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71 lines
2.4 KiB
71 lines
2.4 KiB
8 years ago
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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/op_registry.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 Place, typename T>
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class DropoutKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* x = context.Input<Tensor>("X");
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auto* y = context.Output<Tensor>("Out");
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auto* mask = context.Output<Tensor>("Mask");
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mask->mutable_data<T>(context.GetPlace());
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y->mutable_data<T>(context.GetPlace());
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auto dims = x->dims();
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auto X = EigenMatrix<T>::From(*x);
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auto Y = EigenMatrix<T>::From(*y);
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auto M = EigenMatrix<T>::From(*mask);
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auto place = context.GetEigenDevice<Place>();
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M.device(place).setRandom<UniformRandomGenerator>();
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float dropout_prob = context.op_.GetAttr<float>("dropout_prob");
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M.device(place) = (M > dropout_prob).cast<float>();
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Y.device(place) = X * Y;
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}
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};
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template <typename Place, typename T>
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class DropoutGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
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auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* mask = context.Input<Tensor>("Mask");
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grad_x->mutable_data<T>(context.GetPlace());
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auto dims = grad_x->dims();
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auto M = EigenMatrix<T>::From(*mask);
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auto dX = EigenMatrix<T>::From(*grad_x);
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auto dY = EigenMatrix<T>::From(*grad_y);
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auto place = context.GetEigenDevice<Place>();
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dX.device(place) = dY * M;
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}
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};
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} // namespace operators
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} // namespace paddle
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