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setup.py
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# Welcome to the PyTorch setup.py.
# Environment variables you are probably interested in:
#
# DEBUG
# build with -O0 and -g (debug symbols)
#
# REL_WITH_DEB_INFO
# build with optimizations and -g (debug symbols)
#
# USE_CUSTOM_DEBINFO="path/to/file1.cpp;path/to/file2.cpp"
# build with debug info only for specified files
#
# MAX_JOBS
# maximum number of compile jobs we should use to compile your code
#
# USE_CUDA=0
# disables CUDA build
#
# CFLAGS
# flags to apply to both C and C++ files to be compiled (a quirk of setup.py
# which we have faithfully adhered to in our build system is that CFLAGS
# also applies to C++ files (unless CXXFLAGS is set), in contrast to the
# default behavior of autogoo and cmake build systems.)
#
# A specific flag that can be used is
# -DHAS_TORCH_SHOW_DISPATCH_TRACE
# build with dispatch trace that can be enabled with
# TORCH_SHOW_DISPATCH_TRACE=1 at runtime.
#
# CC
# the C/C++ compiler to use
#
# Environment variables for feature toggles:
#
# DEBUG_CUDA=1
# if used in conjunction with DEBUG or REL_WITH_DEB_INFO, will also
# build CUDA kernels with -lineinfo --source-in-ptx. Note that
# on CUDA 12 this may cause nvcc to OOM, so this is disabled by default.
# USE_CUDNN=0
# disables the cuDNN build
#
# USE_CUSPARSELT=0
# disables the cuSPARSELt build
#
# USE_CUDSS=0
# disables the cuDSS build
#
# USE_CUFILE=0
# disables the cuFile build
#
# USE_FBGEMM=0
# disables the FBGEMM build
#
# USE_KINETO=0
# disables usage of libkineto library for profiling
#
# USE_NUMPY=0
# disables the NumPy build
#
# BUILD_TEST=0
# disables the test build
#
# USE_MKLDNN=0
# disables use of MKLDNN
#
# USE_MKLDNN_ACL
# enables use of Compute Library backend for MKLDNN on Arm;
# USE_MKLDNN must be explicitly enabled.
#
# MKLDNN_CPU_RUNTIME
# MKL-DNN threading mode: TBB or OMP (default)
#
# USE_STATIC_MKL
# Prefer to link with MKL statically - Unix only
# USE_ITT=0
# disable use of Intel(R) VTune Profiler's ITT functionality
#
# USE_NNPACK=0
# disables NNPACK build
#
# USE_DISTRIBUTED=0
# disables distributed (c10d, gloo, mpi, etc.) build
#
# USE_TENSORPIPE=0
# disables distributed Tensorpipe backend build
#
# USE_GLOO=0
# disables distributed gloo backend build
#
# USE_MPI=0
# disables distributed MPI backend build
#
# USE_SYSTEM_NCCL=0
# disables use of system-wide nccl (we will use our submoduled
# copy in third_party/nccl)
#
# USE_OPENMP=0
# disables use of OpenMP for parallelization
#
# USE_FLASH_ATTENTION=0
# disables building flash attention for scaled dot product attention
#
# USE_MEM_EFF_ATTENTION=0
# disables building memory efficient attention for scaled dot product attention
#
# BUILD_BINARY
# enables the additional binaries/ build
#
# ATEN_AVX512_256=TRUE
# ATen AVX2 kernels can use 32 ymm registers, instead of the default 16.
# This option can be used if AVX512 doesn't perform well on a machine.
# The FBGEMM library also uses AVX512_256 kernels on Xeon D processors,
# but it also has some (optimized) assembly code.
#
# PYTORCH_BUILD_VERSION
# PYTORCH_BUILD_NUMBER
# specify the version of PyTorch, rather than the hard-coded version
# in this file; used when we're building binaries for distribution
#
# TORCH_CUDA_ARCH_LIST
# specify which CUDA architectures to build for.
# ie `TORCH_CUDA_ARCH_LIST="6.0;7.0"`
# These are not CUDA versions, instead, they specify what
# classes of NVIDIA hardware we should generate PTX for.
#
# TORCH_XPU_ARCH_LIST
# specify which XPU architectures to build for.
# ie `TORCH_XPU_ARCH_LIST="ats-m150,lnl-m"`
#
# PYTORCH_ROCM_ARCH
# specify which AMD GPU targets to build for.
# ie `PYTORCH_ROCM_ARCH="gfx900;gfx906"`
#
# ONNX_NAMESPACE
# specify a namespace for ONNX built here rather than the hard-coded
# one in this file; needed to build with other frameworks that share ONNX.
#
# BLAS
# BLAS to be used by Caffe2. Can be MKL, Eigen, ATLAS, FlexiBLAS, or OpenBLAS. If set
# then the build will fail if the requested BLAS is not found, otherwise
# the BLAS will be chosen based on what is found on your system.
#
# MKL_THREADING
# MKL threading mode: SEQ, TBB or OMP (default)
#
# USE_ROCM_KERNEL_ASSERT=1
# Enable kernel assert in ROCm platform
#
# Environment variables we respect (these environment variables are
# conventional and are often understood/set by other software.)
#
# CUDA_HOME (Linux/OS X)
# CUDA_PATH (Windows)
# specify where CUDA is installed; usually /usr/local/cuda or
# /usr/local/cuda-x.y
# CUDAHOSTCXX
# specify a different compiler than the system one to use as the CUDA
# host compiler for nvcc.
#
# CUDA_NVCC_EXECUTABLE
# Specify a NVCC to use. This is used in our CI to point to a cached nvcc
#
# CUDNN_LIB_DIR
# CUDNN_INCLUDE_DIR
# CUDNN_LIBRARY
# specify where cuDNN is installed
#
# MIOPEN_LIB_DIR
# MIOPEN_INCLUDE_DIR
# MIOPEN_LIBRARY
# specify where MIOpen is installed
#
# NCCL_ROOT
# NCCL_LIB_DIR
# NCCL_INCLUDE_DIR
# specify where nccl is installed
#
# ACL_ROOT_DIR
# specify where Compute Library is installed
#
# LIBRARY_PATH
# LD_LIBRARY_PATH
# we will search for libraries in these paths
#
# ATEN_THREADING
# ATen parallel backend to use for intra- and inter-op parallelism
# possible values:
# OMP - use OpenMP for intra-op and native backend for inter-op tasks
# NATIVE - use native thread pool for both intra- and inter-op tasks
#
# USE_SYSTEM_LIBS (work in progress)
# Use system-provided libraries to satisfy the build dependencies.
# When turned on, the following cmake variables will be toggled as well:
# USE_SYSTEM_CPUINFO=ON
# USE_SYSTEM_SLEEF=ON
# USE_SYSTEM_GLOO=ON
# BUILD_CUSTOM_PROTOBUF=OFF
# USE_SYSTEM_EIGEN_INSTALL=ON
# USE_SYSTEM_FP16=ON
# USE_SYSTEM_PTHREADPOOL=ON
# USE_SYSTEM_PSIMD=ON
# USE_SYSTEM_FXDIV=ON
# USE_SYSTEM_BENCHMARK=ON
# USE_SYSTEM_ONNX=ON
# USE_SYSTEM_XNNPACK=ON
# USE_SYSTEM_PYBIND11=ON
# USE_SYSTEM_NCCL=ON
# USE_SYSTEM_NVTX=ON
#
# USE_MIMALLOC
# Static link mimalloc into C10, and use mimalloc in alloc_cpu & alloc_free.
# By default, It is only enabled on Windows.
#
# USE_PRIORITIZED_TEXT_FOR_LD
# Uses prioritized text form cmake/prioritized_text.txt for LD
#
# BUILD_LIBTORCH_WHL
# Builds libtorch.so and its dependencies as a wheel
#
# BUILD_PYTHON_ONLY
# Builds pytorch as a wheel using libtorch.so from a seperate wheel
import os
import sys
if sys.platform == "win32" and sys.maxsize.bit_length() == 31:
print(
"32-bit Windows Python runtime is not supported. Please switch to 64-bit Python."
)
sys.exit(-1)
import platform
BUILD_LIBTORCH_WHL = os.getenv("BUILD_LIBTORCH_WHL", "0") == "1"
BUILD_PYTHON_ONLY = os.getenv("BUILD_PYTHON_ONLY", "0") == "1"
python_min_version = (3, 9, 0)
python_min_version_str = ".".join(map(str, python_min_version))
if sys.version_info < python_min_version:
print(
f"You are using Python {platform.python_version()}. Python >={python_min_version_str} is required."
)
sys.exit(-1)
import filecmp
import glob
import importlib
import importlib.util
import json
import shutil
import subprocess
import sysconfig
import time
from collections import defaultdict
import setuptools.command.build_ext
import setuptools.command.install
import setuptools.command.sdist
from setuptools import Extension, find_packages, setup
from setuptools.dist import Distribution
from tools.build_pytorch_libs import build_pytorch
from tools.generate_torch_version import get_torch_version
from tools.setup_helpers.cmake import CMake
from tools.setup_helpers.env import build_type, IS_DARWIN, IS_LINUX, IS_WINDOWS
from tools.setup_helpers.generate_linker_script import gen_linker_script
def _get_package_path(package_name):
spec = importlib.util.find_spec(package_name)
if spec:
# The package might be a namespace package, so get_data may fail
try:
loader = spec.loader
if loader is not None:
file_path = loader.get_filename() # type: ignore[attr-defined]
return os.path.dirname(file_path)
except AttributeError:
pass
return None
# set up appropriate env variables
if BUILD_LIBTORCH_WHL:
# Set up environment variables for ONLY building libtorch.so and not libtorch_python.so
# functorch is not supported without python
os.environ["BUILD_FUNCTORCH"] = "OFF"
if BUILD_PYTHON_ONLY:
os.environ["BUILD_LIBTORCHLESS"] = "ON"
os.environ["LIBTORCH_LIB_PATH"] = f"{_get_package_path('torch')}/lib"
################################################################################
# Parameters parsed from environment
################################################################################
VERBOSE_SCRIPT = True
RUN_BUILD_DEPS = True
# see if the user passed a quiet flag to setup.py arguments and respect
# that in our parts of the build
EMIT_BUILD_WARNING = False
RERUN_CMAKE = False
CMAKE_ONLY = False
filtered_args = []
for i, arg in enumerate(sys.argv):
if arg == "--cmake":
RERUN_CMAKE = True
continue
if arg == "--cmake-only":
# Stop once cmake terminates. Leave users a chance to adjust build
# options.
CMAKE_ONLY = True
continue
if arg == "rebuild" or arg == "build":
arg = "build" # rebuild is gone, make it build
EMIT_BUILD_WARNING = True
if arg == "--":
filtered_args += sys.argv[i:]
break
if arg == "-q" or arg == "--quiet":
VERBOSE_SCRIPT = False
if arg in ["clean", "egg_info", "sdist"]:
RUN_BUILD_DEPS = False
filtered_args.append(arg)
sys.argv = filtered_args
if VERBOSE_SCRIPT:
def report(*args):
print(*args)
else:
def report(*args):
pass
# Make distutils respect --quiet too
setuptools.distutils.log.warn = report
# Constant known variables used throughout this file
cwd = os.path.dirname(os.path.abspath(__file__))
lib_path = os.path.join(cwd, "torch", "lib")
third_party_path = os.path.join(cwd, "third_party")
# CMAKE: full path to python library
if IS_WINDOWS:
cmake_python_library = "{}/libs/python{}.lib".format(
sysconfig.get_config_var("prefix"), sysconfig.get_config_var("VERSION")
)
# Fix virtualenv builds
if not os.path.exists(cmake_python_library):
cmake_python_library = "{}/libs/python{}.lib".format(
sys.base_prefix, sysconfig.get_config_var("VERSION")
)
else:
cmake_python_library = "{}/{}".format(
sysconfig.get_config_var("LIBDIR"), sysconfig.get_config_var("INSTSONAME")
)
cmake_python_include_dir = sysconfig.get_path("include")
################################################################################
# Version, create_version_file, and package_name
################################################################################
package_name = os.getenv("TORCH_PACKAGE_NAME", "torch")
LIBTORCH_PKG_NAME = os.getenv("LIBTORCH_PACKAGE_NAME", "torch_no_python")
if BUILD_LIBTORCH_WHL:
package_name = LIBTORCH_PKG_NAME
package_type = os.getenv("PACKAGE_TYPE", "wheel")
version = get_torch_version()
report(f"Building wheel {package_name}-{version}")
cmake = CMake()
def get_submodule_folders():
git_modules_path = os.path.join(cwd, ".gitmodules")
default_modules_path = [
os.path.join(third_party_path, name)
for name in [
"gloo",
"cpuinfo",
"onnx",
"fbgemm",
"cutlass",
]
]
if not os.path.exists(git_modules_path):
return default_modules_path
with open(git_modules_path) as f:
return [
os.path.join(cwd, line.split("=", 1)[1].strip())
for line in f
if line.strip().startswith("path")
]
def check_submodules():
def check_for_files(folder, files):
if not any(os.path.exists(os.path.join(folder, f)) for f in files):
report("Could not find any of {} in {}".format(", ".join(files), folder))
report("Did you run 'git submodule update --init --recursive'?")
sys.exit(1)
def not_exists_or_empty(folder):
return not os.path.exists(folder) or (
os.path.isdir(folder) and len(os.listdir(folder)) == 0
)
if bool(os.getenv("USE_SYSTEM_LIBS", False)):
return
folders = get_submodule_folders()
# If none of the submodule folders exists, try to initialize them
if all(not_exists_or_empty(folder) for folder in folders):
try:
report(" --- Trying to initialize submodules")
start = time.time()
subprocess.check_call(
["git", "submodule", "update", "--init", "--recursive"], cwd=cwd
)
end = time.time()
report(f" --- Submodule initialization took {end - start:.2f} sec")
except Exception:
report(" --- Submodule initalization failed")
report("Please run:\n\tgit submodule update --init --recursive")
sys.exit(1)
for folder in folders:
check_for_files(
folder,
[
"CMakeLists.txt",
"Makefile",
"setup.py",
"LICENSE",
"LICENSE.md",
"LICENSE.txt",
],
)
check_for_files(
os.path.join(third_party_path, "fbgemm", "third_party", "asmjit"),
["CMakeLists.txt"],
)
# Windows has very bad support for symbolic links.
# Instead of using symlinks, we're going to copy files over
def mirror_files_into_torchgen():
# (new_path, orig_path)
# Directories are OK and are recursively mirrored.
paths = [
(
"torchgen/packaged/ATen/native/native_functions.yaml",
"aten/src/ATen/native/native_functions.yaml",
),
("torchgen/packaged/ATen/native/tags.yaml", "aten/src/ATen/native/tags.yaml"),
("torchgen/packaged/ATen/templates", "aten/src/ATen/templates"),
("torchgen/packaged/autograd", "tools/autograd"),
("torchgen/packaged/autograd/templates", "tools/autograd/templates"),
]
for new_path, orig_path in paths:
# Create the dirs involved in new_path if they don't exist
if not os.path.exists(new_path):
os.makedirs(os.path.dirname(new_path), exist_ok=True)
# Copy the files from the orig location to the new location
if os.path.isfile(orig_path):
shutil.copyfile(orig_path, new_path)
continue
if os.path.isdir(orig_path):
if os.path.exists(new_path):
# copytree fails if the tree exists already, so remove it.
shutil.rmtree(new_path)
shutil.copytree(orig_path, new_path)
continue
raise RuntimeError("Check the file paths in `mirror_files_into_torchgen()`")
# all the work we need to do _before_ setup runs
def build_deps():
report("-- Building version " + version)
check_submodules()
check_pydep("yaml", "pyyaml")
build_python = not BUILD_LIBTORCH_WHL
build_pytorch(
version=version,
cmake_python_library=cmake_python_library,
build_python=build_python,
rerun_cmake=RERUN_CMAKE,
cmake_only=CMAKE_ONLY,
cmake=cmake,
)
if CMAKE_ONLY:
report(
'Finished running cmake. Run "ccmake build" or '
'"cmake-gui build" to adjust build options and '
'"python setup.py install" to build.'
)
sys.exit()
# Use copies instead of symbolic files.
# Windows has very poor support for them.
sym_files = [
"tools/shared/_utils_internal.py",
"torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h",
"torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h",
]
orig_files = [
"torch/_utils_internal.py",
"third_party/valgrind-headers/callgrind.h",
"third_party/valgrind-headers/valgrind.h",
]
for sym_file, orig_file in zip(sym_files, orig_files):
same = False
if os.path.exists(sym_file):
if filecmp.cmp(sym_file, orig_file):
same = True
else:
os.remove(sym_file)
if not same:
shutil.copyfile(orig_file, sym_file)
################################################################################
# Building dependent libraries
################################################################################
missing_pydep = """
Missing build dependency: Unable to `import {importname}`.
Please install it via `conda install {module}` or `pip install {module}`
""".strip()
def check_pydep(importname, module):
try:
importlib.import_module(importname)
except ImportError as e:
raise RuntimeError(
missing_pydep.format(importname=importname, module=module)
) from e
class build_ext(setuptools.command.build_ext.build_ext):
def _embed_libomp(self):
# Copy libiomp5.dylib/libomp.dylib inside the wheel package on MacOS
lib_dir = os.path.join(self.build_lib, "torch", "lib")
libtorch_cpu_path = os.path.join(lib_dir, "libtorch_cpu.dylib")
if not os.path.exists(libtorch_cpu_path):
return
# Parse libtorch_cpu load commands
otool_cmds = (
subprocess.check_output(["otool", "-l", libtorch_cpu_path])
.decode("utf-8")
.split("\n")
)
rpaths, libs = [], []
for idx, line in enumerate(otool_cmds):
if line.strip() == "cmd LC_LOAD_DYLIB":
lib_name = otool_cmds[idx + 2].strip()
assert lib_name.startswith("name ")
libs.append(lib_name.split(" ", 1)[1].rsplit("(", 1)[0][:-1])
if line.strip() == "cmd LC_RPATH":
rpath = otool_cmds[idx + 2].strip()
assert rpath.startswith("path ")
rpaths.append(rpath.split(" ", 1)[1].rsplit("(", 1)[0][:-1])
omplib_path = get_cmake_cache_vars()["OpenMP_libomp_LIBRARY"]
omplib_name = get_cmake_cache_vars()["OpenMP_C_LIB_NAMES"] + ".dylib"
omplib_rpath_path = os.path.join("@rpath", omplib_name)
# This logic is fragile and checks only two cases:
# - libtorch_cpu depends on `@rpath/libomp.dylib`e (happens when built inside miniconda environment)
# - libtorch_cpu depends on `/abs/path/to/libomp.dylib` (happens when built with libomp from homebrew)
if not any(c in libs for c in [omplib_path, omplib_rpath_path]):
return
# Copy libomp/libiomp5 from rpath locations
target_lib = os.path.join(self.build_lib, "torch", "lib", omplib_name)
libomp_relocated = False
for rpath in rpaths:
source_lib = os.path.join(rpath, omplib_name)
if not os.path.exists(source_lib):
continue
self.copy_file(source_lib, target_lib)
# Delete old rpath and add @loader_lib to the rpath
# This should prevent delocate from attempting to package another instance
# of OpenMP library in torch wheel as well as loading two libomp.dylib into
# the address space, as libraries are cached by their unresolved names
install_name_tool_args = [
"-rpath",
rpath,
"@loader_path",
]
libomp_relocated = True
break
if not libomp_relocated and os.path.exists(omplib_path):
self.copy_file(omplib_path, target_lib)
install_name_tool_args = [
"-change",
omplib_path,
omplib_rpath_path,
]
if "@loader_path" not in rpaths:
install_name_tool_args += [
"-add_rpath",
"@loader_path",
]
libomp_relocated = True
if libomp_relocated:
install_name_tool_args.insert(0, "install_name_tool")
install_name_tool_args.append(libtorch_cpu_path)
subprocess.check_call(install_name_tool_args)
# Copy omp.h from OpenMP_C_FLAGS and copy it into include folder
omp_cflags = get_cmake_cache_vars()["OpenMP_C_FLAGS"]
if not omp_cflags:
return
for include_dir in [f[2:] for f in omp_cflags.split(" ") if f.startswith("-I")]:
omp_h = os.path.join(include_dir, "omp.h")
if not os.path.exists(omp_h):
continue
target_omp_h = os.path.join(self.build_lib, "torch", "include", "omp.h")
self.copy_file(omp_h, target_omp_h)
break
def run(self):
# Report build options. This is run after the build completes so # `CMakeCache.txt` exists and we can get an
# accurate report on what is used and what is not.
cmake_cache_vars = defaultdict(lambda: False, cmake.get_cmake_cache_variables())
if cmake_cache_vars["USE_NUMPY"]:
report("-- Building with NumPy bindings")
else:
report("-- NumPy not found")
if cmake_cache_vars["USE_CUDNN"]:
report(
"-- Detected cuDNN at "
+ cmake_cache_vars["CUDNN_LIBRARY"]
+ ", "
+ cmake_cache_vars["CUDNN_INCLUDE_DIR"]
)
else:
report("-- Not using cuDNN")
if cmake_cache_vars["USE_CUDA"]:
report("-- Detected CUDA at " + cmake_cache_vars["CUDA_TOOLKIT_ROOT_DIR"])
else:
report("-- Not using CUDA")
if cmake_cache_vars["USE_XPU"]:
report("-- Detected XPU runtime at " + cmake_cache_vars["SYCL_LIBRARY_DIR"])
else:
report("-- Not using XPU")
if cmake_cache_vars["USE_MKLDNN"]:
report("-- Using MKLDNN")
if cmake_cache_vars["USE_MKLDNN_ACL"]:
report("-- Using Compute Library for the Arm architecture with MKLDNN")
else:
report(
"-- Not using Compute Library for the Arm architecture with MKLDNN"
)
if cmake_cache_vars["USE_MKLDNN_CBLAS"]:
report("-- Using CBLAS in MKLDNN")
else:
report("-- Not using CBLAS in MKLDNN")
else:
report("-- Not using MKLDNN")
if cmake_cache_vars["USE_NCCL"] and cmake_cache_vars["USE_SYSTEM_NCCL"]:
report(
"-- Using system provided NCCL library at {}, {}".format(
cmake_cache_vars["NCCL_LIBRARIES"],
cmake_cache_vars["NCCL_INCLUDE_DIRS"],
)
)
elif cmake_cache_vars["USE_NCCL"]:
report("-- Building NCCL library")
else:
report("-- Not using NCCL")
if cmake_cache_vars["USE_DISTRIBUTED"]:
if IS_WINDOWS:
report("-- Building without distributed package")
else:
report("-- Building with distributed package: ")
report(
" -- USE_TENSORPIPE={}".format(cmake_cache_vars["USE_TENSORPIPE"])
)
report(" -- USE_GLOO={}".format(cmake_cache_vars["USE_GLOO"]))
report(" -- USE_MPI={}".format(cmake_cache_vars["USE_OPENMPI"]))
else:
report("-- Building without distributed package")
if cmake_cache_vars["STATIC_DISPATCH_BACKEND"]:
report(
"-- Using static dispatch with backend {}".format(
cmake_cache_vars["STATIC_DISPATCH_BACKEND"]
)
)
if cmake_cache_vars["USE_LIGHTWEIGHT_DISPATCH"]:
report("-- Using lightweight dispatch")
if cmake_cache_vars["BUILD_EXECUTORCH"]:
report("-- Building Executorch")
if cmake_cache_vars["USE_ITT"]:
report("-- Using ITT")
else:
report("-- Not using ITT")
# Do not use clang to compile extensions if `-fstack-clash-protection` is defined
# in system CFLAGS
c_flags = str(os.getenv("CFLAGS", ""))
if (
IS_LINUX
and "-fstack-clash-protection" in c_flags
and "clang" in os.environ.get("CC", "")
):
os.environ["CC"] = str(os.environ["CC"])
# It's an old-style class in Python 2.7...
setuptools.command.build_ext.build_ext.run(self)
if IS_DARWIN:
self._embed_libomp()
# Copy the essential export library to compile C++ extensions.
if IS_WINDOWS:
build_temp = self.build_temp
ext_filename = self.get_ext_filename("_C")
lib_filename = ".".join(ext_filename.split(".")[:-1]) + ".lib"
export_lib = os.path.join(
build_temp, "torch", "csrc", lib_filename
).replace("\\", "/")
build_lib = self.build_lib
target_lib = os.path.join(build_lib, "torch", "lib", "_C.lib").replace(
"\\", "/"
)
# Create "torch/lib" directory if not exists.
# (It is not created yet in "develop" mode.)
target_dir = os.path.dirname(target_lib)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
self.copy_file(export_lib, target_lib)
def build_extensions(self):
self.create_compile_commands()
# Copy functorch extension
for i, ext in enumerate(self.extensions):
if ext.name != "functorch._C":
continue
fullname = self.get_ext_fullname(ext.name)
filename = self.get_ext_filename(fullname)
fileext = os.path.splitext(filename)[1]
src = os.path.join(os.path.dirname(filename), "functorch" + fileext)
dst = os.path.join(os.path.realpath(self.build_lib), filename)
if os.path.exists(src):
report(f"Copying {ext.name} from {src} to {dst}")
dst_dir = os.path.dirname(dst)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
self.copy_file(src, dst)
setuptools.command.build_ext.build_ext.build_extensions(self)
def get_outputs(self):
outputs = setuptools.command.build_ext.build_ext.get_outputs(self)
outputs.append(os.path.join(self.build_lib, "caffe2"))
report(f"setup.py::get_outputs returning {outputs}")
return outputs
def create_compile_commands(self):
def load(filename):
with open(filename) as f:
return json.load(f)
ninja_files = glob.glob("build/*compile_commands.json")
cmake_files = glob.glob("torch/lib/build/*/compile_commands.json")
all_commands = [entry for f in ninja_files + cmake_files for entry in load(f)]
# cquery does not like c++ compiles that start with gcc.
# It forgets to include the c++ header directories.
# We can work around this by replacing the gcc calls that python
# setup.py generates with g++ calls instead
for command in all_commands:
if command["command"].startswith("gcc "):
command["command"] = "g++ " + command["command"][4:]
new_contents = json.dumps(all_commands, indent=2)
contents = ""
if os.path.exists("compile_commands.json"):
with open("compile_commands.json") as f:
contents = f.read()
if contents != new_contents:
with open("compile_commands.json", "w") as f:
f.write(new_contents)
class concat_license_files:
"""Merge LICENSE and LICENSES_BUNDLED.txt as a context manager
LICENSE is the main PyTorch license, LICENSES_BUNDLED.txt is auto-generated
from all the licenses found in ./third_party/. We concatenate them so there
is a single license file in the sdist and wheels with all of the necessary
licensing info.
"""
def __init__(self, include_files=False):
self.f1 = "LICENSE"
self.f2 = "third_party/LICENSES_BUNDLED.txt"
self.include_files = include_files
def __enter__(self):
"""Concatenate files"""
old_path = sys.path
sys.path.append(third_party_path)
try:
from build_bundled import create_bundled
finally:
sys.path = old_path
with open(self.f1) as f1:
self.bsd_text = f1.read()
with open(self.f1, "a") as f1:
f1.write("\n\n")
create_bundled(
os.path.relpath(third_party_path), f1, include_files=self.include_files
)
def __exit__(self, exception_type, exception_value, traceback):
"""Restore content of f1"""
with open(self.f1, "w") as f:
f.write(self.bsd_text)
try:
from wheel.bdist_wheel import bdist_wheel
except ImportError:
# This is useful when wheel is not installed and bdist_wheel is not
# specified on the command line. If it _is_ specified, parsing the command
# line will fail before wheel_concatenate is needed
wheel_concatenate = None
else:
# Need to create the proper LICENSE.txt for the wheel
class wheel_concatenate(bdist_wheel):
"""check submodules on sdist to prevent incomplete tarballs"""
def run(self):
with concat_license_files(include_files=True):
super().run()
def write_wheelfile(self, *args, **kwargs):
super().write_wheelfile(*args, **kwargs)
if BUILD_LIBTORCH_WHL:
# Remove extraneneous files in the libtorch wheel
for root, dirs, files in os.walk(self.bdist_dir):
for file in files:
if file.endswith((".a", ".so")) and os.path.isfile(
os.path.join(self.bdist_dir, file)
):
os.remove(os.path.join(root, file))
elif file.endswith(".py"):
os.remove(os.path.join(root, file))
# need an __init__.py file otherwise we wouldn't have a package
open(os.path.join(self.bdist_dir, "torch", "__init__.py"), "w").close()
class install(setuptools.command.install.install):
def run(self):
super().run()
class clean(setuptools.Command):
user_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
import glob
import re
with open(".gitignore") as f:
ignores = f.read()
pat = re.compile(r"^#( BEGIN NOT-CLEAN-FILES )?")
for wildcard in filter(None, ignores.split("\n")):
match = pat.match(wildcard)
if match:
if match.group(1):
# Marker is found and stop reading .gitignore.
break
# Ignore lines which begin with '#'.
else:
# Don't remove absolute paths from the system
wildcard = wildcard.lstrip("./")
for filename in glob.glob(wildcard):
try:
os.remove(filename)
except OSError:
shutil.rmtree(filename, ignore_errors=True)
class sdist(setuptools.command.sdist.sdist):
def run(self):
with concat_license_files():
super().run()
def get_cmake_cache_vars():
try:
return defaultdict(lambda: False, cmake.get_cmake_cache_variables())
except FileNotFoundError:
# CMakeCache.txt does not exist. Probably running "python setup.py clean" over a clean directory.
return defaultdict(lambda: False)
def configure_extension_build():
r"""Configures extension build options according to system environment and user's choice.
Returns:
The input to parameters ext_modules, cmdclass, packages, and entry_points as required in setuptools.setup.
"""
cmake_cache_vars = get_cmake_cache_vars()
################################################################################
# Configure compile flags
################################################################################
library_dirs = []
extra_install_requires = []
if IS_WINDOWS:
# /NODEFAULTLIB makes sure we only link to DLL runtime
# and matches the flags set for protobuf and ONNX
extra_link_args = ["/NODEFAULTLIB:LIBCMT.LIB"]
# /MD links against DLL runtime
# and matches the flags set for protobuf and ONNX
# /EHsc is about standard C++ exception handling
extra_compile_args = ["/MD", "/FS", "/EHsc"]
else:
extra_link_args = []
extra_compile_args = [
"-Wall",
"-Wextra",
"-Wno-strict-overflow",
"-Wno-unused-parameter",
"-Wno-missing-field-initializers",
"-Wno-unknown-pragmas",
# Python 2.6 requires -fno-strict-aliasing, see
# http://legacy.python.org/dev/peps/pep-3123/
# We also depend on it in our code (even Python 3).
"-fno-strict-aliasing",
]
library_dirs.append(lib_path)
main_compile_args = []
main_libraries = ["torch_python"]
main_link_args = []
main_sources = ["torch/csrc/stub.c"]
if BUILD_LIBTORCH_WHL:
main_libraries = ["torch"]
main_sources = []
if build_type.is_debug():
if IS_WINDOWS:
extra_compile_args.append("/Z7")
extra_link_args.append("/DEBUG:FULL")
else:
extra_compile_args += ["-O0", "-g"]
extra_link_args += ["-O0", "-g"]
if build_type.is_rel_with_deb_info():
if IS_WINDOWS:
extra_compile_args.append("/Z7")
extra_link_args.append("/DEBUG:FULL")
else:
extra_compile_args += ["-g"]
extra_link_args += ["-g"]
# pypi cuda package that requires installation of cuda runtime, cudnn and cublas
# should be included in all wheels uploaded to pypi
pytorch_extra_install_requirements = os.getenv(
"PYTORCH_EXTRA_INSTALL_REQUIREMENTS", ""