!13818 revert nn.BatchNorm3d.
From: @liu_xiao_93 Reviewed-by: @liangchenghui Signed-off-by: @liangchenghuipull/13818/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
fcce705bcb
@ -1,85 +0,0 @@
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/**
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* Copyright 2021 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 "backend/optimizer/ascend/ir_fusion/batchnorm_grad_to_batchnorm3d_grad.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "backend/session/anf_runtime_algorithm.h"
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#include "ir/primitive.h"
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#include "utils/utils.h"
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#include "base/core_ops.h"
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#include "abstract/abstract_value.h"
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#include "backend/optimizer/common/helper.h"
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namespace mindspore {
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namespace opt {
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namespace {
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constexpr size_t kBN3DGradInputXIndex = 2;
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CNodePtr CreateBatchNorm3DGrad(const FuncGraphPtr &graph, const CNodePtr &batchnorm_grad) {
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MS_EXCEPTION_IF_NULL(graph);
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MS_EXCEPTION_IF_NULL(batchnorm_grad);
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auto prim = std::make_shared<Primitive>(kBatchNorm3DGradOpName);
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std::vector<AnfNodePtr> inputs = {NewValueNode(prim)};
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for (size_t i = 1; i < batchnorm_grad->size() - 1; ++i) {
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inputs.push_back(batchnorm_grad->input(i));
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}
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auto new_node = graph->NewCNode(inputs);
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MS_EXCEPTION_IF_NULL(new_node);
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new_node->set_scope(batchnorm_grad->scope());
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new_node->set_abstract(batchnorm_grad->abstract());
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AnfAlgo::CopyNodeAttrs(batchnorm_grad, new_node);
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return new_node;
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}
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bool NeedFusion(const FuncGraphPtr &graph, const CNodePtr &batchnorm_grad) {
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MS_EXCEPTION_IF_NULL(batchnorm_grad);
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if (AnfAlgo::GetInputTensorNum(batchnorm_grad) < kBNGradInputTensorNum) {
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MS_LOG(INFO) << "BatchNormGrad's input less than " << kBNGradInputTensorNum;
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return false;
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}
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auto format = AnfAlgo::GetNodeAttr<std::string>(batchnorm_grad, kAttrFormat);
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const auto &ori_inputs = batchnorm_grad->inputs();
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auto x_shape = AnfAlgo::GetOutputInferShape(ori_inputs[kBN3DGradInputXIndex], 0);
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if (format != kOpFormat_NCDHW || x_shape.size() != 5) {
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MS_LOG(INFO) << "Only format is NCDHW and the input dim of BatchNormGrad is 5, then do fusion. But format is: "
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<< format << ", size of x_shape is: " << x_shape.size();
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return false;
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}
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return true;
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}
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} // namespace
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const BaseRef BatchNormGrad2BatchNorm3DGRAD::DefinePattern() const {
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VarPtr Xs = std::make_shared<SeqVar>();
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MS_EXCEPTION_IF_NULL(Xs);
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VectorRef pattern({prim::kPrimBatchNormGrad, Xs});
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return pattern;
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}
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const AnfNodePtr BatchNormGrad2BatchNorm3DGRAD::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
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const EquivPtr &) const {
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MS_EXCEPTION_IF_NULL(graph);
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MS_EXCEPTION_IF_NULL(node);
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auto cnode_bn_grad = node->cast<CNodePtr>();
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if (!NeedFusion(graph, cnode_bn_grad)) {
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return nullptr;
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}
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auto bn_3d_grad = CreateBatchNorm3DGrad(graph, cnode_bn_grad);
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TransferDepend(cnode_bn_grad, graph, bn_3d_grad);
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return bn_3d_grad;
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}
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} // namespace opt
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} // namespace mindspore
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@ -1,34 +0,0 @@
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/**
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* Copyright 2021 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_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_GRAD_TO_BATCHNORM_3D_GRAD_H_
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#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_GRAD_TO_BATCHNORM_3D_GRAD_H_
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#include <memory>
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#include "backend/optimizer/common/optimizer.h"
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namespace mindspore {
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namespace opt {
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class BatchNormGrad2BatchNorm3DGRAD : public PatternProcessPass {
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public:
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explicit BatchNormGrad2BatchNorm3DGRAD(bool multigraph = true)
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: PatternProcessPass("batchnorm_grad_to_batchnorm3d_grad", multigraph) {}
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~BatchNormGrad2BatchNorm3DGRAD() override = default;
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const BaseRef DefinePattern() const override;
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const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
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};
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} // namespace opt
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_GRAD_TO_BATCHNORM_3D_GRAD_H_
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@ -1,104 +0,0 @@
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/**
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* Copyright 2021 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 "backend/optimizer/ascend/ir_fusion/batchnorm_to_batchnorm3d.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "backend/session/anf_runtime_algorithm.h"
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#include "ir/primitive.h"
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#include "utils/utils.h"
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#include "base/core_ops.h"
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#include "abstract/abstract_value.h"
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#include "backend/optimizer/common/helper.h"
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namespace mindspore {
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namespace opt {
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namespace {
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constexpr size_t kBN3InputXIndex = 1;
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constexpr size_t kBn3DTrainInputTensorNum = 3;
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CNodePtr CreateBatchNorm3D(const FuncGraphPtr &graph, const CNodePtr &batchnorm) {
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MS_EXCEPTION_IF_NULL(graph);
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MS_EXCEPTION_IF_NULL(batchnorm);
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auto prim = std::make_shared<Primitive>(kBatchNorm3DOpName);
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std::vector<AnfNodePtr> inputs = {NewValueNode(prim)};
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auto is_training = AnfAlgo::GetNodeAttr<bool>(batchnorm, kAttrIsTraining);
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for (size_t i = 1; i < batchnorm->size(); ++i) {
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if (is_training && i > kBn3DTrainInputTensorNum) {
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continue;
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} else {
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inputs.push_back(batchnorm->input(i));
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}
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}
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auto new_node = graph->NewCNode(inputs);
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MS_EXCEPTION_IF_NULL(new_node);
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new_node->set_scope(batchnorm->scope());
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new_node->set_abstract(batchnorm->abstract());
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AnfAlgo::CopyNodeAttrs(batchnorm, new_node);
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return new_node;
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}
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bool NeedFusion(const FuncGraphPtr &graph, const CNodePtr &batchnorm) {
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MS_EXCEPTION_IF_NULL(batchnorm);
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if (!AnfAlgo::HasNodeAttr(kAttrIsTraining, batchnorm)) {
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MS_LOG(INFO) << "BatchNorm has no is_training attr.";
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return false;
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}
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auto is_training = AnfAlgo::GetNodeAttr<bool>(batchnorm, kAttrIsTraining);
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auto format = AnfAlgo::GetNodeAttr<std::string>(batchnorm, kAttrFormat);
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if (is_training && format == kOpFormat_NCDHW) {
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if (AnfAlgo::GetInputTensorNum(batchnorm) < kBn3DTrainInputTensorNum) {
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MS_LOG(INFO) << "When data format is NCDHW and is_training is true, BatchNorm's input less than "
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<< kBn3DTrainInputTensorNum;
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return false;
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}
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} else {
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if (AnfAlgo::GetInputTensorNum(batchnorm) < kBnInputTensorNum) {
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MS_LOG(INFO) << "BatchNorm's input less than " << kBnInputTensorNum;
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return false;
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}
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}
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const auto &ori_inputs = batchnorm->inputs();
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auto x_shape = AnfAlgo::GetOutputInferShape(ori_inputs[kBN3InputXIndex], 0);
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if (format != kOpFormat_NCDHW || x_shape.size() != 5) {
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MS_LOG(INFO) << "Only format is NCDHW and the input dim of BatchNorm is 5, then do fusion. But format is: "
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<< format << ", size of x_shape is: " << x_shape.size();
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return false;
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}
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return true;
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}
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} // namespace
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const BaseRef BatchNorm2BatchNorm3D::DefinePattern() const {
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VarPtr Xs = std::make_shared<SeqVar>();
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MS_EXCEPTION_IF_NULL(Xs);
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VectorRef pattern({prim::kPrimBatchNorm, Xs});
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return pattern;
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}
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const AnfNodePtr BatchNorm2BatchNorm3D::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
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const EquivPtr &) const {
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MS_EXCEPTION_IF_NULL(graph);
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MS_EXCEPTION_IF_NULL(node);
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auto cnode_bn = node->cast<CNodePtr>();
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if (!NeedFusion(graph, cnode_bn)) {
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return nullptr;
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}
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auto bn_3d = CreateBatchNorm3D(graph, cnode_bn);
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TransferDepend(cnode_bn, graph, bn_3d);
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return bn_3d;
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}
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} // namespace opt
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} // namespace mindspore
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@ -1,33 +0,0 @@
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/**
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* Copyright 2021 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_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_TO_BATCHNORM_3D_H_
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#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_TO_BATCHNORM_3D_H_
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#include <memory>
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#include "backend/optimizer/common/optimizer.h"
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namespace mindspore {
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namespace opt {
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class BatchNorm2BatchNorm3D : public PatternProcessPass {
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public:
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explicit BatchNorm2BatchNorm3D(bool multigraph = true) : PatternProcessPass("batchnorm_to_batchnorm3d", multigraph) {}
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~BatchNorm2BatchNorm3D() override = default;
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const BaseRef DefinePattern() const override;
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const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
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};
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} // namespace opt
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_BATCHNORM_TO_BATCHNORM_3D_H_
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@ -1,51 +0,0 @@
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# Copyright 2021 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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"""BatchNorm3D op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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batch_norm3d_op_info = TBERegOp("BatchNorm3D") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("batch_norm3d.so") \
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.compute_cost(10) \
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.kernel_name("batch_norm3d") \
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.partial_flag(True) \
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.attr("epsilon", "optional", "float", "all") \
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.attr("format", "optional", "str", "all") \
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.attr("is_training", "optional", "bool", "all") \
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.input(0, "x", False, "required", "all") \
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.input(1, "scale", False, "required", "all", reshape_type="C") \
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.input(2, "offset", False, "required", "all", reshape_type="C") \
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.input(3, "mean", False, "optional", "all", reshape_type="C") \
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.input(4, "variance", False, "optional", "all", reshape_type="C") \
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.output(0, "y", False, "required", "all") \
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.output(1, "batch_mean", False, "required", "all") \
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.output(2, "batch_variance", False, "required", "all") \
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.output(3, "reserve_space_1", False, "optional", "all") \
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.output(4, "reserve_space_2", False, "optional", "all") \
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.dtype_format(DataType.F16_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
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.dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
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.get_op_info()
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@op_info_register(batch_norm3d_op_info)
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def _batch_norm3d_tbe():
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"""BatchNorm3D TBE register"""
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return
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@ -1,51 +0,0 @@
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# Copyright 2021 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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"""BatchNorm3DGrad op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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batch_norm3d_grad_op_info = TBERegOp("BatchNorm3DGrad") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("batch_norm3d_grad.so") \
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.compute_cost(10) \
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.kernel_name("batch_norm3d_grad") \
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.partial_flag(True) \
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.attr("epsilon", "optional", "float", "all") \
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.attr("format", "optional", "str", "all") \
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.attr("is_training", "optional", "bool", "all") \
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.input(0, "y_backprop", False, "required", "all") \
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.input(1, "x", False, "required", "all") \
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.input(2, "scale", False, "required", "all", reshape_type="C") \
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.input(3, "reserve_space_1", False, "optional", "all") \
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.input(4, "reserve_space_2", False, "optional", "all") \
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.output(0, "x_backprop", False, "required", "all") \
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.output(1, "scale_backprop", False, "required", "all") \
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.output(2, "offset_backprop", False, "required", "all") \
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.output(3, "reserve_space_4", False, "optional", "all") \
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.output(4, "reserve_space_5", False, "optional", "all") \
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.dtype_format(DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
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.dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0,
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DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
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.get_op_info()
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@op_info_register(batch_norm3d_grad_op_info)
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def _batch_norm3d_grad_tbe():
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"""BatchNorm3DGrad TBE register"""
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return
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