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onnxruntime/tools/nuget/generate_nuspec_for_native_nuget.py
Sheil Kumar 4377ff4a1a
Enable .NET Core 2.0 and .NET Framework 4.6.1 in Microsoft.AI.MachineLearning NuGet package (#4125) 
* add project to download cswinrt and build winrt c# interop dll

* Add to nuget package

* reverse if check

* run generation before core compile

* add generated files to compile

* update .net package to binplace native libs

* add props to .netstandard2.0 folder

* auto binplace ml native binaries

* force 'Any CPU' platform build

* Fix anycpu and platform targets

* fix flake errors

* fix variable order

* fix flake pep8 errors, semicolon

Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
2020-06-09 09:08:19 -07:00

362 lines
19 KiB
Python
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import argparse
import sys
import os
def parse_arguments():
parser = argparse.ArgumentParser(description="ONNX Runtime create nuget spec script "
"(for hosting native shared library artifacts)",
usage='')
# Main arguments
parser.add_argument("--package_name", required=True, help="ORT package name. Eg: Microsoft.ML.OnnxRuntime.Gpu")
parser.add_argument("--package_version", required=True, help="ORT package version. Eg: 1.0.0")
parser.add_argument("--target_architecture", required=True, help="Eg: x64")
parser.add_argument("--build_config", required=True, help="Eg: RelWithDebInfo")
parser.add_argument("--ort_build_path", required=True, help="ORT build directory.")
parser.add_argument("--native_build_path", required=True, help="Native build output directory.")
parser.add_argument("--packages_path", required=True, help="Nuget packages output directory.")
parser.add_argument("--sources_path", required=True, help="OnnxRuntime source code root.")
parser.add_argument("--commit_id", required=True, help="The last commit id included in this package.")
parser.add_argument("--is_release_build", required=False, default=None, type=str,
help="Flag indicating if the build is a release build. Accepted values: true/false.")
return parser.parse_args()
def generate_id(list, package_name):
list.append('<id>' + package_name + '</id>')
def generate_version(list, package_version):
list.append('<version>' + package_version + '</version>')
def generate_authors(list, authors):
list.append('<authors>' + authors + '</authors>')
def generate_owners(list, owners):
list.append('<owners>' + owners + '</owners>')
def generate_description(list, description):
list.append('<description>' + description + '</description>')
def generate_copyright(list, copyright):
list.append('<copyright>' + copyright + '</copyright>')
def generate_tags(list, tags):
list.append('<tags>' + tags + '</tags>')
def generate_icon_url(list, icon_url):
list.append('<iconUrl>' + icon_url + '</iconUrl>')
def generate_license(list):
list.append('<license type="file">LICENSE.txt</license>')
def generate_project_url(list, project_url):
list.append('<projectUrl>' + project_url + '</projectUrl>')
def generate_repo_url(list, repo_url, commit_id):
list.append('<repository type="git" url="' + repo_url + '"' + ' commit="' + commit_id + '" />')
def generate_dependencies(list, package_name, version):
if (package_name == 'Microsoft.AI.MachineLearning'):
list.append('<dependencies>')
# Support .Net Core
list.append('<group targetFramework="NETCOREAPP">')
list.append('<dependency id="Microsoft.Windows.SDK.NET"' + ' version="10.0.18362.3-preview"/>')
list.append('</group>')
# Support .Net Standard
list.append('<group targetFramework="NETSTANDARD">')
list.append('<dependency id="Microsoft.Windows.SDK.NET"' + ' version="10.0.18362.3-preview"/>')
list.append('</group>')
# Support .Net Framework
list.append('<group targetFramework="NETFRAMEWORK">')
list.append('<dependency id="Microsoft.Windows.SDK.NET"' + ' version="10.0.18362.3-preview"/>')
list.append('</group>')
list.append('</dependencies>')
else:
list.append('<dependencies>')
# Support .Net Core
list.append('<group targetFramework="NETCOREAPP">')
list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
list.append('</group>')
# Support .Net Standard
list.append('<group targetFramework="NETSTANDARD">')
list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
list.append('</group>')
# Support .Net Framework
list.append('<group targetFramework="NETFRAMEWORK">')
list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
list.append('</group>')
list.append('</dependencies>')
def get_env_var(key):
return os.environ.get(key)
def generate_release_notes(list):
list.append('<releaseNotes>')
list.append('Release Def:')
branch = get_env_var('BUILD_SOURCEBRANCH')
list.append('\t' + 'Branch: ' + (branch if branch is not None else ''))
version = get_env_var('BUILD_SOURCEVERSION')
list.append('\t' + 'Commit: ' + (version if version is not None else ''))
build_id = get_env_var('BUILD_BUILDID')
list.append('\t' + 'Build: https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=' +
(build_id if build_id is not None else ''))
list.append('</releaseNotes>')
def generate_metadata(list, args):
metadata_list = ['<metadata>']
generate_id(metadata_list, args.package_name)
generate_version(metadata_list, args.package_version)
generate_authors(metadata_list, 'Microsoft')
generate_owners(metadata_list, 'Microsoft')
generate_description(metadata_list, 'This package contains native shared library artifacts '
'for all supported platforms of ONNX Runtime.')
generate_copyright(metadata_list, '\xc2\xa9 ' + 'Microsoft Corporation. All rights reserved.')
generate_tags(metadata_list, 'ONNX ONNX Runtime Machine Learning')
generate_icon_url(metadata_list, 'https://go.microsoft.com/fwlink/?linkid=2049168')
generate_license(metadata_list)
generate_project_url(metadata_list, 'https://github.com/Microsoft/onnxruntime')
generate_repo_url(metadata_list, 'https://github.com/Microsoft/onnxruntime.git', args.commit_id)
generate_dependencies(metadata_list, args.package_name, args.package_version)
generate_release_notes(metadata_list)
metadata_list.append('</metadata>')
list += metadata_list
def generate_files(list, args):
files_list = ['<files>']
is_cpu_package = args.package_name == 'Microsoft.ML.OnnxRuntime'
is_mklml_package = args.package_name == 'Microsoft.ML.OnnxRuntime.MKLML'
is_cuda_gpu_package = args.package_name == 'Microsoft.ML.OnnxRuntime.Gpu'
is_dml_package = args.package_name == 'Microsoft.ML.OnnxRuntime.DirectML'
is_windowsai_package = args.package_name == 'Microsoft.AI.MachineLearning'
includes_cuda = is_cuda_gpu_package or is_cpu_package # Why does the CPU package ship the cuda provider headers?
includes_winml = is_windowsai_package
includes_directml = (is_dml_package or is_windowsai_package) and (args.target_architecture == 'x64'
or args.target_architecture == 'x86')
# Process headers
files_list.append('<file src=' + '"' + os.path.join(args.sources_path,
'include\\onnxruntime\\core\\session\\onnxruntime_*.h') +
'" target="build\\native\\include" />')
files_list.append('<file src=' + '"' +
os.path.join(args.sources_path,
'include\\onnxruntime\\core\\providers\\cpu\\cpu_provider_factory.h') +
'" target="build\\native\\include" />')
if includes_cuda:
files_list.append('<file src=' + '"' +
os.path.join(args.sources_path,
'include\\onnxruntime\\core\\providers\\cuda\\cuda_provider_factory.h') +
'" target="build\\native\\include" />')
if includes_directml:
files_list.append('<file src=' + '"' +
os.path.join(args.sources_path,
'include\\onnxruntime\\core\\providers\\dml\\dml_provider_factory.h') +
'" target="build\\native\\include" />')
if includes_winml:
# Add microsoft.ai.machinelearning headers
files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
'microsoft.ai.machinelearning.h') +
'" target="build\\native\\include\\abi\\Microsoft.AI.MachineLearning.h" />')
files_list.append('<file src=' + '"' + os.path.join(args.sources_path,
'winml\\api\\dualapipartitionattribute.h') +
'" target="build\\native\\include\\abi\\dualapipartitionattribute.h" />')
files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
'microsoft.ai.machinelearning.native.h') +
'" target="build\\native\\include\\Microsoft.AI.MachineLearning.Native.h" />')
# Add custom operator headers
mlop_path = 'onnxruntime\\core\\providers\\dml\\dmlexecutionprovider\\inc\\mloperatorauthor.h'
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, mlop_path) +
'" target="build\\native\\include" />')
# Process microsoft.ai.machinelearning.winmd
files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
'microsoft.ai.machinelearning.winmd') +
'" target="lib\\uap10.0\\Microsoft.AI.MachineLearning.winmd" />')
interop_dll = 'Microsoft.AI.MachineLearning.Interop\\netstandard2.0\\Microsoft.AI.MachineLearning.Interop.dll'
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, interop_dll) +
'" target="lib\\netstandard2.0\\Microsoft.AI.MachineLearning.Interop.dll" />')
interop_pdb = 'Microsoft.AI.MachineLearning.Interop\\netstandard2.0\\Microsoft.AI.MachineLearning.Interop.pdb'
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, interop_pdb) +
'" target="lib\\netstandard2.0\\Microsoft.AI.MachineLearning.Interop.pdb" />')
# Process runtimes
# Process onnxruntime import lib, dll, and pdb
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.lib') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.pdb') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
if includes_directml:
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'DirectML.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'DirectML.pdb') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
files_list.append('<file src=' + '"' + os.path.join(args.packages_path, 'DirectML.2.1.0\\LICENSE.txt') +
'" target="DirectML_LICENSE.txt" />')
if includes_winml:
# Process microsoft.ai.machinelearning import lib, dll, and pdb
files_list.append('<file src=' + '"' +
os.path.join(args.native_build_path, 'microsoft.ai.machinelearning.lib') +
'" target="runtimes\\win-' + args.target_architecture +
'\\native\\Microsoft.AI.MachineLearning.lib" />')
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
'microsoft.ai.machinelearning.dll') +
'" target="runtimes\\win-' + args.target_architecture +
'\\native\\Microsoft.AI.MachineLearning.dll" />')
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
'microsoft.ai.machinelearning.pdb') +
'" target="runtimes\\win-' + args.target_architecture +
'\\native\\Microsoft.AI.MachineLearning.pdb" />')
if is_cpu_package or is_cuda_gpu_package or is_dml_package or is_mklml_package:
# Process dnll.dll
if os.path.exists(os.path.join(args.native_build_path, 'dnnl.dll')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'dnnl.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
# Process mklml.dll
if os.path.exists(os.path.join(args.native_build_path, 'mklml.dll')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'mklml.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
# Process libiomp5md.dll
if os.path.exists(os.path.join(args.native_build_path, 'libiomp5md.dll')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'libiomp5md.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
# Process tvm.dll
if os.path.exists(os.path.join(args.native_build_path, 'tvm.dll')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'tvm.dll') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
# Some tools to be packaged in nightly build only, should not be released
# These are copied to the runtimes folder for convenience of loading with the dlls
if args.is_release_build.lower() != 'true' and args.target_architecture == 'x64' and \
os.path.exists(os.path.join(args.native_build_path, 'onnxruntime_perf_test.exe')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime_perf_test.exe') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
if args.is_release_build.lower() != 'true' and args.target_architecture == 'x64' and \
os.path.exists(os.path.join(args.native_build_path, 'onnx_test_runner.exe')):
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnx_test_runner.exe') +
'" target="runtimes\\win-' + args.target_architecture + '\\native" />')
# Process props and targets files
if is_windowsai_package:
windowsai_src = 'Microsoft.AI.MachineLearning'
# Process native props
windowsai_props = 'Microsoft.AI.MachineLearning.props'
windowsai_native_props = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_props)
files_list.append('<file src=' + '"' + windowsai_native_props + '" target="build\\native" />')
# Process native targets
windowsai_targets = 'Microsoft.AI.MachineLearning.targets'
windowsai_native_targets = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_targets)
files_list.append('<file src=' + '"' + windowsai_native_targets + '" target="build\\native" />')
# Process native rules
windowsai_rules = 'Microsoft.AI.MachineLearning.Rules.Project.xml'
windowsai_native_rules = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_rules)
files_list.append('<file src=' + '"' + windowsai_native_rules + '" target="build\\native" />')
# Process .net standard 2.0 targets
interop_src = 'Microsoft.AI.MachineLearning.Interop'
interop_targets = 'Microsoft.AI.MachineLearning.targets'
windowsai_net20_targets = os.path.join(args.sources_path, 'csharp', 'src', interop_src, interop_targets)
files_list.append('<file src=' + '"' + windowsai_net20_targets + '" target="build\\netstandard2.0" />')
if is_cpu_package or is_cuda_gpu_package or is_dml_package or is_mklml_package:
# Process props file
source_props = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime', 'props.xml')
target_props = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime',
args.package_name + '.props')
os.system('copy ' + source_props + ' ' + target_props)
files_list.append('<file src=' + '"' + target_props + '" target="build\\native" />')
files_list.append('<file src=' + '"' + target_props + '" target="build\\netstandard1.1" />')
# Process targets file
source_targets = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime', 'targets.xml')
target_targets = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime',
args.package_name + '.targets')
os.system('copy ' + source_targets + ' ' + target_targets)
files_list.append('<file src=' + '"' + target_targets + '" target="build\\native" />')
files_list.append('<file src=' + '"' + target_targets + '" target="build\\netstandard1.1" />')
# Process License, ThirdPartyNotices, Privacy, README
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'LICENSE.txt') + '" target="LICENSE.txt" />')
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'ThirdPartyNotices.txt') +
'" target="ThirdPartyNotices.txt" />')
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'docs', 'Privacy.md') +
'" target="Privacy.md" />')
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'docs', 'C_API.md') +
'" target="README.md" />')
files_list.append('</files>')
list += files_list
def generate_nuspec(args):
lines = ['<?xml version="1.0"?>']
lines.append('<package>')
generate_metadata(lines, args)
generate_files(lines, args)
lines.append('</package>')
return lines
def is_windows():
return sys.platform.startswith("win")
def main():
if not is_windows():
raise Exception('Native Nuget generation is currently supported only on Windows')
# Parse arguments
args = parse_arguments()
if (args.is_release_build.lower() != 'true' and args.is_release_build.lower() != 'false'):
raise Exception('Only valid options for IsReleaseBuild are: true and false')
# Generate nuspec
lines = generate_nuspec(args)
# Create the nuspec needed to generate the Nuget
with open(os.path.join(args.native_build_path, 'NativeNuget.nuspec'), 'w') as f:
for line in lines:
f.write(line)
f.write('\n')
if __name__ == "__main__":
sys.exit(main())
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