* PyObject* <--> at::Tensor no longer unwraps variables, instead we expect end uses to always work with variable types, and we will only unwrap the variables when we optimize.
* Add torch::CPU, torch::CUDA and torch::getType
* at::CPU -> torch::CPU in extensions
* Support native namespace functions with type dispatch.
Use 'ones' as an example. Note this is a "halfway" solution; i.e. the call chain is:
at::ones(shape, dtype) -> dtype.ones(shape, dtype) -> CPUFloatType.ones(shape, dtype) -> at::native::ones(shape, dtype)
The "nicer" solution would probably be something like:
at::ones(shape, dtype) -> dtype.ones(shape) -> CPUFloatType.ones(shape) -> at::native::ones(shape, this)
* Fix type inference.
* Fix test install.
* Fix extensions.
* Put dtype argument at the beginning.
* Fix extension.cpp.
* Fix rnn.
* Move zeros in the same manner.
* Fix cuda.
* Change randn.
* Change rand.
* Change randperm.
* Fix aten contrib.
* Resize in randperm_out.
* Implement eye.
* Fix sparse zeros.
* linspace, logspace.
* arange.
* range.
* Remove type dispatch from gen_python_functions.
* Properly generate maybe_init_cuda for type dispatch functions not named type.
* Don't duplicate dtype, this parameters for native type dispatched functions.
* Call VariableType factory methods from the base type so it gets version number 0.
* Address review comments.
* 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
