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setup.py
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setup.py
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"""
Copyright 2020 The Microsoft DeepSpeed Team
DeepSpeed library
To build wheel on Windows:
1. Install pytorch, such as pytorch 1.8 + cuda 11.1
2. Install visual cpp build tool
3. Launch cmd console with Administrator privilege for creating required symlink folders
Create a new wheel via the following command:
python setup.py bdist_wheel
The wheel will be located at: dist/*.whl
"""
import os
import sys
import shutil
import subprocess
import warnings
from setuptools import setup, find_packages
import time
torch_available = True
try:
import torch
from torch.utils.cpp_extension import BuildExtension
except ImportError:
torch_available = False
print('[WARNING] Unable to import torch, pre-compiling ops will be disabled. ' \
'Please visit https://pytorch.org/ to see how to properly install torch on your system.')
from op_builder import ALL_OPS, get_default_compute_capatabilities
RED_START = '\033[31m'
RED_END = '\033[0m'
ERROR = f"{RED_START} [ERROR] {RED_END}"
def abort(msg):
print(f"{ERROR} {msg}")
assert False, msg
def fetch_requirements(path):
with open(path, 'r') as fd:
return [r.strip() for r in fd.readlines()]
install_requires = fetch_requirements('requirements/requirements.txt')
extras_require = {
'1bit_adam': fetch_requirements('requirements/requirements-1bit-adam.txt'),
'readthedocs': fetch_requirements('requirements/requirements-readthedocs.txt'),
'dev': fetch_requirements('requirements/requirements-dev.txt'),
}
# If MPI is available add 1bit-adam requirements
if torch_available and torch.cuda.is_available():
if shutil.which('ompi_info') or shutil.which('mpiname'):
cupy = f"cupy-cuda{torch.version.cuda.replace('.','')[:3]}"
extras_require['1bit_adam'].append(cupy)
# Make an [all] extra that installs all needed dependencies
all_extras = set()
for extra in extras_require.items():
for req in extra[1]:
all_extras.add(req)
extras_require['all'] = list(all_extras)
cmdclass = {}
# For any pre-installed ops force disable ninja
if torch_available:
cmdclass['build_ext'] = BuildExtension.with_options(use_ninja=False)
if torch_available:
TORCH_MAJOR = torch.__version__.split('.')[0]
TORCH_MINOR = torch.__version__.split('.')[1]
else:
TORCH_MAJOR = "0"
TORCH_MINOR = "0"
if torch_available and not torch.cuda.is_available():
# Fix to allow docker builds, similar to https://github.com/NVIDIA/apex/issues/486
print(
"[WARNING] Torch did not find cuda available, if cross-compiling or running with cpu only "
"you can ignore this message. Adding compute capability for Pascal, Volta, and Turing "
"(compute capabilities 6.0, 6.1, 6.2)")
if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
os.environ["TORCH_CUDA_ARCH_LIST"] = get_default_compute_capatabilities()
ext_modules = []
# Default to pre-install kernels to false so we rely on JIT on Linux, opposite on Windows.
BUILD_OP_PLATFORM = 1 if sys.platform == "win32" else 0
BUILD_OP_DEFAULT = int(os.environ.get('DS_BUILD_OPS', BUILD_OP_PLATFORM))
print(f"DS_BUILD_OPS={BUILD_OP_DEFAULT}")
if BUILD_OP_DEFAULT:
assert torch_available, "Unable to pre-compile ops without torch installed. Please install torch before attempting to pre-compile ops."
def command_exists(cmd):
if sys.platform == "win32":
result = subprocess.Popen(f'{cmd}', stdout=subprocess.PIPE, shell=True)
return result.wait() == 1
else:
result = subprocess.Popen(f'type {cmd}', stdout=subprocess.PIPE, shell=True)
return result.wait() == 0
def op_envvar(op_name):
assert hasattr(ALL_OPS[op_name], 'BUILD_VAR'), \
f"{op_name} is missing BUILD_VAR field"
return ALL_OPS[op_name].BUILD_VAR
def op_enabled(op_name):
env_var = op_envvar(op_name)
return int(os.environ.get(env_var, BUILD_OP_DEFAULT))
compatible_ops = dict.fromkeys(ALL_OPS.keys(), False)
install_ops = dict.fromkeys(ALL_OPS.keys(), False)
for op_name, builder in ALL_OPS.items():
op_compatible = builder.is_compatible()
compatible_ops[op_name] = op_compatible
# If op is requested but not available, throw an error
if op_enabled(op_name) and not op_compatible:
env_var = op_envvar(op_name)
if env_var not in os.environ:
builder.warning(f"One can disable {op_name} with {env_var}=0")
abort(f"Unable to pre-compile {op_name}")
# If op is compatible update install reqs so it can potentially build/run later
if op_compatible:
reqs = builder.python_requirements()
install_requires += builder.python_requirements()
# If op install enabled, add builder to extensions
if op_enabled(op_name) and op_compatible:
assert torch_available, f"Unable to pre-compile {op_name}, please first install torch"
install_ops[op_name] = op_enabled(op_name)
ext_modules.append(builder.builder())
print(f'Install Ops={install_ops}')
# Write out version/git info
git_hash_cmd = "git rev-parse --short HEAD"
git_branch_cmd = "git rev-parse --abbrev-ref HEAD"
if command_exists('git') and 'DS_BUILD_STRING' not in os.environ:
try:
result = subprocess.check_output(git_hash_cmd, shell=True)
git_hash = result.decode('utf-8').strip()
result = subprocess.check_output(git_branch_cmd, shell=True)
git_branch = result.decode('utf-8').strip()
except subprocess.CalledProcessError:
git_hash = "unknown"
git_branch = "unknown"
else:
git_hash = "unknown"
git_branch = "unknown"
def create_dir_symlink(src, dest):
if not os.path.islink(dest):
if os.path.exists(dest):
os.remove(dest)
assert not os.path.exists(dest)
os.symlink(src, dest)
if sys.platform == "win32":
# This creates a symbolic links on Windows.
# It needs Administrator privilege to create symlinks on Windows.
create_dir_symlink('..\\..\\csrc', '.\\deepspeed\\ops\\csrc')
create_dir_symlink('..\\..\\op_builder', '.\\deepspeed\\ops\\op_builder')
# Parse the DeepSpeed version string from version.txt
version_str = open('version.txt', 'r').read().strip()
# Build specifiers like .devX can be added at install time. Otherwise, add the git hash.
# example: DS_BUILD_STR=".dev20201022" python setup.py sdist bdist_wheel
# Building wheel for distribution, update version file
if 'DS_BUILD_STRING' in os.environ:
# Build string env specified, probably building for distribution
with open('build.txt', 'w') as fd:
fd.write(os.environ.get('DS_BUILD_STRING'))
version_str += os.environ.get('DS_BUILD_STRING')
elif os.path.isfile('build.txt'):
# build.txt exists, probably installing from distribution
with open('build.txt', 'r') as fd:
version_str += fd.read().strip()
else:
# None of the above, probably installing from source
version_str += f'+{git_hash}'
torch_version = ".".join([TORCH_MAJOR, TORCH_MINOR])
# Set cuda_version to 0.0 if cpu-only
cuda_version = "0.0"
if torch_available and torch.version.cuda is not None:
cuda_version = ".".join(torch.version.cuda.split('.')[:2])
torch_info = {"version": torch_version, "cuda_version": cuda_version}
print(f"version={version_str}, git_hash={git_hash}, git_branch={git_branch}")
with open('deepspeed/git_version_info_installed.py', 'w') as fd:
fd.write(f"version='{version_str}'\n")
fd.write(f"git_hash='{git_hash}'\n")
fd.write(f"git_branch='{git_branch}'\n")
fd.write(f"installed_ops={install_ops}\n")
fd.write(f"compatible_ops={compatible_ops}\n")
fd.write(f"torch_info={torch_info}\n")
print(f'install_requires={install_requires}')
print(f'compatible_ops={compatible_ops}')
print(f'ext_modules={ext_modules}')
# Parse README.md to make long_description for PyPI page.
thisdir = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(thisdir, 'README.md'), encoding='utf-8') as fin:
readme_text = fin.read()
start_time = time.time()
setup(name='deepspeed',
version=version_str,
description='DeepSpeed library',
long_description=readme_text,
long_description_content_type='text/markdown',
author='DeepSpeed Team',
author_email='[email protected]',
url='http://deepspeed.ai',
install_requires=install_requires,
extras_require=extras_require,
packages=find_packages(exclude=["docker",
"third_party"]),
include_package_data=True,
scripts=[
'bin/deepspeed',
'bin/deepspeed.pt',
'bin/ds',
'bin/ds_ssh',
'bin/ds_report',
'bin/ds_elastic'
],
classifiers=[
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9'
],
license='MIT',
ext_modules=ext_modules,
cmdclass=cmdclass)
end_time = time.time()
print(f'deepspeed build time = {end_time - start_time} secs')