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121 lines
4.8 KiB
121 lines
4.8 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/fluid/operators/reduce_ops/reduce_mean_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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// NOTE(dengkaipeng): Input(Out) is unnecessary in reduce_mean_grad
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// calcualtion, but will incur a reduce_mean_grad op after
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// reduce_mean_grad_grad, delete Input(Out) here.
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// This change has no effect on reduce_mean_grad calculations.
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template <typename T>
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class ReduceMeanOpGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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std::unique_ptr<T> Apply() const override {
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std::unique_ptr<T> op(new T());
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op->SetType("reduce_mean_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetAttrMap(this->Attrs());
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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return op;
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}
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};
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class ReduceMeanDoubleGradDescMaker : public framework::GradOpDescMakerBase {
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public:
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using framework::GradOpDescMakerBase::GradOpDescMakerBase;
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std::vector<std::unique_ptr<framework::OpDesc>> operator()() const override {
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std::vector<std::unique_ptr<framework::OpDesc>> ops;
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auto x_gg = OutputGrad(framework::GradVarName("X")); // input ddx
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auto out_grads = InputGrad(framework::GradVarName("Out"));
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if (!out_grads.empty()) {
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auto* out_grad_op = new framework::OpDesc();
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out_grad_op->SetType("reduce_mean");
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out_grad_op->SetInput("X", x_gg);
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out_grad_op->SetAttrMap(Attrs());
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out_grad_op->SetOutput("Out", out_grads);
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ops.emplace_back(out_grad_op);
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}
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return ops;
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}
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};
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class ReduceMeanDoubleGradOpBaseMaker : public imperative::GradOpBaseMakerBase {
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public:
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using imperative::GradOpBaseMakerBase::GradOpBaseMakerBase;
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std::vector<std::unique_ptr<imperative::OpBase>> operator()() const override {
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std::vector<std::unique_ptr<imperative::OpBase>> ops;
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auto x_gg = OutputGrad(framework::GradVarName("X")); // input ddx
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auto out_grads = InputGrad(framework::GradVarName("Out"));
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if (!out_grads.empty()) {
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auto* out_grad_op = new imperative::OpBase();
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out_grad_op->SetType("reduce_mean");
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out_grad_op->SetInput("X", x_gg);
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out_grad_op->SetAttrMap(Attrs());
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out_grad_op->SetOutput("Out", out_grads);
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ops.emplace_back(out_grad_op);
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}
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return ops;
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ReduceMeanGradNoNeedBufferVarInference,
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"X");
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} // namespace operators
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} // namespace paddle
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class __reduce_meanMaker__ : public ops::ReduceOpMaker {
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protected:
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virtual std::string GetName() const { return "reduce_mean"; }
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virtual std::string GetOpType() const { return "Reduce reduce_mean"; }
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};
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REGISTER_OPERATOR(reduce_mean, ops::ReduceOp, __reduce_meanMaker__,
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ops::ReduceMeanOpGradMaker<paddle::framework::OpDesc>,
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ops::ReduceMeanOpGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(reduce_mean_grad, ops::ReduceGradOp,
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ops::ReduceMeanDoubleGradDescMaker,
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ops::ReduceMeanDoubleGradOpBaseMaker,
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ops::ReduceMeanGradNoNeedBufferVarInference);
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REGISTER_OP_CPU_KERNEL(reduce_mean,
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ops::ReduceKernel<paddle::platform::CPUDeviceContext,
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float, ops::MeanFunctor>,
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ops::ReduceKernel<paddle::platform::CPUDeviceContext,
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double, ops::MeanFunctor>,
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ops::ReduceKernel<paddle::platform::CPUDeviceContext,
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int, ops::MeanFunctor>,
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ops::ReduceKernel<paddle::platform::CPUDeviceContext,
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int64_t, ops::MeanFunctor>);
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template <typename T>
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using CPUReduceMeanGradKernel =
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ops::ReduceGradKernel<paddle::platform::CPUDeviceContext, T,
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ops::MeanGradFunctor, true>;
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REGISTER_OP_CPU_KERNEL(reduce_mean_grad, CPUReduceMeanGradKernel<float>,
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CPUReduceMeanGradKernel<double>,
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CPUReduceMeanGradKernel<int>,
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CPUReduceMeanGradKernel<int64_t>);
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