Here is an example to compiling extensions with cuda files: https://github.com/longcw/yolo2-pytorch/tree/master/layers/reorg 127
You can compile CUDA kernels manually and link them to pytorch extensions just like @apaszke said .
mask.sh 6
#!/usr/bin/env bash

CUDA_PATH=/usr/local/cuda/

cd layers/reorg/src
echo "Compiling reorg layer kernels by nvcc..."
nvcc -c -o reorg_cuda_kernel.cu.o reorg_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52

cd ../
python build.py

build.py 4
import os
import torch
from torch.utils.ffi import create_extension


sources = ['src/reorg_cpu.c']
headers = ['src/reorg_cpu.h']
defines = []
with_cuda = False

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

this_file = os.path.dirname(os.path.realpath(__file__))
# print(this_file)
extra_objects = ['src/reorg_cuda_kernel.cu.o']
extra_objects = [os.path.join(this_file, fname) for fname in extra_objects]

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

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

Hope it helps you.