Merge branch 'main' into cleanup

2022-11-01 09:43:49 +01:00 · 2022-11-01 09:43:49 +01:00 · 30f28b94a0
commit 30f28b94a0
parent 2a91e15113 8d87c0b852
5 changed files with 59 additions and 43 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -138,3 +138,20 @@ Features:

 Bug fixes:
 - Fixed a problem where warning messages would be displayed even though everything worked correctly.
+
+
+### 0.35.2
+
+Bug fixes:
+ - Fixed a bug where the CUDA setup failed due to a wrong function call.
+
+### 0.35.3
+
+Bug fixes:
+ - Fixed a bug in the CUDA Setup which led to an incomprehensible error if no GPU was detected.
+
+### 0.35.4
+
+Bug fixes:
+ - Fixed a bug in the CUDA Setup failed with the cuda runtime was found, but not the cuda library.
+ - Fixed a bug where not finding the cuda runtime led to an incomprehensible error.
--- a/bitsandbytes/cextension.py
+++ b/bitsandbytes/cextension.py
@ -116,7 +116,7 @@ try:
        CUDASetup.get_instance().generate_instructions()
        CUDASetup.get_instance().print_log_stack()
        raise RuntimeError('''
-        CUDA Setup failed despite GPU being available. Inspect the CUDA SETUP outputs to fix your environment!
+        CUDA Setup failed despite GPU being available. Inspect the CUDA SETUP outputs aboveto fix your environment!
        If you cannot find any issues and suspect a bug, please open an issue with detals about your environment:
        https://github.com/TimDettmers/bitsandbytes/issues''')
    lib.cadam32bit_g32
@ -124,8 +124,6 @@ try:
    lib.get_cusparse.restype = ct.c_void_p
    COMPILED_WITH_CUDA = True
 except AttributeError:
-    warn(
-        "The installed version of bitsandbytes was compiled without GPU support. "
-        "8-bit optimizers and GPU quantization are unavailable."
-    )
+    warn("The installed version of bitsandbytes was compiled without GPU support. "
+        "8-bit optimizers and GPU quantization are unavailable.")
    COMPILED_WITH_CUDA = False
--- a/bitsandbytes/cuda_setup/main.py
+++ b/bitsandbytes/cuda_setup/main.py
@ -17,6 +17,7 @@ evaluation:
 """

 import ctypes
+import torch

 from bitsandbytes.cextension import CUDASetup

@ -28,14 +29,17 @@ def check_cuda_result(cuda, result_val):
    if result_val != 0:
        error_str = ctypes.c_char_p()
        cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
-        CUDASetup.get_instance.add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}")
+        CUDASetup.get_instance().add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}")

+
+# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
 def get_cuda_version(cuda, cudart_path):
-    # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
+    if cuda is None: return None
+
    try:
        cudart = ctypes.CDLL(cudart_path)
    except OSError:
-        CUDASetup.get_instance.add_log_entry(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
+        CUDASetup.get_instance().add_log_entry(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
        return None

    version = ctypes.c_int()
@ -55,7 +59,7 @@ def get_cuda_lib_handle():
    try:
        cuda = ctypes.CDLL("libcuda.so")
    except OSError:
-        CUDA_RUNTIME_LIB.get_instance().add_log_entry('CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!')
+        CUDASetup.get_instance().add_log_entry('CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!')
        return None
    check_cuda_result(cuda, cuda.cuInit(0))

@ -73,7 +77,6 @@ def get_compute_capabilities(cuda):
    # bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
    """

-
    nGpus = ctypes.c_int()
    cc_major = ctypes.c_int()
    cc_minor = ctypes.c_int()
@ -87,9 +90,7 @@ def get_compute_capabilities(cuda):
        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)
-        )
+        check_cuda_result(cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device))
        ccs.append(f"{cc_major.value}.{cc_minor.value}")

    return ccs
@ -102,11 +103,11 @@ def get_compute_capability(cuda):
    capabilities are downwards compatible. If no GPUs are detected, it returns
    None.
    """
+    if cuda is None: return None
+
+    # TODO: handle different compute capabilities; for now, take the max
    ccs = get_compute_capabilities(cuda)
-    if ccs:
-        # TODO: handle different compute capabilities; for now, take the max
-        return ccs[-1]
-    return None
+    if ccs: return ccs[-1]


 def evaluate_cuda_setup():
@ -116,28 +117,31 @@ def evaluate_cuda_setup():
    #print('Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues')
    #print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')
    #print('='*80)
-    #if not torch.cuda.is_available():
-        #print('No GPU detected. Loading CPU library...')
-        #return binary_name
-
-    binary_name = "libbitsandbytes_cpu.so"
+    if not torch.cuda.is_available(): return 'libsbitsandbytes_cpu.so', None, None, None, None

    cuda_setup = CUDASetup.get_instance()
    cudart_path = determine_cuda_runtime_lib_path()
-    if cudart_path is None:
-        cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
-        return binary_name
-
-    cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
    cuda = get_cuda_lib_handle()
    cc = get_compute_capability(cuda)
-    cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
    cuda_version_string = get_cuda_version(cuda, cudart_path)

+    failure = False
+    if cudart_path is None:
+        failure = True
+        cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
+    else:
+        cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")

-    if cc == '':
-        cuda_setup.add_log_entry("WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library...", is_warning=True)
-        return binary_name
+    if cc == '' or cc is None:
+        failure = True
+        cuda_setup.add_log_entry("WARNING: No GPU detected! Check your CUDA paths. Proceeding to load CPU-only library...", is_warning=True)
+    else:
+        cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
+
+    if cuda is None:
+        failure = True
+    else:
+        cuda_setup.add_log_entry(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')

    # 7.5 is the minimum CC vor cublaslt
    has_cublaslt = cc in ["7.5", "8.0", "8.6"]
@ -148,16 +152,13 @@ def evaluate_cuda_setup():

    # 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
-    cuda_setup.add_log_entry(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')

-    def get_binary_name():
+    if failure:
+        binary_name = "libbitsandbytes_cpu.so"
+    elif has_cublaslt:
+        binary_name = f"libbitsandbytes_cuda{cuda_version_string}.so"
+    else:
        "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()
+        binary_name = f"libbitsandbytes_cuda{cuda_version_string}_nocublaslt.so"

    return binary_name, cudart_path, cuda, cc, cuda_version_string
--- a/bitsandbytes/cuda_setup/paths.py
+++ b/bitsandbytes/cuda_setup/paths.py
@ -62,7 +62,7 @@ def warn_in_case_of_duplicates(results_paths: Set[Path]) -> None:
            "If you get `CUDA error: invalid device function` errors, the above "
            "might be the cause and the solution is to make sure only one "
            f"{CUDA_RUNTIME_LIB} in the paths that we search based on your env.")
-        CUDASetup.get_instance.add_log_entry(warning_msg, is_warning=True)
+        CUDASetup.get_instance().add_log_entry(warning_msg, is_warning=True)


 def determine_cuda_runtime_lib_path() -> Union[Path, None]:
@ -88,7 +88,7 @@ def determine_cuda_runtime_lib_path() -> Union[Path, None]:
        if conda_cuda_libs:
            return next(iter(conda_cuda_libs))

-        CUDASetup.get_instance.add_log_entry(f'{candidate_env_vars["CONDA_PREFIX"]} did not contain '
+        CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["CONDA_PREFIX"]} did not contain '
            f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True)

    if "LD_LIBRARY_PATH" in candidate_env_vars:
--- a/setup.py
+++ b/setup.py
@ -18,7 +18,7 @@ def read(fname):

 setup(
    name=f"bitsandbytes",
-    version=f"0.35.1",
+    version=f"0.35.4",
    author="Tim Dettmers",
    author_email="dettmers@cs.washington.edu",
    description="8-bit optimizers and matrix multiplication routines.",