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"""
extract factors the build is dependent on:
[X] compute capability
[ ] TODO: Q - What if we have multiple GPUs of different makes?
- CUDA version
- Software:
- CPU-only: only CPU quantization functions (no optimizer, no matrix multipl)
- CuBLAS-LT: full-build 8-bit optimizer
- no CuBLAS-LT: no 8-bit matrix multiplication (`nomatmul`)
alle Binaries packagen
evaluation:
- if paths faulty, return meaningful error
- else:
- determine CUDA version
- determine capabilities
- based on that set the default path
"""
import ctypes
import os
from pathlib import Path
from typing import Set, Union
from ..utils import print_err, warn_of_missing_prerequisite, execute_and_return
def check_cuda_result(cuda, result_val):
# 3. Check for CUDA errors
if result_val != 0:
error_str = ctypes.c_char_p()
cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
raise Exception(f"CUDA exception! ERROR: {error_str}")
# taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
def get_compute_capability():
# 1. find libcuda.so library (GPU driver) (/usr/lib)
# init_device -> init variables -> call function by reference
# 2. call extern C function to determine CC
# (https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__DEVICE__DEPRECATED.html)
# 3. Check for CUDA errors
# https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
# 1. find libcuda.so library (GPU driver) (/usr/lib)
libnames = ("libcuda.so",)
for libname in libnames:
try:
cuda = ctypes.CDLL(libname)
except OSError:
continue
else:
break
else:
raise OSError("could not load any of: " + " ".join(libnames))
nGpus = ctypes.c_int()
cc_major = ctypes.c_int()
cc_minor = ctypes.c_int()
result = ctypes.c_int()
device = ctypes.c_int()
check_cuda_result(cuda, cuda.cuInit(0))
check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
ccs = []
for i in range(nGpus.value):
check_cuda_result(cuda, cuda.cuDeviceGet(ctypes.byref(device), i))
ref_major = ctypes.byref(cc_major)
ref_minor = ctypes.byref(cc_minor)
# 2. call extern C function to determine CC
check_cuda_result(cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device))
ccs.append(f"{cc_major.value}.{cc_minor.value}")
# TODO: handle different compute capabilities; for now, take the max
ccs.sort()
max_cc = ccs[-1]
return max_cc
CUDA_RUNTIME_LIB: str = "libcudart.so"
def tokenize_paths(paths: str) -> Set[Path]:
return {Path(ld_path) for ld_path in paths.split(":") if ld_path}
def resolve_env_variable(env_var):
'''Searches a given envirionmental library or path for the CUDA runtime library (libcudart.so)'''
paths: Set[Path] = tokenize_paths(env_var)
non_existent_directories: Set[Path] = {
path for path in paths if not path.exists()
}
if non_existent_directories:
print_err(
"WARNING: The following directories listed your path were found to "
f"be non-existent: {non_existent_directories}"
)
cuda_runtime_libs: Set[Path] = {
path / CUDA_RUNTIME_LIB
for path in paths
if (path / CUDA_RUNTIME_LIB).is_file()
} - non_existent_directories
if len(cuda_runtime_libs) > 1:
err_msg = (
f"Found duplicate {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
)
raise FileNotFoundError(err_msg)
elif len(cuda_runtime_libs) == 0: return None # this is not en error, since other envs can contain CUDA
else: return next(iter(cuda_runtime_libs)) # for now just return the first
def get_cuda_runtime_lib_path() -> Union[Path, None]:
'''Searches conda installation and environmental paths for a cuda installations.'''
cuda_runtime_libs = []
# CONDA_PREFIX/lib is the default location for a default conda
# install of pytorch. This location takes priortiy over all
# other defined variables
if 'CONDA_PREFIX' in os.environ:
lib_conda_path = f'{os.environ["CONDA_PREFIX"]}/lib/'
print(lib_conda_path)
cuda_runtime_libs.append(resolve_env_variable(lib_conda_path))
if len(cuda_runtime_libs) == 1: return cuda_runtime_libs[0]
# if CONDA_PREFIX does not have the library, search the environment
# (in particualr LD_LIBRARY PATH)
for var in os.environ:
cuda_runtime_libs.append(resolve_env_variable(var))
if len(cuda_runtime_libs) < 1:
err_msg = (
f"Did not find {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
)
raise FileNotFoundError(err_msg)
return cuda_runtime_libs.pop()
def evaluate_cuda_setup():
cuda_path = get_cuda_runtime_lib_path()
print(f'CUDA SETUP: CUDA path found: {cuda_path}')
cc = get_compute_capability()
binary_name = "libbitsandbytes_cpu.so"
if not (has_gpu := bool(cc)):
print(
"WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..."
)
return binary_name
has_cublaslt = cc in ["7.5", "8.0", "8.6"]
# TODO:
# (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
# (2) Multiple CUDA versions installed
cuda_home = str(Path(cuda_path).parent.parent)
# we use ls -l instead of nvcc to determine the cuda version
# since most installations will have the libcudart.so installed, but not the compiler
ls_output, err = execute_and_return(f"ls -l {cuda_path}")
major, minor, revision = ls_output.split(' ')[-1].replace('libcudart.so.', '').split('.')
cuda_version_string = f"{major}{minor}"
binary_name = f'libbitsandbytes_cuda{cuda_version_string}{("" if has_cublaslt else "_nocublaslt")}.so'
return binary_name
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