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vllm/collect_env.py

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# ruff: noqa
# code borrowed from https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py
# 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` or `python -m torch.utils.collect_env`
import datetime
import locale
import os
import re
import subprocess
import sys
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',
'cuda_module_loading',
'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',
'cpu_info',
'rocm_version', # vllm specific field
'neuron_sdk_version', # vllm specific field
'vllm_version', # vllm specific field
'vllm_build_flags', # vllm specific field
'gpu_topo', # vllm specific field
])
DEFAULT_CONDA_PATTERNS = {
"torch",
"numpy",
"cudatoolkit",
"soumith",
"mkl",
"magma",
"triton",
"optree",
"nccl",
"transformers",
"zmq",
"nvidia",
"pynvml",
}
DEFAULT_PIP_PATTERNS = {
"torch",
"numpy",
"mypy",
"flake8",
"triton",
"optree",
"onnx",
"nccl",
"transformers",
"zmq",
"nvidia",
"pynvml",
}
def run(command):
"""Return (return-code, stdout, stderr)."""
shell = True if type(command) is str else False
p = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=shell)
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):
"""Run 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):
"""Run 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):
"""Run 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, patterns=None):
if patterns is None:
patterns = DEFAULT_CONDA_PATTERNS
conda = os.environ.get('CONDA_EXE', 'conda')
out = run_and_read_all(run_lambda, "{} list".format(conda))
if out is None:
return out
return "\n".join(line for line in out.splitlines()
if not line.startswith("#") and any(name in line
for name in patterns))
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():
if torch.version.hip is not None:
prop = torch.cuda.get_device_properties(0)
if hasattr(prop, "gcnArchName"):
gcnArch = " ({})".format(prop.gcnArchName)
else:
gcnArch = "NoGCNArchNameOnOldPyTorch"
else:
gcnArch = ""
return torch.cuda.get_device_name(None) + gcnArch
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):
"""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 = 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_rocm_version(run_lambda):
"""Returns the ROCm version if available, otherwise 'N/A'."""
return run_and_parse_first_match(run_lambda, 'hipcc --version',
r'HIP version: (\S+)')
def get_neuron_sdk_version(run_lambda):
# Adapted from your install script
try:
result = run_lambda(["neuron-ls"])
return result if result[0] == 0 else 'N/A'
except Exception:
return 'N/A'
def get_vllm_version():
try:
import vllm
return vllm.__version__ + "@" + vllm.__commit__
except Exception:
# old version of vllm does not have __commit__
return 'N/A'
def summarize_vllm_build_flags():
# This could be a static method if the flags are constant, or dynamic if you need to check environment variables, etc.
return 'CUDA Archs: {}; ROCm: {}; Neuron: {}'.format(
os.environ.get('TORCH_CUDA_ARCH_LIST', 'Not Set'),
'Enabled' if os.environ.get('ROCM_HOME') else 'Disabled',
'Enabled' if os.environ.get('NEURON_CORES') else 'Disabled',
)
def get_gpu_topo(run_lambda):
if get_platform() == 'linux':
return run_and_read_all(run_lambda, 'nvidia-smi topo -m')
return None
# example outputs of CPU infos
# * linux
# Architecture: x86_64
# CPU op-mode(s): 32-bit, 64-bit
# Address sizes: 46 bits physical, 48 bits virtual
# Byte Order: Little Endian
# CPU(s): 128
# On-line CPU(s) list: 0-127
# Vendor ID: GenuineIntel
# Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
# CPU family: 6
# Model: 106
# Thread(s) per core: 2
# Core(s) per socket: 32
# Socket(s): 2
# Stepping: 6
# BogoMIPS: 5799.78
# Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
# sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
# xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
# pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
# hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
# fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
# avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
# xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
# avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
# Virtualization features:
# Hypervisor vendor: KVM
# Virtualization type: full
# Caches (sum of all):
# L1d: 3 MiB (64 instances)
# L1i: 2 MiB (64 instances)
# L2: 80 MiB (64 instances)
# L3: 108 MiB (2 instances)
# NUMA:
# NUMA node(s): 2
# NUMA node0 CPU(s): 0-31,64-95
# NUMA node1 CPU(s): 32-63,96-127
# Vulnerabilities:
# Itlb multihit: Not affected
# L1tf: Not affected
# Mds: Not affected
# Meltdown: Not affected
# Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
# Retbleed: Not affected
# Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
# Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
# Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
# Srbds: Not affected
# Tsx async abort: Not affected
# * win32
# Architecture=9
# CurrentClockSpeed=2900
# DeviceID=CPU0
# Family=179
# L2CacheSize=40960
# L2CacheSpeed=
# Manufacturer=GenuineIntel
# MaxClockSpeed=2900
# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
# ProcessorType=3
# Revision=27142
#
# Architecture=9
# CurrentClockSpeed=2900
# DeviceID=CPU1
# Family=179
# L2CacheSize=40960
# L2CacheSpeed=
# Manufacturer=GenuineIntel
# MaxClockSpeed=2900
# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
# ProcessorType=3
# Revision=27142
def get_cpu_info(run_lambda):
rc, out, err = 0, '', ''
if get_platform() == 'linux':
rc, out, err = run_lambda('lscpu')
elif get_platform() == 'win32':
rc, out, err = run_lambda(
'wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE'
)
elif get_platform() == 'darwin':
rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
cpu_info = 'None'
if rc == 0:
cpu_info = out
else:
cpu_info = err
return cpu_info
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, patterns=None):
"""Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
if patterns is None:
patterns = DEFAULT_PIP_PATTERNS
# People generally have `pip` as `pip` or `pip3`
# But here it is invoked as `python -mpip`
def run_with_pip(pip):
out = run_and_read_all(run_lambda, pip + ["list", "--format=freeze"])
return "\n".join(line for line in out.splitlines()
if any(name in line for name in patterns))
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 get_cuda_module_loading_config():
if TORCH_AVAILABLE and torch.cuda.is_available():
torch.cuda.init()
config = os.environ.get('CUDA_MODULE_LOADING', '')
return config
else:
return "N/A"
def is_xnnpack_available():
if TORCH_AVAILABLE:
import torch.backends.xnnpack
return str(
torch.backends.xnnpack.enabled) # type: ignore[attr-defined]
else:
return "N/A"
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
def get_version_or_na(cfg, prefix):
_lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s]
return _lst[0] if _lst else 'N/A'
cfg = torch._C._show_config().split('\n')
hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime')
miopen_runtime_version = get_version_or_na(cfg, 'MIOpen')
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", " ")
conda_packages = get_conda_packages(run_lambda)
rocm_version = get_rocm_version(run_lambda)
neuron_sdk_version = get_neuron_sdk_version(run_lambda)
vllm_version = get_vllm_version()
vllm_build_flags = summarize_vllm_build_flags()
gpu_topo = get_gpu_topo(run_lambda)
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),
cuda_module_loading=get_cuda_module_loading_config(),
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=conda_packages,
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(),
cpu_info=get_cpu_info(run_lambda),
rocm_version=rocm_version,
neuron_sdk_version=neuron_sdk_version,
vllm_version=vllm_version,
vllm_build_flags=vllm_build_flags,
gpu_topo=gpu_topo,
)
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}
CUDA_MODULE_LOADING set to: {cuda_module_loading}
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}
CPU:
{cpu_info}
Versions of relevant libraries:
{pip_packages}
{conda_packages}
""".strip()
# both the above code and the following code use `strip()` to
# remove leading/trailing whitespaces, so we need to add a newline
# in between to separate the two sections
env_info_fmt += "\n"
env_info_fmt += """
ROCM Version: {rocm_version}
Neuron SDK Version: {neuron_sdk_version}
vLLM Version: {vllm_version}
vLLM Build Flags:
{vllm_build_flags}
GPU Topology:
{gpu_topo}
""".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] ')
mutable_dict['cpu_info'] = envinfo.cpu_info
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()
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