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#!/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 <file>.obj <file>.cu
# And the following for linking:
# nvcc --shared -o <file>.dll <file1>.obj <file2>.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})
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