/* 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 <algorithm>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/transform.h"

#ifdef __NVCC__
#include <cuda.h>
#include <thrust/iterator/iterator_adaptor.h>
#include "paddle/fluid/platform/cuda_primitives.h"
constexpr int ELEMWISE_MAX_BLOCK_DIM = 1024;
#endif

#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/for_range.h"

namespace paddle {
namespace operators {

/*
 * Out = X ⊙ Y
 * If Y's shape does not match X' shape, they will be reshaped.
 * For example:
 * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
 *    pre=2, n=3*4, post=5
 *    x.shape(2, 12, 5) * y.shape(1, 12, 1).broadcast(2, 12, 5)
 * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
 *    pre=2*3, n=4*5, post=1
 *    x.shape(6, 20, 1) * y.shape(1, 20, 1).broadcast(6, 20, 1)
 */
inline void get_mid_dims(const framework::DDim& x_dims,
                         const framework::DDim& y_dims, const int axis,
                         int* pre, int* n, int* post) {
  *pre = 1;
  *n = 1;
  *post = 1;
  for (int i = 0; i < axis; ++i) {
    (*pre) *= x_dims[i];
  }

  for (int i = 0; i < y_dims.size(); ++i) {
    PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
                      "Broadcast dimension mismatch.");
    (*n) *= y_dims[i];
  }

  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
    (*post) *= x_dims[i];
  }
}

inline void trim_trailing_singular_dims(framework::DDim* dims) {
  // Remove trailing dimensions of size 1 for y
  auto actual_dims_size = dims->size();
  for (; actual_dims_size != 0; --actual_dims_size) {
    if ((*dims)[actual_dims_size - 1] != 1) break;
  }
  if (actual_dims_size != dims->size()) {
    auto actual_dims = framework::vectorize(*dims);
    actual_dims.resize(actual_dims_size);
    *dims = framework::make_ddim(actual_dims);
  }
}

template <typename T, typename DeviceContext>
class RowwiseTransformIterator;
template <typename T, typename DeviceContext>
class MidWiseTransformIterator;

template <typename T>
class RowwiseTransformIterator<T, platform::CPUDeviceContext> {
 public:
  RowwiseTransformIterator(const T* ptr, int n) : ptr_(ptr), i_(0), n_(n) {}

  RowwiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
    ++i_;
    if (UNLIKELY(i_ == n_)) {
      i_ = 0;
    }
    return *this;
  }

  bool operator==(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) == &(*rhs);
  }

  bool operator!=(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) != &(*rhs);
  }

  const T& operator*() { return ptr_[i_]; }

 private:
  const T* ptr_;
  int i_;
  int64_t n_;
};

template <typename T>
class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
 public:
  MidWiseTransformIterator(const T* ptr, int n, int post)
      : ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}

  MidWiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
    ++j_;
    if (UNLIKELY(j_ == post_)) {
      ++i_;
      j_ = 0;
      if (UNLIKELY(i_ == n_)) {
        i_ = 0;
      }
    }
    return *this;
  }

  bool operator==(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) == &(*rhs);
  }

  bool operator!=(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) != &(*rhs);
  }

  const T& operator*() { return ptr_[i_]; }

 private:
  const T* ptr_;
  int64_t i_;
  int64_t j_;
  int64_t n_;
  int64_t post_;
};

#ifdef __NVCC__
template <typename T>
class RowwiseTransformIterator<T, platform::CUDADeviceContext>
    : public thrust::iterator_adaptor<
          RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
 public:
  typedef thrust::iterator_adaptor<
      RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
      super_t;
  HOSTDEVICE RowwiseTransformIterator(const T* x, int n)
      : super_t(x), begin_(x), n_(n) {}
  friend class thrust::iterator_core_access;

 private:
  unsigned int n_;
  const T* begin_;
  HOSTDEVICE typename super_t::reference dereference() const {
    return *(begin_ + (this->base() - begin_) % n_);
  }
};

template <typename T>
class MidWiseTransformIterator<T, platform::CUDADeviceContext>
    : public thrust::iterator_adaptor<
          MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
 public:
  typedef thrust::iterator_adaptor<
      MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
      super_t;
  HOSTDEVICE MidWiseTransformIterator(const T* x, int n, int post)
      : super_t(x), begin_(x), n_(n), post_(post) {}
  friend class thrust::iterator_core_access;

 private:
  unsigned int post_;
  unsigned int n_;
  const T* begin_;
  HOSTDEVICE typename super_t::reference dereference() const {
    return *(begin_ + (((this->base() - begin_) / post_) % n_));
  }
};
#endif

template <typename Functor, typename T, typename DeviceContext,
          typename OutType = T>
class TransformFunctor {
 public:
  TransformFunctor(const framework::Tensor* x, const framework::Tensor* y,
                   framework::Tensor* z, const DeviceContext& ctx, Functor func)
      : x_(x->data<T>()),
        y_(y->data<T>()),
        z_(z->mutable_data<OutType>(ctx.GetPlace())),
        nx_(x->numel()),
        ctx_(ctx),
        func_(func) {}

  inline void Run() const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_, y_, z_, func_);
  }

  inline void RunRowWise(int n, int pre) const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_, RowwiseTransformIterator<T, DeviceContext>(y_, n),
          z_, func_);
  }

  inline void RunMidWise(int n, int pre, int post) const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_,
          MidWiseTransformIterator<T, DeviceContext>(y_, n, post), z_, func_);
  }

 private:
  const T* x_;
  const T* y_;
  OutType* z_;
  int64_t nx_;
  const DeviceContext& ctx_;
  Functor func_;
};

#define EIGEN_FUNCTOR(name, eigen_op)                                          \
  struct Eigen##name##Functor {                                                \
    template <typename DeviceContext, typename T>                              \
    inline void Run(const framework::Tensor* x, const framework::Tensor* y,    \
                    framework::Tensor* z,                                      \
                    const framework::ExecutionContext& ctx) {                  \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_e);                                                  \
    }                                                                          \
    template <typename DeviceContext, typename T>                              \
    inline void RunBroadCast(const framework::Tensor* x,                       \
                             const framework::Tensor* y, framework::Tensor* z, \
                             const framework::ExecutionContext& ctx, int pre,  \
                             int n) {                                          \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))                  \
                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))             \
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_bcast);                                              \
    }                                                                          \
    template <typename DeviceContext, typename T>                              \
    inline void RunBroadCast2(const framework::Tensor* x,                      \
                              const framework::Tensor* y,                      \
                              framework::Tensor* z,                            \
                              const framework::ExecutionContext& ctx, int pre, \
                              int n, int post) {                               \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))               \
                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))       \
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_bcast);                                              \
    }                                                                          \
  }

#define EIGEN_ADD(x, y) ((x) + (y))
EIGEN_FUNCTOR(Add, EIGEN_ADD);

#define EIGEN_SUB(x, y) ((x) - (y))
EIGEN_FUNCTOR(Sub, EIGEN_SUB);

#define EIGEN_MUL(x, y) ((x) * (y))
EIGEN_FUNCTOR(Mul, EIGEN_MUL);

#define EIGEN_DIV(x, y) ((x) / (y))
EIGEN_FUNCTOR(Div, EIGEN_DIV);

template <typename T, typename DX_OP, typename DY_OP>
struct ElemwiseGradNoBroadcast {
  const T* x_;
  const T* y_;
  const T* out_;
  const T* dout_;

  HOSTDEVICE void operator()(size_t i) {
    if (dx_ != nullptr) {
      dx_[i] = dx_op_(x_[i], y_[i], out_[i], dout_[i]);
    }
    if (dy_ != nullptr) {
      dy_[i] = dy_op_(x_[i], y_[i], out_[i], dout_[i]);
    }
  }

  DX_OP dx_op_;
  DY_OP dy_op_;
  T* dx_;
  T* dy_;
};

template <typename T, typename DX_OP, typename DY_OP>
static void ElemwiseGradBroadcast1CPU(const T* x, const T* y, const T* out,
                                      const T* dout, int h, int w, DX_OP dx_op,
                                      DY_OP dy_op, T* dx, T* dy) {
  for (int i = 0; i < h; ++i) {
    for (int j = 0; j < w; ++j) {
      int x_offset = i * w + j;
      if (dx != nullptr) {
        dx[x_offset] = dx_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
      }
      if (dy != nullptr) {
        T tmp = dy_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
        if (i == 0) {
          dy[j] = tmp;
        } else {
          dy[j] += tmp;
        }
      }
    }
  }
}

#ifdef __NVCC__

template <typename T>
__device__ T reduceSum(T val, int tid, int len) {
  // NOTE(zcd): The warp size should be taken from the
  // parameters of the GPU but not specified as 32 simply.
  // To make the reduceSum more efficiently,
  // I use Warp-Level Parallelism and assume the Warp size
  // is 32 which may be different for different GPU,
  // but most card's warp size is 32.
  const int warpSize = 32;
  __shared__ T shm[warpSize];
  unsigned mask = 0u;
  CREATE_SHFL_MASK(mask, tid < len);

  for (int offset = warpSize / 2; offset > 0; offset /= 2)
    val += platform::__shfl_down_sync(mask, val, offset);

  if (tid < warpSize) shm[tid] = 0;

  __syncthreads();

  if (tid % warpSize == 0) {
    shm[tid / warpSize] = val;
  }
  __syncthreads();

  CREATE_SHFL_MASK(mask, tid < warpSize);

  if (tid < warpSize) {
    val = shm[tid];
    for (int offset = warpSize / 2; offset > 0; offset /= 2)
      val += platform::__shfl_down_sync(mask, val, offset);
  }

  return val;
}

template <typename T, typename DX_OP, typename DY_OP>
static __global__ void ElemwiseGradBroadcast1CUDAKernel(
    const T* x, const T* y, const T* out, const T* dout, int h, int w,
    DX_OP dx_op, DY_OP dy_op, T* dx, T* dy) {
  int j = blockIdx.x;
  int i = threadIdx.x;
  int tid = threadIdx.x;
  T val = 0;

  do {
    int x_offset = i * w + j;
    if (dx) {
      dx[x_offset] = dx_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
    }
    if (dy) {
      val += dy_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
    }
    i += ELEMWISE_MAX_BLOCK_DIM;
  } while (i < h);

  if (dy) {
    h = h > ELEMWISE_MAX_BLOCK_DIM ? ELEMWISE_MAX_BLOCK_DIM : h;
    val = reduceSum(val, tid, h);
    if (threadIdx.x == 0) {
      dy[j] = val;
    }
  }
}

template <typename T, typename DX_OP, typename DY_OP>
static void ElemwiseGradBroadcast1CUDA(cudaStream_t stream, const T* x,
                                       const T* y, const T* out, const T* dout,
                                       int h, int w, DX_OP dx_op, DY_OP dy_op,
                                       T* dx, T* dy) {
  int block_size = std::min(ELEMWISE_MAX_BLOCK_DIM, h);
  int gird_size = w;
  ElemwiseGradBroadcast1CUDAKernel<<<gird_size, block_size, 0, stream>>>(
      x, y, out, dout, h, w, dx_op, dy_op, dx, dy);
}

#endif

template <typename T, typename DX_OP, typename DY_OP>
static void ElemwiseGradBroadcast2CPU(const T* x, const T* y, const T* out,
                                      const T* dout, int pre, int n, int post,
                                      DX_OP dx_op, DY_OP dy_op, T* dx, T* dy) {
  for (int i = 0; i < pre; ++i) {
    for (int j = 0; j < n; ++j) {
      for (int k = 0; k < post; ++k) {
        int x_offset = i * n * post + j * post + k;
        if (dx != nullptr) {
          dx[x_offset] =
              dx_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
        }
        if (dy != nullptr) {
          T tmp = dy_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
          if (i == 0 && k == 0) {
            dy[j] = tmp;
          } else {
            dy[j] += tmp;
          }
        }
      }
    }
  }
}

#ifdef __NVCC__
template <typename T, typename DX_OP, typename DY_OP>
static __global__ void ElemwiseGradBroadcast2CUDAKernel(
    const T* x, const T* y, const T* out, const T* dout, int pre, int n,
    int post, DX_OP dx_op, DY_OP dy_op, T* dx, T* dy) {
  int tid = threadIdx.x;
  int j = blockIdx.x;

  T val = 0;
  int ttid = tid;

  while (true) {
    int i = ttid / post;
    int k = ttid % post;
    if (i >= pre) break;

    int x_offset = i * n * post + j * post + k;

    if (dx != nullptr) {
      dx[x_offset] = dx_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
    }

    if (dy != nullptr) {
      val += dy_op(x[x_offset], y[j], out[x_offset], dout[x_offset]);
    }

    ttid += ELEMWISE_MAX_BLOCK_DIM;
  }

  if (dy) {
    int h = pre * post;
    h = h > ELEMWISE_MAX_BLOCK_DIM ? ELEMWISE_MAX_BLOCK_DIM : h;
    val = reduceSum(val, tid, h);
    if (threadIdx.x == 0) {
      dy[j] = val;
    }
  }
}

template <typename T, typename DX_OP, typename DY_OP>
static void ElemwiseGradBroadcast2CUDA(cudaStream_t stream, const T* x,
                                       const T* y, const T* out, const T* dout,
                                       int pre, int n, int post, DX_OP dx_op,
                                       DY_OP dy_op, T* dx, T* dy) {
  int block_size = std::min(ELEMWISE_MAX_BLOCK_DIM, pre * post);
  int gird_size = n;
  ElemwiseGradBroadcast2CUDAKernel<<<gird_size, block_size, 0, stream>>>(
      x, y, out, dout, pre, n, post, dx_op, dy_op, dx, dy);
}

#endif

template <typename DeviceContext, typename T, typename DX_OP, typename DY_OP>
void ElemwiseGradCompute(const framework::ExecutionContext& ctx,
                         const framework::Tensor& x, const framework::Tensor& y,
                         const framework::Tensor& out,
                         const framework::Tensor& dout, int axis,
                         framework::Tensor* dx, framework::Tensor* dy,
                         DX_OP dx_op, DY_OP dy_op) {
  if (x.dims() == y.dims()) {
    size_t N = static_cast<size_t>(framework::product(x.dims()));
    platform::ForRange<DeviceContext> for_range(
        ctx.template device_context<DeviceContext>(), N);
    for_range(ElemwiseGradNoBroadcast<T, DX_OP, DY_OP>{
        x.data<T>(), y.data<T>(), out.data<T>(), dout.data<T>(), dx_op, dy_op,
        dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()),
        dy == nullptr ? nullptr : dy->mutable_data<T>(ctx.GetPlace())});
  } else {  // Y is a scalar
    auto x_dim = x.dims();
    auto y_dim = y.dims();

    axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
    trim_trailing_singular_dims(&y_dim);
    axis = (y_dim.size() == 0) ? x_dim.size() : axis;

    int pre, n, post;
    get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
    if (post == 1) {
      int h = pre;
      int w = n;
      if (platform::is_gpu_place(ctx.GetPlace())) {
#ifdef __NVCC__
        ElemwiseGradBroadcast1CUDA(
            ctx.template device_context<DeviceContext>().stream(), x.data<T>(),
            y.data<T>(), out.data<T>(), dout.data<T>(), h, w, dx_op, dy_op,
            dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()),
            dy == nullptr ? nullptr : dy->mutable_data<T>(ctx.GetPlace()));
#endif
      } else {
        ElemwiseGradBroadcast1CPU(
            x.data<T>(), y.data<T>(), out.data<T>(), dout.data<T>(), h, w,
            dx_op, dy_op,
            dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()),
            dy == nullptr ? nullptr : dy->mutable_data<T>(ctx.GetPlace()));
      }
    } else {
      if (platform::is_gpu_place(ctx.GetPlace())) {
#ifdef __NVCC__
        ElemwiseGradBroadcast2CUDA(
            ctx.template device_context<DeviceContext>().stream(), x.data<T>(),
            y.data<T>(), out.data<T>(), dout.data<T>(), pre, n, post, dx_op,
            dy_op,
            dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()),
            dy == nullptr ? nullptr : dy->mutable_data<T>(ctx.GetPlace()));
#endif
      } else {
        ElemwiseGradBroadcast2CPU(
            x.data<T>(), y.data<T>(), out.data<T>(), dout.data<T>(), pre, n,
            post, dx_op, dy_op,
            dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()),
            dy == nullptr ? nullptr : dy->mutable_data<T>(ctx.GetPlace()));
      }
    }
  }
}

template <typename DeviceContext, typename T, typename functor,
          typename broadcastfunctor, typename broadcast2functor>
void ElementwiseGradCompute(const framework::ExecutionContext& ctx,
                            const framework::Tensor* x,
                            const framework::Tensor* y,
                            const framework::Tensor* out,
                            const framework::Tensor* dout, int axis,
                            framework::Tensor* dx, framework::Tensor* dy) {
  auto& place = *ctx.template device_context<DeviceContext>().eigen_device();

  auto x_dims = x->dims();
  auto y_dims = y->dims();

  if (dx) {
    dx->mutable_data<T>(ctx.GetPlace());
  }
  if (dy) {
    dy->mutable_data<T>(ctx.GetPlace());
  }

  if (x_dims == y_dims) {
    functor f;
    f(place, x, y, out, dx, dy, dout);
    return;
  }

  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
  trim_trailing_singular_dims(&y_dims);
  axis = (y_dims.size() == 0) ? x_dims.size() : axis;

  int pre, n, post;
  get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);

  if (post == 1) {
    broadcastfunctor f;
    f(place, x, y, out, dx, dy, dout, pre, n);
    return;
  } else {
    broadcast2functor f;
    f(place, x, y, out, dx, dy, dout, pre, n, post);
    return;
  }
}

template <typename Functor, typename DeviceContext, typename T,
          typename OutType = T>
void ElementwiseComputeEx(const framework::ExecutionContext& ctx,
                          const framework::Tensor* x,
                          const framework::Tensor* y, int axis, Functor func,
                          framework::Tensor* z) {
  TransformFunctor<Functor, T, DeviceContext, OutType> functor(
      x, y, z, ctx.template device_context<DeviceContext>(), func);

  auto x_dims = x->dims();
  auto y_dims = y->dims();
  PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
                    "Rank of first input must >= rank of second input.");

  if (x_dims == y_dims) {
    functor.Run();
    return;
  }

  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
  PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
                 "Axis should be in range [0, x_dims)");
  trim_trailing_singular_dims(&y_dims);
  axis = (y_dims.size() == 0) ? x_dims.size() : axis;

  int pre, n, post;
  get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
  if (post == 1) {
    functor.RunRowWise(n, pre);
    return;
  } else {
    functor.RunMidWise(n, pre, post);
    return;
  }
}

}  // namespace operators
}  // namespace paddle
