提交 b64fca6e 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!3091 GPU add fusion: replace batchnormgrad cast

Merge pull request !3091 from VectorSL/batchnorm_grad
/**
* 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 "backend/optimizer/gpu/replace_bn_grad_cast2_fusion.h"
#include <memory>
#include <vector>
#include <string>
#include "backend/session/anf_runtime_algorithm.h"
#include "ir/primitive.h"
#include "utils/utils.h"
#include "backend/optimizer/common/helper.h"
namespace mindspore {
namespace opt {
const BaseRef ReplaceBNGradCast2Fusion::DefinePattern() const {
VectorRef fbn2g = VectorRef({prim::kPrimFusedBatchNormGrad, dy_, x_, scale_, mean_, var_});
VectorRef tupleget = VectorRef({prim::kPrimTupleGetItem, fbn2g, index_});
VectorRef out_cast = VectorRef({prim::kPrimCast, tupleget});
return out_cast;
}
const AnfNodePtr ReplaceBNGradCast2Fusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
const EquivPtr &equiv) const {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(equiv);
auto tuple = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), 0);
auto index_node = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 1);
MS_EXCEPTION_IF_NULL(index_node);
auto value_node = index_node->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
int item_idx = GetValue<int>(value_node->value());
if (item_idx != 0) {
return nullptr;
}
auto fbn2g = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 0);
auto dy_ = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 0);
auto x_ = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 1);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 4);
MS_EXCEPTION_IF_NULL(fbn2g);
MS_EXCEPTION_IF_NULL(dy_);
MS_EXCEPTION_IF_NULL(scale);
MS_EXCEPTION_IF_NULL(x_);
MS_EXCEPTION_IF_NULL(mean);
MS_EXCEPTION_IF_NULL(var);
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(utils::cast<CNodePtr>(node), utils::cast<CNodePtr>(tuple));
std::vector<TypeId> outputs_type;
std::vector<std::vector<size_t>> outputs_shape;
auto output_num = AnfAlgo::GetOutputTensorNum(fbn2g);
for (size_t i = 0; i < output_num; i++) {
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(fbn2g, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(fbn2g, i));
}
outputs_type[0] = AnfAlgo::GetPrevNodeOutputInferDataType(fbn2g, 0);
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fbn2g.get());
outputs_type.clear();
outputs_shape.clear();
outputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(fbn2g, 0));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(tuple, 0));
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, tuple.get());
return tuple;
}
} // namespace opt
} // namespace mindspore
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST2_FUSION_H_
#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST2_FUSION_H_
#include <memory>
#include "backend/optimizer/common/optimizer.h"
namespace mindspore {
namespace opt {
class ReplaceBNGradCast2Fusion : public PatternProcessPass {
public:
explicit ReplaceBNGradCast2Fusion(bool multigraph = true) : PatternProcessPass("replace_grad_cast2", multigraph) {
dy_ = std::make_shared<Var>();
x_ = std::make_shared<Var>();
scale_ = std::make_shared<Var>();
mean_ = std::make_shared<Var>();
var_ = std::make_shared<Var>();
dx_ = std::make_shared<Var>();
bn_scale_ = std::make_shared<Var>();
bn_bias_ = std::make_shared<Var>();
index_ = std::make_shared<Var>();
}
~ReplaceBNGradCast2Fusion() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
private:
VarPtr dy_;
VarPtr x_;
VarPtr scale_;
VarPtr mean_;
VarPtr var_;
VarPtr dx_;
VarPtr bn_scale_;
VarPtr bn_bias_;
VarPtr index_;
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST2_FUSION_H_
/**
* 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 "backend/optimizer/gpu/replace_bn_grad_cast_fusion.h"
#include <memory>
#include <vector>
#include <string>
#include "backend/session/anf_runtime_algorithm.h"
#include "ir/primitive.h"
#include "utils/utils.h"
#include "backend/optimizer/common/helper.h"
namespace mindspore {
namespace opt {
const BaseRef ReplaceBNGradCastFusion::DefinePattern() const {
VectorRef dy_cast = VectorRef({prim::kPrimCast, dy_});
VectorRef fbn2g = VectorRef({prim::kPrimFusedBatchNormGrad, dy_cast, x_, scale_, mean_, var_});
VectorRef tupleget = VectorRef({prim::kPrimTupleGetItem, fbn2g, index_});
VectorRef out_cast = VectorRef({prim::kPrimCast, tupleget});
return out_cast;
}
const AnfNodePtr ReplaceBNGradCastFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
const EquivPtr &equiv) const {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(equiv);
auto tuple = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), 0);
auto index_node = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 1);
MS_EXCEPTION_IF_NULL(index_node);
auto value_node = index_node->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
int item_idx = GetValue<int>(value_node->value());
if (item_idx != 0) {
return nullptr;
}
auto fbn2g = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 0);
auto dy_after = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 0);
auto dy_before = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(dy_after), 0);
auto x_ = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 1);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 4);
MS_EXCEPTION_IF_NULL(fbn2g);
MS_EXCEPTION_IF_NULL(dy_after);
MS_EXCEPTION_IF_NULL(dy_before);
MS_EXCEPTION_IF_NULL(scale);
MS_EXCEPTION_IF_NULL(x_);
MS_EXCEPTION_IF_NULL(mean);
MS_EXCEPTION_IF_NULL(var);
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(utils::cast<CNodePtr>(dy_after), utils::cast<CNodePtr>(dy_before));
manager->Replace(utils::cast<CNodePtr>(node), utils::cast<CNodePtr>(tuple));
std::vector<TypeId> outputs_type;
std::vector<std::vector<size_t>> outputs_shape;
auto output_num = AnfAlgo::GetOutputTensorNum(fbn2g);
for (size_t i = 0; i < output_num; i++) {
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(fbn2g, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(fbn2g, i));
}
outputs_type[0] = kNumberTypeFloat16;
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fbn2g.get());
outputs_type.clear();
outputs_shape.clear();
outputs_type.push_back(kNumberTypeFloat16);
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(tuple, 0));
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, tuple.get());
return tuple;
}
} // namespace opt
} // namespace mindspore
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST_FUSION_H_
#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST_FUSION_H_
#include <memory>
#include "backend/optimizer/common/optimizer.h"
namespace mindspore {
namespace opt {
class ReplaceBNGradCastFusion : public PatternProcessPass {
public:
explicit ReplaceBNGradCastFusion(bool multigraph = true) : PatternProcessPass("replace_bn_grad_cast", multigraph) {
dy_ = std::make_shared<Var>();
x_ = std::make_shared<Var>();
scale_ = std::make_shared<Var>();
mean_ = std::make_shared<Var>();
var_ = std::make_shared<Var>();
dx_ = std::make_shared<Var>();
bn_scale_ = std::make_shared<Var>();
bn_bias_ = std::make_shared<Var>();
index_ = std::make_shared<Var>();
}
~ReplaceBNGradCastFusion() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
private:
VarPtr dy_;
VarPtr x_;
VarPtr scale_;
VarPtr mean_;
VarPtr var_;
VarPtr dx_;
VarPtr bn_scale_;
VarPtr bn_bias_;
VarPtr index_;
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GPU_REPLACE_BN_GRAD_CAST_FUSION_H_
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