# ------------------------------------------------------------------ # Copyright (c) 2020 PyInstaller Development Team. # # This file is distributed under the terms of the GNU General Public # License (version 2.0 or later). # # The full license is available in LICENSE, distributed with # this software. # # SPDX-License-Identifier: GPL-2.0-or-later # ------------------------------------------------------------------ import os from PyInstaller.utils.hooks import ( logger, collect_data_files, is_module_satisfies, collect_dynamic_libs, collect_submodules, get_package_paths, ) if is_module_satisfies("PyInstaller >= 6.0"): from PyInstaller.compat import is_linux, is_win from PyInstaller.utils.hooks import PY_DYLIB_PATTERNS module_collection_mode = "pyz+py" warn_on_missing_hiddenimports = False datas = collect_data_files( "torch", excludes=[ "**/*.h", "**/*.hpp", "**/*.cuh", "**/*.lib", "**/*.cpp", "**/*.pyi", "**/*.cmake", ], ) hiddenimports = collect_submodules("torch") binaries = collect_dynamic_libs( "torch", # Ensure we pick up fully-versioned .so files as well search_patterns=PY_DYLIB_PATTERNS + ['*.so.*'], ) # On Linux, torch wheels built with non-default CUDA version bundle CUDA libraries themselves (and should be handled # by the above `collect_dynamic_libs`). Wheels built with default CUDA version (which are available on PyPI), on the # other hand, use CUDA libraries provided by nvidia-* packages. Due to all possible combinations (CUDA libs from # nvidia-* packages, torch-bundled CUDA libs, CPU-only CUDA libs) we do not add hidden imports directly, but instead # attempt to infer them from requirements listed in the `torch` metadata. if is_linux: def _infer_nvidia_hiddenimports(): import packaging.requirements from _pyinstaller_hooks_contrib.compat import importlib_metadata from _pyinstaller_hooks_contrib.utils import nvidia_cuda as cudautils dist = importlib_metadata.distribution("torch") requirements = [packaging.requirements.Requirement(req) for req in dist.requires or []] requirements = [req.name for req in requirements if req.marker is None or req.marker.evaluate()] return cudautils.infer_hiddenimports_from_requirements(requirements) try: nvidia_hiddenimports = _infer_nvidia_hiddenimports() except Exception: # Log the exception, but make it non-fatal logger.warning("hook-torch: failed to infer NVIDIA CUDA hidden imports!", exc_info=True) nvidia_hiddenimports = [] logger.info("hook-torch: inferred hidden imports for CUDA libraries: %r", nvidia_hiddenimports) hiddenimports += nvidia_hiddenimports # On Linux, prevent binary dependency analysis from generating symbolic links for libraries from `torch/lib` to # the top-level application directory. These symbolic links seem to confuse `torch` about location of its shared # libraries (likely because code in one of the libraries looks up the library file's location, but does not # fully resolve it), and prevent it from finding dynamically-loaded libraries in `torch/lib` directory, such as # `torch/lib/libtorch_cuda_linalg.so`. The issue was observed with earlier versions of `torch` builds provided # by https://download.pytorch.org/whl/torch, specifically 1.13.1+cu117, 2.0.1+cu117, and 2.1.2+cu118; later # versions do not seem to be affected. The wheels provided on PyPI do not seem to be affected, either, even # for torch 1.13.1, 2.01, and 2.1.2. However, these symlinks should be not necessary on linux in general, so # there should be no harm in suppressing them for all versions. # # The `bindepend_symlink_suppression` hook attribute requires PyInstaller >= 6.11, and is no-op in earlier # versions. bindepend_symlink_suppression = ['**/torch/lib/*.so*'] # The Windows nightly build for torch 2.3.0 added dependency on MKL. The `mkl` distribution does not provide an # importable package, but rather installs the DLLs in /Library/bin directory. Therefore, we cannot write a # separate hook for it, and must collect the DLLs here. (Most of these DLLs are missed by PyInstaller's binary # dependency analysis due to being dynamically loaded at run-time). if is_win: def _collect_mkl_dlls(): import packaging.requirements from _pyinstaller_hooks_contrib.compat import importlib_metadata # Check if torch depends on `mkl` dist = importlib_metadata.distribution("torch") requirements = [packaging.requirements.Requirement(req) for req in dist.requires or []] requirements = [req.name for req in requirements if req.marker is None or req.marker.evaluate()] if 'mkl' not in requirements: logger.info('hook-torch: this torch build does not depend on MKL...') return [] # This torch build does not depend on MKL # Find requirements of mkl - this should yield `intel-openmp` and `tbb`, which install DLLs in the same # way as `mkl`. try: dist = importlib_metadata.distribution("mkl") except importlib_metadata.PackageNotFoundError: return [] # For some reason, `mkl` distribution is unavailable. requirements = [packaging.requirements.Requirement(req) for req in dist.requires or []] requirements = [req.name for req in requirements if req.marker is None or req.marker.evaluate()] requirements = ['mkl'] + requirements mkl_binaries = [] logger.info('hook-torch: collecting DLLs from MKL and its dependencies: %r', requirements) for requirement in requirements: try: dist = importlib_metadata.distribution(requirement) except importlib_metadata.PackageNotFoundError: continue # Go over files, and match DLLs in /Library/bin directory for dist_file in dist.files: # NOTE: `importlib_metadata.PackagePath.match()` does not seem to properly normalize the separator, # and on Windows, RECORD can apparently end up with entries that use either Windows or POSIX-style # separators (see pyinstaller/pyinstaller-hooks-contrib#879). This is why we first resolve the # file's location (which yields a `pathlib.Path` instance), and perform matching on resolved path. dll_file = dist.locate_file(dist_file).resolve() if not dll_file.match('**/Library/bin/*.dll'): continue mkl_binaries.append((str(dll_file), '.')) logger.info( 'hook-torch: found MKL DLLs: %r', sorted([os.path.basename(src_name) for src_name, dest_name in mkl_binaries]) ) return mkl_binaries try: mkl_binaries = _collect_mkl_dlls() except Exception: # Log the exception, but make it non-fatal logger.warning("hook-torch: failed to collect MKL DLLs!", exc_info=True) mkl_binaries = [] binaries += mkl_binaries else: datas = [(get_package_paths("torch")[1], "torch")]