move cuda_setup code into subpackage

1 files changed, 0 insertions, 173 deletions

diff --git a/bitsandbytes/cuda_setup.py b/bitsandbytes/cuda_setup.py
deleted file mode 100644
index e68cd5e..0000000
--- a/bitsandbytes/cuda_setup.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-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