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setup.py
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setup.py
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r"""
Parse additional arguments along with the setup.py arguments such as install, build, distribute, sdist, etc.
Usage:
python setup.py install <additional_flags>..<additional_flags> <additional_arg>=<value>..<additional_arg>=<value>
export CC=<C++ compiler>; python setup.py install <additional_flags>..<additional_flags> <additional_arg>=<value>..<additional_arg>=<value>
Examples:
python setup.py install --force_cuda --cuda_home=/usr/local/cuda
export CC=g++7; python setup.py install --force_cuda --cuda_home=/usr/local/cuda
Additional flags:
--cpu_only: Force building only a CPU version. However, if
torch.cuda.is_available() is False, it will default to CPU_ONLY.
--force_cuda: If torch.cuda.is_available() is false, but you have a working
nvcc, compile cuda files. --force_cuda will supercede --cpu_only.
Additional arguments:
--blas=<value> : type of blas library to use for CPU matrix multiplications.
Options: [openblas, mkl, atlas, blas]. By default, it will use the first
numpy blas library it finds.
--cuda_home=<value> : a directory that contains <value>/bin/nvcc and
<value>/lib64/libcudart.so. By default, use
`torch.utils.cpp_extension._find_cuda_home()`.
--blas_include_dirs=<comma_separated_values> : additional include dirs. Only
activated when --blas=<value> is set.
--blas_library_dirs=<comma_separated_values> : additional library dirs. Only
activated when --blas=<value> is set.
"""
import sys
if sys.version_info < (3, 6):
sys.stdout.write(
"Minkowski Engine requires Python 3.6 or higher. Please use anaconda https://www.anaconda.com/distribution/ for an isolated python environment.\n"
)
sys.exit(1)
try:
import torch
except ImportError:
raise ImportError("Pytorch not found. Please install pytorch first.")
import warnings
import codecs
import os
import re
import subprocess
from sys import argv, platform
from setuptools import setup
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from pathlib import Path
if platform == "win32":
raise ImportError("Windows is currently not supported.")
elif platform == "darwin":
# Set the distutils to use clang instead of g++ for valid std
if "CC" not in os.environ:
os.environ["CC"] = "/usr/local/opt/llvm/bin/clang"
here = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
with codecs.open(os.path.join(here, *parts), "r") as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
def run_command(*args):
subprocess.check_call(args)
def _argparse(pattern, argv, is_flag=True):
if is_flag:
found = pattern in argv
if found:
argv.remove(pattern)
return found, argv
else:
arr = [arg for arg in argv if pattern in arg]
if len(arr) == 0: # not found
return False, argv
else:
assert "=" in arr[0], f"{arr[0]} requires a value."
argv.remove(arr[0])
return arr[0].split("=")[1], argv
run_command("rm", "-rf", "build")
run_command("pip", "uninstall", "MinkowskiEngine", "-y")
# For cpu only build
CPU_ONLY, argv = _argparse("--cpu_only", argv)
FORCE_CUDA, argv = _argparse("--force_cuda", argv)
if not torch.cuda.is_available() and not FORCE_CUDA:
warnings.warn(
"torch.cuda.is_available() is False. MinkowskiEngine will compile with CPU_ONLY. Please use `--force_cuda` to compile with CUDA."
)
CPU_ONLY = CPU_ONLY or not torch.cuda.is_available()
if FORCE_CUDA:
CPU_ONLY = False
# args with return value
CUDA_HOME, argv = _argparse("--cuda_home", argv, False)
BLAS, argv = _argparse("--blas", argv, False)
BLAS_INCLUDE_DIRS, argv = _argparse("--blas_include_dirs", argv, False)
BLAS_LIBRARY_DIRS, argv = _argparse("--blas_library_dirs", argv, False)
MAX_COMPILATION_THREADS = 12
Extension = CUDAExtension
extra_link_args = []
include_dirs = []
libraries = []
CC_FLAGS = []
NVCC_FLAGS = []
if CPU_ONLY:
print("--------------------------------")
print("| WARNING: CPU_ONLY build set |")
print("--------------------------------")
Extension = CppExtension
else:
print("--------------------------------")
print("| CUDA compilation set |")
print("--------------------------------")
# system python installation
libraries.append("cusparse")
if not (CUDA_HOME is False): # False when not set, str otherwise
print(f"Using CUDA_HOME={CUDA_HOME}")
if sys.platform == "win32":
vc_version = os.getenv("VCToolsVersion", "")
if vc_version.startswith("14.16."):
CC_FLAGS += ["/sdl"]
else:
CC_FLAGS += ["/sdl", "/permissive-"]
else:
CC_FLAGS += ["-fopenmp"]
if "darwin" in platform:
CC_FLAGS += ["-stdlib=libc++"]
NVCC_FLAGS += ["--expt-relaxed-constexpr", "--expt-extended-lambda"]
FAST_MATH, argv = _argparse("--fast_math", argv)
if FAST_MATH:
NVCC_FLAGS.append("--use_fast_math")
BLAS_LIST = ["openblas", "mkl", "atlas", "blas"]
if not (BLAS is False): # False only when not set, str otherwise
assert BLAS in BLAS_LIST
if BLAS == "mkl":
libraries.append("mkl_rt")
else:
libraries.append(BLAS)
if not (BLAS_INCLUDE_DIRS is False):
include_dirs += BLAS_INCLUDE_DIRS
if not (BLAS_LIBRARY_DIRS is False):
extra_link_args += [f"-Wl,-rpath,{BLAS_LIBRARY_DIRS}"]
else:
# find the default BLAS library
import numpy.distutils.system_info as sysinfo
# Search blas in this order
for blas in BLAS_LIST:
if "libraries" in sysinfo.get_info(blas):
BLAS = blas
libraries += sysinfo.get_info(blas)["libraries"]
break
else:
# BLAS not found
raise ImportError(
' \
\nBLAS not found from numpy.distutils.system_info.get_info. \
\nPlease specify BLAS with: python setup.py install --blas=openblas" \
\nfor more information, please visit https://github.com/NVIDIA/MinkowskiEngine/wiki/Installation'
)
print(f"\nUsing BLAS={BLAS}")
# The Ninja cannot compile the files that have the same name with different
# extensions correctly and uses the nvcc/CC based on the extension. Import a
# .cpp file to the corresponding .cu file to force the nvcc compilation.
SOURCE_SETS = {
"cpu": [
CppExtension,
[
"math_functions_cpu.cpp",
"coordinate_map_manager.cpp",
"convolution_cpu.cpp",
"convolution_transpose_cpu.cpp",
"local_pooling_cpu.cpp",
"local_pooling_transpose_cpu.cpp",
"global_pooling_cpu.cpp",
"broadcast_cpu.cpp",
"pruning_cpu.cpp",
"interpolation_cpu.cpp",
"quantization.cpp",
],
["pybind/minkowski.cpp"],
["-DCPU_ONLY"],
],
"gpu": [
CUDAExtension,
[
"math_functions_cpu.cpp",
"math_functions_gpu.cu",
"coordinate_map_manager.cu",
"coordinate_map_gpu.cu",
"convolution_kernel.cu",
"convolution_gpu.cu",
"convolution_transpose_gpu.cu",
"pooling_avg_kernel.cu",
"pooling_max_kernel.cu",
"local_pooling_gpu.cu",
"local_pooling_transpose_gpu.cu",
"global_pooling_gpu.cu",
"broadcast_kernel.cu",
"broadcast_gpu.cu",
"pruning_gpu.cu",
"interpolation_gpu.cu",
"spmm.cu",
"gpu.cu",
"quantization.cpp",
],
["pybind/minkowski.cu"],
[],
],
}
debug, argv = _argparse("--debug", argv)
USE_NINJA = os.getenv("USE_NINJA") == "0"
HERE = Path(os.path.dirname(__file__)).absolute()
SRC_PATH = HERE / "src"
if "CC" in os.environ or "CXX" in os.environ:
# distutils only checks CC not CXX
if "CXX" in os.environ:
os.environ["CC"] = os.environ["CXX"]
CC = os.environ["CXX"]
else:
CC = os.environ["CC"]
print(f"Using {CC} for c++ compilation")
if torch.__version__ < "1.7.0":
NVCC_FLAGS += [f"-ccbin={CC}"]
else:
print("Using the default compiler")
if debug:
CC_FLAGS += ["-g", "-DDEBUG"]
NVCC_FLAGS += ["-g", "-DDEBUG"]
else:
CC_FLAGS += ["-O3"]
NVCC_FLAGS += ["-O3"]
if "MAX_JOBS" not in os.environ and os.cpu_count() > MAX_COMPILATION_THREADS:
# Clip the num compilation thread to 8
os.environ["MAX_JOBS"] = str(MAX_COMPILATION_THREADS)
target = "cpu" if CPU_ONLY else "gpu"
Extension = SOURCE_SETS[target][0]
SRC_FILES = SOURCE_SETS[target][1]
BIND_FILES = SOURCE_SETS[target][2]
ARGS = SOURCE_SETS[target][3]
CC_FLAGS += ARGS
NVCC_FLAGS += ARGS
ext_modules = [
Extension(
name="MinkowskiEngineBackend._C",
sources=[*[str(SRC_PATH / src_file) for src_file in SRC_FILES], *BIND_FILES],
extra_compile_args={"cxx": CC_FLAGS, "nvcc": NVCC_FLAGS},
libraries=libraries,
),
]
# Python interface
setup(
name="MinkowskiEngine",
version=find_version("MinkowskiEngine", "__init__.py"),
install_requires=["torch", "numpy"],
packages=["MinkowskiEngine", "MinkowskiEngine.utils", "MinkowskiEngine.modules"],
package_dir={"MinkowskiEngine": "./MinkowskiEngine"},
ext_modules=ext_modules,
include_dirs=[str(SRC_PATH), str(SRC_PATH / "3rdparty"), *include_dirs],
cmdclass={"build_ext": BuildExtension},
author="Christopher Choy",
author_email="chrischoy@ai.stanford.edu",
description="a convolutional neural network library for sparse tensors",
long_description=read("README.md"),
long_description_content_type="text/markdown",
url="https://github.com/NVIDIA/MinkowskiEngine",
keywords=[
"pytorch",
"Minkowski Engine",
"Sparse Tensor",
"Convolutional Neural Networks",
"3D Vision",
"Deep Learning",
],
zip_safe=False,
classifiers=[
# https://pypi.org/classifiers/
"Environment :: Console",
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Other Audience",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: C++",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Topic :: Multimedia :: Graphics",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Mathematics",
"Topic :: Scientific/Engineering :: Physics",
"Topic :: Scientific/Engineering :: Visualization",
],
python_requires=">=3.6",
)