""" 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`) evaluation: - if paths faulty, return meaningful error - else: - determine CUDA version - determine capabilities - based on that set the default path """ import ctypes from pathlib import Path from ..utils import execute_and_return from .paths import determine_cuda_runtime_lib_path 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}") def get_compute_capabilities(): """ 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 # bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549 """ # 1. find libcuda.so library (GPU driver) (/usr/lib) try: cuda = ctypes.CDLL("libcuda.so") except OSError: # TODO: shouldn't we error or at least warn here? print('ERROR: libcuda.so not found!') return None 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}") return ccs # def get_compute_capability()-> Union[List[str, ...], None]: # FIXME: error def get_compute_capability(): """ Extracts the highest compute capbility from all available GPUs, as compute capabilities are downwards compatible. If no GPUs are detected, it returns None. """ ccs = get_compute_capabilities() if ccs is not None: # TODO: handle different compute capabilities; for now, take the max return ccs[-1] return None def evaluate_cuda_setup(): cuda_path = determine_cuda_runtime_lib_path() print(f"CUDA SETUP: CUDA path found: {cuda_path}") cc = get_compute_capability() binary_name = "libbitsandbytes_cpu.so" if cc == '': print( "WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..." ) return binary_name # 7.5 is the minimum CC vor cublaslt 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 # FIXME: cuda_home is still unused 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}" print(f'CUDA_SETUP: Detected CUDA version {cuda_version_string}') def get_binary_name(): "if not has_cublaslt (CC < 7.5), then we have to choose _nocublaslt.so" bin_base_name = "libbitsandbytes_cuda" if has_cublaslt: return f"{bin_base_name}{cuda_version_string}.so" else: return f"{bin_base_name}{cuda_version_string}_nocublaslt.so" binary_name = get_binary_name() return binary_name