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sync from mindspore to incubator

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pull/3198/head
yanghaoran 5 years ago
parent 69d1b4c07d 367e5765f4
commit 21c381b366
24 changed files with 720 additions and 15 deletions
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6
.gitmodules vendored
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@ -10,9 +10,9 @@
[submodule "third_party/protobuf"]
path = third_party/protobuf
url = https://github.com/protocolbuffers/protobuf.git
[submodule "graphengine"]
path = graphengine
url = https://gitee.com/mindspore/graphengine.git
[submodule "akg"]
path = akg
url = https://gitee.com/mindspore/akg.git
[submodule "graphengine"]
path = graphengine
url = https://gitee.com/ms-incubator/graphengine.git

2
graphengine

@ -1 +1 @@
Subproject commit dda72a48c7e0033389bd377c5804d485fdf3112d
Subproject commit 8891f0546c4a250095ff68e1262f58772b938fd9

2
mindspore/ccsrc/CMakeLists.txt
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@ -141,7 +141,7 @@ if (ENABLE_GE)
else ()
target_link_libraries(mindspore ge_client)
endif ()
target_link_libraries(mindspore graph tsdclient)
target_link_libraries(mindspore graph tsdclient datatransfer)
endif()
if (ENABLE_D)

1
mindspore/ccsrc/kernel/aicpu/aicpu_util.h
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@ -29,6 +29,7 @@ constexpr auto kInitData = "InitData";
constexpr auto kGetNext = "GetNext";
constexpr auto kPrint = "Print";
constexpr auto kPack = "Pack";
constexpr auto kOutputTypes = "output_types";
constexpr auto kOutputShapes = "output_shapes";
constexpr auto kChannelName = "channel_name";

1
mindspore/ccsrc/optimizer/irpass/ref_eliminate.h
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@ -58,7 +58,6 @@ class GetMakeRefEliminater : public OptimizerCaller {
MATCH_REPLACE(node, PPrimitive(prim::kPrimGetRefKey, PPrimitive(prim::kPrimMakeRef, x, y, z)), x);
MATCH_REPLACE(node, PPrimitive(prim::kPrimGetRefValue, PPrimitive(prim::kPrimMakeRef, x, y, z)), y);
MATCH_REPLACE(node, PPrimitive(prim::kPrimGetRefOrigin, PPrimitive(prim::kPrimMakeRef, x, y, z)), z);
return nullptr;
}
};

2
mindspore/ccsrc/transform/op_declare.cc
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@ -1168,7 +1168,7 @@ INPUT_MAP(SparseApplyAdagradD) = {
{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(grad)}, {4, INPUT_DESC(indices)}};
ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}};
OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}};
// ApplyProximalAdagradD
INPUT_MAP(ApplyProximalAdagradD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(lr)},

11
mindspore/ccsrc/utils/context/ms_context.cc
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@ -181,14 +181,21 @@ bool MsContext::OpenTsd() {
}
MS_LOG(INFO) << "Device id = " << device_id << ", rank size = " << rank_size << ".";
#if (defined(ENABLE_TDTQUE) && defined(ENABLE_GE))
int32_t initStatus = tdt::TdtHostInit(device_id);
if (initStatus != TDT_OK_CODE) {
MS_LOG(EXCEPTION) << "Init tsd failed, status = " << initStatus << ".";
return false;
}
tdt_print_ = std::thread(TensorPrint());
#endif
TDT_StatusT status = tdt::TsdClient::GetInstance()->Open(device_id, rank_size);
if (status != TDT_OK) {
MS_LOG(EXCEPTION) << "Device " << device_id << " is occupied, open tsd failed, status = " << status << ".";
return false;
}
tsd_ref_++;
#ifdef ENABLE_TDTQUE
#if (defined(ENABLE_TDTQUE) && !defined(ENABLE_GE))
int32_t initStatus = tdt::TdtHostInit(device_id);
if (initStatus != TDT_OK_CODE) {
MS_LOG(EXCEPTION) << "Init tsd failed, status = " << initStatus << ".";

1
mindspore/nn/optim/optimizer.py
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@ -342,7 +342,6 @@ class Optimizer(Cell):
current_dynamic_lr = self.gather(self.learning_rate[i], self.global_step, 0)
lr += (current_dynamic_lr,)
F.control_depend(lr, self.assignadd(self.global_step, 1))
else:
lr = self.learning_rate
if self.dynamic_lr:

12
mindspore/ops/_grad/grad_nn_ops.py
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@ -518,6 +518,18 @@ def get_bprop_l2_loss(self):
return bprop
@bprop_getters.register(P.RNNTLoss)
def get_bprop_rnnt_loss(self):
"""Grad definition for `RNNTLoss` operation."""
expand = P.ExpandDims()
def bprop(acts, labels, act_lens, label_lens, out, dout):
grad_loss = out[1]
grad = grad_loss * expand(expand(expand(dout[0], -1), -1), -1)
return grad, zeros_like(labels), zeros_like(act_lens), zeros_like(label_lens)
return bprop
@bprop_getters.register(P.PReLU)
def get_bprop_prelu(self):
"""Grad definition for `PReLU` operation."""

4
mindspore/ops/_op_impl/aicpu/__init__.py
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@ -14,6 +14,7 @@
"""aicpu ops"""
from .init_data_set_queue import _init_data_set_queue_aicpu
from .embedding_lookup import _embedding_lookup_aicpu
from .dropout_genmask import _dropout_genmask_aicpu
from .get_next import _get_next_aicpu
from .print_tensor import _print_aicpu
@ -29,3 +30,6 @@ from .normal import _normal_aicpu
from .ctcloss import _ctcloss_aicpu
from .reverse_sequence import _reverse_sequence_aicpu
from .crop_and_resize import _crop_and_resize_aicpu
from .rnnt_loss import _rnnt_loss_aicpu
from .random_categorical import _random_categorical_aicpu
from .cast import _cast_aicpu

172
mindspore/ops/_op_impl/aicpu/cast.py
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@ -0,0 +1,172 @@
# 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.
# ============================================================================
"""Cast op"""
from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
cast_op_info = AiCPURegOp("Cast") \
.fusion_type("OPAQUE") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U8_Default, DataType.U16_Default) \
.dtype_format(DataType.U8_Default, DataType.U32_Default) \
.dtype_format(DataType.U8_Default, DataType.U64_Default) \
.dtype_format(DataType.U8_Default, DataType.I8_Default) \
.dtype_format(DataType.U8_Default, DataType.I16_Default) \
.dtype_format(DataType.U8_Default, DataType.I32_Default) \
.dtype_format(DataType.U8_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.F16_Default) \
.dtype_format(DataType.U8_Default, DataType.F32_Default) \
.dtype_format(DataType.U8_Default, DataType.F64_Default) \
.dtype_format(DataType.U8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U16_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U16_Default, DataType.U32_Default) \
.dtype_format(DataType.U16_Default, DataType.U64_Default) \
.dtype_format(DataType.U16_Default, DataType.I8_Default) \
.dtype_format(DataType.U16_Default, DataType.I16_Default) \
.dtype_format(DataType.U16_Default, DataType.I32_Default) \
.dtype_format(DataType.U16_Default, DataType.I64_Default) \
.dtype_format(DataType.U16_Default, DataType.F16_Default) \
.dtype_format(DataType.U16_Default, DataType.F32_Default) \
.dtype_format(DataType.U16_Default, DataType.F64_Default) \
.dtype_format(DataType.U16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U32_Default, DataType.U8_Default) \
.dtype_format(DataType.U32_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U32_Default, DataType.U64_Default) \
.dtype_format(DataType.U32_Default, DataType.I8_Default) \
.dtype_format(DataType.U32_Default, DataType.I16_Default) \
.dtype_format(DataType.U32_Default, DataType.I32_Default) \
.dtype_format(DataType.U32_Default, DataType.I64_Default) \
.dtype_format(DataType.U32_Default, DataType.F16_Default) \
.dtype_format(DataType.U32_Default, DataType.F32_Default) \
.dtype_format(DataType.U32_Default, DataType.F64_Default) \
.dtype_format(DataType.U32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U64_Default, DataType.U8_Default) \
.dtype_format(DataType.U64_Default, DataType.U16_Default) \
.dtype_format(DataType.U64_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.U64_Default, DataType.I8_Default) \
.dtype_format(DataType.U64_Default, DataType.I16_Default) \
.dtype_format(DataType.U64_Default, DataType.I32_Default) \
.dtype_format(DataType.U64_Default, DataType.I64_Default) \
.dtype_format(DataType.U64_Default, DataType.F16_Default) \
.dtype_format(DataType.U64_Default, DataType.F32_Default) \
.dtype_format(DataType.U64_Default, DataType.F64_Default) \
.dtype_format(DataType.U64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I8_Default, DataType.U8_Default) \
.dtype_format(DataType.I8_Default, DataType.U16_Default) \
.dtype_format(DataType.I8_Default, DataType.U32_Default) \
.dtype_format(DataType.I8_Default, DataType.U64_Default) \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I8_Default, DataType.I16_Default) \
.dtype_format(DataType.I8_Default, DataType.I32_Default) \
.dtype_format(DataType.I8_Default, DataType.I64_Default) \
.dtype_format(DataType.I8_Default, DataType.F16_Default) \
.dtype_format(DataType.I8_Default, DataType.F32_Default) \
.dtype_format(DataType.I8_Default, DataType.F64_Default) \
.dtype_format(DataType.I8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.U8_Default) \
.dtype_format(DataType.I16_Default, DataType.U16_Default) \
.dtype_format(DataType.I16_Default, DataType.U32_Default) \
.dtype_format(DataType.I16_Default, DataType.U64_Default) \
.dtype_format(DataType.I16_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I16_Default, DataType.I32_Default) \
.dtype_format(DataType.I16_Default, DataType.I64_Default) \
.dtype_format(DataType.I16_Default, DataType.F16_Default) \
.dtype_format(DataType.I16_Default, DataType.F32_Default) \
.dtype_format(DataType.I16_Default, DataType.F64_Default) \
.dtype_format(DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.U8_Default) \
.dtype_format(DataType.I32_Default, DataType.U16_Default) \
.dtype_format(DataType.I32_Default, DataType.U32_Default) \
.dtype_format(DataType.I32_Default, DataType.U64_Default) \
.dtype_format(DataType.I32_Default, DataType.I8_Default) \
.dtype_format(DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.I32_Default, DataType.F16_Default) \
.dtype_format(DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.U8_Default) \
.dtype_format(DataType.I64_Default, DataType.U16_Default) \
.dtype_format(DataType.I64_Default, DataType.U32_Default) \
.dtype_format(DataType.I64_Default, DataType.U64_Default) \
.dtype_format(DataType.I64_Default, DataType.I8_Default) \
.dtype_format(DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.F64_Default) \
.dtype_format(DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F16_Default, DataType.U8_Default) \
.dtype_format(DataType.F16_Default, DataType.U16_Default) \
.dtype_format(DataType.F16_Default, DataType.U32_Default) \
.dtype_format(DataType.F16_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.I8_Default) \
.dtype_format(DataType.F16_Default, DataType.I16_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F16_Default, DataType.F32_Default) \
.dtype_format(DataType.F16_Default, DataType.F64_Default) \
.dtype_format(DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.U8_Default) \
.dtype_format(DataType.F32_Default, DataType.U16_Default) \
.dtype_format(DataType.F32_Default, DataType.U32_Default) \
.dtype_format(DataType.F32_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.I8_Default) \
.dtype_format(DataType.F32_Default, DataType.I16_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F32_Default, DataType.F64_Default) \
.dtype_format(DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.U8_Default) \
.dtype_format(DataType.F64_Default, DataType.U16_Default) \
.dtype_format(DataType.F64_Default, DataType.U32_Default) \
.dtype_format(DataType.F64_Default, DataType.U64_Default) \
.dtype_format(DataType.F64_Default, DataType.I8_Default) \
.dtype_format(DataType.F64_Default, DataType.I16_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default) \
.dtype_format(DataType.F64_Default, DataType.F16_Default) \
.dtype_format(DataType.F64_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.F64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U8_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I8_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(cast_op_info)
def _cast_aicpu():
"""Cast AiCPU register"""
return

102
mindspore/ops/_op_impl/aicpu/embedding_lookup.py
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@ -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.
# ============================================================================
"""EmbeddingLookup op"""
from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
embeddingLookup_op_info = AiCPURegOp("EmbeddingLookup") \
.fusion_type("OPAQUE") \
.input(0, "params", "required") \
.input(1, "indices", "required") \
.input(2, "offset", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I8_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I8_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I64_Default, \
DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I8_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I64_Default, \
DataType.I32_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(embeddingLookup_op_info)
def _embedding_lookup_aicpu():
"""EmbeddingLookup AiCPU register"""
return

48
mindspore/ops/_op_impl/aicpu/random_categorical.py
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@ -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.
# ============================================================================
"""RandomCategorical op"""
from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
random_categorical_op_info = AiCPURegOp("RandomCategorical") \
.fusion_type("OPAQUE") \
.input(0, "logits", "required") \
.input(1, "num_sample", "required") \
.input(2, "seed", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default, DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default, DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(random_categorical_op_info)
def _random_categorical_aicpu():
"""RandomCategorical AiCPU register"""
return

37
mindspore/ops/_op_impl/aicpu/rnnt_loss.py
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@ -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.
# ============================================================================
"""RNNTLoss op"""
from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
rnnt_loss_op_info = AiCPURegOp("RNNTLoss") \
.fusion_type("OPAQUE") \
.input(0, "acts", "required") \
.input(1, "labels", "required") \
.input(2, "input_lengths", "required") \
.input(3, "label_lengths", "required") \
.output(0, "costs", "required") \
.output(1, "grads", "required") \
.attr("blank_label", "int") \
.dtype_format(DataType.F32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW, DataType.F32_NCHW,
DataType.F32_NCHW) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(rnnt_loss_op_info)
def _rnnt_loss_aicpu():
"""RNNTLoss AiCPU register"""
return

11
mindspore/ops/operations/__init__.py
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@ -54,7 +54,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e,
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps)
from .random_ops import (RandomChoiceWithMask, Normal)
from .random_ops import (RandomChoiceWithMask, Normal, RandomCategorical)
from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, ApplyMomentum, BatchNorm,
BiasAdd, Conv2D,
DepthwiseConv2dNative,
@ -69,6 +69,7 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
ResizeBilinear, Sigmoid,
SigmoidCrossEntropyWithLogits,
SmoothL1Loss, Softmax, Softplus,
RNNTLoss,
SoftmaxCrossEntropyWithLogits, ROIAlign,
SparseSoftmaxCrossEntropyWithLogits, Tanh,
TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrl,
@ -77,6 +78,8 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
CheckValid, MakeRefKey, Partial, Depend, CheckBprop)
from . import _quant_ops
from ._quant_ops import *
from .thor_ops import *
__all__ = [
@ -168,6 +171,7 @@ __all__ = [
'Tanh',
'RandomChoiceWithMask',
'Normal',
'RandomCategorical',
'ResizeBilinear',
'ScalarSummary',
'ImageSummary',
@ -198,6 +202,7 @@ __all__ = [
'SmoothL1Loss',
'L2Loss',
'CTCLoss',
'RNNTLoss',
'ReduceAll',
'ScalarToArray',
'ScalarToTensor',
@ -302,6 +307,7 @@ __all__ = [
"ApplyCenteredRMSProp",
"SpaceToBatchND",
"BatchToSpaceND",
"ReverseSequence",
"SquareSumAll",
"BitwiseAnd",
"BitwiseOr",
@ -315,7 +321,8 @@ __all__ = [
"DataFormatDimMap",
"ApproximateEqual",
"InplaceUpdate",
"InTopK"
"InTopK",
"CropAndResize"
]
__all__.sort()

12
mindspore/ops/operations/_grad_ops.py
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@ -1093,8 +1093,18 @@ class StridedSliceGrad(PrimitiveWithInfer):
self.init_prim_io_names(inputs=['dy', 'shapex', 'begin', 'end', 'strides'], outputs=['output'])
def __infer__(self, dy, shapex, begin, end, strides):
args = {"shapex": shapex['dtype'],"begin": begin['dtype'],"end": end['dtype'],"strides": strides['dtype']}
args = {"dy": dy['dtype']}
validator.check_tensor_type_same(args, mstype.number_type, self.name)
for idx, item in enumerate(shapex['value']):
validator.check_value_type("shapex[%d]" % idx, item, [int], self.name)
for idx, item in enumerate(begin['value']):
validator.check_value_type("begin[%d]" % idx, item, [int], self.name)
for idx, item in enumerate(end['value']):
validator.check_value_type("end[%d]" % idx, item, [int], self.name)
for idx, item in enumerate(strides['value']):
validator.check_value_type("strides[%d]" % idx, item, [int], self.name)
return {'shape': shapex['value'],
'dtype': dy['dtype'],
'value': None}

54
mindspore/ops/operations/nn_ops.py
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@ -1697,6 +1697,60 @@ class DataFormatDimMap(PrimitiveWithInfer):
validator.check_tensor_type_same({"x": x_type}, valid_types, self.name)
return x_type
class RNNTLoss(PrimitiveWithInfer):
"""
Computes the RNNTLoss and its gradient with respect to the softmax outputs.
Args:
blank_label (int): blank label. Default: 0.
Inputs:
- **acts** (Tensor[float32]) - Tensor of shape :math:`(B, T, U, V)`.
- **labels** (Tensor[int32]) - Tensor of shape :math:`(B, N)`.
- **input_lengths** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
- **label_lebgths** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
Outputs:
- **costs** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
- **grads** (Tensor[int32]) - Has the same shape as `acts`.
Examples:
>>> B, T, U, V = 1, 2, 3, 5
>>> acts = np.random.random((B, T, U, V)).astype(np.float32)
>>> labels = np.array([[1, 2]]).astype(np.int32)
>>> input_length = np.array([T] * B).astype(np.int32)
>>> label_length = np.array([len(l) for l in labels]).astype(np.int32)
>>> rnnt_loss = P.RNNTLoss(blank_label=blank)
>>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
"""
@prim_attr_register
def __init__(self, blank_label=0):
validator.check_value_type('blank_label', blank_label, [int], self.name)
self.init_prim_io_names(inputs=['acts', 'labels', 'input_length', 'label_length'],
outputs=['costs', 'grads'])
def infer_shape(self, acts_shape, labels_shape, input_length_shape, label_length_shape):
validator.check_integer('acts_rank', len(acts_shape), 4, Rel.EQ, self.name)
validator.check_integer('labels_rank', len(labels_shape), 2, Rel.EQ, self.name)
validator.check_integer('input_length_rank', len(input_length_shape), 1, Rel.EQ, self.name)
validator.check_integer('label_length_rank', len(label_length_shape), 1, Rel.EQ, self.name)
validator.check('labels shape[0]', labels_shape[0], 'acts shape[0]', acts_shape[0], Rel.EQ, self.name)
validator.check('input_length size', input_length_shape[0], 'acts shape[0]', acts_shape[0], Rel.EQ, self.name)
validator.check('label_length size', label_length_shape[0], 'acts shape[0]', acts_shape[0], Rel.EQ, self.name)
costs_shape = (acts_shape[0],)
return (costs_shape, acts_shape)
def infer_dtype(self, acts_type, labels_type, input_length_type, label_length_type):
validator.check_subclass("acts_type", acts_type, mstype.tensor, self.name)
validator.check_subclass("labels_type", labels_type, mstype.tensor, self.name)
validator.check_subclass("input_length_type", input_length_type, mstype.tensor, self.name)
validator.check_subclass("label_length_type", label_length_type, mstype.tensor, self.name)
validator.check_tensor_type_same({"acts_type": acts_type}, [mstype.float32], self.name)
validator.check_tensor_type_same({"labels_type": labels_type}, [mstype.int32], self.name)
validator.check_tensor_type_same({"input_length_type": input_length_type}, [mstype.int32], self.name)
validator.check_tensor_type_same({"label_length_type": label_length_type}, [mstype.int32], self.name)
return (acts_type, acts_type)
class SGD(PrimitiveWithInfer):
"""

57
mindspore/ops/operations/random_ops.py
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@ -108,3 +108,60 @@ class Normal(PrimitiveWithInfer):
"dtype": mstype.float32,
"value": None}
return out
class RandomCategorical(PrimitiveWithInfer):
"""
Generates random samples from a given categorical distribution tensor.
Args:
dtype (mindspore.dtype): The type of output. Its value should be one of [mindspore.int16,
mindspore.int32, mindspore.int64]. Default: mindspore.int64.
Inputs:
- **logits** (Tensor) - The input tensor. 2-D Tensor with shape [batch_size, num_classes].
- **num_sample** (int) - Number of sample to be drawn. Only constant values is allowed.
- **seed** (int) - Random seed. Default: 0.
Outputs:
- **output** (Tensor) - The output Tensor with shape [batch_size, num_samples].
Examples:
>>> class Net(nn.Cell):
>>> def __init__(self, num_sample):
>>> super(Net, self).__init__()
>>> self.random_categorical = P.RandomCategorical(mindspore.int64)
>>> self.num_sample = num_sample
>>> def construct(self, logits, seed=0):
>>> return self.random_categorical(logits, self.num_sample, seed)
>>>
>>> x = np.random.random((10, 5)).astype(np.float32)
>>> net = Net(8)
>>> output = net(Tensor(x))
"""
@prim_attr_register
def __init__(self, dtype=mstype.int64):
"""Init RandomCategorical"""
self.dtype = dtype
valid_values = (mstype.int32, mstype.int16, mstype.int64)
validator.check_type_name("dtype", dtype, valid_values, self.name)
self.init_prim_io_names(inputs=['logits', 'num_samples', 'seed'],
outputs=['output'])
def __infer__(self, logits, num_samples, seed):
logits_dtype = logits['dtype']
valid_types = (mstype.float32, mstype.float16, mstype.float64)
validator.check_tensor_type_same({'logits': logits_dtype}, valid_types, self.name)
num_samples_v = num_samples['value']
seed_v = seed['value']
validator.check_value_type('num_samples', num_samples_v, (int,), self.name)
validator.check_value_type('seed', seed_v, (int,), self.name)
validator.check_integer("num_samples", num_samples_v, 0, Rel.GT, self.name)
x_shape = list(logits['shape'])
if len(x_shape) != 2:
raise ValueError("RandomCategorical shape should be 2-dimension.")
ndim = len(x_shape) - 1
x_shape[ndim] = num_samples_v
return {'shape': (x_shape),
'dtype': (self.dtype),
'value': None}

75
tests/st/ops/ascend/test_aicpu_ops/test_cast.py
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@ -0,0 +1,75 @@
# 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.common.dtype as mstype
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self, x, dtype):
super(Net, self).__init__()
self.cast = P.Cast()
self.x = x
self.dtype = dtype
def construct(self):
return self.cast(self.x, self.dtype)
def test_net_f32_bool():
x = np.random.randn(3,4).astype(np.float32)
x[:,1] = 0
net = Net(Tensor(x), mstype.bool_)
output = net()
print(output.asnumpy())
print(Tensor(x).dtype)
print(output.dtype)
def test_net_f16_bool():
x = np.random.randn(3,4).astype(np.float16)
x[:,1] = 0
net = Net(Tensor(x), mstype.bool_)
output = net()
print(output.asnumpy())
print(Tensor(x).dtype)
print(output.dtype)
def test_net_f64_bool():
x = np.random.randn(3,4).astype(np.float64)
x[:,1] = 0
net = Net(Tensor(x), mstype.bool_)
output = net()
print(output.asnumpy())
print(Tensor(x).dtype)
print(output.dtype)
def test_net_int16_float16():
x = np.random.randint(-512, 512, size=(3,4)).astype(np.int16)
net = Net(Tensor(x), mstype.float16)
output = net()
print(output.asnumpy())
print(Tensor(x).dtype)
print(output.dtype)
def test_net_int64_float16():
x = np.random.randint(-512, 512, size=(3,4)).astype(np.int64)
net = Net(Tensor(x), mstype.float16)
output = net()
print(output.asnumpy())
print(Tensor(x).dtype)
print(output.dtype)

1
tests/st/ops/ascend/test_aicpu_ops/test_pack.py
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@ -127,7 +127,6 @@ def test_net_int64():
print(output.asnumpy())
assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
def test_net_uint64():
x = np.random.randn(3, 5, 4).astype(np.uint64)
y = np.random.randn(3, 5, 4).astype(np.uint64)

38
tests/st/ops/ascend/test_aicpu_ops/test_random_categorical.py
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@ -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.
# ============================================================================
import mindspore
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(mode=context.GRAPH_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self, num_sample):
super(Net, self).__init__()
self.random_categorical = P.RandomCategorical(mindspore.int64)
self.num_sample = num_sample
def construct(self, logits, seed=0):
return self.random_categorical(logits, self.num_sample, seed)
def test_net():
x = np.random.random((10, 5)).astype(np.float32)
net = Net(8)
output = net(Tensor(x))
print(x)
print(output.asnumpy())
print(output.dtype())

43
tests/st/ops/ascend/test_aicpu_ops/test_rnnt_loss.py
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@ -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.
# ============================================================================
import mindspore as ms
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(mode=context.GRAPH_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.rnnt_loss = P.RNNTLoss(blank_label=0)
def construct(self, acts, labels, act_lens, label_lens):
return self.rnnt_loss(acts, labels, act_lens, label_lens)
def test_net():
B, T, U, V = 1, 2, 3, 5
acts = np.random.random((B, T, U, V)).astype(np.float32)
labels = np.array([[np.random.randint(1, V-1) for _ in range(U-1)]]).astype(np.int32)
input_length = np.array([T] * B).astype(np.int32)
label_length = np.array([len(l) for l in labels]).astype(np.int32)
rnnt_loss = Net()
costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
print(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
print(costs.asnumpy())
print(grads.asnumpy())

42
tests/st/ops/ascend/test_embedding_lookup.py
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@ -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.context as context
import mindspore.common.dtype as mstype
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, offset):
super(Net, self).__init__()
self.embedding = P.EmbeddingLookup()
self.offset = offset
def construct(self, param, index):
return self.embedding(param, index, self.offset)
def test_embedding_lookup_sparse():
params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mstype.int32)
indices = Tensor(np.array([[5, 2], [8, 5]]), mstype.int32)
offset = 4
embedding = Net(offset)
out = embedding(params, indices)
assert(out.asnumpy() == [[[10, 11], [0, 0]], [[0, 0], [10, 11]]]).all()

1
tests/ut/python/optimizer/test_optimizer_with_parameter_groups.py
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@ -29,7 +29,6 @@ context.set_context(mode=context.GRAPH_MODE)
class LeNet5(nn.Cell):
""" LeNet5 definition """
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')

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