dangqingqing
7 years ago
parent
5f21709938
commit
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/* 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/tensor.h"
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namespace paddle {
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namespace operators {
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namespace math {
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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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struct ValueClip {
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HOSTDEVICE T operator()(const T& x) const {
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const T kThreshold = -64.;
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return x < kThreshold ? kThreshold : x;
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}
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};
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template <typename Place, typename T>
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void SoftmaxFunctor<Place, T>::operator()(
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const platform::DeviceContext& context, const framework::Tensor* X,
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framework::Tensor* Y) {
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auto logits = EigenMatrix<T>::From(*X);
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auto softmax = EigenMatrix<T>::From(*Y);
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const int kBatchDim = 0;
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const int kClassDim = 1;
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const int batch_size = logits.dimension(kBatchDim);
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const int num_classes = logits.dimension(kClassDim);
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Eigen::DSizes<int, 1> along_class(kClassDim);
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Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
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Eigen::DSizes<int, 2> one_by_class(1, num_classes);
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auto shifted_logits = (logits -
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logits.maximum(along_class)
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.eval()
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.reshape(batch_by_one)
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.broadcast(one_by_class))
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.unaryExpr(ValueClip<T>());
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softmax.device(*context.GetEigenDevice<Place>()) = shifted_logits.exp();
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softmax.device(*context.GetEigenDevice<Place>()) =
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(softmax *
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softmax.sum(along_class)
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.inverse()
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.eval()
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.reshape(batch_by_one)
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.broadcast(one_by_class));
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}
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template <typename Place, typename T>
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void SoftmaxGradFunctor<Place, T>::operator()(
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const platform::DeviceContext& context, const framework::Tensor* y,
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const framework::Tensor* y_grad, framework::Tensor* x_grad) {
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auto softmax = EigenMatrix<T>::From(*y);
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auto softmax_grad = EigenMatrix<T>::From(*y_grad);
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auto logits_grad = EigenMatrix<T>::From(*x_grad);
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const int kBatchDim = 0;
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const int kClassDim = 1;
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const int batch_size = softmax.dimension(kBatchDim);
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const int num_classes = softmax.dimension(kClassDim);
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Eigen::DSizes<int, 1> along_class(kClassDim);
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Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
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Eigen::DSizes<int, 2> one_by_class(1, num_classes);
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auto dot = (softmax * softmax_grad)
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.sum(along_class)
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.eval()
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.reshape(batch_by_one)
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.broadcast(one_by_class);
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logits_grad.device(*context.GetEigenDevice<Place>()) =
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(softmax_grad - dot) * softmax;
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}
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} // namespace math
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} // namespace operators
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} // namespace paddle
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