add range op

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -135,6 +135,7 @@ REGISTER(EluInfo);
 REGISTER(ReLUInfo);
 REGISTER(RepeatElementsInfo);
 REGISTER(TensorDotInfo);
+REGISTER(RangeInfo);
 REGISTER(ReLU6Info);
 REGISTER(ReLUV2Info);
 REGISTER(SoftplusInfo);

mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h

@@ -43,6 +43,7 @@
 #include "frontend/parallel/ops_info/concat_info.h"
 #include "frontend/parallel/ops_info/split_info.h"
 #include "frontend/parallel/ops_info/tensordot_info.h"
+#include "frontend/parallel/ops_info/range_info.h"
 #include "frontend/parallel/ops_info/pack_info.h"
 #include "frontend/parallel/ops_info/broadcast_to_info.h"
 #include "frontend/parallel/ops_info/unique_info.h"

mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h

@@ -104,6 +104,9 @@ constexpr char GROUP[] = "group";
 constexpr char FUSION[] = "fusion";
 constexpr char AXIS[] = "axis";
 constexpr char AXES[] = "axes";
+constexpr char START[] = "start";
+constexpr char LIMIT[] = "limit";
+constexpr char DELTA[] = "delta";
 constexpr char OUTPUT_NUM[] = "output_num";
 constexpr char SPLIT_COUNT[] = "split_count";
 constexpr char SPLIT_DIM[] = "split_dim";
@@ -193,6 +196,7 @@ constexpr char SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS[] = "SparseSoftmaxCrossE
 constexpr char RELU[] = "ReLU";
 constexpr char REPEAT_ELEMENTS[] = "RepeatElements";
 constexpr char TENSOR_DOT[] = "TensorDot";
+constexpr char RANGE[] = "Range";
 constexpr char ONEHOT[] = "OneHot";
 constexpr char DROPOUT_DO_MASK[] = "DropoutDoMask";
 constexpr char DROPOUT_GEN_MASK[] = "DropoutGenMask";

mindspore/ccsrc/frontend/parallel/ops_info/range_info.cc

@@ -0,0 +1,214 @@
+/**
+ * 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 "frontend/parallel/ops_info/range_info.h"
+
+#include <algorithm>
+#include <functional>
+#include <memory>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "ir/value.h"
+#include "frontend/parallel/auto_parallel/graph_costmodel.h"
+#include "frontend/parallel/device_manager.h"
+#include "frontend/parallel/device_matrix.h"
+#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
+#include "frontend/parallel/graph_util/generate_graph.h"
+
+namespace mindspore {
+namespace parallel {
+float RangeInfo::GetRangeAttr(const std::string &arg) {
+  auto iter = attrs_.find(arg);
+  if (iter == attrs_.end()) {
+    MS_LOG(EXCEPTION) << name_ << ": Can not find the attr for " << arg;
+  }
+
+  MS_EXCEPTION_IF_NULL(iter->second);
+  if (!iter->second->isa<FP32Imm>()) {
+    MS_LOG(EXCEPTION) << name_ << ": The type of attr is not float, the attr is " << arg;
+  }
+
+  return iter->second->cast<FP32ImmPtr>()->value();
+}
+
+Status RangeInfo::GetAttrs() {
+  start_ = GetRangeAttr(START);
+  limit_ = GetRangeAttr(LIMIT);
+  delta_ = GetRangeAttr(DELTA);
+  MS_LOG(INFO) << name_ << ": The start is " << start_ << ", the limit is " << limit_ << ", the delta is " << delta_;
+  return SUCCESS;
+}
+
+Status RangeInfo::CheckStrategy(const StrategyPtr &strategy) {
+  MS_EXCEPTION_IF_NULL(strategy);
+  if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status RangeInfo::InferDevMatrixShape() {
+  Strategys stra = strategy_->GetInputDim();
+  dev_matrix_shape_ = stra[0];
+  split_num_ = stra[0][0];
+  return SUCCESS;
+}
+
+Status RangeInfo::InferMirrorOps() { return SUCCESS; }
+
+Status RangeInfo::InferForwardCommunication() { return SUCCESS; }
+
+Status RangeInfo::InferTensorMap() {
+  TensorMap input_tensor_map = {0}, output_tensor_map = {0};
+
+  inputs_tensor_map_.push_back(input_tensor_map);
+  outputs_tensor_map_.push_back(output_tensor_map);
+  return SUCCESS;
+}
+
+Status RangeInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": Invalid args";
+    return FAILED;
+  }
+
+  TensorLayout input_layout, output_layout;
+  for (size_t i = 0; i < inputs_shape_.size(); ++i) {
+    // infer tensor layout
+    if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[i], inputs_shape_[i]) != SUCCESS) {
+      MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed.";
+      return FAILED;
+    }
+    TensorInfo input_tensor_info(input_layout);
+    inputs_tensor_info_.push_back(input_tensor_info);
+  }
+
+  if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed.";
+    return FAILED;
+  }
+  TensorInfo output_tensor_info(output_layout);
+  outputs_tensor_info_.push_back(output_tensor_info);
+
+  for (auto &tensor_info : inputs_tensor_info_) {
+    MS_LOG(INFO) << name_ << ": The input layout: " << tensor_info.tensor_layout().ToString();
+  }
+  MS_LOG(INFO) << name_ << ": The output layout: " << outputs_tensor_info_[0].tensor_layout().ToString();
+  return SUCCESS;
+}
+
+Status RangeInfo::Init(const StrategyPtr &strategy) {
+  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init failed";
+    return FAILED;
+  }
+
+  MS_LOG(INFO) << name_ << ": Init success";
+  return SUCCESS;
+}
+
+Status RangeInfo::InitForCostModel(const StrategyPtr &strategy) {
+  if (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;
+}
+
+Status RangeInfo::InferNewAttr() {
+  CheckGlobalDeviceManager();
+  int64_t rank = g_device_manager->global_rank();
+
+  // If repeated calculation and repeated num as the last dimension of dev-matrix,
+  // the dev-matrix is [split_num_, repeated_calc_num_], so from rank 0 to rank repeated_calc_num_
+  // are repeated calculation, and these rank have the same 'new_start_'.
+  // If repeated calculation and repeated num as the first dimension of dev-matrix,
+  // the dev-matrix is [repeated_calc_num_, split_num_], so rank 0 and rank split_num_ and so on
+  // are repeated calculation, and these rank have the same 'new_start_'.
+  float start_bias = inputs_shape_[0][0] / split_num_ * delta_;
+  if (repeated_num_in_dev_matrix_right_) {
+    new_start_ = start_ + start_bias * (rank / repeated_calc_num_);
+  } else {
+    new_start_ = start_ + start_bias * (rank % split_num_);
+  }
+
+  new_limit_ = new_start_ + start_bias;
+  MS_LOG(INFO) << name_ << ": The new start is " << new_start_ << ", the new limit is " << new_limit_;
+  return SUCCESS;
+}
+
+Status RangeInfo::ComputeReplaceGraph(const CNodePtr &cnode) {
+  GenerateGraph gen_g = GenerateGraph();
+  if (gen_g.Init(cnode) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": GenerateGraph Init failed";
+    return FAILED;
+  }
+
+  if (InferNewAttr() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer new attr failed";
+    return FAILED;
+  }
+
+  Attr attr_start = std::make_pair(START, MakeValue(new_start_));
+  Attr attr_limit = std::make_pair(LIMIT, MakeValue(new_limit_));
+  Attr attr_delta = std::make_pair(DELTA, MakeValue(delta_));
+  OperatorAttrs attrs = {attr_start, attr_limit, attr_delta};
+  auto new_range_op = gen_g.PushBack({gen_g.NewOpInst(RANGE, attrs), gen_g.virtual_input_node()});
+  std::vector<std::pair<AnfNodePtr, int64_t>> input_nodes = {std::make_pair(new_range_op, 1)};
+  replace_graph_ = std::make_shared<std::pair<std::vector<std::pair<AnfNodePtr, int64_t>>, AnfNodePtr>>(
+    std::make_pair(input_nodes, new_range_op));
+
+  return SUCCESS;
+}
+
+ReplaceGraphPtr RangeInfo::replace_graph(const CNodePtr &cnode) {
+  if (ComputeReplaceGraph(cnode) != SUCCESS) {
+    MS_LOG(EXCEPTION) << name_ << ": ComputeReplaceGraph failed.";
+  }
+  return replace_graph_;
+}
+
+Status RangeInfo::GenerateStrategies(int64_t stage_id) {
+  Shape input0_split(inputs_shape_[0].size(), 1);
+  Shapes splittable_inputs = {input0_split};
+
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, inputs_shape_, 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 RangeInfo::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr &strategy) {
+  return SetCostUnderStrategyBase(strategy);
+}
+}  // namespace parallel
+}  // namespace mindspore

mindspore/ccsrc/frontend/parallel/ops_info/range_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_FRONTEND_PARALLEL_OPS_INFO_RANGE_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_RANGE_INFO_H_
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "utils/ms_utils.h"
+#include "ir/value.h"
+#include "frontend/parallel/auto_parallel/operator_costmodel.h"
+#include "frontend/parallel/ops_info/operator_info.h"
+#include "frontend/parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+// Range op:
+//    (start=8.0, limit=16.0, delta=1.0) -> [8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]
+//    (start=8.0, limit=None, delta=1.0) -> [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]
+// when entering the step_parallel, the limit=None has been processed
+// the parallel op need to modify the 'start' and 'limit'
+class RangeInfo : public OperatorInfo {
+ public:
+  RangeInfo(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<ActivationCost>(true)) {}
+  ~RangeInfo() override = default;
+
+  Status Init(const StrategyPtr &strategy) override;
+  Status InitForCostModel(const StrategyPtr &strategy) override;
+
+  Status GenerateStrategies(int64_t stage_id) override;
+  Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
+  ReplaceGraphPtr replace_graph(const CNodePtr &cnode) override;
+
+ protected:
+  Status CheckStrategy(const StrategyPtr &strategy) override;
+  Status InferMirrorOps() override;
+  Status InferForwardCommunication() override;
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+  Status GetAttrs() override;
+  Status InferNewAttr();
+  float GetRangeAttr(const std::string &arg);
+  Status ComputeReplaceGraph(const CNodePtr &cnode);
+
+  float start_ = 0.0;
+  float limit_ = 0.0;
+  float delta_ = 0.0;
+  float new_start_ = 0.0;
+  float new_limit_ = 0.0;
+  int64_t split_num_ = 1;
+};
+}  // namespace parallel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_RANGE_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -316,7 +316,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      EXPM1, LOG1P, SIN, SINH, TAN, RSQRT, INV, RECIPROCAL, ROUND, FLOOR, SIGN, ERF, ERFC, ZEROSLIKE, ONESLIKE,
      BESSELI0E, BESSELI1E, FLOORMOD, ASSIGN, ASSIGN_ADD, ATAN2, DIVNONAN, LOGICALAND, LOGICALOR, ELU, RELU6, RELUV2,
      SOFTPLUS, SOFTSIGN, GREATEREQUAL, LESSEQUAL, LESS, APPROXIMATEEQUAL, MOD, UNIQUE, UNSORTED_SEGMENT_SUM,
-     UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT};
+     UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT, RANGE};
   // clang-format on
 
   auto iter = splittable_op.find(op_name);

tests/ut/python/parallel/test_range.py

@@ -0,0 +1,102 @@
+# 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
+import mindspore.nn as nn
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, Momentum
+from mindspore.ops import operations as P
+from mindspore.train import Model
+from tests.dataset_mock import MindData
+
+
+class Dataset(MindData):
+    def __init__(self, predict, label, length=3):
+        super(Dataset, self).__init__(size=length)
+        self.predict = predict
+        self.label = label
+        self.index = 0
+        self.length = length
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= self.length:
+            raise StopIteration
+        self.index += 1
+        return self.predict, self.label
+
+    def reset(self):
+        self.index = 0
+
+
+class Net(Cell):
+    def __init__(self, weight, start, limit, delta, strategy1=None, strategy2=None, strategy3=None):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.range = nn.Range(start, limit, delta)
+        self.range.range_x.shard(strategy2)
+        self.mul2 = P.Mul().shard(strategy3)
+        self.weight = Parameter(weight, "w")
+
+
+    def construct(self, x, b):
+        r_out = self.range()
+        out = self.mul(x, self.weight)
+        out = self.mul2(out, r_out)
+        return out
+
+dev_num = 4
+_x = Tensor(np.ones([64 // dev_num, 8]), dtype=ms.float32)
+_b = Tensor(np.ones([8]), dtype=ms.float32)
+_w1 = Tensor(np.ones([64, 8]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    learning_rate = 0.1
+    momentum = 0.9
+    epoch_size = 2
+    dataset = Dataset(_x, _b)
+    opt = Momentum(net.trainable_params(), learning_rate, momentum)
+    model = Model(net, optimizer=opt)
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    context.reset_auto_parallel_context()
+
+
+def test_range():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=dev_num, global_rank=2)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((2,),)
+    strategy3 = ((2, 2), (2,))
+    net = Net(_w1, 0, 8, 1, strategy1, strategy2, strategy3)
+    compile_net(net)
+
+
+def test_range2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=dev_num, global_rank=0)
+    strategy1 = ((4, 1), (4, 1))
+    strategy2 = ((1,),)
+    strategy3 = ((4, 1), (1,))
+    net = Net(_w1, 0.0, 4.0, 0.5, strategy1, strategy2, strategy3)
+    compile_net(net)
+
+
+def test_range3():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=dev_num, global_rank=2)
+    net = Net(_w1, 4.0, None, 0.5)
+    compile_net(net)