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89 lines
3.1 KiB
89 lines
3.1 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 <random>
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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 DeviceContext, typename T, typename AttrType>
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class CPUDropoutKernel : 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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auto* x = context.Input<Tensor>("X");
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auto* y = context.Output<Tensor>("Out");
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const auto* x_data = x->data<T>();
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auto* y_data = y->mutable_data<T>(context.GetPlace());
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float dropout_prob = context.Attr<float>("dropout_prob");
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if (!context.Attr<bool>("is_test")) {
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auto* mask = context.Output<Tensor>("Mask");
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auto* mask_data = mask->mutable_data<T>(context.GetPlace());
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int seed = context.Attr<int>("seed");
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std::minstd_rand engine;
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engine.seed(seed);
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std::uniform_real_distribution<float> dist(0, 1);
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size_t size = framework::product(mask->dims());
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for (size_t i = 0; i < size; ++i) {
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if (dist(engine) < dropout_prob) {
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mask_data[i] = 0;
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y_data[i] = 0;
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} else {
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mask_data[i] = 1;
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y_data[i] = x_data[i];
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}
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}
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} else {
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auto X = EigenMatrix<T>::Reshape(*x, 1);
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auto Y = EigenMatrix<T>::Reshape(*y, 1);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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Y.device(place) = X * (1.0f - dropout_prob);
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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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class DropoutGradKernel : 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(!context.Attr<bool>("is_test"),
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"GradOp is only callable when is_test is false");
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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 M = EigenMatrix<T>::Reshape(*mask, 1);
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auto dX = EigenMatrix<T>::Reshape(*grad_x, 1);
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auto dY = EigenMatrix<T>::Reshape(*grad_y, 1);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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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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