Can python distutils compile CUDA code?

Question

I have CUDA code that I want to build in a Python dynamic library using distutils. But distutils does not seem to recognize the ".cu" file, even if the "nvcc" compiler is installed. Not sure how to do this.

Answer 1

Distutils cannot compile CUDA by default, because it does not support the simultaneous use of multiple compilers. By default, it installs the compiler based only on your platform, and not on the type of source code that you have.

I have an example project on github that contains some monkey patches in distutils to crack support for this. An example project is a C ++ class that manages some GPU memory and a CUDA core wrapped in swig, and everything is compiled only with python setup.py install . The focus is on array operations, so we also use numpy. The entire core for this sample project increments each element of the array by one.

Code here: https://github.com/rmcgibbo/npcuda-example . Here's the setup.py script. The key to all code is customize_compiler_for_nvcc() .

 import os from os.path import join as pjoin from setuptools import setup from distutils.extension import Extension from distutils.command.build_ext import build_ext import subprocess import numpy def find_in_path(name, path): "Find a file in a search path" #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ for dir in path.split(os.pathsep): binpath = pjoin(dir, name) if os.path.exists(binpath): return os.path.abspath(binpath) return None def locate_cuda(): """Locate the CUDA environment on the system Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' and values giving the absolute path to each directory. Starts by looking for the CUDAHOME env variable. If not found, everything is based on finding 'nvcc' in the PATH. """ # first check if the CUDAHOME env variable is in use if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = pjoin(home, 'bin', 'nvcc') else: # otherwise, search the PATH for NVCC nvcc = find_in_path('nvcc', os.environ['PATH']) if nvcc is None: raise EnvironmentError('The nvcc binary could not be ' 'located in your $PATH. Either add it to your path, or set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home':home, 'nvcc':nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, 'lib64')} for k, v in cudaconfig.iteritems(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) return cudaconfig CUDA = locate_cuda() # Obtain the numpy include directory. This logic works across numpy versions. try: numpy_include = numpy.get_include() except AttributeError: numpy_include = numpy.get_numpy_include() ext = Extension('_gpuadder', sources=['src/swig_wrap.cpp', 'src/manager.cu'], library_dirs=[CUDA['lib64']], libraries=['cudart'], runtime_library_dirs=[CUDA['lib64']], # this syntax is specific to this build system # we're only going to use certain compiler args with nvcc and not with gcc # the implementation of this trick is in customize_compiler() below extra_compile_args={'gcc': [], 'nvcc': ['-arch=sm_20', '--ptxas-options=-v', '-c', '--compiler-options', "'-fPIC'"]}, include_dirs = [numpy_include, CUDA['include'], 'src']) # check for swig if find_in_path('swig', os.environ['PATH']): subprocess.check_call('swig -python -c++ -o src/swig_wrap.cpp src/swig.i', shell=True) else: raise EnvironmentError('the swig executable was not found in your PATH') def customize_compiler_for_nvcc(self): """inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler, it not trivial to get your subclass injected in, and still have the right customizations (ie distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route, I have this. Note, it kindof like a wierd functional subclassing going on.""" # tell the compiler it can processes .cu self.src_extensions.append('.cu') # save references to the default compiler_so and _comple methods default_compiler_so = self.compiler_so super = self._compile # now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): if os.path.splitext(src)[1] == '.cu': # use the cuda for .cu files self.set_executable('compiler_so', CUDA['nvcc']) # use only a subset of the extra_postargs, which are 1-1 translated # from the extra_compile_args in the Extension class postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['gcc'] super(obj, src, ext, cc_args, postargs, pp_opts) # reset the default compiler_so, which we might have changed for cuda self.compiler_so = default_compiler_so # inject our redefined _compile method into the class self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) setup(name='gpuadder', # random metadata. there more you can supploy author='Robert McGibbon', version='0.1', # this is necessary so that the swigged python file gets picked up py_modules=['gpuadder'], package_dir={'': 'src'}, ext_modules = [ext], # inject our custom trigger cmdclass={'build_ext': custom_build_ext}, # since the package has c code, the egg cannot be zipped zip_safe=False)