Added some more docs and comments.

bitsandbytes/cuda_setup.py

@@ -27,17 +27,24 @@
 
 
 def check_cuda_result(cuda, result_val):
+    # 3. Check for CUDA errors
     if result_val != 0:
-        # TODO: undefined name 'error_str'
+        error_str = ctypes.c_char_p()
         cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
-        print("Count not initialize CUDA - failure!")
-        raise Exception("CUDA exception!")
-    return result_val
+        raise Exception(f"CUDA exception! ERROR: {error_str}")
 
 
 # taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
 def get_compute_capability():
-    libnames = ("libcuda.so", "libcuda.dylib", "cuda.dll")
+    # 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)
@@ -54,31 +61,23 @@ def get_compute_capability():
 
     result = ctypes.c_int()
     device = ctypes.c_int()
-    # TODO: local variable 'context' is assigned to but never used
-    context = ctypes.c_void_p()
-    # TODO: local variable 'error_str' is assigned to but never used
-    error_str = ctypes.c_char_p()
 
-    result = check_cuda_result(cuda, cuda.cuInit(0))
+    check_cuda_result(cuda, cuda.cuInit(0))
 
-    result = check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
+    check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
     ccs = []
     for i in range(nGpus.value):
-        result = check_cuda_result(
-            cuda, cuda.cuDeviceGet(ctypes.byref(device), i)
-        )
-        result = check_cuda_result(
-            cuda,
-            cuda.cuDeviceComputeCapability(
-                ctypes.byref(cc_major), ctypes.byref(cc_minor), device
-            ),
-        )
+        check_cuda_result(cuda, cuda.cuDeviceGet(ctypes.byref(device), i))
+        ref_major = ctypes(cc_major)
+        ref_minor = ctypes(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()
-    # return ccs[-1]
-    return ccs
+    max_cc = ccs[-1]
+    return max_cc
 
 
 CUDA_RUNTIME_LIB: str = "libcudart.so"
@@ -89,6 +88,7 @@ def tokenize_paths(paths: str) -> Set[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] = {
@@ -112,20 +112,25 @@ def resolve_env_variable(env_var):
             f"Found duplicate {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
         )
         raise FileNotFoundError(err_msg)
-    elif len(cuda_runtime_libs) == 0: return None
+    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]:
-    """# TODO: add doc-string"""
+    '''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))
 
@@ -146,17 +151,19 @@ def evaluate_cuda_setup():
 
     if not (has_gpu := bool(cc)):
         print(
-            "WARNING: No GPU detected! Check our CUDA paths. Processing to load CPU-only library..."
+            "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) Model missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
+    # (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}"

tests/test_cuda_setup_evaluator.py

@@ -92,6 +92,9 @@ def test_get_cuda_runtime_lib_path__non_existent_dir(capsys, tmp_path):
 
 def test_full_system():
     ## this only tests the cuda version and not compute capability
+
+    # if CONDA_PREFIX exists, it has priority before all other env variables
+    # but it does not contain the library directly, so we need to look at the a sub-folder
     version = ''
     if 'CONDA_PREFIX' in os.environ:
         ls_output, err = bnb.utils.execute_and_return(f'ls -l {os.environ["CONDA_PREFIX"]}/lib/libcudart.so')