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98 lines
3.2 KiB
98 lines
3.2 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/framework/ddim.h"
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#include "paddle/framework/operator.h"
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#include "paddle/framework/tensor.h"
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#include "paddle/operators/type_alias.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class SoftmaxKernel : public OpKernel {
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public:
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void Compute(const ExecutionContext& context) const override {
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auto input = context.Input<Tensor>("X");
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auto output = context.Output<Tensor>("Y");
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output->mutable_data<T>(context.GetPlace());
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auto logits = EigenMatrix<T>::From(*input);
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auto softmax = EigenMatrix<T>::From(*output);
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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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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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};
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template <typename Place, typename T>
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class SoftmaxGradKernel : public OpKernel {
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public:
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void Compute(const ExecutionContext& context) const override {
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std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
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auto Y = context.Input<Tensor>("Y");
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auto dY = context.Input<Tensor>(framework::GradVarName("Y"));
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auto dX = context.Output<Tensor>(framework::GradVarName("X"));
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dX->mutable_data<T>(context.GetPlace());
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const int batch_size = Y->dims()[0];
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const int class_num = Y->dims()[1];
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Eigen::DSizes<int, 1> along_class(1);
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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, class_num);
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auto Y_eigen = EigenMatrix<T>::From(*Y);
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auto dY_eigen = EigenMatrix<T>::From(*dY);
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auto dX_eigen = EigenMatrix<T>::From(*dX);
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auto place = context.GetEigenDevice<Place>();
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auto dot = (Y_eigen * dY_eigen)
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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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dX_eigen.device(place) = (dY_eigen - dot) * Y_eigen;
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}
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};
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} // namespace operators
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} // namespace paddle
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