* Rename autograd namespace to torch and change torch.h into python.h
* Include torch.h instead of python.h in test/cpp/api
* Change some mentions of torch.h to python.h in C++ extensions
* Set paths directly, without find_path
* Make AT_ASSERT/AT_ERROR non-printf based, other tweaks
- AT_ASSERT/AT_ERROR don't take printf strings anymore; instead,
they take a comma-separated list of things you wanted to print
(bringing it inline with Caffe2's conventions).
Instead of AT_ASSERT(x == 0, "%d is not zero", x)
you write AT_ASSERT(x == 0, x, " is not zero")
This is done by way of a new variadic template at::str(), which
takes a list of arguments and cats their string reps (as per
operator<<) together.
- A bunch of the demangling logic that was in Error.h is now
moved to Error.cpp (better header hygiene.) Also, demangle
has been moved out to its own helper function, and also
a new helper demangle_type (from Caffe2) added.
- A bunch of AT_ASSERT converted into AT_CHECK, to more properly
convey which checks can be caused by user error, and which are
due to logic error in ATen.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* CR
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix test failure.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* buildfix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* More fixes.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* One more fix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Try harder
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Also pass torch includes to nvcc build
* Export ATen/cuda headers with install
* Refactor flags common to C++ and CUDA
* Improve tests for C++/CUDA extensions
* Export .cuh files under THC
* Refactor and clean cpp_extension.py slightly
* Include ATen in cuda extension test
* Clarifying comment in cuda_extension.cu
* Replace cuda_extension.cu with cuda_extension_kernel.cu in setup.py
* Copy compile args in C++ extension and add second kernel
* Conditionally add -std=c++11 to cuda_flags
* Also export cuDNN headers
* Add comment about deepcopy
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
