mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
b2054d3025
Summary:
- Target Sha1: ae108ef49aa5623b896fc93d4298c49d1750d9ba
- Make USE_XNNPACK a dependent option on cmake minimum version 3.12
- Print USE_XNNPACK under cmake options summary, and print the
availability from collet_env.py
- Skip XNNPACK based tests when XNNPACK is not available
- Add SkipIfNoXNNPACK wrapper to skip tests
- Update cmake version for xenial-py3.7-gcc5.4 image to 3.12.4
- This is required for the backwards compatibility test.
The PyTorch op schema is XNNPACK dependent. See,
aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp for
example. The nightly version is assumed to have USE_XNNPACK=ON,
so with this change we ensure that the test build can also
have XNNPACK.
- HACK: skipping test_xnnpack_integration tests on ROCM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72642
Reviewed By: kimishpatel
Differential Revision: D34456794
Pulled By: digantdesai
fbshipit-source-id: 85dbfe0211de7846d8a84321b14fdb061cd6c037
(cherry picked from commit 6cf48e7b64d6979962d701b5d493998262cc8bfa)
476 lines
17 KiB
Python
476 lines
17 KiB
Python
from __future__ import print_function
|
|
|
|
# Unlike the rest of the PyTorch this file must be python2 compliant.
|
|
# This script outputs relevant system environment info
|
|
# Run it with `python collect_env.py`.
|
|
import datetime
|
|
import locale
|
|
import re
|
|
import subprocess
|
|
import sys
|
|
import os
|
|
from collections import namedtuple
|
|
|
|
|
|
try:
|
|
import torch
|
|
TORCH_AVAILABLE = True
|
|
except (ImportError, NameError, AttributeError, OSError):
|
|
TORCH_AVAILABLE = False
|
|
|
|
# System Environment Information
|
|
SystemEnv = namedtuple('SystemEnv', [
|
|
'torch_version',
|
|
'is_debug_build',
|
|
'cuda_compiled_version',
|
|
'gcc_version',
|
|
'clang_version',
|
|
'cmake_version',
|
|
'os',
|
|
'libc_version',
|
|
'python_version',
|
|
'python_platform',
|
|
'is_cuda_available',
|
|
'cuda_runtime_version',
|
|
'nvidia_driver_version',
|
|
'nvidia_gpu_models',
|
|
'cudnn_version',
|
|
'pip_version', # 'pip' or 'pip3'
|
|
'pip_packages',
|
|
'conda_packages',
|
|
'hip_compiled_version',
|
|
'hip_runtime_version',
|
|
'miopen_runtime_version',
|
|
'caching_allocator_config',
|
|
'is_xnnpack_available',
|
|
])
|
|
|
|
|
|
def run(command):
|
|
"""Returns (return-code, stdout, stderr)"""
|
|
p = subprocess.Popen(command, stdout=subprocess.PIPE,
|
|
stderr=subprocess.PIPE, shell=True)
|
|
raw_output, raw_err = p.communicate()
|
|
rc = p.returncode
|
|
if get_platform() == 'win32':
|
|
enc = 'oem'
|
|
else:
|
|
enc = locale.getpreferredencoding()
|
|
output = raw_output.decode(enc)
|
|
err = raw_err.decode(enc)
|
|
return rc, output.strip(), err.strip()
|
|
|
|
|
|
def run_and_read_all(run_lambda, command):
|
|
"""Runs command using run_lambda; reads and returns entire output if rc is 0"""
|
|
rc, out, _ = run_lambda(command)
|
|
if rc != 0:
|
|
return None
|
|
return out
|
|
|
|
|
|
def run_and_parse_first_match(run_lambda, command, regex):
|
|
"""Runs command using run_lambda, returns the first regex match if it exists"""
|
|
rc, out, _ = run_lambda(command)
|
|
if rc != 0:
|
|
return None
|
|
match = re.search(regex, out)
|
|
if match is None:
|
|
return None
|
|
return match.group(1)
|
|
|
|
def run_and_return_first_line(run_lambda, command):
|
|
"""Runs command using run_lambda and returns first line if output is not empty"""
|
|
rc, out, _ = run_lambda(command)
|
|
if rc != 0:
|
|
return None
|
|
return out.split('\n')[0]
|
|
|
|
|
|
def get_conda_packages(run_lambda):
|
|
if get_platform() == 'win32':
|
|
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
|
findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
|
|
grep_cmd = r'{} /R "torch numpy cudatoolkit soumith mkl magma mypy"'.format(findstr_cmd)
|
|
else:
|
|
grep_cmd = r'grep "torch\|numpy\|cudatoolkit\|soumith\|mkl\|magma\|mypy"'
|
|
conda = os.environ.get('CONDA_EXE', 'conda')
|
|
out = run_and_read_all(run_lambda, conda + ' list | ' + grep_cmd)
|
|
if out is None:
|
|
return out
|
|
# Comment starting at beginning of line
|
|
comment_regex = re.compile(r'^#.*\n')
|
|
return re.sub(comment_regex, '', out)
|
|
|
|
|
|
def get_gcc_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)')
|
|
|
|
def get_clang_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)')
|
|
|
|
|
|
def get_cmake_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)')
|
|
|
|
|
|
def get_nvidia_driver_version(run_lambda):
|
|
if get_platform() == 'darwin':
|
|
cmd = 'kextstat | grep -i cuda'
|
|
return run_and_parse_first_match(run_lambda, cmd,
|
|
r'com[.]nvidia[.]CUDA [(](.*?)[)]')
|
|
smi = get_nvidia_smi()
|
|
return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ')
|
|
|
|
|
|
def get_gpu_info(run_lambda):
|
|
if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None):
|
|
if TORCH_AVAILABLE and torch.cuda.is_available():
|
|
return torch.cuda.get_device_name(None)
|
|
return None
|
|
smi = get_nvidia_smi()
|
|
uuid_regex = re.compile(r' \(UUID: .+?\)')
|
|
rc, out, _ = run_lambda(smi + ' -L')
|
|
if rc != 0:
|
|
return None
|
|
# Anonymize GPUs by removing their UUID
|
|
return re.sub(uuid_regex, '', out)
|
|
|
|
|
|
def get_running_cuda_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)')
|
|
|
|
|
|
def get_cudnn_version(run_lambda):
|
|
"""This will return a list of libcudnn.so; it's hard to tell which one is being used"""
|
|
if get_platform() == 'win32':
|
|
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
|
cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%")
|
|
where_cmd = os.path.join(system_root, 'System32', 'where')
|
|
cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
|
|
elif get_platform() == 'darwin':
|
|
# CUDA libraries and drivers can be found in /usr/local/cuda/. See
|
|
# https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
|
|
# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
|
|
# Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
|
|
cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*'
|
|
else:
|
|
cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
|
|
rc, out, _ = run_lambda(cudnn_cmd)
|
|
# find will return 1 if there are permission errors or if not found
|
|
if len(out) == 0 or (rc != 1 and rc != 0):
|
|
l = os.environ.get('CUDNN_LIBRARY')
|
|
if l is not None and os.path.isfile(l):
|
|
return os.path.realpath(l)
|
|
return None
|
|
files_set = set()
|
|
for fn in out.split('\n'):
|
|
fn = os.path.realpath(fn) # eliminate symbolic links
|
|
if os.path.isfile(fn):
|
|
files_set.add(fn)
|
|
if not files_set:
|
|
return None
|
|
# Alphabetize the result because the order is non-deterministic otherwise
|
|
files = list(sorted(files_set))
|
|
if len(files) == 1:
|
|
return files[0]
|
|
result = '\n'.join(files)
|
|
return 'Probably one of the following:\n{}'.format(result)
|
|
|
|
|
|
def get_nvidia_smi():
|
|
# Note: nvidia-smi is currently available only on Windows and Linux
|
|
smi = 'nvidia-smi'
|
|
if get_platform() == 'win32':
|
|
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
|
program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files')
|
|
legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi)
|
|
new_path = os.path.join(system_root, 'System32', smi)
|
|
smis = [new_path, legacy_path]
|
|
for candidate_smi in smis:
|
|
if os.path.exists(candidate_smi):
|
|
smi = '"{}"'.format(candidate_smi)
|
|
break
|
|
return smi
|
|
|
|
|
|
def get_platform():
|
|
if sys.platform.startswith('linux'):
|
|
return 'linux'
|
|
elif sys.platform.startswith('win32'):
|
|
return 'win32'
|
|
elif sys.platform.startswith('cygwin'):
|
|
return 'cygwin'
|
|
elif sys.platform.startswith('darwin'):
|
|
return 'darwin'
|
|
else:
|
|
return sys.platform
|
|
|
|
|
|
def get_mac_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)')
|
|
|
|
|
|
def get_windows_version(run_lambda):
|
|
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
|
wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic')
|
|
findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
|
|
return run_and_read_all(run_lambda, '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))
|
|
|
|
|
|
def get_lsb_version(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)')
|
|
|
|
|
|
def check_release_file(run_lambda):
|
|
return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
|
|
r'PRETTY_NAME="(.*)"')
|
|
|
|
|
|
def get_os(run_lambda):
|
|
from platform import machine
|
|
platform = get_platform()
|
|
|
|
if platform == 'win32' or platform == 'cygwin':
|
|
return get_windows_version(run_lambda)
|
|
|
|
if platform == 'darwin':
|
|
version = get_mac_version(run_lambda)
|
|
if version is None:
|
|
return None
|
|
return 'macOS {} ({})'.format(version, machine())
|
|
|
|
if platform == 'linux':
|
|
# Ubuntu/Debian based
|
|
desc = get_lsb_version(run_lambda)
|
|
if desc is not None:
|
|
return '{} ({})'.format(desc, machine())
|
|
|
|
# Try reading /etc/*-release
|
|
desc = check_release_file(run_lambda)
|
|
if desc is not None:
|
|
return '{} ({})'.format(desc, machine())
|
|
|
|
return '{} ({})'.format(platform, machine())
|
|
|
|
# Unknown platform
|
|
return platform
|
|
|
|
|
|
def get_python_platform():
|
|
import platform
|
|
return platform.platform()
|
|
|
|
|
|
def get_libc_version():
|
|
import platform
|
|
if get_platform() != 'linux':
|
|
return 'N/A'
|
|
return '-'.join(platform.libc_ver())
|
|
|
|
|
|
def get_pip_packages(run_lambda):
|
|
"""Returns `pip list` output. Note: will also find conda-installed pytorch
|
|
and numpy packages."""
|
|
# People generally have `pip` as `pip` or `pip3`
|
|
# But here it is incoved as `python -mpip`
|
|
def run_with_pip(pip):
|
|
if get_platform() == 'win32':
|
|
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
|
findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
|
|
grep_cmd = r'{} /R "numpy torch mypy"'.format(findstr_cmd)
|
|
else:
|
|
grep_cmd = r'grep "torch\|numpy\|mypy"'
|
|
return run_and_read_all(run_lambda, pip + ' list --format=freeze | ' + grep_cmd)
|
|
|
|
pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
|
|
out = run_with_pip(sys.executable + ' -mpip')
|
|
|
|
return pip_version, out
|
|
|
|
|
|
def get_cachingallocator_config():
|
|
ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '')
|
|
return ca_config
|
|
|
|
def is_xnnpack_available():
|
|
import torch.backends.xnnpack
|
|
return str(torch.backends.xnnpack.enabled) # type: ignore[attr-defined]
|
|
|
|
def get_env_info():
|
|
run_lambda = run
|
|
pip_version, pip_list_output = get_pip_packages(run_lambda)
|
|
|
|
if TORCH_AVAILABLE:
|
|
version_str = torch.__version__
|
|
debug_mode_str = str(torch.version.debug)
|
|
cuda_available_str = str(torch.cuda.is_available())
|
|
cuda_version_str = torch.version.cuda
|
|
if not hasattr(torch.version, 'hip') or torch.version.hip is None: # cuda version
|
|
hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
|
|
else: # HIP version
|
|
cfg = torch._C._show_config().split('\n')
|
|
hip_runtime_version = [s.rsplit(None, 1)[-1] for s in cfg if 'HIP Runtime' in s][0]
|
|
miopen_runtime_version = [s.rsplit(None, 1)[-1] for s in cfg if 'MIOpen' in s][0]
|
|
cuda_version_str = 'N/A'
|
|
hip_compiled_version = torch.version.hip
|
|
else:
|
|
version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A'
|
|
hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
|
|
|
|
sys_version = sys.version.replace("\n", " ")
|
|
|
|
return SystemEnv(
|
|
torch_version=version_str,
|
|
is_debug_build=debug_mode_str,
|
|
python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1),
|
|
python_platform=get_python_platform(),
|
|
is_cuda_available=cuda_available_str,
|
|
cuda_compiled_version=cuda_version_str,
|
|
cuda_runtime_version=get_running_cuda_version(run_lambda),
|
|
nvidia_gpu_models=get_gpu_info(run_lambda),
|
|
nvidia_driver_version=get_nvidia_driver_version(run_lambda),
|
|
cudnn_version=get_cudnn_version(run_lambda),
|
|
hip_compiled_version=hip_compiled_version,
|
|
hip_runtime_version=hip_runtime_version,
|
|
miopen_runtime_version=miopen_runtime_version,
|
|
pip_version=pip_version,
|
|
pip_packages=pip_list_output,
|
|
conda_packages=get_conda_packages(run_lambda),
|
|
os=get_os(run_lambda),
|
|
libc_version=get_libc_version(),
|
|
gcc_version=get_gcc_version(run_lambda),
|
|
clang_version=get_clang_version(run_lambda),
|
|
cmake_version=get_cmake_version(run_lambda),
|
|
caching_allocator_config=get_cachingallocator_config(),
|
|
is_xnnpack_available=is_xnnpack_available(),
|
|
)
|
|
|
|
env_info_fmt = """
|
|
PyTorch version: {torch_version}
|
|
Is debug build: {is_debug_build}
|
|
CUDA used to build PyTorch: {cuda_compiled_version}
|
|
ROCM used to build PyTorch: {hip_compiled_version}
|
|
|
|
OS: {os}
|
|
GCC version: {gcc_version}
|
|
Clang version: {clang_version}
|
|
CMake version: {cmake_version}
|
|
Libc version: {libc_version}
|
|
|
|
Python version: {python_version}
|
|
Python platform: {python_platform}
|
|
Is CUDA available: {is_cuda_available}
|
|
CUDA runtime version: {cuda_runtime_version}
|
|
GPU models and configuration: {nvidia_gpu_models}
|
|
Nvidia driver version: {nvidia_driver_version}
|
|
cuDNN version: {cudnn_version}
|
|
HIP runtime version: {hip_runtime_version}
|
|
MIOpen runtime version: {miopen_runtime_version}
|
|
Is XNNPACK available: {is_xnnpack_available}
|
|
|
|
Versions of relevant libraries:
|
|
{pip_packages}
|
|
{conda_packages}
|
|
""".strip()
|
|
|
|
|
|
def pretty_str(envinfo):
|
|
def replace_nones(dct, replacement='Could not collect'):
|
|
for key in dct.keys():
|
|
if dct[key] is not None:
|
|
continue
|
|
dct[key] = replacement
|
|
return dct
|
|
|
|
def replace_bools(dct, true='Yes', false='No'):
|
|
for key in dct.keys():
|
|
if dct[key] is True:
|
|
dct[key] = true
|
|
elif dct[key] is False:
|
|
dct[key] = false
|
|
return dct
|
|
|
|
def prepend(text, tag='[prepend]'):
|
|
lines = text.split('\n')
|
|
updated_lines = [tag + line for line in lines]
|
|
return '\n'.join(updated_lines)
|
|
|
|
def replace_if_empty(text, replacement='No relevant packages'):
|
|
if text is not None and len(text) == 0:
|
|
return replacement
|
|
return text
|
|
|
|
def maybe_start_on_next_line(string):
|
|
# If `string` is multiline, prepend a \n to it.
|
|
if string is not None and len(string.split('\n')) > 1:
|
|
return '\n{}\n'.format(string)
|
|
return string
|
|
|
|
mutable_dict = envinfo._asdict()
|
|
|
|
# If nvidia_gpu_models is multiline, start on the next line
|
|
mutable_dict['nvidia_gpu_models'] = \
|
|
maybe_start_on_next_line(envinfo.nvidia_gpu_models)
|
|
|
|
# If the machine doesn't have CUDA, report some fields as 'No CUDA'
|
|
dynamic_cuda_fields = [
|
|
'cuda_runtime_version',
|
|
'nvidia_gpu_models',
|
|
'nvidia_driver_version',
|
|
]
|
|
all_cuda_fields = dynamic_cuda_fields + ['cudnn_version']
|
|
all_dynamic_cuda_fields_missing = all(
|
|
mutable_dict[field] is None for field in dynamic_cuda_fields)
|
|
if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing:
|
|
for field in all_cuda_fields:
|
|
mutable_dict[field] = 'No CUDA'
|
|
if envinfo.cuda_compiled_version is None:
|
|
mutable_dict['cuda_compiled_version'] = 'None'
|
|
|
|
# Replace True with Yes, False with No
|
|
mutable_dict = replace_bools(mutable_dict)
|
|
|
|
# Replace all None objects with 'Could not collect'
|
|
mutable_dict = replace_nones(mutable_dict)
|
|
|
|
# If either of these are '', replace with 'No relevant packages'
|
|
mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages'])
|
|
mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages'])
|
|
|
|
# Tag conda and pip packages with a prefix
|
|
# If they were previously None, they'll show up as ie '[conda] Could not collect'
|
|
if mutable_dict['pip_packages']:
|
|
mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'],
|
|
'[{}] '.format(envinfo.pip_version))
|
|
if mutable_dict['conda_packages']:
|
|
mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'],
|
|
'[conda] ')
|
|
return env_info_fmt.format(**mutable_dict)
|
|
|
|
|
|
def get_pretty_env_info():
|
|
return pretty_str(get_env_info())
|
|
|
|
|
|
def main():
|
|
print("Collecting environment information...")
|
|
output = get_pretty_env_info()
|
|
print(output)
|
|
|
|
if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'):
|
|
minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
|
|
if sys.platform == "linux" and os.path.exists(minidump_dir):
|
|
dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)]
|
|
latest = max(dumps, key=os.path.getctime)
|
|
ctime = os.path.getctime(latest)
|
|
creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S')
|
|
msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \
|
|
"if this is related to your bug please include it when you file a report ***"
|
|
print(msg, file=sys.stderr)
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|