# ------------------------------------------------------------------------ # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # ------------------------------------------------------------------------ from setuptools import setup, Extension from distutils import log as logger from distutils.command.build_ext import build_ext as _build_ext from glob import glob, iglob from os import path, getcwd, environ, remove, listdir from shutil import copyfile, copytree, rmtree import platform import subprocess import sys import datetime from pathlib import Path nightly_build = False featurizers_build = False package_name = 'onnxruntime' wheel_name_suffix = None def parse_arg_remove_boolean(argv, arg_name): arg_value = False if arg_name in sys.argv: arg_value = True argv.remove(arg_name) return arg_value def parse_arg_remove_string(argv, arg_name_equal): arg_value = None for arg in sys.argv[1:]: if arg.startswith(arg_name_equal): arg_value = arg[len(arg_name_equal):] sys.argv.remove(arg) break return arg_value # Any combination of the following arguments can be applied featurizers_build = parse_arg_remove_boolean(sys.argv, '--use_featurizers') if parse_arg_remove_boolean(sys.argv, '--nightly_build'): package_name = 'ort-nightly' nightly_build = True wheel_name_suffix = parse_arg_remove_string(sys.argv, '--wheel_name_suffix=') cuda_version = None rocm_version = None # The following arguments are mutually exclusive if parse_arg_remove_boolean(sys.argv, '--use_tensorrt'): package_name = 'onnxruntime-gpu-tensorrt' if not nightly_build else 'ort-trt-nightly' elif wheel_name_suffix == 'gpu': # TODO: how to support multiple CUDA versions? cuda_version = parse_arg_remove_string(sys.argv, '--cuda_version=') elif parse_arg_remove_boolean(sys.argv, '--use_rocm'): package_name = 'onnxruntime-rocm' if not nightly_build else 'ort-rocm-nightly' rocm_version = parse_arg_remove_string(sys.argv, '--rocm_version=') elif parse_arg_remove_boolean(sys.argv, '--use_openvino'): package_name = 'onnxruntime-openvino' elif parse_arg_remove_boolean(sys.argv, '--use_dnnl'): package_name = 'onnxruntime-dnnl' elif parse_arg_remove_boolean(sys.argv, '--use_nuphar'): package_name = 'onnxruntime-nuphar' elif parse_arg_remove_boolean(sys.argv, '--use_vitisai'): package_name = 'onnxruntime-vitisai' elif parse_arg_remove_boolean(sys.argv, '--use_acl'): package_name = 'onnxruntime-acl' elif parse_arg_remove_boolean(sys.argv, '--use_armnn'): package_name = 'onnxruntime-armnn' # PEP 513 defined manylinux1_x86_64 and manylinux1_i686 # PEP 571 defined manylinux2010_x86_64 and manylinux2010_i686 # PEP 599 defines the following platform tags: # manylinux2014_x86_64 # manylinux2014_i686 # manylinux2014_aarch64 # manylinux2014_armv7l # manylinux2014_ppc64 # manylinux2014_ppc64le # manylinux2014_s390x manylinux_tags = [ 'manylinux1_x86_64', 'manylinux1_i686', 'manylinux2010_x86_64', 'manylinux2010_i686', 'manylinux2014_x86_64', 'manylinux2014_i686', 'manylinux2014_aarch64', 'manylinux2014_armv7l', 'manylinux2014_ppc64', 'manylinux2014_ppc64le', 'manylinux2014_s390x', ] is_manylinux = environ.get('AUDITWHEEL_PLAT', None) in manylinux_tags class build_ext(_build_ext): def build_extension(self, ext): dest_file = self.get_ext_fullpath(ext.name) logger.info('copying %s -> %s', ext.sources[0], dest_file) copyfile(ext.sources[0], dest_file) try: from wheel.bdist_wheel import bdist_wheel as _bdist_wheel class bdist_wheel(_bdist_wheel): def finalize_options(self): _bdist_wheel.finalize_options(self) if not is_manylinux: self.root_is_pure = False def _rewrite_ld_preload(self, to_preload): with open('onnxruntime/capi/_ld_preload.py', 'rt') as f: ld_preload = f.read().splitlines() with open('onnxruntime/capi/_ld_preload.py', 'wt') as f: for line in ld_preload: f.write(line) f.write('\n') if 'LD_PRELOAD_BEGIN_MARK' in line: break if len(to_preload) > 0: f.write('from ctypes import CDLL, RTLD_GLOBAL\n') for library in to_preload: f.write('_{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split('.')[0], library)) def run(self): if is_manylinux: source = 'onnxruntime/capi/onnxruntime_pybind11_state.so' dest = 'onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so' logger.info('copying %s -> %s', source, dest) copyfile(source, dest) result = subprocess.run(['patchelf', '--print-needed', dest], check=True, stdout=subprocess.PIPE, universal_newlines=True) dependencies = ['librccl.so', 'libamdhip64.so', 'librocblas.so', 'libMIOpen.so', 'libhsa-runtime64.so', 'libhsakmt.so'] to_preload = [] args = ['patchelf', '--debug'] for line in result.stdout.split('\n'): for dependency in dependencies: if dependency in line: to_preload.append(line) args.extend(['--remove-needed', line]) args.append(dest) if len(args) > 3: subprocess.run(args, check=True, stdout=subprocess.PIPE) dest = 'onnxruntime/capi/libonnxruntime_providers_cuda.so' if path.isfile(dest): result = subprocess.run(['patchelf', '--print-needed', dest], check=True, stdout=subprocess.PIPE, universal_newlines=True) cuda_dependencies = ['libcublas.so', 'libcublasLt.so', 'libcudnn.so', 'libcudart.so', 'libcurand.so', 'libcufft.so', 'libnvToolsExt.so'] args = ['patchelf', '--debug'] for line in result.stdout.split('\n'): for dependency in cuda_dependencies: if dependency in line: if dependency not in to_preload: to_preload.append(line) args.extend(['--remove-needed', line]) args.append(dest) if len(args) > 3: subprocess.run(args, check=True, stdout=subprocess.PIPE) self._rewrite_ld_preload(to_preload) _bdist_wheel.run(self) if is_manylinux: file = glob(path.join(self.dist_dir, '*linux*.whl'))[0] logger.info('repairing %s for manylinux1', file) try: subprocess.run(['auditwheel', 'repair', '-w', self.dist_dir, file], check=True, stdout=subprocess.PIPE) finally: logger.info('removing %s', file) remove(file) except ImportError as error: print("Error importing dependencies:") print(error) bdist_wheel = None # Additional binaries if platform.system() == 'Linux': libs = ['onnxruntime_pybind11_state.so', 'libdnnl.so.2', 'libmklml_intel.so', 'libmklml_gnu.so', 'libiomp5.so', 'mimalloc.so'] dl_libs = ['libonnxruntime_providers_shared.so', 'libonnxruntime_providers_cuda.so'] # DNNL, TensorRT & OpenVINO EPs are built as shared libs libs.extend(['libonnxruntime_providers_shared.so']) libs.extend(['libonnxruntime_providers_dnnl.so']) libs.extend(['libonnxruntime_providers_tensorrt.so']) libs.extend(['libonnxruntime_providers_openvino.so']) libs.extend(['libonnxruntime_providers_cuda.so']) # Nuphar Libs libs.extend(['libtvm.so.0.5.1']) if nightly_build: libs.extend(['libonnxruntime_pywrapper.so']) elif platform.system() == "Darwin": libs = ['onnxruntime_pybind11_state.so', 'libdnnl.2.dylib', 'mimalloc.so'] # TODO add libmklml and libiomp5 later. # DNNL & TensorRT EPs are built as shared libs libs.extend(['libonnxruntime_providers_shared.dylib']) libs.extend(['libonnxruntime_providers_dnnl.dylib']) libs.extend(['libonnxruntime_providers_tensorrt.dylib']) libs.extend(['libonnxruntime_providers_cuda.dylib']) if nightly_build: libs.extend(['libonnxruntime_pywrapper.dylib']) else: libs = ['onnxruntime_pybind11_state.pyd', 'dnnl.dll', 'mklml.dll', 'libiomp5md.dll'] # DNNL, TensorRT & OpenVINO EPs are built as shared libs libs.extend(['onnxruntime_providers_shared.dll']) libs.extend(['onnxruntime_providers_dnnl.dll']) libs.extend(['onnxruntime_providers_tensorrt.dll']) libs.extend(['onnxruntime_providers_openvino.dll']) libs.extend(['onnxruntime_providers_cuda.dll']) # DirectML Libs libs.extend(['DirectML.dll']) # Nuphar Libs libs.extend(['tvm.dll']) if nightly_build: libs.extend(['onnxruntime_pywrapper.dll']) if is_manylinux: data = ['capi/libonnxruntime_pywrapper.so'] if nightly_build else [] data += [path.join('capi', x) for x in dl_libs if path.isfile(path.join('onnxruntime', 'capi', x))] ext_modules = [ Extension( 'onnxruntime.capi.onnxruntime_pybind11_state', ['onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so'], ), ] else: data = [path.join('capi', x) for x in libs if path.isfile(path.join('onnxruntime', 'capi', x))] ext_modules = [] # Additional examples examples_names = ["mul_1.onnx", "logreg_iris.onnx", "sigmoid.onnx"] examples = [path.join('datasets', x) for x in examples_names] # Extra files such as EULA and ThirdPartyNotices extra = ["LICENSE", "ThirdPartyNotices.txt", "Privacy.md"] # Description README = path.join(getcwd(), "docs/python/README.rst") if not path.exists(README): this = path.dirname(__file__) README = path.join(this, "docs/python/README.rst") if not path.exists(README): raise FileNotFoundError("Unable to find 'README.rst'") with open(README) as f: long_description = f.read() # Include files in onnxruntime/external if --enable_external_custom_op_schemas build.sh command # line option is specified. # If the options is not specified this following condition fails as onnxruntime/external folder is not created in the # build flow under the build binary directory. if (path.isdir(path.join("onnxruntime", "external"))): # Gather all files under onnxruntime/external directory. extra.extend(list(str(Path(*Path(x).parts[1:])) for x in list(iglob( path.join(path.join("onnxruntime", "external"), '**/*.*'), recursive=True)))) packages = [ 'onnxruntime', 'onnxruntime.backend', 'onnxruntime.capi', 'onnxruntime.capi.training', 'onnxruntime.datasets', 'onnxruntime.tools', 'onnxruntime.tools.ort_format_model', 'onnxruntime.tools.ort_format_model.ort_flatbuffers_py', 'onnxruntime.tools.ort_format_model.ort_flatbuffers_py.experimental', 'onnxruntime.tools.ort_format_model.ort_flatbuffers_py.experimental.fbs', 'onnxruntime.quantization', 'onnxruntime.quantization.operators', 'onnxruntime.quantization.CalTableFlatBuffers', 'onnxruntime.transformers', 'onnxruntime.transformers.longformer', ] requirements_file = "requirements.txt" local_version = None enable_training = parse_arg_remove_boolean(sys.argv, '--enable_training') default_training_package_device = parse_arg_remove_boolean(sys.argv, '--default_training_package_device') package_data = {} data_files = [] classifiers = [ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: POSIX :: Linux', 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Software Development', 'Topic :: Software Development :: Libraries', 'Topic :: Software Development :: Libraries :: Python Modules', 'Programming Language :: Python', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9'] if not enable_training: classifiers.extend([ 'Operating System :: Microsoft :: Windows', 'Operating System :: MacOS']) if enable_training: packages.extend(['onnxruntime.training', 'onnxruntime.training.amp', 'onnxruntime.training.optim', 'onnxruntime.training.ortmodule', 'onnxruntime.training.ortmodule.experimental', 'onnxruntime.training.ortmodule.experimental.json_config', 'onnxruntime.training.ortmodule.torch_cpp_extensions', 'onnxruntime.training.ortmodule.torch_cpp_extensions.aten_op_executor', 'onnxruntime.training.ortmodule.torch_cpp_extensions.torch_gpu_allocator']) package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.aten_op_executor'] = ['*.cc'] package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.torch_gpu_allocator'] = ['*.cc'] requirements_file = "requirements-training.txt" # with training, we want to follow this naming convention: # stable: # onnxruntime-training-1.7.0+cu111-cp36-cp36m-linux_x86_64.whl # nightly: # onnxruntime-training-1.7.0.dev20210408+cu111-cp36-cp36m-linux_x86_64.whl # this is needed immediately by pytorch/ort so that the user is able to # install an onnxruntime training package with matching torch cuda version. package_name = 'onnxruntime-training' # we want put default training packages to pypi. pypi does not accept package with a local version. if not default_training_package_device or nightly_build: def get_torch_version(): try: import torch torch_version = torch.__version__ torch_version_plus_pos = torch_version.find('+') if torch_version_plus_pos != -1: torch_version = torch_version[:torch_version_plus_pos] torch_version = torch_version.replace('.', '') return torch_version except ImportError as error: print("Error importing torch to get torch version:") print(error) return None torch_version = get_torch_version() if cuda_version: # removing '.' to make local Cuda version number in the same form as Pytorch. if torch_version: local_version = '+torch' + torch_version + '.'\ + 'cu' + cuda_version.replace('.', '') else: local_version = '+cu' + cuda_version.replace('.', '') elif rocm_version: # removing '.' to make Cuda version number in the same form as Pytorch. rocm_version = rocm_version.replace('.', '') if torch_version: local_version = '+torch' + torch_version + '.'\ + 'rocm' + rocm_version else: local_version = '+rocm' + rocm_version else: # cpu version for documentation local_version = '+cpu' if package_name == 'onnxruntime-nuphar': packages += ["onnxruntime.nuphar"] extra += [path.join('nuphar', 'NUPHAR_CACHE_VERSION')] if featurizers_build: # Copy the featurizer data from its current directory into the onnx runtime directory so that the # content can be included as module data. # Apparently, the root_dir is different based on how the script is invoked source_root_dir = None dest_root_dir = None for potential_source_prefix, potential_dest_prefix in [ (getcwd(), getcwd()), (path.dirname(__file__), path.dirname(__file__)), (path.join(getcwd(), ".."), getcwd()), ]: potential_dir = path.join(potential_source_prefix, "external", "FeaturizersLibrary", "Data") if path.isdir(potential_dir): source_root_dir = potential_source_prefix dest_root_dir = potential_dest_prefix break if source_root_dir is None: raise Exception("Unable to find the build root dir") assert dest_root_dir is not None featurizer_source_dir = path.join(source_root_dir, "external", "FeaturizersLibrary", "Data") assert path.isdir(featurizer_source_dir), featurizer_source_dir featurizer_dest_dir = path.join(dest_root_dir, "onnxruntime", "FeaturizersLibrary", "Data") if path.isdir(featurizer_dest_dir): rmtree(featurizer_dest_dir) for item in listdir(featurizer_source_dir): this_featurizer_source_fullpath = path.join(featurizer_source_dir) assert path.isdir(this_featurizer_source_fullpath), this_featurizer_source_fullpath copytree(this_featurizer_source_fullpath, featurizer_dest_dir) packages.append("onnxruntime.FeaturizersLibrary.Data.{}".format(item)) package_data[packages[-1]] = listdir(path.join(featurizer_dest_dir, item)) package_data["onnxruntime"] = data + examples + extra version_number = '' with open('VERSION_NUMBER') as f: version_number = f.readline().strip() if nightly_build: # https://docs.microsoft.com/en-us/azure/devops/pipelines/build/variables build_suffix = environ.get('BUILD_BUILDNUMBER') if build_suffix is None: # The following line is only for local testing build_suffix = str(datetime.datetime.now().date().strftime("%Y%m%d")) else: build_suffix = build_suffix.replace('.', '') if len(build_suffix) > 8 and len(build_suffix) < 12: # we want to format the build_suffix to avoid (the 12th run on 20210630 vs the first run on 20210701): # 2021063012 > 202107011 # in above 2021063012 is treated as the latest which is incorrect. # we want to convert the format to: # 20210630012 < 20210701001 # where the first 8 digits are date. the last 3 digits are run count. # as long as there are less than 1000 runs per day, we will not have the problem. # to test this code locally, run: # NIGHTLY_BUILD=1 BUILD_BUILDNUMBER=202107011 python tools/ci_build/build.py --config RelWithDebInfo \ # --enable_training --use_cuda --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ \ # --nccl_home /usr/lib/x86_64-linux-gnu/ --build_dir build/Linux --build --build_wheel --skip_tests \ # --cuda_version 11.1 def check_date_format(date_str): try: datetime.datetime.strptime(date_str, '%Y%m%d') return True except: # noqa return False def reformat_run_count(count_str): try: count = int(count_str) if count >= 0 and count < 1000: return "{:03}".format(count) elif count >= 1000: raise RuntimeError(f'Too many builds for the same day: {count}') return "" except: # noqa return "" build_suffix_is_date_format = check_date_format(build_suffix[:8]) build_suffix_run_count = reformat_run_count(build_suffix[8:]) if build_suffix_is_date_format and build_suffix_run_count: build_suffix = build_suffix[:8] + build_suffix_run_count elif len(build_suffix) >= 12: raise RuntimeError(f'Incorrect build suffix: "{build_suffix}"') if enable_training: from packaging import version from packaging.version import Version # with training package, we need to bump up version minor number so that # nightly releases take precedence over the latest release when --pre is used during pip install. # eventually this shall be the behavior of all onnxruntime releases. # alternatively we may bump up version number right after every release. ort_version = version.parse(version_number) if isinstance(ort_version, Version): # TODO: this is the last time we have to do this!!! # We shall bump up release number right after release cut. if ort_version.major == 1 and ort_version.minor == 8 and ort_version.micro == 0: version_number = '{major}.{minor}.{macro}'.format( major=ort_version.major, minor=ort_version.minor + 1, macro=ort_version.micro) version_number = version_number + ".dev" + build_suffix if local_version: version_number = version_number + local_version if wheel_name_suffix: if not (enable_training and wheel_name_suffix == 'gpu'): # for training packages, local version is used to indicate device types package_name = "{}-{}".format(package_name, wheel_name_suffix) cmd_classes = {} if bdist_wheel is not None: cmd_classes['bdist_wheel'] = bdist_wheel cmd_classes['build_ext'] = build_ext requirements_path = path.join(getcwd(), requirements_file) if not path.exists(requirements_path): this = path.dirname(__file__) requirements_path = path.join(this, requirements_file) if not path.exists(requirements_path): raise FileNotFoundError("Unable to find " + requirements_file) with open(requirements_path) as f: install_requires = f.read().splitlines() if enable_training: def save_build_and_package_info(package_name, version_number, cuda_version, rocm_version): sys.path.append(path.join(path.dirname(__file__), 'onnxruntime', 'python')) from onnxruntime_collect_build_info import find_cudart_versions version_path = path.join('onnxruntime', 'capi', 'build_and_package_info.py') with open(version_path, 'w') as f: f.write("package_name = '{}'\n".format(package_name)) f.write("__version__ = '{}'\n".format(version_number)) if cuda_version: f.write("cuda_version = '{}'\n".format(cuda_version)) # cudart_versions are integers cudart_versions = find_cudart_versions(build_env=True) if cudart_versions and len(cudart_versions) == 1: f.write("cudart_version = {}\n".format(cudart_versions[0])) else: print( "Error getting cudart version. ", "did not find any cudart library" if not cudart_versions or len(cudart_versions) == 0 else "found multiple cudart libraries") elif rocm_version: f.write("rocm_version = '{}'\n".format(rocm_version)) save_build_and_package_info(package_name, version_number, cuda_version, rocm_version) # Setup setup( name=package_name, version=version_number, description='ONNX Runtime is a runtime accelerator for Machine Learning models', long_description=long_description, author='Microsoft Corporation', author_email='onnxruntime@microsoft.com', cmdclass=cmd_classes, license="MIT License", packages=packages, ext_modules=ext_modules, package_data=package_data, url="https://onnxruntime.ai", download_url='https://github.com/microsoft/onnxruntime/tags', data_files=data_files, install_requires=install_requires, keywords='onnx machine learning', entry_points={ 'console_scripts': [ 'onnxruntime_test = onnxruntime.tools.onnxruntime_test:main', ] }, classifiers=classifiers, )