rbox_iou_op.cu

/* Copyright (c) 2021 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 "rbox_iou_op.h"
#include "paddle/extension.h"

// 2D block with 32 * 16 = 512 threads per block
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 16;

/**
   Computes ceil(a / b)
*/

static inline int CeilDiv(const int a, const int b) {
  return (a + b - 1)  / b;
}

template <typename T>
__global__ void rbox_iou_cuda_kernel(
    const int rbox1_num,
    const int rbox2_num,
    const T* rbox1_data_ptr,
    const T* rbox2_data_ptr,
    T* output_data_ptr) {

  // get row_start and col_start
  const int rbox1_block_idx = blockIdx.x * blockDim.x;
  const int rbox2_block_idx = blockIdx.y * blockDim.y;

  const int rbox1_thread_num = min(rbox1_num - rbox1_block_idx, blockDim.x);
  const int rbox2_thread_num = min(rbox2_num - rbox2_block_idx, blockDim.y);

  __shared__ T block_boxes1[BLOCK_DIM_X * 5];
  __shared__ T block_boxes2[BLOCK_DIM_Y * 5];


  // It's safe to copy using threadIdx.x since BLOCK_DIM_X >= BLOCK_DIM_Y
  if (threadIdx.x < rbox1_thread_num && threadIdx.y == 0) {
    block_boxes1[threadIdx.x * 5 + 0] =
        rbox1_data_ptr[(rbox1_block_idx + threadIdx.x) * 5 + 0];
    block_boxes1[threadIdx.x * 5 + 1] =
        rbox1_data_ptr[(rbox1_block_idx + threadIdx.x) * 5 + 1];
    block_boxes1[threadIdx.x * 5 + 2] =
        rbox1_data_ptr[(rbox1_block_idx + threadIdx.x) * 5 + 2];
    block_boxes1[threadIdx.x * 5 + 3] =
        rbox1_data_ptr[(rbox1_block_idx + threadIdx.x) * 5 + 3];
    block_boxes1[threadIdx.x * 5 + 4] =
        rbox1_data_ptr[(rbox1_block_idx + threadIdx.x) * 5 + 4];
  }

  // threadIdx.x < BLOCK_DIM_Y=rbox2_thread_num, just use same condition as above: threadIdx.y == 0
  if (threadIdx.x < rbox2_thread_num && threadIdx.y == 0) {
    block_boxes2[threadIdx.x * 5 + 0] =
        rbox2_data_ptr[(rbox2_block_idx + threadIdx.x) * 5 + 0];
    block_boxes2[threadIdx.x * 5 + 1] =
        rbox2_data_ptr[(rbox2_block_idx + threadIdx.x) * 5 + 1];
    block_boxes2[threadIdx.x * 5 + 2] =
        rbox2_data_ptr[(rbox2_block_idx + threadIdx.x) * 5 + 2];
    block_boxes2[threadIdx.x * 5 + 3] =
        rbox2_data_ptr[(rbox2_block_idx + threadIdx.x) * 5 + 3];
    block_boxes2[threadIdx.x * 5 + 4] =
        rbox2_data_ptr[(rbox2_block_idx + threadIdx.x) * 5 + 4];
  }

  // sync
  __syncthreads();

  if (threadIdx.x < rbox1_thread_num && threadIdx.y < rbox2_thread_num) {
    int offset = (rbox1_block_idx + threadIdx.x) * rbox2_num + rbox2_block_idx + threadIdx.y;
    output_data_ptr[offset] = rbox_iou_single<T>(block_boxes1 + threadIdx.x * 5, block_boxes2 + threadIdx.y * 5);
  }
}

#define CHECK_INPUT_GPU(x) PD_CHECK(x.place() == paddle::PlaceType::kGPU, #x " must be a GPU Tensor.")

std::vector<paddle::Tensor> RboxIouCUDAForward(const paddle::Tensor& rbox1, const paddle::Tensor& rbox2) {
    CHECK_INPUT_GPU(rbox1);
    CHECK_INPUT_GPU(rbox2);

    auto rbox1_num = rbox1.shape()[0];
    auto rbox2_num = rbox2.shape()[0];

    auto output = paddle::Tensor(paddle::PlaceType::kGPU);
    output.reshape({rbox1_num, rbox2_num});

    const int blocks_x = CeilDiv(rbox1_num, BLOCK_DIM_X);
    const int blocks_y = CeilDiv(rbox2_num, BLOCK_DIM_Y);

    dim3 blocks(blocks_x, blocks_y);
    dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y);

    PD_DISPATCH_FLOATING_TYPES(
        rbox1.type(),
        "rbox_iou_cuda_kernel",
        ([&] {
            rbox_iou_cuda_kernel<data_t><<<blocks, threads, 0, rbox1.stream()>>>(
                rbox1_num,
                rbox2_num,
                rbox1.data<data_t>(),
                rbox2.data<data_t>(),
                output.mutable_data<data_t>());
        }));

    return {output};
}