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138 lines
5.0 KiB
138 lines
5.0 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 <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/operators/math/blas.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 = static_cast<T>(-64.);
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return x < kThreshold ? kThreshold : x;
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}
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};
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template <typename DeviceContext, typename T, bool is_test, typename Enable>
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void SoftmaxFunctor<DeviceContext, T, is_test, Enable>::operator()(
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const 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.eigen_device()) = shifted_logits.exp();
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softmax.device(*context.eigen_device()) = (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 <class DeviceContext>
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using enable_if_CPU = typename std::enable_if<
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std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type;
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template <typename DeviceContext>
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class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
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void operator()(const DeviceContext& context, const framework::Tensor* X,
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framework::Tensor* Y) {
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auto in_dims = X->dims();
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const float* in_data = X->data<float>();
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float* out_data = Y->data<float>();
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const int kBatchDim = 0;
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const int kClassDim = 1;
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// 2D data. Batch x C
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const int batch_size = in_dims[kBatchDim];
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const int num_classes = in_dims[kClassDim];
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std::vector<float> entities(batch_size);
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auto blas = math::GetBlas<DeviceContext, float>(context);
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for (int n = 0; n < batch_size; ++n) {
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entities[n] = in_data[n * num_classes];
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for (int c = 1; c < num_classes; ++c) {
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entities[n] = in_data[n * num_classes + c] > entities[n]
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? in_data[n * num_classes + c]
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: entities[n];
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}
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for (int c = 0; c < num_classes; ++c) {
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out_data[n * num_classes + c] =
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in_data[n * num_classes + c] - entities[n];
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}
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}
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blas.VEXP(num_classes * batch_size, out_data, out_data);
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for (int n = 0; n < batch_size; ++n) {
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auto sum = blas.ASUM(num_classes, &out_data[n * num_classes], 1);
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blas.SCAL(num_classes, 1.0f / sum, &out_data[n * num_classes]);
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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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void SoftmaxGradFunctor<DeviceContext, T>::operator()(
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const 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.eigen_device()) = (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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