You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
109 lines
3.8 KiB
109 lines
3.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
#pragma once
|
|
|
|
#include <random>
|
|
#include <string>
|
|
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class CPUDropoutKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* x = context.Input<Tensor>("X");
|
|
auto* y = context.Output<Tensor>("Out");
|
|
const auto* x_data = x->data<T>();
|
|
auto* y_data = y->mutable_data<T>(context.GetPlace());
|
|
float dropout_prob = context.Attr<float>("dropout_prob");
|
|
|
|
auto dropout_implementation =
|
|
context.Attr<std::string>("dropout_implementation");
|
|
if (!context.Attr<bool>("is_test")) {
|
|
auto* mask = context.Output<Tensor>("Mask");
|
|
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
|
|
|
|
// NOTE: fixed seed should only be used in unittest or for debug.
|
|
// Guarantee to use random seed in training.
|
|
std::random_device rnd;
|
|
std::minstd_rand engine;
|
|
int seed =
|
|
context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
|
|
engine.seed(seed);
|
|
|
|
std::uniform_real_distribution<float> dist(0, 1);
|
|
|
|
size_t size = framework::product(mask->dims());
|
|
for (size_t i = 0; i < size; ++i) {
|
|
if (dist(engine) < dropout_prob) {
|
|
mask_data[i] = 0;
|
|
y_data[i] = 0;
|
|
} else {
|
|
if (dropout_implementation == "upscale_in_train") {
|
|
mask_data[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
|
|
y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
|
|
} else {
|
|
mask_data[i] = 1;
|
|
y_data[i] = x_data[i];
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
auto X = EigenMatrix<T>::Reshape(*x, 1);
|
|
auto Y = EigenMatrix<T>::Reshape(*y, 1);
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
if (dropout_implementation == "upscale_in_train") {
|
|
Y.device(place) = X;
|
|
} else {
|
|
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class DropoutGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
PADDLE_ENFORCE(!context.Attr<bool>("is_test"),
|
|
"GradOp is only callable when is_test is false");
|
|
|
|
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
|
|
auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
|
|
auto* mask = context.Input<Tensor>("Mask");
|
|
grad_x->mutable_data<T>(context.GetPlace());
|
|
|
|
auto M = EigenMatrix<T>::Reshape(*mask, 1);
|
|
auto dX = EigenMatrix<T>::Reshape(*grad_x, 1);
|
|
auto dY = EigenMatrix<T>::Reshape(*grad_y, 1);
|
|
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
dX.device(place) = dY * M;
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|