This PR adds support for convenient CUDA integration in our C++ extension mechanism. This mainly involved figuring out how to get setuptools to use nvcc for CUDA files and the regular C++ compiler for C++ files. I've added a mixed C++/CUDA test case which works great.
I've also added a CUDAExtension and CppExtension function that constructs a setuptools.Extension with "usually the right" arguments, which reduces the required boilerplate to write an extension even more. Especially for CUDA, where library_dir (CUDA_HOME/lib64) and libraries (cudart) have to be specified as well.
Next step is to enable this with our "JIT" mechanism.
NOTE: I've had to write a small find_cuda_home function to find the CUDA install directory. This logic is kind of a duplicate of tools/setup_helpers/cuda.py, but that's not available in the shipped PyTorch distribution. The function is also fairly short. Let me know if it's fine to duplicate this logic.
* CUDA support for C++ extensions with setuptools
* Remove printf in CUDA test kernel
* Remove -arch flag in test/cpp_extensions/setup.py
* Put wrap_compile into BuildExtension
* Add guesses for CUDA_HOME directory
* export PATH to CUDA location in test.sh
* On Python2, sys.platform has the linux version number
