/* 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 <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/math/math_function.h"

namespace paddle {
namespace operators {
namespace math {

using framework::To32BitIndex;

template <typename DeviceContext, typename T>
void SetConstant<DeviceContext, T>::operator()(const DeviceContext& context,
                                               framework::Tensor* tensor,
                                               T num) {
  auto t = framework::EigenVector<T>::Flatten(*tensor);
  t.device(*context.eigen_device()) = t.constant(static_cast<T>(num));
}

template <typename DeviceContext, typename T, int Rank>
void Transpose<DeviceContext, T, Rank>::operator()(
    const DeviceContext& context, const framework::Tensor& in,
    framework::Tensor* out, const std::vector<int>& axis) {
  Eigen::array<int, Rank> permute;
  for (int i = 0; i < Rank; i++) {
    permute[i] = axis[i];
  }
  auto eigen_in = framework::EigenTensor<T, Rank>::From(in);
  auto eigen_out = framework::EigenTensor<T, Rank>::From(*out);
  auto* dev = context.eigen_device();
  // use 32bit index to speed up computation
  bool use_32bit_index = eigen_out.size() < Eigen::NumTraits<int>::highest();
  bool is_gpu_place = platform::is_gpu_place(context.GetPlace());
  if (use_32bit_index && is_gpu_place) {
    To32BitIndex(eigen_out).device(*dev) =
        To32BitIndex(eigen_in).shuffle(permute);
  } else {
    eigen_out.device(*dev) = eigen_in.shuffle(permute);
  }
}

template <typename DeviceContext, typename T>
void ColwiseSum<DeviceContext, T>::operator()(const DeviceContext& context,
                                              const framework::Tensor& input,
                                              framework::Tensor* out) {
  auto in_dims = input.dims();
  auto size = input.numel() / in_dims[0];
  PADDLE_ENFORCE_EQ(out->numel(), size);

  auto in = framework::EigenMatrix<T>::From(input);
  auto vec = framework::EigenVector<T>::Flatten(*out);

  vec.device(*context.eigen_device()) = in.sum(Eigen::array<int, 1>({{0}}));
}

// Specialize for CPU, since Eigen implement a general reduce. However,
// colwise-sum can be easily implemented. General reduce has a huge overhead in
// CPU
template <typename T>
class ColwiseSum<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::Tensor& input, framework::Tensor* out) {
    auto& in_dims = input.dims();
    auto height = in_dims[0];
    auto size = in_dims[1];
    PADDLE_ENFORCE_EQ(out->numel(), size);

    T* out_buf = out->mutable_data<T>(out->place());
    const T* in_buf = input.data<T>();

    for (size_t i = 0; i < static_cast<size_t>(height); ++i) {
      for (size_t j = 0; j < static_cast<size_t>(size); ++j) {
        if (i == 0) {
          out_buf[j] = in_buf[i * size + j];
        } else {
          out_buf[j] += in_buf[i * size + j];
        }
      }
    }
  }
};

template <typename DeviceContext, typename T>
void RowwiseMean<DeviceContext, T>::operator()(const DeviceContext& context,
                                               const framework::Tensor& input,
                                               framework::Tensor* out) {
  auto in_dims = input.dims();
  PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
  PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);

  auto in = framework::EigenMatrix<T>::From(input);
  auto vec = framework::EigenVector<T>::Flatten(*out);

  vec.device(*context.eigen_device()) = in.mean(Eigen::array<int, 1>({{1}}));
}
// TODO(zcd): Following ColwiseSum format, need to confirm.
// Specialize for CPU, since Eigen implement a general reduce. However,
// rowwise-sum can be easily implemented. General reduce has a huge overhead in
// CPU
template <typename T>
class RowwiseMean<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::Tensor& input, framework::Tensor* out) {
    auto& in_dims = input.dims();
    PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
    auto height = in_dims[0];
    auto size = in_dims[1];
    PADDLE_ENFORCE_EQ(out->numel(), height);
    auto inv_size = 1.0 / size;
    T* out_buf = out->mutable_data<T>(out->place());
    const T* in_buf = input.data<T>();

    for (size_t i = 0; i < static_cast<size_t>(height); ++i) {
      T sum = 0;
      for (size_t j = 0; j < static_cast<size_t>(size); ++j) {
        sum += in_buf[i * size + j];
      }
      out_buf[i] = sum * inv_size;
    }
  }
};

template <typename DeviceContext, typename T>
void RowwiseSum<DeviceContext, T>::operator()(const DeviceContext& context,
                                              const framework::Tensor& input,
                                              framework::Tensor* out) {
  auto in_dims = input.dims();
  PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
  PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);

  auto in = framework::EigenMatrix<T>::From(input);
  auto vec = framework::EigenVector<T>::Flatten(*out);

  vec.device(*context.eigen_device()) = in.sum(Eigen::array<int, 1>({{1}}));
}
// TODO(zcd): Following ColwiseSum format, need to confirm.
// Specialize for CPU, since Eigen implement a general reduce. However,
// rowwise-sum can be easily implemented. General reduce has a huge overhead in
// CPU
template <typename T>
class RowwiseSum<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::Tensor& input, framework::Tensor* out) {
    auto& in_dims = input.dims();
    PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
    auto height = in_dims[0];
    auto size = in_dims[1];
    PADDLE_ENFORCE_EQ(out->numel(), height);

    T* out_buf = out->mutable_data<T>(out->place());
    const T* in_buf = input.data<T>();

    for (size_t i = 0; i < static_cast<size_t>(height); ++i) {
      T sum = 0;
      for (size_t j = 0; j < static_cast<size_t>(size); ++j) {
        sum += in_buf[i * size + j];
      }
      out_buf[i] = sum;
    }
  }
};

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