sequence_pooling.cc

/* 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. */

#include "paddle/operators/math/sequence_pooling.h"
#include "paddle/operators/math/math_function.h"

namespace paddle {
namespace operators {
namespace math {

template <typename T>
class MaxSeqPoolFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::LoDTensor& input, framework::Tensor* output,
                  framework::Tensor* index) {
    auto in_dims = input.dims();
    auto out_dims = output->dims();
    auto idx_dims = index->dims();
    PADDLE_ENFORCE_GT(in_dims.size(), 1);
    PADDLE_ENFORCE_GT(out_dims.size(), 1);
    for (int64_t i = 1; i < in_dims.size(); ++i) {
      PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
    }
    PADDLE_ENFORCE_EQ(idx_dims, out_dims);

    auto starts = input.lod()[0];
    const T* in_data = input.data<T>();
    T* out_data = output->data<T>();
    int* max_index = index->data<int>();

    int64_t num_seq = out_dims[0];
    int64_t dim = output->numel() / num_seq;
    for (int64_t i = 0; i < num_seq; ++i) {
      for (int64_t k = 0; k < dim; ++k) {
        out_data[i * dim + k] = in_data[starts[i] * dim + k];
        max_index[i * dim + k] = starts[i];
      }
      for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) {
        for (int64_t k = 0; k < dim; ++k) {
          if (in_data[j * dim + k] > out_data[i * dim + k]) {
            out_data[i * dim + k] = in_data[j * dim + k];
            max_index[i * dim + k] = j;
          }
        }
      }
    }
  }
};

template <typename T>
class MaxSeqPoolGradFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::Tensor& out_grad,
                  const framework::Tensor& index,
                  framework::LoDTensor* in_grad) {
    auto og_dims = out_grad.dims();
    auto ig_dims = in_grad->dims();
    auto idx_dims = index.dims();
    PADDLE_ENFORCE_GT(og_dims.size(), 1);
    PADDLE_ENFORCE_GT(ig_dims.size(), 1);
    for (int64_t i = 1; i < og_dims.size(); ++i) {
      PADDLE_ENFORCE_EQ(og_dims[i], ig_dims[i]);
    }
    PADDLE_ENFORCE_EQ(idx_dims, og_dims);

    const T* og_data = out_grad.data<T>();
    const int* max_index = index.data<int>();
    T* ig_data = in_grad->data<T>();

    SetConstant<platform::CPUDeviceContext, T> set_zero;
    set_zero(context, in_grad, static_cast<T>(0.0));
    int64_t num_seq = og_dims[0];
    int64_t dim = out_grad.numel() / num_seq;
    for (int64_t i = 0; i < num_seq; ++i) {
      for (int64_t j = 0; j < dim; ++j) {
        int step_id = max_index[i * dim + j];
        ig_data[step_id * dim + j] = og_data[i * dim + j];
      }
    }
  }
};

template class MaxSeqPoolFunctor<platform::CPUDeviceContext, float>;
template class MaxSeqPoolFunctor<platform::CPUDeviceContext, double>;
template class MaxSeqPoolGradFunctor<platform::CPUDeviceContext, float>;
template class MaxSeqPoolGradFunctor<platform::CPUDeviceContext, double>;

}  // namespace math
}  // namespace operators
}  // namespace paddle