#!/usr/bin/env python import os # on Windows, we need the original PATH without Anaconda's compiler in it: PATH = os.environ.get('PATH') from distutils.spawn import spawn, find_executable from setuptools import setup, find_packages, Extension from setuptools.command.build_ext import build_ext import sys # CUDA specific config # nvcc is assumed to be in user's PATH nvcc_compile_args = ['-O', '--ptxas-options=-v', '--compiler-options=-fPIC'] nvcc_compile_args = os.environ.get('NVCCFLAGS', '').split() + nvcc_compile_args cuda_libs = ['cublas'] cudamat_ext = Extension('cudamat.libcudamat', sources=['cudamat/cudamat.cu', 'cudamat/cudamat_kernels.cu'], libraries=cuda_libs, extra_compile_args=nvcc_compile_args) cudalearn_ext = Extension('cudamat.libcudalearn', sources=['cudamat/learn.cu', 'cudamat/learn_kernels.cu'], libraries=cuda_libs, extra_compile_args=nvcc_compile_args) class CUDA_build_ext(build_ext): """ Custom build_ext command that compiles CUDA files. Note that all extension source files will be processed with this compiler. """ def build_extensions(self): self.compiler.src_extensions.append('.cu') self.compiler.set_executable('compiler_so', 'nvcc') self.compiler.set_executable('linker_so', 'nvcc --shared') if hasattr(self.compiler, '_c_extensions'): self.compiler._c_extensions.append('.cu') # needed for Windows self.compiler.spawn = self.spawn build_ext.build_extensions(self) def spawn(self, cmd, search_path=1, verbose=0, dry_run=0): """ Perform any CUDA specific customizations before actually launching compile/link etc. commands. """ if (sys.platform == 'darwin' and len(cmd) >= 2 and cmd[0] == 'nvcc' and cmd[1] == '--shared' and cmd.count('-arch') > 0): # Versions of distutils on OSX earlier than 2.7.9 inject # '-arch x86_64' which we need to strip while using nvcc for # linking while True: try: index = cmd.index('-arch') del cmd[index:index+2] except ValueError: break elif self.compiler.compiler_type == 'msvc': # There are several things we need to do to change the commands # issued by MSVCCompiler into one that works with nvcc. In the end, # it might have been easier to write our own CCompiler class for # nvcc, as we're only interested in creating a shared library to # load with ctypes, not in creating an importable Python extension. # - First, we replace the cl.exe or link.exe call with an nvcc # call. In case we're running Anaconda, we search cl.exe in the # original search path we captured further above -- Anaconda # inserts a MSVC version into PATH that is too old for nvcc. cmd[:1] = ['nvcc', '--compiler-bindir', os.path.dirname(find_executable("cl.exe", PATH)) or cmd[0]] # - Secondly, we fix a bunch of command line arguments. for idx, c in enumerate(cmd): # create .dll instead of .pyd files if '.pyd' in c: cmd[idx] = c = c.replace('.pyd', '.dll') # replace /c by -c if c == '/c': cmd[idx] = '-c' # replace /DLL by --shared elif c == '/DLL': cmd[idx] = '--shared' # remove --compiler-options=-fPIC elif '-fPIC' in c: del cmd[idx] # replace /Tc... by ... elif c.startswith('/Tc'): cmd[idx] = c[3:] # replace /Fo... by -o ... elif c.startswith('/Fo'): cmd[idx:idx+1] = ['-o', c[3:]] # replace /LIBPATH:... by -L... elif c.startswith('/LIBPATH:'): cmd[idx] = '-L' + c[9:] # replace /OUT:... by -o ... elif c.startswith('/OUT:'): cmd[idx:idx+1] = ['-o', c[5:]] # remove /EXPORT:initlibcudamat or /EXPORT:initlibcudalearn elif c.startswith('/EXPORT:'): del cmd[idx] # replace cublas.lib by -lcublas elif c == 'cublas.lib': cmd[idx] = '-lcublas' # - Finally, we pass on all arguments starting with a '/' to the # compiler or linker, and have nvcc handle all other arguments if '--shared' in cmd: pass_on = '--linker-options=' # we only need MSVCRT for a .dll, remove CMT if it sneaks in: cmd.append('/NODEFAULTLIB:libcmt.lib') else: pass_on = '--compiler-options=' cmd = ([c for c in cmd if c[0] != '/'] + [pass_on + ','.join(c for c in cmd if c[0] == '/')]) # For the future: Apart from the wrongly set PATH by Anaconda, it # would suffice to run the following for compilation on Windows: # nvcc -c -O -o .obj .cu # And the following for linking: # nvcc --shared -o .dll .obj .obj -lcublas # This could be done by a NVCCCompiler class for all platforms. spawn(cmd, search_path, verbose, dry_run) setup(name="cudamat", version="0.3", description="Performs linear algebra computation on the GPU via CUDA", ext_modules=[cudamat_ext, cudalearn_ext], packages=find_packages(exclude=['examples', 'test']), include_package_data=True, package_data={'cudamat': ['rnd_multipliers_32bit.txt']}, author="Volodymyr Mnih", url="https://github.com/cudamat/cudamat", cmdclass={'build_ext': CUDA_build_ext})