// mathutil_cuda_kernel.cu
// 头文件，最后一个是cuda特有的
#include <curand.h>
#include <stdio.h>
#include <math.h>
#include <float.h>
#include "mathutil_cuda_kernel.h"

// 获取GPU线程通道信息
dim3 cuda_gridsize(int n)
{
  int k = (n - 1) / BLOCK + 1;
  int x = k;
  int y = 1;
  if(x > 65535) {
    x = ceil(sqrt(k));
    y = (n - 1) / (x * BLOCK) + 1;
  }
  dim3 d(x, y, 1);
  return d;
}
// 这个函数是cuda执行函数，可以看到细化到了每一个元素
__global__ void broadcast_sum_kernel(float *a, float *b, int x, int y, int size)
{
  int i = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
  if(i >= size) return;
  int j = i % x; i = i / x;
  int k = i % y;
  a[IDX2D(j, k, y)] += b[k];
}


// 这个函数是与c语言函数链接的接口函数
void broadcast_sum_cuda(float *a, float *b, int x, int y, cudaStream_t stream)
{
  int size = x * y;
  cudaError_t err;
  
  // 上面定义的函数
  broadcast_sum_kernel<<<cuda_gridsize(size), BLOCK, 0, stream>(a, b, x, y, size);

  err = cudaGetLastError();
  if (cudaSuccess != err)
  {
    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
    exit(-1);
  }
}

#ifndef _MATHUTIL_CUDA_KERNEL
#define _MATHUTIL_CUDA_KERNEL

#define IDX2D(i, j, dj) (dj * i + j)
#define IDX3D(i, j, k, dj, dk) (IDX2D(IDX2D(i, j, dj), k, dk))

#define BLOCK 512
#define MAX_STREAMS 512

#ifdef __cplusplus
extern "C" {
#endif

void broadcast_sum_cuda(float *a, float *b, int x, int y, cudaStream_t stream);

#ifdef __cplusplus
}
#endif

#endif

// mathutil_cuda.c
// THC是pytorch底层GPU库
#include <THC/THC.h>
#include "mathutil_cuda_kernel.h"

extern THCState *state;

int broadcast_sum(THCudaTensor *a_tensor, THCudaTensor *b_tensor, int x, int y)
{
  float *a = THCudaTensor_data(state, a_tensor);
  float *b = THCudaTensor_data(state, b_tensor);
  cudaStream_t stream = THCState_getCurrentStream(state);

  // 这里调用之前在cuda中编写的接口函数
  broadcast_sum_cuda(a, b, x, y, stream);

  return 1;
}

int broadcast_sum(THCudaTensor *a_tensor, THCudaTensor *b_tensor, int x, int y);

nvcc -c -o mathutil_cuda_kernel.cu.o mathutil_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52

import os
import torch
from torch.utils.ffi import create_extension

this_file = os.path.dirname(__file__)

sources = []
headers = []
defines = []
with_cuda = False

if torch.cuda.is_available():
  print('Including CUDA code.')
  sources += ['src/mathutil_cuda.c']
  headers += ['src/mathutil_cuda.h']
  defines += [('WITH_CUDA', None)]
  with_cuda = True

this_file = os.path.dirname(os.path.realpath(__file__))

extra_objects = ['src/mathutil_cuda_kernel.cu.o']  # 这里是编译好后的.o文件位置
extra_objects = [os.path.join(this_file, fname) for fname in extra_objects]


ffi = create_extension(
  '_ext.cuda_util',
  headers=headers,
  sources=sources,
  define_macros=defines,
  relative_to=__file__,
  with_cuda=with_cuda,
  extra_objects=extra_objects
)

if __name__ == '__main__':
  ffi.build()

from _ext import cuda_util #从对应路径中调用编译好的模块

a = torch.randn(3, 5).cuda()
b = torch.randn(3, 1).cuda()
mathutil.broadcast_sum(a, b, *map(int, a.size()))

# 上面等价于下面的效果：

a = torch.randn(3, 5)
b = torch.randn(3, 1)
a += b