/* 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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/for_range.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename T, bool same_row>
struct CosSimFunctor {
  CosSimFunctor(const T* x, const T* y, T* x_norm, T* y_norm, T* z, int cols)
      : x_norm_(x_norm),
        y_norm_(y_norm),
        x_(x),
        y_(y),
        z_(z),
        cols_(static_cast<size_t>(cols)) {}

  inline HOSTDEVICE void operator()(size_t row_id) const {
    auto* x = x_ + cols_ * row_id;
    T xx = 0, xy = 0, yy = 0;
    if (same_row) {
      auto* y = y_ + cols_ * row_id;
      T tep_x, tep_y;
      for (size_t i = 0; i < cols_; ++i) {
        tep_x = x[i];
        tep_y = y[i];
        xx += tep_x * tep_x;
        yy += tep_y * tep_y;
        xy += tep_x * tep_y;
      }
      xx = sqrt(xx);
      yy = sqrt(yy);
      y_norm_[row_id] = yy;
      x_norm_[row_id] = xx;
      z_[row_id] = xy / (xx * yy);
    } else {  // This can be wrote in a better way.
      T tep_x, tep_y;
      for (size_t i = 0; i < cols_; ++i) {
        tep_x = x[i];
        tep_y = y_[i];
        xx += tep_x * tep_x;
        yy += tep_y * tep_y;
        xy += tep_x * tep_y;
      }
      xx = sqrt(xx);
      yy = sqrt(yy);
      if (row_id == 0) y_norm_[0] = yy;
      x_norm_[row_id] = xx;
      z_[row_id] = xy / (xx * yy);
    }
  }

  T* x_norm_;
  T* y_norm_;
  const T* x_;
  const T* y_;
  T* z_;
  const size_t cols_;
};

template <typename DeviceContext, typename T>
class CosSimKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    // get Tensor
    auto* in_x = context.Input<Tensor>("X");
    auto* in_y = context.Input<Tensor>("Y");
    auto* out_z = context.Output<Tensor>("Out");
    auto* out_x_norm = context.Output<Tensor>("XNorm");
    auto* out_y_norm = context.Output<Tensor>("YNorm");
    out_z->mutable_data<T>(context.GetPlace());
    out_x_norm->mutable_data<T>(context.GetPlace());
    out_y_norm->mutable_data<T>(context.GetPlace());

    int rows_x = in_x->dims()[0];
    int rows_y = in_y->dims()[0];

    int cols = framework::product(in_x->dims()) / rows_x;

    if (rows_x == rows_y) {
      CosSimFunctor<T, true> functor(
          in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
          out_y_norm->data<T>(), out_z->data<T>(), cols);
      platform::ForRange<DeviceContext> for_range(
          static_cast<const DeviceContext&>(context.device_context()), rows_x);
      for_range(functor);
    } else {
      CosSimFunctor<T, false> functor(
          in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
          out_y_norm->data<T>(), out_z->data<T>(), cols);
      platform::ForRange<DeviceContext> for_range(
          static_cast<const DeviceContext&>(context.device_context()), rows_x);
      for_range(functor);
    }
  }
};

template <typename T>
struct CosSimGradFunctor {
  CosSimGradFunctor(const T* x_norm, const T* y_norm, const T* x, const T* y,
                    const T* z, const T* dz, T* dx, int cols)
      : x_norm_(x_norm),
        y_norm_(y_norm),
        x_(x),
        y_(y),
        z_(z),
        dz_(dz),
        dx_(dx),
        cols_(static_cast<size_t>(cols)) {}

  inline HOSTDEVICE void operator()(size_t row_id) const {
    auto x_norm_square = x_norm_[row_id] * x_norm_[row_id];
    auto xy_norm_prod = x_norm_[row_id] * y_norm_[row_id];
    auto dz = dz_[row_id];
    auto z = z_[row_id];

    auto* dx = dx_ + cols_ * row_id;
    auto* x = x_ + cols_ * row_id;
    auto* y = y_ + cols_ * row_id;

    auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
    auto reciprocal_x_norm_square = 1 / x_norm_square;
    for (size_t i = 0; i < cols_; ++i) {
      dx[i] = dz * (y[i] * reciprocal_xy_norm_prod -
                    z * x[i] * reciprocal_x_norm_square);
    }
  }

  const T* x_norm_;
  const T* y_norm_;
  const T* x_;
  const T* y_;
  const T* z_;
  const T* dz_;
  T* dx_;
  const size_t cols_;
};

template <typename T>
struct CosSimDxFunctor {
  CosSimDxFunctor(const T* x_norm, const T* y_norm, const T* x, const T* y,
                  const T* z, const T* dz, T* dx, int cols)
      : x_norm_(x_norm),
        y_norm_(y_norm),
        x_(x),
        y_(y),
        z_(z),
        dz_(dz),
        dx_(dx),
        cols_(static_cast<size_t>(cols)) {}

  inline HOSTDEVICE void operator()(size_t row_id) const {
    auto xy_norm_prod = x_norm_[row_id] * y_norm_[0];
    auto dz = dz_[row_id];
    auto z = z_[row_id];
    auto* x = x_ + cols_ * row_id;
    auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
    auto x_norm_square = x_norm_[row_id] * x_norm_[row_id];
    auto* dx = dx_ + cols_ * row_id;
    auto reciprocal_x_norm_square = 1 / x_norm_square;

    for (size_t i = 0; i < cols_; ++i) {
      dx[i] = dz * (y_[i] * reciprocal_xy_norm_prod -
                    z * x[i] * reciprocal_x_norm_square);
    }
  }
  const T* x_norm_;
  const T* y_norm_;
  const T* x_;
  const T* y_;
  const T* z_;
  const T* dz_;
  T* dx_;
  const size_t cols_;
};

template <typename DeviceContext, typename T>
struct CosSimDyFunctor {
  inline void operator()(const DeviceContext& ctx, const T* x_norm,
                         const T* y_norm, const T* x, const T* y, const T* z,
                         const T* dz, const size_t rows, const size_t cols,
                         T* dy) const;
};

template <typename DeviceContext, typename T>
class CosSimGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    // get Tensor
    auto* in_x = context.Input<Tensor>("X");
    auto* in_y = context.Input<Tensor>("Y");
    auto* in_z = context.Input<Tensor>("Out");
    auto* in_x_norm = context.Input<Tensor>("XNorm");
    auto* in_y_norm = context.Input<Tensor>("YNorm");
    auto* out_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
    auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
    auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));

    // compute gradident
    int rows_x = in_x->dims()[0];
    int rows_y = in_y->dims()[0];
    int cols = framework::product(in_x->dims()) / rows_x;

    if (rows_x == rows_y) {
      if (out_grad_x) {
        CosSimGradFunctor<T> functor(
            in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
            in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
            out_grad_x->mutable_data<T>(context.GetPlace()), cols);
        platform::ForRange<DeviceContext> for_range(
            static_cast<const DeviceContext&>(context.device_context()),
            rows_x);
        for_range(functor);
      }
      if (out_grad_y) {
        CosSimGradFunctor<T> functor(
            in_y_norm->data<T>(), in_x_norm->data<T>(), in_y->data<T>(),
            in_x->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
            out_grad_y->mutable_data<T>(context.GetPlace()), cols);
        platform::ForRange<DeviceContext> for_range(
            static_cast<const DeviceContext&>(context.device_context()),
            rows_x);
        for_range(functor);
      }
    } else {
      if (out_grad_x) {
        CosSimDxFunctor<T> functor(
            in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
            in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
            out_grad_x->mutable_data<T>(context.GetPlace()), cols);
        platform::ForRange<DeviceContext> for_range(
            static_cast<const DeviceContext&>(context.device_context()),
            rows_x);
        for_range(functor);
      }
      if (out_grad_y) {
        out_grad_y->mutable_data<T>(context.GetPlace());
        math::SetConstant<DeviceContext, T> set_zero;
        auto& dev_ctx = context.template device_context<DeviceContext>();
        set_zero(dev_ctx, out_grad_y, static_cast<T>(0));

        CosSimDyFunctor<DeviceContext, T> functor;
        functor(dev_ctx, in_x_norm->data<T>(), in_y_norm->data<T>(),
                in_x->data<T>(), in_y->data<T>(), in_z->data<T>(),
                in_grad_z->data<T>(), static_cast<size_t>(rows_x),
                static_cast<size_t>(cols), out_grad_y->data<T>());
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle