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107 lines
3.8 KiB
107 lines
3.8 KiB
/* Copyright (c) 2019 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 <string>
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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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#include "paddle/fluid/platform/hostdevice.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 EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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using Array1 = Eigen::DSizes<int64_t, 1>;
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template <typename T>
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struct KLDivLossForward {
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HOSTDEVICE KLDivLossForward() {}
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HOSTDEVICE T operator()(const T& target, const T& input) const {
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if (target < 0) {
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return 0;
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} else {
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return target * (std::log(target) - input);
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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 KLDivLossKernel : 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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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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auto* input = ctx.Input<Tensor>("X");
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auto* target = ctx.Input<Tensor>("Target");
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auto* loss = ctx.Output<Tensor>("Loss");
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auto reduction = ctx.Attr<std::string>("reduction");
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const int n = input->dims()[0];
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loss->mutable_data<T>(ctx.GetPlace());
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auto input_t = EigenVector<T>::Flatten(*input);
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auto target_t = EigenVector<T>::Flatten(*target);
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auto loss_t = EigenVector<T>::Flatten(*loss);
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auto output = target_t.binaryExpr(input_t, KLDivLossForward<T>());
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if ("none" == reduction) {
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loss_t.device(place) = output;
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} else if ("batchmean" == reduction) {
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auto output_sum = output.sum().eval();
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loss_t.device(place) = output_sum / output_sum.constant(n);
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} else if ("mean" == reduction) {
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loss_t.device(place) = output.mean();
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} else if ("sum" == reduction) {
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loss_t.device(place) = output.sum();
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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 KLDivLossGradKernel : 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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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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auto* target = ctx.Input<Tensor>("Target");
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auto reduction = ctx.Attr<std::string>("reduction");
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auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
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const int n = input_grad->dims()[0];
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const int numel = input_grad->numel();
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const int expand = numel / loss_grad->numel();
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input_grad->mutable_data<T>(ctx.GetPlace());
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auto target_t = EigenVector<T>::Flatten(*target);
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auto input_grad_t = EigenVector<T>::Flatten(*input_grad);
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auto loss_grad_t = EigenVector<T>::Flatten(*loss_grad);
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auto target_mask = (target_t > target_t.constant(0)).template cast<T>();
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auto loss_grad_expand = loss_grad_t.broadcast(Array1(expand));
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input_grad_t.device(place) =
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target_t * target_t.constant(-1.0) * loss_grad_expand * target_mask;
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if ("mean" == reduction) {
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input_grad_t.device(place) = input_grad_t / static_cast<T>(numel);
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} else if ("batchmean" == reduction) {
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input_grad_t.device(place) = input_grad_t / static_cast<T>(n);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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