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68 lines
2.4 KiB
68 lines
2.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class MeanKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input = context.Input<Tensor>("X");
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auto* output = context.Output<Tensor>("Out");
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output->mutable_data<T>(context.GetPlace());
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auto X = EigenVector<T>::Flatten(*input);
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auto y = EigenScalar<T>::From(*output);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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y.device(place) = X.mean();
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}
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};
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template <typename DeviceContext, typename T>
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class MeanGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto OG = context.Input<Tensor>(framework::GradVarName("Out"));
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PADDLE_ENFORCE(OG->numel() == 1, "Mean Gradient should be scalar");
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auto IG = context.Output<Tensor>(framework::GradVarName("X"));
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IG->mutable_data<T>(context.GetPlace());
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T ig_size = static_cast<T>(IG->numel());
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Eigen::DSizes<int, 1> bcast(ig_size);
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EigenVector<T>::Flatten(*IG).device(
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*context.template device_context<DeviceContext>().eigen_device()) =
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(EigenVector<T>::From(*OG) / ig_size).broadcast(bcast);
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}
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};
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} // namespace operators
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} // namespace paddle
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