// Copyright (c) 2022 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/phi/kernels/grid_sample_grad_kernel.h"

#include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/gpu/grid_sample_utils.h"

namespace phi {

template <typename T>
static __forceinline__ __device__ void AtomicAdd(
    T* data, int h, int w, int sH, int sW, int H, int W, T delta) {
  if (InBounds(h, w, H, W)) {
    paddle::platform::CudaAtomicAdd(data + h * sH + w * sW, delta);
  }
}

template <typename T>
static __forceinline__ __device__ T
UnnormalizeWithMask(T coord, int size, bool align_corners, T* grad_in) {
  if (align_corners) {
    *grad_in = static_cast<T>(size - 1) / 2;
    return ((coord + 1.f) / 2) * (size - 1);
  } else {
    *grad_in = static_cast<T>(size) / 2;
    return ((coord + 1.f) * size - 1) / 2;
  }
}

template <typename T>
static __forceinline__ __device__ T ClipIndexesWithMask(T in,
                                                        int clip_limit,
                                                        T* grad_in) {
  if (in <= static_cast<T>(0)) {
    *grad_in = static_cast<T>(0);
    return static_cast<T>(0);
  } else {
    T max = static_cast<T>(clip_limit - 1);
    if (in >= max) {
      *grad_in = static_cast<T>(0);
      return max;
    } else {
      *grad_in = static_cast<T>(1);
      return in;
    }
  }
}

template <typename T>
static __forceinline__ __device__ T
ReflectIndexesWithMask(T in, int twice_low, int twice_high, T* grad_in) {
  if (twice_low == twice_high) {
    *grad_in = static_cast<T>(0);
    return static_cast<T>(0);
  }
  int grad_in_mult_;
  T min = static_cast<T>(twice_low) / 2;
  T span = static_cast<T>(twice_high - twice_low) / 2;
  in = in - min;
  if (in < static_cast<T>(0)) {
    grad_in_mult_ = -1;
    in = -in;
  } else {
    grad_in_mult_ = 1;
  }
  T extra = fmod(in, span);
  int flips = static_cast<int>(floor(in / span));
  if (flips % 2 == 0) {
    *grad_in = static_cast<T>(grad_in_mult_);
    return extra + min;
  } else {
    *grad_in = static_cast<T>(-grad_in_mult_);
    return span - extra + min;
  }
}

template <typename T>
static __forceinline__ __device__ T
ComputePositionsWithMask(T coord,
                         int size,
                         PaddingMode padding_mode,
                         bool align_corners,
                         T* grad_in) {
  T grad_clip, grad_refl;
  coord = UnnormalizeWithMask<T>(coord, size, align_corners, grad_in);
  if (padding_mode == PaddingMode::border) {
    coord = ClipIndexesWithMask(coord, size, &grad_clip);
    *grad_in = (*grad_in) * grad_clip;
  } else if (padding_mode == PaddingMode::reflect) {
    if (align_corners) {
      coord = ReflectIndexesWithMask(coord, 0, 2 * (size - 1), &grad_refl);
    } else {
      coord = ReflectIndexesWithMask(coord, -1, 2 * size - 1, &grad_refl);
    }
    coord = ClipIndexesWithMask(coord, size, &grad_clip);
    *grad_in = (*grad_in) * grad_refl * grad_clip;
  }

  return coord;
}

template <typename T>
__global__ void GridSamplerCudaBackwardKernel(const int nthreads,
                                              const T* grad_output,
                                              const T* input,
                                              const T* grid,
                                              int n,
                                              int out_c,
                                              int out_h,
                                              int out_w,
                                              int in_h,
                                              int in_w,
                                              T* grad_input,
                                              T* grad_grid,
                                              const Mode mode,
                                              const PaddingMode padding_mode,
                                              bool align_corners) {
  int inp_sN = out_c * in_h * in_w;
  int inp_sC = in_h * in_w;
  int inp_sH = in_w;
  int inp_sW = 1;
  int grid_sN = out_h * out_w * 2;
  int grid_sH = out_w * 2;
  int grid_sW = 2;
  int grid_sCoor = 1;

  int gOut_sN = out_c * out_h * out_w;
  int gOut_sC = out_h * out_w;
  int gOut_sH = out_w;
  int gOut_sW = 1;

  CUDA_KERNEL_LOOP(index, nthreads) {
    const int w = index % out_w;
    const int h = (index / out_w) % out_h;
    const int n = index / (out_h * out_w);
    const int grid_offset = n * grid_sN + h * grid_sH + w * grid_sW;

    T ix = grid[grid_offset];
    T iy = grid[grid_offset + grid_sCoor];

    T gix_mult, giy_mult;
    ix = ComputePositionsWithMask(
        ix, in_w, padding_mode, align_corners, &gix_mult);
    iy = ComputePositionsWithMask(
        iy, in_h, padding_mode, align_corners, &giy_mult);

    if (mode == Mode::bilinear) {
      int ix_nw = static_cast<int>(floor(ix));
      int iy_nw = static_cast<int>(floor(iy));
      int ix_ne = ix_nw + 1;
      int iy_ne = iy_nw;
      int ix_sw = ix_nw;
      int iy_sw = iy_nw + 1;
      int ix_se = ix_nw + 1;
      int iy_se = iy_nw + 1;

      T nw = (ix_se - ix) * (iy_se - iy);
      T ne = (ix - ix_sw) * (iy_sw - iy);
      T sw = (ix_ne - ix) * (iy - iy_ne);
      T se = (ix - ix_nw) * (iy - iy_nw);

      T gix = static_cast<T>(0), giy = static_cast<T>(0);
      int gOut_offset = n * gOut_sN + h * gOut_sH + w * gOut_sW;
      T* gInp_ptr_NC = grad_input + n * inp_sN;
      int inp_offset_NC = n * inp_sN;
      for (int c = 0; c < out_c; ++c,
               inp_offset_NC += inp_sC,
               gInp_ptr_NC += inp_sC,
               gOut_offset += gOut_sC) {
        T gOut = grad_output[gOut_offset];

        AtomicAdd(
            gInp_ptr_NC, iy_nw, ix_nw, inp_sH, inp_sW, in_h, in_w, nw * gOut);
        AtomicAdd(
            gInp_ptr_NC, iy_ne, ix_ne, inp_sH, inp_sW, in_h, in_w, ne * gOut);
        AtomicAdd(
            gInp_ptr_NC, iy_sw, ix_sw, inp_sH, inp_sW, in_h, in_w, sw * gOut);
        AtomicAdd(
            gInp_ptr_NC, iy_se, ix_se, inp_sH, inp_sW, in_h, in_w, se * gOut);

        if (InBounds(iy_nw, ix_nw, in_h, in_w)) {
          T nw_val = input[inp_offset_NC + iy_nw * inp_sH + ix_nw * inp_sW];
          gix -= nw_val * (iy_se - iy) * gOut;
          giy -= nw_val * (ix_se - ix) * gOut;
        }
        if (InBounds(iy_ne, ix_ne, in_h, in_w)) {
          T ne_val = input[inp_offset_NC + iy_ne * inp_sH + ix_ne * inp_sW];
          gix += ne_val * (iy_sw - iy) * gOut;
          giy -= ne_val * (ix - ix_sw) * gOut;
        }
        if (InBounds(iy_sw, ix_sw, in_h, in_w)) {
          T sw_val = input[inp_offset_NC + iy_sw * inp_sH + ix_sw * inp_sW];
          gix -= sw_val * (iy - iy_ne) * gOut;
          giy += sw_val * (ix_ne - ix) * gOut;
        }
        if (InBounds(iy_se, ix_se, in_h, in_w)) {
          T se_val = input[inp_offset_NC + iy_se * inp_sH + ix_se * inp_sW];
          gix += se_val * (iy - iy_nw) * gOut;
          giy += se_val * (ix - ix_nw) * gOut;
        }
      }

      if (grad_grid != nullptr) {
        T* gGrid_ptr_NHW = grad_grid + index * grid_sW;
        gGrid_ptr_NHW[0] = gix_mult * gix;
        gGrid_ptr_NHW[1] = giy_mult * giy;
      }
    } else if (mode == Mode::nearest) {
      int ix_nearest = static_cast<int>(std::nearbyint(ix));
      int iy_nearest = static_cast<int>(std::nearbyint(iy));

      int gOut_offset = n * gOut_sN + h * gOut_sH + w * gOut_sW;
      T* gInp_ptr_NC = grad_input + n * inp_sN;
      for (int c = 0; c < out_c;
           ++c, gInp_ptr_NC += inp_sC, gOut_offset += gOut_sC) {
        AtomicAdd(gInp_ptr_NC,
                  iy_nearest,
                  ix_nearest,
                  inp_sH,
                  inp_sW,
                  in_h,
                  in_w,
                  grad_output[gOut_offset]);
      }

      if (grad_grid != nullptr) {
        T* gGrid_ptr_NHW = grad_grid + index * grid_sW;
        gGrid_ptr_NHW[0] = static_cast<T>(0);
        gGrid_ptr_NHW[1] = static_cast<T>(0);
      }
    }
  }
}

template <typename T, typename Context>
void GridSampleGradKernel(const Context& dev_ctx,
                          const DenseTensor& x,
                          const DenseTensor& grid,
                          const DenseTensor& out_grad,
                          const std::string& mode,
                          const std::string& padding_mode,
                          bool align_corners,
                          DenseTensor* x_grad,
                          DenseTensor* grid_grad) {
  PaddingMode enum_padding_mode;
  Mode enum_mode;
  if (padding_mode == "border") {
    enum_padding_mode = PaddingMode::border;
  } else if (padding_mode == "reflection") {
    enum_padding_mode = PaddingMode::reflect;
  } else {
    enum_padding_mode = PaddingMode::zeros;
  }

  if (mode == "nearest") {
    enum_mode = Mode::nearest;
  } else {
    enum_mode = Mode::bilinear;
  }

  const int n = grid.dims()[0];
  const int out_h = grid.dims()[1];
  const int out_w = grid.dims()[2];
  const int c = x.dims()[1];
  const int in_h = x.dims()[2];
  const int in_w = x.dims()[3];

  dev_ctx.template Alloc<T>(x_grad);
  phi::funcs::SetConstant<Context, T>()(dev_ctx, x_grad, static_cast<T>(0));

  T* grid_grad_data = nullptr;
  if (grid_grad != nullptr) {
    grid_grad_data = dev_ctx.template Alloc<T>(grid_grad);
  }

  int count = static_cast<int>(n * out_h * out_w);
  auto cu_stream = dev_ctx.stream();
  backends::gpu::GpuLaunchConfig config =
      backends::gpu::GetGpuLaunchConfig1D(dev_ctx, count);
  GridSamplerCudaBackwardKernel<T>
      <<<config.block_per_grid, config.thread_per_block, 0, cu_stream>>>(
          count,
          out_grad.data<T>(),
          x.data<T>(),
          grid.data<T>(),
          n,
          c,
          out_h,
          out_w,
          in_h,
          in_w,
          x_grad->data<T>(),
          grid_grad_data,
          enum_mode,
          enum_padding_mode,
          align_corners);
}

}  // namespace phi

PD_REGISTER_KERNEL(grid_sample_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::GridSampleGradKernel,
                   float,
                   double) {}