mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-14 20:48:00 +00:00
b854f2399d
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master. 2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs). 3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11) 4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 . 5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use. 6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04. 7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1 8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build. 9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines. 10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
480 lines
19 KiB
Python
480 lines
19 KiB
Python
# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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from setuptools import setup, Extension
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from distutils import log as logger
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from distutils.command.build_ext import build_ext as _build_ext
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from glob import glob
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from os import path, getcwd, environ, remove, listdir
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from shutil import copyfile, copytree, rmtree
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import platform
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import subprocess
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import sys
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import datetime
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nightly_build = False
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featurizers_build = False
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package_name = 'onnxruntime'
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wheel_name_suffix = None
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def parse_arg_remove_boolean(argv, arg_name):
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arg_value = False
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if arg_name in sys.argv:
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arg_value = True
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argv.remove(arg_name)
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return arg_value
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def parse_arg_remove_string(argv, arg_name_equal):
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arg_value = None
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for arg in sys.argv[1:]:
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if arg.startswith(arg_name_equal):
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arg_value = arg[len(arg_name_equal):]
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sys.argv.remove(arg)
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break
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return arg_value
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# Any combination of the following arguments can be applied
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featurizers_build = parse_arg_remove_boolean(sys.argv, '--use_featurizers')
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if parse_arg_remove_boolean(sys.argv, '--nightly_build'):
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package_name = 'ort-nightly'
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nightly_build = True
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wheel_name_suffix = parse_arg_remove_string(sys.argv, '--wheel_name_suffix=')
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cuda_version = None
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rocm_version = None
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# The following arguments are mutually exclusive
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if parse_arg_remove_boolean(sys.argv, '--use_tensorrt'):
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package_name = 'onnxruntime-gpu-tensorrt' if not nightly_build else 'ort-trt-nightly'
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elif parse_arg_remove_boolean(sys.argv, '--use_cuda'):
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package_name = 'onnxruntime-gpu' if not nightly_build else 'ort-gpu-nightly'
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cuda_version = parse_arg_remove_string(sys.argv, '--cuda_version=')
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elif parse_arg_remove_boolean(sys.argv, '--use_rocm'):
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package_name = 'onnxruntime-rocm' if not nightly_build else 'ort-rocm-nightly'
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rocm_version = parse_arg_remove_string(sys.argv, '--rocm_version=')
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elif parse_arg_remove_boolean(sys.argv, '--use_openvino'):
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package_name = 'onnxruntime-openvino'
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elif parse_arg_remove_boolean(sys.argv, '--use_dnnl'):
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package_name = 'onnxruntime-dnnl'
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elif parse_arg_remove_boolean(sys.argv, '--use_nuphar'):
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package_name = 'onnxruntime-nuphar'
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elif parse_arg_remove_boolean(sys.argv, '--use_vitisai'):
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package_name = 'onnxruntime-vitisai'
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elif parse_arg_remove_boolean(sys.argv, '--use_acl'):
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package_name = 'onnxruntime-acl'
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elif parse_arg_remove_boolean(sys.argv, '--use_armnn'):
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package_name = 'onnxruntime-armnn'
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elif parse_arg_remove_boolean(sys.argv, '--use_dml'):
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package_name = 'onnxruntime-dml'
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# PEP 513 defined manylinux1_x86_64 and manylinux1_i686
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# PEP 571 defined manylinux2010_x86_64 and manylinux2010_i686
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# PEP 599 defines the following platform tags:
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# manylinux2014_x86_64
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# manylinux2014_i686
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# manylinux2014_aarch64
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# manylinux2014_armv7l
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# manylinux2014_ppc64
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# manylinux2014_ppc64le
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# manylinux2014_s390x
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manylinux_tags = [
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'manylinux1_x86_64',
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'manylinux1_i686',
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'manylinux2010_x86_64',
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'manylinux2010_i686',
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'manylinux2014_x86_64',
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'manylinux2014_i686',
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'manylinux2014_aarch64',
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'manylinux2014_armv7l',
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'manylinux2014_ppc64',
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'manylinux2014_ppc64le',
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'manylinux2014_s390x',
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]
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is_manylinux = environ.get('AUDITWHEEL_PLAT', None) in manylinux_tags
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class build_ext(_build_ext):
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def build_extension(self, ext):
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dest_file = self.get_ext_fullpath(ext.name)
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logger.info('copying %s -> %s', ext.sources[0], dest_file)
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copyfile(ext.sources[0], dest_file)
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try:
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from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
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class bdist_wheel(_bdist_wheel):
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def finalize_options(self):
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_bdist_wheel.finalize_options(self)
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if not is_manylinux:
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self.root_is_pure = False
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def _rewrite_ld_preload(self, to_preload):
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with open('onnxruntime/capi/_ld_preload.py', 'rt') as f:
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ld_preload = f.read().splitlines()
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with open('onnxruntime/capi/_ld_preload.py', 'wt') as f:
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for line in ld_preload:
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f.write(line)
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f.write('\n')
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if 'LD_PRELOAD_BEGIN_MARK' in line:
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break
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if len(to_preload) > 0:
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f.write('from ctypes import CDLL, RTLD_GLOBAL\n')
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for library in to_preload:
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f.write('_{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split('.')[0], library))
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def run(self):
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if is_manylinux:
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source = 'onnxruntime/capi/onnxruntime_pybind11_state.so'
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dest = 'onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so'
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logger.info('copying %s -> %s', source, dest)
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copyfile(source, dest)
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result = subprocess.run(['patchelf', '--print-needed', dest], check=True, stdout=subprocess.PIPE, universal_newlines=True)
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dependencies = ['librccl.so', 'libamdhip64.so', 'librocblas.so', 'libMIOpen.so', 'libhsa-runtime64.so', 'libhsakmt.so']
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to_preload = []
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args = ['patchelf', '--debug']
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for line in result.stdout.split('\n'):
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for dependency in dependencies:
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if dependency in line:
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to_preload.append(line)
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args.extend(['--remove-needed', line])
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args.append(dest)
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if len(args) > 3:
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subprocess.run(args, check=True, stdout=subprocess.PIPE)
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dest = 'onnxruntime/capi/libonnxruntime_providers_cuda.so'
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if path.isfile(dest):
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result = subprocess.run(['patchelf', '--print-needed', dest], check=True, stdout=subprocess.PIPE, universal_newlines=True)
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cuda_dependencies = ['libcublas.so', 'libcublasLt.so', 'libcudnn.so', 'libcudart.so', 'libcurand.so', 'libcufft.so', 'libnvToolsExt.so']
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args = ['patchelf', '--debug']
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for line in result.stdout.split('\n'):
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for dependency in cuda_dependencies:
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if dependency in line:
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if not dependency in to_preload:
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to_preload.append(line)
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args.extend(['--remove-needed', line])
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args.append(dest)
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if len(args) > 3:
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subprocess.run(args, check=True, stdout=subprocess.PIPE)
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self._rewrite_ld_preload(to_preload)
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_bdist_wheel.run(self)
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if is_manylinux:
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file = glob(path.join(self.dist_dir, '*linux*.whl'))[0]
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logger.info('repairing %s for manylinux1', file)
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try:
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subprocess.run(['auditwheel', 'repair', '-w', self.dist_dir, file], check=True, stdout=subprocess.PIPE)
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finally:
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logger.info('removing %s', file)
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remove(file)
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except ImportError as error:
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print("Error importing dependencies:")
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print(error)
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bdist_wheel = None
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# Additional binaries
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if platform.system() == 'Linux':
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libs = ['onnxruntime_pybind11_state.so', 'libdnnl.so.2', 'libmklml_intel.so', 'libmklml_gnu.so', 'libiomp5.so', 'mimalloc.so']
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dl_libs = ['libonnxruntime_providers_shared.so', 'libonnxruntime_providers_cuda.so']
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# DNNL, TensorRT & OpenVINO EPs are built as shared libs
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libs.extend(['libonnxruntime_providers_shared.so'])
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libs.extend(['libonnxruntime_providers_dnnl.so'])
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libs.extend(['libonnxruntime_providers_tensorrt.so'])
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libs.extend(['libonnxruntime_providers_openvino.so'])
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libs.extend(['libonnxruntime_providers_cuda.so'])
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# Nuphar Libs
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libs.extend(['libtvm.so.0.5.1'])
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if nightly_build:
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libs.extend(['libonnxruntime_pywrapper.so'])
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elif platform.system() == "Darwin":
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libs = ['onnxruntime_pybind11_state.so', 'libdnnl.2.dylib', 'mimalloc.so'] # TODO add libmklml and libiomp5 later.
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# DNNL & TensorRT EPs are built as shared libs
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libs.extend(['libonnxruntime_providers_shared.dylib'])
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libs.extend(['libonnxruntime_providers_dnnl.dylib'])
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libs.extend(['libonnxruntime_providers_tensorrt.dylib'])
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libs.extend(['libonnxruntime_providers_cuda.dylib'])
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if nightly_build:
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libs.extend(['libonnxruntime_pywrapper.dylib'])
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else:
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libs = ['onnxruntime_pybind11_state.pyd', 'dnnl.dll', 'mklml.dll', 'libiomp5md.dll']
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# DNNL, TensorRT & OpenVINO EPs are built as shared libs
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libs.extend(['onnxruntime_providers_shared.dll'])
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libs.extend(['onnxruntime_providers_dnnl.dll'])
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libs.extend(['onnxruntime_providers_tensorrt.dll'])
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libs.extend(['onnxruntime_providers_openvino.dll'])
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libs.extend(['onnxruntime_providers_cuda.dll'])
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# DirectML Libs
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libs.extend(['DirectML.dll'])
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# Nuphar Libs
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libs.extend(['tvm.dll'])
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if nightly_build:
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libs.extend(['onnxruntime_pywrapper.dll'])
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if is_manylinux:
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data = ['capi/libonnxruntime_pywrapper.so'] if nightly_build else []
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data += [path.join('capi', x) for x in dl_libs if path.isfile(path.join('onnxruntime', 'capi', x))]
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ext_modules = [
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Extension(
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'onnxruntime.capi.onnxruntime_pybind11_state',
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['onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so'],
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),
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]
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else:
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data = [path.join('capi', x) for x in libs if path.isfile(path.join('onnxruntime', 'capi', x))]
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ext_modules = []
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# Additional examples
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examples_names = ["mul_1.onnx", "logreg_iris.onnx", "sigmoid.onnx"]
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examples = [path.join('datasets', x) for x in examples_names]
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# Extra files such as EULA and ThirdPartyNotices
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extra = ["LICENSE", "ThirdPartyNotices.txt", "Privacy.md"]
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# Description
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README = path.join(getcwd(), "docs/python/README.rst")
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if not path.exists(README):
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this = path.dirname(__file__)
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README = path.join(this, "docs/python/README.rst")
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if not path.exists(README):
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raise FileNotFoundError("Unable to find 'README.rst'")
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with open(README) as f:
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long_description = f.read()
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packages = [
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'onnxruntime',
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'onnxruntime.backend',
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'onnxruntime.capi',
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'onnxruntime.capi.training',
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'onnxruntime.datasets',
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'onnxruntime.tools',
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'onnxruntime.tools.ort_format_model',
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'onnxruntime.tools.ort_format_model.ort_flatbuffers_py',
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'onnxruntime.tools.ort_format_model.ort_flatbuffers_py.experimental',
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'onnxruntime.tools.ort_format_model.ort_flatbuffers_py.experimental.fbs',
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'onnxruntime.quantization',
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'onnxruntime.quantization.operators',
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'onnxruntime.quantization.CalTableFlatBuffers',
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'onnxruntime.transformers',
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'onnxruntime.transformers.longformer',
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]
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requirements_file = "requirements.txt"
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local_version = None
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enable_training = parse_arg_remove_boolean(sys.argv, '--enable_training')
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default_training_package_device = parse_arg_remove_boolean(sys.argv, '--default_training_package_device')
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if enable_training:
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packages.extend(['onnxruntime.training',
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'onnxruntime.training.amp',
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'onnxruntime.training.optim',
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'onnxruntime.training.ortmodule'])
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requirements_file = "requirements-training.txt"
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# with training, we want to follow this naming convention:
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# stable:
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# onnxruntime-training-1.7.0+cu111-cp36-cp36m-linux_x86_64.whl
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# nightly:
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# onnxruntime-training-1.7.0.dev20210408+cu111-cp36-cp36m-linux_x86_64.whl
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# this is needed immediately by pytorch/ort so that the user is able to
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# install an onnxruntime training package with matching torch cuda version.
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package_name = 'onnxruntime-training'
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# we want put default training packages to pypi. pypi does not accept package with a local version.
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if not default_training_package_device:
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if cuda_version:
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# removing '.' to make local Cuda version number in the same form as Pytorch.
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local_version = '+cu' + cuda_version.replace('.', '')
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if rocm_version:
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# removing '.' to make Cuda version number in the same form as Pytorch.
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rocm_version = rocm_version.replace('.', '')
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local_version = '+rocm' + rocm_version
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package_data = {}
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data_files = []
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if package_name == 'onnxruntime-nuphar':
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packages += ["onnxruntime.nuphar"]
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extra += [path.join('nuphar', 'NUPHAR_CACHE_VERSION')]
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if featurizers_build:
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# Copy the featurizer data from its current directory into the onnx runtime directory so that the
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# content can be included as module data.
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# Apparently, the root_dir is different based on how the script is invoked
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source_root_dir = None
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dest_root_dir = None
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for potential_source_prefix, potential_dest_prefix in [
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(getcwd(), getcwd()),
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(path.dirname(__file__), path.dirname(__file__)),
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(path.join(getcwd(), ".."), getcwd()),
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]:
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potential_dir = path.join(potential_source_prefix, "external", "FeaturizersLibrary", "Data")
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if path.isdir(potential_dir):
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source_root_dir = potential_source_prefix
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dest_root_dir = potential_dest_prefix
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break
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if source_root_dir is None:
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raise Exception("Unable to find the build root dir")
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assert dest_root_dir is not None
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featurizer_source_dir = path.join(source_root_dir, "external", "FeaturizersLibrary", "Data")
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assert path.isdir(featurizer_source_dir), featurizer_source_dir
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featurizer_dest_dir = path.join(dest_root_dir, "onnxruntime", "FeaturizersLibrary", "Data")
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if path.isdir(featurizer_dest_dir):
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rmtree(featurizer_dest_dir)
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for item in listdir(featurizer_source_dir):
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this_featurizer_source_fullpath = path.join(featurizer_source_dir)
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assert path.isdir(this_featurizer_source_fullpath), this_featurizer_source_fullpath
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copytree(this_featurizer_source_fullpath, featurizer_dest_dir)
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packages.append("onnxruntime.FeaturizersLibrary.Data.{}".format(item))
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package_data[packages[-1]] = listdir(path.join(featurizer_dest_dir, item))
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package_data["onnxruntime"] = data + examples + extra
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version_number = ''
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with open('VERSION_NUMBER') as f:
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version_number = f.readline().strip()
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if nightly_build:
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# https://docs.microsoft.com/en-us/azure/devops/pipelines/build/variables
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build_suffix = environ.get('BUILD_BUILDNUMBER')
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if build_suffix is None:
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# The following line is only for local testing
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build_suffix = str(datetime.datetime.now().date().strftime("%Y%m%d"))
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else:
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build_suffix = build_suffix.replace('.', '')
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if enable_training:
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from packaging import version
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from packaging.version import Version
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# with training package, we need to bump up version minor number so that
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# nightly releases take precedence over the latest release when --pre is used during pip install.
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# eventually this shall be the behavior of all onnxruntime releases.
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# alternatively we may bump up version number right after every release.
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ort_version = version.parse(version_number)
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if isinstance(ort_version, Version):
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version_number = '{major}.{minor}.{macro}'.format(
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major=ort_version.major,
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minor=ort_version.minor + 1,
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macro=ort_version.micro)
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version_number = version_number + ".dev" + build_suffix
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if local_version:
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version_number = version_number + local_version
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if wheel_name_suffix:
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package_name = "{}_{}".format(package_name, wheel_name_suffix)
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cmd_classes = {}
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if bdist_wheel is not None:
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cmd_classes['bdist_wheel'] = bdist_wheel
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cmd_classes['build_ext'] = build_ext
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requirements_path = path.join(getcwd(), requirements_file)
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if not path.exists(requirements_path):
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this = path.dirname(__file__)
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requirements_path = path.join(this, requirements_file)
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if not path.exists(requirements_path):
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raise FileNotFoundError("Unable to find " + requirements_file)
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with open(requirements_path) as f:
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install_requires = f.read().splitlines()
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if is_manylinux:
|
|
AUDITWHEEL_PLAT = environ.get('AUDITWHEEL_PLAT', None)
|
|
if AUDITWHEEL_PLAT == 'manylinux2014_aarch64':
|
|
for i in range(len(install_requires)):
|
|
req = install_requires[i]
|
|
if req.startswith("numpy"):
|
|
install_requires[i] = "numpy >= 1.19.5"
|
|
|
|
if enable_training:
|
|
def save_build_and_package_info(package_name, version_number, cuda_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")
|
|
else:
|
|
# TODO: rocm
|
|
pass
|
|
|
|
save_build_and_package_info(package_name, version_number, cuda_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=[
|
|
'Development Status :: 5 - Production/Stable',
|
|
'Intended Audience :: Developers',
|
|
'License :: OSI Approved :: MIT License',
|
|
'Operating System :: POSIX :: Linux',
|
|
'Operating System :: Microsoft :: Windows',
|
|
'Operating System :: MacOS',
|
|
'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'],
|
|
)
|