dropout_op.h

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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 "paddle/framework/eigen.h"
#include "paddle/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 Place, typename T, typename AttrType>
class CPUDropoutKernel : public framework::OpKernel {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* x = context.Input<Tensor>("X");
    auto* y = context.Output<Tensor>("Out");
    auto* mask = context.Output<Tensor>("Mask");
    auto* mask_data = mask->mutable_data<T>(context.GetPlace());
    auto* y_data = y->mutable_data<T>(context.GetPlace());
    const auto* x_data = x->data<T>();

    AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");

    if (context.Attr<int>("is_training") == 1) {
      int seed = context.Attr<int>("seed");
      std::minstd_rand engine;
      engine.seed(seed);
      std::uniform_real_distribution<AttrType> 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 {
          mask_data[i] = 1;
          y_data[i] = x_data[i];
        }
      }
    } else {
      size_t size = framework::product(mask->dims());
      memset(mask_data, 0, sizeof(T) * size);
      auto X = EigenMatrix<T>::Reshape(*x, 1);
      auto Y = EigenMatrix<T>::Reshape(*y, 1);
      auto place = context.GetEigenDevice<Place>();
      Y.device(place) = X * dropout_prob;
    }
  }
};

template <typename Place, typename T>
class DropoutGradKernel : public framework::OpKernel {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    PADDLE_ENFORCE_EQ(context.Attr<int>("is_training"), 1,
                      "Only callable when is_training is true");
    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.GetEigenDevice<Place>();
    dX.device(place) = dY * M;
  }
};

}  // namespace operators
}  // namespace paddle