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104 lines
3.3 KiB
104 lines
3.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T, typename Functor>
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class ActivationKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Output<framework::Tensor>("Y");
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Y->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, y);
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}
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};
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template <typename Place, typename T, typename Functor>
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class ActivationGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* X = context.Input<framework::Tensor>("X");
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auto* Y = context.Input<framework::Tensor>("Y");
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auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
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dX->mutable_data<T>(context.GetPlace());
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auto dy = framework::EigenVector<T>::Flatten(*dY);
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto y = framework::EigenVector<T>::Flatten(*Y);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, y, dy, dx);
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}
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};
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struct SigmoidFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = 1. / (1. + (-x).exp());
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}
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};
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struct SigmoidGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * y * (1. - y);
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}
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};
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struct ExpFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.exp();
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}
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};
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struct ExpGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = y;
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}
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};
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template <typename T>
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struct ReluFunctor {
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template <typename Device, typename X, typename Y>
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void operator()(Device d, X x, Y y) {
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y.device(d) = x.cwiseMax(static_cast<T>(0));
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}
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};
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template <typename T>
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struct ReluGradFunctor {
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template <typename Device, typename X, typename Y, typename dY, typename dX>
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void operator()(Device d, X x, Y y, dY dy, dX dx) {
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dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>();
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}
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};
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} // namespace operators
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} // namespace paddle
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