.py
@@ -14,93 +14,27 @@
 # ============================================================================
 
 """MaxPoolGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_grad_op_info = TBERegOp("MaxPoolGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_grad") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "grad", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_grad_op_info)
 def _max_pool_grad_tbe():
     """MaxPoolGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
index 3730ee1b93..2d6556ffef 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py
@@ -14,95 +14,28 @@
 # ============================================================================
 
 """MaxPoolGradWithArgmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_grad_with_argmax_op_info = TBERegOp("MaxPoolGradWithArgmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool_grad_with_argmax.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_grad_with_argmax") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "grad", False, "required", "all") \
+    .input(2, "argmax", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.U16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.I64_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolGradWithArgmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool_grad_with_argmax.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_grad_with_argmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "uint16", "int64"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "argmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_grad_with_argmax_op_info)
 def _max_pool_grad_with_argmax_tbe():
     """MaxPoolGradWithArgmax TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
index 2e081c1082..24700d4b42 100644
--- a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
+++ b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py
@@ -14,82 +14,26 @@
 # ============================================================================
 
 """MaxPoolWithArgmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_with_argmax_op_info = TBERegOp("MaxPoolWithArgmax") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("max_pool_with_argmax.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_with_argmax") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "argmax", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.U16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolWithArgmax",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "max_pool_with_argmax.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_with_argmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "uint16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "argmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_with_argmax_op_info)
 def _max_pool_with_argmax_tbe():
     """MaxPoolWithArgmax TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/maximum.py b/mindspore/ops/_op_impl/tbe/maximum.py
index 2a4051f28d..6fb7d05e03 100644
--- a/mindspore/ops/_op_impl/tbe/maximum.py
+++ b/mindspore/ops/_op_impl/tbe/maximum.py
@@ -14,69 +14,28 @@
 # ============================================================================
 
 """Maximum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+maximum_op_info = TBERegOp("Maximum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("maximum.so") \
+    .compute_cost(10) \
+    .kernel_name("maximum") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Maximum",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"maximum.so",
-    "compute_cost":10,
-    "kernel_name":"maximum",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(maximum_op_info)
 def _maximum_tbe():
     """Maximum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/maximum_grad.py b/mindspore/ops/_op_impl/tbe/maximum_grad.py
index f602616da9..b9bc9c09f8 100644
--- a/mindspore/ops/_op_impl/tbe/maximum_grad.py
+++ b/mindspore/ops/_op_impl/tbe/maximum_grad.py
@@ -14,112 +14,38 @@
 # ============================================================================
 
 """MaximumGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+maximum_grad_op_info = TBERegOp("MaximumGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("maximum_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("maximum_grad") \
+    .partial_flag(True) \
+    .attr("grad_x", "optional", "bool", "all") \
+    .attr("grad_y", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y1", False, "required", "all") \
+    .output(1, "y2", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
+                  DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD,
+                  DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"MaximumGrad",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"maximum_grad.so",
-    "compute_cost":10,
-    "kernel_name":"maximum_grad",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"grad_x",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        },
-        {
-            "name":"grad_y",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"grads",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(maximum_grad_op_info)
 def _maximum_grad_tbe():
     """MaximumGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/minimum.py b/mindspore/ops/_op_impl/tbe/minimum.py
index 2f7e5b80d7..1cebfd3dad 100644
--- a/mindspore/ops/_op_impl/tbe/minimum.py
+++ b/mindspore/ops/_op_impl/tbe/minimum.py
@@ -15,74 +15,28 @@
 
 
 """Minimum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+minimum_op_info = TBERegOp("Minimum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("minimum.so") \
+    .compute_cost(10) \
+    .kernel_name("minimum") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD)  \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Minimum",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "minimum.so",
-    "compute_cost": 10,
-    "kernel_name": "minimum",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(minimum_op_info)
 def _minimum_tbe():
     """Minimum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/minimum_grad.py b/mindspore/ops/_op_impl/tbe/minimum_grad.py
index d49b2aa184..c3ea1c3a56 100644
--- a/mindspore/ops/_op_impl/tbe/minimum_grad.py
+++ b/mindspore/ops/_op_impl/tbe/minimum_grad.py
@@ -14,112 +14,38 @@
 # ============================================================================
 
 """MinimumGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+minimum_grad_op_info = TBERegOp("MinimumGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("minimum_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("minimum_grad") \
+    .partial_flag(True) \
+    .attr("grad_x", "optional", "bool", "all") \
+    .attr("grad_y", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y1", False, "required", "all") \
+    .output(1, "y2", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
+                  DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD,
+                  DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"MinimumGrad",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"minimum_grad.so",
-    "compute_cost":10,
-    "kernel_name":"minimum_grad",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"grad_x",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        },
-        {
-            "name":"grad_y",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"grads",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(minimum_grad_op_info)
 def _minimum_grad_tbe():
     """MinimumGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/mul.py b/mindspore/ops/_op_impl/tbe/mul.py
index 912d5e372f..fa74c88de3 100644
--- a/mindspore/ops/_op_impl/tbe/mul.py
+++ b/mindspore/ops/_op_impl/tbe/mul.py
@@ -14,77 +14,37 @@
 # ============================================================================
 
 """Mul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+mul_op_info = TBERegOp("Mul") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("mul.so") \
+    .compute_cost(10) \
+    .kernel_name("mul") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Mul",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "mul.so",
-    "compute_cost": 10,
-    "kernel_name": "mul",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "output",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(mul_op_info)
 def _mul_tbe():
     """Mul TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/neg.py b/mindspore/ops/_op_impl/tbe/neg.py
index bbfcd824ec..feb648f056 100644
--- a/mindspore/ops/_op_impl/tbe/neg.py
+++ b/mindspore/ops/_op_impl/tbe/neg.py
@@ -14,51 +14,29 @@
 # ============================================================================
 
 """Neg op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+neg_op_info = TBERegOp("Neg") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("neg.so") \
+    .compute_cost(10) \
+    .kernel_name("neg") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Neg",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "neg.so",
-    "compute_cost": 10,
-    "kernel_name": "neg",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float","float16","float16","int32","int32","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float","float","float16","float16","int32","int32","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(neg_op_info)
 def _neg_tbe():
     """Neg TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/npu_alloc_float_status.py b/mindspore/ops/_op_impl/tbe/npu_alloc_float_status.py
index 6f65b9064a..a395f38e76 100644
--- a/mindspore/ops/_op_impl/tbe/npu_alloc_float_status.py
+++ b/mindspore/ops/_op_impl/tbe/npu_alloc_float_status.py
@@ -14,39 +14,21 @@
 # ============================================================================
 
 """NPUAllocFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_alloc_float_status_op_info = TBERegOp("NPUAllocFloatStatus") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_alloc_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_alloc_float_status") \
+    .partial_flag(True) \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUAllocFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_alloc_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_alloc_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_alloc_float_status_op_info)
 def _npu_alloc_float_status_tbe():
     """NPUAllocFloatStatus TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/npu_clear_float_status.py b/mindspore/ops/_op_impl/tbe/npu_clear_float_status.py
index d7e69673f2..b67bf5e62b 100644
--- a/mindspore/ops/_op_impl/tbe/npu_clear_float_status.py
+++ b/mindspore/ops/_op_impl/tbe/npu_clear_float_status.py
@@ -14,52 +14,22 @@
 # ============================================================================
 
 """NPUClearFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_clear_float_status_op_info = TBERegOp("NPUClearFloatStatus") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_clear_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_clear_float_status") \
+    .partial_flag(True) \
+    .input(0, "addr", False, "required", "all") \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUClearFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_clear_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_clear_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "addr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_clear_float_status_op_info)
 def _npu_clear_float_status_tbe():
     """NPUClearFloatStatus TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/npu_get_float_status.py b/mindspore/ops/_op_impl/tbe/npu_get_float_status.py
index 441fe3b271..ad3eb4be8c 100644
--- a/mindspore/ops/_op_impl/tbe/npu_get_float_status.py
+++ b/mindspore/ops/_op_impl/tbe/npu_get_float_status.py
@@ -14,52 +14,22 @@
 # ============================================================================
 
 """NPUGetFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_get_float_status_op_info = TBERegOp("NPUGetFloatStatus") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_get_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_get_float_status") \
+    .partial_flag(True) \
+    .input(0, "addr", False, "required", "all") \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUGetFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_get_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_get_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "addr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_get_float_status_op_info)
 def _npu_get_float_status_tbe():
     """NPUGetFloatStatus TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/one_hot.py b/mindspore/ops/_op_impl/tbe/one_hot.py
index 0af406dfc6..81a80bf759 100644
--- a/mindspore/ops/_op_impl/tbe/one_hot.py
+++ b/mindspore/ops/_op_impl/tbe/one_hot.py
@@ -14,96 +14,35 @@
 # ============================================================================
 
 """OneHot op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+one_hot_op_info = TBERegOp("OneHot") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("one_hot.so") \
+    .compute_cost(10) \
+    .kernel_name("one_hot") \
+    .partial_flag(True) \
+    .attr("depth", "required", "int", "all") \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "on_value", False, "required", "all") \
+    .input(2, "off_value", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.U8_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "OneHot",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "one_hot.so",
-    "compute_cost": 10,
-    "kernel_name": "one_hot",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "depth",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32","int32","int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "on_value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "off_value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(one_hot_op_info)
 def _one_hot_tbe():
     """OneHot TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/pad_d.py b/mindspore/ops/_op_impl/tbe/pad_d.py
index 25cb19816d..21d814d6b6 100644
--- a/mindspore/ops/_op_impl/tbe/pad_d.py
+++ b/mindspore/ops/_op_impl/tbe/pad_d.py
@@ -14,57 +14,27 @@
 # ============================================================================
 
 """Pad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+pad_d_op_info = TBERegOp("Pad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("pad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("pad_d") \
+    .partial_flag(True) \
+    .attr("paddings", "optional", "listListInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Pad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "pad_d.so",
-    "compute_cost": 10,
-    "kernel_name": "pad_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "paddings",
-            "param_type": "optional",
-            "type": "listListInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int8","uint8","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int8","uint8","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(pad_d_op_info)
 def _pad_d_tbe():
     """Pad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/pow.py b/mindspore/ops/_op_impl/tbe/pow.py
index aa67a8c942..223a139252 100644
--- a/mindspore/ops/_op_impl/tbe/pow.py
+++ b/mindspore/ops/_op_impl/tbe/pow.py
@@ -14,65 +14,27 @@
 # ============================================================================
 
 """Pow op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+pow_op_info = TBERegOp("Pow") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("pow.so") \
+    .compute_cost(10) \
+    .kernel_name("pow") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Pow",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "pow.so",
-    "compute_cost": 10,
-    "kernel_name": "pow",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(pow_op_info)
 def _pow_tbe():
     """Pow TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/real_div.py b/mindspore/ops/_op_impl/tbe/real_div.py
index 01f870b7cb..b39948971d 100644
--- a/mindspore/ops/_op_impl/tbe/real_div.py
+++ b/mindspore/ops/_op_impl/tbe/real_div.py
@@ -14,64 +14,26 @@
 # ============================================================================
 
 """RealDiv op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+realdiv_op_info = TBERegOp("RealDiv") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("realdiv.so") \
+    .compute_cost(10) \
+    .kernel_name("realdiv") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "RealDiv",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "realdiv.so",
-    "compute_cost": 10,
-    "kernel_name": "realdiv",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "z",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(realdiv_op_info)
 def _real_div_tbe():
     """RealDiv TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reciprocal.py b/mindspore/ops/_op_impl/tbe/reciprocal.py
index ba039be4c5..dfa126384c 100644
--- a/mindspore/ops/_op_impl/tbe/reciprocal.py
+++ b/mindspore/ops/_op_impl/tbe/reciprocal.py
@@ -14,52 +14,27 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reciprocal_op_info = TBERegOp("Reciprocal") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reciprocal.so") \
+    .compute_cost(10) \
+    .kernel_name("reciprocal") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Reciprocal",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reciprocal.so",
-    "compute_cost": 10,
-    "kernel_name": "reciprocal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float32", "float32", "float32"
-            ],
-            "format": [
-                    "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float32", "float32", "float32"
-            ],
-            "format": [
-                    "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(reciprocal_op_info)
 def _reciprocal_tbe():
     """Add TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reduce_max.py b/mindspore/ops/_op_impl/tbe/reduce_max.py
index 9c4981babc..ab0e766f59 100644
--- a/mindspore/ops/_op_impl/tbe/reduce_max.py
+++ b/mindspore/ops/_op_impl/tbe/reduce_max.py
@@ -14,63 +14,29 @@
 # ============================================================================
 
 """ReduceMax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_max_d_op_info = TBERegOp("ReduceMax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_max_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_max_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_max_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_max_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "uint8", "bool", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "uint8", "bool", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_max_d_op_info)
 def _reduce_max_tbe():
     """ReduceMax TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reduce_mean.py b/mindspore/ops/_op_impl/tbe/reduce_mean.py
index c8776fa8b1..47548e9036 100644
--- a/mindspore/ops/_op_impl/tbe/reduce_mean.py
+++ b/mindspore/ops/_op_impl/tbe/reduce_mean.py
@@ -14,63 +14,27 @@
 # ============================================================================
 
 """ReduceMean op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_mean_op_info = TBERegOp("ReduceMean") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_mean.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_mean") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMean",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_mean.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_mean",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_mean_op_info)
 def _reduce_mean_tbe():
     """ReduceMean TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reduce_mean_d.py b/mindspore/ops/_op_impl/tbe/reduce_mean_d.py
index 59cfeb240b..e427b34869 100644
--- a/mindspore/ops/_op_impl/tbe/reduce_mean_d.py
+++ b/mindspore/ops/_op_impl/tbe/reduce_mean_d.py
@@ -14,63 +14,27 @@
 # ============================================================================
 
 """ReduceMeanD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_mean_d_op_info = TBERegOp("ReduceMeanD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_mean_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_mean_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMeanD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_mean_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_mean_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float16","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float16","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_mean_d_op_info)
 def _reduce_mean_d_tbe():
     """Conv2D TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reduce_min.py b/mindspore/ops/_op_impl/tbe/reduce_min.py
index f440b1fff6..f1601ebc94 100644
--- a/mindspore/ops/_op_impl/tbe/reduce_min.py
+++ b/mindspore/ops/_op_impl/tbe/reduce_min.py
@@ -14,63 +14,31 @@
 # ============================================================================
 
 """ReduceMin op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_min_op_info = TBERegOp("ReduceMin") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_min_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_min_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .attr("keep_dims", "required", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMin",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_min_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_min_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_min_op_info)
 def _reduce_min_tbe():
     """ReduceMin TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reduce_sum.py b/mindspore/ops/_op_impl/tbe/reduce_sum.py
index b15a4deccc..2f76f74562 100644
--- a/mindspore/ops/_op_impl/tbe/reduce_sum.py
+++ b/mindspore/ops/_op_impl/tbe/reduce_sum.py
@@ -14,63 +14,25 @@
 # ============================================================================
 
 """ReduceSum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_sum_op_info = TBERegOp("ReduceSum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_sum_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_sum_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceSum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_sum_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_sum_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_sum_op_info)
 def _reduce_sum_tbe():
     """ReduceSum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/relu.py b/mindspore/ops/_op_impl/tbe/relu.py
index 7350f2ae35..03cc381253 100644
--- a/mindspore/ops/_op_impl/tbe/relu.py
+++ b/mindspore/ops/_op_impl/tbe/relu.py
@@ -14,54 +14,29 @@
 # ============================================================================
 
 """ReLU op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+relu_op_info = TBERegOp("ReLU") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relu.so") \
+    .compute_cost(10) \
+    .kernel_name("relu") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReLU",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "relu.so",
-    "compute_cost": 10,
-    "kernel_name": "relu",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float","int32", "int32", "int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(relu_op_info)
 def _relu_tbe():
     """Relu TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/relu_grad.py b/mindspore/ops/_op_impl/tbe/relu_grad.py
index 28b4574e04..0f9e962cfd 100644
--- a/mindspore/ops/_op_impl/tbe/relu_grad.py
+++ b/mindspore/ops/_op_impl/tbe/relu_grad.py
@@ -14,68 +14,32 @@
 # ============================================================================
 
 """ReluGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+relugrad_op_info = TBERegOp("ReluGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relugrad.so") \
+    .compute_cost(10) \
+    .kernel_name("relugrad") \
+    .partial_flag(True) \
+    .input(0, "gradients", False, "required", "all") \
+    .input(1, "features", False, "required", "all") \
+    .output(0, "backprops", True, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReluGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "relugrad.so",
-    "compute_cost": 10,
-    "kernel_name": "relugrad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0","DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "gradients",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "features",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "backprops",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(relugrad_op_info)
 def _relu_grad_tbe():
     """ReluGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/reshape.py b/mindspore/ops/_op_impl/tbe/reshape.py
index 8386b7a6f0..d46fd966d8 100644
--- a/mindspore/ops/_op_impl/tbe/reshape.py
+++ b/mindspore/ops/_op_impl/tbe/reshape.py
@@ -14,57 +14,25 @@
 # ============================================================================
 
 """Reshape op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reshape_op_info = TBERegOp("Reshape") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reshape.so") \
+    .compute_cost(10) \
+    .kernel_name("reshape") \
+    .partial_flag(True) \
+    .attr("shape", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Reshape",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reshape.so",
-    "compute_cost": 10,
-    "kernel_name": "reshape",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reshape_op_info)
 def _reshape_tbe():
     """Reshape TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor.py b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor.py
index 63ecf0ede2..8a66f75dbb 100644
--- a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor.py
+++ b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor.py
@@ -14,67 +14,33 @@
 # ============================================================================
 
 """ResizeNearestNeighbor op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_op_info = TBERegOp("ResizeNearestNeighbor") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "images", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighbor",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8",
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "images",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8",
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_op_info)
 def _resize_nearest_neighbor_d_tbe():
     """ResizeNearestNeighbor TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_d.py b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_d.py
index 9595041401..4b54da991a 100644
--- a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_d.py
+++ b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_d.py
@@ -14,63 +14,28 @@
 # ============================================================================
 
 """ResizeNearestNeighbor op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_d_op_info = TBERegOp("ResizeNearestNeighbor") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "images", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighbor",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0"
-            ],
-            "name": "images",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_d_op_info)
 def _resize_nearest_neighbor_d_tbe():
     """ResizeNearestNeighbor TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_grad_d.py b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_grad_d.py
index 51cfaf5176..6ee6c56146 100644
--- a/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_grad_d.py
+++ b/mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_grad_d.py
@@ -14,63 +14,24 @@
 # ============================================================================
 
 """ResizeNearestNeighborgrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_grad_d_op_info = TBERegOp("ResizeNearestNeighborGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_grad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_grad_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighborGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_grad_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_grad_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_grad_d_op_info)
 def _resize_nearest_neighbor_grad_d_tbe():
     """ResizeNearestNeighborGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/round.py b/mindspore/ops/_op_impl/tbe/round.py
index 1368100a8e..4559a3def0 100644
--- a/mindspore/ops/_op_impl/tbe/round.py
+++ b/mindspore/ops/_op_impl/tbe/round.py
@@ -14,52 +14,27 @@
 # ============================================================================
 
 """Round op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+round_op_info = TBERegOp("Round") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("round.so") \
+    .compute_cost(10) \
+    .kernel_name("round") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Round",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "round.so",
-    "compute_cost": 10,
-    "kernel_name": "round",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(round_op_info)
 def _round_tbe():
     """Round TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/rsqrt.py b/mindspore/ops/_op_impl/tbe/rsqrt.py
index e23d505397..b0830cf484 100644
--- a/mindspore/ops/_op_impl/tbe/rsqrt.py
+++ b/mindspore/ops/_op_impl/tbe/rsqrt.py
@@ -14,94 +14,29 @@
 # ============================================================================
 
 """Rsqrt op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+rsqrt_op_info = TBERegOp("Rsqrt") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("rsqrt.so") \
+    .compute_cost(10) \
+    .kernel_name("rsqrt") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Rsqrt",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"rsqrt.so",
-    "compute_cost":10,
-    "kernel_name":"rsqrt",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0"
-            ],
-            "name":"x",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(rsqrt_op_info)
 def _rsqrt_tbe():
     """Rsqrt TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/scatter_nd.py b/mindspore/ops/_op_impl/tbe/scatter_nd.py
index 947fc57920..6c9eae3ad4 100644
--- a/mindspore/ops/_op_impl/tbe/scatter_nd.py
+++ b/mindspore/ops/_op_impl/tbe/scatter_nd.py
@@ -14,71 +14,28 @@
 # ============================================================================
 
 """ScatterNd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+scatter_nd_op_info = TBERegOp("ScatterNd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_d.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_d") \
+    .partial_flag(True) \
+    .attr("shape", "optional", "listInt", "all") \
+    .input(0, "indices", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-# map to tbe kernel name scatter_nd_d
-@op_info_register("""{
-    "op_name": "ScatterNd",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "scatter_nd_d.so",
-    "compute_cost": 10,
-    "kernel_name": "scatter_nd_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(scatter_nd_op_info)
 def _scatter_nd_tbe():
     """Conv2D TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/scatter_nd_d.py b/mindspore/ops/_op_impl/tbe/scatter_nd_d.py
index ad776fde49..b069b7d8aa 100644
--- a/mindspore/ops/_op_impl/tbe/scatter_nd_d.py
+++ b/mindspore/ops/_op_impl/tbe/scatter_nd_d.py
@@ -14,70 +14,28 @@
 # ============================================================================
 
 """ScatterNdD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+scatter_nd_d_op_info = TBERegOp("ScatterNdD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_d.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_d") \
+    .partial_flag(True) \
+    .attr("shape", "optional", "listInt", "all") \
+    .input(0, "indices", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ScatterNdD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "scatter_nd_d.so",
-    "compute_cost": 10,
-    "kernel_name": "scatter_nd_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(scatter_nd_d_op_info)
 def _scatter_nd_d_tbe():
     """ScatterNdD TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/select.py b/mindspore/ops/_op_impl/tbe/select.py
index c205e0de1d..4af4325312 100644
--- a/mindspore/ops/_op_impl/tbe/select.py
+++ b/mindspore/ops/_op_impl/tbe/select.py
@@ -14,94 +14,33 @@
 # ============================================================================
 
 """Select op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+select_op_info = TBERegOp("Select") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("select.so") \
+    .compute_cost(10) \
+    .kernel_name("select") \
+    .partial_flag(True) \
+    .input(0, "condition", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.BOOL_5HD, DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Select",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "select.so",
-    "compute_cost": 10,
-    "kernel_name": "select",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool",
-                "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "condition",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8",
-                "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(select_op_info)
 def _select_tbe():
     """Select TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sigmoid.py b/mindspore/ops/_op_impl/tbe/sigmoid.py
index cba9561d27..38413c0432 100644
--- a/mindspore/ops/_op_impl/tbe/sigmoid.py
+++ b/mindspore/ops/_op_impl/tbe/sigmoid.py
@@ -14,67 +14,31 @@
 # ============================================================================
 
 """Sigmoid op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_op_info = TBERegOp("Sigmoid") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Sigmoid",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "Sigmoid.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float"
-            ],
-            "format": [
-                "FracZ","FracZ",
-                "FRACTAL_NZ","FRACTAL_NZ",
-                "C1HWNCoC0","C1HWNCoC0",
-                "NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float"
-            ],
-            "format": [
-                "FracZ","FracZ",
-                "FRACTAL_NZ","FRACTAL_NZ",
-                "C1HWNCoC0","C1HWNCoC0",
-                "NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_op_info)
 def _sigmoid_tbe():
     """Sigmoid TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py b/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py
index b20438d5fe..61c81a8a99 100644
--- a/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py
+++ b/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py
@@ -14,64 +14,26 @@
 # ============================================================================
 
 """SigmoidCrossEntropyWithLogits op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_op_info = TBERegOp("SigmoidCrossEntropyWithLogits") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_cross_entropy_with_logits.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_cross_entropy_with_logits") \
+    .partial_flag(True) \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "target", False, "required", "all") \
+    .output(0, "loss", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidCrossEntropyWithLogits",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_cross_entropy_with_logits.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_cross_entropy_with_logits",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "predict",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "target",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "loss",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_op_info)
 def _sigmoid_cross_entropy_with_logits_tbe():
     """SigmoidCrossEntropyWithLogits TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py b/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py
index 6e5df24cfe..cc2a29353d 100644
--- a/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py
+++ b/mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py
@@ -14,77 +14,27 @@
 # ============================================================================
 
 """SigmoidCrossEntropyWithLogitsGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_grad_op_info = TBERegOp("SigmoidCrossEntropyWithLogitsGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_cross_entropy_with_logits_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_cross_entropy_with_logits_grad") \
+    .partial_flag(True) \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "target", False, "required", "all") \
+    .input(2, "dout", False, "required", "all") \
+    .output(0, "gradient", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidCrossEntropyWithLogitsGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_cross_entropy_with_logits_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_cross_entropy_with_logits_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "predict",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "target",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "dout",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "gradient",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_grad_op_info)
 def _sigmoid_cross_entropy_with_logits_grad_tbe():
     """SigmoidCrossEntropyWithLogitsGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sigmoid_grad.py b/mindspore/ops/_op_impl/tbe/sigmoid_grad.py
index f0833e8a80..bc2878ce96 100644
--- a/mindspore/ops/_op_impl/tbe/sigmoid_grad.py
+++ b/mindspore/ops/_op_impl/tbe/sigmoid_grad.py
@@ -14,64 +14,26 @@
 # ============================================================================
 
 """SigmoidGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_op_info = TBERegOp("SigmoidGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_grad") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "z",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_op_info)
 def _sigmoid_grad_tbe():
     """SigmoidGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/slice.py b/mindspore/ops/_op_impl/tbe/slice.py
index 779f19cbc5..402692ca32 100644
--- a/mindspore/ops/_op_impl/tbe/slice.py
+++ b/mindspore/ops/_op_impl/tbe/slice.py
@@ -14,99 +14,33 @@
 # ============================================================================
 
 """Slice op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+slice_op_info = TBERegOp("Slice") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("slice_d.so") \
+    .compute_cost(10) \
+    .kernel_name("slice_d") \
+    .partial_flag(True) \
+    .attr("begin", "required", "listInt", "all") \
+    .attr("size", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Slice",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"slice_d.so",
-    "compute_cost":10,
-    "kernel_name":"slice_d",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"begin",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"size",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float",
-                "float16",
-                "int8",
-                "int16",
-                "int32",
-                "int64",
-                "uint8",
-                "uint16",
-                "uint32",
-                "uint64"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float",
-                "float16",
-                "int8",
-                "int16",
-                "int32",
-                "int64",
-                "uint8",
-                "uint16",
-                "uint32",
-                "uint64"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(slice_op_info)
 def _slice_tbe():
     """Slice TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/softmax.py b/mindspore/ops/_op_impl/tbe/softmax.py
index 5a70d2605a..faefad87ec 100644
--- a/mindspore/ops/_op_impl/tbe/softmax.py
+++ b/mindspore/ops/_op_impl/tbe/softmax.py
@@ -14,57 +14,27 @@
 # ============================================================================
 
 """Softmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+softmax_op_info = TBERegOp("Softmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("softmax.so") \
+    .compute_cost(10) \
+    .kernel_name("softmax") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Softmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "softmax.so",
-    "compute_cost": 10,
-    "kernel_name": "softmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(softmax_op_info)
 def _softmax_tbe():
     """Softmax TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/softmax_cross_entropy_with_logits.py b/mindspore/ops/_op_impl/tbe/softmax_cross_entropy_with_logits.py
index cc9f06948a..386562f9f2 100644
--- a/mindspore/ops/_op_impl/tbe/softmax_cross_entropy_with_logits.py
+++ b/mindspore/ops/_op_impl/tbe/softmax_cross_entropy_with_logits.py
@@ -14,78 +14,25 @@
 # ============================================================================
 
 """SoftmaxCrossEntropyWithLogits op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+softmax_cross_entropy_with_logits_op_info = TBERegOp("SoftmaxCrossEntropyWithLogits") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("softmax_cross_entropy_with_logits.so") \
+    .compute_cost(10) \
+    .kernel_name("softmax_cross_entropy_with_logits") \
+    .partial_flag(True) \
+    .input(0, "input_features", False, "required", "all") \
+    .input(1, "input_labels", False, "required", "all") \
+    .output(0, "output_loss", True, "required", "all") \
+    .output(1, "output_backprop", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SoftmaxCrossEntropyWithLogits",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "softmax_cross_entropy_with_logits.so",
-    "compute_cost": 10,
-    "kernel_name": "softmax_cross_entropy_with_logits",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_features",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_labels",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_loss",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_backprop",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(softmax_cross_entropy_with_logits_op_info)
 def _softmax_cross_entropy_with_logits_tbe():
     """SoftmaxCrossEntropyWithLogits TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/split_d.py b/mindspore/ops/_op_impl/tbe/split_d.py
index 41311ffb90..dcc8219fd4 100644
--- a/mindspore/ops/_op_impl/tbe/split_d.py
+++ b/mindspore/ops/_op_impl/tbe/split_d.py
@@ -14,71 +14,45 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+split_d_op_info = TBERegOp("Split") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("split_d.so") \
+    .compute_cost(10) \
+    .kernel_name("split_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .attr("output_num", "required", "int", "all") \
+    .input(0, "value", False, "required", "all") \
+    .output(0, "output", False, "dynamic", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_NHWC, DataType.BOOL_NHWC) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Split",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "split_d.so",
-    "compute_cost": 10,
-    "kernel_name": "split_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "output_num",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16","float32", "float32", "int32", "int32", "int8", "int8",
-                "int16", "int16", "int64", "int64", "uint8", "uint8", "uint16", "uint16",
-                "uint32", "uint32", "uint64", "uint64", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16","float32", "float32", "int32", "int32", "int8", "int8",
-                "int16", "int16", "int64", "int64", "uint8", "uint8", "uint16", "uint16",
-                "uint32", "uint32", "uint64", "uint64", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-            ],
-            "name": "output",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(split_d_op_info)
 def _split_d_tbe():
     """Add TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sqrt.py b/mindspore/ops/_op_impl/tbe/sqrt.py
index c73092886a..f9e339713b 100644
--- a/mindspore/ops/_op_impl/tbe/sqrt.py
+++ b/mindspore/ops/_op_impl/tbe/sqrt.py
@@ -14,52 +14,27 @@
 # ============================================================================
 
 """Sqrt op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sqrt_op_info = TBERegOp("Sqrt") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("sqrt.so") \
+    .compute_cost(10) \
+    .kernel_name("sqrt") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Sqrt",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "sqrt.so",
-    "compute_cost": 10,
-    "kernel_name": "sqrt",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(sqrt_op_info)
 def _sqrt_tbe():
     """Sqrt TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/square.py b/mindspore/ops/_op_impl/tbe/square.py
index 03a81236cc..c3eeb12780 100644
--- a/mindspore/ops/_op_impl/tbe/square.py
+++ b/mindspore/ops/_op_impl/tbe/square.py
@@ -14,56 +14,30 @@
 # ============================================================================
 
 """Square op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+square_op_info = TBERegOp("Square") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("square.so") \
+    .compute_cost(10) \
+    .kernel_name("square") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Square",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "square.so",
-    "compute_cost": 10,
-    "kernel_name": "sqrt",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC",
-                "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC",
-                "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(square_op_info)
 def _square_tbe():
     """Square TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/square_sum_v1.py b/mindspore/ops/_op_impl/tbe/square_sum_v1.py
index 39b5400298..9d43fe4cc0 100644
--- a/mindspore/ops/_op_impl/tbe/square_sum_v1.py
+++ b/mindspore/ops/_op_impl/tbe/square_sum_v1.py
@@ -14,63 +14,25 @@
 # ============================================================================
 
 """SquareSumV1 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+square_sum_v1_op_info = TBERegOp("SquareSumV1") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("square_sum_v1.so") \
+    .compute_cost(10) \
+    .kernel_name("square_sum_v1") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "input_x", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SquareSumV1",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "square_sum_v1.so",
-    "compute_cost": 10,
-    "kernel_name": "square_sum_v1",
-    "partial_flag": true,
-    "attr":[
-        {
-            "name":"axis",
-            "param_type":"optional",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"keep_dims",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(square_sum_v1_op_info)
 def _square_sum_v1_tbe():
     """SquareSumV1 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/square_sum_v2.py b/mindspore/ops/_op_impl/tbe/square_sum_v2.py
index 2f5ca49cc2..88bb1283d1 100644
--- a/mindspore/ops/_op_impl/tbe/square_sum_v2.py
+++ b/mindspore/ops/_op_impl/tbe/square_sum_v2.py
@@ -14,76 +14,26 @@
 # ============================================================================
 
 """SquareSumV2 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+square_sum_v2_op_info = TBERegOp("SquareSumV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("square_sum_v2.so") \
+    .compute_cost(10) \
+    .kernel_name("square_sum_v2") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "input_x", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .output(1, "output2", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SquareSumV2",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "square_sum_v2.so",
-    "compute_cost": 10,
-    "kernel_name": "square_sum_v2",
-    "partial_flag": true,
-    "attr":[
-        {
-            "name":"axis",
-            "param_type":"optional",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"keep_dims",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(square_sum_v2_op_info)
 def _square_sum_v2_tbe():
     """SquareSumV2 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/squeeze.py b/mindspore/ops/_op_impl/tbe/squeeze.py
index 765ffae2c1..9d585ccabe 100644
--- a/mindspore/ops/_op_impl/tbe/squeeze.py
+++ b/mindspore/ops/_op_impl/tbe/squeeze.py
@@ -14,57 +14,24 @@
 # ============================================================================
 
 """Squeeze op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+squeeze_op_info = TBERegOp("Squeeze") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("squeeze.so") \
+    .compute_cost(10) \
+    .kernel_name("squeeze") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Squeeze",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "squeeze.so",
-    "compute_cost": 10,
-    "kernel_name": "squeeze",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(squeeze_op_info)
 def _squeeze_tbe():
     """Squeeze TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/strideslice_d.py b/mindspore/ops/_op_impl/tbe/strideslice_d.py
index 8f398b4215..e008e6f3d7 100644
--- a/mindspore/ops/_op_impl/tbe/strideslice_d.py
+++ b/mindspore/ops/_op_impl/tbe/strideslice_d.py
@@ -14,99 +14,35 @@
 # ============================================================================
 
 """StridedSlice op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+strided_slice_d_op_info = TBERegOp("StridedSlice") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("strided_slice_d.so") \
+    .compute_cost(10) \
+    .kernel_name("strided_slice_d") \
+    .partial_flag(True) \
+    .attr("begin", "optional", "listInt", "all") \
+    .attr("end", "optional", "listInt", "all") \
+    .attr("strides", "optional", "listInt", "all") \
+    .attr("begin_mask", "required", "int", "all") \
+    .attr("end_mask", "required", "int", "all") \
+    .attr("ellipsis_mask", "required", "int", "all") \
+    .attr("new_axis_mask", "required", "int", "all") \
+    .attr("shrink_axis_mask", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "StridedSlice",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "strided_slice_d.so",
-    "compute_cost": 10,
-    "kernel_name": "strided_slice_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "begin",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "end",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "begin_mask",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "end_mask",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "ellipsis_mask",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "new_axis_mask",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "shrink_axis_mask",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "uint8", "bool", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "uint8", "bool", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(strided_slice_d_op_info)
 def _strided_slice_d_tbe():
     """StridedSlice TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/strideslicegrad_d.py b/mindspore/ops/_op_impl/tbe/strideslicegrad_d.py
index adeeab9a81..e7e1204385 100644
--- a/mindspore/ops/_op_impl/tbe/strideslicegrad_d.py
+++ b/mindspore/ops/_op_impl/tbe/strideslicegrad_d.py
@@ -14,107 +14,40 @@
 # ============================================================================
 
 """StridedSliceGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+strided_slice_grad_d_op_info = TBERegOp("StridedSliceGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("strided_slice_grad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("strided_slice_grad_d") \
+    .partial_flag(True) \
+    .attr("shapex", "optional", "listInt", "all") \
+    .attr("begin", "optional", "listInt", "all") \
+    .attr("end", "optional", "listInt", "all") \
+    .attr("strides", "optional", "listInt", "all") \
+    .attr("begin_mask", "optional", "int", "all") \
+    .attr("end_mask", "optional", "int", "all") \
+    .attr("ellipsis_mask", "optional", "int", "all") \
+    .attr("new_axis_mask", "optional", "int", "all") \
+    .attr("shrink_axis_mask", "optional", "int", "all") \
+    .input(0, "dy", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "StridedSliceGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "strided_slice_grad_d.so",
-    "compute_cost": 10,
-    "kernel_name": "strided_slice_grad_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shapex",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "begin",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "end",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "begin_mask",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "end_mask",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "ellipsis_mask",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "new_axis_mask",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "shrink_axis_mask",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","uint8","uint8","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0",
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","uint8","uint8","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0",
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "output",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(strided_slice_grad_d_op_info)
 def _strided_slice_grad_d_tbe():
     """StridedSliceGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/sub.py b/mindspore/ops/_op_impl/tbe/sub.py
index 8d6ea4aa0e..8e97681c6b 100644
--- a/mindspore/ops/_op_impl/tbe/sub.py
+++ b/mindspore/ops/_op_impl/tbe/sub.py
@@ -14,65 +14,28 @@
 # ============================================================================
 
 """Sub op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sub_op_info = TBERegOp("Sub") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("sub.so") \
+    .compute_cost(10) \
+    .kernel_name("sub") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Sub",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "sub.so",
-    "compute_cost": 10,
-    "kernel_name": "sub",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(sub_op_info)
 def _sub_tbe():
     """Add TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/tanh.py b/mindspore/ops/_op_impl/tbe/tanh.py
index dd2737f2ce..3d0b2704a3 100644
--- a/mindspore/ops/_op_impl/tbe/tanh.py
+++ b/mindspore/ops/_op_impl/tbe/tanh.py
@@ -14,52 +14,25 @@
 # ============================================================================
 
 """Tanh op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+tanh_op_info = TBERegOp("Tanh") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("tanh.so") \
+    .compute_cost(10) \
+    .kernel_name("tanh") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Tanh",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "tanh.so",
-    "compute_cost": 10,
-    "kernel_name": "tanh",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(tanh_op_info)
 def _tanh_tbe():
     """Tanh TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/tanh_grad.py b/mindspore/ops/_op_impl/tbe/tanh_grad.py
index c50b5a3a5a..5796ed7aff 100644
--- a/mindspore/ops/_op_impl/tbe/tanh_grad.py
+++ b/mindspore/ops/_op_impl/tbe/tanh_grad.py
@@ -14,65 +14,26 @@
 # ============================================================================
 
 """TanhGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+tanh_grad_op_info = TBERegOp("TanhGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("tanh_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("tanh_grad") \
+    .partial_flag(True) \
+    .input(0, "y", False, "required", "all") \
+    .input(1, "dy", False, "required", "all") \
+    .output(0, "z", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "TanhGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "tanh_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "tanh_grad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "z",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(tanh_grad_op_info)
 def _tanh_grad_tbe():
     """TanhGrad TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/tensor_add.py b/mindspore/ops/_op_impl/tbe/tensor_add.py
index 26a25c34b2..255c1b1278 100644
--- a/mindspore/ops/_op_impl/tbe/tensor_add.py
+++ b/mindspore/ops/_op_impl/tbe/tensor_add.py
@@ -14,70 +14,28 @@
 # ============================================================================
 
 """TensorAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+tensor_add_op_info = TBERegOp("TensorAdd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("add.so") \
+    .compute_cost(10) \
+    .kernel_name("add") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "TensorAdd",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "add.so",
-    "compute_cost": 10,
-    "kernel_name": "add",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(tensor_add_op_info)
 def _tensor_add_tbe():
     """Add TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/tile.py b/mindspore/ops/_op_impl/tbe/tile.py
index 8299c500db..093e03f6ac 100644
--- a/mindspore/ops/_op_impl/tbe/tile.py
+++ b/mindspore/ops/_op_impl/tbe/tile.py
@@ -14,57 +14,25 @@
 # ============================================================================
 
 """Tile op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+tile_op_info = TBERegOp("Tile") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("tile_d.so") \
+    .compute_cost(10) \
+    .kernel_name("tile_d") \
+    .partial_flag(True) \
+    .attr("multiples", "optional", "listInt", "all")\
+    .input(0, "x1", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Tile",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "tile_d.so",
-    "compute_cost": 10,
-    "kernel_name": "tile_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "multiples",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32", "float16", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32", "float16", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(tile_op_info)
 def _tile_tbe():
     """Tile TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/topkv2.py b/mindspore/ops/_op_impl/tbe/topkv2.py
index 916b246a38..a03871f8b7 100644
--- a/mindspore/ops/_op_impl/tbe/topkv2.py
+++ b/mindspore/ops/_op_impl/tbe/topkv2.py
@@ -14,89 +14,26 @@
 # ============================================================================
 
 """TopKV2 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+top_k_v2_op_info = TBERegOp("TopKV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("top_k_v2.so") \
+    .compute_cost(10) \
+    .kernel_name("top_k_v2") \
+    .partial_flag(True) \
+    .attr("k", "required", "int", "all")\
+    .attr("sorted", "required", "bool", "all")\
+    .input(0, "x", False, "required", "all") \
+    .input(1, "input_indices", False, "optional", "all") \
+    .output(0, "values", False, "required", "all") \
+    .output(1, "indices", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.I32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "TopKV2",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "top_k_v2.so",
-    "compute_cost": 10,
-    "kernel_name": "top_k_v2",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "k",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "sorted",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "input_indices",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "values",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(top_k_v2_op_info)
 def _topk_v2_tbe():
     """TopKV2 TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/trans_data.py b/mindspore/ops/_op_impl/tbe/trans_data.py
index c6628c7638..f961491b37 100644
--- a/mindspore/ops/_op_impl/tbe/trans_data.py
+++ b/mindspore/ops/_op_impl/tbe/trans_data.py
@@ -14,75 +14,54 @@
 # ============================================================================
 
 """TransData op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+trans_data_op_info = TBERegOp("TransData") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("trans_data.so") \
+    .compute_cost(10) \
+    .kernel_name("trans_data") \
+    .partial_flag(True) \
+    .attr("src_format", "required", "str", "DefaultFormat,NC1HWC0,FracZ,FRACTAL_NZ,HWCN,C1HWNCoC0")\
+    .attr("dst_format", "required", "str", "DefaultFormat,NC1HWC0,FracZ,FRACTAL_NZ,HWCN,C1HWNCoC0")\
+    .input(0, "src", False, "required", "all") \
+    .output(0, "dst", False, "required", "all") \
+    .dtype_format(DataType.U16_Default, DataType.U16_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U16_FracZ) \
+    .dtype_format(DataType.U16_Default, DataType.U16_FracNZ) \
+    .dtype_format(DataType.U16_FracZ, DataType.U16_Default) \
+    .dtype_format(DataType.U16_FracZ, DataType.U16_HWCN) \
+    .dtype_format(DataType.U16_FracNZ, DataType.U16_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U16_Default) \
+    .dtype_format(DataType.U16_HWCN, DataType.U16_FracZ) \
+    .dtype_format(DataType.U16_HWCN, DataType.U16_C1HWNCoC0) \
+    .dtype_format(DataType.U16_C1HWNCoC0, DataType.U16_HWCN) \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_HWCN) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_Default) \
+    .dtype_format(DataType.F16_HWCN, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_HWCN, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_HWCN) \
+    .dtype_format(DataType.F32_Default, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_HWCN) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_Default) \
+    .dtype_format(DataType.F32_HWCN, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_HWCN, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_HWCN) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "TransData",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "trans_data.so",
-    "compute_cost": 10,
-    "kernel_name": "trans_data",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "src_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "DefaultFormat,NC1HWC0,FracZ,FRACTAL_NZ,HWCN,C1HWNCoC0"
-        },
-        {
-            "name": "dst_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "DefaultFormat,NC1HWC0,FracZ,FRACTAL_NZ,HWCN,C1HWNCoC0"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool",
-                "float","float","float","float","float","float","float","float","float","float",
-                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16",
-                "uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16"
-            ],
-            "format": [
-                "DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ",
-                "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ",
-                "DefaultFormat","DefaultFormat","DefaultFormat","FracZ","FRACTAL_NZ","NC1HWC0","HWCN","HWCN","C1HWNCoC0","FracZ"
-            ],
-            "name": "src",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool",
-                "float","float","float","float","float","float","float","float","float","float",
-                "float16","float16","float16","float16","float16","float16","float16","float16","float16","float16",
-                "uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16","uint16"
-            ],
-            "format": [
-                "NC1HWC0",
-                "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN",
-                "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN",
-                "NC1HWC0","FRACTAL_NZ","FracZ","DefaultFormat","DefaultFormat","DefaultFormat","FracZ","C1HWNCoC0","HWCN","HWCN"
-            ],
-            "name": "dst",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(trans_data_op_info)
 def _trans_data_tbe():
     """TransData TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/transpose_d.py b/mindspore/ops/_op_impl/tbe/transpose_d.py
index e79a16adeb..fffc95a38f 100644
--- a/mindspore/ops/_op_impl/tbe/transpose_d.py
+++ b/mindspore/ops/_op_impl/tbe/transpose_d.py
@@ -14,59 +14,32 @@
 # ============================================================================
 
 """TransposeD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+transpose_d_op_info = TBERegOp("Transpose") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("transpose_d.so") \
+    .compute_cost(10) \
+    .kernel_name("transpose_d") \
+    .partial_flag(True) \
+    .attr("perm", "optional", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Transpose",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "transpose_d.so",
-    "compute_cost": 10,
-    "kernel_name": "transpose_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "perm",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(transpose_d_op_info)
 def _transpose_d_tbe():
     """TransposeD TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/unsorted_segment_sum.py b/mindspore/ops/_op_impl/tbe/unsorted_segment_sum.py
index 2bc36b9e3d..5dc07dd59f 100644
--- a/mindspore/ops/_op_impl/tbe/unsorted_segment_sum.py
+++ b/mindspore/ops/_op_impl/tbe/unsorted_segment_sum.py
@@ -14,184 +14,33 @@
 # ============================================================================
 
 """UnsortedSegmentSum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+unsorted_segment_sum_op_info = TBERegOp("UnsortedSegmentSum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("unsorted_segment_sum_d.so") \
+    .compute_cost(10) \
+    .kernel_name("unsorted_segment_sum_d") \
+    .partial_flag(True) \
+    .attr("num_segments", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "segment_ids", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I32_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.I32_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.I32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.I32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"UnsortedSegmentSum",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"unsorted_segment_sum_d.so",
-    "compute_cost":10,
-    "kernel_name":"unsorted_segment_sum_d",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"num_segments",
-            "param_type":"required",
-            "type":"int",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "int8",
-                "int8",
-                "int8",
-                "int8",
-                "uint8",
-                "uint8",
-                "uint8",
-                "uint8",
-                "int32",
-                "int32",
-                "int32",
-                "int32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32",
-                "int32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"segment_ids",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "int8",
-                "int8",
-                "int8",
-                "int8",
-                "uint8",
-                "uint8",
-                "uint8",
-                "uint8",
-                "int32",
-                "int32",
-                "int32",
-                "int32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(unsorted_segment_sum_op_info)
 def _unsorted_segment_sum_tbe():
     """UnsortedSegmentSum TBE register"""
     return
diff --git a/mindspore/ops/_op_impl/tbe/zeros_like.py b/mindspore/ops/_op_impl/tbe/zeros_like.py
index 25f48b80a5..144b0c95cb 100644
--- a/mindspore/ops/_op_impl/tbe/zeros_like.py
+++ b/mindspore/ops/_op_impl/tbe/zeros_like.py
@@ -14,53 +14,33 @@
 # ============================================================================
 
 """ZerosLike op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+zeros_like_op_info = TBERegOp("ZerosLike") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("zeros_like.so") \
+    .compute_cost(10) \
+    .kernel_name("zeros_like") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ZerosLike",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "zeros_like.so",
-    "compute_cost": 10,
-    "kernel_name": "zeros_like",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0",
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0",
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(zeros_like_op_info)
 def _zeros_like_tbe():
     """ZerosLike TBE register"""
     return
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 0750094e18..52c05a9435 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -151,7 +151,7 @@ class RegOp():
                     fn_list[idx](element)
                 out_dict[key_list[idx]] = element
         if kwargs:
-            out_dict = dict(out_dict, kwargs)
+            out_dict = dict(out_dict, **kwargs)
         return out_dict
 
     def fusion_type(self, fusion_type):
@@ -177,7 +177,7 @@ class RegOp():
             TypeError: If the type of args is not tuple.
         """
         if len(self.inputs) + len(self.outputs) != len(args):
-            raise ValueError("input size add output size must be equal to detype format size")
+            raise ValueError("input size add output size must be equal to dtype format size")
         dtype_format = []
         for arg in args:
             if not isinstance(arg, tuple) or len(arg) != 2:
@@ -435,6 +435,9 @@ class DataType():
     BOOL_None = ("bool", "")
     BOOL_Default = ("bool", "DefaultFormat")
     BOOL_5HD = ("bool", "NC1HWC0")
+    BOOL_FracZ = ("bool", "FracZ")
+    BOOL_FracNZ = ("bool", "FRACTAL_NZ")
+    BOOL_C1HWNCoC0 = ("bool", "C1HWNCoC0")
     BOOL_NCHW = ("bool", "NCHW")
     BOOL_NHWC = ("bool", "NHWC")
     BOOL_HWCN = ("bool", "HWCN")
@@ -442,8 +445,9 @@ class DataType():
     I8_None = ("int8", "")
     I8_Default = ("int8", "DefaultFormat")
     I8_5HD = ("int8", "NC1HWC0")
-    I8_FracZ = ("int8", "Fracz")
+    I8_FracZ = ("int8", "FracZ")
     I8_FracNZ = ("int8", "FRACTAL_NZ")
+    I8_C1HWNCoC0 = ("int8", "C1HWNCoC0")
     I8_NCHW = ("int8", "NCHW")
     I8_NHWC = ("int8", "NHWC")
     I8_HWCN = ("int8", "HWCN")
@@ -451,8 +455,9 @@ class DataType():
     U8_None = ("uint8", "")
     U8_Default = ("uint8", "DefaultFormat")
     U8_5HD = ("uint8", "NC1HWC0")
-    U8_FracZ = ("uint8", "Fracz")
+    U8_FracZ = ("uint8", "FracZ")
     U8_FracNZ = ("uint8", "FRACTAL_NZ")
+    U8_C1HWNCoC0 = ("uint8", "C1HWNCoC0")
     U8_NCHW = ("uint8", "NCHW")
     U8_NHWC = ("uint8", "NHWC")
     U8_HWCN = ("uint8", "HWCN")
@@ -460,8 +465,9 @@ class DataType():
     I16_None = ("int16", "")
     I16_Default = ("int16", "DefaultFormat")
     I16_5HD = ("int16", "NC1HWC0")
-    I16_FracZ = ("int16", "Fracz")
+    I16_FracZ = ("int16", "FracZ")
     I16_FracNZ = ("int16", "FRACTAL_NZ")
+    I16_C1HWNCoC0 = ("int16", "C1HWNCoC0")
     I16_NCHW = ("int16", "NCHW")
     I16_NHWC = ("int16", "NHWC")
     I16_HWCN = ("int16", "HWCN")
@@ -469,8 +475,9 @@ class DataType():
     U16_None = ("uint16", "")
     U16_Default = ("uint16", "DefaultFormat")
     U16_5HD = ("uint16", "NC1HWC0")
-    U16_FracZ = ("uint16", "Fracz")
+    U16_FracZ = ("uint16", "FracZ")
     U16_FracNZ = ("uint16", "FRACTAL_NZ")
+    U16_C1HWNCoC0 = ("uint16", "C1HWNCoC0")
     U16_NCHW = ("uint16", "NCHW")
     U16_NHWC = ("uint16", "NHWC")
     U16_HWCN = ("uint16", "HWCN")
@@ -478,8 +485,9 @@ class DataType():
     I32_None = ("int32", "")
     I32_Default = ("int32", "DefaultFormat")
     I32_5HD = ("int32", "NC1HWC0")
-    I32_FracZ = ("int32", "Fracz")
+    I32_FracZ = ("int32", "FracZ")
     I32_FracNZ = ("int32", "FRACTAL_NZ")
+    I32_C1HWNCoC0 = ("int32", "C1HWNCoC0")
     I32_NCHW = ("int32", "NCHW")
     I32_NHWC = ("int32", "NHWC")
     I32_HWCN = ("int32", "HWCN")
@@ -487,8 +495,9 @@ class DataType():
     U32_None = ("uint32", "")
     U32_Default = ("uint32", "DefaultFormat")
     U32_5HD = ("uint32", "NC1HWC0")
-    U32_FracZ = ("uint32", "Fracz")
+    U32_FracZ = ("uint32", "FracZ")
     U32_FracNZ = ("uint32", "FRACTAL_NZ")
+    U32_C1HWNCoC0 = ("uint32", "C1HWNCoC0")
     U32_NCHW = ("uint32", "NCHW")
     U32_NHWC = ("uint32", "NHWC")
     U32_HWCN = ("uint32", "HWCN")
@@ -496,8 +505,9 @@ class DataType():
     I64_None = ("int64", "")
     I64_Default = ("int64", "DefaultFormat")
     I64_5HD = ("int64", "NC1HWC0")
-    I64_FracZ = ("int64", "Fracz")
+    I64_FracZ = ("int64", "FracZ")
     I64_FracNZ = ("int64", "FRACTAL_NZ")
+    I64_C1HWNCoC0 = ("int64", "C1HWNCoC0")
     I64_NCHW = ("int64", "NCHW")
     I64_NHWC = ("int64", "NHWC")
     I64_HWCN = ("int64", "HWCN")
@@ -505,8 +515,9 @@ class DataType():
     U64_None = ("uint64", "")
     U64_Default = ("uint64", "DefaultFormat")
     U64_5HD = ("uint64", "NC1HWC0")
-    U64_FracZ = ("uint64", "Fracz")
+    U64_FracZ = ("uint64", "FracZ")
     U64_FracNZ = ("uint64", "FRACTAL_NZ")
+    U64_C1HWNCoC0 = ("uint64", "C1HWNCoC0")
     U64_NCHW = ("uint64", "NCHW")
     U64_NHWC = ("uint64", "NHWC")
     U64_HWCN = ("uint64", "HWCN")
@@ -514,7 +525,7 @@ class DataType():
     F16_None = ("float16", "")
     F16_Default = ("float16", "DefaultFormat")
     F16_5HD = ("float16", "NC1HWC0")
-    F16_FracZ = ("float16", "Fracz")
+    F16_FracZ = ("float16", "FracZ")
     F16_FracNZ = ("float16", "FRACTAL_NZ")
     F16_C1HWNCoC0 = ("float16", "C1HWNCoC0")
     F16_NCHW = ("float16", "NCHW")
@@ -524,7 +535,7 @@ class DataType():
     F32_None = ("float32", "")
     F32_Default = ("float32", "DefaultFormat")
     F32_5HD = ("float32", "NC1HWC0")
-    F32_FracZ = ("float32", "Fracz")
+    F32_FracZ = ("float32", "FracZ")
     F32_FracNZ = ("float32", "FRACTAL_NZ")
     F32_C1HWNCoC0 = ("float32", "C1HWNCoC0")
     F32_NCHW = ("float32", "NCHW")

From 791442288cd7dc12b959f5109f566cec0734978c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Tue, 14 Apr 2020 17:21:00 +0800
Subject: [PATCH 224/367] fix format typo in examples

---
 mindspore/ops/operations/control_ops.py | 4 ++--
 mindspore/ops/operations/other_ops.py   | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index d4e8b279ba..30f1e25a34 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -50,7 +50,7 @@ class ControlDepend(Primitive):
         >>> # step should be increased, so the add operation should depend on the data calculation operation.
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
-        >>>        super(Net, self).__init__()
+        >>>         super(Net, self).__init__()
         >>>         self.global_step = mindspore.Parameter(initializer(0, [1]), name="global_step")
         >>>         self.rate = 0.2
         >>>         self.control_depend = P.ControlDepend()
@@ -89,7 +89,7 @@ class GeSwitch(PrimitiveWithInfer):
     Examples:
         >>> class Net(nn.Cell):
         >>> 	def __init__(self):
-        >>>	    super(Net, self).__init__()
+        >>>         super(Net, self).__init__()
         >>>         self.square = P.Square()
         >>>         self.add = P.TensorAdd()
         >>>         self.value = Tensor(np.full((1), 3), mindspore.float32)
diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py
index 4ac7f2e554..b6182f0476 100644
--- a/mindspore/ops/operations/other_ops.py
+++ b/mindspore/ops/operations/other_ops.py
@@ -39,7 +39,7 @@ class Assign(PrimitiveWithInfer):
         >>>         self.y = mindspore.Parameter(Tensor([1.0], mindspore.float32), name="y")
         >>>
         >>>     def construct(self, x):
-        >>>         Assign()(self.y, x)
+        >>>         P.Assign()(self.y, x)
         >>>         return x
         >>> x = Tensor([2.0], mindspore.float32)
         >>> net = Net()

From a007e4812bd83f5997fce8fd5b7f5e46c67d88e0 Mon Sep 17 00:00:00 2001
From: maoweiyong <maoweiyong@huawei.com>
Date: Fri, 10 Apr 2020 10:20:37 +0800
Subject: [PATCH 225/367] modify gpu operator information registration

---
 mindspore/ops/__init__.py                     |  4 +-
 mindspore/ops/_op_impl/akg/gpu/cast.py        | 50 ++++------------
 mindspore/ops/_op_impl/akg/gpu/equal.py       | 55 ++++--------------
 mindspore/ops/_op_impl/akg/gpu/hsigmoid.py    | 44 ++++----------
 .../ops/_op_impl/akg/gpu/hsigmoid_grad.py     | 55 ++++--------------
 mindspore/ops/_op_impl/akg/gpu/hswish.py      | 44 ++++----------
 mindspore/ops/_op_impl/akg/gpu/hswish_grad.py | 55 ++++--------------
 mindspore/ops/_op_impl/akg/gpu/mean.py        | 44 ++++----------
 mindspore/ops/_op_impl/akg/gpu/mean_grad.py   | 50 ++++------------
 mindspore/ops/_op_impl/akg/gpu/mul.py         | 55 ++++--------------
 mindspore/ops/_op_impl/akg/gpu/relu6.py       | 44 ++++----------
 mindspore/ops/_op_impl/akg/gpu/relu6_grad.py  | 55 ++++--------------
 mindspore/ops/_op_impl/akg/gpu/squeeze.py     | 50 ++++------------
 .../ops/_op_impl/akg/gpu/squeeze_grad.py      | 56 +++++-------------
 mindspore/ops/_op_impl/akg/gpu/tile.py        | 50 ++++------------
 mindspore/ops/op_info_register.py             | 58 +++++++++++++++++++
 16 files changed, 225 insertions(+), 544 deletions(-)

diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py
index 0e6c114566..01ca039862 100644
--- a/mindspore/ops/__init__.py
+++ b/mindspore/ops/__init__.py
@@ -30,7 +30,7 @@ Note:
 
 from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry
-from .op_info_register import op_info_register, AiCPURegOp, TBERegOp, DataType
+from .op_info_register import op_info_register, AkgRegOp, AiCPURegOp, TBERegOp, DataType
 from .primitive import constexpr
 from .._c_expression import signature_rw, signature_kind
 
@@ -40,6 +40,6 @@ __primitive__ = [
 ]
 
 __all__ = ["get_vm_impl_fn", "vm_impl_registry",
-           "op_info_register", "AiCPURegOp", "TBERegOp", "DataType",
+           "op_info_register", "AkgRegOp", "AiCPURegOp", "TBERegOp", "DataType",
            "constexpr"]
 __all__.extend(__primitive__)
diff --git a/mindspore/ops/_op_impl/akg/gpu/cast.py b/mindspore/ops/_op_impl/akg/gpu/cast.py
index fb4b221be6..b9ce4cf464 100644
--- a/mindspore/ops/_op_impl/akg/gpu/cast.py
+++ b/mindspore/ops/_op_impl/akg/gpu/cast.py
@@ -13,45 +13,19 @@
 # limitations under the License.
 
 """Cast op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Cast",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-        {
-            "name": "dst_type",
-            "param_type": "required",
-            "type": "str"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+cast_op_info = AkgRegOp("Cast") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .attr("dst_type", "required", "str") \
+    .dtype_format(DataType.F16_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(cast_op_info)
 def _cast_akg():
     """Cast AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/equal.py b/mindspore/ops/_op_impl/akg/gpu/equal.py
index c6cffdad24..fa20392411 100644
--- a/mindspore/ops/_op_impl/akg/gpu/equal.py
+++ b/mindspore/ops/_op_impl/akg/gpu/equal.py
@@ -13,50 +13,19 @@
 # limitations under the License.
 
 """Equal op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Equal",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+equal_op_info = AkgRegOp("Equal") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .input(1, "y") \
+    .output(0, "output") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .get_op_info()
+
+
+@op_info_register(equal_op_info)
 def _equal_akg():
     """Equal AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
index 29dd8d6251..31fe332206 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
@@ -13,40 +13,18 @@
 # limitations under the License.
 
 """HSigmoid op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "HSigmoid",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+hsigmoid_op_info = AkgRegOp("HSigmoid") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(hsigmoidgrad_op_info)
 def _hsigmoid_akg():
     """HSigmoid AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py b/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
index d29df9c946..39b819138e 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hsigmoid_grad.py
@@ -13,50 +13,19 @@
 # limitations under the License.
 
 """HSigmoidGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "HSigmoidGrad",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y_grad"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+hsigmoidgrad_op_info = AkgRegOp("HSigmoidGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "y_grad") \
+    .input(1, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(hsigmoidgrad_op_info)
 def _hsigmoid_grad_akg():
     """HSigmoidGrad AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish.py b/mindspore/ops/_op_impl/akg/gpu/hswish.py
index 619575920f..17364de6cb 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hswish.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish.py
@@ -13,40 +13,18 @@
 # limitations under the License.
 
 """HSwish op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "HSwish",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+hswish_op_info = AkgRegOp("HSwish") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(hsigmoidgrad_op_info)
 def _hswish_akg():
     """HSwish AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
index 6d3556b969..503dd9a5f1 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
@@ -13,50 +13,19 @@
 # limitations under the License.
 
 """HSwishGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "HSwishGrad",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y_grad"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+hswishgrad_op_info = AkgRegOp("HSwishGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "y_grad") \
+    .input(1, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(hsigmoidgrad_op_info)
 def _hswish_grad_akg():
     """HSwishGrad AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/mean.py b/mindspore/ops/_op_impl/akg/gpu/mean.py
index 244af290bb..b46b701b91 100644
--- a/mindspore/ops/_op_impl/akg/gpu/mean.py
+++ b/mindspore/ops/_op_impl/akg/gpu/mean.py
@@ -13,40 +13,18 @@
 # limitations under the License.
 
 """SimpleMean op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "SimpleMean",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+mean_op_info = AkgRegOp("SimpleMean") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(mean_op_info)
 def _simple_mean_akg():
     """SimpleMean AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/mean_grad.py b/mindspore/ops/_op_impl/akg/gpu/mean_grad.py
index 27c0674632..e3e0121c20 100644
--- a/mindspore/ops/_op_impl/akg/gpu/mean_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/mean_grad.py
@@ -13,45 +13,19 @@
 # limitations under the License.
 
 """SimpleMeanGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "SimpleMeanGrad",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-        {
-            "name": "input_shape",
-            "param_type": "required",
-            "type": "listInt"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "HEAD"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+mean_grad_op_info = AkgRegOp("SimpleMeanGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "HEAD") \
+    .output(0, "output") \
+    .attr("input_shape", "required", "listInt") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(mean_grad_op_info)
 def _simple_mean_grad_akg():
     """SimpleMeanGrad AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/mul.py b/mindspore/ops/_op_impl/akg/gpu/mul.py
index d9e1a0b5d6..db5b1460ed 100644
--- a/mindspore/ops/_op_impl/akg/gpu/mul.py
+++ b/mindspore/ops/_op_impl/akg/gpu/mul.py
@@ -13,50 +13,19 @@
 # limitations under the License.
 
 """Mul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Mul",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+mul_op_info = AkgRegOp("Mul") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .input(1, "y") \
+    .output(0, "output") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(mul_op_info)
 def _mul_akg():
     """Mul AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/relu6.py b/mindspore/ops/_op_impl/akg/gpu/relu6.py
index 0de0a7e400..31bfebcd8d 100644
--- a/mindspore/ops/_op_impl/akg/gpu/relu6.py
+++ b/mindspore/ops/_op_impl/akg/gpu/relu6.py
@@ -13,40 +13,18 @@
 # limitations under the License.
 
 """ReLU6 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "ReLU6",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+relu_op_info = AkgRegOp("ReLU6") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(relu_op_info)
 def _relu6_akg():
     """ReLU6 AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/relu6_grad.py b/mindspore/ops/_op_impl/akg/gpu/relu6_grad.py
index 4d3c5e9a00..83d93f3077 100644
--- a/mindspore/ops/_op_impl/akg/gpu/relu6_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/relu6_grad.py
@@ -13,50 +13,19 @@
 # limitations under the License.
 
 """ReLU6Grad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "ReLU6Grad",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y_grad"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+relu_grad_op_info = AkgRegOp("ReLU6Grad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "y_grad") \
+    .input(1, "x") \
+    .output(0, "output") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(relu_grad_op_info)
 def _relu6_grad_akg():
     """ReLU6Grad AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/squeeze.py b/mindspore/ops/_op_impl/akg/gpu/squeeze.py
index 9e766cdfd7..378d096d5c 100644
--- a/mindspore/ops/_op_impl/akg/gpu/squeeze.py
+++ b/mindspore/ops/_op_impl/akg/gpu/squeeze.py
@@ -13,45 +13,19 @@
 # limitations under the License.
 
 """Squeeze op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Squeeze",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+squeeze_op_info = AkgRegOp("SqueezeGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .attr("axis", "optional", "listInt") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(squeeze_op_info)
 def _squeeze_akg():
     """Squeeze AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/squeeze_grad.py b/mindspore/ops/_op_impl/akg/gpu/squeeze_grad.py
index 7584bd05f9..ef397ea0a7 100644
--- a/mindspore/ops/_op_impl/akg/gpu/squeeze_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/squeeze_grad.py
@@ -13,50 +13,20 @@
 # limitations under the License.
 
 """SqueezeGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "SqueezeGrad",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-        {
-            "name": "x_shape",
-            "param_type": "required",
-            "type": "listInt"
-        },
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y_grad"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+squeeze_grad_op_info = AkgRegOp("SqueezeGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "y_grad") \
+    .output(0, "output") \
+    .attr("x_shape", "required", "listInt") \
+    .attr("axis", "optional", "listInt") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(squeeze_grad_op_info)
 def _squeeze_grad_akg():
     """SqueezeGrad AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/tile.py b/mindspore/ops/_op_impl/akg/gpu/tile.py
index f110e9314e..8c9de00979 100644
--- a/mindspore/ops/_op_impl/akg/gpu/tile.py
+++ b/mindspore/ops/_op_impl/akg/gpu/tile.py
@@ -13,45 +13,19 @@
 # limitations under the License.
 
 """Tile op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-@op_info_register("""{
-    "op_name": "Tile",
-    "imply_type": "AutoDiff",
-    "fusion_type": "OPAQUE",
-    "processor": "cuda",
-    "attr": [
-        {
-            "name": "multiples",
-            "param_type": "required",
-            "type": "listInt"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32", "float16"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output"
-        }
-    ]
-}""")
+tile_op_info = AkgRegOp("Tile") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x") \
+    .output(0, "output") \
+    .attr("multiples", "required", "listInt") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(tile_op_info)
 def _tile_akg():
     """Tile AutoDiff register"""
     return
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 0750094e18..daf3192d4f 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -205,6 +205,64 @@ class RegOp():
         return op_info
 
 
+class AkgRegOp(RegOp):
+    """Class for Akg op info register"""
+
+    def __init__(self, op_name):
+        super(AkgRegOp, self).__init__(op_name)
+        self.imply_type = "AutoDiff"
+        self.processor = "cuda"
+
+    def input(self, index=None, name=None, **kwargs):
+        """
+        Register Akg op input information.
+
+        Args:
+            index (int): Order of the input. Default: None.
+            name (str): Name of the input. Default: None.
+            kwargs (dict): Other information for the input.
+        """
+        param_list = [index, name]
+        key_list = ["index", "name"]
+        fn_list = [self._is_int, self._is_string]
+        input_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.inputs.append(input_dict)
+        return self
+
+    def output(self, index=None, name=None, **kwargs):
+        """
+        Register Akg op output information.
+
+        Args:
+            index (int): Order of the output. Default: None.
+            name (str): Name of the output. Default: None.
+            kwargs (dict): Other information for the output.
+        """
+        param_list = [index, name]
+        key_list = ["index", "name"]
+        fn_list = [self._is_int, self._is_string]
+        output_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.outputs.append(output_dict)
+        return self
+
+    def attr(self, name=None, param_type=None, value_type=None, **kwargs):
+        """
+        Register Akg op attribute information.
+
+        Args:
+            name (str): Name of the attribute. Default: None.
+            param_type (str): Param type of the attribute. Default: None.
+            value_type (str): Value type of the attribute. Default: None.
+            kwargs (dict): Other information for the attribute.
+        """
+        param_list = [name, param_type, value_type]
+        key_list = ["name", "param_type", "type"]
+        fn_list = [self._is_string]
+        attr_dict = self._check_param(param_list, key_list, fn_list, kwargs)
+        self.attr_.append(attr_dict)
+        return self
+
+
 class AiCPURegOp(RegOp):
     """Class for AiCPU op info register"""
 

From d248b05a985e76aff8feebdede7dca250ac36c86 Mon Sep 17 00:00:00 2001
From: VectorSL <shiliang10@huawei.com>
Date: Tue, 14 Apr 2020 19:41:00 +0800
Subject: [PATCH 226/367] gpu add kernel select

---
 .../kernel/gpu/arrays/select_gpu_kernel.cc    | 43 +++++++++
 .../kernel/gpu/arrays/select_gpu_kernel.h     | 95 +++++++++++++++++++
 .../ccsrc/kernel/gpu/cuda_impl/select_impl.cu | 42 ++++++++
 .../kernel/gpu/cuda_impl/select_impl.cuh      | 25 +++++
 tests/st/ops/gpu/test_select_op.py            | 47 +++++++++
 5 files changed, 252 insertions(+)
 create mode 100644 mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cu
 create mode 100644 mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cuh
 create mode 100644 tests/st/ops/gpu/test_select_op.py

diff --git a/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.cc
new file mode 100644
index 0000000000..41c9c2243f
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.cc
@@ -0,0 +1,43 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/arrays/select_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(Select,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeBool)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      SelectGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Select,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeBool)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      SelectGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(Select,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeBool)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddInputAttr(kNumberTypeInt32)
+                        .AddOutputAttr(kNumberTypeInt32),
+                      SelectGpuKernel, int)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.h
new file mode 100644
index 0000000000..ba0bea4dee
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/arrays/select_gpu_kernel.h
@@ -0,0 +1,95 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_SELECT_GPU_KERNEL_H
+#define MINDSPORE_CCSRC_KERNEL_GPU_SELECT_GPU_KERNEL_H
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/select_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class SelectGpuKernel : public GpuKernel {
+ public:
+  SelectGpuKernel() : input_size_(0), output_size_(0) {}
+  ~SelectGpuKernel() override = default;
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
+    bool *input_cond = GetDeviceAddress<bool>(inputs, 0);
+    T *input_x = GetDeviceAddress<T>(inputs, 1);
+    T *input_y = GetDeviceAddress<T>(inputs, 2);
+    T *output = GetDeviceAddress<T>(outputs, 0);
+    CalSelect(output_size_ / sizeof(T), input_cond, input_x, input_y, output,
+              reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    if (!CheckParam(kernel_node)) {
+      return false;
+    }
+    auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    input_size_ = sizeof(bool);
+    output_size_ = sizeof(T);
+    for (size_t x : shape) {
+      input_size_ = input_size_ * x;
+      output_size_ = output_size_ * x;
+    }
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    input_size_list_.push_back(output_size_);
+    input_size_list_.push_back(output_size_);
+    output_size_list_.push_back(output_size_);
+  }
+
+ private:
+  bool CheckParam(const CNodePtr &kernel_node) {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 3) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but SelectGpuKernel needs 3 output.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but SelectGpuKernel needs 1 output.";
+      return false;
+    }
+    return true;
+  }
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  size_t input_size_;
+  size_t output_size_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_SELECT_GPU_KERNEL_H
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cu
new file mode 100644
index 0000000000..f07a820e75
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cu
@@ -0,0 +1,42 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdio.h>
+#include <stdint.h>
+#include <include/cuda_runtime.h>
+#include "kernel/gpu/cuda_impl/select_impl.cuh"
+
+template <typename T>
+__global__ void Select(const size_t size, const bool* cond, const T* input_x, const T* input_y, T* output) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
+    output[pos] = cond[pos] ? input_x[pos] : input_y[pos];
+  }
+  return;
+}
+
+template <typename T>
+void CalSelect(const size_t size, const bool* cond, const T* input_x, const T* input_y, T* output,
+               cudaStream_t cuda_stream) {
+  Select<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, cond, input_x, input_y, output);
+  return;
+}
+
+template void CalSelect<float>(const size_t size, const bool* cond, const float* input_X, const float* input_y,
+                               float* output, cudaStream_t cuda_stream);
+template void CalSelect<int>(const size_t size, const bool* cond, const int* input_X, const int* input_y, int* output,
+                             cudaStream_t cuda_stream);
+template void CalSelect<half>(const size_t size, const bool* cond, const half* input_X, const half* input_y,
+                              half* output, cudaStream_t cuda_stream);
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cuh
new file mode 100644
index 0000000000..da2d7d9a7f
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/select_impl.cuh
@@ -0,0 +1,25 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SELECT_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SELECT_IMPL_H_
+
+#include "device/gpu/cuda_common.h"
+
+template <typename T>
+void CalSelect(const size_t size, const bool* cond, const T* input_x, const T* input_y, T* output,
+               cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SELECT_IMPL_H_
diff --git a/tests/st/ops/gpu/test_select_op.py b/tests/st/ops/gpu/test_select_op.py
new file mode 100644
index 0000000000..5cac6a6ad3
--- /dev/null
+++ b/tests/st/ops/gpu/test_select_op.py
@@ -0,0 +1,47 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.select = P.Select()
+
+    def construct(self, cond, x, y):
+        return self.select(cond, x, y)
+
+cond = np.array([[True, False], [True, False]]).astype(np.bool)
+x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
+y = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_select():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    select = Net()
+    output = select(Tensor(cond), Tensor(x), Tensor(y))
+    expect = [[1.2, 2], [1, 4.0]]
+    error = np.ones(shape=[2, 2]) * 1.0e-6
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)

From 9e372073e2e0718dddfa6b291af4f001d3edd7de Mon Sep 17 00:00:00 2001
From: VectorSL <shiliang10@huawei.com>
Date: Tue, 14 Apr 2020 20:11:44 +0800
Subject: [PATCH 227/367] gpu add assigin

---
 .../kernel/gpu/other/assign_gpu_kernel.cc     | 33 +++++++
 .../kernel/gpu/other/assign_gpu_kernel.h      | 93 +++++++++++++++++++
 tests/st/ops/gpu/test_assign_op.py            | 50 ++++++++++
 3 files changed, 176 insertions(+)
 create mode 100644 mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.h
 create mode 100644 tests/st/ops/gpu/test_assign_op.py

diff --git a/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.cc
new file mode 100644
index 0000000000..0f3e0c95f4
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.cc
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/other/assign_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(
+  Assign,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  AssignGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(
+  Assign,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  AssignGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(
+  Assign, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  AssignGpuKernel, int)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.h
new file mode 100644
index 0000000000..1c1cde4fd4
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.h
@@ -0,0 +1,93 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H
+#define MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class AssignGpuKernel : public GpuKernel {
+ public:
+  AssignGpuKernel() : input_size_(0) {}
+  ~AssignGpuKernel() override = default;
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+              const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
+    T *var = GetDeviceAddress<T>(inputs, 0);
+    T *value = GetDeviceAddress<T>(inputs, 1);
+    T *output = GetDeviceAddress<T>(outputs, 0);
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      cudaMemcpyAsync(var, value, input_size_, cudaMemcpyDeviceToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemxcpyAsync failed.");
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      cudaMemcpyAsync(output, value, input_size_, cudaMemcpyDeviceToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemxcpyAsync failed.");
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    if (!CheckParam(kernel_node)) {
+      return false;
+    }
+    auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    input_size_ = sizeof(T);
+    for (size_t x : shape) {
+      input_size_ = input_size_ * x;
+    }
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(input_size_);
+  }
+
+ private:
+  bool CheckParam(const CNodePtr &kernel_node) {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 2) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but AssignGpuKernel needs 2 output.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but AssignGpuKernel needs 1 output.";
+      return false;
+    }
+    return true;
+  }
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  size_t input_size_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H
diff --git a/tests/st/ops/gpu/test_assign_op.py b/tests/st/ops/gpu/test_assign_op.py
new file mode 100644
index 0000000000..4cf730d763
--- /dev/null
+++ b/tests/st/ops/gpu/test_assign_op.py
@@ -0,0 +1,50 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.assign = P.Assign()
+
+    def construct(self, var, value):
+        return self.assign(var, value)
+
+x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
+value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_assign():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    assign = Net()
+    var = Tensor(x)
+    output = assign(var, Tensor(value))
+
+    error = np.ones(shape=[2, 2]) * 1.0e-6
+    diff1 = output.asnumpy() - value
+    diff2 = var.asnumpy() - value
+    assert np.all(diff1 < error)
+    assert np.all(-diff1 < error)
+    assert np.all(diff2 < error)
+    assert np.all(-diff2 < error)

From 48f90eb7bcb1b6a68f2cdafa8be66b8d6e023c13 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Tue, 14 Apr 2020 16:14:20 +0800
Subject: [PATCH 228/367] add custom op st to ci

---
 tests/st/ops/custom_ops_tbe/cus_square.py  | 2 +-
 tests/st/ops/custom_ops_tbe/test_square.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/st/ops/custom_ops_tbe/cus_square.py b/tests/st/ops/custom_ops_tbe/cus_square.py
index 6a9e769f51..d006f75b4c 100644
--- a/tests/st/ops/custom_ops_tbe/cus_square.py
+++ b/tests/st/ops/custom_ops_tbe/cus_square.py
@@ -24,7 +24,7 @@ class CusSquare(PrimitiveWithInfer):
     def __init__(self):
         """init CusSquare"""
         self.init_prim_io_names(inputs=['x'], outputs=['y'])
-        from .square_impl import CusSquare
+        from square_impl import CusSquare
 
     def vm_impl(self, x):
         x = x.asnumpy()
diff --git a/tests/st/ops/custom_ops_tbe/test_square.py b/tests/st/ops/custom_ops_tbe/test_square.py
index c67edae307..d8439000f8 100644
--- a/tests/st/ops/custom_ops_tbe/test_square.py
+++ b/tests/st/ops/custom_ops_tbe/test_square.py
@@ -16,7 +16,7 @@ import numpy as np
 import mindspore.nn as nn
 import mindspore.context as context
 from mindspore import Tensor
-from .cus_square import CusSquare
+from cus_square import CusSquare
 import pytest
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
@@ -32,6 +32,7 @@ class Net(nn.Cell):
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_onecard
 def test_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)

From 3ea3d9e5a433e47196b9dc087bd00a50bdcf1163 Mon Sep 17 00:00:00 2001
From: ZPaC <zhoupeichen@huawei.com>
Date: Mon, 13 Apr 2020 21:04:33 +0800
Subject: [PATCH 229/367] 1.GPU supports multiple streams. 2.GPU commnication
 stream and compute stream overlap.

---
 .../ccsrc/device/gpu/gpu_device_manager.cc    |  20 +-
 .../ccsrc/device/gpu/gpu_device_manager.h     |  16 +-
 .../ccsrc/device/gpu/gpu_stream_assign.cc     | 181 ++++++++++++++++++
 .../ccsrc/device/gpu/gpu_stream_assign.h      |  73 +++++++
 .../ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h   |  25 +--
 mindspore/ccsrc/session/gpu_session.cc        |   8 +
 mindspore/ccsrc/session/gpu_session.h         |   2 +
 mindspore/ccsrc/utils/utils.h                 |   2 +
 8 files changed, 304 insertions(+), 23 deletions(-)
 create mode 100644 mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
 create mode 100644 mindspore/ccsrc/device/gpu/gpu_stream_assign.h

diff --git a/mindspore/ccsrc/device/gpu/gpu_device_manager.cc b/mindspore/ccsrc/device/gpu/gpu_device_manager.cc
index 59c8fde5a2..b25ba2906b 100644
--- a/mindspore/ccsrc/device/gpu/gpu_device_manager.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_device_manager.cc
@@ -25,7 +25,7 @@ namespace device {
 namespace gpu {
 void GPUDeviceManager::InitDevice() {
   CHECK_OP_RET_WITH_EXCEPT(CudaDriver::set_current_device(SizeToInt(cur_dev_id_)), "Failed to set current device id");
-  CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateStream(&stream_), "Failed to create CUDA stream.");
+  CHECK_OP_RET_WITH_EXCEPT(CreateStream(&default_stream_), "Failed to create CUDA stream.");
   CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreate(&cudnn_handle_), "Failed to create cuDNN handle");
   CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetStream(cudnn_handle_, reinterpret_cast<cudaStream_t>(default_stream())),
                               "Failed to set stream for cuDNN handle.");
@@ -36,19 +36,27 @@ void GPUDeviceManager::InitDevice() {
 }
 
 void GPUDeviceManager::ReleaseDevice() {
-  if (stream_ != nullptr) {
-    CHECK_OP_RET_WITH_ERROR(CudaDriver::DestroyStream(stream_), "Failed to destroy cuda stream.");
+  for (DeviceStream stream : gpu_streams_) {
+    if (stream != nullptr) {
+      CHECK_OP_RET_WITH_ERROR(CudaDriver::DestroyStream(stream), "Failed to destroy CUDA stream.");
+    }
   }
   if (cudnn_handle_ != nullptr) {
-    CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroy(cudnn_handle_), "Failed to destroy cudnn handle");
+    CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroy(cudnn_handle_), "Failed to destroy cuDNN handle");
   }
   if (cublas_handle_ != nullptr) {
-    CHECK_CUBLAS_RET_WITH_ERROR(cublasDestroy(cublas_handle_), "Failed to destroy cublas handle.");
+    CHECK_CUBLAS_RET_WITH_ERROR(cublasDestroy(cublas_handle_), "Failed to destroy cuBLAS handle.");
   }
   CHECK_OP_RET_WITH_ERROR(GPUMemoryAllocator::GetInstance().Finalize(), "Failed to destroy gpu memory allocator");
 }
 
-const DeviceStream& GPUDeviceManager::default_stream() const { return stream_; }
+bool GPUDeviceManager::CreateStream(DeviceStream* stream) {
+  CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateStream(stream), "Failed to create CUDA stream");
+  gpu_streams_.emplace_back(*stream);
+  return true;
+}
+
+const DeviceStream& GPUDeviceManager::default_stream() const { return default_stream_; }
 
 int GPUDeviceManager::device_count() const { return CudaDriver::device_count(); }
 
diff --git a/mindspore/ccsrc/device/gpu/gpu_device_manager.h b/mindspore/ccsrc/device/gpu/gpu_device_manager.h
index 6bfaf85673..3b3d2aecb5 100644
--- a/mindspore/ccsrc/device/gpu/gpu_device_manager.h
+++ b/mindspore/ccsrc/device/gpu/gpu_device_manager.h
@@ -19,6 +19,7 @@
 
 #include <cudnn.h>
 #include <cublas_v2.h>
+#include <vector>
 #include <memory>
 #include "device/gpu/cuda_driver.h"
 #include "device/gpu/gpu_memory_allocator.h"
@@ -36,13 +37,15 @@ class GPUDeviceManager {
   uint32_t cur_device_id() const;
   bool is_device_id_init() const;
 
+  bool CreateStream(DeviceStream* stream);
+  bool SyncStream(const DeviceStream& stream) const;
   const DeviceStream& default_stream() const;
+
   const cudnnHandle_t& GetCudnnHandle() const;
   const cublasHandle_t& GetCublasHandle() const;
 
   bool CopyDeviceMemToHost(const HostMemPtr& dst, const DeviceMemPtr& src, size_t size) const;
   bool CopyHostMemToDevice(const DeviceMemPtr& dst, const void* src, size_t size) const;
-  bool SyncStream(const DeviceStream& stream) const;
 
   static GPUDeviceManager& GetInstance() {
     static GPUDeviceManager instance;
@@ -55,13 +58,16 @@ class GPUDeviceManager {
   GPUDeviceManager(const GPUDeviceManager&) = delete;
   GPUDeviceManager& operator=(const GPUDeviceManager&) = delete;
 
-  // default cuda stream used for all the kernels.
-  DeviceStream stream_{nullptr};
+  // default CUDA stream used for all the kernels.
+  DeviceStream default_stream_{nullptr};
+
+  // all gpu CUDA streams including default_stream_.
+  std::vector<DeviceStream> gpu_streams_;
 
-  // handle used for cudnn kernels.
+  // handle used for cuDNN kernels.
   cudnnHandle_t cudnn_handle_{nullptr};
 
-  // handle used for cublas kernels.
+  // handle used for cuBLAS kernels.
   cublasHandle_t cublas_handle_{nullptr};
 
   bool dev_id_init_;
diff --git a/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
new file mode 100644
index 0000000000..39d5ca3fe6
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
@@ -0,0 +1,181 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <set>
+#include <string>
+#include <memory>
+#include <algorithm>
+#include "device/gpu/gpu_common.h"
+#include "device/gpu/kernel_info_setter.h"
+#include "device/gpu/gpu_device_manager.h"
+#include "device/gpu/gpu_stream_assign.h"
+
+namespace mindspore {
+namespace device {
+namespace gpu {
+void AssignGpuStream(const std::shared_ptr<session::KernelGraph> &kernel_graph) {
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  std::vector<CNodePtr> allreduce_cnodes;
+  auto execution_kernels = kernel_graph->execution_order();
+  for (auto kernel : execution_kernels) {
+    std::string kernel_name = AnfAlgo::GetCNodeName(kernel);
+    if (kernel_name == kAllReduceOpName) {
+      allreduce_cnodes.emplace_back(kernel);
+    }
+  }
+  if (allreduce_cnodes.size() > 1) {
+    DeviceStream comm_stream = nullptr;
+    GPUDeviceManager::GetInstance().CreateStream(&comm_stream);
+    std::transform(allreduce_cnodes.begin(), allreduce_cnodes.end(), allreduce_cnodes.begin(), [&](CNodePtr node) {
+      AnfAlgo::SetNodeAttr("stream_id", MakeValue(reinterpret_cast<uintptr_t>(comm_stream)), node);
+      return node;
+    });
+
+    std::vector<SendRecvPair> send_recv_pairs;
+    FindAllReduceStreamSwitchPos(kernel_graph, &send_recv_pairs);
+    InsertStreamSwitchNode(kernel_graph, send_recv_pairs);
+  }
+}
+
+void FindAllReduceStreamSwitchPos(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                                  std::vector<SendRecvPair> *send_recv_pairs) {
+  auto execution_kernels = kernel_graph->execution_order();
+  std::vector<CNodePtr>::iterator iter, iter_begin;
+  iter = iter_begin = execution_kernels.begin();
+  std::vector<CNodePtr>::iterator iter_end = execution_kernels.end();
+  for (; iter != execution_kernels.end(); ++iter) {
+    std::string kernel_name = AnfAlgo::GetCNodeName(*iter);
+    if (kernel_name == kAllReduceOpName) {
+      // Find AllReduce node's last input node.
+      std::vector<CNodePtr>::iterator mock_send_node_iter =
+        FindSendNodePos(iter_begin, iter + 1, *iter, kAllReduceStreamSwitch);
+      if (mock_send_node_iter == iter + 1) {
+        MS_LOG(WARNING) << "Can't find send node place before AllReduce node.";
+        continue;
+      }
+      SendRecvPair pair1 = {kAllReduceStreamSwitch, *mock_send_node_iter, *iter,
+                            IntToSize(mock_send_node_iter - iter_begin + 1), IntToSize(iter - iter_begin)};
+      send_recv_pairs->push_back(pair1);
+      // Find node which uses AllReduce as input[0].
+      std::vector<CNodePtr>::iterator mock_recv_node_iter =
+        FindRecvNodePos(iter, iter_end, *iter, kAllReduceStreamSwitch);
+      if (mock_recv_node_iter == iter_end) {
+        MS_LOG(WARNING) << "Can't find send node place before AllReduce node.";
+        continue;
+      }
+      SendRecvPair pair2 = {kAllReduceStreamSwitch, *iter, *mock_recv_node_iter, IntToSize(iter - iter_begin + 1),
+                            IntToSize(mock_recv_node_iter - iter_begin)};
+      send_recv_pairs->push_back(pair2);
+    }
+  }
+}
+
+std::vector<CNodePtr>::iterator FindSendNodePos(std::vector<CNodePtr>::iterator begin,
+                                                std::vector<CNodePtr>::iterator end, const CNodePtr mock_recv_node,
+                                                StreamSwitchType stream_switch_type) {
+  MS_EXCEPTION_IF_NULL(mock_recv_node);
+  if (stream_switch_type == kAllReduceStreamSwitch) {
+    for (auto iter = begin; iter != end; iter++) {
+      if (*(iter + 1) == mock_recv_node) {
+        return iter;
+      }
+    }
+  }
+  return end;
+}
+
+std::vector<CNodePtr>::iterator FindRecvNodePos(std::vector<CNodePtr>::iterator begin,
+                                                std::vector<CNodePtr>::iterator end, const CNodePtr mock_send_node,
+                                                StreamSwitchType stream_switch_type) {
+  MS_EXCEPTION_IF_NULL(mock_send_node);
+  for (auto iter = begin; iter != end; iter++) {
+    auto node = *iter;
+    if (stream_switch_type == kAllReduceStreamSwitch) {
+      for (auto input : node->inputs()) {
+        if (mock_send_node == AnfAlgo::VisitKernel(input, 0).first) {
+          return iter;
+        }
+      }
+    }
+  }
+  return end;
+}
+
+void InsertStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                            const std::vector<SendRecvPair> &send_recv_pairs) {
+  std::set<StreamSwitchNode> ordered_stream_switch_nodes;
+  for (SendRecvPair pair : send_recv_pairs) {
+    StreamSwitchType stream_switch_type = pair.stream_switch_type;
+    CNodePtr mock_send_node = pair.mock_send_node;
+    CNodePtr mock_recv_node = pair.mock_recv_node;
+    size_t send_node_offset = pair.send_node_offset;
+    size_t recv_node_offset = pair.recv_node_offset;
+    CNodePtr send_node = nullptr;
+    CNodePtr recv_node = nullptr;
+    // Step 1: generate Send and Recv CNodes.
+    if (stream_switch_type == kAllReduceStreamSwitch) {
+      if (!GenSendRecvCNodesForAllReduce(kernel_graph, mock_send_node, mock_recv_node, &send_node, &recv_node)) {
+        MS_LOG(EXCEPTION) << "Generating CNodes for send and recv failed. Stream switch type: kAllReduceStreamSwitch";
+      }
+    }
+    // Step 2: sort send and recv CNodes by offset.
+    ordered_stream_switch_nodes.insert({send_node_offset, send_node});
+    ordered_stream_switch_nodes.insert({recv_node_offset, recv_node});
+  }
+  // Step 3: insert stream switch CNodes into execution kernel list.
+  auto execution_kernels = kernel_graph->execution_order();
+  for (auto node = ordered_stream_switch_nodes.begin(); node != ordered_stream_switch_nodes.end(); node++) {
+    execution_kernels.insert(execution_kernels.begin() + node->offset, node->cnode);
+  }
+  kernel_graph->set_execution_order(execution_kernels);
+}
+
+bool GenSendRecvCNodesForAllReduce(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                                   const CNodePtr &mock_send_node, const CNodePtr &mock_recv_node, CNodePtr *send_node,
+                                   CNodePtr *recv_node) {
+  *send_node = CreateStreamSwitchNode(kernel_graph, kSendOpName);
+  MS_EXCEPTION_IF_NULL(*send_node);
+  *recv_node = CreateStreamSwitchNode(kernel_graph, kRecvOpName);
+  MS_EXCEPTION_IF_NULL(*recv_node);
+
+  cudaEvent_t event = nullptr;
+  CHECK_CUDA_RET_WITH_EXCEPT(cudaEventCreate(&event, cudaEventDisableTiming), "Creating cuda event failed.");
+  AnfAlgo::SetNodeAttr("record_event", MakeValue(reinterpret_cast<uintptr_t>(event)), *send_node);
+  AnfAlgo::SetNodeAttr("wait_event", MakeValue(reinterpret_cast<uintptr_t>(event)), *recv_node);
+
+  uintptr_t send_stream = AnfAlgo::GetNodeAttr<uintptr_t>(mock_send_node, "stream_id");
+  AnfAlgo::SetNodeAttr("record_event_stream", MakeValue(send_stream), *send_node);
+  uintptr_t recv_stream = AnfAlgo::GetNodeAttr<uintptr_t>(mock_recv_node, "stream_id");
+  AnfAlgo::SetNodeAttr("wait_event_stream", MakeValue(recv_stream), *recv_node);
+  return true;
+}
+
+CNodePtr CreateStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &kernel_graph, const std::string &name) {
+  auto op = std::make_shared<Primitive>(name);
+  auto apply = std::make_shared<ValueNode>(op);
+  std::vector<AnfNodePtr> input_list = {apply};
+  CNodePtr node = kernel_graph->NewCNode(input_list);
+  MS_EXCEPTION_IF_NULL(node);
+  kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder;
+  AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_builder.Build(), node.get());
+  auto abstract_none = std::make_shared<abstract::AbstractNone>();
+  node->set_abstract(abstract_none);
+  SetKernelInfo(node);
+  return node;
+}
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/gpu/gpu_stream_assign.h b/mindspore/ccsrc/device/gpu/gpu_stream_assign.h
new file mode 100644
index 0000000000..c7d2fe40e2
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/gpu_stream_assign.h
@@ -0,0 +1,73 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_DEVICE_GPU_GPU_STREAM_ASSIGN_H_
+#define MINDSPORE_CCSRC_DEVICE_GPU_GPU_STREAM_ASSIGN_H_
+
+#include <vector>
+#include <string>
+#include <memory>
+#include "session/kernel_graph.h"
+#include "session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace device {
+namespace gpu {
+enum StreamSwitchType { kAllReduceStreamSwitch, kStreamSwitchInvalidType = 255 };
+struct SendRecvPair {
+  StreamSwitchType stream_switch_type;
+  CNodePtr mock_send_node;
+  CNodePtr mock_recv_node;
+  size_t send_node_offset;
+  size_t recv_node_offset;
+};
+struct StreamSwitchNode {
+  size_t offset;
+  CNodePtr cnode;
+  bool operator<(const StreamSwitchNode &n) const {
+    if (offset < n.offset) {
+      return true;
+    } else if (offset == n.offset) {
+      return AnfAlgo::GetCNodeName(cnode) == kSendOpName ? true : false;
+    } else {
+      return false;
+    }
+  }
+};
+void AssignGpuStream(const std::shared_ptr<session::KernelGraph> &kernel_graph);
+void FindAllReduceStreamSwitchPos(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                                  std::vector<SendRecvPair> *send_recv_pairs);
+// Find Send node position according to "mock" recv node.
+// "mock" recv node is a gpu kernel node after a real Recv node, e.g. AllReduce node.
+std::vector<CNodePtr>::iterator FindSendNodePos(std::vector<CNodePtr>::iterator begin,
+                                                std::vector<CNodePtr>::iterator end, const CNodePtr mock_recv_node,
+                                                StreamSwitchType stream_switch_type);
+// Find Recv node position according to "mock" send node.
+// "mock" send node is a gpu kernel node before a real send node, e.g. AllReduce node.
+std::vector<CNodePtr>::iterator FindRecvNodePos(std::vector<CNodePtr>::iterator begin,
+                                                std::vector<CNodePtr>::iterator end, const CNodePtr mock_send_node,
+                                                StreamSwitchType stream_switch_type);
+void InsertStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                            const std::vector<SendRecvPair> &send_recv_pairs);
+bool GenSendRecvCNodesForAllReduce(const std::shared_ptr<session::KernelGraph> &kernel_graph,
+                                   const CNodePtr &mock_send_node, const CNodePtr &mock_recv_node, CNodePtr *send_node,
+                                   CNodePtr *recv_node);
+CNodePtr CreateStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &kernel_graph, const std::string &name);
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
+
+#endif
diff --git a/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
index 54e4eb9213..cea56b9878 100644
--- a/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
@@ -52,7 +52,8 @@ class NcclGpuKernel : public GpuKernel {
         nccl_reduce_type_(ncclSum),
         input_size_(0),
         output_size_(0),
-        collective_handle_(nullptr) {}
+        collective_handle_(nullptr),
+        comm_stream_(nullptr) {}
   ~NcclGpuKernel() override = default;
 
   const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@@ -63,34 +64,33 @@ class NcclGpuKernel : public GpuKernel {
     T *input_addr = GetDeviceAddress<T>(inputs, 0);
     T *output_addr = GetDeviceAddress<T>(outputs, 0);
 
+    cudaStream_t stream = comm_stream_ ? comm_stream_ : reinterpret_cast<cudaStream_t>(stream_ptr);
     switch (nccl_kernel_type_) {
       case NCCL_ALL_REDUCE: {
         auto all_reduce_funcptr =
           reinterpret_cast<AllReduce>(dlsym(const_cast<void *>(collective_handle_), "AllReduce"));
         MS_EXCEPTION_IF_NULL(all_reduce_funcptr);
-        CHECK_NCCL_RET_WITH_EXCEPT(
-          (*all_reduce_funcptr)(input_addr, output_addr, output_size_ / sizeof(T), nccl_data_type_, nccl_reduce_type_,
-                                reinterpret_cast<cudaStream_t>(stream_ptr)),
-          "ncclAllReduce failed");
+        CHECK_NCCL_RET_WITH_EXCEPT((*all_reduce_funcptr)(input_addr, output_addr, output_size_ / sizeof(T),
+                                                         nccl_data_type_, nccl_reduce_type_, stream),
+                                   "ncclAllReduce failed");
         break;
       }
       case NCCL_ALL_GATHER: {
         auto all_gather_funcptr =
           reinterpret_cast<AllGather>(dlsym(const_cast<void *>(collective_handle_), "AllGather"));
         MS_EXCEPTION_IF_NULL(all_gather_funcptr);
-        CHECK_NCCL_RET_WITH_EXCEPT((*all_gather_funcptr)(input_addr, output_addr, input_size_ / sizeof(T),
-                                                         nccl_data_type_, reinterpret_cast<cudaStream_t>(stream_ptr)),
-                                   "ncclAllGather failed");
+        CHECK_NCCL_RET_WITH_EXCEPT(
+          (*all_gather_funcptr)(input_addr, output_addr, input_size_ / sizeof(T), nccl_data_type_, stream),
+          "ncclAllGather failed");
         break;
       }
       case NCCL_REDUCE_SCATTER: {
         auto reduce_scatter_funcptr =
           reinterpret_cast<ReduceScatter>(dlsym(const_cast<void *>(collective_handle_), "ReduceScatter"));
         MS_EXCEPTION_IF_NULL(reduce_scatter_funcptr);
-        CHECK_NCCL_RET_WITH_EXCEPT(
-          (*reduce_scatter_funcptr)(input_addr, output_addr, output_size_ / sizeof(T), nccl_data_type_,
-                                    nccl_reduce_type_, reinterpret_cast<cudaStream_t>(stream_ptr)),
-          "ncclReduceScatter failed");
+        CHECK_NCCL_RET_WITH_EXCEPT((*reduce_scatter_funcptr)(input_addr, output_addr, output_size_ / sizeof(T),
+                                                             nccl_data_type_, nccl_reduce_type_, stream),
+                                   "ncclReduceScatter failed");
         break;
       }
       default: {
@@ -167,6 +167,7 @@ class NcclGpuKernel : public GpuKernel {
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;
   const void *collective_handle_;
+  cudaStream_t comm_stream_;
 };
 }  // namespace kernel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index bbcf2228cc..c0b2323e04 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -17,6 +17,7 @@
 #include "device/gpu/kernel_info_setter.h"
 #include "device/gpu/gpu_kernel_build.h"
 #include "device/gpu/gpu_kernel_runtime.h"
+#include "device/gpu/gpu_stream_assign.h"
 #include "pre_activate/common/optimizer.h"
 #include "pre_activate/common/pass_manager.h"
 #include "pre_activate/common/ir_fusion/allreduce_fusion.h"
@@ -55,6 +56,11 @@ void GPUSession::Optimize(const std::shared_ptr<KernelGraph> &kernel_graph) {
   kernel_graph->SetExecOrderByDefault();
 }
 
+void GPUSession::AssignStream(const std::shared_ptr<KernelGraph> &kernel_graph) {
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  device::gpu::AssignGpuStream(kernel_graph);
+}
+
 void GPUSession::BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
   device::gpu::GpuBuild(kernel_graph);
 }
@@ -94,6 +100,8 @@ GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
   StartKernelRT();
   // AllReduce Optimize
   Optimize(graph);
+  // Assign CUDA streams
+  AssignStream(graph);
   // Build kernel if node is cnode
   BuildKernel(graph);
   // Set graph execution order before memory alloc, ensure that memory alloc is according to the reorder graph
diff --git a/mindspore/ccsrc/session/gpu_session.h b/mindspore/ccsrc/session/gpu_session.h
index e443c1e701..d81a6c58f9 100644
--- a/mindspore/ccsrc/session/gpu_session.h
+++ b/mindspore/ccsrc/session/gpu_session.h
@@ -49,6 +49,8 @@ class GPUSession : public SessionBasic {
 
   void Optimize(const std::shared_ptr<KernelGraph> &kernel_graph);
 
+  void AssignStream(const std::shared_ptr<KernelGraph> &kernel_graph);
+
   void BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const;
 
   void AllocateMemory(KernelGraph *kernel_graph) const;
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 39b4b7a160..646fb36871 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -112,6 +112,8 @@ constexpr auto kFusedMulAddNOpName = "FusedMulAddN";
 constexpr auto kFusedMulApplyMomentumOpName = "FusedMulApplyMomentum";
 constexpr auto kBiasAddOpName = "BiasAdd";
 constexpr auto kConfusionMulGradOpName = "ConfusionMulGrad";
+constexpr auto kSendOpName = "Send";
+constexpr auto kRecvOpName = "Recv";
 
 // attr key name
 constexpr auto kAttrInputNames = "input_names";

From 950d75e3761762541c0c8947c1ebcb3ce7ca243d Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Tue, 14 Apr 2020 20:42:20 +0800
Subject: [PATCH 230/367] add get_class_indexing exception info

---
 mindspore/dataset/engine/datasets.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index f92f6f28a8..819013031f 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -782,7 +782,7 @@ class Dataset:
         """
         if self.input:
             return self.input[0].get_class_indexing()
-        return None
+        raise NotImplementedError("Dataset {} has not supported api get_class_indexing yet.".format(type(self)))
 
     def reset(self):
         """Reset the dataset for next epoch"""

From 2dd60b4ce38a92a40dc07443a7d6767811cdfe25 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Mon, 13 Apr 2020 15:30:25 +0800
Subject: [PATCH 231/367] pack via cpack

---
 CMakeLists.txt                 |   4 +-
 build.sh                       |  33 +-----
 cmake/package.cmake            | 185 +++++++++++++++++++++++++++++++++
 cmake/package_script.cmake     |  92 ++++++++++++++++
 mindspore/ccsrc/CMakeLists.txt |  88 +---------------
 package.sh                     | 128 -----------------------
 6 files changed, 283 insertions(+), 247 deletions(-)
 create mode 100644 cmake/package.cmake
 create mode 100644 cmake/package_script.cmake
 delete mode 100755 package.sh

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6fe159590f..a02fcd1caf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,7 +25,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include/flatbuffers)
 
 include(${CMAKE_SOURCE_DIR}/cmake/dependency_utils.cmake)
-find_package(Python3 COMPONENTS Interpreter Development)
+find_package(Python3 3.7 COMPONENTS Interpreter Development)
 if(Python3_FOUND)
     set(PYTHON_INCLUDE_DIRS "${Python3_INCLUDE_DIRS}")
     set(PYTHON_LIBRARIES "${Python3_LIBRARIES}")
@@ -60,3 +60,5 @@ add_subdirectory(mindspore/ccsrc)
 if (ENABLE_TESTCASES)
     add_subdirectory(tests)
 endif()
+
+include(cmake/package.cmake)
\ No newline at end of file
diff --git a/build.sh b/build.sh
index 3c6de1cf77..d555188c20 100755
--- a/build.sh
+++ b/build.sh
@@ -16,7 +16,6 @@
 
 set -e
 BASEPATH=$(cd "$(dirname $0)"; pwd)
-PROJECT_PATH="${BASEPATH}"
 CUDA_PATH=""
 CUDNN_PATH=""
 export BUILD_PATH="${BASEPATH}/build/"
@@ -24,7 +23,7 @@ export BUILD_PATH="${BASEPATH}/build/"
 usage()
 {
   echo "Usage:"
-  echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-s] [-b ge|cpu] [-m infer|train] \\"
+  echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-b ge|cpu] [-m infer|train] \\"
   echo "              [-a on|off] [-g on|off] [-p on|off] [-i] [-L] [-R] [-D on|off] [-j[n]] [-e gpu|d|cpu] \\"
   echo "              [-P on|off] [-z [on|off]] [-M on|off] [-V 9.2|10.1] [-I] [-K]"
   echo ""
@@ -36,7 +35,6 @@ usage()
   echo "    -t Run testcases switch, default on"
   echo "    -g Use glog to output log, default on"
   echo "    -h Print usage"
-  echo "    -s Install or setup"
   echo "    -b Select other backend, available: \\"
   echo "           ge:graph engine, cpu"
   echo "    -m Select mode, available: infer, train, default is infer "
@@ -77,7 +75,6 @@ checkopts()
   VERBOSE=""
   ENABLE_COVERAGE="off"
   RUN_TESTCASES="off"
-  EXECUTE_SETUP="off"
   ENABLE_BACKEND=""
   TRAIN_MODE="INFER"
   ENABLE_ASAN="off"
@@ -129,9 +126,6 @@ checkopts()
         usage
         exit 0
         ;;
-      s)
-        EXECUTE_SETUP="on"
-        ;;
       b)
         if [[ "X$OPTARG" != "Xge" && "X$OPTARG" != "Xcpu" ]]; then
           echo "Invalid value ${OPTARG} for option -b"
@@ -139,9 +133,6 @@ checkopts()
           exit 1
         fi
         ENABLE_BACKEND=$(echo "$OPTARG" | tr '[a-z]' '[A-Z]')
-        if [[ "X$ENABLE_BACKEND" == "XGE" ]]; then
-          ENABLE_GE="on"
-        fi
         if [[ "X$ENABLE_BACKEND" != "XCPU" ]]; then
           ENABLE_CPU="on"
         fi
@@ -323,10 +314,7 @@ build_mindspore()
     if [[ "X$INC_BUILD" = "Xoff" ]]; then
       cmake ${CMAKE_ARGS} ../..
     fi
-    make ${VERBOSE} -j$THREAD_NUM
-    if [[ "X$EXECUTE_SETUP" = "Xon" ]]; then
-      make install
-    fi
+    cmake --build . --target package ${VERBOSE} -j$THREAD_NUM
     echo "success to build mindspore project!"
 }
 
@@ -457,24 +445,7 @@ else
     build_mindspore
 fi
 
-if [[ "X$INC_BUILD" = "Xoff" ]]; then
-    if [[ "X$ENABLE_GE" = "Xon" ]]; then
-        bash "${PROJECT_PATH}/package.sh" ge
-    elif [[ "X$ENABLE_GPU" = "Xon" ]]; then
-        bash "${PROJECT_PATH}/package.sh" ms gpu
-    elif [[ "X$ENABLE_D" = "Xon" ]]; then
-        bash "${PROJECT_PATH}/package.sh" ms ascend
-    elif [[ "X$ENABLE_CPU" = "Xon" ]]; then
-        bash "${PROJECT_PATH}/package.sh" ms cpu
-    else
-        bash "${PROJECT_PATH}/package.sh" debug
-    fi
-fi
-
 cp -rf ${BUILD_PATH}/package/mindspore/lib ${BUILD_PATH}/../mindspore
 cp -rf ${BUILD_PATH}/package/mindspore/*.so ${BUILD_PATH}/../mindspore
 
-if [[ -d "${BUILD_PATH}/package/build" ]]; then
-    rm -rf "${BUILD_PATH}/package/build"
-fi
 echo "---------------- mindspore: build end   ----------------"
diff --git a/cmake/package.cmake b/cmake/package.cmake
new file mode 100644
index 0000000000..8a44d392cc
--- /dev/null
+++ b/cmake/package.cmake
@@ -0,0 +1,185 @@
+# include dependency
+include(CMakePackageConfigHelpers)
+include(GNUInstallDirs)
+
+# set package information
+set(CPACK_PACKAGE_NAME ${PROJECT_NAME})
+set(CPACK_GENERATOR "External")
+set(CPACK_EXTERNAL_PACKAGE_SCRIPT ${CMAKE_SOURCE_DIR}/cmake/package_script.cmake)
+set(CPACK_EXTERNAL_ENABLE_STAGING true)
+set(CPACK_TEMPORARY_PACKAGE_FILE_NAME ${CMAKE_SOURCE_DIR}/build/package/mindspore)
+set(CPACK_TEMPORARY_INSTALL_DIRECTORY ${CMAKE_SOURCE_DIR}/build/package/mindspore)
+if (ENABLE_GE)
+    set(CPACK_MS_BACKEND "ge")
+    set(CPACK_MS_PACKAGE_NAME "mindspore")
+elseif (ENABLE_GPU)
+    set(CPACK_MS_BACKEND "ms")
+    set(CPACK_MS_PACKAGE_NAME "mindspore-gpu")
+elseif (ENABLE_D)
+    set(CPACK_MS_BACKEND "ms")
+    set(CPACK_MS_PACKAGE_NAME "mindspore-ascend")
+elseif (ENABLE_CPU)
+    set(CPACK_MS_BACKEND "ms")
+    set(CPACK_MS_PACKAGE_NAME "mindspore")
+else ()
+    set(CPACK_MS_BACKEND "debug")
+    set(CPACK_MS_PACKAGE_NAME "mindspore")
+endif ()
+include(CPack)
+
+# set install path
+set(INSTALL_LIB_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Installation directory for libraries")
+set(INSTALL_PY_DIR ".")
+set(INSTALL_LIB_DIR "lib")
+set(INSTALL_BASE_DIR ".")
+
+# set package files
+install(
+    TARGETS _c_expression
+    DESTINATION ${INSTALL_BASE_DIR}
+    COMPONENT mindspore
+)
+
+install(
+    TARGETS mindspore_gvar
+    DESTINATION ${INSTALL_LIB_DIR}
+    COMPONENT mindspore
+)
+
+if (USE_GLOG)
+    file(GLOB_RECURSE GLOG_LIB_LIST ${glog_LIBPATH}/libglog*)
+    install(
+        FILES ${GLOG_LIB_LIST}
+        DESTINATION ${INSTALL_LIB_DIR}
+        COMPONENT mindspore
+    )
+endif ()
+
+if (ENABLE_MINDDATA)
+    install(
+        TARGETS _c_dataengine _c_mindrecord
+        DESTINATION ${INSTALL_BASE_DIR}
+        COMPONENT mindspore
+    )
+
+    file(GLOB_RECURSE OPENCV_LIB_LIST
+            ${opencv_LIBPATH}/libopencv_core*
+            ${opencv_LIBPATH}/libopencv_imgcodecs*
+            ${opencv_LIBPATH}/libopencv_imgproc*
+    )
+    install(
+        FILES ${OPENCV_LIB_LIST}
+        DESTINATION ${INSTALL_LIB_DIR}
+        COMPONENT mindspore
+    )
+endif ()
+
+if (ENABLE_CPU)
+    if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+        file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/libdnnl${CMAKE_SHARED_LIBRARY_SUFFIX}*)
+    elseif (CMAKE_SYSTEM_NAME MATCHES "Darwin")
+        file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/libdnnl*${CMAKE_SHARED_LIBRARY_SUFFIX}*)
+    elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")
+        file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/dnnl.lib)
+    endif ()
+    install(
+        FILES ${DNNL_LIB_LIST}
+        DESTINATION ${INSTALL_LIB_DIR}
+        COMPONENT mindspore
+    )
+endif ()
+
+if (ENABLE_GPU)
+  if (ENABLE_MPI)
+      install(
+          TARGETS _ms_mpi gpu_collective
+          DESTINATION ${INSTALL_LIB_DIR}
+          COMPONENT mindspore
+      )
+  endif ()
+
+  install(
+      TARGETS gpu_queue
+      DESTINATION ${INSTALL_LIB_DIR}
+      COMPONENT mindspore
+  )
+endif ()
+
+if (NOT ENABLE_GE)
+    if (ENABLE_D)
+        if (DEFINED ENV{ASCEND_CUSTOM_PATH})
+            set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
+        else ()
+            set(ASCEND_PATH /usr/local/Ascend)
+        endif ()
+        set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
+
+        install(
+            FILES
+                ${CMAKE_BINARY_DIR}/graphengine/src/common/graph/libgraph.so
+                ${CMAKE_BINARY_DIR}/graphengine/src/ge/common/libge_common.so
+                ${CMAKE_BINARY_DIR}/graphengine/src/ge/ge_runtime/libge_runtime.so
+                ${ASCEND_DRIVER_PATH}/libslog.so
+                ${ASCEND_DRIVER_PATH}/libc_sec.so
+            DESTINATION ${INSTALL_LIB_DIR}
+            COMPONENT mindspore
+        )
+    elseif (ENABLE_TESTCASES)
+        install(
+            FILES
+                ${CMAKE_BINARY_DIR}/graphengine/src/common/graph/libgraph.so
+                ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libslog.so
+                ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libc_sec.so
+            DESTINATION ${INSTALL_LIB_DIR}
+            COMPONENT mindspore
+        )
+    endif ()
+endif ()
+
+# set python files
+file(GLOB MS_PY_LIST ${CMAKE_SOURCE_DIR}/mindspore/*.py)
+install(
+    FILES ${MS_PY_LIST}
+    DESTINATION ${INSTALL_PY_DIR}
+    COMPONENT mindspore
+)
+
+install(
+    DIRECTORY
+        ${CMAKE_SOURCE_DIR}/mindspore/nn
+        ${CMAKE_SOURCE_DIR}/mindspore/_extends
+        ${CMAKE_SOURCE_DIR}/mindspore/parallel
+        ${CMAKE_SOURCE_DIR}/mindspore/mindrecord
+        ${CMAKE_SOURCE_DIR}/mindspore/train
+        ${CMAKE_SOURCE_DIR}/mindspore/model_zoo
+        ${CMAKE_SOURCE_DIR}/mindspore/common
+        ${CMAKE_SOURCE_DIR}/mindspore/ops
+        ${CMAKE_SOURCE_DIR}/mindspore/communication
+    DESTINATION ${INSTALL_PY_DIR}
+    COMPONENT mindspore
+)
+
+if (ENABLE_GPU)
+    install(
+        DIRECTORY ${CMAKE_SOURCE_DIR}/mindspore/_akg
+        DESTINATION ${INSTALL_PY_DIR}
+        COMPONENT mindspore
+    )
+    if (EXISTS ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm)
+        install(
+            DIRECTORY
+                ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/topi/python/topi
+                ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/python/tvm
+            DESTINATION ${INSTALL_PY_DIR}/_akg
+            COMPONENT mindspore
+        )
+    endif ()
+endif ()
+
+if (EXISTS ${CMAKE_SOURCE_DIR}/mindspore/dataset)
+    install(
+        DIRECTORY ${CMAKE_SOURCE_DIR}/mindspore/dataset
+        DESTINATION ${INSTALL_PY_DIR}
+        COMPONENT mindspore
+    )
+endif ()
diff --git a/cmake/package_script.cmake b/cmake/package_script.cmake
new file mode 100644
index 0000000000..565154a38d
--- /dev/null
+++ b/cmake/package_script.cmake
@@ -0,0 +1,92 @@
+# find exec
+find_package(Python3 3.7 COMPONENTS Interpreter Development)
+if (NOT Python3_FOUND)
+    message("No python3 found.")
+    return ()
+endif ()
+
+set(PYTHON ${Python3_EXECUTABLE})
+set(PYTHON_VERSION ${Python3_VERSION_MAJOR}.${Python3_VERSION_MINOR})
+
+find_package(Git)
+if (NOT GIT_FOUND)
+    message("No git found.")
+    return ()
+endif ()
+set(GIT ${GIT_EXECUTABLE})
+
+# set path
+set(MS_ROOT_DIR ${CPACK_PACKAGE_DIRECTORY}/../../)
+set(MS_PACK_ROOT_DIR ${MS_ROOT_DIR}/build/package)
+
+# set package file name
+if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+    if (PYTHON_VERSION MATCHES "3.7")
+        set(PY_TAGS "cp37-cp37m")
+    else ()
+        message("Could not find 'Python 3.7'")
+        return()
+    endif ()
+    string(TOLOWER linux_${CMAKE_HOST_SYSTEM_PROCESSOR} PLATFORM_TAG)
+elseif (CMAKE_SYSTEM_NAME MATCHES "Darwin")
+    if (PYTHON_VERSION MATCHES "3.7")
+        set(PY_TAGS "py37-none")
+    else ()
+        message("Could not find 'Python 3.7'")
+        return()
+    endif ()
+    set(PLATFORM_TAG "any")
+elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    if (PYTHON_VERSION MATCHES "3.7")
+        set(PY_TAGS "cp37-cp37m")
+    else ()
+        message("Could not find 'Python 3.7'")
+        return()
+    endif ()
+    set(PLATFORM_TAG "win_amd64")
+else ()
+    message(FATAL_ERROR "other platform: ${CMAKE_SYSTEM_NAME}")
+endif ()
+
+# get git commit id
+set(GIT_COMMIT_ID "")
+execute_process(
+    COMMAND ${GIT} log --format='[sha1]:%h,[branch]:%d' -1
+    OUTPUT_VARIABLE GIT_COMMIT_ID
+    WORKING_DIRECTORY ${MS_ROOT_DIR}
+    ERROR_QUIET)
+string(REPLACE " " "" GIT_COMMIT_ID ${GIT_COMMIT_ID})
+
+set(ENV{BACKEND_POLICY} ${CPACK_MS_BACKEND})
+set(ENV{MS_PACKAGE_NAME} ${CPACK_MS_PACKAGE_NAME})
+set(ENV{COMMIT_ID} ${GIT_COMMIT_ID})
+
+execute_process(
+    COMMAND ${PYTHON} ${MS_ROOT_DIR}/setup.py "bdist_wheel"
+    WORKING_DIRECTORY ${MS_PACK_ROOT_DIR}
+)
+
+# finally
+set(PACKAGE_NAME ${CPACK_MS_PACKAGE_NAME})
+if (NOT CMAKE_SYSTEM_NAME MATCHES "Windows")
+    string(REPLACE "-" "_" PACKAGE_NAME ${PACKAGE_NAME})
+    execute_process(
+        COMMAND chmod -R 700 ${MS_PACK_ROOT_DIR}/mindspore/
+        COMMAND chmod -R 700 ${MS_PACK_ROOT_DIR}/${PACKAGE_NAME}.egg-info/
+    )
+endif ()
+
+set(PACKAGE_NAME "mindspore")
+
+file(GLOB WHL_FILE ${MS_PACK_ROOT_DIR}/dist/*.whl)
+get_filename_component(ORIGIN_FILE_NAME ${WHL_FILE} NAME)
+string(REPLACE "-" ";" ORIGIN_FILE_NAME ${ORIGIN_FILE_NAME})
+list(GET ORIGIN_FILE_NAME 1 VERSION)
+set(NEW_FILE_NAME ${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG}.whl)
+file(RENAME ${WHL_FILE} ${MS_PACK_ROOT_DIR}/${NEW_FILE_NAME})
+file(REMOVE_RECURSE ${MS_ROOT_DIR}/output)
+file(MAKE_DIRECTORY ${MS_ROOT_DIR}/output)
+file(COPY ${MS_PACK_ROOT_DIR}/${NEW_FILE_NAME} DESTINATION ${MS_ROOT_DIR}/output/)
+
+file(SHA256 ${MS_ROOT_DIR}/output/${NEW_FILE_NAME} SHA256_VAR)
+file(WRITE ${MS_ROOT_DIR}/output/${NEW_FILE_NAME}.sha256 ${SHA256_VAR} " " ${NEW_FILE_NAME})
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 3a04d9b3fb..f8af2a4f18 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -492,93 +492,7 @@ if(ENABLE_MINDDATA)
     add_subdirectory(mindrecord)
     add_subdirectory(dataset)
 endif()
-set(MS_PACK_PATH ${CMAKE_SOURCE_DIR}/build/package/mindspore/)
-set(MS_LIB_PATH ${CMAKE_SOURCE_DIR}/build/package/mindspore/lib/)
-
-add_custom_target(add_ms_lib ALL
-    COMMAND mkdir -pv ${MS_LIB_PATH}
-    COMMAND cp ${MS_CCSRC_BUILD_PATH}/_c_expression* ${MS_PACK_PATH}
-    COMMAND cp ${MS_CCSRC_BUILD_PATH}/libmindspore_gvar${CMAKE_SHARED_LIBRARY_SUFFIX} ${MS_LIB_PATH}
-)
-add_dependencies(add_ms_lib _c_expression)
-
-if (NOT ENABLE_GE)
-    if (ENABLE_D)
-        if(DEFINED ENV{ASCEND_CUSTOM_PATH})
-            set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
-        else()
-            set(ASCEND_PATH /usr/local/Ascend)
-        endif()
-        set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
-        add_custom_target(add_ge_lib ALL
-                COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH}
-                COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/common/libge_common.so ${MS_LIB_PATH}
-                COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/ge_runtime/libge_runtime.so ${MS_LIB_PATH}
-                COMMAND cp ${ASCEND_DRIVER_PATH}/libslog.so ${MS_LIB_PATH}
-                COMMAND cp ${ASCEND_DRIVER_PATH}/libc_sec.so ${MS_LIB_PATH}
-        )
-        add_dependencies(add_ge_lib add_ms_lib)
-        add_dependencies(add_ge_lib graph)
-        add_dependencies(add_ge_lib ge_runtime)
-    elseif(ENABLE_TESTCASES)
-        add_custom_target(add_ge_lib ALL
-                COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so  ${MS_LIB_PATH}
-                COMMAND cp ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libslog.so ${MS_LIB_PATH}
-                COMMAND cp ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libc_sec.so ${MS_LIB_PATH}
-        )
-        add_dependencies(add_ge_lib add_ms_lib)
-        add_dependencies(add_ge_lib graph)
-    endif()
-endif()
-
-if (ENABLE_GPU)
-    if (ENABLE_MPI)
-        add_custom_target(add_mpi_lib ALL
-            COMMAND cp ${MS_CCSRC_BUILD_PATH}/_ms_mpi* ${MS_PACK_PATH}
-        )
-        add_dependencies(add_mpi_lib _ms_mpi)
-        add_custom_target(add_gpu_collective_lib ALL
-            COMMAND mkdir -pv ${MS_LIB_PATH}
-            COMMAND cp ${MS_CCSRC_BUILD_PATH}/libgpu_collective* ${MS_LIB_PATH}
-            )
-        add_dependencies(add_gpu_collective_lib gpu_collective)
-    endif()   
-    add_custom_target(add_gpu_queue_lib ALL
-        COMMAND cp ${MS_CCSRC_BUILD_PATH}/libgpu_queue* ${MS_LIB_PATH}
-            )
-    add_dependencies(add_gpu_queue_lib add_ms_lib)
-endif()
-
-if (ENABLE_CPU)
-    if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
-        add_custom_target(add_cpu_lib ALL COMMAND cp ${onednn_LIBPATH}/libdnnl.1.1.dylib ${MS_LIB_PATH}/libdnnl.1.1.dylib)
-    else ()
-        add_custom_target(add_cpu_lib ALL COMMAND cp ${onednn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1)
-    endif ()
-    add_dependencies(add_cpu_lib add_ms_lib)
-endif()
-
-if (ENABLE_MINDDATA)
-    add_custom_target(add_minddata_lib ALL
-        COMMAND cp ${MS_CCSRC_BUILD_PATH}/dataset/*.so ${MS_PACK_PATH}
-        COMMAND cp ${MS_CCSRC_BUILD_PATH}/mindrecord/*.so ${MS_PACK_PATH}
-        COMMAND cp ${opencv_LIBPATH}/libopencv_core.so.4.2.0 ${MS_LIB_PATH}/libopencv_core.so.4.2
-        COMMAND cp ${opencv_LIBPATH}/libopencv_imgcodecs.so.4.2.0 ${MS_LIB_PATH}/libopencv_imgcodecs.so.4.2
-        COMMAND cp ${opencv_LIBPATH}/libopencv_imgproc.so.4.2.0 ${MS_LIB_PATH}/libopencv_imgproc.so.4.2
-    )
-    add_dependencies(add_minddata_lib add_ms_lib)
-    add_dependencies(add_minddata_lib _c_mindrecord)
-    add_dependencies(add_minddata_lib _c_dataengine)
-
-    add_dependencies(_c_mindrecord mindspore)
-    add_dependencies(_c_dataengine mindspore)
-endif()
 
 if (USE_GLOG)
     target_link_libraries(_c_expression PRIVATE mindspore::glog)
-    if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
-        add_custom_target(add_glog_lib ALL COMMAND cp ${glog_LIBPATH}/libglog*.dylib ${MS_LIB_PATH})
-    else ()
-        add_custom_target(add_glog_lib ALL COMMAND cp ${glog_LIBPATH}/libglog*.so.0 ${MS_LIB_PATH})
-    endif ()
-endif()
+endif()
\ No newline at end of file
diff --git a/package.sh b/package.sh
deleted file mode 100755
index 056ca8c25d..0000000000
--- a/package.sh
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/bin/bash
-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-set -e
-
-BASEPATH=$(cd "$(dirname $0)"; pwd)
-echo "${BASEPATH}"
-cd "${BASEPATH}"
-BUILD_PATH="${BASEPATH}/build"
-PACKAGE_PATH="${BUILD_PATH}/package"
-OUTPUT_PATH="${BASEPATH}/output"
-
-mk_new_dir() {
-    local create_dir="$1"  # the target to make
-
-    if [[ -d "${create_dir}" ]];then
-        rm -rf "${create_dir}"
-    fi
-
-    mkdir -pv "${create_dir}"
-}
-
-to_lower () {
-    echo "$1" | tr '[:upper:]' '[:lower:]'
-}
-
-COMMIT_ID=$(git log --format='[sha1]:%h,[branch]:%d' -1 | sed 's/ //g')
-export COMMIT_ID
-
-PYTHON=$(which python3)
-PYTHON_VERSION=$("${PYTHON}" -V 2>&1 | awk '{print $2}' | cut -d. -f-2)
-if [[ $(uname) == "Linux" ]]; then
-    if [[ "${PYTHON_VERSION}" == "3.7" ]]; then
-        PY_TAGS="cp37-cp37m"
-    else
-        echo "Could not find 'Python 3.7'"
-        exit 1
-    fi
-    PLATFORM_TAG=$(to_lower "$(uname)_$(uname -m)")
-elif [[ $(uname) == "Darwin" ]]; then
-    if [[ "${PYTHON_VERSION}" == "3.7" ]]; then
-        PY_TAGS="py3-none"
-    else
-        echo "Could not find 'Python 3.7'"
-        exit 1
-    fi
-    PLATFORM_TAG="any"
-fi
-echo "=========${BASEPATH}==================="
-mk_new_dir "${OUTPUT_PATH}"
-
-#copy necessary file to pack_path
-cp ${BASEPATH}/mindspore/*.py "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/nn" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/_extends" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/parallel" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/mindrecord" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/train" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/model_zoo" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/common" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/ops" "${PACKAGE_PATH}/mindspore"
-cp -rf "${BUILD_PATH}/../mindspore/communication" "${PACKAGE_PATH}/mindspore"
-
-if [[ "X$2" = "Xgpu" ]]; then
-    echo "package _akg when gpu enable."
-    cp -rf "${BASEPATH}/mindspore/_akg" "${PACKAGE_PATH}"
-    if [[ -d "${BUILD_PATH}/mindspore/incubator-tvm" ]]; then
-        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/topi/python/topi" "${PACKAGE_PATH}/_akg"
-        cp -rf "${BUILD_PATH}/mindspore/incubator-tvm/python/tvm" "${PACKAGE_PATH}/_akg"
-    fi
-fi
-
-# move dataset
-if [[ -d "${BASEPATH}/mindspore/dataset" ]]; then
-    cp -rf "${BASEPATH}/mindspore/dataset" "${PACKAGE_PATH}/mindspore"
-fi
-
-cd "${PACKAGE_PATH}"
-if [ -n "$1" ];then
-    export BACKEND_POLICY=$1
-else
-    export BACKEND_POLICY="ms"
-fi
-
-# package name
-if [[ "X$1" = "Xge" ]]; then
-    export MS_PACKAGE_NAME="mindspore"
-elif [[ "X$1" = "Xms" && "X$2" = "Xgpu" ]]; then
-    export MS_PACKAGE_NAME="mindspore-gpu"
-elif [[ "X$1" = "Xms" && "X$2" = "Xascend" ]]; then
-    export MS_PACKAGE_NAME="mindspore-ascend"
-elif [[ "X$1" = "Xms" && "X$2" = "Xcpu" ]]; then
-    export MS_PACKAGE_NAME="mindspore"
-else
-    export MS_PACKAGE_NAME="mindspore"
-fi
-
-${PYTHON} "${BASEPATH}/setup.py" bdist_wheel
-
-chmod -R 700 ${PACKAGE_PATH}/mindspore/
-chmod -R 700 ${PACKAGE_PATH}/${MS_PACKAGE_NAME//-/_}.egg-info/
-
-# rename package
-PACKAGE_FULL_NAME=$(find "${PACKAGE_PATH}" -iname "*.whl")
-PACKAGE_BASE_NAME=$(echo ${PACKAGE_FULL_NAME} | awk -F / '{print $NF}' | awk -F - '{print $1"-"$2}')
-PACKAGE_BASE_NAME=${PACKAGE_BASE_NAME//_*-/-}
-
-PACKAGE_NEW_NAME="${PACKAGE_BASE_NAME}-${PY_TAGS}-${PLATFORM_TAG}.whl"
-cp -rf "${PACKAGE_PATH}/dist"/*.whl "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}"
-cp -f "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}" "${OUTPUT_PATH}"
-find ${OUTPUT_PATH} -name "*.whl" -print0 | xargs -0 -I {} sh -c "sha256sum {} | awk '{printf \$1}' > {}.sha256"
-
-cd "${BASEPATH}"
-
-echo "------Successfully created mindspore package------"

From 99f12f9105da9fe8d787d90b8d5f5f0e88627c67 Mon Sep 17 00:00:00 2001
From: limingqi107 <limingqi@huawei.com>
Date: Tue, 14 Apr 2020 16:34:28 +0800
Subject: [PATCH 232/367] gpu uses dynamic memory pool by default

---
 .../ccsrc/device/gpu/gpu_kernel_runtime.cc    | 25 +++++-------
 mindspore/ccsrc/device/memory_manager.cc      |  7 ++++
 mindspore/ccsrc/device/memory_manager.h       |  1 +
 .../ccsrc/pre_activate/mem_reuse/mem_reuse.cc | 39 +++++++------------
 mindspore/ccsrc/utils/context/ms_context.cc   |  2 +-
 5 files changed, 34 insertions(+), 40 deletions(-)

diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
index 671a11f776..584f66eee7 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
@@ -127,9 +127,10 @@ bool GPUKernelRuntime::Run(session::KernelGraph *graph) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   bool is_enable_dynamic_mem = context_ptr->enable_dynamic_mem_pool();
+  bool is_enable_pynative_infer = context_ptr->enable_pynative_infer();
   struct timeval start_time, end_time;
   (void)gettimeofday(&start_time, nullptr);
-  if (is_enable_dynamic_mem) {
+  if (is_enable_dynamic_mem && !is_enable_pynative_infer) {
     ret = LaunchKernelDynamic(graph);
   } else {
     ret = LaunchKernel(graph);
@@ -152,7 +153,7 @@ void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) {
   }
   mem_reuse_util_ptr->SetKernelDefMap();
   mem_reuse_util_ptr->SetReuseRefCount();
-  // Can't free the device address of graph output, so set the reference count of graph output specially,
+  // Can't free the device address of graph output, so set the reference count of graph output specially.
   mem_reuse_util_ptr->SetGraphOutputRefCount();
   mem_reuse_util_ptr_ = mem_reuse_util_ptr;
 }
@@ -351,6 +352,10 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
     if (kernel_ref_count_ptr == nullptr) {
       continue;
     }
+    // Can't free the output of graph.
+    if (kernel_ref_count_ptr->ref_count_dynamic_use_ == memreuse::kMaxRefCount) {
+      continue;
+    }
     kernel_ref_count_ptr->ref_count_dynamic_use_--;
     if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
       // Reset the reference count.
@@ -360,14 +365,10 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
       FreeCommunicationOpDynamicRes(kernel, i, &is_communication_op);
       if (!is_communication_op) {
         auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
-        MS_EXCEPTION_IF_NULL(device_address);
-        MS_EXCEPTION_IF_NULL(device_address->ptr_);
-        mem_manager_->FreeMemFromMemPool(device_address->ptr_);
-        device_address->ptr_ = nullptr;
+        mem_manager_->FreeMemFromMemPool(device_address);
       }
     }
   }
-
   // Free the workspace of kernel.
   for (size_t i = 0; i < kernel_workspaces.size(); ++i) {
     auto workspace = kernel_workspaces[i];
@@ -388,10 +389,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
     communication_op_input_ref_count_--;
     if (communication_op_input_ref_count_ == 0) {
       auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0);
-      MS_EXCEPTION_IF_NULL(device_address);
-      MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      mem_manager_->FreeMemFromMemPool(device_address->ptr_);
-      device_address->ptr_ = nullptr;
+      mem_manager_->FreeMemFromMemPool(device_address);
     }
     *is_communication_op = true;
     return;
@@ -410,10 +408,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr
     communication_op_output_ref_count_--;
     if (communication_op_output_ref_count_ == 0) {
       auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0);
-      MS_EXCEPTION_IF_NULL(device_address);
-      MS_EXCEPTION_IF_NULL(device_address->ptr_);
-      mem_manager_->FreeMemFromMemPool(device_address->ptr_);
-      device_address->ptr_ = nullptr;
+      mem_manager_->FreeMemFromMemPool(device_address);
     }
     *is_communication_op = true;
   }
diff --git a/mindspore/ccsrc/device/memory_manager.cc b/mindspore/ccsrc/device/memory_manager.cc
index 6977628eb1..2fad5fc10e 100644
--- a/mindspore/ccsrc/device/memory_manager.cc
+++ b/mindspore/ccsrc/device/memory_manager.cc
@@ -155,6 +155,13 @@ void *MemoryManager::MallocMemFromMemPool(size_t size) {
   return nullptr;
 }
 
+void MemoryManager::FreeMemFromMemPool(const DeviceAddressPtr address) {
+  MS_EXCEPTION_IF_NULL(address);
+  MS_EXCEPTION_IF_NULL(address->ptr_);
+  FreeMemFromMemPool(address->ptr_);
+  address->ptr_ = nullptr;
+}
+
 void MemoryManager::FreeMemFromMemPool(void *device_ptr) {
   if (device_ptr == nullptr) {
     MS_LOG(ERROR) << "FreeMemFromMemPool device_ptr is null.";
diff --git a/mindspore/ccsrc/device/memory_manager.h b/mindspore/ccsrc/device/memory_manager.h
index 82c22f4548..c90ffc380e 100644
--- a/mindspore/ccsrc/device/memory_manager.h
+++ b/mindspore/ccsrc/device/memory_manager.h
@@ -47,6 +47,7 @@ class MemoryManager {
 
   virtual void MallocMemFromMemPool(const DeviceAddressPtr address, size_t size);
   virtual void *MallocMemFromMemPool(size_t size);
+  virtual void FreeMemFromMemPool(const DeviceAddressPtr address);
   virtual void FreeMemFromMemPool(void *device_ptr);
 
   size_t GetCommonAlignSize(size_t input_size) const;
diff --git a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
index 2113fec653..d25b60003f 100644
--- a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
+++ b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc
@@ -273,30 +273,21 @@ void MemReuseUtil::SetReuseRefCount() {
 }
 
 void MemReuseUtil::SetGraphOutputRefCount() {
-  for (const auto &output : graph_->outputs()) {
-    MS_EXCEPTION_IF_NULL(output);
-    for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(output); ++i) {
-      if (!(output->isa<CNode>())) {
-        continue;
-      }
-      auto cnode = output->cast<CNodePtr>();
-      MS_EXCEPTION_IF_NULL(cnode);
-      auto input_node = cnode->input(i + 1);
-      MS_EXCEPTION_IF_NULL(input_node);
-      auto kernel_input = AnfAlgo::VisitKernel(input_node, 0);
-      MS_EXCEPTION_IF_NULL(kernel_input.first);
-      if (!(kernel_input.first->isa<CNode>())) {
-        continue;
-      }
-      auto ak_node = kernel_input.first->cast<CNodePtr>();
-      auto key = ak_node.get();
-      auto iter = kernel_output_refs_.find(key);
-      if ((iter != kernel_output_refs_.end()) && (kernel_input.second < iter->second.size())) {
-        auto kernel_ref_count_ptr = kernel_output_refs_[key][kernel_input.second];
-        MS_EXCEPTION_IF_NULL(kernel_ref_count_ptr);
-        kernel_ref_count_ptr->ref_count_ = kMaxRefCount;
-        kernel_ref_count_ptr->ref_count_dynamic_use_ = kMaxRefCount;
-      }
+  auto nodes = AnfAlgo::GetAllOutput(graph_->output(), {prim::kPrimTupleGetItem});
+  for (const auto &node : nodes) {
+    auto kernel_input = AnfAlgo::VisitKernelWithReturnType(node, 0);
+    MS_EXCEPTION_IF_NULL(kernel_input.first);
+    if (!kernel_input.first->isa<CNode>() || !AnfAlgo::IsRealKernel(kernel_input.first)) {
+      continue;
+    }
+    auto ak_node = kernel_input.first->cast<CNodePtr>();
+    auto key = ak_node.get();
+    auto iter = kernel_output_refs_.find(key);
+    if ((iter != kernel_output_refs_.end()) && (kernel_input.second < iter->second.size())) {
+      auto kernel_ref_count_ptr = kernel_output_refs_[key][kernel_input.second];
+      MS_EXCEPTION_IF_NULL(kernel_ref_count_ptr);
+      kernel_ref_count_ptr->ref_count_ = kMaxRefCount;
+      kernel_ref_count_ptr->ref_count_dynamic_use_ = kMaxRefCount;
     }
   }
 #ifdef MEM_REUSE_DEBUG
diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index 6c15e16714..b1ab0205f2 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -75,7 +75,7 @@ MsContext::MsContext(const std::string& policy, const std::string& target) {
   precompile_only_ = false;
   auto_mixed_precision_flag_ = true;
   enable_pynative_infer_ = false;
-  enable_dynamic_mem_pool_ = false;
+  enable_dynamic_mem_pool_ = true;
   graph_memory_max_size_ = "0";
   variable_memory_max_size_ = "0";
   MS_LOG(INFO) << "Create context with backend policy:" << policy << ", device target:" << target << ".";

From 1a1cbc6814a068f52298134a86e786a9a241256d Mon Sep 17 00:00:00 2001
From: xiefangqi <xiefangqi2@huawei.com>
Date: Tue, 14 Apr 2020 19:21:15 +0800
Subject: [PATCH 233/367] implemention of new api: apply

---
 mindspore/dataset/engine/datasets.py  |  45 +++++
 tests/ut/python/dataset/test_apply.py | 236 ++++++++++++++++++++++++++
 2 files changed, 281 insertions(+)
 create mode 100644 tests/ut/python/dataset/test_apply.py

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index f92f6f28a8..76b23eb292 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -499,6 +499,51 @@ class Dataset:
 
         return ProjectDataset(self, columns)
 
+    def apply(self, apply_func):
+        """
+        Apply a function in this dataset.
+
+        The specified apply_func is a function that must take one 'Dataset' as an argument
+        and return a preprogressing 'Dataset'.
+
+        Args:
+            apply_func (function): A function that must take one 'Dataset' as an argument and
+                                   return a preprogressing 'Dataset'.
+
+        Returns:
+            Dataset, applied by the function.
+
+        Examples:
+            >>> import numpy as np
+            >>> import mindspore.dataset as ds
+            >>> # Generate 1d int numpy array from 0 - 6
+            >>> def generator_1d():
+            >>>     for i in range(6):
+            >>>         yield (np.array([i]),)
+            >>> # 1) get all data from dataset
+            >>> data = ds.GeneratorDataset(generator_1d, ["data"])
+            >>> # 2) declare a apply_func function
+            >>> def apply_func(ds):
+            >>>     ds = ds.batch(2)
+            >>>     return ds
+            >>> # 3) use apply to call apply_func
+            >>> data = data.apply(apply_func)
+            >>> for item in data.create_dict_iterator():
+            >>>     print(item["data"])
+
+        Raises:
+            TypeError: If apply_func is not a function.
+            TypeError: If apply_func doesn't return a Dataset.
+        """
+
+        if not hasattr(apply_func, '__call__'):
+            raise TypeError("apply_func must be a function.")
+
+        dataset = apply_func(self)
+        if not isinstance(dataset, Dataset):
+            raise TypeError("apply_func must return a dataset.")
+        return dataset
+
     def device_que(self, prefetch_size=None):
         """
         Returns a transferredDataset that transfer data through tdt.
diff --git a/tests/ut/python/dataset/test_apply.py b/tests/ut/python/dataset/test_apply.py
new file mode 100644
index 0000000000..f2e7a79011
--- /dev/null
+++ b/tests/ut/python/dataset/test_apply.py
@@ -0,0 +1,236 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import mindspore.dataset as ds
+from mindspore import log as logger
+import mindspore.dataset.transforms.vision.c_transforms as vision
+import numpy as np
+
+DATA_DIR = "../data/dataset/testPK/data"
+
+# Generate 1d int numpy array from 0 - 64
+def generator_1d():
+    for i in range(64):
+        yield (np.array([i]),)
+
+def test_apply_generator_case():
+    # apply dataset operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+    data2 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        ds = ds.repeat(2)
+        return ds.batch(4)
+
+    data1 = data1.apply(dataset_fn)
+    data2 = data2.repeat(2)
+    data2 = data2.batch(4)
+
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        assert np.array_equal(item1["data"], item2["data"])
+
+def test_apply_imagefolder_case():
+    # apply dataset map operations
+    data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_shards=4, shard_id=3)
+    data2 = ds.ImageFolderDatasetV2(DATA_DIR, num_shards=4, shard_id=3)
+
+    decode_op = vision.Decode()
+    normalize_op = vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
+
+    def dataset_fn(ds):
+        ds = ds.map(operations = decode_op)
+        ds = ds.map(operations = normalize_op)
+        ds = ds.repeat(2)
+        return ds
+    
+    data1 = data1.apply(dataset_fn)
+    data2 = data2.map(operations = decode_op)
+    data2 = data2.map(operations = normalize_op)
+    data2 = data2.repeat(2)
+    
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        assert np.array_equal(item1["image"], item2["image"])
+
+def test_apply_flow_case_0(id=0):
+    # apply control flow operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        if id == 0:
+            ds = ds.batch(4)
+        elif id == 1:
+            ds = ds.repeat(2)
+        elif id == 2:
+            ds = ds.batch(4)
+            ds = ds.repeat(2)
+        else:
+            ds = ds.shuffle(buffer_size=4)
+        return ds
+
+    data1 = data1.apply(dataset_fn)
+    num_iter = 0
+    for _ in data1.create_dict_iterator():
+        num_iter = num_iter + 1
+
+    if id == 0:
+        assert num_iter == 16
+    elif id == 1:
+        assert num_iter == 128
+    elif id == 2:
+        assert num_iter == 32
+    else:
+        assert num_iter == 64
+
+def test_apply_flow_case_1(id=1):
+    # apply control flow operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        if id == 0:
+            ds = ds.batch(4)
+        elif id == 1:
+            ds = ds.repeat(2)
+        elif id == 2:
+            ds = ds.batch(4)
+            ds = ds.repeat(2)
+        else:
+            ds = ds.shuffle(buffer_size=4)
+        return ds
+
+    data1 = data1.apply(dataset_fn)
+    num_iter = 0
+    for _ in data1.create_dict_iterator():
+        num_iter = num_iter + 1
+
+    if id == 0:
+        assert num_iter == 16
+    elif id == 1:
+        assert num_iter == 128
+    elif id == 2:
+        assert num_iter == 32
+    else:
+        assert num_iter == 64
+
+def test_apply_flow_case_2(id=2):
+    # apply control flow operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        if id == 0:
+            ds = ds.batch(4)
+        elif id == 1:
+            ds = ds.repeat(2)
+        elif id == 2:
+            ds = ds.batch(4)
+            ds = ds.repeat(2)
+        else:
+            ds = ds.shuffle(buffer_size=4)
+        return ds
+
+    data1 = data1.apply(dataset_fn)
+    num_iter = 0
+    for _ in data1.create_dict_iterator():
+        num_iter = num_iter + 1
+
+    if id == 0:
+        assert num_iter == 16
+    elif id == 1:
+        assert num_iter == 128
+    elif id == 2:
+        assert num_iter == 32
+    else:
+        assert num_iter == 64
+
+def test_apply_flow_case_3(id=3):
+    # apply control flow operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        if id == 0:
+            ds = ds.batch(4)
+        elif id == 1:
+            ds = ds.repeat(2)
+        elif id == 2:
+            ds = ds.batch(4)
+            ds = ds.repeat(2)
+        else:
+            ds = ds.shuffle(buffer_size=4)
+        return ds
+
+    data1 = data1.apply(dataset_fn)
+    num_iter = 0
+    for _ in data1.create_dict_iterator():
+        num_iter = num_iter + 1
+
+    if id == 0:
+        assert num_iter == 16
+    elif id == 1:
+        assert num_iter == 128
+    elif id == 2:
+        assert num_iter == 32
+    else:
+        assert num_iter == 64
+
+def test_apply_exception_case():
+    # apply exception operations
+    data1 = ds.GeneratorDataset(generator_1d, ["data"])
+
+    def dataset_fn(ds):
+        ds = ds.repeat(2)
+        return ds.batch(4)
+
+    def exception_fn(ds):
+        return np.array([[0], [1], [3], [4], [5]])
+
+    try:
+        data1 = data1.apply("123")
+        for _ in data1.create_dict_iterator():
+            pass
+        assert False
+    except TypeError:
+        pass
+
+    try:
+        data1 = data1.apply(exception_fn)
+        for _ in data1.create_dict_iterator():
+            pass
+        assert False
+    except TypeError:
+        pass
+
+    try:
+        data2 = data1.apply(dataset_fn)
+        data3 = data1.apply(dataset_fn)
+        for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+            pass
+        assert False
+    except ValueError:
+        pass
+ 
+if __name__ == '__main__':
+    logger.info("Running test_apply.py test_apply_generator_case() function")
+    test_apply_generator_case()
+
+    logger.info("Running test_apply.py test_apply_imagefolder_case() function")
+    test_apply_imagefolder_case()
+
+    logger.info("Running test_apply.py test_apply_flow_case(id) function")
+    test_apply_flow_case_0()
+    test_apply_flow_case_1()
+    test_apply_flow_case_2()
+    test_apply_flow_case_3()
+
+    logger.info("Running test_apply.py test_apply_exception_case() function")
+    test_apply_exception_case()
+

From 19a5e14b31ad4f0dc38d425c25f8abff8cb758f0 Mon Sep 17 00:00:00 2001
From: fary86 <fary.fanrui@huawei.com>
Date: Tue, 14 Apr 2020 21:38:00 +0800
Subject: [PATCH 234/367] Fix BatchNorm1d and BatchNorm2d doc bug, and slog
 print source path twice in log

---
 mindspore/ccsrc/utils/log_adapter.cc | 7 +++++++
 mindspore/nn/layer/normalization.py  | 2 ++
 2 files changed, 9 insertions(+)

diff --git a/mindspore/ccsrc/utils/log_adapter.cc b/mindspore/ccsrc/utils/log_adapter.cc
index 19482ec193..eed68cf859 100644
--- a/mindspore/ccsrc/utils/log_adapter.cc
+++ b/mindspore/ccsrc/utils/log_adapter.cc
@@ -96,6 +96,13 @@ static int GetGlogLevel(MsLogLevel level) {
   }
 }
 #else
+
+#undef Dlog
+#define Dlog(module_id, level, format, ...)                   \
+  do {                                                        \
+    DlogInner((module_id), (level), (format), ##__VA_ARGS__); \
+  } while (0)
+
 // convert MsLogLevel to corresponding slog level
 static int GetSlogLevel(MsLogLevel level) {
   switch (level) {
diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index d5082371c1..2df064353f 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -136,6 +136,7 @@ class BatchNorm1d(_BatchNorm):
         eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
         momentum (float): A floating hyperparameter of the momentum for the
             running_mean and running_var computation. Default: 0.9.
+        affine (bool): A bool value when set to True, gamma and beta can be learnable. Default: True.
         gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.
@@ -187,6 +188,7 @@ class BatchNorm2d(_BatchNorm):
         eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
         momentum (float): A floating hyperparameter of the momentum for the
             running_mean and running_var computation. Default: 0.9.
+        affine (bool): A bool value when set to True, gamma and beta can be learnable. Default: True.
         gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.

From 6a2cf4b6e698041dfffa1bbd61954c4f2ee677ef Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Thu, 9 Apr 2020 16:58:18 +0800
Subject: [PATCH 235/367] ssim impl code

---
 mindspore/nn/layer/__init__.py             |   6 +-
 mindspore/nn/layer/basic.py                |  45 -----
 mindspore/nn/layer/image.py                | 197 +++++++++++++++++++++
 tests/ut/python/nn/test_image_gradients.py |   2 +-
 4 files changed, 202 insertions(+), 48 deletions(-)
 create mode 100644 mindspore/nn/layer/image.py

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index f51eff2b31..9c2c30c914 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -22,9 +22,10 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
-from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients, Pad
+from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, Pad
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
+from .image import ImageGradients, SSIM
 
 __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'PReLU', 'get_activation', 'LeakyReLU', 'HSigmoid', 'HSwish', 'ELU',
@@ -32,7 +33,8 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
-           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'ImageGradients',
+           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot',
            'Embedding',
            'AvgPool2d', 'MaxPool2d', 'Pad',
+           'ImageGradients', 'SSIM',
            ]
diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 5b36755d16..64c4cfd93b 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -372,51 +372,6 @@ class OneHot(Cell):
         return self.onehot(indices, self.depth, self.on_value, self.off_value)
 
 
-class ImageGradients(Cell):
-    r"""
-    Returns two tensors, the first is along the height dimension and the second is along the width dimension.
-
-    Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`,
-    respectively.
-
-    .. math::
-        dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i<h-1 \cr
-        0, &if\ i==h-1\end{cases}
-
-        dx[i] = \begin{cases} image[:, i+1]-image[:, i], &if\ 0<=i<w-1 \cr
-        0, &if\ i==w-1\end{cases}
-
-    Inputs:
-        - **images** (Tensor) - The input image data, with format 'NCHW'.
-
-    Outputs:
-        - **dy** (Tensor) - vertical image gradients, the same type and shape as input.
-        - **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
-
-    Examples:
-        >>> net = nn.ImageGradients()
-        >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
-        >>> net(image)
-        [[[[2,2]
-           [0,0]]]]
-        [[[[1,0]
-           [1,0]]]]
-    """
-    def __init__(self):
-        super(ImageGradients, self).__init__()
-
-    def construct(self, images):
-        batch_size, depth, height, width = P.Shape()(images)
-        dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
-        dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
-        dy = P.Concat(2)((dy, dy_last))
-
-        dx = images[:, :, :, 1:] - images[:, :, :, :width - 1]
-        dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
-        dx = P.Concat(3)((dx, dx_last))
-        return dy, dx
-
-
 class Pad(Cell):
     """
     Pads the input tensor according to the paddings and mode.
diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py
new file mode 100644
index 0000000000..6121776f59
--- /dev/null
+++ b/mindspore/nn/layer/image.py
@@ -0,0 +1,197 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""image"""
+import numpy as np
+import mindspore.common.dtype as mstype
+from mindspore.common.tensor import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops.primitive import constexpr
+from mindspore._checkparam import ParamValidator as validator
+from mindspore._checkparam import Rel
+from ..cell import Cell
+
+
+class ImageGradients(Cell):
+    r"""
+    Returns two tensors, the first is along the height dimension and the second is along the width dimension.
+
+    Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`,
+    respectively.
+
+    .. math::
+        dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i<h-1 \cr
+        0, &if\ i==h-1\end{cases}
+
+        dx[i] = \begin{cases} image[:, i+1]-image[:, i], &if\ 0<=i<w-1 \cr
+        0, &if\ i==w-1\end{cases}
+
+    Inputs:
+        - **images** (Tensor) - The input image data, with format 'NCHW'.
+
+    Outputs:
+        - **dy** (Tensor) - vertical image gradients, the same type and shape as input.
+        - **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
+
+    Examples:
+        >>> net = nn.ImageGradients()
+        >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+        >>> net(image)
+        [[[[2,2]
+           [0,0]]]]
+        [[[[1,0]
+           [1,0]]]]
+    """
+    def __init__(self):
+        super(ImageGradients, self).__init__()
+
+    def construct(self, images):
+        batch_size, depth, height, width = P.Shape()(images)
+        dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
+        dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
+        dy = P.Concat(2)((dy, dy_last))
+
+        dx = images[:, :, :, 1:] - images[:, :, :, :width - 1]
+        dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
+        dx = P.Concat(3)((dx, dx_last))
+        return dy, dx
+
+
+@constexpr
+def _gauss_kernel_helper(filter_size):
+    """gauss kernel helper"""
+    filter_size = F.scalar_cast(filter_size, mstype.int32)
+    coords = ()
+    for i in range(filter_size):
+        i_cast = F.scalar_cast(i, mstype.float32)
+        offset = F.scalar_cast(filter_size-1, mstype.float32)/2.0
+        element = i_cast-offset
+        coords = coords+(element,)
+    g = np.square(coords).astype(np.float32)
+    g = Tensor(g)
+    return filter_size, g
+
+
+class SSIM(Cell):
+    r"""
+    Returns SSIM index between img1 and img2.
+
+    Its implementation is based on Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). `Image quality
+    assessment: from error visibility to structural similarity <https://ieeexplore.ieee.org/document/1284395>`_.
+    IEEE transactions on image processing.
+
+    .. math::
+
+        l(x,y)&=\frac{2\mu_x\mu_y+C_1}{\mu_x^2+\mu_y^2+C_1}, C_1=(K_1L)^2.\\
+        c(x,y)&=\frac{2\sigma_x\sigma_y+C_2}{\sigma_x^2+\sigma_y^2+C_2}, C_2=(K_2L)^2.\\
+        s(x,y)&=\frac{\sigma_{xy}+C_3}{\sigma_x\sigma_y+C_3}, C_3=C_2/2.\\
+        SSIM(x,y)&=l*c*s\\&=\frac{(2\mu_x\mu_y+C_1)(2\sigma_{xy}+C_2}{(\mu_x^2+\mu_y^2+C_1)(\sigma_x^2+\sigma_y^2+C_2)}.
+
+    Args:
+        max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images).
+          Default: 1.0.
+        filter_size (int): The size of the Gaussian filter. Default: 11.
+        filter_sigma (float): The standard deviation of Gaussian kernel. Default: 1.5.
+        k1 (float): The constant used to generate c1 in the luminance comparison function. Default: 0.01.
+        k2 (float): The constant used to generate c2 in the contrast comparison function. Default: 0.03.
+
+    Inputs:
+        - **img1** (Tensor) - The first image batch with format 'NCHW'. It should be the same shape and dtype as img2.
+        - **img2** (Tensor) - The second image batch with format 'NCHW'. It should be the same shape and dtype as img1.
+
+    Outputs:
+        Tensor, has the same dtype as img1. It is a 1-D tensor with shape N, where N is the batch num of img1.
+
+    Examples:
+        >>> net = nn.SSIM()
+        >>> img1 = Tensor(np.random.random((1,3,16,16)))
+        >>> img2 = Tensor(np.random.random((1,3,16,16)))
+        >>> ssim = net(img1, img2)
+    """
+    def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
+        super(SSIM, self).__init__()
+        validator.check_type('max_val', max_val, [int, float])
+        validator.check('max_val', max_val, '', 0.0, Rel.GT)
+        self.max_val = max_val
+        self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE)
+        self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma)
+        validator.check_type('k1', k1, [float])
+        self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0, Rel.INC_NEITHER)
+        validator.check_type('k2', k2, [float])
+        self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0, Rel.INC_NEITHER)
+        self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
+
+    def construct(self, img1, img2):
+        max_val = self._convert_img_dtype_to_float32(self.max_val, self.max_val)
+        img1 = self._convert_img_dtype_to_float32(img1, self.max_val)
+        img2 = self._convert_img_dtype_to_float32(img2, self.max_val)
+
+        kernel = self._fspecial_gauss(self.filter_size, self.filter_sigma)
+        kernel = P.Tile()(kernel, (1, P.Shape()(img1)[1], 1, 1))
+
+        mean_ssim = self._calculate_mean_ssim(img1, img2, kernel, max_val, self.k1, self.k2)
+
+        return mean_ssim
+
+    def _convert_img_dtype_to_float32(self, img, max_val):
+        """convert img dtype to float32"""
+        # Ususally max_val is 1.0 or 255, we will do the scaling if max_val > 1.
+        # We will scale img pixel value if max_val > 1. and just cast otherwise.
+        ret = P.Cast()(img, mstype.float32)
+        max_val = F.scalar_cast(max_val, mstype.float32)
+        if max_val > 1.:
+            scale = 1./max_val
+            ret = ret*scale
+        return ret
+
+    def _calculate_mean_ssim(self, x, y, kernel, max_val, k1, k2):
+        """calculate mean ssim"""
+        c1 = (k1*max_val)*(k1*max_val)
+        c2 = (k2*max_val)*(k2*max_val)
+
+        # SSIM luminance formula
+        # (2 * mean_{x} * mean_{y} + c1) / (mean_{x}**2 + mean_{y}**2 + c1)
+        mean_x = self.mean(x, kernel)
+        mean_y = self.mean(y, kernel)
+        square_sum = F.square(mean_x)+F.square(mean_y)
+        luminance = (2*mean_x*mean_y+c1)/(square_sum+c1)
+
+        # SSIM contrast*structure formula (when c3 = c2/2)
+        # (2 * conv_{xy} + c2) / (conv_{xx} + conv_{yy} + c2), equals to
+        # (2 * (mean_{xy} - mean_{x}*mean_{y}) + c2) / (mean_{xx}-mean_{x}**2 + mean_{yy}-mean_{y}**2 + c2)
+        mean_xy = self.mean(x*y, kernel)
+        mean_square_add = self.mean(F.square(x)+F.square(y), kernel)
+
+        cs = (2*(mean_xy-mean_x*mean_y)+c2)/(mean_square_add-square_sum+c2)
+
+        # SSIM formula
+        # luminance * cs
+        ssim = luminance*cs
+
+        mean_ssim = P.ReduceMean()(ssim, (-3, -2, -1))
+
+        return mean_ssim
+
+    def _fspecial_gauss(self, filter_size, filter_sigma):
+        """get gauss kernel"""
+        filter_size, g = _gauss_kernel_helper(filter_size)
+
+        square_sigma_scale = -0.5/(filter_sigma * filter_sigma)
+        g = g*square_sigma_scale
+        g = F.reshape(g, (1, -1))+F.reshape(g, (-1, 1))
+        g = F.reshape(g, (1, -1))
+        g = P.Softmax()(g)
+        ret = F.reshape(g, (1, 1, filter_size, filter_size))
+        return ret
diff --git a/tests/ut/python/nn/test_image_gradients.py b/tests/ut/python/nn/test_image_gradients.py
index f65f38ec0a..a2b9495443 100644
--- a/tests/ut/python/nn/test_image_gradients.py
+++ b/tests/ut/python/nn/test_image_gradients.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-""" test loss """
+""" test image gradients """
 import numpy as np
 import mindspore.nn as nn
 import mindspore.context as context

From 0fc23eee0f13a709687673e4f0489cf9c3b6562a Mon Sep 17 00:00:00 2001
From: hesham <h.farahat@huawei.com>
Date: Sat, 11 Apr 2020 18:00:14 -0400
Subject: [PATCH 236/367] Support nested repeat

---
 .../dataset/engine/datasetops/dataset_op.cc   |  17 ++-
 .../dataset/engine/datasetops/dataset_op.h    |  10 +-
 .../dataset/engine/datasetops/parallel_op.h   |  16 +-
 .../dataset/engine/datasetops/pipeline_op.h   |  15 +-
 .../dataset/engine/datasetops/repeat_op.cc    |  36 ++---
 .../dataset/engine/datasetops/repeat_op.h     |  10 +-
 .../ccsrc/dataset/engine/execution_tree.cc    |  25 ++--
 mindspore/dataset/engine/datasets.py          |   2 +
 tests/ut/cpp/dataset/repeat_op_test.cc        |  27 +++-
 tests/ut/python/dataset/test_repeat.py        | 137 +++++++++++++++++-
 10 files changed, 233 insertions(+), 62 deletions(-)

diff --git a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
index f51c2a1539..7edf1dd288 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
@@ -161,15 +161,18 @@ Status DatasetOp::EofReceived(int32_t worker_id) {
   return (out_connector_->Add(static_cast<int>(worker_id), std::move(eof_buffer)));
 }
 
-// During tree prepare phase, operators may have specific operations to perform depending on
+// During tree prepare phase, operators may have specific pre-operations to perform depending on
 // their role.
-Status DatasetOp::PrepareNodeAction() {
+Status DatasetOp::PrepareNodePreAction() {
+  if (BitTest(tree_->PrepareFlags(), ExecutionTree::kDePrepRepeat)) set_control_flag(kDeOpRepeated);
+  return Status::OK();
+}
+// During tree prepare phase, operators may have specific post-operations to perform depending on
+// their role.
+Status DatasetOp::PrepareNodePostAction() {
   // If this op does not have any children and it is in a repeat path of the tree...
-  if (child_.size() == 0 && BitTest(tree_->PrepareFlags(), ExecutionTree::kDePrepRepeat)) {
-    // Then, flag this operator as a leaf node in a repeat path of tree execution.
-    BitSet(&op_ctrl_flags_, kDeOpRepeated);
-
-    // Secondly, push ourselves onto the tree repeat stack.  Later, the repeat operator
+  if (child_.empty() && BitTest(op_ctrl_flags_, kDeOpRepeated)) {
+    // push ourselves onto the tree repeat stack.  Later, the repeat operator
     // above us will consume them.
     tree_->AddToRepeatStack(shared_from_this());
   }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.h b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.h
index a7d87c3092..0111f5239a 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.h
@@ -150,11 +150,17 @@ class DatasetOp : public std::enable_shared_from_this<DatasetOp> {
     return Status::OK();
   }
 
-  // During tree prepare phase, operators may have specific operations to perform depending on
+  // During tree prepare phase, operators may have specific pre-operations to perform depending on
   // their role.
   // @notes Derived versions of this function should always call it's superclass version first
   // before providing their own implementations.
-  virtual Status PrepareNodeAction();
+  virtual Status PrepareNodePreAction();
+
+  // During tree prepare phase, operators may have specific post-operations to perform depending on
+  // their role.
+  // @notes Derived versions of this function should always call it's superclass version first
+  // before providing their own implementations.
+  virtual Status PrepareNodePostAction();
 
   // Getter function
   // @return The operator id
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.h b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.h
index ceb7f2c4ac..142ec78360 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.h
@@ -64,14 +64,24 @@ class ParallelOp : public DatasetOp {
     return out;
   }
 
-  // During tree prepare phase, operators may have specific operations to perform depending on
+  // During tree prepare phase, operators may have specific pre-operations to perform depending on
   // their role.
   // @notes Derived versions of this function should always call it's superclass version first
   // before providing their own implementations.
   // @return Status - The error return code
-  Status PrepareNodeAction() override {
+  Status PrepareNodePreAction() override {
     // Run common code from super class before adding ParallelOp specific logic
-    return (DatasetOp::PrepareNodeAction());
+    return (DatasetOp::PrepareNodePreAction());
+  }
+
+  // During tree prepare phase, operators may have specific post-operations to perform depending on
+  // their role.
+  // @notes Derived versions of this function should always call it's superclass version first
+  // before providing their own implementations.
+  // @return Status - The error return code
+  Status PrepareNodePostAction() override {
+    // Run common code from super class before adding ParallelOp specific logic
+    return (DatasetOp::PrepareNodePostAction());
   }
 
   // Override base class reset to provide reset actions specific to the ParallelOp class.
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/pipeline_op.h b/mindspore/ccsrc/dataset/engine/datasetops/pipeline_op.h
index ee20f1d373..a14279032d 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/pipeline_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/pipeline_op.h
@@ -64,13 +64,22 @@ class PipelineOp : public DatasetOp {
   // @return The number of threads that push data to the output connector
   int32_t num_producers() const override { return 1; }
 
-  // During tree prepare phase, operators may have specific operations to perform depending on
+  // During tree prepare phase, operators may have specific pre-operations to perform depending on
   // their role.
   // @notes Derived versions of this function should always call it's superclass version first
   // before providing their own implementations.
-  Status PrepareNodeAction() override {
+  Status PrepareNodePreAction() override {
     // Run common code from super class before adding PipelineOp specific logic
-    return (DatasetOp::PrepareNodeAction());
+    return (DatasetOp::PrepareNodePreAction());
+  }
+
+  // During tree prepare phase, operators may have specific post-operations to perform depending on
+  // their role.
+  // @notes Derived versions of this function should always call it's superclass version first
+  // before providing their own implementations.
+  Status PrepareNodePostAction() override {
+    // Run common code from super class before adding PipelineOp specific logic
+    return (DatasetOp::PrepareNodePostAction());
   }
 
  protected:
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.cc
index 32723a9bd4..33c731c400 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.cc
@@ -58,10 +58,10 @@ void RepeatOp::Print(std::ostream &out, bool show_all) const {
   out << "RepeatOp:"
       << "\nCurrent repeat count: " << repeat_count_ << "\nMax repeat count: " << max_repeats_
       << "\nLeaf Nodes in my execution path:";
-  if (!leaf_ops_.empty()) {
+  if (!eoe_ops_.empty()) {
     out << "\n";
-    for (size_t i = 0; i < leaf_ops_.size(); i++) {
-      out << "  Operator: " << leaf_ops_[i]->id() << "\n";
+    for (size_t i = 0; i < eoe_ops_.size(); i++) {
+      out << "  Operator: " << eoe_ops_[i]->id() << "\n";
     }
   } else {
     out << " kNone.";
@@ -71,21 +71,17 @@ void RepeatOp::Print(std::ostream &out, bool show_all) const {
 
 // Base-class override for executing specific RepeatOp configurations. This code will be called
 // during the execution tree prepare phase when it is visiting this operator.
-Status RepeatOp::PrepareNodeAction() {
+Status RepeatOp::PrepareNodePostAction() {
   // Run any common code from super class first before adding our own specific logic
-  RETURN_IF_NOT_OK(PipelineOp::PrepareNodeAction());
+  RETURN_IF_NOT_OK(PipelineOp::PrepareNodePostAction());
   std::shared_ptr<DatasetOp> leaf_op = tree_->PopFromRepeatStack();
   while (leaf_op != nullptr) {
     // Track the leaf operators that are under this repeat op.
-    leaf_ops_.push_back(leaf_op);
-
-    // Special case.  If the repeat count is 1, then pre-flag the leaf nodes
-    // to tell them they are already at their last op:
-    if (max_repeats_ == 1) {
-      leaf_op->set_control_flag(kDeOpLastRepeat);
-    }
+    eoe_ops_.push_back(leaf_op);
     leaf_op = tree_->PopFromRepeatStack();
   }
+  // Push ourselves to the stack in case one of our ascendants is repeat too.
+  tree_->AddToRepeatStack(shared_from_this());
   return Status::OK();
 }
 
@@ -127,16 +123,20 @@ Status RepeatOp::GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t wo
 Status RepeatOp::EoeReceived(int32_t worker_id) {
   repeat_count_++;
   MS_LOG(INFO) << "Repeat operator end of epoch message received. Repeat count is now: " << repeat_count_ << ".";
-
-  // If we've reached the requested repeat count, then flag the leaf nodes
+  bool repeated = BitTest(op_ctrl_flags_, kDeOpRepeated);
+  bool last_repeat = BitTest(op_ctrl_flags_, kDeOpLastRepeat);
+  // If we've reached the requested repeat count, then flag the eoe nodes
   // to tell them they've got one more epoch to perform.  When they reach the end
-  // of the last epoch, they quit rather than loop again.
-  if (max_repeats_ != kInfiniteRepeat && repeat_count_ == (max_repeats_ - 1)) {
-    for (size_t i = 0; i < leaf_ops_.size(); i++) {
-      leaf_ops_[i]->set_control_flag(kDeOpLastRepeat);
+  // of the last epoch, they quit rather than loop again. This happens in two cases:
+  // 1- We are also repeated (by another repeat op) and we are at the last repetition. Or,
+  // 2- We are not repeated
+  if (max_repeats_ != kInfiniteRepeat && repeat_count_ == (max_repeats_ - 1) && (!repeated || last_repeat)) {
+    for (auto &eoe_op : eoe_ops_) {
+      eoe_op->set_control_flag(kDeOpLastRepeat);
     }
   }
   if (repeat_count_ == max_repeats_) {
+    repeat_count_ = 0;
     state_ = OpState::kDeOpIdle;
     return Status::OK();
   }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.h b/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.h
index 5cc7ec2efa..8497b4cf3c 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/repeat_op.h
@@ -87,8 +87,8 @@ class RepeatOp : public PipelineOp {
   uint32_t PrepareFlags() const override;
 
   // Base-class override for executing specific RepeatOp configurations. This code will be called
-  // during the execution tree prepare phase when it is visiting this operator.
-  Status PrepareNodeAction() override;
+  // during the execution tree post-prepare phase when it is visiting this operator.
+  Status PrepareNodePostAction() override;
 
   // This function returns the buffer that is at the top of our output connector. The caller is
   // typically our parent node, when the parent is asking us to provide the next buffer of data.
@@ -119,9 +119,9 @@ class RepeatOp : public PipelineOp {
   int32_t num_producers() const override;
 
  private:
-  int32_t max_repeats_;                               // The number of repeats that the user requested
-  int32_t repeat_count_;                              // A counter for the current number of executed repeats
-  std::vector<std::shared_ptr<DatasetOp>> leaf_ops_;  // List of leaf operators underneath this repeat.
+  int32_t max_repeats_;                              // The number of repeats that the user requested
+  int32_t repeat_count_;                             // A counter for the current number of executed repeats
+  std::vector<std::shared_ptr<DatasetOp>> eoe_ops_;  // List of operators that can generate EOE underneath this repeat.
 };
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/engine/execution_tree.cc b/mindspore/ccsrc/dataset/engine/execution_tree.cc
index 20fcb836c5..ebfa532195 100644
--- a/mindspore/ccsrc/dataset/engine/execution_tree.cc
+++ b/mindspore/ccsrc/dataset/engine/execution_tree.cc
@@ -162,30 +162,25 @@ Status ExecutionTree::Prepare() {
 // Recursive function used during prepare phase to visit a node and drive any pre- and post-
 // node actions during a tree walk.
 Status ExecutionTree::PrepareNode(const std::shared_ptr<DatasetOp> &dataset_op) {
-  int32_t num_children = dataset_op->child_.size();
+  // execute PreAction
+  RETURN_IF_NOT_OK(dataset_op->PrepareNodePreAction());
 
-  // Before going down into children, make any prepare flags updates based on this
-  // operator.
+  // Before going down into children, make any prepare flags updates based on this operator.
   uint32_t op_prep_flags = dataset_op->PrepareFlags();
-  // Sanity check.  In future we can support nested repeats.  for now it's not allowed.
-  // If somebody above us already set the repeat flag, and now we are another repeat...
-  if (BitTest(op_prep_flags, kDePrepRepeat) && BitTest(prepare_flags_, kDePrepRepeat)) {
-    std::string err_msg("Nested RepeatOp detected! This is not supported yet.");
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
   BitSet(&prepare_flags_, op_prep_flags);
 
   // Now, descend to children
-  for (int32_t i = 0; i < num_children; ++i) {
-    RETURN_IF_NOT_OK(this->PrepareNode(dataset_op->child_[i]));
+  for (const auto &i : dataset_op->child_) {
+    RETURN_IF_NOT_OK(this->PrepareNode(i));
   }
 
-  // No more children, now we execute any prepare actions before going back up the
-  // the tree on recursive function exit
-  RETURN_IF_NOT_OK(dataset_op->PrepareNodeAction());
-
   // Then clear the flags from this op now that we have prepared it.
   BitClear(&prepare_flags_, op_prep_flags);
+
+  // No more children, now we execute any prepare actions before going back up the
+  // the tree on recursive function
+  RETURN_IF_NOT_OK(dataset_op->PrepareNodePostAction());
+
   return Status::OK();
 }
 
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 3d660d58a8..e40c24c140 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -417,6 +417,8 @@ class Dataset:
             >>> repeat_and_shuffle = data.repeat(50)
             >>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)
         """
+        if count == 1:
+            return self
         return RepeatDataset(self, count)
 
     @check_zip_dataset
diff --git a/tests/ut/cpp/dataset/repeat_op_test.cc b/tests/ut/cpp/dataset/repeat_op_test.cc
index 99e91afe81..e32e98cbd7 100644
--- a/tests/ut/cpp/dataset/repeat_op_test.cc
+++ b/tests/ut/cpp/dataset/repeat_op_test.cc
@@ -33,18 +33,29 @@ TEST_F(MindDataTestrepeat_op, Testrepeat_opFuntions) {
   auto my_tree = std::make_shared<ExecutionTree>();
 
   std::shared_ptr<DatasetOp> parent_op = std::make_shared<RepeatOp>(32);
-
-  std::shared_ptr<DatasetOp> leaf_op = std::make_shared<RepeatOp>(16);
+  std::string dataset_path;
+  dataset_path = datasets_root_path_ + "/testTFTestAllTypes/test.data";
+// TFReaderOp
+  std::shared_ptr<TFReaderOp> my_tfreader_op;
+  TFReaderOp::Builder builder;
+  builder.SetDatasetFilesList({dataset_path})
+    .SetRowsPerBuffer(16)
+    .SetWorkerConnectorSize(16)
+    .SetNumWorkers(16);
+  Status rc= builder.Build(&my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+  rc = my_tree->AssociateNode(my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
   my_tree->AssociateNode(parent_op);
-  my_tree->AssociateNode(leaf_op);
   ASSERT_NE(parent_op, nullptr);
-  ASSERT_NE(leaf_op, nullptr);
-  parent_op->AddChild(std::move(leaf_op));
-  parent_op->Print(std::cout, false);
-  parent_op->PrepareNodeAction();
+  ASSERT_NE(my_tfreader_op, nullptr);
+  parent_op->AddChild(std::move(my_tfreader_op));
+  MS_LOG(INFO) << parent_op;
+  my_tree->Prepare();
+
   RepeatOp RepeatOpOp();
 
   std::shared_ptr<RepeatOp> repeat_op;
-  Status rc = RepeatOp::Builder(3).Build(&repeat_op);
+  rc = RepeatOp::Builder(3).Build(&repeat_op);
   ASSERT_NE(repeat_op, nullptr);
 }
diff --git a/tests/ut/python/dataset/test_repeat.py b/tests/ut/python/dataset/test_repeat.py
index 196a62c315..cb7a80e3d1 100644
--- a/tests/ut/python/dataset/test_repeat.py
+++ b/tests/ut/python/dataset/test_repeat.py
@@ -16,6 +16,7 @@ import mindspore.dataset.transforms.vision.c_transforms as vision
 from util import save_and_check
 
 import mindspore.dataset as ds
+import numpy as np
 from mindspore import log as logger
 
 DATA_DIR_TF = ["../data/dataset/testTFTestAllTypes/test.data"]
@@ -95,6 +96,141 @@ def test_tf_repeat_03():
     assert num_iter == 2
 
 
+def generator():
+    for i in range(3):
+        yield np.array([i]),
+
+
+def test_nested_repeat1():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(2)
+    data = data.repeat(3)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data]) == 2 * 3 * 3
+
+
+def test_nested_repeat2():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(1)
+    data = data.repeat(1)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data]) == 3
+
+
+def test_nested_repeat3():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(1)
+    data = data.repeat(2)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data]) == 2 * 3
+
+
+def test_nested_repeat4():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(2)
+    data = data.repeat(1)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data]) == 2 * 3
+
+
+def test_nested_repeat5():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.batch(3)
+    data = data.repeat(2)
+    data = data.repeat(3)
+
+    for i, d in enumerate(data):
+        assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
+
+    assert sum([1 for _ in data]) == 6
+
+
+def test_nested_repeat6():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(2)
+    data = data.batch(3)
+    data = data.repeat(3)
+
+    for i, d in enumerate(data):
+        assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
+
+    assert sum([1 for _ in data]) == 6
+
+
+def test_nested_repeat7():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(2)
+    data = data.repeat(3)
+    data = data.batch(3)
+
+    for i, d in enumerate(data):
+        assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
+
+    assert sum([1 for _ in data]) == 6
+
+
+def test_nested_repeat8():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.batch(2, drop_remainder=False)
+    data = data.repeat(2)
+    data = data.repeat(3)
+
+    for i, d in enumerate(data):
+        if i % 2 == 0:
+            assert np.array_equal(d[0], np.asarray([[0], [1]]))
+        else:
+            assert np.array_equal(d[0], np.asarray([[2]]))
+
+    assert sum([1 for _ in data]) == 6 * 2
+
+
+def test_nested_repeat9():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat()
+    data = data.repeat(3)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+        if i == 10:
+            break
+
+
+def test_nested_repeat10():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(3)
+    data = data.repeat()
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+        if i == 10:
+            break
+
+
+def test_nested_repeat11():
+    data = ds.GeneratorDataset(generator, ["data"])
+    data = data.repeat(2)
+    data = data.repeat(3)
+    data = data.repeat(4)
+    data = data.repeat(5)
+
+    for i, d in enumerate(data):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data]) == 2 * 3 * 4 * 5 * 3
+
+
 if __name__ == "__main__":
     logger.info("--------test tf repeat 01---------")
     # test_repeat_01()
@@ -104,4 +240,3 @@ if __name__ == "__main__":
 
     logger.info("--------test tf repeat 03---------")
     test_tf_repeat_03()
-

From 0099f0108ede55c8f723d6b69be73afedab57853 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Tue, 14 Apr 2020 22:15:44 +0800
Subject: [PATCH 237/367] fix akg register bug

---
 mindspore/ops/_op_impl/akg/gpu/hsigmoid.py    | 2 +-
 mindspore/ops/_op_impl/akg/gpu/hswish.py      | 2 +-
 mindspore/ops/_op_impl/akg/gpu/hswish_grad.py | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
index 31fe332206..4e802c1cad 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hsigmoid.py
@@ -24,7 +24,7 @@ hsigmoid_op_info = AkgRegOp("HSigmoid") \
     .get_op_info()
 
 
-@op_info_register(hsigmoidgrad_op_info)
+@op_info_register(hsigmoid_op_info)
 def _hsigmoid_akg():
     """HSigmoid AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish.py b/mindspore/ops/_op_impl/akg/gpu/hswish.py
index 17364de6cb..29f20bafae 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hswish.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish.py
@@ -24,7 +24,7 @@ hswish_op_info = AkgRegOp("HSwish") \
     .get_op_info()
 
 
-@op_info_register(hsigmoidgrad_op_info)
+@op_info_register(hswish_op_info)
 def _hswish_akg():
     """HSwish AutoDiff register"""
     return
diff --git a/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
index 503dd9a5f1..38e8c78e28 100644
--- a/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
+++ b/mindspore/ops/_op_impl/akg/gpu/hswish_grad.py
@@ -15,7 +15,7 @@
 """HSwishGrad op"""
 from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-hswishgrad_op_info = AkgRegOp("HSwishGrad") \
+hswish_grad_op_info = AkgRegOp("HSwishGrad") \
     .fusion_type("OPAQUE") \
     .input(0, "y_grad") \
     .input(1, "x") \
@@ -25,7 +25,7 @@ hswishgrad_op_info = AkgRegOp("HSwishGrad") \
     .get_op_info()
 
 
-@op_info_register(hsigmoidgrad_op_info)
+@op_info_register(hswish_grad_op_info)
 def _hswish_grad_akg():
     """HSwishGrad AutoDiff register"""
     return

From 9739d3b048cd2471b6032339def5e54e20f60f6f Mon Sep 17 00:00:00 2001
From: Junhan Hu <junhan.hu@huawei.com>
Date: Sun, 29 Mar 2020 23:21:21 -0400
Subject: [PATCH 238/367] Add CPP sampler support for GeneratorDataset

---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    |   2 +-
 .../ccsrc/dataset/api/python_bindings.cc      |  11 +-
 mindspore/ccsrc/dataset/core/tensor.cc        |   2 +
 .../engine/datasetops/source/celeba_op.cc     |   2 +-
 .../engine/datasetops/source/cifar_op.cc      |   2 +-
 .../datasetops/source/image_folder_op.cc      |   2 +-
 .../engine/datasetops/source/manifest_op.cc   |   2 +-
 .../engine/datasetops/source/mnist_op.cc      |   2 +-
 .../source/sampler/distributed_sampler.cc     |   5 +-
 .../source/sampler/distributed_sampler.h      |   6 +-
 .../datasetops/source/sampler/pk_sampler.cc   |  12 +-
 .../datasetops/source/sampler/pk_sampler.h    |   5 +-
 .../source/sampler/random_sampler.cc          |   5 +-
 .../source/sampler/random_sampler.h           |   6 +-
 .../datasetops/source/sampler/sampler.cc      |  49 ++++-
 .../datasetops/source/sampler/sampler.h       |  18 +-
 .../source/sampler/sequential_sampler.cc      |   4 +-
 .../source/sampler/sequential_sampler.h       |   6 +-
 .../source/sampler/subset_random_sampler.cc   |   5 +-
 .../source/sampler/subset_random_sampler.h    |   3 +-
 .../source/sampler/weighted_random_sampler.cc |  32 +--
 .../source/sampler/weighted_random_sampler.h  |   5 +-
 .../engine/datasetops/source/voc_op.cc        |   2 +-
 mindspore/dataset/engine/datasets.py          | 194 ++++++++++++++----
 mindspore/dataset/engine/samplers.py          |   1 -
 mindspore/dataset/engine/validators.py        |  44 ++--
 .../cpp/dataset/stand_alone_samplers_test.cc  |   4 +-
 .../cpp/dataset/subset_random_sampler_test.cc |  12 +-
 .../dataset/weighted_random_sampler_test.cc   |  24 +--
 tests/ut/python/dataset/test_generator.py     |  71 +++++++
 tests/ut/python/dataset/test_sampler.py       |  21 ++
 31 files changed, 432 insertions(+), 127 deletions(-)

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index f572db0cdf..b64d40125e 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -517,7 +517,7 @@ Status DEPipeline::ParseGeneratorOp(const py::dict &args, std::shared_ptr<Datase
     std::string key = py::str(arg.first);
     py::handle value = arg.second;
     if (!value.is_none()) {
-      if (key == "generator_function") {
+      if (key == "source") {
         py::object obj = py::cast(&value);
         if (!py::isinstance<py::function>(obj)) {
           std::string err_msg = "Error: generator is invalid or not set.";
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index 3d543f946b..0633af4914 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -384,7 +384,16 @@ void bindTensorOps4(py::module *m) {
 }
 
 void bindSamplerOps(py::module *m) {
-  (void)py::class_<Sampler, std::shared_ptr<Sampler>>(*m, "Sampler");
+  (void)py::class_<Sampler, std::shared_ptr<Sampler>>(*m, "Sampler")
+    .def("set_num_rows", [](Sampler &self, int64_t rows) { THROW_IF_ERROR(self.SetNumRowsInDataset(rows)); })
+    .def("set_num_samples", [](Sampler &self, int64_t samples) { THROW_IF_ERROR(self.SetNumSamples(samples)); })
+    .def("initialize", [](Sampler &self) { THROW_IF_ERROR(self.InitSampler()); })
+    .def("get_indices", [](Sampler &self) {
+      py::array ret;
+      THROW_IF_ERROR(self.GetAllIdsThenReset(&ret));
+      return ret;
+    });
+
   (void)py::class_<mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardOperator>>(*m, "ShardOperator");
 
   (void)py::class_<DistributedSampler, Sampler, std::shared_ptr<DistributedSampler>>(*m, "DistributedSampler")
diff --git a/mindspore/ccsrc/dataset/core/tensor.cc b/mindspore/ccsrc/dataset/core/tensor.cc
index 8f0eae459a..8fd1f8d48d 100644
--- a/mindspore/ccsrc/dataset/core/tensor.cc
+++ b/mindspore/ccsrc/dataset/core/tensor.cc
@@ -491,6 +491,8 @@ Status Tensor::GetItemAt(T *o, const std::vector<dsize_t> &index) const {
 
 // return data as numpy, should return status
 Status Tensor::GetDataAsNumpy(py::array *data) {
+  RETURN_UNEXPECTED_IF_NULL(data_);
+  RETURN_UNEXPECTED_IF_NULL(data);
   if (type_ == DataType::DE_BOOL) {
     *data = py::array_t<bool>(shape_.AsVector(), reinterpret_cast<bool *>(data_));
   } else if (type_ == DataType::DE_INT8) {
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
index 0c2e20729e..87a7b3c687 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc
@@ -100,7 +100,7 @@ Status CelebAOp::LaunchThreadsAndInitOp() {
   RETURN_IF_NOT_OK(tree_->LaunchWorkers(num_workers_, std::bind(&CelebAOp::WorkerEntry, this, std::placeholders::_1)));
   TaskManager::FindMe()->Post();
   RETURN_IF_NOT_OK(ParseImageAttrInfo());
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
 
   return Status::OK();
 }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
index 3e64c8a3e6..60de5a6bdf 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc
@@ -240,7 +240,7 @@ Status CifarOp::Reset() {
 
 // hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
 Status CifarOp::InitSampler() {
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
index f6cf377666..0ac579a865 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc
@@ -258,7 +258,7 @@ Status ImageFolderOp::Reset() {
 
 // hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
 Status ImageFolderOp::InitSampler() {
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
index 6907647952..0139af4d9d 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc
@@ -254,7 +254,7 @@ Status ManifestOp::Reset() {
 
 // hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
 Status ManifestOp::InitSampler() {
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
index 3431e58aea..71900f8a91 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc
@@ -205,7 +205,7 @@ Status MnistOp::Reset() {
 
 // hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
 Status MnistOp::InitSampler() {
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
index 28a5705648..5b5a9321df 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc
@@ -31,8 +31,9 @@ DistributedSampler::DistributedSampler(int64_t num_dev, int64_t dev_id, bool shu
       num_devices_(num_dev),
       shuffle_(shuffle) {}
 
-Status DistributedSampler::Init(const RandomAccessOp *op) {
-  RETURN_IF_NOT_OK(Sampler::Init(op));
+Status DistributedSampler::InitSampler() {
+  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0, "num_samples <= 0\n");
+  CHECK_FAIL_RETURN_UNEXPECTED(num_rows_ > 0, "num_rows <= 0\n");
   CHECK_FAIL_RETURN_UNEXPECTED(device_id_ < num_devices_ && device_id_ >= 0 && num_rows_ > 0 && num_samples_ > 0,
                                "fail to init DistributedSampler");
   rnd_.seed(seed_++);
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.h
index ef25b6bccf..58b469dcc8 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.h
@@ -41,10 +41,8 @@ class DistributedSampler : public Sampler {
   // @return - The error code return
   Status GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) override;
 
-  // first handshake between StorageOp and Sampler
-  // @param op - StorageOp pointer, pass in so Sampler can call GetNumSamples() and get ClassIds()
-  // @return
-  Status Init(const RandomAccessOp *) override;
+  // Init sampler, called by base class or python
+  Status InitSampler() override;
 
   // for next epoch of sampleIds
   // @return - The error code return
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
index 8c8c12fce2..8198204437 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc
@@ -28,9 +28,7 @@ PKSampler::PKSampler(int64_t val, bool shuffle, int64_t samples_per_buffer)
       num_pk_samples_(0),
       samples_per_class_(val) {}
 
-Status PKSampler::Init(const RandomAccessOp *op) {
-  RETURN_UNEXPECTED_IF_NULL(op);
-  RETURN_IF_NOT_OK(op->GetClassIds(&label_to_ids_));
+Status PKSampler::InitSampler() {
   labels_.reserve(label_to_ids_.size());
   for (const auto &pair : label_to_ids_) {
     if (pair.second.empty() == false) {
@@ -79,5 +77,13 @@ Status PKSampler::Reset() {
   rnd_.seed(seed_++);
   return Status::OK();
 }
+
+Status PKSampler::HandshakeRandomAccessOp(const RandomAccessOp *op) {
+  RETURN_UNEXPECTED_IF_NULL(op);
+  RETURN_IF_NOT_OK(op->GetClassIds(&label_to_ids_));
+  RETURN_IF_NOT_OK(InitSampler());
+  return Status::OK();
+}
+
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.h
index fa2b4ed0c7..14f598a9ce 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.h
@@ -45,7 +45,10 @@ class PKSampler : public Sampler {  // NOT YET FINISHED
   // first handshake between StorageOp and Sampler
   // @param op - StorageOp pointer, pass in so Sampler can call GetNumSamples() and get ClassIds()
   // @return
-  Status Init(const RandomAccessOp *op) override;
+  Status HandshakeRandomAccessOp(const RandomAccessOp *op) override;
+
+  // init sampler, to be called by python or Handshake
+  Status InitSampler() override;
 
   // for next epoch of sampleIds
   // @return - The error code return
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
index 216f322052..de8cde409f 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc
@@ -49,10 +49,9 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) {
   return Status::OK();
 }
 
-Status RandomSampler::Init(const RandomAccessOp *op) {
-  RETURN_IF_NOT_OK(Sampler::Init(op));
+Status RandomSampler::InitSampler() {
   num_samples_ = (user_num_samples_ < num_samples_) ? user_num_samples_ : num_samples_;
-  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && num_rows_ > 0, "Fail to init RandomSampler");
+  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && num_rows_ > 0, "both num_samples & num_rows need to be positive");
   samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
   if (replacement_ == false) {
     shuffled_ids_.reserve(num_rows_);
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.h
index 54f26f352b..84a07e9fc6 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.h
@@ -42,10 +42,8 @@ class RandomSampler : public Sampler {
   // @return - The error code return
   Status GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) override;
 
-  // first handshake between StorageOp and Sampler
-  // @param op - StorageOp pointer, pass in so Sampler can call GetNumSamples() and get ClassIds()
-  // @return
-  Status Init(const RandomAccessOp *op) override;
+  // meant to be called by base class or python
+  Status InitSampler() override;
 
   // for next epoch of sampleIds
   // @return - The error code return
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
index aa3838f8b5..3c3f5f48e8 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc
@@ -20,12 +20,13 @@ namespace dataset {
 Sampler::Sampler(int64_t samples_per_buffer)
     : DatasetOp(0), num_rows_(0), num_samples_(0), samples_per_buffer_(samples_per_buffer), col_desc_(nullptr) {}
 
-Status Sampler::Init(const RandomAccessOp *op) {
-  CHECK_FAIL_RETURN_UNEXPECTED(op != nullptr && samples_per_buffer_ > 0, "Fail to init Sampler()\n");
+Status Sampler::HandshakeRandomAccessOp(const RandomAccessOp *op) {
+  CHECK_FAIL_RETURN_UNEXPECTED(op != nullptr, "RandomAccessOp is nullptr\n");
   RETURN_IF_NOT_OK(op->GetNumSamples(&num_samples_));
   RETURN_IF_NOT_OK(op->GetNumRowsInDataset(&num_rows_));
   // It's up to the derived class to check the validity of the two args
   // Because some sampler only needs one of the arg (weighted_random_sampler)
+  RETURN_IF_NOT_OK(InitSampler());  // init sampler after callback
   return Status::OK();
 }
 
@@ -42,5 +43,49 @@ Status Sampler::CreateSamplerTensor(std::shared_ptr<Tensor> *sample_ids, int64_t
   (void)(*sample_ids)->StartAddr();  // allocate memory in case user forgets!
   return Status::OK();
 }
+
+Status Sampler::GetAllIdsThenReset(py::array *data) {
+  std::unique_ptr<DataBuffer> db;
+  std::shared_ptr<Tensor> sample_ids;
+
+  // check samples_per_buffer is properly set and doesn't overflow
+  CHECK_FAIL_RETURN_UNEXPECTED(samples_per_buffer_ + 1 > 1, "samples_per_buffer invalid");
+
+  // A call to derived class to get sample ids wrapped inside a buffer
+  RETURN_IF_NOT_OK(GetNextBuffer(&db));
+  // Get the only tensor inside the buffer that contains the actual SampleIds for the entire epoch
+  RETURN_IF_NOT_OK(db->GetTensor(&sample_ids, 0, 0));
+  // check this buffer is not a ctrl buffer
+  CHECK_FAIL_RETURN_UNEXPECTED(db->buffer_flags() == DataBuffer::kDeBFlagNone, "ERROR ctrl buffer received");
+  {
+    py::gil_scoped_acquire gil_acquire;
+    if (Py_IsInitialized() == 0) {
+      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized");
+    }
+    try {
+      RETURN_IF_NOT_OK(sample_ids->GetDataAsNumpy(data));
+    } catch (const std::runtime_error &e) {
+      return Status(StatusCode::kPyFuncException, e.what());
+    }
+  }
+  // perform error checking! Next buffer supposed to be EOE since last one already contains all ids for current epoch
+  RETURN_IF_NOT_OK(GetNextBuffer(&db));
+  CHECK_FAIL_RETURN_UNEXPECTED(db->eoe(), "ERROR Non EOE received");
+  // Reset Sampler since this is the end of the epoch
+  RETURN_IF_NOT_OK(Reset());
+  return Status::OK();
+}
+
+Status Sampler::SetNumSamples(int64_t num_samples) {
+  CHECK_FAIL_RETURN_UNEXPECTED(num_samples > 0, "num_samples is negative or 0");
+  num_samples_ = num_samples;
+  return Status::OK();
+}
+
+Status Sampler::SetNumRowsInDataset(int64_t num_rows) {
+  CHECK_FAIL_RETURN_UNEXPECTED(num_rows > 0, "num_rows is negative or 0");
+  num_rows_ = num_rows;
+  return Status::OK();
+}
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
index 801565508b..4ea221027a 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h
@@ -78,14 +78,26 @@ class Sampler : public DatasetOp {
   // @return - The error code return
   Status GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer) override = 0;
 
+  // return all ids in one epoch as a numpy array, then call reset
+  Status GetAllIdsThenReset(py::array *data);
+
   // for next epoch of sampleIds
   // @return - The error code return
   Status Reset() override = 0;
 
-  // first handshake between StorageOp and Sampler. Base class init will call both GetNumRows and GetNumSamples
-  // @param op - StorageOp pointer, pass in so Sampler can call GetNumSamples() and get ClassIds()
+  // setter function for num_rows_
+  Status SetNumRowsInDataset(int64_t num_rows);
+
+  // setter function for num_samples_
+  Status SetNumSamples(int64_t num_samples);
+
+  // first handshake between StorageOp and Sampler. This func will call getNumRows and getNumSamples
+  // @param op - StorageOp pointer, pass in so Sampler can call getNumSamples() and get ClassIds()
   // @return
-  virtual Status Init(const RandomAccessOp *op);
+  virtual Status HandshakeRandomAccessOp(const RandomAccessOp *op);
+
+  // initialize sampler and perform checks on certain vars
+  virtual Status InitSampler() { return Status::OK(); }
 
   // Not meant to be called
   // @return
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
index 72131a6de1..a3c4fe2256 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
@@ -41,9 +41,7 @@ Status SequentialSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buffer)
   return Status::OK();
 }
 
-Status SequentialSampler::Init(const RandomAccessOp *op) {
-  RETURN_UNEXPECTED_IF_NULL(op);
-  RETURN_IF_NOT_OK(op->GetNumSamples(&num_samples_));
+Status SequentialSampler::InitSampler() {
   CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && samples_per_buffer_ > 0, "Fail to init Sequential Sampler");
   samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
   return Status::OK();
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.h
index d119fd8d08..c38a9ed2f9 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.h
@@ -32,10 +32,8 @@ class SequentialSampler : public Sampler {
   // Destructor.
   ~SequentialSampler() = default;
 
-  // Initialize the sampler.
-  // @param op
-  // @return Status
-  Status Init(const RandomAccessOp *op) override;
+  // init sampler, called by python
+  Status InitSampler() override;
 
   // for next epoch of sampleIds
   // @return - The error code return
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
index 16603939b3..c377fddb49 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc
@@ -31,9 +31,8 @@ SubsetRandomSampler::SubsetRandomSampler(const std::vector<int64_t> &indices, in
     : Sampler(samples_per_buffer), indices_(indices), sample_id_(0), buffer_id_(0) {}
 
 // Initialized this Sampler.
-Status SubsetRandomSampler::Init(const RandomAccessOp *op) {
-  // Calling base class init.
-  RETURN_IF_NOT_OK(Sampler::Init(op));
+Status SubsetRandomSampler::InitSampler() {
+  CHECK_FAIL_RETURN_UNEXPECTED(num_rows_ > 0, "num_rows <= 0\n");
 
   // Initialize random generator with seed from config manager
   rand_gen_.seed(GetSeed());
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.h
index 38fae6b20b..1f4c155748 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.h
@@ -38,9 +38,8 @@ class SubsetRandomSampler : public Sampler {
   ~SubsetRandomSampler() = default;
 
   // Initialize the sampler.
-  // @param op (Not used in this sampler)
   // @return Status
-  Status Init(const RandomAccessOp *op) override;
+  Status InitSampler() override;
 
   // Reset the internal variable to the initial state and reshuffle the indices.
   // @return Status
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
index f2957e74be..06afc219e6 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc
@@ -29,21 +29,21 @@ namespace dataset {
 //  Constructor.
 WeightedRandomSampler::WeightedRandomSampler(const std::vector<double> &weights, int64_t num_samples, bool replacement,
                                              int64_t samples_per_buffer)
-    : Sampler(samples_per_buffer), weights_(weights), replacement_(replacement), sample_id_(0), buffer_id_(0) {
-  num_samples_ = num_samples;  // this variable is defined in base class sampler
-}
+    : Sampler(samples_per_buffer),
+      weights_(weights),
+      replacement_(replacement),
+      sample_id_(0),
+      buffer_id_(0),
+      user_num_samples_(num_samples) {}
 
 // Initialized this Sampler.
-Status WeightedRandomSampler::Init(const RandomAccessOp *op) {
-  RETURN_UNEXPECTED_IF_NULL(op);
-  RETURN_IF_NOT_OK(op->GetNumRowsInDataset(&num_rows_));
-
+Status WeightedRandomSampler::InitSampler() {
+  CHECK_FAIL_RETURN_UNEXPECTED(num_rows_ > 0 && user_num_samples_, "num_samples & num_rows need to be positive");
+  CHECK_FAIL_RETURN_UNEXPECTED(samples_per_buffer_ > 0, "samples_per_buffer<=0\n");
   // Initialize random generator with seed from config manager
   rand_gen_.seed(GetSeed());
 
-  samples_per_buffer_ = (samples_per_buffer_ > num_samples_) ? num_samples_ : samples_per_buffer_;
-
-  CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && samples_per_buffer_ > 0, "Fail to init WeightedRandomSampler");
+  samples_per_buffer_ = (samples_per_buffer_ > user_num_samples_) ? user_num_samples_ : samples_per_buffer_;
 
   if (!replacement_) {
     exp_dist_ = std::make_unique<std::exponential_distribution<>>(1);
@@ -65,8 +65,8 @@ void WeightedRandomSampler::InitOnePassSampling() {
   }
 
   // Partial sort the first `numSamples` elements.
-  std::partial_sort(val_idx.begin(), val_idx.begin() + num_samples_, val_idx.end());
-  for (int64_t i = 0; i < num_samples_; i++) {
+  std::partial_sort(val_idx.begin(), val_idx.begin() + user_num_samples_, val_idx.end());
+  for (int64_t i = 0; i < user_num_samples_; i++) {
     onepass_ids_.push_back(val_idx[i].second);
   }
 }
@@ -91,11 +91,11 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buf
                   "number of samples weights is more than num of rows. Might generate id out of bound OR other errors");
   }
 
-  if (!replacement_ && (weights_.size() < static_cast<size_t>(num_samples_))) {
+  if (!replacement_ && (weights_.size() < static_cast<size_t>(user_num_samples_))) {
     RETURN_STATUS_UNEXPECTED("Without replacement, sample weights less than numSamples");
   }
 
-  if (sample_id_ == num_samples_) {
+  if (sample_id_ == user_num_samples_) {
     (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
   } else {
     (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
@@ -103,8 +103,8 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr<DataBuffer> *out_buf
 
     int64_t last_id = sample_id_ + samples_per_buffer_;
     // Handling the return all samples at once, and when last draw is not a full batch.
-    if (last_id > num_samples_) {
-      last_id = num_samples_;
+    if (last_id > user_num_samples_) {
+      last_id = user_num_samples_;
     }
 
     // Allocate tensor.
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h
index bccc9e599d..5381bb64b0 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h
@@ -43,7 +43,7 @@ class WeightedRandomSampler : public Sampler {
   // Initialize the sampler.
   // @param op (Not used in this sampler)
   // @return Status
-  Status Init(const RandomAccessOp *op) override;
+  Status InitSampler() override;
 
   // Reset the internal variable to the initial state and reshuffle the indices.
   Status Reset() override;
@@ -69,6 +69,9 @@ class WeightedRandomSampler : public Sampler {
   // Random engine and device
   std::mt19937 rand_gen_;
 
+  // num_samples from user
+  int64_t user_num_samples_;
+
   // Discrete distribution for generating weighted random numbers with replacement.
   std::unique_ptr<std::discrete_distribution<int64_t>> discrete_dist_;
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
index 71b4c47cf5..1731ed14ba 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc
@@ -220,7 +220,7 @@ Status VOCOp::ParseImageIds() {
 }
 
 Status VOCOp::InitSampler() {
-  RETURN_IF_NOT_OK(sampler_->Init(this));
+  RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
   return Status::OK();
 }
 
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 8e6545375b..4480bbc462 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1748,14 +1748,70 @@ class MindDataset(SourceDataset):
         return num_rows
 
 
-def ds_fn(dataset):
-    for val in dataset:
-        # convert output tensors to ndarrays
-        yield tuple([np.array(x) for x in val])
+def _iter_fn(dataset, num_samples):
+    """
+    Generator function wrapper for iterable dataset
+    """
+    if num_samples is not None:
+        ds_iter = iter(dataset)
+        for _ in range(num_samples):
+            try:
+                val = next(ds_iter)
+            except StopIteration:
+                return
+            # convert output tensors to ndarrays
+            yield tuple([np.array(x) for x in val])
+    else:
+        for val in dataset:
+            # convert output tensors to ndarrays
+            yield tuple([np.array(x) for x in val])
+
+
+def _generator_fn(generator, num_samples):
+    """
+    Generator function wrapper for generator function dataset
+    """
+    if num_samples is not None:
+        gen_iter = generator()
+        for _ in range(num_samples):
+            try:
+                val = next(gen_iter)
+            except StopIteration:
+                return
+            yield val
+    else:
+        gen_iter = generator()
+        for val in gen_iter:
+            yield val
 
 
-def sampler_fn(sampler, dataset):
-    for i in sampler:
+def _py_sampler_fn(sampler, num_samples, dataset):
+    """
+    Generator function wrapper for mappable dataset with python sampler
+    """
+    if num_samples is not None:
+        sampler_iter = iter(sampler)
+        for _ in range(num_samples):
+            try:
+                idx = next(sampler_iter)
+            except StopIteration:
+                return
+            val = dataset[idx]
+            # convert output tensors to ndarrays
+            yield tuple([np.array(x) for x in val])
+    else:
+        for i in sampler:
+            val = dataset[i]
+            # convert output tensors to ndarrays
+            yield tuple([np.array(x) for x in val])
+
+
+def _cpp_sampler_fn(sampler, dataset):
+    """
+    Generator function wrapper for mappable dataset with cpp sampler
+    """
+    indices = sampler.get_indices()
+    for i in indices:
         val = dataset[i]
         # convert output tensors to ndarrays
         yield tuple([np.array(x) for x in val])
@@ -1763,49 +1819,122 @@ def sampler_fn(sampler, dataset):
 
 class GeneratorDataset(SourceDataset):
     """
-    A source dataset that generate data from calling generator function each epoch.
+    A source dataset that generate data from python by invoking python data source each epoch.
+
+    This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table
+    below shows what input args are allowed and their expected behavior.
+
+    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
+       :widths: 25 25 50
+       :header-rows: 1
+
+       * - Parameter 'sampler'
+         - Parameter 'shuffle'
+         - Expected Order Behavior
+       * - None
+         - None
+         - random order
+       * - None
+         - True
+         - random order
+       * - None
+         - False
+         - sequential order
+       * - Sampler object
+         - None
+         - order defined by sampler
+       * - Sampler object
+         - True
+         - not allowed
+       * - Sampler object
+         - False
+         - not allowed
 
     Args:
-        generator_function (callable):
-            A callable object that returns an Generator object that supports the iter() protocol.
-            Generator object is required to return a tuple of numpy array as a row of the dataset on next().
+        source (Callable/Iterable/Random Accessible):
+            A generator callable object, an iterable python object or a random accessible python object.
+            Callable source is required to return a tuple of numpy array as a row of the dataset on source().next().
+            Iterable source is required to return a tuple of numpy array as a row of the dataset on iter(source).next().
+            Random accessible source is required to return a tuple of numpy array as a row of the dataset on
+            source[idx].
         column_names (list[str]): List of column names of the dataset.
         column_types (list[mindspore.dtype], optional): List of column data types of the dataset (default=None).
             If provided, sanity check will be performed on generator output.
-        prefetch_size (int, optional): Prefetch number of records ahead of the user's request (default=None).
-        sampler (Sampler, optional): Object used to choose samples from the dataset (default=None).
+        schema (Schema/String, optional): Path to the json schema file or schema object (default=None).
+            If the schema is not provided, the meta data from column_names and column_types is considered the schema.
+        num_samples (int, optional): The number of samples to be included in the dataset
+            (default=None, all images).
+        shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
+            (default=None, expected order behavior shown in the table).
+        sampler (Sampler/Iterable, optional): Object used to choose samples from the dataset. Random accessible input is
+        required.
+            (default=None, expected order behavior shown in the table).
+        num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
+            This argument should be specified only when 'num_samples' is "None". Random accessible input is required.
+        shard_id (int, optional): The shard ID within num_shards (default=None). This argument should be specified only
+            when num_shards is also specified. Random accessible input is required.
 
     Examples:
-        >>> import mindspore.dataset as ds
-        >>> # 1) generator function that generates multi-dimensional data
+        >>> import mindspore.dataengine as de
+        >>> # 1) Multidimensional generator function as callable input
         >>> def generator_md():
         >>>     for i in range(64):
         >>>         yield (np.array([[i, i + 1], [i + 2, i + 3]]),)
-        >>> # create multi_dimension_generator_dataset with GeneratorMD() and column name "multi_dimensional_data"
-        >>> multi_dimension_generator_dataset = ds.GeneratorDataset(generator_md, ["multi_dimensional_data"])
-        >>> # 2) generator function that generates multi-columns data
+        >>> # create multi_dimension_generator_dataset with GeneratorMD and column name "multi_dimensional_data"
+        >>> multi_dimension_generator_dataset = de.GeneratorDataset(generator_md, ["multi_dimensional_data"])
+        >>> # 2) Multi-column generator function as callable input
         >>> def generator_mc(maxid = 64):
         >>>     for i in range(maxid):
         >>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
-        >>> # create multi_column_generator_dataset with GeneratorMC() and column names "col1" and "col2"
-        >>> multi_column_generator_dataset = ds.GeneratorDataset(generator_mc, ["col1, col2"])
+        >>> # create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
+        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1, col2"])
+        >>> # 3) Iterable dataset as iterable input
+        >>> class MyIterable():
+        >>>     def __iter__(self):
+        >>>         return # User implementation
+        >>> # create iterable_generator_dataset with MyIterable object
+        >>> iterable_generator_dataset = de.GeneratorDataset(MyIterable(), ["col1"])
+        >>> # 4) Random accessible dataset as Random accessible input
+        >>> class MyRA():
+        >>>     def __getitem__(self, index):
+        >>>         return # User implementation
+        >>> # create ra_generator_dataset with MyRA object
+        >>> ra_generator_dataset = de.GeneratorDataset(MyRA(), ["col1"])
+        >>> # List/Dict/Tuple is also random accessible
+        >>> list_generator = de.GeneratorDataset([(np.array(0),), (np.array(1)), (np.array(2))], ["col1"])
+        >>> # 5) Built-in Sampler
+        >>> my_generator = de.GeneratorDataset(my_ds, ["img", "label"], sampler=samplers.RandomSampler())
+        >>>
     """
 
     @check_generatordataset
-    def __init__(self, generator_function, column_names, column_types=None, prefetch_size=None, sampler=None):
-        super().__init__(1)
-        if sampler is not None:
-            self.generator_function = (lambda: sampler_fn(sampler, generator_function))
+    def __init__(self, source, column_names, column_types=None, schema=None, num_samples=None, num_parallel_workers=1,
+                 shuffle=None, sampler=None, num_shards=None, shard_id=None):
+        super().__init__(num_parallel_workers)
+        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
+        if self.sampler is not None and hasattr(source, "__getitem__"):
+            if isinstance(self.sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
+                                         samplers.RandomSampler, samplers.SubsetRandomSampler,
+                                         samplers.WeightedRandomSampler)):
+                if num_samples is None:
+                    num_samples = len(source)
+                sampler_instance = self.sampler.create()
+                sampler_instance.set_num_rows(len(source))
+                sampler_instance.set_num_samples(num_samples)
+                sampler_instance.initialize()
+                self.source = (lambda: _cpp_sampler_fn(sampler_instance, source))
+            else:
+                self.source = (lambda: _py_sampler_fn(self.sampler, num_samples, source))
         else:
             try:
-                # test to see if generator_function is iterable
-                iter(generator_function)
+                iter(source)
             except TypeError:
-                # generator_function was not iterable, assume it is a function
-                self.generator_function = generator_function
+                # Use generator function if input callable
+                self.source = (lambda: _generator_fn(source, num_samples))
             else:
-                # generator_function was iterable, build a function around it
-                self.generator_function = (lambda: ds_fn(generator_function))
+                # Use iterator function if input is iterable
+                # Random accessible input is also iterable
+                self.source = (lambda: _iter_fn(source, num_samples))
 
         self.column_names = column_names
 
@@ -1813,17 +1942,12 @@ class GeneratorDataset(SourceDataset):
             self.column_types = mstypelist_to_detypelist(column_types)
         else:
             self.column_types = column_types
-        self.distribution = ""
-        self.prefetch_size = prefetch_size
-        self.sampler = sampler
 
     def get_args(self):
         args = super().get_args()
-        args["generator_function"] = self.generator_function
+        args["source"] = self.source
         args["column_names"] = self.column_names
         args["column_types"] = self.column_types
-        args["prefetch_size"] = self.prefetch_size
-        args["sampler"] = self.sampler
         return args
 
     def get_dataset_size(self):
diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py
index fd9c50e951..f9c74f151d 100644
--- a/mindspore/dataset/engine/samplers.py
+++ b/mindspore/dataset/engine/samplers.py
@@ -20,7 +20,6 @@ SequentialSampler, SubsetRandomSampler, WeightedRandomSampler.
 
 import mindspore._c_dataengine as cde
 
-
 class DistributedSampler():
     """
     Sampler that access a shard of the dataset.
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 63d7c58270..165a160e77 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -543,28 +543,48 @@ def check_generatordataset(method):
     def new_method(*args, **kwargs):
         param_dict = make_param_dict(method, args, kwargs)
 
-        nreq_param_int = ['prefetch_size']
-        nreq_param_list = ['column_names', 'column_types']
-
         # check generator_function; required argument
-        generator_function = param_dict.get('generator_function')
-        if generator_function is None:
-            raise ValueError("generator_function is not provided.")
+        source = param_dict.get('source')
+        if source is None:
+            raise ValueError("source is not provided.")
+        if not callable(source):
+            try:
+                iter(source)
+            except TypeError:
+                raise TypeError("source should be callable, iterable or random accessible")
 
         # check column_names; required argument
         column_names = param_dict.get('column_names')
         if column_names is None:
             raise ValueError("column_names is not provided.")
 
-        # check prefetch_size range
-        prefetch_size = param_dict.get('prefetch_size')
-        if prefetch_size is not None and (prefetch_size <= 0 or prefetch_size > 1024):
-            raise ValueError("prefetch_size exceeds the boundary.")
-
+        # check optional argument
+        nreq_param_int = ["num_samples", "num_parallel_workers", "num_shards", "shard_id"]
         check_param_type(nreq_param_int, param_dict, int)
-
+        nreq_param_list = ["column_types"]
         check_param_type(nreq_param_list, param_dict, list)
 
+        num_shards = param_dict.get("num_shards")
+        shard_id = param_dict.get("shard_id")
+        if (num_shards is None) != (shard_id is None):
+            # These two parameters appear together.
+            raise ValueError("num_shards and shard_id need to be passed in together")
+        if num_shards is not None:
+            if shard_id >= num_shards:
+                raise ValueError("shard_id should be less than num_shards")
+
+        sampler = param_dict.get("sampler")
+        if sampler is not None:
+            if isinstance(sampler, samplers.PKSampler):
+                raise ValueError("PKSampler is not supported by GeneratorDataset")
+            if not isinstance(sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
+                                        samplers.RandomSampler, samplers.SubsetRandomSampler,
+                                        samplers.WeightedRandomSampler)):
+                try:
+                    iter(sampler)
+                except TypeError:
+                    raise TypeError("sampler should be either iterable or from dataset.samplers.py")
+
         return method(*args, **kwargs)
 
     return new_method
diff --git a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
index 48cc811615..ea0ae78aef 100644
--- a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
+++ b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc
@@ -75,7 +75,7 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) {
   std::shared_ptr<Tensor> tensor;
   for (int i = 0; i < 6; i++) {
     std::unique_ptr<Sampler> sampler = std::make_unique<DistributedSampler>(3, i % 3, (i < 3 ? false : true));
-    sampler->Init(&mock);
+    sampler->HandshakeRandomAccessOp(&mock);
     sampler->GetNextBuffer(&db);
     db->GetTensor(&tensor, 0, 0);
     MS_LOG(DEBUG) << (*tensor);
@@ -95,7 +95,7 @@ TEST_F(MindDataTestStandAloneSampler, TestStandAoneSequentialSampler) {
   std::shared_ptr<Sampler> sampler = std::make_shared<SequentialSampler>(3);
   std::unique_ptr<DataBuffer> db;
   std::shared_ptr<Tensor> tensor;
-  sampler->Init(&mock);
+  sampler->HandshakeRandomAccessOp(&mock);
   sampler->GetNextBuffer(&db);
   db->GetTensor(&tensor, 0, 0);
   EXPECT_TRUE((*tensor) == (*label1));
diff --git a/tests/ut/cpp/dataset/subset_random_sampler_test.cc b/tests/ut/cpp/dataset/subset_random_sampler_test.cc
index 5142a6d399..bb8b3439d5 100644
--- a/tests/ut/cpp/dataset/subset_random_sampler_test.cc
+++ b/tests/ut/cpp/dataset/subset_random_sampler_test.cc
@@ -52,8 +52,8 @@ TEST_F(MindDataTestSubsetRandomSampler, TestAllAtOnce) {
   std::unordered_set<int64_t> in_set(in.begin(), in.end());
   SubsetRandomSampler sampler(in);
 
-  DummyRandomAccessOp dummy_random_access_op(5);
-  sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(5);
+  sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -80,8 +80,8 @@ TEST_F(MindDataTestSubsetRandomSampler, TestGetNextBuffer) {
   std::vector<int64_t> input(total_samples, 1);
   SubsetRandomSampler sampler(input, samples_per_buffer);
 
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -111,8 +111,8 @@ TEST_F(MindDataTestSubsetRandomSampler, TestReset) {
   std::unordered_set<int64_t> in_set(in.begin(), in.end());
   SubsetRandomSampler sampler(in);
 
-  DummyRandomAccessOp dummy_random_access_op(5);
-  sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(5);
+  sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
diff --git a/tests/ut/cpp/dataset/weighted_random_sampler_test.cc b/tests/ut/cpp/dataset/weighted_random_sampler_test.cc
index 1c5d73613f..51a4bc3cb3 100644
--- a/tests/ut/cpp/dataset/weighted_random_sampler_test.cc
+++ b/tests/ut/cpp/dataset/weighted_random_sampler_test.cc
@@ -60,8 +60,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotReplacement) {
 
   // create sampler with replacement = true
   WeightedRandomSampler m_sampler(weights, num_samples, true);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -90,8 +90,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotNoReplacement) {
 
   // create sampler with replacement = replacement
   WeightedRandomSampler m_sampler(weights, num_samples, false);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -126,8 +126,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestGetNextBufferReplacement) {
 
   // create sampler with replacement = replacement
   WeightedRandomSampler m_sampler(weights, num_samples, true, samples_per_buffer);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -162,8 +162,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestGetNextBufferNoReplacement) {
 
   // create sampler with replacement = replacement
   WeightedRandomSampler m_sampler(weights, num_samples, false, samples_per_buffer);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -203,8 +203,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetReplacement) {
 
   // create sampler with replacement = true
   WeightedRandomSampler m_sampler(weights, num_samples, true);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
@@ -248,8 +248,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
 
   // create sampler with replacement = true
   WeightedRandomSampler m_sampler(weights, num_samples, false);
-  DummyRandomAccessOp dummy_random_access_op(total_samples);
-  m_sampler.Init(&dummy_random_access_op);
+  DummyRandomAccessOp dummyRandomAccessOp(total_samples);
+  m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
   std::unique_ptr<DataBuffer> db;
   TensorRow row;
diff --git a/tests/ut/python/dataset/test_generator.py b/tests/ut/python/dataset/test_generator.py
index 07556d9c7f..c224c5a2ea 100644
--- a/tests/ut/python/dataset/test_generator.py
+++ b/tests/ut/python/dataset/test_generator.py
@@ -439,6 +439,74 @@ def test_case_error_4():
     assert "Unexpected error. Result of a tensorOp doesn't match output column names" in str(info.value)
 
 
+def test_sequential_sampler():
+    source = [(np.array([x]),) for x in range(64)]
+    ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler())
+    i = 0
+    for data in ds1.create_dict_iterator():  # each data is a dictionary
+        golden = np.array([i])
+        assert np.array_equal(data["data"], golden)
+        i = i + 1
+
+
+def test_random_sampler():
+    source = [(np.array([x]),) for x in range(64)]
+    ds1 = ds.GeneratorDataset(source, ["data"], shuffle = True)
+    for data in ds1.create_dict_iterator():  # each data is a dictionary
+        pass
+
+
+def test_distributed_sampler():
+    source = [(np.array([x]),) for x in range(64)]
+    for sid in range(8):
+        ds1 = ds.GeneratorDataset(source, ["data"], shuffle = False, num_shards=8, shard_id=sid)
+        i = sid
+        for data in ds1.create_dict_iterator():  # each data is a dictionary
+            golden = np.array([i])
+            assert np.array_equal(data["data"], golden)
+            i = i + 8
+
+
+def test_num_samples():
+    source = [(np.array([x]),) for x in range(64)]
+    num_samples = 32
+    ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler(), num_samples = num_samples)
+    ds2 = ds.GeneratorDataset(source, ["data"], sampler=[i for i in range(32)], num_samples = num_samples)
+    ds3 = ds.GeneratorDataset(generator_1d, ["data"], num_samples = num_samples)
+
+    count = 0
+    for _ in ds1.create_dict_iterator():
+        count = count + 1
+    assert count == num_samples
+
+    count = 0
+    for _ in ds2.create_dict_iterator():
+        count = count + 1
+    assert count == num_samples
+
+    count = 0
+    for _ in ds3.create_dict_iterator():
+        count = count + 1
+    assert count == num_samples
+
+
+def test_num_samples_underflow():
+    source = [(np.array([x]),) for x in range(64)]
+    num_samples = 256
+    ds2 = ds.GeneratorDataset(source, ["data"], sampler=[i for i in range(64)], num_samples = num_samples)
+    ds3 = ds.GeneratorDataset(generator_1d, ["data"], num_samples = num_samples)
+
+    count = 0
+    for _ in ds2.create_dict_iterator():
+        count = count + 1
+    assert count == 64
+
+    count = 0
+    for _ in ds3.create_dict_iterator():
+        count = count + 1
+    assert count == 64
+
+
 if __name__ == "__main__":
     test_case_0()
     test_case_1()
@@ -458,3 +526,6 @@ if __name__ == "__main__":
     test_case_error_2()
     test_case_error_3()
     test_case_error_4()
+    test_sequential_sampler()
+    test_distributed_sampler()
+    test_random_sampler()
diff --git a/tests/ut/python/dataset/test_sampler.py b/tests/ut/python/dataset/test_sampler.py
index ca618311cb..7a58249f9c 100644
--- a/tests/ut/python/dataset/test_sampler.py
+++ b/tests/ut/python/dataset/test_sampler.py
@@ -87,7 +87,28 @@ def test_random_sampler_multi_iter(print_res=False):
     test_config(replacement=True, num_samples=5, num_repeats=5, validate=[0, 1, 2, 3, 4, 5])
 
 
+def test_sampler_py_api():
+    sampler = ds.SequentialSampler().create()
+    sampler.set_num_rows(128)
+    sampler.set_num_samples(64)
+    sampler.initialize()
+    sampler.get_indices()
+
+    sampler = ds.RandomSampler().create()
+    sampler.set_num_rows(128)
+    sampler.set_num_samples(64)
+    sampler.initialize()
+    sampler.get_indices()
+
+    sampler = ds.DistributedSampler(8, 4).create()
+    sampler.set_num_rows(128)
+    sampler.set_num_samples(64)
+    sampler.initialize()
+    sampler.get_indices()
+
+
 if __name__ == '__main__':
     test_sequential_sampler(True)
     test_random_sampler(True)
     test_random_sampler_multi_iter(True)
+    test_sampler_py_api()

From 59604af98b6027b936c14aecf5741d9c39f74531 Mon Sep 17 00:00:00 2001
From: zhaoting <zhaoting23@huawei.com>
Date: Tue, 14 Apr 2020 16:27:12 +0800
Subject: [PATCH 239/367] change some settings in YOLOv3

---
 example/yolov3_coco2017/dataset.py              | 4 +---
 example/yolov3_coco2017/run_distribute_train.sh | 9 ++++++++-
 example/yolov3_coco2017/train.py                | 5 +++--
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/example/yolov3_coco2017/dataset.py b/example/yolov3_coco2017/dataset.py
index 826fe16c53..9c6a0f362d 100644
--- a/example/yolov3_coco2017/dataset.py
+++ b/example/yolov3_coco2017/dataset.py
@@ -22,7 +22,6 @@ from PIL import Image
 from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
 import mindspore.dataset as de
 from mindspore.mindrecord import FileWriter
-import mindspore.dataset.transforms.vision.py_transforms as P
 import mindspore.dataset.transforms.vision.c_transforms as C
 from config import ConfigYOLOV3ResNet18
 
@@ -301,13 +300,12 @@ def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num
     compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
 
     if is_training:
-        hwc_to_chw = P.HWC2CHW()
+        hwc_to_chw = C.HWC2CHW()
         ds = ds.map(input_columns=["image", "annotation"],
                     output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
                     columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
                     operations=compose_map_func, num_parallel_workers=num_parallel_workers)
         ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
-        ds = ds.shuffle(buffer_size=256)
         ds = ds.batch(batch_size, drop_remainder=True)
         ds = ds.repeat(repeat_num)
     else:
diff --git a/example/yolov3_coco2017/run_distribute_train.sh b/example/yolov3_coco2017/run_distribute_train.sh
index 0c43e776b9..201f19ca16 100644
--- a/example/yolov3_coco2017/run_distribute_train.sh
+++ b/example/yolov3_coco2017/run_distribute_train.sh
@@ -19,6 +19,7 @@ echo "Please run the scipt as: "
 echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
 echo "for example: sh run_distribute_train.sh 8 100 /data/Mindrecord_train /data /data/train.txt /data/hccl.json"
 echo "It is better to use absolute path."
+echo "The learning rate is 0.005 as default, if you want other lr, please change the value in this script."
 echo "=============================================================================================================="
 
 EPOCH_SIZE=$2
@@ -38,6 +39,11 @@ export RANK_SIZE=$1
 for((i=0;i<RANK_SIZE;i++))
 do
     export DEVICE_ID=$i
+
+    start=`expr $i \* 12`
+    end=`expr $start \+ 11`
+    cmdopt=$start"-"$end
+
     rm -rf LOG$i
     mkdir ./LOG$i
     cp  *.py ./LOG$i
@@ -45,8 +51,9 @@ do
     export RANK_ID=$i
     echo "start training for rank $i, device $DEVICE_ID"
     env > env.log
-    python ../train.py  \
+    taskset -c $cmdopt python ../train.py  \
     --distribute=1  \
+    --lr=0.005 \
     --device_num=$RANK_SIZE  \
     --device_id=$DEVICE_ID  \
     --mindrecord_dir=$MINDRECORD_DIR  \
diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py
index 121e2aa810..c7d28a8350 100644
--- a/example/yolov3_coco2017/train.py
+++ b/example/yolov3_coco2017/train.py
@@ -67,6 +67,7 @@ if __name__ == '__main__':
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
+    parser.add_argument("--lr", type=float, default=0.001, help="Learning rate, default is 0.001.")
     parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
@@ -137,8 +138,8 @@ if __name__ == '__main__':
         ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
         ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
 
-        lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
-                           decay_step=1000, decay_rate=0.95))
+        lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=0, global_step=args_opt.epoch_size * dataset_size,
+                           decay_step=1000, decay_rate=0.95, steps=True))
         opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
         net = TrainingWrapper(net, opt, loss_scale)
 

From 5c2ded8d8eb714d22291f37945e35a4504fd17e7 Mon Sep 17 00:00:00 2001
From: anzhengqi <anzhengqi.huawei.com>
Date: Tue, 14 Apr 2020 21:17:11 +0800
Subject: [PATCH 240/367] fix schema.parse_columns function

---
 mindspore/dataset/engine/datasets.py | 76 +++++++++++++++-------------
 1 file changed, 41 insertions(+), 35 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 4b02004086..683102aecf 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2464,47 +2464,53 @@ class Schema:
         Parse the columns and add it to self.
 
         Args:
-            columns (dict or list[str]): names of columns.
+            columns (dict or list[dict]): dataset attribution information, decoded from schema file.
+            if list: columns element must be dict, 'name' and 'type' must be in keys, 'shape' optional.
+            if dict: columns.keys() as name, element in columns.values() is dict, and 'type' inside, 'shape' optional.
+              example 1)
+                [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
+                 {'name': 'label', 'type': 'int8', 'shape': [1]}]
+              example 2)
+                {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
 
         Raises:
-            RuntimeError: If failed to parse schema file.
-            RuntimeError: If unknown items in schema file.
+            RuntimeError: If failed to parse columns.
+            RuntimeError: If unknown items in columns.
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
-        if columns is None:
-            raise TypeError("Expected non-empty dict or string list.")
         self.columns = []
-        for col in columns:
-            name = None
-            shape = None
-            data_type = None
-            col_details = None
-            if isinstance(columns, list):
-                col_details = col
-                if "name" in col:
-                    name = col["name"]
-            elif isinstance(columns, dict):
-                col_details = columns[col]
-                name = col
-            else:
-                raise RuntimeError("Error parsing the schema file")
-
-            for k, v in col_details.items():
-                if k == "shape":
-                    shape = v
-                elif k == "type":
-                    data_type = v
-                elif k in ("t_impl", "rank"):
-                    pass
-                else:
-                    raise RuntimeError("Unknown field %s" % k)
-
-            if name is None:
-                raise RuntimeError("Column's name field is missing.")
-            if data_type is None:
-                raise RuntimeError("Column's type field is missing.")
-            self.add_column(name, data_type, shape)
+        if isinstance(columns, list):
+            for column in columns:
+                try:
+                    name = column.pop("name")
+                except KeyError:
+                    raise RuntimeError("Column's name is missing")
+                try:
+                    de_type = column.pop("type")
+                except KeyError:
+                    raise RuntimeError("Column' type is missing")
+                shape = column.pop("shape", None)
+                column.pop("t_impl", None)
+                column.pop("rank", None)
+                if column:
+                    raise RuntimeError("Unknown field {}".format(",".join(column.keys())))
+                self.add_column(name, de_type, shape)
+        elif isinstance(columns, dict):
+            for key, value in columns.items():            
+                name = key
+                try:
+                    de_type = value.pop("type")
+                except KeyError:
+                    raise RuntimeError("Column' type is missing")
+                shape = value.pop("shape", None)
+                value.pop("t_impl", None)
+                value.pop("rank", None)
+                if value:
+                    raise RuntimeError("Unknown field {}".format(",".join(value.keys())))
+                self.add_column(name, de_type, shape)
+        else:
+            raise RuntimeError("columns must be dict or list, columns contain name, type, shape(optional).")
 
     def from_json(self, json_obj):
         """

From a1a2182b8aa1a1d1b0f728fd49f603f52aa16fc4 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Wed, 15 Apr 2020 09:48:04 +0800
Subject: [PATCH 241/367] modify whl file name to be same as package name

---
 cmake/package_script.cmake | 2 --
 1 file changed, 2 deletions(-)

diff --git a/cmake/package_script.cmake b/cmake/package_script.cmake
index 565154a38d..dcc8ee0ad0 100644
--- a/cmake/package_script.cmake
+++ b/cmake/package_script.cmake
@@ -76,8 +76,6 @@ if (NOT CMAKE_SYSTEM_NAME MATCHES "Windows")
     )
 endif ()
 
-set(PACKAGE_NAME "mindspore")
-
 file(GLOB WHL_FILE ${MS_PACK_ROOT_DIR}/dist/*.whl)
 get_filename_component(ORIGIN_FILE_NAME ${WHL_FILE} NAME)
 string(REPLACE "-" ";" ORIGIN_FILE_NAME ${ORIGIN_FILE_NAME})

From ffb2cb03a45812cf7e864ea4d32f302111e74981 Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Tue, 14 Apr 2020 17:06:37 +0800
Subject: [PATCH 242/367] Change 'NOT_FULLY_USE_DEVICES' to 'FULLY_USE_DEVICES'
 and make ALL-1 user-specified-strategy valid in auto-parallel

---
 .../parallel/auto_parallel/edge_costmodel.cc  |  6 +--
 .../parallel/auto_parallel/graph_costmodel.cc | 14 +++----
 .../parallel/auto_parallel/graph_costmodel.h  |  4 +-
 mindspore/ccsrc/parallel/costmodel_context.cc |  4 +-
 mindspore/ccsrc/parallel/costmodel_context.h  | 10 ++---
 .../ccsrc/parallel/ops_info/matmul_info.cc    |  2 +-
 .../ccsrc/parallel/ops_info/operator_info.cc  |  2 +-
 .../ccsrc/parallel/step_auto_parallel.cc      | 38 +++++++------------
 mindspore/ccsrc/pipeline/init.cc              |  8 ++--
 mindspore/parallel/algo_parameter_config.py   | 16 ++++----
 .../parallel/test_auto_parallel_two_matmul.py | 10 ++---
 tests/ut/python/parallel/test_reshape.py      |  6 +--
 12 files changed, 55 insertions(+), 65 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
index 895646f409..21e67f9f7b 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc
@@ -85,10 +85,10 @@ Status Edge::InitEdgeCost() {
     }
   }
   if (!has_available_cost) {
-    if (!NOT_FULLY_USE_DEVICES) {
+    if (FULLY_USE_DEVICES) {
       MS_LOG(EXCEPTION) << "Generating cost for edge: " << edge_name_
-                        << " failed, it may be caused by setting 'not_fully_use_devices' false. Try to set "
-                           "'not_fully_use_devices' true.";
+                        << " failed, it may be caused by setting 'fully_use_devices' true. Try to set "
+                           "'fully_use_devices' false.";
     } else if (ELEMENTWISE_OP_STRA_FOLLOW) {
       MS_LOG(EXCEPTION) << "Generating cost for edge: " << edge_name_
                         << " failed, it may be caused by setting 'elementwise_op_strategy_follow' true. "
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
index 82dd723039..c56d3a6fbd 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc
@@ -36,7 +36,7 @@ double COST_MODEL_COMMUNI_CONST = DEFAULT_COST_MODEL_COMMUNI_CONST;
 double COST_MODEL_COMMUNI_BIAS = DEFAULT_COST_MODEL_COMMUNI_BIAS;
 bool TENSOR_SLICE_ALIGNMENT_ENABLE = DEFAULT_TENSOR_SLICE_ALIGNMENT_ENABLE;
 size_t TENSOR_SLICE_ALIGNMENT_SIZE = DEFAULT_TENSOR_SLICE_ALIGNMENT_SIZE;
-bool NOT_FULLY_USE_DEVICES = DEFAULT_NOT_FULLY_USE_DEVICES;
+bool FULLY_USE_DEVICES = DEFAULT_FULLY_USE_DEVICES;
 bool ELEMENTWISE_OP_STRA_FOLLOW = DEFAULT_ELEMENTWISE_OP_STRA_FOLLOW;
 
 void CostGraph::SetDeviceMemoryAndCostParameter() {
@@ -125,13 +125,13 @@ void CostGraph::SetDeviceMemoryAndCostParameter() {
   TENSOR_SLICE_ALIGNMENT_SIZE = align_size;
   MS_LOG(INFO) << "tensor_slice_align_size: " << TENSOR_SLICE_ALIGNMENT_SIZE << ".";
 
-  // NOT_FULLY_USE_DEVICES
-  auto not_fully_devices = CostModelContext::GetInstance()->not_fully_use_device();
-  NOT_FULLY_USE_DEVICES = not_fully_devices;
-  if (NOT_FULLY_USE_DEVICES) {
-    MS_LOG(INFO) << "not_fully_use_devices: true.";
+  // FULLY_USE_DEVICES
+  auto fully_devices = CostModelContext::GetInstance()->fully_use_device();
+  FULLY_USE_DEVICES = fully_devices;
+  if (FULLY_USE_DEVICES) {
+    MS_LOG(INFO) << "fully_use_devices: true.";
   } else {
-    MS_LOG(INFO) << "not_fully_use_devices: false.";
+    MS_LOG(INFO) << "fully_use_devices: false.";
   }
 
   // ELEMENTWISE_OP_STRA_FOLLOW
diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
index 65aeb210ea..b6591c0741 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
@@ -42,7 +42,7 @@ namespace parallel {
 #define DEFAULT_COST_MODEL_COMMUNI_BIAS 1024.0
 #define DEFAULT_TENSOR_SLICE_ALIGNMENT_ENABLE false
 #define DEFAULT_TENSOR_SLICE_ALIGNMENT_SIZE 16
-#define DEFAULT_NOT_FULLY_USE_DEVICES false
+#define DEFAULT_FULLY_USE_DEVICES true
 #define DEFAULT_ELEMENTWISE_OP_STRA_FOLLOW false
 
 class CostGraph;
@@ -57,7 +57,7 @@ extern double COST_MODEL_COMMUNI_CONST;
 extern double COST_MODEL_COMMUNI_BIAS;
 extern bool TENSOR_SLICE_ALIGNMENT_ENABLE;
 extern size_t TENSOR_SLICE_ALIGNMENT_SIZE;
-extern bool NOT_FULLY_USE_DEVICES;
+extern bool FULLY_USE_DEVICES;
 extern bool ELEMENTWISE_OP_STRA_FOLLOW;
 
 class CostGraph {
diff --git a/mindspore/ccsrc/parallel/costmodel_context.cc b/mindspore/ccsrc/parallel/costmodel_context.cc
index 0ebbd2c626..82b260f967 100644
--- a/mindspore/ccsrc/parallel/costmodel_context.cc
+++ b/mindspore/ccsrc/parallel/costmodel_context.cc
@@ -60,7 +60,7 @@ void CostModelContext::ResetAlgoParameters() {
   costmodel_simplify_cal_ = DEFAULT_COST_MODEL_SIMPLIFY_CALCULATION;
   tensor_slice_alignment_enable_ = DEFAULT_TENSOR_SLICE_ALIGNMENT_ENABLE;
   tensor_slice_alignment_size_ = DEFAULT_TENSOR_SLICE_ALIGNMENT_SIZE;
-  not_fully_use_device_ = DEFAULT_NOT_FULLY_USE_DEVICES;
+  fully_use_device_ = DEFAULT_FULLY_USE_DEVICES;
   elementwise_stra_follow_ = DEFAULT_ELEMENTWISE_OP_STRA_FOLLOW;
 }
 
@@ -118,7 +118,7 @@ void CostModelContext::set_tensor_slice_alignment_size(size_t ts_align_size) {
   tensor_slice_alignment_size_ = ts_align_size;
 }
 
-void CostModelContext::set_not_fully_use_device(bool not_fully_use) { not_fully_use_device_ = not_fully_use; }
+void CostModelContext::set_fully_use_device(bool fully_use) { fully_use_device_ = fully_use; }
 
 void CostModelContext::set_elementwise_stra_follow(bool elementwise_follow) {
   elementwise_stra_follow_ = elementwise_follow;
diff --git a/mindspore/ccsrc/parallel/costmodel_context.h b/mindspore/ccsrc/parallel/costmodel_context.h
index 04782fa366..23c9f7cc8d 100644
--- a/mindspore/ccsrc/parallel/costmodel_context.h
+++ b/mindspore/ccsrc/parallel/costmodel_context.h
@@ -102,9 +102,9 @@ class CostModelContext {
   void set_tensor_slice_alignment_size(size_t);
   size_t tensor_slice_alignment_size() const { return tensor_slice_alignment_size_; }
 
-  // NOT_FULLY_USE_DEVICES
-  void set_not_fully_use_device(bool);
-  bool not_fully_use_device() const { return not_fully_use_device_; }
+  // FULLY_USE_DEVICES
+  void set_fully_use_device(bool);
+  bool fully_use_device() const { return fully_use_device_; }
 
   // ELEMENTWISE_OP_STRA_FOLLOW
   void set_elementwise_stra_follow(bool);
@@ -158,8 +158,8 @@ class CostModelContext {
   // TENSOR_SLICE_ALIGNMENT_SIZE
   size_t tensor_slice_alignment_size_;
 
-  // NOT_FULLY_USE_DEVICES
-  bool not_fully_use_device_;
+  // FULLY_USE_DEVICES
+  bool fully_use_device_;
 
   // ELEMENTWISE_OP_STRA_FOLLOW
   bool elementwise_stra_follow_;
diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
index e617ae6c24..8d1264482b 100644
--- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc
@@ -465,7 +465,7 @@ Status MatMulBase::PrepareStrategy(int32_t stage_id, size_t dev_num,
                                    mindspore::parallel::Dimensions combined_partitions, size_t input0_shape_size,
                                    size_t input1_shape_size, mindspore::parallel::StrategyPtr* const sp) {
   int32_t product = std::accumulate(combined_partitions.begin(), combined_partitions.end(), 1, std::multiplies<int>());
-  if (NOT_FULLY_USE_DEVICES) {
+  if (!FULLY_USE_DEVICES) {
     if (IntToSize(product) > dev_num) {
       return FAILED;
     }
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 23b6a5190a..5842a9149f 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -675,7 +675,7 @@ Status PrepareStrategyBase(int32_t stage_id, size_t dev_num, const Shapes& input
   for (auto& input_partition : inputs_partitions) {
     product *= std::accumulate(input_partition.begin(), input_partition.end(), 1, std::multiplies<int>());
   }
-  if (NOT_FULLY_USE_DEVICES) {
+  if (!FULLY_USE_DEVICES) {
     if (IntToSize(product) > dev_num) {
       return FAILED;
     }
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index a42ce612fb..495c3a8d39 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -110,8 +110,6 @@ std::vector<std::string> splittable_op_ = {MATMUL,
 std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,
                                             CAST,       POW,  EXP,  LOG,     COS,         ACOS, LOGICALNOT};
 
-std::vector<std::string> ignore_manual_strategy_op_ = {BATCH_NORM};
-
 bool StepAutoParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &) {
   MS_EXCEPTION_IF_NULL(root);
   MS_EXCEPTION_IF_NULL(ParallelContext::GetInstance());
@@ -308,16 +306,6 @@ std::vector<TypePtr> ExtractOutputTypeByNode(const CNodePtr &node) {
   return outputs_type;
 }
 
-// Be careful the argument is cnode_full_name, not the op_name
-bool IsIgnoreStrategyOperator(const std::string &cnode_full_name) {
-  for (auto &ignore_op : ignore_manual_strategy_op_) {
-    if (cnode_full_name.find(ignore_op) != std::string::npos) {
-      return true;
-    }
-  }
-  return false;
-}
-
 bool IsElementWiseOperator(const std::string &op_name) {
   auto iter = std::find(elementwise_op_.begin(), elementwise_op_.end(), op_name);
   return (iter != elementwise_op_.end());
@@ -414,18 +402,20 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
       // Set cost for this configured strategy
       if (operator_info->SetCostUnderStrategy(strategyPtr) != SUCCESS) {
         MS_LOG(EXCEPTION) << "Failure: operator " << prim->name() << " SetCostUnderStrategy failed";
-      } else if (!NOT_FULLY_USE_DEVICES) {
-        if (!IsIgnoreStrategyOperator(cnode->fullname_with_scope())) {
-          // If configured to fully use devices, then checking for the user-specified strategy
-          int32_t used_devices = operator_info->used_devices();
-          MS_EXCEPTION_IF_NULL(g_device_manager);
-          auto total_device_num = g_device_manager->GetDeviceListByStageId(0).size();
-          // 'used_devices == -1' means that 'used_devices_' is not set
-          if ((used_devices == -1) || IntToSize(used_devices) != total_device_num) {
-            MS_LOG(EXCEPTION) << "In configuration 'NOT_FULLY_USE_DEVICES' = False, "
-                              << "but the specified strategy uses device: " << used_devices
-                              << ", total devices: " << total_device_num;
-          }
+      } else if (FULLY_USE_DEVICES) {
+        // If configured to fully use devices, then checking for the user-specified strategy
+        int32_t used_devices = operator_info->used_devices();
+        MS_EXCEPTION_IF_NULL(g_device_manager);
+        auto total_device_num = g_device_manager->GetDeviceListByStageId(0).size();
+        // 'used_devices == 1' means that ALL-1 strategy, which is valid in auto-parallel
+        if (used_devices == 1) {
+          return operator_info;
+        }
+        // 'used_devices == -1' means that 'used_devices_' is not set
+        if ((used_devices == -1) || IntToSize(used_devices) != total_device_num) {
+          MS_LOG(EXCEPTION) << "In configuration 'FULLY_USE_DEVICES' = True, "
+                            << "but the specified strategy uses device: " << used_devices
+                            << ", total devices: " << total_device_num;
         }
       }
     }
diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc
index 24ead047d3..4aab6e2a5e 100644
--- a/mindspore/ccsrc/pipeline/init.cc
+++ b/mindspore/ccsrc/pipeline/init.cc
@@ -261,10 +261,10 @@ PYBIND11_MODULE(_c_expression, m) {
          "Set the parameter tensor_slice_size in strategy generation.")
     .def("get_tensor_slice_align_size", &CostModelContext::tensor_slice_alignment_size,
          "Get the parameter tensor_slice_size in strategy generation.")
-    .def("set_not_fully_use_devices", &CostModelContext::set_not_fully_use_device,
-         "Set the parameter not_fully_use_devices in the DP algorithm.")
-    .def("get_not_fully_use_devices", &CostModelContext::not_fully_use_device,
-         "Get the parameter not_fully_use_devices in the DP algorithm.")
+    .def("set_fully_use_devices", &CostModelContext::set_fully_use_device,
+         "Set the parameter fully_use_devices in the DP algorithm.")
+    .def("get_fully_use_devices", &CostModelContext::fully_use_device,
+         "Get the parameter fully_use_devices in the DP algorithm.")
     .def("set_elementwise_op_strategy_follow", &CostModelContext::set_elementwise_stra_follow,
          "Set the parameter elementwise_op_strategy_follow in the DP algorithm.")
     .def("get_elementwise_op_strategy_follow", &CostModelContext::elementwise_stra_follow,
diff --git a/mindspore/parallel/algo_parameter_config.py b/mindspore/parallel/algo_parameter_config.py
index aafc02367f..d1e4aa87a9 100644
--- a/mindspore/parallel/algo_parameter_config.py
+++ b/mindspore/parallel/algo_parameter_config.py
@@ -53,13 +53,13 @@ class _AlgoParameterConfig():
         self.check_config_handle()
         return self._config_handle.get_simplify_cal()
 
-    def set_not_fully_use_devices(self, not_fully):
+    def set_fully_use_devices(self, not_fully):
         self.check_config_handle()
-        self._config_handle.set_not_fully_use_devices(not_fully)
+        self._config_handle.set_fully_use_devices(not_fully)
 
-    def get_not_fully_use_devices(self):
+    def get_fully_use_devices(self):
         self.check_config_handle()
-        return self._config_handle.get_not_fully_use_devices()
+        return self._config_handle.get_fully_use_devices()
 
     def set_elementwise_op_strategy_follow(self, element_strategy_follow):
         self.check_config_handle()
@@ -119,7 +119,7 @@ def _algo_parameter_config():
 
 set_algo_parameters_config_func_map = {
     "simplify_cal": _algo_parameter_config().set_simplify_cal,
-    "not_fully_use_devices": _algo_parameter_config().set_not_fully_use_devices,
+    "fully_use_devices": _algo_parameter_config().set_fully_use_devices,
     "elementwise_op_strategy_follow": _algo_parameter_config().set_elementwise_op_strategy_follow,
     "tensor_slice_align_enable": _algo_parameter_config().set_tensor_slice_align_enable,
     "tensor_slice_align_size": _algo_parameter_config().set_tensor_slice_align_size}
@@ -127,14 +127,14 @@ set_algo_parameters_config_func_map = {
 
 get_algo_parameters_config_func_map = {
     "simplify_cal": _algo_parameter_config().get_simplify_cal,
-    "not_fully_use_devices": _algo_parameter_config().get_not_fully_use_devices,
+    "fully_use_devices": _algo_parameter_config().get_fully_use_devices,
     "elementwise_op_strategy_follow": _algo_parameter_config().get_elementwise_op_strategy_follow,
     "tensor_slice_align_enable": _algo_parameter_config().get_tensor_slice_align_enable,
     "tensor_slice_align_size": _algo_parameter_config().get_tensor_slice_align_size}
 
 
 @args_type_check(simplify_cal=bool, tensor_slice_align_enable=bool, tensor_slice_align_size=int,
-                 not_fully_use_devices=bool, elementwise_op_strategy_follow=bool)
+                 fully_use_devices=bool, elementwise_op_strategy_follow=bool)
 def set_algo_parameters(**kwargs):
     """
     Set algo parameter config.
@@ -146,7 +146,7 @@ def set_algo_parameters(**kwargs):
         simplify_cal (bool): Whether simplifying calculations in strategy-searching algorithm. Default: True
         tensor_slice_align_enable (bool): Whether checking tensor slice shape. Default: False
         tensor_slice_align_size (int): The minimum tensor slice shape, the value must be in [1, 1024]. Default: 16
-        not_fully_use_devices (bool): Whether generating strategies that not fully use devices. Default: False
+        fully_use_devices (bool): Whether generating strategies that fully use all available devices. Default: True
         elementwise_op_strategy_follow (bool): Whether the elementwise operator have the same strategies as its
             subsequent operators. Default: False
 
diff --git a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
index bd6639a501..db6190ab89 100644
--- a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
+++ b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py
@@ -100,7 +100,7 @@ def test_two_matmul():
     set_algo_parameters(simplify_cal=True,
                                           tensor_slice_align_enable=False,
                                           tensor_slice_align_size=32,
-                                          not_fully_use_devices=True,
+                                          fully_use_devices=False,
                                           elementwise_op_strategy_follow=False)
     para_simplify_cal = get_algo_parameters("simplify_cal")
     assert para_simplify_cal == True
@@ -108,8 +108,8 @@ def test_two_matmul():
     assert para_slice_align_enable == False
     para_slice_align_size = get_algo_parameters("tensor_slice_align_size")
     assert para_slice_align_size == 32
-    not_fully_use_devices  = get_algo_parameters("not_fully_use_devices")
-    assert not_fully_use_devices == True
+    fully_use_devices = get_algo_parameters("fully_use_devices")
+    assert fully_use_devices == False
     elementwise_op_strategy_follow = get_algo_parameters("elementwise_op_strategy_follow")
     assert elementwise_op_strategy_follow == False
 
@@ -120,8 +120,8 @@ def test_two_matmul():
     assert para_slice_align_enable == False
     para_slice_align_size = get_algo_parameters("tensor_slice_align_size")
     assert para_slice_align_size == 16
-    not_fully_use_devices  = get_algo_parameters("not_fully_use_devices")
-    assert not_fully_use_devices == False
+    fully_use_devices = get_algo_parameters("fully_use_devices")
+    assert fully_use_devices == True
     elementwise_op_strategy_follow = get_algo_parameters("elementwise_op_strategy_follow")
     assert elementwise_op_strategy_follow == False
 
diff --git a/tests/ut/python/parallel/test_reshape.py b/tests/ut/python/parallel/test_reshape.py
index 43906aec23..f72e5f909b 100644
--- a/tests/ut/python/parallel/test_reshape.py
+++ b/tests/ut/python/parallel/test_reshape.py
@@ -576,7 +576,7 @@ def test_flatten_reshape2(parallel_mode="auto_parallel"):
     epoch_size = 2
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
-    set_algo_parameters(not_fully_use_devices=True)
+    set_algo_parameters(fully_use_devices=False)
     net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_axis=(2, 3), strategy=((4, 1, 1, 1),))
     loss = CrossEntropyLoss()
     predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
@@ -617,7 +617,7 @@ def test_flatten_reshape3(parallel_mode="auto_parallel"):
     epoch_size = 2
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
-    set_algo_parameters(not_fully_use_devices=True)
+    set_algo_parameters(fully_use_devices=False)
     net = ParallelReshapeNet(dense_in_channel=2048, dense_out_channel=1000, shape=(128, 1000), strategy=((16, 1),))
     loss = CrossEntropyLoss()
     predict = Tensor(np.ones([batch_size, 1, 2, 1024]), dtype=ms.float32)
@@ -646,7 +646,7 @@ def test_flatten_reshape4(parallel_mode="semi_auto_parallel"):
     epoch_size = 2
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
-    set_algo_parameters(not_fully_use_devices=True)
+    set_algo_parameters(fully_use_devices=False)
     net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_keep_dims=True, strategy=((4, 1, 1, 1),))
     loss = CrossEntropyLoss2()
     predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)

From b02e871c1aa277217111a4fa2f3ce2ced40cf921 Mon Sep 17 00:00:00 2001
From: huanghui <huanghui44@huawei.com>
Date: Tue, 14 Apr 2020 20:30:44 +0800
Subject: [PATCH 243/367] [IRFusion] add derelu_fusion pass

---
 .../ascend/ir_fusion/derelu_fusion.cc         | 105 ++++++++++++++++++
 .../ascend/ir_fusion/derelu_fusion.h          |  33 ++++++
 mindspore/ccsrc/pre_activate/common/helper.h  |   1 +
 mindspore/ccsrc/utils/utils.h                 |   2 +
 .../ascend/ir_fusion/derelu_fusion_test.cc    |  54 +++++++++
 .../gtest_input/pre_activate/derelu_fusion.py |  56 ++++++++++
 6 files changed, 251 insertions(+)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.h
 create mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fusion/derelu_fusion_test.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/derelu_fusion.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.cc
new file mode 100644
index 0000000000..d5ea315de1
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.cc
@@ -0,0 +1,105 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/ir_fusion/derelu_fusion.h"
+#include <memory>
+#include <vector>
+#include "session/anf_runtime_algorithm.h"
+#include "ir/primitive.h"
+#include "utils/utils.h"
+#include "pipeline/static_analysis/abstract_value.h"
+#include "pre_activate/common/helper.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+const size_t kReluV2OutputNum = 2;
+
+CNodePtr GetRelu(const CNodePtr &relu_grad) {
+  MS_EXCEPTION_IF_NULL(relu_grad);
+  if (relu_grad->size() != kReluGradInputNum) {
+    MS_LOG_EXCEPTION << "ReluGrad has wrong input size " << relu_grad->size();
+  }
+  auto relu_anf = relu_grad->input(2);
+  MS_EXCEPTION_IF_NULL(relu_anf);
+  return relu_anf->cast<CNodePtr>();
+}
+
+CNodePtr CreateReluV2(const FuncGraphPtr &graph, const CNodePtr &relu) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(relu);
+  if (relu->size() != kReluInputNum) {
+    MS_LOG_EXCEPTION << "Relu has wrong input size " << relu->size();
+  }
+
+  auto prim = std::make_shared<Primitive>(kReluV2OpName);
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim), relu->input(1)};
+  auto new_node = graph->NewCNode(inputs);
+  MS_EXCEPTION_IF_NULL(new_node);
+  new_node->set_scope(relu->scope());
+
+  // ReluV2's 2rd output is mask whose data type is uint8 and value is 0 or 1, so shape is an empty vector
+  TypeId mask_dtype = kNumberTypeUInt8;
+  std::vector<size_t> mask_shape;
+  auto types = {AnfAlgo::GetOutputInferDataType(relu, 0), mask_dtype};
+  auto shapes = {AnfAlgo::GetOutputInferShape(relu, 0), mask_shape};
+  AnfAlgo::SetOutputInferTypeAndShape(types, shapes, new_node.get());
+  return new_node;
+}
+
+CNodePtr CreateReluGradV2(const FuncGraphPtr &graph, const CNodePtr &relu_grad, const AnfNodePtr &second_input) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(relu_grad);
+  MS_EXCEPTION_IF_NULL(second_input);
+
+  auto prim = std::make_shared<Primitive>(kReluGradV2OpName);
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim), relu_grad->input(1), second_input};
+  auto new_node = graph->NewCNode(inputs);
+  MS_EXCEPTION_IF_NULL(new_node);
+  new_node->set_scope(relu_grad->scope());
+  new_node->set_abstract(relu_grad->abstract());
+  return new_node;
+}
+}  // namespace
+
+const BaseRef DereluFusion::DefinePattern() const {
+  VarPtr i0 = std::make_shared<Var>();
+  VarPtr i1 = std::make_shared<Var>();
+  VectorRef relu({prim::kPrimRelu, i1});
+  VectorRef relu_grad({prim::kPrimReluGrad, i0, relu});
+  return relu_grad;
+}
+
+const AnfNodePtr DereluFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(node);
+  auto relu_grad = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(relu_grad);
+  auto relu = GetRelu(relu_grad);
+  MS_EXCEPTION_IF_NULL(relu);
+
+  auto relu_v2 = CreateReluV2(graph, relu);
+  std::vector<AnfNodePtr> relu_v2_node_outputs;
+  CreateMultipleOutputsOfAnfNode(graph, relu_v2, kReluV2OutputNum, &relu_v2_node_outputs);
+
+  auto relu_grad_v2 = CreateReluGradV2(graph, relu_grad, relu_v2_node_outputs[1]);
+
+  auto manage = graph->manager();
+  MS_EXCEPTION_IF_NULL(manage);
+  manage->Replace(relu, relu_v2_node_outputs[0]);
+  return relu_grad_v2;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.h
new file mode 100644
index 0000000000..e1811f4db4
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/derelu_fusion.h
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_DERELU_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_DERELU_FUSION_H_
+
+#include <memory>
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class DereluFusion : public PatternProcessPass {
+ public:
+  explicit DereluFusion(bool multigraph = true) : PatternProcessPass("derelu_fusion", multigraph) {}
+  ~DereluFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_DERELU_FUSION_H_
diff --git a/mindspore/ccsrc/pre_activate/common/helper.h b/mindspore/ccsrc/pre_activate/common/helper.h
index 4f30a935af..4cacd6fbcc 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.h
+++ b/mindspore/ccsrc/pre_activate/common/helper.h
@@ -29,6 +29,7 @@ constexpr size_t kTransOpInputNum = 2;
 constexpr size_t kCastInputNum = 2;
 constexpr size_t kDependInputNum = 3;
 constexpr size_t kReluInputNum = 2;
+constexpr size_t kReluGradInputNum = 3;
 constexpr size_t kAddInputNum = 3;
 constexpr size_t kAddNInputNum = 3;
 constexpr size_t kTupleGetitemInputNum = 3;
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 08a98a3129..60d5830933 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -115,6 +115,8 @@ constexpr auto kBiasAddOpName = "BiasAdd";
 constexpr auto kConfusionMulGradOpName = "ConfusionMulGrad";
 constexpr auto kSendOpName = "Send";
 constexpr auto kRecvOpName = "Recv";
+constexpr auto kReluV2OpName = "ReluV2";
+constexpr auto kReluGradV2OpName = "ReluGradV2";
 
 // attr key name
 constexpr auto kAttrInputNames = "input_names";
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/derelu_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/derelu_fusion_test.cc
new file mode 100644
index 0000000000..ffa5a42b4d
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/derelu_fusion_test.cc
@@ -0,0 +1,54 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/ascend/ir_fusion/derelu_fusion.h"
+#include "debug/anf_ir_dump.h"
+
+namespace mindspore {
+namespace opt {
+class TestHWOptimizeDereluFusion : public BackendCommon {
+ public:
+  TestHWOptimizeDereluFusion() : get_py_fun_("gtest_input.pre_activate.derelu_fusion", true) {}
+  ~TestHWOptimizeDereluFusion() override = default;
+
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWOptimizeDereluFusion, test_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_derelu_fusion", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{1, 1, 1, 1};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 2; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::DereluFusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_derelu_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+}  // namespace opt
+}  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/derelu_fusion.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/derelu_fusion.py
new file mode 100644
index 0000000000..497975542b
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/derelu_fusion.py
@@ -0,0 +1,56 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+
+relu = P.ReLU()
+relu_grad = Primitive('ReluGrad')
+relu_v2 = Primitive('ReluV2')
+relu_grad_v2 = Primitive('ReluGradV2')
+make_tuple = Primitive('make_tuple')
+tuple_getitem = Primitive('tuple_getitem')
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+def test_derelu_fusion(tag):
+    fns = FnDict()
+
+    @fns
+    def before(i0, i1):
+        relu_res = relu(i1)
+        res = relu_grad(i0, relu_res)
+        other = relu(relu_res)
+        res = make_tuple(res, other)
+        return res
+
+    @fns
+    def after(i0, i1):
+        relu_res = relu_v2(i1)
+        item0 = tuple_getitem(relu_res, 0)
+        item1 = tuple_getitem(relu_res, 1)
+        other = relu(item0)
+        res = relu_grad_v2(i0, item1)
+        res = make_tuple(res, other)
+        return make_tuple(res)
+
+    return fns[tag]

From f72b84ffb819e50cdce2d0265ce3b46d27b93801 Mon Sep 17 00:00:00 2001
From: jonyguo <guozhijian@huawei.com>
Date: Wed, 15 Apr 2020 11:09:04 +0800
Subject: [PATCH 244/367] Revert "fix schema.parse_columns function"

This reverts commit 5c2ded8d8eb714d22291f37945e35a4504fd17e7.
---
 mindspore/dataset/engine/datasets.py | 76 +++++++++++++---------------
 1 file changed, 35 insertions(+), 41 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 683102aecf..4b02004086 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2464,53 +2464,47 @@ class Schema:
         Parse the columns and add it to self.
 
         Args:
-            columns (dict or list[dict]): dataset attribution information, decoded from schema file.
-            if list: columns element must be dict, 'name' and 'type' must be in keys, 'shape' optional.
-            if dict: columns.keys() as name, element in columns.values() is dict, and 'type' inside, 'shape' optional.
-              example 1)
-                [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
-                 {'name': 'label', 'type': 'int8', 'shape': [1]}]
-              example 2)
-                {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
+            columns (dict or list[str]): names of columns.
 
         Raises:
-            RuntimeError: If failed to parse columns.
-            RuntimeError: If unknown items in columns.
+            RuntimeError: If failed to parse schema file.
+            RuntimeError: If unknown items in schema file.
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
+        if columns is None:
+            raise TypeError("Expected non-empty dict or string list.")
         self.columns = []
-        if isinstance(columns, list):
-            for column in columns:
-                try:
-                    name = column.pop("name")
-                except KeyError:
-                    raise RuntimeError("Column's name is missing")
-                try:
-                    de_type = column.pop("type")
-                except KeyError:
-                    raise RuntimeError("Column' type is missing")
-                shape = column.pop("shape", None)
-                column.pop("t_impl", None)
-                column.pop("rank", None)
-                if column:
-                    raise RuntimeError("Unknown field {}".format(",".join(column.keys())))
-                self.add_column(name, de_type, shape)
-        elif isinstance(columns, dict):
-            for key, value in columns.items():            
-                name = key
-                try:
-                    de_type = value.pop("type")
-                except KeyError:
-                    raise RuntimeError("Column' type is missing")
-                shape = value.pop("shape", None)
-                value.pop("t_impl", None)
-                value.pop("rank", None)
-                if value:
-                    raise RuntimeError("Unknown field {}".format(",".join(value.keys())))
-                self.add_column(name, de_type, shape)
-        else:
-            raise RuntimeError("columns must be dict or list, columns contain name, type, shape(optional).")
+        for col in columns:
+            name = None
+            shape = None
+            data_type = None
+            col_details = None
+            if isinstance(columns, list):
+                col_details = col
+                if "name" in col:
+                    name = col["name"]
+            elif isinstance(columns, dict):
+                col_details = columns[col]
+                name = col
+            else:
+                raise RuntimeError("Error parsing the schema file")
+
+            for k, v in col_details.items():
+                if k == "shape":
+                    shape = v
+                elif k == "type":
+                    data_type = v
+                elif k in ("t_impl", "rank"):
+                    pass
+                else:
+                    raise RuntimeError("Unknown field %s" % k)
+
+            if name is None:
+                raise RuntimeError("Column's name field is missing.")
+            if data_type is None:
+                raise RuntimeError("Column's type field is missing.")
+            self.add_column(name, data_type, shape)
 
     def from_json(self, json_obj):
         """

From b804d9103d5d69fea39701e79097d0b8b4b3b0a3 Mon Sep 17 00:00:00 2001
From: chujinjin <chujinjin52@hauwei.com>
Date: Tue, 14 Apr 2020 10:16:17 +0800
Subject: [PATCH 245/367] set default execution mode to pynative

---
 mindspore/ccsrc/utils/context/ms_context.cc | 2 +-
 tests/st/pynative/test_ascend_lenet.py      | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc
index b1ab0205f2..bee5875f60 100644
--- a/mindspore/ccsrc/utils/context/ms_context.cc
+++ b/mindspore/ccsrc/utils/context/ms_context.cc
@@ -65,7 +65,7 @@ MsContext::MsContext(const std::string& policy, const std::string& target) {
   }
   backend_policy_ = policy_map_[policy];
   device_target_ = target;
-  execution_mode_ = kGraphMode;
+  execution_mode_ = kPynativeMode;
   enable_task_sink_ = true;
   ir_fusion_flag_ = true;
   enable_hccl_ = false;
diff --git a/tests/st/pynative/test_ascend_lenet.py b/tests/st/pynative/test_ascend_lenet.py
index 4681454489..4009844791 100644
--- a/tests/st/pynative/test_ascend_lenet.py
+++ b/tests/st/pynative/test_ascend_lenet.py
@@ -122,8 +122,9 @@ class GradWrap(nn.Cell):
 
 
 @pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
+@pytest.mark.env_onecard
 def test_ascend_pynative_lenet():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
 
@@ -152,6 +153,5 @@ def test_ascend_pynative_lenet():
         total_time = total_time + cost_time
 
         print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
-    assert(total_time < 20.0)
-    assert(loss_output.asnumpy() < 0.01)
+    assert(loss_output.asnumpy() < 0.1)
     
\ No newline at end of file

From 5617143fb871aecd36418c787d6981006bc44284 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Wed, 15 Apr 2020 11:20:48 +0800
Subject: [PATCH 246/367] Fix arg_min_with_value bug

---
 mindspore/ops/_op_impl/tbe/arg_min_with_value.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/_op_impl/tbe/arg_min_with_value.py b/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
index a23005403b..b0c23ef301 100644
--- a/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
+++ b/mindspore/ops/_op_impl/tbe/arg_min_with_value.py
@@ -16,7 +16,7 @@
 """ArgMinWithValue op"""
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-arg_min_with_value_op_info = TBERegOp("ArgMaxWithValue") \
+arg_min_with_value_op_info = TBERegOp("ArgMinWithValue") \
     .fusion_type("ELEMWISE") \
     .async_flag(False) \
     .binfile_name("arg_min_with_value.so") \

From e0393b482f0c07af120daf9ca07c0c7f36a9024c Mon Sep 17 00:00:00 2001
From: anzhengqi <anzhengqi.huawei.com>
Date: Wed, 15 Apr 2020 14:44:56 +0800
Subject: [PATCH 247/367] fix schema.parse_columns function

---
 mindspore/dataset/engine/datasets.py | 76 +++++++++++++++-------------
 1 file changed, 41 insertions(+), 35 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 4b02004086..f2d0f59f82 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2464,47 +2464,53 @@ class Schema:
         Parse the columns and add it to self.
 
         Args:
-            columns (dict or list[str]): names of columns.
+            columns (dict or list[dict]): dataset attribution information, decoded from schema file.
+            if list: columns element must be dict, 'name' and 'type' must be in keys, 'shape' optional.
+            if dict: columns.keys() as name, element in columns.values() is dict, and 'type' inside, 'shape' optional.
+              example 1)
+                [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
+                 {'name': 'label', 'type': 'int8', 'shape': [1]}]
+              example 2)
+                {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
 
         Raises:
-            RuntimeError: If failed to parse schema file.
-            RuntimeError: If unknown items in schema file.
+            RuntimeError: If failed to parse columns.
+            RuntimeError: If unknown items in columns.
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
         """
-        if columns is None:
-            raise TypeError("Expected non-empty dict or string list.")
         self.columns = []
-        for col in columns:
-            name = None
-            shape = None
-            data_type = None
-            col_details = None
-            if isinstance(columns, list):
-                col_details = col
-                if "name" in col:
-                    name = col["name"]
-            elif isinstance(columns, dict):
-                col_details = columns[col]
-                name = col
-            else:
-                raise RuntimeError("Error parsing the schema file")
-
-            for k, v in col_details.items():
-                if k == "shape":
-                    shape = v
-                elif k == "type":
-                    data_type = v
-                elif k in ("t_impl", "rank"):
-                    pass
-                else:
-                    raise RuntimeError("Unknown field %s" % k)
-
-            if name is None:
-                raise RuntimeError("Column's name field is missing.")
-            if data_type is None:
-                raise RuntimeError("Column's type field is missing.")
-            self.add_column(name, data_type, shape)
+        if isinstance(columns, list):
+            for column in columns:
+                try:
+                    name = column.pop("name")
+                except KeyError:
+                    raise RuntimeError("Column's name is missing")
+                try:
+                    de_type = column.pop("type")
+                except KeyError:
+                    raise RuntimeError("Column' type is missing")
+                shape = column.pop("shape", None)
+                column.pop("t_impl", None)
+                column.pop("rank", None)
+                if column:
+                    raise RuntimeError("Unknown field {}".format(",".join(column.keys())))
+                self.add_column(name, de_type, shape)
+        elif isinstance(columns, dict):
+            for key, value in columns.items():
+                name = key
+                try:
+                    de_type = value.pop("type")
+                except KeyError:
+                    raise RuntimeError("Column' type is missing")
+                shape = value.pop("shape", None)
+                value.pop("t_impl", None)
+                value.pop("rank", None)
+                if value:
+                    raise RuntimeError("Unknown field {}".format(",".join(value.keys())))
+                self.add_column(name, de_type, shape)
+        else:
+            raise RuntimeError("columns must be dict or list, columns contain name, type, shape(optional).")
 
     def from_json(self, json_obj):
         """

From b55f1324a31290da15dce172d3cba524522aebc7 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Wed, 15 Apr 2020 14:59:49 +0800
Subject: [PATCH 248/367] Fix kernel query log level

---
 mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc | 2 +-
 mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc   | 2 +-
 mindspore/ccsrc/kernel/mng/rt_kernel_info.cc          | 2 +-
 mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc       | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
index 9f91c1bdd2..e8636ffa2e 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc
@@ -34,7 +34,7 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<
   }
   auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU);
   if (op_info_ptr == nullptr) {
-    MS_LOG(WARNING) << "Aicpu doestn't have metadata of op [" << op_name << "]";
+    MS_LOG(DEBUG) << "Aicpu does not have op [" << op_name << "]";
     return;
   }
   // For compatibility with the current framework
diff --git a/mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc b/mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc
index 80e87b0c6d..dc26045374 100755
--- a/mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc
+++ b/mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc
@@ -27,7 +27,7 @@ void HcclMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<K
   MS_EXCEPTION_IF_NULL(kernel_node);
   std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
   if (op_name != kAllGather && op_name != kAllReduce && op_name != kBroadcast && op_name != kReduceScatter) {
-    MS_LOG(INFO) << "Hccl can't find op [" << op_name << "]";
+    MS_LOG(DEBUG) << "Hccl does not have op [" << op_name << "]";
     return;
   }
 
diff --git a/mindspore/ccsrc/kernel/mng/rt_kernel_info.cc b/mindspore/ccsrc/kernel/mng/rt_kernel_info.cc
index 9bd22712d9..a87bb4d514 100755
--- a/mindspore/ccsrc/kernel/mng/rt_kernel_info.cc
+++ b/mindspore/ccsrc/kernel/mng/rt_kernel_info.cc
@@ -60,7 +60,7 @@ void GetRtKelInfo(const CNodePtr &kernel_node,
   MS_EXCEPTION_IF_NULL(ker_desc_ptr);
   auto kernel_info = ker_desc_ptr->GetKernelInfo();
   if (kernel_info.empty()) {
-    MS_LOG(WARNING) << "Rt dose not has op[" << opNameLower << "].";
+    MS_LOG(DEBUG) << "Rt dose not have op [" << opNameLower << "].";
     return;
   }
   *kernel_info_list = kernel_info;
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
index 92798aa6bc..e818f503c0 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
@@ -542,7 +542,7 @@ void TbeMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<Ke
     }
   }
   if (kernel_info_list->empty()) {
-    MS_LOG(DEBUG) << "Tbe dose not has metadata of op[" << op_name << "].";
+    MS_LOG(DEBUG) << "Tbe dose not have op [" << op_name << "].";
   }
 }
 }  // namespace kernel

From 5365678eee1bcf53dc0ef045f76957c21fc67568 Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Tue, 14 Apr 2020 15:59:48 +0800
Subject: [PATCH 249/367] refactor kernel select

---
 .../device/ascend/kernel_select_ascend.cc     | 83 ++++++++-----------
 1 file changed, 35 insertions(+), 48 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index 06af3d2ca7..dafe958348 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -31,12 +31,13 @@ namespace mindspore {
 namespace device {
 namespace ascend {
 namespace {
+const float kWegihtBaseScore = 1;
+const float kFeatureMapBaseScore = 10;
 enum MatchCountPriority : int {
   MATCH_COUNT_PRIORITY_BEGIN = 0,
   MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
   MATCH_FORMAT_COUNT,
   MATCH_SPECIAL_FORMAT_COUNT,
-  MATCH_5D_FORMAT_COUNT,
   MATCH_OUTPUT_DTYPE_COUNT,
   MATCH_COUNT_PRIORITY_END
 };
@@ -82,13 +83,6 @@ bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::
     }
     return true;
   };
-  if (AnfAlgo::GetCNodeName(kernel_node) == "Adam") {
-    auto input_num = AnfAlgo::GetInputTensorNum(kernel_node);
-    if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_num - 1) !=
-        kernel_build_info.GetInputFormat(input_num - 1)) {
-      return false;
-    }
-  }
   if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
     return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
            AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
@@ -112,21 +106,7 @@ bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildIn
   MS_EXCEPTION_IF_NULL(cnode);
   // Check input data type
   for (size_t input_index = 0; input_index < kernel_build_info.GetInputNum(); ++input_index) {
-    AnfNodePtr cur_input = AnfAlgo::GetInputNode(cnode, input_index);
-    MS_EXCEPTION_IF_NULL(cur_input);
-    TypeId input_origin_type;
-    if (cur_input->isa<Parameter>() && AnfAlgo::IsParameterWeight(cur_input->cast<ParameterPtr>())) {
-      // weight
-      input_origin_type = AnfAlgo::GetOutputDeviceDataType(cur_input, 0);
-    } else if (cur_input->isa<ValueNode>()) {
-      input_origin_type = AnfAlgo::GetOutputDeviceDataType(cur_input, 0);
-    } else {
-      // feature map
-      input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
-    }
-    if (input_origin_type == kTypeUnknown) {
-      continue;
-    }
+    TypeId input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
     if (kernel_build_info.GetInputDeviceType(input_index) != input_origin_type) {
       return false;
     }
@@ -140,6 +120,29 @@ bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildIn
   return true;
 }
 
+string GetPriorityMatchFormat(const CNodePtr &cnode) {
+  string priority_matched_format = kOpFormat_NC1HWC0;
+  bool is_init = false;
+  bool need_change_nd = false;
+  for (size_t index = 0; index < AnfAlgo::GetInputTensorNum(cnode); ++index) {
+    auto pre_output_format = AnfAlgo::GetPrevNodeOutputFormat(cnode, index);
+    if (AnfAlgo::IsFeatureMapInput(cnode, index) &&
+        kNeedTransFormatSet.find(pre_output_format) != kNeedTransFormatSet.end()) {
+      priority_matched_format = !is_init ? priority_matched_format : pre_output_format;
+      is_init = true;
+    }
+    // feature map has two or more special format;
+    if (priority_matched_format != pre_output_format && pre_output_format != kOpFormat_DEFAULT) {
+      priority_matched_format = kOpFormat_DEFAULT;
+    }
+    auto input_shape_size = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index).size();
+    need_change_nd = (need_change_nd || (input_shape_size != 4 && input_shape_size > 1));
+  }
+  if (need_change_nd) {
+    priority_matched_format = kOpFormat_DEFAULT;
+  }
+  return priority_matched_format;
+}
 /**
  * compare two vector by priority, select a better vector, like compare two num, first compare highest num location,
  * if equal then next num location
@@ -172,34 +175,18 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
   if (cur_kernelinfo_match_counts->size() < MATCH_COUNT_PRIORITY_END) {
     MS_LOG(EXCEPTION) << "Out of range cur_kernelinfo_match_counts " << MATCH_COUNT_PRIORITY_END;
   }
+  auto pri_match_format = GetPriorityMatchFormat(kernel_node);
   for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
-    AnfNodePtr input_anf_node = AnfAlgo::GetInputNode(kernel_node, input_index);
-    MS_EXCEPTION_IF_NULL(input_anf_node);
-    // if a input parameter is a weight with default format, the input shouldn't participate the judge
-    if (input_anf_node->isa<Parameter>()) {
-      auto para = input_anf_node->cast<ParameterPtr>();
-      if (AnfAlgo::IsParameterWeight(para) && AnfAlgo::GetOutputDeviceDataType(para, 0) == kTypeUnknown) {
-        continue;
-      }
-    }
+    auto base_score = AnfAlgo::IsFeatureMapInput(kernel_node, input_index) ? kFeatureMapBaseScore : kWegihtBaseScore;
     if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
-      if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) &&
-          kNeedTransFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kNeedTransFormatSet.end()) {
-        (*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT]++;
-      }
-      (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++;
+      (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT] += base_score;
     }
     if (kernel_build_info.GetInputDeviceType(input_index) ==
         AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index)) {
-      (*cur_kernelinfo_match_counts)[MATCH_DTYPE_COUNT]++;
+      (*cur_kernelinfo_match_counts)[MATCH_DTYPE_COUNT] += base_score;
     }
-    if (kernel_build_info.GetInputFormat(input_index) == kOpFormat_NC1HWC0) {
-      // input is from a feature map & this input's shape is not 4d
-      if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) &&
-          AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index).size() != kShape4dDims) {
-        continue;
-      }
-      (*cur_kernelinfo_match_counts)[MATCH_5D_FORMAT_COUNT]++;
+    if (kernel_build_info.GetInputFormat(input_index) == pri_match_format) {
+      (*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT] += base_score;
     }
   }
 
@@ -207,7 +194,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
     // cal count of same output dtype between abstract and kernel info
     if (kernel_build_info.GetOutputDeviceType(output_index) ==
         AnfAlgo::GetOutputInferDataType(kernel_node, output_index)) {
-      (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++;
+      (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT] += 1;
     }
   }
 }
@@ -517,7 +504,7 @@ void SelectKernelInfo(const CNodePtr &kernel_node) {
   std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
   MS_EXCEPTION_IF_NULL(kernel_node);
   kernel::KernelQuery(kernel_node, &kernel_info_list);
-  std::vector<int> most_match_counts = {-1, -1, -1, -1, -1};
+  std::vector<int> most_match_counts = {-1, -1, -1, -1};
   int selected_index = -1;
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
@@ -527,7 +514,7 @@ void SelectKernelInfo(const CNodePtr &kernel_node) {
   std::vector<int> node_mix_precision_datatype_index;
   std::vector<TypeId> node_mix_precision_datatype;
   for (size_t info_index = 0; info_index < kernel_info_list.size(); ++info_index) {
-    std::vector<int> cur_kernel_info_match_counts = {0, 0, 0, 0, 0};
+    std::vector<int> cur_kernel_info_match_counts = {0, 0, 0, 0};
     auto kernel_build_info = *(kernel_info_list[info_index]);
     if (!IsValidKernelInfo(kernel_node, kernel_build_info)) {
       continue;

From 62807da0c4fa6caa0c941524483959c0263f51c5 Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Wed, 25 Mar 2020 18:38:10 +0800
Subject: [PATCH 250/367] =?UTF-8?q?Develop=20operator=20Unfold=EF=BC=8Ctak?=
 =?UTF-8?q?e=20the=20ge=20backend=EF=BC=8Cdock=20with=20tbe's=20ExtractIma?=
 =?UTF-8?q?gePatches=20operator?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 mindspore/ccsrc/kernel/tbe/tbe_adapter.h |   6 --
 mindspore/ccsrc/transform/convert.cc     |   2 +
 mindspore/ccsrc/transform/op_adapter.h   |  26 +++---
 mindspore/ccsrc/transform/op_declare.cc  |  14 ++--
 mindspore/ccsrc/transform/op_declare.h   |   2 +
 mindspore/ccsrc/utils/utils.h            |   1 +
 mindspore/nn/layer/__init__.py           |   4 +-
 mindspore/nn/layer/basic.py              |  48 +++++++++++
 mindspore/ops/_grad/grad_nn_ops.py       |  57 ++++++++++++-
 mindspore/ops/operations/__init__.py     |   3 +-
 mindspore/ops/operations/math_ops.py     |   2 +-
 mindspore/ops/operations/nn_ops.py       | 100 ++++++++++++++++++++++-
 tests/ut/python/ops/test_math_ops.py     |  32 ++++++--
 tests/ut/python/ops/test_nn_ops.py       |  55 +++++++++++++
 14 files changed, 310 insertions(+), 42 deletions(-)

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.h b/mindspore/ccsrc/kernel/tbe/tbe_adapter.h
index 3997318c86..27f6d315f6 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.h
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.h
@@ -45,12 +45,6 @@ class TbeAdapter {
                                          std::vector<nlohmann::json> *input_list, kCreaterType creater_type);
 
  private:
-  static void MaxPoolWithArgmaxAttrJsonPass(const AnfNodePtr &anf_node,
-                                            const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
-                                            nlohmann::json *attrs_json);
-  static void MaxPoolGradWithArgmaxAttrJsonPass(const AnfNodePtr &anf_node,
-                                                const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
-                                                nlohmann::json *attrs_json);
   static void Conv2DAttrJsonPass(const AnfNodePtr &anf_node, const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
                                  nlohmann::json *attrs_json);
   static void Conv2DBackpropFilterAttrJsonPass(const AnfNodePtr &anf_node,
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 251946f6fd..2daa86b960 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -96,6 +96,7 @@ const char kNameConfusionMatrix[] = "ConfusionMatrix";
 const char kNameResizeNearestNeighborD[] = "ResizeNearestNeighbor";
 const char kNameResizeNearestNeighborGrad[] = "ResizeNearestNeighborGrad";
 const char kNameApplyAdam[] = "Adam";
+const char kNameExtractImagePatches[] = "ExtractImagePatches";
 const char kNameReLU6[] = "ReLU6";
 const char kNameReLU6Grad[] = "ReLU6Grad";
 const char kNameElu[] = "Elu";
@@ -214,6 +215,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameMaxPoolGrad), ADPT_DESC(MaxPoolGrad)},
     {string(kNameAvgPoolGrad), ADPT_DESC(AvgPoolGrad)},
     {string(kNameMaxPoolGradWithArgmax), ADPT_DESC(MaxPoolGradWithArgmax)},
+    {string(kNameExtractImagePatches), ADPT_DESC(ExtractImagePatches)},
     {prim::kPrimAssign->name(), ADPT_DESC(Assign)},
     {prim::kPrimStateSetItem->name(), ADPT_DESC(Assign)},
     {prim::kPrimReluGrad->name(), ADPT_DESC(ReluGrad)},
diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h
index 7f20a88035..421e4c4569 100644
--- a/mindspore/ccsrc/transform/op_adapter.h
+++ b/mindspore/ccsrc/transform/op_adapter.h
@@ -322,18 +322,12 @@ class OpAdapter : public BaseOpAdapter {
 
   Status UpdateSingleOutputDesc(const OperatorPtr& op, const abstract::BaseShapePtr& shp, const TypePtr& type) {
     MS_EXCEPTION_IF_NULL(type);
-    TypeId me_type = type->type_id();
-    if (kObjectTypeTensorType == me_type) {
-      me_type = dyn_cast<TensorType>(type)->element()->type_id();
-    }
-
-    std::vector<int> shape;
-    auto normal_shape_ptr = dyn_cast<abstract::Shape>(shp);
-    if (nullptr != normal_shape_ptr) {
-      shape = normal_shape_ptr->shape();
+    std::string format = "NCHW";
+    if (op->GetOpType() == kExtractImagePatchesOpName) {
+      format = "NHWC";
     }
 
-    auto desc = TransformUtil::GetGeTensorDesc(shape, me_type, "NCHW");
+    auto desc = CreateOutputDesc(dyn_cast<abstract::Shape>(shp), type, format);
     if (desc == nullptr) {
       MS_LOG(ERROR) << "Update output descriptor failed!";
       return FAILED;
@@ -410,14 +404,15 @@ class OpAdapter : public BaseOpAdapter {
       MS_LOG(ERROR) << "output_map is not equal tuple_shape size";
       return FAILED;
     }
+    std::string format = "NCHW";
+    if (op->GetOpType() == kTopKOpName) {
+      format = "NHWC";
+    }
     for (size_t i = 0; i < tuple_shp->shape().size(); ++i) {
       auto tuple_type = dyn_cast<Tuple>(type);
       MS_EXCEPTION_IF_NULL(tuple_type);
       TypePtr type_elem = tuple_type->elements()[i];
-      std::string format = "NCHW";
-      if (op->GetOpType() == kTopKOpName) {
-        format = "NHWC";
-      }
+
       auto desc = CreateOutputDesc(dyn_cast<abstract::Shape>(tuple_shp->shape()[i]), type_elem, format);
       if (desc == nullptr) {
         MS_LOG(ERROR) << "Create output descriptor failed!";
@@ -476,6 +471,9 @@ class OpAdapter : public BaseOpAdapter {
         if (desc == nullptr) {
           continue;
         }
+        if (op->GetOpType() == kExtractImagePatchesOpName) {
+          desc->SetFormat(ge::Format::FORMAT_NHWC);
+        }
         it->second.update_input_desc(op, *desc);
       }
     }
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index f821c71d87..420edc685a 100644
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -751,16 +751,20 @@ ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyT
 OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}};
 
 // MaxPoolGradWithArgmax
-INPUT_MAP(MaxPoolGradWithArgmax) = {
-  {1, INPUT_DESC(x)},
-  {2, INPUT_DESC(grad)},
-  {3, INPUT_DESC(argmax)},
-};
+INPUT_MAP(MaxPoolGradWithArgmax) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(grad)}, {3, INPUT_DESC(argmax)}};
 ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 
+// ExtractImagePatches
+INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(images)}};
+ATTR_MAP(ExtractImagePatches) = {{"ksizes", ATTR_DESC(ksizes, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                                 {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                                 {"rates", ATTR_DESC(rates, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                                 {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+OUTPUT_MAP(ExtractImagePatches) = {{0, OUTPUT_DESC(y)}};
+
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(Conv2D) = {
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 8f6dda9430..8b32e16b35 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -95,6 +95,8 @@ DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax)
 DECLARE_OP_ADAPTER(Conv2D)
 DECLARE_OP_USE_ENUM(Conv2D)
 DECLARE_OP_USE_OUTPUT(Conv2D)
+DECLARE_OP_ADAPTER(ExtractImagePatches)
+DECLARE_OP_USE_OUTPUT(ExtractImagePatches)
 DECLARE_OP_ADAPTER(Conv2DBackpropInputD)
 DECLARE_OP_USE_ENUM(Conv2DBackpropInputD)
 DECLARE_OP_USE_INPUT_ATTR(Conv2DBackpropInputD)
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 08a98a3129..44e7b4d4c2 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -49,6 +49,7 @@ constexpr auto kBroadcastOpName = "Broadcast";
 constexpr auto kReduceScatterOpName = "ReduceScatter";
 constexpr auto kMemCpyAsyncOpName = "memcpy_async";
 constexpr auto kTopKOpName = "TopK";
+constexpr auto kExtractImagePatchesOpName = "ExtractImagePatches";
 constexpr auto kBNTrainingReduceOpName = "BNTrainingReduce";
 constexpr auto kBNTrainingUpdateOpName = "BNTrainingUpdate";
 constexpr auto kSimpleMeanGradOpName = "SimpleMeanGrad";
diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index 9c2c30c914..3d729edcd3 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -22,7 +22,7 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
-from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, Pad
+from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, Pad, Unfold
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
 from .image import ImageGradients, SSIM
@@ -35,6 +35,6 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'LSTM',
            'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot',
            'Embedding',
-           'AvgPool2d', 'MaxPool2d', 'Pad',
+           'AvgPool2d', 'MaxPool2d', 'Pad', 'Unfold',
            'ImageGradients', 'SSIM',
            ]
diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 64c4cfd93b..5ac52acac7 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -439,3 +439,51 @@ class Pad(Cell):
         else:
             x = self.pad(x, self.paddings)
         return x
+
+
+class Unfold(Cell):
+    """
+    Extract patches from images.
+    The input tensor must be a 4-D tensor and the data format is NCHW.
+
+    Args:
+        ksizes (Union[tuple[int], list[int]]): The size of sliding window, should be a tuple or list of int,
+            and the format is [1, ksize_row, ksize_col, 1].
+        strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
+            should be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
+        rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dim
+            pixel positions, should be a tuple or list of int, and the format is [1, rate_row, rate_col, 1].
+        padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
+            not case sensitive. Default: "valid".
+
+            - same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
+
+            - valid: Means that the patch area taken must be completely contained in the original image.
+
+    Inputs:
+        - **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_depth, in_row, in_col] and
+          data type is int8, float16, uint8.
+
+    Outputs:
+        Tensor, a 4-D tensor whose data type is same as 'input_x',
+        and the shape is [out_batch, out_depth, out_row, out_col], the out_batch is same as the in_batch.
+
+    Examples:
+        >>> net = Unfold(ksizes=[1, 2, 2, 1], strides=[1, 1, 1, 1], rates=[1, 1, 1, 1])
+        >>> image = Tensor(np.ones([1, 1, 3, 3]), dtype=mstype.float16)
+        >>> net(image)
+        Tensor ([[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1], [1, 1]]]],
+                shape=(1, 4, 2, 2), dtype=mstype.float16)
+    """
+    def __init__(self, ksizes, strides, rates, padding="valid"):
+        super(Unfold, self).__init__()
+        self.extract_image_patches = P.ExtractImagePatches(ksizes, strides, rates, padding)
+        self.transpose = P.Transpose()
+        self.format_NHWC = (0, 2, 3, 1)
+        self.format_NCHW = (0, 3, 1, 2)
+
+    def construct(self, input_x):
+        x_transpose = self.transpose(input_x, self.format_NHWC)
+        ret = self.extract_image_patches(x_transpose)
+        ret_transpose = self.transpose(ret, self.format_NCHW)
+        return ret_transpose
diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py
index 149dd6caec..ae730d78a7 100755
--- a/mindspore/ops/_grad/grad_nn_ops.py
+++ b/mindspore/ops/_grad/grad_nn_ops.py
@@ -14,7 +14,7 @@
 # ============================================================================
 
 """Define the grad rules of neural network related operations."""
-
+from mindspore.common import dtype as mstype
 from .. import functional as F
 from .. import operations as P
 from ..operations import _grad_ops as G
@@ -52,6 +52,61 @@ def get_bprop_conv2d(self):
     return bprop
 
 
+@bprop_getters.register(P.ExtractImagePatches)
+def get_bprop_extract_image_patches(self):
+    """Grad definition for `ExtractImagePatches` operation."""
+    get_shape = P.Shape()
+    reshape = P.Reshape()
+    extract_image_patches = P.ExtractImagePatches(ksizes=self.ksizes,
+                                                  strides=self.strides,
+                                                  rates=self.rates,
+                                                  padding=self.padding)
+    concat = P.Concat(axis=-1)
+    expand_dims = P.ExpandDims()
+    scatter_nd = P.ScatterNd()
+    dtype = P.DType()
+    fill = P.Fill()
+    slice_op = P.Slice()
+    transpose = P.Transpose()
+    matmul = P.MatMul()
+    cast = P.Cast()
+    _, ksizes_row, ksizes_col, _ = self.ksizes
+
+    def bprop(x, out, dout):
+        x_shape = get_shape(x)
+        x_batch, x_row, x_col, x_depth = x_shape
+        x_indices_num = x_row * x_col + 1
+        x_idx = F.tuple_to_array(range(1, x_indices_num))
+        x_idx = reshape(x_idx, (1, x_row, x_col, 1))
+        x_idx = cast(x_idx, mstype.float16)
+        x_idx_patch = extract_image_patches(x_idx)
+        x_idx_patch = transpose(x_idx_patch, (0, 3, 1, 2))
+        x_idx_patch = cast(x_idx_patch, mstype.int32)
+
+        out_shape = get_shape(out)
+        _, out_row, out_col, _ = out_shape
+        out_indices_num = out_row * out_col * ksizes_row * ksizes_col
+        out_idx = F.tuple_to_array(range(out_indices_num))
+        out_idx = reshape(out_idx, (1, ksizes_row * ksizes_col, out_row, out_col))
+
+        idx_tensor = concat((expand_dims(x_idx_patch, -1), expand_dims(out_idx, -1)))
+        idx_tensor = reshape(idx_tensor, (-1, 2))
+        sp_shape = (x_indices_num, out_indices_num)
+        sp_tensor = scatter_nd(idx_tensor, fill(dtype(dout), (out_indices_num,), 1), sp_shape)
+        sp_tensor = slice_op(sp_tensor, (1, 0), (x_indices_num - 1, out_indices_num))
+
+        grad = reshape(dout, (x_batch, out_row, out_col, ksizes_row, ksizes_col, x_depth))
+        grad = transpose(grad, (1, 2, 3, 4, 0, 5))
+        grad = reshape(grad, (-1, x_batch * x_depth))
+
+        jac = matmul(sp_tensor, grad)
+        dx = reshape(jac, (x_row, x_col, x_batch, x_depth))
+        dx = transpose(dx, (2, 0, 1, 3))
+
+        return (dx,)
+    return bprop
+
+
 @bprop_getters.register(P.DepthwiseConv2dNative)
 def get_bprop_depthwise_conv2d_native(self):
     """Grad definition for `DepthwiseConv2dNative` operation."""
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 40cbfc3381..492ebae444 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -57,7 +57,7 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      Gelu, Elu,
                      GetNext, L2Normalize, LayerNorm,
                      LogSoftmax,
-                     MaxPool,
+                     MaxPool, ExtractImagePatches,
                      AvgPool, Conv2DBackpropInput,
                      MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, HSwish, HSigmoid,
                      ResizeBilinear, Sigmoid,
@@ -89,6 +89,7 @@ __all__ = [
     'Sqrt',
     'Square',
     'Conv2D',
+    'ExtractImagePatches',
     'Flatten',
     'MaxPoolWithArgmax',
     'FusedBatchNorm',
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index f6feb1af18..e390b6b589 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1475,7 +1475,7 @@ class LogicalNot(PrimitiveWithInfer):
     Computes the "logical NOT" of a tensor element-wise.
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor whose dtype is bool
+        - **input_x** (Tensor) - The input tensor whose dtype is bool.
 
     Outputs:
         Tensor, the shape is same as the `input_x`, and the dtype is bool.
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 377ef19417..a92a75c781 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2546,6 +2546,7 @@ class ApplyFtrl(PrimitiveWithInfer):
     Outputs:
         Tensor, representing the updated var.
     """
+
     @prim_attr_register
     def __init__(self, use_locking=False):
         self.init_prim_io_names(inputs=['var', 'accum', 'linear', 'grad', 'lr', 'l1', 'l2', 'lr_power'],
@@ -2566,8 +2567,99 @@ class ApplyFtrl(PrimitiveWithInfer):
         args = {'var_type': var_type, 'accum_type': accum_type, 'linear_type': linear_type, 'grad_type': grad_type}
         validator.check_type_same(args, (mstype.float32, mstype.float16))
 
-        validator.check_typename("lr", lr_type,[mstype.float16, mstype.float32])
-        validator.check_typename("l1", l1_type,[mstype.float16, mstype.float32])
-        validator.check_typename("l2", l2_type,[mstype.float16, mstype.float32])
-        validator.check_typename("lr_power", lr_power_type,[mstype.float16, mstype.float32])
+        validator.check_typename("lr", lr_type, [mstype.float16, mstype.float32])
+        validator.check_typename("l1", l1_type, [mstype.float16, mstype.float32])
+        validator.check_typename("l2", l2_type, [mstype.float16, mstype.float32])
+        validator.check_typename("lr_power", lr_power_type, [mstype.float16, mstype.float32])
         return var_type
+
+
+class ExtractImagePatches(PrimitiveWithInfer):
+    """
+    Extract patches from images.
+    The input tensor must be a 4-D tensor and the data format is NHWC.
+
+    Args:
+        ksizes (Union[tuple[int], list[int]]): The size of sliding window, should be a tuple or list of int,
+            and the format is [1, ksize_row, ksize_col, 1].
+        strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
+            should be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
+        rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dim
+            pixel positions, should be a tuple or list of int, and the format is [1, rate_row, rate_col, 1].
+        padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
+            not case sensitive. Default: "valid".
+
+            - same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
+
+            - valid: Means that the patch area taken must be completely contained in the original image.
+
+    Inputs:
+        - **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_row, in_col, in_depth] and
+          data type is int8, float16, uint8.
+
+    Outputs:
+        Tensor, a 4-D tensor whose data type is same as 'input_x',
+        and the shape is [out_batch, out_row, out_col, out_depth], the out_batch is same as the in_batch.
+    """
+
+    @prim_attr_register
+    def __init__(self, ksizes, strides, rates, padding="valid"):
+        """init"""
+        validator.check_type("ksizes", ksizes, [tuple, list])
+        validator.check_type("strides", strides, [tuple, list])
+        validator.check_type("rates", rates, [tuple, list])
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'])
+        self.add_prim_attr("padding", self.padding)
+
+        if len(ksizes) != 4 or ksizes[0] != 1 or ksizes[3] != 1:
+            raise ValueError("The format of ksizes should be [1, ksize_row, ksize_col, 1], "
+                             f"but got {ksizes}.")
+        if not isinstance(ksizes[1], int) or not isinstance(ksizes[2], int) or \
+                ksizes[1] < 1 or ksizes[2] < 1:
+            raise ValueError("The ksize_row and ksize_col in ksizes should be an positive integer number, "
+                             f"but got ksize_row is {ksizes[1]}, ksize_col is {ksizes[2]}")
+
+        if len(strides) != 4 or strides[0] != 1 or strides[3] != 1:
+            raise ValueError("The format of strides should be [1, stride_row, stride_col, 1], "
+                             f"but got {strides}.")
+        if not isinstance(strides[1], int) or not isinstance(strides[2], int) or \
+                strides[1] < 1 or strides[2] < 1:
+            raise ValueError("The stride_row and stride_col in strides should be an positive integer number, "
+                             f"but got stride_row is {strides[1]}, stride_col is {strides[2]}")
+
+        if len(rates) != 4 or rates[0] != 1 or rates[3] != 1:
+            raise ValueError("The format of rates should be [1, rate_row, rate_col, 1], "
+                             f"but got {rates}.")
+        if not isinstance(rates[1], int) or not isinstance(rates[2], int) or \
+                rates[1] < 1 or rates[2] < 1:
+            raise ValueError("The rate_row and rate_col in rates should be an positive integer number, "
+                             f"but got rate_row is {rates[1]}, rate_col is {rates[2]}")
+
+    def infer_shape(self, input_x):
+        in_batch, in_row, in_col, in_depth = input_x
+        _, ksize_row, ksize_col, _ = self.ksizes
+        _, stride_row, stride_col, _ = self.strides
+        _, rate_row, rate_col, _ = self.rates
+        if len(input_x) != 4:
+            raise ValueError("The `input_x` should be a 4-D tensor, "
+                             f"but got a {len(input_x)}-D tensor whose shape is {input_x}")
+
+        out_batch = in_batch
+        out_depth = ksize_row * ksize_col * in_depth
+
+        if self.padding == "VALID":
+            out_row = \
+                (in_row - (ksize_row + (ksize_row - 1) * (rate_row - 1))) // stride_row + 1
+            out_col = \
+                (in_col - (ksize_col + (ksize_col - 1) * (rate_col - 1))) // stride_col + 1
+        else:
+            out_row = (in_row - 1) // stride_row + 1
+            out_col = (in_col - 1) // stride_col + 1
+
+        out_shape = [out_batch, out_row, out_col, out_depth]
+        return out_shape
+
+    def infer_dtype(self, input_x):
+        validator.check_subclass("input_x", input_x, mstype.tensor)
+        validator.check_typename("input_x_dtype", input_x, (mstype.int8, mstype.float16, mstype.float32))
+        return input_x
diff --git a/tests/ut/python/ops/test_math_ops.py b/tests/ut/python/ops/test_math_ops.py
index ad1642228d..8b7f627e81 100755
--- a/tests/ut/python/ops/test_math_ops.py
+++ b/tests/ut/python/ops/test_math_ops.py
@@ -30,6 +30,8 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
 from ....mindspore_test_framework.pipeline.forward.verify_exception \
     import pipeline_for_verify_exception_for_case_by_case_config
+
+
 # pylint: disable=W0613
 # pylint: disable=W0231
 # W0613: unused-argument
@@ -106,7 +108,7 @@ def test_realdiv():
     result = div(x, y)
     x = x.asnumpy()
     y = y.asnumpy()
-    expect = x/y
+    expect = x / y
     assert np.all(result.asnumpy() == expect)
 
 
@@ -122,6 +124,7 @@ def test_eye():
 
 class VirtualLossGrad(PrimitiveWithInfer):
     """ VirtualLossGrad definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLossGrad"""
@@ -138,6 +141,7 @@ class VirtualLossGrad(PrimitiveWithInfer):
 
 class VirtualLoss(PrimitiveWithInfer):
     """ VirtualLoss definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLoss"""
@@ -151,6 +155,7 @@ class VirtualLoss(PrimitiveWithInfer):
         def bprop(x, out, dout):
             dx = loss_grad(x, out, dout)
             return (dx,)
+
         return bprop
 
     def infer_shape(self, x_shape):
@@ -162,6 +167,7 @@ class VirtualLoss(PrimitiveWithInfer):
 
 class NetWithLoss(nn.Cell):
     """ NetWithLoss definition """
+
     def __init__(self, network):
         super(NetWithLoss, self).__init__()
         self.loss = VirtualLoss()
@@ -174,6 +180,7 @@ class NetWithLoss(nn.Cell):
 
 class GradWrap(nn.Cell):
     """ GradWrap definition """
+
     def __init__(self, network):
         super(GradWrap, self).__init__()
         self.network = network
@@ -184,6 +191,7 @@ class GradWrap(nn.Cell):
 
 class MatMulNet(nn.Cell):
     """ MatMulNet definition """
+
     def __init__(self):
         super(MatMulNet, self).__init__()
         self.matmul = P.MatMul()
@@ -195,6 +203,7 @@ class MatMulNet(nn.Cell):
 
 class NetWithLossSub(nn.Cell):
     """ NetWithLossSub definition """
+
     def __init__(self, network):
         super(NetWithLossSub, self).__init__()
         self.loss = VirtualLoss()
@@ -207,6 +216,7 @@ class NetWithLossSub(nn.Cell):
 
 class GradWrapSub(nn.Cell):
     """ GradWrapSub definition """
+
     def __init__(self, network):
         super(GradWrapSub, self).__init__()
         self.network = network
@@ -217,6 +227,7 @@ class GradWrapSub(nn.Cell):
 
 class SubNet(nn.Cell):
     """ SubNet definition """
+
     def __init__(self):
         super(SubNet, self).__init__()
         self.sub = P.Sub()
@@ -227,6 +238,7 @@ class SubNet(nn.Cell):
 
 class NpuFloatNet(nn.Cell):
     """ NpuFloat definition """
+
     def __init__(self):
         super(NpuFloatNet, self).__init__()
         self.mul = P.Mul()
@@ -258,6 +270,7 @@ class NpuFloatNet(nn.Cell):
 
 class DiagNet(nn.Cell):
     """ DiagNet definition """
+
     def __init__(self):
         super(DiagNet, self).__init__()
         self.fill = P.Fill()
@@ -269,6 +282,7 @@ class DiagNet(nn.Cell):
 
 class NetWithLossCumSum(nn.Cell):
     """ NetWithLossCumSum definition """
+
     def __init__(self, network):
         super(NetWithLossCumSum, self).__init__()
         self.loss = VirtualLoss()
@@ -281,6 +295,7 @@ class NetWithLossCumSum(nn.Cell):
 
 class GradWrapCumSum(nn.Cell):
     """ GradWrap definition """
+
     def __init__(self, network):
         super(GradWrapCumSum, self).__init__()
         self.network = network
@@ -291,6 +306,7 @@ class GradWrapCumSum(nn.Cell):
 
 class NetCumSum(nn.Cell):
     """ NetCumSum definition """
+
     def __init__(self):
         super(NetCumSum, self).__init__()
         self.cumsum = P.CumSum()
@@ -321,8 +337,8 @@ test_case_math_ops = [
         'skip': ['backward']}),
     ('CumSumGrad', {
         'block': GradWrapCumSum(NetWithLossCumSum(NetCumSum())),
-        'desc_inputs': [Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float16))],
-        'desc_bprop': [Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float16))],
+        'desc_inputs': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
+        'desc_bprop': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
         'skip': ['backward']}),
     ('Diag', {
         'block': DiagNet(),
@@ -351,7 +367,6 @@ test_case_math_ops = [
         'skip': ['backward']}),
 ]
 
-
 test_case_lists = [test_case_math_ops]
 test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 # use -k to select certain testcast
@@ -360,6 +375,7 @@ test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 
 import mindspore.context as context
 
+
 @non_graph_engine
 @mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
 def test_exec():
@@ -369,16 +385,16 @@ def test_exec():
 
 raise_set = [
     ('StridedSlice_1_Error', {
-        'block': (lambda x : P.StridedSlice(begin_mask="1"), {'exception': ValueError}),
+        'block': (lambda x: P.StridedSlice(begin_mask="1"), {'exception': ValueError}),
         'desc_inputs': [0]}),
     ('StridedSlice_2_Error', {
-        'block': (lambda x : P.StridedSlice(end_mask="1"), {'exception': ValueError}),
+        'block': (lambda x: P.StridedSlice(end_mask="1"), {'exception': ValueError}),
         'desc_inputs': [0]}),
     ('StridedSlice_3_Error', {
-        'block': (lambda x : P.StridedSlice(ellipsis_mask=1.1), {'exception': ValueError}),
+        'block': (lambda x: P.StridedSlice(ellipsis_mask=1.1), {'exception': ValueError}),
         'desc_inputs': [0]}),
     ('StridedSlice_4_Error', {
-        'block': (lambda x : P.StridedSlice(new_axis_mask="1.1"), {'exception': ValueError}),
+        'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': ValueError}),
         'desc_inputs': [0]}),
 ]
 
diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py
index cadac6dfb4..7364893503 100644
--- a/tests/ut/python/ops/test_nn_ops.py
+++ b/tests/ut/python/ops/test_nn_ops.py
@@ -382,6 +382,46 @@ def test_max_pool_with_arg_max():
     print(ret)
 
 
+class GradWrapUnfold(nn.Cell):
+    """ GradWrapUnfold definition """
+
+    def __init__(self, network):
+        super(GradWrapUnfold, self).__init__()
+        self.network = network
+        self.sens = Tensor(np.ones([1, 4, 2, 2], np.float32))
+
+    def construct(self, x):
+        return C.grad_all_with_sens(self.network)(x, self.sens)
+
+
+class UnfoldNetValid(nn.Cell):
+    """ UnfoldNetValid definition """
+
+    def __init__(self):
+        super(UnfoldNetValid, self).__init__()
+        self.unfold = nn.Unfold(ksizes=[1, 2, 2, 1],
+                                strides=[1, 1, 1, 1],
+                                rates=[1, 1, 1, 1],
+                                padding='VALID')
+
+    def construct(self, x):
+        return self.unfold(x)
+
+
+class UnfoldNetSame(nn.Cell):
+    """ UnfoldNetSame definition """
+
+    def __init__(self):
+        super(UnfoldNetSame, self).__init__()
+        self.unfold = nn.Unfold(ksizes=[1, 2, 2, 1],
+                                strides=[1, 1, 1, 1],
+                                rates=[1, 1, 1, 1],
+                                padding='SAME')
+
+    def construct(self, x):
+        return self.unfold(x)
+
+
 test_cases = [
     ('SoftMaxGrad', {
         'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
@@ -440,6 +480,21 @@ test_cases = [
         'block': ComparisonNet(),
         'desc_inputs': [Tensor(np.ones([6, 9, 10], np.int32)), Tensor(np.ones([6, 9, 10], np.int32))],
     }),
+    ('UnfoldValid', {
+        'block': UnfoldNetValid(),
+        'desc_inputs': [Tensor(np.ones([1, 1, 3, 3], np.float32))],
+        'desc_bprop': [Tensor(np.ones([1, 4, 2, 2], np.float32))],
+        'skip': ['backward']}),
+    ('UnfoldSame', {
+        'block': UnfoldNetSame(),
+        'desc_inputs': [Tensor(np.ones([1, 1, 3, 3], np.float32))],
+        'desc_bprop': [Tensor(np.ones([1, 4, 3, 3], np.float32))],
+        'skip': ['backward']}),
+    ('UnfoldGrad', {
+        'block': GradWrapUnfold(UnfoldNetValid()),
+        'desc_inputs': [Tensor(np.ones([1, 1, 3, 3], np.float32))],
+        'desc_bprop': [Tensor(np.ones([1, 4, 2, 2], np.float32))],
+        'skip': ['backward']}),
 ]
 
 test_cases_for_verify_exception = [

From 734fabb780327a78f4b607f85e9b84426777f14e Mon Sep 17 00:00:00 2001
From: chenfei <chenfei52@huawei.com>
Date: Wed, 15 Apr 2020 15:24:16 +0800
Subject: [PATCH 251/367] solve problem when create parameter from 'call' cnode

---
 mindspore/ccsrc/session/ascend_session.cc | 2 +-
 mindspore/ccsrc/session/session_basic.cc  | 6 +++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 93ae99f4d2..ba0ef836e1 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -822,7 +822,7 @@ GraphId AscendSession::GetGraphIdByNode(const AnfNodePtr &front_anf) const {
     }
   }
   MS_EXCEPTION_IF_NULL(front_anf);
-  MS_LOG(WARNING) << "front_anf " << front_anf->DebugString() << " is not exist in any graph";
+  MS_LOG(DEBUG) << "front_anf " << front_anf->DebugString() << " is not exist in any graph";
   return kInvalidGraphId;
 }
 
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index bea51037bf..33ef78c13d 100755
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -264,7 +264,11 @@ std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, bool va
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(graph);
   std::vector<AnfNodePtr> parameters;
-  std::vector<AnfNodePtr> pre_graph_out = AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem});
+  std::vector<AnfNodePtr> pre_graph_out = {node};
+  // If a cnode is a call, it's input0 is a cnode too, so it doesn't have primitive
+  if (!AnfAlgo::IsRealKernel(node)) {
+    pre_graph_out = AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem});
+  }
   auto valid_inputs = graph->MutableValidInputs();
   MS_EXCEPTION_IF_NULL(valid_inputs);
   auto graph_inputs = graph->MutableInputs();

From 8765810528442d6a1431de818ae67b21b3dfd49d Mon Sep 17 00:00:00 2001
From: c00425699 <chentingting13@huawei.com>
Date: Wed, 15 Apr 2020 16:15:45 +0800
Subject: [PATCH 252/367] fix_coding_style_check_warning

---
 mindspore/ccsrc/parallel/device_manager.cc                  | 1 -
 mindspore/ccsrc/parallel/device_matrix.cc                   | 1 -
 mindspore/ccsrc/parallel/dynamic_creator.h                  | 1 -
 mindspore/ccsrc/parallel/ops_info/activation_info.cc        | 3 ++-
 mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc    | 1 -
 mindspore/ccsrc/parallel/ops_info/loss_info.cc              | 3 ++-
 mindspore/ccsrc/parallel/ops_info/operator_info.cc          | 1 -
 mindspore/ccsrc/parallel/ops_info/prelu_info.cc             | 6 ++++--
 mindspore/ccsrc/parallel/ops_info/reshape_info.cc           | 5 +++--
 mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.cc   | 6 +++---
 mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h    | 1 -
 mindspore/ccsrc/parallel/status.h                           | 1 -
 mindspore/ccsrc/parallel/step_auto_parallel.cc              | 2 --
 mindspore/ccsrc/parallel/step_auto_parallel.h               | 1 -
 mindspore/ccsrc/parallel/step_parallel.cc                   | 2 --
 mindspore/ccsrc/parallel/strategy.h                         | 1 -
 mindspore/ccsrc/parallel/tensor_layout/arrangement.cc       | 1 -
 mindspore/ccsrc/parallel/tensor_layout/arrangement.h        | 2 --
 mindspore/ccsrc/parallel/tensor_layout/array.cc             | 1 -
 mindspore/ccsrc/parallel/tensor_layout/array.h              | 2 --
 mindspore/ccsrc/parallel/tensor_layout/construct_operator.h | 1 -
 mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc   | 2 --
 mindspore/ccsrc/parallel/tensor_layout/layout_transfer.h    | 2 --
 mindspore/ccsrc/parallel/tensor_layout/map.cc               | 1 -
 mindspore/ccsrc/parallel/tensor_layout/map.h                | 1 -
 .../tensor_layout/redistribution_layout_transfer.cc         | 2 --
 .../parallel/tensor_layout/redistribution_layout_transfer.h | 2 --
 .../parallel/tensor_layout/redistribution_operator_infer.cc | 2 --
 .../parallel/tensor_layout/redistribution_operator_infer.h  | 2 --
 .../ccsrc/parallel/tensor_layout/reshape_layout_transfer.cc | 1 -
 .../ccsrc/parallel/tensor_layout/reshape_layout_transfer.h  | 2 --
 mindspore/ccsrc/parallel/tensor_layout/shape_util.cc        | 2 --
 mindspore/ccsrc/parallel/tensor_layout/shape_util.h         | 2 --
 mindspore/ccsrc/parallel/tensor_layout/tensor_info.h        | 2 --
 mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc     | 3 +--
 mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h      | 2 --
 .../ccsrc/parallel/tensor_layout/tensor_redistribution.cc   | 1 -
 .../ccsrc/parallel/tensor_layout/tensor_redistribution.h    | 2 --
 38 files changed, 15 insertions(+), 59 deletions(-)

diff --git a/mindspore/ccsrc/parallel/device_manager.cc b/mindspore/ccsrc/parallel/device_manager.cc
index 3a553e08ec..0b34cedc00 100644
--- a/mindspore/ccsrc/parallel/device_manager.cc
+++ b/mindspore/ccsrc/parallel/device_manager.cc
@@ -370,6 +370,5 @@ void DeviceManager::Clear() {
   stage_devices_.clear();
   gm_.Clear();
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/device_matrix.cc b/mindspore/ccsrc/parallel/device_matrix.cc
index a581dbf275..3fdc3dd15a 100644
--- a/mindspore/ccsrc/parallel/device_matrix.cc
+++ b/mindspore/ccsrc/parallel/device_matrix.cc
@@ -29,7 +29,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 DeviceMatrix::DeviceMatrix(int32_t rank, RankList dev_list, Shape dev_shape)
     : rank_(rank), dev_list_(std::move(dev_list)), dev_shape_(std::move(dev_shape)) {
   if (!std::any_of(dev_list_.begin(), dev_list_.end(), [rank](int32_t a) { return a == rank; })) {
diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 145a8a0840..1b864cd8bf 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 #define REGISTER(className)                                                                                  \
   OperatorInfoPtr objectCreator##className(std::string name, Shapes in, Shapes out, PrimitiveAttrs& attrs) { \
     return std::make_shared<className>(name, in, out, attrs);                                                \
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
index c59ca8402b..c11db56082 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
@@ -229,7 +229,8 @@ Status Softmax::GenerateStrategies(int32_t stage_id) {
   }
 
   is_auto_parallel_ = true;
-  Shape input0_split(inputs_shape_[0].size(), 1);
+  Shape input0_split;
+  (void)input0_split.insert(input0_split.begin(), inputs_shape_[0].size(), 1);
   for (auto& element : axis_) {
     int32_t axis_index = element;
     if (element < 0) {
diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
index b1d9b8b60e..9d356cd573 100644
--- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.cc
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status BatchParallelInfo::CheckStrategy(const StrategyPtr& strategy) {
   if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) {
     if (is_auto_parallel_) {
diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.cc b/mindspore/ccsrc/parallel/ops_info/loss_info.cc
index 31f80e338b..28ea19f120 100644
--- a/mindspore/ccsrc/parallel/ops_info/loss_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/loss_info.cc
@@ -194,7 +194,8 @@ Status SoftmaxCrossEntropyWithLogitsInfo::GenerateStrategies(int32_t stage_id) {
   }
   is_auto_parallel_ = true;
 
-  Shape input0_split(inputs_shape_[0].size(), 1);
+  Shape input0_split;
+  (void)input0_split.insert(input0_split.begin(), inputs_shape_[0].size(), 1);
   input0_split[IntToSize(axis_index)] = 0;
   Shapes splittable_inputs = {input0_split, input0_split};
   std::vector<StrategyPtr> sp_vector;
diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
index 23b6a5190a..e066142589 100644
--- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc
@@ -1255,6 +1255,5 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra
 double OperatorInfo::GetForwardMemoryCostFromCNode() {
   return operator_cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0);
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
index 1a44501f42..a4d601dbe9 100644
--- a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc
@@ -212,8 +212,10 @@ Status PReLUInfo::GenerateStrategies(int32_t stage_id) {
     return FAILED;
   }
   is_auto_parallel_ = true;
-  Shape input0_split(inputs_shape_[0].size(), 1);
-  input0_split[1] = 0;
+  Shape input0_split;
+  input0_split.emplace_back(1);
+  input0_split.emplace_back(0);
+  (void)input0_split.insert(input0_split.end(), inputs_shape_[0].size() - 2, 1);
   Shape input1_split(inputs_shape_[1].size(), 0);
   Shapes splittable_inputs = {input0_split, input1_split};
   std::vector<StrategyPtr> sp_vector;
diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.cc b/mindspore/ccsrc/parallel/ops_info/reshape_info.cc
index 0c95ee9c05..4cb81ee769 100644
--- a/mindspore/ccsrc/parallel/ops_info/reshape_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.cc
@@ -413,8 +413,9 @@ Status ReshapeInfo::GenerateStrategies(int32_t stage_id) {
     return FAILED;
   }
   is_auto_parallel_ = true;
-  Shape input0_split(inputs_shape_[0].size(), 0);
-  input0_split[0] = 1;
+  Shape input0_split;
+  input0_split.emplace_back(1);
+  (void)input0_split.insert(input0_split.end(), inputs_shape_[0].size() - 1, 0);
   Shapes splittable_inputs = {input0_split};
   std::vector<StrategyPtr> sp_vector;
   if (GenerateStrategiesForIndependentInputs(stage_id, inputs_shape_, splittable_inputs, &sp_vector) != SUCCESS) {
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.cc b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.cc
index acb39247d4..cd3b40315c 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.cc
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status VirtualDatasetInfo::CheckStrategy(const StrategyPtr& strategy) {
   if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) {
     if (is_auto_parallel_) {
@@ -225,8 +224,9 @@ Status VirtualDatasetInfo::GenerateStrategies(int32_t stage_id) {
   StrategyPtr sp;
   std::vector<Dimensions> strategy;
   for (auto& shape : inputs_shape_) {
-    Shape temp(shape.size(), 1);
-    temp[0] = SizeToInt(total_dev_num);
+    Shape temp;
+    temp.emplace_back(SizeToInt(total_dev_num));
+    (void)temp.insert(temp.end(), shape.size() - 1, 1);
     strategy.push_back(temp);
   }
   sp = std::make_shared<Strategy>(stage_id, strategy);
diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
index bf17e678a3..398bae3585 100644
--- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h
@@ -51,7 +51,6 @@ class VirtualDatasetInfo : public OperatorInfo {
   Status GetAttrs() override;
   Status InferAsLossDivisor() override;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/status.h b/mindspore/ccsrc/parallel/status.h
index 9d773f0d9b..6bfe9f0e72 100644
--- a/mindspore/ccsrc/parallel/status.h
+++ b/mindspore/ccsrc/parallel/status.h
@@ -21,7 +21,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 enum Status {
   SUCCESS = 0,
   FAILED,
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index a42ce612fb..a56fca1ae6 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -487,7 +487,6 @@ Status ConstructCostGraphNodes(const std::vector<AnfNodePtr> &all_nodes, const F
         bool is_find_wrong = (current_op_ptr->name().find(VIRTUAL_DATA_SET_INFO) == std::string::npos) &&
                              (current_op_ptr->name().find(BATCH_PARALLEL) == std::string::npos) &&
                              (current_op_ptr->name().find(prim->name()) == std::string::npos);
-
         if (is_find_wrong) {
           MS_LOG(EXCEPTION) << "The OperatorInfo: " << current_op_ptr->name()
                             << " does not match the Prim: " << prim->name();
@@ -947,7 +946,6 @@ Status ParallelStrategyRecSearch(const std::vector<AnfNodePtr> &all_nodes, const
 
   graph = EliminateGraph(graph, eli_list, index_list);
   size_t num_device = g_device_manager->DeviceNum();
-
   if (PartitionForAllDevices(num_device, graph) == SUCCESS) {
     MS_LOG(INFO) << "Partition Success With " << num_device << " devices.";
   } else {
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.h b/mindspore/ccsrc/parallel/step_auto_parallel.h
index 349af7c956..f120edcc61 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.h
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.h
@@ -55,7 +55,6 @@ Status ParallelStrategyRecSearch(const std::vector<AnfNodePtr> &all_nodes, const
 
 std::vector<std::vector<std::string>> RecInputTensorNames(const std::map<std::string, std::string>::iterator &it,
                                                           std::vector<std::vector<std::string>> input_tensor_names);
-
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_STEP_AUTO_PARALLEL_H_
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index f0126a4027..bcd4dc3763 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -2094,7 +2094,6 @@ CNodePtr FindLossCNodeFromRoot(const FuncGraphPtr& root) {
   MS_EXCEPTION_IF_NULL(root_return_node);
   const auto& all_nodes = root->nodes();
   FuncGraphPtr func_graph = FindForwardGraphByRootNodes(all_nodes);
-
   if (func_graph == nullptr) {
     return FindLossCNode(root);
   } else {
@@ -2109,7 +2108,6 @@ FuncGraphPtr ForwardGraph(const FuncGraphPtr& root) {
   MS_EXCEPTION_IF_NULL(root_return_node);
   const auto& all_nodes = root->nodes();
   FuncGraphPtr func_graph = FindForwardGraphByRootNodes(all_nodes);
-
   if (func_graph != nullptr) {
     forward_graph = func_graph;
   }
diff --git a/mindspore/ccsrc/parallel/strategy.h b/mindspore/ccsrc/parallel/strategy.h
index acc6ca928f..93d4d4dff1 100644
--- a/mindspore/ccsrc/parallel/strategy.h
+++ b/mindspore/ccsrc/parallel/strategy.h
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 #define MIN_SLICE_NUM 1
 
 using Dimensions = std::vector<int32_t>;
diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
index 68acae87f3..b42ba30242 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc
@@ -26,7 +26,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status Arrangement::Init(const std::vector<int32_t>& array) {
   Status status = Array::Init(array);
   if (status != Status::SUCCESS) {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
index 6d64e07f03..2dc13038c1 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h
@@ -28,7 +28,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class Arrangement : public Array {
  public:
   Arrangement() : size_(1) {}
@@ -53,7 +52,6 @@ class Arrangement : public Array {
   void ComputeSize();
   int32_t size_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/array.cc b/mindspore/ccsrc/parallel/tensor_layout/array.cc
index ce1b9b8ecf..ba3858ae00 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/array.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/array.cc
@@ -21,7 +21,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 std::string Array::ToString() const {
   std::ostringstream buffer;
   buffer << "[ ";
diff --git a/mindspore/ccsrc/parallel/tensor_layout/array.h b/mindspore/ccsrc/parallel/tensor_layout/array.h
index 3a47f0d818..f7d9c3c673 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/array.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/array.h
@@ -26,7 +26,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class Array {
  public:
   Array() = default;
@@ -43,7 +42,6 @@ class Array {
  protected:
   std::vector<int32_t> array_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
index 91f5236037..cf6cff456a 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h
@@ -52,7 +52,6 @@ class ConstructOperator {
   Shape dev_matrix_shape_;
   Status CreateGroupByDim(size_t axis, std::vector<Group>* group);
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
index b2ee51b40b..190a5846ba 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc
@@ -20,7 +20,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 std::string LayoutTransfer::ToString() const {
   std::ostringstream buffer;
   buffer << std::endl << std::string("from_in_ tensor layout:" + from_in_.ToString());
@@ -37,6 +36,5 @@ Status LayoutTransfer::Init(const TensorLayout& from_in, const TensorLayout& to_
   Status status = CheckValidTransfer();
   return status;
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.h b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.h
index b892a87d30..b05128f5b8 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.h
@@ -23,7 +23,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class LayoutTransfer {
  public:
   LayoutTransfer() = default;
@@ -43,7 +42,6 @@ class LayoutTransfer {
  private:
   virtual Status CheckValidTransfer() = 0;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.cc b/mindspore/ccsrc/parallel/tensor_layout/map.cc
index 4f3f2369c7..320dbe6ebd 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/map.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/map.cc
@@ -26,7 +26,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status Map::Init(const std::vector<int32_t>& array) {
   Status status = Array::Init(array);
   if (status != Status::SUCCESS) {
diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.h b/mindspore/ccsrc/parallel/tensor_layout/map.h
index f7bc061aa1..3f839ef198 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/map.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/map.h
@@ -46,7 +46,6 @@ class Map : public Array {
  private:
   bool IsValidMap();
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
index 2ee682fad8..7ed07ac02e 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc
@@ -21,7 +21,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status RedistributionLayoutTransfer::CheckValidTransfer() { return Status::SUCCESS; }
 
 /*
@@ -66,6 +65,5 @@ std::shared_ptr<ReshapeLayoutTransfer> RedistributionLayoutTransfer::UnifyDevice
   }
   return unified_device_arrangement_ptr->UnifyDeviceArrangementAndTensorShape();
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.h
index 6522b7f8c2..7b57f46dd6 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.h
@@ -24,7 +24,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class RedistributionLayoutTransfer : public LayoutTransfer {
  public:
   RedistributionLayoutTransfer() = default;
@@ -35,7 +34,6 @@ class RedistributionLayoutTransfer : public LayoutTransfer {
   Status CheckValidTransfer() override;
   std::shared_ptr<ReshapeLayoutTransfer> UnifyDeviceArrangement() const;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.cc b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.cc
index 028fb5874a..b4ec6a016f 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.cc
@@ -22,7 +22,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status RedistributionOperatorInfer::Init(const TensorLayout& tensor_layout, const Map& out_tensor_map,
                                          RankList dev_list) {
   in_tensor_map_ = tensor_layout.tensor_map();
@@ -273,6 +272,5 @@ Status RedistributionOperatorInfer::TransferConcatByAxis(Args args) {
   }
   return Status::SUCCESS;
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
index 13f9e7af24..b4ec0c4633 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h
@@ -28,7 +28,6 @@
 #include "utils/convert_utils.h"
 namespace mindspore {
 namespace parallel {
-
 using DeviceArrangement = std::vector<int32_t>;
 using TensorMap = std::vector<int32_t>;
 using TensorShape = std::vector<int32_t>;
@@ -69,7 +68,6 @@ class RedistributionOperatorInfer {
   RankList dev_list_;
   bool construct_op_flag_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.cc
index 1d56aa2220..39a6bef92d 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.cc
@@ -20,7 +20,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status ReshapeLayoutTransfer::CheckValidTransfer() {
   if (!IsSameDeviceArrangement()) {
     return Status::FAILED;
diff --git a/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.h b/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.h
index 9ad8e67635..8aae71631d 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/reshape_layout_transfer.h
@@ -23,7 +23,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class ReshapeLayoutTransfer : public LayoutTransfer {
  public:
   ReshapeLayoutTransfer() = default;
@@ -43,7 +42,6 @@ class ReshapeLayoutTransfer : public LayoutTransfer {
   bool FromTensorShapeCanBeExpandByTo() const;
   bool ToTensorShapeCanBeExpandByFrom() const;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/shape_util.cc b/mindspore/ccsrc/parallel/tensor_layout/shape_util.cc
index 54bb976032..a26627fb3c 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/shape_util.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/shape_util.cc
@@ -21,7 +21,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 /*
  * example:
  * shape = [2, 8, 32]
@@ -260,6 +259,5 @@ Status ExpandShape(const std::vector<int32_t>& in, const std::vector<int32_t>& e
   }
   return status;
 }
-
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
index 85ca70969b..e83156500c 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 /*
  * compute the accumulating product of all the values in shape from left to right,
  * the accumulating results are saved in shape_accum from left to right
@@ -167,7 +166,6 @@ Status ExpandAccumulateProduct(const std::vector<int64_t>& in_accum_reverse,
  * out = [2, 4, 2, 4, 8]
  */
 Status ExpandShape(const std::vector<int32_t>& in, const std::vector<int32_t>& expand, std::vector<int32_t>* out);
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
index 9fc6a229e2..4a64ab472c 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h
@@ -28,7 +28,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 using Shapes = std::vector<Shape>;
 
 class TensorInfo {
@@ -55,7 +54,6 @@ class TensorInfo {
   // reduce method's reduce dim
   std::vector<int32_t> reduce_dim_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
index f49b967abc..5fbd04431c 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc
@@ -27,7 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 std::string TensorLayout::ToString() const { return StandardToString() + OriginToString(); }
 
 std::string TensorLayout::StandardToString() const {
@@ -337,7 +336,7 @@ Status TensorLayout::UpdateTensorMap(uint32_t index, int32_t value) {
     MS_LOG(ERROR) << "Index is out of the size of the tensor map!";
     return Status::FAILED;
   }
-  Shape shape = tensor_map_.array();
+  auto shape = tensor_map_.array();
   shape[index] = value;
   if (tensor_map_.Init(shape) == Status::FAILED) {
     MS_LOG(ERROR) << "Update tensor map failed!";
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
index 238c9373d9..e6ddc2a708 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h
@@ -30,7 +30,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class TensorLayout {
  public:
   TensorLayout() = default;
@@ -94,7 +93,6 @@ class TensorLayout {
   Map tensor_map_;
   Arrangement tensor_shape_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
index be5eaa40ba..d8eef7e7a5 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc
@@ -24,7 +24,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 Status TensorRedistribution::Init(const TensorLayout& from, const TensorLayout& to, const RankList& dev_list) {
   from_origin_ = from;
   to_origin_ = to;
diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
index 7e2b3682e6..ebaccadf53 100644
--- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
+++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h
@@ -33,7 +33,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 class TensorRedistribution {
  public:
   explicit TensorRedistribution(bool construct_op_flag = true, bool keep_reshape = false)
@@ -83,7 +82,6 @@ class TensorRedistribution {
   bool construct_op_flag_;
   bool keep_reshape_;
 };
-
 }  // namespace parallel
 }  // namespace mindspore
 

From 4a292d48f9ff1ddc033abe797ec7c803da9e1bde Mon Sep 17 00:00:00 2001
From: "wangnan39@huawei.com" <wangnan39@huawei.com>
Date: Tue, 14 Apr 2020 16:03:55 +0800
Subject: [PATCH 253/367] modify annotation of DepthwiseConv2d

---
 mindspore/ops/operations/nn_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index ec2c2aeecd..b799420620 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -679,7 +679,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
         - **weight** (Tensor) - Set size of kernel is :math:`(K_1, K_2)`, then the shape is
-          :math:`(channel_{multiplier}, C_{in}, K_1, K_2)`.
+          :math:`(\text{channel_multiplier}, C_{in}, K_1, K_2)`.
 
     Outputs:
         Tensor of shape :math:`(N, C_{in} * \text{channel_multiplier}, H_{out}, W_{out})`.

From c926c36d95e7dc20b1de5f927cc58aab3e0dc35c Mon Sep 17 00:00:00 2001
From: huanghui <huanghui44@huawei.com>
Date: Wed, 15 Apr 2020 16:21:53 +0800
Subject: [PATCH 254/367] fix export mslite model

---
 .../converter/lite_model/operations/conv2d_packer.cc | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mindspore/ccsrc/predict/converter/lite_model/operations/conv2d_packer.cc b/mindspore/ccsrc/predict/converter/lite_model/operations/conv2d_packer.cc
index 0fefb89c59..176b235f5f 100644
--- a/mindspore/ccsrc/predict/converter/lite_model/operations/conv2d_packer.cc
+++ b/mindspore/ccsrc/predict/converter/lite_model/operations/conv2d_packer.cc
@@ -28,8 +28,8 @@ bool Conv2dPacker(const CNodePtr &c_node_ptr, OpDefT *ms_op) {
   std::vector<int> kernel_size_value = AnfAlgo::GetNodeAttr<std::vector<int>>(c_node_ptr, "kernel_size");
   std::string kernel_pad_mode_value = AnfAlgo::GetNodeAttr<std::string>(c_node_ptr, "pad_mode");
   int kernel_pad_value = AnfAlgo::GetNodeAttr<int>(c_node_ptr, "pad");
-  int kernel_stride_value = AnfAlgo::GetNodeAttr<int>(c_node_ptr, "stride");
-  int kernel_dilation_value = AnfAlgo::GetNodeAttr<int>(c_node_ptr, "dilation");
+  auto kernel_stride_value = AnfAlgo::GetNodeAttr<std::vector<int>>(c_node_ptr, "stride");
+  auto kernel_dilation_value = AnfAlgo::GetNodeAttr<std::vector<int>>(c_node_ptr, "dilation");
   std::string kernel_data_format_value = AnfAlgo::GetNodeAttr<std::string>(c_node_ptr, "data_format");
   std::unique_ptr<Conv2DT> attr(new Conv2DT());
   MS_EXCEPTION_IF_NULL(attr);
@@ -43,15 +43,15 @@ bool Conv2dPacker(const CNodePtr &c_node_ptr, OpDefT *ms_op) {
   attr->channelOut = kernel_channel_value;
   attr->kernelW = kernel_size_value[0];
   attr->kernelH = kernel_size_value[1];
-  attr->strideW = kernel_stride_value;
-  attr->strideH = kernel_stride_value;
+  attr->strideW = kernel_stride_value[0];
+  attr->strideH = kernel_stride_value[1];
   attr->padMode = GetAttrPadMode(kernel_pad_mode_value);
   attr->padUp = kernel_pad_value;
   attr->padDown = kernel_pad_value;
   attr->padLeft = kernel_pad_value;
   attr->padRight = kernel_pad_value;
-  attr->dilateW = kernel_dilation_value;
-  attr->dilateH = kernel_dilation_value;
+  attr->dilateW = kernel_dilation_value[0];
+  attr->dilateH = kernel_dilation_value[1];
   attr->hasBias = false;
   ms_op->name = c_node_ptr->fullname_with_scope();
   ms_op->attr.type = OpT_Conv2D;

From c3e0ce68d5b4ad6e1058e1f7d24550ef10d58f15 Mon Sep 17 00:00:00 2001
From: wanghua <wanghua36@huawei.com>
Date: Wed, 15 Apr 2020 16:43:35 +0800
Subject: [PATCH 255/367] modify GetNext OP example

---
 mindspore/ops/operations/nn_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index ec2c2aeecd..e87551344c 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1949,7 +1949,7 @@ class GetNext(PrimitiveWithInfer):
         and the type is described is `types`.
 
     Examples:
-        >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name')
+        >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 2, 'shared_name')
         >>> feature, label = get_next()
     """
 

From 4a9f497283ca1882aad6ce28b8d2a8699e53e538 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Wed, 15 Apr 2020 17:03:48 +0800
Subject: [PATCH 256/367] move akg out

---
 cmake/package.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/package.cmake b/cmake/package.cmake
index 8a44d392cc..e4f8fa7706 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -162,7 +162,7 @@ install(
 if (ENABLE_GPU)
     install(
         DIRECTORY ${CMAKE_SOURCE_DIR}/mindspore/_akg
-        DESTINATION ${INSTALL_PY_DIR}
+        DESTINATION ${INSTALL_PY_DIR}/../
         COMPONENT mindspore
     )
     if (EXISTS ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm)
@@ -170,7 +170,7 @@ if (ENABLE_GPU)
             DIRECTORY
                 ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/topi/python/topi
                 ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/python/tvm
-            DESTINATION ${INSTALL_PY_DIR}/_akg
+            DESTINATION ${INSTALL_PY_DIR}/../_akg
             COMPONENT mindspore
         )
     endif ()

From 1286767d0e083ad5ac6a9f50a56451b843463805 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Wed, 15 Apr 2020 09:36:48 +0000
Subject: [PATCH 257/367] support building on windows

---
 CMakeLists.txt                                | 13 +++
 build.bat                                     | 36 ++++++++
 cmake/external_libs/flatbuffers.cmake         |  3 +
 cmake/external_libs/mkl_dnn.cmake             | 13 ++-
 cmake/external_libs/opencv.cmake              | 89 +++++++++++++------
 cmake/external_libs/protobuf.cmake            | 44 +++++----
 cmake/external_libs/sqlite.cmake              | 41 ++++++---
 cmake/mind_expression.cmake                   |  6 +-
 cmake/options.cmake                           |  8 +-
 cmake/utils.cmake                             | 50 +++++++----
 mindspore/ccsrc/CMakeLists.txt                | 62 ++++++++++---
 mindspore/ccsrc/dataset/CMakeLists.txt        | 18 +++-
 .../ccsrc/dataset/api/python_bindings.cc      |  4 +
 mindspore/ccsrc/dataset/core/tensor.h         |  4 +
 .../dataset/engine/datasetops/shuffle_op.cc   |  4 +
 .../dataset/kernels/image/CMakeLists.txt      | 68 +++++++++-----
 .../dataset/kernels/image/image_utils.cc      |  4 +
 .../ccsrc/dataset/kernels/image/image_utils.h |  4 +
 mindspore/ccsrc/dataset/util/path.cc          |  6 +-
 mindspore/ccsrc/dataset/util/random.cc        |  4 +
 mindspore/ccsrc/dataset/util/services.cc      |  8 ++
 mindspore/ccsrc/dataset/util/services.h       |  2 +
 mindspore/ccsrc/dataset/util/sig_handler.cc   |  4 +
 mindspore/ccsrc/dataset/util/sig_handler.h    |  2 +
 mindspore/ccsrc/debug/anf_ir_dump.cc          |  9 ++
 mindspore/ccsrc/debug/anf_ir_utils.cc         | 16 +++-
 mindspore/ccsrc/debug/info.cc                 |  7 +-
 .../ccsrc/device/cpu/cpu_resource_manager.cc  |  1 +
 .../gpu/distribution/collective_fake_init.cc  | 28 ++++++
 .../gpu/distribution/collective_fake_init.h   | 37 ++++++++
 mindspore/ccsrc/device/kernel_runtime.cc      | 20 +++++
 mindspore/ccsrc/kernel/common_utils.cc        | 11 +++
 mindspore/ccsrc/mindrecord/CMakeLists.txt     |  6 +-
 .../ccsrc/mindrecord/common/shard_utils.cc    | 24 +++++
 .../mindrecord/include/common/shard_utils.h   |  2 +
 .../ccsrc/mindrecord/include/shard_reader.h   |  2 +
 mindspore/ccsrc/mindrecord/io/shard_reader.cc |  4 +
 mindspore/ccsrc/mindrecord/io/shard_writer.cc | 10 +++
 .../allreduce_fusion/step_allreduce_fusion.cc | 13 ++-
 .../ccsrc/parallel/step_auto_parallel.cc      |  2 +-
 mindspore/ccsrc/pipeline/action.cc            |  2 +-
 mindspore/ccsrc/pipeline/init.cc              | 14 ++-
 .../ccsrc/pre_activate/common/pass_manager.cc | 10 +++
 mindspore/ccsrc/utils/log_adapter.cc          | 10 ++-
 mindspore/ccsrc/vm/transform.cc               |  6 +-
 mindspore/ccsrc/vm/transform.h                |  2 +-
 mindspore/log.py                              |  7 +-
 third_party/securec/CMakeLists.txt            | 12 ++-
 48 files changed, 619 insertions(+), 133 deletions(-)
 create mode 100644 build.bat
 create mode 100644 mindspore/ccsrc/device/gpu/distribution/collective_fake_init.cc
 create mode 100644 mindspore/ccsrc/device/gpu/distribution/collective_fake_init.h

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a02fcd1caf..9c6ee73687 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -12,6 +12,7 @@ else()
 endif()
 
 set(CMAKE_CXX_FLAGS_DEBUG "$ENV{CXXFLAGS} -O0 -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -Wl,--allow-shlib-undefined -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)=' -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2 -Wno-cpp")
+
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -I/usr/local/include -std=c++17 -Werror -Wall -Wno-deprecated-declarations -fPIC")
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
@@ -29,6 +30,18 @@ find_package(Python3 3.7 COMPONENTS Interpreter Development)
 if(Python3_FOUND)
     set(PYTHON_INCLUDE_DIRS "${Python3_INCLUDE_DIRS}")
     set(PYTHON_LIBRARIES "${Python3_LIBRARIES}")
+    if (WIN32)
+        if (Python3_DIR)
+            message("Python3_DIR set already: " ${Python3_DIR})
+        else()
+            string(LENGTH ${PYTHON_LIBRARIES} PYTHON_LIBRARIES_LEN)
+            string(LENGTH "libpythonxx.a" Python3_NAME_LEN)
+            math(EXPR Python3_DIR_LEN  ${PYTHON_LIBRARIES_LEN}-${Python3_NAME_LEN})
+            string(SUBSTRING ${Python3_LIBRARIES} 0 ${Python3_DIR_LEN} Python3_DIR)
+            message("Python3_DIR: " ${Python3_DIR})
+        endif()
+        link_directories(${Python3_DIR})
+    endif()
 else()
     find_python_package(py_inc py_lib)
     set(PYTHON_INCLUDE_DIRS "${py_inc}")
diff --git a/build.bat b/build.bat
new file mode 100644
index 0000000000..b4f3420e14
--- /dev/null
+++ b/build.bat
@@ -0,0 +1,36 @@
+@echo off
+@title mindspore_build
+ 
+SET BASEPATH=%CD%
+IF NOT EXIST %BASEPATH%/build (
+         md "build"
+         )
+ 
+cd %BASEPATH%/build
+SET BUILD_PATH=%CD%
+ 
+IF NOT EXIST %BUILD_PATH%/mindspore (
+         md "mindspore"
+         )
+ 
+cd %CD%/mindspore
+ 
+cmake -DCMAKE_BUILD_TYPE=Release -DENABLE_CPU=ON -DENABLE_MINDDATA=ON -DUSE_GLOG=ON -G "CodeBlocks - MinGW Makefiles" ../..
+IF NOT %errorlevel% == 0 (
+    goto run_fail
+    )
+ 
+cmake --build . --target all -- -j6
+IF NOT %errorlevel% == 0 (
+    goto run_fail
+    )
+
+cd %BASEPATH%
+
+goto run_eof
+
+:run_fail
+    cd %BASEPATH%
+    echo "build fail."
+
+:run_eof
diff --git a/cmake/external_libs/flatbuffers.cmake b/cmake/external_libs/flatbuffers.cmake
index 7d7c74b9e1..18549ed1b5 100644
--- a/cmake/external_libs/flatbuffers.cmake
+++ b/cmake/external_libs/flatbuffers.cmake
@@ -1,5 +1,8 @@
 set(flatbuffers_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
 set(flatbuffers_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
+if (WIN32)
+ set(flatbuffers_USE_STATIC_LIBS ON)
+endif()
 mindspore_add_pkg(flatbuffers
         VER 1.11.0
         LIBS flatbuffers
diff --git a/cmake/external_libs/mkl_dnn.cmake b/cmake/external_libs/mkl_dnn.cmake
index 6f033fa565..4b2c46670a 100644
--- a/cmake/external_libs/mkl_dnn.cmake
+++ b/cmake/external_libs/mkl_dnn.cmake
@@ -1,11 +1,22 @@
 set(onednn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
 set(onednn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
-mindspore_add_pkg(onednn
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    mindspore_add_pkg(onednn
+        VER 1.1.1
+        LIBS dnnl mkldnn
+        HEAD_ONLY ./
+        RELEASE on
+        URL https://github.com/oneapi-src/oneDNN/releases/download/v1.1.1/dnnl_win_1.1.1_cpu_vcomp.zip
+        MD5 ecaab9ed549643067699c80e5cea1c23)
+else()
+    mindspore_add_pkg(onednn
         VER 1.1.2
         LIBS dnnl mkldnn
         URL https://github.com/oneapi-src/oneDNN/archive/v1.1.2.tar.gz
         MD5 ab40d52230f3ad1d7a6f06ce0f6bc17a
         CMAKE_OPTION -DDNNL_ARCH_OPT_FLAGS='' -DDNNL_CPU_RUNTIME='SEQ' -DDNNL_BUILD_EXAMPLES=OFF -DDNNL_BUILD_TESTS=OFF)
+endif()
+
 include_directories(${onednn_INC})
 add_library(mindspore::dnnl ALIAS onednn::dnnl)
 add_library(mindspore::mkldnn ALIAS onednn::mkldnn)
diff --git a/cmake/external_libs/opencv.cmake b/cmake/external_libs/opencv.cmake
index 5c60a2fa61..1c40769f62 100644
--- a/cmake/external_libs/opencv.cmake
+++ b/cmake/external_libs/opencv.cmake
@@ -2,35 +2,72 @@ if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
     set(opencv_CXXFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_LDFLAGS "-Wl")
+elseif (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
+    set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
 else()
     set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 endif()
 
-mindspore_add_pkg(opencv
-        VER 4.2.0
-        LIBS opencv_core opencv_imgcodecs opencv_imgproc
-        URL https://github.com/opencv/opencv/archive/4.2.0.tar.gz
-        MD5 e8cb208ce2723481408b604b480183b6
-        CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -DWITH_PROTOBUF=OFF -DWITH_WEBP=OFF -DWITH_IPP=OFF -DWITH_ADE=OFF
-        -DBUILD_ZLIB=ON
-        -DBUILD_JPEG=ON
-        -DBUILD_PNG=ON
-        -DBUILD_OPENEXR=ON
-        -DBUILD_TESTS=OFF
-        -DBUILD_PERF_TESTS=OFF
-        -DBUILD_opencv_apps=OFF
-        -DCMAKE_SKIP_RPATH=TRUE
-        -DBUILD_opencv_python3=OFF
-        -DWITH_FFMPEG=OFF
-        -DWITH_TIFF=ON
-        -DBUILD_TIFF=OFF
-        -DWITH_JASPER=OFF
-        -DBUILD_JASPER=OFF
-        -DTIFF_INCLUDE_DIR=${tiff_INC}
-        -DTIFF_LIBRARY=${tiff_LIB})
-include_directories(${opencv_INC}/opencv4)
-add_library(mindspore::opencv_core ALIAS opencv::opencv_core)
-add_library(mindspore::opencv_imgcodecs ALIAS opencv::opencv_imgcodecs)
-add_library(mindspore::opencv_imgproc ALIAS opencv::opencv_imgproc)
+if (WIN32)
+    mindspore_add_pkg(opencv
+            VER 4.2.0
+            LIBS libopencv_core420.dll.a libopencv_imgcodecs420.dll.a libopencv_imgproc420.dll.a
+            LIB_PATH x64/mingw/lib
+            URL https://github.com/opencv/opencv/archive/4.2.0.tar.gz
+            MD5 e8cb208ce2723481408b604b480183b6
+            CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -DWITH_PROTOBUF=OFF -DWITH_WEBP=OFF -DWITH_IPP=OFF -DWITH_ADE=OFF
+            -DBUILD_ZLIB=ON
+            -DBUILD_JPEG=ON
+            -DBUILD_PNG=ON
+            -DBUILD_OPENEXR=ON
+            -DBUILD_TESTS=OFF
+            -DBUILD_PERF_TESTS=OFF
+            -DBUILD_opencv_apps=OFF
+            -DCMAKE_SKIP_RPATH=TRUE
+            -DBUILD_opencv_python3=OFF
+            -DWITH_FFMPEG=OFF
+            -DWITH_TIFF=ON
+            -DBUILD_TIFF=OFF
+            -DWITH_JASPER=OFF
+            -DBUILD_JASPER=OFF
+            -DTIFF_INCLUDE_DIR=${tiff_INC}
+            -DTIFF_LIBRARY=${tiff_LIB})
+else()
+    mindspore_add_pkg(opencv
+            VER 4.2.0
+            LIBS opencv_core opencv_imgcodecs opencv_imgproc
+            URL https://github.com/opencv/opencv/archive/4.2.0.tar.gz
+            MD5 e8cb208ce2723481408b604b480183b6
+            CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -DWITH_PROTOBUF=OFF -DWITH_WEBP=OFF -DWITH_IPP=OFF -DWITH_ADE=OFF
+            -DBUILD_ZLIB=ON
+            -DBUILD_JPEG=ON
+            -DBUILD_PNG=ON
+            -DBUILD_OPENEXR=ON
+            -DBUILD_TESTS=OFF
+            -DBUILD_PERF_TESTS=OFF
+            -DBUILD_opencv_apps=OFF
+            -DCMAKE_SKIP_RPATH=TRUE
+            -DBUILD_opencv_python3=OFF
+            -DWITH_FFMPEG=OFF
+            -DWITH_TIFF=ON
+            -DBUILD_TIFF=OFF
+            -DWITH_JASPER=OFF
+            -DBUILD_JASPER=OFF
+            -DTIFF_INCLUDE_DIR=${tiff_INC}
+            -DTIFF_LIBRARY=${tiff_LIB})
+endif()
+
+if (WIN32)
+    include_directories(${opencv_INC})
+    add_library(mindspore::opencv_core ALIAS opencv::libopencv_core420.dll.a)
+    add_library(mindspore::opencv_imgcodecs ALIAS opencv::libopencv_imgcodecs420.dll.a)
+    add_library(mindspore::opencv_imgproc ALIAS opencv::libopencv_imgproc420.dll.a)
+else()
+    include_directories(${opencv_INC}/opencv4)
+    add_library(mindspore::opencv_core ALIAS opencv::opencv_core)
+    add_library(mindspore::opencv_imgcodecs ALIAS opencv::opencv_imgcodecs)
+    add_library(mindspore::opencv_imgproc ALIAS opencv::opencv_imgproc)
+endif()
diff --git a/cmake/external_libs/protobuf.cmake b/cmake/external_libs/protobuf.cmake
index 987d5c6a8b..a574e789db 100644
--- a/cmake/external_libs/protobuf.cmake
+++ b/cmake/external_libs/protobuf.cmake
@@ -77,22 +77,36 @@ function(ms_protobuf_generate_py c_var h_var py_var)
         list(APPEND ${c_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc")
         list(APPEND ${h_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h")
         list(APPEND ${py_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py")
-
-        add_custom_command(
-                OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
-                "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
-                "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
-                WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
-                COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
-                COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
-                COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
-                COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
-                COMMAND perl -pi -e "s/import (.+_pb2.*)/from . import \\1/"  "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
-                COMMAND cp "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py" "${PROJECT_SOURCE_DIR}/mindspore/train/"
-                DEPENDS protobuf::protoc ${abs_file}
-                COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
+        if (WIN32)
+            add_custom_command(
+                    OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
+                    "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
+                    "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
+                    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
+                    COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
+                    COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND perl -pi.bak -e "s/import (.+_pb2.*)/from . import \\1/"  "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
+                    COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py" "${PROJECT_SOURCE_DIR}/mindspore/train/"
+                    DEPENDS protobuf::protoc ${abs_file}
+                    COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
+        else()
+            add_custom_command(
+                    OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
+                    "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
+                    "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
+                    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
+                    COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
+                    COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
+                    COMMAND perl -pi -e "s/import (.+_pb2.*)/from . import \\1/"  "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
+                    COMMAND cp "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py" "${PROJECT_SOURCE_DIR}/mindspore/train/"
+                    DEPENDS protobuf::protoc ${abs_file}
+                    COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
+        endif()
     endforeach()
-
     set_source_files_properties(${${c_var}} ${${h_var}} ${${py_var}} PROPERTIES GENERATED TRUE)
     set(${c_var} ${${c_var}} PARENT_SCOPE)
     set(${h_var} ${${h_var}} PARENT_SCOPE)
diff --git a/cmake/external_libs/sqlite.cmake b/cmake/external_libs/sqlite.cmake
index e3fe77d96b..69692f284d 100644
--- a/cmake/external_libs/sqlite.cmake
+++ b/cmake/external_libs/sqlite.cmake
@@ -1,19 +1,36 @@
+if (WIN32)
+    mindspore_add_pkg(sqlite-head
+            VER 3.31.1
+            HEAD_ONLY ./
+            URL https://sqlite.org/2020/sqlite-amalgamation-3310100.zip
+            MD5 2b7bfcdd97dc281903a9aee966213fe4)
+    include_directories(${sqlite-head_INC})
+    mindspore_add_pkg(sqlite
+            VER 3.31.1
+            LIBS sqlite3
+            LIB_PATH ./
+            HEAD_ONLY ./
+            RELEASE ON
+            URL https://sqlite.org/2020/sqlite-dll-win64-x64-3310100.zip
+            MD5 662c9d2b05467d590ba5c0443e7fd6bd)
 
-set(sqlite_USE_STATIC_LIBS ON)
-set(sqlite_CXXFLAGS)
-if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-    set(sqlite_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
-else()
-    set(sqlite_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
-endif()
-set(sqlite_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
-
-mindspore_add_pkg(sqlite
+else ()
+    set(sqlite_USE_STATIC_LIBS ON) 
+    set(sqlite_CXXFLAGS)
+    if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+        set(sqlite_CFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+    else()
+        set(sqlite_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
+    endif()
+    set(sqlite_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
+    mindspore_add_pkg(sqlite
         VER 3.31.1
         LIBS sqlite3
         URL https://github.com/sqlite/sqlite/archive/version-3.31.1.tar.gz
         MD5 5f4e7b4016c15f4fb5855615279819da
         PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/sqlite/sqlite.patch001
         CONFIGURE_COMMAND ./configure --enable-shared=no --disable-tcl --disable-editline --enable-json1)
-include_directories(${sqlite_INC})
-add_library(mindspore::sqlite ALIAS sqlite::sqlite3)
\ No newline at end of file
+    include_directories(${sqlite_INC})
+endif ()
+
+add_library(mindspore::sqlite ALIAS sqlite::sqlite3)
diff --git a/cmake/mind_expression.cmake b/cmake/mind_expression.cmake
index af122d4117..7e5e07bdbb 100644
--- a/cmake/mind_expression.cmake
+++ b/cmake/mind_expression.cmake
@@ -1,6 +1,10 @@
 set(SECURE_CXX_FLAGS "")
 if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(SECURE_CXX_FLAGS "-fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
+    if (WIN32)
+        set(SECURE_CXX_FLAGS "-fstack-protector-all")
+    else()
+	set(SECURE_CXX_FLAGS "-fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
+    endif()
 endif()
 set(_ms_tmp_CMAKE_CXX_FLAGS_F ${CMAKE_CXX_FLAGS})
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden")
diff --git a/cmake/options.cmake b/cmake/options.cmake
index 6ec577f312..2f44e9a17c 100644
--- a/cmake/options.cmake
+++ b/cmake/options.cmake
@@ -19,7 +19,11 @@ option(ENABLE_MPI "enable mpi" OFF)
 option(ENABLE_AKG "enable akg" OFF)
 
 if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-    set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
+    if (WIN32)
+        set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fstack-protector-all")
+    else()
+        set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
+    endif()
 endif()
 
 if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
@@ -106,4 +110,4 @@ endif()
 
 if(ENABLE_DUMP_E2E)
     add_compile_definitions(ENABLE_DUMP_E2E)
-endif()
\ No newline at end of file
+endif()
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index c9b7d944f0..f0181de5ea 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -103,7 +103,7 @@ function(__download_pkg_with_git pkg_name pkg_url pkg_git_commit pkg_md5)
 endfunction()
 
 
-function(__find_pkg_then_add_target pkg_name pkg_exe)
+function(__find_pkg_then_add_target pkg_name pkg_exe lib_path)
 
     unset(${pkg_name}_LIBS)
 
@@ -129,15 +129,24 @@ function(__find_pkg_then_add_target pkg_name pkg_exe)
             set(_LIB_TYPE STATIC)
         endif ()
         set(${_LIB_NAME}_LIB ${_LIB_NAME}_LIB-NOTFOUND)
-        find_library(${_LIB_NAME}_LIB ${_LIB_SEARCH_NAME} PATHS ${${pkg_name}_BASE_DIR}/lib NO_DEFAULT_PATH)
+        find_library(${_LIB_NAME}_LIB ${_LIB_SEARCH_NAME} PATHS ${${pkg_name}_BASE_DIR}/${lib_path} NO_DEFAULT_PATH)
+
         if(NOT ${_LIB_NAME}_LIB)
             return()
         endif()
+
         add_library(${pkg_name}::${_LIB_NAME} ${_LIB_TYPE} IMPORTED GLOBAL)
-        set_target_properties(${pkg_name}::${_LIB_NAME} PROPERTIES
-                INTERFACE_INCLUDE_DIRECTORIES "${${pkg_name}_BASE_DIR}/include"
-                IMPORTED_LOCATION ${${_LIB_NAME}_LIB}
-                )
+        if (WIN32 AND ${_LIB_TYPE} STREQUAL "SHARED")
+            set_target_properties(${pkg_name}::${_LIB_NAME} PROPERTIES IMPORTED_IMPLIB_RELEASE ${${_LIB_NAME}_LIB})
+        else()
+            set_target_properties(${pkg_name}::${_LIB_NAME} PROPERTIES IMPORTED_LOCATION ${${_LIB_NAME}_LIB})
+        endif()
+
+        if (EXISTS ${${pkg_name}_BASE_DIR}/include)
+            set_target_properties(${pkg_name}::${_LIB_NAME} PROPERTIES 
+                INTERFACE_INCLUDE_DIRECTORIES "${${pkg_name}_BASE_DIR}/include")
+        endif ()
+
         list(APPEND ${pkg_name}_LIBS ${pkg_name}::${_LIB_NAME})
         message("found ${${_LIB_NAME}_LIB}")
         STRING( REGEX REPLACE "(.+)/(.+)" "\\1" LIBPATH ${${_LIB_NAME}_LIB})
@@ -192,12 +201,18 @@ set(MS_FIND_NO_DEFAULT_PATH ${MS_FIND_NO_DEFAULT_PATH} PARENT_SCOPE)
 function(mindspore_add_pkg pkg_name )
 
     set(options )
-    set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY CMAKE_PATH)
+    set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY CMAKE_PATH RELEASE LIB_PATH)
     set(multiValueArgs CMAKE_OPTION LIBS PRE_CONFIGURE_COMMAND CONFIGURE_COMMAND BUILD_OPTION INSTALL_INCS INSTALL_LIBS PATCHES)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN} )
-    if (NOT PKG_CMAKE_PATH)
-        set(PKG_CMAKE_PATH .)
+
+    if (NOT PKG_LIB_PATH)
+        set(PKG_LIB_PATH lib)
     endif ()
+
+    if(NOT PKG_EXE)
+        set(PKG_EXE 0)
+    endif()
+
     set(__FIND_PKG_NAME ${pkg_name})
     string(TOLOWER ${pkg_name} pkg_name)
     message("pkg name:${__FIND_PKG_NAME},${pkg_name}")
@@ -225,18 +240,17 @@ function(mindspore_add_pkg pkg_name )
         set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/${PKG_HEAD_ONLY} PARENT_SCOPE)
         add_library(${pkg_name} INTERFACE)
         target_include_directories(${pkg_name} INTERFACE ${${pkg_name}_INC})
+        if (${PKG_RELEASE})
+            __find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIB_PATH} ${PKG_LIBS})
+        endif ()
         return()
     endif ()
 
-    if(NOT PKG_EXE)
-        set(PKG_EXE 0)
-    endif()
-
     set(${__FIND_PKG_NAME}_ROOT ${${pkg_name}_BASE_DIR})
     set(${__FIND_PKG_NAME}_ROOT ${${pkg_name}_BASE_DIR} PARENT_SCOPE)
 
     if (PKG_LIBS)
-        __find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIBS})
+        __find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIB_PATH} ${PKG_LIBS})
         if(${pkg_name}_LIBS)
             set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
             message("Found libs: ${${pkg_name}_LIBS}")
@@ -283,8 +297,10 @@ function(mindspore_add_pkg pkg_name )
             file(GLOB ${pkg_name}_SOURCE_SUBDIRS ${${pkg_name}_SOURCE_DIR}/*)
             file(COPY ${${pkg_name}_SOURCE_SUBDIRS} DESTINATION ${${pkg_name}_BASE_DIR})
             set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/${PKG_HEAD_ONLY} PARENT_SCOPE)
-            add_library(${pkg_name} INTERFACE)
-            target_include_directories(${pkg_name} INTERFACE ${${pkg_name}_INC})
+            if (NOT PKG_RELEASE)
+                add_library(${pkg_name} INTERFACE)
+                target_include_directories(${pkg_name} INTERFACE ${${pkg_name}_INC})
+            endif ()
 
         elseif (PKG_CMAKE_OPTION)
             # in cmake
@@ -355,7 +371,7 @@ function(mindspore_add_pkg pkg_name )
     endif()
 
     if (PKG_LIBS)
-        __find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIBS})
+        __find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIB_PATH} ${PKG_LIBS})
         set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
         if(NOT ${pkg_name}_LIBS)
             message(FATAL_ERROR "Can not find pkg: ${pkg_name}")
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index d4103f30c0..500972b0d6 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -5,6 +5,10 @@ if(ENABLE_CPU)
     file(GLOB_RECURSE CPU_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
     "device/cpu/*.cc"
     )
+    if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-attributes -DHAVE_SNPRINTF")
+        add_compile_definitions(BUILDING_DLL)
+    endif()
 endif()
 
 if(ENABLE_GPU)
@@ -150,7 +154,15 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "kernel/kash/*.cc"
         "device/gpu/distribution/collective_init.cc"
         )
-
+if (ENABLE_CPU)
+    list(REMOVE_ITEM MINDSPORE_SRC_LIST "device/gpu/distribution/collective_init.cc")
+    if (WIN32)
+        list(REMOVE_ITEM MINDSPORE_SRC_LIST "kernel/kernel_query.cc")
+    endif()
+endif()
+if (NOT ENABLE_GPU)
+    list(APPEND MINDSPORE_SRC_LIST "device/gpu/distribution/collective_fake_init.cc")
+endif()
 file(GLOB_RECURSE MEM_REUSE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "pre_activate/mem_reuse/*.cc"
         )
@@ -248,6 +260,7 @@ file(GLOB_RECURSE MS_GVAR_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
 
 add_library(mindspore_gvar SHARED ${MS_GVAR_SRC_LIST})
 add_library(mindspore STATIC ${MINDSPORE_SRC_LIST})
+add_dependencies(mindspore GENERATED_OUTPUT_DIR)
 
 if(ENABLE_D)
     list(APPEND MINDSPORE_PROTO_LIST ${MINDSPORE_PROTO_AICPU_LIST})
@@ -315,19 +328,33 @@ if(ENABLE_D)
 endif()
 
 target_link_libraries(mindspore securec)
-target_link_libraries(mindspore dl)
+if (NOT WIN32)
+  target_link_libraries(mindspore dl)
+endif()
 target_link_libraries(mindspore mindspore::flatbuffers)
 # link protobuf
 if (ENABLE_D)
     target_link_libraries(mindspore mindspore::protobuf)
 endif()
 
+if (WIN32)
+    target_link_libraries(mindspore ${PYTHON_LIBRARIES} mindspore_gvar)
+endif()
+
 # set c_expression building
+if (WIN32)
+set(PYTHON_MODULE_SOURCE ${MS_GVAR_SRC_LIST}
+        pipeline/init.cc
+        kernel/oplib/oplib.cc
+        ${MINDSPORE_SRC_LIST} ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
+        ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
+else()
 set(PYTHON_MODULE_SOURCE
         pipeline/init.cc
         kernel/oplib/oplib.cc
-    ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
-    ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
+        ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
+        ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
+endif()
 
 set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE)
 pybind11_add_module(_c_expression ${PYTHON_MODULE_SOURCE})
@@ -339,6 +366,8 @@ if (CMAKE_SYSTEM_NAME MATCHES "Linux")
 elseif (CMAKE_SYSTEM_NAME MATCHES "Darwin")
     set_target_properties(_c_expression PROPERTIES MACOSX_RPATH ON)
     set(ORIGIN_PATH @loader_path)
+elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    set(ORIGIN_PATH $ORIGIN)
 else ()
     MESSAGE(FATAL_ERROR "other platform: ${CMAKE_SYSTEM_NAME}")
 endif ()
@@ -346,11 +375,22 @@ endif ()
 
 set(ORIGIN_PATH ${ORIGIN_PATH}/lib)
 set_target_properties(_c_expression PROPERTIES INSTALL_RPATH ${ORIGIN_PATH})
-target_link_libraries(_c_expression PRIVATE
-        mindspore::pybind11_module
-        mindspore
-        mindspore_gvar
-        )
+if (WIN32)
+    target_link_libraries(_c_expression PRIVATE
+            mindspore::pybind11_module
+            securec
+            proto_input
+            mindspore::flatbuffers
+            mindspore::glog
+            )
+else()
+    target_link_libraries(_c_expression PRIVATE
+            mindspore::pybind11_module
+            mindspore
+            mindspore_gvar
+            mindspore::glog
+            )
+endif()
 
 if(ENABLE_GPU)
 	execute_process(COMMAND bash ${CMAKE_SOURCE_DIR}/third_party/apply_patches.sh 
@@ -493,7 +533,3 @@ if(ENABLE_MINDDATA)
     add_subdirectory(mindrecord)
     add_subdirectory(dataset)
 endif()
-
-if (USE_GLOG)
-    target_link_libraries(_c_expression PRIVATE mindspore::glog)
-endif()
\ No newline at end of file
diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index 52ba328828..b3ac34de70 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -80,14 +80,19 @@ set_target_properties(_c_dataengine PROPERTIES
 ######################################################################
 
 ################# Link with external libraries ########################
-target_link_libraries(_c_dataengine PRIVATE mindspore mindspore_gvar)
-target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module -ldl mindspore::protobuf ${SECUREC_LIBRARY})
+if (WIN32)
+    target_link_libraries(_c_dataengine PRIVATE mindspore)
+    target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module ${PYTHON_LIBRARIES} mindspore::protobuf ${SECUREC_LIBRARY})
+else()
+    target_link_libraries(_c_dataengine PRIVATE mindspore mindspore_gvar)
+    target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module -ldl mindspore::protobuf ${SECUREC_LIBRARY})
+endif()
 target_link_libraries(_c_dataengine PUBLIC mindspore::jpeg_turbo mindspore::opencv_core mindspore::opencv_imgcodecs
         mindspore::opencv_imgproc)
 if (ENABLE_GPUQUE)
     target_link_libraries(_c_dataengine PRIVATE gpu_queue
                                      ${CUDNN_PATH}/lib64/libcudnn.so
-                                     ${CUDA_PATH}/lib64/libcudart.so
+				     ${CUDA_PATH}/lib64/libcudart.so
                                      ${CUDA_PATH}/lib64/stubs/libcuda.so)
 endif ()
 
@@ -96,7 +101,12 @@ if (ENABLE_TDTQUE)
 endif ()
 
 add_dependencies(_c_dataengine _c_mindrecord)
-target_link_libraries(_c_dataengine PRIVATE _c_mindrecord)
+if (WIN32)
+    set(MINDRECORD_LINK_OBJECT ${CMAKE_BINARY_DIR}/mindspore/ccsrc/mindrecord/CMakeFiles/_c_mindrecord.dir/objects.a)
+    target_link_libraries(_c_dataengine PRIVATE _c_mindrecord ${MINDRECORD_LINK_OBJECT} mindspore::sqlite)
+else()
+    target_link_libraries(_c_dataengine PRIVATE _c_mindrecord)
+endif()
 
 if (USE_GLOG)
     target_link_libraries(_c_dataengine PRIVATE mindspore::glog)
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index 3d543f946b..e2675ee217 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -19,7 +19,9 @@
 #include "dataset/kernels/no_op.h"
 #include "dataset/kernels/data/one_hot_op.h"
 #include "dataset/kernels/image/center_crop_op.h"
+#if !defined(_WIN32) && !defined(_WIN64)
 #include "dataset/kernels/image/change_mode_op.h"
+#endif
 #include "dataset/kernels/image/cut_out_op.h"
 #include "dataset/kernels/image/decode_op.h"
 #include "dataset/kernels/image/distort_bounding_box_crop_op.h"
@@ -279,9 +281,11 @@ void bindTensorOps2(py::module *m) {
          py::arg("fillG") = RandomCropOp::kDefFillG, py::arg("fillB") = RandomCropOp::kDefFillB);
   (void)py::class_<HwcToChwOp, TensorOp, std::shared_ptr<HwcToChwOp>>(*m, "ChannelSwapOp").def(py::init<>());
 
+#if !defined(_WIN32) && !defined(_WIN64)
   (void)py::class_<ChangeModeOp, TensorOp, std::shared_ptr<ChangeModeOp>>(
     *m, "ChangeModeOp", "Tensor operation to change colors from BGR to RGB")
     .def(py::init<>());
+#endif
 
   (void)py::class_<OneHotOp, TensorOp, std::shared_ptr<OneHotOp>>(
     *m, "OneHotOp", "Tensor operation to apply one hot encoding. Takes number of classes.")
diff --git a/mindspore/ccsrc/dataset/core/tensor.h b/mindspore/ccsrc/dataset/core/tensor.h
index 2017c2dfab..3409354d19 100644
--- a/mindspore/ccsrc/dataset/core/tensor.h
+++ b/mindspore/ccsrc/dataset/core/tensor.h
@@ -22,6 +22,10 @@
 #include <vector>
 #include "./securec.h"
 #include "utils/log_adapter.h"
+#if defined(_WIN32) || defined(_WIN64)
+#undef HAVE_STDDEF_H
+#undef HAVE_STDLIB_H
+#endif
 #include "pybind11/numpy.h"
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
index 2afafe2128..bdf39b6a39 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc
@@ -85,7 +85,11 @@ Status ShuffleOp::SelfReset() {
   if (!reshuffle_each_epoch_) {
     rng_ = std::mt19937_64(shuffle_seed_);
   } else {
+#if defined(_WIN32) || defined(_WIN64)
+    std::random_device random_device;
+#else
     std::random_device random_device("/dev/urandom");
+#endif
     std::uniform_int_distribution<int32_t> distribution(0, std::numeric_limits<int32_t>::max());
     shuffle_seed_ = distribution(random_device);
     rng_ = std::mt19937_64(shuffle_seed_);
diff --git a/mindspore/ccsrc/dataset/kernels/image/CMakeLists.txt b/mindspore/ccsrc/dataset/kernels/image/CMakeLists.txt
index 7a6240d173..23a26d5214 100644
--- a/mindspore/ccsrc/dataset/kernels/image/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/kernels/image/CMakeLists.txt
@@ -1,22 +1,46 @@
-add_library(kernels-image OBJECT
-    center_crop_op.cc
-    change_mode_op.cc
-    cut_out_op.cc
-    decode_op.cc
-    distort_bounding_box_crop_op.cc
-    hwc_to_chw_op.cc
-    image_utils.cc
-    normalize_op.cc
-    pad_op.cc
-    random_color_adjust_op.cc
-    random_crop_decode_resize_op.cc
-    random_crop_and_resize_op.cc
-    random_crop_op.cc
-    random_horizontal_flip_op.cc
-    random_resize_op.cc
-    random_rotation_op.cc
-    random_vertical_flip_op.cc
-    rescale_op.cc
-    resize_bilinear_op.cc
-    resize_op.cc
-    )
+if (WIN32)
+  add_library(kernels-image OBJECT
+      center_crop_op.cc
+      cut_out_op.cc
+      decode_op.cc
+      distort_bounding_box_crop_op.cc
+      hwc_to_chw_op.cc
+      image_utils.cc
+      normalize_op.cc
+      pad_op.cc
+      random_color_adjust_op.cc
+      random_crop_decode_resize_op.cc
+      random_crop_and_resize_op.cc
+      random_crop_op.cc
+      random_horizontal_flip_op.cc
+      random_resize_op.cc
+      random_rotation_op.cc
+      random_vertical_flip_op.cc
+      rescale_op.cc
+      resize_bilinear_op.cc
+      resize_op.cc
+      )
+else()
+  add_library(kernels-image OBJECT
+      center_crop_op.cc
+      change_mode_op.cc
+      cut_out_op.cc
+      decode_op.cc
+      distort_bounding_box_crop_op.cc
+      hwc_to_chw_op.cc
+      image_utils.cc
+      normalize_op.cc
+      pad_op.cc
+      random_color_adjust_op.cc
+      random_crop_decode_resize_op.cc
+      random_crop_and_resize_op.cc
+      random_crop_op.cc
+      random_horizontal_flip_op.cc
+      random_resize_op.cc
+      random_rotation_op.cc
+      random_vertical_flip_op.cc
+      rescale_op.cc
+      resize_bilinear_op.cc
+      resize_op.cc
+      )
+endif()
diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
index 8735cf7a05..63c9bb2641 100644
--- a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
+++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc
@@ -186,7 +186,11 @@ void JpegSetSource(j_decompress_ptr cinfo, const void *data, int64_t datasize) {
     (*cinfo->mem->alloc_small)(reinterpret_cast<j_common_ptr>(cinfo), JPOOL_PERMANENT, sizeof(struct jpeg_source_mgr)));
   cinfo->src->init_source = JpegInitSource;
   cinfo->src->fill_input_buffer = JpegFillInputBuffer;
+#if defined(_WIN32) || defined(_WIN64)
+  cinfo->src->skip_input_data = reinterpret_cast<void (*)(j_decompress_ptr, long)>(JpegSkipInputData);
+#else
   cinfo->src->skip_input_data = JpegSkipInputData;
+#endif
   cinfo->src->resync_to_restart = jpeg_resync_to_restart;
   cinfo->src->term_source = JpegTermSource;
   cinfo->src->bytes_in_buffer = datasize;
diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.h b/mindspore/ccsrc/dataset/kernels/image/image_utils.h
index a4ddef40d6..51090fb9ea 100644
--- a/mindspore/ccsrc/dataset/kernels/image/image_utils.h
+++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.h
@@ -22,6 +22,10 @@
 #include <random>
 #include <string>
 #include <vector>
+#if defined(_WIN32) || defined(_WIN64)
+#undef HAVE_STDDEF_H
+#undef HAVE_STDLIB_H
+#endif
 #include "./jpeglib.h"
 #include "./jerror.h"
 #include <opencv2/imgproc/imgproc.hpp>
diff --git a/mindspore/ccsrc/dataset/util/path.cc b/mindspore/ccsrc/dataset/util/path.cc
index dd72f80766..24c8db5b9c 100644
--- a/mindspore/ccsrc/dataset/util/path.cc
+++ b/mindspore/ccsrc/dataset/util/path.cc
@@ -27,7 +27,7 @@
 namespace mindspore {
 namespace dataset {
 #ifdef _WIN32
-char Path::_separator = '\\';
+char Path::separator_ = '\\';
 #else
 char Path::separator_ = '/';
 #endif
@@ -129,7 +129,11 @@ bool Path::IsDirectory() {
 
 Status Path::CreateDirectory() {
   if (!Exists()) {
+#if defined(_WIN32) || defined(_WIN64)
+    int rc = mkdir(common::SafeCStr(path_));
+#else
     int rc = mkdir(common::SafeCStr(path_), 0700);
+#endif
     if (rc) {
       std::ostringstream oss;
       oss << "Unable to create directory " << path_ << ". Errno = " << errno;
diff --git a/mindspore/ccsrc/dataset/util/random.cc b/mindspore/ccsrc/dataset/util/random.cc
index e4bab6094c..2a0762c920 100644
--- a/mindspore/ccsrc/dataset/util/random.cc
+++ b/mindspore/ccsrc/dataset/util/random.cc
@@ -32,7 +32,11 @@ namespace dataset {
 uint32_t GetSeed() {
   uint32_t seed = GlobalContext::config_manager()->seed();
   if (seed == std::mt19937::default_seed) {
+#if defined(_WIN32) || defined(_WIN64)
+    std::random_device random_device;
+#else
     std::random_device random_device("/dev/urandom");
+#endif
     std::uniform_int_distribution<uint32_t> distribution(0, std::numeric_limits<uint32_t>::max());
     seed = distribution(random_device);
   }
diff --git a/mindspore/ccsrc/dataset/util/services.cc b/mindspore/ccsrc/dataset/util/services.cc
index 7dcb5b14c9..ea7b11014c 100644
--- a/mindspore/ccsrc/dataset/util/services.cc
+++ b/mindspore/ccsrc/dataset/util/services.cc
@@ -16,7 +16,9 @@
 #include "dataset/util/services.h"
 
 #include <limits.h>
+#if !defined(_WIN32) && !defined(_WIN64)
 #include <sys/syscall.h>
+#endif
 #include <unistd.h>
 #include <random>
 #include "dataset/util/circular_pool.h"
@@ -28,6 +30,7 @@ namespace dataset {
 std::unique_ptr<Services> Services::instance_ = nullptr;
 std::once_flag Services::init_instance_flag_;
 
+#if !defined(_WIN32) && !defined(_WIN64)
 std::string Services::GetUserName() {
   char user[LOGIN_NAME_MAX];
   (void)getlogin_r(user, sizeof(user));
@@ -41,10 +44,15 @@ std::string Services::GetHostName() {
 }
 
 int Services::GetLWP() { return syscall(SYS_gettid); }
+#endif
 
 std::string Services::GetUniqueID() {
   const std::string kStr = "abcdefghijklmnopqrstuvwxyz0123456789";
+#if defined(_WIN32) || defined(_WIN64)
+  std::mt19937 gen{std::random_device{}()};
+#else
   std::mt19937 gen{std::random_device{"/dev/urandom"}()};
+#endif
   std::uniform_int_distribution<> dist(0, kStr.size() - 1);
   char buffer[UNIQUEID_LEN];
   for (int i = 0; i < UNIQUEID_LEN; i++) {
diff --git a/mindspore/ccsrc/dataset/util/services.h b/mindspore/ccsrc/dataset/util/services.h
index 65a302af91..5e81c4816e 100644
--- a/mindspore/ccsrc/dataset/util/services.h
+++ b/mindspore/ccsrc/dataset/util/services.h
@@ -62,11 +62,13 @@ class Services {
 
   std::shared_ptr<MemoryPool> GetServiceMemPool() { return pool_; }
 
+#if !defined(_WIN32) && !defined(_WIN64)
   static std::string GetUserName();
 
   static std::string GetHostName();
 
   static int GetLWP();
+#endif
 
   static std::string GetUniqueID();
 
diff --git a/mindspore/ccsrc/dataset/util/sig_handler.cc b/mindspore/ccsrc/dataset/util/sig_handler.cc
index 1b6a3701c3..644a633066 100644
--- a/mindspore/ccsrc/dataset/util/sig_handler.cc
+++ b/mindspore/ccsrc/dataset/util/sig_handler.cc
@@ -16,13 +16,16 @@
 #include "dataset/util/sig_handler.h"
 #include <signal.h>
 #include <sys/types.h>
+#if !defined(_WIN32) && !defined(_WIN64)
 #include <ucontext.h>
+#endif
 #include <unistd.h>
 #include "dataset/util/task_manager.h"
 
 namespace mindspore {
 namespace dataset {
 // Register the custom signal handlers
+#if !defined(_WIN32) && !defined(_WIN64)
 void RegisterHandlers() {
   struct sigaction new_int_action;
 
@@ -40,5 +43,6 @@ extern void IntHandler(int sig_num,          // The signal that was raised
   // Wake up the watchdog which is designed as async-signal-safe.
   TaskManager::WakeUpWatchDog();
 }
+#endif
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/util/sig_handler.h b/mindspore/ccsrc/dataset/util/sig_handler.h
index 6c5e1f015c..af40738feb 100644
--- a/mindspore/ccsrc/dataset/util/sig_handler.h
+++ b/mindspore/ccsrc/dataset/util/sig_handler.h
@@ -22,12 +22,14 @@
 namespace mindspore {
 namespace dataset {
 // Register the custom signal handlers
+#if !defined(_WIN32) && !defined(_WIN64)
 extern void RegisterHandlers();
 
 // A signal handler for SIGINT.  Drives interrupt to watchdog
 extern void IntHandler(int sig_num,          // The signal that was raised
                        siginfo_t *sig_info,  // The siginfo structure.
                        void *context);       // context info
+#endif
 }  // namespace dataset
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/debug/anf_ir_dump.cc b/mindspore/ccsrc/debug/anf_ir_dump.cc
index fcf0777d16..e977084ab8 100644
--- a/mindspore/ccsrc/debug/anf_ir_dump.cc
+++ b/mindspore/ccsrc/debug/anf_ir_dump.cc
@@ -14,6 +14,9 @@
  * limitations under the License.
  */
 #include "debug/anf_ir_dump.h"
+#if defined(_WIN32) || defined(_WIN64)
+#include <stdlib.h>
+#endif
 #include <fstream>
 #include <map>
 #include <memory>
@@ -434,9 +437,15 @@ void DumpIR(const std::string &filename, const FuncGraphPtr &graph, bool dump_fu
     return;
   }
   char real_path[PATH_MAX] = {0};
+#if defined(_WIN32) || defined(_WIN64)
+  if (_fullpath(real_path, filename.c_str(), PATH_MAX) == nullptr) {
+    MS_LOG(DEBUG) << "dir " << filename << " does not exit.";
+  }
+#else
   if (nullptr == realpath(filename.c_str(), real_path)) {
     MS_LOG(DEBUG) << "Dir " << filename << " does not exit.";
   }
+#endif
 
   OrderedMap<AnfNodePtr, int32_t> para_map;
   std::string path_string = real_path;
diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc
index c25ad862df..8e626d6f9a 100644
--- a/mindspore/ccsrc/debug/anf_ir_utils.cc
+++ b/mindspore/ccsrc/debug/anf_ir_utils.cc
@@ -48,9 +48,15 @@ std::string GetMsIrPath(void) {
   if (path_ptr != nullptr) {
     path = path_ptr;
     char real_path[PATH_MAX] = {0};
+#if defined(_WIN32) || defined(_WIN64)
+    if (path.size() > PATH_MAX || _fullpath(real_path, path.c_str(), PATH_MAX) == nullptr) {
+      MS_LOG(EXCEPTION) << "MS IR Path error, " << path_ptr;
+    }
+#else
     if (path.size() > PATH_MAX || nullptr == realpath(path.c_str(), real_path)) {
       MS_LOG(EXCEPTION) << "MS IR path error, " << path_ptr;
     }
+#endif
     path = real_path;
   }
   return path;
@@ -2247,8 +2253,14 @@ void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix) {
     return;
   }
   char real_path[PATH_MAX] = {0};
-  if (nullptr == realpath(file_path.c_str(), real_path)) {
-    MS_LOG(DEBUG) << "Dir " << file_path << " does not exit.";
+  char* real_path_ret = nullptr;
+#if defined(_WIN32) || defined(_WIN64)
+  real_path_ret = _fullpath(real_path, file_path.c_str(), PATH_MAX);
+#else
+  real_path_ret = realpath(file_path.c_str(), real_path);
+#endif
+  if (nullptr == real_path_ret) {
+    MS_LOG(DEBUG) << "dir " << file_path << " does not exit.";
   } else {
     std::string path_string = real_path;
     if (chmod(common::SafeCStr(path_string), S_IRUSR | S_IWUSR) == -1) {
diff --git a/mindspore/ccsrc/debug/info.cc b/mindspore/ccsrc/debug/info.cc
index 5c1fc372c5..3c43bfa9b1 100644
--- a/mindspore/ccsrc/debug/info.cc
+++ b/mindspore/ccsrc/debug/info.cc
@@ -53,10 +53,15 @@ std::string Location::ToString(SourceLineTip tip) {
   }
 
   char path[PATH_MAX + 1] = {0x00};
+#if defined(_WIN32) || defined(_WIN64)
+  if (file_name_.size() > PATH_MAX || _fullpath(path, file_name_.c_str(), PATH_MAX) == nullptr) {
+    return debug_info_ss.str();
+  }
+#else
   if (file_name_.size() > PATH_MAX || realpath(file_name_.c_str(), path) == nullptr) {
     return debug_info_ss.str();
   }
-
+#endif
   auto src_path = std::string(path);
   std::ifstream file(src_path);
   if (!file.is_open()) {
diff --git a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
index 97df7d4487..45b9ea5bed 100644
--- a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
+++ b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc
@@ -60,6 +60,7 @@ void CPUResourceManager::MemMalloc(const session::KernelGraph *graph) {
 void *CPUResourceManager::MemMalloc(size_t mem_size) {
   void *ptr = malloc(mem_size);
   if (ptr != nullptr) {
+    memset_s(ptr, mem_size, 0, mem_size);
     dynamic_mem_[ptr] = mem_size;
     return ptr;
   } else {
diff --git a/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.cc b/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.cc
new file mode 100644
index 0000000000..06497a2e82
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.cc
@@ -0,0 +1,28 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "device/gpu/distribution/collective_fake_init.h"
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace device {
+namespace gpu {
+void CollectiveFakeInitializer::InitCollective() { MS_LOG(EXCEPTION) << "build without enable gpu!"; }
+
+void CollectiveFakeInitializer::FinalizeCollective() { MS_LOG(EXCEPTION) << "build without enable gpu!"; }
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.h b/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.h
new file mode 100644
index 0000000000..65467139c0
--- /dev/null
+++ b/mindspore/ccsrc/device/gpu/distribution/collective_fake_init.h
@@ -0,0 +1,37 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_DEVICE_GPU_DISTRIBUTION_COLLECTIVE_FAKE_INIT_H_
+#define MINDSPORE_CCSRC_DEVICE_GPU_DISTRIBUTION_COLLECTIVE_FAKE_INIT_H_
+
+namespace mindspore {
+namespace device {
+namespace gpu {
+
+class CollectiveFakeInitializer {
+ public:
+  CollectiveFakeInitializer() = default;
+  ~CollectiveFakeInitializer() = default;
+  CollectiveFakeInitializer(CollectiveFakeInitializer const &) = delete;
+  CollectiveFakeInitializer &operator=(const CollectiveFakeInitializer &) = delete;
+  static void InitCollective();
+  static void FinalizeCollective();
+};
+}  // namespace gpu
+}  // namespace device
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_DEVICE_GPU_DISTRIBUTION_COLLECTIVE_FAKE_INIT_H_
diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc
index 3b0d84eada..7f3d31d8d0 100644
--- a/mindspore/ccsrc/device/kernel_runtime.cc
+++ b/mindspore/ccsrc/device/kernel_runtime.cc
@@ -44,19 +44,29 @@ bool KernelRuntime::Run(session::KernelGraph *graph) {
   bool ret = false;
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
+#if defined(_WIN32) || defined(_WIN64)
+  auto start_time = std::chrono::steady_clock::now();
+#else
   struct timeval start_time, end_time;
   (void)gettimeofday(&start_time, nullptr);
+#endif
   bool is_task_sink = context_ptr->enable_task_sink();
   if (is_task_sink) {
     ret = RunTask(graph);
   } else {
     ret = LaunchKernel(graph);
   }
+#if defined(_WIN32) || defined(_WIN64)
+  auto end_time = std::chrono::steady_clock::now();
+  std::chrono::duration<double, std::ratio<1, 1000000>> cost = end_time - start_time;
+  MS_LOG(INFO) << "Call MS Run Success in " << cost.count() << " us";
+#else
   (void)gettimeofday(&end_time, nullptr);
   const uint64_t kUSecondInSecond = 1000000;
   uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
   cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
   MS_LOG(INFO) << "Call MS Run Success in " << cost << " us";
+#endif
   return ret;
 }
 
@@ -561,8 +571,12 @@ bool KernelRuntime::LaunchKernelMod(const session::KernelGraph &graph) {
     AddressPtrList kernel_workspaces;
     AddressPtrList kernel_outputs;
     GenLaunchArgs(*kernel_mod, kernel, &kernel_inputs, &kernel_workspaces, &kernel_outputs);
+#if defined(_WIN32) || defined(_WIN64)
+    auto start_time = std::chrono::steady_clock::now();
+#else
     struct timeval start_time, end_time;
     (void)gettimeofday(&start_time, nullptr);
+#endif
     auto ret =
       kernel_mod->Launch(kernel_inputs, kernel_workspaces, kernel_outputs, reinterpret_cast<uintptr_t>(stream_));
     if (!ret) {
@@ -572,11 +586,17 @@ bool KernelRuntime::LaunchKernelMod(const session::KernelGraph &graph) {
       if (AnfAlgo::GetKernelType(kernel) == TBE_KERNEL && !SyncStream()) {
         MS_LOG(EXCEPTION) << "SyncStream failed.";
       }
+#if defined(_WIN32) || defined(_WIN64)
+      auto end_time = std::chrono::steady_clock::now();
+      std::chrono::duration<double, std::ratio<1, 1000000>> cost = end_time - start_time;
+      MS_LOG(DEBUG) << "d " << kernel->fullname_with_scope() << " in  " << cost.count() << " us";
+#else
       (void)gettimeofday(&end_time, nullptr);
       const uint64_t kUSecondInSecond = 1000000;
       uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
       cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
       MS_LOG(DEBUG) << "d " << kernel->fullname_with_scope() << " in  " << cost << " us";
+#endif
     }
   }
   return true;
diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc
index 065cfaddc8..5abaff412e 100644
--- a/mindspore/ccsrc/kernel/common_utils.cc
+++ b/mindspore/ccsrc/kernel/common_utils.cc
@@ -117,7 +117,11 @@ bool IsAtomicNode(const CNodePtr &kernel_node) {
 bool KernelMeta::ReadIndex(const std::string &bin_dir) {
   DIR *dir = opendir(bin_dir.c_str());
   if (dir == nullptr) {
+#if defined(_WIN32) || defined(_WIN64)
+    auto ret = mkdir(bin_dir.c_str());
+#else
     auto ret = mkdir(bin_dir.c_str(), S_IRWXG | S_IRWXU);
+#endif
     if (ret != 0) {
       MS_LOG(INFO) << "kernel dir not exist[" << bin_dir << "].";
       return false;
@@ -500,10 +504,17 @@ void SaveJsonInfo(const std::string &json_name, const std::string &info) {
   }
   filewrite << info << std::endl;
   filewrite.close();
+#if defined(_WIN32) || defined(_WIN64)
+  if (nullptr == _fullpath(real_path, path.c_str(), PATH_MAX)) {
+    MS_LOG(DEBUG) << "dir " << path << " does not exit.";
+    return;
+  }
+#else
   if (nullptr == realpath(path.c_str(), real_path)) {
     MS_LOG(DEBUG) << "dir " << path << " does not exit.";
     return;
   }
+#endif
   MS_LOG(INFO) << "real path is :" << real_path;
   if (chmod(real_path, S_IRUSR) == -1) {
     MS_LOG(DEBUG) << "modify file:" << real_path << " to read only fail.";
diff --git a/mindspore/ccsrc/mindrecord/CMakeLists.txt b/mindspore/ccsrc/mindrecord/CMakeLists.txt
index 4b8448287d..a2b8897b16 100644
--- a/mindspore/ccsrc/mindrecord/CMakeLists.txt
+++ b/mindspore/ccsrc/mindrecord/CMakeLists.txt
@@ -26,7 +26,11 @@ set_target_properties(_c_mindrecord PROPERTIES
     )
 
 # add link library
-target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite ${PYTHON_LIB} ${SECUREC_LIBRARY} mindspore mindspore_gvar mindspore::protobuf)
+if (WIN32)
+    target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite mindspore mindspore::protobuf)
+else()
+    target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite ${PYTHON_LIB} ${SECUREC_LIBRARY} mindspore mindspore_gvar mindspore::protobuf)
+endif()
 
 if (USE_GLOG)
     target_link_libraries(_c_mindrecord PRIVATE mindspore::glog)
diff --git a/mindspore/ccsrc/mindrecord/common/shard_utils.cc b/mindspore/ccsrc/mindrecord/common/shard_utils.cc
index ca4bb8a261..51de0c5f64 100644
--- a/mindspore/ccsrc/mindrecord/common/shard_utils.cc
+++ b/mindspore/ccsrc/mindrecord/common/shard_utils.cc
@@ -65,6 +65,15 @@ std::pair<MSRStatus, std::string> GetFileName(const std::string &path) {
     return {FAILED, ""};
   }
   char tmp[PATH_MAX] = {0};
+#if defined(_WIN32) || defined(_WIN64)
+  if (_fullpath(tmp, dirname(&(buf[0])), PATH_MAX) == nullptr) {
+    MS_LOG(ERROR) << "Invalid file path, path: " << buf;
+    return {FAILED, ""};
+  }
+  if (_fullpath(real_path, common::SafeCStr(path), PATH_MAX) == nullptr) {
+    MS_LOG(DEBUG) << "Path: " << common::SafeCStr(path) << "check successfully";
+  }
+#else
   if (realpath(dirname(&(buf[0])), tmp) == nullptr) {
     MS_LOG(ERROR) << "Invalid file path, path: " << buf;
     return {FAILED, ""};
@@ -72,6 +81,7 @@ std::pair<MSRStatus, std::string> GetFileName(const std::string &path) {
   if (realpath(common::SafeCStr(path), real_path) == nullptr) {
     MS_LOG(DEBUG) << "Path: " << path << "check successfully";
   }
+#endif
   std::string s = real_path;
   char sep = '/';
   size_t i = s.rfind(sep, s.length());
@@ -91,6 +101,15 @@ std::pair<MSRStatus, std::string> GetParentDir(const std::string &path) {
     return {FAILED, ""};
   }
   char tmp[PATH_MAX] = {0};
+#if defined(_WIN32) || defined(_WIN64)
+  if (_fullpath(tmp, dirname(&(buf[0])), PATH_MAX) == nullptr) {
+    MS_LOG(ERROR) << "Invalid file path, path: " << buf;
+    return {FAILED, ""};
+  }
+  if (_fullpath(real_path, common::SafeCStr(path), PATH_MAX) == nullptr) {
+    MS_LOG(DEBUG) << "Path: " << common::SafeCStr(path) << "check successfully";
+  }
+#else
   if (realpath(dirname(&(buf[0])), tmp) == nullptr) {
     MS_LOG(ERROR) << "Invalid file path, path: " << buf;
     return {FAILED, ""};
@@ -98,6 +117,7 @@ std::pair<MSRStatus, std::string> GetParentDir(const std::string &path) {
   if (realpath(common::SafeCStr(path), real_path) == nullptr) {
     MS_LOG(DEBUG) << "Path: " << path << "check successfully";
   }
+#endif
   std::string s = real_path;
   if (s.rfind('/') + 1 <= s.size()) {
     return {SUCCESS, s.substr(0, s.rfind('/') + 1)};
@@ -144,6 +164,9 @@ bool IsLegalFile(const std::string &path) {
 }
 
 std::pair<MSRStatus, uint64_t> GetDiskSize(const std::string &str_dir, const DiskSizeType &disk_type) {
+#if defined(_WIN32) || defined(_WIN64)
+  return {SUCCESS, 100};
+#else
   uint64_t ll_count = 0;
   struct statfs disk_info;
   if (statfs(common::SafeCStr(str_dir), &disk_info) == -1) {
@@ -166,6 +189,7 @@ std::pair<MSRStatus, uint64_t> GetDiskSize(const std::string &str_dir, const Dis
   }
 
   return {SUCCESS, ll_count};
+#endif
 }
 
 uint32_t GetMaxThreadNum() {
diff --git a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
index e18cbb75b9..d31037c8ad 100644
--- a/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
+++ b/mindspore/ccsrc/mindrecord/include/common/shard_utils.h
@@ -21,8 +21,10 @@
 #include <limits.h>
 #include <stdlib.h>
 #include <sys/stat.h>
+#if !defined(_WIN32) && !defined(_WIN64)
 #include <sys/statfs.h>
 #include <sys/wait.h>
+#endif
 #include <unistd.h>
 #include <cassert>
 #include <cmath>
diff --git a/mindspore/ccsrc/mindrecord/include/shard_reader.h b/mindspore/ccsrc/mindrecord/include/shard_reader.h
index c114b17951..5548473cd7 100644
--- a/mindspore/ccsrc/mindrecord/include/shard_reader.h
+++ b/mindspore/ccsrc/mindrecord/include/shard_reader.h
@@ -19,7 +19,9 @@
 
 #include <dirent.h>
 #include <signal.h>
+#if !defined(_WIN32) && !defined(_WIN64)
 #include <sys/prctl.h>
+#endif
 #include <sys/stat.h>
 #include <sys/types.h>
 #include <unistd.h>
diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
index 085f148a88..fd3fede5a2 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc
@@ -987,8 +987,10 @@ TASK_RETURN_CONTENT ShardReader::ConsumerOneTask(int task_id, uint32_t consumer_
 
 MSRStatus ShardReader::ConsumerByRow(int consumer_id) {
   // Set thread name
+#if !defined(_WIN32) && !defined(_WIN64)
   auto thread_id = kThreadName + std::to_string(consumer_id);
   prctl(PR_SET_NAME, common::SafeCStr(thread_id), 0, 0, 0);
+#endif
 
   // Loop forever
   for (;;) {
@@ -1040,8 +1042,10 @@ MSRStatus ShardReader::ReadBlob(const int &shard_id, const uint64_t &page_offset
 
 MSRStatus ShardReader::ConsumerByBlock(int consumer_id) {
   // Set thread name
+#if !defined(_WIN32) && !defined(_WIN64)
   auto thread_id = kThreadName + std::to_string(consumer_id);
   prctl(PR_SET_NAME, common::SafeCStr(thread_id), 0, 0, 0);
+#endif
 
   // Loop forever
   for (;;) {
diff --git a/mindspore/ccsrc/mindrecord/io/shard_writer.cc b/mindspore/ccsrc/mindrecord/io/shard_writer.cc
index 3d4259ebbd..864e6697d0 100644
--- a/mindspore/ccsrc/mindrecord/io/shard_writer.cc
+++ b/mindspore/ccsrc/mindrecord/io/shard_writer.cc
@@ -63,6 +63,15 @@ MSRStatus ShardWriter::Open(const std::vector<std::string> &paths, bool append)
       MS_LOG(ERROR) << "Securec func failed";
       return FAILED;
     }
+#if defined(_WIN32) || defined(_WIN64)
+    if (_fullpath(resolved_path, dirname(&(buf[0])), PATH_MAX) == nullptr) {
+      MS_LOG(ERROR) << "Invalid file path";
+      return FAILED;
+    }
+    if (_fullpath(resolved_path, common::SafeCStr(path), PATH_MAX) == nullptr) {
+      MS_LOG(DEBUG) << "Path " << resolved_path;
+    }
+#else
     if (realpath(dirname(&(buf[0])), resolved_path) == nullptr) {
       MS_LOG(ERROR) << "Invalid file path";
       return FAILED;
@@ -70,6 +79,7 @@ MSRStatus ShardWriter::Open(const std::vector<std::string> &paths, bool append)
     if (realpath(common::SafeCStr(path), resolved_path) == nullptr) {
       MS_LOG(DEBUG) << "Path " << resolved_path;
     }
+#endif
     file_paths_.emplace_back(string(resolved_path));
   }
 
diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
index 9dbd3a0246..8ab0895216 100644
--- a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
+++ b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc
@@ -38,10 +38,12 @@ bool StepAllreduceFusion(const FuncGraphPtr &root, const opt::OptimizerPtr &opti
       (root->has_flag(ALLREDUCE_FUSION_RUN_ONCE_ONLY))) {
     return changes;
   }
-
+#if defined(_WIN32) || defined(_WIN64)
+  auto start_time = std::chrono::steady_clock::now();
+#else
   struct timeval start_time, end_time;
   (void)gettimeofday(&start_time, nullptr);
-
+#endif
   MS_LOG(INFO) << "Now entering allreduce fusion";
   DumpGraph(root, std::string(ALLREDUCE_FUSION_BEGIN));
 
@@ -63,11 +65,16 @@ bool StepAllreduceFusion(const FuncGraphPtr &root, const opt::OptimizerPtr &opti
   // allreduce fusion only run once
   root->flags()[ALLREDUCE_FUSION_RUN_ONCE_ONLY] = true;
   res->results()[pipeline::kStepParallelGraph] = root;
-
+#if defined(_WIN32) || defined(_WIN64)
+  auto end_time = std::chrono::steady_clock::now();
+  std::chrono::duration<double, std::ratio<1, 1000000>> cost = end_time - start_time;
+  MS_LOG(INFO) << "Now leaving allreduce fusion, used time: " << cost.count() << " us";
+#else
   (void)gettimeofday(&end_time, nullptr);
   uint64_t time = 1000000 * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
   time += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
   MS_LOG(INFO) << "Now leaving allreduce fusion, used time: " << time << " us";
+#endif
   return changes;
 }
 }  // namespace parallel
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index a42ce612fb..1b881e5d23 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -229,7 +229,7 @@ size_t GetLengthOfDataType(const TypePtr &type) {
     case kNumberTypeInt:
       return sizeof(int);
     case kNumberTypeUInt:
-      return sizeof(uint);
+      return sizeof(unsigned int);
     case kNumberTypeFloat:
       return sizeof(float);
     default:
diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc
index d77fee84aa..3e0f8804e7 100644
--- a/mindspore/ccsrc/pipeline/action.cc
+++ b/mindspore/ccsrc/pipeline/action.cc
@@ -264,7 +264,7 @@ bool TaskEmitAction(const ResourcePtr& res) {
   auto bc_ptr = res->results()[kBackend].cast<compile::BackendPtr>();
   std::vector<PrimitivePtr> cut_list = compile::nonlinear_ops;
   if (bc_ptr->name() == kMsConvert) {
-    cut_list = compile::ms_nonlinear_ops;
+    cut_list = compile::GetMsNonlinearOps();
   }
   std::shared_ptr<CompileGraphs> compile = std::make_shared<CompileGraphs>(bc_ptr, cut_list);
   res->results()[kOutput] = compile->CompileAndLink(func_graph);
diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc
index 24ead047d3..aa370254bb 100644
--- a/mindspore/ccsrc/pipeline/init.cc
+++ b/mindspore/ccsrc/pipeline/init.cc
@@ -29,8 +29,11 @@
 #include "parallel/context.h"
 #include "parallel/device_manager.h"
 #include "parallel/costmodel_context.h"
+#ifdef ENABLE_GPUQUE
 #include "device/gpu/distribution/collective_init.h"
-
+#else
+#include "device/gpu/distribution/collective_fake_init.h"
+#endif
 namespace py = pybind11;
 
 using FuncGraph = mindspore::FuncGraph;
@@ -297,9 +300,16 @@ PYBIND11_MODULE(_c_expression, m) {
   (void)py::class_<OpLib, std::shared_ptr<OpLib>>(m, "Oplib")
     .def(py::init())
     .def("reg_op", &OpLib::RegOp, "Register op info.");
-
+#ifdef ENABLE_GPUQUE
   (void)m.def("init_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::InitCollective,
               "Init gpu collective communication mode.");
   (void)m.def("finalize_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::FinalizeCollective,
               "Finalize gpu collective communication mode.");
+#else
+  (void)m.def("init_gpu_collective", &mindspore::device::gpu::CollectiveFakeInitializer::InitCollective,
+              "Init gpu collective communication mode.");
+  (void)m.def("finalize_gpu_collective", &mindspore::device::gpu::CollectiveFakeInitializer::FinalizeCollective,
+              "Finalize gpu collective communication mode.");
+
+#endif
 }
diff --git a/mindspore/ccsrc/pre_activate/common/pass_manager.cc b/mindspore/ccsrc/pre_activate/common/pass_manager.cc
index 0dfe4c763f..f93a1ad51d 100644
--- a/mindspore/ccsrc/pre_activate/common/pass_manager.cc
+++ b/mindspore/ccsrc/pre_activate/common/pass_manager.cc
@@ -52,17 +52,27 @@ bool PassManager::Run(const FuncGraphPtr &func_graph, const std::vector<PassPtr>
   size_t num = 0;
   for (const auto &pass : passes) {
     if (pass != nullptr) {
+#if defined(_WIN32) || defined(_WIN64)
+      auto start_time = std::chrono::steady_clock::now();
+#else
       struct timeval start_time {};
       struct timeval end_time {};
       (void)gettimeofday(&start_time, nullptr);
+#endif
       if (pass->Run(func_graph)) {
         changed = true;
       }
+#if defined(_WIN32) || defined(_WIN64)
+      auto end_time = std::chrono::steady_clock::now();
+      std::chrono::duration<double, std::ratio<1, 1000000>> cost = end_time - start_time;
+      MS_LOG(INFO) << "Run pass hwopt_" + name() + "_" << num << "_" + pass->name() + " in " << cost.count() << " us";
+#else
       (void)gettimeofday(&end_time, nullptr);
       const uint64_t kUSecondInSecond = 1000000;
       uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
       cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
       MS_LOG(INFO) << "Run pass hwopt_" + name() + "_" << num << "_" + pass->name() + " in " << cost << " us";
+#endif
       if (save_graphs) {
         auto dump_file_path =
           save_graphs_path + "/" + "hwopt_" + name() + "_" + std::to_string(num) + "_" + pass->name() + ".ir";
diff --git a/mindspore/ccsrc/utils/log_adapter.cc b/mindspore/ccsrc/utils/log_adapter.cc
index 19482ec193..5d6e734fea 100644
--- a/mindspore/ccsrc/utils/log_adapter.cc
+++ b/mindspore/ccsrc/utils/log_adapter.cc
@@ -26,12 +26,19 @@ namespace mindspore {
 #ifdef USE_GLOG
 static std::string GetTime() {
 #define BUFLEN 80
+  static char buf[BUFLEN];
+#if defined(_WIN32) || defined(_WIN64)
+  time_t time_seconds = time(0);
+  struct tm now_time;
+  localtime_s(&now_time, &time_seconds);
+  sprintf_s(buf, BUFLEN, "%d-%d-%d %d:%d:%d", now_time.tm_year + 1900, now_time.tm_mon + 1, now_time.tm_mday,
+            now_time.tm_hour, now_time.tm_min, now_time.tm_sec);
+#else
   struct timeval cur_time;
   (void)gettimeofday(&cur_time, NULL);
 
   struct tm now;
   (void)localtime_r(&cur_time.tv_sec, &now);
-  static char buf[BUFLEN];
   (void)strftime(buf, BUFLEN, "%Y-%m-%d-%H:%M:%S", &now);  // format date and time
   // set micro-second
   buf[27] = '\0';
@@ -44,6 +51,7 @@ static std::string GetTime() {
       buf[idx--] = '.';
     }
   }
+#endif
   return std::string(buf);
 }
 
diff --git a/mindspore/ccsrc/vm/transform.cc b/mindspore/ccsrc/vm/transform.cc
index be7aaf5baa..92976e0ddb 100644
--- a/mindspore/ccsrc/vm/transform.cc
+++ b/mindspore/ccsrc/vm/transform.cc
@@ -41,8 +41,10 @@ using TypedPrimitiveAbstractClosurePtr = std::shared_ptr<abstract::TypedPrimitiv
 
 std::vector<PrimitivePtr> nonlinear_ops = {prim::kPrimReturn, prim::kPrimPartial, prim::kPrimSwitch,
                                            prim::kPrimMakeTuple};
-
-std::vector<PrimitivePtr> ms_nonlinear_ops = {prim::kPrimReturn, prim::kPrimPartial, prim::kPrimSwitch};
+const std::vector<PrimitivePtr>& GetMsNonlinearOps() {
+  static const std::vector<PrimitivePtr> ms_nonlinear_ops = {prim::kPrimReturn, prim::kPrimPartial, prim::kPrimSwitch};
+  return ms_nonlinear_ops;
+}
 
 CompileGraph::CompileGraph(const BackendPtr& backend, const std::vector<PrimitivePtr>& cut_list)
     : backend_(backend), cut_list_(cut_list) {
diff --git a/mindspore/ccsrc/vm/transform.h b/mindspore/ccsrc/vm/transform.h
index f862444a82..290af10049 100644
--- a/mindspore/ccsrc/vm/transform.h
+++ b/mindspore/ccsrc/vm/transform.h
@@ -42,7 +42,7 @@ extern const char kGeVm[];
 // A sub namespace in ME to support compile related definition.
 namespace compile {
 extern std::vector<PrimitivePtr> nonlinear_ops;
-extern std::vector<PrimitivePtr> ms_nonlinear_ops;
+const std::vector<PrimitivePtr>& GetMsNonlinearOps();
 
 using VmEvalFunc = std::function<BaseRef(const VectorRef&)>;
 using VmEvalFuncPtr = std::shared_ptr<std::function<BaseRef(const VectorRef&)>>;
diff --git a/mindspore/log.py b/mindspore/log.py
index 38455e2e18..9731b04ac1 100644
--- a/mindspore/log.py
+++ b/mindspore/log.py
@@ -19,11 +19,13 @@ import sys
 import os
 import stat
 import time
-import fcntl
 import logging
 from logging.handlers import RotatingFileHandler
 import traceback
 import threading
+import platform
+if platform.system() != "Windows":
+    import fcntl
 
 __all__ = ['get_level', 'get_log_config']
 
@@ -90,7 +92,8 @@ class _MultiCompatibleRotatingFileHandler(RotatingFileHandler):
 
         # Attain an exclusive lock with bloking mode by `fcntl` module.
         with open(self.baseFilename, 'a') as file_pointer:
-            fcntl.lockf(file_pointer.fileno(), fcntl.LOCK_EX)
+            if platform.system() != "Windows":
+                fcntl.lockf(file_pointer.fileno(), fcntl.LOCK_EX)
 
         if self.backupCount > 0:
             self.rolling_rename()
diff --git a/third_party/securec/CMakeLists.txt b/third_party/securec/CMakeLists.txt
index e360a6ebae..b2f29c488b 100644
--- a/third_party/securec/CMakeLists.txt
+++ b/third_party/securec/CMakeLists.txt
@@ -1,10 +1,18 @@
 SET(CMAKE_BUILD_TYPE "Debug")
-SET(CMAKE_C_FLAGS_DEBUG "$ENV{CFLAGS} -fPIC -O0 -Wall -Wno-deprecated-declarations -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)='")
+if (WIN32)
+    SET(CMAKE_C_FLAGS_DEBUG "$ENV{CFLAGS} -fPIC -O0 -Wall -Wno-deprecated-declarations -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer")
+else()
+    SET(CMAKE_C_FLAGS_DEBUG "$ENV{CFLAGS} -fPIC -O0 -Wall -Wno-deprecated-declarations -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)='")
+endif()
 SET(CMAKE_C_FLAGS_RELEASE "$ENV{CFLAGS} -fPIC -O3 -Wall -Wno-deprecated-declarations")
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 #add flags
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
+if (WIN32)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include")
+else()
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
+endif()
 
 
 include_directories(./include)

From 593c4fc7002e962374e8f3ee15e190fef0477372 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Wed, 15 Apr 2020 16:17:08 +0800
Subject: [PATCH 258/367] fix shape used for dump

---
 .../device/ascend/ascend_kernel_runtime.cc    | 24 ++++++++++++-------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 311abb634e..c093446969 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -151,14 +151,18 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path,
     auto output_size = AnfAlgo::GetOutputTensorNum(node);
     for (size_t j = 0; j < output_size; ++j) {
       auto addr = AnfAlgo::GetOutputAddr(node, j);
-      auto shape = trans::GetRuntimePaddingShape(node, j);
+      std::vector<int> int_shapes;
+      if (trans_flag) {
+        int_shapes = trans::GetRuntimePaddingShape(node, j);
+      } else {
+        auto shape = AnfAlgo::GetOutputDeviceShape(node, j);
+        (void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
+                             [](size_t inner_item) { return SizeToInt(inner_item); });
+      }
       auto type = AnfAlgo::GetOutputInferDataType(node, j);
       auto format = kOpFormat_DEFAULT;
       string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
       auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
-      std::vector<int> int_shapes;
-      (void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
-                           [](size_t inner_item) { return SizeToInt(inner_item); });
       auto ret = ascend_addr->DumpMemToFile(trans_flag, filepath, format, int_shapes, type);
       if (!ret) {
         MS_LOG(ERROR) << "DumpMemToFile Failed: flag:" << trans_flag << ", path:" << filepath
@@ -182,14 +186,18 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p
       continue;
     }
     auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX);
-    auto shape = trans::GetRuntimePaddingShape(item, PRAMATER_OUTPUT_INDEX);
+    std::vector<int> int_shapes;
+    if (trans_flag) {
+      int_shapes = trans::GetRuntimePaddingShape(item, PRAMATER_OUTPUT_INDEX);
+    } else {
+      auto shape = AnfAlgo::GetOutputDeviceShape(item, PRAMATER_OUTPUT_INDEX);
+      (void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
+                           [](size_t inner_item) { return SizeToInt(inner_item); });
+    }
     auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX);
     auto format = kOpFormat_DEFAULT;
     string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
     auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
-    std::vector<int> int_shapes;
-    (void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
-                         [](size_t inner_item) { return SizeToInt(inner_item); });
     auto ret = ascend_addr->DumpMemToFile(trans_flag, filepath, format, int_shapes, type);
     if (!ret) {
       MS_LOG(ERROR) << "DumpMemToFile Failed: flag:" << trans_flag << ", path:" << filepath

From 2122a28e1a1bb87a6a629ee8d8cdae6253e1cf97 Mon Sep 17 00:00:00 2001
From: "wangnan39@huawei.com" <wangnan39@huawei.com>
Date: Wed, 15 Apr 2020 17:44:05 +0800
Subject: [PATCH 259/367] fix bug in tests of vm conv2d

---
 tests/vm_impl/vm_me.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/vm_impl/vm_me.py b/tests/vm_impl/vm_me.py
index a189aa8b90..da7fc1ecbe 100644
--- a/tests/vm_impl/vm_me.py
+++ b/tests/vm_impl/vm_me.py
@@ -161,7 +161,7 @@ def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
     elif isinstance(stride, tuple) and len(stride) == 2:
         stride_h = stride[0]
         stride_w = stride[1]
-    elif isinstance(stride, tuple) and len(stride) == 3:
+    elif isinstance(stride, tuple) and len(stride) == 4:
         stride_h = stride[2]
         stride_w = stride[3]
     else:
@@ -328,7 +328,7 @@ def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
     elif isinstance(stride, tuple) and len(stride) == 2:
         stride_h = stride[0]
         stride_w = stride[1]
-    elif isinstance(stride, tuple) and len(stride) == 3:
+    elif isinstance(stride, tuple) and len(stride) == 4:
         stride_h = stride[2]
         stride_w = stride[3]
     else:
@@ -340,7 +340,7 @@ def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
     elif isinstance(dilation, tuple) and len(dilation) == 2:
         dilation_h = dilation[0]
         dilation_w = dilation[1]
-    elif isinstance(dilation, tuple) and len(dilation) == 3:
+    elif isinstance(dilation, tuple) and len(dilation) == 4:
         dilation_h = dilation[2]
         dilation_w = dilation[3]
     else:

From c4d0bb266a59f13ecfd680b3d839fac3dcede50f Mon Sep 17 00:00:00 2001
From: leilei_snow <nilc@qq.com>
Date: Wed, 15 Apr 2020 10:00:27 +0000
Subject: [PATCH 260/367] fix optimizer.decay_weight bug

---
 mindspore/nn/optim/optimizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index c2a419c565..bfbde78fff 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -109,7 +109,7 @@ class Optimizer(Cell):
             tuple[Tensor], The gradients after weight decay.
         """
         if self.weight_decay > 0:
-            params = self.params
+            params = self.parameters
             gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_flags, params, gradients)
 
         return gradients

From 762bf9ac2538a4746c434f4cc29c1ab64ea76aea Mon Sep 17 00:00:00 2001
From: jojobugfree <caifubi1@huawei.com>
Date: Wed, 15 Apr 2020 16:37:11 +0800
Subject: [PATCH 261/367] fix tensoradd grad op run fail

---
 .../device/ascend/ascend_kernel_runtime.cc    | 35 ++++++++++++++++---
 .../device/ascend/ascend_kernel_runtime.h     |  2 ++
 2 files changed, 33 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 311abb634e..69b9e00457 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -260,6 +260,19 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
   auto anf_node_list = graph->execution_order();
   TaskGenerator::GenTasks(anf_node_list, &task_info_list, graph->graph_id());
 
+  // Store the task_info_list
+  auto iter = task_map_.find(graph);
+  if (iter != task_map_.end()) {
+    MS_LOG(EXCEPTION) << "graph TaskInfo list already exist";
+  }
+  task_map_[graph] = task_info_list;
+
+  // Graph may have no compute node, such TensorAddGrad.
+  if (task_info_list.empty()) {
+    MS_LOG(WARNING) << "graph " << graph->graph_id() << " have no compute node";
+    return true;
+  }
+
   AscendStreamAssign &assign_instance = AscendStreamAssign::GetInstance();
   // the streams' flag not HEAD_STREAM
   std::vector<uint32_t> wait_active_stream_list = assign_instance.GetWaitStreams();
@@ -278,10 +291,6 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
   graph_model_map_[graph] = model;
   graph_model_id_map_[graph] = graph->graph_id();
   MS_LOG(INFO) << "TaskGenerator GetTaskInfo end...";
-
-  // Store the task_info_list
-  task_map_.insert(std::make_pair(graph, task_info_list));
-
   return true;
 }
 
@@ -305,6 +314,11 @@ bool AscendKernelRuntime::LoadTask(const session::KernelGraph *graph) {
     return true;
   }
 
+  if (GraphWithEmptyTaskList(graph)) {
+    MS_LOG(WARNING) << "LoadTask end, task list is empty";
+    return true;
+  }
+
   auto task_iter = graph_model_map_.find(graph);
   if (task_iter == graph_model_map_.end()) {
     MS_LOG(ERROR) << "task not exist";
@@ -333,6 +347,11 @@ bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(context_ptr);
   ge::InputData input_tensors = ge::InputData();
   ge::OutputData *output_tensors = nullptr;
+  if (GraphWithEmptyTaskList(graph)) {
+    MS_LOG(WARNING) << "RunTask end, no task info found";
+    return true;
+  }
+
   auto model_id = GetGraphModelId(graph);
   bool status = ge::model_runner::ModelRunner::Instance().RunModel(model_id, input_tensors, output_tensors);
   if (!status) {
@@ -468,6 +487,14 @@ bool AscendKernelRuntime::DestroyHccl() {
   context_ptr->set_enable_hccl(false);
   return true;
 }
+
+bool AscendKernelRuntime::GraphWithEmptyTaskList(const session::KernelGraph *graph) const {
+  auto iter = task_map_.find(graph);
+  if (iter == task_map_.end()) {
+    MS_LOG(EXCEPTION) << "Unknown graph ptr";
+  }
+  return iter->second.empty();
+}
 }  // namespace ascend
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
index 0eedad3d2b..547228d32f 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
@@ -55,6 +55,8 @@ class AscendKernelRuntime : public KernelRuntime {
   void ClearGraphModelMap();
   void ReleaseDeviceRes() override;
   uint32_t GetGraphModelId(const session::KernelGraph *kernel_graph);
+  bool GraphWithEmptyTaskList(const session::KernelGraph *graph) const;
+
   rtContext_t rt_context_{nullptr};
   bool initialized_{false};
   unordered_map<const session::KernelGraph *, vector<std::shared_ptr<TaskInfo>>> task_map_;

From 6d522f0a4fef6f1192e1bb9dbefc63226df3470b Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Tue, 14 Apr 2020 20:13:14 +0800
Subject: [PATCH 262/367] add parallel op for layernorm

---
 .../auto_parallel/operator_costmodel.cc       | 162 +++++----
 .../auto_parallel/operator_costmodel.h        | 246 ++++++-------
 mindspore/ccsrc/parallel/dynamic_creator.h    |   1 +
 .../parallel/ops_info/activation_info.cc      |   4 +-
 .../parallel/ops_info/layer_norm_info.cc      | 324 ++++++++++++++++++
 .../ccsrc/parallel/ops_info/layer_norm_info.h |  76 ++++
 .../parallel/ops_info/ops_info_head_files.h   |   1 +
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |   3 +
 .../ccsrc/parallel/step_auto_parallel.cc      |   1 +
 .../redistribution_layout_transfer_test.cc    |   4 +-
 .../reshape_layout_transfer_test.cc           |   4 +-
 .../tensor_layout/util_layout_gen_test.cc     |   8 +-
 .../tensor_layout/util_layout_gen_test.h      |   8 +-
 tests/ut/python/parallel/test_layer_norm.py   |  96 ++++++
 14 files changed, 745 insertions(+), 193 deletions(-)
 create mode 100644 mindspore/ccsrc/parallel/ops_info/layer_norm_info.cc
 create mode 100644 mindspore/ccsrc/parallel/ops_info/layer_norm_info.h
 create mode 100644 tests/ut/python/parallel/test_layer_norm.py

diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index ecd42db6bb..9ea583293b 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -65,7 +65,7 @@ double OperatorCost::GetMemoryCost(const std::vector<TensorInfo>& inputs,
 
 // return the per device communication cost in the forward phase.
 double MatMulCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                      const int32_t&) const {
+                                      int32_t) const {
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
   Shape input0_shape = input0.shape();
@@ -81,7 +81,7 @@ double MatMulCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, con
 
 // return the per device communication cost in the forward phase.
 double MatMulCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                       const int32_t& stage_id) const {
+                                       int32_t stage_id) const {
   // In backward phase, the communication cost is incurred only when tensor B is a Parameter and tensor B does not
   // fully utilize all devices
   double result = 0.0;
@@ -108,7 +108,7 @@ double MatMulCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, co
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double MatMulCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                             const std::vector<TensorInfo>& outputs, const int32_t&) const {
+                                             const std::vector<TensorInfo>& outputs, int32_t) const {
   // In forward phase, the compuatation cost = slice(A) + slice(B) + (0 or 1) allreduce(slice(C))
   double result = 0.0;
   TensorInfo output0 = outputs[0];
@@ -127,7 +127,7 @@ double MatMulCost::GetForwardComputationCost(const std::vector<TensorInfo>& inpu
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double MatMulCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                              const int32_t& stage_id) const {
+                                              int32_t stage_id) const {
   // In backward phase, the computation cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
@@ -152,14 +152,14 @@ double MatMulCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inp
 
 // Return the per device communication cost in the forward phase.
 double ActivationCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                          const int32_t&) const {
+                                          int32_t) const {
   // ReLU is the element-wise operator, thus it does not need communication in the forward phase
   return 0.0;
 }
 
 // Return the per device communication cost in the backward phase.
 double ActivationCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                           const int32_t& stage_id) const {
+                                           int32_t stage_id) const {
   double result = 0.0;
   if (is_parameter_[0]) {
     TensorInfo input1 = inputs[0];
@@ -181,7 +181,7 @@ double ActivationCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                                 const int32_t&) const {
+                                                 int32_t) const {
   TensorInfo input0_info = inputs[0];
   Shape input0_slice_shape = input0_info.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
@@ -190,20 +190,19 @@ double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo>&
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ActivationCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                                  const int32_t&) const {
+                                                  int32_t) const {
   return 0.0;
 }
 
 // Return the per device communication cost in the forward phase.
-double SoftmaxCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                       const int32_t&) const {
+double SoftmaxCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // In the forward phase, the communication cost = 0
   return 0.0;
 }
 
 // Return the per device communication cost in the backward phase.
 double SoftmaxCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                        const int32_t& stage_id) const {
+                                        int32_t stage_id) const {
   double result = 0.0;
   if (is_parameter_[0]) {
     TensorInfo input1 = inputs[0];
@@ -225,7 +224,7 @@ double SoftmaxCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, c
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                              const int32_t&) const {
+                                              int32_t) const {
   // In the forward phase, the computation cost = slice(A)
   TensorInfo input0 = inputs[0];
   Shape input0_slice_shape = input0.slice_shape();
@@ -235,21 +234,20 @@ double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                               const std::vector<mindspore::parallel::TensorInfo>&,
-                                               const int32_t&) const {
+                                               const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
 // return the per device communication cost in the forward phase.
 double TmpIdentityCost::GetForwardCommCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                           const std::vector<mindspore::parallel::TensorInfo>&, const int32_t&) const {
+                                           const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   // Identity is the element-wise operator, thus it does not need communication in the forward phase
   return 0.0;
 }
 
 // return the per device communication cost in the backward phase.
 double TmpIdentityCost::GetBackwardCommCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                            const std::vector<mindspore::parallel::TensorInfo>&, const int32_t&) const {
+                                            const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   // Identity is the element-wise operator, thus it does not need communication in the backward phase
   return 0.0;
 }
@@ -257,16 +255,14 @@ double TmpIdentityCost::GetBackwardCommCost(const std::vector<mindspore::paralle
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double TmpIdentityCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                                  const std::vector<mindspore::parallel::TensorInfo>&,
-                                                  const int32_t&) const {
+                                                  const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
 // Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                                   const std::vector<mindspore::parallel::TensorInfo>&,
-                                                   const int32_t&) const {
+                                                   const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
@@ -277,7 +273,7 @@ double TmpIdentityCost::GetMemoryCost(const std::vector<TensorInfo>&, const std:
 
 double BatchParallelCost::GetForwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
                                                     const std::vector<mindspore::parallel::TensorInfo>&,
-                                                    const int32_t&) const {
+                                                    int32_t) const {
   double cost = 0.0;
   for (size_t i = 0; i < inputs.size(); ++i) {
     cost += ListProduct(inputs[i].slice_shape()) * static_cast<double>(inputs_type_lengths_[i]);
@@ -287,20 +283,19 @@ double BatchParallelCost::GetForwardComputationCost(const std::vector<mindspore:
 
 double BatchParallelCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
                                                      const std::vector<mindspore::parallel::TensorInfo>&,
-                                                     const int32_t&) const {
+                                                     int32_t) const {
   return 0.0;
 }
 
 // return the per device communication cost in the forward phase.
-double PReLUCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                     const int32_t&) const {
+double PReLUCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // prelu does not need communication in the forward phase
   return 0.0;
 }
 
 // return the per device communication cost in the backward phase.
 double PReLUCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                      const int32_t& stage_id) const {
+                                      int32_t stage_id) const {
   double result = 0.0;
   if (is_parameter_[1]) {
     TensorInfo input1 = inputs[1];
@@ -323,7 +318,7 @@ double PReLUCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, con
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double PReLUCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                            const int32_t&) const {
+                                            int32_t) const {
   // In forward phase, the computation cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
@@ -336,7 +331,7 @@ double PReLUCost::GetForwardComputationCost(const std::vector<TensorInfo>& input
 // this operator uses
 double PReLUCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>& inputs,
                                              const std::vector<mindspore::parallel::TensorInfo>&,
-                                             const int32_t& stage_id) const {
+                                             int32_t stage_id) const {
   // In backward phase, the computation cost = (0 or 1) allreduce(slice(B))
   double result = 0.0;
   if (is_parameter_[1]) {
@@ -360,15 +355,13 @@ double PReLUCost::GetBackwardComputationCost(const std::vector<mindspore::parall
 }
 
 // return the per device communication cost in the forward phase.
-double OneHotCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                      const int32_t&) const {
+double OneHotCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // onehot does not need communication in the forward phase
   return 0.0;
 }
 
 // return the per device communication cost in the backward phase.
-double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                       const int32_t&) const {
+double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // onehot does not need communication in the backward phase
   return 0.0;
 }
@@ -376,7 +369,7 @@ double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double OneHotCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                             const int32_t&) const {
+                                             int32_t) const {
   // In onehot's forward phase, the computation cost = slice(A)
   Shape input0_slice_shape = inputs[0].slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
@@ -385,20 +378,20 @@ double OneHotCost::GetForwardComputationCost(const std::vector<TensorInfo>& inpu
 // Return the per  device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double OneHotCost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                              const int32_t&) const {
+                                              int32_t) const {
   return 0.0;
 }
 
 // return the per device communication cost in the forward phase.
 double SoftmaxCrossEntropyWithLogitsCost::GetForwardCommCost(const std::vector<TensorInfo>&,
-                                                             const std::vector<TensorInfo>&, const int32_t&) const {
+                                                             const std::vector<TensorInfo>&, int32_t) const {
   // SoftmaxCrossEntropyWithLogitsCost does not need communication in the forward phase
   return 0.0;
 }
 
 // return the per device communication cost in the backward phase.
 double SoftmaxCrossEntropyWithLogitsCost::GetBackwardCommCost(const std::vector<TensorInfo>&,
-                                                              const std::vector<TensorInfo>&, const int32_t&) const {
+                                                              const std::vector<TensorInfo>&, int32_t) const {
   // SoftmaxCrossEntropyWithLogitsCost does not need communication in the backward phase
   return 0.0;
 }
@@ -406,8 +399,7 @@ double SoftmaxCrossEntropyWithLogitsCost::GetBackwardCommCost(const std::vector<
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                                                    const std::vector<TensorInfo>&,
-                                                                    const int32_t&) const {
+                                                                    const std::vector<TensorInfo>&, int32_t) const {
   // In forward phase, the computation cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
@@ -419,14 +411,13 @@ double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::v
 // Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double SoftmaxCrossEntropyWithLogitsCost::GetBackwardComputationCost(const std::vector<TensorInfo>&,
-                                                                     const std::vector<TensorInfo>&,
-                                                                     const int32_t&) const {
+                                                                     const std::vector<TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
 // return the per device communication cost in the forward phase.
 double ReshapeCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                       const int32_t& stage_id) const {
+                                       int32_t stage_id) const {
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
   RankList dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
@@ -441,15 +432,14 @@ double ReshapeCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, co
 }
 
 // return the per device communication cost in the backward phase.
-double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                        const int32_t&) const {
+double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
 double ReshapeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                              const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
+                                              const std::vector<TensorInfo>& outputs, int32_t stage_id) const {
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
   RankList dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
@@ -466,13 +456,12 @@ double ReshapeCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
 // Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
 // this operator uses
 double ReshapeCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo>&,
-                                               const std::vector<mindspore::parallel::TensorInfo>&,
-                                               const int32_t&) const {
+                                               const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const {
   return 0.0;
 }
 
 double ArithmeticCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                                 const int32_t&) const {
+                                                 int32_t) const {
   double result;
   result = ListProduct(inputs[0].slice_shape()) * static_cast<double>(inputs_type_lengths_[0]) +
            ListProduct(inputs[1].slice_shape()) * static_cast<double>(inputs_type_lengths_[1]);
@@ -480,7 +469,7 @@ double ArithmeticCost::GetForwardComputationCost(const std::vector<TensorInfo>&
 }
 
 double ArithmeticCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                                  const int32_t& stage_id) const {
+                                                  int32_t stage_id) const {
   double result = 0.0;
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
@@ -515,7 +504,7 @@ double ArithmeticCost::GetBackwardComputationCost(const std::vector<TensorInfo>&
 }
 
 double ArithmeticCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                           const int32_t& stage_id) const {
+                                           int32_t stage_id) const {
   double result = 0.0;
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
@@ -550,7 +539,7 @@ double ArithmeticCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs
   return result;
 }
 
-bool IsDataParallel(const Shape& shape, const Shape& slice_shape, const int32_t& stage_id) {
+bool IsDataParallel(const Shape& shape, const Shape& slice_shape, int32_t stage_id) {
   CheckGlobalDeviceManager();
   MS_EXCEPTION_IF_NULL(g_device_manager);
   auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
@@ -560,7 +549,7 @@ bool IsDataParallel(const Shape& shape, const Shape& slice_shape, const int32_t&
 }
 
 double ReduceMethodCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs,
-                                            const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const {
+                                            const std::vector<TensorInfo>& outputs, int32_t stage_id) const {
   double result = 0.0;
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
@@ -571,7 +560,7 @@ double ReduceMethodCost::GetForwardCommCost(const std::vector<TensorInfo>& input
   }
   std::vector<int32_t> dim_list = input0.reduce_dim();
   std::vector<int>::iterator pos;
-  pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](const int32_t& index) {
+  pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](int32_t index) {
     return input0_shape[IntToSize(index)] != input0_slice_shape[IntToSize(index)];
   });
   if (pos != dim_list.end()) {
@@ -582,7 +571,7 @@ double ReduceMethodCost::GetForwardCommCost(const std::vector<TensorInfo>& input
 }
 
 double ReduceMethodCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                             const int32_t& stage_id) const {
+                                             int32_t stage_id) const {
   double result = 0.0;
   if (is_parameter_[0]) {
     TensorInfo input_tensor_info = inputs[0];
@@ -605,8 +594,7 @@ double ReduceMethodCost::GetBackwardCommCost(const std::vector<TensorInfo>& inpu
 }
 
 double ReduceMethodCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                                   const std::vector<TensorInfo>& outputs,
-                                                   const int32_t& stage_id) const {
+                                                   const std::vector<TensorInfo>& outputs, int32_t stage_id) const {
   double result = 0.0;
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
@@ -615,7 +603,7 @@ double ReduceMethodCost::GetForwardComputationCost(const std::vector<TensorInfo>
   Shape input0_shape = input0.shape();
   if (!cross_batch_ || !IsDataParallel(input0_shape, input0_slice_shape, stage_id)) {
     std::vector<int>::iterator pos;
-    pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](const int32_t& index) {
+    pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](int32_t index) {
       return input0_shape[IntToSize(index)] != input0_slice_shape[IntToSize(index)];
     });
     if (pos != dim_list.end()) {
@@ -628,8 +616,7 @@ double ReduceMethodCost::GetForwardComputationCost(const std::vector<TensorInfo>
 }
 
 double ReduceMeanCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                                 const std::vector<TensorInfo>& outputs,
-                                                 const int32_t& stage_id) const {
+                                                 const std::vector<TensorInfo>& outputs, int32_t stage_id) const {
   double result = 0.0;
   TensorInfo input0 = inputs[0];
   TensorInfo output0 = outputs[0];
@@ -638,7 +625,7 @@ double ReduceMeanCost::GetForwardComputationCost(const std::vector<TensorInfo>&
   Shape input0_shape = input0.shape();
   if (!cross_batch_ || !IsDataParallel(input0_shape, input0_slice_shape, stage_id)) {
     std::vector<int>::iterator pos;
-    pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](const int32_t& index) {
+    pos = std::find_if(dim_list.begin(), dim_list.end(), [input0_shape, input0_slice_shape](int32_t index) {
       return input0_shape[IntToSize(index)] != input0_slice_shape[IntToSize(index)];
     });
     if (pos != dim_list.end()) {
@@ -651,7 +638,7 @@ double ReduceMeanCost::GetForwardComputationCost(const std::vector<TensorInfo>&
 }
 
 double DropOutCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                              const int32_t&) const {
+                                              int32_t) const {
   if (inputs.empty()) {
     return 0.0;
   }
@@ -661,21 +648,20 @@ double DropOutCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
 }
 
 // return the per device communication cost in the forward phase.
-double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                        const int32_t&) const {
+double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // GatherV2Cost does not need communication in the forward phase
   return 0.0;
 }
 
 // return the per device communication cost in the backward phase.
 double GatherV2Cost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                         const int32_t&) const {
+                                         int32_t) const {
   // GatherV2Cost does not need communication in the backward phase
   return 0.0;
 }
 
 double GatherV2Cost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
-                                               const int32_t&) const {
+                                               int32_t) const {
   // In forward phase, the computation cost = slice(A) + slice(B)
   Shape input0_slice_shape = inputs[0].slice_shape();
   Shape input1_slice_shape = inputs[1].slice_shape();
@@ -685,8 +671,56 @@ double GatherV2Cost::GetForwardComputationCost(const std::vector<TensorInfo>& in
 }
 
 double GatherV2Cost::GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                                const int32_t&) const {
+                                                int32_t) const {
   return 0.0;
 }
+
+double LayerNormCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                          int32_t stage_id) const {
+  double result = 0.0;
+  if (is_parameter_.size() != inputs.size()) {
+    MS_LOG(EXCEPTION) << "Invalid parameter size " << is_parameter_.size() << " for layer norm cost";
+  }
+  if (inputs_type_lengths_.size() != inputs.size()) {
+    MS_LOG(EXCEPTION) << "Invalid inputs type size " << inputs_type_lengths_.size() << " for layer norm cost";
+  }
+
+  MS_EXCEPTION_IF_NULL(g_device_manager);
+  auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+
+  for (size_t index = 0; index < inputs.size(); ++index) {
+    if (is_parameter_[index]) {
+      TensorInfo tensor_info = inputs[index];
+      Shape shape = tensor_info.shape();
+      Shape slice_shape = tensor_info.slice_shape();
+      int32_t used_device_num = 1;
+      for (size_t i = 0; i < shape.size(); ++i) {
+        if (slice_shape[i] == 0) {
+          MS_LOG(EXCEPTION) << "Invalid slice shape " << ShapeToString(slice_shape);
+        }
+        used_device_num *= shape[i] / slice_shape[i];
+      }
+      if (total_device_num != IntToSize(used_device_num)) {
+        result += ListProduct(slice_shape) * static_cast<double>(inputs_type_lengths_[index]);
+      }
+    }
+  }
+  return result;
+}
+
+double LayerNormCost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                                int32_t) const {
+  double result = 0.0;
+  if (inputs_type_lengths_.size() != inputs.size()) {
+    MS_LOG(EXCEPTION) << "Invalid inputs type size " << inputs_type_lengths_.size() << " for layer norm cost";
+  }
+
+  for (size_t index = 0; index < inputs.size(); ++index) {
+    TensorInfo tensor_info = inputs[index];
+    Shape slice_shape = tensor_info.slice_shape();
+    result += ListProduct(slice_shape) * static_cast<double>(inputs_type_lengths_[index]);
+  }
+  return result;
+}
 }  // namespace parallel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index 7dc45bae71..f16dfa21fc 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -72,18 +72,18 @@ class OperatorCost {
 
   // per device communication cost
   virtual double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const = 0;
+                             int32_t stage_id) const = 0;
   virtual double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const = 0;
+                                    int32_t stage_id) const = 0;
   virtual double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                     const int32_t& stage_id) const = 0;
+                                     int32_t stage_id) const = 0;
   // per device computation cost
   virtual double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const = 0;
+                                    int32_t stage_id) const = 0;
   virtual double GetForwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                           const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
+                                           const std::vector<TensorInfo>& outputs, int32_t stage_id) const = 0;
   virtual double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs,
-                                            const std::vector<TensorInfo>& outputs, const int32_t& stage_id) const = 0;
+                                            const std::vector<TensorInfo>& outputs, int32_t stage_id) const = 0;
   // per device PEAK memory cost in a training iteration
   // Typically, the PEAK memory cost contributed by an operator is its output (if the output is parameter-invovled),
   // plus necessary inputs.
@@ -114,23 +114,23 @@ class MatMulCost : public OperatorCost {
 
   // per device communication cost
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
 
   // per device computation cost
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using MatMulCostPtr = std::shared_ptr<MatMulCost>;
 
@@ -141,21 +141,21 @@ class ActivationCost : public OperatorCost {
   ~ActivationCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using ActivationCostPtr = std::shared_ptr<ActivationCost>;
 using TransposeCost = ActivationCost;
@@ -168,21 +168,21 @@ class SoftmaxCost : public OperatorCost {
   ~SoftmaxCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t&) const override;
+                                    int32_t) const override;
 };
 using SoftmaxCostPtr = std::shared_ptr<SoftmaxCost>;
 
@@ -193,21 +193,21 @@ class TmpIdentityCost : public OperatorCost {
   ~TmpIdentityCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
   // per device PEAK memory cost in a training iteration
   double GetMemoryCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs) const override;
 };
@@ -220,25 +220,23 @@ class BatchParallelCost : public OperatorCost {
   ~BatchParallelCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override {
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using BatchParallelCostPtr = std::shared_ptr<BatchParallelCost>;
 
@@ -249,27 +247,25 @@ class VirtualDatasetCost : public OperatorCost {
   ~VirtualDatasetCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override {
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                   const int32_t&) const override {
+                                   int32_t) const override {
     return 0.0;
   }
   double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                    const int32_t&) const override {
+                                    int32_t) const override {
     return 0.0;
   }
   // per device PEAK memory cost in a training iteration
@@ -286,29 +282,27 @@ class GeneratorBaseCost : public OperatorCost {
   ~GeneratorBaseCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override {
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   // Inputs vector is empty for generator ops.
   double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                   const int32_t&) const override {
+                                   int32_t) const override {
     return 0.0;
   }
   // Generator ops don't have backward steps.
   double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                    const int32_t&) const override {
+                                    int32_t) const override {
     return 0.0;
   }
 };
@@ -322,23 +316,23 @@ class PReLUCost : public OperatorCost {
 
   // per device communication cost
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
 
   // per device computation cost
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using PReLUCostPtr = std::shared_ptr<PReLUCost>;
 
@@ -350,23 +344,23 @@ class OneHotCost : public OperatorCost {
 
   // per device communication cost
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
 
   // per device computation cost
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using OneHotCostPtr = std::shared_ptr<OneHotCost>;
 
@@ -378,23 +372,23 @@ class SoftmaxCrossEntropyWithLogitsCost : public OperatorCost {
 
   // per device communication cost
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
 
   // per device computation cost
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using SoftmaxCrossEntropyWithLogitsCostPtr = std::shared_ptr<SoftmaxCrossEntropyWithLogitsCost>;
 
@@ -407,27 +401,27 @@ class ReshapeCost : public OperatorCost {
 
   // per device communication cost
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
 
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
 
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
 
   // per device computation cost
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
 
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
 
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using ReshapeCostPtr = std::shared_ptr<ReshapeCost>;
 
@@ -438,24 +432,22 @@ class ArithmeticCost : public OperatorCost {
   ~ArithmeticCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override;
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override;
 
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t& stage_id) const override;
+                                    int32_t stage_id) const override;
 };
 using ArithmeticCostPtr = std::shared_ptr<ArithmeticCost>;
 using BiasAddCost = ArithmeticCost;
@@ -468,21 +460,21 @@ class ReduceMethodCost : public OperatorCost {
   ~ReduceMethodCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                    const int32_t&) const override {
+                                    int32_t) const override {
     return 0.0;
   }
   void set_cross_batch(bool cb) { cross_batch_ = cb; }
@@ -499,7 +491,7 @@ class ReduceMeanCost : public ReduceMethodCost {
   ~ReduceMeanCost() override = default;
 
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
 };
 using ReduceMeanCostPtr = std::shared_ptr<ReduceMeanCost>;
 
@@ -510,29 +502,27 @@ class GetNextCost : public OperatorCost {
   ~GetNextCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override {
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   // Inputs vector is empty for generator ops.
   double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                   const int32_t&) const override {
+                                   int32_t) const override {
     return 0.0;
   }
   // Generator ops don't have backward steps.
   double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                    const int32_t&) const override {
+                                    int32_t) const override {
     return 0.0;
   }
 };
@@ -545,25 +535,51 @@ class DropOutCost : public OperatorCost {
   ~DropOutCost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
-  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                            const int32_t&) const override {
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
+    return 0.0;
+  }
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
+    return 0.0;
+  }
+  double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                            int32_t stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                   int32_t) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
+                                    int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                             const int32_t&) const override {
+};
+
+using DropOutCostPtr = std::shared_ptr<DropOutCost>;
+
+class LayerNormCost : public OperatorCost {
+ public:
+  explicit LayerNormCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  LayerNormCost() : OperatorCost(true) {}
+  ~LayerNormCost() override = default;
+
+  double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
+                     int32_t stage_id) const override {
+    return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
+  }
+  double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                   const int32_t&) const override;
+                                   int32_t) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                    const int32_t&) const override {
+                                    int32_t) const override {
     return 0.0;
   }
 };
@@ -577,21 +593,21 @@ class GatherV2Cost : public OperatorCost {
   ~GatherV2Cost() override = default;
 
   double GetCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                     const int32_t& stage_id) const override {
+                     int32_t stage_id) const override {
     return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
   }
   double GetForwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override;
+                            int32_t stage_id) const override;
   double GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                             const int32_t& stage_id) const override;
+                             int32_t stage_id) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                            const int32_t& stage_id) const override {
+                            int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
   }
   double GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                   const int32_t& stage_id) const override;
+                                   int32_t stage_id) const override;
   double GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
-                                    const int32_t&) const override;
+                                    int32_t) const override;
 };
 
 using GatherV2CostPtr = std::shared_ptr<GatherV2Cost>;
diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 1b864cd8bf..953380fb32 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -101,6 +101,7 @@ REGISTER(CosInfo);
 REGISTER(ACosInfo);
 REGISTER(LogicalNotInfo);
 REGISTER(L2NormalizeInfo);
+REGISTER(LayerNormInfo);
 REGISTER(ReduceMaxInfo);
 REGISTER(ArgMaxWithValueInfo);
 REGISTER(ArgMinWithValueInfo);
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
index c11db56082..e659759de2 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc
@@ -195,8 +195,8 @@ Status Softmax::GetAttrs() {
 
   // for example: tensor dimension is 4, then axis range [-4, 3]
   int32_t dim = SizeToInt(inputs_shape_.at(0).size());
-  auto it = std::find_if(axis_.begin(), axis_.end(),
-                         [dim](const int32_t& element) { return ((element >= dim) || (element < -dim)); });
+  auto it =
+    std::find_if(axis_.begin(), axis_.end(), [dim](int32_t element) { return ((element >= dim) || (element < -dim)); });
   if (it != axis_.end()) {
     MS_LOG(ERROR) << name_ << " : The axis(" << *it << ") is out of range[" << -dim << ", " << dim - 1 << "].";
     return FAILED;
diff --git a/mindspore/ccsrc/parallel/ops_info/layer_norm_info.cc b/mindspore/ccsrc/parallel/ops_info/layer_norm_info.cc
new file mode 100644
index 0000000000..3abfc3d2ed
--- /dev/null
+++ b/mindspore/ccsrc/parallel/ops_info/layer_norm_info.cc
@@ -0,0 +1,324 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parallel/ops_info/layer_norm_info.h"
+#include <algorithm>
+#include <vector>
+#include "parallel/device_matrix.h"
+#include "parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+Status LayerNormInfo::GetAttrs() {
+  auto iter = attrs_.find(BEGIN_NORM_AXIS);
+  if (iter == attrs_.end()) {
+    MS_LOG(ERROR) << name_ << ": Can not find the attr of begin norm axis";
+    return FAILED;
+  }
+  if ((iter->second == nullptr) || !iter->second->isa<Int32Imm>()) {
+    MS_LOG(ERROR) << name_ << ": The axis type is not int";
+    return FAILED;
+  }
+
+  int32_t dim = SizeToInt(input_shape_.size());
+  auto axis = GetValue<int32_t>(iter->second);
+  if ((axis >= dim) || (axis < -dim)) {
+    MS_LOG(ERROR) << name_ << ": The axis(" << axis << ") is out of range[" << -dim << ", " << dim - 1 << "]";
+    return FAILED;
+  }
+
+  if (axis < 0) {
+    axis = axis + dim;
+  }
+  begin_norm_axis_ = IntToSize(axis);
+  return SUCCESS;
+}
+
+Status LayerNormInfo::CheckStrategy(const StrategyPtr &strategy) {
+  MS_EXCEPTION_IF_NULL(strategy);
+  std::vector<Dimensions> stra = strategy->GetInputDim();
+  if (stra.size() != LAYER_NORM_INPUT_SIZE) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy size " << stra.size();
+    return FAILED;
+  }
+
+  if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy value";
+    return FAILED;
+  }
+
+  Dimensions input_strategy = stra[LAYER_NORM_INPUT_INDEX];
+  Dimensions gamma_strategy = stra[LAYER_NORM_GAMMA_INDEX];
+  Dimensions beta_strategy = stra[LAYER_NORM_BETA_INDEX];
+  if (begin_norm_axis_ >= input_strategy.size()) {
+    MS_LOG(ERROR) << name_ << ": Invalid begin norm axis " << begin_norm_axis_;
+    return FAILED;
+  }
+  // check input strategy
+  for (size_t i = begin_norm_axis_; i < input_strategy.size(); ++i) {
+    if (input_strategy[begin_norm_axis_] != NO_SPLIT_STRATEGY) {
+      MS_LOG(ERROR) << name_ << ": Invalid input strategy " << ShapeToString(input_strategy);
+      return FAILED;
+    }
+  }
+
+  // check gamma and beta strategy
+  if ((gamma_strategy.size() > input_strategy.size()) || (beta_strategy.size() > input_strategy.size())) {
+    MS_LOG(ERROR) << name_ << " : The strategy size of gamma or beta is lager than input strategy";
+    return FAILED;
+  }
+
+  size_t gamma_diff = input_strategy.size() - gamma_strategy.size();
+  for (size_t j = 0; j < gamma_strategy.size(); ++j) {
+    if (gamma_strategy[j] != input_strategy[gamma_diff + j]) {
+      MS_LOG(ERROR) << name_ << ": Invalid gamma strategy " << ShapeToString(gamma_strategy);
+      return FAILED;
+    }
+  }
+
+  size_t beta_diff = input_strategy.size() - beta_strategy.size();
+  for (size_t k = 0; k < beta_strategy.size(); ++k) {
+    if (beta_strategy[k] != input_strategy[beta_diff + k]) {
+      MS_LOG(ERROR) << name_ << ": Invalid beta strategy " << ShapeToString(beta_strategy);
+      return FAILED;
+    }
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InferDevMatrixShape() {
+  if (strategy_ == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
+    return FAILED;
+  }
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
+  if (stra.empty()) {
+    MS_LOG(ERROR) << name_ << ": The strategy is empty";
+    return FAILED;
+  }
+  dev_matrix_shape_ = stra[0];
+  return SUCCESS;
+}
+
+Status LayerNormInfo::CreateTensorMap(size_t input_index) {
+  if (inputs_shape_.size() <= input_index) {
+    MS_LOG(ERROR) << name_ << ": Invalid index" << input_index;
+    return FAILED;
+  }
+  Shape shape = inputs_shape_[input_index];
+  Shape tensor_map;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    tensor_map.push_back(SizeToInt(shape.size() - i - 1));
+  }
+  inputs_tensor_map_.push_back(tensor_map);
+  outputs_tensor_map_.push_back(tensor_map);
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InferTensorMap() {
+  if ((CreateTensorMap(LAYER_NORM_INPUT_INDEX) != SUCCESS) || (CreateTensorMap(LAYER_NORM_GAMMA_INDEX) != SUCCESS) ||
+      (CreateTensorMap(LAYER_NORM_BETA_INDEX) != SUCCESS)) {
+    MS_LOG(ERROR) << name_ << ": Create tensor map failed";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::CreateMirrorOp(size_t input_index) {
+  if (inputs_tensor_map_.size() <= input_index) {
+    MS_LOG(ERROR) << name_ << ": Invalid index " << input_index;
+    return FAILED;
+  }
+  Shape tensor_map = inputs_tensor_map_[input_index];
+  std::vector<Group> group;
+  if (CreateGroupByTensorMap(tensor_map, &group) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Create group for input " << input_index << " failed";
+    return FAILED;
+  }
+  OperatorVector mirror_op;
+  if (!group.empty()) {
+    mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
+    MS_LOG(INFO) << name_ << " : Create the mirror ops for input " << input_index << " success, group is "
+                 << group[0].name();
+  }
+  mirror_ops_.push_back(mirror_op);
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InferMirrorOps() {
+  if ((CreateMirrorOp(LAYER_NORM_INPUT_INDEX) != SUCCESS) || (CreateMirrorOp(LAYER_NORM_GAMMA_INDEX) != SUCCESS) ||
+      (CreateMirrorOp(LAYER_NORM_BETA_INDEX) != SUCCESS)) {
+    MS_LOG(ERROR) << name_ << ": Create mirror op failed";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::CreateTensorInfo(size_t input_index) {
+  if ((inputs_shape_.size() <= input_index) || (inputs_tensor_map_.size() <= input_index)) {
+    MS_LOG(ERROR) << name_ << ": Invalid input index" << input_index;
+    return FAILED;
+  }
+  Shape tensor_map = inputs_tensor_map_[input_index];
+  Shape shape = inputs_shape_[input_index];
+  TensorLayout tensor_layout;
+  if (tensor_layout.InitFromVector(dev_matrix_shape_, tensor_map, shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for input " << input_index << " failed";
+    return FAILED;
+  }
+
+  TensorInfo tensor_info(tensor_layout);
+  inputs_tensor_info_.push_back(tensor_info);
+  outputs_tensor_info_.push_back(tensor_info);
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InferTensorInfo() {
+  if ((CreateTensorInfo(LAYER_NORM_INPUT_INDEX) != SUCCESS) || (CreateTensorInfo(LAYER_NORM_GAMMA_INDEX) != SUCCESS) ||
+      (CreateTensorInfo(LAYER_NORM_BETA_INDEX) != SUCCESS)) {
+    MS_LOG(ERROR) << name_ << ": Create tensor info failed";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InferAsLossDivisor() {
+  if (outputs_tensor_map_.size() != LAYER_NORM_INPUT_SIZE) {
+    MS_LOG(ERROR) << name_ << ": The size of outputs tensor map " << outputs_tensor_map_.size() << " is error";
+    return FAILED;
+  }
+  as_loss_divisor_ = ComputeRepeatDeviceNumByTensorMap(dev_matrix_shape_, outputs_tensor_map_[0]);
+  MS_LOG(INFO) << name_ << " : The dev matrix shape is " << ShapeToString(dev_matrix_shape_)
+               << ", the output[0]'s tensor map is " << ShapeToString(outputs_tensor_map_[0])
+               << ", as_loss_divisor_ is " << as_loss_divisor_;
+  return SUCCESS;
+}
+
+Status LayerNormInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
+  if (SetCostUnderStrategyBase(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Set cost failed";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::GenerateGammaAndBetaStrategies(const std::vector<StrategyPtr> &sp_vector) {
+  if ((gamma_shape_.size() > input_shape_.size()) || (beta_shape_.size() > input_shape_.size())) {
+    MS_LOG(ERROR) << name_ << ": The dimension of gamma or beta is lager than input";
+    return FAILED;
+  }
+
+  size_t gamma_diff = input_shape_.size() - gamma_shape_.size();
+  size_t beta_diff = input_shape_.size() - beta_shape_.size();
+  for (auto &sp : sp_vector) {
+    if ((sp == nullptr) || sp->GetInputDim().empty()) {
+      MS_LOG(ERROR) << name_ << ": Invalid strategy";
+      return FAILED;
+    }
+    std::vector<Dimensions> tmp_strategy;
+    Dimensions input_strategy = sp->GetInputDim()[0];
+    Dimensions gamma_strategy = input_strategy;
+    (void)gamma_strategy.erase(gamma_strategy.begin(),
+                               gamma_strategy.begin() + static_cast<different_type>(gamma_diff));
+    Dimensions beta_strategy = input_strategy;
+    (void)beta_strategy.erase(beta_strategy.begin(), beta_strategy.begin() + static_cast<different_type>(beta_diff));
+
+    // reset the strategy
+    tmp_strategy.push_back(input_strategy);
+    tmp_strategy.push_back(gamma_strategy);
+    tmp_strategy.push_back(beta_strategy);
+    sp->ResetInputs(tmp_strategy);
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::GenerateStrategies(int32_t stage_id) {
+  if (InitShapes() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init shapes failed";
+    return FAILED;
+  }
+  if (GetAttrs() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Get attrs failed";
+    return FAILED;
+  }
+  Shape input_split(input_shape_.size(), SPLIT_FLAG);
+  if (begin_norm_axis_ >= input_split.size()) {
+    MS_LOG(ERROR) << name_ << ": Invalid begin norm axis " << begin_norm_axis_;
+    return FAILED;
+  }
+
+  // Can not split the dimensions from begin norm axis
+  for (size_t i = begin_norm_axis_; i < input_split.size(); ++i) {
+    input_split[i] = NO_SPLIT_FLAG;
+  }
+
+  // Generate strategy for input
+  Shapes splittable_inputs = {input_split};
+  Shapes tmp_inputs_shape = {input_shape_};
+  std::vector<StrategyPtr> sp_vector;
+  is_auto_parallel_ = true;
+  if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_inputs, &sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Generate input strategy failed";
+    return FAILED;
+  }
+
+  // Generate the strategies for gamma and beta
+  if (GenerateGammaAndBetaStrategies(sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Generate gamma and beta strategies failed";
+    return FAILED;
+  }
+
+  size_t success = 0;
+  for (auto &sp : sp_vector) {
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(DEBUG) << name_ << ": Successfully generated " << success << " strategy";
+    }
+  }
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InitShapes() {
+  if (inputs_shape_.size() != LAYER_NORM_INPUT_SIZE) {
+    MS_LOG(ERROR) << name_ << ": Invalid inputs size";
+    return FAILED;
+  }
+  input_shape_ = inputs_shape_[LAYER_NORM_INPUT_INDEX];
+  gamma_shape_ = inputs_shape_[LAYER_NORM_GAMMA_INDEX];
+  beta_shape_ = inputs_shape_[LAYER_NORM_BETA_INDEX];
+  return SUCCESS;
+}
+
+Status LayerNormInfo::Init(const StrategyPtr &strategy) {
+  if ((InitShapes() != SUCCESS) || (InitWithAutoRepeatCalc(strategy)) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init failed";
+    return FAILED;
+  }
+  MS_LOG(INFO) << name_ << ": Init success";
+  return SUCCESS;
+}
+
+Status LayerNormInfo::InitForCostModel(const StrategyPtr &strategy) {
+  if ((InitShapes() != SUCCESS) || (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS)) {
+    MS_LOG(ERROR) << name_ << ": Init for cost model failed";
+    return FAILED;
+  }
+
+  MS_LOG(INFO) << name_ << ": Init for cost model success";
+  return SUCCESS;
+}
+}  // namespace parallel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/parallel/ops_info/layer_norm_info.h b/mindspore/ccsrc/parallel/ops_info/layer_norm_info.h
new file mode 100644
index 0000000000..c52645ade2
--- /dev/null
+++ b/mindspore/ccsrc/parallel/ops_info/layer_norm_info.h
@@ -0,0 +1,76 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LAYER_NORM_INFO_H_
+#define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LAYER_NORM_INFO_H_
+
+#include <string>
+#include <memory>
+#include <unordered_map>
+#include <vector>
+#include "ir/value.h"
+#include "parallel/auto_parallel/operator_costmodel.h"
+#include "parallel/ops_info/operator_info.h"
+#include "parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+constexpr size_t LAYER_NORM_INPUT_SIZE = 3;
+constexpr size_t LAYER_NORM_INPUT_INDEX = 0;
+constexpr size_t LAYER_NORM_GAMMA_INDEX = 1;
+constexpr size_t LAYER_NORM_BETA_INDEX = 2;
+constexpr char BEGIN_NORM_AXIS[] = "begin_norm_axis";
+
+// The dimensions of input tensor starting from begin norm axis cannot be split. Other dimensions can be split
+// arbitrarily. Gamma and beta should match input to meet the broadcast requirements of mul and add.
+class LayerNormInfo : public OperatorInfo {
+ public:
+  LayerNormInfo(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                const PrimitiveAttrs& attrs)
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<LayerNormCost>(true)),
+        begin_norm_axis_(0) {}
+  ~LayerNormInfo() override = default;
+
+  Status Init(const StrategyPtr& strategy) override;
+  Status InitForCostModel(const StrategyPtr& strategy) override;
+  Status GenerateStrategies(int32_t) override;
+  Status SetCostUnderStrategy(const StrategyPtr&) override;
+
+ protected:
+  Status GetAttrs() override;
+  Status CheckStrategy(const StrategyPtr& strategy) override;
+  Status InferMirrorOps() override;
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+  Status InferAsLossDivisor() override;
+  Status CreateTensorMap(size_t input_index);
+  Status CreateTensorInfo(size_t input_index);
+  Status CreateMirrorOp(size_t input_index);
+  Status GenerateGammaAndBetaStrategies(const std::vector<StrategyPtr>& sp_vector);
+  Status InitShapes();
+
+ private:
+  size_t begin_norm_axis_;
+  Shape input_shape_;
+  Shape gamma_shape_;
+  Shape beta_shape_;
+};
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LAYER_NORM_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
index 27b434ecca..aec25f7f41 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h
@@ -27,6 +27,7 @@
 #include "parallel/ops_info/gather_v2_info.h"
 #include "parallel/ops_info/get_next_info.h"
 #include "parallel/ops_info/l2_normalize_info.h"
+#include "parallel/ops_info/layer_norm_info.h"
 #include "parallel/ops_info/loss_info.h"
 #include "parallel/ops_info/matmul_info.h"
 #include "parallel/ops_info/onehot_info.h"
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 88377d237b..50920e5954 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -26,6 +26,8 @@ constexpr int32_t PRELU_CHANNEL_INDEX = 1;
 constexpr int32_t PRELU_CHANNEL_STRATEGY = 1;
 constexpr int32_t NO_SPLIT_MAP = -1;
 constexpr int32_t NO_SPLIT_STRATEGY = 1;
+constexpr int32_t SPLIT_FLAG = 1;
+constexpr int32_t NO_SPLIT_FLAG = 0;
 constexpr size_t MATMUL_ATTRS_SIZE = 2;
 constexpr size_t MATMUL_INPUTS_SIZE = 2;
 constexpr size_t MATMUL_OUTPUTS_SIZE = 1;
@@ -173,6 +175,7 @@ constexpr char ARGMINWITHVALUE[] = "ArgMinWithValue";
 constexpr char CONV2D[] = "Conv2D";
 constexpr char FUSE_BATCH_NORM[] = "FusedBatchNorm";
 constexpr char BATCH_NORM[] = "BatchNorm";
+constexpr char LAYER_NORM[] = "LayerNorm";
 constexpr char POOLING[] = "Pooling";
 constexpr char CAST[] = "Cast";
 constexpr char MAX_POOL_WITH_ARGMAX[] = "MaxPoolWithArgmax";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index 33097bf2b7..5caf6573f2 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -82,6 +82,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SIMPLE_MEAN,
                                            FLATTEN,
                                            BATCH_NORM,
+                                           LAYER_NORM,
                                            BIAS_ADD,
                                            ASSIGN_SUB,
                                            COS,
diff --git a/tests/ut/cpp/parallel/tensor_layout/redistribution_layout_transfer_test.cc b/tests/ut/cpp/parallel/tensor_layout/redistribution_layout_transfer_test.cc
index 4e34847582..5291e2f48d 100644
--- a/tests/ut/cpp/parallel/tensor_layout/redistribution_layout_transfer_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/redistribution_layout_transfer_test.cc
@@ -245,8 +245,8 @@ void ValidRedistributionLayoutCheck(const DeviceArrangement& in_device_arrangeme
                          unified_out_tensor_map, unified_tensor_shape);
 }
 
-void ValidRedistributionLayoutCheckAll(const int32_t& device_pow_size, const int32_t& tensor_pow_size,
-                                       const int32_t& max_device_dim, const int32_t& max_shape_dim) {
+void ValidRedistributionLayoutCheckAll(int32_t device_pow_size, int32_t tensor_pow_size,
+                                       int32_t max_device_dim, int32_t max_shape_dim) {
   std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>> layout_list;
   GenerateValidLayoutByDeviceSizeAndTensorSize(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim,
                                                &layout_list);
diff --git a/tests/ut/cpp/parallel/tensor_layout/reshape_layout_transfer_test.cc b/tests/ut/cpp/parallel/tensor_layout/reshape_layout_transfer_test.cc
index 36b89684f6..9d6152721e 100644
--- a/tests/ut/cpp/parallel/tensor_layout/reshape_layout_transfer_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/reshape_layout_transfer_test.cc
@@ -260,8 +260,8 @@ TEST_F(TestReshapeLayoutTransfer, ValidInferUnifiedLayoutCheck11) {
   ValidUnifiedLayoutCheck(device_arrangement, in_tensor_map, in_tensor_shape, out_tensor_map, out_tensor_shape);
 }
 
-void ValidInferUnifiedLayoutCheckAll(const int32_t& device_pow_size, const int32_t& tensor_pow_size,
-                                     const int32_t& max_device_dim, const int32_t& max_shape_dim) {
+void ValidInferUnifiedLayoutCheckAll(int32_t device_pow_size, int32_t tensor_pow_size,
+                                     int32_t max_device_dim, int32_t max_shape_dim) {
   std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>> layout_list;
   GenerateValidLayoutByDeviceSizeAndTensorSize(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim,
                                                &layout_list);
diff --git a/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.cc b/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.cc
index 07d270c95c..93147c486b 100644
--- a/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.cc
+++ b/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.cc
@@ -51,7 +51,7 @@ std::vector<std::vector<int32_t>> combine(const std::vector<int32_t>& in, int32_
   return output;
 }
 
-void GenerateValidShapeBySizeAndDim(const int32_t& pow_size, const int32_t& dim,
+void GenerateValidShapeBySizeAndDim(int32_t pow_size, int32_t dim,
                                     std::vector<std::vector<int32_t>>* out) {
   out->clear();
   std::vector<int32_t> in;
@@ -78,7 +78,7 @@ void GenerateValidShapeBySizeAndDim(const int32_t& pow_size, const int32_t& dim,
   return;
 }
 
-void GenerateValidShapeBySize(const int32_t& pow_size, std::vector<std::vector<int32_t>>* out) {
+void GenerateValidShapeBySize(int32_t pow_size, std::vector<std::vector<int32_t>>* out) {
   out->clear();
   for (int32_t dim = 1; dim <= pow_size; dim++) {
     std::vector<std::vector<int32_t>> combine_result;
@@ -148,8 +148,8 @@ void GenerateValidTensorMap(const std::vector<int32_t>& device_arrangement, cons
 }
 
 void GenerateValidLayoutByDeviceSizeAndTensorSize(
-  const int32_t& device_pow_size, const int32_t& tensor_pow_size, const int32_t& max_device_dim,
-  const int32_t& max_shape_dim,
+  int32_t device_pow_size, int32_t tensor_pow_size, int32_t max_device_dim,
+  int32_t max_shape_dim,
   std::vector<std::tuple<std::vector<int32_t>, std::vector<int32_t>, std::vector<int32_t>>>* layout_list) {
   layout_list->clear();
   std::vector<std::vector<int32_t>> device_arrangement_list;
diff --git a/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.h b/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.h
index e14556378f..a359cadbea 100644
--- a/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.h
+++ b/tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.h
@@ -27,10 +27,10 @@ namespace parallel {
 
 std::vector<std::vector<int32_t>> combine(const std::vector<int32_t>& in, int32_t target);
 
-void GenerateValidShapeBySizeAndDim(const int32_t& pow_size, const int32_t& dim,
+void GenerateValidShapeBySizeAndDim(int32_t pow_size, int32_t dim,
                                     std::vector<std::vector<int32_t>>* out);
 
-void GenerateValidShapeBySize(const int32_t& pow_size, std::vector<std::vector<int32_t>>* out);
+void GenerateValidShapeBySize(int32_t pow_size, std::vector<std::vector<int32_t>>* out);
 
 std::vector<int32_t> GenerateTensorMap(const uint32_t& map_size, const std::vector<int32_t>& pos_index,
                                        const std::vector<int32_t>& pos_value);
@@ -39,8 +39,8 @@ void GenerateValidTensorMap(const std::vector<int32_t>& device_arrangement, cons
                             std::vector<std::vector<int32_t>>* tensor_map_list);
 
 void GenerateValidLayoutByDeviceSizeAndTensorSize(
-  const int32_t& device_pow_size, const int32_t& tensor_pow_size, const int32_t& max_device_dim,
-  const int32_t& max_shape_dim,
+  int32_t device_pow_size, int32_t tensor_pow_size, int32_t max_device_dim,
+  int32_t max_shape_dim,
   std::vector<std::tuple<std::vector<int32_t>, std::vector<int32_t>, std::vector<int32_t>>>* layout_list);
 
 uint32_t ComputeNoneNumber(const std::vector<int32_t>& tensor_map);
diff --git a/tests/ut/python/parallel/test_layer_norm.py b/tests/ut/python/parallel/test_layer_norm.py
new file mode 100644
index 0000000000..c65ee5fc8e
--- /dev/null
+++ b/tests/ut/python/parallel/test_layer_norm.py
@@ -0,0 +1,96 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+from mindspore.common.initializer import initializer
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None, strategy3=None):
+        super().__init__()
+        self.begin_norm_axis = -1
+        self.begin_params_axis = 1
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.layer_norm = P.LayerNorm(self.begin_norm_axis, self.begin_params_axis).set_strategy(strategy2)
+        self.mul2 = P.Mul().set_strategy(strategy3)
+        self.mul_weight = Parameter(mul_weight, "w1")
+        self.normalized_shape = [64, 32, 16]
+        self.gamma = Parameter(initializer('ones', self.normalized_shape), name="gamma")
+        self.beta = Parameter(initializer('zeros', self.normalized_shape), name="beta")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out, _, _ = self.layer_norm(out, self.gamma, self.beta)
+        out = self.mul2(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32, 16]), dtype=ms.float32)
+_w = Tensor(np.ones([128, 64, 32, 16]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32, 16]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_layer_norm_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1, 1), (16, 1, 1, 1))
+    strategy2 = ((16, 1, 1, 1), (1, 1, 1), (1, 1, 1))
+    strategy3 = ((16, 1, 1, 1), (16, 1, 1, 1))
+    net = Net(_w, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_layer_norm_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16, 1), (1, 1, 16, 1))
+    strategy2 = ((1, 1, 16, 1), (1, 16, 1), (1, 16, 1))
+    strategy3 = ((1, 1, 16, 1), (1, 1, 16, 1))
+    net = Net(_w, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_layer_norm_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    strategy2 = ((2, 2, 4, 1), (2, 4, 1), (2, 4, 1))
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_layer_norm_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w)
+    compile(net)
+
+
+def test_layer_norm_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    strategy2 = ((1, 2, 2, 1), (2, 2, 1), (2, 2, 1))
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w, strategy1, strategy2, strategy3)
+    compile(net)
+

From 3e05f50f5fa9ae89c6d2cdcc4a3669a3fc33f455 Mon Sep 17 00:00:00 2001
From: laiyongqiang <laiyongqiang1@huawei.com>
Date: Thu, 9 Apr 2020 09:47:14 +0800
Subject: [PATCH 263/367] getnext_memcpy_elimination

---
 .../ascend/ascend_backend_optimization.cc     |   7 +-
 .../enhancer/getnext_memcpy_elimination.cc    |  72 +++++++++++
 .../enhancer/getnext_memcpy_elimination.h     |  33 +++++
 .../ir_fusion => pass}/allreduce_fusion.cc    |   2 +-
 .../ir_fusion => pass}/allreduce_fusion.h     |   6 +-
 mindspore/ccsrc/session/gpu_session.cc        |   2 +-
 mindspore/ccsrc/utils/utils.h                 |   1 +
 .../enhancer/getnext_memcpy_elimination.cc    |  98 +++++++++++++++
 .../common/ir_fusion/allreduce_fusion_test.cc |   2 +-
 .../getnext_memcpy_elimination_test.py        | 117 ++++++++++++++++++
 10 files changed, 333 insertions(+), 7 deletions(-)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.h
 rename mindspore/ccsrc/pre_activate/{common/ir_fusion => pass}/allreduce_fusion.cc (97%)
 rename mindspore/ccsrc/pre_activate/{common/ir_fusion => pass}/allreduce_fusion.h (86%)
 create mode 100644 tests/ut/cpp/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/getnext_memcpy_elimination_test.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 60ae2cd6cc..394c5ce281 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -21,7 +21,7 @@
 #include "pre_activate/ascend/ir_fission/bn_grad_split.h"
 #include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h"
 #include "pre_activate/ascend/ir_fission/layer_norm_grad_split.h"
-#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/pass/allreduce_fusion.h"
 #include "pre_activate/ascend/ir_fusion/square_sum_fusion.h"
 #include "pre_activate/ascend/ir_fusion/clip_by_norm_no_div_square_sum_fusion.h"
 #include "pre_activate/ascend/ir_fusion/lamb_update_with_lr_rule_fusion.h"
@@ -58,8 +58,10 @@
 #include "pre_activate/ascend/ir_fission/add_memcpy_async.h"
 #include "pre_activate/ascend/format_type/insert_cast_for_runop.h"
 #include "pre_activate/ascend/format_type/insert_transdata_for_runop.h"
+#include "pre_activate/ascend/enhancer/getnext_memcpy_elimination.h"
 #include "pre_activate/ascend/ir_fission/addn_fission.h"
 #include "utils/context/ms_context.h"
+#include "utils/config_manager.h"
 #include "debug/anf_ir_dump.h"
 #include "debug/anf_ir_utils.h"
 
@@ -244,6 +246,9 @@ void AscendBackendOptimization(const std::shared_ptr<session::KernelGraph> &kern
   other_pm->AddPass(std::make_shared<BufferFusion>());
   other_pm->AddPass(std::make_shared<GetitemTuple>());
   other_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
+  if (context_ptr->enable_task_sink() && context_ptr->loop_sink_flag() && ConfigManager::GetInstance().iter_num() > 1) {
+    other_pm->AddPass(std::make_shared<GetnextMemcpyElimination>());
+  }
   other_pm->AddPass(std::make_shared<CheckConsistency>());
   optimizer->AddPassManager(other_pm);
   (void)optimizer->Optimize(kernel_graph);
diff --git a/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc b/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
new file mode 100644
index 0000000000..a39918ecee
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
@@ -0,0 +1,72 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "pre_activate/ascend/enhancer/getnext_memcpy_elimination.h"
+#include <memory>
+#include "session/anf_runtime_algorithm.h"
+#include "optimizer/opt.h"
+
+namespace mindspore::opt {
+
+const BaseRef GetnextMemcpyElimination::DefinePattern() const {
+  auto prim_memcpy = std::make_shared<Primitive>(kMemCpyAsyncOpName);
+  VarPtr x = std::make_shared<SeqVar>();
+  VectorRef memcpy_async({prim_memcpy, x});
+  return memcpy_async;
+}
+
+const AnfNodePtr GetnextMemcpyElimination::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
+                                                   const EquivPtr &equiv) const {
+  if (graph == nullptr || node == nullptr || equiv == nullptr) {
+    return nullptr;
+  }
+  auto memcpy_cnode = node->cast<CNodePtr>();
+  if (memcpy_cnode == nullptr) {
+    return nullptr;
+  }
+
+  // 1. memcpy has attr kAttrLabelForInsertStreamActive
+  if (!AnfAlgo::HasNodeAttr(kAttrLabelForInsertStreamActive, node)) {
+    MS_LOG(DEBUG) << "node has no label_for_insert_stream_active attr";
+    return nullptr;
+  }
+
+  // 2. memcpy's output has only one user next_node
+  auto manager = graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+  if (manager->node_users().find(memcpy_cnode) == manager->node_users().end()) {
+    MS_LOG(EXCEPTION) << "memcpy has no output in manager";
+  }
+  auto next_nodes = manager->node_users()[memcpy_cnode];
+  if (next_nodes.size() > 1) {
+    MS_LOG(DEBUG) << "node's output has more than one users";
+    return nullptr;
+  }
+
+  // 3. next_node has only one input which is memcpy's output
+  for (auto &item : next_nodes) {
+    auto next_node = item.first->cast<CNodePtr>();
+    if (next_node->inputs().size() != 2) {
+      MS_LOG(DEBUG) << "next node has more than one input";
+      return nullptr;
+    }
+    // add attr label_for_insert_stream_active for next_node
+    AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), next_node);
+  }
+
+  return memcpy_cnode->input(1);
+}
+}  // namespace mindspore::opt
diff --git a/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.h b/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.h
new file mode 100644
index 0000000000..523fc87a38
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.h
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_GETNEXT_MEMCPY_ELIMINATION_H
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_GETNEXT_MEMCPY_ELIMINATION_H
+
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class GetnextMemcpyElimination : public PatternProcessPass {
+ public:
+  explicit GetnextMemcpyElimination(bool multigraph = true)
+      : PatternProcessPass("getnext_memcpy_elimination", multigraph) {}
+  ~GetnextMemcpyElimination() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_GETNEXT_MEMCPY_ELIMINATION_H
diff --git a/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc b/mindspore/ccsrc/pre_activate/pass/allreduce_fusion.cc
similarity index 97%
rename from mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
rename to mindspore/ccsrc/pre_activate/pass/allreduce_fusion.cc
index 55efcf9058..70a8974eca 100644
--- a/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc
+++ b/mindspore/ccsrc/pre_activate/pass/allreduce_fusion.cc
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/pass/allreduce_fusion.h"
 
 #include <vector>
 #include <string>
diff --git a/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h b/mindspore/ccsrc/pre_activate/pass/allreduce_fusion.h
similarity index 86%
rename from mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
rename to mindspore/ccsrc/pre_activate/pass/allreduce_fusion.h
index b49b8373c6..e443767e43 100644
--- a/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h
+++ b/mindspore/ccsrc/pre_activate/pass/allreduce_fusion.h
@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
-#define MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_PASS_ALLREDUCE_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_PASS_ALLREDUCE_FUSION_H_
 #include <vector>
 
 #include "pre_activate/common/pass.h"
@@ -46,4 +46,4 @@ class AllReduceFusion : public Pass {
 };
 }  // namespace opt
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_PASS_ALLREDUCE_FUSION_H_
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index c0b2323e04..c6ab1e4664 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -20,7 +20,7 @@
 #include "device/gpu/gpu_stream_assign.h"
 #include "pre_activate/common/optimizer.h"
 #include "pre_activate/common/pass_manager.h"
-#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/pass/allreduce_fusion.h"
 #include "device/kernel_runtime_manager.h"
 #include "predict/predict.h"
 #include "common/utils.h"
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 08a98a3129..2859b5613f 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -147,6 +147,7 @@ constexpr auto kAttrSrcFormat = "src_format";
 constexpr auto kAttrOutputUsedNum = "output_used_num";
 constexpr auto kAttrHasBias = "has_bias";
 constexpr auto kAttrN = "N";
+constexpr auto kAttrLabelForInsertStreamActive = "label_for_insert_stream_active";
 
 // attr value
 constexpr auto kValueTargetSwitch = "target_switch";
diff --git a/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc b/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
new file mode 100644
index 0000000000..93885a4b3a
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/enhancer/getnext_memcpy_elimination.cc
@@ -0,0 +1,98 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#include "session/anf_runtime_algorithm.h"
+#include "operator/ops.h"
+#include "ir/meta_tensor.h"
+#include "debug/anf_ir_dump.h"
+#include "utils/utils.h"
+#include "kernel/kernel_build_info.h"
+#include "pre_activate/common/optimizer.h"
+#include "mindspore/ccsrc/pre_activate/ascend/enhancer/getnext_memcpy_elimination.h"
+
+namespace mindspore {
+namespace opt {
+class TestGetNextMemcpyElimination : public BackendCommon {
+ public:
+  TestGetNextMemcpyElimination() : get_py_fun_("gtest_input.pre_activate.getnext_memcpy_elimination_test", true) {}
+
+ public:
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestGetNextMemcpyElimination, test_getnext_memcpy_elimination) {
+  FuncGraphPtr g_before = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination", "before");
+  ASSERT_TRUE(g_before != nullptr);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto pass = std::make_shared<opt::GetnextMemcpyElimination>();
+  pm->AddPass(pass);
+  optimizer->AddPassManager(pm);
+  auto new_graph = optimizer->Optimize(g_before);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestGetNextMemcpyElimination, test_getnext_memcpy_elimination_no_attr) {
+  FuncGraphPtr g_before = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_no_attr", "before");
+  ASSERT_TRUE(g_before != nullptr);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto pass = std::make_shared<opt::GetnextMemcpyElimination>();
+  pm->AddPass(pass);
+  optimizer->AddPassManager(pm);
+  auto new_graph = optimizer->Optimize(g_before);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_no_attr", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestGetNextMemcpyElimination, test_getnext_memcpy_elimination_memcpy_multi_users) {
+  FuncGraphPtr g_before = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_memcpy_multi_users", "before");
+  ASSERT_TRUE(g_before != nullptr);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto pass = std::make_shared<opt::GetnextMemcpyElimination>();
+  pm->AddPass(pass);
+  optimizer->AddPassManager(pm);
+  auto new_graph = optimizer->Optimize(g_before);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_memcpy_multi_users", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestGetNextMemcpyElimination, test_getnext_memcpy_elimination_next_multi_inputs) {
+  FuncGraphPtr g_before = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_next_multi_inputs", "before");
+  ASSERT_TRUE(g_before != nullptr);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto pass = std::make_shared<opt::GetnextMemcpyElimination>();
+  pm->AddPass(pass);
+  optimizer->AddPassManager(pm);
+  auto new_graph = optimizer->Optimize(g_before);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_getnext_memcpy_elimination_next_multi_inputs", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+}  // namespace opt
+}  // namespace mindspore
diff --git a/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
index 79a1cf1a8a..d5f2fa636d 100644
--- a/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
+++ b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc
@@ -20,7 +20,7 @@
 #include "ir/manager.h"
 #include "debug/anf_ir_dump.h"
 #include "session/anf_runtime_algorithm.h"
-#include "pre_activate/common/ir_fusion/allreduce_fusion.h"
+#include "pre_activate/pass/allreduce_fusion.h"
 #include "pre_activate/common/optimizer.h"
 #include "device/kernel_info.h"
 #include "pre_activate/common/pass_manager.h"
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/getnext_memcpy_elimination_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/getnext_memcpy_elimination_test.py
new file mode 100644
index 0000000000..39b60d72d6
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/getnext_memcpy_elimination_test.py
@@ -0,0 +1,117 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+import mindspore as ms
+
+get_next = P.GetNext([ms.float32], [[1, 64, 112, 112]], 1, "")
+memcpy_async_attr = Primitive('memcpy_async')
+memcpy_async_attr.add_prim_attr("label_for_insert_stream_active", True)
+memcpy_async = Primitive('memcpy_async')
+cast = P.Cast()
+add = P.TensorAdd()
+
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_getnext_memcpy_elimination(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        res = get_next()
+        res = memcpy_async_attr(res)
+        res = cast(res)
+        return res
+
+    @fns
+    def after(x):
+        res = get_next()
+        res = cast(res)
+        return res
+
+    return fns[tag]
+
+
+def test_getnext_memcpy_elimination_no_attr(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        res = get_next()
+        res = memcpy_async(res)
+        res = cast(res)
+        return res
+
+    @fns
+    def after(x):
+        res = get_next()
+        res = memcpy_async(res)
+        res = cast(res)
+        return res
+
+    return fns[tag]
+
+
+def test_getnext_memcpy_elimination_memcpy_multi_users(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        res = get_next()
+        memcpy_out = memcpy_async_attr(res)
+        res = cast(memcpy_out)
+        res = add(memcpy_out, res)
+        return res
+
+    @fns
+    def after(x):
+        res = get_next()
+        memcpy_out = memcpy_async_attr(res)
+        res = cast(memcpy_out)
+        res = add(memcpy_out, res)
+        return res
+
+    return fns[tag]
+
+
+def test_getnext_memcpy_elimination_next_multi_inputs(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        res = get_next()
+        memcpy_out = memcpy_async_attr(res)
+        res = add(memcpy_out, res)
+        return res
+
+    @fns
+    def after(x):
+        res = get_next()
+        memcpy_out = memcpy_async_attr(res)
+        res = add(memcpy_out, res)
+        return res
+
+    return fns[tag]

From ffe8b5d3ecb02bfca90b933858356cabca2348cc Mon Sep 17 00:00:00 2001
From: lvliang <lvliang18@huawei.com>
Date: Mon, 13 Apr 2020 19:58:50 +0800
Subject: [PATCH 264/367] pynative-add-op-supported

---
 mindspore/ccsrc/pre_activate/common/helper.cc |  57 +++++
 mindspore/ccsrc/pre_activate/common/helper.h  |   5 +
 .../pass/const_input_to_attr_registry.cc      |  34 +++
 .../pass/const_input_to_attr_registry.h       |   2 +-
 .../pass/convert_const_input_to_attr.cc       |  32 ---
 .../pass/convert_const_input_to_attr.h        |   5 +-
 .../convert_const_input_to_tensor_input.cc    |  58 +----
 mindspore/ccsrc/pynative/pynative_execute.cc  |   5 +-
 mindspore/ccsrc/session/ascend_session.cc     |  12 +-
 mindspore/ccsrc/session/ascend_session.h      |   6 +-
 mindspore/ccsrc/session/gpu_session.cc        |  12 +-
 mindspore/ccsrc/session/gpu_session.h         |   6 +-
 mindspore/ccsrc/session/session_basic.cc      | 204 ++++++++++--------
 mindspore/ccsrc/session/session_basic.h       |  12 +-
 14 files changed, 240 insertions(+), 210 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/common/helper.cc b/mindspore/ccsrc/pre_activate/common/helper.cc
index e5f0fafbe0..3b7d95a6f2 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.cc
+++ b/mindspore/ccsrc/pre_activate/common/helper.cc
@@ -28,6 +28,7 @@
 
 namespace mindspore {
 namespace opt {
+constexpr size_t kType32Len = 4;
 std::vector<int> Convert2Int(const std::vector<size_t> &v) {
   std::vector<int> result;
   (void)std::transform(v.begin(), v.end(), std::back_inserter(result), SizeToInt);
@@ -264,6 +265,62 @@ void CreateMultipleOutputsOfAnfNode(const FuncGraphPtr &func_graph, const AnfNod
   }
 }
 
+template <typename T>
+tensor::TensorPtr CreateTensorWithValueTuple(const ValueTuplePtr &value_tuple_ptr, const TypePtr &type_ptr,
+                                             size_t data_length) {
+  MS_EXCEPTION_IF_NULL(value_tuple_ptr);
+  MS_EXCEPTION_IF_NULL(type_ptr);
+  std::vector<T> values;
+  for (const auto &v : value_tuple_ptr->value()) {
+    MS_EXCEPTION_IF_NULL(v);
+    if (v->isa<Scalar>()) {
+      ScalarPtr scalar = v->cast<ScalarPtr>();
+      values.push_back(GetValue<T>(scalar));
+    } else {
+      MS_LOG(WARNING) << "The value " << v << "of tuple is not a scalar";
+      return nullptr;
+    }
+  }
+  std::vector<int> tensor_shape = {SizeToInt(values.size())};
+  tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_ptr->type_id(), tensor_shape);
+  MS_EXCEPTION_IF_NULL(tensor);
+  tensor::DeviceInfo device_info{kOpFormat_DEFAULT, type_ptr};
+  tensor->set_device_info(device_info);
+  auto data_ptr = tensor->data_c(true);
+  MS_EXCEPTION_IF_NULL(data_ptr);
+  auto elem_num = values.size() * data_length;
+  auto ret_code = memcpy_s(data_ptr, static_cast<size_t>(tensor->data().nbytes()), values.data(), elem_num);
+  if (ret_code != 0) {
+    MS_LOG(EXCEPTION) << "Failed to copy data into Tensor.";
+  }
+  return tensor;
+}
+
+tensor::TensorPtr CreateTupleTensor(const ValueTuplePtr &value_tuple) {
+  MS_EXCEPTION_IF_NULL(value_tuple);
+  tensor::TensorPtr tensor = nullptr;
+  ValuePtr v = *(value_tuple->value().begin());
+  MS_EXCEPTION_IF_NULL(v);
+  // Currently we only deal with the scalar tuple
+  if (!v->isa<Scalar>()) {
+    MS_LOG(WARNING) << "The value " << v << "of tuple is not a scalar";
+    return nullptr;
+  }
+  ScalarPtr scalar = v->cast<ScalarPtr>();
+  MS_EXCEPTION_IF_NULL(scalar);
+  if (scalar->isa<IntergerImm>()) {
+    tensor = CreateTensorWithValueTuple<int>(value_tuple, kInt32, kType32Len);
+  } else if (scalar->isa<FloatImm>()) {
+    tensor = CreateTensorWithValueTuple<float>(value_tuple, kFloat32, kType32Len);
+  } else {
+    auto type = scalar->type();
+    auto type_str = (type == nullptr) ? "nullptr" : type->ToString();
+    MS_LOG(ERROR) << "Invalid scalar type: " << type_str;
+    return nullptr;
+  }
+  return tensor;
+}
+
 bool IsNopNode(const AnfNodePtr &node) {
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
diff --git a/mindspore/ccsrc/pre_activate/common/helper.h b/mindspore/ccsrc/pre_activate/common/helper.h
index 4f30a935af..8d174a1ad0 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.h
+++ b/mindspore/ccsrc/pre_activate/common/helper.h
@@ -135,6 +135,11 @@ void CreateOutputsOfFusedBn3(const FuncGraphPtr &graph, const AnfNodePtr &data_i
 void CreateMultipleOutputsOfAnfNode(const FuncGraphPtr &kernel_graph, const AnfNodePtr &anf_node_ptr, size_t output_num,
                                     std::vector<AnfNodePtr> *outputs);
 
+tensor::TensorPtr CreateTensorWithValueTuple(const ValueTuplePtr &value_tuple_ptr, const TypePtr &type_ptr,
+                                             size_t data_length);
+
+tensor::TensorPtr CreateTupleTensor(const ValueTuplePtr &value_tuple);
+
 bool IsNopNode(const AnfNodePtr &node);
 
 void HideNopNode(session::KernelGraph *const graph);
diff --git a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
index 42d373392c..88edfd3019 100644
--- a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
+++ b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
@@ -17,10 +17,44 @@
 
 #include <utility>
 
+#include "utils/utils.h"
 #include "utils/log_adapter.h"
+#include "operator/ops.h"
 
 namespace mindspore {
 namespace opt {
+ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() {
+  Register(prim::kPrimCast->name(), {1});
+  Register(prim::kPrimConv2DBackpropInput->name(), {2});
+  Register(prim::kPrimConv2DBackpropFilter->name(), {2});
+  Register(prim::kPrimReshape->name(), {1});
+  Register(prim::kPrimReduceMax->name(), {1});
+  Register(prim::kPrimReduceMin->name(), {1});
+  Register(prim::kPrimReduceSum->name(), {1});
+  Register(prim::kPrimReduceMean->name(), {1});
+  Register(prim::kPrimGatherV2->name(), {2});
+  Register(prim::kPrimTranspose->name(), {1});
+  Register(prim::kPrimUnsortedSegmentSum->name(), {2});
+  Register(prim::kPrimOneHot->name(), {1});
+  Register(kUnsortedSegmentProdOpName, {2});
+  Register(kUnsortedSegmentMinOpName, {2});
+  Register(kSimpleMeanGradOpName, {1});
+  Register(kMeanGradOpName, {1});
+  Register(kSliceOpName, {1, 2});
+  Register(kSliceGradOpName, {2, 3});
+  Register(kTileOpName, {1});
+  Register(kScatterNdOpName, {2});
+  Register(kStridedSliceAssignOpName, {1, 2, 3});
+  Register(kStridedSliceOpName, {1, 2, 3});
+  Register(kStridedSliceGradOpName, {1, 2, 3, 4});
+  Register(kFlattenGradOpName, {1});
+  Register(kExpandDimsOpName, {1});
+  Register(kSplitOpName, {0});
+  Register(kTopKOpName, {1});
+  Register(kSparseApplyAdagradOpName, {2});
+  Register(kResizeNearestNeighborGrad, {1});
+}
+
 ConstInputToAttrInfoRegistry &ConstInputToAttrInfoRegistry::Instance() {
   static ConstInputToAttrInfoRegistry instance;
   return instance;
diff --git a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.h b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.h
index 48007929fb..bd6cac1322 100644
--- a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.h
+++ b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.h
@@ -54,7 +54,7 @@ class ConstInputToAttrInfoRegistry {
   bool GetRegisterByOpName(const std::string &op_name, ConstInputToAttrInfoRegister *reg) const;
 
  private:
-  ConstInputToAttrInfoRegistry() = default;
+  ConstInputToAttrInfoRegistry();
   ~ConstInputToAttrInfoRegistry() = default;
   DISABLE_COPY_AND_ASSIGN(ConstInputToAttrInfoRegistry)
   std::unordered_map<std::string, ConstInputToAttrInfoRegister> op_input_to_attr_map_;
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.cc b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.cc
index 2bef0d36ca..15d62a164f 100644
--- a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.cc
+++ b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.cc
@@ -87,37 +87,5 @@ const AnfNodePtr ConvertConstInputToAttr::Process(const FuncGraphPtr &, const An
   ConstInputToAttr(cnode, reg.GetConstInputAttrInfo());
   return cnode;
 }
-
-void ConvertConstInputToAttr::Init() {
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimCast->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimConv2DBackpropInput->name(), {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimConv2DBackpropFilter->name(), {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimReshape->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimReduceMax->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimReduceMin->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimReduceSum->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimReduceMean->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimGatherV2->name(), {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimTranspose->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimUnsortedSegmentSum->name(), {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(prim::kPrimOneHot->name(), {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kUnsortedSegmentProdOpName, {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(kUnsortedSegmentMinOpName, {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(kSimpleMeanGradOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kMeanGradOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kSliceOpName, {1, 2});
-  ConstInputToAttrInfoRegistry::Instance().Register(kSliceGradOpName, {2, 3});
-  ConstInputToAttrInfoRegistry::Instance().Register(kTileOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kScatterNdOpName, {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(kStridedSliceAssignOpName, {1, 2, 3});
-  ConstInputToAttrInfoRegistry::Instance().Register(kStridedSliceOpName, {1, 2, 3});
-  ConstInputToAttrInfoRegistry::Instance().Register(kStridedSliceGradOpName, {1, 2, 3, 4});
-  ConstInputToAttrInfoRegistry::Instance().Register(kFlattenGradOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kExpandDimsOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kSplitOpName, {0});
-  ConstInputToAttrInfoRegistry::Instance().Register(kTopKOpName, {1});
-  ConstInputToAttrInfoRegistry::Instance().Register(kSparseApplyAdagradOpName, {2});
-  ConstInputToAttrInfoRegistry::Instance().Register(kResizeNearestNeighborGrad, {1});
-}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.h b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.h
index 54caa1633c..e124ff8cf4 100644
--- a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.h
+++ b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_attr.h
@@ -27,14 +27,11 @@ namespace opt {
 class ConvertConstInputToAttr : public PatternProcessPass {
  public:
   explicit ConvertConstInputToAttr(bool multigraph = true)
-      : PatternProcessPass("convert_const_input_to_attr", multigraph) {
-    Init();
-  }
+      : PatternProcessPass("convert_const_input_to_attr", multigraph) {}
   ~ConvertConstInputToAttr() override = default;
   const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
 
  private:
-  void Init();
   std::unordered_map<std::string, std::unordered_set<size_t>> op_input_attr_map_;
 };
 }  // namespace opt
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_tensor_input.cc b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_tensor_input.cc
index 431a67792d..56be2e273d 100644
--- a/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_tensor_input.cc
+++ b/mindspore/ccsrc/pre_activate/pass/convert_const_input_to_tensor_input.cc
@@ -19,69 +19,13 @@
 #include <memory>
 
 #include "utils/graph_utils.h"
+#include "pre_activate/common/helper.h"
 #include "session/anf_runtime_algorithm.h"
 #include "session/kernel_graph.h"
 
 namespace mindspore {
 namespace opt {
 namespace {
-constexpr size_t kType32Len = 4;
-template <typename T>
-tensor::TensorPtr CreateTensorWithValueTuple(const ValueTuplePtr &value_tuple_ptr, const TypePtr &type_ptr,
-                                             size_t data_length) {
-  MS_EXCEPTION_IF_NULL(value_tuple_ptr);
-  MS_EXCEPTION_IF_NULL(type_ptr);
-  std::vector<T> values;
-  for (const auto &v : value_tuple_ptr->value()) {
-    MS_EXCEPTION_IF_NULL(v);
-    if (v->isa<Scalar>()) {
-      ScalarPtr scalar = v->cast<ScalarPtr>();
-      values.push_back(GetValue<T>(scalar));
-    } else {
-      MS_LOG(WARNING) << "The value " << v << "of tuple is not a scalar";
-      return nullptr;
-    }
-  }
-  std::vector<int> tensor_shape = {SizeToInt(values.size())};
-  tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_ptr->type_id(), tensor_shape);
-  MS_EXCEPTION_IF_NULL(tensor);
-  tensor::DeviceInfo device_info{kOpFormat_DEFAULT, type_ptr};
-  tensor->set_device_info(device_info);
-  auto data_ptr = tensor->data_c(true);
-  MS_EXCEPTION_IF_NULL(data_ptr);
-  auto elem_num = values.size() * data_length;
-  auto ret_code = memcpy_s(data_ptr, static_cast<size_t>(tensor->data().nbytes()), values.data(), elem_num);
-  if (ret_code != 0) {
-    MS_LOG(EXCEPTION) << "Failed to copy data into Tensor.";
-  }
-  return tensor;
-}
-
-tensor::TensorPtr CreateTupleTensor(const ValueTuplePtr &value_tuple) {
-  MS_EXCEPTION_IF_NULL(value_tuple);
-  tensor::TensorPtr tensor = nullptr;
-  ValuePtr v = *(value_tuple->value().begin());
-  MS_EXCEPTION_IF_NULL(v);
-  // Currently we only deal with the scalar tuple
-  if (!v->isa<Scalar>()) {
-    MS_LOG(WARNING) << "The value " << v << "of tuple is not a scalar";
-    return nullptr;
-  }
-  ScalarPtr scalar = v->cast<ScalarPtr>();
-  MS_EXCEPTION_IF_NULL(scalar);
-  if (scalar->isa<IntergerImm>()) {
-    tensor = CreateTensorWithValueTuple<int>(value_tuple, kInt32, kType32Len);
-  } else if (scalar->isa<FloatImm>()) {
-    tensor = CreateTensorWithValueTuple<float>(value_tuple, kFloat32, kType32Len);
-  } else {
-    auto type = scalar->type();
-    auto type_str = (type == nullptr) ? "nullptr" : type->ToString();
-    MS_LOG(ERROR) << "Invalid scalar type: " << type_str;
-    return nullptr;
-  }
-  return tensor;
-}
-
 AnfNodePtr CreateTensorInput(const KernelGraphPtr &kernel_graph, const AnfNodePtr &input_node) {
   MS_EXCEPTION_IF_NULL(input_node);
   auto value_node = input_node->cast<ValueNodePtr>();
diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index e5fb0c6949..4144ad2d6b 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -158,8 +158,9 @@ py::object RunOpInMs(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* stat
   session->Init(ms_context->device_id());
 
   std::string graph_info = GetSingleOpGraphInfo(op_exec_info);
-  session->BuildOp(*op_exec_info, graph_info);
-  py::tuple result = session->RunOp(*op_exec_info, graph_info);
+  std::vector<tensor::TensorPtr> input_tensors;
+  session->BuildOp(*op_exec_info, graph_info, &input_tensors);
+  py::tuple result = session->RunOp(*op_exec_info, graph_info, input_tensors);
   ms_context->set_enable_pynative_infer(false);
   *status = PYNATIVE_SUCCESS;
   return result;
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index ba0ef836e1..4455d33ee9 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -204,10 +204,12 @@ void AscendSession::RunOpExecTask(const std::shared_ptr<KernelGraph> &kernel_gra
   MS_LOG(INFO) << "Finish!";
 }
 
-void AscendSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) {
+void AscendSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                            std::vector<tensor::TensorPtr> *input_tensors) {
+  MS_EXCEPTION_IF_NULL(input_tensors);
   MS_LOG(INFO) << "Build op " << op_run_info.op_name << " start !";
   // construct graph include one op
-  auto graph = ConstructSingleOpGraph(op_run_info);
+  auto graph = ConstructSingleOpGraph(op_run_info, input_tensors);
   MS_EXCEPTION_IF_NULL(graph);
   opt::RunOpAscendBackendIRFusionOptimization(graph);
   // kernel select
@@ -222,14 +224,12 @@ void AscendSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph
   run_op_graphs_[graph_info] = graph;
 }
 
-py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) {
+py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                               const std::vector<tensor::TensorPtr> &input_tensors) {
   auto graph = run_op_graphs_[graph_info];
   MS_EXCEPTION_IF_NULL(graph);
   MS_LOG(INFO) << "Run op " << op_run_info.op_name << " start!";
   // malloc mem
-  std::vector<tensor::TensorPtr> input_tensors = {};
-  std::vector<bool> tensors_mask = {};
-  ToTensorPtr(op_run_info, &input_tensors, &tensors_mask);
   RunOpMemoryAlloc(input_tensors, graph.get());
   // load input data to device
   LoadInputData(graph, input_tensors);
diff --git a/mindspore/ccsrc/session/ascend_session.h b/mindspore/ccsrc/session/ascend_session.h
index c45ab6630a..2d24691404 100755
--- a/mindspore/ccsrc/session/ascend_session.h
+++ b/mindspore/ccsrc/session/ascend_session.h
@@ -41,8 +41,10 @@ class AscendSession : public SessionBasic {
   GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
   void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
   void BuildGraph(GraphId) override;
-  void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) override;
-  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) override;
+  void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+               std::vector<tensor::TensorPtr> *input_tensors) override;
+  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                  const std::vector<tensor::TensorPtr> &input_tensors) override;
 
   // set parameters of final graph
   GraphId SetFinalGraphInput(const std::vector<AnfNodePtr> &args) override;
diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc
index c0b2323e04..98a84cd6d2 100644
--- a/mindspore/ccsrc/session/gpu_session.cc
+++ b/mindspore/ccsrc/session/gpu_session.cc
@@ -132,9 +132,11 @@ void GPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::Ten
   }
 }
 
-void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) {
+void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                         std::vector<tensor::TensorPtr> *input_tensors) {
   // Prepare the graph
-  auto kernel_graph = ConstructSingleOpGraph(op_run_info);
+  MS_EXCEPTION_IF_NULL(input_tensors);
+  auto kernel_graph = ConstructSingleOpGraph(op_run_info, input_tensors);
   MS_EXCEPTION_IF_NULL(kernel_graph);
   SelectKernel(kernel_graph);
   StartKernelRT();
@@ -142,12 +144,10 @@ void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_in
   run_op_graphs_[graph_info] = kernel_graph;
 }
 
-py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) {
+py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                            const std::vector<tensor::TensorPtr> &input_tensors) {
   auto kernel_graph = run_op_graphs_[graph_info];
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  std::vector<tensor::TensorPtr> input_tensors = {};
-  std::vector<bool> tensors_mask = {};
-  ToTensorPtr(op_run_info, &input_tensors, &tensors_mask);
   RunOpAllocateMemory(input_tensors, kernel_graph.get());
   // Execute the computation
   LoadInputData(kernel_graph, input_tensors);
diff --git a/mindspore/ccsrc/session/gpu_session.h b/mindspore/ccsrc/session/gpu_session.h
index d81a6c58f9..470c9b4799 100644
--- a/mindspore/ccsrc/session/gpu_session.h
+++ b/mindspore/ccsrc/session/gpu_session.h
@@ -39,8 +39,10 @@ class GPUSession : public SessionBasic {
   GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
 
   void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
-  void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) override;
-  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info) override;
+  void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+               std::vector<tensor::TensorPtr> *input_tensors) override;
+  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                  const std::vector<tensor::TensorPtr> &input_tensors) override;
 
  private:
   void SelectKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const;
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index 33ef78c13d..01a836fad1 100755
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -17,6 +17,7 @@
 #include <utility>
 #include <algorithm>
 #include <unordered_map>
+#include <unordered_set>
 #include "pipeline/parse/data_converter.h"
 #include "ir/manager.h"
 #include "operator/ops.h"
@@ -26,6 +27,7 @@
 #include "session/anf_runtime_algorithm.h"
 #include "kernel/oplib/oplib.h"
 #include "pre_activate/common/common_backend_optimization.h"
+#include "pre_activate/pass/const_input_to_attr_registry.h"
 #include "pre_activate/common/helper.h"
 #include "common/utils.h"
 #include "ir/dtype.h"
@@ -178,56 +180,113 @@ BaseRef CreatTupleForOutput(const AnfNodePtr &anf, const KernelGraph &graph,
   return ret;
 }
 
-std::string FindOpInputParameterType(const std::string &op_name, kernel::OpImplyType implyType, size_t index) {
-  std::string para_type;
-  auto op_info = kernel::OpLib::FindOp(op_name, implyType);
-  if (op_info == nullptr) {
-    return para_type;
+bool RunOpConvertConstInputToAttr(const py::object &input_object, size_t input_index, const PrimitivePtr &op_prim,
+                                  const std::unordered_set<size_t> &input_attrs) {
+  MS_EXCEPTION_IF_NULL(op_prim);
+  auto input_names_value = op_prim->GetAttr(kAttrInputNames);
+  if (input_names_value == nullptr) {
+    return false;
+  }
+  auto input_names_vec = GetValue<std::vector<std::string>>(input_names_value);
+  if (input_index >= input_names_vec.size()) {
+    MS_LOG(EXCEPTION) << "The input index: " << input_index << " is large than the input names vector size!";
   }
-  auto op_inputs_info_vec = op_info->inputs_ptr();
-  if (index >= op_inputs_info_vec.size()) {
-    return para_type;
+
+  if (input_attrs.find(input_index) != input_attrs.end()) {
+    ValuePtr value = parse::data_converter::PyDataToValue(input_object);
+    MS_EXCEPTION_IF_NULL(value);
+    auto input_name = input_names_vec[input_index];
+    op_prim->set_attr(input_name, value);
+    return true;
   }
-  auto op_io_info = op_inputs_info_vec[index];
-  MS_EXCEPTION_IF_NULL(op_io_info);
-  para_type = op_io_info->param_type();
-  return para_type;
+  return false;
 }
 
-void RunOpConvertConstInputToAttr(const OpRunInfo &op_run_info, const std::shared_ptr<CNode> &cnode) {
-  MS_EXCEPTION_IF_NULL(cnode);
-  auto op_inputs = op_run_info.op_inputs;
-  // get input names vector from attrs
-  auto primitive = AnfAlgo::GetCNodePrimitive(cnode);
-  MS_EXCEPTION_IF_NULL(primitive);
-  auto input_names_value = primitive->GetAttr(kAttrInputNames);
-  if (input_names_value == nullptr) {
+void PlantTensorTupleToVector(const py::tuple &tuple_inputs, const PrimitivePtr &op_prim,
+                              std::vector<tensor::TensorPtr> *input_tensor) {
+  MS_EXCEPTION_IF_NULL(op_prim);
+  MS_EXCEPTION_IF_NULL(input_tensor);
+  for (const auto &input_object : tuple_inputs) {
+    if (!py::isinstance<tensor::Tensor>(input_object)) {
+      MS_LOG(EXCEPTION) << "The input object is not a tensor!";
+    }
+    auto tensor = py::cast<tensor::TensorPtr>(input_object);
+    MS_EXCEPTION_IF_NULL(tensor);
+    input_tensor->push_back(tensor);
+  }
+  op_prim->set_attr(kAttrDynInputSizes, MakeValue(std::vector<int>{SizeToInt(tuple_inputs.size())}));
+}
+
+void ConvertValueTupleToTensor(const py::object &input_object, std::vector<tensor::TensorPtr> *input_tensor) {
+  MS_EXCEPTION_IF_NULL(input_tensor);
+  ValuePtr input_value = parse::data_converter::PyDataToValue(input_object);
+  MS_EXCEPTION_IF_NULL(input_value);
+  if (!input_value->isa<ValueTuple>()) {
+    MS_LOG(EXCEPTION) << "The input object is not a value tuple!";
+  }
+  auto value_tuple = input_value->cast<ValueTuplePtr>();
+  MS_EXCEPTION_IF_NULL(value_tuple);
+  tensor::TensorPtr tensor_ptr = nullptr;
+  tensor_ptr = opt::CreateTupleTensor(value_tuple);
+  MS_EXCEPTION_IF_NULL(tensor_ptr);
+  input_tensor->push_back(tensor_ptr);
+}
+
+void ConvertPyObjectToTensor(const py::object &input_object, const PrimitivePtr &op_prim,
+                             std::vector<tensor::TensorPtr> *input_tensor) {
+  MS_EXCEPTION_IF_NULL(op_prim);
+  MS_EXCEPTION_IF_NULL(input_tensor);
+  tensor::TensorPtr tensor_ptr = nullptr;
+  if (py::isinstance<tensor::Tensor>(input_object)) {
+    tensor_ptr = py::cast<tensor::TensorPtr>(input_object);
+  } else if (py::isinstance<py::float_>(input_object)) {
+    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::float_>(input_object), kFloat32);
+  } else if (py::isinstance<py::int_>(input_object)) {
+    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::int_>(input_object), nullptr);
+  } else if (py::isinstance<py::list>(input_object)) {
+    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::list>(input_object), nullptr);
+  } else if (py::isinstance<py::array>(input_object)) {
+    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::array>(input_object), nullptr);
+  } else if (py::isinstance<py::tuple>(input_object)) {
+    auto tuple_inputs = py::cast<py::tuple>(input_object);
+    if (py::isinstance<tensor::Tensor>(tuple_inputs[0])) {
+      PlantTensorTupleToVector(tuple_inputs, op_prim, input_tensor);
+    } else {
+      ConvertValueTupleToTensor(input_object, input_tensor);
+    }
     return;
+  } else {
+    MS_LOG(EXCEPTION) << "Run op inputs type is invalid!";
   }
-  auto input_names_vec = GetValue<std::vector<std::string>>(input_names_value);
-  // convert const input to attr
-  size_t input_num = op_inputs.size();
-  if (input_num != input_names_vec.size()) {
-    MS_LOG(EXCEPTION) << "input name number " << input_names_vec.size() << "is not equal to input value number "
-                      << input_num;
+  MS_EXCEPTION_IF_NULL(tensor_ptr);
+  input_tensor->push_back(tensor_ptr);
+}
+
+void ConvertInputPyobject(const OpRunInfo &op_run_info, const PrimitivePtr &op_prim,
+                          std::vector<tensor::TensorPtr> *input_tensors, std::vector<bool> *tensors_mask) {
+  MS_EXCEPTION_IF_NULL(op_prim);
+  MS_EXCEPTION_IF_NULL(input_tensors);
+  MS_EXCEPTION_IF_NULL(tensors_mask);
+  if (op_run_info.op_inputs.size() != op_run_info.inputs_mask.size()) {
+    MS_LOG(EXCEPTION) << "Op input size " << op_run_info.op_inputs.size() << " should be equal to op input mask size "
+                      << op_run_info.inputs_mask.size();
   }
+  opt::ConstInputToAttrInfoRegister reg;
+  bool reg_exist = opt::ConstInputToAttrInfoRegistry::Instance().GetRegisterByOpName(op_run_info.op_name, &reg);
+  size_t input_num = op_run_info.op_inputs.size();
+  MS_LOG(INFO) << "py input size: " << input_num;
   for (size_t index = 0; index < input_num; ++index) {
-    // skip tensor
-    if (py::isinstance<tensor::Tensor>(op_inputs[index])) {
-      continue;
-    }
-    // convert to attr
-    auto para_type = FindOpInputParameterType(op_run_info.op_name, kernel::OpImplyType::kTBE, index);
-    if (!para_type.empty() && para_type == kAttrDynInput) {
-      auto tuple_inputs = py::cast<py::tuple>(op_inputs[index]);
-      primitive->set_attr(kAttrDynInputSizes, MakeValue(std::vector<int>{SizeToInt(tuple_inputs.size())}));
+    // convert const input to attr
+    if (reg_exist &&
+        RunOpConvertConstInputToAttr(op_run_info.op_inputs[index], index, op_prim, reg.GetConstInputAttrInfo())) {
       continue;
     }
-    ValuePtr value = parse::data_converter::PyDataToValue(op_inputs[index]);
-    MS_EXCEPTION_IF_NULL(value);
-    auto input_name = input_names_vec[index];
-    // set the input node as attr of the cnode, key is name of input node,value is input node's value
-    primitive->set_attr(input_name, value);
+    // convert const and tuple input to tensor
+    ConvertPyObjectToTensor(op_run_info.op_inputs[index], op_prim, input_tensors);
+    // make tensors, weight : 1, data : 0
+    std::vector<bool> new_mask(input_tensors->size() - tensors_mask->size(),
+                               py::cast<bool>(op_run_info.inputs_mask[index]));
+    tensors_mask->insert(tensors_mask->end(), new_mask.begin(), new_mask.end());
   }
 }
 
@@ -638,40 +697,6 @@ void SessionBasic::Summary(KernelGraph *graph) {
   summary_callback_(0, params_list);
 }
 
-void SessionBasic::ToTensorPtr(const OpRunInfo &op_run_info, std::vector<tensor::TensorPtr> *inputs,
-                               std::vector<bool> *tensor_mask) {
-  MS_EXCEPTION_IF_NULL(inputs);
-  MS_EXCEPTION_IF_NULL(tensor_mask);
-  if (op_run_info.op_inputs.size() != op_run_info.inputs_mask.size()) {
-    MS_LOG(EXCEPTION) << "Op input size " << op_run_info.op_inputs.size() << " should be equal to op input mask size "
-                      << op_run_info.inputs_mask.size();
-  }
-  size_t input_num = op_run_info.op_inputs.size();
-  // get tensors from op_inputs
-  for (size_t i = 0; i < input_num; ++i) {
-    tensor::TensorPtr tensor_ptr = nullptr;
-    auto param_type = FindOpInputParameterType(op_run_info.op_name, kernel::OpImplyType::kTBE, i);
-    if (py::isinstance<tensor::Tensor>(op_run_info.op_inputs[i])) {
-      tensor_ptr = py::cast<tensor::TensorPtr>(op_run_info.op_inputs[i]);
-    } else if (!param_type.empty() && param_type == kAttrDynInput) {
-      auto tuple_inputs = py::cast<py::tuple>(op_run_info.op_inputs[i]);
-      for (auto &&tuple_input : tuple_inputs) {
-        tensor_ptr = py::cast<tensor::TensorPtr>(tuple_input);
-        MS_EXCEPTION_IF_NULL(tensor_ptr);
-        inputs->push_back(tensor_ptr);
-        tensor_mask->push_back(py::cast<bool>(op_run_info.inputs_mask[i]));
-      }
-      continue;
-    } else if (op_run_info.op_name == kApplyMomentumOpName && py::isinstance<py::float_>(op_run_info.op_inputs[i])) {
-      tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::float_>(op_run_info.op_inputs[i]), kFloat32);
-    }
-    if (tensor_ptr != nullptr) {
-      inputs->push_back(tensor_ptr);
-      tensor_mask->push_back(py::cast<bool>(op_run_info.inputs_mask[i]));
-    }
-  }
-}
-
 CNodePtr SessionBasic::ConstructOutput(const AnfNodePtrList &outputs, const std::shared_ptr<KernelGraph> &graph) {
   MS_EXCEPTION_IF_NULL(graph);
   std::vector<AnfNodePtr> output_args;
@@ -724,30 +749,27 @@ void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr
   MS_LOG(INFO) << "Finish!";
 }
 
-std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info) {
+std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info,
+                                                                  std::vector<tensor::TensorPtr> *input_tensors) {
+  MS_EXCEPTION_IF_NULL(input_tensors);
   auto graph = std::make_shared<KernelGraph>();
   std::vector<AnfNodePtr> inputs;
-  if (op_run_info.op_inputs.size() != op_run_info.inputs_mask.size()) {
-    MS_LOG(EXCEPTION) << "op_run_info inputs.size" << op_run_info.op_inputs.size()
-                      << " should be equal to parameter_mask.size " << op_run_info.inputs_mask.size();
-  }
   // set input[0]
-  if (op_run_info.py_primitive == nullptr) {
-    inputs.push_back(std::make_shared<ValueNode>(std::make_shared<Primitive>(op_run_info.op_name)));
-  } else {
-    inputs.push_back(std::make_shared<ValueNode>(op_run_info.py_primitive));
+  PrimitivePtr op_prim = op_run_info.py_primitive;
+  if (op_prim == nullptr) {
+    op_prim = std::make_shared<Primitive>(op_run_info.op_name);
   }
+  inputs.push_back(std::make_shared<ValueNode>(op_prim));
   // set input parameter
-  std::vector<tensor::TensorPtr> input_tensors;
   std::vector<bool> tensors_mask;
-  ToTensorPtr(op_run_info, &input_tensors, &tensors_mask);
-  MS_LOG(INFO) << "Input tensor size" << input_tensors.size();
-  if (input_tensors.size() != tensors_mask.size()) {
-    MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors.size() << " should be equal to tensors mask size "
+  ConvertInputPyobject(op_run_info, op_prim, input_tensors, &tensors_mask);
+  MS_LOG(INFO) << "Input tensor size: " << input_tensors->size();
+  if (input_tensors->size() != tensors_mask.size()) {
+    MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors->size() << " should be equal to tensors mask size "
                       << tensors_mask.size();
   }
-  for (size_t i = 0; i < input_tensors.size(); ++i) {
-    auto parameter = ConstructRunOpParameter(graph, input_tensors[i], tensors_mask[i]);
+  for (size_t i = 0; i < input_tensors->size(); ++i) {
+    auto parameter = ConstructRunOpParameter(graph, input_tensors->at(i), tensors_mask[i]);
     inputs.push_back(parameter);
     graph->MutableInputs()->push_back(parameter);
   }
@@ -756,8 +778,6 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInf
   MS_EXCEPTION_IF_NULL(cnode);
   // set abstract,which include inferred shapes and types
   cnode->set_abstract(op_run_info.abstract);
-  // set const input to attr if value is not a tensor,such as scalar or tuple
-  RunOpConvertConstInputToAttr(op_run_info, cnode);
   // set execution order
   std::vector<CNodePtr> exe_order = {cnode};
   graph->set_execution_order(exe_order);
diff --git a/mindspore/ccsrc/session/session_basic.h b/mindspore/ccsrc/session/session_basic.h
index f1872e375c..aa359c74d9 100755
--- a/mindspore/ccsrc/session/session_basic.h
+++ b/mindspore/ccsrc/session/session_basic.h
@@ -61,9 +61,11 @@ class SessionBasic {
 
   virtual void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) = 0;
 
-  virtual void BuildOp(const OpRunInfo &, const GraphInfo &) {}
+  virtual void BuildOp(const OpRunInfo &, const GraphInfo &, std::vector<tensor::TensorPtr> *input_tensors) {}
 
-  virtual py::tuple RunOp(const OpRunInfo &, const GraphInfo &) { return py::tuple(); }
+  virtual py::tuple RunOp(const OpRunInfo &, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors) {
+    return py::tuple();
+  }
 
   virtual void RegisterSummaryCallBackFunc(const CallBackFunc &callback);
 
@@ -96,10 +98,8 @@ class SessionBasic {
   void CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr<KernelGraph> &graph);
   CNodePtr ConstructOutput(const AnfNodePtrList &outputs, const std::shared_ptr<KernelGraph> &graph);
   // create a single run op graph
-  std::shared_ptr<KernelGraph> ConstructSingleOpGraph(const OpRunInfo &op_run_info);
-  // get tensors from op inputs
-  void ToTensorPtr(const OpRunInfo &op_run_info, std::vector<tensor::TensorPtr> *inputs,
-                   std::vector<bool> *tensor_mask);
+  std::shared_ptr<KernelGraph> ConstructSingleOpGraph(const OpRunInfo &op_run_info,
+                                                      std::vector<tensor::TensorPtr> *input_tensor);
   // trans BaseRef list to py::tuple
   BaseRef TransformBaseRefListToTuple(const BaseRef &base_ref);
 

From a66b0c1e111903e27b6c45d5f221060346a6fe11 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Wed, 15 Apr 2020 18:58:36 +0800
Subject: [PATCH 265/367] fix tvm copy path

---
 cmake/package.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cmake/package.cmake b/cmake/package.cmake
index e4f8fa7706..7c9c92b23c 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -165,11 +165,11 @@ if (ENABLE_GPU)
         DESTINATION ${INSTALL_PY_DIR}/../
         COMPONENT mindspore
     )
-    if (EXISTS ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm)
+    if (EXISTS ${CMAKE_BINARY_DIR}/incubator-tvm)
         install(
             DIRECTORY
-                ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/topi/python/topi
-                ${CMAKE_SOURCE_DIR}/mindspore/incubator-tvm/python/tvm
+                ${CMAKE_BINARY_DIR}/incubator-tvm/topi/python/topi
+                ${CMAKE_BINARY_DIR}/incubator-tvm/python/tvm
             DESTINATION ${INSTALL_PY_DIR}/../_akg
             COMPONENT mindspore
         )

From b08a90ab9b964883f0ab2304cabeb74e3ca80f30 Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Wed, 15 Apr 2020 20:18:03 +0800
Subject: [PATCH 266/367] add tdt transfer data limit notice

---
 mindspore/dataset/engine/datasets.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index f2d0f59f82..517ec6c385 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -548,12 +548,16 @@ class Dataset:
 
     def device_que(self, prefetch_size=None):
         """
-        Returns a transferredDataset that transfer data through tdt.
+        Returns a transferredDataset that transfer data through device.
 
         Args:
             prefetch_size (int, optional): prefetch number of records ahead of the
                 user's request (default=None).
 
+        Note:
+            If device is Ascend, features of data will be transferred one by one. The limitation
+            of data transferation per time is 256M.
+
         Return:
             TransferDataset, dataset for transferring.
         """
@@ -566,6 +570,10 @@ class Dataset:
         Args:
             num_batch (int, optional): limit the number of batch to be sent to device (default=None).
 
+        Note:
+            If device is Ascend, features of data will be transferred one by one. The limitation
+            of data transferation per time is 256M.
+
         Returns:
             TransferDataset, dataset for transferring.
 

From b77f41d6589043cb7ff6f23f69198db1ecc40c52 Mon Sep 17 00:00:00 2001
From: chenzomi <chenzomi12@gmail.com>
Date: Wed, 15 Apr 2020 21:37:34 +0800
Subject: [PATCH 267/367] clear the warmming scan by package

---
 .../ccsrc/device/gpu/gpu_memory_manager.cc    |  2 +-
 .../ccsrc/device/gpu/gpu_stream_assign.cc     |  2 +-
 .../ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc |  1 -
 .../gpu/quant/batchnorm_fold2_gpu_kernel.cc   |  1 -
 .../gpu/quant/batchnorm_fold2_gpu_kernel.h    |  1 -
 .../quant/batchnorm_fold2_grad_gpu_kernel.cc  |  1 -
 .../gpu/quant/batchnorm_fold_gpu_kernel.cc    |  1 -
 .../gpu/quant/correction_mul_gpu_kernel.h     |  1 -
 .../quant/correction_mul_grad_gpu_kernel.cc   |  1 -
 .../quant/correction_mul_grad_gpu_kernel.h    |  1 -
 .../fake_quant_per_channel_gpu_kernel.cc      | 62 +++++++++++--------
 .../quant/fake_quant_per_channel_gpu_kernel.h |  5 ++
 12 files changed, 42 insertions(+), 37 deletions(-)

diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
index 7d042264b6..8bb65963d8 100644
--- a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc
@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
index 39d5ca3fe6..08a19aa469 100644
--- a/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
index eeec8365da..937f38137f 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc
@@ -19,7 +19,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 DropoutGpuFwdKernel::DropoutGpuFwdKernel()
     : cudnn_handle_(nullptr),
       is_null_input_(false),
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
index a95c1b78dd..af95767407 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.cc
@@ -18,7 +18,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 MS_REG_GPU_KERNEL_ONE(BatchNormFold2,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat32)
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
index 3e246f18f6..beeeb12a9a 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_gpu_kernel.h
@@ -132,7 +132,6 @@ class BatchNormFold2GpuKernel : public GpuKernel {
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;
 };
-
 }  // namespace kernel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
index d5932f1984..93862aeedd 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold2_grad_gpu_kernel.cc
@@ -18,7 +18,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 MS_REG_GPU_KERNEL_ONE(BatchNormFold2Grad,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat32)
diff --git a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
index b5fbfe4927..4f968a0fa3 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/quant/batchnorm_fold_gpu_kernel.cc
@@ -18,7 +18,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 MS_REG_GPU_KERNEL_ONE(BatchNormFold,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat32)
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
index 7608ae5d3c..eeab872ab3 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_gpu_kernel.h
@@ -54,7 +54,6 @@ class CorrectionMulGpuKernel : public GpuKernel {
     }
 
     auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-
     if (input_shape.size() != 4) {
       MS_LOG(ERROR) << "CorrectionMulGpuKernel input shape needs (N,C,H,W).";
       return false;
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
index 211c515e02..28b5d56e68 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.cc
@@ -19,7 +19,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 MS_REG_GPU_KERNEL_ONE(CorrectionMulGrad,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat32)
diff --git a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
index 2439826cc3..29aeb3be13 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/correction_mul_grad_gpu_kernel.h
@@ -61,7 +61,6 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
     }
 
     auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-
     if (input_shape.size() != 4) {
       MS_LOG(ERROR) << "CorrectionMulGradGpuKernel input shape needs (N,C,H,W).";
       return false;
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
index 302ef8d99f..1da9f457a1 100644
--- a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.cc
@@ -114,6 +114,36 @@ void FakeQuantPerChannelGpuKernel::InitSizeLists() {
   workspace_size_list_.push_back(workspace_size_);
 }
 
+void FakeQuantPerChannelGpuKernel::CalFakeQuantizeForTraining(float *input, float *output, float *input_min,
+                                                              float *input_max, float *d_nudge_min, float *d_nudge_max,
+                                                              float *d_scale, uintptr_t stream_ptr) {
+  // calculate the input min and max according by the parameter ema and ema_decay.
+  CalMinMaxPerChannel(input, input_min, input_max, input_size_ / sizeof(float), channel_out_, ema_decay_, ema_,
+                      reinterpret_cast<cudaStream_t>(stream_ptr));
+  // control flow for quant_delay
+  if (global_step_ >= quant_delay_) {
+    // real launch
+    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
+                       reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantizePerChannel(input, output, input_size_ / sizeof(float), channel_out_, d_nudge_min, d_nudge_max,
+                              d_scale, symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
+  } else {
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, input, input_size_, cudaMemcpyDeviceToDevice),
+                              "Copy gpu memory failed.");
+  }
+  global_step_++;
+}
+
+void FakeQuantPerChannelGpuKernel::CalFakeQuantizeForInfer(float *input, float *output, float *input_min,
+                                                           float *input_max, float *d_nudge_min, float *d_nudge_max,
+                                                           float *d_scale, uintptr_t stream_ptr) {
+  // real launch
+  CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
+                     reinterpret_cast<cudaStream_t>(stream_ptr));
+  CalFakeQuantizePerChannel(input, output, input_size_ / sizeof(float), channel_out_, d_nudge_min, d_nudge_max, d_scale,
+                            symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
+}
+
 bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
                                           const std::vector<AddressPtr> &workspace,
                                           const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
@@ -126,11 +156,8 @@ bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
   if (input == nullptr) {
     MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input is null.";
   }
-  if (input_min == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input min is null.";
-  }
-  if (input_max == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input max is null.";
+  if (input_min == nullptr || input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input min or max is null.";
   }
 
   // Allocate space for device copies
@@ -143,30 +170,11 @@ bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
                             "Malloc gpu memory failed");
   CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), sizeof(float) * channel_out_),
                             "Malloc gpu memory failed");
-  int total_size = input_size_ / sizeof(float);
-  bool symmetric = false;
+
   if (training_) {
-    // calculate the input min and max according by the parameter ema and ema_decay.
-    CalMinMaxPerChannel(input, input_min, input_max, total_size, channel_out_, ema_decay_, ema_,
-                        reinterpret_cast<cudaStream_t>(stream_ptr));
-    // control flow for quant_delay
-    if (global_step_ >= quant_delay_) {
-      // real launch
-      CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
-                         reinterpret_cast<cudaStream_t>(stream_ptr));
-      CalFakeQuantizePerChannel(input, output, total_size, channel_out_, d_nudge_min, d_nudge_max, d_scale, symmetric,
-                                reinterpret_cast<cudaStream_t>(stream_ptr));
-    } else {
-      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, input, input_size_, cudaMemcpyDeviceToDevice),
-                                "Copy gpu memory failed.");
-    }
-    global_step_++;
+    CalFakeQuantizeForTraining(input, output, input_min, input_max, d_nudge_min, d_nudge_max, d_scale, stream_ptr);
   } else {
-    // real launch
-    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
-                       reinterpret_cast<cudaStream_t>(stream_ptr));
-    CalFakeQuantizePerChannel(input, output, total_size, channel_out_, d_nudge_min, d_nudge_max, d_scale, symmetric,
-                              reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantizeForInfer(input, output, input_min, input_max, d_nudge_min, d_nudge_max, d_scale, stream_ptr);
   }
 
   // Cleanup
diff --git a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
index faf8684fca..8a1bb7293a 100755
--- a/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h
@@ -39,6 +39,11 @@ class FakeQuantPerChannelGpuKernel : public GpuKernel {
   void InitSizeLists() override;
 
  private:
+  void CalFakeQuantizeForTraining(float *input, float *output, float *input_min, float *input_max, float *d_nudge_min,
+                                  float *d_nudge_max, float *d_scale, uintptr_t stream_ptr);
+  void CalFakeQuantizeForInfer(float *input, float *output, float *input_min, float *input_max, float *d_nudge_min,
+                               float *d_nudge_max, float *d_scale, uintptr_t stream_ptr);
+
   size_t input_size_;
   size_t min_size_;
   size_t max_size_;

From b6493af6b7531f3d460d581e27415dd67b0bd6d3 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Wed, 15 Apr 2020 14:18:50 +0000
Subject: [PATCH 268/367] fix gpu init fail

---
 cmake/options.cmake              | 1 +
 mindspore/ccsrc/pipeline/init.cc | 4 ++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/cmake/options.cmake b/cmake/options.cmake
index 2f44e9a17c..3677418a98 100644
--- a/cmake/options.cmake
+++ b/cmake/options.cmake
@@ -64,6 +64,7 @@ endif()
 
 if (ENABLE_GPU)
     set(ENABLE_GPUQUE ON)
+    add_compile_definitions(ENABLE_GPU_COLLECTIVE)
 endif()
 
 if (ENABLE_GE)
diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc
index 7c77949fda..b709199c87 100644
--- a/mindspore/ccsrc/pipeline/init.cc
+++ b/mindspore/ccsrc/pipeline/init.cc
@@ -29,7 +29,7 @@
 #include "parallel/context.h"
 #include "parallel/device_manager.h"
 #include "parallel/costmodel_context.h"
-#ifdef ENABLE_GPUQUE
+#ifdef ENABLE_GPU_COLLECTIVE
 #include "device/gpu/distribution/collective_init.h"
 #else
 #include "device/gpu/distribution/collective_fake_init.h"
@@ -300,7 +300,7 @@ PYBIND11_MODULE(_c_expression, m) {
   (void)py::class_<OpLib, std::shared_ptr<OpLib>>(m, "Oplib")
     .def(py::init())
     .def("reg_op", &OpLib::RegOp, "Register op info.");
-#ifdef ENABLE_GPUQUE
+#ifdef ENABLE_GPU_COLLECTIVE
   (void)m.def("init_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::InitCollective,
               "Init gpu collective communication mode.");
   (void)m.def("finalize_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::FinalizeCollective,

From bbf5d4b4998896ef2d24128d5fa41724ae02d194 Mon Sep 17 00:00:00 2001
From: seatea <t.cai@huawei.com>
Date: Wed, 15 Apr 2020 22:27:12 +0800
Subject: [PATCH 269/367] Fix the error in the example of
 `RandomChoiceWithMask` operation.

---
 mindspore/ops/operations/random_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index c8d7c75768..18c2212b3d 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -45,7 +45,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
 
     Examples:
         >>> rnd_choice_mask = P.RandomChoiceWithMask()
-        >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_)
+        >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool))
         >>> output_y, output_mask = rnd_choice_mask(input_x)
     """
 

From 108ac9cda7d51882c67b5ced6a3bfa4a2a99b0a0 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Thu, 16 Apr 2020 09:22:47 +0800
Subject: [PATCH 270/367] fix package path

---
 cmake/package.cmake | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/cmake/package.cmake b/cmake/package.cmake
index 7c9c92b23c..d04efca085 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -90,19 +90,23 @@ if (ENABLE_CPU)
 endif ()
 
 if (ENABLE_GPU)
-  if (ENABLE_MPI)
-      install(
-          TARGETS _ms_mpi gpu_collective
-          DESTINATION ${INSTALL_LIB_DIR}
-          COMPONENT mindspore
-      )
-  endif ()
-
-  install(
-      TARGETS gpu_queue
-      DESTINATION ${INSTALL_LIB_DIR}
-      COMPONENT mindspore
-  )
+    if (ENABLE_MPI)
+        install(
+            TARGETS _ms_mpi
+            DESTINATION ${INSTALL_BASE_DIR}
+            COMPONENT mindspore
+        )
+        install(
+            TARGETS gpu_collective
+            DESTINATION ${INSTALL_LIB_DIR}
+            COMPONENT mindspore
+        )
+    endif ()
+    install(
+        TARGETS gpu_queue
+        DESTINATION ${INSTALL_LIB_DIR}
+        COMPONENT mindspore
+    )
 endif ()
 
 if (NOT ENABLE_GE)

From 8e8f56cbdc67fa0559f7205c3eafd92bc0aa7a24 Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Thu, 16 Apr 2020 10:14:09 +0800
Subject: [PATCH 271/367] only link to glog when enable GE

---
 mindspore/ccsrc/CMakeLists.txt | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 500972b0d6..c99809f90f 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -381,17 +381,19 @@ if (WIN32)
             securec
             proto_input
             mindspore::flatbuffers
-            mindspore::glog
             )
 else()
     target_link_libraries(_c_expression PRIVATE
             mindspore::pybind11_module
             mindspore
             mindspore_gvar
-            mindspore::glog
             )
 endif()
 
+if(USE_GLOG)
+    target_link_libraries(_c_expression PRIVATE mindspore::glog)
+endif()
+
 if(ENABLE_GPU)
 	execute_process(COMMAND bash ${CMAKE_SOURCE_DIR}/third_party/apply_patches.sh 
 		${CMAKE_BINARY_DIR} 

From e017fd891679af3fa17be9a60a27dcc11049f2c8 Mon Sep 17 00:00:00 2001
From: chenfei <chenfei52@huawei.com>
Date: Thu, 16 Apr 2020 10:29:10 +0800
Subject: [PATCH 272/367] share mem of paramter between child graph

---
 mindspore/ccsrc/pre_activate/common/helper.cc |  4 ----
 mindspore/ccsrc/session/ascend_session.cc     | 19 +++++++++----------
 2 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/common/helper.cc b/mindspore/ccsrc/pre_activate/common/helper.cc
index 3b7d95a6f2..3f157d6a64 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.cc
+++ b/mindspore/ccsrc/pre_activate/common/helper.cc
@@ -337,10 +337,6 @@ bool IsNopNode(const AnfNodePtr &node) {
   if (nop_nodes.find(AnfAlgo::GetCNodeName(cnode)) == nop_nodes.end()) {
     return false;
   }
-  if (cnode->inputs().size() != 2) {
-    MS_LOG(EXCEPTION) << "Nop node(" + cnode->DebugString() + ") should have only 1 input, but it has "
-                      << cnode->inputs().size() - 1 << " inputs.";
-  }
   return true;
 }
 
diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index 4455d33ee9..b0855feb49 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -709,18 +709,17 @@ void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const An
                    << "of graph " << AnfAlgo::GetGraphId(backend_arg.get());
       return;
     }
-  }
-  // if a parameter is a weight and not linked to any executable node,device type will be kTypeUnknown,set it's device
-  // type same to arg
-  if (AnfAlgo::GetOutputDeviceDataType(backend_parameter, 0) == kTypeUnknown) {
-    AnfAlgo::SetSelectKernelBuildInfo(AnfAlgo::GetSelectKernelBuildInfo(backend_arg), backend_parameter.get());
+    // if a parameter is a weight and not linked to any executable node,device type will be kTypeUnknown,set it's device
+    // type same to arg
+    if (AnfAlgo::GetOutputDeviceDataType(backend_parameter, 0) == kTypeUnknown) {
+      AnfAlgo::SetSelectKernelBuildInfo(AnfAlgo::GetSelectKernelBuildInfo(backend_arg), backend_parameter.get());
+    }
+    // if front anf is a parameter,we can assign the value back,because backend_parameter won't be change in it's graph
+    // unless it's a weight.If backend_parameter is a weight,we should assign the value back.
+    AnfAlgo::SetOutputAddr(AnfAlgo::GetMutableOutputAddr(backend_arg, 0), 0, backend_parameter.get());
+    return;
   }
   InsertAssignToGraph(from_graph_id, backend_arg, backend_parameter);
-  // if front anf is a parameter,we can assign the value back,because backend_parameter won't be change in it's graph
-  // unless it's a weigth.If backend_parameter is a weight,we do should assign the value back
-  if (backend_arg->isa<Parameter>() && !to_graph->execution_order().empty()) {
-    InsertAssignToGraph(to_graph_id, backend_parameter, backend_arg);
-  }
   MS_LOG(INFO) << "Finish!";
 }
 

From 4cd237eee4495ad838a312164d0d4b3e43be9232 Mon Sep 17 00:00:00 2001
From: ZPaC <zhoupeichen@huawei.com>
Date: Thu, 16 Apr 2020 09:56:43 +0800
Subject: [PATCH 273/367] Add GPU NCCL ci test cases.

---
 tests/st/nccl/test_nccl_all.py               | 44 ++++++++++++++++++++
 tests/st/nccl/test_nccl_all_reduce_op.py     |  2 +-
 tests/st/nccl/test_nccl_lenet.py             |  3 +-
 tests/st/nccl/test_nccl_reduce_scatter_op.py |  2 -
 4 files changed, 47 insertions(+), 4 deletions(-)
 create mode 100644 tests/st/nccl/test_nccl_all.py

diff --git a/tests/st/nccl/test_nccl_all.py b/tests/st/nccl/test_nccl_all.py
new file mode 100644
index 0000000000..99494bb741
--- /dev/null
+++ b/tests/st/nccl/test_nccl_all.py
@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import os
+import pytest
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_lenet():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_lenet.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_all_reduce_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_all_reduce_op.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_all_gather_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_all_gather_op.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_reduce_scatter_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_reduce_scatter_op.py")
+    assert(return_code == 0)
diff --git a/tests/st/nccl/test_nccl_all_reduce_op.py b/tests/st/nccl/test_nccl_all_reduce_op.py
index 3ba8b219e4..7c2e579463 100644
--- a/tests/st/nccl/test_nccl_all_reduce_op.py
+++ b/tests/st/nccl/test_nccl_all_reduce_op.py
@@ -20,7 +20,7 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
-context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+context.set_context(mode=context.GRAPH_MODE, device_target='GPU', enable_dynamic_memory=False)
 
 init('nccl')
 rank = get_rank()
diff --git a/tests/st/nccl/test_nccl_lenet.py b/tests/st/nccl/test_nccl_lenet.py
index 5642603d42..2aebc5da50 100644
--- a/tests/st/nccl/test_nccl_lenet.py
+++ b/tests/st/nccl/test_nccl_lenet.py
@@ -27,7 +27,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 init('nccl')
 
 epoch = 2
-total = 50000
+total = 5000
 batch_size = 32
 mini_batch = total // batch_size
 
@@ -94,3 +94,4 @@ def test_lenet_nccl():
     with open("ms_loss.txt", "w") as fo2:
         fo2.write("loss:")
         fo2.write(str(losses[-5:]))
+    assert(losses[-1] < 0.01)
diff --git a/tests/st/nccl/test_nccl_reduce_scatter_op.py b/tests/st/nccl/test_nccl_reduce_scatter_op.py
index af22c7690f..32c1f31788 100644
--- a/tests/st/nccl/test_nccl_reduce_scatter_op.py
+++ b/tests/st/nccl/test_nccl_reduce_scatter_op.py
@@ -62,8 +62,6 @@ def test_ReduceScatter():
     expect1 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * size
     diff1 = output[1].asnumpy() - expect1
     error1 = np.ones(shape=expect1.shape) * 1.0e-5
-    print(expect1)
-    print(output[1])
     assert np.all(diff1 < error1)
     assert (output[1].shape() == expect1.shape)
 

From b8a91215974bd7590028c667d982751ede344604 Mon Sep 17 00:00:00 2001
From: ZPaC <zhoupeichen@huawei.com>
Date: Wed, 15 Apr 2020 11:19:19 +0800
Subject: [PATCH 274/367] Add GPU send and recv controlling kernels.

---
 mindspore/ccsrc/device/gpu/cuda_driver.cc     |  2 +-
 .../ccsrc/device/gpu/gpu_stream_assign.cc     | 24 ++++---
 .../ccsrc/device/gpu/gpu_stream_assign.h      |  4 +-
 .../kernel/gpu/control/recv_gpu_kernel.cc     | 23 +++++++
 .../kernel/gpu/control/recv_gpu_kernel.h      | 66 +++++++++++++++++++
 .../kernel/gpu/control/send_gpu_kernel.cc     | 23 +++++++
 .../kernel/gpu/control/send_gpu_kernel.h      | 66 +++++++++++++++++++
 .../ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h   |  6 ++
 8 files changed, 201 insertions(+), 13 deletions(-)
 create mode 100644 mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.h
 create mode 100644 mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.cc
 create mode 100644 mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.h

diff --git a/mindspore/ccsrc/device/gpu/cuda_driver.cc b/mindspore/ccsrc/device/gpu/cuda_driver.cc
index 3693157d2b..3b265a4d5e 100644
--- a/mindspore/ccsrc/device/gpu/cuda_driver.cc
+++ b/mindspore/ccsrc/device/gpu/cuda_driver.cc
@@ -96,7 +96,7 @@ size_t CudaDriver::free_mem_size() {
 }
 
 bool CudaDriver::CreateStream(DeviceStream *stream) {
-  auto ret = cudaStreamCreate(reinterpret_cast<CUstream_st **>(stream));
+  auto ret = cudaStreamCreateWithFlags(reinterpret_cast<CUstream_st **>(stream), cudaStreamNonBlocking);
   if (ret != cudaSuccess) {
     MS_LOG(ERROR) << "cudaStreamCreate failed, ret[" << static_cast<int>(ret) << "], " << cudaGetErrorString(ret);
     return false;
diff --git a/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
index 08a19aa469..2550b543ec 100644
--- a/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_stream_assign.cc
@@ -28,21 +28,25 @@ namespace device {
 namespace gpu {
 void AssignGpuStream(const std::shared_ptr<session::KernelGraph> &kernel_graph) {
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  std::vector<CNodePtr> allreduce_cnodes;
+  std::vector<CNodePtr> allreduce_kernels;
   auto execution_kernels = kernel_graph->execution_order();
-  for (auto kernel : execution_kernels) {
-    std::string kernel_name = AnfAlgo::GetCNodeName(kernel);
+  for (auto kernel_node : execution_kernels) {
+    std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
     if (kernel_name == kAllReduceOpName) {
-      allreduce_cnodes.emplace_back(kernel);
+      allreduce_kernels.emplace_back(kernel_node);
+    } else {
+      DeviceStream compute_stream = GPUDeviceManager::GetInstance().default_stream();
+      AnfAlgo::SetNodeAttr("stream_id", MakeValue(reinterpret_cast<uintptr_t>(compute_stream)), kernel_node);
     }
   }
-  if (allreduce_cnodes.size() > 1) {
+  if (allreduce_kernels.size() > 1) {
     DeviceStream comm_stream = nullptr;
     GPUDeviceManager::GetInstance().CreateStream(&comm_stream);
-    std::transform(allreduce_cnodes.begin(), allreduce_cnodes.end(), allreduce_cnodes.begin(), [&](CNodePtr node) {
-      AnfAlgo::SetNodeAttr("stream_id", MakeValue(reinterpret_cast<uintptr_t>(comm_stream)), node);
-      return node;
-    });
+    std::transform(
+      allreduce_kernels.begin(), allreduce_kernels.end(), allreduce_kernels.begin(), [&](CNodePtr allreduce_kernel) {
+        AnfAlgo::SetNodeAttr("stream_id", MakeValue(reinterpret_cast<uintptr_t>(comm_stream)), allreduce_kernel);
+        return allreduce_kernel;
+      });
 
     std::vector<SendRecvPair> send_recv_pairs;
     FindAllReduceStreamSwitchPos(kernel_graph, &send_recv_pairs);
@@ -137,7 +141,7 @@ void InsertStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &kernel_
   }
   // Step 3: insert stream switch CNodes into execution kernel list.
   auto execution_kernels = kernel_graph->execution_order();
-  for (auto node = ordered_stream_switch_nodes.begin(); node != ordered_stream_switch_nodes.end(); node++) {
+  for (auto node = ordered_stream_switch_nodes.rbegin(); node != ordered_stream_switch_nodes.rend(); node++) {
     execution_kernels.insert(execution_kernels.begin() + node->offset, node->cnode);
   }
   kernel_graph->set_execution_order(execution_kernels);
diff --git a/mindspore/ccsrc/device/gpu/gpu_stream_assign.h b/mindspore/ccsrc/device/gpu/gpu_stream_assign.h
index c7d2fe40e2..e3d98d68da 100644
--- a/mindspore/ccsrc/device/gpu/gpu_stream_assign.h
+++ b/mindspore/ccsrc/device/gpu/gpu_stream_assign.h
@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -70,4 +70,4 @@ CNodePtr CreateStreamSwitchNode(const std::shared_ptr<session::KernelGraph> &ker
 }  // namespace device
 }  // namespace mindspore
 
-#endif
+#endif  // MINDSPORE_CCSRC_DEVICE_GPU_GPU_STREAM_ASSIGN_H_
diff --git a/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.cc
new file mode 100644
index 0000000000..5468aa6500
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.cc
@@ -0,0 +1,23 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/control/recv_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_REGULAR(Recv, KernelAttr(), RecvGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.h
new file mode 100644
index 0000000000..206eac5bd9
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/control/recv_gpu_kernel.h
@@ -0,0 +1,66 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_RECV_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_RECV_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class RecvGpuKernel : public GpuKernel {
+ public:
+  RecvGpuKernel() {}
+  ~RecvGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
+              uintptr_t) override {
+    CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamWaitEvent(wait_stream_, wait_event_, 0), "Waiting cuda event failed.");
+    return true;
+  }
+  bool Init(const CNodePtr &kernel_node) override {
+    wait_stream_ = reinterpret_cast<cudaStream_t>(GetAttr<uintptr_t>(kernel_node, "wait_event_stream"));
+    wait_event_ = reinterpret_cast<cudaEvent_t>(GetAttr<uintptr_t>(kernel_node, "wait_event"));
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.clear();
+    output_size_list_.clear();
+    workspace_size_list_.clear();
+    return;
+  }
+
+ private:
+  cudaStream_t wait_stream_{nullptr};
+  cudaEvent_t wait_event_{nullptr};
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_RECV_GPU_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.cc
new file mode 100644
index 0000000000..c417c30bb3
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.cc
@@ -0,0 +1,23 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernel/gpu/control/send_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_REGULAR(Send, KernelAttr(), SendGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.h
new file mode 100644
index 0000000000..156ec4160d
--- /dev/null
+++ b/mindspore/ccsrc/kernel/gpu/control/send_gpu_kernel.h
@@ -0,0 +1,66 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_SEND_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_SEND_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class SendGpuKernel : public GpuKernel {
+ public:
+  SendGpuKernel() {}
+  ~SendGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &, const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
+              uintptr_t) override {
+    CHECK_CUDA_RET_WITH_EXCEPT(cudaEventRecord(record_event_, record_stream_), "Recording cuda event failed.");
+    return true;
+  }
+  bool Init(const CNodePtr &kernel_node) override {
+    record_stream_ = reinterpret_cast<cudaStream_t>(GetAttr<uintptr_t>(kernel_node, "record_event_stream"));
+    record_event_ = reinterpret_cast<cudaEvent_t>(GetAttr<uintptr_t>(kernel_node, "record_event"));
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.clear();
+    output_size_list_.clear();
+    workspace_size_list_.clear();
+    return;
+  }
+
+ private:
+  cudaStream_t record_stream_{nullptr};
+  cudaEvent_t record_event_{nullptr};
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CONTROL_SEND_GPU_KERNEL_H_
diff --git a/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
index cea56b9878..4ea332784d 100644
--- a/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/nccl/nccl_gpu_kernel.h
@@ -124,6 +124,12 @@ class NcclGpuKernel : public GpuKernel {
     InferCommType(kernel_node);
     collective_handle_ = device::gpu::CollectiveInitializer::instance().collective_handle();
     MS_EXCEPTION_IF_NULL(collective_handle_);
+
+    auto comm_stream_attr = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stream_id");
+    if (comm_stream_attr) {
+      comm_stream_ = reinterpret_cast<cudaStream_t>(GetValue<uintptr_t>(comm_stream_attr));
+      MS_EXCEPTION_IF_NULL(comm_stream_);
+    }
     return true;
   }
 

From ebe6efff719bec947f2f73d42cdd542ff67f932e Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 01:42:32 -0400
Subject: [PATCH 275/367] Add Group Normalization

---
 mindspore/nn/layer/__init__.py       |  4 +-
 mindspore/nn/layer/normalization.py  | 73 +++++++++++++++++++++++++++-
 tests/ut/python/nn/test_batchnorm.py | 12 +++++
 3 files changed, 85 insertions(+), 4 deletions(-)

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index 3d729edcd3..cf601f03ff 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -18,7 +18,7 @@ Layer.
 The high-level components(Cells) used to construct the neural network.
 """
 from .activation import Softmax, LogSoftmax, ReLU, ReLU6, Tanh, GELU, ELU, Sigmoid, PReLU, get_activation, LeakyReLU, HSigmoid, HSwish
-from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
+from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm, GroupNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
@@ -29,7 +29,7 @@ from .image import ImageGradients, SSIM
 
 __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'PReLU', 'get_activation', 'LeakyReLU', 'HSigmoid', 'HSwish', 'ELU',
-           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm',
+           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'GroupNorm',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 2df064353f..cac73d239e 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -18,8 +18,9 @@ from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
-import mindspore.common.dtype as DT
+import mindspore.common.dtype as mstype
 import mindspore.context as context
+from mindspore._checkparam import  check_int_positive, check_bool,check_typename
 from mindspore._extends import cell_attr_register
 from ..cell import Cell
 
@@ -58,7 +59,7 @@ class _BatchNorm(Cell):
 
         if context.get_context("enable_ge"):
             self.is_ge_backend = True
-            self.momentum = Tensor(1.0 - momentum, DT.float32)
+            self.momentum = Tensor(1.0 - momentum, mstype.float32)
             self.bn_train = P.BatchNorm(is_training=True,
                                         epsilon=self.eps)
         else:
@@ -289,3 +290,71 @@ class LayerNorm(Cell):
         s = 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format(
             self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
         return s
+
+class GroupNorm(Cell):
+    r"""
+    Group  Normalization over a mini-batch of inputs.
+
+    Group normalization is widely used in recurrent neural networks. It applies
+    normalization over a mini-batch of inputs for each single training case as described
+    in the paper `Group Normalization <https://arxiv.org/pdf/1803.08494.pdf>`_. Group normalization
+    divides the channels into groups and computes within each group the mean and variance for normalization,
+    and it performs very stable over a wide range of batch size. It can be described using the following formula.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    Args:
+        num_groups (int): The number of groups to be divided along the channel dimension.
+        num_channels (int): The number of channels per group.
+        eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
+        affine (bool): A bool value, this layer will has learnable affine parameters when set to true. Default: True.
+
+    Inputs:
+        - **input_x** (Tensor) - The input feature with shape [N, C, H, W].
+
+    Outputs:
+        Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
+
+    Examples:
+        >>> goup_norm_op = nn.GroupNorm(16, 64)
+        >>> x = Tensor(np.ones([1, 64, 256, 256], np.float32))
+        >>> goup_norm_op(x)
+    """
+    def __init__(self, num_groups, num_channels, eps=1e-05, affine=True):
+        super(GroupNorm, self).__init__()
+        self.num_groups = check_int_positive(num_groups)
+        self.num_channels = check_int_positive(num_channels)
+        if num_channels % num_groups != 0:
+            raise ValueError("num_channels should be divided by num_groups")
+        self.eps = Tensor(check_typename('eps', eps, (float,)),mstype.float32)
+        self.affine = check_bool(affine)
+
+        gamma = initializer('ones', [num_channels, 1, 1], mstype.float32)
+        beta = initializer('zeros', [num_channels, 1, 1], mstype.float32)
+        if self.affine:
+            self.gamma = Parameter(gamma, name='gamma')
+            self.beta = Parameter(beta, name='beta')
+        else:
+            self.gamma = gamma
+            self.beta = beta
+        self.shape = F.shape
+        self.reshape = F.reshape
+        self.reduce_mean = P.ReduceMean(keep_dims=True)
+        self.square = F.square
+        self.reduce_sum = P.ReduceSum(keep_dims=True)
+        self.sqrt = P.Sqrt()
+
+    def construct(self, x):
+        batch,channel,height,width = self.shape(x)
+        x = self.reshape(x,(batch, self.num_groups,channel*height*width/self.num_groups))
+        mean = self.reduce_mean(x, 2)
+        var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups - 1)
+        std = self.sqrt(var + self.eps)
+        x = (x - mean) / std
+        x = self.reshape(x, (batch, channel, height, width))
+        output = x * self.gamma + self.beta
+        return output
+
+    def extend_repr(self):
+        return 'num_groups={}, num_channels={}'.format(self.num_groups,self.num_channels)
\ No newline at end of file
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index eaafdd81b4..efccfa4b33 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -56,3 +56,15 @@ def test_compile():
     net = Net()
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
     _executor.compile(net, input_data)
+
+class GroupNet(nn.Cell):
+    def __init__(self):
+        super(GroupNet, self).__init__()
+        self.group_bn = nn.GroupNorm()
+    def construct(self, x):
+        return self.group_bn(x)
+
+def test_compile_groupnorm():
+    net = nn.GroupNorm(16, 64)
+    input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))
+    _executor.compile(net, input_data)
\ No newline at end of file

From 898acc3201f65b3943afc6b7a187857ad4471a06 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 01:51:36 -0400
Subject: [PATCH 276/367] Add Group Normalization

---
 mindspore/nn/layer/normalization.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index cac73d239e..58f926cdcf 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -20,7 +20,7 @@ from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
 import mindspore.common.dtype as mstype
 import mindspore.context as context
-from mindspore._checkparam import  check_int_positive, check_bool,check_typename
+from mindspore._checkparam import  check_int_positive, check_bool, check_typename
 from mindspore._extends import cell_attr_register
 from ..cell import Cell
 
@@ -327,7 +327,7 @@ class GroupNorm(Cell):
         self.num_channels = check_int_positive(num_channels)
         if num_channels % num_groups != 0:
             raise ValueError("num_channels should be divided by num_groups")
-        self.eps = Tensor(check_typename('eps', eps, (float,)),mstype.float32)
+        self.eps = Tensor(check_typename('eps', eps, (float,)), mstype.float32)
         self.affine = check_bool(affine)
 
         gamma = initializer('ones', [num_channels, 1, 1], mstype.float32)
@@ -346,8 +346,8 @@ class GroupNorm(Cell):
         self.sqrt = P.Sqrt()
 
     def construct(self, x):
-        batch,channel,height,width = self.shape(x)
-        x = self.reshape(x,(batch, self.num_groups,channel*height*width/self.num_groups))
+        batch, channel, height,width = self.shape(x)
+        x = self.reshape(x, (batch, self.num_groups, channel*height*width/self.num_groups))
         mean = self.reduce_mean(x, 2)
         var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups - 1)
         std = self.sqrt(var + self.eps)
@@ -357,4 +357,6 @@ class GroupNorm(Cell):
         return output
 
     def extend_repr(self):
-        return 'num_groups={}, num_channels={}'.format(self.num_groups,self.num_channels)
\ No newline at end of file
+        """Display instance object as string."""
+        s = 'num_groups={}, num_channels={}'.format(self.num_groups, self.num_channels)
+        return s
\ No newline at end of file

From 0b7de6968fe1dcbc9c78f292bdc2b673502b5f2c Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 01:56:12 -0400
Subject: [PATCH 277/367] Add Group Normalization

---
 mindspore/nn/layer/normalization.py  | 4 ++--
 tests/ut/python/nn/test_batchnorm.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 58f926cdcf..b286eaae1b 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -346,7 +346,7 @@ class GroupNorm(Cell):
         self.sqrt = P.Sqrt()
 
     def construct(self, x):
-        batch, channel, height,width = self.shape(x)
+        batch, channel, height, width = self.shape(x)
         x = self.reshape(x, (batch, self.num_groups, channel*height*width/self.num_groups))
         mean = self.reduce_mean(x, 2)
         var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups - 1)
@@ -359,4 +359,4 @@ class GroupNorm(Cell):
     def extend_repr(self):
         """Display instance object as string."""
         s = 'num_groups={}, num_channels={}'.format(self.num_groups, self.num_channels)
-        return s
\ No newline at end of file
+        return s
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index efccfa4b33..4bd8c996d6 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -67,4 +67,4 @@ class GroupNet(nn.Cell):
 def test_compile_groupnorm():
     net = nn.GroupNorm(16, 64)
     input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))
-    _executor.compile(net, input_data)
\ No newline at end of file
+    _executor.compile(net, input_data)

From 04c522d0c6edfe4074c9bdecf16146fbe617644d Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 01:59:32 -0400
Subject: [PATCH 278/367] Add Group Normalization

---
 tests/ut/python/nn/test_batchnorm.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index 4bd8c996d6..e73b7ebbf0 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -57,6 +57,7 @@ def test_compile():
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
     _executor.compile(net, input_data)
 
+
 class GroupNet(nn.Cell):
     def __init__(self):
         super(GroupNet, self).__init__()
@@ -64,6 +65,7 @@ class GroupNet(nn.Cell):
     def construct(self, x):
         return self.group_bn(x)
 
+
 def test_compile_groupnorm():
     net = nn.GroupNorm(16, 64)
     input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))

From a9426cbd89e2148a813f7bda1a36ae9b3194bb98 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Thu, 16 Apr 2020 06:18:25 +0000
Subject: [PATCH 279/367] build.bat support input -j value

---
 build.bat | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/build.bat b/build.bat
index b4f3420e14..6ada61cab2 100644
--- a/build.bat
+++ b/build.bat
@@ -20,7 +20,11 @@ IF NOT %errorlevel% == 0 (
     goto run_fail
     )
  
-cmake --build . --target all -- -j6
+IF "%1%" == "" (
+    cmake --build . --target all -- -j6
+    ) ELSE (
+        cmake --build . --target all -- -j%1%
+    )
 IF NOT %errorlevel% == 0 (
     goto run_fail
     )

From ee5f3fa2406104cec0c90afc72d4fa7b2f923947 Mon Sep 17 00:00:00 2001
From: wenchunjiang <wenchunjiang@huawei.com>
Date: Thu, 16 Apr 2020 11:23:10 +0800
Subject: [PATCH 280/367] add pass to insert memcpy_async for get_next outputs

---
 .../pre_activate/ascend/ascend_helper.cc      | 12 +++
 .../ccsrc/pre_activate/ascend/ascend_helper.h |  2 +
 .../insert_memcpy_async_for_getnext.cc        | 74 +++++++++++++++++++
 .../insert_memcpy_async_for_getnext.h         | 35 +++++++++
 .../ascend/ir_fission/add_memcpy_async.cc     | 13 +---
 .../insert_memcpy_async_for_getnext.cc        | 67 +++++++++++++++++
 .../insert_memcpy_async_for_getnext.py        | 55 ++++++++++++++
 7 files changed, 246 insertions(+), 12 deletions(-)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h
 create mode 100644 tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
index 490a905a45..745ed4460f 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
@@ -360,5 +360,17 @@ AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePt
   AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get());
   return tuple_getitem;
 }
+
+AnfNodePtr CreateMemcpyAsyncOp(const FuncGraphPtr &graph, const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(node);
+  auto prim = std::make_shared<Primitive>(kMemCpyAsyncOpName);
+  std::vector<AnfNodePtr> new_node_inputs = {NewValueNode(prim), node};
+  auto new_node = graph->NewCNode(new_node_inputs);
+  MS_EXCEPTION_IF_NULL(new_node);
+  new_node->set_abstract(node->abstract());
+  new_node->set_scope(node->scope());
+  return new_node;
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
index 8925a52a7d..7f5e86d726 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
@@ -65,6 +65,8 @@ AnfNodePtr InsertTransOpForOutput(const FuncGraphPtr &func_graph, const AnfNodeP
 CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnode);
 
 AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node, size_t output_idx);
+
+AnfNodePtr CreateMemcpyAsyncOp(const FuncGraphPtr &graph, const AnfNodePtr &node);
 }  // namespace opt
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ASCEND_HELPER_H_
diff --git a/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
new file mode 100644
index 0000000000..5065bab0f2
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
@@ -0,0 +1,74 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h"
+#include <vector>
+#include <memory>
+#include "pre_activate/ascend/ascend_helper.h"
+#include "session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace opt {
+AnfNodePtr InsertMemcpyAsyncForGetNextOutputs(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
+  if (func_graph == nullptr || node == nullptr) {
+    return nullptr;
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(node);
+  if (output_num == 0) {
+    MS_LOG(DEBUG) << "Output number is zero, no need to insert memcpy_async!";
+    return node;
+  }
+
+  // getnext output is tuple and dynamic
+  std::vector<AnfNodePtr> make_tuple_inputs;
+  make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
+
+  for (size_t output_index = 0; output_index < output_num; ++output_index) {
+    auto tuple_get_item = CreatTupleGetItemNode(func_graph, node, output_index);
+    auto new_node = CreateMemcpyAsyncOp(func_graph, tuple_get_item);
+    if (new_node == nullptr) {
+      MS_LOG(EXCEPTION) << "Create memcpy_async op failed!";
+    }
+    AnfAlgo::SetNodeAttr(kAttrLabelForInsertStreamActive, MakeValue(true), new_node);
+    make_tuple_inputs.push_back(new_node);
+  }
+  AnfNodePtr make_tuple = func_graph->NewCNode(make_tuple_inputs);
+  return make_tuple;
+}
+
+const BaseRef InsertMemcpyAsyncForGetNext::DefinePattern() const {
+  std::shared_ptr<Var> Xs = std::make_shared<SeqVar>();
+  auto prim = std::make_shared<Primitive>(kGetNextOpName);
+
+  return VectorRef({prim, Xs});
+}
+
+const AnfNodePtr InsertMemcpyAsyncForGetNext::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
+                                                      const EquivPtr &) const {
+  if (func_graph == nullptr || node == nullptr || !AnfAlgo::IsRealKernel(node)) {
+    return nullptr;
+  }
+
+  if (AnfAlgo::HasNodeAttr(kAttrVisited, node)) {
+    MS_LOG(DEBUG) << "Node op_name[" << kGetNextOpName << "] has visited.";
+    return nullptr;
+  }
+  AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node);
+
+  return InsertMemcpyAsyncForGetNextOutputs(func_graph, node);
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h
new file mode 100644
index 0000000000..eb3b78d33f
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h
@@ -0,0 +1,35 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_INSERT_MEMCPY_ASYNC_FOR_GETNEXT_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_INSERT_MEMCPY_ASYNC_FOR_GETNEXT_H_
+
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class InsertMemcpyAsyncForGetNext : public PatternProcessPass {
+ public:
+  explicit InsertMemcpyAsyncForGetNext(bool multigraph = true)
+      : PatternProcessPass("insert_memcpy_async_for_getnext", multigraph) {}
+  ~InsertMemcpyAsyncForGetNext() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_ENHANCER_INSERT_MEMCPY_ASYNC_FOR_GETNEXT_H_
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fission/add_memcpy_async.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fission/add_memcpy_async.cc
index 2ab11b6032..bbea944750 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fission/add_memcpy_async.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fission/add_memcpy_async.cc
@@ -18,22 +18,11 @@
 #include "utils/utils.h"
 #include "session/anf_runtime_algorithm.h"
 #include "optimizer/opt.h"
+#include "pre_activate/ascend/ascend_helper.h"
 
 namespace mindspore {
 namespace opt {
 namespace {
-AnfNodePtr CreateMemcpyAsyncOp(const FuncGraphPtr &graph, const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(graph);
-  MS_EXCEPTION_IF_NULL(node);
-  auto prim = std::make_shared<Primitive>(kMemCpyAsyncOpName);
-  std::vector<AnfNodePtr> new_node_inputs = {NewValueNode(prim), node};
-  auto new_node = graph->NewCNode(new_node_inputs);
-  MS_EXCEPTION_IF_NULL(new_node);
-  new_node->set_abstract(node->abstract());
-  new_node->set_scope(node->scope());
-  return new_node;
-}
-
 const AnfNodePtr AddMemcpyAsyncIfInputIsUsedByOthers(const FuncGraphPtr &graph, const CNodePtr &node) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(node);
diff --git a/tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
new file mode 100644
index 0000000000..2616354e4c
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
@@ -0,0 +1,67 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#include "session/ascend_session.h"
+#include "pipeline/resource.h"
+#include "operator/ops.h"
+#include "ir/manager.h"
+#include "debug/anf_ir_dump.h"
+#include "utils/utils.h"
+#include "kernel/kernel_build_info.h"
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.h"
+
+namespace mindspore {
+namespace opt {
+using KernelBuildInfoBuilder = kernel::KernelBuildInfo::KernelBuildInfoBuilder;
+
+class TestHWInsertMemcpyAsyncForGetNext : public BackendCommon {
+ public:
+  TestHWInsertMemcpyAsyncForGetNext() : get_py_fun_("gtest_input.pre_activate.insert_memcpy_async_for_getnext", true) {}
+  ~TestHWInsertMemcpyAsyncForGetNext() override = default;
+
+ public:
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWInsertMemcpyAsyncForGetNext, test_insert_memcpy_async_for_getnext_multi_output) {
+  FuncGraphPtr g_before = get_py_fun_.CallAndParseRet("test_insert_memcpy_async_for_getnext", "getnext_multi_output_before");
+
+  AbstractBasePtrList args_spec_list{};
+  auto kernel_graph = GetKernelGraph(g_before, args_spec_list);
+
+  KernelBuildInfoBuilder builder;
+  builder.SetOutputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT});
+  builder.SetOutputsDeviceType({kFloat32->type_id(), kInt32->type_id()});
+  auto ret = kernel_graph->get_return();
+  EXPECT_NE(ret->input(1), nullptr);
+  EXPECT_NE(ret->input(1)->cast<CNodePtr>()->input(1), nullptr);
+  auto get_next = ret->input(1)->cast<CNodePtr>()->input(1);
+  get_next->set_kernel_info(std::make_shared<device::KernelInfo>());
+  AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), get_next.get());
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::InsertMemcpyAsyncForGetNext>());
+  optimizer->AddPassManager(pm);
+  auto new_graph = optimizer->Optimize(kernel_graph);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_insert_memcpy_async_for_getnext", "getnext_multi_output_after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+}  // namespace opt
+}  // namespace mindspore
\ No newline at end of file
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py
new file mode 100644
index 0000000000..902fd636de
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py
@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+import mindspore as ms
+
+get_next = P.GetNext([ms.float32, ms.int32], [[32, 64], [32]], 2, "")
+memcpy_async = Primitive('memcpy_async')
+make_tuple = Primitive('make_tuple')
+tuple_getitem = Primitive('tuple_getitem')
+
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_insert_memcpy_async_for_getnext(tag):
+    fns = FnDict()
+
+    @fns
+    def getnext_multi_output_before():
+        res = get_next()
+        return res
+
+    @fns
+    def getnext_multi_output_after():
+        res = get_next()
+        data = tuple_getitem(res, 0)
+        label = tuple_getitem(res, 1)
+        memcpy_async_data = memcpy_async(data)
+        memcpy_async_label = memcpy_async(label)
+        tuple = make_tuple(make_tuple(memcpy_async_data, memcpy_async_label))
+        return tuple
+
+    return fns[tag]

From 57cd9f81886d59e2ee4c4c650ffcd11c792db53c Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Thu, 16 Apr 2020 14:32:43 +0800
Subject: [PATCH 281/367] add parallel op for sigmoidloss

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |  1 +
 .../ccsrc/parallel/ops_info/arithmetic_info.h |  9 ++
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |  1 +
 .../ccsrc/parallel/step_auto_parallel.cc      |  1 +
 .../test_sigmoid_cross_entropy_with_logits.py | 83 +++++++++++++++++++
 5 files changed, 95 insertions(+)
 create mode 100644 tests/ut/python/parallel/test_sigmoid_cross_entropy_with_logits.py

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 953380fb32..723c018d7f 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -127,6 +127,7 @@ REGISTER(NegInfo);
 REGISTER(BatchMatMulInfo);
 REGISTER(ExpandDimsInfo);
 REGISTER(SqueezeInfo);
+REGISTER(SigmoidCrossEntropyWithLogitsInfo);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
index 376a1fb4cf..78dfc23803 100644
--- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h
@@ -120,6 +120,15 @@ class AssignSubInfo : public ArithmeticBase {
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~AssignSubInfo() override = default;
 };
+
+// All dimensions can be split arbitrarily, but the split method of Logits should be the same as that of label.
+class SigmoidCrossEntropyWithLogitsInfo : public ArithmeticBase {
+ public:
+  SigmoidCrossEntropyWithLogitsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                                    const PrimitiveAttrs& attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~SigmoidCrossEntropyWithLogitsInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 50920e5954..0b52d8e83c 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -138,6 +138,7 @@ constexpr char ALL_GATHER[] = "AllGather";
 constexpr char REDUCE_SCATTER[] = "ReduceScatter";
 constexpr char CONCAT[] = "Concat";
 constexpr char SOFTMAX_CROSS_ENTROPY_WITH_LOGITS[] = "SoftmaxCrossEntropyWithLogits";
+constexpr char SIGMOID_CROSS_ENTROPY_WITH_LOGITS[] = "SigmoidCrossEntropyWithLogits";
 constexpr char MATMUL[] = "MatMul";
 constexpr char GELU[] = "Gelu";
 constexpr char TANH[] = "Tanh";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index 5caf6573f2..1d52eac82d 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -78,6 +78,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            FUSE_BATCH_NORM,
                                            POOLING,
                                            SOFTMAX_CROSS_ENTROPY_WITH_LOGITS,
+                                           SIGMOID_CROSS_ENTROPY_WITH_LOGITS,
                                            MAX_POOL_WITH_ARGMAX,
                                            SIMPLE_MEAN,
                                            FLATTEN,
diff --git a/tests/ut/python/parallel/test_sigmoid_cross_entropy_with_logits.py b/tests/ut/python/parallel/test_sigmoid_cross_entropy_with_logits.py
new file mode 100644
index 0000000000..d59d053b07
--- /dev/null
+++ b/tests/ut/python/parallel/test_sigmoid_cross_entropy_with_logits.py
@@ -0,0 +1,83 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.loss = P.SigmoidCrossEntropyWithLogits().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.loss(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_sigmoid_cross_entropy_with_logits_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1), (16, 1))
+    strategy2 = ((16, 1), (16, 1))
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_sigmoid_cross_entropy_with_logits_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 16), (1, 16))
+    strategy2 = ((1, 16), (1, 16))
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_sigmoid_cross_entropy_with_logits_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 8), (2, 8))
+    strategy2 = ((2, 8), (2, 8))
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_sigmoid_cross_entropy_with_logits_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_sigmoid_cross_entropy_with_logits_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 8), (2, 8))
+    strategy2 = ((2, 2), (2, 2))
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)

From 77fd2e841eab957a08b766a210e2db45942263a4 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 03:54:32 -0400
Subject: [PATCH 282/367] Add Group Normalization

---
 mindspore/nn/layer/normalization.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index b286eaae1b..4aafaf031e 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -20,7 +20,7 @@ from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
 import mindspore.common.dtype as mstype
 import mindspore.context as context
-from mindspore._checkparam import  check_int_positive, check_bool, check_typename
+from mindspore._checkparam import check_int_positive, check_bool, check_typename
 from mindspore._extends import cell_attr_register
 from ..cell import Cell
 
@@ -293,7 +293,7 @@ class LayerNorm(Cell):
 
 class GroupNorm(Cell):
     r"""
-    Group  Normalization over a mini-batch of inputs.
+    Group Normalization over a mini-batch of inputs.
 
     Group normalization is widely used in recurrent neural networks. It applies
     normalization over a mini-batch of inputs for each single training case as described

From 257464ef7b3ac24ed2fb1d0760fd93b44c8bfd17 Mon Sep 17 00:00:00 2001
From: maoweiyong <maoweiyong@huawei.com>
Date: Thu, 16 Apr 2020 16:16:17 +0800
Subject: [PATCH 283/367] fix gpu op register

---
 mindspore/ops/_op_impl/akg/gpu/squeeze.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ops/_op_impl/akg/gpu/squeeze.py b/mindspore/ops/_op_impl/akg/gpu/squeeze.py
index 378d096d5c..cebf6ff1f3 100644
--- a/mindspore/ops/_op_impl/akg/gpu/squeeze.py
+++ b/mindspore/ops/_op_impl/akg/gpu/squeeze.py
@@ -15,7 +15,7 @@
 """Squeeze op"""
 from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType
 
-squeeze_op_info = AkgRegOp("SqueezeGrad") \
+squeeze_op_info = AkgRegOp("Squeeze") \
     .fusion_type("OPAQUE") \
     .input(0, "x") \
     .output(0, "output") \

From 8bdcd8c267bbff59c0c97198067263b202395084 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Thu, 16 Apr 2020 16:18:47 +0800
Subject: [PATCH 284/367] modify test_cell_bprop.py ut

---
 tests/ut/python/pynative_mode/test_cell_bprop.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/ut/python/pynative_mode/test_cell_bprop.py b/tests/ut/python/pynative_mode/test_cell_bprop.py
index 054afe36c9..da1e14974f 100644
--- a/tests/ut/python/pynative_mode/test_cell_bprop.py
+++ b/tests/ut/python/pynative_mode/test_cell_bprop.py
@@ -64,14 +64,15 @@ def test_grad_inline_mul_add():
 class WithParameter(nn.Cell):
     def __init__(self):
         super(WithParameter, self).__init__()
-        self.param = Parameter(2, 'param')
+        self.param1 = Parameter(1, 'param1')
+        self.param2 = Parameter(2, 'param2')
 
     def construct(self, x, y):
-        return self.param * x + y
+        return self.param1 * self.param2 * x + y
 
     def bprop(self, x, y, out, dout):
         # In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
-        return self.param * dout, 2 * y
+        return self.param1 * self.param2 * dout, 2 * y
 
 def test_with_param():
     with_param = WithParameter()

From c95a04f215d5316a1be1e716c52ad0de23a6bd27 Mon Sep 17 00:00:00 2001
From: leonwanghui <wanghui71leon@gmail.com>
Date: Thu, 16 Apr 2020 16:10:46 +0800
Subject: [PATCH 285/367] Refactor docker image framework

Signed-off-by: leonwanghui <wanghui71leon@gmail.com>
---
 README.md                                   | 10 +--
 docker/mindspore-cpu/0.1.0-alpha/Dockerfile | 18 +----
 docker/mindspore-cpu/devel/Dockerfile       | 11 +--
 docker/mindspore-cpu/runtime/Dockerfile     | 64 +++++++++++++++++
 docker/mindspore-gpu/0.1.0-alpha/Dockerfile | 19 +----
 docker/mindspore-gpu/devel/Dockerfile       | 31 ++------
 docker/mindspore-gpu/runtime/Dockerfile     | 80 +++++++++++++++++++++
 7 files changed, 160 insertions(+), 73 deletions(-)
 create mode 100644 docker/mindspore-cpu/runtime/Dockerfile
 create mode 100644 docker/mindspore-gpu/runtime/Dockerfile

diff --git a/README.md b/README.md
index 169f1985e5..c9abd82da0 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ MindSpore offers build options across multiple backends:
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
 
-For installation using `pip`, take `Ubuntu-x86` and `CPU` build version as an example:
+For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
 
 1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
 
@@ -97,9 +97,11 @@ currently the containerized build options are supported as follows:
 | :---------------- | :---------------------- | :-- | :---------- |
 | CPU | `mindspore/mindspore-cpu` | `0.1.0-alpha` | Production environment with pre-installed MindSpore `0.1.0-alpha` CPU release. |
 |  |  | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to https://www.mindspore.cn/install/en for installation details. |
+|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `CPU` backend. |
 | GPU | `mindspore/mindspore-gpu` | `0.1.0-alpha` | Production environment with pre-installed MindSpore `0.1.0-alpha` GPU release. |
 |  |  | `devel` | Development environment provided to build MindSpore (with `GPU CUDA10.1` backend) from the source, refer to https://www.mindspore.cn/install/en for installation details. |
-| Ascend | <center>—</center> | <center>—</center> | Coming soon. |
+|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `GPU` backend. |
+| Ascend | <center>&mdash;</center> | <center>&mdash;</center> | Coming soon. |
 
 * CPU
 
@@ -111,7 +113,7 @@ currently the containerized build options are supported as follows:
 
 * GPU
 
-    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for Ubuntu users:
+    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for `Ubuntu` users:
     ```
     DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
     curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
@@ -153,7 +155,7 @@ currently the containerized build options are supported as follows:
     [ 2.  2.  2.  2.]]]
     ```
 
-If anyone wants to learn more about the build process of MindSpore docker images,
+If you want to learn more about the building process of MindSpore docker images,
 please check out `docker` folder for the details.
 
 ## Quickstart
diff --git a/docker/mindspore-cpu/0.1.0-alpha/Dockerfile b/docker/mindspore-cpu/0.1.0-alpha/Dockerfile
index d24d23cf6b..c9fb7c2b88 100644
--- a/docker/mindspore-cpu/0.1.0-alpha/Dockerfile
+++ b/docker/mindspore-cpu/0.1.0-alpha/Dockerfile
@@ -4,8 +4,7 @@ MAINTAINER leonwanghui <leon.wanghui@huawei.com>
 
 # Set env
 ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
-ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
-ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV PATH /usr/local/bin:$PATH
 
 # Install base tools
 RUN apt update \
@@ -64,20 +63,5 @@ RUN mkdir -pv /root/.pip \
     && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
     && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
 
-# Install pip package
-RUN pip install --no-cache-dir \
-    numpy \
-    wheel \
-    nose \
-    pytest \
-    pytest-xdist
-
-# Install cmake (v3.14.1)
-RUN cd /tmp \
-    && wget https://github.com/Kitware/CMake/releases/download/v3.14.1/cmake-3.14.1-Linux-x86_64.sh \
-    && mkdir -p ${CMAKE_ROOT_PATH} \
-    && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
-    && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
-
 # Install MindSpore cpu whl package
 RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
diff --git a/docker/mindspore-cpu/devel/Dockerfile b/docker/mindspore-cpu/devel/Dockerfile
index a48326af9b..77c422ef90 100644
--- a/docker/mindspore-cpu/devel/Dockerfile
+++ b/docker/mindspore-cpu/devel/Dockerfile
@@ -62,15 +62,8 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
 RUN mkdir -pv /root/.pip \
     && echo "[global]" > /root/.pip/pip.conf \
     && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
-    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
-
-# Install pip package
-RUN pip install --no-cache-dir \
-    numpy \
-    wheel \
-    nose \
-    pytest \
-    pytest-xdist
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf \
+    && pip install --no-cache-dir wheel
 
 # Install cmake (v3.14.1)
 RUN cd /tmp \
diff --git a/docker/mindspore-cpu/runtime/Dockerfile b/docker/mindspore-cpu/runtime/Dockerfile
new file mode 100644
index 0000000000..ccf5f297a0
--- /dev/null
+++ b/docker/mindspore-cpu/runtime/Dockerfile
@@ -0,0 +1,64 @@
+FROM ubuntu:18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV PATH /usr/local/bin:$PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
diff --git a/docker/mindspore-gpu/0.1.0-alpha/Dockerfile b/docker/mindspore-gpu/0.1.0-alpha/Dockerfile
index 7b82b21a1e..50ca2b9f08 100644
--- a/docker/mindspore-gpu/0.1.0-alpha/Dockerfile
+++ b/docker/mindspore-gpu/0.1.0-alpha/Dockerfile
@@ -4,9 +4,9 @@ MAINTAINER leonwanghui <leon.wanghui@huawei.com>
 
 # Set env
 ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
-ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
 ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
-ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV PATH ${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV LD_LIBRARY_PATH ${OMPI_ROOT_PATH}/lib:$LD_LIBRARY_PATH
 
 # Install base tools
 RUN apt update \
@@ -67,21 +67,6 @@ RUN mkdir -pv /root/.pip \
     && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
     && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
 
-# Install pip package
-RUN pip install --no-cache-dir \
-    numpy \
-    wheel \
-    nose \
-    pytest \
-    pytest-xdist
-
-# Install cmake (v3.14.1)
-RUN cd /tmp \
-    && wget https://github.com/Kitware/CMake/releases/download/v3.14.1/cmake-3.14.1-Linux-x86_64.sh \
-    && mkdir -p ${CMAKE_ROOT_PATH} \
-    && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
-    && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
-
 # Install openmpi (v3.1.5)
 RUN cd /tmp \
     && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
diff --git a/docker/mindspore-gpu/devel/Dockerfile b/docker/mindspore-gpu/devel/Dockerfile
index 0b07605d9f..113bed5b06 100644
--- a/docker/mindspore-gpu/devel/Dockerfile
+++ b/docker/mindspore-gpu/devel/Dockerfile
@@ -5,8 +5,8 @@ MAINTAINER leonwanghui <leon.wanghui@huawei.com>
 # Set env
 ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
 ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
-ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
-ENV PATH ${PYTHON_ROOT_PATH}/bin:${CMAKE_ROOT_PATH}/bin:${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV PATH ${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
 
 # Install base tools
 RUN apt update \
@@ -35,9 +35,7 @@ RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     autoconf \
     libtool \
     automake \
-    flex \
-    libnccl2=2.4.8-1+cuda10.1 \
-    libnccl-dev=2.4.8-1+cuda10.1
+    flex
 
 # Set bash
 RUN echo "dash dash/sh boolean false" | debconf-set-selections
@@ -65,15 +63,8 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
 RUN mkdir -pv /root/.pip \
     && echo "[global]" > /root/.pip/pip.conf \
     && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
-    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
-
-# Install pip package
-RUN pip install --no-cache-dir \
-    numpy \
-    wheel \
-    nose \
-    pytest \
-    pytest-xdist
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf \
+    && pip install --no-cache-dir wheel
 
 # Install cmake (v3.14.1)
 RUN cd /tmp \
@@ -81,15 +72,3 @@ RUN cd /tmp \
     && mkdir -p ${CMAKE_ROOT_PATH} \
     && bash ./cmake-3.14.1-Linux-x86_64.sh --prefix=${CMAKE_ROOT_PATH} --exclude-subdir --skip-license \
     && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh
-
-# Install openmpi (v3.1.5)
-RUN cd /tmp \
-    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
-    && tar -xvf openmpi-3.1.5.tar.gz \
-    && cd /tmp/openmpi-3.1.5 \
-    && mkdir -p ${OMPI_ROOT_PATH} \
-    && ./configure --prefix=${OMPI_ROOT_PATH} \
-    && make -j4 \
-    && make install -j4 \
-    && rm -rf /tmp/openmpi-3.1.5 \
-    && rm -f /tmp/openmpi-3.1.5.tar.gz
diff --git a/docker/mindspore-gpu/runtime/Dockerfile b/docker/mindspore-gpu/runtime/Dockerfile
new file mode 100644
index 0000000000..9e8dabe594
--- /dev/null
+++ b/docker/mindspore-gpu/runtime/Dockerfile
@@ -0,0 +1,80 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
+ENV PATH ${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV LD_LIBRARY_PATH ${OMPI_ROOT_PATH}/lib:$LD_LIBRARY_PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex \
+    libnccl2=2.4.8-1+cuda10.1 \
+    libnccl-dev=2.4.8-1+cuda10.1
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install openmpi (v3.1.5)
+RUN cd /tmp \
+    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
+    && tar -xvf openmpi-3.1.5.tar.gz \
+    && cd /tmp/openmpi-3.1.5 \
+    && mkdir -p ${OMPI_ROOT_PATH} \
+    && ./configure --prefix=${OMPI_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -rf /tmp/openmpi-3.1.5 \
+    && rm -f /tmp/openmpi-3.1.5.tar.gz

From efedcfb5303076de2a1754d17a4e095055bc941d Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Tue, 14 Apr 2020 21:28:42 +0800
Subject: [PATCH 286/367] add test case of Bprop and tester

add test case for grad concat

fix usage in test framework

fix testcase

format code
---
 .../utils/block_util.py                       |  23 +--
 .../python/parameter_feature/test_var_grad.py | 143 +++++++++++++++++-
 2 files changed, 136 insertions(+), 30 deletions(-)

diff --git a/tests/mindspore_test_framework/utils/block_util.py b/tests/mindspore_test_framework/utils/block_util.py
index b4a926c15d..75946c3559 100644
--- a/tests/mindspore_test_framework/utils/block_util.py
+++ b/tests/mindspore_test_framework/utils/block_util.py
@@ -65,32 +65,11 @@ class IthOutputCell(nn.Cell):
         self.output_index = output_index
 
     def construct(self, *inputs):
-        raise NotImplementedError
-
-    def construct1(self, x1):
-        predict = self.network(x1)[self.output_index]
-        return predict
-
-    def construct2(self, x1, x2):
-        predict = self.network(x1, x2)[self.output_index]
-        return predict
-
-    def construct3(self, x1, x2, x3):
-        predict = self.network(x1, x2, x3)[self.output_index]
-        return predict
-
-    def construct4(self, x1, x2, x3, x4):
-        predict = self.network(x1, x2, x3, x4)[self.output_index]
-        return predict
-
-    def construct5(self, x1, x2, x3, x4, x5):
-        predict = self.network(x1, x2, x3, x4, x5)[self.output_index]
+        predict = self.network(*inputs)[self.output_index]
         return predict
 
 def get_output_cell(network, num_input, output_index, training=True):
     net = IthOutputCell(network, output_index)
-    f = getattr(net, 'construct%d' % num_input)
-    setattr(net, "construct", f)
     set_block_training(net, training)
     return net
 
diff --git a/tests/ut/python/parameter_feature/test_var_grad.py b/tests/ut/python/parameter_feature/test_var_grad.py
index 12c05d0594..528456d02e 100644
--- a/tests/ut/python/parameter_feature/test_var_grad.py
+++ b/tests/ut/python/parameter_feature/test_var_grad.py
@@ -24,11 +24,14 @@ from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE)
 
+
 def test_net_vargs_expand():
     class AddNet(Cell):
         def __init__(self):
             super(AddNet, self).__init__()
-            self.w = Parameter(Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
+            self.w = Parameter(
+                Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
+
         def construct(self, x, y):
             return x + y
     x = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
@@ -37,22 +40,59 @@ def test_net_vargs_expand():
     net = AddNet()
     out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
 
+
 class VarNet(Cell):
     def __init__(self, net):
         super(VarNet, self).__init__()
-        self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
-        self.w = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
+        self.b = Parameter(
+            Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+        self.w = Parameter(
+            Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
         self.net = net
+
     def construct(self, *args):
         return self.net(*args)*self.w + self.b
-    
+
+
 class SecondNet(Cell):
     def __init__(self):
         super(SecondNet, self).__init__()
-        self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+        self.b2 = Parameter(
+            Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+
     def construct(self, *args):
         res = args[0] + args[1]
         return res + self.b2
+
+
+class Bprop(Cell):
+    def __init__(self, func, wrt_params, params, grad_op, sens=None):
+        super(Bprop, self).__init__(auto_prefix=False)
+        self.func = func
+        self.wrt_params = wrt_params
+        self.params = None
+        if self.wrt_params and params:
+            self.params = ParameterTuple(params)
+        self.grad = grad_op
+        self.with_sens = False
+        self.sens = sens
+        if sens:
+            self.sens = Tensor(sens, dtype=mstype.float32)
+            self.with_sens = True
+
+    def construct(self, *inputs):
+        # pylint: disable=no-else-return
+        if self.wrt_params:
+            if self.with_sens:
+                return self.grad(self.func, self.params)(*inputs, self.sens)
+            else:
+                return self.grad(self.func, self.params)(*inputs)
+        elif self.with_sens:
+            return self.grad(self.func)(*inputs, self.sens)
+        else:
+            return self.grad(self.func)(*inputs)
+
+
 def test_all_var_args_grad_with_sens():
     """"test grad_by_list_with_sens with all var args input"""
     class GradNet(Cell):
@@ -60,6 +100,7 @@ def test_all_var_args_grad_with_sens():
             super(GradNet, self).__init__()
             self.weights = ParameterTuple(net.trainable_params())
             self.net = net
+
         def construct(self, *inputs):
             return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -69,12 +110,14 @@ def test_all_var_args_grad_with_sens():
     grad_net = GradNet(net)
     out = grad_net(x, y, sens)
 
+
 def test_grad_list_var_args():
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
             self.weights = ParameterTuple(net.trainable_params())
             self.net = net
+
         def construct(self, *inputs):
             return C.grad_by_list(self.net, self.weights)(*inputs)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -83,12 +126,14 @@ def test_grad_list_var_args():
     grad_net = GradNet(net)
     out = grad_net(x, y)
 
+
 def test_grad_all_var_args():
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
             self.weights = ParameterTuple(net.trainable_params())
             self.net = net
+
         def construct(self, *inputs):
             return C.grad_all(self.net)(*inputs)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -97,12 +142,14 @@ def test_grad_all_var_args():
     grad_net = GradNet(net)
     out = grad_net(x, y)
 
+
 def test_grad_all_var_args_with_sens():
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
             self.weights = ParameterTuple(net.trainable_params())
             self.net = net
+
         def construct(self, *inputs):
             return C.grad_all_with_sens(self.net)(*inputs)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -112,12 +159,14 @@ def test_grad_all_var_args_with_sens():
     grad_net = GradNet(net)
     out = grad_net(x, y, sens)
 
+
 def test_grad_var_args_with_sens():
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
             self.weights = ParameterTuple(net.trainable_params())
             self.net = net
+
         def construct(self, *inputs):
             return C.grad_with_sens(self.net)(*inputs)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -127,27 +176,34 @@ def test_grad_var_args_with_sens():
     grad_net = GradNet(net)
     out = grad_net(x, y, sens)
 
+
 def test_var_args_grad():
     class VarNet(Cell):
         def __init__(self, net):
             super(VarNet, self).__init__()
-            self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+            self.b = Parameter(
+                Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
             self.net = net
+
         def construct(self, *args):
             return self.net(*args) + self.b
-        
+
     class SecondNet(Cell):
         def __init__(self):
             super(SecondNet, self).__init__()
-            self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+            self.b2 = Parameter(
+                Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+
         def construct(self, *args):
             res = args[0] + args[1]
             return res + self.b2
+
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
             self.net = net
             self.weights = ParameterTuple(net.trainable_params())
+
         def construct(self, x, y, sens):
             return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -164,12 +220,14 @@ def test_var_args_positional():
         def __init__(self, net):
             super(VarNet, self).__init__()
             self.net = net
+
         def construct(self, x, y):
             return self.net(x, y)*x
 
     class SecondNet(Cell):
         def __init__(self):
             super(SecondNet, self).__init__()
+
         def construct(self, *args):
             return args[0] + args[1]
 
@@ -178,6 +236,7 @@ def test_var_args_positional():
             super(GradNet, self).__init__()
             self.net = net
             self.weights = ParameterTuple(net.trainable_params())
+
         def construct(self, x, y):
             return C.grad_all(self.net)(x, y)
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
@@ -185,3 +244,71 @@ def test_var_args_positional():
     net = VarNet(SecondNet())
     grad_net = GradNet(net)
     out = grad_net(x, y)
+
+
+def test_grad_within_if_else():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+            grad_op = C.GradOperation(
+                name='grad', get_all=False, get_by_list=True, sens_param=True)
+            self.grad = Bprop(self.net, True, self.weights, grad_op, 1.0)
+
+        def construct(self, *inputs):
+            return self.grad(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)
+    print("test_grad_var_args_with_sens out=", out)
+
+
+def test_grad_for_concat():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+            grad_op = C.GradOperation(
+                name='grad', get_all=True, get_by_list=False, sens_param=True)
+            self.grad = Bprop(self.net, False, self.weights, grad_op)
+
+        def construct(self, *inputs):
+            return self.grad(*inputs)
+
+    class Concat(Cell):
+        def __init__(self, axis):
+            super().__init__()
+            self.concat = P.Concat(axis=axis)
+
+        def construct(self, *input1):
+            return self.concat(input1)
+
+    class ConcatFactory:
+        def __init__(self, input_shape, axis, dtype=np.float32):
+            super(ConcatFactory, self).__init__()
+            self.inputs_np = []
+            for s in input_shape:
+                self.inputs_np.append(np.random.randn(*s).astype(dtype))
+            self.axis = axis
+            self.out_numpy = np.concatenate(self.inputs_np, axis=self.axis)
+            self.out_grad_np = self.out_numpy
+
+        def grad_mindspore_impl(self):
+            inputs = []
+            for i in self.inputs_np:
+                inputs.append(Tensor(i))
+            net = Concat(axis=self.axis)
+            grad_net = GradNet(net)
+            grad_net.set_train()
+            input_grad = grad_net(*inputs, Tensor(self.out_grad_np))
+
+        def grad_cmp(self):
+            input_grad_mindspore = self.grad_mindspore_impl()
+    fact = ConcatFactory(input_shape=(
+        (2, 184320, 1), (2, 46080, 1), (2, 11520, 1), (2, 2880, 1), (2, 720, 1)), axis=1)
+    fact.grad_cmp()

From 80978cf3ccee7570ee77dbf9f1ab3a043ebab0ca Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Thu, 9 Apr 2020 19:07:14 +0800
Subject: [PATCH 287/367] support operator ** // % for scalar and tensor, and
 in not in for dict, ang str concat

---
 mindspore/_extends/parse/resources.py         | 10 +--
 .../ccsrc/operator/cc_implementations.cc      | 40 ++++++++---
 mindspore/ccsrc/operator/cc_implementations.h |  3 +-
 .../ccsrc/operator/composite/do_signature.cc  | 25 ++++---
 mindspore/ccsrc/operator/ops.cc               |  4 ++
 mindspore/ccsrc/operator/ops.h                |  6 +-
 mindspore/ccsrc/operator/prim_nn.cc           | 10 +--
 mindspore/ccsrc/operator/prim_statement.cc    | 31 +++++++++
 mindspore/ccsrc/operator/prim_structures.cc   | 63 ++++++++++-------
 mindspore/ccsrc/operator/prim_to_function.cc  | 59 ++++++++--------
 .../ccsrc/pipeline/static_analysis/prim.cc    |  5 ++
 .../ccsrc/pipeline/static_analysis/prim.h     |  6 ++
 .../ops/composite/multitype_ops/__init__.py   |  6 ++
 .../ops/composite/multitype_ops/add_impl.py   | 21 ++++--
 .../ops/composite/multitype_ops/div_impl.py   |  6 +-
 .../composite/multitype_ops/floordiv_impl.py  | 50 ++++++++++++++
 .../ops/composite/multitype_ops/mod_impl.py   | 50 ++++++++++++++
 .../ops/composite/multitype_ops/mul_impl.py   |  6 +-
 .../ops/composite/multitype_ops/pow_impl.py   | 50 ++++++++++++++
 .../ops/composite/multitype_ops/sub_impl.py   |  6 +-
 mindspore/ops/functional.py                   |  8 ++-
 mindspore/ops/operations/math_ops.py          |  4 +-
 .../ut/python/pipeline/parse/test_operator.py | 69 +++++++++++++++++++
 23 files changed, 435 insertions(+), 103 deletions(-)
 create mode 100644 mindspore/ops/composite/multitype_ops/floordiv_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/mod_impl.py
 create mode 100644 mindspore/ops/composite/multitype_ops/pow_impl.py

diff --git a/mindspore/_extends/parse/resources.py b/mindspore/_extends/parse/resources.py
index 5dd24ccf80..9fb357597e 100644
--- a/mindspore/_extends/parse/resources.py
+++ b/mindspore/_extends/parse/resources.py
@@ -83,9 +83,9 @@ convert_object_map = {
     T.mul:          multitype_ops.mul,
     T.truediv:      multitype_ops.div,
     T.getitem:      multitype_ops.getitem,
-    T.floordiv:     NO_IMPLEMENT,
-    T.mod:          F.scalar_mod,
-    T.pow:          F.scalar_pow,
+    T.floordiv:     multitype_ops.floordiv,
+    T.mod:          multitype_ops.mod,
+    T.pow:          multitype_ops.pow_,
     T.matmul:       F.dot,
     T.lshift:       NO_IMPLEMENT,
     T.rshift:       NO_IMPLEMENT,
@@ -104,8 +104,8 @@ convert_object_map = {
     T.ge:           multitype_ops.greater_equal,
     T.is_:          F.is_,
     T.is_not:       F.is_not,
-    T.contains:     NO_IMPLEMENT,
-    T.not_contains: NO_IMPLEMENT,
+    T.contains:     F.in_dict,
+    T.not_contains: F.not_in_dict,
 
     # system function
     T.len:          M.ms_len,
diff --git a/mindspore/ccsrc/operator/cc_implementations.cc b/mindspore/ccsrc/operator/cc_implementations.cc
index 5ff49758b4..62b23b346f 100644
--- a/mindspore/ccsrc/operator/cc_implementations.cc
+++ b/mindspore/ccsrc/operator/cc_implementations.cc
@@ -103,7 +103,7 @@ T InnerScalarMul(T x, T y) {
 }
 
 template <typename T>
-T InnerScalarDiv(T x, T y) {
+float InnerScalarDiv(T x, T y) {
   if (y == 0) {
     MS_LOG(EXCEPTION) << "Divisor could not be zero";
   }
@@ -111,23 +111,41 @@ T InnerScalarDiv(T x, T y) {
     MS_LOG(EXCEPTION) << "Overflow of the div of two signed number x: " << std::to_string(x)
                       << ", y: " << std::to_string(y) << ".";
   }
-  return x / y;
+  return static_cast<float>(x) / static_cast<float>(y);
 }
 
-int32_t InnerScalarMod(int32_t x, int32_t y) {
+template <typename T>
+T InnerScalarFloordiv(T x, T y) {
+  auto ret = std::floor(InnerScalarDiv(x, y));
+  if (std::is_integral<T>::value) {
+    return static_cast<int>(ret);
+  }
+  return ret;
+}
+
+template <typename T>
+T InnerScalarMod(T x, T y) {
   if (y == 0) {
     MS_LOG(EXCEPTION) << "Could not mod to zero.";
   }
-  if (IsSignedIntOverflow(x, y, OpType::MOD)) {
+  if (std::is_integral<T>::value && std::is_signed<T>::value && IsSignedIntOverflow(x, y, OpType::MOD)) {
     MS_LOG(EXCEPTION) << "Overflow of the mod of two signed number x: " << std::to_string(x)
                       << ", y: " << std::to_string(y) << ".";
   }
-  return x % y;
+  if (std::is_integral<T>::value) {
+    return static_cast<int>(x) % static_cast<int>(y);
+  }
+  float x_int = std::floor(x);
+  float y_int = std::ceil(y);
+  float max = x_int / y_int;
+  float ret = x - y * max;
+  return ret;
 }
 
-float InnerScalarMod(float, float) { MS_LOG(EXCEPTION) << "Float does not support mod operator."; }
-
-double InnerScalarMod(double, double) { MS_LOG(EXCEPTION) << "Double does not support mod operator."; }
+template <typename T, typename U>
+T InnerScalarPow(T x, U y) {
+  return std::pow(x, y);
+}
 
 template <typename T, typename U>
 bool InnerScalarEq(T x, U y) {
@@ -193,6 +211,8 @@ SCALAR_OP(Sub)
 SCALAR_OP(Mul)
 SCALAR_OP(Div)
 SCALAR_OP(Mod)
+SCALAR_OP(Pow)
+SCALAR_OP(Floordiv)
 
 #define LOGIC_OP(op_t)                                                                       \
   ValuePtr Scalar##op_t(const ValuePtrList& list) {                                          \
@@ -227,6 +247,10 @@ SCALAR_OP(Mod)
       bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int>(y));                    \
       return MakeValue(sum);                                                                 \
     }                                                                                        \
+    if (x->isa<Int32Imm>() && y->isa<FP32Imm>()) {                                           \
+      bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<float>(y));                    \
+      return MakeValue(sum);                                                                 \
+    }                                                                                        \
     if (x->isa<Int64Imm>() && y->isa<Int32Imm>()) {                                          \
       bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<int>(y));                  \
       return MakeValue(sum);                                                                 \
diff --git a/mindspore/ccsrc/operator/cc_implementations.h b/mindspore/ccsrc/operator/cc_implementations.h
index 2c2936fc92..69981cea7d 100644
--- a/mindspore/ccsrc/operator/cc_implementations.h
+++ b/mindspore/ccsrc/operator/cc_implementations.h
@@ -37,9 +37,10 @@ ValuePtr ScalarSub(const ValuePtrList& list);
 ValuePtr ScalarMul(const ValuePtrList& list);
 ValuePtr ScalarDiv(const ValuePtrList& list);
 ValuePtr ScalarMod(const ValuePtrList& list);
+ValuePtr ScalarPow(const ValuePtrList& list);
+ValuePtr ScalarFloordiv(const ValuePtrList& list);
 ValuePtr ScalarUAdd(const ValuePtrList& list);
 ValuePtr ScalarUSub(const ValuePtrList& list);
-ValuePtr ScalarUSub(const ValuePtrList& list);
 ValuePtr ScalarLog(const ValuePtrList& list);
 ValuePtr ScalarEq(const ValuePtrList& list);
 ValuePtr ScalarLt(const ValuePtrList& list);
diff --git a/mindspore/ccsrc/operator/composite/do_signature.cc b/mindspore/ccsrc/operator/composite/do_signature.cc
index 62de1c71f2..a4a26377f5 100644
--- a/mindspore/ccsrc/operator/composite/do_signature.cc
+++ b/mindspore/ccsrc/operator/composite/do_signature.cc
@@ -88,14 +88,17 @@ std::map<SignatureEnumDType, size_t> GetMaxDtypeIndex(const std::vector<Signatur
     if (indexs.size() < 2) {
       continue;
     }
-    size_t m_index = indexs[0];
-    for (size_t i = 1; i < indexs.size(); ++i) {
-      if (args_spec_list[indexs[i]]->isa<abstract::AbstractTensor>()) {
-        m_index = indexs[i];
+
+    for (const auto& index : indexs) {
+      AbstractBasePtr arg_value = args_spec_list[index];
+      if (arg_value->isa<abstract::AbstractRef>()) {
+        arg_value = arg_value->cast<abstract::AbstractRefPtr>()->ref();
+      }
+
+      if (arg_value->isa<abstract::AbstractTensor>()) {
+        (void)dst_type.insert(std::make_pair(type, index));
+        break;
       }
-    }
-    if (args_spec_list[m_index]->isa<abstract::AbstractTensor>()) {
-      (void)dst_type.insert(std::make_pair(type, m_index));
     }
   }
   return dst_type;
@@ -119,15 +122,19 @@ void DoAutoCast(const std::vector<Signature>& signature, const abstract::Abstrac
   (void)std::transform(signature.begin(), signature.end(), std::back_inserter(dtypes),
                        [](const Signature& sig) { return sig.dtype; });
   int empty_dtype_count = std::count(dtypes.begin(), dtypes.end(), SignatureEnumDType::kDTypeEmptyDefaultValue);
-  if (dtypes.size() == 0 || static_cast<int>(dtypes.size()) == empty_dtype_count) {
+  if (dtypes.empty() || static_cast<int>(dtypes.size()) == empty_dtype_count) {
     return;
   }
   // Stat the index of the arguments with the largest type in the same SignatureEnumDType.
   std::map<SignatureEnumDType, size_t> dst_type = GetMaxDtypeIndex(dtypes, args_spec_list);
   // Identify which arg requires auto cast
   for (size_t i = 0; i < args_spec_list.size(); ++i) {
+    AbstractBasePtr arg_value = args_spec_list[i];
+    if (arg_value->isa<abstract::AbstractRef>()) {
+      arg_value = arg_value->cast<abstract::AbstractRefPtr>()->ref();
+    }
     auto it = dst_type.find(dtypes[i]);
-    if (it == dst_type.end() || it->second == i || !args_spec_list[i]->isa<abstract::AbstractScalar>()) {
+    if (it == dst_type.end() || it->second == i || !arg_value->isa<abstract::AbstractScalar>()) {
       continue;
     }
     // get source node for cast
diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc
index f3053cac7d..e190d7d0b2 100755
--- a/mindspore/ccsrc/operator/ops.cc
+++ b/mindspore/ccsrc/operator/ops.cc
@@ -28,6 +28,7 @@ const PrimitivePtr kPrimScalarAdd = std::make_shared<Primitive>("scalar_add");
 const PrimitivePtr kPrimScalarSub = std::make_shared<Primitive>("scalar_sub");
 const PrimitivePtr kPrimScalarMul = std::make_shared<Primitive>("scalar_mul");
 const PrimitivePtr kPrimScalarDiv = std::make_shared<Primitive>("scalar_div");
+const PrimitivePtr kPrimScalarFloordiv = std::make_shared<Primitive>("scalar_floordiv");
 const PrimitivePtr kPrimScalarMod = std::make_shared<Primitive>("scalar_mod");
 const PrimitivePtr kPrimScalarPow = std::make_shared<Primitive>("scalar_pow");
 const PrimitivePtr kPrimScalarTrunc = std::make_shared<Primitive>("scalar_trunc");
@@ -78,6 +79,7 @@ const PrimitivePtr kPrimCreateInstance = std::make_shared<Primitive>("create_ins
 
 // Structure
 const PrimitivePtr kPrimStringEqual = std::make_shared<Primitive>("string_equal");
+const PrimitivePtr kPrimStringConcat = std::make_shared<Primitive>("string_concat");
 const PrimitivePtr kPrimMakeTuple = std::make_shared<Primitive>("make_tuple");
 const PrimitivePtr kPrimMakeList = std::make_shared<Primitive>("make_list");
 const PrimitivePtr kPrimMakeDict = std::make_shared<Primitive>("make_dict");
@@ -221,6 +223,8 @@ const PrimitivePtr kPrimBroadcastGradientArgs = std::make_shared<Primitive>("Bro
 const PrimitivePtr kPrimControlDepend = std::make_shared<Primitive>("ControlDepend");
 const PrimitivePtr kPrimIs_ = std::make_shared<Primitive>("is_");
 const PrimitivePtr kPrimIsNot = std::make_shared<Primitive>("is_not");
+const PrimitivePtr kPrimInDict = std::make_shared<Primitive>("in_dict");
+const PrimitivePtr kPrimNotInDict = std::make_shared<Primitive>("not_in_dict");
 
 // Comm ops
 const PrimitivePtr kPrimMirror = std::make_shared<Primitive>("_MirrorOperator");
diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
index 2dc7072972..0148e073e0 100755
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -34,6 +34,7 @@ extern const PrimitivePtr kPrimScalarAdd;
 extern const PrimitivePtr kPrimScalarSub;
 extern const PrimitivePtr kPrimScalarMul;
 extern const PrimitivePtr kPrimScalarDiv;
+extern const PrimitivePtr kPrimScalarFloordiv;
 extern const PrimitivePtr kPrimScalarMod;
 extern const PrimitivePtr kPrimScalarPow;
 extern const PrimitivePtr kPrimScalarTrunc;
@@ -84,6 +85,7 @@ extern const PrimitivePtr kPrimCreateInstance;
 
 // Structure
 extern const PrimitivePtr kPrimStringEqual;
+extern const PrimitivePtr kPrimStringConcat;
 extern const PrimitivePtr kPrimMakeTuple;
 extern const PrimitivePtr kPrimMakeList;
 extern const PrimitivePtr kPrimMakeDict;
@@ -227,8 +229,8 @@ extern const PrimitivePtr kPrimBroadcastGradientArgs;
 extern const PrimitivePtr kPrimControlDepend;
 extern const PrimitivePtr kPrimIs_;
 extern const PrimitivePtr kPrimIsNot;
-extern const PrimitivePtr kPrimMinimumGrad;
-extern const PrimitivePtr kPrimMaximumGrad;
+extern const PrimitivePtr kPrimInDict;
+extern const PrimitivePtr kPrimNotInDict;
 
 // Comm ops
 extern const PrimitivePtr kPrimMirror;
diff --git a/mindspore/ccsrc/operator/prim_nn.cc b/mindspore/ccsrc/operator/prim_nn.cc
index 892bf2921e..3591168187 100644
--- a/mindspore/ccsrc/operator/prim_nn.cc
+++ b/mindspore/ccsrc/operator/prim_nn.cc
@@ -114,12 +114,12 @@ void FusedBatchNormCheckDim(const PrimitivePtr &primitive, const AbstractBasePtr
     AbstractTensorPtr arg = CheckArg<AbstractTensor>(op_name, args_spec_list, i);
     ShapePtr arg_shape = dyn_cast<Shape>(arg->GetShapeTrack());
     if (arg_shape == nullptr) {
-      MS_LOG(EXCEPTION) << "" << op_name << " type of args[" << i << "] should be Shape, but " << arg->ToString();
+      MS_LOG(EXCEPTION) << op_name << " type of args[" << i << "] should be Shape, but " << arg->ToString();
     }
 
     if (i == 0) {
       if (arg_shape->shape().size() < 2) {
-        MS_LOG(EXCEPTION) << "" << op_name << " shape of args[" << i
+        MS_LOG(EXCEPTION) << op_name << " shape of args[" << i
                           << "] should be TensorShape with dimension greater than 1, but shape: "
                           << arg_shape->ToString();
       }
@@ -127,7 +127,7 @@ void FusedBatchNormCheckDim(const PrimitivePtr &primitive, const AbstractBasePtr
     }
 
     if (arg_shape->shape().size() != 1) {
-      MS_LOG(EXCEPTION) << "" << op_name << " shape of args[" << i
+      MS_LOG(EXCEPTION) << op_name << " shape of args[" << i
                         << "] should be TensorShape with dimension: 1, but shape: " << arg_shape->ToString();
     }
   }
@@ -159,7 +159,7 @@ AbstractBasePtr InferImplFusedBatchNorm(const AnalysisEnginePtr &, const Primiti
       MS_LOG(EXCEPTION) << "Arg shape size should >= 1.";
     }
     if (arg_shape_list[0] != input_shape_list[1]) {
-      MS_LOG(EXCEPTION) << "" << op_name << " size of tensor param[" << i << "](which is " << arg_shape_list[0]
+      MS_LOG(EXCEPTION) << op_name << " size of tensor param[" << i << "](which is " << arg_shape_list[0]
                         << ") should match the second dimension of tensor"
                            " param[0](which is "
                         << input_shape_list[1] << ").";
@@ -378,7 +378,7 @@ AbstractBasePtr InferImplDropoutGenMask(const AnalysisEnginePtr &, const Primiti
 
   TypePtr prob_type = keep_prob->element()->BuildType();
   if ((prob_type->type_id() != kNumberTypeFloat16) && (prob_type->type_id() != kNumberTypeFloat32)) {
-    MS_LOG(EXCEPTION) << "" << op_name << " keep_prob type should be float16 or float32, but " << prob_type->ToString()
+    MS_LOG(EXCEPTION) << op_name << " keep_prob type should be float16 or float32, but " << prob_type->ToString()
                       << ".";
   }
 
diff --git a/mindspore/ccsrc/operator/prim_statement.cc b/mindspore/ccsrc/operator/prim_statement.cc
index 7d5038d4e1..239aed5bde 100644
--- a/mindspore/ccsrc/operator/prim_statement.cc
+++ b/mindspore/ccsrc/operator/prim_statement.cc
@@ -169,5 +169,36 @@ AbstractBasePtr InferImplIsNot(const AnalysisEnginePtr &, const PrimitivePtr &pr
 
   return std::make_shared<AbstractScalar>(!(*t == *x));
 }
+
+bool IsInDict(const PrimitivePtr &primitive, const AbstractBasePtrList &args_spec_list) {
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 2);
+  auto key = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
+  auto dict = CheckArg<AbstractDictionary>(op_name, args_spec_list, 1);
+
+  ValuePtr key_value = key->BuildValue();
+  if (!key_value->isa<StringImm>()) {
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
+  }
+  auto key_str = GetValue<std::string>(key_value);
+  std::vector<AbstractAttribute> dict_elems = dict->elements();
+  auto it = std::find_if(dict_elems.begin(), dict_elems.end(),
+                         [key_str](const AbstractAttribute &item) { return item.first == key_str; });
+  return it != dict_elems.end();
+}
+
+AbstractBasePtr InferImplInDict(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                const AbstractBasePtrList &args_spec_list) {
+  // statement: x in t
+  // Inputs: x, t
+  return std::make_shared<AbstractScalar>(IsInDict(primitive, args_spec_list));
+}
+
+AbstractBasePtr InferImplNotInDict(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                   const AbstractBasePtrList &args_spec_list) {
+  // statement: x not in t
+  // Inputs: x, t
+  return std::make_shared<AbstractScalar>(!IsInDict(primitive, args_spec_list));
+}
 }  // namespace abstract
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/operator/prim_structures.cc b/mindspore/ccsrc/operator/prim_structures.cc
index 88699c4d38..31d2bff43d 100644
--- a/mindspore/ccsrc/operator/prim_structures.cc
+++ b/mindspore/ccsrc/operator/prim_structures.cc
@@ -36,7 +36,7 @@ AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitiveP
   ValuePtr value_x = scalar_x->BuildValue();
   ValuePtr value_y = scalar_y->BuildValue();
   if (!value_x->isa<StringImm>() || !value_y->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
+    MS_LOG(EXCEPTION) << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
                       << ", param1: " << value_y->ToString();
   }
 
@@ -44,6 +44,25 @@ AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitiveP
   return std::make_shared<AbstractScalar>(ret);
 }
 
+AbstractBasePtr InferImplStringConcat(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two scalars whose value is a string.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 2);
+  AbstractScalarPtr scalar_x = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
+  AbstractScalarPtr scalar_y = CheckArg<AbstractScalar>(op_name, args_spec_list, 1);
+
+  ValuePtr value_x = scalar_x->BuildValue();
+  ValuePtr value_y = scalar_y->BuildValue();
+  if (!value_x->isa<StringImm>() || !value_y->isa<StringImm>()) {
+    MS_LOG(EXCEPTION) << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
+                      << ", param1: " << value_y->ToString();
+  }
+
+  std::string ret = (value_x->cast<StringImmPtr>()->value() + value_y->cast<StringImmPtr>()->value());
+  return std::make_shared<AbstractScalar>(ret);
+}
+
 AbstractBasePtr InferImplMakeTuple(const AnalysisEnginePtr &, const PrimitivePtr &,
                                    const AbstractBasePtrList &args_spec_list) {
   return std::make_shared<AbstractTuple>(args_spec_list);
@@ -64,7 +83,7 @@ AbstractBasePtr InferImplMakeDict(const AnalysisEnginePtr &, const PrimitivePtr
 
   size_t keys_size = keys->size();
   if (values->size() != keys_size) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator keys' size is not equal with values' size";
+    MS_LOG(EXCEPTION) << op_name << " evaluator keys' size is not equal with values' size";
   }
 
   std::vector<AbstractAttribute> key_value;
@@ -76,7 +95,7 @@ AbstractBasePtr InferImplMakeDict(const AnalysisEnginePtr &, const PrimitivePtr
     ValuePtr keyPtr = key->BuildValue();
     MS_EXCEPTION_IF_NULL(keyPtr);
     if (!keyPtr->isa<StringImm>()) {
-      MS_LOG(EXCEPTION) << "" << op_name << " evaluator keys should be string, but got " << keyPtr->ToString();
+      MS_LOG(EXCEPTION) << op_name << " evaluator keys should be string, but got " << keyPtr->ToString();
     }
     std::string key_string = GetValue<std::string>(keyPtr);
     key_value.emplace_back(key_string, value_list[index]);
@@ -93,7 +112,7 @@ AbstractBasePtr InferImplMakeKwarg(const AnalysisEnginePtr &, const PrimitivePtr
 
   ValuePtr keyPtr = key->BuildValue();
   if (!keyPtr->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << keyPtr->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << keyPtr->ToString();
   }
   std::string key_string = GetValue<std::string>(keyPtr);
   return std::make_shared<AbstractKeywordArg>(key_string, args_spec_list[1]);
@@ -109,14 +128,13 @@ AbstractBasePtr InferImplExtractKwarg(const AnalysisEnginePtr &, const Primitive
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
   std::string key_input = GetValue<std::string>(key_value);
   std::string key_actual = kwarg->get_key();
   if (key_actual != key_input) {
-    MS_LOG(EXCEPTION) << "" << op_name
-                      << " evaluator input key should be same as AbstractKeywordArg' key, but input is " << key_input
-                      << ", AbstractKeywordArg' key is " << key_actual;
+    MS_LOG(EXCEPTION) << op_name << " evaluator input key should be same as AbstractKeywordArg' key, but input is "
+                      << key_input << ", AbstractKeywordArg' key is " << key_actual;
   }
   return kwarg->get_arg();
 }
@@ -187,13 +205,12 @@ AbstractBasePtr InferTupleOrListGetItem(const std::string &op_name, const Abstra
 
   ValuePtr index_value = index->BuildValue();
   if (!index_value->isa<Int32Imm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be an int32 number, but got "
-                      << index_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be an int32 number, but got " << index_value->ToString();
   }
   int idx_v = GetValue<int>(index_value);
   std::size_t nelems = queue->elements().size();
   if (idx_v >= SizeToInt(nelems) || idx_v < -SizeToInt(nelems)) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be in range[-" << SizeToInt(nelems) << ", "
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be in range[-" << SizeToInt(nelems) << ", "
                       << SizeToInt(nelems) << "), but got " << idx_v << ".";
   }
 
@@ -215,8 +232,7 @@ AbstractBasePtr InferTupleOrListSetItem(const std::string &op_name, const Abstra
 
   ValuePtr index_value = index->BuildValue();
   if (!index_value->isa<Int32Imm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be an int32 number, but got "
-                      << index_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be an int32 number, but got " << index_value->ToString();
   }
   int idx_v = GetValue<int>(index_value);
   if (idx_v < 0) {
@@ -227,8 +243,7 @@ AbstractBasePtr InferTupleOrListSetItem(const std::string &op_name, const Abstra
   AbstractBasePtrList elements = queue->elements();
   std::size_t nelems = elements.size();
   if (uidx_v >= nelems) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator the index: " << uidx_v << " to set out of range: " << nelems - 1
-                      << ".";
+    MS_LOG(EXCEPTION) << op_name << " evaluator the index: " << uidx_v << " to set out of range: " << nelems - 1 << ".";
   }
   elements[uidx_v] = args_spec_list[2];
   return std::make_shared<T>(elements);
@@ -264,12 +279,12 @@ AbstractBasePtr InferImplDictGetItem(const AnalysisEnginePtr &, const PrimitiveP
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
-  std::string key_str = GetValue<std::string>(key_value);
+  auto key_str = GetValue<std::string>(key_value);
   std::vector<AbstractAttribute> dict_elems = dict->elements();
   auto it = std::find_if(dict_elems.begin(), dict_elems.end(),
-                         [key_str](AbstractAttribute &item) { return item.first == key_str; });
+                         [key_str](const AbstractAttribute &item) { return item.first == key_str; });
 
   if (it == dict_elems.end()) {
     MS_LOG(EXCEPTION) << "The key " << key_str << " does not exist in the dict:" << args_spec_list[0]->ToString();
@@ -287,7 +302,7 @@ AbstractBasePtr InferImplDictSetItem(const AnalysisEnginePtr &, const PrimitiveP
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
   std::string key_str = GetValue<std::string>(key_value);
   std::vector<AbstractAttribute> dict_elems = dict->elements();
@@ -446,27 +461,27 @@ AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitiveP
 
   auto x_shp_value = shape_x->BuildValue();
   if (x_shp_value->isa<AnyValue>()) {
-    MS_LOG(EXCEPTION) << "" << op_name
+    MS_LOG(EXCEPTION) << op_name
                       << " evaluator shape's data field can't be anything: " << args_spec_list[1]->ToString();
   }
 
   // Axis can be scalar, tuple or None
   AbstractTuplePtr axis = nullptr;
   if (args_spec_list[1]->isa<AbstractScalar>()) {
-    MS_LOG(DEBUG) << "" << op_name << " evaluator second parameter is scalar";
+    MS_LOG(DEBUG) << op_name << " evaluator second parameter is scalar";
     AbstractBasePtrList axis_list = {dyn_cast<AbstractScalar>(args_spec_list[1])};
     axis = std::make_shared<AbstractTuple>(axis_list);
   } else if (args_spec_list[1]->isa<AbstractTuple>()) {
-    MS_LOG(DEBUG) << "" << op_name << " evaluator second parameter is tuple";
+    MS_LOG(DEBUG) << op_name << " evaluator second parameter is tuple";
     axis = args_spec_list[1]->cast<AbstractTuplePtr>();
   } else {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator second parameter should be a scalar or tuple, but got "
+    MS_LOG(EXCEPTION) << op_name << " evaluator second parameter should be a scalar or tuple, but got "
                       << args_spec_list[1]->ToString();
   }
 
   auto axis_value = axis->BuildValue();
   if (axis_value->isa<AnyValue>()) {
-    MS_LOG(EXCEPTION) << "" << op_name
+    MS_LOG(EXCEPTION) << op_name
                       << " evaluator shape's data field can't be anything: " << args_spec_list[1]->ToString();
   }
   auto axis_value_ptr = axis_value->cast<ValueTuplePtr>();
diff --git a/mindspore/ccsrc/operator/prim_to_function.cc b/mindspore/ccsrc/operator/prim_to_function.cc
index 234c829d44..bdfe48157c 100644
--- a/mindspore/ccsrc/operator/prim_to_function.cc
+++ b/mindspore/ccsrc/operator/prim_to_function.cc
@@ -24,36 +24,35 @@ namespace mindspore {
 namespace prim {
 
 PrimToFunction::PrimToFunction()
-    : prim_func_type_map_({
-        // ONE_ARG prim
-        {"bool_not", kPrimTypeOneArg},
-        {"scalar_cos", kPrimTypeOneArg},
-        {"scalar_exp", kPrimTypeOneArg},
-        {"scalar_floor", kPrimTypeOneArg},
-        {"scalar_log", kPrimTypeOneArg},
-        {"scalar_sin", kPrimTypeOneArg},
-        {"scalar_tan", kPrimTypeOneArg},
-        {"scalar_trunc", kPrimTypeOneArg},
-        {"typeof", kPrimTypeOneArg},
-        {"scalar_uadd", kPrimTypeOneArg},
-        {"scalar_usub", kPrimTypeOneArg},
-        // TWO_ARGS prim
-        {"scalar_add", kPrimTypeTwoArgs},
-        {"bool_and", kPrimTypeTwoArgs},
-        {"bool_eq", kPrimTypeTwoArgs},
-        {"bool_or", kPrimTypeTwoArgs},
-        {"scalar_div", kPrimTypeTwoArgs},
-        {"scalar_eq", kPrimTypeTwoArgs},
-        {"scalar_ge", kPrimTypeTwoArgs},
-        {"scalar_gt", kPrimTypeTwoArgs},
-        {"scalar_le", kPrimTypeTwoArgs},
-        {"scalar_lt", kPrimTypeTwoArgs},
-        {"scalar_ne", kPrimTypeTwoArgs},
-        {"scalar_mod", kPrimTypeTwoArgs},
-        {"scalar_mul", kPrimTypeTwoArgs},
-        {"scalar_pow", kPrimTypeTwoArgs},
-        {"scalar_sub", kPrimTypeTwoArgs},
-      }) {}
+    : prim_func_type_map_({// ONE_ARG prim
+                           {"bool_not", kPrimTypeOneArg},
+                           {"scalar_cos", kPrimTypeOneArg},
+                           {"scalar_exp", kPrimTypeOneArg},
+                           {"scalar_floor", kPrimTypeOneArg},
+                           {"scalar_log", kPrimTypeOneArg},
+                           {"scalar_sin", kPrimTypeOneArg},
+                           {"scalar_tan", kPrimTypeOneArg},
+                           {"scalar_trunc", kPrimTypeOneArg},
+                           {"typeof", kPrimTypeOneArg},
+                           {"scalar_uadd", kPrimTypeOneArg},
+                           {"scalar_usub", kPrimTypeOneArg},
+                           // TWO_ARGS prim
+                           {"scalar_add", kPrimTypeTwoArgs},
+                           {"bool_and", kPrimTypeTwoArgs},
+                           {"bool_eq", kPrimTypeTwoArgs},
+                           {"bool_or", kPrimTypeTwoArgs},
+                           {"scalar_div", kPrimTypeTwoArgs},
+                           {"scalar_eq", kPrimTypeTwoArgs},
+                           {"scalar_ge", kPrimTypeTwoArgs},
+                           {"scalar_gt", kPrimTypeTwoArgs},
+                           {"scalar_le", kPrimTypeTwoArgs},
+                           {"scalar_lt", kPrimTypeTwoArgs},
+                           {"scalar_ne", kPrimTypeTwoArgs},
+                           {"scalar_mod", kPrimTypeTwoArgs},
+                           {"scalar_mul", kPrimTypeTwoArgs},
+                           {"scalar_pow", kPrimTypeTwoArgs},
+                           {"scalar_sub", kPrimTypeTwoArgs},
+                           {"scalar_floordiv", kPrimTypeTwoArgs}}) {}
 
 bool PrimToFunction::GetFunction(const PrimitivePtr& prim, FunctionPtr* const func) const {
   bool result = false;
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 403bbdf433..1512596cb4 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -52,6 +52,8 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimSwitch, {InferImplSwitch, true}},
     {prim::kPrimIs_, {InferImplIs_, true}},
     {prim::kPrimIsNot, {InferImplIsNot, true}},
+    {prim::kPrimInDict, {InferImplInDict, true}},
+    {prim::kPrimNotInDict, {InferImplNotInDict, true}},
     // Maths
     {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}},
     {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}},
@@ -91,6 +93,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimMakeRange, {InferImplMakeRange, false}},
     {prim::kPrimStopGradient, {InferImplStopGradient, false}},
     {prim::kPrimStringEqual, {InferImplStringEqual, false}},
+    {prim::kPrimStringConcat, {InferImplStringConcat, false}},
     {prim::kPrimDictLen, {InferImplDictLen, false}},
     // NN
     {prim::kPrimPooling, {InferImplPooling, true}},
@@ -988,6 +991,8 @@ PrimitiveToImplMap &GetUniformPrimitiveToImplMap() {
     {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}},
     {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}},
     {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}},
+    {prim::kPrimScalarPow, {prim::ScalarPow, true, nullptr, true}},
+    {prim::kPrimScalarFloordiv, {prim::ScalarFloordiv, true, nullptr, true}},
     {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}},
     {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}},
     {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}},
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.h b/mindspore/ccsrc/pipeline/static_analysis/prim.h
index e154473dbb..be71f3200a 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.h
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.h
@@ -178,6 +178,10 @@ AbstractBasePtr InferImplIs_(const AnalysisEnginePtr &, const PrimitivePtr &,
                              const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplIsNot(const AnalysisEnginePtr &, const PrimitivePtr &,
                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplInDict(const AnalysisEnginePtr &, const PrimitivePtr &,
+                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplNotInDict(const AnalysisEnginePtr &, const PrimitivePtr &,
+                                   const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplPooling(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                  const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplPoolingGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
@@ -287,6 +291,8 @@ AbstractBasePtr InferImplStopGradient(const AnalysisEnginePtr &, const Primitive
                                       const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                      const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplStringConcat(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplDictLen(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                  const AbstractBasePtrList &args_spec_list);
 
diff --git a/mindspore/ops/composite/multitype_ops/__init__.py b/mindspore/ops/composite/multitype_ops/__init__.py
index db28b1b5f6..40bf71d49a 100644
--- a/mindspore/ops/composite/multitype_ops/__init__.py
+++ b/mindspore/ops/composite/multitype_ops/__init__.py
@@ -19,6 +19,9 @@ from .add_impl import add
 from .sub_impl import sub
 from .mul_impl import mul
 from .div_impl import div
+from .pow_impl import pow_
+from .floordiv_impl import floordiv
+from .mod_impl import mod
 from .getitem_impl import getitem
 from .zeros_like_impl import zeros_like
 from .ones_like_impl import ones_like
@@ -38,6 +41,9 @@ __all__ = [
     'sub',
     'mul',
     'div',
+    'pow_',
+    'floordiv',
+    'mod',
     'uadd',
     'zeros_like',
     'ones_like',
diff --git a/mindspore/ops/composite/multitype_ops/add_impl.py b/mindspore/ops/composite/multitype_ops/add_impl.py
index 2b1f83679e..2ad81bfc93 100644
--- a/mindspore/ops/composite/multitype_ops/add_impl.py
+++ b/mindspore/ops/composite/multitype_ops/add_impl.py
@@ -69,6 +69,21 @@ def _scalar_add_scalar(x, y):
     return F.scalar_add(x, y)
 
 
+@add.register("String", "String")
+def _string_concat_string(x, y):
+    """
+    Concatenate the string y to the string x.
+
+    Args:
+        x (str): The first input string.
+        y (str): the second input string.
+
+    Returns:
+        str, concatenate the y to the x.
+    """
+    return F.string_concat(x, y)
+
+
 @add.register("Number", "Tensor")
 def _scalar_add_tensor(x, y):
     """
@@ -81,8 +96,7 @@ def _scalar_add_tensor(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_add(z, y)
+    return F.tensor_add(x, y)
 
 
 @add.register("Tensor", "Number")
@@ -97,8 +111,7 @@ def _tensor_add_scalar(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_add(x, z)
+    return F.tensor_add(x, y)
 
 
 @add.register("Tensor", "Tensor")
diff --git a/mindspore/ops/composite/multitype_ops/div_impl.py b/mindspore/ops/composite/multitype_ops/div_impl.py
index 3edf3c8d9f..c37fcb9c36 100644
--- a/mindspore/ops/composite/multitype_ops/div_impl.py
+++ b/mindspore/ops/composite/multitype_ops/div_impl.py
@@ -68,8 +68,7 @@ def _scalar_div_tensor(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_div(z, y)
+    return F.tensor_div(x, y)
 
 
 @div.register("Tensor", "Number")
@@ -84,5 +83,4 @@ def _tensor_div_scalar(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_div(x, z)
+    return F.tensor_div(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/floordiv_impl.py b/mindspore/ops/composite/multitype_ops/floordiv_impl.py
new file mode 100644
index 0000000000..c1a47f881f
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/floordiv_impl.py
@@ -0,0 +1,50 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Implementation for internal polymorphism `floordiv` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+floordiv = base.MultitypeFuncGraph("floordiv")
+"""
+`floordiv` is a metafuncgraph object which will compute the floordiv of two objects
+using ".register" decorator.
+"""
+
+
+@floordiv.register("Number", "Number")
+def _floordiv_scalar(x, y):
+    """Returns x // y where x and y are all scalars."""
+    return F.scalar_floordiv(x, y)
+
+
+@floordiv.register("Tensor", "Tensor")
+def _floordiv_tensor(x, y):
+    """Returns x // y where x and y are all tensors and have save dtype."""
+    return F.tensor_floordiv(x, y)
+
+
+@floordiv.register("Tensor", "Number")
+def _tensor_floordiv_scalar(x, y):
+    """Returns x // y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_floordiv(x, y)
+
+
+@floordiv.register("Number", "Tensor")
+def _scalar_floordiv_tensor(x, y):
+    """Returns x // y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_floordiv(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/mod_impl.py b/mindspore/ops/composite/multitype_ops/mod_impl.py
new file mode 100644
index 0000000000..e9947677ac
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/mod_impl.py
@@ -0,0 +1,50 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Implementation for internal polymorphism `mod` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+mod = base.MultitypeFuncGraph("mod")
+"""
+`mod` is a metafuncgraph object which will compute the mod of two objects
+using ".register" decorator.
+"""
+
+
+@mod.register("Number", "Number")
+def _mod_scalar(x, y):
+    """Returns x % y where x and y are all scalars."""
+    return F.scalar_mod(x, y)
+
+
+@mod.register("Tensor", "Tensor")
+def _mod_tensor(x, y):
+    """Returns x % y where x and y are all tensors and have save dtype."""
+    return F.tensor_mod(x, y)
+
+
+@mod.register("Tensor", "Number")
+def _tensor_mod_scalar(x, y):
+    """Returns x % y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_mod(x, y)
+
+
+@mod.register("Number", "Tensor")
+def _scalar_mod_tensor(x, y):
+    """Returns x % y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_mod(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/mul_impl.py b/mindspore/ops/composite/multitype_ops/mul_impl.py
index 1d4733a46b..ce9ec391af 100644
--- a/mindspore/ops/composite/multitype_ops/mul_impl.py
+++ b/mindspore/ops/composite/multitype_ops/mul_impl.py
@@ -56,8 +56,7 @@ def _scalar_mul_tensor(x, y):
     Outputs:
        Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_mul(z, y)
+    return F.tensor_mul(x, y)
 
 
 @mul.register("Tensor", "Number")
@@ -68,5 +67,4 @@ def _tensor_mul_scalar(x, y):
     Outputs:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_mul(x, z)
+    return F.tensor_mul(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/pow_impl.py b/mindspore/ops/composite/multitype_ops/pow_impl.py
new file mode 100644
index 0000000000..8d73335c98
--- /dev/null
+++ b/mindspore/ops/composite/multitype_ops/pow_impl.py
@@ -0,0 +1,50 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Implementation for internal polymorphism `pow` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+pow_ = base.MultitypeFuncGraph("pow")
+"""
+`pow` is a metafuncgraph object which will compute the pow of two objects
+using ".register" decorator.
+"""
+
+
+@pow_.register("Number", "Number")
+def _pow_scalar(x, y):
+    """Returns x ** y where x and y are all scalars."""
+    return F.scalar_pow(x, y)
+
+
+@pow_.register("Tensor", "Tensor")
+def _pow_tensor(x, y):
+    """Returns x ** y where x and y are all tensors and have save dtype."""
+    return F.tensor_pow(x, y)
+
+
+@pow_.register("Tensor", "Number")
+def _tensor_pow_scalar(x, y):
+    """Returns x ** y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_pow(x, y)
+
+
+@pow_.register("Number", "Tensor")
+def _scalar_pow_tensor(x, y):
+    """Returns x ** y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_pow(x, y)
diff --git a/mindspore/ops/composite/multitype_ops/sub_impl.py b/mindspore/ops/composite/multitype_ops/sub_impl.py
index 4a3224a859..431a58b991 100644
--- a/mindspore/ops/composite/multitype_ops/sub_impl.py
+++ b/mindspore/ops/composite/multitype_ops/sub_impl.py
@@ -41,12 +41,10 @@ def _sub_tensor(x, y):
 @sub.register("Number", "Tensor")
 def _scalar_sub_tensor(x, y):
     """Returns x - y where x is a scalar and y is a tensor. x and y should have same dtype."""
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_sub(z, y)
+    return F.tensor_sub(x, y)
 
 
 @sub.register("Tensor", "Number")
 def _tensor_sub_scalar(x, y):
     """Returns x - y where x is a tensor and y is a scalar. x and y should have same dtype."""
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_sub(x, z)
+    return F.tensor_sub(x, y)
diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py
index 4da725145f..611c569553 100644
--- a/mindspore/ops/functional.py
+++ b/mindspore/ops/functional.py
@@ -48,6 +48,9 @@ tensor_ge = P.GreaterEqual()
 tensor_sub = P.Sub()
 tensor_mul = P.Mul()
 tensor_div = P.RealDiv()
+tensor_floordiv = P.FloorDiv()
+tensor_pow = P.Pow()
+tensor_mod = P.FloorMod()
 strided_slice = P.StridedSlice()
 same_type_shape = P.SameTypeShape()
 equal = P.Equal()
@@ -83,6 +86,7 @@ scalar_add = Primitive('scalar_add')
 scalar_mul = Primitive('scalar_mul')
 scalar_sub = Primitive('scalar_sub')
 scalar_div = Primitive('scalar_div')
+scalar_floordiv = Primitive('scalar_floordiv')
 scalar_log = Primitive('scalar_log')
 scalar_pow = Primitive('scalar_pow')
 scalar_gt = Primitive('scalar_gt')
@@ -95,6 +99,7 @@ scalar_uadd = Primitive('scalar_uadd')
 scalar_usub = Primitive('scalar_usub')
 scalar_mod = Primitive('scalar_mod')
 string_eq = Primitive('string_equal')
+string_concat = Primitive('string_concat')
 bool_not = Primitive("bool_not")
 bool_or = Primitive("bool_or")
 bool_and = Primitive("bool_and")
@@ -104,7 +109,8 @@ logical_not = P.LogicalNot()
 array_to_scalar = Primitive('array_to_scalar')
 is_ = Primitive("is_")
 is_not = Primitive("is_not")
-
+in_dict = Primitive("in_dict")
+not_in_dict = Primitive("not_in_dict")
 broadcast_gradient_args = Primitive('BroadcastGradientArgs')
 dot = Primitive('dot')
 array_reduce = Primitive('array_reduce')
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index e390b6b589..ce3449a8b7 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -667,8 +667,8 @@ class AddN(PrimitiveWithInfer):
         >>>         return self.addN(z)
         >>>
         >>> net = NetAddN()
-        >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
-        >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32)
+        >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
+        >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.float32)
         >>> net(input_x, input_y, input_x, input_y)
         Tensor([10, 14, 18], shape=(3,), dtype=mindspore.int32)
     """
diff --git a/tests/ut/python/pipeline/parse/test_operator.py b/tests/ut/python/pipeline/parse/test_operator.py
index a3412a6f8f..a3c5f7e422 100644
--- a/tests/ut/python/pipeline/parse/test_operator.py
+++ b/tests/ut/python/pipeline/parse/test_operator.py
@@ -131,3 +131,72 @@ def test_ME_arithmetic_operator_0070():
 def test_ME_logical_operator_0020():
     """ test_ME_logical_operator_0020 """
     logical_operator_base('or')
+
+
+def test_ops():
+    class OpsNet(Cell):
+        """ OpsNet definition """
+
+        def __init__(self, x, y):
+            super(OpsNet, self).__init__()
+            self.x = x
+            self.y = y
+            self.int = 4
+            self.float = 3.2
+            self.str_a = "hello"
+            self.str_b = "world"
+
+        def construct(self, x, y):
+            h = x // y
+            m = x ** y
+            n = x % y
+            r = self.x // self.y
+            s = self.x ** self.y
+            t = self.x % self.y
+            p = h + m + n
+            q = r + s + t
+            ret_pow = p ** q + q ** p
+            ret_mod = p % q + q % p
+            ret_floor = p // q + q // p
+            ret = ret_pow + ret_mod + ret_floor
+            if self.int > self.float:
+                if self.str_a + self.str_b == "helloworld":
+                    return ret
+            return x
+
+    net = OpsNet(9, 2)
+    x = Tensor(np.random.randint(low=1, high=10, size=(2, 3, 4), dtype=np.int32))
+    y = Tensor(np.random.randint(low=10, high=20, size=(2, 3, 4), dtype=np.int32))
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    net(x, y)
+
+
+def test_in_dict():
+    class InDictNet(Cell):
+        """ InDictNet definition """
+
+        def __init__(self, key_in, key_not_in):
+            super(InDictNet, self).__init__()
+            self.key_in = key_in
+            self.key_not_in = key_not_in
+
+        def construct(self, x, y, z):
+            d = {"a": x, "b": y}
+            ret_in = 1
+            ret_not_in = 2
+            if self.key_in in d:
+                ret_in = d[self.key_in]
+            if self.key_not_in not in d:
+                ret_not_in = z
+            ret = ret_in + ret_not_in
+            return ret
+
+    net = InDictNet("a", "c")
+    x = Tensor(np.random.randint(low=1, high=10, size=(2, 3, 4), dtype=np.int32))
+    y = Tensor(np.random.randint(low=10, high=20, size=(2, 3, 4), dtype=np.int32))
+    z = Tensor(np.random.randint(low=20, high=30, size=(2, 3, 4), dtype=np.int32))
+    context.set_context(mode=context.GRAPH_MODE)
+    net(x, y, z)
+
+
+

From 4ba6f7884d3eaa144f8a896674f409f5a836d9ea Mon Sep 17 00:00:00 2001
From: zhangz0911gm <zhangzheng44@huawei.com>
Date: Tue, 14 Apr 2020 21:58:04 -0400
Subject: [PATCH 288/367] Fixing problem issues including class slice example
 cannot run, adding an example for class SigmoidCrossEntropyWithLogits etc.

---
 mindspore/nn/optim/optimizer.py       | 14 ++++++++++++++
 mindspore/nn/optim/sgd.py             | 12 +++++++++++-
 mindspore/ops/operations/array_ops.py |  5 ++++-
 mindspore/ops/operations/nn_ops.py    |  5 +++--
 4 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index bfbde78fff..6c6d14ed7a 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -81,8 +81,22 @@ class Optimizer(Cell):
             else:
                 raise TypeError("Learning rate should be float, Tensor or Iterable.")
 
+        if isinstance(weight_decay, int):
+            weight_decay = float(weight_decay)
+
+        if not isinstance(weight_decay, float):
+            raise TypeError("weight_decay should be a float number!")
+
+        if isinstance(loss_scale, int):
+            loss_scale = float(loss_scale)
+
+        if not isinstance(loss_scale, float):
+            raise TypeError("loss_scale should be a float number!")
+
         if loss_scale <= 0.0:
             raise ValueError("Loss scale should be greater than 0, but got {}".format(loss_scale))
+        self.loss_scale = loss_scale
+
         if weight_decay < 0.0:
             raise ValueError("Weight decay should be equal or greater than 0, but got {}".format(weight_decay))
 
diff --git a/mindspore/nn/optim/sgd.py b/mindspore/nn/optim/sgd.py
index a18adb8184..983be4bf80 100755
--- a/mindspore/nn/optim/sgd.py
+++ b/mindspore/nn/optim/sgd.py
@@ -61,7 +61,8 @@ class SGD(Optimizer):
         dampening (float): A floating point value of dampening for momentum. Default: 0.
         weight_decay (float): Weight decay (L2 penalty). Default: 0.
         nesterov (bool): Enables the Nesterov momentum. Default: False.
-        loss_scale (float): A floating point value for the loss scale. Default: 1.0.
+        loss_scale (float): A floating point value for the loss scale, which should be larger
+        than 0.0. Default: 1.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -83,9 +84,18 @@ class SGD(Optimizer):
 
         super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale)
 
+        if not isinstance(momentum, float):
+            raise TypeError("momentum should be float number!")
+
         if isinstance(momentum, float) and momentum < 0.0:
             raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
 
+        if not isinstance(dampening, float):
+            raise TypeError("dampening should be float number")
+
+        if isinstance(dampening, int):
+            dampening = float(dampening)
+
         if dampening < 0.0:
             raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening))
         self.dampening = dampening
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index a7c3f50440..499f5d4f57 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1008,6 +1008,7 @@ class Argmax(PrimitiveWithInfer):
 
     def infer_dtype(self, x_dtype):
         validator.check_subclass("input_x", x_dtype, mstype.tensor)
+        validator.check_typename('input_x', x_dtype, [mstype.float32, mstype.float16])
         return mstype.tensor_type(self.output_type)
 
 
@@ -1500,7 +1501,9 @@ class Slice(PrimitiveWithInfer):
         Tensor.
 
     Examples:
-        >>> data = Tensor(np.array([3,2,3]).astype(np.int32))
+        >>> data = Tensor(np.array([[[1, 1, 1], [2, 2, 2]],
+        >>>                         [[3, 3, 3], [4, 4, 4]],
+        >>>                         [[5, 5, 5], [6, 6, 6]]]).astype(np.int32))
         >>> type = P.Slice()(data, (1, 0, 0), (1, 1, 3))
     """
 
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index f5037882f1..180e4cfe33 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1436,9 +1436,9 @@ class SGD(PrimitiveWithInfer):
         nesterov (bool): Enable Nesterov momentum. Default: False.
 
     Inputs:
-        - **parameters** (Tensor) - Parameters to be updated.
+        - **parameters** (Tensor) - Parameters to be updated. Their data type can be list or tuple.
         - **gradient** (Tensor) - Gradients.
-        - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
+        - **learning_rate** (Tensor) - Learning rate. Must be float value. e.g. Tensor(0.1, mindspore.float32).
         - **accum** (Tensor) - Accum(velocity) to be updated.
         - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
         - **stat** (Tensor) - States to be updated with the same shape as gradient.
@@ -1449,6 +1449,7 @@ class SGD(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, dampening=0.0, weight_decay=0.0, nesterov=False):
+        validator.check_type("nesterov", nesterov, [bool])
         self.init_prim_io_names(inputs=['parameters', 'gradient', 'learning_rate', 'accum', 'momentum', 'stat'],
                                 outputs=['output'])
 

From bce5f577526e68e0d8852d8934a33aa8917f05e1 Mon Sep 17 00:00:00 2001
From: caifubi <caifubi1@huawei.com>
Date: Thu, 16 Apr 2020 16:52:27 +0800
Subject: [PATCH 289/367] use GraphId as key of DavinciModel in
 ascend_kernel_runtime.cc

---
 .../device/ascend/ascend_kernel_runtime.cc    | 69 ++++++++++---------
 .../device/ascend/ascend_kernel_runtime.h     |  8 +--
 2 files changed, 40 insertions(+), 37 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
index 6d849bd2a5..935e694636 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
@@ -54,9 +54,9 @@ static const size_t PRAMATER_OUTPUT_INDEX = 0;
 AscendKernelRuntime::~AscendKernelRuntime() { graph_model_map_.clear(); }
 
 void AscendKernelRuntime::ClearGraphModelMap() {
-  for (auto &iter : graph_model_id_map_) {
-    MS_LOG(INFO) << "Ge UnloadModel " << iter.second;
-    auto ret = ge::model_runner::ModelRunner::Instance().UnloadModel(iter.second);
+  for (auto &iter : graph_model_map_) {
+    MS_LOG(INFO) << "Ge UnloadModel " << iter.first;
+    auto ret = ge::model_runner::ModelRunner::Instance().UnloadModel(iter.first);
     if (!ret) {
       MS_LOG(ERROR) << "UnloadModel failed";
     }
@@ -249,6 +249,10 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size
 }
 
 bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
+  if (graph == nullptr) {
+    MS_EXCEPTION(NotExistsError) << "session::KernelGraph is NULL!";
+  }
+  MS_LOG(INFO) << "GenTask start. GraphId:" << graph->graph_id();
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   bool is_task_sink = context_ptr->enable_task_sink();
@@ -261,19 +265,15 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
     mindspore::memreuse::MemReuseChecker::GetInstance().CheckNormalIR(graph);
   }
 #endif
-  if (graph == nullptr) {
-    MS_EXCEPTION(NotExistsError) << "session::KernelGraph is NULL!";
-  }
   vector<std::shared_ptr<TaskInfo>> task_info_list;
   auto anf_node_list = graph->execution_order();
   TaskGenerator::GenTasks(anf_node_list, &task_info_list, graph->graph_id());
 
   // Store the task_info_list
-  auto iter = task_map_.find(graph);
-  if (iter != task_map_.end()) {
-    MS_LOG(EXCEPTION) << "graph TaskInfo list already exist";
+  auto insert_ret = task_map_.insert(std::make_pair(graph->graph_id(), task_info_list));
+  if (!insert_ret.second) {
+    MS_LOG(EXCEPTION) << "Duplicate GraphId! Please check in ascend_session.";
   }
-  task_map_[graph] = task_info_list;
 
   // Graph may have no compute node, such TensorAddGrad.
   if (task_info_list.empty()) {
@@ -296,25 +296,19 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
     task_info_list, empty_list, empty_list, empty_list, empty_list, wait_active_stream_list, force_copy_stream_list, 0,
     0, 0, 0, 0, 0, assign_instance.GetTotalStreamNum(), 1, assign_instance.GetTotalEventNum(), 0);
 
-  graph_model_map_[graph] = model;
-  graph_model_id_map_[graph] = graph->graph_id();
+  auto ret = graph_model_map_.insert(std::make_pair(graph->graph_id(), model));
+  if (!ret.second) {
+    MS_LOG(EXCEPTION) << "Duplicate GraphId! Please check in ascend_session.";
+  }
   MS_LOG(INFO) << "TaskGenerator GetTaskInfo end...";
   return true;
 }
 
-uint32_t AscendKernelRuntime::GetGraphModelId(const session::KernelGraph *kernel_graph) {
-  MS_EXCEPTION_IF_NULL(kernel_graph);
-  auto iter = graph_model_id_map_.find(kernel_graph);
-  if (iter == graph_model_id_map_.end()) {
-    MS_LOG(EXCEPTION) << "graph not in the map";
-  }
-  return iter->second;
-}
-
 bool AscendKernelRuntime::LoadTask(const session::KernelGraph *graph) {
   if (graph == nullptr) {
     MS_EXCEPTION(NotExistsError) << "Null pointer graph, LoadTask failed. ";
   }
+  MS_LOG(INFO) << "LoadTask start. GraphId:" << graph->graph_id();
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   bool is_task_sink = context_ptr->enable_task_sink();
@@ -327,23 +321,22 @@ bool AscendKernelRuntime::LoadTask(const session::KernelGraph *graph) {
     return true;
   }
 
-  auto task_iter = graph_model_map_.find(graph);
-  if (task_iter == graph_model_map_.end()) {
-    MS_LOG(ERROR) << "task not exist";
+  auto model_iter = graph_model_map_.find(graph->graph_id());
+  if (model_iter == graph_model_map_.end()) {
+    MS_LOG(ERROR) << "GraphId:" << graph->graph_id() << " Invalid! Graph LoadTask without GenTask.";
     return false;
   }
 
-  auto model_id = GetGraphModelId(graph);
   std::shared_ptr<ge::ModelListener> listener;
-  MS_LOG(INFO) << "LoadDavinciModel mode_id:" << model_id;
-  bool status =
-    ge::model_runner::ModelRunner::Instance().LoadDavinciModel(device_id_, 0, model_id, task_iter->second, listener);
+  MS_LOG(INFO) << "LoadDavinciModel mode_id:" << model_iter->first;
+  bool status = ge::model_runner::ModelRunner::Instance().LoadDavinciModel(device_id_, 0, model_iter->first,
+                                                                           model_iter->second, listener);
   if (!status) {
-    MS_LOG(INFO) << "load task failed";
+    MS_LOG(ERROR) << "load task failed";
     return false;
   }
   if (ProfilingManager::GetInstance().IsProfiling()) {
-    std::vector<uint32_t> task_ids = ge::model_runner::ModelRunner::Instance().GetTaskIdList(model_id);
+    std::vector<uint32_t> task_ids = ge::model_runner::ModelRunner::Instance().GetTaskIdList(model_iter->first);
     ProfilingUtils::ReportProfilingData(graph->graph_id(), task_ids);
   }
   return true;
@@ -351,6 +344,8 @@ bool AscendKernelRuntime::LoadTask(const session::KernelGraph *graph) {
 
 bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  MS_LOG(INFO) << "RunTask start. GraphId:" << graph->graph_id();
+
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
   ge::InputData input_tensors = ge::InputData();
@@ -360,8 +355,12 @@ bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
     return true;
   }
 
-  auto model_id = GetGraphModelId(graph);
-  bool status = ge::model_runner::ModelRunner::Instance().RunModel(model_id, input_tensors, output_tensors);
+  if (!CheckGraphIdValid(graph->graph_id())) {
+    MS_LOG(ERROR) << "GraphId:" << graph->graph_id() << " Invalid! Graph RunTask without GenTask.";
+    return false;
+  }
+
+  bool status = ge::model_runner::ModelRunner::Instance().RunModel(graph->graph_id(), input_tensors, output_tensors);
   if (!status) {
     MS_LOG(INFO) << "run task failed";
     return false;
@@ -497,12 +496,16 @@ bool AscendKernelRuntime::DestroyHccl() {
 }
 
 bool AscendKernelRuntime::GraphWithEmptyTaskList(const session::KernelGraph *graph) const {
-  auto iter = task_map_.find(graph);
+  auto iter = task_map_.find(graph->graph_id());
   if (iter == task_map_.end()) {
     MS_LOG(EXCEPTION) << "Unknown graph ptr";
   }
   return iter->second.empty();
 }
+
+bool AscendKernelRuntime::CheckGraphIdValid(GraphId graph_id) const {
+  return task_map_.find(graph_id) != task_map_.end() && graph_model_map_.find(graph_id) != graph_model_map_.end();
+}
 }  // namespace ascend
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
index 547228d32f..5d0f61d0a6 100644
--- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
+++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h
@@ -23,6 +23,7 @@
 #include "runtime/context.h"
 #include "framework/ge_runtime/davinci_model.h"
 #include "device/kernel_runtime_manager.h"
+#include "session/session_basic.h"
 
 using ge::model_runner::TaskInfo;
 using std::unordered_map;
@@ -54,14 +55,13 @@ class AscendKernelRuntime : public KernelRuntime {
 
   void ClearGraphModelMap();
   void ReleaseDeviceRes() override;
-  uint32_t GetGraphModelId(const session::KernelGraph *kernel_graph);
   bool GraphWithEmptyTaskList(const session::KernelGraph *graph) const;
+  bool CheckGraphIdValid(GraphId graph_id) const;
 
   rtContext_t rt_context_{nullptr};
   bool initialized_{false};
-  unordered_map<const session::KernelGraph *, vector<std::shared_ptr<TaskInfo>>> task_map_;
-  unordered_map<const session::KernelGraph *, std::shared_ptr<ge::model_runner::DavinciModel>> graph_model_map_;
-  unordered_map<const session::KernelGraph *, uint32_t> graph_model_id_map_;
+  unordered_map<GraphId, vector<std::shared_ptr<TaskInfo>>> task_map_;
+  unordered_map<GraphId, std::shared_ptr<ge::model_runner::DavinciModel>> graph_model_map_;
 };
 
 MS_REG_KERNEL_RUNTIME(kAscendDevice, AscendKernelRuntime);

From a6146a6d2f227458823b342d661e7b72a55eac56 Mon Sep 17 00:00:00 2001
From: leonwanghui <leon.wanghui@huawei.com>
Date: Thu, 16 Apr 2020 17:34:53 +0800
Subject: [PATCH 290/367] Update mindspore-gpu:devel docker image

---
 docker/mindspore-gpu/devel/Dockerfile | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docker/mindspore-gpu/devel/Dockerfile b/docker/mindspore-gpu/devel/Dockerfile
index 113bed5b06..fe88bd9a2c 100644
--- a/docker/mindspore-gpu/devel/Dockerfile
+++ b/docker/mindspore-gpu/devel/Dockerfile
@@ -6,7 +6,6 @@ MAINTAINER leonwanghui <leon.wanghui@huawei.com>
 ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
 ENV CMAKE_ROOT_PATH /usr/local/cmake-3.14.1
 ENV PATH ${CMAKE_ROOT_PATH}/bin:/usr/local/bin:$PATH
-ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
 
 # Install base tools
 RUN apt update \
@@ -37,6 +36,9 @@ RUN DEBIAN_FRONTEND=noninteractive apt install -y \
     automake \
     flex
 
+# Configure cuDNN (v7.6.5)
+RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5 /usr/local/cuda/lib64/libcudnn.so
+
 # Set bash
 RUN echo "dash dash/sh boolean false" | debconf-set-selections
 RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash

From 51de42bc050f44acedf3cd08717b8f81cb327f4e Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Thu, 16 Apr 2020 16:53:58 +0800
Subject: [PATCH 291/367] modify examples of ApplyMomentum/ArgMax

---
 mindspore/ops/operations/array_ops.py | 3 +--
 mindspore/ops/operations/nn_ops.py    | 6 +-----
 2 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index a7c3f50440..0af284b947 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -983,8 +983,7 @@ class Argmax(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]))
-        >>> index = P.Argmax()(input_x)
-        >>> assert index == Tensor(1, mindspore.int64)
+        >>> index = P.Argmax(output_type=mindspore.int32)(input_x)
     """
 
     @prim_attr_register
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index f5037882f1..42ae394289 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1345,11 +1345,7 @@ class ApplyMomentum(PrimitiveWithInfer):
         Tensor, parameters to be updated.
 
     Examples:
-        >>> net = ResNet50()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = P.ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])),
-                                filter(lambda x: x.requires_grad, net.get_parameters()))
-        >>> model = Model(net, loss, opt)
+        Please refer to the usage in nn.ApplyMomentum.
     """
     __mindspore_signature__ = (
         ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),

From 3684ec4967f977d8c56c901e99c9447354f12e54 Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Thu, 16 Apr 2020 19:28:42 +0800
Subject: [PATCH 292/367] fix example of ControlDepend to ensure pass in gpu
 and cpu

---
 mindspore/ops/operations/control_ops.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index 30f1e25a34..ca161cfad0 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -51,16 +51,18 @@ class ControlDepend(Primitive):
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.global_step = mindspore.Parameter(initializer(0, [1]), name="global_step")
-        >>>         self.rate = 0.2
         >>>         self.control_depend = P.ControlDepend()
+        >>>         self.softmax = P.Softmax()
         >>>
-        >>>     def construct(self, x):
-        >>>         data = self.rate * self.global_step + x
-        >>>         added_global_step = self.global_step + 1
-        >>>         self.global_step = added_global_step
-        >>>         self.control_depend(data, added_global_step)
-        >>>         return data
+        >>>     def construct(self, x, y):
+        >>>         mul = x * y
+        >>>         softmax = self.softmax(x)
+        >>>         ret = self.control_depend(mul, softmax)
+        >>>         return ret
+        >>> x = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
+        >>> y = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
+        >>> net = Net()
+        >>> output = net(x, y)
     """
 
     @prim_attr_register

From 85205ea80be9cbe0905c530adfb51e246b9d002f Mon Sep 17 00:00:00 2001
From: panyifeng <panyifeng@huawei.com>
Date: Thu, 16 Apr 2020 19:58:37 +0800
Subject: [PATCH 293/367] fix document example for insert_grad_of

---
 mindspore/ops/operations/debug_ops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py
index a69dcc2df1..e4467f5ce1 100644
--- a/mindspore/ops/operations/debug_ops.py
+++ b/mindspore/ops/operations/debug_ops.py
@@ -123,6 +123,7 @@ class InsertGradientOf(PrimitiveWithInfer):
         >>>     return ret
         >>>
         >>> clip = P.InsertGradientOf(clip_gradient)
+        >>> grad_all = C.GradOperation('get_all', get_all=True)
         >>> def InsertGradientOfClipDemo():
         >>>     def clip_test(x, y):
         >>>         x = clip(x)
@@ -135,7 +136,7 @@ class InsertGradientOf(PrimitiveWithInfer):
         >>>         return clip_test(x, y)
         >>>
         >>>     def fd(x, y):
-        >>>         return C.grad_all(clip_test)(x, y)
+        >>>         return grad_all(clip_test)(x, y)
         >>>
         >>>     print("forward: ", f(1.1, 0.1))
         >>>     print("clip_gradient:", fd(1.1, 0.1))

From 37042d5b6782baaab926a194d6f5b48dd2905df4 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 08:08:07 -0400
Subject: [PATCH 294/367] add global batch normalization

---
 mindspore/nn/layer/__init__.py       |   4 +-
 mindspore/nn/layer/normalization.py  | 130 ++++++++++++++++++++++++---
 tests/ut/python/nn/test_batchnorm.py |  15 ++++
 3 files changed, 133 insertions(+), 16 deletions(-)

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index cf601f03ff..714b517a84 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -18,7 +18,7 @@ Layer.
 The high-level components(Cells) used to construct the neural network.
 """
 from .activation import Softmax, LogSoftmax, ReLU, ReLU6, Tanh, GELU, ELU, Sigmoid, PReLU, get_activation, LeakyReLU, HSigmoid, HSwish
-from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm, GroupNorm
+from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm, GroupNorm, GlobalBatchNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
@@ -29,7 +29,7 @@ from .image import ImageGradients, SSIM
 
 __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'PReLU', 'get_activation', 'LeakyReLU', 'HSigmoid', 'HSwish', 'ELU',
-           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'GroupNorm',
+           'BatchNorm1d', 'BatchNorm2d', 'LayerNorm', 'GroupNorm', 'GlobalBatchNorm',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 4aafaf031e..4bfa222986 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -20,16 +20,28 @@ from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
 import mindspore.common.dtype as mstype
 import mindspore.context as context
-from mindspore._checkparam import check_int_positive, check_bool, check_typename
+from mindspore._checkparam import check_bool, check_typename
 from mindspore._extends import cell_attr_register
+from mindspore.communication.management import get_local_rank_size, get_rank
+from mindspore.communication import management
+from mindspore._checkparam import check_int_positive
 from ..cell import Cell
 
+class _GlobalBNHelper(Cell):
+    def __init__(self, group):
+        super(_GlobalBNHelper, self).__init__()
+        self.group = group
+        self.reduce = P.AllReduce(P.ReduceOp.SUM, group=self.group).add_prim_attr('fusion', 1)
+    def construct(self, x):
+        x = self.reduce(x)
+        return x
 
 class _BatchNorm(Cell):
     """Batch Normalization base class."""
     @cell_attr_register
     def __init__(self,
                  num_features,
+                 group=1,
                  eps=1e-5,
                  momentum=0.9,
                  affine=True,
@@ -56,6 +68,20 @@ class _BatchNorm(Cell):
             gamma_init, num_features), name="gamma", requires_grad=affine)
         self.beta = Parameter(initializer(
             beta_init, num_features), name="beta", requires_grad=affine)
+        self.group = check_int_positive(group)
+        self.rank_id = get_rank()
+        self.rank_size = get_local_rank_size()
+        self.device_list = [i for i in range(0, self.rank_size)]
+        self.rank_list = self.list_group(self.device_list, self.group)
+        self.rank_list_idx = len(self.rank_list)
+        for i in range(self.rank_list_idx):
+            if self.rank_id in self.rank_list[i] and self.group != 1:
+                self.is_global = True
+                management.create_group('group' + str(i), self.rank_list[i])
+                self.all_reduce = _GlobalBNHelper('group' + str(i))
+        self.shape = P.Shape()
+        self.reduce_mean = P.ReduceMean()
+        self.square = P.Square()
 
         if context.get_context("enable_ge"):
             self.is_ge_backend = True
@@ -82,22 +108,52 @@ class _BatchNorm(Cell):
     def _check_data_dim(self, x):
         raise NotImplementedError
 
+    def list_group(self, world_rank, group_size):
+        if group_size > get_local_rank_size():
+            raise ValueError("group size can not be greater than local rank size, group size is {}, local_rank_size is {}".format(group_size, get_local_rank_size()))
+        if len(world_rank) % group_size != 0:
+            raise ValueError("please make your group size correct.")
+        world_rank_list = zip(*(iter(world_rank),) *group_size)
+        group_list = [list(i) for i in world_rank_list]
+        return group_list
+
     def construct(self, x):
         if self.training and self.use_batch_statistics:
             if self.is_ge_backend:
-                y, batch_mean, batch_var, _, _ = \
-                    self.bn_train(x,
-                                  self.gamma,
-                                  self.beta,
-                                  None,
-                                  None)
-
-                mean_sub = self.sub_mean(self.moving_mean, batch_mean)
-                temp_mean = self.mul_mean(mean_sub, self.momentum)
-                mean_sub2 = self.sub_var(self.moving_variance, batch_var)
-                temp_variance = self.mul_var(mean_sub2, self.momentum)
-                y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
-                y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
+                if self.is_global:
+                    x_mean = self.reduce_mean(x)
+                    x_mean_square = self.reduce_mean(self.square(x))
+                    global_batch_mean = self.all_reduce(x_mean) / self.group
+                    global_batch_mean_square = self.all_reduce(x_mean_square) / self.group
+                    global_mean = global_batch_mean
+                    global_var = global_batch_mean_square - self.square(global_batch_mean)
+                    y, batch_mean, batch_var, _, _ = \
+                        self.bn_train(x,
+                                      self.gamma,
+                                      self.beta,
+                                      None,
+                                      None)
+
+                    mean_sub = self.sub_mean(self.moving_mean, global_mean)
+                    temp_mean = self.mul_mean(mean_sub, self.momentum)
+                    mean_sub2 = self.sub_var(self.moving_variance, global_var)
+                    temp_variance = self.mul_var(mean_sub2, self.momentum)
+                    y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
+                    y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
+                else:
+                    y, batch_mean, batch_var, _, _ = \
+                        self.bn_train(x,
+                                      self.gamma,
+                                      self.beta,
+                                      None,
+                                      None)
+
+                    mean_sub = self.sub_mean(self.moving_mean, batch_mean)
+                    temp_mean = self.mul_mean(mean_sub, self.momentum)
+                    mean_sub2 = self.sub_var(self.moving_variance, batch_var)
+                    temp_variance = self.mul_var(mean_sub2, self.momentum)
+                    y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
+                    y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
             else:
                 y = self.bn_train(x,
                                   self.gamma,
@@ -221,6 +277,52 @@ class BatchNorm2d(_BatchNorm):
             pass
 
 
+class GlobalBatchNorm(_BatchNorm):
+    r"""
+    Global normalization layer over a N-dimension input.
+
+    Global Normalization is cross device synchronized batch normalization. Batch Normalization implementation
+    only normalize the data within each device. Global normalization will normalize the input within the group.
+    It has been described in the paper `Batch Normalization: Accelerating Deep Network Training by
+    Reducing Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the
+    feature using a mini-batch of data and the learned parameters which can be described in the following formula.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    Args:
+        num_features (int): `C` from an expected input of size (N, C, H, W).
+        group (int): The number of device in each group.
+        eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
+        momentum (float): A floating hyperparameter of the momentum for the
+            running_mean and running_var computation. Default: 0.9.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data, else use
+            the mean value and variance value of specified value. Default: True.
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+
+    Examples:
+        >>> global_bn_op = nn.GlobalBatchNorm(num_features=3, group=4)
+        >>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
+        >>> global_bn_op(input)
+    """
+
 class LayerNorm(Cell):
     r"""
     Applies Layer Normalization over a mini-batch of inputs.
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index e73b7ebbf0..23ca79e8e1 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -19,6 +19,7 @@ import pytest
 import mindspore.nn as nn
 from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.communication.management import init
 
 
 def test_bn_pars_valid1():
@@ -70,3 +71,17 @@ def test_compile_groupnorm():
     net = nn.GroupNorm(16, 64)
     input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))
     _executor.compile(net, input_data)
+
+class GlobalBNNet(nn.Cell):
+    def __init__(self):
+        super(GlobalBNNet, self).__init__()
+        self.bn = nn.GlobalBatchNorm(num_features = 2, group = 4)
+    def construct(self, x):
+        return self.bn(x)
+
+def test_gloabl_bn():
+    init("hccl")
+    net = GlobalBNNet()
+    input_data = Tensor(np.array([[2.4, 2.1], [3.2, 5.4]], dtype=np.float32))
+    net.set_train()
+    out = net(input_data)

From 75fec82b5248f656f0a97701ce32f54cc0c9be79 Mon Sep 17 00:00:00 2001
From: kingfo <wangqiuliang@huawei.com>
Date: Tue, 14 Apr 2020 11:52:56 +0800
Subject: [PATCH 295/367] resolve pynative operator issue

---
 mindspore/_extends/builtin_operations.py      |  8 +-
 mindspore/ccsrc/pipeline/pipeline.cc          | 12 +--
 mindspore/ccsrc/pynative/pynative_execute.cc  | 87 ++++++++++++++-----
 mindspore/common/parameter.py                 | 18 ++--
 mindspore/common/tensor.py                    | 43 ++++++---
 mindspore/ops/_grad/grad_array_ops.py         |  2 +-
 mindspore/ops/_utils/__init__.py              |  4 +-
 .../ops/_utils/{broadcast.py => utils.py}     | 29 ++++++-
 mindspore/ops/operations/__init__.py          |  3 +-
 mindspore/ops/operations/_grad_ops.py         | 28 ++++++
 mindspore/ops/operations/array_ops.py         | 53 +----------
 mindspore/ops/operations/other_ops.py         |  3 +
 tests/ut/python/ir/test_tensor.py             | 22 +++++
 tests/vm_impl/array_ops_vm_impl.py            |  2 +-
 14 files changed, 208 insertions(+), 106 deletions(-)
 rename mindspore/ops/_utils/{broadcast.py => utils.py} (62%)

diff --git a/mindspore/_extends/builtin_operations.py b/mindspore/_extends/builtin_operations.py
index 087b704719..6fea07425e 100644
--- a/mindspore/_extends/builtin_operations.py
+++ b/mindspore/_extends/builtin_operations.py
@@ -125,7 +125,7 @@ def list_len(x):
     return len(x)
 
 
-# only used in PyNative modes
+# only used in PyNative mode
 def partial(*args):
     """Implement `partial`."""
     func = args[0].__call__
@@ -133,10 +133,14 @@ def partial(*args):
     return partial_func
 
 
-# only used in PyNative modes
+# only used in PyNative mode
 def depend(value, expr):
     return value
 
+# only used in PyNative mode
+def make_ref(key, value, ref):
+    return value
+
 
 def scalar_cast(x, t):
     """Implement scalar_cast."""
diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc
index 003d4c15e9..cd4fe28db9 100644
--- a/mindspore/ccsrc/pipeline/pipeline.cc
+++ b/mindspore/ccsrc/pipeline/pipeline.cc
@@ -616,17 +616,19 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
     return ExecDFGraph(info_, args, phase_s);
   }
 #else
-  if (backend == "ge") {
-    std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
+  if (backend == "ms" || backend == "ge") {
+    auto ret_val = std::make_shared<py::object>();
     if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
       if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
         return *ret_val;
       }
     }
-    if (args.size() > 0) {
-      return args[0];
+    if (backend == "ge") {
+      if (args.size() > 0) {
+        return args[0];
+      }
+      return args;
     }
-    return args;
   }
 #endif
   std::size_t full_arg_size = ArgListSize(phase_s);
diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index 4144ad2d6b..5620634bcc 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -20,11 +20,13 @@
 #include <map>
 #include <set>
 #include <unordered_set>
+#include <algorithm>
 
 #include "utils/any.h"
 #include "utils/utils.h"
 #include "utils/context/ms_context.h"
 #include "operator/ops.h"
+#include "operator/composite/do_signature.h"
 #include "pipeline/parse/data_converter.h"
 #include "pipeline/static_analysis/prim.h"
 #include "session/session_factory.h"
@@ -50,6 +52,57 @@ inline ValuePtr PyAttrValue(const py::object& obj) {
   return converted_ret;
 }
 
+py::tuple ConvertInputs(const PrimitivePyPtr& prim, const py::tuple& py_args) {
+  auto signature = prim->signatures();
+  std::vector<SignatureEnumDType> dtypes;
+  (void)std::transform(signature.begin(), signature.end(), std::back_inserter(dtypes),
+                       [](const Signature& sig) { return sig.dtype; });
+  int empty_dtype_count = std::count(dtypes.begin(), dtypes.end(), SignatureEnumDType::kDTypeEmptyDefaultValue);
+  if (dtypes.size() == 0 || static_cast<int>(dtypes.size()) == empty_dtype_count) {
+    return py_args;
+  }
+  std::map<SignatureEnumDType, std::vector<size_t>> type_indexs;
+  for (size_t i = 0; i < dtypes.size(); ++i) {
+    auto it = type_indexs.find(dtypes[i]);
+    if (it == type_indexs.end()) {
+      (void)type_indexs.insert(std::make_pair(dtypes[i], std::vector<size_t>{i}));
+    } else {
+      it->second.push_back(i);
+    }
+  }
+  std::map<SignatureEnumDType, size_t> dst_type;
+  for (auto it = type_indexs.begin(); it != type_indexs.end(); (void)++it) {
+    auto type = it->first;
+    auto indexs = it->second;
+    if (indexs.size() < 2) {
+      continue;
+    }
+    size_t m_index = indexs[0];
+    for (size_t i = 1; i < indexs.size(); ++i) {
+      if (py::isinstance<tensor::Tensor>(py_args[indexs[i]])) {
+        m_index = indexs[i];
+      }
+    }
+    (void)dst_type.insert(std::make_pair(type, m_index));
+  }
+  py::tuple py_inputs(py_args.size());
+  for (size_t i = 0; i < py_args.size(); ++i) {
+    auto it = dst_type.find(dtypes[i]);
+    if (it != dst_type.end() && it->second != i &&
+        (py::isinstance<py::int_>(py_args[i]) || py::isinstance<py::float_>(py_args[i]))) {
+      auto tensor_ptr = py::cast<tensor::TensorPtr>(py_args[it->second]);
+      if (py::isinstance<py::int_>(py_args[i])) {
+        py_inputs[i] = std::make_shared<tensor::Tensor>(py::cast<py::int_>(py_args[i]), tensor_ptr->Dtype());
+      } else {
+        py_inputs[i] = std::make_shared<tensor::Tensor>(py::cast<py::float_>(py_args[i]), tensor_ptr->Dtype());
+      }
+      continue;
+    }
+    py_inputs[i] = py_args[i];
+  }
+  return py_inputs;
+}
+
 void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecInfo* const op_exec_info) {
   size_t size = py_args.size();
   AbstractBasePtrList args_spec_list;
@@ -73,30 +126,22 @@ OpExecInfoPtr GenerateOpExecInfo(const py::args& args) {
   auto op_exec_info = std::make_shared<OpExecInfo>();
   MS_EXCEPTION_IF_NULL(op_exec_info);
   op_exec_info->op_name = py::cast<std::string>(args[PY_NAME]);
-  if (py::isinstance<py::none>(args[PY_PRIM])) {
-    py::module ops_mod = py::module::import("mindspore.ops.operations");
-    py::object py_primitive = ops_mod.attr(op_exec_info->op_name.c_str())();
-    op_exec_info->py_primitive = py::cast<PrimitivePyPtr>(py_primitive);
-    py::dict none_attrs = py::dict();
-    op_exec_info->op_attrs = none_attrs;
-  } else {
-    PrimitivePyPtr prim = py::cast<PrimitivePyPtr>(args[PY_PRIM]);
-    auto pyobj = prim->GetPyObj();
-    if (pyobj == nullptr) {
-      MS_LOG(EXCEPTION) << "pyobj is empty";
-    }
-    py::tuple py_args = args[PY_INPUTS];
-    // use python infer method
-    if (ignore_infer_prim.find(op_exec_info->op_name) == ignore_infer_prim.end()) {
-      PynativeInfer(prim, py_args, op_exec_info.get());
-    }
-    op_exec_info->py_primitive = prim;
-    op_exec_info->op_attrs = py::getattr(args[PY_PRIM], "attrs");
+  auto prim = py::cast<PrimitivePyPtr>(args[PY_PRIM]);
+  auto pyobj = prim->GetPyObj();
+  if (pyobj == nullptr) {
+    MS_LOG(EXCEPTION) << "pyobj is empty";
+  }
+  py::tuple py_args = ConvertInputs(prim, args[PY_INPUTS]);
+  // use python infer method
+  if (ignore_infer_prim.find(op_exec_info->op_name) == ignore_infer_prim.end()) {
+    PynativeInfer(prim, py_args, op_exec_info.get());
   }
-  op_exec_info->op_inputs = args[PY_INPUTS];
+  op_exec_info->py_primitive = prim;
+  op_exec_info->op_attrs = py::getattr(args[PY_PRIM], "attrs");
+  op_exec_info->op_inputs = py_args;
   op_exec_info->inputs_mask = args[PY_INPUT_MASK];
   if (op_exec_info->op_inputs.size() != op_exec_info->inputs_mask.size()) {
-    MS_LOG(ERROR) << "" << op_exec_info->op_name << " op_inputs size not equal op_mask";
+    MS_LOG(ERROR) << "op:" << op_exec_info->op_name << " inputs size not equal op_mask";
     return nullptr;
   }
   return op_exec_info;
diff --git a/mindspore/common/parameter.py b/mindspore/common/parameter.py
index c8ddf0eac6..c354bcd235 100644
--- a/mindspore/common/parameter.py
+++ b/mindspore/common/parameter.py
@@ -14,7 +14,7 @@
 # ============================================================================
 
 """Parameter for cell."""
-from copy import copy
+from copy import copy, deepcopy
 import numpy as np
 from .initializer import initializer
 from .tensor import Tensor
@@ -156,16 +156,24 @@ class Parameter:
         return self.default_input
 
     def __add__(self, other):
-        return self.default_input + other
+        res = deepcopy(self)
+        res.default_input = res.default_input + other
+        return res
 
     def __sub__(self, other):
-        return self.default_input - other
+        res = deepcopy(self)
+        res.default_input = res.default_input - other
+        return res
 
     def __mul__(self, other):
-        return self.default_input * other
+        res = deepcopy(self)
+        res.default_input = res.default_input * other
+        return res
 
     def __truediv__(self, other):
-        return self.default_input / other
+        res = deepcopy(self)
+        res.default_input = res.default_input / other
+        return res
 
     def set_parameter_data(self, data):
         if isinstance(data, (Tensor, list, int, float,
diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index 709b2ae280..70b8b169ca 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -70,45 +70,60 @@ class Tensor(Tensor_):
         return str(self.__str__())
 
     def __add__(self, other):
-        if not isinstance(other, Tensor):
-            raise TypeError("input_data must be a tensor")
+        check_type('tensor input_data', other, (Tensor, float, int))
         out = tensor_operator_registry.get('__add__')(self, other)
         return out
 
     def __mul__(self, other):
-        if not isinstance(other, Tensor):
-            raise TypeError("input_data must be a tensor")
+        check_type('tensor input_data', other, (Tensor, float, int))
         out = tensor_operator_registry.get('__mul__')(self, other)
         return out
 
+    def __neg__(self):
+        return Tensor(-self.asnumpy())
+
     def __iadd__(self, other):
         out = self.__add__(other)
         return out
 
+    def __radd__(self, other):
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = tensor_operator_registry.get('__add__')(other, self)
+        return out
+
     def __imul__(self, other):
         out = self.__mul__(other)
         return out
 
+    def __rmul__(self, other):
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = tensor_operator_registry.get('__mul__')(other, self)
+        return out
+
     def __truediv__(self, other):
-        if isinstance(other, (int, float)):
-            other_tensor = Tensor(other, self.dtype())
-        elif isinstance(other, Tensor):
-            other_tensor = other
-        else:
-            raise TypeError("unsupported type for div operation")
-        out = tensor_operator_registry.get('__div__')(self, other_tensor)
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = tensor_operator_registry.get('__div__')(self, other)
+        return out
+
+    def __rtruediv__(self, other):
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = tensor_operator_registry.get('__div__')(other, self)
         return out
 
     def __sub__(self, other):
-        if not isinstance(other, Tensor):
-            raise TypeError("input_data must be a tensor")
-        out = self.__add__(Tensor(-other.asnumpy()))
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = self.__add__(-other)
         return out
 
     def __isub__(self, other):
         out = self.__sub__(other)
         return out
 
+    def __rsub__(self, other):
+        check_type('tensor operation input', other, (Tensor, float, int))
+        out = tensor_operator_registry.get('__add__')(other, Tensor(-self.asnumpy()))
+        return out
+
     def __str__(self):
         if self.dtype() == mstype.type_none:
             return "Unknown Tensor type!"
diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index abad030ae9..35d37b3ada 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -191,7 +191,7 @@ def get_bprop_concat(self):
 
     def bprop(x, out, dout):
         dx = ()
-        out_offset = P.ConcatOffset(F.tuple_len(x), axis)(x)
+        out_offset = G.ConcatOffset(F.tuple_len(x), axis)(x)
         for i in range(F.tuple_len(x)):
             slice_out = P.Slice()(dout, out_offset[i], shape_op(x[i]))
             dx = dx + (slice_out,)
diff --git a/mindspore/ops/_utils/__init__.py b/mindspore/ops/_utils/__init__.py
index 00ce07453a..8fe1102968 100644
--- a/mindspore/ops/_utils/__init__.py
+++ b/mindspore/ops/_utils/__init__.py
@@ -14,6 +14,6 @@
 # ============================================================================
 
 """ops utils."""
-from .broadcast import _get_broadcast_shape
+from .utils import _get_broadcast_shape, _get_concat_offset
 
-__all__ = ['_get_broadcast_shape']
+__all__ = ['_get_broadcast_shape', '_get_concat_offset']
diff --git a/mindspore/ops/_utils/broadcast.py b/mindspore/ops/_utils/utils.py
similarity index 62%
rename from mindspore/ops/_utils/broadcast.py
rename to mindspore/ops/_utils/utils.py
index c71158de57..fbd81c4f0d 100644
--- a/mindspore/ops/_utils/broadcast.py
+++ b/mindspore/ops/_utils/utils.py
@@ -13,8 +13,11 @@
 # limitations under the License.
 # ============================================================================
 
-"""broadcast"""
+"""utils for operator"""
 
+from ..._checkparam import ParamValidator as validator
+from ..._checkparam import Rel
+from ...common import dtype as mstype
 
 def _get_broadcast_shape(x_shape, y_shape, prim_name):
     """
@@ -57,3 +60,27 @@ def _get_broadcast_shape(x_shape, y_shape, prim_name):
     broadcast_shape_front = y_shape[0: y_len - length] if length == x_len else x_shape[0: x_len - length]
     broadcast_shape = broadcast_shape_front + broadcast_shape_back
     return broadcast_shape
+
+
+def _get_concat_offset(x_shp, x_type, axis):
+    """for concat and concatoffset check args and compute offset"""
+    validator.check_type("shape", x_shp, [tuple])
+    validator.check_integer("len of input_x shape", len(x_shp), 0, Rel.GT)
+    validator.check_subclass("shape0", x_type[0], mstype.tensor)
+    validator.check_integer("len of input_x0 shape", len(x_shp[0]), 0, Rel.GT)
+    rank_base = len(x_shp[0])
+    validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH)
+    if axis < 0:
+        axis = axis + rank_base
+    all_shp = x_shp[0][axis]
+    offset = [0,]
+    for i in range(1, len(x_shp)):
+        v = x_shp[i]
+        validator.check('len of x_shp[%d]' % i, len(v), 'len of base', len(x_shp[0]))
+        validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0])
+        for j in range(rank_base):
+            if j != axis and v[j] != x_shp[0][j]:
+                raise ValueError("Concat evaluator element %d shape in input can not concat with first element" % i)
+        offset.append(all_shp)
+        all_shp += v[axis]
+    return offset, all_shp, axis
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 492ebae444..e1dd8e36c5 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -19,7 +19,7 @@ Primitive operator classes.
 A collection of operators to build nerual networks or computing functions.
 """
 
-from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Pack, Unpack,
+from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, GatherNd, GatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
@@ -200,7 +200,6 @@ __all__ = [
     'LogicalOr',
     'Size',
     'DepthwiseConv2dNative',
-    'ConcatOffset',
     'UnsortedSegmentSum',
     "AllGather",
     "AllReduce",
diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index 6143f9d0a0..48d1a2a89c 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -20,6 +20,7 @@ from ..._c_expression import signature_kind as sig_kind
 from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 from ..._checkparam import ParamValidator as validator
 from ..._checkparam import Rel, check_int_positive, check_bool
+from .._utils import _get_concat_offset
 from ...common import dtype as mstype
 
 
@@ -107,6 +108,33 @@ class BinaryCrossEntropyGrad(PrimitiveWithInfer):
             validator.check_two_types_same('x_type', x_type, 'weight_type', weight_type)
         return x_type
 
+class ConcatOffset(PrimitiveWithInfer):
+    """primitive for computing Concat's gradient."""
+
+    @prim_attr_register
+    def __init__(self, N=2, axis=0):
+        """init ConcatOffset"""
+
+    def __infer__(self, input_x):
+        axis = self.axis
+        x_shp = input_x['shape']
+        x_type = input_x['dtype']
+        offset, _, axis = _get_concat_offset(x_shp, x_type, axis)
+        self.add_prim_attr('T', x_type[0].element_type())
+        offset_values = []
+        for i in range(len(x_shp)):
+            values = []
+            for j in range(len(x_shp[0])):
+                value = 0
+                if j == axis:
+                    value = offset[i]
+                values.append(value)
+            offset_values.append(tuple(values))
+        out = {'shape': None,
+               'dtype': None,
+               'value': tuple(offset_values)}
+        return out
+
 
 class Conv2DBackpropFilter(PrimitiveWithInfer):
     """
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index a7c3f50440..da16a2ab29 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -29,6 +29,7 @@ from ..._checkparam import Rel
 from ...common import dtype as mstype
 from ...common.tensor import Tensor
 from ..operations.math_ops import _infer_shape_reduce
+from .._utils import _get_concat_offset
 from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
 
 def _check_infer_attr_reduce(axis, keep_dims):
@@ -1275,30 +1276,6 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
         return out
 
 
-def _get_concat_offset(x_shp, x_type, axis):
-    """for concat and concatoffset check args and compute offset"""
-    validator.check_type("shape", x_shp, [tuple])
-    validator.check_integer("len of input_x shape", len(x_shp), 0, Rel.GT)
-    validator.check_subclass("shape0", x_type[0], mstype.tensor)
-    validator.check_integer("len of input_x0 shape", len(x_shp[0]), 0, Rel.GT)
-    rank_base = len(x_shp[0])
-    validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH)
-    if axis < 0:
-        axis = axis + rank_base
-    all_shp = x_shp[0][axis]
-    offset = [0,]
-    for i in range(1, len(x_shp)):
-        v = x_shp[i]
-        validator.check('len of x_shp[%d]' % i, len(v), 'len of base', len(x_shp[0]))
-        validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0])
-        for j in range(rank_base):
-            if j != axis and v[j] != x_shp[0][j]:
-                raise ValueError("Concat evaluator element %d shape in input can not concat with first element" % i)
-        offset.append(all_shp)
-        all_shp += v[axis]
-    return offset, all_shp, axis
-
-
 class Concat(PrimitiveWithInfer):
     r"""
     Concat tensor in specified axis.
@@ -1531,34 +1508,6 @@ class Slice(PrimitiveWithInfer):
                 'value': None}
 
 
-class ConcatOffset(PrimitiveWithInfer):
-    """primitive for computing Concat's gradient."""
-
-    @prim_attr_register
-    def __init__(self, N=2, axis=0):
-        """init ConcatOffset"""
-
-    def __infer__(self, input_x):
-        axis = self.axis
-        x_shp = input_x['shape']
-        x_type = input_x['dtype']
-        offset, _, axis = _get_concat_offset(x_shp, x_type, axis)
-        self.add_prim_attr('T', x_type[0].element_type())
-        offset_values = []
-        for i in range(len(x_shp)):
-            values = []
-            for j in range(len(x_shp[0])):
-                value = 0
-                if j == axis:
-                    value = offset[i]
-                values.append(value)
-            offset_values.append(tuple(values))
-        out = {'shape': None,
-               'dtype': None,
-               'value': tuple(offset_values)}
-        return out
-
-
 class Select(PrimitiveWithInfer):
     r"""
 
diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py
index b6182f0476..ff66e80972 100644
--- a/mindspore/ops/operations/other_ops.py
+++ b/mindspore/ops/operations/other_ops.py
@@ -271,3 +271,6 @@ class MakeRefKey(Primitive):
     @prim_attr_register
     def __init__(self, tag):
         validator.check_type('tag', tag, (str,))
+
+    def __call__(self):
+        pass
diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py
index 1757567db5..b7bf1bebf5 100644
--- a/tests/ut/python/ir/test_tensor.py
+++ b/tests/ut/python/ir/test_tensor.py
@@ -24,6 +24,7 @@ import pytest
 import mindspore as ms
 import mindspore.common.api as me
 import mindspore.nn as nn
+from mindspore import Tensor
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
 from ..ut_filter import non_graph_engine
@@ -396,3 +397,24 @@ def test_tensor_dtype_fp32_to_bool():
         input = ms.Tensor(input)
         input_me = ms.Tensor(input, dtype=ms.bool_)
 
+
+def test_tensor_operation():
+    x = Tensor(np.ones((3,3)) * 4)
+    res = x + 1
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 5)
+    res = 1 + x
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 5)
+    res = x - 2
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 2)
+    res = 6 - x
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 2)
+    res = x * 3
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 12)
+    res = 3 * x
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 12)
+    res = x / 2
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 2)
+    res = 8 / x
+    assert np.all(res.asnumpy() == np.ones((3, 3)) * 2)
+    with pytest.raises(TypeError):
+        res = x * (2, 3)
diff --git a/tests/vm_impl/array_ops_vm_impl.py b/tests/vm_impl/array_ops_vm_impl.py
index 4258dadc62..38c613012e 100644
--- a/tests/vm_impl/array_ops_vm_impl.py
+++ b/tests/vm_impl/array_ops_vm_impl.py
@@ -190,7 +190,7 @@ def vm_impl_slice(self):
     return vm_impl
 
 
-@vm_impl_getters.register(P.ConcatOffset)
+@vm_impl_getters.register(P._grad_ops.ConcatOffset)
 def vm_impl_concatOffset(self):
     """Generate vm_impl function for ConcatOffset"""
     def vm_impl(x):

From c8da1cf8e97632fdc420a1d003de97f040b400f1 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Thu, 16 Apr 2020 13:44:20 +0000
Subject: [PATCH 296/367] support building on windows|support package

---
 README.md                          |  1 +
 build.bat                          |  4 ++--
 cmake/external_libs/libtiff.cmake  |  3 +++
 cmake/external_libs/opencv.cmake   |  3 +++
 cmake/package.cmake                | 25 +++++++++++++++++++++++--
 mindspore/ccsrc/CMakeLists.txt     | 10 +++++++++-
 mindspore/ops/_op_impl/__init__.py |  6 ++++--
 setup.py                           |  6 ++++++
 third_party/securec/CMakeLists.txt |  6 +++---
 9 files changed, 54 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index c9abd82da0..cf6eece1e7 100644
--- a/README.md
+++ b/README.md
@@ -69,6 +69,7 @@ MindSpore offers build options across multiple backends:
 | GPU CUDA 9.2 | Ubuntu-x86 | ✔️ |
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
+| CPU | Windows-x86 | ✔️ |
 
 For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
 
diff --git a/build.bat b/build.bat
index 6ada61cab2..5d423720fd 100644
--- a/build.bat
+++ b/build.bat
@@ -21,9 +21,9 @@ IF NOT %errorlevel% == 0 (
     )
  
 IF "%1%" == "" (
-    cmake --build . --target all -- -j6
+    cmake --build . --target package -- -j6
     ) ELSE (
-        cmake --build . --target all -- -j%1%
+        cmake --build . --target package -- -j%1%
     )
 IF NOT %errorlevel% == 0 (
     goto run_fail
diff --git a/cmake/external_libs/libtiff.cmake b/cmake/external_libs/libtiff.cmake
index c9934bfaa1..4086004e33 100644
--- a/cmake/external_libs/libtiff.cmake
+++ b/cmake/external_libs/libtiff.cmake
@@ -8,6 +8,9 @@ else()
         -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
     set(tiff_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
         -Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
+    if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
+	set(tiff_CFLAGS "${tiff_CFLAGS} -Wno-int-to-pointer-cast -Wno-implicit-fallthrough -Wno-pointer-to-int-cast")
+    endif()
 endif()
 
 set(tiff_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
diff --git a/cmake/external_libs/opencv.cmake b/cmake/external_libs/opencv.cmake
index 1c40769f62..b4f8d55a9e 100644
--- a/cmake/external_libs/opencv.cmake
+++ b/cmake/external_libs/opencv.cmake
@@ -5,6 +5,8 @@ if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
 elseif (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
     set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
+    set(opencv_CXXFLAGS "${opencv_CXXFLAGS} -Wno-attributes -Wno-unknown-pragmas")
+    set(opencv_CXXFLAGS "${opencv_CXXFLAGS} -Wno-unused-value -Wno-implicit-fallthrough")
 else()
     set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
     set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
@@ -28,6 +30,7 @@ if (WIN32)
             -DBUILD_opencv_apps=OFF
             -DCMAKE_SKIP_RPATH=TRUE
             -DBUILD_opencv_python3=OFF
+            -DBUILD_opencv_videoio=OFF
             -DWITH_FFMPEG=OFF
             -DWITH_TIFF=ON
             -DBUILD_TIFF=OFF
diff --git a/cmake/package.cmake b/cmake/package.cmake
index d04efca085..7d1fdc6d8a 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -30,9 +30,18 @@ include(CPack)
 # set install path
 set(INSTALL_LIB_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Installation directory for libraries")
 set(INSTALL_PY_DIR ".")
-set(INSTALL_LIB_DIR "lib")
 set(INSTALL_BASE_DIR ".")
 
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    set(INSTALL_LIB_DIR ".")
+    set(onednn_LIBPATH ${onednn_LIBPATH}/../bin/)
+    set(glog_LIBPATH ${glog_LIBPATH}/../bin/)
+    set(opencv_LIBPATH ${opencv_LIBPATH}/../bin/)
+    set(jpeg_turbo_LIBPATH ${jpeg_turbo_LIBPATH}/../bin/)
+else ()
+    set(INSTALL_LIB_DIR "lib")
+endif ()
+
 # set package files
 install(
     TARGETS _c_expression
@@ -80,7 +89,7 @@ if (ENABLE_CPU)
     elseif (CMAKE_SYSTEM_NAME MATCHES "Darwin")
         file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/libdnnl*${CMAKE_SHARED_LIBRARY_SUFFIX}*)
     elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")
-        file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/dnnl.lib)
+        file(GLOB_RECURSE DNNL_LIB_LIST ${onednn_LIBPATH}/dnnl.dll)
     endif ()
     install(
         FILES ${DNNL_LIB_LIST}
@@ -140,6 +149,18 @@ if (NOT ENABLE_GE)
     endif ()
 endif ()
 
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    get_filename_component(CXX_DIR ${CMAKE_CXX_COMPILER} PATH)
+    file(GLOB CXX_LIB_LIST ${CXX_DIR}/*.dll)
+    file(GLOB JPEG_LIB_LIST ${jpeg_turbo_LIBPATH}/*.dll)
+    file(GLOB SQLITE_LIB_LIST ${sqlite_LIBPATH}/*.dll)
+    install(
+        FILES ${CXX_LIB_LIST} ${JPEG_LIB_LIST} ${SQLITE_LIB_LIST}
+        DESTINATION ${INSTALL_LIB_DIR}
+        COMPONENT mindspore
+    )
+endif ()
+
 # set python files
 file(GLOB MS_PY_LIST ${CMAKE_SOURCE_DIR}/mindspore/*.py)
 install(
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index c99809f90f..77008aee5f 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -533,5 +533,13 @@ endif()
 
 if(ENABLE_MINDDATA)
     add_subdirectory(mindrecord)
-    add_subdirectory(dataset)
+    if (WIN32)
+	set(_md_tmp_CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE})
+	set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O0 -Wl,--allow-shlib-undefined -DHALF_ENABLE_CPP11_USER_LITERALS=0")
+	set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -D_FORTIFY_SOURCE=2")
+        add_subdirectory(dataset)
+	set(CMAKE_CXX_FLAGS_RELEASE ${_md_tmp_CMAKE_CXX_FLAGS_RELEASE})
+    else()
+	add_subdirectory(dataset)
+    endif()
 endif()
diff --git a/mindspore/ops/_op_impl/__init__.py b/mindspore/ops/_op_impl/__init__.py
index 76444881cc..65a12cd73c 100644
--- a/mindspore/ops/_op_impl/__init__.py
+++ b/mindspore/ops/_op_impl/__init__.py
@@ -14,8 +14,10 @@
 # ============================================================================
 """Operators info register."""
 
-from .akg.gpu import *
-from .tbe import *
+import platform
 from .aicpu import *
+if "Windows" not in platform.system():
+    from .akg.gpu import *
+    from .tbe import *
 
 __all__ = []
diff --git a/setup.py b/setup.py
index 221c6dc4f2..82e6d70fcc 100644
--- a/setup.py
+++ b/setup.py
@@ -17,6 +17,7 @@
 """setup package."""
 import os
 import stat
+import platform
 
 from setuptools import setup, find_packages
 from setuptools.command.egg_info import egg_info
@@ -97,6 +98,8 @@ required_package = [
 package_data = {
     '': [
         '*.so*',
+        '*.pyd',
+        '*.dll',
         'lib/*.so*',
         'lib/*.a',
         '.commit_id',
@@ -111,6 +114,9 @@ def update_permissions(path):
     Args:
         path (str): Target directory path.
     """
+    if platform.system() == "Windows":
+        return
+
     for dirpath, dirnames, filenames in os.walk(path):
         for dirname in dirnames:
             dir_fullpath = os.path.join(dirpath, dirname)
diff --git a/third_party/securec/CMakeLists.txt b/third_party/securec/CMakeLists.txt
index b2f29c488b..b195cc9555 100644
--- a/third_party/securec/CMakeLists.txt
+++ b/third_party/securec/CMakeLists.txt
@@ -1,5 +1,5 @@
 SET(CMAKE_BUILD_TYPE "Debug")
-if (WIN32)
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
     SET(CMAKE_C_FLAGS_DEBUG "$ENV{CFLAGS} -fPIC -O0 -Wall -Wno-deprecated-declarations -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer")
 else()
     SET(CMAKE_C_FLAGS_DEBUG "$ENV{CFLAGS} -fPIC -O0 -Wall -Wno-deprecated-declarations -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)='")
@@ -8,8 +8,8 @@ SET(CMAKE_C_FLAGS_RELEASE "$ENV{CFLAGS} -fPIC -O3 -Wall -Wno-deprecated-declarat
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 #add flags
-if (WIN32)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include")
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Wno-attributes")
 else()
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
 endif()

From 4907abbc2448311b6cd75d2660fa435885e4ee63 Mon Sep 17 00:00:00 2001
From: chenfei <chenfei52@huawei.com>
Date: Thu, 16 Apr 2020 19:41:40 +0800
Subject: [PATCH 297/367] fix bug of real input arg num of set child graph
 input

---
 mindspore/ccsrc/session/ascend_session.cc | 83 ++++++++++++++++-------
 1 file changed, 58 insertions(+), 25 deletions(-)

diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc
index b0855feb49..ad6c58bc93 100755
--- a/mindspore/ccsrc/session/ascend_session.cc
+++ b/mindspore/ccsrc/session/ascend_session.cc
@@ -92,6 +92,51 @@ GraphId GetDistinctionLabel(const KernelGraphPtr &graph) {
   // else use first node of execution order as label
   return AnfAlgo::GetStreamDistinctionLabel(graph->execution_order()[0].get());
 }
+
+std::vector<BaseRef> GetRealArgs(const KernelGraphPtr graph, const VectorRef &args) {
+  MS_EXCEPTION_IF_NULL(graph);
+  std::vector<AnfNodePtr> graph_inputs = graph->inputs();
+  auto valid_inputs = graph->ValidInputs();
+  size_t real_args_size = 0;
+  std::vector<BaseRef> real_args = {};
+  for (size_t i = 0; i < args.size(); i++) {
+    if (utils::isa<AnfNodePtr>(args[i])) {
+      auto tmp_args = AnfAlgo::GetAllOutput(utils::cast<AnfNodePtr>(args[i]), {prim::kPrimTupleGetItem});
+      for (auto &real_arg : tmp_args) {
+        auto anf_node = utils::cast<AnfNodePtr>(real_arg);
+        MS_EXCEPTION_IF_NULL(anf_node);
+        auto abstract = anf_node->abstract();
+        MS_EXCEPTION_IF_NULL(abstract);
+        // create multiple parameters if is a tuple output real kernel
+        if (abstract->isa<abstract::AbstractTuple>() &&
+            !AnfAlgo::CheckPrimitiveType(anf_node, prim::kPrimTupleGetItem)) {
+          auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
+          real_args_size += tuple_abstract->size();
+          continue;
+        }
+        real_args_size += 1;
+        real_args.push_back(real_arg);
+      }
+    } else {
+      real_args_size += 1;
+      real_args.push_back(args[i]);
+    }
+  }
+  if (graph_inputs.size() != valid_inputs.size()) {
+    MS_LOG(EXCEPTION) << "graph_inputs.size(): " << graph_inputs.size()
+                      << ", valid_inputs.size(): " << valid_inputs.size() << " not equal";
+  }
+  if (real_args_size != graph_inputs.size()) {
+    for (size_t j = 0; j < valid_inputs.size(); j++) {
+      if (valid_inputs[j]) {
+        MS_LOG(INFO) << "index: " << j << ", nodes: " << graph_inputs[j]->DebugString();
+      }
+    }
+    MS_LOG(WARNING) << "real_args_size: " << real_args_size << ", graph_inputs.size(): " << graph_inputs.size()
+                    << " not equal";
+  }
+  return real_args;
+}
 }  // namespace
 
 GraphId AscendSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
@@ -763,38 +808,26 @@ void AscendSession::SetChildGraphInput(GraphId g, const VectorRef &args) {
   UpdateGraphOrder(g);
   std::vector<AnfNodePtr> graph_inputs = to_graph->inputs();
   auto valid_inputs = to_graph->ValidInputs();
-  size_t real_args_size = 0;
-  for (size_t i = 0; i < args.size(); i++) {
-    real_args_size += AnfAlgo::GetAllOutput(utils::cast<AnfNodePtr>(args[i]), {prim::kPrimTupleGetItem}).size();
-  }
-  if (real_args_size != graph_inputs.size()) {
-    for (size_t j = 0; j < valid_inputs.size(); j++) {
-      if (valid_inputs[j]) {
-        MS_LOG(INFO) << "index: " << j << ", nodes: " << graph_inputs[j]->DebugString();
-      }
-    }
-    MS_LOG(WARNING) << "real_args_size: " << real_args_size << ", graph_inputs.size(): " << graph_inputs.size()
-                    << " not equal";
-  }
+  auto real_args = GetRealArgs(to_graph, args);
   size_t input_index = 0;
-  if (graph_inputs.size() != valid_inputs.size()) {
-    MS_LOG(EXCEPTION) << "graph_inputs.size(): " << graph_inputs.size()
-                      << ", valid_inputs.size(): " << valid_inputs.size() << " not equal";
-  }
-  for (size_t i = 0; i < args.size(); i++) {
+  for (size_t i = 0; i < real_args.size(); i++) {
     if (input_index >= graph_inputs.size()) {
       MS_LOG(EXCEPTION) << "input_index " << input_index << " out of range size " << graph_inputs.size();
     }
-    if (utils::isa<AnfNodePtr>(args[i])) {
+    if (utils::isa<AnfNodePtr>(real_args[i])) {
       // arg is a anf node
-      for (const auto &real_arg : AnfAlgo::GetAllOutput(utils::cast<AnfNodePtr>(args[i]), {prim::kPrimTupleGetItem})) {
-        if (!valid_inputs[input_index]) {
-          MS_LOG(DEBUG) << "Invalid input arg" << real_arg->DebugString();
-          continue;
-        }
+      auto real_arg = utils::cast<AnfNodePtr>(real_args[i]);
+      auto real_arg_output_num = AnfAlgo::GetOutputTensorNum(real_arg);
+      if (!AnfAlgo::CheckPrimitiveType(real_arg, prim::kPrimTupleGetItem) && real_arg_output_num > 1) {
+        input_index += real_arg_output_num;
+        continue;
+      }
+      if (valid_inputs[input_index]) {
         SetChildGraphParameter(real_arg, graph_inputs[input_index]);
-        input_index++;
+      } else {
+        MS_LOG(DEBUG) << "Invalid input arg" << real_arg->DebugString();
       }
+      input_index++;
     } else if (utils::isa<ValuePtr>(args[i])) {
       auto value = utils::cast<ValuePtr>(args[i]);
       MS_EXCEPTION_IF_NULL(value);

From 27c307684901a9ef7ee1ee8327191f59e1012748 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 10:00:35 -0400
Subject: [PATCH 298/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py  | 27 ++++++++++++++++-----------
 tests/ut/python/nn/test_batchnorm.py | 11 +++++++++--
 2 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 4bfa222986..32e4be5998 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -69,16 +69,17 @@ class _BatchNorm(Cell):
         self.beta = Parameter(initializer(
             beta_init, num_features), name="beta", requires_grad=affine)
         self.group = check_int_positive(group)
-        self.rank_id = get_rank()
-        self.rank_size = get_local_rank_size()
-        self.device_list = [i for i in range(0, self.rank_size)]
-        self.rank_list = self.list_group(self.device_list, self.group)
-        self.rank_list_idx = len(self.rank_list)
-        for i in range(self.rank_list_idx):
-            if self.rank_id in self.rank_list[i] and self.group != 1:
-                self.is_global = True
-                management.create_group('group' + str(i), self.rank_list[i])
-                self.all_reduce = _GlobalBNHelper('group' + str(i))
+        if self.group != 1:
+            self.rank_id = get_rank()
+            self.rank_size = get_local_rank_size()
+            self.device_list = [i for i in range(0, self.rank_size)]
+            self.rank_list = self.list_group(self.device_list, self.group)
+            self.rank_list_idx = len(self.rank_list)
+            for i in range(self.rank_list_idx):
+                if self.rank_id in self.rank_list[i] and self.group != 1:
+                    self.is_global = True
+                    management.create_group('group' + str(i), self.rank_list[i])
+                    self.all_reduce = _GlobalBNHelper('group' + str(i))
         self.shape = P.Shape()
         self.reduce_mean = P.ReduceMean()
         self.square = P.Square()
@@ -110,7 +111,8 @@ class _BatchNorm(Cell):
 
     def list_group(self, world_rank, group_size):
         if group_size > get_local_rank_size():
-            raise ValueError("group size can not be greater than local rank size, group size is {}, local_rank_size is {}".format(group_size, get_local_rank_size()))
+            raise ValueError("group size can not be greater than local rank size, group size is {}, local_rank_size is {}".format(
+                group_size, get_local_rank_size()))
         if len(world_rank) % group_size != 0:
             raise ValueError("please make your group size correct.")
         world_rank_list = zip(*(iter(world_rank),) *group_size)
@@ -322,6 +324,9 @@ class GlobalBatchNorm(_BatchNorm):
         >>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
         >>> global_bn_op(input)
     """
+    def _check_data_dim(self, x):
+        if x.dim == 0:
+            pass
 
 class LayerNorm(Cell):
     r"""
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index 23ca79e8e1..24f0de85f7 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -20,6 +20,8 @@ import mindspore.nn as nn
 from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
 from mindspore.communication.management import init
+from mindspore import context
+from mindspore import ParallelMode
 
 
 def test_bn_pars_valid1():
@@ -75,12 +77,17 @@ def test_compile_groupnorm():
 class GlobalBNNet(nn.Cell):
     def __init__(self):
         super(GlobalBNNet, self).__init__()
-        self.bn = nn.GlobalBatchNorm(num_features = 2, group = 4)
+        self.bn = nn.GlobalBatchNorm(num_features = 2, group = 2)
     def construct(self, x):
         return self.bn(x)
 
-def test_gloabl_bn():
+def test_global_bn():
     init("hccl")
+    size = 4
+    context.set_context(mode=context.GRAPH_MODE)
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
+                                      device_num=size, parameter_broadcast=True)
     net = GlobalBNNet()
     input_data = Tensor(np.array([[2.4, 2.1], [3.2, 5.4]], dtype=np.float32))
     net.set_train()

From b5e98042c531650c639c9975b9709c384cfde933 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 10:06:21 -0400
Subject: [PATCH 299/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 32e4be5998..dddf32ec48 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -111,8 +111,8 @@ class _BatchNorm(Cell):
 
     def list_group(self, world_rank, group_size):
         if group_size > get_local_rank_size():
-            raise ValueError("group size can not be greater than local rank size, group size is {}, local_rank_size is {}".format(
-                group_size, get_local_rank_size()))
+            raise ValueError("group size can not be greater than local rank size, group size is {}, "
+                             "local_rank_size is {}".format(group_size, get_local_rank_size()))
         if len(world_rank) % group_size != 0:
             raise ValueError("please make your group size correct.")
         world_rank_list = zip(*(iter(world_rank),) *group_size)

From 616b9ea394d4d90b8caf6e5fc2453eb822ead721 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 10:34:41 -0400
Subject: [PATCH 300/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py     | 4 ++--
 tests/ut/python/hccl_test/manage/api.py | 8 ++++++++
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index dddf32ec48..2b55147cf1 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -22,7 +22,7 @@ import mindspore.common.dtype as mstype
 import mindspore.context as context
 from mindspore._checkparam import check_bool, check_typename
 from mindspore._extends import cell_attr_register
-from mindspore.communication.management import get_local_rank_size, get_rank
+from mindspore.communication.management import get_group_size, get_rank
 from mindspore.communication import management
 from mindspore._checkparam import check_int_positive
 from ..cell import Cell
@@ -71,7 +71,7 @@ class _BatchNorm(Cell):
         self.group = check_int_positive(group)
         if self.group != 1:
             self.rank_id = get_rank()
-            self.rank_size = get_local_rank_size()
+            self.rank_size = get_group_size()
             self.device_list = [i for i in range(0, self.rank_size)]
             self.rank_list = self.list_group(self.device_list, self.group)
             self.rank_list_idx = len(self.rank_list)
diff --git a/tests/ut/python/hccl_test/manage/api.py b/tests/ut/python/hccl_test/manage/api.py
index 8dac167a3f..b684df5263 100644
--- a/tests/ut/python/hccl_test/manage/api.py
+++ b/tests/ut/python/hccl_test/manage/api.py
@@ -65,6 +65,14 @@ def get_rank_size(group=None):
         return int(group.split("-")[0])
     raise ValueError
 
+def get_group_size(group=None):
+    hccl = Hccl()
+    if group is None:
+        return hccl.rank_size
+    if isinstance(group, str):
+        return int(group.split("-")[0])
+    raise ValueError
+
 # pylint: disable=unused-argument
 def get_world_rank_from_group_rank(group, group_rank_id):
     return group_rank_id

From c91a58dc55e31e3becf39f038f2fb8db527eedf3 Mon Sep 17 00:00:00 2001
From: hesham <h.farahat@huawei.com>
Date: Thu, 16 Apr 2020 00:09:42 -0400
Subject: [PATCH 301/367] end() iterator is causing performance problem

---
 mindspore/ccsrc/dataset/core/tensor.cc | 6 ++++++
 mindspore/ccsrc/dataset/core/tensor.h  | 4 +++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/dataset/core/tensor.cc b/mindspore/ccsrc/dataset/core/tensor.cc
index 8f0eae459a..8b70fe690d 100644
--- a/mindspore/ccsrc/dataset/core/tensor.cc
+++ b/mindspore/ccsrc/dataset/core/tensor.cc
@@ -85,6 +85,7 @@ Tensor &Tensor::operator=(Tensor &&other) noexcept {
     shape_ = other.shape();
     type_ = other.type();
     data_ = other.StartAddr();
+    data_end_ = other.data_end_;
     data_allocator_ = std::move(other.data_allocator_);
     other.Invalidate();
   }
@@ -208,11 +209,13 @@ Tensor::~Tensor() {
     if (data_allocator_ != nullptr) {
       data_allocator_->deallocate(data_);
       data_ = nullptr;
+      data_end_ = nullptr;
     } else {
       // If we didn't have an allocator, but data_ is not null then it must
       // be a stand-alone tensor that used malloc directly.
       free(data_);
       data_ = nullptr;
+      data_end_ = nullptr;
     }
   }
 }
@@ -338,8 +341,10 @@ unsigned char *Tensor::StartAddr() {
     // on the shape and type and allocate it.
     if (data_allocator_ != nullptr) {
       data_ = data_allocator_->allocate(this->SizeInBytes());
+      data_end_ = data_ + SizeInBytes();
     } else {
       data_ = static_cast<unsigned char *>(malloc(this->SizeInBytes()));
+      data_end_ = data_ + SizeInBytes();
       if (data_ == nullptr) {
         return nullptr;
       }
@@ -362,6 +367,7 @@ void Tensor::Invalidate() {
   shape_ = TensorShape::CreateUnknownRankShape();
   type_ = DataType(DataType::DE_UNKNOWN);
   data_ = nullptr;
+  data_end_ = nullptr;
   data_allocator_ = nullptr;
 }
 
diff --git a/mindspore/ccsrc/dataset/core/tensor.h b/mindspore/ccsrc/dataset/core/tensor.h
index 3409354d19..74da40c293 100644
--- a/mindspore/ccsrc/dataset/core/tensor.h
+++ b/mindspore/ccsrc/dataset/core/tensor.h
@@ -363,7 +363,7 @@ class Tensor {
   // @return TensorIterator
   template <typename T>
   TensorIterator<T> end() {
-    return TensorIterator<T>(data_ + SizeInBytes());
+    return TensorIterator<T>(data_end_);
   }
 
  protected:
@@ -402,6 +402,8 @@ class Tensor {
   unsigned char *data_;
   // An allocator for data_
   CharAllocPtr data_allocator_;
+  // pointer to the end of the physical data
+  unsigned char *data_end_ = nullptr;
 };
 }  // namespace dataset
 }  // namespace mindspore

From fb6c7ba2e127be6632ddb8f0497df54bfaedb59a Mon Sep 17 00:00:00 2001
From: hesham <h.farahat@huawei.com>
Date: Thu, 16 Apr 2020 00:07:28 -0400
Subject: [PATCH 302/367] Fix two problem when we create multiple instances of
 the same dataset (2 for-loops) -- Iterator list is keeping  all created
 iterators wihtout cleaning them up -- alter tree modifies the original.

---
 mindspore/ccsrc/dataset/api/python_bindings.cc |  6 ++++--
 mindspore/dataset/engine/iterators.py          | 12 +++++++++---
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index e2675ee217..d9e0ccbba8 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -225,11 +225,13 @@ void bindTensor(py::module *m) {
   (void)py::class_<DataType>(*m, "DataType")
     .def(py::init<std::string>())
     .def(py::self == py::self)
-    .def("__str__", &DataType::ToString);
+    .def("__str__", &DataType::ToString)
+    .def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
 }
 
 void bindTensorOps1(py::module *m) {
-  (void)py::class_<TensorOp, std::shared_ptr<TensorOp>>(*m, "TensorOp");
+  (void)py::class_<TensorOp, std::shared_ptr<TensorOp>>(*m, "TensorOp")
+    .def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
 
   (void)py::class_<NormalizeOp, TensorOp, std::shared_ptr<NormalizeOp>>(
     *m, "NormalizeOp", "Tensor operation to normalize an image. Takes mean and std.")
diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index 268a66c0cf..69dd9ce0a9 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -15,6 +15,8 @@
 """Built-in iterators.
 """
 from abc import abstractmethod
+import copy
+import weakref
 
 from mindspore._c_dataengine import DEPipeline
 from mindspore._c_dataengine import OpName
@@ -27,7 +29,9 @@ ITERATORS_LIST = list()
 
 def _cleanup():
     for itr in ITERATORS_LIST:
-        itr.release()
+        iter_ref = itr()
+        if itr is not None:
+            iter_ref.release()
 
 
 def alter_tree(node):
@@ -73,8 +77,10 @@ class Iterator:
     """
 
     def __init__(self, dataset):
-        ITERATORS_LIST.append(self)
-        self.dataset = alter_tree(dataset)
+        ITERATORS_LIST.append(weakref.ref(self))
+        # create a copy of tree and work on it.
+        self.dataset = copy.deepcopy(dataset)
+        self.dataset = alter_tree(self.dataset)
         if not self.__is_tree():
             raise ValueError("The data pipeline is not a tree (i.e., one node has 2 consumers)")
         self.depipeline = DEPipeline()

From d2b04664cad59608eb4754d2df93eaeaf84d7aca Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 20:58:59 -0400
Subject: [PATCH 303/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 2b55147cf1..c85b945a0d 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -110,9 +110,9 @@ class _BatchNorm(Cell):
         raise NotImplementedError
 
     def list_group(self, world_rank, group_size):
-        if group_size > get_local_rank_size():
+        if group_size > get_group_size():
             raise ValueError("group size can not be greater than local rank size, group size is {}, "
-                             "local_rank_size is {}".format(group_size, get_local_rank_size()))
+                             "local_rank_size is {}".format(group_size, get_group_size()))
         if len(world_rank) % group_size != 0:
             raise ValueError("please make your group size correct.")
         world_rank_list = zip(*(iter(world_rank),) *group_size)

From d8bd5a09c4281c467e2576d140056c29b6826867 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Thu, 16 Apr 2020 21:13:45 -0400
Subject: [PATCH 304/367] add global batch normalization

---
 tests/ut/python/hccl_test/manage/api.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tests/ut/python/hccl_test/manage/api.py b/tests/ut/python/hccl_test/manage/api.py
index b684df5263..04ce7da6d5 100644
--- a/tests/ut/python/hccl_test/manage/api.py
+++ b/tests/ut/python/hccl_test/manage/api.py
@@ -21,6 +21,7 @@ class Hccl():
     _instance = None
     _rank_id = 0
     _rank_size = 1
+    _group_size = 4
 
     def __init__(self):
         pass
@@ -47,6 +48,10 @@ class Hccl():
     def rank_size(self):
         return self._rank_size
 
+    @property
+    def group_size(self):
+        return self._group_size
+
     @rank_size.setter
     def rank_size(self, size):
         self._rank_size = size
@@ -68,7 +73,7 @@ def get_rank_size(group=None):
 def get_group_size(group=None):
     hccl = Hccl()
     if group is None:
-        return hccl.rank_size
+        return hccl.group_size
     if isinstance(group, str):
         return int(group.split("-")[0])
     raise ValueError

From aa8fbcc06eb5801811ec008803dc933017ac5273 Mon Sep 17 00:00:00 2001
From: zhaozhenlong <zhaozhenlong1@huawei.com>
Date: Wed, 15 Apr 2020 11:22:21 +0800
Subject: [PATCH 305/367] add cell psnr

---
 mindspore/nn/layer/__init__.py  |  4 +-
 mindspore/nn/layer/image.py     | 82 ++++++++++++++++++++++------
 tests/ut/python/nn/test_psnr.py | 61 +++++++++++++++++++++
 tests/ut/python/nn/test_ssim.py | 95 +++++++++++++++++++++++++++++++++
 4 files changed, 224 insertions(+), 18 deletions(-)
 create mode 100644 tests/ut/python/nn/test_psnr.py
 create mode 100644 tests/ut/python/nn/test_ssim.py

diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py
index cf601f03ff..6c1b19a110 100644
--- a/mindspore/nn/layer/__init__.py
+++ b/mindspore/nn/layer/__init__.py
@@ -25,7 +25,7 @@ from .lstm import LSTM
 from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, Pad, Unfold
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
-from .image import ImageGradients, SSIM
+from .image import ImageGradients, SSIM, PSNR
 
 __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'PReLU', 'get_activation', 'LeakyReLU', 'HSigmoid', 'HSwish', 'ELU',
@@ -36,5 +36,5 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot',
            'Embedding',
            'AvgPool2d', 'MaxPool2d', 'Pad', 'Unfold',
-           'ImageGradients', 'SSIM',
+           'ImageGradients', 'SSIM', 'PSNR',
            ]
diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py
index 6121776f59..72c4c6d8e2 100644
--- a/mindspore/nn/layer/image.py
+++ b/mindspore/nn/layer/image.py
@@ -69,6 +69,18 @@ class ImageGradients(Cell):
         return dy, dx
 
 
+def _convert_img_dtype_to_float32(img, max_val):
+    """convert img dtype to float32"""
+    # Ususally max_val is 1.0 or 255, we will do the scaling if max_val > 1.
+    # We will scale img pixel value if max_val > 1. and just cast otherwise.
+    ret = F.cast(img, mstype.float32)
+    max_val = F.scalar_cast(max_val, mstype.float32)
+    if max_val > 1.:
+        scale = 1. / max_val
+        ret = ret * scale
+    return ret
+
+
 @constexpr
 def _gauss_kernel_helper(filter_size):
     """gauss kernel helper"""
@@ -134,9 +146,9 @@ class SSIM(Cell):
         self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
 
     def construct(self, img1, img2):
-        max_val = self._convert_img_dtype_to_float32(self.max_val, self.max_val)
-        img1 = self._convert_img_dtype_to_float32(img1, self.max_val)
-        img2 = self._convert_img_dtype_to_float32(img2, self.max_val)
+        max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
+        img1 = _convert_img_dtype_to_float32(img1, self.max_val)
+        img2 = _convert_img_dtype_to_float32(img2, self.max_val)
 
         kernel = self._fspecial_gauss(self.filter_size, self.filter_sigma)
         kernel = P.Tile()(kernel, (1, P.Shape()(img1)[1], 1, 1))
@@ -145,21 +157,10 @@ class SSIM(Cell):
 
         return mean_ssim
 
-    def _convert_img_dtype_to_float32(self, img, max_val):
-        """convert img dtype to float32"""
-        # Ususally max_val is 1.0 or 255, we will do the scaling if max_val > 1.
-        # We will scale img pixel value if max_val > 1. and just cast otherwise.
-        ret = P.Cast()(img, mstype.float32)
-        max_val = F.scalar_cast(max_val, mstype.float32)
-        if max_val > 1.:
-            scale = 1./max_val
-            ret = ret*scale
-        return ret
-
     def _calculate_mean_ssim(self, x, y, kernel, max_val, k1, k2):
         """calculate mean ssim"""
-        c1 = (k1*max_val)*(k1*max_val)
-        c2 = (k2*max_val)*(k2*max_val)
+        c1 = (k1 * max_val) * (k1 * max_val)
+        c2 = (k2 * max_val) * (k2 * max_val)
 
         # SSIM luminance formula
         # (2 * mean_{x} * mean_{y} + c1) / (mean_{x}**2 + mean_{y}**2 + c1)
@@ -195,3 +196,52 @@ class SSIM(Cell):
         g = P.Softmax()(g)
         ret = F.reshape(g, (1, 1, filter_size, filter_size))
         return ret
+
+
+class PSNR(Cell):
+    r"""
+    Returns Peak Signal-to-Noise Ratio of two image batches.
+
+    It produces a PSNR value for each image in batch.
+    Assume inputs are :math:`I` and :math:`K`, both with shape :math:`h*w`.
+    :math:`MAX` represents the dynamic range of pixel values.
+
+    .. math::
+
+        MSE&=\frac{1}{hw}\sum\limits_{i=0}^{h-1}\sum\limits_{j=0}^{w-1}[I(i,j)-K(i,j)]^2\\
+        PSNR&=10*log_{10}(\frac{MAX^2}{MSE})
+
+    Args:
+        max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images).
+          Default: 1.0.
+
+    Inputs:
+        - **img1** (Tensor) - The first image batch with format 'NCHW'. It should be the same shape and dtype as img2.
+        - **img2** (Tensor) - The second image batch with format 'NCHW'. It should be the same shape and dtype as img1.
+
+    Outputs:
+        Tensor, with dtype mindspore.float32. It is a 1-D tensor with shape N, where N is the batch num of img1.
+
+    Examples:
+        >>> net = nn.PSNR()
+        >>> img1 = Tensor(np.random.random((1,3,16,16)))
+        >>> img2 = Tensor(np.random.random((1,3,16,16)))
+        >>> psnr = net(img1, img2)
+
+    """
+    def __init__(self, max_val=1.0):
+        super(PSNR, self).__init__()
+        validator.check_type('max_val', max_val, [int, float])
+        validator.check('max_val', max_val, '', 0.0, Rel.GT)
+        self.max_val = max_val
+
+    def construct(self, img1, img2):
+        max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
+        img1 = _convert_img_dtype_to_float32(img1, self.max_val)
+        img2 = _convert_img_dtype_to_float32(img2, self.max_val)
+
+        mse = P.ReduceMean()(F.square(img1 - img2), (-3, -2, -1))
+        # 10*log_10(max_val^2/MSE)
+        psnr = 10 * P.Log()(F.square(max_val) / mse) / F.scalar_log(10.0)
+
+        return psnr
diff --git a/tests/ut/python/nn/test_psnr.py b/tests/ut/python/nn/test_psnr.py
new file mode 100644
index 0000000000..5a908b308d
--- /dev/null
+++ b/tests/ut/python/nn/test_psnr.py
@@ -0,0 +1,61 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+test psnr
+"""
+import numpy as np
+import pytest
+import mindspore.nn as nn
+from mindspore.common.api import _executor
+from mindspore import Tensor
+
+
+class PSNRNet(nn.Cell):
+    def __init__(self, max_val=1.0):
+        super(PSNRNet, self).__init__()
+        self.net = nn.PSNR(max_val)
+
+    def construct(self, img1, img2):
+        return self.net(img1, img2)
+
+
+def test_compile_psnr():
+    max_val = 1.0
+    net = PSNRNet(max_val)
+    img1 = Tensor(np.random.random((8, 3, 16, 16)))
+    img2 = Tensor(np.random.random((8, 3, 16, 16)))
+    _executor.compile(net, img1, img2)
+
+def test_compile_psnr_grayscale():
+    max_val = 255
+    net = PSNRNet(max_val)
+    img1 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
+    img2 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
+    _executor.compile(net, img1, img2)
+
+def test_psnr_max_val_negative():
+    max_val = -1
+    with pytest.raises(ValueError):
+        net = PSNRNet(max_val)
+
+def test_psnr_max_val_bool():
+    max_val = True
+    with pytest.raises(ValueError):
+        net = PSNRNet(max_val)
+
+def test_psnr_max_val_zero():
+    max_val = 0
+    with pytest.raises(ValueError):
+        net = PSNRNet(max_val)
diff --git a/tests/ut/python/nn/test_ssim.py b/tests/ut/python/nn/test_ssim.py
new file mode 100644
index 0000000000..a698b59f69
--- /dev/null
+++ b/tests/ut/python/nn/test_ssim.py
@@ -0,0 +1,95 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+test ssim
+"""
+import numpy as np
+import pytest
+import mindspore.nn as nn
+from mindspore.common.api import _executor
+from mindspore import Tensor
+
+
+class SSIMNet(nn.Cell):
+    def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
+        super(SSIMNet, self).__init__()
+        self.net = nn.SSIM(max_val, filter_size, filter_sigma, k1, k2)
+
+    def construct(self, img1, img2):
+        return self.net(img1, img2)
+
+
+def test_compile():
+    net = SSIMNet()
+    img1 = Tensor(np.random.random((8, 3, 16, 16)))
+    img2 = Tensor(np.random.random((8, 3, 16, 16)))
+    _executor.compile(net, img1, img2)
+
+def test_compile_grayscale():
+    max_val = 255
+    net = SSIMNet(max_val = max_val)
+    img1 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
+    img2 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
+    _executor.compile(net, img1, img2)
+
+def test_ssim_max_val_negative():
+    max_val = -1
+    with pytest.raises(ValueError):
+        net = SSIMNet(max_val)
+
+def test_ssim_max_val_bool():
+    max_val = True
+    with pytest.raises(ValueError):
+        net = SSIMNet(max_val)
+
+def test_ssim_max_val_zero():
+    max_val = 0
+    with pytest.raises(ValueError):
+        net = SSIMNet(max_val)
+
+def test_ssim_filter_size_float():
+    with pytest.raises(ValueError):
+        net = SSIMNet(filter_size=1.1)
+
+def test_ssim_filter_size_zero():
+    with pytest.raises(ValueError):
+        net = SSIMNet(filter_size=0)
+
+def test_ssim_filter_sigma_zero():
+    with pytest.raises(ValueError):
+        net = SSIMNet(filter_sigma=0.0)
+
+def test_ssim_filter_sigma_negative():
+    with pytest.raises(ValueError):
+        net = SSIMNet(filter_sigma=-0.1)
+
+def test_ssim_k1_k2_wrong_value():
+    with pytest.raises(ValueError):
+        net = SSIMNet(k1=1.1)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k1=1.0)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k1=0.0)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k1=-1.0)
+
+    with pytest.raises(ValueError):
+        net = SSIMNet(k2=1.1)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k2=1.0)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k2=0.0)
+    with pytest.raises(ValueError):
+        net = SSIMNet(k2=-1.0)
\ No newline at end of file

From 87e71f26cccb9e820ca200d349fb55100994e93a Mon Sep 17 00:00:00 2001
From: YuJianfeng <yujianfeng5@huawei.com>
Date: Fri, 17 Apr 2020 09:21:55 +0800
Subject: [PATCH 306/367] Erase the visited attr after transdata split

---
 .../ccsrc/pre_activate/ascend/ascend_backend_optimization.cc    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index 394c5ce281..a72fb9dc9a 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -114,8 +114,8 @@ void AscendDataLayout(const std::shared_ptr<session::KernelGraph> &kernel_graph)
   data_layout_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
   data_layout_pm->AddPass(std::make_shared<EliminateRedundantOp>());
   data_layout_pm->AddPass(std::make_shared<OptimizeDependence>());
-  data_layout_pm->AddPass(std::make_shared<EraseVisitAttr>());
   data_layout_pm->AddPass(std::make_shared<TransDataSplit>());
+  data_layout_pm->AddPass(std::make_shared<EraseVisitAttr>());
   optimizer->AddPassManager(data_layout_pm);
   (void)optimizer->Optimize(kernel_graph);
   kernel_graph->SetExecOrderByDefault();

From cce61d462c5d3b1cef5e1c93732548f2506dc318 Mon Sep 17 00:00:00 2001
From: wenkai <wenkai8@huawei.com>
Date: Sun, 12 Apr 2020 16:32:36 +0800
Subject: [PATCH 307/367] histogram python dev

---
 mindspore/ccsrc/utils/summary.proto           |  25 +++
 mindspore/train/summary/_summary_adapter.py   |  78 ++++++-
 mindspore/train/summary/_summary_scheduler.py |   3 +
 .../ut/python/train/summary/summary_reader.py |  43 ++++
 .../train/summary/test_histogram_summary.py   | 210 ++++++++++++++++++
 5 files changed, 358 insertions(+), 1 deletion(-)
 create mode 100644 tests/ut/python/train/summary/summary_reader.py
 create mode 100644 tests/ut/python/train/summary/test_histogram_summary.py

diff --git a/mindspore/ccsrc/utils/summary.proto b/mindspore/ccsrc/utils/summary.proto
index f7fb733597..6ea6ce08b8 100644
--- a/mindspore/ccsrc/utils/summary.proto
+++ b/mindspore/ccsrc/utils/summary.proto
@@ -61,6 +61,30 @@ message Summary {
         required bytes encoded_image = 4;
     }
 
+    message Histogram {
+      message bucket{
+          // Count number of values fallen in [left, left + width).
+          // For the right most bucket, range is [left, left + width].
+          required double left = 1;
+          required double width = 2;
+          required int64 count = 3;
+      }
+  
+      repeated bucket buckets = 1;
+      optional int64 nan_count = 2;
+      optional int64 pos_inf_count = 3;
+      optional int64 neg_inf_count = 4;
+
+      // max, min, sum will not take nan and inf into account.
+      // If there is no valid value in tensor, max will be nan, min will be nan, sum will be 0.
+      optional double max = 5;
+      optional double min = 6;
+      optional double sum = 7;
+
+      // total number of values, including nan and inf
+      optional int64 count = 8;
+    }
+
     message Value {
         // Tag name for the data.
         required string tag = 1;
@@ -70,6 +94,7 @@ message Summary {
             float scalar_value = 3;
             Image image = 4;
             TensorProto tensor = 8;
+            Histogram histogram = 9;
         }
     }
 
diff --git a/mindspore/train/summary/_summary_adapter.py b/mindspore/train/summary/_summary_adapter.py
index 29a5774271..7db80de693 100644
--- a/mindspore/train/summary/_summary_adapter.py
+++ b/mindspore/train/summary/_summary_adapter.py
@@ -71,12 +71,14 @@ class SummaryType(Enum):
         TENSOR (Number): Summary TENSOR enum.
         IMAGE (Number): Summary image enum.
         GRAPH (Number): Summary graph enum.
+        HISTOGRAM (Number): Summary histogram enum.
         INVALID (Number): Unknow type.
     """
     SCALAR = 1      # Scalar summary
     TENSOR = 2      # Tensor summary
     IMAGE = 3       # Image summary
     GRAPH = 4       # graph
+    HISTOGRAM = 5   # Histogram Summary
     INVALID = 0xFF  # unknow type
 
 
@@ -148,7 +150,7 @@ def package_summary_event(data_id, step):
     """
     data_list = get_summary_data(data_id)
     if data_list is None:
-        logger.error("The step(%r) does not have record data.", self.step)
+        logger.error("The step(%r) does not have record data.", step)
     del_summary_data(data_id)
     # create the event of summary
     summary_event = Event()
@@ -177,6 +179,12 @@ def package_summary_event(data_id, step):
             summary_value.tag = tag
             summary_image = summary_value.image
             _get_image_summary(tag, data, summary_image, MS_IMAGE_TENSOR_FORMAT)
+        elif summary_type is SummaryType.HISTOGRAM:
+            logger.debug("Now process Histogram summary, tag = %r", tag)
+            summary_value = summary.value.add()
+            summary_value.tag = tag
+            summary_histogram = summary_value.histogram
+            _fill_histogram_summary(tag, data, summary_histogram)
         else:
             # The data is invalid ,jump the data
             logger.error("Summary type is error, tag = %r", tag)
@@ -284,6 +292,74 @@ def _get_tensor_summary(tag: str, np_value, summary_tensor):
     return summary_tensor
 
 
+def _fill_histogram_summary(tag: str, np_value: np.array, summary_histogram) -> None:
+    """
+    Package the histogram summary.
+
+    Args:
+        tag (str): Summary tag describe.
+        np_value (np.array): Summary data.
+        summary_histogram (summary_pb2.Summary.Histogram): Summary histogram data.
+    """
+    logger.debug("Set(%r) the histogram summary value", tag)
+    # Default bucket for tensor with no valid data.
+    default_bucket_left = -0.5
+    default_bucket_width = 1.0
+
+    if np_value.size == 0:
+        bucket = summary_histogram.buckets.add()
+        bucket.left = default_bucket_left
+        bucket.width = default_bucket_width
+        bucket.count = 0
+
+        summary_histogram.nan_count = 0
+        summary_histogram.pos_inf_count = 0
+        summary_histogram.neg_inf_count = 0
+
+        summary_histogram.max = 0
+        summary_histogram.min = 0
+        summary_histogram.sum = 0
+
+        summary_histogram.count = 0
+
+        return
+
+    summary_histogram.nan_count = np.count_nonzero(np.isnan(np_value))
+    summary_histogram.pos_inf_count = np.count_nonzero(np.isposinf(np_value))
+    summary_histogram.neg_inf_count = np.count_nonzero(np.isneginf(np_value))
+    summary_histogram.count = np_value.size
+
+    masked_value = np.ma.masked_invalid(np_value)
+    tensor_max = masked_value.max()
+    tensor_min = masked_value.min()
+    tensor_sum = masked_value.sum()
+
+    # No valid value in tensor.
+    if tensor_max is np.ma.masked:
+        bucket = summary_histogram.buckets.add()
+        bucket.left = default_bucket_left
+        bucket.width = default_bucket_width
+        bucket.count = 0
+
+        summary_histogram.max = np.nan
+        summary_histogram.min = np.nan
+        summary_histogram.sum = 0
+
+        return
+
+    counts, edges = np.histogram(np_value, bins='auto', range=(tensor_min, tensor_max))
+
+    for ind, count in enumerate(counts):
+        bucket = summary_histogram.buckets.add()
+        bucket.left = edges[ind]
+        bucket.width = edges[ind + 1] - edges[ind]
+        bucket.count = count
+
+    summary_histogram.max = tensor_max
+    summary_histogram.min = tensor_min
+    summary_histogram.sum = tensor_sum
+
+
 def _get_image_summary(tag: str, np_value, summary_image, input_format='NCHW'):
     """
     Package the image summary.
diff --git a/mindspore/train/summary/_summary_scheduler.py b/mindspore/train/summary/_summary_scheduler.py
index fa5a228e6a..3327b02fa7 100644
--- a/mindspore/train/summary/_summary_scheduler.py
+++ b/mindspore/train/summary/_summary_scheduler.py
@@ -23,6 +23,7 @@ from ._summary_adapter import SummaryType, package_summary_event, save_summary_d
 FORMAT_SCALAR_STR = "Scalar"
 FORMAT_TENSOR_STR = "Tensor"
 FORMAT_IMAGE_STR = "Image"
+FORMAT_HISTOGRAM_STR = "Histogram"
 FORMAT_BEGIN_SLICE = "[:"
 FORMAT_END_SLICE = "]"
 
@@ -95,6 +96,8 @@ def _parse_tag_format(tag: str):
         summary_type = SummaryType.TENSOR
     elif type_str == FORMAT_IMAGE_STR:
         summary_type = SummaryType.IMAGE
+    elif type_str == FORMAT_HISTOGRAM_STR:
+        summary_type = SummaryType.HISTOGRAM
     else:
         logger.error("The tag(%s) type is invalid.", tag)
         summary_type = SummaryType.INVALID
diff --git a/tests/ut/python/train/summary/summary_reader.py b/tests/ut/python/train/summary/summary_reader.py
new file mode 100644
index 0000000000..647c25f25c
--- /dev/null
+++ b/tests/ut/python/train/summary/summary_reader.py
@@ -0,0 +1,43 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Summary reader."""
+import struct
+
+import mindspore.train.summary_pb2 as summary_pb2
+
+_HEADER_SIZE = 8
+_HEADER_CRC_SIZE = 4
+_DATA_CRC_SIZE = 4
+
+
+class SummaryReader:
+    """Read events from summary file."""
+
+    def __init__(self, file_name):
+        self._file_name = file_name
+        self._file_handler = open(self._file_name, "rb")
+        # skip version event
+        self.read_event()
+
+    def read_event(self):
+        """Read next event."""
+        file_handler = self._file_handler
+        header = file_handler.read(_HEADER_SIZE)
+        data_len = struct.unpack('Q', header)[0]
+        file_handler.read(_HEADER_CRC_SIZE)
+        event_str = file_handler.read(data_len)
+        file_handler.read(_DATA_CRC_SIZE)
+        summary_event = summary_pb2.Event.FromString(event_str)
+        return summary_event
diff --git a/tests/ut/python/train/summary/test_histogram_summary.py b/tests/ut/python/train/summary/test_histogram_summary.py
new file mode 100644
index 0000000000..50204cd757
--- /dev/null
+++ b/tests/ut/python/train/summary/test_histogram_summary.py
@@ -0,0 +1,210 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Test histogram summary."""
+
+import logging
+import os
+import tempfile
+
+import numpy as np
+
+from mindspore.common.tensor import Tensor
+from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
+from .summary_reader import SummaryReader
+
+CUR_DIR = os.getcwd()
+SUMMARY_DIR = os.path.join(CUR_DIR, "/test_temp_summary_event_file/")
+
+LOG = logging.getLogger("test")
+LOG.setLevel(level=logging.ERROR)
+
+
+def _wrap_test_data(input_data: Tensor):
+    """
+    Wraps test data to summary format.
+
+    Args:
+        input_data (Tensor): Input data.
+
+    Returns:
+        dict, the wrapped data.
+    """
+
+    return [{
+        "name": "test_data[:Histogram]",
+        "data": input_data
+    }]
+
+
+def test_histogram_summary():
+    """Test histogram summary."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        test_data = _wrap_test_data(Tensor([[1, 2, 3], [4, 5, 6]]))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        assert event.summary.value[0].histogram.count == 6
+
+
+def test_histogram_multi_summary():
+    """Test histogram multiple step."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        rng = np.random.RandomState(10)
+        size = 50
+        num_step = 5
+
+        for i in range(num_step):
+            arr = rng.normal(size=size)
+
+            test_data = _wrap_test_data(Tensor(arr))
+            _cache_summary_tensor_data(test_data)
+            test_writer.record(step=i)
+
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        for _ in range(num_step):
+            event = reader.read_event()
+            assert event.summary.value[0].histogram.count == size
+
+
+def test_histogram_summary_scalar_tensor():
+    """Test histogram summary, input is a scalar tensor."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        test_data = _wrap_test_data(Tensor(1))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        assert event.summary.value[0].histogram.count == 1
+
+
+def test_histogram_summary_empty_tensor():
+    """Test histogram summary, input is an empty tensor."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        test_data = _wrap_test_data(Tensor([]))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        assert event.summary.value[0].histogram.count == 0
+
+
+def test_histogram_summary_same_value():
+    """Test histogram summary, input is an ones tensor."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        dim1 = 100
+        dim2 = 100
+
+        test_data = _wrap_test_data(Tensor(np.ones([dim1, dim2])))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        LOG.debug(event)
+
+        assert len(event.summary.value[0].histogram.buckets) == 1
+
+
+def test_histogram_summary_high_dims():
+    """Test histogram summary, input is a 4-dimension tensor."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+        dim = 10
+
+        rng = np.random.RandomState(0)
+        tensor_data = rng.normal(size=[dim, dim, dim, dim])
+        test_data = _wrap_test_data(Tensor(tensor_data))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        LOG.debug(event)
+
+        assert event.summary.value[0].histogram.count == tensor_data.size
+
+
+def test_histogram_summary_nan_inf():
+    """Test histogram summary, input tensor has nan."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        dim1 = 100
+        dim2 = 100
+
+        arr = np.ones([dim1, dim2])
+        arr[0][0] = np.nan
+        arr[0][1] = np.inf
+        arr[0][2] = -np.inf
+        test_data = _wrap_test_data(Tensor(arr))
+
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        LOG.debug(event)
+
+        assert event.summary.value[0].histogram.nan_count == 1
+
+
+def test_histogram_summary_all_nan_inf():
+    """Test histogram summary, input tensor has no valid number."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
+
+        test_data = _wrap_test_data(Tensor(np.array([np.nan, np.nan, np.nan, np.inf, -np.inf])))
+        _cache_summary_tensor_data(test_data)
+        test_writer.record(step=1)
+        test_writer.close()
+
+        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
+        reader = SummaryReader(file_name)
+        event = reader.read_event()
+        LOG.debug(event)
+
+        histogram = event.summary.value[0].histogram
+        assert histogram.nan_count == 3
+        assert histogram.pos_inf_count == 1
+        assert histogram.neg_inf_count == 1

From c72bafaec9ccf1d1c5560bd1bac3e97189e8a48f Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Fri, 17 Apr 2020 09:50:00 +0800
Subject: [PATCH 308/367] fix compiles fail with cmake version greater than
 3.17 on windows

---
 README.md         | 2 +-
 cmake/utils.cmake | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index cf6eece1e7..e465f8e3e1 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ MindSpore offers build options across multiple backends:
 | GPU CUDA 9.2 | Ubuntu-x86 | ✔️ |
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
-| CPU | Windows-x86 | ✔️ |
+|  | Windows-x86 | ✔️ |
 
 For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
 
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index f0181de5ea..21a766dc8c 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -1,6 +1,10 @@
 include(FetchContent)
 set(FETCHCONTENT_QUIET OFF)
 
+if (CMAKE_SYSTEM_NAME MATCHES "Windows" AND ${CMAKE_VERSION} VERSION_GREATER_EQUAL 3.17.0)
+    set(CMAKE_FIND_LIBRARY_SUFFIXES .dll ${CMAKE_FIND_LIBRARY_SUFFIXES})
+endif ()
+
 function(mindspore_add_submodule_obj des_submodule_objs sub_dir submodule_name_obj)
 
     add_subdirectory(${sub_dir})

From 523fde0e25a5ef67d798a362328a85219b100eb1 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Fri, 17 Apr 2020 02:33:37 +0000
Subject: [PATCH 309/367] eliminate securec warning

---
 cmake/dependency_securec.cmake         | 3 +++
 third_party/securec/CMakeLists.txt     | 2 +-
 third_party/securec/src/CMakeLists.txt | 7 +++++--
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/cmake/dependency_securec.cmake b/cmake/dependency_securec.cmake
index 81714c21d4..7ff5acad06 100644
--- a/cmake/dependency_securec.cmake
+++ b/cmake/dependency_securec.cmake
@@ -9,6 +9,9 @@ if (NOT TARGET securec)
   set(_ms_tmp_CMAKE_C_FLAGS ${CMAKE_C_FLAGS})
 
   set(CMAKE_C_FLAGS "${SECURE_CXX_FLAGS}")
+  if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    add_compile_definitions(SECUREC_ONLY_DECLARE_MEMSET)
+  endif()
   add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/securec ${CMAKE_BINARY_DIR}/securec)
   set(CMAKE_POSITION_INDEPENDENT_CODE ${_ms_tmp_CMAKE_POSITION_INDEPENDENT_CODE})
   set(CMAKE_C_FLAGS ${_ms_tmp_CMAKE_C_FLAGS})
diff --git a/third_party/securec/CMakeLists.txt b/third_party/securec/CMakeLists.txt
index b195cc9555..99345c6eb3 100644
--- a/third_party/securec/CMakeLists.txt
+++ b/third_party/securec/CMakeLists.txt
@@ -9,7 +9,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 #add flags
 if (CMAKE_SYSTEM_NAME MATCHES "Windows")
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Wno-attributes")
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include")
 else()
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
 endif()
diff --git a/third_party/securec/src/CMakeLists.txt b/third_party/securec/src/CMakeLists.txt
index 60ec0a90ee..2af0f764c8 100644
--- a/third_party/securec/src/CMakeLists.txt
+++ b/third_party/securec/src/CMakeLists.txt
@@ -1,3 +1,6 @@
-aux_source_directory(. SECUREC_SRCS)
-
+if (CMAKE_SYSTEM_NAME MATCHES "Windows")
+    list(APPEND SECUREC_SRCS "memset_s.c")
+else()
+    aux_source_directory(. SECUREC_SRCS)
+endif()
 add_library(securec STATIC ${SECUREC_SRCS})

From 10877f077e8f2db69487e4b3eeb5924722c9df5d Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Fri, 17 Apr 2020 10:33:46 +0800
Subject: [PATCH 310/367] modify examples of Cast/NMSWithMask

---
 mindspore/ops/operations/array_ops.py | 3 +--
 mindspore/ops/operations/math_ops.py  | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 0af284b947..57dbfc621d 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -174,10 +174,9 @@ class Cast(PrimitiveWithInfer):
     Examples:
         >>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
         >>> input_x = Tensor(input_np)
-        >>> type_dst = mindspore.int32
+        >>> type_dst = mindspore.float16
         >>> cast = P.Cast()
         >>> result = cast(input_x, type_dst)
-        >>> expect = input_np.astype(type_dst)
     """
 
     @prim_attr_register
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index e390b6b589..3665f3c023 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1872,7 +1872,7 @@ class NMSWithMask(PrimitiveWithInfer):
         >>> bbox = np.random.rand(128, 5)
         >>> bbox[:, 2] += bbox[:, 0]
         >>> bbox[:, 3] += bbox[:, 1]
-        >>> inputs = Tensor(bbox)
+        >>> inputs = Tensor(bbox, mindspore.float32)
         >>> nms = P.NMSWithMask(0.5)
         >>> output_boxes, indices, mask = nms(inputs)
     """

From 36ffb66782175388dbe200e74cc0cb61a1881db1 Mon Sep 17 00:00:00 2001
From: yangzhenzhang <285824651@qq.com>
Date: Thu, 16 Apr 2020 21:52:18 +0800
Subject: [PATCH 311/367] add parallel op for square

---
 mindspore/ccsrc/parallel/dynamic_creator.h    |  1 +
 .../ccsrc/parallel/ops_info/activation_info.h |  8 ++
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |  3 +-
 .../ccsrc/parallel/step_auto_parallel.cc      |  7 +-
 tests/ut/python/parallel/test_square.py       | 85 +++++++++++++++++++
 5 files changed, 100 insertions(+), 4 deletions(-)
 create mode 100644 tests/ut/python/parallel/test_square.py

diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h
index 723c018d7f..bad947687d 100644
--- a/mindspore/ccsrc/parallel/dynamic_creator.h
+++ b/mindspore/ccsrc/parallel/dynamic_creator.h
@@ -128,6 +128,7 @@ REGISTER(BatchMatMulInfo);
 REGISTER(ExpandDimsInfo);
 REGISTER(SqueezeInfo);
 REGISTER(SigmoidCrossEntropyWithLogitsInfo);
+REGISTER(SquareInfo);
 }  // namespace parallel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h
index 8dca036f9e..887be5ea33 100644
--- a/mindspore/ccsrc/parallel/ops_info/activation_info.h
+++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h
@@ -203,6 +203,14 @@ class SqueezeInfo : public ActivationOther {
  private:
   ValueTuplePtr axis_;
 };
+
+class SquareInfo : public ActivationOther {
+ public:
+  SquareInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+             const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SquareInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ACTIVATION_INFO_H_
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 0b52d8e83c..89b174d6b0 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -202,9 +202,10 @@ constexpr char SQRT[] = "Sqrt";
 constexpr char ASSIGN[] = "Assign";
 constexpr char GET_NEXT[] = "GetNext";
 constexpr char SQUEEZE[] = "Squeeze";
-constexpr char Neg[] = "Neg";
+constexpr char NEG[] = "Neg";
 constexpr char BATCH_MATMUL[] = "BatchMatMul";
 constexpr char EXPAND_DIMS[] = "ExpandDims";
+constexpr char SQUARE[] = "Square";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";
diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index 1d52eac82d..81aae04c73 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -104,13 +104,14 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SQRT,
                                            GET_NEXT,
                                            CAST,
-                                           Neg,
+                                           NEG,
+                                           SQUARE,
                                            BATCH_MATMUL,
                                            EXPAND_DIMS,
                                            SQUEEZE};
 
-std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,
-                                            CAST,       POW,  EXP,  LOG,     COS,         ACOS, LOGICALNOT};
+std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU,       SQRT, CAST,
+                                            POW,        EXP,  LOG,  COS,     ACOS,        LOGICALNOT, NEG,  SQUARE};
 
 bool StepAutoParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &) {
   MS_EXCEPTION_IF_NULL(root);
diff --git a/tests/ut/python/parallel/test_square.py b/tests/ut/python/parallel/test_square.py
new file mode 100644
index 0000000000..e9c182a439
--- /dev/null
+++ b/tests/ut/python/parallel/test_square.py
@@ -0,0 +1,85 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super(Net, self).__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.square = P.Square().set_strategy(strategy2)
+        self.mul2 = P.Mul().set_strategy(strategy1)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.square(out)
+        out = self.mul2(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_square_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile_net(net)
+
+
+def test_square_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)

From 79de8f4bdf84005b4bb88907432a7dab349f7e78 Mon Sep 17 00:00:00 2001
From: Xiaoda Zhang <zhangxiaoda@huawei.com>
Date: Thu, 16 Apr 2020 15:10:06 +0800
Subject: [PATCH 312/367] Adjusting backward communication cost of some
 operators

---
 .../auto_parallel/operator_costmodel.cc       | 73 +++++++++++++++++--
 .../auto_parallel/operator_costmodel.h        |  4 +-
 .../ccsrc/parallel/ops_info/gather_v2_info.cc |  2 +-
 3 files changed, 69 insertions(+), 10 deletions(-)

diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
index 9ea583293b..0192dce8b8 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
@@ -287,6 +287,31 @@ double BatchParallelCost::GetBackwardComputationCost(const std::vector<mindspore
   return 0.0;
 }
 
+double BatchParallelCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                              int32_t stage_id) const {
+  double result = 0.0;
+  CheckGlobalDeviceManager();
+  MS_EXCEPTION_IF_NULL(g_device_manager);
+  auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+
+  for (size_t j = 0; j < inputs.size(); ++j) {
+    if (!is_parameter_[j]) {
+      continue;
+    }
+    TensorInfo input_a_tensor_info = inputs[j];
+    Shape input_a_shape = input_a_tensor_info.shape();
+    Shape input_a_slice_shape = input_a_tensor_info.slice_shape();
+    int32_t used_device_num = 1;
+    for (size_t i = 0; i < input_a_shape.size(); ++i) {
+      used_device_num *= input_a_shape[i] / input_a_slice_shape[i];
+    }
+    if (total_device_num != IntToSize(used_device_num)) {
+      result += ListProduct(input_a_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+    }
+  }
+
+  return result;
+}
 // return the per device communication cost in the forward phase.
 double PReLUCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
   // prelu does not need communication in the forward phase
@@ -432,8 +457,24 @@ double ReshapeCost::GetForwardCommCost(const std::vector<TensorInfo>& inputs, co
 }
 
 // return the per device communication cost in the backward phase.
-double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const {
-  return 0.0;
+double ReshapeCost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                        int32_t stage_id) const {
+  double result = 0.0;
+  if (is_parameter_[0]) {
+    TensorInfo input1 = inputs[0];
+    MS_EXCEPTION_IF_NULL(g_device_manager);
+    auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+    Shape input1_shape = input1.shape();
+    Shape input1_slice_shape = input1.slice_shape();
+    int32_t used_device_num = 1;
+    for (size_t i = 0; i < input1_shape.size(); ++i) {
+      used_device_num *= input1_shape[i] / input1_slice_shape[i];
+    }
+    if (total_device_num != IntToSize(used_device_num)) {
+      result = ListProduct(input1_slice_shape) * static_cast<double>(inputs_type_lengths_[1]);
+    }
+  }
+  return result;
 }
 
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
@@ -654,10 +695,30 @@ double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo>&, const st
 }
 
 // return the per device communication cost in the backward phase.
-double GatherV2Cost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&,
-                                         int32_t) const {
-  // GatherV2Cost does not need communication in the backward phase
-  return 0.0;
+double GatherV2Cost::GetBackwardCommCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
+                                         int32_t stage_id) const {
+  double result = 0.0;
+  CheckGlobalDeviceManager();
+  MS_EXCEPTION_IF_NULL(g_device_manager);
+  auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+
+  for (size_t j = 0; j < inputs.size(); ++j) {
+    if (!is_parameter_[j]) {
+      continue;
+    }
+    TensorInfo input_a_tensor_info = inputs[j];
+    Shape input_a_shape = input_a_tensor_info.shape();
+    Shape input_a_slice_shape = input_a_tensor_info.slice_shape();
+    int32_t used_device_num = 1;
+    for (size_t i = 0; i < input_a_shape.size(); ++i) {
+      used_device_num *= input_a_shape[i] / input_a_slice_shape[i];
+    }
+    if (total_device_num != IntToSize(used_device_num)) {
+      result += ListProduct(input_a_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+    }
+  }
+
+  return result;
 }
 
 double GatherV2Cost::GetForwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&,
diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
index f16dfa21fc..37b054aa98 100644
--- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
+++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h
@@ -226,9 +226,7 @@ class BatchParallelCost : public OperatorCost {
   double GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
     return 0.0;
   }
-  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override {
-    return 0.0;
-  }
+  double GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const override;
   double GetComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>& outputs,
                             int32_t stage_id) const override {
     return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
diff --git a/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
index 2010d1ed46..c315991849 100644
--- a/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
+++ b/mindspore/ccsrc/parallel/ops_info/gather_v2_info.cc
@@ -291,7 +291,7 @@ Status GatherV2Info::GenerateStrategies(int32_t stage_id) {
   }
 
   is_auto_parallel_ = true;
-  Shape input0_split(inputs_shape_[0].size());
+  Shape input0_split(inputs_shape_[0].size(), 1);
   Shapes splittable_inputs = {input0_split};
 
   std::vector<StrategyPtr> sp_vector;

From 1d1e085b0b85de13a30e8d850f15e84dbc79d512 Mon Sep 17 00:00:00 2001
From: xiefangqi <xiefangqi2@huawei.com>
Date: Fri, 17 Apr 2020 11:47:39 +0800
Subject: [PATCH 313/367] fix mindspore.datasets.apply comments

---
 mindspore/dataset/engine/datasets.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 1c6ac634f2..d62901d146 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -516,22 +516,14 @@ class Dataset:
             Dataset, applied by the function.
 
         Examples:
-            >>> import numpy as np
             >>> import mindspore.dataset as ds
-            >>> # Generate 1d int numpy array from 0 - 6
-            >>> def generator_1d():
-            >>>     for i in range(6):
-            >>>         yield (np.array([i]),)
-            >>> # 1) get all data from dataset
-            >>> data = ds.GeneratorDataset(generator_1d, ["data"])
-            >>> # 2) declare a apply_func function
+            >>> # data is an instance of Dataset object
+            >>> # declare an apply_func function which returns a Dataset object
             >>> def apply_func(ds):
             >>>     ds = ds.batch(2)
             >>>     return ds
-            >>> # 3) use apply to call apply_func
+            >>> # use apply to call apply_func
             >>> data = data.apply(apply_func)
-            >>> for item in data.create_dict_iterator():
-            >>>     print(item["data"])
 
         Raises:
             TypeError: If apply_func is not a function.

From 34298e21358b7eca2995d3643fc55288b709733e Mon Sep 17 00:00:00 2001
From: anzhengqi <anzhengqi.huawei.com>
Date: Fri, 17 Apr 2020 12:04:50 +0800
Subject: [PATCH 314/367] fix commit format error

---
 mindspore/dataset/engine/datasets.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 1c6ac634f2..4cff9c57b9 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2597,19 +2597,24 @@ class Schema:
 
         Args:
             columns (dict or list[dict]): dataset attribution information, decoded from schema file.
-            if list: columns element must be dict, 'name' and 'type' must be in keys, 'shape' optional.
-            if dict: columns.keys() as name, element in columns.values() is dict, and 'type' inside, 'shape' optional.
-              example 1)
-                [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
-                 {'name': 'label', 'type': 'int8', 'shape': [1]}]
-              example 2)
-                {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
+
+                - list[dict], 'name' and 'type' must be in keys, 'shape' optional.
+
+                - dict, columns.keys() as name, columns.values() is dict, and 'type' inside, 'shape' optional.
 
         Raises:
             RuntimeError: If failed to parse columns.
             RuntimeError: If unknown items in columns.
             RuntimeError: If column's name field is missing.
             RuntimeError: If column's type field is missing.
+
+        Example:
+            >>> schema = Schema()
+            >>> columns1 = [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
+            >>>             {'name': 'label', 'type': 'int8', 'shape': [1]}]
+            >>> schema.parse_columns(columns1)
+            >>> columns2 = {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
+            >>> schema.parse_columns(columns2)
         """
         self.columns = []
         if isinstance(columns, list):

From 149901ce2eb19e947492561d755853a05f6a2ed8 Mon Sep 17 00:00:00 2001
From: liyong <liyong126@huawei.com>
Date: Thu, 16 Apr 2020 14:29:23 +0800
Subject: [PATCH 315/367] remove validation in write raw data

---
 mindspore/mindrecord/filewriter.py | 55 ++++--------------------------
 1 file changed, 6 insertions(+), 49 deletions(-)

diff --git a/mindspore/mindrecord/filewriter.py b/mindspore/mindrecord/filewriter.py
index 4056825ff3..90bca48038 100644
--- a/mindspore/mindrecord/filewriter.py
+++ b/mindspore/mindrecord/filewriter.py
@@ -26,8 +26,7 @@ from .shardheader import ShardHeader
 from .shardindexgenerator import ShardIndexGenerator
 from .shardutils import MIN_SHARD_COUNT, MAX_SHARD_COUNT, VALID_ATTRIBUTES, VALID_ARRAY_ATTRIBUTES, \
     check_filename, VALUE_TYPE_MAP
-from .common.exceptions import ParamValueError, ParamTypeError, MRMInvalidSchemaError, MRMDefineIndexError, \
-    MRMValidateDataError
+from .common.exceptions import ParamValueError, ParamTypeError, MRMInvalidSchemaError, MRMDefineIndexError
 
 __all__ = ['FileWriter']
 
@@ -201,52 +200,13 @@ class FileWriter:
             raw_data.pop(i)
             logger.warning(v)
 
-    def _verify_based_on_blob_fields(self, raw_data):
+    def write_raw_data(self, raw_data):
         """
-        Verify data according to blob fields which is sub set of schema's fields.
-
-        Raise exception if validation failed.
-        1) allowed data type contains: "int32", "int64", "float32", "float64", "string", "bytes".
-
-        Args:
-            raw_data (list[dict]): List of raw data.
-
-        Raises:
-            MRMValidateDataError: If data does not match blob fields.
-        """
-        schema_content = self._header.schema
-        for field in schema_content:
-            for i, v in enumerate(raw_data):
-                if field not in v:
-                    raise MRMValidateDataError("for schema, {} th data is wrong: "\
-                    "there is not '{}' object in the raw data.".format(i, field))
-                if field in self._header.blob_fields:
-                    field_type = type(v[field]).__name__
-                    if field_type not in VALUE_TYPE_MAP:
-                        raise MRMValidateDataError("for schema, {} th data is wrong: "\
-                        "data type for '{}' is not matched.".format(i, field))
-                    if schema_content[field]["type"] not in VALUE_TYPE_MAP[field_type]:
-                        raise MRMValidateDataError("for schema, {} th data is wrong: "\
-                        "data type for '{}' is not matched.".format(i, field))
-                    if field_type == 'ndarray':
-                        if 'shape' not in schema_content[field]:
-                            raise MRMValidateDataError("for schema, {} th data is wrong: " \
-                                                       "data type for '{}' is not matched.".format(i, field))
-                        try:
-                            # tuple or list
-                            np.reshape(v[field], schema_content[field]['shape'])
-                        except ValueError:
-                            raise MRMValidateDataError("for schema, {} th data is wrong: " \
-                                                      "data type for '{}' is not matched.".format(i, field))
-
-    def write_raw_data(self, raw_data, validate=True):
-        """
-        Write raw data and generate sequential pair of MindRecord File.
+        Write raw data and generate sequential pair of MindRecord File and \
+        validate data based on predefined schema by default.
 
         Args:
            raw_data (list[dict]): List of raw data.
-           validate (bool, optional): Validate data according schema if it equals to True,
-               or validate data according to blob fields (default=True).
 
         Raises:
             ParamTypeError: If index field is invalid.
@@ -264,11 +224,8 @@ class FileWriter:
         for each_raw in raw_data:
             if not isinstance(each_raw, dict):
                 raise ParamTypeError('raw_data item', 'dict')
-        if validate is True:
-            self._verify_based_on_schema(raw_data)
-        elif validate is False:
-            self._verify_based_on_blob_fields(raw_data)
-        return self._writer.write_raw_data(raw_data, validate)
+        self._verify_based_on_schema(raw_data)
+        return self._writer.write_raw_data(raw_data, True)
 
     def set_header_size(self, header_size):
         """

From f9ef78609f67569c203290eefd5dbe2f543945e5 Mon Sep 17 00:00:00 2001
From: jjfeing <jiangjianfei3@huawei.com>
Date: Fri, 17 Apr 2020 11:37:35 +0800
Subject: [PATCH 316/367] add nc1hwc0_c04 format

---
 mindspore/ccsrc/device/ascend/kernel_select_ascend.cc | 7 ++++---
 mindspore/ccsrc/utils/utils.h                         | 1 +
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index dafe958348..f02e677163 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -45,9 +45,10 @@ enum MatchCountPriority : int {
 const size_t kMaxCount = 0xffffffff;
 const int kUnSupportMixedDataTypeIndex = -1;
 
-const std::set<std::string> kOpFormatList = {
-  kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,     kOpFormat_NCHW,      kOpFormat_NHWC,
-  kOpFormat_HWCN,    kOpFormat_NC1HWC0,     kOpFormat_FRAC_Z, kOpFormat_C1HWNCoC0, kOpFormat_FRAC_NZ};
+const std::set<std::string> kOpFormatList = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
+                                             kOpFormat_NCHW,    kOpFormat_NHWC,        kOpFormat_HWCN,
+                                             kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,      kOpFormat_C1HWNCoC0,
+                                             kOpFormat_FRAC_NZ, kOpFormat_NC1HWC0_C04};
 
 bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format) {
   // if format is default, it remarkes support all format
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index e346a9e901..4661deed3a 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -183,6 +183,7 @@ constexpr auto kOpFormat_NC1HWC0 = "NC1HWC0";
 constexpr auto kOpFormat_FRAC_Z = "FracZ";
 constexpr auto kOpFormat_FRAC_NZ = "FRACTAL_NZ";
 constexpr auto kOpFormat_C1HWNCoC0 = "C1HWNCoC0";
+constexpr auto kOpFormat_NC1HWC0_C04 = "NC1HWC0_C04";
 const std::set<std::string> k1DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NCHW,        kOpFormat_NHWC,
                                                 kOpFormat_FRAC_Z,  kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0};
 const std::set<std::string> k2DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_FRAC_Z,

From 83eeac9310819b1db8bad7051252f95dfcde4bdf Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Wed, 15 Apr 2020 11:12:14 +0800
Subject: [PATCH 317/367] optimize execute order sort

---
 .../device/ascend/ascend_memory_manager.h     |   2 +-
 .../ccsrc/device/ascend/ascend_memory_pool.h  |   8 +-
 .../ccsrc/session/anf_runtime_algorithm.cc    |   8 +
 .../ccsrc/session/anf_runtime_algorithm.h     |   1 +
 mindspore/ccsrc/session/kernel_graph.cc       | 177 +++++++++++-------
 mindspore/ccsrc/session/kernel_graph.h        |   7 +-
 tests/st/networks/test_gpu_lstm.py            |   1 +
 7 files changed, 127 insertions(+), 77 deletions(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
index dea88ac10a..90c8b2dfca 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h
@@ -23,7 +23,7 @@ namespace ascend {
 class AscendMemoryManager : public MemoryManager {
  public:
   AscendMemoryManager() = default;
-  virtual ~AscendMemoryManager() = default;
+  ~AscendMemoryManager() override = default;
 
   void MallocDeviceMemory() override;
   void FreeDeviceMemory() override;
diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_pool.h b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h
index c2a29725f4..a02bd453b2 100644
--- a/mindspore/ccsrc/device/ascend/ascend_memory_pool.h
+++ b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h
@@ -26,6 +26,8 @@ namespace ascend {
 class AscendMemoryPool : public DynamicMemPoolBestFit {
  public:
   ~AscendMemoryPool() override = default;
+  AscendMemoryPool(const AscendMemoryPool&) = delete;
+  AscendMemoryPool& operator=(const AscendMemoryPool&) = delete;
 
   size_t AllocDeviceMem(size_t size, DeviceMemPtr* addr) override;
   bool FreeDeviceMem(const DeviceMemPtr& addr) override;
@@ -51,13 +53,11 @@ class AscendMemoryPool : public DynamicMemPoolBestFit {
 
  private:
   AscendMemoryPool() = default;
-  AscendMemoryPool(const AscendMemoryPool&) = delete;
-  AscendMemoryPool& operator=(const AscendMemoryPool&) = delete;
   bool has_malloc_{false};
   uint8_t* device_mem_pool_base_{nullptr};
   uint64_t device_mem_pool_size_{0};
-  size_t free_mem_size_;
-  size_t total_mem_size_;
+  size_t free_mem_size_{0};
+  size_t total_mem_size_{0};
 };
 }  // namespace ascend
 }  // namespace device
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 44472a9a6f..4a38c5fa09 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -858,6 +858,14 @@ bool AnfRuntimeAlgorithm::IsCommunicationOp(const AnfNodePtr &node) {
   return false;
 }
 
+bool AnfRuntimeAlgorithm::IsAllReduceOp(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  if (node->isa<CNode>() && AnfAlgo::GetCNodeName(node) == kAllReduceOpName) {
+    return true;
+  }
+  return false;
+}
+
 bool AnfRuntimeAlgorithm::IsGetNext(const NotNull<AnfNodePtr> &node) {
   auto kernel_name = AnfAlgo::GetCNodeName(node);
   return kernel_name == kGetNextOpName;
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h
index a70a63b678..78359cdd5a 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.h
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h
@@ -176,6 +176,7 @@ class AnfRuntimeAlgorithm {
   // get real input index for some tbe ops which input order is different between me and tbe impl
   static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index);
   static bool IsCommunicationOp(const AnfNodePtr &node);
+  static bool IsAllReduceOp(const AnfNodePtr &node);
   static bool IsGetNext(const NotNull<AnfNodePtr> &node);
 };
 }  // namespace session
diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc
index bbcc04e14b..49d482d6d4 100755
--- a/mindspore/ccsrc/session/kernel_graph.cc
+++ b/mindspore/ccsrc/session/kernel_graph.cc
@@ -50,90 +50,127 @@ std::vector<AnfNodePtr> KernelGraph::outputs() const {
 }
 
 void KernelGraph::SetExecOrderByDefault() {
-  BfsToUpdateNodeOutput();
+  std::stack<AnfNodePtr> seed_nodes;
+  UpdateNodeEdgeList(&seed_nodes);
   execution_order_.clear();
-  std::queue<AnfNodePtr> allreduce_nodes;
-  std::queue<AnfNodePtr> zero_output_nodes;
   std::unordered_set<AnfNodePtr> visited_nodes;
-  auto clear_output = [&zero_output_nodes, &allreduce_nodes, &visited_nodes, this](const AnfNodePtr &input) -> void {
-    if (node_output_num_[input] == 0 && visited_nodes.find(input) == visited_nodes.end()) {
-      MS_EXCEPTION_IF_NULL(input);
-      MS_LOG(DEBUG) << "Clear output num:" << input->DebugString();
-      (void)visited_nodes.insert(input);
-      if (input->isa<CNode>() && AnfAlgo::GetCNodeName(input) == kAllReduceOpName) {
-        allreduce_nodes.push(input);
-      } else {
-        zero_output_nodes.push(input);
+  std::queue<AnfNodePtr> zero_input_nodes;
+
+  auto visit_node_descendant = [&visited_nodes, this](const AnfNodePtr &node, std::queue<AnfNodePtr> *visit_queue) {
+    auto it = node_output_edges_.find(node);
+    if (it == node_output_edges_.end()) {
+      // value node and parameter has no input,no need to print log
+      if (node->isa<CNode>()) {
+        MS_LOG(DEBUG) << "Can not find node [" << node->DebugString() << "]";
       }
+      return;
+    }
+
+    // visit all reduce node first, then other nodes
+    std::vector<AnfNodePtr> active_nodes;
+    for (const auto &output_edge : it->second) {
+      auto next_node = output_edge.first;
+      if (node_input_num_.find(next_node) == node_input_num_.end()) {
+        MS_EXCEPTION_IF_NULL(next_node);
+        MS_LOG(EXCEPTION) << "Can't find node[" << next_node->DebugString() << "]";
+      }
+      MS_EXCEPTION_IF_NULL(next_node);
+      MS_LOG(DEBUG) << "Decrease input:" << next_node->DebugString() << ",node:" << node->DebugString()
+                    << ",num: " << node_input_num_[next_node] << ",decrease num:" << output_edge.second;
+      if (node_input_num_[next_node] < output_edge.second) {
+        MS_LOG(EXCEPTION) << "Input node:" << next_node->DebugString() << ",node_output_num"
+                          << node_input_num_[next_node] << ",depend edge:" << output_edge.second;
+      }
+      node_input_num_[next_node] = node_input_num_[next_node] - output_edge.second;
+      // allreduce first
+      if (node_input_num_[next_node] == 0 && visited_nodes.find(next_node) == visited_nodes.end()) {
+        (void)visited_nodes.insert(next_node);
+        if (AnfAlgo::IsAllReduceOp(next_node)) {
+          MS_LOG(DEBUG) << "visit node:" << next_node->DebugString();
+          visit_queue->push(next_node);
+        } else {
+          active_nodes.emplace_back(next_node);
+        }
+      }
+    }
+
+    for (auto &node : active_nodes) {
+      MS_LOG(DEBUG) << "visit node:" << node->DebugString();
+      visit_queue->push(node);
     }
   };
-  zero_output_nodes.emplace(get_return());
-  while (!zero_output_nodes.empty() || !allreduce_nodes.empty()) {
-    AnfNodePtr node;
-    if (!zero_output_nodes.empty()) {
-      node = zero_output_nodes.front();
-      zero_output_nodes.pop();
+
+  AnfNodePtr last_allreduce_node = nullptr;
+  std::queue<AnfNodePtr> allreduce_descendants;
+  while (!seed_nodes.empty() || last_allreduce_node != nullptr) {
+    // seed nodes first, then visit last all reduce node descendant
+    if (seed_nodes.empty()) {
+      visit_node_descendant(last_allreduce_node, &allreduce_descendants);
+      last_allreduce_node = nullptr;
     } else {
-      node = allreduce_nodes.front();
-      allreduce_nodes.pop();
-    }
-    MS_EXCEPTION_IF_NULL(node);
-    if (node->isa<CNode>() && AnfAlgo::IsRealKernel(node)) {
-      execution_order_.push_back(node->cast<CNodePtr>());
+      zero_input_nodes.push(seed_nodes.top());
+      seed_nodes.pop();
     }
-    auto it = node_input_edges_.find(node);
-    if (it == node_input_edges_.end()) {
-      // value node and parameter has no input,no need to print log
-      if (node->isa<CNode>()) {
-        MS_LOG(DEBUG) << "Can not find node [" << node->DebugString() << "]";
+
+    // all reduce node descendant first, then common queue
+    while (!zero_input_nodes.empty() || !allreduce_descendants.empty()) {
+      AnfNodePtr node = nullptr;
+      bool is_allreduce_descendant = false;
+      if (allreduce_descendants.empty()) {
+        node = zero_input_nodes.front();
+        zero_input_nodes.pop();
+      } else {
+        node = allreduce_descendants.front();
+        allreduce_descendants.pop();
+        is_allreduce_descendant = true;
       }
-      continue;
-    }
-    for (const auto &input_edge : it->second) {
-      if (node_output_num_.find(input_edge.first) == node_output_num_.end()) {
-        MS_EXCEPTION_IF_NULL(input_edge.first);
-        MS_LOG(EXCEPTION) << "Can't find node[" << input_edge.first->DebugString() << "]";
+      // add execute node
+      MS_EXCEPTION_IF_NULL(node);
+      if (node->isa<CNode>() && AnfAlgo::IsRealKernel(node)) {
+        execution_order_.push_back(node->cast<CNodePtr>());
       }
-      MS_EXCEPTION_IF_NULL(input_edge.first);
-      MS_LOG(DEBUG) << "Decrease input:" << input_edge.first->DebugString() << ",node:" << node->DebugString()
-                    << ",num: " << node_output_num_[input_edge.first] << ",decrease num:" << input_edge.second;
-      if (node_output_num_[input_edge.first] < input_edge.second) {
-        MS_LOG(EXCEPTION) << "Input node:" << input_edge.first->DebugString() << ",node_output_num"
-                          << node_output_num_[input_edge.first] << "depend edge:" << input_edge.second;
+      // for all reduce node, visit last all reduce node descendant
+      if (AnfAlgo::IsAllReduceOp(node)) {
+        if (last_allreduce_node != nullptr) {
+          visit_node_descendant(last_allreduce_node, &allreduce_descendants);
+        }
+        last_allreduce_node = node;
+      } else if (is_allreduce_descendant) {
+        visit_node_descendant(node, &allreduce_descendants);
+      } else {
+        visit_node_descendant(node, &zero_input_nodes);
       }
-      node_output_num_[input_edge.first] = node_output_num_[input_edge.first] - input_edge.second;
-      clear_output(input_edge.first);
     }
   }
+
   CheckLoop();
-  std::reverse(execution_order_.begin(), execution_order_.end());
 }
 
 void KernelGraph::CheckLoop() {
-  std::map<AnfNodePtr, size_t> none_zero_output;
-  if (node_output_edges_.size() != node_output_num_.size()) {
-    MS_LOG(EXCEPTION) << "node_output_edges_ size :" << node_output_edges_.size()
-                      << "not equal to node_output_num_ size:" << node_output_num_.size();
+  std::map<AnfNodePtr, size_t> none_zero_nodes;
+  if (node_input_edges_.size() != node_input_num_.size()) {
+    MS_LOG(EXCEPTION) << "node_input_edges_ size :" << node_input_edges_.size()
+                      << "not equal to node_input_num_ size:" << node_input_num_.size();
   }
-  for (auto &it : node_output_num_) {
+  for (auto &it : node_input_num_) {
     MS_EXCEPTION_IF_NULL(it.first);
     string str;
-    auto node_output_it = node_output_edges_.find(it.first);
-    if (node_output_it == node_output_edges_.end()) {
+    auto node_input_it = node_input_edges_.find(it.first);
+    if (node_input_it == node_input_edges_.end()) {
       MS_LOG(EXCEPTION) << "Can't find node [" << it.first->DebugString() << "]";
     }
-    for (const auto &output_edge : node_output_edges_[it.first]) {
-      MS_EXCEPTION_IF_NULL(output_edge.first);
-      str = str.append(output_edge.first->DebugString()).append("|");
+    for (const auto &input_edge : node_input_edges_[it.first]) {
+      MS_EXCEPTION_IF_NULL(input_edge.first);
+      str = str.append(input_edge.first->DebugString()).append("|");
     }
     if (it.second != 0) {
-      MS_LOG(WARNING) << "Node:" << it.first->DebugString() << ",outputs:" << str << ",output num:" << it.second;
-      none_zero_output[it.first] = it.second;
+      MS_LOG(WARNING) << "Node:" << it.first->DebugString() << ",inputs:" << str << ",input num:" << it.second;
+      none_zero_nodes[it.first] = it.second;
     }
   }
   // if don't consider control depend and loop exit,a exception will be throw
-  if (!none_zero_output.empty()) {
-    MS_LOG(EXCEPTION) << "Nodes have loop, left node num:" << none_zero_output.size();
+  if (!none_zero_nodes.empty()) {
+    MS_LOG(EXCEPTION) << "Nodes have loop, left node num:" << none_zero_nodes.size();
   }
 }
 
@@ -346,12 +383,13 @@ void KernelGraph::AddDependEdge(const AnfNodePtr &node, const AnfNodePtr &input,
   } else {
     input_it->second.push_back(input_depend_edge);
   }
-  // add the depend sum of node
-  auto depend_it = node_output_num_.find(input);
-  if (depend_it == node_output_num_.end()) {
-    node_output_num_[input] = 0;
+  // add node input depend num
+  auto depend_it = node_input_num_.find(node);
+  if (depend_it == node_input_num_.end()) {
+    node_input_num_[node] = depend_edge_num;
+  } else {
+    depend_it->second += depend_edge_num;
   }
-  node_output_num_[input] += depend_edge_num;
 }
 
 std::vector<AnfNodePtr> KernelGraph::GetOutputNodes(const AnfNodePtr &node) {
@@ -429,9 +467,9 @@ bool KernelGraph::HandleControlDependNode(const AnfNodePtr &node, std::queue<Anf
   return true;
 }
 
-void KernelGraph::BfsToUpdateNodeOutput() {
+void KernelGraph::UpdateNodeEdgeList(std::stack<AnfNodePtr> *seed_nodes) {
   node_output_edges_.clear();
-  node_output_num_.clear();
+  node_input_num_.clear();
   node_input_edges_.clear();
   std::vector<AnfNodePtr> control_depends;
   std::unordered_set<AnfNodePtr> visited_nodes;
@@ -441,6 +479,11 @@ void KernelGraph::BfsToUpdateNodeOutput() {
     auto node = que.front();
     que.pop();
     MS_EXCEPTION_IF_NULL(node);
+    if (node->isa<Parameter>() || node->isa<ValueNode>()) {
+      seed_nodes->push(node);
+      continue;
+    }
+
     if (!node->isa<CNode>()) {
       continue;
     }
@@ -454,10 +497,6 @@ void KernelGraph::BfsToUpdateNodeOutput() {
         control_depends.push_back(input);
         depend_edge_num = 0;
       }
-      // the 2rd input of depend is no depend edge
-      if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) && input == cnode->input(kDependAttachNodeIndex)) {
-        depend_edge_num = 0;
-      }
       PushNoVisitedNode(input, &que, &visited_nodes);
       AddDependEdge(node, input, depend_edge_num);
     }
diff --git a/mindspore/ccsrc/session/kernel_graph.h b/mindspore/ccsrc/session/kernel_graph.h
index ff964482bb..54b16014a3 100755
--- a/mindspore/ccsrc/session/kernel_graph.h
+++ b/mindspore/ccsrc/session/kernel_graph.h
@@ -22,6 +22,7 @@
 #include <utility>
 #include <string>
 #include <queue>
+#include <stack>
 #include <map>
 #include <unordered_set>
 #include "ir/func_graph.h"
@@ -93,8 +94,8 @@ class KernelGraph : public FuncGraph {
  private:
   // remove value node form graph
   bool RemoveValueNodeFromGraph(const ValueNodePtr &value_node);
-  // BFS to update all nodes' output
-  void BfsToUpdateNodeOutput();
+  // update node edge list
+  void UpdateNodeEdgeList(std::stack<AnfNodePtr> *seed_nodes);
   // add node depend edge by data edge or control depend
   void AddDependEdge(const AnfNodePtr &node, const AnfNodePtr &input, size_t depend_edge_num);
   // handle control depend
@@ -114,7 +115,7 @@ class KernelGraph : public FuncGraph {
   std::unordered_map<tensor::TensorPtr, ValueNodePtr> tensor_to_value_node_map_;
   // include all value nodes
   std::unordered_set<ValueNodePtr> graph_value_nodes_;
-  std::unordered_map<AnfNodePtr, size_t> node_output_num_;
+  std::unordered_map<AnfNodePtr, size_t> node_input_num_;
   std::unordered_map<AnfNodePtr, std::vector<std::pair<AnfNodePtr, size_t>>> node_input_edges_;
   // record map between ref final output anf with index and ref origin input with index
   std::map<AnfWithOutIndex, AnfWithOutIndex> ref_out_in_map_;
diff --git a/tests/st/networks/test_gpu_lstm.py b/tests/st/networks/test_gpu_lstm.py
index 4387179812..e5208ff669 100644
--- a/tests/st/networks/test_gpu_lstm.py
+++ b/tests/st/networks/test_gpu_lstm.py
@@ -135,4 +135,5 @@ def test_LSTM():
     for epoch in range(num_epochs):
         loss = train_network(train_features, train_labels)
         losses.append(loss)
+        print("loss:", loss.asnumpy())
     assert(losses[-1].asnumpy() < 0.01)

From b9742de3a3f13a8690b7ecf719c37d49fa486094 Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Fri, 17 Apr 2020 15:24:10 +0800
Subject: [PATCH 318/367] repeair GeneratorDataset to_device problem

---
 mindspore/dataset/engine/datasets.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 4cff9c57b9..77b7dc54c7 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -607,9 +607,9 @@ class Dataset:
 
         def get_distribution(output_dataset):
             dev_id = 0
-            if isinstance(output_dataset, (StorageDataset, GeneratorDataset, MindDataset)):
+            if isinstance(output_dataset, (StorageDataset, MindDataset)):
                 return output_dataset.distribution, dev_id
-            if isinstance(output_dataset, (Cifar10Dataset, Cifar100Dataset, ImageFolderDatasetV2,
+            if isinstance(output_dataset, (Cifar10Dataset, Cifar100Dataset, GeneratorDataset, ImageFolderDatasetV2,
                                            ManifestDataset, MnistDataset, VOCDataset, CelebADataset)):
                 sampler = output_dataset.sampler
                 if isinstance(sampler, samplers.DistributedSampler):

From b8210eb685c64aa2cd779aae70a347440fbaa14b Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Fri, 17 Apr 2020 15:19:07 +0800
Subject: [PATCH 319/367] fix no wrap in ge bankend in model.train

---
 mindspore/train/model.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 46e4f421f7..8ce51562fa 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -254,7 +254,8 @@ class Model:
         """
         # remove later to deal with loop sink
         need_wrap = False
-        if not hasattr(train_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink"):
+        if not hasattr(train_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink") \
+               and not context.get_context("enable_ge"):
             need_wrap = True
 
         dataset_helper = DatasetHelper(train_dataset)
@@ -418,7 +419,8 @@ class Model:
 
         # remove later to deal with loop sink
         need_wrap = False
-        if not hasattr(valid_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink"):
+        if not hasattr(valid_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink") \
+               and not context.get_context("enable_ge"):
             need_wrap = True
 
         valid_dataset.__loop_size__ = 1

From 9cb665e634b7c7fb53ceaef5cea1d452550dc4e5 Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Fri, 17 Apr 2020 15:16:55 +0800
Subject: [PATCH 320/367] add suport for parameter of const value pass as mixed
 precision args

fix pylint
---
 mindspore/ccsrc/pipeline/parse/parse.cc       |  4 +-
 mindspore/ops/composite/base.py               |  9 ++++
 .../parameter_feature/test_parameter.py       | 42 ++++++++++++++++++-
 3 files changed, 51 insertions(+), 4 deletions(-)

diff --git a/mindspore/ccsrc/pipeline/parse/parse.cc b/mindspore/ccsrc/pipeline/parse/parse.cc
index 231b98ab00..51c4fc17ec 100644
--- a/mindspore/ccsrc/pipeline/parse/parse.cc
+++ b/mindspore/ccsrc/pipeline/parse/parse.cc
@@ -68,9 +68,7 @@ AnfNodePtr GetMixedPrecisionCastHelp(const FuncGraphPtr &func_graph, const AnfNo
     return param;
   }
   auto cast_helper = prim::GetPythonOps("_mp_cast_helper", "mindspore.ops.composite.base");
-  auto partial =
-    func_graph->NewCNode({NewValueNode(prim::kPrimPartial), NewValueNode(cast_helper), NewValueNode(dst_type)});
-  auto cast = func_graph->NewCNode({NewValueNode(prim::kCompositeHyperMap), partial, param});
+  auto cast = func_graph->NewCNode({NewValueNode(cast_helper), NewValueNode(dst_type), param});
   return cast;
 }
 
diff --git a/mindspore/ops/composite/base.py b/mindspore/ops/composite/base.py
index 8670f4aa7c..4b559d1605 100644
--- a/mindspore/ops/composite/base.py
+++ b/mindspore/ops/composite/base.py
@@ -307,3 +307,12 @@ def _mixed_precision_cast_helper_2(type_, x):
     if F.issubclass_(F.dtype(x), mstype.float_):
         return P.Cast()(x, type_)
     return x
+
+@_mp_cast_helper.register("TypeType", "Tuple")
+@core
+def _mixed_precision_cast_helper_3(type_, x):
+    """if x is a tuple"""
+    t = ()
+    for item in x:
+        t = t + (_mp_cast_helper(type_, item),)
+    return t
diff --git a/tests/ut/python/parameter_feature/test_parameter.py b/tests/ut/python/parameter_feature/test_parameter.py
index 696b107f56..1409fef386 100644
--- a/tests/ut/python/parameter_feature/test_parameter.py
+++ b/tests/ut/python/parameter_feature/test_parameter.py
@@ -19,7 +19,7 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 import mindspore.ops.composite as C
 
-context.set_context(mode=context.GRAPH_MODE)
+context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 
 def test_parser_three_default_mixed_args_subnet():
 
@@ -227,3 +227,43 @@ def test_net_vargs_expand():
 
     net.set_train()
     net(x, y, sens)
+    
+
+def test_mixed_precision_const_parameter():
+    class NetLoss(Cell):
+        def __init__(self):
+            super(NetLoss, self).__init__()
+            self.shape = P.Shape()
+            self.up_sample1 = P.ResizeBilinear((14, 14))
+            self.up_sample2 = P.ResizeBilinear((28, 28))
+            self.up_sample3 = P.ResizeBilinear((36, 36))
+        def construct(self, x, y, z, *args):
+            ret = 0
+            if args[0] == self.shape(z)[2]:
+                if args[0] == 14:
+                    ret = self.up_sample1(y) + x
+                elif args[0] == 28:
+                    ret = self.up_sample2(y) - x
+                else:
+                    ret = x / y
+            else:
+                ret = x * y
+            ret = ret * z
+            return ret
+    class NetMain(Cell):
+        def __init__(self, loss_fn):
+            super(NetMain, self).__init__()
+            self.loss_fn = loss_fn
+            self.shape = P.Shape()
+        def construct(self, x, y, z):
+            size_x = self.shape(x)[2]
+            size_y = self.shape(y)[2]
+            ret = self.loss_fn(x, y, z, size_x, size_y)
+            return ret
+    loss_fn = NetLoss()
+    net = NetMain(loss_fn)
+    net.add_flags_recursive(fp32=True)
+    x = Tensor(np.ones((1, 3, 28, 28), np.float32))
+    y = Tensor(np.ones((1, 3, 14, 14), np.float32))
+    z = Tensor(np.ones((1, 3, 28, 28), np.float32))
+    out = net(x, y, z)
\ No newline at end of file

From 00e4306518934c96774e5f7ca7004a23bbd1c13d Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Fri, 17 Apr 2020 16:45:50 +0800
Subject: [PATCH 321/367] convert all tuple output to maketuple

---
 cmake/options.cmake                           |  2 +-
 mindspore/ccsrc/common/trans.cc               |  4 +-
 .../pre_activate/ascend/ascend_helper.cc      | 16 +---
 .../ccsrc/pre_activate/ascend/ascend_helper.h |  2 -
 .../insert_memcpy_async_for_getnext.cc        |  1 +
 .../common/common_backend_optimization.cc     |  2 +
 mindspore/ccsrc/pre_activate/common/helper.cc | 15 ++++
 mindspore/ccsrc/pre_activate/common/helper.h  |  2 +
 .../convert_tuple_input_to_dynamic_input.cc   |  9 +--
 .../pass/convert_tuple_output_to_maketuple.cc | 79 +++++++++++++++++++
 .../pass/convert_tuple_output_to_maketuple.h  | 40 ++++++++++
 mindspore/ccsrc/session/kernel_graph.cc       |  2 +-
 .../convert_tuple_output_to_maketuple_test.cc | 65 +++++++++++++++
 .../convert_tuple_output_to_maketuple_test.py | 54 +++++++++++++
 .../insert_memcpy_async_for_getnext.py        |  7 +-
 15 files changed, 270 insertions(+), 30 deletions(-)
 create mode 100644 mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.cc
 create mode 100644 mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.h
 create mode 100644 tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc
 create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/convert_tuple_output_to_maketuple_test.py

diff --git a/cmake/options.cmake b/cmake/options.cmake
index 3677418a98..3e03ed3339 100644
--- a/cmake/options.cmake
+++ b/cmake/options.cmake
@@ -45,8 +45,8 @@ endif()
 
 if (DEBUG_MODE)
     set(CMAKE_BUILD_TYPE "Debug")
-else()
     add_compile_definitions(MEM_REUSE_DEBUG)
+else()
     set(CMAKE_BUILD_TYPE "Release")
 endif()
 
diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index a2b9f7ef24..5eb21f09bd 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -205,8 +205,8 @@ std::vector<int> GetRuntimePaddingShape(const AnfNodePtr &node, size_t index) {
     if (tensor == nullptr) {
       MS_LOG(EXCEPTION) << " the node[ " << node->DebugString() << "]'s cannot convert ";
     }
-    shape = tensor->shape();
-    (void)std::transform(shape.begin(), shape.end(), std::back_inserter(host_shape), IntToSize);
+    auto shape_temp = tensor->shape();
+    (void)std::transform(shape_temp.begin(), shape_temp.end(), std::back_inserter(host_shape), IntToSize);
     if (host_shape.empty()) {
       host_shape.push_back(1);
     }
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
index 745ed4460f..fbb3e345df 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc
@@ -18,6 +18,7 @@
 #include <set>
 #include "common/trans.h"
 #include "common/utils.h"
+#include "pre_activate/common/helper.h"
 #include "utils/utils.h"
 #include "device/kernel_info.h"
 #include "kernel/oplib/oplib.h"
@@ -346,21 +347,6 @@ CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnod
   return new_node;
 }
 
-AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node, size_t output_idx) {
-  auto idx = NewValueNode(SizeToInt(output_idx));
-  MS_EXCEPTION_IF_NULL(idx);
-  auto imm = std::make_shared<Int32Imm>(SizeToInt(output_idx));
-  auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(imm);
-  idx->set_abstract(abstract_scalar);
-  AnfNodePtr tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx});
-  MS_EXCEPTION_IF_NULL(tuple_getitem);
-  tuple_getitem->set_scope(node->scope());
-  std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
-  TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx);
-  AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get());
-  return tuple_getitem;
-}
-
 AnfNodePtr CreateMemcpyAsyncOp(const FuncGraphPtr &graph, const AnfNodePtr &node) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(node);
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
index 7f5e86d726..a8fd7dc514 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.h
@@ -64,8 +64,6 @@ AnfNodePtr InsertTransOpForOutput(const FuncGraphPtr &func_graph, const AnfNodeP
 
 CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnode);
 
-AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node, size_t output_idx);
-
 AnfNodePtr CreateMemcpyAsyncOp(const FuncGraphPtr &graph, const AnfNodePtr &node);
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
index 5065bab0f2..fb8b19047c 100644
--- a/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/enhancer/insert_memcpy_async_for_getnext.cc
@@ -17,6 +17,7 @@
 #include <vector>
 #include <memory>
 #include "pre_activate/ascend/ascend_helper.h"
+#include "pre_activate/common/helper.h"
 #include "session/anf_runtime_algorithm.h"
 
 namespace mindspore {
diff --git a/mindspore/ccsrc/pre_activate/common/common_backend_optimization.cc b/mindspore/ccsrc/pre_activate/common/common_backend_optimization.cc
index c3fd292aa9..f622f2f06f 100644
--- a/mindspore/ccsrc/pre_activate/common/common_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/common/common_backend_optimization.cc
@@ -18,6 +18,7 @@
 #include <string>
 #include "pre_activate/common/optimizer.h"
 #include "pre_activate/pass/convert_const_input_to_attr.h"
+#include "pre_activate/pass/convert_tuple_output_to_maketuple.h"
 #include "pre_activate/pass/convert_const_input_to_tensor_input.h"
 #include "pre_activate/pass/convert_tuple_input_to_dynamic_input.h"
 #include "utils/context/ms_context.h"
@@ -42,6 +43,7 @@ void BackendCommonOptimization(const std::shared_ptr<session::KernelGraph> &kern
   common_pm->AddPass(std::make_shared<ConvertConstInputToAttr>());
   common_pm->AddPass(std::make_shared<ConvertConstInputToTensorInput>());
   common_pm->AddPass(std::make_shared<ConvertTupleInputToDynamicInput>());
+  common_pm->AddPass(std::make_shared<ConvertTupleOutputToMaketuple>());
   optimizer->AddPassManager(common_pm);
   (void)optimizer->Optimize(kernel_graph);
   kernel_graph->SetExecOrderByDefault();
diff --git a/mindspore/ccsrc/pre_activate/common/helper.cc b/mindspore/ccsrc/pre_activate/common/helper.cc
index 3f157d6a64..de45239268 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.cc
+++ b/mindspore/ccsrc/pre_activate/common/helper.cc
@@ -407,5 +407,20 @@ bool IsUsedByOthers(const FuncGraphPtr &graph, const AnfNodePtr &node) {
   }
   return manager->node_users()[node].size() > 1;
 }
+
+AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node, size_t output_idx) {
+  auto idx = NewValueNode(SizeToInt(output_idx));
+  MS_EXCEPTION_IF_NULL(idx);
+  auto imm = std::make_shared<Int32Imm>(SizeToInt(output_idx));
+  auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(imm);
+  idx->set_abstract(abstract_scalar);
+  AnfNodePtr tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx});
+  MS_EXCEPTION_IF_NULL(tuple_getitem);
+  tuple_getitem->set_scope(node->scope());
+  std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
+  TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx);
+  AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get());
+  return tuple_getitem;
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/common/helper.h b/mindspore/ccsrc/pre_activate/common/helper.h
index 8d174a1ad0..2b95bec824 100644
--- a/mindspore/ccsrc/pre_activate/common/helper.h
+++ b/mindspore/ccsrc/pre_activate/common/helper.h
@@ -146,6 +146,8 @@ void HideNopNode(session::KernelGraph *const graph);
 
 void RemoveNopNode(session::KernelGraph *const graph);
 
+AnfNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node, size_t output_idx);
+
 bool IsUsedByOthers(const FuncGraphPtr &graph, const AnfNodePtr &node);
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_tuple_input_to_dynamic_input.cc b/mindspore/ccsrc/pre_activate/pass/convert_tuple_input_to_dynamic_input.cc
index 92579111f6..ccc4fd5265 100644
--- a/mindspore/ccsrc/pre_activate/pass/convert_tuple_input_to_dynamic_input.cc
+++ b/mindspore/ccsrc/pre_activate/pass/convert_tuple_input_to_dynamic_input.cc
@@ -19,6 +19,7 @@
 #include <memory>
 
 #include "session/anf_runtime_algorithm.h"
+#include "pre_activate/common/helper.h"
 #include "session/kernel_graph.h"
 
 namespace mindspore {
@@ -40,13 +41,7 @@ void ConvertTupleOuputToPlantInputs(const FuncGraphPtr &graph, const AnfNodePtr
     convert_inputs = kernel_graph->SplitTupleValueNodeToNodeList(value_node);
   } else {
     for (size_t index = 0; index < output_size; ++index) {
-      auto idx = NewValueNode(SizeToInt(index));
-      MS_EXCEPTION_IF_NULL(idx);
-      auto imm = std::make_shared<Int32Imm>(SizeToInt(index));
-      auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(imm);
-      idx->set_abstract(abstract_scalar);
-      auto tuple_get_item =
-        graph->NewCNode(std::vector<AnfNodePtr>{NewValueNode(prim::kPrimTupleGetItem), input_node, idx});
+      auto tuple_get_item = CreatTupleGetItemNode(graph, input_node, index);
       AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input_node, index)},
                                           {AnfAlgo::GetOutputInferShape(input_node, index)}, tuple_get_item.get());
       convert_inputs.emplace_back(tuple_get_item);
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.cc b/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.cc
new file mode 100644
index 0000000000..3f283e5d24
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.cc
@@ -0,0 +1,79 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "pre_activate/pass/convert_tuple_output_to_maketuple.h"
+
+#include <algorithm>
+#include <memory>
+
+#include "session/anf_runtime_algorithm.h"
+#include "pre_activate/common/helper.h"
+#include "session/kernel_graph.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+CNodePtr ConvertTupleInputToMakeTuple(const FuncGraphPtr &graph, const CNodePtr &cnode_ptr) {
+  MS_EXCEPTION_IF_NULL(cnode_ptr);
+  MS_EXCEPTION_IF_NULL(graph);
+  std::vector<AnfNodePtr> convert_inputs = {cnode_ptr->input(0)};
+  for (size_t index = 0; index < AnfAlgo::GetInputTensorNum(cnode_ptr); ++index) {
+    auto input_node = AnfAlgo::GetInputNode(cnode_ptr, index);
+    if (AnfAlgo::IsTupleOutput(input_node)) {
+      std::vector<TypeId> types;
+      std::vector<std::vector<size_t>> shapes;
+      std::vector<AnfNodePtr> make_tuple_inputs_list = {NewValueNode(prim::kPrimMakeTuple)};
+      for (size_t tuple_out_index = 0; tuple_out_index < AnfAlgo::GetOutputTensorNum(input_node); ++tuple_out_index) {
+        make_tuple_inputs_list.emplace_back(CreatTupleGetItemNode(graph, input_node, tuple_out_index));
+        types.push_back(AnfAlgo::GetOutputInferDataType(input_node, tuple_out_index));
+        shapes.emplace_back(AnfAlgo::GetOutputInferShape(input_node, tuple_out_index));
+      }
+      auto make_tuple = graph->NewCNode(make_tuple_inputs_list);
+      AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get());
+      convert_inputs.emplace_back(make_tuple);
+    } else {
+      convert_inputs.push_back(input_node);
+    }
+  }
+  cnode_ptr->set_inputs(convert_inputs);
+  return cnode_ptr;
+}
+}  // namespace
+
+const BaseRef ConvertTupleOutputToMaketuple::DefinePattern() const {
+  VarPtr V = std::make_shared<Var>();
+  VarPtr Xs = std::make_shared<SeqVar>();
+  return VectorRef({V, Xs});
+}
+
+const AnfNodePtr ConvertTupleOutputToMaketuple::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
+                                                        const EquivPtr &) const {
+  if (node == nullptr || !node->isa<CNode>()) {
+    return nullptr;
+  }
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimTupleGetItem->name()) {
+    return nullptr;
+  }
+  if (std::any_of(cnode->inputs().begin() + 1, cnode->inputs().end(), [](const AnfNodePtr &node) {
+        return AnfAlgo::IsTupleOutput(node) && AnfAlgo::GetCNodeName(node) != prim::kPrimMakeTuple->name();
+      })) {
+    return ConvertTupleInputToMakeTuple(func_graph, cnode);
+  }
+  return nullptr;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.h b/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.h
new file mode 100644
index 0000000000..a16ffaf674
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/pass/convert_tuple_output_to_maketuple.h
@@ -0,0 +1,40 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
+#define MINDSPORE_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
+#include <string>
+#include <vector>
+
+#include "ir/anf.h"
+#include "pre_activate/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class ConvertTupleOutputToMaketuple : public PatternProcessPass {
+ public:
+  explicit ConvertTupleOutputToMaketuple(bool multigraph = true)
+      : PatternProcessPass("convert_tuple_output_to_maketuple", multigraph) {}
+
+  ~ConvertTupleOutputToMaketuple() override = default;
+
+  const BaseRef DefinePattern() const override;
+
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc
index bbcc04e14b..3c647bf21d 100755
--- a/mindspore/ccsrc/session/kernel_graph.cc
+++ b/mindspore/ccsrc/session/kernel_graph.cc
@@ -239,7 +239,7 @@ std::vector<AnfNodePtr> KernelGraph::SplitTupleValueNodeToNodeList(const ValueNo
     AddValueNodeToGraph(new_value_node);
     convert_inputs.emplace_back(new_value_node);
   }
-  if (RemoveValueNodeFromGraph(value_node)) {
+  if (!RemoveValueNodeFromGraph(value_node)) {
     MS_LOG(WARNING) << "failed to remove the value_node " << value_node->DebugString();
   }
   return convert_inputs;
diff --git a/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc b/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc
new file mode 100644
index 0000000000..da01a74d76
--- /dev/null
+++ b/tests/ut/cpp/pre_activate/pass/convert_tuple_output_to_maketuple_test.cc
@@ -0,0 +1,65 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/backend_common_test.h"
+#include "ir/anf.h"
+#include "ir/meta_tensor.h"
+#include "debug/anf_ir_dump.h"
+#include "common/py_func_graph_fetcher.h"
+#include "session/anf_runtime_algorithm.h"
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/common/pass_manager.h"
+#include "pre_activate/pass/convert_tuple_output_to_maketuple.h"
+#include "utils/utils.h"
+
+namespace mindspore {
+namespace opt {
+class TestHWTupleOutputToMakeTuple : public BackendCommon {
+ public:
+  TestHWTupleOutputToMakeTuple()
+      : getPyFun_("gtest_input.pre_activate.convert_tuple_output_to_maketuple_test", true) {}
+  ~TestHWTupleOutputToMakeTuple() override = default;
+
+ public:
+  UT::PyFuncGraphFetcher getPyFun_;
+};
+
+TEST_F(TestHWTupleOutputToMakeTuple, test_convert_tuple_output_to_maketuple) {
+  FuncGraphPtr g = getPyFun_.CallAndParseRet("test_convert_tuple_output_to_maketuple", "before");
+  ASSERT_TRUE(g != nullptr);
+  std::vector<int> shp_x{5, 2, 10};
+  std::vector<int> shp_h{1, 2, 2};
+  std::vector<int> shp_c{1, 2, 2};
+  std::vector<int> shp_w{112, 1, 1};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
+  auto h_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_h);
+  auto c_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_c);
+  auto w_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_w);
+  AbstractBasePtrList args_spec_list{x_abstract, h_abstract, c_abstract, w_abstract};
+  auto func_graph = GetKernelGraph(g, args_spec_list);
+  ASSERT_TRUE(func_graph != nullptr);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::ConvertTupleOutputToMaketuple>());
+  optimizer->AddPassManager(pm);
+  optimizer->Optimize(func_graph);
+
+  FuncGraphPtr g_after = getPyFun_.CallAndParseRet("test_convert_tuple_output_to_maketuple", "after");
+  ASSERT_TRUE(g_after != nullptr);
+  EXPECT_TRUE(CheckEqualGraph(func_graph, g_after));
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/convert_tuple_output_to_maketuple_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/convert_tuple_output_to_maketuple_test.py
new file mode 100644
index 0000000000..961f7b6232
--- /dev/null
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/convert_tuple_output_to_maketuple_test.py
@@ -0,0 +1,54 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+from mindspore.ops import operations as P
+from mindspore.ops import Primitive
+import mindspore as ms
+import mindspore.common.dtype as mstype
+from mindspore.common.tensor import Tensor
+import numpy as np
+
+make_tuple = Primitive('make_tuple')
+tuple_get_item = Primitive("tuple_getitem");
+LSTM = P.LSTM(input_size=10,hidden_size=2,num_layers=1,has_bias=True,bidirectional=False,dropout=0.0)
+add = P.TensorAdd()
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_convert_tuple_output_to_maketuple(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x, h, c, w):
+        res = LSTM(x, h, c, w)
+        return res
+
+    @fns
+    def after(x, h, c, w):
+        res = LSTM(x, h, c, w)
+        res = make_tuple(
+            make_tuple(tuple_get_item(res, 0), tuple_get_item(res, 1), tuple_get_item(res, 2), tuple_get_item(res, 3),
+                       tuple_get_item(res, 4)));
+        return res
+
+    return fns[tag]
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py
index 902fd636de..f0320fef79 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_getnext.py
@@ -49,7 +49,10 @@ def test_insert_memcpy_async_for_getnext(tag):
         label = tuple_getitem(res, 1)
         memcpy_async_data = memcpy_async(data)
         memcpy_async_label = memcpy_async(label)
-        tuple = make_tuple(make_tuple(memcpy_async_data, memcpy_async_label))
-        return tuple
+        bind_tuple = make_tuple(memcpy_async_data, memcpy_async_label)
+        get_item0 = tuple_getitem(bind_tuple, 0)
+        get_item1 = tuple_getitem(bind_tuple, 1)
+        bind_tuple = make_tuple(make_tuple(get_item0, get_item1))
+        return bind_tuple
 
     return fns[tag]

From 6cc5e87126be19ea4b4115d22286ad89d1dcb6a6 Mon Sep 17 00:00:00 2001
From: zjun <zhangjun0@huawei.com>
Date: Thu, 16 Apr 2020 17:27:33 +0800
Subject: [PATCH 322/367] Fix oplib codexx

---
 .../ccsrc/kernel/aicpu/aicpu_kernel_build.cc  | 13 ++--------
 mindspore/ccsrc/kernel/oplib/oplib.cc         | 26 +++++++++++--------
 mindspore/ccsrc/kernel/oplib/oplib.h          |  1 +
 mindspore/ops/op_info_register.py             |  6 ++---
 4 files changed, 21 insertions(+), 25 deletions(-)

diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
index cf23779415..808e87edc0 100644
--- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
+++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc
@@ -39,8 +39,6 @@ namespace mindspore {
 namespace kernel {
 using FNodeAttrHandle = std::function<void(const std::shared_ptr<AnfNode> &anf_node, mindspore::NodeDef *proto)>;
 
-const std::vector<std::string> local_framework_op_vec = {kInitData, kGetNext, kDropoutGenMask, kPrint};
-
 bool SetIOIputSize(const std::shared_ptr<AnfNode> &anf_node, const size_t &input_num,
                    std::vector<size_t> *input_size_list) {
   MS_EXCEPTION_IF_NULL(anf_node);
@@ -298,19 +296,12 @@ KernelModPtr AicpuOpBuild(const std::shared_ptr<AnfNode> &anf_node) {
   MS_EXCEPTION_IF_NULL(kernel_mod_ptr);
   kernel_mod_ptr->SetAnfNode(anf_node);
   kernel_mod_ptr->SetNodeName(op_name);
-  auto iter = std::find(local_framework_op_vec.begin(), local_framework_op_vec.end(), op_name);
-  if (iter != local_framework_op_vec.end()) {
-    if (!CreateNodeDefBytes(anf_node, kernel_mod_ptr)) {
-      MS_LOG(EXCEPTION) << "Create nodeDefBytes faild!";
-    }
-  } else {
-    MS_LOG(EXCEPTION) << "Aicpu don't support node [" << op_name << "]";
+  if (!CreateNodeDefBytes(anf_node, kernel_mod_ptr)) {
+    MS_LOG(EXCEPTION) << "Create nodeDefBytes faild!";
   }
-
   if (!SetIOSize(anf_node, kernel_mod_ptr)) {
     MS_LOG(EXCEPTION) << "Set input output size list failed.";
   }
-
   return kernel_mod_ptr;
 }
 }  // namespace kernel
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc
index d2464bce47..c8cc1530ce 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.cc
+++ b/mindspore/ccsrc/kernel/oplib/oplib.cc
@@ -94,6 +94,20 @@ bool OpLib::RegOp(const std::string& json_string, const std::string& impl_path)
   return ret;
 }
 
+void OpLib::DecodeTBESpecificInfo(const nlohmann::json& obj, const std::shared_ptr<OpInfo>& op_info) {
+  op_info->set_async_flag(obj.at(kAsyncFlag));
+  op_info->set_binfile_name(obj.at(kBinfileName));
+  op_info->set_compute_cost(obj.at(kComputeCost));
+  op_info->set_kernel_name(obj.at(kKernelName));
+  op_info->set_partial_flag(obj.at(kPartialFlag));
+  if (obj.find(kOpPattern) != obj.end()) {
+    op_info->set_op_pattern(obj.at(kOpPattern));
+  }
+  if (obj.find(kDynamicFormat) != obj.end()) {
+    op_info->set_dynamic_format(obj.at(kDynamicFormat));
+  }
+}
+
 bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpImplyType imply_type,
                          const std::string& impl_path) {
   std::shared_ptr<OpInfo> op_info = std::make_shared<OpInfo>();
@@ -103,17 +117,7 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI
   op_info->set_imply_type(imply_type);
   op_info->set_fusion_type(obj.at(kFusionType));
   if (imply_type == kTBE) {
-    op_info->set_async_flag(obj.at(kAsyncFlag));
-    op_info->set_binfile_name(obj.at(kBinfileName));
-    op_info->set_compute_cost(obj.at(kComputeCost));
-    op_info->set_kernel_name(obj.at(kKernelName));
-    op_info->set_partial_flag(obj.at(kPartialFlag));
-    if (obj.find(kOpPattern) != obj.end()) {
-      op_info->set_op_pattern(obj.at(kOpPattern));
-    }
-    if (obj.find(kDynamicFormat) != obj.end()) {
-      op_info->set_dynamic_format(obj.at(kDynamicFormat));
-    }
+    DecodeTBESpecificInfo(obj, op_info);
   }
   auto attrs = obj.at(kAttr);
   for (const auto& attr : attrs) {
diff --git a/mindspore/ccsrc/kernel/oplib/oplib.h b/mindspore/ccsrc/kernel/oplib/oplib.h
index a4c5e04bb1..0e11e28d58 100644
--- a/mindspore/ccsrc/kernel/oplib/oplib.h
+++ b/mindspore/ccsrc/kernel/oplib/oplib.h
@@ -40,6 +40,7 @@ class OpLib {
                          const std::shared_ptr<OpInfo>& op_info);
   static bool DecodeDtypeFormat(const nlohmann::json& dtype_format, const std::shared_ptr<OpIOInfo>& op_io,
                                 size_t index);
+  static void DecodeTBESpecificInfo(const nlohmann::json& obj, const std::shared_ptr<OpInfo>& op_info);
   static bool DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply_type, const OpIOType io_type,
                                 const std::shared_ptr<OpInfo>& op_info, const nlohmann::json& dtype_format);
   static bool GetRefInfo(const std::shared_ptr<OpInfo>& op_info);
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 7dd7a9f729..28821b621e 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -57,7 +57,7 @@ def op_info_register(op_info):
     return register_decorator
 
 
-class RegOp():
+class RegOp:
     """
     Base class for op info register.
 
@@ -483,9 +483,9 @@ class TBERegOp(RegOp):
         return self
 
 
-class DataType():
+class DataType:
     """
-    Various combinations of dtype and formatself.
+    Various combinations of dtype and format.
 
     The current list below maybe not completed. If necessary, please add it.
     """

From 7fae76d10dbb9324154a6d0963cc99627152c5f2 Mon Sep 17 00:00:00 2001
From: huangdongrun <huangdongrun@huawei.com>
Date: Thu, 16 Apr 2020 16:57:08 +0800
Subject: [PATCH 323/367] add support for calculation between float and int

format code

remove test case for fp64

add back scalar manipulation

remove save graph
---
 .../ccsrc/operator/cc_implementations.cc      |  8 +++
 .../ccsrc/pipeline/static_analysis/prim.cc    |  3 +
 mindspore/ccsrc/utils/convert_utils.h         |  1 +
 tests/ut/python/ops/test_python_operators.py  | 59 +++++++++++++++++--
 4 files changed, 66 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/operator/cc_implementations.cc b/mindspore/ccsrc/operator/cc_implementations.cc
index 62b23b346f..49dc3ab791 100644
--- a/mindspore/ccsrc/operator/cc_implementations.cc
+++ b/mindspore/ccsrc/operator/cc_implementations.cc
@@ -201,6 +201,14 @@ bool InnerScalarGe(T x, U y) {
         int sum = InnerScalar##op_t(GetValue<int>(x), GetValue<int>(y));                       \
         return MakeValue(sum);                                                                 \
       }                                                                                        \
+      if (x->isa<Int32Imm>() && y->isa<FP32Imm>()) {                                           \
+        float sum = InnerScalar##op_t(IntToFloat(GetValue<int>(x)), GetValue<float>(y));       \
+        return MakeValue(sum);                                                                 \
+      }                                                                                        \
+      if (x->isa<FP32Imm>() && y->isa<Int32Imm>()) {                                           \
+        float sum = InnerScalar##op_t(GetValue<float>(x), IntToFloat(GetValue<int>(y)));       \
+        return MakeValue(sum);                                                                 \
+      }                                                                                        \
       MS_LOG(EXCEPTION) << "Unsupported Value for Scalar" << #op_t << ", x: " << x->ToString() \
                         << ", y: " << y->ToString();                                           \
     } while (0);                                                                               \
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 1512596cb4..b6cb57a9b8 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -445,6 +445,9 @@ AbstractBasePtr UniformPrimEvaluator::EvalPrim(const AnalysisEnginePtr &, const
   }
 
   ValuePtr inferred_value = RunImpl(value_list);
+  if (!(*inferred_value == *kAnyValue)) {
+    ret_value_type = inferred_value->type();
+  }
   // for comparison primitives , return type shall have be specified to be bool.
   if (specify_out_type_ != nullptr) {
     ret_value_type = specify_out_type_;
diff --git a/mindspore/ccsrc/utils/convert_utils.h b/mindspore/ccsrc/utils/convert_utils.h
index fbd4485a3f..55f478d5fe 100644
--- a/mindspore/ccsrc/utils/convert_utils.h
+++ b/mindspore/ccsrc/utils/convert_utils.h
@@ -81,6 +81,7 @@ inline size_t FloatToSize(float u) {
   }
   return static_cast<size_t>(u);
 }
+inline float IntToFloat(int32_t v) { return static_cast<float>(v); }
 
 inline uint32_t IntToUint(int32_t u) {
   if (u < 0) {
diff --git a/tests/ut/python/ops/test_python_operators.py b/tests/ut/python/ops/test_python_operators.py
index eb65a7f373..705774068d 100644
--- a/tests/ut/python/ops/test_python_operators.py
+++ b/tests/ut/python/ops/test_python_operators.py
@@ -25,11 +25,13 @@ from ....mindspore_test_framework.mindspore_test import mindspore_test
 from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
 
-context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+context.set_context(mode=context.GRAPH_MODE)
+
 
 class ComparisonOpsNet(nn.Cell):
     def __init__(self):
         super(ComparisonOpsNet, self).__init__()
+
     def construct(self, x, y):
         a = x <= y
         b = x <= 1.0
@@ -46,22 +48,60 @@ class ComparisonOpsNet(nn.Cell):
         m = k != l
         return a or b or c or d or e or f or g or h or i or j or m
 
+
+class MathOpsNet(nn.Cell):
+    def __init__(self):
+        super(MathOpsNet, self).__init__()
+        self.relu = P.ReLU()
+
+    def construct(self, x, y):
+        x = x - (-1)
+        return self.relu(x)
+
+
+class ScalarCompareNet(nn.Cell):
+    def __init__(self):
+        super(ScalarCompareNet, self).__init__()
+        self.relu = P.ReLU()
+
+    def construct(self, x, y):
+        t = 0
+        if 3 > 3.2:
+            t = x + y
+        else:
+            t = x - y
+        if 3.1 <= 5:
+            t = t - x
+        else:
+            t = t + x
+        a = 32.0 * 12
+        b = 12/3.0
+        if a > b:
+            t = t * x
+        else:
+            t = t / x
+        return t
+
+
 class LogicalNumberOpsNet(nn.Cell):
     def __init__(self):
         super(LogicalNumberOpsNet, self).__init__()
         self.cond = True
         self.one = 0
         self.zero = 0.0
+
     def construct(self, x, y):
         if self.cond and self.one or self.zero and not self.one:
             return x + y
         return x - y
 
+
 class LogicalTensorOpsNet(nn.Cell):
     def __init__(self):
         """"""
         super(LogicalTensorOpsNet, self).__init__()
         self.const_true = Tensor(True, dtype=mstype.bool_)
+
     def construct(self, x, y):
         ret = x and y and (y or self.const_true) and (not self.const_true)
         return ret
@@ -71,20 +111,29 @@ test_case_ops = [
     ('CompareOpsNet', {
         'block': ComparisonOpsNet(),
         'desc_inputs': [Tensor(np.ones([6, 9, 10]), dtype=mstype.float32),
-         Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
+                        Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
+    ('MathOpsNet', {
+        'block': MathOpsNet(),
+        'desc_inputs': [Tensor(np.ones([6, 9, 10]), dtype=mstype.float32),
+                        Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
+    ('ScalarCompareNet', {
+        'block': ScalarCompareNet(),
+        'desc_inputs': [Tensor(np.ones([6, 9, 10]), dtype=mstype.float32),
+                        Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
     ('LogicalNumberOps', {
         'block': LogicalNumberOpsNet(),
         'desc_inputs': [Tensor(np.ones([6, 9, 10]), dtype=mstype.float32),
-         Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
+                        Tensor(np.zeros([6, 9, 10]), dtype=mstype.float32)]}),
     ('LogicalTensorOps', {
         'block': LogicalTensorOpsNet(),
         'desc_inputs': [Tensor(np.ones([6, 9, 10]).astype(np.bool_), dtype=mstype.bool_),
-         Tensor(np.zeros([6, 9, 10]).astype(np.bool_), dtype=mstype.bool_)]}),
+                        Tensor(np.zeros([6, 9, 10]).astype(np.bool_), dtype=mstype.bool_)]}),
 ]
 
 test_case_lists = [test_case_ops]
 test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 
+
 @mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
 def test_compile():
-    return test_exec_case
\ No newline at end of file
+    return test_exec_case

From fc07cd908e85415e1e695fb63e2f8e9612b9f50c Mon Sep 17 00:00:00 2001
From: liubuyu <liubuyu1@huawei.com>
Date: Fri, 17 Apr 2020 18:07:36 +0800
Subject: [PATCH 324/367] add 6d format transfer

---
 mindspore/ccsrc/common/trans.cc               | 243 +++++++++++++++---
 mindspore/ccsrc/common/trans.h                |   2 +
 .../device/ascend/ascend_device_address.cc    |  14 +-
 mindspore/ccsrc/utils/utils.h                 |   6 +-
 4 files changed, 228 insertions(+), 37 deletions(-)

diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index a2b9f7ef24..5c982166dd 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -231,7 +231,98 @@ std::vector<size_t> PaddingShapeTo4d(const std::vector<size_t> &shape, const std
   return shape_4d;
 }
 
+namespace {
+bool CheckDims(const std::vector<size_t> &shape) {
+  if (shape.size() != 4) {
+    MS_LOG(ERROR) << "Host shape dims shoud be 4";
+    return false;
+  }
+  return true;
+}
+
+std::vector<size_t> NchwDeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  return shape;
+}
+
+std::vector<size_t> NhwcDeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Ccheck dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  device_shape.push_back(shape[0]);
+  device_shape.push_back(shape[2]);
+  device_shape.push_back(shape[3]);
+  device_shape.push_back(shape[1]);
+  return device_shape;
+}
+
+std::vector<size_t> HwchDeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  device_shape.push_back(shape[2]);
+  device_shape.push_back(shape[3]);
+  device_shape.push_back(shape[1]);
+  device_shape.push_back(shape[0]);
+  return device_shape;
+}
+
+std::vector<size_t> FracZDeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  size_t cout16 = ((shape[0] + kCubeSize - 1) / kCubeSize) * kCubeSize;
+  size_t cin16 = ((shape[1] + kCubeSize - 1) / kCubeSize) * kCubeSize;
+  device_shape.push_back(shape[2] * shape[3] * cin16 / kCubeSize);
+  device_shape.push_back(cout16 / kCubeSize);
+  device_shape.push_back(kCubeSize);
+  device_shape.push_back(kCubeSize);
+  return device_shape;
+}
+
+std::vector<size_t> Nc1hwc0DeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  size_t C1 = (shape[1] + kCubeSize - 1) / kCubeSize;
+  size_t C0 = kCubeSize;
+  device_shape.push_back(shape[0]);
+  device_shape.push_back(C1);
+  device_shape.push_back(shape[2]);
+  device_shape.push_back(shape[3]);
+  device_shape.push_back(C0);
+  return device_shape;
+}
+
+std::vector<size_t> C1hwncoc0DeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  device_shape.push_back((shape[1] - 1) / kCubeSize + 1);
+  device_shape.push_back(shape[2]);
+  device_shape.push_back(shape[3]);
+  device_shape.push_back(shape[0]);
+  device_shape.push_back(kCubeSize);
+  device_shape.push_back(kCubeSize);
+  return device_shape;
+}
+}  // namespace
+
 std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const std::string &format) {
+  using DeviceShapeTransfer = std::function<std::vector<size_t>(const std::vector<size_t> &)>;
+  const std::map<std::string, DeviceShapeTransfer> device_shape_map{
+    {kOpFormat_NCHW, NchwDeviceShape},       {kOpFormat_NHWC, NhwcDeviceShape},
+    {kOpFormat_HWCN, HwchDeviceShape},       {kOpFormat_FRAC_Z, FracZDeviceShape},
+    {kOpFormat_NC1HWC0, Nc1hwc0DeviceShape}, {kOpFormat_C1HWNCoC0, C1hwncoc0DeviceShape},
+  };
+
   if (format == kOpFormat_ND || format == kOpFormat_DEFAULT) {
     return shape;
   }
@@ -255,37 +346,31 @@ std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const s
     MS_LOG(WARNING) << "Get Device Shape using a shape size is less than 4 ,should be Padding shape by Default firstly";
     temp_shape = PaddingShapeTo4dByDefault(shape);
   }
-  if (format == kOpFormat_NC1HWC0) {
-    size_t C1 = (temp_shape[1] + kCubeSize - 1) / kCubeSize;
-    size_t C0 = kCubeSize;
-    device_shape.push_back(temp_shape[0]);
-    device_shape.push_back(C1);
-    device_shape.push_back(temp_shape[2]);
-    device_shape.push_back(temp_shape[3]);
-    device_shape.push_back(C0);
-    return device_shape;
-  } else if (format == kOpFormat_FRAC_Z) {
-    size_t cout16 = ((temp_shape[0] + kCubeSize - 1) / kCubeSize) * kCubeSize;
-    size_t cin16 = ((temp_shape[1] + kCubeSize - 1) / kCubeSize) * kCubeSize;
-    device_shape.push_back(temp_shape[2] * temp_shape[3] * cin16 / kCubeSize);
-    device_shape.push_back(cout16 / kCubeSize);
-    device_shape.push_back(kCubeSize);
-    device_shape.push_back(kCubeSize);
-    return device_shape;
-  } else if (format == kOpFormat_NHWC) {
-    device_shape.push_back(temp_shape[0]);
-    device_shape.push_back(temp_shape[2]);
-    device_shape.push_back(temp_shape[3]);
-    device_shape.push_back(temp_shape[1]);
-    return device_shape;
-  } else if (format == kOpFormat_HWCN) {
-    return {temp_shape[2], temp_shape[3], temp_shape[1], temp_shape[0]};
-  } else if (format == kOpFormat_NCHW) {
-    return temp_shape;
+  auto iter = device_shape_map.find(format);
+  if (iter != device_shape_map.end()) {
+    return iter->second(temp_shape);
   }
   MS_LOG(EXCEPTION) << "Unexpected format[" << format << "]";
 }
 
+bool CheckArgs(const FormatArgs &args, size_t *size, size_t *total_size) {
+  if (args.host_shape.size() != kNchwDims) {
+    MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims;
+    return false;
+  }
+  *size = TypeIdSize(args.src_data_type);
+  if (*size < 1) {
+    MS_LOG(ERROR) << "Illegal dtype.";
+    return false;
+  }
+  *total_size = ShapeSize(args.device_shape) * (*size);
+  if (*total_size != args.device_size) {
+    MS_LOG(ERROR) << "Illegal total data size, total_size:" << *total_size << ", device_size:" << args.device_size;
+    return false;
+  }
+  return true;
+}
+
 bool TransDataType(const TypeIdArgs &args, void *result) {
   MS_LOG(DEBUG) << "Begin trans datatype from " << TypeIdLabel(args.host_data_type) << " to "
                 << TypeIdLabel(args.device_data_type);
@@ -320,13 +405,14 @@ bool TransFormat(const FormatArgs &args, void *result) {
     MS_LOG(ERROR) << "Invalid datatype..";
     return false;
   }
-  if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) &&
-      args.device_format == kOpFormat_FRAC_Z) {
+  if (args.device_format == kOpFormat_FRAC_Z) {
     return NchwToFracZ(args, result);
   } else if (args.device_format == kOpFormat_FRAC_NZ) {
     return NchwToFracNz(args, result);
   } else if (args.device_format == kOpFormat_NC1HWC0) {
     return NchwToNc1hwc0(args, result);
+  } else if (args.device_format == kOpFormat_C1HWNCoC0) {
+    return NchwToC1hwncoc0(args, result);
   }
   return true;
 }
@@ -337,13 +423,14 @@ bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result) {
     MS_LOG(ERROR) << "Invalid datatype..";
     return false;
   }
-  if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) &&
-      args.device_format == kOpFormat_FRAC_Z) {
+  if (args.device_format == kOpFormat_FRAC_Z) {
     return FracZToNchw(args, result);
   } else if (args.device_format == kOpFormat_FRAC_NZ) {
     return FracNzToNchw(args, result);
   } else if (args.device_format == kOpFormat_NC1HWC0) {
     return Nc1hwc0ToNchw(args, result);
+  } else if (args.device_format == kOpFormat_C1HWNCoC0) {
+    return C1hwncoc0ToNchw(args, result);
   }
   return true;
 }
@@ -801,5 +888,99 @@ bool Nc1hwc0ToNchw(const FormatArgs &args, void *result) {
   }
   return true;
 }
+
+bool NchwToC1hwncoc0(const FormatArgs &args, void *result) {
+  // trans nchw to c1hwncoc0
+  MS_LOG(DEBUG) << "Trans format from nchw to c1hwncoc0.";
+  MS_EXCEPTION_IF_NULL(result);
+  size_t size = 0;
+  size_t total_size = 0;
+  if (!CheckArgs(args, &size, &total_size)) {
+    MS_LOG(ERROR) << "Check args failed.";
+    return false;
+  }
+  auto n = args.host_shape[0];
+  auto c = args.host_shape[1];
+  auto h = args.host_shape[2];
+  auto w = args.host_shape[3];
+  auto c1 = args.device_shape[0];
+  auto co = args.device_shape[4];
+  auto c0 = args.device_shape[5];
+  for (size_t c1_i = 0; c1_i < c1; c1_i++) {
+    for (size_t h_i = 0; h_i < h; h_i++) {
+      for (size_t w_i = 0; w_i < w; w_i++) {
+        for (size_t n_i = 0; n_i < n; n_i++) {
+          for (size_t co_i = 0; co_i < co; co_i++) {
+            for (size_t c0_i = 0; c0_i < c0; c0_i++) {
+              size_t dst_offset = (c1_i * h * w * n * co * c0 + h_i * w * n * co * c0 + w_i * n * co * c0 +
+                                   n_i * co * c0 + co_i * c0 + c0_i) *
+                                  size;
+              size_t protected_size = total_size - dst_offset < static_cast<size_t>(SECUREC_MEM_MAX_LEN)
+                                        ? total_size - dst_offset
+                                        : static_cast<size_t>(SECUREC_MEM_MAX_LEN);
+              size_t c_i = c0_i + c1_i * c0;
+              size_t src_offset = (n_i * c * h * w + c_i * h * w + h_i * w + w_i) * size;
+              error_t ret;
+              if (c_i < c && c0_i == co_i) {
+                ret = memcpy_s(static_cast<uint8_t *>(result) + dst_offset, protected_size,
+                               static_cast<uint8_t const *>(args.data) + src_offset, size);
+              } else {
+                ret = memset_s(static_cast<uint8_t *>(result) + dst_offset, protected_size, 0, size);
+              }
+              if (ret != EOK) {
+                MS_LOG(ERROR) << "Failed to operate the dst memory, error-code:" << ret;
+                return false;
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return true;
+}
+
+bool C1hwncoc0ToNchw(const FormatArgs &args, void *result) {
+  // trans c1hwncoc0 to nchw
+  MS_LOG(DEBUG) << "Trans format from c1hwncoc0 to nchw";
+  MS_EXCEPTION_IF_NULL(result);
+  size_t size = 0;
+  size_t total_size = 0;
+  if (!CheckArgs(args, &size, &total_size)) {
+    MS_LOG(ERROR) << "Check args failed.";
+    return false;
+  }
+  auto n = args.host_shape[0];
+  auto c = args.host_shape[1];
+  auto h = args.host_shape[2];
+  auto w = args.host_shape[3];
+  auto co = args.device_shape[4];
+  auto c0 = args.device_shape[5];
+  for (size_t n_i = 0; n_i < n; n_i++) {
+    for (size_t c_i = 0; c_i < c; c_i++) {
+      for (size_t h_i = 0; h_i < h; h_i++) {
+        for (size_t w_i = 0; w_i < w; w_i++) {
+          size_t dst_offset = (n_i * c * h * w + c_i * h * w + h_i * w + w_i) * size;
+          size_t c1_i = c_i / kCubeSize;
+          size_t c0_i = c_i % kCubeSize;
+          size_t co_i = c0_i;
+          size_t src_offset = (c1_i * h * w * n * co * c0 + h_i * w * n * co * c0 + w_i * n * co * c0 + n_i * co * c0 +
+                               co_i * c0 + c0_i) *
+                              size;
+          size_t protected_size = total_size - dst_offset < static_cast<size_t>(SECUREC_MEM_MAX_LEN)
+                                    ? total_size - dst_offset
+                                    : static_cast<size_t>(SECUREC_MEM_MAX_LEN);
+          auto ret = memcpy_s(static_cast<uint8_t *>(result) + dst_offset, protected_size,
+                              static_cast<uint8_t const *>(args.data) + src_offset, size);
+          if (ret != EOK) {
+            MS_LOG(ERROR) << "Failed to operate the dst memory, error-code:" << ret;
+            return false;
+          }
+        }
+      }
+    }
+  }
+  return true;
+}
 }  // namespace trans
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/common/trans.h b/mindspore/ccsrc/common/trans.h
index 4bebdde814..054fa89a06 100644
--- a/mindspore/ccsrc/common/trans.h
+++ b/mindspore/ccsrc/common/trans.h
@@ -63,10 +63,12 @@ bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result);
 bool NchwToFracZ(const FormatArgs &args, void *result);
 bool NchwToFracNz(const FormatArgs &args, void *result);
 bool NchwToNc1hwc0(const FormatArgs &args, void *result);
+bool NchwToC1hwncoc0(const FormatArgs &args, void *result);
 // device to host
 bool FracZToNchw(const FormatArgs &args, void *result);
 bool FracNzToNchw(const FormatArgs &args, void *result);
 bool Nc1hwc0ToNchw(const FormatArgs &args, void *result);
+bool C1hwncoc0ToNchw(const FormatArgs &args, void *result);
 }  // namespace trans
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
index 69d1918163..f0a30e4c42 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
@@ -114,8 +114,11 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector<int> &shape, size_t
         return false;
       }
     }
-  } else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) {
-    sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr);
+  } else {
+    auto iter = kNeedTransFormatSet.find(format_);
+    if (iter != kNeedTransFormatSet.end()) {
+      sync_ok = ConvertFormatAndSyncHostToDevice(shape, size, type, host_ptr);
+    }
   }
   if (!sync_ok) {
     MS_LOG(ERROR) << "Not support to trans, dev_format:" << format_ << ", dev_type:" << TypeIdLabel(type_id_)
@@ -199,8 +202,11 @@ bool AscendDeviceAddress::SyncHostToDevice(const std::vector<int> &shape, size_t
       }
       SyncMemory(ptr_, host_tmp.data(), size_, RT_MEMCPY_HOST_TO_DEVICE);
     }
-  } else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) {
-    sync_ok = ConvertFormatAndSyncHostToDevice(shape, size, type, host_ptr);
+  } else {
+    auto iter = kNeedTransFormatSet.find(format_);
+    if (iter != kNeedTransFormatSet.end()) {
+      sync_ok = ConvertFormatAndSyncHostToDevice(shape, size, type, host_ptr);
+    }
   }
   if (!sync_ok) {
     MS_LOG(ERROR) << "Not support to trans, dev_format:" << format_ << ", dev_type:" << TypeIdLabel(type_id_)
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 9405d0d334..10ef4abf62 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -186,8 +186,10 @@ constexpr auto kOpFormat_FRAC_Z = "FracZ";
 constexpr auto kOpFormat_FRAC_NZ = "FRACTAL_NZ";
 constexpr auto kOpFormat_C1HWNCoC0 = "C1HWNCoC0";
 constexpr auto kOpFormat_NC1HWC0_C04 = "NC1HWC0_C04";
-const std::set<std::string> k1DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NCHW,        kOpFormat_NHWC,
-                                                kOpFormat_FRAC_Z,  kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0};
+const std::set<std::string> k1DSupportFormat = {kOpFormat_DEFAULT,  kOpFormat_NCHW,        kOpFormat_NHWC,
+                                                kOpFormat_FRAC_Z,   kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
+                                                kOpFormat_C1HWNCoC0};
+
 const std::set<std::string> k2DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_FRAC_Z,
                                                 kOpFormat_NC1KHKWHWC0};
 const std::set<std::string> k3DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0};

From 3f7054dccbe7e9ab18750a650037007dd81d7b94 Mon Sep 17 00:00:00 2001
From: jzw <jzw@DESKTOP-2ORPQ19.localdomain>
Date: Thu, 9 Apr 2020 17:13:46 +0800
Subject: [PATCH 325/367] add skip dataset op

---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    |  12 ++
 mindspore/ccsrc/dataset/api/de_pipeline.h     |   3 +
 .../ccsrc/dataset/api/python_bindings.cc      |   1 +
 mindspore/ccsrc/dataset/core/client.h         |   1 +
 .../dataset/engine/datasetops/CMakeLists.txt  |   1 +
 .../dataset/engine/datasetops/skip_op.cc      | 128 +++++++++++++++++
 .../ccsrc/dataset/engine/datasetops/skip_op.h |  95 +++++++++++++
 mindspore/dataset/engine/datasets.py          |  54 +++++++-
 mindspore/dataset/engine/iterators.py         |   2 +
 .../dataset/engine/serializer_deserializer.py |   3 +
 mindspore/dataset/engine/validators.py        |  14 ++
 tests/ut/cpp/dataset/CMakeLists.txt           |   1 +
 tests/ut/cpp/dataset/skip_op_test.cc          |  91 ++++++++++++
 tests/ut/python/dataset/test_skip.py          | 130 ++++++++++++++++++
 14 files changed, 535 insertions(+), 1 deletion(-)
 create mode 100644 mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
 create mode 100644 mindspore/ccsrc/dataset/engine/datasetops/skip_op.h
 create mode 100644 tests/ut/cpp/dataset/skip_op_test.cc
 create mode 100644 tests/ut/python/dataset/test_skip.py

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index b64d40125e..a62994cb51 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -47,6 +47,7 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &D
                                                                    {kMap, &DEPipeline::ParseMapOp},
                                                                    {kBatch, &DEPipeline::ParseBatchOp},
                                                                    {kRepeat, &DEPipeline::ParseRepeatOp},
+                                                                   {kSkip, &DEPipeline::ParseSkipOp},
                                                                    {kZip, &DEPipeline::ParseZipOp},
                                                                    {kRename, &DEPipeline::ParseRenameOp},
                                                                    {kDeviceQueue, &DEPipeline::ParseDeviceQueueOp},
@@ -511,6 +512,17 @@ Status DEPipeline::ParseRepeatOp(const py::dict &args, std::shared_ptr<DatasetOp
   return Status::OK();
 }
 
+Status DEPipeline::ParseSkipOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
+  if (args["count"].is_none()) {
+    std::string err_msg = "Error: count is invalid or not set.";
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  std::shared_ptr<SkipOp> op;
+  RETURN_IF_NOT_OK(SkipOp::Builder(ToInt(args["count"])).Build(&op));
+  *ptr = op;
+  return Status::OK();
+}
+
 Status DEPipeline::ParseGeneratorOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   std::shared_ptr<GeneratorOp::Builder> builder = std::make_shared<GeneratorOp::Builder>();
   for (auto arg : args) {
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index acffc390cc..35276e5b74 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -42,6 +42,7 @@ enum OpName {
   kBatch,
   kCache,
   kRepeat,
+  kSkip,
   kTake,
   kZip,
   kMap,
@@ -107,6 +108,8 @@ class DEPipeline {
 
   Status ParseRepeatOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
+  Status ParseSkipOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
+
   Status ParseBatchOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
   Status ParseGeneratorOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc
index acfe6d9988..076f2ecc36 100644
--- a/mindspore/ccsrc/dataset/api/python_bindings.cc
+++ b/mindspore/ccsrc/dataset/api/python_bindings.cc
@@ -446,6 +446,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("MINDRECORD", OpName::kMindrecord)
     .value("CACHE", OpName::kCache)
     .value("REPEAT", OpName::kRepeat)
+    .value("SKIP", OpName::kSkip)
     .value("TAKE", OpName::kTake)
     .value("ZIP", OpName::kZip)
     .value("MAP", OpName::kMap)
diff --git a/mindspore/ccsrc/dataset/core/client.h b/mindspore/ccsrc/dataset/core/client.h
index ac289a0e07..b39ba3442b 100644
--- a/mindspore/ccsrc/dataset/core/client.h
+++ b/mindspore/ccsrc/dataset/core/client.h
@@ -32,6 +32,7 @@
 #include "dataset/engine/datasetops/project_op.h"
 #include "dataset/engine/datasetops/rename_op.h"
 #include "dataset/engine/datasetops/repeat_op.h"
+#include "dataset/engine/datasetops/skip_op.h"
 #include "dataset/engine/datasetops/shuffle_op.h"
 #include "dataset/engine/datasetops/source/generator_op.h"
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt b/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
index d23d6bccb8..9e511f78f4 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
@@ -11,6 +11,7 @@ add_library(engine-datasetops OBJECT
     project_op.cc
     rename_op.cc
     repeat_op.cc
+    skip_op.cc
     shuffle_op.cc
     zip_op.cc
     )
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
new file mode 100644
index 0000000000..5b0433b6c8
--- /dev/null
+++ b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
@@ -0,0 +1,128 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <iostream>
+#include <utility>
+
+#include "dataset/engine/data_buffer.h"
+#include "dataset/engine/datasetops/skip_op.h"
+#include "dataset/engine/db_connector.h"
+#include "dataset/engine/execution_tree.h"
+
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace dataset {
+// Builder constructor.  Creates the builder object.
+SkipOp::Builder::Builder(int32_t count) : build_max_skips_(count) {}
+
+Status SkipOp::Builder::SanityCheck() const {
+  if (build_max_skips_ < 0) {
+    std::string err_msg("Skip count must be positive integer or 0.");
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
+// The builder "build" method creates the final object.
+Status SkipOp::Builder::Build(std::shared_ptr<SkipOp> *ptr) {
+  RETURN_IF_NOT_OK(SanityCheck());
+  *ptr = std::make_shared<SkipOp>(build_max_skips_);
+  return Status::OK();
+}
+
+// Constructor of the SkipOp.
+SkipOp::SkipOp(int32_t count) : PipelineOp(0), max_skips_(count), skip_count_(0) {}
+
+// Destructor
+SkipOp::~SkipOp() {}
+
+// A print method typically used for debugging
+void SkipOp::Print(std::ostream &out, bool show_all) const {
+  // Call base class printer first
+  PipelineOp::Print(out, show_all);
+
+  // Then display our own stuff
+  out << "SkipOp:"
+      << "\nCurrent skip count: " << skip_count_ << "\nMax skip count: " << max_skips_;
+}
+
+// Since the buffer may contain multi rows, this function will drop the rows
+// that need to skip in it, and then return the buffer.
+Status SkipOp::GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t worker_id, bool retry_if_eoe) {
+  if (child_.empty()) {
+    RETURN_STATUS_UNEXPECTED("SkipOp can't be the leaf node.");
+  }
+
+  std::unique_ptr<DataBuffer> buf;
+  // Drop first max_skips_ rows
+  while (skip_count_ < max_skips_) {
+    RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
+    if (buf->eoe() || buf->eof()) {
+      break;
+    }
+
+    // Consider the rows of buffer more than 1
+    TensorRow drop_row;
+    int row_num = buf->NumRows();
+    for (int i = 0; i < row_num; i++) {
+      RETURN_IF_NOT_OK(buf->PopRow(&drop_row));
+      if (++skip_count_ == max_skips_) {
+        break;
+      }
+    }
+  }
+
+  // If buffer is none or the rows of buffer is 0,
+  // then get a buffer from child.
+  if (!buf || buf->NumRows() == 0) {
+    RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
+  }
+
+  // Handling eoe and eof
+  if (buf->eoe() || buf->eof()) {
+    RETURN_IF_NOT_OK(EoeReceived(worker_id));
+    if (state_ == OpState::kDeOpIdle) {
+      *p_buffer = std::move(buf);
+      return Status::OK();
+    }
+  }
+
+  *p_buffer = std::move(buf);
+  return Status::OK();
+}
+
+// Base-class override for handling cases when an eoe is received.
+Status SkipOp::EoeReceived(int32_t worker_id) {
+  skip_count_ = 0;
+  state_ = OpState::kDeOpIdle;
+  return Status::OK();
+}
+
+// Class functor operator () override.
+// Most dataset ops operate by launching a thread (see ExecutionTree).
+// However, the SkipOp is defined as a inlined operator, so it is invalid to
+// launch the functor since this op runs inlined inside another operator.  The
+// function is overloaded to ensure that it is not called by mistake (it will
+// generate an error).
+Status SkipOp::operator()() { RETURN_STATUS_UNEXPECTED("Logic error. SkipOp is an inlined operator."); }
+
+// Base-class override for handling cases when an eof is received.
+Status SkipOp::EofReceived(int32_t worker_id) {
+  MS_LOG(INFO) << "Skip operator EOF received, do nothing now.";
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/skip_op.h b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.h
new file mode 100644
index 0000000000..0ae520c3ad
--- /dev/null
+++ b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.h
@@ -0,0 +1,95 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef DATASET_ENGINE_DATASETOPS_SKIP_OP_H_
+#define DATASET_ENGINE_DATASETOPS_SKIP_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "dataset/engine/datasetops/pipeline_op.h"
+
+namespace mindspore {
+namespace dataset {
+class SkipOp : public PipelineOp {
+ public:
+  class Builder {
+   public:
+    // Builder constructor.  Creates the builder object.
+    // @note No default args
+    // @param count - The number of skip to do
+    // @return This is a constructor.
+    explicit Builder(int32_t count);
+
+    // Default destructor
+    ~Builder() = default;
+
+    // The builder "build" method creates the final object.
+    // @return shared_ptr to the new StorageOp object
+    Status Build(std::shared_ptr<SkipOp> *);
+
+   private:
+    int32_t build_max_skips_;
+
+    Status SanityCheck() const;
+  };
+
+  // Constructor of the SkipOp.
+  // @note The builder class should be used to call it
+  // @param count - The number of skips to do
+  explicit SkipOp(int32_t count);
+
+  // Destructor
+  ~SkipOp();
+
+  // A print method typically used for debugging
+  // @param out - The output stream to write output to
+  // @param show_all - A bool to control if you want to show all info or just a summary
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // Class functor operator () override.
+  // Most dataset ops operate by launching a thread (see ExecutionTree).
+  // However, the SkipOp is defined as a inlined operator, so it is invalid to launch the
+  // functor since this op runs inlined inside another operator.  The function is overloaded to
+  // ensure that it is not called by mistake (it will generate an error).
+  // @return Status - The error code return
+  Status operator()() override;
+
+  // This function returns the buffer that is at the top of our output connector. The caller is
+  // typically our parent node, when the parent is asking us to provide the next buffer of data.
+  // Since SkipOp is an inlined op, getting a buffer from us will simply bounce you to get
+  // a buffer from our child.
+  // @param p_buffer - output pointer to the buffer that it will fetch.
+  // @param worker_id - The worker id
+  // @param retry_if_eoe Set this flag to true to allow calling pop() again after the first pop() returns EOE.
+  // @return Status - The error code return
+  Status GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t worker_id, bool retry_if_eoe) override;
+
+  // Base-class override for handling cases when an eoe is received.
+  // @param worker_id - The worker id
+  Status EoeReceived(int32_t worker_id) override;
+
+  // Base-class override for handling cases when an eof is received.
+  // @param worker_id - The worker id
+  Status EofReceived(int32_t worker_id) override;
+
+ private:
+  int32_t max_skips_;   // The number of skips that the user requested
+  int32_t skip_count_;  // A counter for the current number of executed skips
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_ENGINE_DATASETOPS_SKIP_OP_H_
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 63b0fa4405..4a41e3f778 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -35,7 +35,7 @@ from mindspore._c_expression import typing
 from mindspore import log as logger
 from . import samplers
 from .iterators import DictIterator, TupleIterator
-from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \
+from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_skip, check_zip, check_rename, \
     check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
     check_zip_dataset, check_add_column
@@ -423,6 +423,25 @@ class Dataset:
             return self
         return RepeatDataset(self, count)
 
+    @check_skip
+    def skip(self, count):
+        """
+        Skip the first N elements of this dataset.
+
+        Args:
+            count (int): Number of elements the dataset should be skipped.
+
+        Returns:
+            SkipDataset, dataset skipped.
+
+        Examples:
+            >>> import mindspore.dataset as ds
+            >>> # data is an instance of Dataset object.
+            >>> # creates a dataset which skips first 3 elements from data
+            >>> data = data.skip(3)
+        """
+        return SkipDataset(self, count)
+
     @check_zip_dataset
     def zip(self, datasets):
         """
@@ -1081,6 +1100,39 @@ class RepeatDataset(DatasetOp):
         """
         return self.count
 
+class SkipDataset(DatasetOp):
+    """
+    The result of applying Skip operator to the input Dataset.
+
+    Args:
+        datasets (tuple): A tuple of datasets to be skipped.
+        count (int): Number of rows the dataset should be skipped.
+    """
+
+    def __init__(self, input_dataset, count):
+        super().__init__()
+        self.count = count
+        self.input.append(input_dataset)
+        input_dataset.output.append(self)
+        self._input_indexs = input_dataset.input_indexs
+
+    def get_args(self):
+        args = super().get_args()
+        args["count"] = self.count
+        return args
+
+    def get_dataset_size(self):
+        """
+        Get the number of batches in an epoch.
+
+        Return:
+            Number, number of batches.
+        """
+        child_size = self.input[0].get_dataset_size()
+        output_size = 0
+        if self.count >= 0 and self.count < child_size:
+            output_size = child_size - self.count
+        return output_size
 
 class ZipDataset(DatasetOp):
     """
diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index 69dd9ce0a9..7227dbb2f0 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -127,6 +127,8 @@ class Iterator:
             op_type = OpName.MAP
         elif isinstance(dataset, de.RepeatDataset):
             op_type = OpName.REPEAT
+        elif isinstance(dataset, de.SkipDataset):
+            op_type = OpName.SKIP
         elif isinstance(dataset, de.StorageDataset):
             op_type = OpName.STORAGE
         elif isinstance(dataset, de.ImageFolderDatasetV2):
diff --git a/mindspore/dataset/engine/serializer_deserializer.py b/mindspore/dataset/engine/serializer_deserializer.py
index d1ed5c47cd..15f430aeb8 100644
--- a/mindspore/dataset/engine/serializer_deserializer.py
+++ b/mindspore/dataset/engine/serializer_deserializer.py
@@ -297,6 +297,9 @@ def create_node(node):
     elif dataset_op == 'RepeatDataset':
         pyobj = de.Dataset().repeat(node.get('count'))
 
+    elif dataset_op == 'SkipDataset':
+        pyobj = de.Dataset().skip(node.get('count'))
+
     elif dataset_op == 'MapDataset':
         tensor_ops = construct_tensor_ops(node.get('operations'))
         pyobj = de.Dataset().map(node.get('input_columns'), tensor_ops, node.get('output_columns'),
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 165a160e77..3502cbb204 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -709,6 +709,20 @@ def check_repeat(method):
 
     return new_method
 
+def check_skip(method):
+    """check the input arguments of skip."""
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        count = param_dict.get('count')
+        check_type(count, 'count', int)
+        if count < 0:
+            raise ValueError("Skip count must be positive integer or 0.")
+
+        return method(*args, **kwargs)
+
+    return new_method
 
 def check_zip(method):
     """check the input arguments of zip."""
diff --git a/tests/ut/cpp/dataset/CMakeLists.txt b/tests/ut/cpp/dataset/CMakeLists.txt
index 086a67c7d7..db207363a8 100644
--- a/tests/ut/cpp/dataset/CMakeLists.txt
+++ b/tests/ut/cpp/dataset/CMakeLists.txt
@@ -41,6 +41,7 @@ SET(DE_UT_SRCS
     random_vertical_flip_op_test.cc
     rename_op_test.cc
     repeat_op_test.cc
+    skip_op_test.cc
     rescale_op_test.cc
     resize_bilinear_op_test.cc
     resize_op_test.cc
diff --git a/tests/ut/cpp/dataset/skip_op_test.cc b/tests/ut/cpp/dataset/skip_op_test.cc
new file mode 100644
index 0000000000..c2168b24d4
--- /dev/null
+++ b/tests/ut/cpp/dataset/skip_op_test.cc
@@ -0,0 +1,91 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "dataset/util/circular_pool.h"
+#include "dataset/core/client.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "utils/log_adapter.h"
+
+using namespace mindspore::dataset;
+using mindspore::MsLogLevel::INFO;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+
+class MindDataTestSkipOp : public UT::DatasetOpTesting {};
+
+TEST_F(MindDataTestSkipOp, TestSkipOpFuntions) {
+  // Start with an empty execution tree
+  auto my_tree = std::make_shared<ExecutionTree>();
+
+  std::string dataset_path;
+  dataset_path = datasets_root_path_ + "/testTFTestAllTypes/test.data";
+
+  std::shared_ptr<TFReaderOp> my_tfreader_op;
+  TFReaderOp::Builder builder;
+  builder.SetDatasetFilesList({dataset_path})
+    .SetRowsPerBuffer(16)
+    .SetWorkerConnectorSize(16)
+    .SetNumWorkers(16);
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
+  schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
+  builder.SetDataSchema(std::move(schema));
+  Status rc = builder.Build(&my_tfreader_op);  ASSERT_TRUE(rc.IsOk());
+  rc = my_tree->AssociateNode(my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  // SkipOp
+  std::shared_ptr<SkipOp> skip_op = std::make_shared<SkipOp>(5);
+  rc = my_tree->AssociateNode(skip_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  // Set children/root layout.
+  rc = skip_op->AddChild(my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+  rc = my_tree->AssignRoot(skip_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  MS_LOG(INFO) << "Launching tree and begin iteration.";
+  rc = my_tree->Prepare();
+
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = my_tree->Launch();
+  ASSERT_TRUE(rc.IsOk());
+
+  // Start the loop of reading tensors from our pipeline
+  DatasetIterator di(my_tree);
+  TensorRow tensor_list;
+  rc = di.FetchNextTensorRow(&tensor_list);
+  ASSERT_TRUE(rc.IsOk());
+
+  int row_count = 0;
+  while (!tensor_list.empty()) {
+    MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
+
+    // Display the tensor by calling the printer on it
+    for (int i = 0; i < tensor_list.size(); i++) {
+      std::ostringstream ss;
+      ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
+      MS_LOG(INFO) << "Tensor print: " << ss.str() << ".";
+    }
+
+    rc = di.FetchNextTensorRow(&tensor_list);
+    ASSERT_TRUE(rc.IsOk());
+    row_count++;
+  }
+
+  ASSERT_EQ(row_count, 7);
+}
\ No newline at end of file
diff --git a/tests/ut/python/dataset/test_skip.py b/tests/ut/python/dataset/test_skip.py
new file mode 100644
index 0000000000..bea7db4e05
--- /dev/null
+++ b/tests/ut/python/dataset/test_skip.py
@@ -0,0 +1,130 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import numpy as np
+
+import mindspore.dataset.transforms.vision.c_transforms as vision
+import mindspore.dataset as ds
+from mindspore import log as logger
+
+DATA_DIR_TF2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+SCHEMA_DIR_TF2 = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
+
+def test_tf_skip():
+    data1 = ds.TFRecordDataset(DATA_DIR_TF2, SCHEMA_DIR_TF2, shuffle=False)
+
+    resize_height, resize_width = 32, 32
+    decode_op = vision.Decode()
+    resize_op = vision.Resize((resize_height, resize_width), interpolation=ds.transforms.vision.Inter.LINEAR)
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=resize_op)
+    data1 = data1.skip(2)
+
+    num_iter = 0
+    for item in data1.create_dict_iterator():
+        num_iter += 1
+    assert num_iter == 1
+
+def generator_md():
+    # Create a dataset with [0, 1, 2, 3, 4]
+    for i in range(5):
+        yield (np.array([i]), )
+
+def test_generator_skip():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be [3, 4]
+    ds1 = ds1.skip(3)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 2
+
+def test_skip_1():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be []
+    ds1 = ds1.skip(7)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 0
+
+def test_skip_2():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be [0, 1, 2, 3, 4]
+    ds1 = ds1.skip(0)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 5
+
+def test_skip_repeat_1():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
+    ds1 = ds1.repeat(2)
+
+    # Here ds1 should be [3, 4, 0, 1, 2, 3, 4]
+    ds1 = ds1.skip(3)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 7
+
+def test_skip_repeat_2():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be [3, 4]
+    ds1 = ds1.skip(3)
+
+    # Here ds1 should be [3, 4, 3, 4]
+    ds1 = ds1.repeat(2)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 4
+
+def test_skip_repeat_3():
+    ds1 = ds.GeneratorDataset(generator_md, ["data"])
+
+    # Here ds1 should be [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
+    ds1 = ds1.repeat(2)
+
+    # Here ds1 should be [3, 4]
+    ds1 = ds1.skip(8)
+
+    # Here ds1 should be [3, 4, 3, 4, 3, 4]
+    ds1 = ds1.repeat(3)
+
+    buf = []
+    for data in ds1:
+        buf.append(data[0][0])
+    assert len(buf) == 6
+
+if __name__ == "__main__":
+    test_tf_skip()
+    test_generator_skip()
+    test_skip_1()
+    test_skip_2()
+    test_skip_repeat_1()
+    test_skip_repeat_2()
+    test_skip_repeat_3()
\ No newline at end of file

From f7872774f3ebb44e345af364bac9230adf6aac32 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Fri, 17 Apr 2020 08:33:40 -0400
Subject: [PATCH 326/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py  | 2 +-
 tests/ut/python/nn/test_batchnorm.py | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index c85b945a0d..04de71f71c 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -79,7 +79,7 @@ class _BatchNorm(Cell):
                 if self.rank_id in self.rank_list[i] and self.group != 1:
                     self.is_global = True
                     management.create_group('group' + str(i), self.rank_list[i])
-                    self.all_reduce = _GlobalBNHelper('group' + str(i))
+                    self.all_reduce = P.AllReduce(P.ReduceOp.SUM, 'group' + str(i)).add_prim_attr('fusion', 1)
         self.shape = P.Shape()
         self.reduce_mean = P.ReduceMean()
         self.square = P.Square()
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index 24f0de85f7..b6e27e6950 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -90,5 +90,4 @@ def test_global_bn():
                                       device_num=size, parameter_broadcast=True)
     net = GlobalBNNet()
     input_data = Tensor(np.array([[2.4, 2.1], [3.2, 5.4]], dtype=np.float32))
-    net.set_train()
-    out = net(input_data)
+    _executor.compile(net,input_data)

From c5120e770caa1e04aeefb287497a9674d2a05127 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Fri, 17 Apr 2020 08:37:08 -0400
Subject: [PATCH 327/367] add global batch normalization

---
 mindspore/nn/layer/normalization.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 04de71f71c..6456a3603d 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -27,14 +27,6 @@ from mindspore.communication import management
 from mindspore._checkparam import check_int_positive
 from ..cell import Cell
 
-class _GlobalBNHelper(Cell):
-    def __init__(self, group):
-        super(_GlobalBNHelper, self).__init__()
-        self.group = group
-        self.reduce = P.AllReduce(P.ReduceOp.SUM, group=self.group).add_prim_attr('fusion', 1)
-    def construct(self, x):
-        x = self.reduce(x)
-        return x
 
 class _BatchNorm(Cell):
     """Batch Normalization base class."""

From b1585f862d9730b95e06c73c21f9b4e441cb05ae Mon Sep 17 00:00:00 2001
From: liubuyu <liubuyu1@huawei.com>
Date: Thu, 16 Apr 2020 10:03:58 +0800
Subject: [PATCH 328/367] auto mix precision

---
 .../device/ascend/kernel_select_ascend.cc     |  89 ++++---------
 .../ccsrc/kernel/tbe/tbe_kernel_select.cc     |  61 ++++++++-
 .../ascend/ascend_backend_optimization.cc     |   2 +
 .../ir_fusion/parameter_and_transop_fusion.cc | 120 ++++++++++++++++++
 .../ir_fusion/parameter_and_transop_fusion.h  |  41 ++++++
 mindspore/ops/_op_impl/tbe/cast.py            |   6 +
 6 files changed, 253 insertions(+), 66 deletions(-)
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.cc
 create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h

diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
index f02e677163..36c622cbc5 100644
--- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
+++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
@@ -45,64 +45,6 @@ enum MatchCountPriority : int {
 const size_t kMaxCount = 0xffffffff;
 const int kUnSupportMixedDataTypeIndex = -1;
 
-const std::set<std::string> kOpFormatList = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
-                                             kOpFormat_NCHW,    kOpFormat_NHWC,        kOpFormat_HWCN,
-                                             kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,      kOpFormat_C1HWNCoC0,
-                                             kOpFormat_FRAC_NZ, kOpFormat_NC1HWC0_C04};
-
-bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format) {
-  // if format is default, it remarkes support all format
-  if (kOpFormatList.find(format) == kOpFormatList.end()) {
-    MS_LOG(EXCEPTION) << "got the unknown format " << format;
-  }
-  if (format == kOpFormat_DEFAULT) {
-    return true;
-  }
-  // if shape size is 0, the shape will be a scalar
-  if (shape.empty()) {
-    return true;
-  }
-  if (shape.size() > kShapeSupportFormatMap.size()) {
-    return false;
-  }
-  if (format == kOpFormat_FRAC_NZ && shape.size() >= 2) {
-    return true;
-  }
-  return !(kShapeSupportFormatMap[shape.size() - 1].find(format) == kShapeSupportFormatMap[shape.size() - 1].end());
-}
-
-bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::KernelBuildInfo &kernel_build_info) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  auto check_function = [](const std::vector<size_t> &shape, const std::string &format) -> bool {
-    if (!IsShapeMatchFormat(shape, format)) {
-      return false;
-    }
-    for (auto shape_value : shape) {
-      if (shape_value == 0) {
-        MS_LOG(EXCEPTION) << "dimension size of the tensor shape should be a positive integer, but got " << shape_value;
-      }
-    }
-    return true;
-  };
-  if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
-    return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
-           AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
-  }
-  for (size_t index = 0; index < kernel_build_info.GetOutputNum(); ++index) {
-    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, index);
-    if (!check_function(output_shape, kernel_build_info.GetOutputFormat(index))) {
-      return false;
-    }
-  }
-  for (size_t index = 0; index < kernel_build_info.GetInputNum(); ++index) {
-    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index);
-    if (!check_function(input_shape, kernel_build_info.GetInputFormat(index))) {
-      return false;
-    }
-  }
-  return true;
-}
-
 bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildInfo &kernel_build_info) {
   MS_EXCEPTION_IF_NULL(cnode);
   // Check input data type
@@ -459,6 +401,29 @@ int PrecisionReduce(const std::vector<int> &node_mix_precision_datatype_index,
   // raise precision
   int selected_index = RaiseDataTypePrecisionSelect(node_mix_precision_datatype_index, node_mix_precision_datatype,
                                                     kernel_support_datatype, kernel_match_datatype_idx);
+  if (selected_index != -1) {
+    int max_match = 0;
+    auto iter = kernel_match_datatype_idx->begin();
+    int match_count = 0;
+    while (iter != kernel_match_datatype_idx->end()) {
+      auto kernel_datatypes = kernel_support_datatype.find(iter->first);
+      if (kernel_datatypes == kernel_support_datatype.end()) {
+        MS_LOG(EXCEPTION) << "Can not find kernel index" << iter->first << "'s datatype.";
+      }
+      if (kernel_datatypes->second.size() < node_mix_precision_datatype.size()) {
+        MS_LOG(EXCEPTION) << "Kernel datatype size is not equal to node datatype size!";
+      }
+      for (size_t i = 0; i < node_mix_precision_datatype.size(); ++i) {
+        if (node_mix_precision_datatype[i] == kernel_datatypes->second[i]) {
+          ++match_count;
+        }
+      }
+      if (match_count > max_match) {
+        selected_index = SizeToInt(iter->first);
+      }
+      ++iter;
+    }
+  }
   if (selected_index == -1 && context_ptr->enable_reduce_precision()) {
     selected_index =
       RaiseOrReduceDataTypePrecisionSelect(node_mix_precision_datatype_index, node_mix_precision_datatype,
@@ -507,9 +472,6 @@ void SelectKernelInfo(const CNodePtr &kernel_node) {
   kernel::KernelQuery(kernel_node, &kernel_info_list);
   std::vector<int> most_match_counts = {-1, -1, -1, -1};
   int selected_index = -1;
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  bool auto_mixed_precision = context_ptr->auto_mixed_precision_flag();
   std::unordered_map<size_t, std::vector<int>> kernel_match_datatype_idx;
   std::unordered_map<size_t, std::vector<TypeId>> kernel_support_datatype;
   std::vector<int> node_mix_precision_datatype_index;
@@ -517,16 +479,13 @@ void SelectKernelInfo(const CNodePtr &kernel_node) {
   for (size_t info_index = 0; info_index < kernel_info_list.size(); ++info_index) {
     std::vector<int> cur_kernel_info_match_counts = {0, 0, 0, 0};
     auto kernel_build_info = *(kernel_info_list[info_index]);
-    if (!IsValidKernelInfo(kernel_node, kernel_build_info)) {
-      continue;
-    }
     std::vector<int> support_indexes;
     std::vector<TypeId> support_datatypes;
     AddNodeAndKernelDataType(kernel_node, kernel_build_info, &support_indexes, &node_mix_precision_datatype,
                              &support_datatypes, &node_mix_precision_datatype_index);
     kernel_match_datatype_idx[info_index] = support_indexes;
     kernel_support_datatype[info_index] = support_datatypes;
-    if (!auto_mixed_precision && !MatchInferOutputDataType(kernel_node, kernel_build_info)) {
+    if (!MatchInferOutputDataType(kernel_node, kernel_build_info)) {
       continue;
     }
     std::shared_ptr<kernel::KernelBuildInfo> kernel_info_ptr = kernel_info_list[info_index];
diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
index e818f503c0..127451851e 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc
@@ -19,6 +19,7 @@
 #include <unordered_map>
 #include <memory>
 #include <map>
+#include <set>
 
 #include "session/anf_runtime_algorithm.h"
 #include "kernel/oplib/oplib.h"
@@ -510,6 +511,64 @@ bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr<const OpIn
   return true;
 }
 
+bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format) {
+  const std::set<std::string> kOpFormatList = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
+                                               kOpFormat_NCHW,    kOpFormat_NHWC,        kOpFormat_HWCN,
+                                               kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,      kOpFormat_C1HWNCoC0,
+                                               kOpFormat_FRAC_NZ, kOpFormat_NC1HWC0_C04};
+
+  // if format is default, it remarkes support all format
+  if (kOpFormatList.find(format) == kOpFormatList.end()) {
+    MS_LOG(EXCEPTION) << "Got the unknown format " << format;
+  }
+  if (format == kOpFormat_DEFAULT) {
+    return true;
+  }
+  // if shape size is 0, the shape will be a scalar
+  if (shape.empty()) {
+    return true;
+  }
+  if (shape.size() > kShapeSupportFormatMap.size()) {
+    return false;
+  }
+  if (format == kOpFormat_FRAC_NZ && shape.size() >= 2) {
+    return true;
+  }
+  return !(kShapeSupportFormatMap[shape.size() - 1].find(format) == kShapeSupportFormatMap[shape.size() - 1].end());
+}
+
+bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::KernelBuildInfo &kernel_build_info) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  auto check_function = [](const std::vector<size_t> &shape, const std::string &format) -> bool {
+    if (!IsShapeMatchFormat(shape, format)) {
+      return false;
+    }
+    for (auto shape_value : shape) {
+      if (shape_value == 0) {
+        MS_LOG(EXCEPTION) << "Dimension size of the tensor shape should be a positive integer, but got " << shape_value;
+      }
+    }
+    return true;
+  };
+  for (size_t index = 0; index < kernel_build_info.GetOutputNum(); ++index) {
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, index);
+    if (!check_function(output_shape, kernel_build_info.GetOutputFormat(index))) {
+      return false;
+    }
+  }
+  for (size_t index = 0; index < kernel_build_info.GetInputNum(); ++index) {
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index);
+    if (!check_function(input_shape, kernel_build_info.GetInputFormat(index))) {
+      return false;
+    }
+  }
+  if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
+    return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
+           AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
+  }
+  return true;
+}
+
 void TbeMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<KernelBuildInfo>> *kernel_info_list) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   MS_EXCEPTION_IF_NULL(kernel_info_list);
@@ -534,7 +593,7 @@ void TbeMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<Ke
     if (context_ptr->execution_mode() == kPynativeMode) {
       kernel_info_list->push_back(parse_info);
     } else {
-      if (CheckSupported(kernel_node, parse_info)) {
+      if (IsValidKernelInfo(kernel_node, *(parse_info)) && CheckSupported(kernel_node, parse_info)) {
         kernel_info_list->push_back(parse_info);
       } else {
         MS_LOG(INFO) << "CheckSupported Failed for TBE op" << op_name << " kernel info.";
diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
index a72fb9dc9a..7a35627e25 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
@@ -37,6 +37,7 @@
 #include "pre_activate/ascend/ir_fusion/transpose_reshape_fusion.h"
 #include "pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h"
 #include "pre_activate/ascend/ir_fusion/adam_apply_one_with_decay_rule.h"
+#include "pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h"
 #include "pre_activate/ascend/ir_fusion/transpose_transdata_fusion.h"
 #include "pre_activate/ascend/ir_fusion/transdata_split.h"
 #include "pre_activate/ascend/ir_fission/topk_split.h"
@@ -243,6 +244,7 @@ void AscendBackendOptimization(const std::shared_ptr<session::KernelGraph> &kern
   auto optimizer = std::make_shared<GraphOptimizer>();
   auto other_pm = std::make_shared<PassManager>("other_pm");
   other_pm->AddPass(std::make_shared<AllReduceFusion>());
+  other_pm->AddPass(std::make_shared<ParameterTransOpFusion>());
   other_pm->AddPass(std::make_shared<BufferFusion>());
   other_pm->AddPass(std::make_shared<GetitemTuple>());
   other_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.cc
new file mode 100644
index 0000000000..faa1308f8b
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.cc
@@ -0,0 +1,120 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h"
+#include <memory>
+#include "session/anf_runtime_algorithm.h"
+#include "utils/utils.h"
+#include "operator/ops.h"
+#include "device/kernel_info.h"
+#include "pre_activate/common/helper.h"
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/ascend/ascend_helper.h"
+
+namespace mindspore {
+namespace opt {
+const AnfNodePtr ParamTransRoad(const FuncGraphPtr &func_graph, const AnfNodePtr &node, bool first_flag,
+                                std::vector<CNodePtr> *trans_road) {
+  if (node == nullptr) {
+    MS_LOG(ERROR) << "nullptr";
+    return nullptr;
+  }
+  if (node->isa<CNode>()) {
+    auto cnode = node->cast<CNodePtr>();
+    auto op_name = AnfAlgo::GetCNodeName(cnode);
+    auto manager = func_graph->manager();
+    if (manager == nullptr) {
+      return nullptr;
+    }
+    if (op_name == prim::kPrimCast->name() || op_name == prim::kPrimTranspose->name() ||
+        op_name == prim::kPrimReshape->name() || op_name == kTransDataOpName) {
+      auto users = manager->node_users()[node];
+      if (users.size() > 1 && !first_flag) {
+        return nullptr;
+      }
+      trans_road->push_back(cnode);
+      first_flag = false;
+      auto next_node = AnfAlgo::GetInputNode(cnode, 0);
+      if (next_node->isa<Parameter>() || next_node->isa<ValueNode>()) {
+        return next_node;
+      }
+      return ParamTransRoad(func_graph, next_node, first_flag, trans_road);
+    }
+  } else if (node->isa<Parameter>() || node->isa<ValueNode>()) {
+    return node;
+  }
+  return nullptr;
+}
+
+bool ParameterTransOpFusion::Run(const FuncGraphPtr &func_graph) {
+  if (func_graph == nullptr) {
+    MS_LOG(ERROR) << "Func graph is nullptr";
+    return false;
+  }
+  auto manager = func_graph->manager();
+  if (manager == nullptr) {
+    return false;
+  }
+  std::vector<AnfNodePtr> node_list = TopoSort(func_graph->get_return());
+  bool changed = false;
+  for (auto node : node_list) {
+    if (node == nullptr || !node->isa<CNode>()) {
+      continue;
+    }
+    auto cnode = node->cast<CNodePtr>();
+    auto node_name = AnfAlgo::GetCNodeName(cnode);
+    if (node_name == prim::kPrimCast->name() || node_name == prim::kPrimTranspose->name() ||
+        node_name == prim::kPrimReshape->name() || node_name == kTransDataOpName) {
+      MS_LOG(DEBUG) << "Skip trans op";
+      continue;
+    }
+    for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(cnode); input_index++) {
+      std::vector<CNodePtr> trans_road;
+      bool first_flag = true;
+      auto final_node = ParamTransRoad(func_graph, AnfAlgo::GetInputNode(cnode, input_index), first_flag, &trans_road);
+      if (final_node != nullptr && trans_road.size() == 3 && AnfAlgo::GetCNodeName(trans_road[0]) == kTransDataOpName &&
+          AnfAlgo::GetCNodeName(trans_road[1]) == prim::kPrimCast->name() &&
+          AnfAlgo::GetCNodeName(trans_road[2]) == kTransDataOpName) {
+        auto cur_transop = trans_road[0];
+        auto format = AnfAlgo::GetOutputFormat(cur_transop, 0);
+        auto dtype = AnfAlgo::GetOutputDeviceDataType(cur_transop, 0);
+        auto param_format = AnfAlgo::GetOutputFormat(final_node, 0);
+        auto param_dtype = AnfAlgo::GetOutputDeviceDataType(final_node, 0);
+
+        auto cast = trans_road[1];
+        auto cast_format = AnfAlgo::GetOutputFormat(cast, 0);
+        auto cast_build_info = cast->kernel_info()->select_kernel_build_info();
+        kernel::KernelBuildInfo::KernelBuildInfoBuilder builder;
+        builder.SetOutputsFormat({format});
+        builder.SetInputsFormat({format});
+        builder.SetInputsDeviceType({param_dtype});
+        builder.SetOutputsDeviceType({dtype});
+        builder.SetKernelType(cast_build_info->kernel_type());
+        builder.SetFusionType(cast_build_info->fusion_type());
+        builder.SetProcessor(cast_build_info->processor());
+        AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cast.get());
+        if (param_format == format && param_dtype != dtype) {
+          manager->Replace(trans_road[2], final_node);
+          manager->Replace(cur_transop, cast);
+        }
+        changed = true;
+      }
+    }
+  }
+  return changed;
+}
+}  // namespace opt
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h
new file mode 100644
index 0000000000..823ec083b1
--- /dev/null
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/parameter_and_transop_fusion.h
@@ -0,0 +1,41 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_PARAMETER_AND_TRANSOP_FUSION_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_PARAMETER_AND_TRANSOP_FUSION_H_
+
+#include <vector>
+#include <string>
+#include <utility>
+#include <memory>
+#include "ir/anf.h"
+#include "pre_activate/common/pass.h"
+
+namespace mindspore {
+namespace opt {
+class ParameterTransOpFusion : public Pass {
+ public:
+  explicit ParameterTransOpFusion(size_t groups = 1) : Pass("Parameter_and_transop_fusion"), groups_(groups) {}
+  ~ParameterTransOpFusion() override = default;
+  bool Run(const FuncGraphPtr &graph) override;
+
+ private:
+  size_t groups_ = 1;
+};
+}  // namespace opt
+}  // namespace mindspore
+
+#endif
diff --git a/mindspore/ops/_op_impl/tbe/cast.py b/mindspore/ops/_op_impl/tbe/cast.py
index a18dcddfbf..07e14139da 100644
--- a/mindspore/ops/_op_impl/tbe/cast.py
+++ b/mindspore/ops/_op_impl/tbe/cast.py
@@ -44,6 +44,12 @@ cast_op_info = TBERegOp("Cast") \
     .dtype_format(DataType.F16_Default, DataType.U8_Default) \
     .dtype_format(DataType.F16_Default, DataType.F32_Default) \
     .dtype_format(DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F16_FracNZ) \
     .dtype_format(DataType.F32_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.I32_Default) \
     .get_op_info()

From 3c02c82771ad4075d90875a00619688f0901ca30 Mon Sep 17 00:00:00 2001
From: hesham <h.farahat@huawei.com>
Date: Fri, 17 Apr 2020 10:12:50 -0400
Subject: [PATCH 329/367] Bug in weak reference. Add new test cases

---
 mindspore/dataset/engine/iterators.py    |  6 ++--
 tests/ut/python/dataset/test_iterator.py | 40 ++++++++++++++++++++++++
 2 files changed, 43 insertions(+), 3 deletions(-)

diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index 69dd9ce0a9..d498d57c85 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -28,10 +28,10 @@ ITERATORS_LIST = list()
 
 
 def _cleanup():
-    for itr in ITERATORS_LIST:
-        iter_ref = itr()
+    for itr_ref in ITERATORS_LIST:
+        itr = itr_ref()
         if itr is not None:
-            iter_ref.release()
+            itr.release()
 
 
 def alter_tree(node):
diff --git a/tests/ut/python/dataset/test_iterator.py b/tests/ut/python/dataset/test_iterator.py
index d2518e1119..102fd0eea1 100644
--- a/tests/ut/python/dataset/test_iterator.py
+++ b/tests/ut/python/dataset/test_iterator.py
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 import numpy as np
+import pytest
 
 import mindspore.dataset as ds
+from mindspore.dataset.engine.iterators import ITERATORS_LIST, _cleanup
 
 DATA_DIR = ["../data/dataset/testTFTestAllTypes/test.data"]
 SCHEMA_DIR = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
@@ -41,3 +43,41 @@ def test_case_iterator():
     check(COLUMNS[0:7])
     check(COLUMNS[7:8])
     check(COLUMNS[0:2:8])
+
+
+def test_iterator_weak_ref():
+    ITERATORS_LIST.clear()
+    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR)
+    itr1 = data.create_tuple_iterator()
+    itr2 = data.create_tuple_iterator()
+    itr3 = data.create_tuple_iterator()
+
+    assert len(ITERATORS_LIST) == 3
+    assert sum(itr() is not None for itr in ITERATORS_LIST) == 3
+
+    del itr1
+    assert len(ITERATORS_LIST) == 3
+    assert sum(itr() is not None for itr in ITERATORS_LIST) == 2
+
+    del itr2
+    assert len(ITERATORS_LIST) == 3
+    assert sum(itr() is not None for itr in ITERATORS_LIST) == 1
+
+    del itr3
+    assert len(ITERATORS_LIST) == 3
+    assert sum(itr() is not None for itr in ITERATORS_LIST) == 0
+
+    itr1 = data.create_tuple_iterator()
+    itr2 = data.create_tuple_iterator()
+    itr3 = data.create_tuple_iterator()
+
+    _cleanup()
+    with pytest.raises(AttributeError) as info:
+        itr2.get_next()
+    assert "object has no attribute 'depipeline'" in str(info.value)
+
+    del itr1
+    assert len(ITERATORS_LIST) == 6
+    assert sum(itr() is not None for itr in ITERATORS_LIST) == 2
+
+    _cleanup()

From ea297c0889596791f6222732f9806bc4a1d3cc34 Mon Sep 17 00:00:00 2001
From: anthonyaje <anthony2@huawei.com>
Date: Fri, 17 Apr 2020 13:24:02 -0400
Subject: [PATCH 330/367] Fix dataset serdes for MindDataset

---
 .../dataset/engine/serializer_deserializer.py | 49 ++++++++++---------
 .../ut/python/dataset/test_serdes_dataset.py  | 35 ++++++++++++-
 2 files changed, 60 insertions(+), 24 deletions(-)

diff --git a/mindspore/dataset/engine/serializer_deserializer.py b/mindspore/dataset/engine/serializer_deserializer.py
index d1ed5c47cd..4de5f8101e 100644
--- a/mindspore/dataset/engine/serializer_deserializer.py
+++ b/mindspore/dataset/engine/serializer_deserializer.py
@@ -127,9 +127,12 @@ def serialize_operations(node_repr, key, val):
 
 def serialize_sampler(node_repr, val):
     """Serialize sampler object to dictionary."""
-    node_repr['sampler'] = val.__dict__
-    node_repr['sampler']['sampler_module'] = type(val).__module__
-    node_repr['sampler']['sampler_name'] = type(val).__name__
+    if val is None:
+        node_repr['sampler'] = None
+    else:
+        node_repr['sampler'] = val.__dict__
+        node_repr['sampler']['sampler_module'] = type(val).__module__
+        node_repr['sampler']['sampler_name'] = type(val).__name__
 
 
 def traverse(node):
@@ -253,9 +256,10 @@ def create_node(node):
                         node.get('shuffle'), sampler, node.get('num_shards'), node.get('shard_id'))
 
     elif dataset_op == 'MindDataset':
-        pyobj = pyclass(node['dataset_file'], node.get('column_list'),
+        sampler = construct_sampler(node.get('sampler'))
+        pyobj = pyclass(node['dataset_file'], node.get('columns_list'),
                         node.get('num_parallel_workers'), node.get('seed'), node.get('num_shards'),
-                        node.get('shard_id'), node.get('block_reader'))
+                        node.get('shard_id'), node.get('block_reader'), sampler)
 
     elif dataset_op == 'TFRecordDataset':
         pyobj = pyclass(node['dataset_files'], node.get('schema'), node.get('column_list'),
@@ -341,24 +345,25 @@ def create_node(node):
 
 def construct_sampler(in_sampler):
     """Instantiate Sampler object based on the information from dictionary['sampler']"""
-    sampler_name = in_sampler['sampler_name']
-    sampler_module = in_sampler['sampler_module']
-    sampler_class = getattr(sys.modules[sampler_module], sampler_name)
     sampler = None
-    if sampler_name == 'DistributedSampler':
-        sampler = sampler_class(in_sampler['num_shards'], in_sampler['shard_id'], in_sampler.get('shuffle'))
-    elif sampler_name == 'PKSampler':
-        sampler = sampler_class(in_sampler['num_val'], in_sampler.get('num_class'), in_sampler('shuffle'))
-    elif sampler_name == 'RandomSampler':
-        sampler = sampler_class(in_sampler.get('replacement'), in_sampler.get('num_samples'))
-    elif sampler_name == 'SequentialSampler':
-        sampler = sampler_class()
-    elif sampler_name == 'SubsetRandomSampler':
-        sampler = sampler_class(in_sampler['indices'])
-    elif sampler_name == 'WeightedRandomSampler':
-        sampler = sampler_class(in_sampler['weights'], in_sampler['num_samples'], in_sampler.get('replacement'))
-    else:
-        raise ValueError("Sampler type is unknown: " + sampler_name)
+    if in_sampler is not None:
+        sampler_name = in_sampler['sampler_name']
+        sampler_module = in_sampler['sampler_module']
+        sampler_class = getattr(sys.modules[sampler_module], sampler_name)
+        if sampler_name == 'DistributedSampler':
+            sampler = sampler_class(in_sampler['num_shards'], in_sampler['shard_id'], in_sampler.get('shuffle'))
+        elif sampler_name == 'PKSampler':
+            sampler = sampler_class(in_sampler['num_val'], in_sampler.get('num_class'), in_sampler('shuffle'))
+        elif sampler_name == 'RandomSampler':
+            sampler = sampler_class(in_sampler.get('replacement'), in_sampler.get('num_samples'))
+        elif sampler_name == 'SequentialSampler':
+            sampler = sampler_class()
+        elif sampler_name == 'SubsetRandomSampler':
+            sampler = sampler_class(in_sampler['indices'])
+        elif sampler_name == 'WeightedRandomSampler':
+            sampler = sampler_class(in_sampler['weights'], in_sampler['num_samples'], in_sampler.get('replacement'))
+        else:
+            raise ValueError("Sampler type is unknown: " + sampler_name)
 
     return sampler
 
diff --git a/tests/ut/python/dataset/test_serdes_dataset.py b/tests/ut/python/dataset/test_serdes_dataset.py
index 2ef93dbcd6..7fdb0f1dde 100644
--- a/tests/ut/python/dataset/test_serdes_dataset.py
+++ b/tests/ut/python/dataset/test_serdes_dataset.py
@@ -19,7 +19,7 @@ import filecmp
 import glob
 import json
 import os
-
+import pytest
 import numpy as np
 
 import mindspore.dataset as ds
@@ -28,7 +28,6 @@ import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore.dataset.transforms.vision import Inter
 from mindspore import log as logger
 
-
 def test_imagefolder(remove_json_files=True):
     """
     Test simulating resnet50 dataset pipeline.
@@ -217,6 +216,38 @@ def delete_json_files():
         except IOError:
             logger.info("Error while deleting: {}".format(f))
 
+# Test save load minddataset
+from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME, FILES_NUM, \
+    FileWriter, Inter
+
+def test_minddataset(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["data", "file_name", "label"]
+    num_readers = 4
+    indices = [1, 2, 3, 5, 7]
+    sampler = ds.SubsetRandomSampler(indices)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                              sampler=sampler)
+
+    # Serializing into python dictionary
+    ds1_dict = ds.serialize(data_set)
+    # Serializing into json object
+    ds1_json = json.dumps(ds1_dict, sort_keys=True)
+
+    # Reconstruct dataset pipeline from its serialized form
+    data_set = ds.deserialize(input_dict=ds1_dict)
+    ds2_dict = ds.serialize(data_set)
+    # Serializing into json object
+    ds2_json = json.dumps(ds2_dict, sort_keys=True)
+
+    assert ds1_json == ds2_json
+
+    data = get_data(CV_DIR_NAME)
+    assert data_set.get_dataset_size() == 10
+    num_iter = 0
+    for item in data_set.create_dict_iterator():
+        num_iter += 1
+    assert num_iter == 5
 
 
 if __name__ == '__main__':

From 36e87c26306eef4361f8a6585ababfb915f506ce Mon Sep 17 00:00:00 2001
From: lianliguang <lianliguang@huawei.com>
Date: Sat, 18 Apr 2020 09:36:25 +0800
Subject: [PATCH 331/367] fix bug of deal ref trans

---
 .../ascend/format_type/deal_ref_trans_and_cast.cc             | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
index 2d44bf8f8f..83a44029a7 100644
--- a/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/format_type/deal_ref_trans_and_cast.cc
@@ -101,9 +101,9 @@ AnfNodePtr AddAdditionalToRefOutput(const FuncGraphPtr &func_graph, const CNodeP
   auto origin_type = AnfAlgo::GetOutputDeviceDataType(origin_pair.first, origin_pair.second);
   auto cur_format = AnfAlgo::GetOutputFormat(cnode, output_index);
   auto cur_type = AnfAlgo::GetOutputDeviceDataType(cnode, output_index);
-  auto cur_shape = AnfAlgo::GetOutputInferShape(cnode, 0);
+  auto cur_shape = AnfAlgo::GetOutputInferShape(cnode, output_index);
   // insert trans
-  if (origin_format != cur_format) {
+  if (origin_format != cur_format && cur_shape.size() > 1) {
     auto kernel_select = std::make_shared<KernelSelect>();
     final_node = AddTransOpNodeToGraph(func_graph, final_node, kernel_select, 0, cur_format, origin_format,
                                        kTransDataOpName, false);

From 780aa6f87b594e80cef0e345f5e9e69aed656782 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Sat, 18 Apr 2020 01:41:25 +0000
Subject: [PATCH 332/367] Add license head in build.bat

---
 build.bat                          | 14 ++++++++++++++
 third_party/securec/CMakeLists.txt |  7 +------
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/build.bat b/build.bat
index 5d423720fd..76d7f19262 100644
--- a/build.bat
+++ b/build.bat
@@ -1,3 +1,17 @@
+@rem Copyright 2020 Huawei Technologies Co., Ltd
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem http://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem ============================================================================
 @echo off
 @title mindspore_build
  
diff --git a/third_party/securec/CMakeLists.txt b/third_party/securec/CMakeLists.txt
index 99345c6eb3..f012b05860 100644
--- a/third_party/securec/CMakeLists.txt
+++ b/third_party/securec/CMakeLists.txt
@@ -8,12 +8,7 @@ SET(CMAKE_C_FLAGS_RELEASE "$ENV{CFLAGS} -fPIC -O3 -Wall -Wno-deprecated-declarat
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 #add flags
-if (CMAKE_SYSTEM_NAME MATCHES "Windows")
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include")
-else()
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
-endif()
-
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -I/usr/local/include -Werror")
 
 include_directories(./include)
 add_subdirectory(src)

From 17e27824c54c163cb11bd1d4b8d3b257b149b123 Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Fri, 17 Apr 2020 21:47:39 -0400
Subject: [PATCH 333/367] add global batch normalization

---
 tests/ut/python/nn/test_batchnorm.py | 18 ------------------
 1 file changed, 18 deletions(-)

diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index b6e27e6950..10b4cb00a1 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -73,21 +73,3 @@ def test_compile_groupnorm():
     net = nn.GroupNorm(16, 64)
     input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))
     _executor.compile(net, input_data)
-
-class GlobalBNNet(nn.Cell):
-    def __init__(self):
-        super(GlobalBNNet, self).__init__()
-        self.bn = nn.GlobalBatchNorm(num_features = 2, group = 2)
-    def construct(self, x):
-        return self.bn(x)
-
-def test_global_bn():
-    init("hccl")
-    size = 4
-    context.set_context(mode=context.GRAPH_MODE)
-    context.reset_auto_parallel_context()
-    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
-                                      device_num=size, parameter_broadcast=True)
-    net = GlobalBNNet()
-    input_data = Tensor(np.array([[2.4, 2.1], [3.2, 5.4]], dtype=np.float32))
-    _executor.compile(net,input_data)

From 852e61d46cf3995c74af4d1b0b7878daa5a56299 Mon Sep 17 00:00:00 2001
From: liubuyu <liubuyu1@huawei.com>
Date: Sat, 18 Apr 2020 09:49:12 +0800
Subject: [PATCH 334/367] bug fix

---
 mindspore/ccsrc/device/ascend/ascend_device_address.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
index f0a30e4c42..79241df612 100644
--- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc
+++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc
@@ -117,7 +117,7 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector<int> &shape, size_t
   } else {
     auto iter = kNeedTransFormatSet.find(format_);
     if (iter != kNeedTransFormatSet.end()) {
-      sync_ok = ConvertFormatAndSyncHostToDevice(shape, size, type, host_ptr);
+      sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr);
     }
   }
   if (!sync_ok) {

From 97e250d4f1898000cbc44a2620cd36f6e52abd2f Mon Sep 17 00:00:00 2001
From: zhaojichen <zhaojichen1@huawei.com>
Date: Fri, 17 Apr 2020 21:51:45 -0400
Subject: [PATCH 335/367] add global batch normalization

---
 tests/ut/python/hccl_test/manage/api.py | 13 -------------
 tests/ut/python/nn/test_batchnorm.py    |  3 ---
 2 files changed, 16 deletions(-)

diff --git a/tests/ut/python/hccl_test/manage/api.py b/tests/ut/python/hccl_test/manage/api.py
index 04ce7da6d5..8dac167a3f 100644
--- a/tests/ut/python/hccl_test/manage/api.py
+++ b/tests/ut/python/hccl_test/manage/api.py
@@ -21,7 +21,6 @@ class Hccl():
     _instance = None
     _rank_id = 0
     _rank_size = 1
-    _group_size = 4
 
     def __init__(self):
         pass
@@ -48,10 +47,6 @@ class Hccl():
     def rank_size(self):
         return self._rank_size
 
-    @property
-    def group_size(self):
-        return self._group_size
-
     @rank_size.setter
     def rank_size(self, size):
         self._rank_size = size
@@ -70,14 +65,6 @@ def get_rank_size(group=None):
         return int(group.split("-")[0])
     raise ValueError
 
-def get_group_size(group=None):
-    hccl = Hccl()
-    if group is None:
-        return hccl.group_size
-    if isinstance(group, str):
-        return int(group.split("-")[0])
-    raise ValueError
-
 # pylint: disable=unused-argument
 def get_world_rank_from_group_rank(group, group_rank_id):
     return group_rank_id
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index 10b4cb00a1..e73b7ebbf0 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -19,9 +19,6 @@ import pytest
 import mindspore.nn as nn
 from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
-from mindspore.communication.management import init
-from mindspore import context
-from mindspore import ParallelMode
 
 
 def test_bn_pars_valid1():

From 0ed6d9178e0a411d4f809451665d187482953337 Mon Sep 17 00:00:00 2001
From: ougongchang <ougongchang@huawei.com>
Date: Mon, 13 Apr 2020 11:24:43 +0800
Subject: [PATCH 336/367] add Histogram summary operator

clean clang format errors and cpplint errors

add some test cases for histogram summary operator
---
 mindspore/ccsrc/ir/anf.cc                     | 11 ++-
 mindspore/ccsrc/operator/ops.cc               |  1 +
 mindspore/ccsrc/operator/ops.h                |  1 +
 mindspore/ccsrc/operator/prim_debug.cc        |  2 +-
 .../ccsrc/optimizer/irpass/branch_culling.cc  | 27 ++-----
 mindspore/ccsrc/parallel/node_check.cc        |  1 +
 mindspore/ccsrc/parallel/ops_info/ops_utils.h |  1 +
 .../ccsrc/pipeline/static_analysis/prim.cc    |  1 +
 .../ccsrc/session/anf_runtime_algorithm.cc    |  3 +-
 mindspore/ccsrc/session/session_basic.cc      |  4 +-
 mindspore/ccsrc/transform/convert.cc          |  1 +
 mindspore/ccsrc/utils/callbacks_ge.cc         |  2 +-
 mindspore/ops/_grad/grad_debug_ops.py         |  9 +++
 mindspore/ops/operations/__init__.py          |  3 +-
 mindspore/ops/operations/debug_ops.py         | 27 +++++++
 tests/st/summary/test_gpu_summary.py          | 57 ++-----------
 tests/ut/cpp/transform/convert_test.cc        |  6 ++
 tests/ut/cpp/transform/transform_base_test.cc |  3 +-
 tests/ut/python/ops/test_nn_ops.py            | 17 ++++
 tests/ut/python/ops/test_ops.py               | 19 +++++
 tests/ut/python/train/summary/test_summary.py |  2 +
 .../test_summary_ops_params_valid_check.py    | 81 ++++++++++++++-----
 22 files changed, 181 insertions(+), 98 deletions(-)

diff --git a/mindspore/ccsrc/ir/anf.cc b/mindspore/ccsrc/ir/anf.cc
index 924453a7a6..658fb578b7 100644
--- a/mindspore/ccsrc/ir/anf.cc
+++ b/mindspore/ccsrc/ir/anf.cc
@@ -103,7 +103,8 @@ std::string CNode::fullname_with_scope() {
     return fullname_with_scope_;
   }
 
-  if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary)) {
+  if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary) ||
+      IsApply(prim::kPrimHistogramSummary)) {
     std::string tag = GetValue<std::string>(GetValueNode(input(1)));
     if (tag == "") {
       MS_LOG(EXCEPTION) << "The tag name is null, should be valid string";
@@ -111,10 +112,12 @@ std::string CNode::fullname_with_scope() {
     std::string name;
     if (IsApply(prim::kPrimScalarSummary)) {
       name = tag + "[:Scalar]";
-    } else if (IsApply(prim::kPrimTensorSummary)) {
-      name = tag + "[:Tensor]";
-    } else {
+    } else if (IsApply(prim::kPrimImageSummary)) {
       name = tag + "[:Image]";
+    } else if (IsApply(prim::kPrimHistogramSummary)) {
+      name = tag + "[:Histogram]";
+    } else {
+      name = tag + "[:Tensor]";
     }
     fullname_with_scope_ = name;
   } else {
diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc
index e190d7d0b2..b1a8a9b782 100755
--- a/mindspore/ccsrc/operator/ops.cc
+++ b/mindspore/ccsrc/operator/ops.cc
@@ -235,6 +235,7 @@ const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDa
 const PrimitivePtr kPrimScalarSummary = std::make_shared<Primitive>("ScalarSummary");
 const PrimitivePtr kPrimImageSummary = std::make_shared<Primitive>("ImageSummary");
 const PrimitivePtr kPrimTensorSummary = std::make_shared<Primitive>("TensorSummary");
+const PrimitivePtr kPrimHistogramSummary = std::make_shared<Primitive>("HistogramSummary");
 
 ValuePtr GetPythonOps(const std::string& op_name, const std::string& module_name) {
   py::object obj = parse::python_adapter::GetPyFn(module_name, op_name);
diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
index 0148e073e0..26c13993e0 100755
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -225,6 +225,7 @@ extern const PrimitivePtr kPrimStateSetItem;
 extern const PrimitivePtr kPrimScalarSummary;
 extern const PrimitivePtr kPrimImageSummary;
 extern const PrimitivePtr kPrimTensorSummary;
+extern const PrimitivePtr kPrimHistogramSummary;
 extern const PrimitivePtr kPrimBroadcastGradientArgs;
 extern const PrimitivePtr kPrimControlDepend;
 extern const PrimitivePtr kPrimIs_;
diff --git a/mindspore/ccsrc/operator/prim_debug.cc b/mindspore/ccsrc/operator/prim_debug.cc
index 28f7e92303..c8db775320 100644
--- a/mindspore/ccsrc/operator/prim_debug.cc
+++ b/mindspore/ccsrc/operator/prim_debug.cc
@@ -69,7 +69,7 @@ AbstractBasePtr InferImplTensorSummary(const AnalysisEnginePtr &, const Primitiv
 
   int tensor_rank = SizeToInt(tensor_value->shape()->shape().size());
   if (tensor_rank == 0) {
-    MS_LOG(EXCEPTION) << "Tensor/Image Summary evaluator second arg should be an tensor, but got a scalar";
+    MS_LOG(EXCEPTION) << op_name << " summary evaluator second arg should be an tensor, but got a scalar, rank is 0";
   }
 
   // Reomve the force check to support batch set summary use 'for' loop
diff --git a/mindspore/ccsrc/optimizer/irpass/branch_culling.cc b/mindspore/ccsrc/optimizer/irpass/branch_culling.cc
index 7c4ada8b3b..d90b2bd44c 100644
--- a/mindspore/ccsrc/optimizer/irpass/branch_culling.cc
+++ b/mindspore/ccsrc/optimizer/irpass/branch_culling.cc
@@ -51,25 +51,14 @@ bool InConvertWhiteList(const AnfNodePtr &node, size_t index) {
   // node because it is attribute or ge specific reason.
   // Example : when convert CNode(kPrimReduceSum, x, axis), node of index 2 in CNode->inputs is axis which should not be
   // converted to switch guarded.
-  std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list({{prim::kPrimApplyMomentum, {1, 2}},
-                                                                        {prim::kPrimMomentum, {2, 3}},
-                                                                        {prim::kPrimStateSetItem, {1}},
-                                                                        {prim::kPrimEnvGetItem, {1}},
-                                                                        {prim::kPrimEnvSetItem, {1}},
-                                                                        {prim::kPrimReduceSum, {2}},
-                                                                        {prim::kPrimReduceMean, {2}},
-                                                                        {prim::kPrimReduceAll, {2}},
-                                                                        {prim::kPrimCast, {2}},
-                                                                        {prim::kPrimTranspose, {2}},
-                                                                        {prim::kPrimOneHot, {2}},
-                                                                        {prim::kPrimGatherV2, {3}},
-                                                                        {prim::kPrimReshape, {2}},
-                                                                        {prim::kPrimAssign, {1}},
-                                                                        {prim::kPrimAssignAdd, {1}},
-                                                                        {prim::kPrimAssignSub, {1}},
-                                                                        {prim::kPrimTensorSummary, {1}},
-                                                                        {prim::kPrimImageSummary, {1}},
-                                                                        {prim::kPrimScalarSummary, {1}}});
+  std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list(
+    {{prim::kPrimApplyMomentum, {1, 2}}, {prim::kPrimMomentum, {2, 3}},     {prim::kPrimStateSetItem, {1}},
+     {prim::kPrimEnvGetItem, {1}},       {prim::kPrimEnvSetItem, {1}},      {prim::kPrimReduceSum, {2}},
+     {prim::kPrimReduceMean, {2}},       {prim::kPrimReduceAll, {2}},       {prim::kPrimCast, {2}},
+     {prim::kPrimTranspose, {2}},        {prim::kPrimOneHot, {2}},          {prim::kPrimGatherV2, {3}},
+     {prim::kPrimReshape, {2}},          {prim::kPrimAssign, {1}},          {prim::kPrimAssignAdd, {1}},
+     {prim::kPrimAssignSub, {1}},        {prim::kPrimTensorSummary, {1}},   {prim::kPrimImageSummary, {1}},
+     {prim::kPrimScalarSummary, {1}},    {prim::kPrimHistogramSummary, {1}}});
   for (auto &item : white_list) {
     auto matched = std::any_of(item.second.begin(), item.second.end(), [&item, &node, &index](size_t idx) {
       return IsPrimitiveCNode(node, item.first) && idx == index;
diff --git a/mindspore/ccsrc/parallel/node_check.cc b/mindspore/ccsrc/parallel/node_check.cc
index ffd40e421d..e43d03c29c 100644
--- a/mindspore/ccsrc/parallel/node_check.cc
+++ b/mindspore/ccsrc/parallel/node_check.cc
@@ -66,6 +66,7 @@ const std::set<std::string> BLACK_LIST = {TUPLE_GETITEM,
                                           SCALARSUMMARY,
                                           IMAGESUMMARY,
                                           TENSORSUMMARY,
+                                          HISTOGRAMSUMMARY,
                                           COL2IMV1,
                                           RESOLVE,
                                           BROADCASTGRADIENTARGS,
diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
index 89b174d6b0..bdae87858d 100644
--- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h
+++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h
@@ -246,6 +246,7 @@ constexpr char STATESETITEM[] = "state_setitem";
 constexpr char SCALARSUMMARY[] = "ScalarSummary";
 constexpr char IMAGESUMMARY[] = "ImageSummary";
 constexpr char TENSORSUMMARY[] = "TensorSummary";
+constexpr char HISTOGRAMSUMMARY[] = "HistogramSummary";
 constexpr char BROADCASTGRADIENTARGS[] = "BroadcastGradientArgs";
 constexpr char INVERTPERMUTATION[] = "InvertPermutation";
 constexpr char CONTROLDEPEND[] = "ControlDepend";
diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
index 1512596cb4..2a2e90d675 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc
@@ -131,6 +131,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
     {prim::kPrimImageSummary, {InferImplTensorSummary, true}},
     {prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
+    {prim::kPrimHistogramSummary, {InferImplTensorSummary, true}},
   };
   return prim_eval_implement_map;
 }
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 44472a9a6f..a11122bbde 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -714,7 +714,8 @@ bool AnfRuntimeAlgorithm::IsRealKernel(const AnfNodePtr &node) {
   }
   auto input = cnode->inputs()[0];
   bool is_virtual_node = IsPrimitive(input, prim::kPrimImageSummary) || IsPrimitive(input, prim::kPrimScalarSummary) ||
-                         IsPrimitive(input, prim::kPrimTensorSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
+                         IsPrimitive(input, prim::kPrimTensorSummary) ||
+                         IsPrimitive(input, prim::kPrimHistogramSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
                          IsPrimitive(input, prim::kPrimStateSetItem) || IsPrimitive(input, prim::kPrimDepend) ||
                          IsPrimitive(input, prim::kPrimTupleGetItem) || IsPrimitive(input, prim::kPrimControlDepend) ||
                          IsPrimitive(input, prim::kPrimReturn);
diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc
index 01a836fad1..0ef0ad97ea 100755
--- a/mindspore/ccsrc/session/session_basic.cc
+++ b/mindspore/ccsrc/session/session_basic.cc
@@ -45,7 +45,7 @@ void GetSummaryNodes(const KernelGraph *graph, std::unordered_map<std::string, s
   for (auto &n : apply_list) {
     MS_EXCEPTION_IF_NULL(n);
     if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
-        IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
+        IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
       int index = 0;
       auto cnode = n->cast<CNodePtr>();
       MS_EXCEPTION_IF_NULL(cnode);
@@ -83,7 +83,7 @@ bool ExistSummaryNode(const KernelGraph *graph) {
   auto all_nodes = DeepLinkedGraphSearch(ret);
   for (auto &n : all_nodes) {
     if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
-        IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
+        IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
       return true;
     }
   }
diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 2daa86b960..417989247e 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -353,6 +353,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimScalarSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimImageSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimTensorSummary->name(), ADPT_DESC(Summary)},
+    {prim::kPrimHistogramSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimTensorAdd->name(),
      std::make_shared<OpAdapterDesc>(std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})),
                                      std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})))},
diff --git a/mindspore/ccsrc/utils/callbacks_ge.cc b/mindspore/ccsrc/utils/callbacks_ge.cc
index 50fd2f0b11..36bbcbf297 100644
--- a/mindspore/ccsrc/utils/callbacks_ge.cc
+++ b/mindspore/ccsrc/utils/callbacks_ge.cc
@@ -131,7 +131,7 @@ static TensorPtr GetMeTensorForSummary(const std::string& name, const std::share
     auto shape = std::vector<int>({ONE_SHAPE});
     return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape);
   }
-  if (tname == "[:Tensor]") {
+  if (tname == "[:Tensor]" || tname == "[:Histogram]") {
     MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor";
     // process the tensor summary
     // Now we can't get the real shape, so we keep same shape with GE
diff --git a/mindspore/ops/_grad/grad_debug_ops.py b/mindspore/ops/_grad/grad_debug_ops.py
index 431d82192f..1cb756219a 100644
--- a/mindspore/ops/_grad/grad_debug_ops.py
+++ b/mindspore/ops/_grad/grad_debug_ops.py
@@ -49,6 +49,15 @@ def get_bprop_image_summary(self):
     return bprop
 
 
+@bprop_getters.register(P.HistogramSummary)
+def get_bprop_histogram_summary(self):
+    """Generate bprop for HistogramSummary"""
+
+    def bprop(tag, x, out, dout):
+        return tag, zeros_like(x)
+    return bprop
+
+
 @bprop_getters.register(P.InsertGradientOf)
 def get_bprop_insert_gradient_of(self):
     """Generate bprop for InsertGradientOf"""
diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index e1dd8e36c5..1f0ee8a04d 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -34,7 +34,7 @@ from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice)
 from .debug_ops import (ImageSummary, InsertGradientOf, ScalarSummary,
-                        TensorSummary, Print)
+                        TensorSummary, HistogramSummary, Print)
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
@@ -148,6 +148,7 @@ __all__ = [
     'ScalarSummary',
     'ImageSummary',
     'TensorSummary',
+    'HistogramSummary',
     "Print",
     'InsertGradientOf',
     'InvertPermutation',
diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py
index e4467f5ce1..1a3248cb15 100644
--- a/mindspore/ops/operations/debug_ops.py
+++ b/mindspore/ops/operations/debug_ops.py
@@ -98,6 +98,33 @@ class TensorSummary(Primitive):
         """init"""
 
 
+class HistogramSummary(Primitive):
+    """
+    Output tensor to protocol buffer through histogram summary operator.
+
+    Inputs:
+        - **name** (str) - The name of the input variable.
+        - **value** (Tensor) - The value of tensor, and the rank of tensor should be greater than 0.
+
+    Examples:
+        >>> class SummaryDemo(nn.Cell):
+        >>>     def __init__(self,):
+        >>>         super(SummaryDemo, self).__init__()
+        >>>         self.summary = P.HistogramSummary()
+        >>>         self.add = P.TensorAdd()
+        >>>
+        >>>     def construct(self, x, y):
+        >>>         x = self.add(x, y)
+        >>>         name = "x"
+        >>>         self.summary(name, x)
+        >>>         return x
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init"""
+
+
 class InsertGradientOf(PrimitiveWithInfer):
     """
     Attach callback to graph node that will be invoked on the node's gradient.
diff --git a/tests/st/summary/test_gpu_summary.py b/tests/st/summary/test_gpu_summary.py
index ef645fcb2e..c97c08c4e1 100644
--- a/tests/st/summary/test_gpu_summary.py
+++ b/tests/st/summary/test_gpu_summary.py
@@ -24,17 +24,6 @@ from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
 from mindspore.train.summary.summary_record import SummaryRecord
 
-'''
-    This testcase is used for save summary data only. You need install MindData first and uncomment the commented 
-    packages to analyse summary data.
-    Using "minddata start --datalog='./test_me_summary_event_file/' --host=0.0.0.0" to make data visible. 
-'''
-# from minddata.datavisual.data_transform.data_manager import DataManager
-# from minddata.datavisual.visual.train_visual.train_task_manager import TrainTaskManager
-# from minddata.datavisual.visual.train_visual.scalars_processor import ScalarsProcessor
-# from minddata.datavisual.common.enums import PluginNameEnum
-# from minddata.datavisual.common.enums import DataManagerStatus
-
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -43,6 +32,7 @@ CUR_DIR = os.getcwd()
 SUMMARY_DIR_ME = CUR_DIR + "/test_me_summary_event_file/"
 SUMMARY_DIR_ME_TEMP = CUR_DIR + "/test_me_temp_summary_event_file/"
 
+
 def clean_environment_file(srcDir):
     if os.path.exists(srcDir):
         ls = os.listdir(srcDir)
@@ -50,6 +40,8 @@ def clean_environment_file(srcDir):
             filePath = os.path.join(srcDir, line)
             os.remove(filePath)
         os.removedirs(srcDir)
+
+
 def save_summary_events_file(srcDir, desDir):
     if not os.path.exists(desDir):
         print("-- create desDir")
@@ -64,12 +56,14 @@ def save_summary_events_file(srcDir, desDir):
         os.remove(filePath)
     os.removedirs(srcDir)
 
+
 class SummaryNet(nn.Cell):
     def __init__(self, tag_tuple=None, scalar=1):
         super(SummaryNet, self).__init__()
         self.summary_s = P.ScalarSummary()
         self.summary_i = P.ImageSummary()
         self.summary_t = P.TensorSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag_tuple = tag_tuple
         self.scalar = scalar
@@ -79,8 +73,10 @@ class SummaryNet(nn.Cell):
         self.summary_s("x1", x)
         z = self.add(x, y)
         self.summary_t("z1", z)
+        self.histogram_summary("histogram", z)
         return z
 
+
 def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
     net = SummaryNet()
     out_me_dict = {}
@@ -93,6 +89,7 @@ def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
         out_me_dict[i] = out_put.asnumpy()
     return out_me_dict
 
+
 def me_scalar_summary(steps, tag=None, value=None):
     test_writer = SummaryRecord(SUMMARY_DIR_ME_TEMP)
 
@@ -104,44 +101,6 @@ def me_scalar_summary(steps, tag=None, value=None):
     test_writer.close()
     return out_me_dict
 
-def print_scalar_data():
-    print("============start print_scalar_data\n")
-    data_manager = DataManager()
-    data_manager.start_load_data(path=SUMMARY_DIR_ME)
-    while data_manager.get_status() != DataManagerStatus.DONE:
-        time.sleep(0.1)
-    task_manager = TrainTaskManager(data_manager)
-    train_jobs = task_manager.get_all_train_tasks(PluginNameEnum.scalar)
-    print(train_jobs)
-    """
-        train_jobs
-        ['train_jobs': {
-	    'id': '12-123',
-	    'name': 'train_job_name',
-	    'tags': ['x1', 'y1']
-	                    }]
-    """
-    scalar_processor = ScalarsProcessor(data_manager)
-    metadata = scalar_processor.get_metadata_list(train_job_ids=train_jobs['train_jobs'][0]['id'], tag=train_jobs['train_jobs'][0]['tags'][0])
-    print(metadata)
-    '''
-    metadata
-    {
-        'scalars' : [
-            {
-                'train_job_id' : '12-12',
-                'metadatas' : [
-                    {
-                        'wall_time' : 0.1,
-                        'step' : 1,
-                        'value' : 0.1
-                    }
-                ]
-            }
-        ]
-    }
-    '''
-    print("============end print_scalar_data\n")
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
diff --git a/tests/ut/cpp/transform/convert_test.cc b/tests/ut/cpp/transform/convert_test.cc
index c7cd394002..4388312592 100644
--- a/tests/ut/cpp/transform/convert_test.cc
+++ b/tests/ut/cpp/transform/convert_test.cc
@@ -621,6 +621,12 @@ TEST_F(TestConvert, TestTensorSummaryOps) {
   ASSERT_TRUE(ret);
 }
 
+TEST_F(TestConvert, TestHistogramSummaryOps) {
+  auto prim = prim::kPrimHistogramSummary;
+  bool ret = MakeDfGraph(prim, 2);
+  ASSERT_TRUE(ret);
+}
+
 TEST_F(TestConvert, TestGreaterOps) {
   auto prim = std::make_shared<Primitive>("Greater");
   bool ret = MakeDfGraph(prim, 2);
diff --git a/tests/ut/cpp/transform/transform_base_test.cc b/tests/ut/cpp/transform/transform_base_test.cc
index 944721ec83..eb083e2dd1 100644
--- a/tests/ut/cpp/transform/transform_base_test.cc
+++ b/tests/ut/cpp/transform/transform_base_test.cc
@@ -73,7 +73,8 @@ FuncGraphPtr MakeFuncGraph(const PrimitivePtr prim, unsigned int nparam) {
   std::vector<AnfNodePtr> inputs;
   inputs.push_back(NewValueNode(prim));
   for (unsigned int i = 0; i < nparam; i++) {
-    if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" || prim->name() == "ImageSummary") &&
+    if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" || 
+        prim->name() == "ImageSummary" || prim->name() == "HistogramSummary") &&
         i == 0) {
       auto input = NewValueNode("testSummary");
       inputs.push_back(input);
diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py
index 7364893503..bb2bb3ea9f 100644
--- a/tests/ut/python/ops/test_nn_ops.py
+++ b/tests/ut/python/ops/test_nn_ops.py
@@ -198,6 +198,19 @@ class ScalarSummaryNet(nn.Cell):
         return out
 
 
+class HistogramSummaryNet(nn.Cell):
+    """HistogramSummaryNet definition"""
+
+    def __init__(self):
+        super(HistogramSummaryNet, self).__init__()
+        self.summary = P.HistogramSummary()
+
+    def construct(self, tensor):
+        string_in = "wight_value"
+        out = self.summary(string_in, tensor)
+        return out
+
+
 class FusedBatchNormGrad(nn.Cell):
     """ FusedBatchNormGrad definition """
 
@@ -443,6 +456,10 @@ test_cases = [
         'block': ScalarSummaryNet(),
         'desc_inputs': [2.2],
     }),
+    ('HistogramSummary', {
+        'block': HistogramSummaryNet(),
+        'desc_inputs': [[1,2,3]],
+    }),
     ('FusedBatchNormGrad', {
         'block': FusedBatchNormGrad(nn.BatchNorm2d(num_features=512, eps=1e-5, momentum=0.1)),
         'desc_inputs': [[64, 512, 7, 7], [64, 512, 7, 7]],
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 078ada8406..3345f77862 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -160,6 +160,19 @@ class SummaryNet(nn.Cell):
         return self.add(x, y)
 
 
+class HistogramSummaryNet(nn.Cell):
+    def __init__(self,):
+        super(HistogramSummaryNet, self).__init__()
+        self.summary = P.HistogramSummary()
+        self.add = P.TensorAdd()
+
+    def construct(self, x, y):
+        out = self.add(x, y)
+        string_in = "out"
+        self.summary(string_in, out)
+        return out
+
+
 test_case_math_ops = [
     ('Neg', {
         'block': P.Neg(),
@@ -1104,6 +1117,12 @@ test_case_other_ops = [
         'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
                         Tensor(np.array([1.2]).astype(np.float32))],
         'skip': ['backward']}),
+    ('HistogramSummary', {
+        'block': HistogramSummaryNet(),
+        'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
+                        Tensor(np.array([1.2]).astype(np.float32))],
+        'skip': ['backward']}),
+    
 ]
 
 test_case_lists = [test_case_nn_ops, test_case_math_ops, test_case_array_ops, test_case_other_ops]
diff --git a/tests/ut/python/train/summary/test_summary.py b/tests/ut/python/train/summary/test_summary.py
index cc595ea883..82287c4290 100644
--- a/tests/ut/python/train/summary/test_summary.py
+++ b/tests/ut/python/train/summary/test_summary.py
@@ -132,6 +132,7 @@ class SummaryDemo(nn.Cell):
     def __init__(self,):
         super(SummaryDemo, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
 
     def construct(self, x, y):
@@ -139,6 +140,7 @@ class SummaryDemo(nn.Cell):
         z = self.add(x, y)
         self.s("z1", z)
         self.s("y1", y)
+        self.histogram_summary("histogram", z)
         return z
 
 
diff --git a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
index 98dfd6aaef..23c85d398c 100644
--- a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
+++ b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
@@ -40,6 +40,7 @@ class SummaryDemoTag(nn.Cell):
     def __init__(self, tag1, tag2, tag3):
         super(SummaryDemoTag, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag1 = tag1
         self.tag2 = tag2
@@ -50,6 +51,7 @@ class SummaryDemoTag(nn.Cell):
         z = self.add(x, y)
         self.s(self.tag2, z)
         self.s(self.tag3, y)
+        self.histogram_summary(self.tag1, x)
         return z
 
 
@@ -58,6 +60,7 @@ class SummaryDemoTagForSet(nn.Cell):
     def __init__(self, tag_tuple):
         super(SummaryDemoTagForSet, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag_tuple = tag_tuple
 
@@ -65,6 +68,7 @@ class SummaryDemoTagForSet(nn.Cell):
         z = self.add(x, y)
         for tag in self.tag_tuple:
             self.s(tag, x)
+            self.histogram_summary(tag, x)
         return z
 
 
@@ -98,6 +102,19 @@ class SummaryDemoValueForSet(nn.Cell):
             self.s(tag, self.v)
         return z
 
+
+class HistogramSummaryNet(nn.Cell):
+    "HistogramSummaryNet definition"
+    def __init__(self, value):
+        self.histogram_summary = P.HistogramSummary()
+        self.add = P.TensorAdd()
+        self.value = value
+    
+    def construct(self, tensors1, tensor2):
+        self.histogram_summary("value", self.value)
+        return self.add(tensors1, tensor2)
+
+
 def run_case(net):
     """ run_case """
     # step 0: create the thread
@@ -121,8 +138,8 @@ def run_case(net):
 
 
 # Test 1: use the repeat tag
-def test_scalar_summary_use_repeat_tag():
-    log.debug("begin test_scalar_summary_use_repeat_tag")
+def test_summary_use_repeat_tag():
+    log.debug("begin test_summary_use_repeat_tag")
     net = SummaryDemoTag("x", "x", "x")
     try:
         run_case(net)
@@ -130,12 +147,12 @@ def test_scalar_summary_use_repeat_tag():
         assert False
     else:
         assert True
-    log.debug("finished test_scalar_summary_use_repeat_tag")
+    log.debug("finished test_summary_use_repeat_tag")
 
 
 # Test 2: repeat tag use for set summary
-def test_scalar_summary_use_repeat_tag_for_set():
-    log.debug("begin test_scalar_summary_use_repeat_tag_for_set")
+def test_summary_use_repeat_tag_for_set():
+    log.debug("begin test_summary_use_repeat_tag_for_set")
     net = SummaryDemoTagForSet(("x", "x", "x"))
     try:
         run_case(net)
@@ -143,12 +160,12 @@ def test_scalar_summary_use_repeat_tag_for_set():
         assert False
     else:
         assert True
-    log.debug("finished test_scalar_summary_use_repeat_tag_for_set")
+    log.debug("finished test_summary_use_repeat_tag_for_set")
 
 
 # Test3: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_None():
-    log.debug("begin test_scalar_summary_use_invalid_tag_None")
+def test_summary_use_invalid_tag_None():
+    log.debug("begin test_summary_use_invalid_tag_None")
     net = SummaryDemoTag(None, None, None)
     try:
         run_case(net)
@@ -156,31 +173,31 @@ def test_scalar_summary_use_invalid_tag_None():
         assert True
     else:
         assert False
-    log.debug("finished test_scalar_summary_use_invalid_tag_None")
+    log.debug("finished test_summary_use_invalid_tag_None")
 
 
 # Test4: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_Bool():
-    log.debug("begin test_scalar_summary_use_invalid_tag_Bool")
+def test_summary_use_invalid_tag_Bool():
+    log.debug("begin test_summary_use_invalid_tag_Bool")
     net = SummaryDemoTag(True, True, True)
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_Bool")
+    log.debug("finished test_summary_use_invalid_tag_Bool")
 
 
 # Test5: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_null():
-    log.debug("begin test_scalar_summary_use_invalid_tag_null")
+def test_summary_use_invalid_tag_null():
+    log.debug("begin test_summary_use_invalid_tag_null")
     net = SummaryDemoTag("", "", "")
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_null")
+    log.debug("finished test_summary_use_invalid_tag_null")
 
 
 # Test6: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_Int():
-    log.debug("begin test_scalar_summary_use_invalid_tag_Int")
+def test_summary_use_invalid_tag_Int():
+    log.debug("begin test_summary_use_invalid_tag_Int")
     net = SummaryDemoTag(1, 2, 3)
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_Int")
+    log.debug("finished test_summary_use_invalid_tag_Int")
 
 
 # Test7: test with invalid value(None, "")
@@ -196,7 +213,6 @@ def test_scalar_summary_use_invalid_value_None():
     log.debug("finished test_scalar_summary_use_invalid_tag_Int")
 
 
-
 # Test8: test with invalid value(None, "")
 def test_scalar_summary_use_invalid_value_None_ForSet():
     log.debug("begin test_scalar_summary_use_invalid_value_None_ForSet")
@@ -221,3 +237,30 @@ def test_scalar_summary_use_invalid_value_null():
     else:
         assert False
     log.debug("finished test_scalar_summary_use_invalid_value_null")
+
+
+def test_histogram_summary_use_valid_value():
+    """Test histogram summary with valid value"""
+    log.debug("Begin test_histogram_summary_use_valid_value")
+    try:
+        net = HistogramSummaryNet(Tensor(np.array([1,2,3])))
+        run_case(net)
+    except:
+        assert True
+    else:
+        assert False
+    log.debug("Finished test_histogram_summary_use_valid_value")
+
+
+def test_histogram_summary_use_scalar_value():
+    """Test histogram summary use scalar value"""
+    log.debug("Begin test_histogram_summary_use_scalar_value")
+    try:
+        scalar = Tensor(1)
+        net = HistogramSummaryNet(scalar)
+        run_case(net)
+    except:
+        assert True
+    else:
+        assert False
+    log.debug("Finished test_histogram_summary_use_scalar_value")

From 7cded1ec32ad0455532b5ba9ae545134d0b7ea7f Mon Sep 17 00:00:00 2001
From: huanghui <huanghui44@huawei.com>
Date: Fri, 17 Apr 2020 16:50:16 +0800
Subject: [PATCH 337/367] bugfix: confusion_softmax_grad need to be set with
 axis and keep_dims attr

---
 .../ir_fusion/confusion_softmax_grad_rule.cc  | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_softmax_grad_rule.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_softmax_grad_rule.cc
index 1270ae77c1..8078247c2a 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_softmax_grad_rule.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/confusion_softmax_grad_rule.cc
@@ -21,9 +21,30 @@
 #include "session/anf_runtime_algorithm.h"
 #include "ir/primitive.h"
 #include "utils/utils.h"
+#include "pre_activate/common/helper.h"
 
 namespace mindspore {
 namespace opt {
+namespace {
+void SetAttrsForFusionNode(const AnfNodePtr &sub_anf, const AnfNodePtr &fusion_node) {
+  MS_EXCEPTION_IF_NULL(sub_anf);
+  MS_EXCEPTION_IF_NULL(fusion_node);
+  auto sub = sub_anf->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(sub);
+  if (sub->size() != kSubInputNum) {
+    MS_LOG(EXCEPTION) << "Sub's size is not equal with 3";
+  }
+  auto reduce_sum_anf = sub->input(2);
+  MS_EXCEPTION_IF_NULL(reduce_sum_anf);
+  auto reduce_sum = reduce_sum_anf->cast<CNodePtr>();
+  if (reduce_sum == nullptr) {
+    MS_LOG(EXCEPTION) << "Sub's second input is not a cnode";
+  }
+  AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node);
+  AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node);
+}
+}  // namespace
+
 const BaseRef ConfusionSoftmaxGradRule::DefinePattern() const {
   return VectorRef(
     {prim::kPrimSub, input0_, VectorRef({prim::kPrimReduceSum, VectorRef({prim::kPrimMul, input0_, input1_})})});
@@ -48,6 +69,7 @@ const AnfNodePtr ConfusionSoftmaxGradRule::Process(const FuncGraphPtr &graph, co
   auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)};
   AnfAlgo::SetOutputInferTypeAndShape(types, shapes, confusion_softmax_grad.get());
   confusion_softmax_grad->set_scope(node->scope());
+  SetAttrsForFusionNode(node, confusion_softmax_grad);
   return confusion_softmax_grad;
 }
 }  // namespace opt

From c69f3be75873d786465e9884a3651dd2607809ff Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Fri, 17 Apr 2020 15:40:16 +0800
Subject: [PATCH 338/367] fix dataset api description

---
 mindspore/dataset/engine/datasets.py | 6 +++---
 mindspore/dataset/engine/samplers.py | 3 +--
 mindspore/train/model.py             | 4 ++++
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 1c6ac634f2..df05da7032 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -556,7 +556,7 @@ class Dataset:
 
         Note:
             If device is Ascend, features of data will be transferred one by one. The limitation
-            of data transferation per time is 256M.
+            of data transmission per time is 256M.
 
         Return:
             TransferDataset, dataset for transferring.
@@ -572,7 +572,7 @@ class Dataset:
 
         Note:
             If device is Ascend, features of data will be transferred one by one. The limitation
-            of data transferation per time is 256M.
+            of data transmission per time is 256M.
 
         Returns:
             TransferDataset, dataset for transferring.
@@ -1897,7 +1897,7 @@ class GeneratorDataset(SourceDataset):
         >>>     for i in range(maxid):
         >>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
         >>> # create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
-        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1, col2"])
+        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1", "col2"])
         >>> # 3) Iterable dataset as iterable input
         >>> class MyIterable():
         >>>     def __iter__(self):
diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py
index f9c74f151d..0bba559210 100644
--- a/mindspore/dataset/engine/samplers.py
+++ b/mindspore/dataset/engine/samplers.py
@@ -112,8 +112,7 @@ class RandomSampler():
 
     Args:
         replacement (bool, optional): If True, put the sample ID back for the next draw (default=False).
-        num_samples (int, optional): Number of elements to sample (default=None, all elements). This
-            argument should be specified only when 'replacement' is "True".
+        num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
         >>> import mindspore.dataset as ds
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 46e4f421f7..851cd63af9 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -362,6 +362,8 @@ class Model:
             If dataset_sink_mode is True, epoch of training should be equal to the count of repeat
             operation in dataset processing. Otherwise, errors could occur since the amount of data
             is not the amount training requires.
+            If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
+            of data will be transferred one by one. The limitation of data transmission per time is 256M.
 
         Args:
             epoch (int): Total number of iterations on the data.
@@ -485,6 +487,8 @@ class Model:
 
         Note:
             CPU is not supported when dataset_sink_mode is true.
+            If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
+            of data will be transferred one by one. The limitation of data transmission per time is 256M.
 
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.

From 8f4cf323f84951d8c192f76ae41c98261128d4a5 Mon Sep 17 00:00:00 2001
From: kingfo <wangqiuliang@huawei.com>
Date: Fri, 17 Apr 2020 18:39:57 +0800
Subject: [PATCH 339/367] fix cell output issue and vm operator in pynative
 mode

---
 mindspore/ccsrc/pynative/pynative_execute.cc     | 6 +++---
 mindspore/ccsrc/session/anf_runtime_algorithm.cc | 2 +-
 mindspore/nn/cell.py                             | 5 ++++-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index 5620634bcc..6a1ddf6a7e 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -39,7 +39,7 @@
 
 const char SINGLE_OP_GRAPH[] = "single_op_graph";
 // primitive unable to infer value for constant input in PyNative mode
-const std::unordered_set<std::string> vm_operators = {"partial", "depend"};
+const std::unordered_set<std::string> vm_operators = {"partial", "depend", "make_ref"};
 
 namespace mindspore {
 namespace pynative {
@@ -141,7 +141,7 @@ OpExecInfoPtr GenerateOpExecInfo(const py::args& args) {
   op_exec_info->op_inputs = py_args;
   op_exec_info->inputs_mask = args[PY_INPUT_MASK];
   if (op_exec_info->op_inputs.size() != op_exec_info->inputs_mask.size()) {
-    MS_LOG(ERROR) << "op:" << op_exec_info->op_name << " inputs size not equal op_mask";
+    MS_LOG(ERROR) << "Op:" << op_exec_info->op_name << " inputs size not equal op_mask";
     return nullptr;
   }
   return op_exec_info;
@@ -163,7 +163,7 @@ std::string GetSingleOpGraphInfo(const OpExecInfoPtr& op_exec_info) {
   // get prim and abstract info
   (void)graph_info.append(std::to_string((uintptr_t)(op_exec_info->py_primitive.get())) + "_" +
                           op_exec_info->abstract->ToString());
-  MS_LOG(INFO) << "graph info [" << graph_info << "]";
+  MS_LOG(INFO) << "Graph info [" << graph_info << "]";
   return graph_info;
 }
 
diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
index 44472a9a6f..0fcb3ce39e 100644
--- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc
+++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc
@@ -457,7 +457,7 @@ TypeId AnfRuntimeAlgorithm::GetOutputInferDataType(const AnfNodePtr &node, size_
     } else if (tuple_i->isa<Number>()) {
       return tuple_i->type_id();
     } else {
-      MS_LOG(EXCEPTION) << "Not support type " << tuple_i->ToString();
+      MS_LOG(WARNING) << "Not support type " << tuple_i->ToString();
       return tuple_i->type_id();
     }
   } else if (type_ptr->isa<Number>()) {
diff --git a/mindspore/nn/cell.py b/mindspore/nn/cell.py
index 088f3f3e57..5507d12af8 100755
--- a/mindspore/nn/cell.py
+++ b/mindspore/nn/cell.py
@@ -140,7 +140,10 @@ class Cell:
         if context.get_context("mode") == context.GRAPH_MODE:
             out = self.compile_and_run(*inputs)
             return out
-        return self.construct(*inputs)
+        output = self.construct(*inputs)
+        if isinstance(output, Parameter):
+            output = output.data
+        return output
 
     def __setattr__(self, name, value):
         cells = self.__dict__.get('_cells')

From b600991cc32ce713fa8601e8449d0b96a269a6f3 Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Fri, 17 Apr 2020 16:16:10 +0800
Subject: [PATCH 340/367] dock DepthwiseConv2dBackprop
 DepthwiseConv2dBackpropFilter DepthwiseConv2dBackpropInput

---
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     |  3 ++
 .../pass/const_input_to_attr_registry.cc      |  2 +
 mindspore/ops/_op_impl/tbe/__init__.py        |  3 ++
 .../ops/_op_impl/tbe/depthwise_conv2d.py      | 44 +++++++++++++++++++
 .../tbe/depthwise_conv2d_backprop_filter.py   | 41 +++++++++++++++++
 .../tbe/depthwise_conv2d_backprop_input.py    | 41 +++++++++++++++++
 mindspore/ops/operations/nn_ops.py            |  1 +
 7 files changed, 135 insertions(+)
 create mode 100644 mindspore/ops/_op_impl/tbe/depthwise_conv2d.py
 create mode 100644 mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py
 create mode 100644 mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index e11ff7d6e6..9e4553e057 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -39,6 +39,9 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"reduce_min", "reduce_min_d"},
   {"conv2d_backprop_filter", "conv2d_backprop_filter_d"},
   {"conv2d_backprop_input", "conv2d_backprop_input_d"},
+  {"depthwise_conv2d_native", "depthwise_conv2d"},
+  {"depthwise_conv2d_native_backprop_filter", "depthwise_conv2d_backprop_filter_d"},
+  {"depthwise_conv2d_native_backprop_input", "depthwise_conv2d_backprop_input_d"},
   {"top_kv2", "top_k"},
   {"scatter_nd", "scatter_nd_d"},
   {"tile", "tile_d"},
diff --git a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
index 88edfd3019..c2f96e54c6 100644
--- a/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
+++ b/mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc
@@ -27,6 +27,8 @@ ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() {
   Register(prim::kPrimCast->name(), {1});
   Register(prim::kPrimConv2DBackpropInput->name(), {2});
   Register(prim::kPrimConv2DBackpropFilter->name(), {2});
+  Register(prim::kPrimDepthwiseConv2dNativeBackpropFilter->name(), {1});
+  Register(prim::kPrimDepthwiseConv2dNativeBackpropInput->name(), {0});
   Register(prim::kPrimReshape->name(), {1});
   Register(prim::kPrimReduceMax->name(), {1});
   Register(prim::kPrimReduceMin->name(), {1});
diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py
index 9ec5446165..340cf9efe3 100644
--- a/mindspore/ops/_op_impl/tbe/__init__.py
+++ b/mindspore/ops/_op_impl/tbe/__init__.py
@@ -133,3 +133,6 @@ from .arg_min_with_value import _arg_min_with_value_tbe
 from .fused_mul_add import _fused_mul_add_tbe
 from .fused_mul_add_n import _fused_mul_add_n_tbe
 from .fused_mul_apply_momentum import _fused_mul_apply_momentum_tbe
+from .depthwise_conv2d import _depthwise_conv2d_tbe
+from .depthwise_conv2d_backprop_filter import _depthwise_conv2d_backprop_filter_tbe
+from .depthwise_conv2d_backprop_input import _depthwise_conv2d_backprop_input_tbe
diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py
new file mode 100644
index 0000000000..fdafcd3fa4
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py
@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""DepthwiseConv2D op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+depthwise_conv2d_op_info = TBERegOp("DepthwiseConv2dNative") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("depthwise_conv2d.so") \
+    .compute_cost(10) \
+    .kernel_name("depthwise_conv2d") \
+    .partial_flag(True) \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .attr("pads", "required", "listInt", "all") \
+    .attr("data_format", "required", "str", "all") \
+    .attr("offset_a", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .input(3, "offset_w", False, "optional", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_5HD) \
+    .get_op_info()
+
+
+@op_info_register(depthwise_conv2d_op_info)
+def _depthwise_conv2d_tbe():
+    """DepthwiseConv2D TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py
new file mode 100644
index 0000000000..c19a311009
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py
@@ -0,0 +1,41 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""DepthwiseConv2DBackpropFilter op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+depthwise_conv2d_backprop_filter_op_info = TBERegOp("DepthwiseConv2dNativeBackpropFilter") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("depthwise_conv2d_backprop_filter_d.so") \
+    .compute_cost(10) \
+    .kernel_name("depthwise_conv2d_backprop_filter_d") \
+    .partial_flag(True) \
+    .attr("filter_size", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .attr("pads", "required", "str", "all") \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "input", False, "required", "all") \
+    .input(1, "out_backprop", False, "required", "all") \
+    .output(0, "filter_grad", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
+
+
+@op_info_register(depthwise_conv2d_backprop_filter_op_info)
+def _depthwise_conv2d_backprop_filter_tbe():
+    """DepthwiseConv2DBackpropFilter TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py
new file mode 100644
index 0000000000..9e671f18e2
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py
@@ -0,0 +1,41 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""DepthwiseConv2DBackpropInput op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+depthwise_conv2d_backprop_input_op_info = TBERegOp("DepthwiseConv2dNativeBackpropInput") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("depthwise_conv2d_backprop_input_d.so") \
+    .compute_cost(10) \
+    .kernel_name("depthwise_conv2d_backprop_input_d") \
+    .partial_flag(True) \
+    .attr("input_size", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .attr("pads", "required", "str", "all") \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "filter", False, "required", "all") \
+    .input(1, "out_backprop", False, "required", "all") \
+    .output(0, "input_grad", False, "required", "all") \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
+
+
+@op_info_register(depthwise_conv2d_backprop_input_op_info)
+def _depthwise_conv2d_backprop_input_tbe():
+    """DepthwiseConv2DBackpropInput TBE register"""
+    return
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index a4efd5a47b..9827975fd0 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -696,6 +696,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
                  dilation=1,
                  group=1):
         """init DepthwiseConv2dNative"""
+        self.init_prim_io_names(inputs=['x', 'w'], outputs=['output'])
         validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad)
         self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple))
         if isinstance(kernel_size, int):

From 5eb53798894ca2be54cbd33bfe5f0088a04e0875 Mon Sep 17 00:00:00 2001
From: YuJianfeng <yujianfeng5@huawei.com>
Date: Fri, 17 Apr 2020 17:06:16 +0800
Subject: [PATCH 341/367] Add AdamApplyOne fusion pass

---
 .../ascend/ir_fusion/adam_apply_one_fusion.cc |  56 ++++++-
 .../ascend/ir_fusion/adam_apply_one_fusion.h  |  50 +++++-
 .../ir_fusion/adam_apply_one_fusion_test.cc   | 151 ++++++++++++++++++
 .../adam_apply_one_fusion_test.py             |  72 +++++++++
 4 files changed, 321 insertions(+), 8 deletions(-)

diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
index 3f905fedf9..4645167191 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.cc
@@ -42,17 +42,69 @@ AnfNodePtr AdamApplyOneFusion::CreateAdamApplyOneNode(const FuncGraphPtr &func_g
 
 const BaseRef AdamApplyOneFusion::DefinePattern() const {
   const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
-  const auto prim_deal_div = std::make_shared<Primitive>(kRealDivOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
   VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
   VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
   VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_deal_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
 }
 
+const BaseRef AdamApplyOneCond1Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
+}
+
+const BaseRef AdamApplyOneCond2Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, VectorRef({prim::kPrimSquare, input_vars_[0]}), mul_x_input_vars_[3]});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+}
+
+const BaseRef AdamApplyOneCond3Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+}
+
+const BaseRef AdamApplyOneCond4Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, VectorRef({add1_var_, mul2, mul3})});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+}
+
 const AnfNodePtr AdamApplyOneFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                              const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(func_graph);
diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
index 77f6641463..5ee8a86cfb 100644
--- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
+++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/adam_apply_one_fusion.h
@@ -18,21 +18,23 @@
 
 #include <vector>
 #include <memory>
+#include <string>
 #include "pre_activate/common/optimizer.h"
 #include "utils/utils.h"
 
 namespace mindspore {
 namespace opt {
-constexpr size_t kAdamApplyOneInputNum = 5;
-constexpr size_t kAdamApplyOneMulInputNum = 4;
+constexpr size_t kAdamApplyOneInputVarNum = 5;
+constexpr size_t kAdamApplyOneMulInputVarNum = 4;
 
 class AdamApplyOneFusion : public PatternProcessPass {
  public:
-  explicit AdamApplyOneFusion(bool multigraph = true) : PatternProcessPass("adam_apply_one_fusion", multigraph) {
-    for (size_t i = 0; i < kAdamApplyOneInputNum; ++i) {
+  explicit AdamApplyOneFusion(const std::string &name = "adam_apply_one_fusion", bool multigraph = true)
+      : PatternProcessPass(name, multigraph) {
+    for (size_t i = 0; i < kAdamApplyOneInputVarNum; ++i) {
       input_vars_.push_back(std::make_shared<Var>());
     }
-    for (size_t i = 0; i < kAdamApplyOneMulInputNum; ++i) {
+    for (size_t i = 0; i < kAdamApplyOneMulInputVarNum; ++i) {
       mul_x_input_vars_.push_back(std::make_shared<Var>());
     }
     add2_y_ = std::make_shared<Var>();
@@ -44,7 +46,7 @@ class AdamApplyOneFusion : public PatternProcessPass {
   const BaseRef DefinePattern() const override;
   const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
 
- private:
+ protected:
   AnfNodePtr CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv) const;
   std::vector<VarPtr> input_vars_;
   std::vector<VarPtr> mul_x_input_vars_;
@@ -52,6 +54,42 @@ class AdamApplyOneFusion : public PatternProcessPass {
   VarPtr add0_var_;
   VarPtr add1_var_;
 };
+
+class AdamApplyOneCond1Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneCond1Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_cond1_fusion", multigraph) {}
+
+  ~AdamApplyOneCond1Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneCond2Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneCond2Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_cond2_fusion", multigraph) {}
+
+  ~AdamApplyOneCond2Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneCond3Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneCond3Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_cond3_fusion", multigraph) {}
+
+  ~AdamApplyOneCond3Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneCond4Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneCond4Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_cond4_fusion", multigraph) {}
+
+  ~AdamApplyOneCond4Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
 }  // namespace opt
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ADAM_APPLY_ONE_FUSION_H_
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
index f4e418bed1..c2ee7b6519 100644
--- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
@@ -66,5 +66,156 @@ TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_fusion) {
   EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
 }
 
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_cond1_fusion) {
+  /*
+   * def before_cond1(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+   *    square0 = Square(input0)
+   *    mul1 = Mul(mul1_x, input0)
+   *    mul0 = Mul(mul0_x, input2)
+   *    mul2 = Mul(mul2_x, input1)
+   *    mul3 = Mul(mul3_x, square0)
+   *    add0 = Add(mul0, mul1)
+   *    add1 = Add(mul2, mul3)
+   *    sqrt0 = Sqrt(add1)
+   *    add2 = Add(add2_y, sqrt0)
+   *    true_div0 = RealDiv(add0, add2)
+   *    mul4 = Mul(input4, true_div0)
+   *    sub0 = Sub(input3, mul4)
+   *    outputs = make_tuple(add1, add0, sub0)
+   *    output = tuple_getitem(outputs, 0)
+   *    return output
+   */
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "before_cond1");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneCond1Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_cond2_fusion) {
+  /*
+   * def before_cond2(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+   *    square0 = Square(input0)
+   *    mul1 = Mul(mul1_x, input0)
+   *    mul0 = Mul(mul0_x, input2)
+   *    mul2 = Mul(mul2_x, input1)
+   *    mul3 = Mul(square0, mul3_x)
+   *    add0 = Add(mul0, mul1)
+   *    add1 = Add(mul2, mul3)
+   *    sqrt0 = Sqrt(add1)
+   *    add2 = Add(sqrt0, add2_y)
+   *    true_div0 = RealDiv(add0, add2)
+   *    mul4 = Mul(true_div0, input4)
+   *    sub0 = Sub(input3, mul4)
+   *    outputs = make_tuple(add1, add0, sub0)
+   *    output = tuple_getitem(outputs, 0)
+   *    return output
+   */
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "before_cond2");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneCond2Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_cond3_fusion) {
+  /*
+   * def before_cond3(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+   *    square0 = Square(input0)
+   *    mul1 = Mul(mul1_x, input0)
+   *    mul0 = Mul(mul0_x, input2)
+   *    mul2 = Mul(mul2_x, input1)
+   *    mul3 = Mul(mul3_x, square0)
+   *    add0 = Add(mul0, mul1)
+   *    add1 = Add(mul2, mul3)
+   *    sqrt0 = Sqrt(add1)
+   *    add2 = Add(sqrt0, add2_y)
+   *    true_div0 = RealDiv(add0, add2)
+   *    mul4 = Mul(true_div0, input4)
+   *    sub0 = Sub(input3, mul4)
+   *    outputs = make_tuple(add1, add0, sub0)
+   *    output = tuple_getitem(outputs, 0)
+   *    return output
+   */
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "before_cond3");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneCond3Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_cond4_fusion) {
+  /*
+   * def before_cond4(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+   *    square0 = Square(input0)
+   *    mul1 = Mul(mul1_x, input0)
+   *    mul0 = Mul(mul0_x, input2)
+   *    mul2 = Mul(mul2_x, input1)
+   *    mul3 = Mul(mul3_x, square0)
+   *    add0 = Add(mul0, mul1)
+   *    add1 = Add(mul2, mul3)
+   *    sqrt0 = Sqrt(add1)
+   *    add2 = Add(add2_y, sqrt0)
+   *    true_div0 = RealDiv(add0, add2)
+   *    mul4 = Mul(true_div0, input4)
+   *    sub0 = Sub(input3, mul4)
+   *    outputs = make_tuple(add1, add0, sub0)
+   *    output = tuple_getitem(outputs, 0)
+   *    return output
+   */
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "before_cond4");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneCond4Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
index b55764b18d..225964ee38 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
@@ -58,6 +58,78 @@ def test_adam_apply_one_fusion(tag):
         output = tuple_getitem(outputs, 0)
         return output
 
+    @fns
+    def before_cond1(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(add2_y, sqrt0)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(input4, true_div0)
+        sub0 = Sub(input3, mul4)
+        outputs = make_tuple(add1, add0, sub0)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond2(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(square0, mul3_x)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(sqrt0, add2_y)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        outputs = make_tuple(add1, add0, sub0)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond3(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(sqrt0, add2_y)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        outputs = make_tuple(add1, add0, sub0)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond4(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(add2_y, sqrt0)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        outputs = make_tuple(add1, add0, sub0)
+        output = tuple_getitem(outputs, 0)
+        return output
+
     @fns
     def after(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
         adam_apply_one = AdamApplyOne(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y)

From c853f985cc8c7e696f9d3e5e23de446d37a6663a Mon Sep 17 00:00:00 2001
From: buxue <yiren19920727@163.com>
Date: Sat, 18 Apr 2020 10:03:26 +0800
Subject: [PATCH 342/367] support equal list with none

---
 .../ccsrc/operator/composite/composite.cc     |  2 +-
 .../ops/composite/multitype_ops/equal_impl.py | 31 +++++++++++++++++++
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/operator/composite/composite.cc b/mindspore/ccsrc/operator/composite/composite.cc
index 347641829d..9a665e8a30 100644
--- a/mindspore/ccsrc/operator/composite/composite.cc
+++ b/mindspore/ccsrc/operator/composite/composite.cc
@@ -743,7 +743,7 @@ FuncGraphPtr MultitypeFuncGraph::GenerateFromTypes(const TypePtrList& types) {
     }
     oss << ++idx << ". " << item.first << "\n  " << trace::GetDebugInfo(func_graph->debug_info()) << "\n";
   }
-  MS_LOG(EXCEPTION) << "Fail to find overload function for `" << name_ << "` with type " << buffer.str() << "\n"
+  MS_LOG(EXCEPTION) << "The '" << name_ << "' operation does not support the type " << buffer.str() << "\n"
                     << oss.str();
 }
 
diff --git a/mindspore/ops/composite/multitype_ops/equal_impl.py b/mindspore/ops/composite/multitype_ops/equal_impl.py
index 428cdf4705..ff54c34fad 100644
--- a/mindspore/ops/composite/multitype_ops/equal_impl.py
+++ b/mindspore/ops/composite/multitype_ops/equal_impl.py
@@ -190,6 +190,7 @@ def _none_equal_tuple(x, y):
    """
     return False
 
+
 @equal.register("Tensor", "Number")
 @equal.register("Number", "Tensor")
 @equal.register("Tensor", "Tensor")
@@ -235,3 +236,33 @@ def _none_equal_tensor(x, y):
        bool, return false.
    """
     return False
+
+
+@equal.register("List", "None")
+def _list_equal_none(x, y):
+    """
+    Determine if list equal none.
+
+    Args:
+       x (list): The first input which is a list.
+       y (none): The second input which is none.
+
+    Returns:
+       bool, return false.
+   """
+    return False
+
+
+@equal.register("None", "List")
+def _none_equal_list(x, y):
+    """
+    Determine if none equal list.
+
+    Args:
+       x (none): The first input which is none.
+       y (list): The second input which is a list.
+
+    Returns:
+       bool, return false.
+   """
+    return False

From d8feaa9c19e7acb89c17186f90ac202756d25f1f Mon Sep 17 00:00:00 2001
From: gengdongjie <gengdongjie@huawei.com>
Date: Sat, 18 Apr 2020 11:44:08 +0800
Subject: [PATCH 343/367] remove list append to avoid memory explosion

---
 mindspore/train/callback.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py
index dcf630342c..b9635acc62 100644
--- a/mindspore/train/callback.py
+++ b/mindspore/train/callback.py
@@ -686,9 +686,6 @@ class TimeMonitor(Callback):
     def __init__(self, data_size):
         super(TimeMonitor, self).__init__()
         self.data_size = data_size
-        self.step_time_cost = []
-        self.epoch_time_cost = []
-        self.per_step_time = []
 
     def epoch_begin(self, run_context):
         self.epoch_time = time.time()
@@ -696,8 +693,6 @@ class TimeMonitor(Callback):
     def epoch_end(self, run_context):
         epoch_mseconds = (time.time() - self.epoch_time) * 1000
         per_step_mseconds = epoch_mseconds / self.data_size
-        self.epoch_time_cost.append(epoch_mseconds)
-        self.per_step_time.append(per_step_mseconds)
         print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)
 
     def step_begin(self, run_context):
@@ -705,6 +700,5 @@ class TimeMonitor(Callback):
 
     def step_end(self, run_context):
         step_mseconds = (time.time() - self.step_time) * 1000
-        self.step_time_cost.append(step_mseconds)
         print('step time', step_mseconds, flush=True)
 

From f4164169faea1a125428be716b9a4a0dd61b1691 Mon Sep 17 00:00:00 2001
From: dengwentao <dengwentao1@huawei.com>
Date: Mon, 13 Apr 2020 11:22:45 +0800
Subject: [PATCH 344/367] modify  gpu tvm cmake

---
 cmake/external_libs/dmlc_core.cmake |   2 +-
 cmake/external_libs/tvm_gpu.cmake   |  18 +++--
 cmake/package.cmake                 |  12 ++-
 cmake/utils.cmake                   |  20 +++--
 mindspore/ccsrc/CMakeLists.txt      | 109 +---------------------------
 5 files changed, 39 insertions(+), 122 deletions(-)

diff --git a/cmake/external_libs/dmlc_core.cmake b/cmake/external_libs/dmlc_core.cmake
index 386a52429d..e07df83fd6 100644
--- a/cmake/external_libs/dmlc_core.cmake
+++ b/cmake/external_libs/dmlc_core.cmake
@@ -1,4 +1,4 @@
-mindspore_add_pkg(dmlc_core
+mindspore_add_pkg(dmlc-core
         VER 0.3
         HEAD_ONLY ./
 	URL https://github.com/dmlc/dmlc-core/archive/808f485387f9a03f78fa9f1159f387d0d91b7a28.zip
diff --git a/cmake/external_libs/tvm_gpu.cmake b/cmake/external_libs/tvm_gpu.cmake
index 57a045cb03..2edec52ee1 100644
--- a/cmake/external_libs/tvm_gpu.cmake
+++ b/cmake/external_libs/tvm_gpu.cmake
@@ -1,8 +1,16 @@
-set(incubator_tvm_gpu_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
-set(incubator_tvm_gpu_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
+set(incubator_tvm_gpu_CFLAGS "-pipe -Wall -fPIC -fstack-protector-all -D_FORTIFY_SOURCE=2 -O2")
+set(incubator_tvm_gpu_CXXFLAGS "-std=c++11 -pipe -Wall -fPIC -fstack-protector-all -D_FORTIFY_SOURCE=2 -O2")
+set(USE_CUDA "ON")
 mindspore_add_pkg(incubator_tvm_gpu
         VER 0.6.0
-        HEAD_ONLY ./
+        LIBS tvm
         URL https://github.com/apache/incubator-tvm/archive/v0.6.0.tar.gz
-        MD5 9cbbd32545a776023acabbba270449fe)
-
+        MD5 9cbbd32545a776023acabbba270449fe
+        SUBMODULES ${dlpack_DIRPATH} ${dmlc-core_DIRPATH} ${rang_DIRPATH}
+        SOURCEMODULES topi/python/topi python/tvm
+        PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/find_library.patch
+                ${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/include.patch
+                ${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/src_pass.patch
+        CMAKE_OPTION -DBUILD_TESTING=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_SHARED_LIBS=ON)
+include_directories(${incubator_tvm_gpu_INC})
+add_library(mindspore::tvm ALIAS incubator_tvm_gpu::tvm)
diff --git a/cmake/package.cmake b/cmake/package.cmake
index 7d1fdc6d8a..81d58ccb92 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -190,11 +190,17 @@ if (ENABLE_GPU)
         DESTINATION ${INSTALL_PY_DIR}/../
         COMPONENT mindspore
     )
-    if (EXISTS ${CMAKE_BINARY_DIR}/incubator-tvm)
+    if (EXISTS ${incubator_tvm_gpu_ROOT})
+        file(GLOB_RECURSE GLOG_LIB_LIST ${incubator_tvm_gpu_LIBPATH}/lib*)
+        install(
+                FILES ${GLOG_LIB_LIST}
+                DESTINATION ${INSTALL_LIB_DIR}
+                COMPONENT mindspore
+        )
         install(
             DIRECTORY
-                ${CMAKE_BINARY_DIR}/incubator-tvm/topi/python/topi
-                ${CMAKE_BINARY_DIR}/incubator-tvm/python/tvm
+                ${incubator_tvm_gpu_ROOT}/topi/python/topi
+                ${incubator_tvm_gpu_ROOT}/python/tvm
             DESTINATION ${INSTALL_PY_DIR}/../_akg
             COMPONENT mindspore
         )
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index 21a766dc8c..5516fa9543 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -206,7 +206,7 @@ function(mindspore_add_pkg pkg_name )
 
     set(options )
     set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY CMAKE_PATH RELEASE LIB_PATH)
-    set(multiValueArgs CMAKE_OPTION LIBS PRE_CONFIGURE_COMMAND CONFIGURE_COMMAND BUILD_OPTION INSTALL_INCS INSTALL_LIBS PATCHES)
+    set(multiValueArgs CMAKE_OPTION LIBS PRE_CONFIGURE_COMMAND CONFIGURE_COMMAND BUILD_OPTION INSTALL_INCS INSTALL_LIBS PATCHES SUBMODULES SOURCEMODULES)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN} )
 
     if (NOT PKG_LIB_PATH)
@@ -270,11 +270,21 @@ function(mindspore_add_pkg pkg_name )
     endif ()
 
     if (NOT PKG_DIR)
-	if (PKG_GIT_REPOSITORY)
-	    __download_pkg_with_git(${pkg_name} ${PKG_GIT_REPOSITORY} ${PKG_GIT_TAG} ${PKG_MD5})
-	else()
+        if (PKG_GIT_REPOSITORY)
+            __download_pkg_with_git(${pkg_name} ${PKG_GIT_REPOSITORY} ${PKG_GIT_TAG} ${PKG_MD5})
+        else()
             __download_pkg(${pkg_name} ${PKG_URL} ${PKG_MD5})
-	endif()
+        endif()
+        foreach(_SUBMODULE_FILE ${PKG_SUBMODULES})
+            STRING( REGEX REPLACE "(.+)_(.+)" "\\1" _SUBMODEPATH ${_SUBMODULE_FILE})
+            STRING( REGEX REPLACE "(.+)/(.+)" "\\2" _SUBMODENAME ${_SUBMODEPATH})
+            file(GLOB ${pkg_name}_INSTALL_SUBMODULE ${_SUBMODULE_FILE}/*)
+            file(COPY ${${pkg_name}_INSTALL_SUBMODULE} DESTINATION ${${pkg_name}_SOURCE_DIR}/3rdparty/${_SUBMODENAME})
+        endforeach (_SUBMODULE_FILE)
+        foreach(_SOURCE_DIR ${PKG_SOURCEMODULES})
+            file(GLOB ${pkg_name}_INSTALL_SOURCE ${${pkg_name}_SOURCE_DIR}/${_SOURCE_DIR}/*)
+            file(COPY ${${pkg_name}_INSTALL_SOURCE} DESTINATION ${${pkg_name}_BASE_DIR}/${_SOURCE_DIR}/)
+        endforeach (_SUBMODULE_FILE)
     else()
         set(${pkg_name}_SOURCE_DIR ${PKG_DIR})
     endif ()
diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index 77008aee5f..e227969162 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -395,114 +395,7 @@ if(USE_GLOG)
 endif()
 
 if(ENABLE_GPU)
-	execute_process(COMMAND bash ${CMAKE_SOURCE_DIR}/third_party/apply_patches.sh 
-		${CMAKE_BINARY_DIR} 
-		${dlpack_DIRPATH} 
-		${dmlc_core_DIRPATH} 
-		${rang_DIRPATH} 
-		${incubator_tvm_gpu_DIRPATH})
-    set(TVM_DIR "${CMAKE_BINARY_DIR}/incubator-tvm")
-    # Utility functions
-    include(${TVM_DIR}/cmake/util/Util.cmake)
-    include(${TVM_DIR}/cmake/util/FindCUDA.cmake)
-
-    # include directories
-    include_directories(AFTER "${TVM_DIR}/include")
-    include_directories(AFTER "${TVM_DIR}/src")
-    include_directories(AFTER "${TVM_DIR}")
-    include_directories(AFTER "${TVM_DIR}/src/schedule")
-
-    include_directories(AFTER "${TVM_DIR}/3rdparty/dmlc-core/include")
-    include_directories(AFTER "${TVM_DIR}/3rdparty/dlpack/include")
-    include_directories(AFTER "${TVM_DIR}/3rdparty/compiler-rt")
-    include_directories(AFTER "${TVM_DIR}/3rdparty/rang/include")
-
-    # lib contain dlopen and dlclose
-    set(TVM_RUNTIME_LINKER_LIBS ${CMAKE_DL_LIBS})
-
-    # add source group
-    file(GLOB_RECURSE GROUP_SOURCE "${TVM_DIR}/src/*.cc" "src/*.cc")
-    file(GLOB_RECURSE GROUP_INCLUDE "${TVM_DIR}/src/*.h"
-        "${TVM_DIR}/include/*.h" "src/*.h" "include/*.h")
-    assign_source_group("Source" ${GROUP_SOURCE})
-    assign_source_group("Include" ${GROUP_INCLUDE})
-
-    file(GLOB COMPILER_SRCS
-        "pre_activate/gpu/*.cc"
-        ${TVM_DIR}/src/api/*.cc
-        ${TVM_DIR}/src/arithmetic/*.cc
-        ${TVM_DIR}/src/autotvm/*.cc
-        ${TVM_DIR}/src/codegen/*.cc
-        ${TVM_DIR}/src/lang/*.cc
-        ${TVM_DIR}/src/pass/*.cc
-        ${TVM_DIR}/src/op/*.cc
-        ${TVM_DIR}/src/node/*.cc
-        ${TVM_DIR}/src/schedule/*.cc
-        ${TVM_DIR}/src/runtime/*.cc
-        ${TVM_DIR}/src/runtime/vm/*.cc
-        ${TVM_DIR}/src/runtime/vm/profiler/*.cc
-        ${TVM_DIR}/src/codegen/stackvm/*.cc)
-
-    file(GLOB_RECURSE RELAY_SRCS ${TVM_DIR}/src/relay/*.cc)
-    list(APPEND COMPILER_SRCS ${RELAY_SRCS})
-
-    file(GLOB DATATYPE_SRCS ${TVM_DIR}/src/codegen/datatype/*.cc)
-    list(APPEND COMPILER_SRCS ${DATATYPE_SRCS})
-
-    file(GLOB COMPILER_VERILOG_SRCS ${TVM_DIR}/src/codegen/verilog/*.cc)
-    list(APPEND COMPILER_SRCS ${COMPILER_VERILOG_SRCS})
-
-    file(GLOB TOPI_SRCS ${TVM_DIR}/topi/src/*.cc)
-
-    file(GLOB RUNTIME_SRCS
-        ${TVM_DIR}/src/runtime/*.cc
-        ${TVM_DIR}/src/runtime/vm/*.cc
-        ${TVM_DIR}/src/runtime/stub/*.cc
-        ${TVM_DIR}/src/runtime/stackvm/*.cc)
-
-
-    file(GLOB COMPILER_OFF_SRCS
-        ${TVM_DIR}/src/codegen/opt/build_*_off.cc)
-    set(USE_CUDA "OFF")
-    if(ENABLE_GPU)
-        list(REMOVE_ITEM COMPILER_OFF_SRCS
-            ${TVM_DIR}/src/codegen/opt/build_cuda_off.cc)
-        set(USE_CUDA "ON")
-    endif()
-    list(APPEND COMPILER_SRCS ${COMPILER_OFF_SRCS})
-    # Module rules
-    include(${TVM_DIR}/cmake/modules/CUDA.cmake)
-
-    set(CMAKE_C_FLAGS_AKG  -pipe -Wall -fPIC -fstack-protector-all)
-    set(CMAKE_C_FLAGS_AKG ${CMAKE_C_FLAGS_AKG} -Wl,-z,relro,-z,now,-z,noexecstack)
-
-    set(CMAKE_CXX_FLAGS_AKG  -std=c++11 -pipe -Wall -fPIC -fstack-protector-all)
-    set(CMAKE_CXX_FLAGS_AKG ${CMAKE_CXX_FLAGS_AKG} -Wl,-z,relro,-z,now,-z,noexecstack)
-
-    if("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
-        message("-- Build in Debug mode")
-        set(CMAKE_C_FLAGS_AKG ${CMAKE_C_FLAGS_AKG} -O0 -g -rdynamic)
-        set(CMAKE_CXX_FLAGS_AKG ${CMAKE_CXX_FLAGS_AKG} -O0 -g -rdynamic)
-    else()
-        message("-- Build in Release mode")
-        set(CMAKE_C_FLAGS_AKG ${CMAKE_C_FLAGS_AKG} -O2 -Werror)
-        set(CMAKE_CXX_FLAGS_AKG ${CMAKE_CXX_FLAGS_AKG} -O2 -Werror)
-    endif()
-    if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION
-                                               VERSION_GREATER 7.0)
-        set(CMAKE_CXX_FLAGS_AKG ${CMAKE_CXX_FLAGS_AKG} -faligned-new)
-    endif()
-
-    add_library(akg OBJECT ${COMPILER_SRCS} ${RUNTIME_SRCS} ${TOPI_SRCS})
-
-    target_link_libraries(akg ${TVM_LINKER_LIBS} ${TVM_RUNTIME_LINKER_LIBS})
-    target_compile_options(akg PRIVATE
-        $<$<COMPILE_LANGUAGE:C>:${CMAKE_C_FLAGS_AKG}>
-        $<$<COMPILE_LANGUAGE:CXX>:${CMAKE_CXX_FLAGS_AKG}>)
-    target_include_directories(akg PRIVATE "${TVM_DIR}/topi/include")
-
-    add_dependencies(_c_expression akg)
-    target_link_libraries(_c_expression PRIVATE akg)
+    target_link_libraries(_c_expression PRIVATE mindspore::tvm)
 endif()
 
 if(ENABLE_DUMP_PROTO)

From f182edfd448ebe478d6bff58113978abeaf5f5aa Mon Sep 17 00:00:00 2001
From: Ziyan <gongziyan1@huawei.com>
Date: Sat, 18 Apr 2020 12:50:47 +0800
Subject: [PATCH 345/367] fix lars base class type

---
 mindspore/nn/optim/lars.py      | 7 +++----
 mindspore/nn/optim/optimizer.py | 2 +-
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py
index c0cb71cfa6..02538aa61a 100755
--- a/mindspore/nn/optim/lars.py
+++ b/mindspore/nn/optim/lars.py
@@ -21,8 +21,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
-from mindspore.nn.cell import Cell
-from .optimizer import grad_scale
+from .optimizer import grad_scale, Optimizer
 
 lars_opt = C.MultitypeFuncGraph("lars_opt")
 
@@ -61,7 +60,7 @@ def _tensor_run_opt_v2(lars, weight_decay, learning_rate, gradient, weight, deca
     return gradient
 
 
-class LARS(Cell):
+class LARS(Optimizer):
     """
     Implements the LARS algorithm with LARSUpdate Operator.
 
@@ -98,7 +97,7 @@ class LARS(Cell):
     def __init__(self, optimizer, epsilon=1e-05, hyperpara=0.001, weight_decay=0.0, use_clip=False,
                  decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name,
                  lars_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name, loss_scale=1.0):
-        super(LARS, self).__init__(auto_prefix=False)
+        super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="trivial")])
         self.opt = optimizer
         self.parameters = optimizer.parameters
         self.learning_rate = optimizer.learning_rate
diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index 6c6d14ed7a..00d3fd3b7b 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -57,7 +57,7 @@ class Optimizer(Cell):
 
     def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(Optimizer, self).__init__()
+        super(Optimizer, self).__init__(auto_prefix=False)
         if isinstance(learning_rate, float):
             self.dynamic_lr = False
             self.gather = None

From 9773cf024d39a03dd7bfdf08285b668bf3677850 Mon Sep 17 00:00:00 2001
From: chenjianping <jpc.chen@huawei.com>
Date: Sat, 18 Apr 2020 06:22:23 +0000
Subject: [PATCH 346/367] fix windows building fail

---
 mindspore/ccsrc/common/trans.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc
index e0c70e1b01..b4e02c8fe6 100644
--- a/mindspore/ccsrc/common/trans.cc
+++ b/mindspore/ccsrc/common/trans.cc
@@ -920,7 +920,7 @@ bool NchwToC1hwncoc0(const FormatArgs &args, void *result) {
                                         : static_cast<size_t>(SECUREC_MEM_MAX_LEN);
               size_t c_i = c0_i + c1_i * c0;
               size_t src_offset = (n_i * c * h * w + c_i * h * w + h_i * w + w_i) * size;
-              error_t ret;
+              errno_t ret;
               if (c_i < c && c0_i == co_i) {
                 ret = memcpy_s(static_cast<uint8_t *>(result) + dst_offset, protected_size,
                                static_cast<uint8_t const *>(args.data) + src_offset, size);

From 93e909594ddec13256b16f66e3b9d72553dff09c Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Fri, 17 Apr 2020 15:50:30 +0800
Subject: [PATCH 347/367] checpoint support customize network

---
 mindspore/train/serialization.py | 56 +++++++++++++++++++++-----------
 1 file changed, 37 insertions(+), 19 deletions(-)

diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 90d8816094..8ec1b38804 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -224,42 +224,60 @@ def load_param_into_net(net, parameter_dict):
         msg = ("Argument parameter_dict should be a dict, but got {}.".format(type(parameter_dict)))
         raise TypeError(msg)
 
-    logger.info("Execute parameter into net process.")
-    param_name_net_not_have = []
+    logger.info("Execute load parameter into net process.")
     for name in parameter_dict:
-        b_par_dict_have_par_of_net = False
         for _, param in net.parameters_and_names():
-            if name == param.name:
-                b_par_dict_have_par_of_net = True
+            if name == param.name and param.layerwise_parallel:
                 # layerwise parallel parameter data loaded from checkpoint file,
                 # was a complete(merged) data, need to be splited
-                if param.layerwise_parallel:
-                    new_param = parameter_dict[param.name]
-                    _load_tensor_for_layerwise(new_param, param)
+                new_param = parameter_dict[param.name]
+                _load_tensor_for_layerwise(new_param, param)
                 break
-        if not b_par_dict_have_par_of_net:
-            param_name_net_not_have.append(name)
 
-    param_name_param_dict_not_have = []
+    param_not_load = []
     for _, param in net.parameters_and_names():
         if param.name in parameter_dict:
             new_param = parameter_dict[param.name]
-
             if not isinstance(new_param, Parameter):
                 logger.error("Failed to combine the net and the parameters.")
                 msg = ("Argument parameter_dict element should be a Parameter, but got {}.".format(type(new_param)))
                 raise TypeError(msg)
             _update_param(param, new_param)
         else:
-            param_name_param_dict_not_have.append(param.name)
+            param_not_load.append(param.name)
+
+    if param_not_load:
+        _load_dismatch_prefix_params(net, parameter_dict, param_not_load)
 
     logger.debug("Params not matched(in net but not in parameter_dict):")
-    for paramname in param_name_param_dict_not_have:
-        logger.debug("%s", paramname)
-    logger.debug("Params not matched(in parameter_dict but not in net):")
-    for paramname in param_name_net_not_have:
-        logger.debug("%s", paramname)
-    logger.info("Load parameter into net process finish.")
+    for param_name in param_not_load:
+        logger.debug("%s", param_name)
+
+    logger.info("Load parameter into net finish, {} parameters has not been loaded.".format(len(param_not_load)))
+
+
+def _load_dismatch_prefix_params(net, parameter_dict, param_not_load):
+    """When some net parameter did not load, try to continue load."""
+    prefix_name = ""
+    longest_name = param_not_load[0]
+    while prefix_name != longest_name and param_not_load:
+        logger.debug("Count: {} parameters has not been loaded, try to load continue.".format(len(param_not_load)))
+        longest_name = sorted(param_not_load, key=len, reverse=True)[0]
+        prefix_name = longest_name
+        for net_param_name in param_not_load:
+            for dict_name in parameter_dict:
+                if dict_name.endswith(net_param_name):
+                    tmp_name = dict_name[:-len(net_param_name)]
+                    prefix_name = prefix_name if len(prefix_name) < len(tmp_name) else tmp_name
+
+        if prefix_name != longest_name:
+            logger.info("Remove parameter prefix name: {}, continue to load.".format(prefix_name))
+            for _, param in net.parameters_and_names():
+                new_param_name = prefix_name + param.name
+                if param.name in param_not_load and new_param_name in parameter_dict:
+                    new_param = parameter_dict[new_param_name]
+                    _update_param(param, new_param)
+                    param_not_load.remove(param.name)
 
 
 def _save_graph(network, file_name):

From 6775190e480197243de557c3811679072392e1b1 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Sat, 18 Apr 2020 11:20:26 +0800
Subject: [PATCH 348/367] add cpu one hot

---
 .../device/cpu/kernel/one_hot_cpu_kernel.cc   | 74 +++++++++++++++++
 .../device/cpu/kernel/one_hot_cpu_kernel.h    | 47 +++++++++++
 .../device/cpu/kernel/reshape_cpu_kernel.h    |  2 +
 tests/st/ops/cpu/test_one_hot_op.py           | 82 +++++++++++++++++++
 4 files changed, 205 insertions(+)
 create mode 100644 mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.cc
 create mode 100644 mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.h
 create mode 100644 tests/st/ops/cpu/test_one_hot_op.py

diff --git a/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.cc b/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.cc
new file mode 100644
index 0000000000..e4b3f03f58
--- /dev/null
+++ b/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.cc
@@ -0,0 +1,74 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "device/cpu/kernel/one_hot_cpu_kernel.h"
+#include "device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace device {
+namespace cpu {
+void OneHotCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+  if (output_shape.size() < 2) {
+    MS_LOG(EXCEPTION) << "invalid output shape size: " << output_shape.size();
+  }
+  int axis = AnfAlgo::GetNodeAttr<int>(kernel_node, AXIS);
+  if (axis != -1 && IntToSize(axis) >= output_shape.size()) {
+    MS_LOG(EXCEPTION) << "invalid axis: " << axis;
+  }
+  if (axis == -1) {
+    axis_ = output_shape.size() - 1;
+  } else {
+    axis_ = IntToSize(axis);
+  }
+  depth_ = output_shape[axis_];
+  stride_ = 1;
+  for (size_t i = axis_ + 1; i < output_shape.size(); ++i) {
+    stride_ *= output_shape[i];
+  }
+}
+
+bool OneHotCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                             const std::vector<kernel::AddressPtr> & /*workspace*/,
+                             const std::vector<kernel::AddressPtr> &outputs) {
+  if (inputs.size() < 3 || outputs.empty()) {
+    MS_LOG(EXCEPTION) << "input or output invalid!";
+  }
+  auto indices = reinterpret_cast<int *>(inputs[0]->addr);
+  auto on_value = reinterpret_cast<float *>(inputs[1]->addr)[0];
+  auto off_value = reinterpret_cast<float *>(inputs[2]->addr)[0];
+  auto output = reinterpret_cast<float *>(outputs[0]->addr);
+  size_t elem_num = inputs[0]->size / sizeof(int);
+
+  for (size_t i = 0; i < elem_num; i++) {
+    size_t stride_num = i / stride_;
+    size_t output_index = stride_num * depth_ * stride_ + i % stride_;
+    size_t index = IntToSize(indices[i]);
+    for (size_t j = 0; j < depth_; j++) {
+      if (index == j) {
+        output[output_index] = on_value;
+      } else {
+        output[output_index] = off_value;
+      }
+      output_index += stride_;
+    }
+  }
+
+  return true;
+}
+}  // namespace cpu
+}  // namespace device
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.h b/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.h
new file mode 100644
index 0000000000..f41ac63265
--- /dev/null
+++ b/mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.h
@@ -0,0 +1,47 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include "device/cpu/cpu_kernel.h"
+#include "device/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace device {
+namespace cpu {
+class OneHotCPUKernel : public CPUKernel {
+ public:
+  OneHotCPUKernel() = default;
+  ~OneHotCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  size_t depth_;
+  size_t stride_;
+  size_t axis_;
+};
+
+MS_REG_CPU_KERNEL(OneHot, OneHotCPUKernel);
+}  // namespace cpu
+}  // namespace device
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_
diff --git a/mindspore/ccsrc/device/cpu/kernel/reshape_cpu_kernel.h b/mindspore/ccsrc/device/cpu/kernel/reshape_cpu_kernel.h
index 908c3df2d9..d371e3a7ac 100644
--- a/mindspore/ccsrc/device/cpu/kernel/reshape_cpu_kernel.h
+++ b/mindspore/ccsrc/device/cpu/kernel/reshape_cpu_kernel.h
@@ -35,6 +35,8 @@ class ReshapeCPUKernel : public CPUKernel {
 };
 
 MS_REG_CPU_KERNEL(Reshape, ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(Flatten, ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(ExpandDims, ReshapeCPUKernel);
 }  // namespace cpu
 }  // namespace device
 }  // namespace mindspore
diff --git a/tests/st/ops/cpu/test_one_hot_op.py b/tests/st/ops/cpu/test_one_hot_op.py
new file mode 100644
index 0000000000..3f2c54b3cb
--- /dev/null
+++ b/tests/st/ops/cpu/test_one_hot_op.py
@@ -0,0 +1,82 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+
+context.set_context(device_target='CPU')
+
+
+class NetOneHot(nn.Cell):
+    def __init__(self):
+        super(NetOneHot, self).__init__()
+        self.on_value = 2.0
+        self.off_value = 3.0
+
+        self.depth_1 = 6
+        self.one_hot_1 = nn.OneHot(-1, self.depth_1, self.on_value, self.off_value)
+
+        self.depth_2 = 4
+        self.one_hot_2 = nn.OneHot(0, self.depth_1, self.on_value, self.off_value)
+        self.one_hot_3 = nn.OneHot(0, self.depth_2, self.on_value, self.off_value)
+        self.one_hot_4 = nn.OneHot(1, self.depth_1, self.on_value, self.off_value)
+
+    @ms_function
+    def construct(self, indices1, indices2, indices3, indices4):
+        return (self.one_hot_1(indices1), self.one_hot_2(indices2),
+                self.one_hot_3(indices3), self.one_hot_4(indices4))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_one_hot():
+    one_hot = NetOneHot()
+    indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
+    indices2 = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(np.int32))
+    indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
+    output = one_hot(indices1, indices2, indices3, indices4)
+    expect_0 = np.array([
+        [[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]],
+        [[3., 3., 3., 3., 2., 3.], [3., 3., 3., 3., 3., 2.]],
+        [[3., 3., 2., 3., 3., 3.], [3., 3., 3., 3., 3., 3.]]
+    ]).astype(np.float32)
+    expect_1 = np.array([
+        [3., 3., 3.],
+        [2., 3., 3.],
+        [3., 2., 3.],
+        [3., 3., 2.],
+        [3., 3., 3.],
+        [3., 3., 3.]
+    ]).astype(np.float32)
+    expect_2 = np.array([
+        [[2., 3.], [3., 2.]], [[3., 2.], [2., 3.]], [[3., 3.], [3., 3.]],
+        [[3., 3.], [3., 3.]]
+    ]).astype(np.float32)
+    expect_3 = np.array([
+        [[2., 3.], [3., 2.], [3., 3.], [3., 3.], [3., 3.], [3., 3.]],
+        [[3., 3.], [3., 3.], [3., 3.], [3., 3.], [2., 3.], [3., 2.]],
+        [[3., 3.], [3., 3.], [2., 3.], [3., 3.], [3., 3.], [3., 3.]]
+    ]).astype(np.float32)
+    assert (output[0].asnumpy() == expect_0).all()
+    assert (output[1].asnumpy() == expect_1).all()
+    assert (output[2].asnumpy() == expect_2).all()
+    assert (output[3].asnumpy() == expect_3).all()

From 638b11909907338fa7acb0a59130c2f6650eb915 Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Fri, 17 Apr 2020 16:05:21 +0800
Subject: [PATCH 349/367] windows sqlite patch

---
 CMakeLists.txt                                |   3 +
 cmake/external_libs/sqlite.cmake              |  22 ++-
 cmake/package.cmake                           |   1 +
 cmake/utils.cmake                             |  10 +-
 .../patch/sqlite/sqlite.windows.patch001      | 133 ++++++++++++++++++
 5 files changed, 152 insertions(+), 17 deletions(-)
 create mode 100644 third_party/patch/sqlite/sqlite.windows.patch001

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9c6ee73687..46804c8dde 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -20,6 +20,9 @@ set(PYBIND11_CPP_STANDARD -std=c++17)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPTION_CXX_FLAGS}")
 
 find_package(Threads)
+find_package(Patch)
+message(PATCH_EXECUTABLE = ${Patch_EXECUTABLE})
+
 include(${CMAKE_SOURCE_DIR}/cmake/mind_expression.cmake)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include)
diff --git a/cmake/external_libs/sqlite.cmake b/cmake/external_libs/sqlite.cmake
index 69692f284d..aa6f236917 100644
--- a/cmake/external_libs/sqlite.cmake
+++ b/cmake/external_libs/sqlite.cmake
@@ -1,18 +1,12 @@
 if (WIN32)
-    mindspore_add_pkg(sqlite-head
-            VER 3.31.1
-            HEAD_ONLY ./
-            URL https://sqlite.org/2020/sqlite-amalgamation-3310100.zip
-            MD5 2b7bfcdd97dc281903a9aee966213fe4)
-    include_directories(${sqlite-head_INC})
     mindspore_add_pkg(sqlite
-            VER 3.31.1
-            LIBS sqlite3
-            LIB_PATH ./
-            HEAD_ONLY ./
-            RELEASE ON
-            URL https://sqlite.org/2020/sqlite-dll-win64-x64-3310100.zip
-            MD5 662c9d2b05467d590ba5c0443e7fd6bd)
+        VER 3.31.1
+        LIBS sqlite3
+        URL https://sqlite.org/2020/sqlite-amalgamation-3310100.zip
+        MD5 2b7bfcdd97dc281903a9aee966213fe4
+        PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/sqlite/sqlite.windows.patch001
+        CMAKE_OPTION " "
+    )
 
 else ()
     set(sqlite_USE_STATIC_LIBS ON) 
@@ -30,7 +24,7 @@ else ()
         MD5 5f4e7b4016c15f4fb5855615279819da
         PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/sqlite/sqlite.patch001
         CONFIGURE_COMMAND ./configure --enable-shared=no --disable-tcl --disable-editline --enable-json1)
-    include_directories(${sqlite_INC})
 endif ()
 
+include_directories(${sqlite_INC})
 add_library(mindspore::sqlite ALIAS sqlite::sqlite3)
diff --git a/cmake/package.cmake b/cmake/package.cmake
index 7d1fdc6d8a..531dff29ca 100644
--- a/cmake/package.cmake
+++ b/cmake/package.cmake
@@ -38,6 +38,7 @@ if (CMAKE_SYSTEM_NAME MATCHES "Windows")
     set(glog_LIBPATH ${glog_LIBPATH}/../bin/)
     set(opencv_LIBPATH ${opencv_LIBPATH}/../bin/)
     set(jpeg_turbo_LIBPATH ${jpeg_turbo_LIBPATH}/../bin/)
+    set(sqlite_LIBPATH ${sqlite_LIBPATH}/../bin/)
 else ()
     set(INSTALL_LIB_DIR "lib")
 endif ()
diff --git a/cmake/utils.cmake b/cmake/utils.cmake
index 21a766dc8c..894a0de1b8 100644
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@@ -282,12 +282,16 @@ function(mindspore_add_pkg pkg_name )
     message("${pkg_name}_SOURCE_DIR : ${${pkg_name}_SOURCE_DIR}")
 
     foreach(_PATCH_FILE ${PKG_PATCHES})
-        message("patching ${${pkg_name}_SOURCE_DIR} -p1 < ${_PATCH_FILE}")
-        execute_process(COMMAND patch -p1 INPUT_FILE ${_PATCH_FILE}
+        get_filename_component(_PATCH_FILE_NAME ${_PATCH_FILE} NAME)
+        set(_LF_PATCH_FILE ${CMAKE_BINARY_DIR}/_ms_patch/${_PATCH_FILE_NAME})
+        configure_file(${_PATCH_FILE} ${_LF_PATCH_FILE} NEWLINE_STYLE LF)
+
+        message("patching ${${pkg_name}_SOURCE_DIR} -p1 < ${_LF_PATCH_FILE}")
+        execute_process(COMMAND ${Patch_EXECUTABLE} -p1 INPUT_FILE ${_LF_PATCH_FILE}
                 WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}
                 RESULT_VARIABLE Result)
         if(NOT Result EQUAL "0")
-            message(FATAL_ERROR "Failed patch: ${_PATCH_FILE}")
+            message(FATAL_ERROR "Failed patch: ${_LF_PATCH_FILE}")
         endif()
     endforeach(_PATCH_FILE)
         
diff --git a/third_party/patch/sqlite/sqlite.windows.patch001 b/third_party/patch/sqlite/sqlite.windows.patch001
new file mode 100644
index 0000000000..f92548e15e
--- /dev/null
+++ b/third_party/patch/sqlite/sqlite.windows.patch001
@@ -0,0 +1,133 @@
+diff -uprN sqlite-amalgamation-3310100/CMakeLists.txt sqlite-patch/CMakeLists.txt
+--- sqlite-amalgamation-3310100/CMakeLists.txt	1970-01-01 08:00:00.000000000 +0800
++++ sqlite-patch/CMakeLists.txt	2020-04-18 09:16:28.258637600 +0800
+@@ -0,0 +1,6 @@
++cmake_minimum_required(VERSION 3.14)
++project (Sqlite[C])
++add_library(sqlite3 SHARED sqlite3.c)
++set_target_properties(sqlite3 PROPERTIES PUBLIC_HEADER "sqlite3.h;sqlite3ext.h")
++include(GNUInstallDirs)
++install(TARGETS sqlite3 PUBLIC_HEADER)
+diff -uprN sqlite-amalgamation-3310100/sqlite3.c sqlite-patch/sqlite3.c
+--- sqlite-amalgamation-3310100/sqlite3.c	2020-01-28 03:25:14.000000000 +0800
++++ sqlite-patch/sqlite3.c	2020-04-17 15:40:21.005440300 +0800
+@@ -1167,7 +1167,7 @@ extern "C" {
+ */
+ #define SQLITE_VERSION        "3.31.1"
+ #define SQLITE_VERSION_NUMBER 3031001
+-#define SQLITE_SOURCE_ID      "2020-01-27 19:55:54 3bfa9cc97da10598521b342961df8f5f68c7388fa117345eeb516eaa837bb4d6"
++#define SQLITE_SOURCE_ID      "2020-02-17 19:25:07 387240fc85ea3549ff8a6ed060ef07c6184548457fb91cd7c6fc39ddb678alt1"
+ 
+ /*
+ ** CAPI3REF: Run-Time Library Version Numbers
+@@ -17428,8 +17428,11 @@ struct Table {
+ */
+ #ifndef SQLITE_OMIT_VIRTUALTABLE
+ #  define IsVirtual(X)      ((X)->nModuleArg)
++#  define ExprIsVtab(X)  \
++              ((X)->op==TK_COLUMN && (X)->y.pTab!=0 && (X)->y.pTab->nModuleArg)
+ #else
+ #  define IsVirtual(X)      0
++#  define ExprIsVtab(X)     0
+ #endif
+ 
+ /*
+@@ -104133,19 +104136,25 @@ static int impliesNotNullRow(Walker *pWa
+     case TK_LT:
+     case TK_LE:
+     case TK_GT:
+-    case TK_GE:
++    case TK_GE: {
++      Expr *pLeft = pExpr->pLeft;
++      Expr *pRight = pExpr->pRight;
+       testcase( pExpr->op==TK_EQ );
+       testcase( pExpr->op==TK_NE );
+       testcase( pExpr->op==TK_LT );
+       testcase( pExpr->op==TK_LE );
+       testcase( pExpr->op==TK_GT );
+       testcase( pExpr->op==TK_GE );
+-      if( (pExpr->pLeft->op==TK_COLUMN && IsVirtual(pExpr->pLeft->y.pTab))
+-       || (pExpr->pRight->op==TK_COLUMN && IsVirtual(pExpr->pRight->y.pTab))
++      /* The y.pTab=0 assignment in wherecode.c always happens after the
++      ** impliesNotNullRow() test */
++      if( (pLeft->op==TK_COLUMN && ALWAYS(pLeft->y.pTab!=0)
++                               && IsVirtual(pLeft->y.pTab))
++       || (pRight->op==TK_COLUMN && ALWAYS(pRight->y.pTab!=0)
++                               && IsVirtual(pRight->y.pTab))
+       ){
+-       return WRC_Prune;
++        return WRC_Prune;
+       }
+-
++    }
+     default:
+       return WRC_Continue;
+   }
+@@ -142591,7 +142600,8 @@ static int isAuxiliaryVtabOperator(
+     **       MATCH(expression,vtab_column)
+     */
+     pCol = pList->a[1].pExpr;
+-    if( pCol->op==TK_COLUMN && IsVirtual(pCol->y.pTab) ){
++    testcase( pCol->op==TK_COLUMN && pCol->y.pTab==0 );
++    if( ExprIsVtab(pCol) ){
+       for(i=0; i<ArraySize(aOp); i++){
+         if( sqlite3StrICmp(pExpr->u.zToken, aOp[i].zOp)==0 ){
+           *peOp2 = aOp[i].eOp2;
+@@ -142613,7 +142623,8 @@ static int isAuxiliaryVtabOperator(
+     ** with function names in an arbitrary case.
+     */
+     pCol = pList->a[0].pExpr;
+-    if( pCol->op==TK_COLUMN && IsVirtual(pCol->y.pTab) ){
++    testcase( pCol->op==TK_COLUMN && pCol->y.pTab==0 );
++    if( ExprIsVtab(pCol) ){
+       sqlite3_vtab *pVtab;
+       sqlite3_module *pMod;
+       void (*xNotUsed)(sqlite3_context*,int,sqlite3_value**);
+@@ -142636,10 +142647,12 @@ static int isAuxiliaryVtabOperator(
+     int res = 0;
+     Expr *pLeft = pExpr->pLeft;
+     Expr *pRight = pExpr->pRight;
+-    if( pLeft->op==TK_COLUMN && IsVirtual(pLeft->y.pTab) ){
++    testcase( pLeft->op==TK_COLUMN && pLeft->y.pTab==0 );
++    if( ExprIsVtab(pLeft) ){
+       res++;
+     }
+-    if( pRight && pRight->op==TK_COLUMN && IsVirtual(pRight->y.pTab) ){
++    testcase( pRight && pRight->op==TK_COLUMN && pRight->y.pTab==0 );
++    if( pRight && ExprIsVtab(pRight) ){
+       res++;
+       SWAP(Expr*, pLeft, pRight);
+     }
+@@ -223667,7 +223680,7 @@ static void fts5SourceIdFunc(
+ ){
+   assert( nArg==0 );
+   UNUSED_PARAM2(nArg, apUnused);
+-  sqlite3_result_text(pCtx, "fts5: 2020-01-27 19:55:54 3bfa9cc97da10598521b342961df8f5f68c7388fa117345eeb516eaa837bb4d6", -1, SQLITE_TRANSIENT);
++  sqlite3_result_text(pCtx, "fts5: 2020-02-17 19:25:07 abc473fb8fb999005dc79a360e34f97b3b25429decf1820dd2afa5c19577753d", -1, SQLITE_TRANSIENT);
+ }
+ 
+ /*
+@@ -228440,9 +228453,9 @@ SQLITE_API int sqlite3_stmt_init(
+ #endif /* !defined(SQLITE_CORE) || defined(SQLITE_ENABLE_STMTVTAB) */
+ 
+ /************** End of stmt.c ************************************************/
+-#if __LINE__!=228443
++#if __LINE__!=228456
+ #undef SQLITE_SOURCE_ID
+-#define SQLITE_SOURCE_ID      "2020-01-27 19:55:54 3bfa9cc97da10598521b342961df8f5f68c7388fa117345eeb516eaa837balt2"
++#define SQLITE_SOURCE_ID      "2020-02-17 19:25:07 387240fc85ea3549ff8a6ed060ef07c6184548457fb91cd7c6fc39ddb678alt2"
+ #endif
+ /* Return the source-id for this library */
+ SQLITE_API const char *sqlite3_sourceid(void){ return SQLITE_SOURCE_ID; }
+diff -uprN sqlite-amalgamation-3310100/sqlite3.h sqlite-patch/sqlite3.h
+--- sqlite-amalgamation-3310100/sqlite3.h	2020-01-28 03:25:14.000000000 +0800
++++ sqlite-patch/sqlite3.h	2020-04-17 15:40:21.005440300 +0800
+@@ -125,7 +125,7 @@ extern "C" {
+ */
+ #define SQLITE_VERSION        "3.31.1"
+ #define SQLITE_VERSION_NUMBER 3031001
+-#define SQLITE_SOURCE_ID      "2020-01-27 19:55:54 3bfa9cc97da10598521b342961df8f5f68c7388fa117345eeb516eaa837bb4d6"
++#define SQLITE_SOURCE_ID      "2020-02-17 19:25:07 387240fc85ea3549ff8a6ed060ef07c6184548457fb91cd7c6fc39ddb678alt1"
+ 
+ /*
+ ** CAPI3REF: Run-Time Library Version Numbers

From 5c9791a802507a7a387810845c65a36cd2a1b551 Mon Sep 17 00:00:00 2001
From: liuxiao <liuxiao93@huawei.com>
Date: Wed, 15 Apr 2020 17:50:34 +0800
Subject: [PATCH 350/367] Add Abs\AbsGrad\Sign\SmoothL1Loss\SmoothL1LossGrad
 and modify TopKV2->TopK for VM

---
 mindspore/ccsrc/kernel/tbe/tbe_adapter.cc     |  1 -
 mindspore/ops/_op_impl/tbe/__init__.py        |  7 ++-
 mindspore/ops/_op_impl/tbe/abs.py             | 41 ++++++++++++++
 mindspore/ops/_op_impl/tbe/abs_grad.py        | 44 +++++++++++++++
 mindspore/ops/_op_impl/tbe/sign.py            | 41 ++++++++++++++
 mindspore/ops/_op_impl/tbe/smooth_l1_loss.py  | 44 +++++++++++++++
 .../ops/_op_impl/tbe/smooth_l1_loss_grad.py   | 45 +++++++++++++++
 .../ops/_op_impl/tbe/{topkv2.py => top_k.py}  | 14 ++---
 mindspore/ops/op_info_register.py             |  1 +
 .../test_tbe_ops/test_smooth_l1_loss.py       | 42 ++++++++++++++
 .../test_tbe_ops/test_smooth_l1_loss_grad.py  | 55 +++++++++++++++++++
 .../{test_topkv2.py => test_topk.py}          |  2 +-
 12 files changed, 327 insertions(+), 10 deletions(-)
 create mode 100644 mindspore/ops/_op_impl/tbe/abs.py
 create mode 100644 mindspore/ops/_op_impl/tbe/abs_grad.py
 create mode 100644 mindspore/ops/_op_impl/tbe/sign.py
 create mode 100644 mindspore/ops/_op_impl/tbe/smooth_l1_loss.py
 create mode 100644 mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py
 rename mindspore/ops/_op_impl/tbe/{topkv2.py => top_k.py} (86%)
 create mode 100644 tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss.py
 create mode 100644 tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss_grad.py
 rename tests/st/ops/davinci/test_tbe_ops/{test_topkv2.py => test_topk.py} (97%)

diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
index 9e4553e057..3fda554759 100644
--- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
+++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
@@ -42,7 +42,6 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"depthwise_conv2d_native", "depthwise_conv2d"},
   {"depthwise_conv2d_native_backprop_filter", "depthwise_conv2d_backprop_filter_d"},
   {"depthwise_conv2d_native_backprop_input", "depthwise_conv2d_backprop_input_d"},
-  {"top_kv2", "top_k"},
   {"scatter_nd", "scatter_nd_d"},
   {"tile", "tile_d"},
   {"gather_v2", "gather_v2_d"},
diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py
index 340cf9efe3..2cffc37491 100644
--- a/mindspore/ops/_op_impl/tbe/__init__.py
+++ b/mindspore/ops/_op_impl/tbe/__init__.py
@@ -14,6 +14,8 @@
 # ============================================================================
 
 """tbe ops"""
+from .abs import _abs_tbe
+from .abs_grad import _abs_grad_tbe
 from .adam_apply_one_with_decay import _adam_apply_one_with_decay_tbe
 from .add import _add_tbe
 from .add_n import _add_n_tbe
@@ -49,7 +51,7 @@ from .sigmoid_cross_entropy_with_logits import _sigmoid_cross_entropy_with_logit
 from .sigmoid_cross_entropy_with_logits_grad import _sigmoid_cross_entropy_with_logits_grad_tbe
 from .tensor_add import _tensor_add_tbe
 from .trans_data import _trans_data_tbe
-from .topkv2 import _topk_v2_tbe
+from .top_k import _top_k_tbe
 from .matmul import _matmul_tbe
 from .sub import _sub_tbe
 from .reduce_mean_d import _reduce_mean_d_tbe
@@ -107,6 +109,7 @@ from .minimum_grad import _minimum_grad_tbe
 from .maximum_grad import _maximum_grad_tbe
 from .concat import _concat_tbe
 from .slice import _slice_tbe
+from .sign import _sign_tbe
 from .greater import _greater_tbe
 from .clip_by_norm_no_div_sum import _clip_by_norm_no_div_sum_tbe
 from .clip_by_value import _clip_by_value_tbe
@@ -130,6 +133,8 @@ from .resize_nearest_neighbor_grad_d import _resize_nearest_neighbor_grad_d_tbe
 from .pad_d import _pad_d_tbe
 from .arg_max_with_value import _arg_max_with_value_tbe
 from .arg_min_with_value import _arg_min_with_value_tbe
+from .smooth_l1_loss import _smooth_l1_loss_tbe
+from .smooth_l1_loss_grad import _smooth_l1_loss_grad_tbe
 from .fused_mul_add import _fused_mul_add_tbe
 from .fused_mul_add_n import _fused_mul_add_n_tbe
 from .fused_mul_apply_momentum import _fused_mul_apply_momentum_tbe
diff --git a/mindspore/ops/_op_impl/tbe/abs.py b/mindspore/ops/_op_impl/tbe/abs.py
new file mode 100644
index 0000000000..30a75812bd
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/abs.py
@@ -0,0 +1,41 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Abs op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+abs_op_info = TBERegOp("Abs") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("abs.so") \
+    .compute_cost(10) \
+    .kernel_name("abs") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .input(0, "x", None, "required", None) \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(abs_op_info)
+def _abs_tbe():
+    """Abs TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/abs_grad.py b/mindspore/ops/_op_impl/tbe/abs_grad.py
new file mode 100644
index 0000000000..ba630f6570
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/abs_grad.py
@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""AbsGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+abs_grad_op_info = TBERegOp("AbsGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("abs_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("abs_grad") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .input(0, "y", None, "required", None) \
+    .input(1, "dy", None, "required", None) \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(abs_grad_op_info)
+def _abs_grad_tbe():
+    """AbsGrad TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/sign.py b/mindspore/ops/_op_impl/tbe/sign.py
new file mode 100644
index 0000000000..823715aa9f
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/sign.py
@@ -0,0 +1,41 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Sign op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+sign_op_info = TBERegOp("Sign") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("sign.so") \
+    .compute_cost(10) \
+    .kernel_name("sign") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .input(0, "x", None, "required", None) \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(sign_op_info)
+def _sign_tbe():
+    """Sign TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py b/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py
new file mode 100644
index 0000000000..3723b30c04
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py
@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""SmoothL1Loss op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+smooth_l1_loss_op_info = TBERegOp("SmoothL1Loss") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("smooth_l1_loss.so") \
+    .compute_cost(10) \
+    .kernel_name("smooth_l1_loss") \
+    .partial_flag(True) \
+    .attr("sigma", "required", "float", "all") \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "label", False, "required", "all") \
+    .output(0, "loss", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
+
+
+@op_info_register(smooth_l1_loss_op_info)
+def _smooth_l1_loss_tbe():
+    """SmoothL1Loss TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py b/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py
new file mode 100644
index 0000000000..fa1ae1ec34
--- /dev/null
+++ b/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py
@@ -0,0 +1,45 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""SmoothL1LossGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+smooth_l1_loss_grad_op_info = TBERegOp("SmoothL1LossGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("smooth_l1_loss_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("smooth_l1_loss_grad") \
+    .partial_flag(True) \
+    .attr("sigma", "required", "float", "all") \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "label", False, "required", "all") \
+    .input(2, "dout", False, "required", "all") \
+    .output(0, "loss", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
+
+
+@op_info_register(smooth_l1_loss_grad_op_info)
+def _smooth_l1_loss_grad_tbe():
+    """SmoothL1LossGrad TBE register"""
+    return
diff --git a/mindspore/ops/_op_impl/tbe/topkv2.py b/mindspore/ops/_op_impl/tbe/top_k.py
similarity index 86%
rename from mindspore/ops/_op_impl/tbe/topkv2.py
rename to mindspore/ops/_op_impl/tbe/top_k.py
index a03871f8b7..92733bbf46 100644
--- a/mindspore/ops/_op_impl/tbe/topkv2.py
+++ b/mindspore/ops/_op_impl/tbe/top_k.py
@@ -13,15 +13,15 @@
 # limitations under the License.
 # ============================================================================
 
-"""TopKV2 op"""
+"""TopK op"""
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-top_k_v2_op_info = TBERegOp("TopKV2") \
+top_k_op_info = TBERegOp("TopK") \
     .fusion_type("OPAQUE") \
     .async_flag(False) \
-    .binfile_name("top_k_v2.so") \
+    .binfile_name("top_k.so") \
     .compute_cost(10) \
-    .kernel_name("top_k_v2") \
+    .kernel_name("top_k") \
     .partial_flag(True) \
     .attr("k", "required", "int", "all")\
     .attr("sorted", "required", "bool", "all")\
@@ -33,7 +33,7 @@ top_k_v2_op_info = TBERegOp("TopKV2") \
     .get_op_info()
 
 
-@op_info_register(top_k_v2_op_info)
-def _topk_v2_tbe():
-    """TopKV2 TBE register"""
+@op_info_register(top_k_op_info)
+def _top_k_tbe():
+    """TopK TBE register"""
     return
diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py
index 28821b621e..e4b0bfdbfe 100644
--- a/mindspore/ops/op_info_register.py
+++ b/mindspore/ops/op_info_register.py
@@ -599,3 +599,4 @@ class DataType:
     F32_NCHW = ("float32", "NCHW")
     F32_NHWC = ("float32", "NHWC")
     F32_HWCN = ("float32", "HWCN")
+ 
\ No newline at end of file
diff --git a/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss.py b/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss.py
new file mode 100644
index 0000000000..cc0c0e0fc2
--- /dev/null
+++ b/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss.py
@@ -0,0 +1,42 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, sigma=1.0):
+        super(Net, self).__init__()
+        self.SmoothL1Loss = P.SmoothL1Loss(sigma)
+
+    def construct(self, pred, gt):
+        return self.SmoothL1Loss(pred, gt)
+
+
+def test_net():
+    pred = np.random.randn(2, 4).astype(np.float32)
+    gt = np.random.randn(2, 4).astype(np.float32)
+    smooth_l1_loss = Net()
+    loss = smooth_l1_loss(Tensor(pred), Tensor(gt))
+    print("------------- input ---------------")
+    print("predict:\n", pred)
+    print("grount truth:\n", gt)
+    print("------------- output ---------------")
+    print("loss:\n", loss.asnumpy())
diff --git a/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss_grad.py b/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss_grad.py
new file mode 100644
index 0000000000..1ab9d998a1
--- /dev/null
+++ b/tests/st/ops/davinci/test_tbe_ops/test_smooth_l1_loss_grad.py
@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore.ops.composite import GradOperation
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, sigma=1.0):
+        super(Net, self).__init__()
+        self.SmoothL1Loss = P.SmoothL1Loss(sigma)
+
+    def construct(self, pred, gt):
+        return self.SmoothL1Loss(pred, gt)
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
+        self.network = network
+
+    def construct(self, pred, gt, dout):
+        return self.grad(self.network)(pred, gt, dout)
+
+
+def test_net():
+    pred = np.random.randn(2, 4).astype(np.float32)
+    gt = np.random.randn(2, 4).astype(np.float32)
+    dout = np.random.randn(2, 4).astype(np.float32)
+    smooth_l1_loss_grad = Grad(Net())
+    output = smooth_l1_loss_grad(Tensor(pred), Tensor(gt), Tensor(dout))
+    print("------------- input ---------------")
+    print("predict:\n", pred)
+    print("grount truth:\n", gt)
+    print("dout:\n", dout)
+    print("------------- output ---------------")
+    print("predict grad:\n", output[0].asnumpy())
diff --git a/tests/st/ops/davinci/test_tbe_ops/test_topkv2.py b/tests/st/ops/davinci/test_tbe_ops/test_topk.py
similarity index 97%
rename from tests/st/ops/davinci/test_tbe_ops/test_topkv2.py
rename to tests/st/ops/davinci/test_tbe_ops/test_topk.py
index a505865637..275ef50038 100644
--- a/tests/st/ops/davinci/test_tbe_ops/test_topkv2.py
+++ b/tests/st/ops/davinci/test_tbe_ops/test_topk.py
@@ -24,7 +24,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 class Net(nn.Cell):
     def __init__(self, k):
         super(Net, self).__init__()
-        self.topk = P.TopK()
+        self.topk = P.TopK(True)
         self.k = k
 
     def construct(self, x):

From 8a2a1eb310560d5ce1d4ceba5dccf6cf530b3695 Mon Sep 17 00:00:00 2001
From: simson <526422051@qq.com>
Date: Sat, 18 Apr 2020 15:31:50 +0800
Subject: [PATCH 351/367] fix the error in example of mindspore.model

---
 mindspore/train/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 46e4f421f7..a9f59207bb 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -71,7 +71,7 @@ class Model:
         >>>         self.bn = nn.BatchNorm2d(64)
         >>>         self.relu = nn.ReLU()
         >>>         self.flatten = nn.Flatten()
-        >>>         self.fc = nn.Dense(64*222*222, 3) # padding=0
+        >>>         self.fc = nn.Dense(64*224*224, 12) # padding=0
         >>>
         >>>     def construct(self, x):
         >>>         x = self.conv(x)

From d4822137f36c706ced937b6c4ee5028f77cbe181 Mon Sep 17 00:00:00 2001
From: xiefangqi <xiefangqi2@huawei.com>
Date: Sat, 18 Apr 2020 15:21:06 +0800
Subject: [PATCH 352/367] fix segment bug on windows

---
 mindspore/ccsrc/CMakeLists.txt                | 20 +++++------------
 mindspore/ccsrc/dataset/CMakeLists.txt        |  7 ++++--
 .../dataset/engine/datasetops/dataset_op.cc   |  4 ++++
 .../ccsrc/dataset/engine/datasetops/map_op.cc | 22 +++++++++++++++++++
 .../ccsrc/dataset/engine/datasetops/map_op.h  |  4 ++++
 mindspore/ccsrc/mindrecord/CMakeLists.txt     |  2 +-
 6 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt
index e227969162..8c33b9051c 100644
--- a/mindspore/ccsrc/CMakeLists.txt
+++ b/mindspore/ccsrc/CMakeLists.txt
@@ -156,7 +156,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         )
 if (ENABLE_CPU)
     list(REMOVE_ITEM MINDSPORE_SRC_LIST "device/gpu/distribution/collective_init.cc")
-    if (WIN32)
+    if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
         list(REMOVE_ITEM MINDSPORE_SRC_LIST "kernel/kernel_query.cc")
     endif()
 endif()
@@ -337,19 +337,19 @@ if (ENABLE_D)
     target_link_libraries(mindspore mindspore::protobuf)
 endif()
 
-if (WIN32)
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
     target_link_libraries(mindspore ${PYTHON_LIBRARIES} mindspore_gvar)
 endif()
 
 # set c_expression building
-if (WIN32)
-set(PYTHON_MODULE_SOURCE ${MS_GVAR_SRC_LIST}
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
+    set(PYTHON_MODULE_SOURCE ${MS_GVAR_SRC_LIST}
         pipeline/init.cc
         kernel/oplib/oplib.cc
         ${MINDSPORE_SRC_LIST} ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
         ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST})
 else()
-set(PYTHON_MODULE_SOURCE
+    set(PYTHON_MODULE_SOURCE
         pipeline/init.cc
         kernel/oplib/oplib.cc
         ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST}
@@ -426,13 +426,5 @@ endif()
 
 if(ENABLE_MINDDATA)
     add_subdirectory(mindrecord)
-    if (WIN32)
-	set(_md_tmp_CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE})
-	set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O0 -Wl,--allow-shlib-undefined -DHALF_ENABLE_CPP11_USER_LITERALS=0")
-	set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -D_FORTIFY_SOURCE=2")
-        add_subdirectory(dataset)
-	set(CMAKE_CXX_FLAGS_RELEASE ${_md_tmp_CMAKE_CXX_FLAGS_RELEASE})
-    else()
-	add_subdirectory(dataset)
-    endif()
+    add_subdirectory(dataset)
 endif()
diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt
index b3ac34de70..0bc4065ac9 100644
--- a/mindspore/ccsrc/dataset/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/CMakeLists.txt
@@ -12,6 +12,9 @@ endif()
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-format")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-attributes")
 
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,--image-base -Wl,0x10000000")
+endif()
 ############################# Options ################################
 if (ENABLE_GPUQUE)
     add_definitions(-D ENABLE_GPUQUE)
@@ -80,7 +83,7 @@ set_target_properties(_c_dataengine PROPERTIES
 ######################################################################
 
 ################# Link with external libraries ########################
-if (WIN32)
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
     target_link_libraries(_c_dataengine PRIVATE mindspore)
     target_link_libraries(_c_dataengine PRIVATE mindspore::pybind11_module ${PYTHON_LIBRARIES} mindspore::protobuf ${SECUREC_LIBRARY})
 else()
@@ -101,7 +104,7 @@ if (ENABLE_TDTQUE)
 endif ()
 
 add_dependencies(_c_dataengine _c_mindrecord)
-if (WIN32)
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
     set(MINDRECORD_LINK_OBJECT ${CMAKE_BINARY_DIR}/mindspore/ccsrc/mindrecord/CMakeFiles/_c_mindrecord.dir/objects.a)
     target_link_libraries(_c_dataengine PRIVATE _c_mindrecord ${MINDRECORD_LINK_OBJECT} mindspore::sqlite)
 else()
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
index 7edf1dd288..5e3ea3dc44 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc
@@ -109,11 +109,15 @@ void DatasetOp::Print(std::ostream &out, bool show_all) const {
 
 // Gets the next buffer from the given child
 Status DatasetOp::GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t worker_id, bool retry_if_eoe) {
+#if defined(_WIN32) || defined(_WIN64)
+  RETURN_IF_NOT_OK(out_connector_->PopWithRetry(static_cast<int>(worker_id), p_buffer, retry_if_eoe));
+#else
   std::unique_ptr<DataBuffer> next_buff;
   // pop is a blocked call and will throw an interruption if the whole group shuts down.
   RETURN_IF_NOT_OK(out_connector_->PopWithRetry(static_cast<int>(worker_id), &next_buff, retry_if_eoe));
 
   *p_buffer = std::move(next_buff);
+#endif
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
index b6d603bac9..3f8d70b606 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc
@@ -65,6 +65,9 @@ MapOp::MapOp(const std::vector<std::string> &in_col_names, const std::vector<std
       tfuncs_(std::move(tensor_funcs)),
       in_columns_(in_col_names),
       out_columns_(out_col_names),
+#if defined(_WIN32) || defined(_WIN64)
+      eof_worker_id_(0),
+#endif
       perf_mode_(perf_mode) {
   // If caller didn't specify the out_col_names, assume they are same as the in_columns.
   if (out_columns_.empty() || out_columns_[0].empty()) {
@@ -120,6 +123,17 @@ Status MapOp::operator()() {
       RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buff, 0));
       is_eof = buff->eof();
       RETURN_IF_NOT_OK(local_queues_[que_id]->Add(std::move(buff)));
+#if defined(_WIN32) || defined(_WIN64)
+      if (is_eof) {
+        eof_worker_id_ = que_id;
+        for (int32_t id = 0; id < num_workers_; id++) {
+          if (id != eof_worker_id_) {
+            auto eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+            RETURN_IF_NOT_OK(local_queues_[id]->Add(std::move(eof_buffer)));
+          }
+        }
+      }
+#endif
       que_id = (que_id + 1) % num_workers_;
     }
   }
@@ -159,6 +173,14 @@ Status MapOp::WorkerEntry(int32_t worker_id) {
       continue;
     } else if (in_buffer->eof()) {
       // Calling base class EofReceived to forward eof buffer.
+#if defined(_WIN32) || defined(_Win64)
+      if (perf_mode_) {
+        if (eof_worker_id_ == worker_id) {
+          RETURN_IF_NOT_OK(EofReceived(worker_id));
+        }
+        break;
+      }
+#endif
       RETURN_IF_NOT_OK(EofReceived(worker_id));
       break;
     }
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/map_op.h b/mindspore/ccsrc/dataset/engine/datasetops/map_op.h
index 4c9d27f9c7..5e16bc3fed 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/map_op.h
+++ b/mindspore/ccsrc/dataset/engine/datasetops/map_op.h
@@ -193,6 +193,10 @@ class MapOp : public ParallelOp {
   // cause additional blocking because pop calls to Connector from the threads are synchronized to enforce the order.
   bool perf_mode_;
 
+#if defined(_WIN32) || defined(_WIN64)
+  // EOF worker id is only work on Performance mode, to record the worker id of queue which gets EOF
+  int32_t eof_worker_id_;
+#endif
   // Private function for worker/thread to loop continuously. It comprises the main
   // logic of MapOp: getting the data from previous Op, validating user specified column names,
   // applying a list of TensorOps to each of the data, process the results and then
diff --git a/mindspore/ccsrc/mindrecord/CMakeLists.txt b/mindspore/ccsrc/mindrecord/CMakeLists.txt
index a2b8897b16..fdd648a50f 100644
--- a/mindspore/ccsrc/mindrecord/CMakeLists.txt
+++ b/mindspore/ccsrc/mindrecord/CMakeLists.txt
@@ -26,7 +26,7 @@ set_target_properties(_c_mindrecord PROPERTIES
     )
 
 # add link library
-if (WIN32)
+if (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
     target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite mindspore mindspore::protobuf)
 else()
     target_link_libraries(_c_mindrecord PRIVATE mindspore::sqlite ${PYTHON_LIB} ${SECUREC_LIBRARY} mindspore mindspore_gvar mindspore::protobuf)

From 8c1939f1b04f642563204dafd1decec9f176c7c3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?=
 <yuhuijun1@huawei.com>
Date: Sat, 18 Apr 2020 16:09:40 +0800
Subject: [PATCH 353/367] fix examples issues

---
 mindspore/ops/__init__.py             | 2 ++
 mindspore/ops/operations/array_ops.py | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py
index 01ca039862..b73d683284 100644
--- a/mindspore/ops/__init__.py
+++ b/mindspore/ops/__init__.py
@@ -26,6 +26,8 @@ Note:
     - The Primitive operators in operations need to be used after instantiation.
     - The composite operators are pre-defined combination of operator.
     - The functional operators are the pre-instantiated Primitive operators, which can be used directly like a function.
+    - For functional operators usage, please refer to
+      https://gitee.com/mindspore/mindspore/blob/master/mindspore/ops/functional.py
 """
 
 from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 7605c2d1e8..4047a19b23 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -795,7 +795,7 @@ class ZerosLike(PrimitiveWithInfer):
 
     Examples:
         >>> zeroslike = P.ZerosLike()
-        >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
+        >>> x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
         >>> output = zeroslike(x)
     """
 

From ea6958c50a02c87350b26ec2080abc6650f1a045 Mon Sep 17 00:00:00 2001
From: Wei Luning <weiluning@huawei.com>
Date: Sun, 12 Apr 2020 23:18:04 +0800
Subject: [PATCH 354/367] add pattern AdjustAllReduceMulAdd

---
 mindspore/ccsrc/operator/ops.cc               |  1 +
 mindspore/ccsrc/operator/ops.h                |  1 +
 mindspore/ccsrc/optimizer/irpass.cc           |  2 +-
 .../optimizer/irpass/arithmetic_simplify.h    | 78 +++++++++++++++++++
 mindspore/ops/operations/array_ops.py         |  2 +-
 mindspore/ops/operations/nn_ops.py            |  2 +-
 tests/ut/cpp/optimizer/lib_test.cc            | 19 +++++
 .../gtest_input/optimizer/opt_test.py         | 45 ++++++++++-
 8 files changed, 145 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc
index f3053cac7d..8cf2d1290f 100755
--- a/mindspore/ccsrc/operator/ops.cc
+++ b/mindspore/ccsrc/operator/ops.cc
@@ -226,6 +226,7 @@ const PrimitivePtr kPrimIsNot = std::make_shared<Primitive>("is_not");
 const PrimitivePtr kPrimMirror = std::make_shared<Primitive>("_MirrorOperator");
 const PrimitivePtr kPrimVirtualDiv = std::make_shared<Primitive>("_VirtualDiv");
 const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDataset");
+const PrimitivePtr kPrimAllReduce = std::make_shared<Primitive>("AllReduce");
 
 // Debug ops
 const PrimitivePtr kPrimScalarSummary = std::make_shared<Primitive>("ScalarSummary");
diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h
index 2dc7072972..548980bf2d 100755
--- a/mindspore/ccsrc/operator/ops.h
+++ b/mindspore/ccsrc/operator/ops.h
@@ -231,6 +231,7 @@ extern const PrimitivePtr kPrimMinimumGrad;
 extern const PrimitivePtr kPrimMaximumGrad;
 
 // Comm ops
+extern const PrimitivePtr kPrimAllReduce;
 extern const PrimitivePtr kPrimMirror;
 extern const PrimitivePtr kPrimVirtualDiv;
 extern const PrimitivePtr kPrimVirtualDataset;
diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc
index 96d88f6e61..d64df33f99 100644
--- a/mindspore/ccsrc/optimizer/irpass.cc
+++ b/mindspore/ccsrc/optimizer/irpass.cc
@@ -48,7 +48,7 @@ namespace irpass {
 OptimizeIRPassLib::OptimizeIRPassLib() {
   arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify",
                                           {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd,
-                                           prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul});
+                                           prim::kPrimAddN, prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul});
   special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate",
                                            {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType,
                                             prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv});
diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
index ab191aab20..0d48fc1463 100644
--- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
+++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h
@@ -228,6 +228,82 @@ class ConstantDuplicateMul : public AnfVisitor {
   CNodePtr cnode_;
 };
 
+// grad = AllReduce(grad) / worker_number
+// grad = grad + weight * decy
+// ->
+// grad = grad + weight * decy
+// grad = AllReduce(grad) / worker_number
+
+// {prim::kPrimAddN, {prim::kPrimMakeTuple, {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}, Z}} ->
+// {prim::kPrimMul, {prim::kPrimAllReduce, {prim::kPrimAddN,{prim::kPrimMakeTuple, Z, X}}}, Y}
+class AdjustAllReduceMulAdd : public AnfVisitor {
+ public:
+  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
+    Reset();
+    // {prim::kPrimAddN, Zs}
+    if (!IsPrimitiveCNode(node, prim::kPrimAddN)) {
+      return nullptr;
+    }
+    auto addn = node->cast<CNodePtr>();
+    if (addn->size() != 2) {
+      return nullptr;
+    }
+
+    AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1));
+    if (x_ == nullptr || y_ == nullptr || z_ == nullptr) {
+      return nullptr;
+    }
+
+    auto fg = node->func_graph();
+    AnfNodePtr tuple = NewCNode({NewValueNode(prim::kPrimMakeTuple), z_, x_}, fg);
+    AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimAddN), tuple}, fg);
+    AnfNodePtr all_reduce = NewCNode({NewValueNode(prim::kPrimAllReduce), add}, fg);
+    return NewCNode({NewValueNode(prim::kPrimMul), all_reduce, y_}, fg);
+  }
+
+  void Visit(const AnfNodePtr &node) override {
+    if (level_ == 0) {
+      level_ = 1;
+      is_reduce_match_ = false;
+      // {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}
+      AnfVisitor::Match(prim::kPrimMul)(node);
+      level_ = 0;
+      if (is_reduce_match_) {
+        y_ = tmp_;
+      } else {
+        z_ = node;
+      }
+    }
+
+    if (level_ == 1) {
+      // {prim::kPrimAllReduce, X}
+      if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) {
+        auto cnode = node->cast<CNodePtr>();
+        if (cnode->size() > 1) {
+          x_ = cnode->input(1);
+          is_reduce_match_ = true;
+        }
+      } else {
+        tmp_ = node;
+      }
+    }
+  }
+
+  void Reset() {
+    level_ = 0;
+    is_reduce_match_ = false;
+    x_ = nullptr;
+    y_ = nullptr;
+    z_ = nullptr;
+    tmp_ = nullptr;
+  }
+
+ private:
+  int level_{0};
+  bool is_reduce_match_{false};
+  AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr};
+};
+
 class ArithmeticSimplify {
  public:
   ArithmeticSimplify()
@@ -243,6 +319,7 @@ class ArithmeticSimplify {
     eliminaters_.emplace_back(identity_);
     eliminaters_.emplace_back(opt_update_zero_tensor_);
     eliminaters_.emplace_back(constant_duplicate_mul_);
+    eliminaters_.emplace_back(adjust_allreduce_mul_add_);
   }
   ~ArithmeticSimplify() = default;
 
@@ -264,6 +341,7 @@ class ArithmeticSimplify {
   PrimEliminater identity_;
   OptUpdateZeroTensor opt_update_zero_tensor_;
   ConstantDuplicateMul constant_duplicate_mul_;
+  AdjustAllReduceMulAdd adjust_allreduce_mul_add_;
   std::vector<TransformFuncType> eliminaters_{};
 };
 }  // namespace irpass
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index a7c3f50440..b348e9a700 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1235,7 +1235,7 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
         Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`.
 
     Examples:
-        >>> input_x = Tensor([1, 2, 3, 4], mindspore.float)
+        >>> input_x = Tensor([1, 2, 3, 4], mindspore.float32)
         >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32)
         >>> num_segments = 4
         >>> P.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 62265162a9..3f24996b1b 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1572,7 +1572,7 @@ class LayerNorm(Primitive):
     `Layer Normalization <https://arxiv.org/abs/1607.06450>`_.
 
     .. math::
-        y = \frac{x - mean]}{\sqrt{variance + \epsilon}} * \gamma + \beta
+        y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
 
     where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
 
diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc
index 2d4cf0e78e..8e348c698a 100644
--- a/tests/ut/cpp/optimizer/lib_test.cc
+++ b/tests/ut/cpp/optimizer/lib_test.cc
@@ -556,5 +556,24 @@ TEST_F(TestOptLib, test_constant_duplicate_mul) {
   ASSERT_TRUE(CheckOpt(beforerl, after, patterns));
   ASSERT_TRUE(CheckOpt(beforerr, after, patterns));
 }
+
+TEST_F(TestOptLib, test_adjust_allreduce_mul_add) {
+  FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforell");
+  FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforelr");
+  FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerl");
+  FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerr");
+  FuncGraphPtr after1 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after1");
+  FuncGraphPtr before2r = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2r");
+  FuncGraphPtr before2l = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2l");
+  FuncGraphPtr after2 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after2");
+  auto patterns = std::vector<SubstitutionPtr>({irpass.arithmetic_simplify_});
+  ASSERT_TRUE(CheckOpt(beforell, after1, patterns));
+  ASSERT_TRUE(CheckOpt(beforelr, after1, patterns));
+  ASSERT_TRUE(CheckOpt(beforerl, after1, patterns));
+  ASSERT_TRUE(CheckOpt(beforerr, after1, patterns));
+  ASSERT_TRUE(CheckOpt(before2l, after2, patterns));
+  ASSERT_TRUE(CheckOpt(before2r, after2, patterns));
+}
+
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
index d494ad27d3..d74aa15952 100644
--- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
@@ -908,8 +908,8 @@ def test_print_tuple_wrapper(tag):
 
 def test_constant_duplicate_mul(tag):
     fns = FnDict()
-    Mul = Primitive('Mul');
-    Sqrt = Primitive('Sqrt');
+    Mul = Primitive('Mul')
+    Sqrt = Primitive('Sqrt')
 
     x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32'))
     tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
@@ -936,3 +936,44 @@ def test_constant_duplicate_mul(tag):
         return Mul(Sqrt(x), Mul(tensor1, tensor2))
 
     return fns[tag]
+
+
+def test_adjust_allreduce_mul_add(tag):
+    fns = FnDict()
+    Mul = Primitive('Mul')
+    AddN = Primitive('AddN')
+    AllReduce = Primitive('AllReduce')
+
+    @fns
+    def beforell(x, y, z):
+        return AddN((z, Mul(y, AllReduce(x))))
+
+    @fns
+    def beforelr(x, y, z):
+        return AddN((z, Mul(AllReduce(x), y)))
+
+    @fns
+    def beforerl(x, y, z):
+        return AddN((Mul(y, AllReduce(x)), z))
+
+    @fns
+    def beforerr(x, y, z):
+        return AddN((Mul(AllReduce(x), y), z))
+
+    @fns
+    def after1(x, y, z):
+        return Mul(AllReduce(AddN((z, x))), y)
+
+    @fns
+    def before2r(x, y, z):
+        return AddN((Mul(AllReduce(x), y), Mul(z, z)))
+
+    @fns
+    def before2l(x, y, z):
+        return AddN((Mul(z, z), Mul(AllReduce(x), y)))
+
+    @fns
+    def after2(x, y, z):
+        return Mul(AllReduce(AddN((Mul(z, z), x))), y)
+
+    return fns[tag]

From 08775a67e7d377cce596bb20e8edd1a24d32dd08 Mon Sep 17 00:00:00 2001
From: guohongzilong <2713219276@qq.com>
Date: Sat, 18 Apr 2020 16:50:09 +0800
Subject: [PATCH 355/367] add print comment and fix some example

---
 mindspore/ops/operations/comm_ops.py  | 8 ++++----
 mindspore/ops/operations/debug_ops.py | 3 +++
 mindspore/ops/operations/other_ops.py | 2 +-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py
index 441e441c2c..a5a4c9f236 100644
--- a/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/ops/operations/comm_ops.py
@@ -65,7 +65,7 @@ class AllReduce(PrimitiveWithInfer):
         The contents depend on the specified operation.
 
     Examples:
-        >>> from mindspore.communication.management import init
+        >>> from mindspore.communication import init
         >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
@@ -130,7 +130,7 @@ class AllGather(PrimitiveWithInfer):
         then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
 
     Examples:
-        >>> from mindspore.communication.management import init
+        >>> from mindspore.communication import init
         >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
@@ -187,7 +187,7 @@ class ReduceScatter(PrimitiveWithInfer):
         ValueError: If the first dimension of input can not be divided by rank size.
 
     Examples:
-        >>> from mindspore.communication.management import init
+        >>> from mindspore.communication import init
         >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
@@ -252,7 +252,7 @@ class Broadcast(PrimitiveWithInfer):
         TypeError: If root_rank is not a integer or group is not a string.
 
     Examples:
-        >>> from mindspore.communication.management import init
+        >>> from mindspore.communication import init
         >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py
index e4467f5ce1..ec3f10a696 100644
--- a/mindspore/ops/operations/debug_ops.py
+++ b/mindspore/ops/operations/debug_ops.py
@@ -161,6 +161,9 @@ class Print(PrimitiveWithInfer):
     """
     Output tensor or string to stdout.
 
+    Note:
+        The print operation cannot support float64 and bool types currently.
+
     Inputs:
         - **input_x** (Union[Tensor, str]) - The graph node to attach to. The input supports
           multiple strings and tensors which are separated by ','.
diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py
index ff66e80972..2ece6b7088 100644
--- a/mindspore/ops/operations/other_ops.py
+++ b/mindspore/ops/operations/other_ops.py
@@ -120,7 +120,7 @@ class BoundingBoxDecode(PrimitiveWithInfer):
 
     Examples:
         >>> boundingbox_decode = P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
-                                                   max_shape=(768, 1280), wh_ratio_clip=0.016)
+        >>>                                          max_shape=(768, 1280), wh_ratio_clip=0.016)
         >>> bbox = boundingbox_decode(anchor_box, deltas)
     """
 

From 5e01b94ccd457e04eb2e82a9729712a0986fde75 Mon Sep 17 00:00:00 2001
From: limingqi107 <limingqi@huawei.com>
Date: Fri, 17 Apr 2020 10:41:49 +0800
Subject: [PATCH 356/367] gpu dynamic memory pool suppoerts multi-graph

---
 .../ccsrc/device/gpu/gpu_kernel_runtime.cc    | 24 +++++++++++++++----
 .../ccsrc/device/gpu/gpu_kernel_runtime.h     |  6 +++--
 2 files changed, 23 insertions(+), 7 deletions(-)

diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
index 584f66eee7..11b8bdc162 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc
@@ -155,7 +155,8 @@ void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) {
   mem_reuse_util_ptr->SetReuseRefCount();
   // Can't free the device address of graph output, so set the reference count of graph output specially.
   mem_reuse_util_ptr->SetGraphOutputRefCount();
-  mem_reuse_util_ptr_ = mem_reuse_util_ptr;
+  auto graph_id = graph->graph_id();
+  mem_reuse_util_map_[graph_id] = mem_reuse_util_ptr;
 }
 
 void GPUKernelRuntime::InitKernelOutputAddress(const session::KernelGraph *graph) {
@@ -179,6 +180,7 @@ void GPUKernelRuntime::InitKernelOutputAddress(const session::KernelGraph *graph
 
 bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  auto graph_id = graph->graph_id();
   // The inputs and outputs memory of communication kernel are special, so separate processing.
   AllocCommunicationOpDynamicRes(graph);
 
@@ -194,7 +196,7 @@ bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph) {
       MS_LOG(ERROR) << "Launch kernel failed.";
       return false;
     }
-    FreeKernelDynamicRes(kernel, kernel_workspaces);
+    FreeKernelDynamicRes(kernel, kernel_workspaces, graph_id);
   }
 
   if (!SyncStream()) {
@@ -341,14 +343,16 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf
 }
 
 void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
-                                            const AddressPtrList &kernel_workspaces) {
+                                            const AddressPtrList &kernel_workspaces, uint32_t graph_id) {
   MS_EXCEPTION_IF_NULL(kernel);
   MS_EXCEPTION_IF_NULL(mem_manager_);
+  auto mem_reuse_util_ptr = mem_reuse_util_map_[graph_id];
+  MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
   auto cnode = kernel->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   // Free the input of kernel by reference count.
   for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
-    auto kernel_ref_count_ptr = mem_reuse_util_ptr_->GetKernelInputRef(cnode, i);
+    auto kernel_ref_count_ptr = mem_reuse_util_ptr->GetKernelInputRef(cnode, i);
     if (kernel_ref_count_ptr == nullptr) {
       continue;
     }
@@ -361,7 +365,6 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
       // Reset the reference count.
       kernel_ref_count_ptr->ref_count_dynamic_use_ = kernel_ref_count_ptr->ref_count_;
       bool is_communication_op = false;
-      // The inputs and outputs memory of communication kernel are special, so separate processing.
       FreeCommunicationOpDynamicRes(kernel, i, &is_communication_op);
       if (!is_communication_op) {
         auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i);
@@ -369,6 +372,17 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel,
       }
     }
   }
+  // Free the output of kernel, if output has no reference.
+  for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(kernel); ++i) {
+    auto kernel_ref_count_ptr = mem_reuse_util_ptr->GetRef(cnode, i);
+    if (kernel_ref_count_ptr == nullptr) {
+      continue;
+    }
+    if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
+      auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i);
+      mem_manager_->FreeMemFromMemPool(device_address);
+    }
+  }
   // Free the workspace of kernel.
   for (size_t i = 0; i < kernel_workspaces.size(); ++i) {
     auto workspace = kernel_workspaces[i];
diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
index 6f761342d3..e0eb2dc3f1 100644
--- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
+++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h
@@ -21,6 +21,7 @@
 #include <memory>
 #include <vector>
 #include <utility>
+#include <unordered_map>
 #include "device/kernel_runtime.h"
 #include "device/kernel_runtime_manager.h"
 
@@ -57,11 +58,12 @@ class GPUKernelRuntime : public KernelRuntime {
   void AllocCommunicationOpDynamicRes(const session::KernelGraph *graph);
   void AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel);
   void AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel);
-  void FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, const AddressPtrList &kernel_workspaces);
+  void FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, const AddressPtrList &kernel_workspaces,
+                            uint32_t graph_id);
   void FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx, bool *is_communication_op);
   size_t communication_op_input_ref_count_{0};
   size_t communication_op_output_ref_count_{0};
-  MemReuseUtilPtr mem_reuse_util_ptr_{nullptr};
+  std::unordered_map<uint32_t, MemReuseUtilPtr> mem_reuse_util_map_;
 };
 MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime);
 }  // namespace gpu

From 852039b7265cccf975a5d9246ebbe4b305e27a31 Mon Sep 17 00:00:00 2001
From: caojian05 <caojian5@huawei.com>
Date: Sat, 18 Apr 2020 18:16:29 +0800
Subject: [PATCH 357/367] fix dataset deepcopy failed while depipline
 referenced in the iterator

---
 mindspore/dataset/engine/iterators.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index d670de508c..cb56a435f1 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -231,6 +231,9 @@ class Iterator:
     def num_classes(self):
         return self.depipeline.GetNumClasses()
 
+    def __deepcopy__(self, memo):
+        return Iterator(copy.deepcopy(self.dataset, memo))
+
 
 class DictIterator(Iterator):
     """

From f0c07c3fa633f70cab54793ee0edbbc9bd3dba6d Mon Sep 17 00:00:00 2001
From: ms_yan <6576637+ms_yan@user.noreply.gitee.com>
Date: Sat, 11 Apr 2020 18:59:37 +0800
Subject: [PATCH 358/367] Realize take op and add ut

---
 mindspore/ccsrc/dataset/api/de_pipeline.cc    |  12 +-
 mindspore/ccsrc/dataset/api/de_pipeline.h     |   2 +-
 mindspore/ccsrc/dataset/core/client.h         |   1 +
 .../dataset/engine/datasetops/CMakeLists.txt  |   2 +-
 .../dataset/engine/datasetops/skip_op.cc      |   4 +
 .../dataset/engine/datasetops/take_op.cc      | 146 ++++++++
 .../ccsrc/dataset/engine/datasetops/take_op.h | 107 ++++++
 mindspore/dataset/engine/datasets.py          |  65 +++-
 mindspore/dataset/engine/iterators.py         |   2 +
 .../dataset/engine/serializer_deserializer.py |   3 +
 mindspore/dataset/engine/validators.py        |  19 +-
 tests/ut/cpp/dataset/CMakeLists.txt           |   1 +
 tests/ut/cpp/dataset/take_op_test.cc          | 103 ++++++
 tests/ut/python/dataset/test_take.py          | 317 ++++++++++++++++++
 14 files changed, 779 insertions(+), 5 deletions(-)
 create mode 100644 mindspore/ccsrc/dataset/engine/datasetops/take_op.cc
 create mode 100644 mindspore/ccsrc/dataset/engine/datasetops/take_op.h
 create mode 100644 tests/ut/cpp/dataset/take_op_test.cc
 create mode 100644 tests/ut/python/dataset/test_take.py

diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc
index a62994cb51..5f61c86f06 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc
@@ -54,6 +54,7 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &D
                                                                    {kGenerator, &DEPipeline::ParseGeneratorOp},
                                                                    {kTfReader, &DEPipeline::ParseTFReaderOp},
                                                                    {kProject, &DEPipeline::ParseProjectOp},
+                                                                   {kTake, &DEPipeline::ParseTakeOp},
                                                                    {kImageFolder, &DEPipeline::ParseImageFolderOp},
                                                                    {kMnist, &DEPipeline::ParseMnistOp},
                                                                    {kManifest, &DEPipeline::ParseManifestOp},
@@ -650,7 +651,16 @@ Status DEPipeline::ParseRenameOp(const py::dict &args, std::shared_ptr<DatasetOp
   return Status::OK();
 }
 
-DsOpPtr DEPipeline::ParseTakeOp(const py::dict &args) const { return DsOpPtr(); }
+Status DEPipeline::ParseTakeOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
+  if (args["count"].is_none()) {
+    std::string err_msg = "Error: count is invalid or not set.";
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  std::shared_ptr<TakeOp> op;
+  RETURN_IF_NOT_OK(TakeOp::Builder(ToInt(args["count"])).Build(&op));
+  *ptr = op;
+  return Status::OK();
+}
 
 Status DEPipeline::ParseZipOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   std::shared_ptr<ZipOp::Builder> builder = std::make_shared<ZipOp::Builder>();
diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h
index 35276e5b74..6ff7bb091c 100644
--- a/mindspore/ccsrc/dataset/api/de_pipeline.h
+++ b/mindspore/ccsrc/dataset/api/de_pipeline.h
@@ -116,7 +116,7 @@ class DEPipeline {
 
   Status ParseRenameOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
-  DsOpPtr ParseTakeOp(const py::dict &args) const;
+  Status ParseTakeOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
   Status ParseZipOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
diff --git a/mindspore/ccsrc/dataset/core/client.h b/mindspore/ccsrc/dataset/core/client.h
index b39ba3442b..b865c54260 100644
--- a/mindspore/ccsrc/dataset/core/client.h
+++ b/mindspore/ccsrc/dataset/core/client.h
@@ -38,6 +38,7 @@
 #include "dataset/engine/datasetops/source/mindrecord_op.h"
 #include "dataset/engine/datasetops/source/storage_op.h"
 #include "dataset/engine/datasetops/source/tf_reader_op.h"
+#include "dataset/engine/datasetops/take_op.h"
 #include "dataset/engine/datasetops/zip_op.h"
 #include "dataset/engine/execution_tree.h"
 #include "dataset/util/status.h"
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt b/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
index 9e511f78f4..655a739ada 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
+++ b/mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt
@@ -5,13 +5,13 @@ add_library(engine-datasetops OBJECT
     parallel_op.cc
     pipeline_op.cc
     batch_op.cc
-    batch_op.cc
     device_queue_op.cc
     map_op.cc
     project_op.cc
     rename_op.cc
     repeat_op.cc
     skip_op.cc
+    take_op.cc
     shuffle_op.cc
     zip_op.cc
     )
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
index 5b0433b6c8..90c160b5bf 100644
--- a/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
+++ b/mindspore/ccsrc/dataset/engine/datasetops/skip_op.cc
@@ -88,6 +88,10 @@ Status SkipOp::GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t work
   // If buffer is none or the rows of buffer is 0,
   // then get a buffer from child.
   if (!buf || buf->NumRows() == 0) {
+    if (buf && buf->eof()) {
+      *p_buffer = std::move(buf);
+      return Status::OK();
+    }
     RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
   }
 
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/take_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/take_op.cc
new file mode 100644
index 0000000000..d9625b6c26
--- /dev/null
+++ b/mindspore/ccsrc/dataset/engine/datasetops/take_op.cc
@@ -0,0 +1,146 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <utility>
+
+#include "common/utils.h"
+#include "dataset/engine/data_buffer.h"
+#include "dataset/engine/datasetops/take_op.h"
+#include "dataset/engine/db_connector.h"
+#include "dataset/engine/execution_tree.h"
+
+namespace mindspore {
+namespace dataset {
+// Builder constructor. Creates the builder object.
+TakeOp::Builder::Builder(int32_t count) : build_max_takes_(count) {}
+
+Status TakeOp::Builder::SanityCheck() const {
+  if (build_max_takes_ <= 0) {
+    std::string err_msg("Take count must be greater than 0.");
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
+// The builder "build" method creates the final object.
+Status TakeOp::Builder::Build(std::shared_ptr<TakeOp> *ptr) {
+  RETURN_IF_NOT_OK(SanityCheck());
+  *ptr = std::make_shared<TakeOp>(build_max_takes_);
+  return Status::OK();
+}
+
+// Constructor of the TakeOp.
+TakeOp::TakeOp(int32_t count) : PipelineOp(0), max_takes_(count), take_count_(0) {}
+
+// A print method typically used for debugging
+void TakeOp::Print(std::ostream &out, bool show_all) const {
+  // Call base class printer first
+  PipelineOp::Print(out, show_all);
+
+  // Then display our own stuff
+  out << "TakeOp:"
+      << "\nCurrent take count: " << take_count_ << "\nMax take count: " << max_takes_;
+}
+
+// This function will be call muti times to returns the buffer, when meet required max take count or meet
+// EOF buffer then this will stop.
+Status TakeOp::GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t worker_id, bool retry_if_eoe) {
+  if (child_.empty()) {
+    RETURN_STATUS_UNEXPECTED("TakeOp can't be the leaf node.");
+  }
+
+  std::unique_ptr<DataBuffer> buf;
+
+  bool last_repeat = !BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat);
+  if (take_count_ == max_takes_) {
+    if (state_ == OpState::kDeOpRunning) {
+      MS_LOG(INFO) << "meet max count and push-back eoe buffer.";
+      auto eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+      *p_buffer = std::move(eoe_buffer);
+      state_ = OpState::kDeOpIdle;
+
+      // Reset the count and drain
+      if (!last_repeat) {
+        take_count_ = 0;
+        RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
+        while (!buf->eoe() && !buf->eof()) {
+          RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
+        }
+      }
+    } else {
+      MS_LOG(INFO) << "meet max count and push-back eof buffer.";
+      auto eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+      *p_buffer = std::move(eof_buffer);
+      take_count_ = 0;
+    }
+    return Status::OK();
+  }
+  RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf, worker_id, true));
+  // Loop until non EOE is received
+  if (buf->eoe()) {
+    take_count_ = 0;
+    *p_buffer = std::move(buf);
+    return Status::OK();
+  }
+
+  // Check if the last buf is next eof
+  if (buf->eof()) {
+    *p_buffer = std::move(buf);
+    return Status::OK();
+  }
+
+  // Get buffer and push back when take_count is still small
+  if (take_count_ < max_takes_) {
+    RETURN_IF_NOT_OK(FillBuffer(&buf, p_buffer));
+  }
+  return Status::OK();
+}
+
+// Function FillBuffer mainly prepare the buffer for returning
+Status TakeOp::FillBuffer(std::unique_ptr<DataBuffer> *buffer, std::unique_ptr<DataBuffer> *data_buffer) {
+  int32_t buffer_size = (*buffer)->NumRows();
+  if (take_count_ + buffer_size < max_takes_) {
+    *data_buffer = std::move(*buffer);
+    take_count_ = take_count_ + buffer_size;
+  } else {
+    MS_LOG(INFO) << "In last buffer: Push one buffer.";
+    std::unique_ptr<TensorQTable> new_tensor_table = std::make_unique<TensorQTable>();
+    while (take_count_ < max_takes_) {
+      TensorRow new_row;
+      RETURN_IF_NOT_OK((*buffer)->PopRow(&new_row));
+      take_count_++;
+      new_tensor_table->push_back(new_row);
+    }
+    (*buffer)->set_tensor_table(std::move(new_tensor_table));
+    *data_buffer = std::move(*buffer);
+  }
+  return Status::OK();
+}
+
+// Class functor operator () override.
+// Most dataset ops operate by launching a thread (see ExecutionTree).
+// However, the TakeOp is defined as a inlined operator, so it is invalid to launch the
+// functor since this op runs inlined inside another operator.  The function is overloaded to
+// ensure that it is not called by mistake (it will generate an error).
+Status TakeOp::operator()() { RETURN_STATUS_UNEXPECTED("Logic error. TakeOp is an inlined operator."); }
+
+Status TakeOp::PrepareNodePostAction() {
+  RETURN_IF_NOT_OK(PipelineOp::PrepareNodePostAction());
+  tree_->AddToRepeatStack(shared_from_this());
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/dataset/engine/datasetops/take_op.h b/mindspore/ccsrc/dataset/engine/datasetops/take_op.h
new file mode 100644
index 0000000000..02218cf610
--- /dev/null
+++ b/mindspore/ccsrc/dataset/engine/datasetops/take_op.h
@@ -0,0 +1,107 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef DATASET_ENGINE_DATASETOPS_TAKE_OP_H_
+#define DATASET_ENGINE_DATASETOPS_TAKE_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "dataset/engine/datasetops/pipeline_op.h"
+
+namespace mindspore {
+namespace dataset {
+class TakeOp : public PipelineOp {
+ public:
+  // The nested builder class inside of the TakeOp is used to help manage all of the arguments
+  // for constructing it.  This take op is very simple though, so this builder is really just
+  // provided for a consistent look and feel for creators of Dataset operators overall.
+  class Builder {
+   public:
+    // Builder constructor.  Creates the builder object.
+    // @note No default args
+    // @param count - The number of takes to do
+    // @return This is a constructor.
+    explicit Builder(int32_t count);
+
+    // Default destructor
+    ~Builder() = default;
+
+    // The builder "build" method creates the final object.
+    // @return shared_ptr to the new StorageOp object
+    Status Build(std::shared_ptr<TakeOp> *);
+
+   private:
+    int32_t build_max_takes_;
+
+    Status SanityCheck() const;
+  };
+
+  // Constructor of the TakeOp.
+  // @note The builder class should be used to call it
+  // @param count - The number of takes to do
+  explicit TakeOp(int32_t count);
+
+  // Destructor
+  ~TakeOp() = default;
+
+  // A print method typically used for debugging
+  // @param out - The output stream to write output to
+  // @param show_all - A bool to control if you want to show all info or just a summary
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // << Stream output operator overload
+  // @notes This allows you to write the debug print info using stream operators
+  // @param out - reference to the output stream being overloaded
+  // @param ro - reference to the TakeOp to display
+  // @return - the output stream must be returned
+  friend std::ostream &operator<<(std::ostream &out, const TakeOp &ro) {
+    ro.Print(out, false);
+    return out;
+  }
+
+  // Class functor operator () override.
+  // Most dataset ops operate by launching a thread (see ExecutionTree).
+  // However, the TakeOp is defined as a inlined operator, so it is invalid to launch the
+  // functor since this op runs inlined inside another operator.  The function is overloaded to
+  // ensure that it is not called by mistake (it will generate an error).
+  // @return Status - The error code return
+  Status operator()() override;
+
+  // Gets a buffer from the child node. The caller is typically our parent node.
+  // @note This function sets the `retryIfEoe` flag when popping from the child connector. This way,
+  // this function will retry to pop the connector again and will get the non-EOE buffer if any.
+  // @param p_buffer - output pointer to the buffer that it will fetch.
+  // @param worker_id - The worker id
+  // @param retry_if_eoe Set this flag to true to allow calling pop() again after the first pop() returns EOE.
+  // @return Status - The error code return
+  Status GetNextBuffer(std::unique_ptr<DataBuffer> *p_buffer, int32_t worker_id, bool retry_if_eoe) override;
+
+  // During tree prepare phase, operators may have specific post-operations to perform depending on
+  // their role.
+  // @notes Derived versions of this function should always call it's superclass version first
+  // before providing their own implementations.
+  Status PrepareNodePostAction() override;
+
+ private:
+  int32_t max_takes_;   // The number of takes that the user requested
+  int32_t take_count_;  // A counter for the current number of executed takes
+
+  Status FillBuffer(std::unique_ptr<DataBuffer> *buffer, std::unique_ptr<DataBuffer> *data_buffer);
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_ENGINE_DATASETOPS_TAKE_OP_H_
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 642e2beec8..8de56a6dff 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -36,7 +36,7 @@ from mindspore import log as logger
 from . import samplers
 from .iterators import DictIterator, TupleIterator
 from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_skip, check_zip, check_rename, \
-    check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
+    check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
     check_zip_dataset, check_add_column
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
@@ -442,6 +442,33 @@ class Dataset:
         """
         return SkipDataset(self, count)
 
+    @check_take
+    def take(self, count=-1):
+        """
+        Takes at most given numbers of elements from the dataset.
+
+        Note:
+            1. If count is greater than the number of element in dataset or equal to -1,
+            all the element in dataset will be taken.
+            2. The order of using take and batch effects. If take before batch operation,
+            then taken given number of rows, otherwise take given number of batches.
+
+        Args:
+            count (int, optional): Number of elements to be taken from the dataset (default=-1).
+
+        Returns:
+            TakeDataset, dataset taken.
+
+        Examples:
+            >>> import mindspore.dataset as ds
+            >>> # data is an instance of Dataset object.
+            >>> # creates a dataset where the dataset including 50 elements.
+            >>> data = data.take(50)
+        """
+        if count == -1:
+            return self
+        return TakeDataset(self, count)
+
     @check_zip_dataset
     def zip(self, datasets):
         """
@@ -1100,6 +1127,7 @@ class RepeatDataset(DatasetOp):
         """
         return self.count
 
+
 class SkipDataset(DatasetOp):
     """
     The result of applying Skip operator to the input Dataset.
@@ -1134,6 +1162,41 @@ class SkipDataset(DatasetOp):
             output_size = child_size - self.count
         return output_size
 
+
+class TakeDataset(DatasetOp):
+    """
+    The result of applying Take operator to the input Dataset.
+
+    Args:
+        input_dataset (Dataset): Input Dataset to be taken element from.
+        count (int): Number of elements to be taken from the dataset.
+    """
+
+    def __init__(self, input_dataset, count):
+        super().__init__()
+        self.count = count
+        self.input.append(input_dataset)
+        input_dataset.output.append(self)
+        self._input_indexs = input_dataset.input_indexs
+
+    def get_args(self):
+        args = super().get_args()
+        args["count"] = self.count
+        return args
+
+    def get_dataset_size(self):
+        """
+        Get the number of batches in an epoch.
+
+        Return:
+            Number, number of batches.
+        """
+        child_size = self.input[0].get_dataset_size()
+        if child_size < self.count:
+            return child_size
+        return self.count
+
+
 class ZipDataset(DatasetOp):
     """
     The result of applying Zip operator to the input Dataset.
diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py
index d670de508c..3d6873d04c 100644
--- a/mindspore/dataset/engine/iterators.py
+++ b/mindspore/dataset/engine/iterators.py
@@ -129,6 +129,8 @@ class Iterator:
             op_type = OpName.REPEAT
         elif isinstance(dataset, de.SkipDataset):
             op_type = OpName.SKIP
+        elif isinstance(dataset, de.TakeDataset):
+            op_type = OpName.TAKE
         elif isinstance(dataset, de.StorageDataset):
             op_type = OpName.STORAGE
         elif isinstance(dataset, de.ImageFolderDatasetV2):
diff --git a/mindspore/dataset/engine/serializer_deserializer.py b/mindspore/dataset/engine/serializer_deserializer.py
index a54a7a6b32..61417e4d52 100644
--- a/mindspore/dataset/engine/serializer_deserializer.py
+++ b/mindspore/dataset/engine/serializer_deserializer.py
@@ -304,6 +304,9 @@ def create_node(node):
     elif dataset_op == 'SkipDataset':
         pyobj = de.Dataset().skip(node.get('count'))
 
+    elif dataset_op == 'TakeDataset':
+        pyobj = de.Dataset().take(node.get('count'))
+
     elif dataset_op == 'MapDataset':
         tensor_ops = construct_tensor_ops(node.get('operations'))
         pyobj = de.Dataset().map(node.get('input_columns'), tensor_ops, node.get('output_columns'),
diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py
index 3502cbb204..b74e913202 100644
--- a/mindspore/dataset/engine/validators.py
+++ b/mindspore/dataset/engine/validators.py
@@ -602,7 +602,7 @@ def check_batch_size(batch_size):
 def check_count(count):
     check_type(count, 'count', int)
     if (count <= 0 and count != -1) or count > INT32_MAX:
-        raise ValueError("repeat count should be either -1 or positive integer.")
+        raise ValueError("count should be either -1 or positive integer.")
 
 
 def check_columns(columns, name):
@@ -709,6 +709,7 @@ def check_repeat(method):
 
     return new_method
 
+
 def check_skip(method):
     """check the input arguments of skip."""
     @wraps(method)
@@ -724,6 +725,21 @@ def check_skip(method):
 
     return new_method
 
+
+def check_take(method):
+    """check the input arguments of take."""
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        count = param_dict.get('count')
+        check_count(count)
+
+        return method(*args, **kwargs)
+
+    return new_method
+
+
 def check_zip(method):
     """check the input arguments of zip."""
     @wraps(method)
@@ -759,6 +775,7 @@ def check_zip_dataset(method):
 
     return new_method
 
+
 def check_rename(method):
     """check the input arguments of rename."""
     @wraps(method)
diff --git a/tests/ut/cpp/dataset/CMakeLists.txt b/tests/ut/cpp/dataset/CMakeLists.txt
index db207363a8..ae9c46e62c 100644
--- a/tests/ut/cpp/dataset/CMakeLists.txt
+++ b/tests/ut/cpp/dataset/CMakeLists.txt
@@ -64,6 +64,7 @@ SET(DE_UT_SRCS
     voc_op_test.cc
     cifar_op_test.cc
     celeba_op_test.cc
+    take_op_test.cc
     )
 
 add_executable(de_ut_tests ${DE_UT_SRCS})
diff --git a/tests/ut/cpp/dataset/take_op_test.cc b/tests/ut/cpp/dataset/take_op_test.cc
new file mode 100644
index 0000000000..7f8508de20
--- /dev/null
+++ b/tests/ut/cpp/dataset/take_op_test.cc
@@ -0,0 +1,103 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <iostream>
+#include <memory>
+#include <vector>
+
+#include "common/common.h"
+#include "common/utils.h"
+#include "dataset/core/client.h"
+#include "gtest/gtest.h"
+#include "utils/log_adapter.h"
+
+namespace common = mindspore::common;
+
+using namespace mindspore::dataset;
+using mindspore::MsLogLevel::INFO;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+
+class MindDataTestTakeOp : public UT::DatasetOpTesting {};
+
+TEST_F(MindDataTestTakeOp, TestTakeProject) {
+  // Start with an empty execution tree
+  auto my_tree = std::make_shared<ExecutionTree>();
+
+  std::string dataset_path;
+  dataset_path = datasets_root_path_ + "/testTFTestAllTypes/test.data";
+
+  // TFReaderOp
+  std::shared_ptr<TFReaderOp> my_tfreader_op;
+  TFReaderOp::Builder builder;
+  builder.SetDatasetFilesList({dataset_path})
+    .SetRowsPerBuffer(16)
+    .SetWorkerConnectorSize(16)
+    .SetNumWorkers(16);
+  std::unique_ptr<DataSchema> schema = std::make_unique<DataSchema>();
+  schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
+  builder.SetDataSchema(std::move(schema));
+  Status rc = builder.Build(&my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  // TakeOp
+  std::shared_ptr<TakeOp> my_take_op;
+  TakeOp::Builder builder_take(5);
+  rc = builder_take.Build(&my_take_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = my_tree->AssociateNode(my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+  rc = my_tree->AssociateNode(my_take_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  // Set children/root layout.
+  rc = my_take_op->AddChild(my_tfreader_op);
+  ASSERT_TRUE(rc.IsOk());
+  rc = my_tree->AssignRoot(my_take_op);
+  ASSERT_TRUE(rc.IsOk());
+
+  MS_LOG(INFO) << "Launching tree and begin iteration.";
+  rc = my_tree->Prepare();
+
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = my_tree->Launch();
+  ASSERT_TRUE(rc.IsOk());
+
+  // Start the loop of reading tensors from our pipeline
+  DatasetIterator di(my_tree);
+  TensorRow tensor_list;
+  rc = di.FetchNextTensorRow(&tensor_list);
+  ASSERT_TRUE(rc.IsOk());
+
+  int row_count = 0;
+  while (!tensor_list.empty()) {
+    MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
+
+    // Display the tensor by calling the printer on it
+    for (int i = 0; i < tensor_list.size(); i++) {
+      std::ostringstream ss;
+      ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
+      MS_LOG(INFO) << "Tensor print: " << ss.str() << ".";
+    }
+
+    rc = di.FetchNextTensorRow(&tensor_list);
+    ASSERT_TRUE(rc.IsOk());
+    row_count++;
+  }
+
+  ASSERT_EQ(row_count, 5);
+}
diff --git a/tests/ut/python/dataset/test_take.py b/tests/ut/python/dataset/test_take.py
new file mode 100644
index 0000000000..ed71f67e26
--- /dev/null
+++ b/tests/ut/python/dataset/test_take.py
@@ -0,0 +1,317 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from mindspore import log as logger
+import numpy as np
+
+
+# In generator dataset: Number of rows is 3, its value is 0, 1, 2
+def generator():
+    for i in range(3):
+        yield np.array([i]),
+
+
+# In generator dataset: Number of rows is 10, its value is 0, 1, 2 ... 10
+def generator_10():
+    for i in range(10):
+        yield np.array([i]),
+
+
+def test_take_01():
+    """
+    Test take: origin there are 3 row, and take 1 row, in this case: will not meet eoe and eof
+    """
+    logger.info("test_take_01")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(1)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert 0 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_02():
+    """
+    Test take: origin there are 3 row, and take 2 row, in this case: will meet eoe
+    """
+    logger.info("test_take_02")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(2)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 2 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 4
+
+
+def test_take_03():
+    """
+    Test take: origin there are 3 row, and take 3 row, in this case: will meet eoe and eof
+    """
+    logger.info("test_take_03")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(3)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 6
+
+
+def test_take_04():
+    """
+    Test take: origin there are 3 row, and take 4 row, this is more than the total rows
+    """
+    logger.info("test_take_04")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(4)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 6
+
+
+def test_take_05():
+    """
+    Test take: there is no repeat op
+    """
+    logger.info("test_take_05")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i == d[0][0]
+
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_06():
+    """
+    Test take: repeat is before take
+    """
+    logger.info("test_take_06")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+    
+    data1 = data1.repeat(2)
+    data1 = data1.take(4)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 4
+
+
+def test_take_07():
+    """
+    Test take: take is before batch, that mean take(N), N refer to rows num
+    """
+    logger.info("test_take_07")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+    
+    data1 = data1.take(2)
+    data1 = data1.batch(2)
+    assert sum([1 for _ in data1]) == 1
+
+
+def test_take_08():
+    """
+    Test take: take is after batch, that mean take(N), N refer to batches num
+    """
+    logger.info("test_take_08")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.batch(2)
+    data1 = data1.take(2)
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_09():
+    """
+    Test take: repeat count is -1, and read the whole dataset, take after repeat
+    """
+    logger.info("test_take_09")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+    
+    data1 = data1.repeat(2)
+    data1 = data1.take(-1)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 6
+
+
+def test_take_10():
+    """
+    Test take: repeat count is -1, and read the whole dataset, take before repeat
+    """
+    logger.info("test_take_10")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(-1)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert i % 3 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 6
+
+
+def test_take_11():
+    """
+    Test take: batch first, then do repeat and take operation
+    """
+    logger.info("test_take_11")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.batch(2)
+    data1 = data1.repeat(2)
+    data1 = data1.take(-1)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert 2 * (i % 2) == d[0][0]
+
+    assert sum([1 for _ in data1]) == 4
+
+
+def test_take_12():
+    """
+    Test take: take first, then do batch and repeat operation
+    """
+    logger.info("test_take_12")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(2)
+    data1 = data1.batch(2)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert 0 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_13():
+    """
+    Test take: skip first, then do take, batch and repeat operation
+    """
+    logger.info("test_take_13")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.skip(2)
+    data1 = data1.take(-1)
+    data1 = data1.batch(2)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert 2 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_14():
+    """
+    Test take: take first, then do batch, skip and repeat operation
+    """
+    logger.info("test_take_14")
+    data1 = ds.GeneratorDataset(generator, ["data"])
+
+    data1 = data1.take(-1)
+    data1 = data1.batch(2)
+    data1 = data1.skip(1)
+    data1 = data1.repeat(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert 2 == d[0][0]
+
+    assert sum([1 for _ in data1]) == 2
+
+
+def test_take_15():
+    """
+    Test take: large amount data, take a part, then do skip operation
+    """
+    logger.info("test_take_15")
+    data1 = ds.GeneratorDataset(generator_10, ["data"])
+
+    data1 = data1.take(6)
+    data1 = data1.skip(2)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert (i + 2) == d[0][0]
+
+    assert sum([1 for _ in data1]) == 4
+
+
+def test_take_16():
+    """
+    Test take: large amount data, skip a part, then do take operation
+    """
+    logger.info("test_take_16")
+    data1 = ds.GeneratorDataset(generator_10, ["data"])
+
+    data1 = data1.skip(3)
+    data1 = data1.take(5)
+
+    # Here i refers to index, d refers to data element 
+    for i, d in enumerate(data1):
+        assert (i + 3) == d[0][0]
+
+    assert sum([1 for _ in data1]) == 5
+
+
+if __name__ == '__main__':
+    test_take_01()
+    test_take_02()
+    test_take_03()
+    test_take_04()
+    test_take_05()
+    test_take_06()
+    test_take_07()
+    test_take_08()
+    test_take_09()
+    test_take_10()
+    test_take_11()
+    test_take_12()
+    test_take_13()
+    test_take_14()
+    test_take_15()
+    test_take_16()
+    logger.info('== test take operation finished ==')
\ No newline at end of file

From c889320f4e1bc014e84eba8d286a2291a731cb4b Mon Sep 17 00:00:00 2001
From: zhoufeng <zhoufeng54@huawei.com>
Date: Sun, 19 Apr 2020 12:14:46 +0800
Subject: [PATCH 359/367] fix cmake verbose

---
 build.sh | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/build.sh b/build.sh
index d555188c20..7550d76c8f 100755
--- a/build.sh
+++ b/build.sh
@@ -314,7 +314,10 @@ build_mindspore()
     if [[ "X$INC_BUILD" = "Xoff" ]]; then
       cmake ${CMAKE_ARGS} ../..
     fi
-    cmake --build . --target package ${VERBOSE} -j$THREAD_NUM
+    if [[ -n "$VERBOSE" ]]; then
+      CMAKE_VERBOSE="--verbose"
+    fi
+    cmake --build . --target package ${CMAKE_VERBOSE} -j$THREAD_NUM
     echo "success to build mindspore project!"
 }
 

From 7b5a31dc831bbf40578788b387b1383f32f24757 Mon Sep 17 00:00:00 2001
From: yangjie159 <yangjie159@huawei.com>
Date: Sun, 19 Apr 2020 23:42:40 +0800
Subject: [PATCH 360/367] add comment for export lite model

---
 mindspore/context.py             | 4 ++--
 mindspore/train/serialization.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mindspore/context.py b/mindspore/context.py
index 2938b87119..ba0ac36b66 100644
--- a/mindspore/context.py
+++ b/mindspore/context.py
@@ -487,8 +487,8 @@ def set_context(**kwargs):
         enable_loop_sink (bool): Whether to enable loop sink. Default: False.
         enable_task_sink (bool): Whether to enable task sink. Default: True.
         enable_mem_reuse (bool): Whether to enable memory reuse. Default: True.
-        save_ms_model (bool): Whether to save model converted by graph. Default: False.
-        save_ms_model_path (str): Path to save converted model. Default: "."
+        save_ms_model (bool): Whether to save lite model converted by graph. Default: False.
+        save_ms_model_path (str): Path to save converted lite model. Default: "."
         enable_gpu_summary (bool): Whether to enable gpu summary. Default: True.
         save_graphs_path (str): Path to save graphs. Default: "."
         enable_auto_mixed_precision (bool): Whether to enable auto mixed precision. Default: True.
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 8ec1b38804..ae17bf8116 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -426,7 +426,7 @@ def export(net, *inputs, file_name, file_format='GEIR'):
             - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
               Ascend model.
             - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
-            - LITE: Huawei model format for mobile.
+            - LITE: Huawei model format for mobile. A lite model only for the MindSpore Lite
     """
     logger.info("exporting model file:%s format:%s.", file_name, file_format)
     check_input_data(*inputs, data_class=Tensor)

From 698c29eedfaf7922036130878946b75252410099 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Mon, 20 Apr 2020 17:47:34 +0800
Subject: [PATCH 361/367] del graph

---
 graphengine | 1 -
 1 file changed, 1 deletion(-)
 delete mode 160000 graphengine

diff --git a/graphengine b/graphengine
deleted file mode 160000
index 5869ad8541..0000000000
--- a/graphengine
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 5869ad8541403235113812f872f462bf19ecd11e

From aadf06d70bdbde6e409e22520c755ece6c4bcfbf Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Mon, 20 Apr 2020 17:48:43 +0800
Subject: [PATCH 362/367] add graph

---
 graphengine | 1 +
 1 file changed, 1 insertion(+)
 create mode 160000 graphengine

diff --git a/graphengine b/graphengine
new file mode 160000
index 0000000000..5869ad8541
--- /dev/null
+++ b/graphengine
@@ -0,0 +1 @@
+Subproject commit 5869ad8541403235113812f872f462bf19ecd11e

From a67ff264a86087070ee11bedabce659a116c7037 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Mon, 20 Apr 2020 20:15:34 +0800
Subject: [PATCH 363/367] fetch graph

---
 graphengine | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphengine b/graphengine
index 5869ad8541..70bb745b45 160000
--- a/graphengine
+++ b/graphengine
@@ -1 +1 @@
-Subproject commit 5869ad8541403235113812f872f462bf19ecd11e
+Subproject commit 70bb745b459ff9a0e7fc1008d15fe4b510f03da7

From 9dc561b7c68db208c65a99ca116b9eb12c2e67c6 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 21 Apr 2020 10:00:45 +0800
Subject: [PATCH 364/367] modify topk

---
 mindspore/ccsrc/transform/op_declare.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 8b32e16b35..2d15bfa181 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -217,8 +217,8 @@ DECLARE_OP_USE_OUTPUT(Merge)
 DECLARE_OP_ADAPTER(Switch)
 DECLARE_OP_USE_OUTPUT(Switch)
 
-DECLARE_OP_ADAPTER(TopKV2)
-DECLARE_OP_USE_OUTPUT(TopKV2)
+DECLARE_OP_ADAPTER(TopK)
+DECLARE_OP_USE_OUTPUT(TopK)
 
 DECLARE_OP_ADAPTER(RealDiv)
 DECLARE_OP_USE_OUTPUT(RealDiv)

From ea3fa240b5fdb90e29bf99bb87ed3825f1038fcb Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 21 Apr 2020 11:26:02 +0800
Subject: [PATCH 365/367] modify LogSoftmaxV2

---
 mindspore/ccsrc/transform/op_declare.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 2d15bfa181..6fdb4b5434 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -268,8 +268,8 @@ DECLARE_OP_ADAPTER(Select)
 DECLARE_OP_USE_OUTPUT(Select)
 DECLARE_OP_ADAPTER(LessEqual)
 DECLARE_OP_USE_OUTPUT(LessEqual)
-DECLARE_OP_ADAPTER(LogSoftmax)
-DECLARE_OP_USE_OUTPUT(LogSoftmax)
+DECLARE_OP_ADAPTER(LogSoftmaxV2)
+DECLARE_OP_USE_OUTPUT(LogSoftmaxV2)
 DECLARE_OP_ADAPTER(TruncatedNormal)
 DECLARE_OP_USE_OUTPUT(TruncatedNormal)
 DECLARE_OP_ADAPTER(StridedSliceGrad)

From fd344cb8f7a843b077fc950b940d981b876d5c3a Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 21 Apr 2020 11:38:18 +0800
Subject: [PATCH 366/367] add softmaxv2

---
 mindspore/ccsrc/transform/op_declare.h | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 6fdb4b5434..933896a275 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -403,8 +403,8 @@ DECLARE_OP_ADAPTER(Sigmoid)
 DECLARE_OP_USE_OUTPUT(Sigmoid)
 DECLARE_OP_ADAPTER(SigmoidGrad)
 DECLARE_OP_USE_OUTPUT(SigmoidGrad)
-DECLARE_OP_ADAPTER(Softmax)
-DECLARE_OP_USE_OUTPUT(Softmax)
+DECLARE_OP_ADAPTER(SoftmaxV2)
+DECLARE_OP_USE_OUTPUT(SoftmaxV2)
 DECLARE_OP_ADAPTER(SoftmaxGrad)
 DECLARE_OP_USE_OUTPUT(SoftmaxGrad)
 DECLARE_OP_ADAPTER(Greater)
@@ -447,6 +447,8 @@ DECLARE_OP_ADAPTER(Round)
 DECLARE_OP_USE_OUTPUT(Round)
 DECLARE_OP_ADAPTER(ApplyFtrl)
 DECLARE_OP_USE_OUTPUT(ApplyFtrl)
+DECLARE_OP_ADAPTER(SparseApplyFtrlD)
+DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD)
 DECLARE_OP_ADAPTER(Diag)
 DECLARE_OP_USE_OUTPUT(Diag)
 DECLARE_OP_ADAPTER(DiagPart)

From e5dc50533c4ba3b9925948a8842ff08960936cc2 Mon Sep 17 00:00:00 2001
From: chang zherui <760161589@qq.com>
Date: Tue, 21 Apr 2020 11:50:35 +0800
Subject: [PATCH 367/367] modify op_declare.h

---
 mindspore/ccsrc/transform/convert.cc    |   8 +-
 mindspore/ccsrc/transform/op_declare.cc | 212 ++++++++++--------------
 mindspore/ccsrc/transform/op_declare.h  |   6 -
 3 files changed, 95 insertions(+), 131 deletions(-)

diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc
index 417989247e..46e5586122 100755
--- a/mindspore/ccsrc/transform/convert.cc
+++ b/mindspore/ccsrc/transform/convert.cc
@@ -176,6 +176,7 @@ const char kNameAbsGrad[] = "AbsGrad";
 const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy";
 const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad";
 const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad";
+const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD";
 const char kNameAcosh[] = "Acosh";
 const char kNameFloorMod[] = "FloorMod";
 const char kNameSpaceToDepth[] = "SpaceToDepth";
@@ -204,7 +205,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameMaxPool), ADPT_DESC(MaxPool)},
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
     {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
-    {string(kNameTopK), ADPT_DESC(TopKV2)},
+    {string(kNameTopK), ADPT_DESC(TopK)},
     {string(kNamePack), ADPT_DESC(Pack)},
     {string(kNameUnpack), ADPT_DESC(Unpack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},
@@ -329,7 +330,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimMinimum->name(), ADPT_DESC(Minimum)},
     {prim::kPrimSelect->name(), ADPT_DESC(Select)},
     {string(kNameLessEqual), ADPT_DESC(LessEqual)},
-    {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmax)},
+    {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmaxV2)},
     {string(kNameTruncatedNormal), ADPT_DESC(TruncatedNormal)},
     {string(kNameStridedSliceGrad), ADPT_DESC(StridedSliceGrad)},
     {prim::kPrimGelu->name(), ADPT_DESC(Gelu)},
@@ -363,7 +364,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimMatMul->name(), ADPT_DESC(MatMul)},
 
     {string(kNameConst), ADPT_DESC(Constant, Const)},
-    {string(kNameSoftmax), ADPT_DESC(Softmax)},
+    {string(kNameSoftmax), ADPT_DESC(SoftmaxV2)},
     {string(kNameSoftmaxGrad), ADPT_DESC(SoftmaxGrad)},
     {string(kNameParam), ADPT_DESC(Data)},
     {string(kNameROIAlign), ADPT_DESC(ROIAlign)},
@@ -373,6 +374,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)},
     {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)},
     {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)},
+    {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)},
     {string(kNameAcosh), ADPT_DESC(Acosh)},
     {string(kNameFloorMod), ADPT_DESC(FloorMod)},
     {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)},
diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index 420edc685a..858b9b6b39 100644
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -138,11 +138,10 @@ OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}};
 INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}};
 ATTR_MAP(Summary) = EMPTY_ATTR_MAP;
 
-// data
+// Data
 INPUT_MAP(Data) = EMPTY_INPUT_MAP;
 ATTR_MAP(Data) = EMPTY_ATTR_MAP;
 
-// resnet ops in ge
 // BatchNorm
 INPUT_MAP(BatchNorm) = {{1, INPUT_DESC(x)},
                         {2, INPUT_DESC(scale)},
@@ -193,10 +192,9 @@ ATTR_MAP(PRelu) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(PRelu) = {{0, OUTPUT_DESC(y)}};
 
 // PReluGrad
-INPUT_MAP(PReluGrad) = {
-  {1, INPUT_DESC(input_gradients)}, {2, INPUT_DESC(input_features)}, {3, INPUT_DESC(input_weights)}};
+INPUT_MAP(PReluGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}};
 ATTR_MAP(PReluGrad) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(output_backprops_dx)}, {1, OUTPUT_DESC(output_backprops_da)}};
+OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(da)}};
 
 // Sigmoid
 INPUT_MAP(Sigmoid) = {{1, INPUT_DESC(x)}};
@@ -241,12 +239,12 @@ ATTR_MAP(CumsumD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits<bool>())},
                      {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 OUTPUT_MAP(CumsumD) = {{0, OUTPUT_DESC(y)}};
 
-// softmax
-INPUT_MAP(Softmax) = {{1, INPUT_DESC(x)}};
-ATTR_MAP(Softmax) = {
-  {"axis", ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+// SoftmaxV2
+INPUT_MAP(SoftmaxV2) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(SoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
-OUTPUT_MAP(Softmax) = {{0, OUTPUT_DESC(y)}};
+OUTPUT_MAP(SoftmaxV2) = {{0, OUTPUT_DESC(y)}};
 
 // SoftmaxGrad
 INPUT_MAP(SoftmaxGrad) = {{1, INPUT_DESC(softmax)}, {2, INPUT_DESC(grad_softmax)}};
@@ -269,21 +267,21 @@ ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceSum
-INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}};
+INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
 ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceSumD
 INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceSumD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceProdD
 INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceProdD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}};
 
@@ -294,7 +292,7 @@ ATTR_MAP(CumprodD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits<bool>())},
                       {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 OUTPUT_MAP(CumprodD) = {{0, OUTPUT_DESC(y)}};
 
-// SoftmaxCrossEntropyWithLogits/
+// SoftmaxCrossEntropyWithLogits
 INPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(labels)}};
 ATTR_MAP(SoftmaxCrossEntropyWithLogits) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{0, OUTPUT_DESC(loss)}, {1, OUTPUT_DESC(backprop)}};
@@ -306,7 +304,7 @@ INPUT_ATTR_MAP(MeanGrad) = {{2, ATTR_DESC(mean_grad_output_shape_value, kOpForma
 ATTR_MAP(MeanGrad) = {{"mode", ATTR_DESC(mode, AnyTraits<int64_t>())}};
 
 INPUT_MAP(SliceD) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(begin, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(offsets, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                           {3, ATTR_DESC(size, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(SliceD) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SliceD) = {{0, OUTPUT_DESC(y)}};
@@ -411,42 +409,10 @@ ATTR_MAP(BoundingBoxDecode) = {
 };
 OUTPUT_MAP(BoundingBoxDecode) = {{0, OUTPUT_DESC(bboxes)}};
 
-#ifdef VALID_CODE
-
-// Less
-INPUT_MAP(Less) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
-ATTR_MAP(Less) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(Less) = {{0, OUTPUT_DESC(z)}};
-
-// Cast
-INPUT_MAP(Cast) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(Cast) = {{2, ATTR_DESC(dst_type, AnyTraits<GEType>())}};
-ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits<bool>())}};
-OUTPUT_MAP(Cast) = {{0, OUTPUT_DESC(y)}};
-
-// Minimum
-INPUT_MAP(Minimum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
-ATTR_MAP(Minimum) = {{"alpha", ATTR_DESC(alpha, AnyTraits<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-OUTPUT_MAP(Minimum) = {{0, OUTPUT_DESC(z)}};
-
-// Sub
-INPUT_MAP(Sub) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(Sub) = {{"alpha", ATTR_DESC(alpha, AnyTraits<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-
-#endif
-
-// TopKV2
-INPUT_MAP(TopKV2) = {
-  {1, INPUT_DESC(input)},
-  {2, INPUT_DESC(k)},
-};
-
-ATTR_MAP(TopKV2) = {{"T", ATTR_DESC(T, AnyTraits<GEType>())}, {"sorted", ATTR_DESC(sorted, AnyTraits<bool>())}};
-
-OUTPUT_MAP(TopKV2) = {
-  {0, OUTPUT_DESC(values)},
-  {1, OUTPUT_DESC(indices)},
-};
+// TopK
+INPUT_MAP(TopK) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(k)}};
+ATTR_MAP(TopK) = {{"sorted", ATTR_DESC(sorted, AnyTraits<bool>())}};
+OUTPUT_MAP(TopK) = {{0, OUTPUT_DESC(values)}, {1, OUTPUT_DESC(indices)}};
 
 // Multiply
 INPUT_MAP(Multiply) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
@@ -486,7 +452,7 @@ ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
 OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}};
 
 // ResizeNearestNeighborD
-INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(images)}};
+INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ResizeNearestNeighborD) = {
   {"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -511,9 +477,9 @@ OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}};
 #endif
 
 // Relu6
-INPUT_MAP(Relu6) = {{1, INPUT_DESC(features)}};
+INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Relu6) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}};
+OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(y)}};
 
 // Relu6Grad
 INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}};
@@ -525,8 +491,8 @@ INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original
 ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
 OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}};
 
-// ResizeBilinear
-INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(images)}};
+// ResizeBilinearD
+INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ResizeBilinearD) = {
   {"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -549,9 +515,9 @@ OUTPUT_MAP(NMSWithMask) = {
   {0, OUTPUT_DESC(selected_boxes)}, {1, OUTPUT_DESC(selected_idx)}, {2, OUTPUT_DESC(selected_mask)}};
 
 // Unpack
-INPUT_MAP(Unpack) = {{1, INPUT_DESC(value)}};
+INPUT_MAP(Unpack) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Unpack) = {{"axis", ATTR_DESC(axis, AnyTraits<int>())}, {"num", ATTR_DESC(num, AnyTraits<int>())}};
-DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(output)}};
+DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(y)}};
 
 // ScatterNdUpdate
 INPUT_MAP(ScatterNdUpdate) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(indices)}, {3, INPUT_DESC(updates)}};
@@ -584,8 +550,8 @@ INPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {
 ATTR_MAP(SigmoidCrossEntropyWithLogitsGrad) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {{0, OUTPUT_DESC(gradient)}};
 
-// ScatterNd
-INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(updates)}};
+// ScatterNdD
+INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ScatterNdD) = {
   {3, ATTR_DESC(shape, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ScatterNdD) = EMPTY_ATTR_MAP;
@@ -596,24 +562,14 @@ INPUT_MAP(PadD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::vector<int64_t>>>())}};
 OUTPUT_MAP(PadD) = {{0, OUTPUT_DESC(y)}};
 
-// MirrorPad
-INPUT_MAP(MirrorPad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
-ATTR_MAP(MirrorPad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
-OUTPUT_MAP(MirrorPad) = {{0, OUTPUT_DESC(y)}};
-
-// MirrorPadGrad
-INPUT_MAP(MirrorPadGrad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
-ATTR_MAP(MirrorPadGrad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
-OUTPUT_MAP(MirrorPadGrad) = {{0, OUTPUT_DESC(y)}};
-
 // GatherNd
-INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
+INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}};
 ATTR_MAP(GatherNd) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(GatherNd) = {{0, OUTPUT_DESC(y)}};
 
 // ROIAlign
 INPUT_MAP(ROIAlign) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(rois)}};
-OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(output)}};
+OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(y)}};
 ATTR_MAP(ROIAlign) = {{"pooled_height", ATTR_DESC(pooled_height, AnyTraits<int>())},
                       {"pooled_width", ATTR_DESC(pooled_width, AnyTraits<int>())},
                       {"spatial_scale", ATTR_DESC(spatial_scale, AnyTraits<float>())},
@@ -632,13 +588,13 @@ ATTR_MAP(ROIAlignGrad) = {
 // ArgMaxD
 INPUT_MAP(ArgMaxD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMaxD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMaxD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMinD
 INPUT_MAP(ArgMinD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMinD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMaxWithValue
@@ -654,14 +610,14 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
 OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}};
 
 // ReduceAll
-INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}};
+INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
 ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceMeanD
 INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMeanD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMeanD) = {{0, OUTPUT_DESC(y)}};
 
@@ -728,11 +684,12 @@ INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
 OUTPUT_MAP(BiasAddGrad) = {{0, OUTPUT_DESC(y)}};
 
-// maxpoolgrad
+// MaxPoolGrad
 INPUT_MAP(MaxPoolGrad) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}, {3, INPUT_DESC(grad)}};
 ATTR_MAP(MaxPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                          {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())},
+                         {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}};
 
 // avgpoolgrad
@@ -751,57 +708,55 @@ ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyT
 OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}};
 
 // MaxPoolGradWithArgmax
-INPUT_MAP(MaxPoolGradWithArgmax) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(grad)}, {3, INPUT_DESC(argmax)}};
+INPUT_MAP(MaxPoolGradWithArgmax) = {
+  {1, INPUT_DESC(x)},
+  {2, INPUT_DESC(grad)},
+  {3, INPUT_DESC(argmax)},
+};
 ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 
-// ExtractImagePatches
-INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(images)}};
-ATTR_MAP(ExtractImagePatches) = {{"ksizes", ATTR_DESC(ksizes, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"rates", ATTR_DESC(rates, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
-OUTPUT_MAP(ExtractImagePatches) = {{0, OUTPUT_DESC(y)}};
-
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
-ATTR_MAP(Conv2D) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-};
+ATTR_MAP(Conv2D) = {{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+                    {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+                    {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+                    {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+                    {"group", ATTR_DESC(groups, AnyTraits<int>())}};
 OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}};
 
 // Conv2DBackpropInputD
-INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filters)}};
+INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filter)}};
 INPUT_ATTR_MAP(Conv2DBackpropInputD) = {
-  {3, ATTR_DESC(input_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropInputD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-};
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+  {"group", ATTR_DESC(groups, AnyTraits<int>())}};
 OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}};
 
 // Conv2DBackpropFilterD
 INPUT_MAP(Conv2DBackpropFilterD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(Conv2DBackpropFilterD) = {
-  {3, ATTR_DESC(filter_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropFilterD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-};
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+  {"group", ATTR_DESC(groups, AnyTraits<int>())}};
 OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}};
 
 // DepthwiseConv2D
 INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(DepthwiseConv2D) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
   {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
 };
 OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}};
@@ -811,9 +766,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
   {1, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}};
 
@@ -822,16 +777,16 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
   {2, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}};
 
 // MatMul
 INPUT_MAP(MatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_a, AnyTraits<bool>())},
-                    {"transpose_b", ATTR_DESC(transpose_b, AnyTraits<bool>())}};
+ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_x1, AnyTraits<bool>())},
+                    {"transpose_b", ATTR_DESC(transpose_x2, AnyTraits<bool>())}};
 OUTPUT_MAP(MatMul) = {{0, OUTPUT_DESC(y)}};
 
 // Merge
@@ -878,10 +833,10 @@ ATTR_MAP(Sub) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Sub) = {{0, OUTPUT_DESC(y)}};
 
 // SplitD
-INPUT_MAP(SplitD) = {{1, INPUT_DESC(value)}};
+INPUT_MAP(SplitD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(SplitD) = {{"axis", ATTR_DESC(split_dim, AnyTraits<int>())},
                     {"output_num", ATTR_DESC(num_split, AnyTraits<int>())}};
-DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(output)}};
+DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(y)}};
 
 // Neg
 INPUT_MAP(Neg) = {{1, INPUT_DESC(x)}};
@@ -908,12 +863,12 @@ OUTPUT_MAP(Pack) = {{0, OUTPUT_DESC(y)}};
 
 // ConcatD
 INPUT_MAP(ConcatD) = EMPTY_INPUT_MAP;
-DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(input_values)}};
+DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(x)}};
 ATTR_MAP(ConcatD) = {
   {"axis", ATTR_DESC(concat_dim, AnyTraits<int>())},
   {"inputNums", ATTR_DESC(N, AnyTraits<int>())},
 };
-OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(output_data)}};
+OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(y)}};
 
 // Less
 INPUT_MAP(Less) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
@@ -948,14 +903,14 @@ OUTPUT_MAP(TanhGrad) = {{0, OUTPUT_DESC(z)}};
 // ReduceMinD
 INPUT_MAP(ReduceMinD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMinD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceMaxD
 INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMaxD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMaxD) = {{0, OUTPUT_DESC(y)}};
 
@@ -1040,11 +995,11 @@ INPUT_MAP(LessEqual) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(LessEqual) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(LessEqual) = {{0, OUTPUT_DESC(y)}};
 
-// LogSoftmax
-INPUT_MAP(LogSoftmax) = {{1, INPUT_DESC(logits)}};
-ATTR_MAP(LogSoftmax) = {
-  {"axis", ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
-OUTPUT_MAP(LogSoftmax) = {{0, OUTPUT_DESC(logsoftmax)}};
+// LogSoftmaxV2
+INPUT_MAP(LogSoftmaxV2) = {{1, INPUT_DESC(logits)}};
+ATTR_MAP(LogSoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+OUTPUT_MAP(LogSoftmaxV2) = {{0, OUTPUT_DESC(logsoftmax)}};
 
 // RandomChoiceWithMask
 INPUT_MAP(RandomChoiceWithMask) = {{1, INPUT_DESC(x)}};
@@ -1126,8 +1081,8 @@ OUTPUT_MAP(LayerNormGrad) = {{0, OUTPUT_DESC(pd_x)}, {1, OUTPUT_DESC(pd_gamma)},
 
 // BatchMatMul
 INPUT_MAP(BatchMatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x, AnyTraits<bool>())},
-                         {"transpose_x2", ATTR_DESC(adj_y, AnyTraits<bool>())}};
+ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x1, AnyTraits<bool>())},
+                         {"transpose_x2", ATTR_DESC(adj_x2, AnyTraits<bool>())}};
 OUTPUT_MAP(BatchMatMul) = {{0, OUTPUT_DESC(y)}};
 
 // DropoutDoMask
@@ -1178,6 +1133,19 @@ ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
                                  {"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}};
 
+// SparseApplyFtrlD
+INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)},
+                               {2, INPUT_DESC(accum)},
+                               {3, INPUT_DESC(linear)},
+                               {4, INPUT_DESC(grad)},
+                               {5, INPUT_DESC(indices)}};
+ATTR_MAP(SparseApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())},
+                              {"lr", ATTR_DESC(lr, AnyTraits<float>())},
+                              {"l1", ATTR_DESC(l1, AnyTraits<float>())},
+                              {"l2", ATTR_DESC(l2, AnyTraits<float>())},
+                              {"lr_power", ATTR_DESC(lr_power, AnyTraits<float>())}};
+OUTPUT_MAP(SparseApplyFtrlD) = {{0, OUTPUT_DESC(var)}};
+
 // SpaceToDepth
 INPUT_MAP(SpaceToDepth) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(SpaceToDepth) = {{"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())}};
diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h
index 933896a275..9fbc97f3c9 100755
--- a/mindspore/ccsrc/transform/op_declare.h
+++ b/mindspore/ccsrc/transform/op_declare.h
@@ -95,8 +95,6 @@ DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax)
 DECLARE_OP_ADAPTER(Conv2D)
 DECLARE_OP_USE_ENUM(Conv2D)
 DECLARE_OP_USE_OUTPUT(Conv2D)
-DECLARE_OP_ADAPTER(ExtractImagePatches)
-DECLARE_OP_USE_OUTPUT(ExtractImagePatches)
 DECLARE_OP_ADAPTER(Conv2DBackpropInputD)
 DECLARE_OP_USE_ENUM(Conv2DBackpropInputD)
 DECLARE_OP_USE_INPUT_ATTR(Conv2DBackpropInputD)
@@ -157,10 +155,6 @@ DECLARE_OP_USE_INPUT_ATTR(ScatterNdD)
 DECLARE_OP_USE_OUTPUT(ScatterNdD)
 DECLARE_OP_ADAPTER(PadD)
 DECLARE_OP_USE_OUTPUT(PadD)
-DECLARE_OP_ADAPTER(MirrorPad)
-DECLARE_OP_USE_OUTPUT(MirrorPad)
-DECLARE_OP_ADAPTER(MirrorPadGrad)
-DECLARE_OP_USE_OUTPUT(MirrorPadGrad)
 DECLARE_OP_ADAPTER(BoundingBoxEncode)
 DECLARE_OP_USE_OUTPUT(BoundingBoxEncode)
 DECLARE_OP_ADAPTER(BoundingBoxDecode)