# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. # See https://llvm.org/LICENSE.txt for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception # Also available under a BSD-style license. See LICENSE. # Script for generating the torch-mlir wheel. # ``` # $ python setup.py bdist_wheel # ``` # # It is recommended to build with Ninja and ccache. To do so, set environment # variables by prefixing to above invocations: # ``` # CMAKE_GENERATOR=Ninja CMAKE_C_COMPILER_LAUNCHER=ccache CMAKE_CXX_COMPILER_LAUNCHER=ccache # ``` # # On CIs, it is often advantageous to re-use/control the CMake build directory. # This can be set with the TORCH_MLIR_CMAKE_BUILD_DIR env var. # Additionally, the TORCH_MLIR_CMAKE_BUILD_DIR_ALREADY_BUILT env var will # prevent this script from attempting to build the directory, and will simply # use the (presumed already built) directory as-is. # # The package version can be set with the TORCH_MLIR_PYTHON_PACKAGE_VERSION # environment variable. For example, this can be "20220330.357" for a snapshot # release on 2022-03-30 with build number 357. # # Implementation notes: # The contents of the wheel is just the contents of the `python_packages` # directory that our CMake build produces. We go through quite a bit of effort # on the CMake side to organize that directory already, so we avoid duplicating # that here, and just package up its contents. import os import pathlib import shutil import subprocess import sys import multiprocessing from distutils.command.build import build as _build from setuptools import setup, Extension from setuptools.command.build_ext import build_ext from setuptools.command.build_py import build_py def check_env_flag(name: str, default=None) -> bool: return str(os.getenv(name, default)).upper() in ["ON", "1", "YES", "TRUE", "Y"] PACKAGE_VERSION = os.environ.get("TORCH_MLIR_PYTHON_PACKAGE_VERSION") or "0.0.1" # If true, enable LTC build by default TORCH_MLIR_ENABLE_LTC_DEFAULT = True TORCH_MLIR_ENABLE_ONLY_MLIR_PYTHON_BINDINGS = check_env_flag( 'TORCH_MLIR_ENABLE_ONLY_MLIR_PYTHON_BINDINGS', False) LLVM_INSTALL_DIR = os.getenv('LLVM_INSTALL_DIR', None) SRC_DIR = pathlib.Path(__file__).parent.absolute() CMAKE_BUILD_TYPE = os.getenv("CMAKE_BUILD_TYPE", "Release") # Build phase discovery is unreliable. Just tell it what phases to run. class CustomBuild(_build): def initialize_options(self): _build.initialize_options(self) # Make setuptools not steal the build directory name, # because the mlir c++ developers are quite # used to having build/ be for cmake self.build_base = "setup_build" def run(self): self.run_command("build_py") self.run_command("build_ext") self.run_command("build_scripts") class CMakeBuild(build_py): def cmake_build(self, cmake_build_dir): llvm_dir = str(SRC_DIR / "externals" / "llvm-project" / "llvm") enable_ltc = check_env_flag('TORCH_MLIR_ENABLE_LTC', TORCH_MLIR_ENABLE_LTC_DEFAULT) max_jobs = os.getenv("MAX_JOBS") or str(multiprocessing.cpu_count()) cmake_config_args = [ f"cmake", f"-DCMAKE_BUILD_TYPE={CMAKE_BUILD_TYPE}", f"-DPython3_EXECUTABLE={sys.executable}", f"-DPython3_FIND_VIRTUALENV=ONLY", f"-DMLIR_ENABLE_BINDINGS_PYTHON=ON", f"-DLLVM_TARGETS_TO_BUILD=host", f"-DLLVM_ENABLE_ZSTD=OFF", # Optimization options for building wheels. f"-DCMAKE_VISIBILITY_INLINES_HIDDEN=ON", f"-DCMAKE_C_VISIBILITY_PRESET=hidden", f"-DCMAKE_CXX_VISIBILITY_PRESET=hidden", f"-DTORCH_MLIR_ENABLE_LTC={'ON' if enable_ltc else 'OFF'}", f"-DTORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS={'OFF' if TORCH_MLIR_ENABLE_ONLY_MLIR_PYTHON_BINDINGS else 'ON'}", ] if LLVM_INSTALL_DIR: cmake_config_args += [ f"-DMLIR_DIR='{LLVM_INSTALL_DIR}/lib/cmake/mlir/'", f"-DLLVM_DIR='{LLVM_INSTALL_DIR}/lib/cmake/llvm/'", f"{SRC_DIR}", ] else: cmake_config_args += [ f"-DLLVM_ENABLE_PROJECTS=mlir", f"-DLLVM_EXTERNAL_PROJECTS='torch-mlir'", f"-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR={SRC_DIR}", f"{llvm_dir}", ] cmake_build_args = [ f"cmake", f"--build", f".", f"--config", f"{CMAKE_BUILD_TYPE}", f"--target", f"TorchMLIRPythonModules", f"--", f"-j{max_jobs}" ] try: subprocess.check_call(cmake_config_args, cwd=cmake_build_dir) subprocess.check_call(cmake_build_args, cwd=cmake_build_dir) except subprocess.CalledProcessError as e: print("cmake build failed with\n", e) print("debug by follow cmake command:") sys.exit(e.returncode) finally: print(f"cmake config: {' '.join(cmake_config_args)}") print(f"cmake build: {' '.join(cmake_build_args)}") print(f"cmake workspace: {cmake_build_dir}") def run(self): target_dir = self.build_lib cmake_build_dir = os.getenv("TORCH_MLIR_CMAKE_BUILD_DIR") if not cmake_build_dir: cmake_build_dir = os.path.abspath( os.path.join(target_dir, "..", "cmake_build")) if LLVM_INSTALL_DIR: python_package_dir = os.path.join(cmake_build_dir, "python_packages", "torch_mlir") else: python_package_dir = os.path.join(cmake_build_dir, "tools", "torch-mlir", "python_packages", "torch_mlir") if not os.getenv("TORCH_MLIR_CMAKE_BUILD_DIR_ALREADY_BUILT"): os.makedirs(cmake_build_dir, exist_ok=True) cmake_cache_file = os.path.join(cmake_build_dir, "CMakeCache.txt") if os.path.exists(cmake_cache_file): os.remove(cmake_cache_file) # NOTE: With repeated builds for different Python versions, the # prior version binaries will continue to accumulate. IREE uses # a separate install step and cleans the install directory to # keep this from happening. That is the most robust. Here we just # delete the directory where we build native extensions to keep # this from happening but still take advantage of most of the # build cache. mlir_libs_dir = os.path.join(python_package_dir, "torch_mlir", "_mlir_libs") if os.path.exists(mlir_libs_dir): print(f"Removing _mlir_mlibs dir to force rebuild: {mlir_libs_dir}") shutil.rmtree(mlir_libs_dir) else: print(f"Not removing _mlir_libs dir (does not exist): {mlir_libs_dir}") self.cmake_build(cmake_build_dir) if os.path.exists(target_dir): shutil.rmtree(target_dir, ignore_errors=False, onerror=None) shutil.copytree(python_package_dir, target_dir, symlinks=False) class CMakeExtension(Extension): def __init__(self, name, sourcedir=""): Extension.__init__(self, name, sources=[]) self.sourcedir = os.path.abspath(sourcedir) class NoopBuildExtension(build_ext): def build_extension(self, ext): pass with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() # Requires and extension modules depend on whether building PyTorch # extensions. INSTALL_REQUIRES = [ "numpy", "packaging", ] EXT_MODULES = [ CMakeExtension("torch_mlir._mlir_libs._torchMlir"), ] NAME = "torch-mlir-core" # If building PyTorch extensions, customize. if not TORCH_MLIR_ENABLE_ONLY_MLIR_PYTHON_BINDINGS: import torch NAME = "torch-mlir" INSTALL_REQUIRES.extend([ f"torch=={torch.__version__}".split("+", 1)[0], ]) EXT_MODULES.extend([ CMakeExtension("torch_mlir._mlir_libs._jit_ir_importer"), ]) setup( name=NAME, version=f"{PACKAGE_VERSION}", author="Sean Silva", author_email="silvasean@google.com", description="First-class interop between PyTorch and MLIR", long_description=long_description, long_description_content_type="text/markdown", include_package_data=True, cmdclass={ "build": CustomBuild, "built_ext": NoopBuildExtension, "build_py": CMakeBuild, }, ext_modules=EXT_MODULES, python_requires=">=3.8", install_requires=INSTALL_REQUIRES, extras_require={ "onnx": [ "onnx>=1.15.0", ], }, entry_points={ "console_scripts": [ "torch-mlir-import-onnx = torch_mlir.tools.import_onnx:_cli_main", ], }, zip_safe=False, )