import os
import re
import subprocess
import warnings
from typing import List
def print_rank_0(message: str) -> None:
"""
Print on only one process to avoid spamming.
"""
try:
import torch.distributed as dist
if not dist.is_initialized():
is_main_rank = True
else:
is_main_rank = dist.get_rank() == 0
except ImportError:
is_main_rank = True
if is_main_rank:
print(message)
def get_cuda_version_in_pytorch() -> List[int]:
"""
This function returns the CUDA version in the PyTorch build.
Returns:
The CUDA version required by PyTorch, in the form of tuple (major, minor).
"""
import torch
try:
torch_cuda_major = torch.version.cuda.split(".")[0]
torch_cuda_minor = torch.version.cuda.split(".")[1]
except:
raise ValueError(
"[extension] Cannot retrieve the CUDA version in the PyTorch binary given by torch.version.cuda")
return torch_cuda_major, torch_cuda_minor
def get_cuda_bare_metal_version(cuda_dir) -> List[int]:
"""
Get the System CUDA version from nvcc.
Args:
cuda_dir (str): the directory for CUDA Toolkit.
Returns:
The CUDA version required by PyTorch, in the form of tuple (major, minor).
"""
nvcc_path = os.path.join(cuda_dir, 'bin/nvcc')
if cuda_dir is None:
raise ValueError(
f"[extension] The argument cuda_dir is None, but expected to be a string. Please make sure your have exported the environment variable CUDA_HOME correctly."
)
# check for nvcc path
if not os.path.exists(nvcc_path):
raise FileNotFoundError(
f"[extension] The nvcc compiler is not found in {nvcc_path}, please make sure you have set the correct value for CUDA_HOME."
)
# parse the nvcc -v output to obtain the system cuda version
try:
raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
output = raw_output.split()
release_idx = output.index("release") + 1
release = output[release_idx].split(".")
bare_metal_major = release[0]
bare_metal_minor = release[1][0]
except:
raise ValueError(
f"[extension] Failed to parse the nvcc output to obtain the system CUDA bare metal version. The output for 'nvcc -v' is \n{raw_output}"
)
return bare_metal_major, bare_metal_minor
def check_system_pytorch_cuda_match(cuda_dir):
bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
torch_cuda_major, torch_cuda_minor = get_cuda_version_in_pytorch()
if bare_metal_major != torch_cuda_major:
raise Exception(
f'[extension] Failed to build PyTorch extension because the detected CUDA version ({bare_metal_major}.{bare_metal_minor}) '
f'mismatches the version that was used to compile PyTorch ({torch_cuda_major}.{torch_cuda_minor}).'
'Please make sure you have set the CUDA_HOME correctly and installed the correct PyTorch in https://pytorch.org/get-started/locally/ .'
)
if bare_metal_minor != torch_cuda_minor:
warnings.warn(
f"[extension] The CUDA version on the system ({bare_metal_major}.{bare_metal_minor}) does not match with the version ({torch_cuda_major}.{torch_cuda_minor}) torch was compiled with. "
"The mismatch is found in the minor version. As the APIs are compatible, we will allow compilation to proceed. "
"If you encounter any issue when using the built kernel, please try to build it again with fully matched CUDA versions"
)
return True
def get_pytorch_version() -> List[int]:
"""
This functions finds the PyTorch version.
Returns:
A tuple of integers in the form of (major, minor, patch).
"""
import torch
torch_version = torch.__version__.split('+')[0]
TORCH_MAJOR = int(torch_version.split('.')[0])
TORCH_MINOR = int(torch_version.split('.')[1])
TORCH_PATCH = int(torch_version.split('.')[2], 16)
return TORCH_MAJOR, TORCH_MINOR, TORCH_PATCH
def check_pytorch_version(min_major_version, min_minor_version) -> bool:
"""
Compare the current PyTorch version with the minium required version.
Args:
min_major_version (int): the minimum major version of PyTorch required
min_minor_version (int): the minimum minor version of PyTorch required
Returns:
A boolean value. The value is True if the current pytorch version is acceptable and False otherwise.
"""
# get pytorch version
torch_major, torch_minor, _ = get_pytorch_version()
# if the
if torch_major < min_major_version or (torch_major == min_major_version and torch_minor < min_minor_version):
raise RuntimeError(
f"[extension] Colossal-AI requires Pytorch {min_major_version}.{min_minor_version} or newer.\n"
"The latest stable release can be obtained from https://pytorch.org/get-started/locally/")
def check_cuda_availability():
"""
Check if CUDA is available on the system.
Returns:
A boolean value. True if CUDA is available and False otherwise.
"""
import torch
return torch.cuda.is_available()
def set_cuda_arch_list(cuda_dir):
"""
This function sets the PyTorch TORCH_CUDA_ARCH_LIST variable for ahead-of-time extension compilation.
Ahead-of-time compilation occurs when CUDA_EXT=1 is set when running 'pip install'.
"""
cuda_available = check_cuda_availability()
# we only need to set this when CUDA is not available for cross-compilation
if not cuda_available:
warnings.warn('\n[extension] PyTorch did not find available GPUs on this system.\n'
'If your intention is to cross-compile, this is not an error.\n'
'By default, Colossal-AI will cross-compile for \n'
'1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
'2. Volta (compute capability 7.0)\n'
'3. Turing (compute capability 7.5),\n'
'4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n'
'\nIf you wish to cross-compile for a single specific architecture,\n'
'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
arch_list = ['6.0', '6.1', '6.2', '7.0', '7.5']
if int(bare_metal_major) == 11:
if int(bare_metal_minor) == 0:
arch_list.append('8.0')
else:
arch_list.append('8.0')
arch_list.append('8.6')
arch_list_str = ';'.join(arch_list)
os.environ["TORCH_CUDA_ARCH_LIST"] = arch_list_str
return False
return True
def get_cuda_cc_flag() -> List[str]:
"""
This function produces the cc flags for your GPU arch
Returns:
The CUDA cc flags for compilation.
"""
# only import torch when needed
# this is to avoid importing torch when building on a machine without torch pre-installed
# one case is to build wheel for pypi release
import torch
cc_flag = []
for arch in torch.cuda.get_arch_list():
res = re.search(r'sm_(\d+)', arch)
if res:
arch_cap = res[1]
if int(arch_cap) >= 60:
cc_flag.extend(['-gencode', f'arch=compute_{arch_cap},code={arch}'])
return cc_flag
def append_nvcc_threads(nvcc_extra_args: List[str]) -> List[str]:
"""
This function appends the threads flag to your nvcc args.
Returns:
The nvcc compilation flags including the threads flag.
"""
from torch.utils.cpp_extension import CUDA_HOME
bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
return nvcc_extra_args + ["--threads", "4"]
return nvcc_extra_args