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91 lines
3.5 KiB
91 lines
3.5 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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#include "paddle/fluid/operators/math/cross_entropy.h"
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#include "paddle/fluid/operators/math/softmax.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 EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename T>
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class SoftmaxWithCrossEntropyKernel : 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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PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
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"This kernel only runs on CPU.");
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const Tensor* logits = context.Input<Tensor>("Logits");
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const Tensor* labels = context.Input<Tensor>("Label");
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Tensor* softmax = context.Output<Tensor>("Softmax");
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Tensor* loss = context.Output<Tensor>("Loss");
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softmax->mutable_data<T>(context.GetPlace());
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loss->mutable_data<T>(context.GetPlace());
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auto& dev_ctx =
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context.template device_context<platform::CPUDeviceContext>();
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math::SoftmaxFunctor<platform::CPUDeviceContext, T>()(dev_ctx, logits,
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softmax);
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math::CrossEntropyFunctor<platform::CPUDeviceContext, T>()(
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dev_ctx, loss, softmax, labels, context.Attr<bool>("soft_label"));
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}
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};
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template <typename T>
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class SoftmaxWithCrossEntropyGradKernel : 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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const Tensor* out_grad =
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context.Input<Tensor>(framework::GradVarName("Loss"));
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const Tensor* labels = context.Input<Tensor>("Label");
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Tensor* logit_grad =
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context.Output<Tensor>(framework::GradVarName("Logits"));
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logit_grad->ShareDataWith(*context.Input<Tensor>("Softmax"));
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const int class_num = logit_grad->dims()[1];
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auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
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auto logit_grad_mat = EigenMatrix<T>::From(*logit_grad);
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auto& place = *context.template device_context<platform::CPUDeviceContext>()
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.eigen_device();
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if (context.Attr<bool>("soft_label")) {
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auto lbl_mat = EigenMatrix<T>::From(*labels);
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logit_grad_mat.device(place) =
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out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, class_num)) *
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(logit_grad_mat - lbl_mat);
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} else {
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logit_grad_mat.device(place) =
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logit_grad_mat *
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out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, class_num));
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const int batch_size = logit_grad->dims()[0];
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const int64_t* label_data = labels->data<int64_t>();
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T* logit_grad_data = logit_grad->data<T>();
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const T* out_grad_data = out_grad->data<T>();
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for (int i = 0; i < batch_size; ++i) {
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logit_grad_data[i * class_num + label_data[i]] -= out_grad_data[i];
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}
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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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