diff options
Diffstat (limited to 'bitsandbytes')
| -rw-r--r-- | bitsandbytes/cextension.py | 46 | ||||
| -rw-r--r-- | bitsandbytes/cuda_setup/main.py | 40 | ||||
| -rw-r--r-- | bitsandbytes/cuda_setup/paths.py | 27 | ||||
| -rw-r--r-- | bitsandbytes/nn/__init__.py | 2 | ||||
| -rw-r--r-- | bitsandbytes/nn/modules.py | 44 |
5 files changed, 61 insertions, 98 deletions
diff --git a/bitsandbytes/cextension.py b/bitsandbytes/cextension.py index af23c8f..abb3054 100644 --- a/bitsandbytes/cextension.py +++ b/bitsandbytes/cextension.py @@ -2,33 +2,49 @@ import ctypes as ct from pathlib import Path from warnings import warn -from .cuda_setup.main import evaluate_cuda_setup -class CUDALibrary_Singleton(object): +class CUDASetup(object): _instance = None def __init__(self): raise RuntimeError("Call get_instance() instead") def initialize(self): + self.cuda_setup_log = [] + + from .cuda_setup.main import evaluate_cuda_setup binary_name = evaluate_cuda_setup() package_dir = Path(__file__).parent binary_path = package_dir / binary_name - if not binary_path.exists(): - print(f"CUDA SETUP: TODO: compile library for specific version: {binary_name}") - legacy_binary_name = "libbitsandbytes.so" - print(f"CUDA SETUP: Defaulting to {legacy_binary_name}...") - binary_path = package_dir / legacy_binary_name + try: if not binary_path.exists(): - print('CUDA SETUP: CUDA detection failed. Either CUDA driver not installed, CUDA not installed, or you have multiple conflicting CUDA libraries!') - print('CUDA SETUP: If you compiled from source, try again with `make CUDA_VERSION=DETECTED_CUDA_VERSION` for example, `make CUDA_VERSION=113`.') - raise Exception('CUDA SETUP: Setup Failed!') - self.lib = ct.cdll.LoadLibrary(binary_path) - else: - print(f"CUDA SETUP: Loading binary {binary_path}...") - self.lib = ct.cdll.LoadLibrary(binary_path) + self.add_log_entry(f"CUDA SETUP: TODO: compile library for specific version: {binary_name}") + legacy_binary_name = "libbitsandbytes.so" + self.add_log_entry(f"CUDA SETUP: Defaulting to {legacy_binary_name}...") + binary_path = package_dir / legacy_binary_name + if not binary_path.exists(): + self.add_log_entry('CUDA SETUP: CUDA detection failed. Either CUDA driver not installed, CUDA not installed, or you have multiple conflicting CUDA libraries!') + self.add_log_entry('CUDA SETUP: If you compiled from source, try again with `make CUDA_VERSION=DETECTED_CUDA_VERSION` for example, `make CUDA_VERSION=113`.') + self.print_log_stack() + raise Exception('CUDA SETUP: Setup Failed!') + self.lib = ct.cdll.LoadLibrary(binary_path) + else: + self.add_log_entry(f"CUDA SETUP: Loading binary {binary_path}...") + self.lib = ct.cdll.LoadLibrary(binary_path) + except: + self.print_log_stack() + + def add_log_entry(self, msg, is_warning=False): + self.cuda_setup_log.append((msg, is_warning)) + + def print_log_stack(self): + for msg, is_warning in self.cuda_setup_log: + if is_warning: + warn(msg) + else: + print(msg) @classmethod def get_instance(cls): @@ -38,7 +54,7 @@ class CUDALibrary_Singleton(object): return cls._instance -lib = CUDALibrary_Singleton.get_instance().lib +lib = CUDASetup.get_instance().lib try: lib.cadam32bit_g32 lib.get_context.restype = ct.c_void_p diff --git a/bitsandbytes/cuda_setup/main.py b/bitsandbytes/cuda_setup/main.py index f11b430..f8f35f0 100644 --- a/bitsandbytes/cuda_setup/main.py +++ b/bitsandbytes/cuda_setup/main.py @@ -19,6 +19,7 @@ evaluation: import ctypes from .paths import determine_cuda_runtime_lib_path +from bitsandbytes.cextension import CUDASetup def check_cuda_result(cuda, result_val): @@ -26,15 +27,14 @@ 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)) - print(f"CUDA exception! Error code: {error_str.value.decode()}") + CUDASetup.get_instance.add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}") def get_cuda_version(cuda, cudart_path): # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION try: cudart = ctypes.CDLL(cudart_path) except OSError: - # TODO: shouldn't we error or at least warn here? - print(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() @@ -44,7 +44,7 @@ def get_cuda_version(cuda, cudart_path): minor = (version-(major*1000))//10 if major < 11: - print('CUDA SETUP: CUDA version lower than 11 are currenlty not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!') + CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currenlty not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!') return f'{major}{minor}' @@ -54,8 +54,7 @@ def get_cuda_lib_handle(): try: cuda = ctypes.CDLL("libcuda.so") except OSError: - # TODO: shouldn't we error or at least warn here? - print('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!') + 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!') return None check_cuda_result(cuda, cuda.cuInit(0)) @@ -110,34 +109,33 @@ def get_compute_capability(cuda): def evaluate_cuda_setup(): - print('') - print('='*35 + 'BUG REPORT' + '='*35) - 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) - binary_name = "libbitsandbytes_cpu.so" + # we remove this for now and see how things go + #print('') + #print('='*35 + 'BUG REPORT' + '='*35) + #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" + + cuda_setup = CUDASetup.get_instance() cudart_path = determine_cuda_runtime_lib_path() if cudart_path is None: - print( - "WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!" - ) + cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True) return binary_name - print(f"CUDA SETUP: CUDA runtime path found: {cudart_path}") + cuda_setup.add_log_entry((f"CUDA SETUP: CUDA runtime path found: {cudart_path}")) cuda = get_cuda_lib_handle() cc = get_compute_capability(cuda) - print(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}") + cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}") cuda_version_string = get_cuda_version(cuda, cudart_path) if cc == '': - print( - "WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..." - ) + 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 # 7.5 is the minimum CC vor cublaslt @@ -149,7 +147,7 @@ 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 - print(f'CUDA SETUP: Detected CUDA version {cuda_version_string}') + cuda_setup.add_log_entry(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" diff --git a/bitsandbytes/cuda_setup/paths.py b/bitsandbytes/cuda_setup/paths.py index ba3f97f..3223359 100644 --- a/bitsandbytes/cuda_setup/paths.py +++ b/bitsandbytes/cuda_setup/paths.py @@ -1,7 +1,7 @@ import errno from pathlib import Path from typing import Set, Union -from warnings import warn +from bitsandbytes.cextension import CUDASetup from .env_vars import get_potentially_lib_path_containing_env_vars @@ -24,10 +24,8 @@ def remove_non_existent_dirs(candidate_paths: Set[Path]) -> Set[Path]: non_existent_directories: Set[Path] = candidate_paths - existent_directories if non_existent_directories: - warn( - "WARNING: The following directories listed in your path were found to " - f"be non-existent: {non_existent_directories}" - ) + CUDASetup.get_instance().add_log_entry("WARNING: The following directories listed in your path were found to " + f"be non-existent: {non_existent_directories}", is_warning=True) return existent_directories @@ -62,9 +60,8 @@ def warn_in_case_of_duplicates(results_paths: Set[Path]) -> None: "Either way, this might cause trouble in the future:\n" "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." - ) - warn(warning_msg) + 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) def determine_cuda_runtime_lib_path() -> Union[Path, None]: @@ -90,10 +87,8 @@ def determine_cuda_runtime_lib_path() -> Union[Path, None]: if conda_cuda_libs: return next(iter(conda_cuda_libs)) - warn( - f'{candidate_env_vars["CONDA_PREFIX"]} did not contain ' - f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...' - ) + 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: lib_ld_cuda_libs = find_cuda_lib_in(candidate_env_vars["LD_LIBRARY_PATH"]) @@ -102,10 +97,8 @@ def determine_cuda_runtime_lib_path() -> Union[Path, None]: return next(iter(lib_ld_cuda_libs)) warn_in_case_of_duplicates(lib_ld_cuda_libs) - warn( - f'{candidate_env_vars["LD_LIBRARY_PATH"]} did not contain ' - f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...' - ) + CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["LD_LIBRARY_PATH"]} did not contain ' + f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True) remaining_candidate_env_vars = { env_var: value for env_var, value in candidate_env_vars.items() @@ -117,7 +110,7 @@ def determine_cuda_runtime_lib_path() -> Union[Path, None]: cuda_runtime_libs.update(find_cuda_lib_in(value)) if len(cuda_runtime_libs) == 0: - print('CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...') + CUDASetup.get_instance().add_log_entry('CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...') cuda_runtime_libs.update(find_cuda_lib_in('/usr/local/cuda/lib64')) warn_in_case_of_duplicates(cuda_runtime_libs) diff --git a/bitsandbytes/nn/__init__.py b/bitsandbytes/nn/__init__.py index 98d4aa0..edc595a 100644 --- a/bitsandbytes/nn/__init__.py +++ b/bitsandbytes/nn/__init__.py @@ -2,4 +2,4 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .modules import Int8Params, Linear8bit, Linear8bitLt, StableEmbedding +from .modules import Int8Params, Linear8bitLt, StableEmbedding diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py index 9250fec..4f82cdc 100644 --- a/bitsandbytes/nn/modules.py +++ b/bitsandbytes/nn/modules.py @@ -271,47 +271,3 @@ class Linear8bitLt(nn.Linear): del self.state.CxB return out - - -class Linear8bit(nn.Linear): - def __init__( - self, - input_features, - output_features, - bias=True, - quant_type="vector", - index=None, - args=None, - sparse_decomp=False, - ): - super(Linear8bit, self).__init__(input_features, output_features, bias) - self.quant_type = quant_type - self.index = index - self.args = args - self.iter = 0 - - def forward(self, x): - self.iter += 1 - if self.iter % self.args.clip_freq == 0: - with torch.no_grad(): - maxval, maxidx = torch.topk( - torch.abs(self.weight.flatten()), k=self.args.clip_idx - ) - if not dist.is_initialized() or dist.get_rank() == 0: - print("clip", maxval[-1].item()) - self.weight.clip_(-maxval[-1], maxval[-1]) - - if self.args is not None: - out = bnb.nn.functional.sparse_decomposed_linear8bit( - x, - self.weight, - self.bias, - qval=self.args.sparse_decomp_val, - quant_type=self.args.quant_type, - ) - else: - out = bnb.nn.functional.linear8bit( - x, self.weight, self.bias, quant_type=self.args.quant_type - ) - - return out |