deformable_conv_grad_kernel.cu

// 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/deformable_conv_grad_kernel.h"

#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/deformable_conv_grad_kernel_impl.h"

namespace phi {

static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaximumNumBlocks = 4096;

static inline int NumBlocks(const int N) {
  return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
                  kNumMaximumNumBlocks);
}

template <typename T, typename MT>
__global__ void ModulatedDeformableCol2imGpuKernel(
    const int nthreads,
    const T* data_col,
    const T* data_offset,
    const T* data_mask,
    const int channels,
    const int height,
    const int width,
    const int kernel_h,
    const int kernel_w,
    const int pad_h,
    const int pad_w,
    const int stride_h,
    const int stride_w,
    const int dilation_h,
    const int dilation_w,
    const int channel_per_deformable_group,
    const int batch_size,
    const int deformable_group,
    const int height_col,
    const int width_col,
    MT* grad_im) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int offset = blockDim.x * gridDim.x;
  // using MT = typename phi::dtype::MPTypeTrait<T>::Type;
  for (size_t thread = index; thread < nthreads; thread += offset) {
    const int j = (thread / width_col / height_col / batch_size) % kernel_w;
    const int i =
        (thread / width_col / height_col / batch_size / kernel_w) % kernel_h;
    const int c =
        thread / width_col / height_col / batch_size / kernel_w / kernel_h;

    const int deformable_group_index = c / channel_per_deformable_group;

    int w_out = thread % width_col;
    int h_out = (thread / width_col) % height_col;
    int b = (thread / width_col / height_col) % batch_size;
    int w_in = w_out * stride_w - pad_w;
    int h_in = h_out * stride_h - pad_h;

    const T* data_offset_ptr =
        data_offset + (b * deformable_group + deformable_group_index) * 2 *
                          kernel_h * kernel_w * height_col * width_col;
    const int data_offset_h_ptr =
        ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
    const int data_offset_w_ptr =
        ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
    const int data_mask_hw_ptr =
        ((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
    const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
    const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
    const MT cur_inv_h_data = h_in + i * dilation_h + offset_h;
    const MT cur_inv_w_data = w_in + j * dilation_w + offset_w;

    MT cur_top_grad = static_cast<MT>(data_col[thread]);
    if (data_mask) {
      const T* data_mask_ptr =
          data_mask + (b * deformable_group + deformable_group_index) *
                          kernel_h * kernel_w * height_col * width_col;
      const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
      cur_top_grad *= mask;
    }
    const int cur_h = static_cast<int>(cur_inv_h_data);
    const int cur_w = static_cast<int>(cur_inv_w_data);
    for (int dy = -2; dy <= 2; dy++) {
      for (int dx = -2; dx <= 2; dx++) {
        if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 &&
            cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
            abs(cur_inv_w_data - (cur_w + dx)) < 1) {
          int cur_bottom_grad_pos =
              ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
          MT weight = DmcnGetGradientWeight(cur_inv_h_data,
                                            cur_inv_w_data,
                                            cur_h + dy,
                                            cur_w + dx,
                                            height,
                                            width);
          paddle::platform::CudaAtomicAdd(grad_im + cur_bottom_grad_pos,
                                          weight * cur_top_grad);
        }
      }
    }
  }
}

template <typename T, typename MT, typename Context>
void ModulatedDeformableCol2im(const Context& dev_ctx,
                               const T* data_col,
                               const T* data_offset,
                               const T* data_mask,
                               const std::vector<int64_t>& im_shape,
                               const std::vector<int64_t>& col_shape,
                               const std::vector<int64_t>& kernel_shape,
                               const std::vector<int>& pad,
                               const std::vector<int>& stride,
                               const std::vector<int>& dilation,
                               const int deformable_group,
                               MT* grad_im) {
  int channel_per_deformable_group = im_shape[0] / deformable_group;
  int num_kernels = col_shape[0] * col_shape[1] * col_shape[2] * col_shape[3];
  int blocks = NumBlocks(num_kernels);
  int threads = kNumCUDAThreads;

  ModulatedDeformableCol2imGpuKernel<T, MT>
      <<<blocks, threads, 0, dev_ctx.stream()>>>(num_kernels,
                                                 data_col,
                                                 data_offset,
                                                 data_mask,
                                                 im_shape[0],
                                                 im_shape[1],
                                                 im_shape[2],
                                                 kernel_shape[2],
                                                 kernel_shape[3],
                                                 pad[0],
                                                 pad[1],
                                                 stride[0],
                                                 stride[1],
                                                 dilation[0],
                                                 dilation[1],
                                                 channel_per_deformable_group,
                                                 col_shape[1],
                                                 deformable_group,
                                                 col_shape[2],
                                                 col_shape[3],
                                                 grad_im);
}

template <typename T>
__global__ void ModulatedDeformableCol2imCoordGpuKernel(
    const int nthreads,
    const T* data_col,
    const T* data_im,
    const T* data_offset,
    const T* data_mask,
    const int channels,
    const int height,
    const int width,
    const int kernel_h,
    const int kernel_w,
    const int pad_h,
    const int pad_w,
    const int stride_h,
    const int stride_w,
    const int dilation_h,
    const int dilation_w,
    const int channel_per_deformable_group,
    const int batch_size,
    const int offset_channels,
    const int deformable_group,
    const int height_col,
    const int width_col,
    T* grad_offset,
    T* grad_mask) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int offset = blockDim.x * gridDim.x;
  using MT = typename phi::dtype::MPTypeTrait<T>::Type;
  for (size_t i = index; i < nthreads; i += offset) {
    MT val = 0, mval = 0;
    const int w = i % width_col;
    const int h = (i / width_col) % height_col;
    const int c = (i / width_col / height_col) % offset_channels;
    const int b = (i / width_col / height_col) / offset_channels;

    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
    const int col_step = kernel_h * kernel_w;
    int cnt = 0;
    const T* data_col_ptr = data_col + deformable_group_index *
                                           channel_per_deformable_group *
                                           batch_size * width_col * height_col;
    const T* data_im_ptr =
        data_im + (b * deformable_group + deformable_group_index) *
                      channel_per_deformable_group / kernel_h / kernel_w *
                      height * width;
    const T* data_offset_ptr =
        data_offset + (b * deformable_group + deformable_group_index) * 2 *
                          kernel_h * kernel_w * height_col * width_col;
    const T* data_mask_ptr =
        data_mask
            ? data_mask + (b * deformable_group + deformable_group_index) *
                              kernel_h * kernel_w * height_col * width_col
            : nullptr;

    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;

    for (int col_c = offset_c / 2; col_c < channel_per_deformable_group;
         col_c += col_step) {
      const int col_pos =
          (((col_c * batch_size + b) * height_col) + h) * width_col + w;
      const int bp_dir = offset_c % 2;

      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
      int i =
          (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
      int w_out = col_pos % width_col;
      int h_out = (col_pos / width_col) % height_col;
      int w_in = w_out * stride_w - pad_w;
      int h_in = h_out * stride_h - pad_h;
      const int data_offset_h_ptr =
          (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
      const int data_offset_w_ptr =
          (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col +
           w_out);

      const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
      const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
      MT inv_h = h_in + i * dilation_h + offset_h;
      MT inv_w = w_in + j * dilation_w + offset_w;
      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) {
        inv_h = inv_w = -2;
      } else {
        mval +=
            static_cast<MT>(data_col_ptr[col_pos]) *
            funcs::DmcnIm2colBilinear<T, MT>(data_im_ptr + cnt * height * width,
                                             width,
                                             height,
                                             width,
                                             inv_h,
                                             inv_w);
      }
      const MT weight =
          DmcnGetCoordinateWeight<T, MT>(inv_h,
                                         inv_w,
                                         height,
                                         width,
                                         data_im_ptr + cnt * height * width,
                                         width,
                                         bp_dir);
      if (data_mask_ptr) {
        const int data_mask_hw_ptr =
            (((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
        const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
        val += weight * static_cast<MT>(data_col_ptr[col_pos]) * mask;
      } else {
        val += weight * static_cast<MT>(data_col_ptr[col_pos]);
      }
      cnt += 1;
    }
    grad_offset[i] = static_cast<T>(val);
    if (grad_mask && offset_c % 2 == 0)
      grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h *
                      kernel_w +
                  offset_c / 2) *
                     height_col +
                 h) *
                    width_col +
                w] = mval;
  }
}

template <typename T, typename Context>
void ModulatedDeformableCol2imCoord(const Context& dev_ctx,
                                    const T* data_col,
                                    const T* data_im,
                                    const T* data_offset,
                                    const T* data_mask,
                                    const std::vector<int64_t>& im_shape,
                                    const std::vector<int64_t>& col_shape,
                                    const std::vector<int64_t>& kernel_shape,
                                    const std::vector<int>& paddings,
                                    const std::vector<int>& strides,
                                    const std::vector<int>& dilations,
                                    const int deformable_groups,
                                    T* grad_offset,
                                    T* grad_mask) {
  int num_kernels = 2 * kernel_shape[2] * kernel_shape[3] * col_shape[1] *
                    col_shape[2] * col_shape[3] * deformable_groups;
  int channel_per_deformable_group = col_shape[0] / deformable_groups;
  int blocks = NumBlocks(num_kernels);
  int threads = kNumCUDAThreads;

  ModulatedDeformableCol2imCoordGpuKernel<T>
      <<<blocks, threads, 0, dev_ctx.stream()>>>(
          num_kernels,
          data_col,
          data_im,
          data_offset,
          data_mask,
          im_shape[0],
          im_shape[1],
          im_shape[2],
          kernel_shape[2],
          kernel_shape[3],
          paddings[0],
          paddings[1],
          strides[0],
          strides[1],
          dilations[0],
          dilations[1],
          channel_per_deformable_group,
          col_shape[1],
          2 * kernel_shape[2] * kernel_shape[3] * deformable_groups,
          deformable_groups,
          col_shape[2],
          col_shape[3],
          grad_offset,
          grad_mask);
}

template <typename T>
__global__ void FilterGradAddupGpuKernel(const int nthreads,
                                         const int n,
                                         const int height,
                                         const int width,
                                         const T* dweight_3d,
                                         T* filter_grad) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int offset = blockDim.x * gridDim.x;
  for (size_t i = index; i < nthreads; i += offset) {
    filter_grad[i] = filter_grad[i] + dweight_3d[i];
  }
}

template <typename T, typename Context>
void FilterGradAddup(const Context& dev_ctx,
                     const int nthreads,
                     const int n,
                     const int height,
                     const int width,
                     const T* dweight_3d,
                     T* filter_grad) {
  FilterGradAddupGpuKernel<T>
      <<<NumBlocks(nthreads), kNumCUDAThreads, 0, dev_ctx.stream()>>>(
          nthreads, n, height, width, dweight_3d, filter_grad);
}

}  // namespace phi

PD_REGISTER_KERNEL(deformable_conv_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::DeformableConvGradKernel,
                   float,
                   double,
                   paddle::platform::float16) {}