/* 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. */

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

namespace paddle {
namespace operators {
namespace math {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;

template <typename T>
class MaxSeqPoolFunctor {
 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 {
 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 <typename T>
class SequencePoolFunctor<platform::CPUDeviceContext, T> {
 public:
  /* max pool has index output */
  void operator()(const platform::CPUDeviceContext& context,
                  const std::string pooltype, const framework::LoDTensor& input,
                  framework::Tensor* output,
                  framework::Tensor* index = nullptr) {
    if (pooltype == "MAX") {
      math::MaxSeqPoolFunctor<T> max_pool;
      max_pool(context, input, output, index);
      return;
    }
    auto lod = input.lod()[0];
    auto& place = *context.eigen_device();
    for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
      Tensor in_t =
          input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
      Tensor out_t = output->Slice(i, i + 1);
      int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
      int64_t w = input.numel() / input.dims()[0];
      auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
      auto out_e = EigenVector<T>::Flatten(out_t);
      if (pooltype == "AVERAGE") {
        out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
      } else if (pooltype == "SUM") {
        out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
      } else if (pooltype == "SQRT") {
        out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
                              std::sqrt(static_cast<T>(h));
      } else if (pooltype == "LAST") {
        out_e.device(place) = in_e.chip(h - 1, 0);
      } else if (pooltype == "FIRST") {
        out_e.device(place) = in_e.chip(0, 0);
      } else {
        PADDLE_THROW("unsupported pooling pooltype");
      }
    }
  }
};

template <typename T>
class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const std::string pooltype, const framework::Tensor& out_grad,
                  framework::LoDTensor* in_grad,
                  /* max pool has index */
                  const framework::Tensor* index = nullptr) {
    if (pooltype == "MAX") {
      math::MaxSeqPoolGradFunctor<T> max_pool_grad;
      max_pool_grad(context, out_grad, *index, in_grad);
      return;
    }

    if (pooltype == "LAST" || pooltype == "FIRST") {
      // set X@Grad be zero at first when pooltype is LAST/FIRST
      math::SetConstant<platform::CPUDeviceContext, T> functor;
      functor(context, in_grad, 0);
    }
    auto lod = in_grad->lod()[0];
    auto& place = *context.eigen_device();
    for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
      auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
                                   static_cast<int>(lod[i + 1]));
      auto out_g_t = out_grad.Slice(i, i + 1);
      int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
      int64_t w = in_grad->numel() / in_grad->dims()[0];
      auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
      auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
      auto out_g_e_v = EigenVector<T>::Flatten(out_g_t);
      Eigen::DSizes<int, 2> bcast(h, 1);

      if (pooltype == "AVERAGE") {
        in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
      } else if (pooltype == "SUM") {
        in_g_e.device(place) = (out_g_e).broadcast(bcast);
      } else if (pooltype == "SQRT") {
        in_g_e.device(place) =
            (out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
      } else if (pooltype == "LAST") {
        in_g_e.chip(h - 1, 0).device(place) = out_g_e_v;
      } else if (pooltype == "FIRST") {
        in_g_e.chip(0, 0).device(place) = out_g_e_v;
      } else {
        PADDLE_THROW("unsupported pooling pooltype");
      }
    }
  }
};

template class SequencePoolFunctor<platform::CPUDeviceContext, float>;
template class SequencePoolFunctor<platform::CPUDeviceContext, double>;
template class SequencePoolGradFunctor<platform::CPUDeviceContext, float>;
template class SequencePoolGradFunctor<platform::CPUDeviceContext, double>;

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