forked from mindspore-Ecosystem/mindspore
implement parallel Split
This commit is contained in:
parent
97e8742f84
commit
18ed2bec53
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@ -201,6 +201,8 @@ using TileCost = SoftmaxCost;
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using TileCostPtr = std::shared_ptr<TileCost>;
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using ConcatCost = TileCost;
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using ConcatCostPtr = std::shared_ptr<ConcatCost>;
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using SplitCost = TileCost;
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using SplitCostPtr = std::shared_ptr<SplitCost>;
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class TmpIdentityCost : public OperatorCost {
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public:
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@ -179,6 +179,7 @@ REGISTER(TileInfo);
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REGISTER(StridedSliceInfo);
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REGISTER(DropoutInfo);
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REGISTER(ConcatInfo);
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REGISTER(SplitInfo);
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} // namespace parallel
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} // namespace mindspore
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@ -40,5 +40,6 @@
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#include "frontend/parallel/ops_info/tile_info.h"
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#include "frontend/parallel/ops_info/strided_slice_info.h"
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#include "frontend/parallel/ops_info/concat_info.h"
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#include "frontend/parallel/ops_info/split_info.h"
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#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_
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@ -0,0 +1,294 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "frontend/parallel/ops_info/split_info.h"
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#include <string>
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#include <memory>
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#include <vector>
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#include "frontend/parallel/device_matrix.h"
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#include "frontend/parallel/strategy.h"
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#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
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#include "frontend/parallel/context.h"
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#include "pipeline/jit/resource.h"
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namespace mindspore {
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namespace parallel {
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Status SplitInfo::GetAttrs() {
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int axis = 0;
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int output_num = 0;
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auto axis_iter = attrs_.find(AXIS);
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if (axis_iter != attrs_.end()) {
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MS_EXCEPTION_IF_NULL(axis_iter->second);
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if (axis_iter->second->isa<Int32Imm>()) {
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axis = axis_iter->second->cast<Int32ImmPtr>()->value();
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} else {
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MS_LOG(ERROR) << name_ << ": The value of axis is not int";
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return FAILED;
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}
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} else {
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MS_LOG(ERROR) << name_ << ": Can not find the axis attr";
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return FAILED;
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}
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if (inputs_shape_.empty()) {
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MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
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return FAILED;
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}
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int dim = SizeToInt(inputs_shape_[0].size());
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if (axis < 0) {
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axis = axis + dim;
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}
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axis_ = SizeToInt(axis);
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auto output_num_iter = attrs_.find(OUTPUT_NUM);
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if (output_num_iter != attrs_.end()) {
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MS_EXCEPTION_IF_NULL(output_num_iter->second);
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if (output_num_iter->second->isa<Int32Imm>()) {
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output_num = output_num_iter->second->cast<Int32ImmPtr>()->value();
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} else {
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MS_LOG(ERROR) << name_ << ": The value of output_num is not int";
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return FAILED;
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}
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} else {
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MS_LOG(ERROR) << name_ << ": Can not find the output_num attr";
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return FAILED;
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}
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output_num_ = output_num;
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return SUCCESS;
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}
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Status SplitInfo::CheckStrategy(const StrategyPtr &strategy) {
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MS_EXCEPTION_IF_NULL(strategy);
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if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Invalid strategy";
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return FAILED;
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}
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std::vector<Dimensions> stra = strategy->GetInputDim();
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if (stra.empty()) {
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MS_LOG(ERROR) << name_ << ": The strategy is empty";
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return FAILED;
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}
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if (axis_ >= stra[0].size()) {
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MS_LOG(ERROR) << name_ << ": The axis is out of range, the axis is " << axis_;
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return FAILED;
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}
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if (stra[0][axis_] != 1) {
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MS_LOG(ERROR) << name_ << ": The axis can not be split";
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return FAILED;
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}
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return SUCCESS;
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}
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Status SplitInfo::InferDevMatrixShape() {
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MS_EXCEPTION_IF_NULL(strategy_);
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std::vector<Dimensions> stra = strategy_->GetInputDim();
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if (stra.empty()) {
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MS_LOG(ERROR) << name_ << "The strategy is empty";
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return FAILED;
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}
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dev_matrix_shape_ = stra[0];
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return SUCCESS;
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}
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Status SplitInfo::InferTensorMap() {
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TensorMap tensor_map;
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if (inputs_shape_.empty()) {
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MS_LOG(ERROR) << name_ << "The inputs shape is empty";
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return FAILED;
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}
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int32_t size = SizeToInt(inputs_shape_[0].size());
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for (int i = 0; i < size; ++i) {
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tensor_map.push_back(size - i - 1);
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}
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inputs_tensor_map_.push_back(tensor_map);
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for (size_t i = 0; i < outputs_shape_.size(); ++i) {
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outputs_tensor_map_.push_back(tensor_map);
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}
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return SUCCESS;
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}
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Status SplitInfo::InferMirrorOps() {
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mirror_ops_.clear();
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if (inputs_tensor_map_.empty()) {
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MS_LOG(ERROR) << name_ << ": The inputs tensor map is empty";
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return FAILED;
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}
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Shape input_tensor_map = inputs_tensor_map_[0];
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std::vector<Group> group;
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if (CreateGroupByTensorMap(input_tensor_map, &group) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Create group for input failed.";
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return FAILED;
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}
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OperatorVector mirror_op;
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if (group.empty()) {
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MS_LOG(INFO) << name_ << ": The mirror group is empty.";
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return SUCCESS;
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} else {
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mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
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mirror_ops_.push_back(mirror_op);
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std::string group_name = group[0].name();
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MS_LOG(INFO) << name_ << " : Create the mirror ops success, the group name is " << group_name;
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}
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return SUCCESS;
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}
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Status SplitInfo::InferTensorInfo() {
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if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
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MS_LOG(ERROR) << name_ << ": Invalid args";
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return FAILED;
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}
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TensorLayout input_layout, output_layout;
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// infer tensor layout
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if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], inputs_shape_[0]) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed.";
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return FAILED;
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}
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TensorInfo input_tensor_info(input_layout);
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inputs_tensor_info_.push_back(input_tensor_info);
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if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed.";
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return FAILED;
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}
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for (size_t i = 0; i < outputs_shape_.size(); ++i) {
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TensorInfo output_tensor_info(output_layout);
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outputs_tensor_info_.push_back(output_tensor_info);
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}
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return SUCCESS;
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}
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Status SplitInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
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Status SplitInfo::GenerateStrategies(int32_t stage_id) {
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if (InferAttrs() != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Infer attrs failed";
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return FAILED;
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}
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if (inputs_shape_.empty()) {
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MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
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return FAILED;
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}
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Shape input_split;
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for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
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if (i == axis_) {
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input_split.push_back(0);
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} else {
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input_split.push_back(1);
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}
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}
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Shapes splittable_input = {input_split};
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Shapes tmp_inputs_shape = {inputs_shape_[0]};
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std::vector<StrategyPtr> sp_vector;
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if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_input, &sp_vector) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Generate strategies failed";
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return FAILED;
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}
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size_t success = 0;
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for (auto &sp : sp_vector) {
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PrintStrategy(sp);
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if (SetCostUnderStrategy(sp) == SUCCESS) {
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success++;
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MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy.";
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PrintStrategy(sp);
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}
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}
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return SUCCESS;
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}
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std::shared_ptr<Strategys> SplitInfo::GenerateBatchStrategies() {
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if (GetAttrs() != SUCCESS) {
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MS_LOG(EXCEPTION) << name_ << ": Get attr failed";
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}
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CheckGlobalDeviceManager();
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size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
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Dimensions input_strategy(inputs_shape_[0].size(), 1);
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// axis can't split
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if (inputs_shape_[0].size() > 1) {
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if (axis_ == 0) {
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input_strategy[1] = dev_num;
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} else {
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input_strategy[0] = dev_num;
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}
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}
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Strategys strategy_v = {input_strategy};
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return std::make_shared<Strategys>(strategy_v);
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}
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Status SplitInfo::InferAsLossDivisor() {
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if (!ParallelContext::GetInstance()->loss_repeated_mean()) {
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as_loss_divisor_ = 1;
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return SUCCESS;
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}
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if (outputs_tensor_map_.empty()) {
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MS_LOG(ERROR) << name_ << ": The outputs tensor map is empty.";
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return FAILED;
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}
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if (outputs_tensor_map_[0].empty()) {
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as_loss_divisor_ = SizeToInt(global_device_list_.size());
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MS_LOG(INFO) << name_ << ": The output is a scalar, use the dev size " << as_loss_divisor_ << ", loss divisor.";
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return SUCCESS;
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}
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as_loss_divisor_ = ComputeRepeatDeviceNumByTensorMap(dev_matrix_shape_, outputs_tensor_map_[0]);
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MS_LOG(INFO) << name_ << ": the dev matrix shape is " << ShapeToString(dev_matrix_shape_)
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<< ", the output tensor map is " << ShapeToString(outputs_tensor_map_[0]) << ", loss divisor is "
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<< as_loss_divisor_;
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return SUCCESS;
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}
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Status SplitInfo::Init(const StrategyPtr &strategy) {
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if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Init failed.";
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return FAILED;
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}
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MS_LOG(INFO) << name_ << ": Init success.";
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return SUCCESS;
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}
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Status SplitInfo::InitForCostModel(const StrategyPtr &strategy) {
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if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
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MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
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return FAILED;
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}
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MS_LOG(INFO) << name_ << ": Init for cost model success.";
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return SUCCESS;
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}
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} // namespace parallel
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} // namespace mindspore
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@ -0,0 +1,60 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_
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#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_
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#include <string>
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#include <memory>
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#include "ir/value.h"
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#include "frontend/parallel/auto_parallel/operator_costmodel.h"
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#include "frontend/parallel/ops_info/operator_info.h"
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#include "frontend/parallel/strategy.h"
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namespace mindspore {
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namespace parallel {
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class SplitInfo : public OperatorInfo {
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public:
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SplitInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
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const PrimitiveAttrs &attrs)
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: OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<ConcatCost>(false)) {}
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~SplitInfo() override = default;
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Status Init(const StrategyPtr &strategy) override;
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Status InitForCostModel(const StrategyPtr &strategy) override;
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Status GenerateStrategies(int32_t) override;
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std::shared_ptr<Strategys> GenerateBatchStrategies() override;
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Status SetCostUnderStrategy(const StrategyPtr &) override;
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protected:
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Status GetAttrs() override;
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Status CheckStrategy(const StrategyPtr &strategy) override;
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Status InferMirrorOps() override;
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Status InferForwardCommunication() override { return SUCCESS; }
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Status InferTensorInfo() override;
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Status InferDevMatrixShape() override;
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Status InferTensorMap() override;
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Status InferAsLossDivisor() override;
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private:
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size_t axis_ = 0;
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size_t output_num_ = 0;
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};
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} // namespace parallel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_
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@ -263,7 +263,7 @@ bool IsSplittableOperator(const std::string &op_name) {
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LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT, CONCAT,
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STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2, TILE, DROPOUT,
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SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, SIGMOID_CROSS_ENTROPY_WITH_LOGITS, SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS,
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EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX};
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EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX, SPLIT};
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// clang-format on
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auto iter = splittable_op.find(op_name);
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@ -0,0 +1,147 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None, strategy3=None):
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super(Net, self).__init__()
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self.split = P.Split(axis, out_nums).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.matmul = P.MatMul(transpose_b=True).shard(strategy2)
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self.matmul2 = P.MatMul().shard(strategy3)
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.weight)
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out1, out2, out3 = self.split(out)
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out = self.matmul(out1, out2)
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out = self.matmul2(out, out3)
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return out
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class Net1(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
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super(Net1, self).__init__()
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self.split = P.Split(axis, out_nums).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out1, out2 = self.split(self.weight)
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out = self.mul(x, out1)
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out = self.mul(out, out2)
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return out
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class Net2(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
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super(Net2, self).__init__()
|
||||
self.split = P.Split(axis, out_nums).shard(strategy1)
|
||||
self.mul = P.Mul().shard(strategy2)
|
||||
self.weight = Parameter(mul_weight, "w1")
|
||||
|
||||
def construct(self, x):
|
||||
out = self.mul(x, self.weight)
|
||||
out1, _ = self.split(out)
|
||||
return out1
|
||||
|
||||
|
||||
_w = Tensor(np.ones([48, 64]), dtype=ms.float32)
|
||||
_x = Tensor(np.ones([48, 64]), dtype=ms.float32)
|
||||
|
||||
_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
|
||||
_x1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
|
||||
|
||||
_w2 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
|
||||
|
||||
def compile_net(net):
|
||||
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
||||
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
train_net = TrainOneStepCell(net, optimizer)
|
||||
train_net.set_auto_parallel()
|
||||
_executor.compile(train_net, _x)
|
||||
context.reset_auto_parallel_context()
|
||||
|
||||
|
||||
def compile_net1(net):
|
||||
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
||||
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
train_net = TrainOneStepCell(net, optimizer)
|
||||
train_net.set_auto_parallel()
|
||||
_executor.compile(train_net, _x1)
|
||||
context.reset_auto_parallel_context()
|
||||
|
||||
|
||||
def test_split_parameter():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 4, 2),)
|
||||
strategy2 = ((1, 4, 2), (1, 4, 2))
|
||||
net = Net1(_w1, 0, 2, strategy1, strategy2)
|
||||
compile_net1(net)
|
||||
|
||||
|
||||
def test_split_parameter_no_full_split():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 2, 2),)
|
||||
strategy2 = ((1, 4, 2), (1, 4, 2))
|
||||
net = Net1(_w1, 0, 2, strategy1, strategy2)
|
||||
compile_net1(net)
|
||||
|
||||
|
||||
def test_split_tensor():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 8),)
|
||||
strategy2 = ((1, 8), (1, 8))
|
||||
strategy3 = ((1, 1), (1, 8))
|
||||
net = Net(_w, 0, 3, strategy1, strategy2, strategy3)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_split_output():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 4, 2),)
|
||||
strategy2 = ((1, 4, 2), (1, 4, 2))
|
||||
net = Net2(_w2, 0, 2, strategy1, strategy2)
|
||||
compile_net1(net)
|
||||
|
||||
|
||||
def test_split_output_no_full_split():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 2, 2),)
|
||||
strategy2 = ((1, 4, 2), (1, 4, 2))
|
||||
net = Net2(_w2, 0, 2, strategy1, strategy2)
|
||||
compile_net1(net)
|
||||
|
||||
|
||||
def test_split_no_strategy():
|
||||
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = None
|
||||
strategy2 = ((1, 4, 2), (1, 4, 2))
|
||||
net = Net2(_w2, 0, 2, strategy1, strategy2)
|
||||
compile_net1(net)
|
||||
|
||||
|
||||
def test_split_auto_parallel():
|
||||
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
|
||||
net = Net2(_w2, 0, 2)
|
||||
compile_net1(net)
|
Loading…
Reference in New Issue