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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pip install twine
import io
import os
import sys
import re
import shutil
from shutil import rmtree
import textwrap
import shlex
import subprocess
from setuptools import find_packages, setup, Command, Extension
from setuptools.command.build_ext import build_ext
from setuptools.command.sdist import sdist as sdist_orig
from distutils.errors import CompileError, DistutilsError, DistutilsPlatformError, LinkError, DistutilsSetupError, DistutilsExecError
from distutils import log as distutils_logger
from distutils.version import LooseVersion
import traceback
if os.path.isfile('./pre_setup_local.py'):
import pre_setup_local as pre_setup
else:
import pre_setup as pre_setup
server_lib = Extension('byteps.server.c_lib', [])
tensorflow_lib = Extension('byteps.tensorflow.c_lib', [])
mxnet_lib = Extension('byteps.mxnet.c_lib', [])
pytorch_lib = Extension('byteps.torch.c_lib', [])
# Package meta-data.
NAME = 'byteps'
DESCRIPTION = 'A high-performance cross-framework Parameter Server for Deep Learning'
URL = 'https://github.com/bytedance/byteps'
EMAIL = 'lab-hr@bytedance.com'
AUTHOR = 'Bytedance Inc.'
REQUIRES_PYTHON = '>=2.7.0'
VERSION = '0.2.5'
# What packages are required for this module to be executed?
REQUIRED = [
'cloudpickle',
# 'cffi>=1.4.0',
]
# What packages are optional?
EXTRAS = {
# 'fancy feature': ['django'],
}
# The rest you shouldn't have to touch too much :)
# ------------------------------------------------
# Except, perhaps the License and Trove Classifiers!
# If you do change the License, remember to change the Trove Classifier for that!
here = os.path.abspath(os.path.dirname(__file__))
# Import the README and use it as the long-description.
# Note: this will only work if 'README.md' is present in your MANIFEST.in file!
try:
with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = '\n' + f.read()
except OSError:
long_description = DESCRIPTION
# Load the package's __version__.py module as a dictionary.
about = {}
if not VERSION:
with open(os.path.join(here, NAME, '__version__.py')) as f:
exec(f.read(), about)
else:
about['__version__'] = VERSION
def is_build_action():
if len(sys.argv) <= 1:
return False
if sys.argv[1].startswith('build'):
return True
if sys.argv[1].startswith('bdist'):
return True
if sys.argv[1].startswith('install'):
return True
class UploadCommand(Command):
"""Support setup.py upload."""
description = 'Build and publish the package.'
user_options = []
@staticmethod
def status(s):
"""Prints things in bold."""
print('\033[1m{0}\033[0m'.format(s))
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
try:
self.status('Removing previous builds…')
rmtree(os.path.join(here, 'dist'))
except OSError:
pass
self.status('Building Source and Wheel (universal) distribution…')
os.system(
'{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
self.status('Uploading the package to PyPI via Twine…')
os.system('twine upload dist/*')
self.status('Pushing git tags…')
os.system('git tag v{0}'.format(about['__version__']))
os.system('git push --tags')
sys.exit()
# Start to build c libs
# ------------------------------------------------
def test_compile(build_ext, name, code, libraries=None, include_dirs=None, library_dirs=None,
macros=None, extra_compile_preargs=None, extra_link_preargs=None):
test_compile_dir = os.path.join(build_ext.build_temp, 'test_compile')
if not os.path.exists(test_compile_dir):
os.makedirs(test_compile_dir)
source_file = os.path.join(test_compile_dir, '%s.cc' % name)
with open(source_file, 'w') as f:
f.write(code)
compiler = build_ext.compiler
[object_file] = compiler.object_filenames([source_file])
shared_object_file = compiler.shared_object_filename(
name, output_dir=test_compile_dir)
compiler.compile([source_file], extra_preargs=extra_compile_preargs,
include_dirs=include_dirs, macros=macros)
compiler.link_shared_object(
[object_file], shared_object_file, libraries=libraries, library_dirs=library_dirs,
extra_preargs=extra_link_preargs)
return shared_object_file
def get_mpi_flags():
show_command = os.environ.get('BYTEPS_MPICXX_SHOW', 'mpicxx -show')
try:
mpi_show_output = subprocess.check_output(
shlex.split(show_command), universal_newlines=True).strip()
mpi_show_args = shlex.split(mpi_show_output)
if not mpi_show_args[0].startswith('-'):
# Open MPI and MPICH print compiler name as a first word, skip it
mpi_show_args = mpi_show_args[1:]
# strip off compiler call portion and always escape each arg
return ' '.join(['"' + arg.replace('"', '"\'"\'"') + '"'
for arg in mpi_show_args])
except Exception:
raise DistutilsPlatformError(
'%s failed (see error below), is MPI in $PATH?\n'
'Note: If your version of MPI has a custom command to show compilation flags, '
'please specify it with the BYTEPS_MPICXX_SHOW environment variable.\n\n'
'%s' % (show_command, traceback.format_exc()))
def get_cpp_flags(build_ext):
last_err = None
default_flags = ['-std=c++11', '-fPIC', '-Ofast', '-Wall', '-fopenmp', '-march=native', '-mno-avx512f']
flags_to_try = []
if sys.platform == 'darwin':
# Darwin most likely will have Clang, which has libc++.
flags_to_try = [default_flags + ['-stdlib=libc++'],
default_flags]
else:
flags_to_try = [default_flags ,
default_flags + ['-stdlib=libc++']]
for cpp_flags in flags_to_try:
try:
test_compile(build_ext, 'test_cpp_flags', extra_compile_preargs=cpp_flags,
code=textwrap.dedent('''\
#include <unordered_map>
void test() {
}
'''))
return cpp_flags
except (CompileError, LinkError):
last_err = 'Unable to determine C++ compilation flags (see error above).'
except Exception:
last_err = 'Unable to determine C++ compilation flags. ' \
'Last error:\n\n%s' % traceback.format_exc()
raise DistutilsPlatformError(last_err)
def get_link_flags(build_ext):
last_err = None
libtool_flags = ['-Wl,-exported_symbols_list,byteps.exp']
ld_flags = ['-Wl,--version-script=byteps.lds', '-fopenmp']
flags_to_try = []
if sys.platform == 'darwin':
flags_to_try = [libtool_flags, ld_flags]
else:
flags_to_try = [ld_flags, libtool_flags]
for link_flags in flags_to_try:
try:
test_compile(build_ext, 'test_link_flags', extra_link_preargs=link_flags,
code=textwrap.dedent('''\
void test() {
}
'''))
return link_flags
except (CompileError, LinkError):
last_err = 'Unable to determine C++ link flags (see error above).'
except Exception:
last_err = 'Unable to determine C++ link flags. ' \
'Last error:\n\n%s' % traceback.format_exc()
raise DistutilsPlatformError(last_err)
def has_rdma_header():
ret_code = subprocess.call(
"echo '#include <rdma/rdma_cma.h>' | cpp -H -o /dev/null 2>/dev/null", shell=True)
if ret_code != 0:
import warnings
warnings.warn("\n\n No RDMA header file detected. Will disable RDMA for compilation! \n\n")
return ret_code==0
def use_ucx():
byteps_with_ucx = int(os.environ.get('BYTEPS_WITH_UCX', 0))
return byteps_with_ucx
def with_pre_setup():
return int(os.environ.get('BYTEPS_WITHOUT_PRESETUP', 0)) == 0
def with_tensorflow():
return int(os.environ.get('BYTEPS_WITH_TENSORFLOW', 0))
def without_tensorflow():
return int(os.environ.get('BYTEPS_WITHOUT_TENSORFLOW', 0))
def with_pytorch():
return int(os.environ.get('BYTEPS_WITH_PYTORCH', 0))
def without_pytorch():
return int(os.environ.get('BYTEPS_WITHOUT_PYTORCH', 0))
def should_build_ucx():
has_prebuilt_ucx = os.environ.get('BYTEPS_UCX_HOME', '')
return use_ucx() and not has_prebuilt_ucx
ucx_default_home = '/usr/local'
def get_ucx_prefix():
""" specify where to install ucx """
ucx_prefix = os.getenv('BYTEPS_UCX_PREFIX', ucx_default_home)
return ucx_prefix
def get_ucx_home():
""" pre-installed ucx path """
if should_build_ucx():
return get_ucx_prefix()
return os.environ.get('BYTEPS_UCX_HOME', ucx_default_home)
def get_common_options(build_ext):
cpp_flags = get_cpp_flags(build_ext)
link_flags = get_link_flags(build_ext)
MACROS = [('EIGEN_MPL2_ONLY', 1)]
INCLUDES = ['3rdparty/ps-lite/include']
SOURCES = ['byteps/common/common.cc',
'byteps/common/operations.cc',
'byteps/common/core_loops.cc',
'byteps/common/global.cc',
'byteps/common/logging.cc',
'byteps/common/communicator.cc',
'byteps/common/scheduled_queue.cc',
'byteps/common/ready_table.cc',
'byteps/common/shared_memory.cc',
'byteps/common/nccl_manager.cc',
'byteps/common/cpu_reducer.cc'] + [
'byteps/common/compressor/compressor_registry.cc',
'byteps/common/compressor/error_feedback.cc',
'byteps/common/compressor/momentum.cc',
'byteps/common/compressor/impl/dithering.cc',
'byteps/common/compressor/impl/onebit.cc',
'byteps/common/compressor/impl/randomk.cc',
'byteps/common/compressor/impl/topk.cc',
'byteps/common/compressor/impl/vanilla_error_feedback.cc',
'byteps/common/compressor/impl/nesterov_momentum.cc']
if "BYTEPS_USE_MPI" in os.environ and os.environ["BYTEPS_USE_MPI"] == "1":
mpi_flags = get_mpi_flags()
COMPILE_FLAGS = cpp_flags + \
shlex.split(mpi_flags) + ["-DBYTEPS_USE_MPI"]
LINK_FLAGS = link_flags + shlex.split(mpi_flags)
else:
COMPILE_FLAGS = cpp_flags
LINK_FLAGS = link_flags
LIBRARY_DIRS = []
LIBRARIES = []
nccl_include_dirs, nccl_lib_dirs, nccl_libs = get_nccl_vals()
INCLUDES += nccl_include_dirs
LIBRARY_DIRS += nccl_lib_dirs
LIBRARIES += nccl_libs
# RDMA and NUMA libs
LIBRARIES += ['numa']
# auto-detect rdma
if has_rdma_header():
LIBRARIES += ['rdmacm', 'ibverbs', 'rt']
if use_ucx():
LIBRARIES += ['ucp', 'uct', 'ucs', 'ucm']
ucx_home = get_ucx_home()
if ucx_home:
INCLUDES += [f'{ucx_home}/include']
LIBRARY_DIRS += [f'{ucx_home}/lib']
# ps-lite
EXTRA_OBJECTS = ['3rdparty/ps-lite/build/libps.a',
'3rdparty/ps-lite/deps/lib/libzmq.a']
return dict(MACROS=MACROS,
INCLUDES=INCLUDES,
SOURCES=SOURCES,
COMPILE_FLAGS=COMPILE_FLAGS,
LINK_FLAGS=LINK_FLAGS,
LIBRARY_DIRS=LIBRARY_DIRS,
LIBRARIES=LIBRARIES,
EXTRA_OBJECTS=EXTRA_OBJECTS)
def build_server(build_ext, options):
server_lib.define_macros = options['MACROS']
server_lib.include_dirs = options['INCLUDES']
server_lib.sources = ['byteps/server/server.cc',
'byteps/common/cpu_reducer.cc',
'byteps/common/logging.cc',
'byteps/common/common.cc'] + [
'byteps/common/compressor/compressor_registry.cc',
'byteps/common/compressor/error_feedback.cc',
'byteps/common/compressor/impl/dithering.cc',
'byteps/common/compressor/impl/onebit.cc',
'byteps/common/compressor/impl/randomk.cc',
'byteps/common/compressor/impl/topk.cc',
'byteps/common/compressor/impl/vanilla_error_feedback.cc']
server_lib.extra_compile_args = options['COMPILE_FLAGS'] + \
['-DBYTEPS_BUILDING_SERVER']
server_lib.extra_link_args = options['LINK_FLAGS']
server_lib.extra_objects = options['EXTRA_OBJECTS']
server_lib.library_dirs = options['LIBRARY_DIRS']
# auto-detect rdma
if has_rdma_header():
server_lib.libraries = ['rdmacm', 'ibverbs', 'rt']
else:
server_lib.libraries = []
if use_ucx():
server_lib.libraries += ['ucp', 'uct', 'ucs', 'ucm']
ucx_home = get_ucx_home()
if ucx_home:
server_lib.include_dirs += [f'{ucx_home}/include']
server_lib.library_dirs += [f'{ucx_home}/lib']
build_ext.build_extension(server_lib)
def check_tf_version():
try:
import tensorflow as tf
if LooseVersion(tf.__version__) < LooseVersion('1.1.0'):
raise DistutilsPlatformError(
'Your TensorFlow version %s is outdated. '
'BytePS requires tensorflow>=1.1.0' % tf.__version__)
except ImportError:
raise DistutilsPlatformError(
'import tensorflow failed, is it installed?\n\n%s' % traceback.format_exc())
except AttributeError:
# This means that tf.__version__ was not exposed, which makes it *REALLY* old.
raise DistutilsPlatformError(
'Your TensorFlow version is outdated. BytePS requires tensorflow>=1.1.0')
def get_tf_include_dirs():
import tensorflow as tf
tf_inc = tf.sysconfig.get_include()
return [tf_inc, '%s/external/nsync/public' % tf_inc]
def get_tf_lib_dirs():
import tensorflow as tf
tf_lib = tf.sysconfig.get_lib()
return [tf_lib]
def get_tf_libs(build_ext, lib_dirs, cpp_flags):
last_err = None
for tf_libs in [['tensorflow_framework'], []]:
try:
lib_file = test_compile(build_ext, 'test_tensorflow_libs',
library_dirs=lib_dirs, libraries=tf_libs,
extra_compile_preargs=cpp_flags,
code=textwrap.dedent('''\
void test() {
}
'''))
from tensorflow.python.framework import load_library
load_library.load_op_library(lib_file)
return tf_libs
except (CompileError, LinkError):
last_err = 'Unable to determine -l link flags to use with TensorFlow (see error above).'
except Exception:
last_err = 'Unable to determine -l link flags to use with TensorFlow. ' \
'Last error:\n\n%s' % traceback.format_exc()
raise DistutilsPlatformError(last_err)
def get_tf_abi(build_ext, include_dirs, lib_dirs, libs, cpp_flags):
last_err = None
cxx11_abi_macro = '_GLIBCXX_USE_CXX11_ABI'
for cxx11_abi in ['0', '1']:
try:
lib_file = test_compile(build_ext, 'test_tensorflow_abi',
macros=[(cxx11_abi_macro, cxx11_abi)],
include_dirs=include_dirs, library_dirs=lib_dirs,
libraries=libs, extra_compile_preargs=cpp_flags,
code=textwrap.dedent('''\
#include <string>
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
void test() {
auto ignore = tensorflow::strings::StrCat("a", "b");
}
'''))
from tensorflow.python.framework import load_library
load_library.load_op_library(lib_file)
return cxx11_abi_macro, cxx11_abi
except (CompileError, LinkError):
last_err = 'Unable to determine CXX11 ABI to use with TensorFlow (see error above).'
except Exception:
last_err = 'Unable to determine CXX11 ABI to use with TensorFlow. ' \
'Last error:\n\n%s' % traceback.format_exc()
raise DistutilsPlatformError(last_err)
def get_tf_flags(build_ext, cpp_flags):
import tensorflow as tf
try:
return tf.sysconfig.get_compile_flags(), tf.sysconfig.get_link_flags()
except AttributeError:
# fallback to the previous logic
tf_include_dirs = get_tf_include_dirs()
tf_lib_dirs = get_tf_lib_dirs()
tf_libs = get_tf_libs(build_ext, tf_lib_dirs, cpp_flags)
tf_abi = get_tf_abi(build_ext, tf_include_dirs,
tf_lib_dirs, tf_libs, cpp_flags)
compile_flags = []
for include_dir in tf_include_dirs:
compile_flags.append('-I%s' % include_dir)
if tf_abi:
compile_flags.append('-D%s=%s' % tf_abi)
link_flags = []
for lib_dir in tf_lib_dirs:
link_flags.append('-L%s' % lib_dir)
for lib in tf_libs:
link_flags.append('-l%s' % lib)
return compile_flags, link_flags
def build_tf_extension(build_ext, options):
check_tf_version()
tf_compile_flags, tf_link_flags = get_tf_flags(
build_ext, options['COMPILE_FLAGS'])
# We assume we have CUDA
cuda_include_dirs, cuda_lib_dirs = get_cuda_dirs(
build_ext, options['COMPILE_FLAGS'])
options['MACROS'] += [('HAVE_CUDA', '1')]
options['INCLUDES'] += cuda_include_dirs
options['LIBRARY_DIRS'] += cuda_lib_dirs
options['LIBRARIES'] += ['cudart']
tensorflow_lib.define_macros = options['MACROS']
tensorflow_lib.include_dirs = options['INCLUDES']
tensorflow_lib.sources = options['SOURCES'] + \
['byteps/tensorflow/ops.cc']
tensorflow_lib.extra_compile_args = options['COMPILE_FLAGS'] + \
tf_compile_flags
tensorflow_lib.extra_link_args = options['LINK_FLAGS'] + tf_link_flags
tensorflow_lib.library_dirs = options['LIBRARY_DIRS']
tensorflow_lib.libraries = options['LIBRARIES']
tensorflow_lib.extra_objects = options['EXTRA_OBJECTS']
build_ext.build_extension(tensorflow_lib)
def check_mx_version():
try:
import mxnet as mx
if mx.__version__ < '1.4.0':
raise DistutilsPlatformError(
'Your MXNet version %s is outdated. '
'BytePS requires mxnet>=1.4.0' % mx.__version__)
except ImportError:
raise DistutilsPlatformError(
'import mxnet failed, is it installed?\n\n%s' % traceback.format_exc())
except AttributeError:
raise DistutilsPlatformError(
'Your MXNet version is outdated. BytePS requires mxnet>=1.4.0')
def get_mx_include_dirs():
try:
import mxnet as mx
path = mx.libinfo.find_include_path()
return path
except:
# Try to find the path automatically
tmp_mxnet_dir = os.getenv(
"BYTEPS_SERVER_MXNET_PATH", "/root/mxnet15-rdma")
MXNET_ROOT = os.getenv("MXNET_SOURCE_ROOT", tmp_mxnet_dir)
return os.path.join(MXNET_ROOT, 'include/')
def get_mx_lib_dirs():
import mxnet as mx
mx_libs = mx.libinfo.find_lib_path()
mx_lib_dirs = [os.path.dirname(mx_lib) for mx_lib in mx_libs]
return mx_lib_dirs
def get_mx_libs(build_ext, lib_dirs, cpp_flags):
last_err = None
cpp_flags.append('-DDMLC_USE_RDMA')
for mx_libs in [['mxnet'], []]:
try:
lib_file = test_compile(build_ext, 'test_mx_libs',
library_dirs=lib_dirs, libraries=mx_libs,
extra_compile_preargs=cpp_flags,
code=textwrap.dedent('''\
void test() {
}
'''))
return mx_libs
except (CompileError, LinkError):
last_err = 'Unable to determine -l link flags to use with MXNet (see error above).'
except Exception:
last_err = 'Unable to determine -l link flags to use with MXNet. ' \
'Last error:\n\n%s' % traceback.format_exc()
raise DistutilsPlatformError(last_err)
def get_mx_flags(build_ext, cpp_flags):
mx_include_dirs = [get_mx_include_dirs()]
mx_lib_dirs = get_mx_lib_dirs()
mx_libs = get_mx_libs(build_ext, mx_lib_dirs, cpp_flags)
compile_flags = []
has_mkldnn = is_mx_mkldnn()
for include_dir in mx_include_dirs:
compile_flags.append('-I%s' % include_dir)
if has_mkldnn:
mkldnn_include = os.path.join(include_dir, 'mkldnn')
compile_flags.append('-I%s' % mkldnn_include)
link_flags = []
for lib_dir in mx_lib_dirs:
link_flags.append('-Wl,-rpath,%s' % lib_dir)
link_flags.append('-L%s' % lib_dir)
for lib in mx_libs:
link_flags.append('-l%s' % lib)
return compile_flags, link_flags
def check_macro(macros, key):
return any(k == key and v for k, v in macros)
def set_macro(macros, key, new_value):
if any(k == key for k, _ in macros):
return [(k, new_value if k == key else v) for k, v in macros]
else:
return macros + [(key, new_value)]
def is_mx_cuda():
try:
from mxnet import runtime
features = runtime.Features()
return features.is_enabled('CUDA')
except Exception:
if 'linux' in sys.platform:
try:
import mxnet as mx
mx_libs = mx.libinfo.find_lib_path()
for mx_lib in mx_libs:
output = subprocess.check_output(['readelf', '-d', mx_lib])
if 'cuda' in str(output):
return True
return False
except Exception:
return False
return False
def get_cuda_dirs(build_ext, cpp_flags):
cuda_include_dirs = []
cuda_lib_dirs = []
cuda_home = os.environ.get('BYTEPS_CUDA_HOME')
if cuda_home:
cuda_include_dirs += ['%s/include' % cuda_home]
cuda_lib_dirs += ['%s/lib' % cuda_home, '%s/lib64' % cuda_home]
cuda_include = os.environ.get('BYTEPS_CUDA_INCLUDE')
if cuda_include:
cuda_include_dirs += [cuda_include]
cuda_lib = os.environ.get('BYTEPS_CUDA_LIB')
if cuda_lib:
cuda_lib_dirs += [cuda_lib]
if not cuda_include_dirs and not cuda_lib_dirs:
# default to /usr/local/cuda
cuda_include_dirs += ['/usr/local/cuda/include']
cuda_lib_dirs += ['/usr/local/cuda/lib', '/usr/local/cuda/lib64']
try:
test_compile(build_ext, 'test_cuda', libraries=['cudart'], include_dirs=cuda_include_dirs,
library_dirs=cuda_lib_dirs, extra_compile_preargs=cpp_flags,
code=textwrap.dedent('''\
#include <cuda_runtime.h>
void test() {
cudaSetDevice(0);
}
'''))
except (CompileError, LinkError):
raise DistutilsPlatformError(
'CUDA library was not found (see error above).\n'
'Please specify correct CUDA location with the BYTEPS_CUDA_HOME '
'environment variable or combination of BYTEPS_CUDA_INCLUDE and '
'BYTEPS_CUDA_LIB environment variables.\n\n'
'BYTEPS_CUDA_HOME - path where CUDA include and lib directories can be found\n'
'BYTEPS_CUDA_INCLUDE - path to CUDA include directory\n'
'BYTEPS_CUDA_LIB - path to CUDA lib directory')
return cuda_include_dirs, cuda_lib_dirs
def get_nccl_vals():
nccl_include_dirs = []
nccl_lib_dirs = []
nccl_libs = []
nccl_home = os.environ.get('BYTEPS_NCCL_HOME', '/usr/local/nccl')
if nccl_home:
nccl_include_dirs += ['%s/include' % nccl_home]
nccl_lib_dirs += ['%s/lib' % nccl_home, '%s/lib64' % nccl_home]
nccl_link_mode = os.environ.get('BYTEPS_NCCL_LINK', 'SHARED')
if nccl_link_mode.upper() == 'SHARED':
nccl_libs += ['nccl']
else:
nccl_libs += ['nccl_static']
return nccl_include_dirs, nccl_lib_dirs, nccl_libs
def is_mx_mkldnn():
try:
from mxnet import runtime
features = runtime.Features()
return features.is_enabled('MKLDNN')
except Exception:
msg = 'INFO: Cannot detect if MKLDNN is enabled in MXNet. Please \
set MXNET_USE_MKLDNN=1 if MKLDNN is enabled in your MXNet build.'
if 'linux' not in sys.platform:
# MKLDNN is only enabled by default in MXNet Linux build. Return
# False by default for non-linux build but still allow users to
# enable it by using MXNET_USE_MKLDNN env variable.
print(msg)
return os.environ.get('MXNET_USE_MKLDNN', '0') == '1'
else:
try:
import mxnet as mx
mx_libs = mx.libinfo.find_lib_path()
for mx_lib in mx_libs:
output = subprocess.check_output(['readelf', '-d', mx_lib])
if 'mkldnn' in str(output):
return True
return False
except Exception:
print(msg)
return os.environ.get('MXNET_USE_MKLDNN', '0') == '1'
def build_mx_extension(build_ext, options):
# clear ROLE -- installation does not need this
os.environ.pop("DMLC_ROLE", None)
check_mx_version()
mx_compile_flags, mx_link_flags = get_mx_flags(
build_ext, options['COMPILE_FLAGS'])
mx_have_cuda = is_mx_cuda()
macro_have_cuda = check_macro(options['MACROS'], 'HAVE_CUDA')
if not mx_have_cuda and macro_have_cuda:
raise DistutilsPlatformError(
'BytePS build with GPU support was requested, but this MXNet '
'installation does not support CUDA.')
# Update HAVE_CUDA to mean that MXNet supports CUDA.
if mx_have_cuda and not macro_have_cuda:
cuda_include_dirs, cuda_lib_dirs = get_cuda_dirs(
build_ext, options['COMPILE_FLAGS'])
options['MACROS'] += [('HAVE_CUDA', '1')]
options['INCLUDES'] += cuda_include_dirs
options['LIBRARY_DIRS'] += cuda_lib_dirs
options['LIBRARIES'] += ['cudart']
mxnet_lib.define_macros = options['MACROS']
if check_macro(options['MACROS'], 'HAVE_CUDA'):
mxnet_lib.define_macros += [('MSHADOW_USE_CUDA', '1')]
else:
mxnet_lib.define_macros += [('MSHADOW_USE_CUDA', '0')]
if is_mx_mkldnn():
mxnet_lib.define_macros += [('MXNET_USE_MKLDNN', '1')]
else:
mxnet_lib.define_macros += [('MXNET_USE_MKLDNN', '0')]
mxnet_lib.define_macros += [('MSHADOW_USE_MKL', '0')]
# use MXNet's DMLC headers first instead of ps-lite's
options['INCLUDES'].insert(0, get_mx_include_dirs())
mxnet_lib.include_dirs = options['INCLUDES']
mxnet_lib.sources = options['SOURCES'] + \
['byteps/mxnet/ops.cc',
'byteps/mxnet/ready_event.cc',
'byteps/mxnet/tensor_util.cc',
'byteps/mxnet/cuda_util.cc',
'byteps/mxnet/adapter.cc']
mxnet_lib.extra_compile_args = options['COMPILE_FLAGS'] + \
mx_compile_flags
mxnet_lib.extra_link_args = options['LINK_FLAGS'] + mx_link_flags
mxnet_lib.extra_objects = options['EXTRA_OBJECTS']
mxnet_lib.library_dirs = options['LIBRARY_DIRS']
mxnet_lib.libraries = options['LIBRARIES']
build_ext.build_extension(mxnet_lib)
def dummy_import_torch():
try:
import torch
except:
pass
def parse_version(version_str):
if "dev" in version_str:
return 9999999999
m = re.match('^(\d+)(?:\.(\d+))?(?:\.(\d+))?(?:\.(\d+))?', version_str)
if m is None:
return None
# turn version string to long integer
version = int(m.group(1)) * 10 ** 9
if m.group(2) is not None:
version += int(m.group(2)) * 10 ** 6
if m.group(3) is not None:
version += int(m.group(3)) * 10 ** 3
if m.group(4) is not None:
version += int(m.group(4))
return version
def check_torch_version():
try:
import torch
if torch.__version__ < '1.0.1':
raise DistutilsPlatformError(
'Your torch version %s is outdated. '
'BytePS requires torch>=1.0.1' % torch.__version__)
except ImportError:
print('import torch failed, is it installed?\n\n%s' %
traceback.format_exc())
# parse version
version = parse_version(torch.__version__)
if version is None:
raise DistutilsPlatformError(
'Unable to determine PyTorch version from the version string \'%s\'' % torch.__version__)
return version
def is_torch_cuda(build_ext, include_dirs, extra_compile_args):
try:
from torch.utils.cpp_extension import include_paths
test_compile(build_ext, 'test_torch_cuda', include_dirs=include_dirs + include_paths(cuda=True),
extra_compile_preargs=extra_compile_args, code=textwrap.dedent('''\
#include <THC/THC.h>
void test() {
}
'''))
return True
except (CompileError, LinkError, EnvironmentError):
print('INFO: Above error indicates that this PyTorch installation does not support CUDA.')
return False
def build_torch_extension(build_ext, options, torch_version):
pytorch_compile_flags = ["-std=c++14" if flag == "-std=c++11"
else flag for flag in options['COMPILE_FLAGS']]
have_cuda = is_torch_cuda(build_ext, include_dirs=options['INCLUDES'],
extra_compile_args=pytorch_compile_flags)
if not have_cuda and check_macro(options['MACROS'], 'HAVE_CUDA'):
raise DistutilsPlatformError(
'byteps build with GPU support was requested, but this PyTorch '
'installation does not support CUDA.')
# Update HAVE_CUDA to mean that PyTorch supports CUDA.
updated_macros = set_macro(
options['MACROS'], 'HAVE_CUDA', str(int(have_cuda)))
# Export TORCH_VERSION equal to our representation of torch.__version__. Internally it's
# used for backwards compatibility checks.
updated_macros = set_macro(
updated_macros, 'TORCH_VERSION', str(torch_version))
# Always set _GLIBCXX_USE_CXX11_ABI, since PyTorch can only detect whether it was set to 1.
import torch
updated_macros = set_macro(updated_macros, '_GLIBCXX_USE_CXX11_ABI',
str(int(torch.compiled_with_cxx11_abi())))
# PyTorch requires -DTORCH_API_INCLUDE_EXTENSION_H
updated_macros = set_macro(
updated_macros, 'TORCH_API_INCLUDE_EXTENSION_H', '1')
if have_cuda:
from torch.utils.cpp_extension import CUDAExtension as TorchExtension
else:
# CUDAExtension fails with `ld: library not found for -lcudart` if CUDA is not present
from torch.utils.cpp_extension import CppExtension as TorchExtension
ext = TorchExtension(pytorch_lib.name,
define_macros=updated_macros,
include_dirs=options['INCLUDES'],
sources=options['SOURCES'] + ['byteps/torch/ops.cc',
'byteps/torch/ready_event.cc',
'byteps/torch/cuda_util.cc',
'byteps/torch/adapter.cc',
'byteps/torch/handle_manager.cc'],
extra_compile_args=pytorch_compile_flags,
extra_link_args=options['LINK_FLAGS'],
extra_objects=options['EXTRA_OBJECTS'],
library_dirs=options['LIBRARY_DIRS'],
libraries=options['LIBRARIES'])
# Patch an existing pytorch_lib extension object.
for k, v in ext.__dict__.items():
pytorch_lib.__dict__[k] = v
build_ext.build_extension(pytorch_lib)
def build_ucx():
ucx_tarball_path = os.getenv("BYTEPS_UCX_TARBALL_PATH", "")
if not ucx_tarball_path and with_pre_setup() \
and hasattr(pre_setup, 'ucx_tarball_path'):
ucx_tarball_path = pre_setup.ucx_tarball_path.strip()
if not ucx_tarball_path:
if os.path.exists("./ucx.tar.gz"):
ucx_tarball_path = os.path.join(here, './ucx.tar.gz')
if not ucx_tarball_path:
cmd = "curl -kL {} -o ucx.tar.gz".format("https://github.com/openucx/ucx/archive/refs/tags/v1.11.0.tar.gz")
subprocess.run(cmd, shell=True)
ucx_tarball_path = os.path.join(here, './ucx.tar.gz')
print("ucx_tarball_path is", ucx_tarball_path)
ucx_prefix = get_ucx_prefix()
sudo_str = "" if os.access(ucx_prefix, os.W_OK) else "sudo"
cmd = "mkdir -p tmp; tar xzf {} -C tmp; ".format(ucx_tarball_path) + \
"rm -rf ucx-build; mkdir -p ucx-build; mv tmp/ucx-*/* ucx-build/; " + \
"cd ucx-build; pwd; which libtoolize; " + \
"./autogen.sh; ./autogen.sh && " + \
"./contrib/configure-release --enable-mt --prefix={0} && make -j && {1} make install -j".format(ucx_prefix, sudo_str)
make_process = subprocess.Popen(cmd,
cwd='3rdparty',
stdout=sys.stdout,
stderr=sys.stderr,
shell=True)
make_process.communicate()
if make_process.returncode:
raise DistutilsSetupError('An ERROR occured while running the '
'Makefile for the ucx library. '
'Exit code: {0}'.format(make_process.returncode))
# run the customize_compiler
class custom_build_ext(build_ext):
def build_extensions(self):
if with_pre_setup():
pre_setup.setup()
ucx_home = get_ucx_home()
ucx_prefix = get_ucx_prefix()
make_option = ""
# To resolve tf-gcc incompatibility
has_cxx_flag = False
glibcxx_flag = False
if not without_tensorflow():
try:
import tensorflow as tf
make_option += 'ADD_CFLAGS="'
for flag in tf.sysconfig.get_compile_flags():
if 'D_GLIBCXX_USE_CXX11_ABI' in flag:
has_cxx_flag = True
glibcxx_flag = False if (flag[-1]=='0') else True
make_option += flag + ' '
break
make_option += '" '
except:
pass
# To resolve torch-gcc incompatibility
if not without_pytorch():
try:
import torch
torch_flag = torch.compiled_with_cxx11_abi()
if has_cxx_flag:
if glibcxx_flag != torch_flag:
raise DistutilsError(
'-D_GLIBCXX_USE_CXX11_ABI is not consistent between TensorFlow and PyTorch, '
'consider install them separately.')
else:
pass
else:
make_option += 'ADD_CFLAGS=-D_GLIBCXX_USE_CXX11_ABI=' + \
str(int(torch_flag)) + ' '
has_cxx_flag = True
glibcxx_flag = torch_flag
except:
pass
if not os.path.exists("3rdparty/ps-lite/build/libps.a") or \
not os.path.exists("3rdparty/ps-lite/deps/lib"):
print("should_build_ucx is", should_build_ucx())
if should_build_ucx():
build_ucx()
if os.environ.get('CI', 'false') == 'false':
make_option += "-j "
if has_rdma_header():
make_option += "USE_RDMA=1 "
if use_ucx():
make_option += 'USE_UCX=1 '
if ucx_home:
make_option += f'UCX_PATH={ucx_home} '
if with_pre_setup():
make_option += pre_setup.extra_make_option()
if os.path.exists("./zeromq-4.1.4.tar.gz"):
zmq_tarball_path = os.path.join(here, './zeromq-4.1.4.tar.gz')
make_option += " WGET='curl -O ' ZMQ_URL=file://" + zmq_tarball_path + " "
make_process = subprocess.Popen('make ' + make_option,
cwd='3rdparty/ps-lite',
stdout=sys.stdout,
stderr=sys.stderr,
shell=True)
make_process.communicate()
if make_process.returncode:
raise DistutilsSetupError('An ERROR occured while running the '
'Makefile for the ps-lite library. '
'Exit code: {0}'.format(make_process.returncode))
options = get_common_options(self)
if has_cxx_flag:
options['COMPILE_FLAGS'] += ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(glibcxx_flag))]
built_plugins = []
try:
build_server(self, options)
except:
raise DistutilsSetupError('An ERROR occured while building the server module.\n\n'
'%s' % traceback.format_exc())
# If PyTorch is installed, it must be imported before others, otherwise
# we may get an error: dlopen: cannot load any more object with static TLS
if not without_pytorch():
dummy_import_torch()
if not without_tensorflow():
try:
build_tf_extension(self, options)
built_plugins.append(True)
print('INFO: Tensorflow extension is built successfully.')
except:
if not with_tensorflow():
print('INFO: Unable to build TensorFlow plugin, will skip it.\n\n'
'%s' % traceback.format_exc())
built_plugins.append(False)
else:
raise
if not without_pytorch():
try:
torch_version = check_torch_version()
build_torch_extension(self, options, torch_version)
built_plugins.append(True)
print('INFO: PyTorch extension is built successfully.')
except:
if not with_pytorch():
print('INFO: Unable to build PyTorch plugin, will skip it.\n\n'
'%s' % traceback.format_exc())
built_plugins.append(False)
else:
raise
if not int(os.environ.get('BYTEPS_WITHOUT_MXNET', 0)):
# fix "libcuda.so.1 not found" issue
cuda_home = os.environ.get('BYTEPS_CUDA_HOME', '/usr/local/cuda')
cuda_stub_path = cuda_home + '/lib64/stubs'
ln_command = "cd " + cuda_stub_path + "; ln -sf libcuda.so libcuda.so.1"
os.system(ln_command)
try:
build_mx_extension(self, options)
built_plugins.append(True)
print('INFO: MXNet extension is built successfully.')
except:
if not int(os.environ.get('BYTEPS_WITH_MXNET', 0)):
print('INFO: Unable to build MXNet plugin, will skip it.\n\n'
'%s' % traceback.format_exc())
built_plugins.append(False)
else:
raise
finally:
os.system("rm -rf " + cuda_stub_path + "/libcuda.so.1")
if not built_plugins:
print('INFO: Only server module is built.')
return
if not any(built_plugins):
raise DistutilsError(
'None of TensorFlow, MXNet, PyTorch plugins were built. See errors above.')
# Where the magic happens:
if not os.path.exists('3rdparty/ps-lite/src'):
msg = "Missing ./3rdparty/ps-lite, ps-lite is required to build BytePS."
raise ValueError(msg)
if os.path.exists('launcher/launch.py'):
if not os.path.exists('bin'):
os.mkdir('bin')
shutil.copyfile('launcher/launch.py', 'bin/bpslaunch')
extensions_to_build = [server_lib, tensorflow_lib, mxnet_lib, pytorch_lib]
if int(os.environ.get('BYTEPS_WITHOUT_MXNET', 0)):
extensions_to_build.remove(mxnet_lib)
if without_tensorflow():
extensions_to_build.remove(tensorflow_lib)
if without_pytorch():
extensions_to_build.remove(pytorch_lib)
class sdist(sdist_orig):
def run(self):
try:
if not os.path.isfile("./ucx.tar.gz"):
self.spawn(['curl', '-kL', 'https://github.com/openucx/ucx/archive/refs/tags/v1.11.0.tar.gz', '-o', 'ucx.tar.gz'])
if not os.path.isfile("./zeromq-4.1.4.tar.gz"):
self.spawn(['curl', '-kL', '-O', 'https://github.com/zeromq/zeromq4-1/releases/download/v4.1.4/zeromq-4.1.4.tar.gz'])
except DistutilsExecError:
self.warn('failed to download required tarballs')
super().run()
setup(
name=NAME,
version=about['__version__'],
description=DESCRIPTION,
long_description=long_description,
long_description_content_type='text/markdown',
author=AUTHOR,
author_email=EMAIL,
python_requires=REQUIRES_PYTHON,
url=URL,
packages=find_packages(exclude=('tests',)),
install_requires=REQUIRED,
extras_require=EXTRAS,
include_package_data=True,
license='Apache',
classifiers=[
# Trove classifiers
# Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
'License :: OSI Approved :: Apache Software License',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Operating System :: POSIX :: Linux'
],
ext_modules=extensions_to_build,
# $ setup.py publish support.
cmdclass={
'upload': UploadCommand,
'build_ext': custom_build_ext,
'sdist': sdist
},
# cffi is required for PyTorch
# If cffi is specified in setup_requires, it will need libffi to be installed on the machine,
# which is undesirable. Luckily, `install` action will install cffi before executing build,
# so it's only necessary for `build*` or `bdist*` actions.
setup_requires=[],
scripts=['bin/bpslaunch']
)
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