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118 lines
4.2 KiB
118 lines
4.2 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/operators/elementwise_op_function.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class ElementwiseDivKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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ElementwiseCompute<EigenDivFunctor, DeviceContext, T>(ctx);
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}
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};
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template <typename T>
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struct ElementwiseDivGradFunctor {
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template <typename Device, typename X, typename Y, typename Z, typename dX,
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typename dY, typename dZ>
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void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
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auto y_e = framework::EigenVector<T>::Flatten(*y);
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auto z_e = framework::EigenVector<T>::Flatten(*z);
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auto dz_e = framework::EigenVector<T>::Flatten(*dz);
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if (dx) {
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auto dx_e = framework::EigenVector<T>::Flatten(*dx);
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dx_e.device(d) = dz_e / y_e;
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}
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if (dy) {
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auto dy_e = framework::EigenVector<T>::Flatten(*dy);
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dy_e.device(d) = -1.0 * dz_e * z_e / y_e;
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}
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}
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};
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template <typename T>
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struct ElementwiseDivBroadCastGradFunctor {
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template <typename Device, typename X, typename Y, typename Z, typename dX,
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typename dY, typename dZ, typename Pre, typename N>
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void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
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auto x_e = framework::EigenVector<T>::Flatten(*x);
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auto y_e = framework::EigenVector<T>::Flatten(*y);
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auto dz_e = framework::EigenVector<T>::Flatten(*dz);
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auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
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.broadcast(Eigen::DSizes<int, 2>(pre, 1))
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.reshape(Eigen::DSizes<int, 1>(x_e.size()));
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if (dx) {
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auto dx_e = framework::EigenVector<T>::Flatten(*dx);
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dx_e.device(d) = dz_e / y_e_bcast;
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}
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if (dy) {
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auto dy_e = framework::EigenVector<T>::Flatten(*dy);
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dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
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.reshape(Eigen::DSizes<int, 2>(pre, n))
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.sum(Eigen::array<int, 1>{{0}});
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}
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}
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};
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template <typename T>
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struct ElementwiseDivBroadCast2GradFunctor {
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template <typename Device, typename X, typename Y, typename Z, typename dX,
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typename dY, typename dZ, typename Pre, typename N, typename Post>
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void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
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Post post) {
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auto x_e = framework::EigenVector<T>::Flatten(*x);
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auto y_e = framework::EigenVector<T>::Flatten(*y);
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auto dz_e = framework::EigenVector<T>::Flatten(*dz);
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auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
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.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
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.reshape(Eigen::DSizes<int, 1>(x_e.size()));
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if (dx) {
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auto dx_e = framework::EigenVector<T>::Flatten(*dx);
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dx_e.device(d) = dz_e / y_e_bcast;
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}
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if (dy) {
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auto dy_e = framework::EigenVector<T>::Flatten(*dy);
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dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
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.reshape(Eigen::DSizes<int, 3>(pre, n, post))
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.sum(Eigen::array<int, 2>{{0, 2}});
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}
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}
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};
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template <typename DeviceContext, typename T>
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class ElementwiseDivGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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ElementwiseGradCompute<DeviceContext, T, ElementwiseDivGradFunctor<T>,
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ElementwiseDivGradFunctor<T>,
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ElementwiseDivBroadCastGradFunctor<T>,
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ElementwiseDivBroadCast2GradFunctor<T>>(ctx);
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}
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};
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} // namespace operators
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} // namespace paddle
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