424 lines
19 KiB
Python
424 lines
19 KiB
Python
"""
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extract factors the build is dependent on:
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[X] compute capability
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[ ] TODO: Q - What if we have multiple GPUs of different makes?
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- CUDA version
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- Software:
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- CPU-only: only CPU quantization functions (no optimizer, no matrix multipl)
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- CuBLAS-LT: full-build 8-bit optimizer
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- no CuBLAS-LT: no 8-bit matrix multiplication (`nomatmul`)
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evaluation:
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- if paths faulty, return meaningful error
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- else:
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- determine CUDA version
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- determine capabilities
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- based on that set the default path
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"""
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import ctypes as ct
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import os
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import errno
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import torch
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from warnings import warn
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from itertools import product
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from pathlib import Path
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from typing import Set, Union
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from .env_vars import get_potentially_lib_path_containing_env_vars
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# these are the most common libs names
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# libcudart.so is missing by default for a conda install with PyTorch 2.0 and instead
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# we have libcudart.so.11.0 which causes a lot of errors before
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# not sure if libcudart.so.12.0 exists in pytorch installs, but it does not hurt
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CUDA_RUNTIME_LIBS: list = ["libcudart.so", 'libcudart.so.11.0', 'libcudart.so.12.0']
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# this is a order list of backup paths to search CUDA in, if it cannot be found in the main environmental paths
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backup_paths = []
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backup_paths.append('$CONDA_PREFIX/lib/libcudart.so.11.0')
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class CUDASetup:
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_instance = None
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def __init__(self):
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raise RuntimeError("Call get_instance() instead")
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def generate_instructions(self):
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if self.cuda is None:
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self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA library was not detected.')
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self.add_log_entry('CUDA SETUP: Solution 1): Your paths are probably not up-to-date. You can update them via: sudo ldconfig.')
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self.add_log_entry('CUDA SETUP: Solution 2): If you do not have sudo rights, you can do the following:')
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self.add_log_entry('CUDA SETUP: Solution 2a): Find the cuda library via: find / -name libcuda.so 2>/dev/null')
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self.add_log_entry('CUDA SETUP: Solution 2b): Once the library is found add it to the LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:FOUND_PATH_FROM_2a')
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self.add_log_entry('CUDA SETUP: Solution 2c): For a permanent solution add the export from 2b into your .bashrc file, located at ~/.bashrc')
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return
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if self.cudart_path is None:
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self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA runtime library was not detected.')
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self.add_log_entry('CUDA SETUP: Solution 1: To solve the issue the libcudart.so location needs to be added to the LD_LIBRARY_PATH variable')
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self.add_log_entry('CUDA SETUP: Solution 1a): Find the cuda runtime library via: find / -name libcudart.so 2>/dev/null')
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self.add_log_entry('CUDA SETUP: Solution 1b): Once the library is found add it to the LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:FOUND_PATH_FROM_1a')
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self.add_log_entry('CUDA SETUP: Solution 1c): For a permanent solution add the export from 1b into your .bashrc file, located at ~/.bashrc')
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self.add_log_entry('CUDA SETUP: Solution 2: If no library was found in step 1a) you need to install CUDA.')
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self.add_log_entry('CUDA SETUP: Solution 2a): Download CUDA install script: wget https://github.com/TimDettmers/bitsandbytes/blob/main/cuda_install.sh')
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self.add_log_entry('CUDA SETUP: Solution 2b): Install desired CUDA version to desired location. The syntax is bash cuda_install.sh CUDA_VERSION PATH_TO_INSTALL_INTO.')
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self.add_log_entry('CUDA SETUP: Solution 2b): For example, "bash cuda_install.sh 113 ~/local/" will download CUDA 11.3 and install into the folder ~/local')
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return
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make_cmd = f'CUDA_VERSION={self.cuda_version_string}'
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if len(self.cuda_version_string) < 3:
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make_cmd += ' make cuda92'
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elif self.cuda_version_string == '110':
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make_cmd += ' make cuda110'
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elif self.cuda_version_string[:2] == '11' and int(self.cuda_version_string[2]) > 0:
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make_cmd += ' make cuda11x'
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elif self.cuda_version_string == '100':
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self.add_log_entry('CUDA SETUP: CUDA 10.0 not supported. Please use a different CUDA version.')
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self.add_log_entry('CUDA SETUP: Before you try again running bitsandbytes, make sure old CUDA 10.0 versions are uninstalled and removed from $LD_LIBRARY_PATH variables.')
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return
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has_cublaslt = is_cublasLt_compatible(self.cc)
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if not has_cublaslt:
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make_cmd += '_nomatmul'
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self.add_log_entry('CUDA SETUP: Something unexpected happened. Please compile from source:')
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self.add_log_entry('git clone git@github.com:TimDettmers/bitsandbytes.git')
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self.add_log_entry('cd bitsandbytes')
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self.add_log_entry(make_cmd)
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self.add_log_entry('python setup.py install')
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def initialize(self):
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if not getattr(self, 'initialized', False):
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self.has_printed = False
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self.lib = None
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self.initialized = False
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def run_cuda_setup(self):
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self.initialized = True
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self.cuda_setup_log = []
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binary_name, cudart_path, cuda, cc, cuda_version_string = evaluate_cuda_setup()
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self.cudart_path = cudart_path
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self.cuda = cuda
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self.cc = cc
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self.cuda_version_string = cuda_version_string
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package_dir = Path(__file__).parent.parent
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binary_path = package_dir / binary_name
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print('bin', binary_path)
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try:
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if not binary_path.exists():
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self.add_log_entry(f"CUDA SETUP: Required library version not found: {binary_name}. Maybe you need to compile it from source?")
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legacy_binary_name = "libbitsandbytes_cpu.so"
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self.add_log_entry(f"CUDA SETUP: Defaulting to {legacy_binary_name}...")
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binary_path = package_dir / legacy_binary_name
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if not binary_path.exists() or torch.cuda.is_available():
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self.add_log_entry('')
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self.add_log_entry('='*48 + 'ERROR' + '='*37)
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self.add_log_entry('CUDA SETUP: CUDA detection failed! Possible reasons:')
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self.add_log_entry('1. CUDA driver not installed')
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self.add_log_entry('2. CUDA not installed')
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self.add_log_entry('3. You have multiple conflicting CUDA libraries')
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self.add_log_entry('4. Required library not pre-compiled for this bitsandbytes release!')
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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`.')
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self.add_log_entry('CUDA SETUP: The CUDA version for the compile might depend on your conda install. Inspect CUDA version via `conda list | grep cuda`.')
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self.add_log_entry('='*80)
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self.add_log_entry('')
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self.generate_instructions()
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raise Exception('CUDA SETUP: Setup Failed!')
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self.lib = ct.cdll.LoadLibrary(binary_path)
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else:
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self.add_log_entry(f"CUDA SETUP: Loading binary {binary_path}...")
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self.lib = ct.cdll.LoadLibrary(binary_path)
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except Exception as ex:
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self.add_log_entry(str(ex))
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def add_log_entry(self, msg, is_warning=False):
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self.cuda_setup_log.append((msg, is_warning))
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def print_log_stack(self):
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for msg, is_warning in self.cuda_setup_log:
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if is_warning:
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warn(msg)
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else:
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print(msg)
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@classmethod
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def get_instance(cls):
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if cls._instance is None:
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cls._instance = cls.__new__(cls)
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cls._instance.initialize()
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return cls._instance
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def is_cublasLt_compatible(cc):
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has_cublaslt = False
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if cc is not None:
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cc_major, cc_minor = cc.split('.')
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if int(cc_major) < 7 or (int(cc_major) == 7 and int(cc_minor) < 5):
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CUDASetup.get_instance().add_log_entry("WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!", is_warning=True)
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else:
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has_cublaslt = True
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return has_cublaslt
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def extract_candidate_paths(paths_list_candidate: str) -> Set[Path]:
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return {Path(ld_path) for ld_path in paths_list_candidate.split(":") if ld_path}
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def remove_non_existent_dirs(candidate_paths: Set[Path]) -> Set[Path]:
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existent_directories: Set[Path] = set()
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for path in candidate_paths:
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try:
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if path.exists():
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existent_directories.add(path)
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except OSError as exc:
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if exc.errno != errno.ENAMETOOLONG:
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raise exc
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non_existent_directories: Set[Path] = candidate_paths - existent_directories
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if non_existent_directories:
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CUDASetup.get_instance().add_log_entry("WARNING: The following directories listed in your path were found to "
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f"be non-existent: {non_existent_directories}", is_warning=True)
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return existent_directories
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def get_cuda_runtime_lib_paths(candidate_paths: Set[Path]) -> Set[Path]:
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paths = set()
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for libname in CUDA_RUNTIME_LIBS:
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for path in candidate_paths:
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if (path / libname).is_file():
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paths.add(path / libname)
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return paths
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def resolve_paths_list(paths_list_candidate: str) -> Set[Path]:
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"""
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Searches a given environmental var for the CUDA runtime library,
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i.e. `libcudart.so`.
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"""
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return remove_non_existent_dirs(extract_candidate_paths(paths_list_candidate))
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def find_cuda_lib_in(paths_list_candidate: str) -> Set[Path]:
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return get_cuda_runtime_lib_paths(
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resolve_paths_list(paths_list_candidate)
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)
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def warn_in_case_of_duplicates(results_paths: Set[Path]) -> None:
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if len(results_paths) > 1:
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warning_msg = (
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f"Found duplicate {CUDA_RUNTIME_LIB} files: {results_paths}.. "
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"We'll flip a coin and try one of these, in order to fail forward.\n"
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"Either way, this might cause trouble in the future:\n"
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"If you get `CUDA error: invalid device function` errors, the above "
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"might be the cause and the solution is to make sure only one "
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f"{CUDA_RUNTIME_LIB} in the paths that we search based on your env.")
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CUDASetup.get_instance().add_log_entry(warning_msg, is_warning=True)
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def determine_cuda_runtime_lib_path() -> Union[Path, None]:
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"""
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Searches for a cuda installations, in the following order of priority:
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1. active conda env
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2. LD_LIBRARY_PATH
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3. any other env vars, while ignoring those that
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- are known to be unrelated (see `bnb.cuda_setup.env_vars.to_be_ignored`)
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- don't contain the path separator `/`
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If multiple libraries are found in part 3, we optimistically try one,
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while giving a warning message.
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"""
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candidate_env_vars = get_potentially_lib_path_containing_env_vars()
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if "CONDA_PREFIX" in candidate_env_vars:
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conda_libs_path = Path(candidate_env_vars["CONDA_PREFIX"]) / "lib"
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conda_cuda_libs = find_cuda_lib_in(str(conda_libs_path))
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warn_in_case_of_duplicates(conda_cuda_libs)
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if conda_cuda_libs:
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return next(iter(conda_cuda_libs))
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CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["CONDA_PREFIX"]} did not contain '
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f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True)
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if "LD_LIBRARY_PATH" in candidate_env_vars:
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lib_ld_cuda_libs = find_cuda_lib_in(candidate_env_vars["LD_LIBRARY_PATH"])
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if lib_ld_cuda_libs:
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return next(iter(lib_ld_cuda_libs))
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warn_in_case_of_duplicates(lib_ld_cuda_libs)
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CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["LD_LIBRARY_PATH"]} did not contain '
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f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True)
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remaining_candidate_env_vars = {
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env_var: value for env_var, value in candidate_env_vars.items()
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if env_var not in {"CONDA_PREFIX", "LD_LIBRARY_PATH"}
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}
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cuda_runtime_libs = set()
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for env_var, value in remaining_candidate_env_vars.items():
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cuda_runtime_libs.update(find_cuda_lib_in(value))
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if len(cuda_runtime_libs) == 0:
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CUDASetup.get_instance().add_log_entry('CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...')
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cuda_runtime_libs.update(find_cuda_lib_in('/usr/local/cuda/lib64'))
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warn_in_case_of_duplicates(cuda_runtime_libs)
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return next(iter(cuda_runtime_libs)) if cuda_runtime_libs else None
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def check_cuda_result(cuda, result_val):
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# 3. Check for CUDA errors
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if result_val != 0:
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error_str = ct.c_char_p()
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cuda.cuGetErrorString(result_val, ct.byref(error_str))
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if error_str.value is not None:
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CUDASetup.get_instance().add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}")
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else:
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CUDASetup.get_instance().add_log_entry(f"Unknown CUDA exception! Please check your CUDA install. It might also be that your GPU is too old.")
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# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
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def get_cuda_version(cuda, cudart_path):
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if cuda is None: return None
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try:
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cudart = ct.CDLL(cudart_path)
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except OSError:
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CUDASetup.get_instance().add_log_entry(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
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return None
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version = ct.c_int()
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try:
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check_cuda_result(cuda, cudart.cudaRuntimeGetVersion(ct.byref(version)))
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except AttributeError as e:
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CUDASetup.get_instance().add_log_entry(f'ERROR: {str(e)}')
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CUDASetup.get_instance().add_log_entry(f'CUDA SETUP: libcudart.so path is {cudart_path}')
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CUDASetup.get_instance().add_log_entry(f'CUDA SETUP: Is seems that your cuda installation is not in your path. See https://github.com/TimDettmers/bitsandbytes/issues/85 for more information.')
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version = int(version.value)
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major = version//1000
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minor = (version-(major*1000))//10
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if major < 11:
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CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currently not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!')
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return f'{major}{minor}'
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def get_cuda_lib_handle():
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# 1. find libcuda.so library (GPU driver) (/usr/lib)
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try:
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cuda = ct.CDLL("libcuda.so")
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except OSError:
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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!')
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return None
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check_cuda_result(cuda, cuda.cuInit(0))
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return cuda
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def get_compute_capabilities(cuda):
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"""
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1. find libcuda.so library (GPU driver) (/usr/lib)
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init_device -> init variables -> call function by reference
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2. call extern C function to determine CC
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(https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__DEVICE__DEPRECATED.html)
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3. Check for CUDA errors
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https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
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# bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
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"""
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nGpus = ct.c_int()
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cc_major = ct.c_int()
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cc_minor = ct.c_int()
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device = ct.c_int()
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check_cuda_result(cuda, cuda.cuDeviceGetCount(ct.byref(nGpus)))
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ccs = []
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for i in range(nGpus.value):
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check_cuda_result(cuda, cuda.cuDeviceGet(ct.byref(device), i))
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ref_major = ct.byref(cc_major)
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ref_minor = ct.byref(cc_minor)
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# 2. call extern C function to determine CC
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check_cuda_result(cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device))
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ccs.append(f"{cc_major.value}.{cc_minor.value}")
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return ccs
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# def get_compute_capability()-> Union[List[str, ...], None]: # FIXME: error
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def get_compute_capability(cuda):
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"""
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Extracts the highest compute capbility from all available GPUs, as compute
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capabilities are downwards compatible. If no GPUs are detected, it returns
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None.
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"""
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if cuda is None: return None
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# TODO: handle different compute capabilities; for now, take the max
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ccs = get_compute_capabilities(cuda)
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if ccs: return ccs[-1]
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def evaluate_cuda_setup():
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if 'BITSANDBYTES_NOWELCOME' not in os.environ or str(os.environ['BITSANDBYTES_NOWELCOME']) == '0':
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print('')
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print('='*35 + 'BUG REPORT' + '='*35)
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print(('Welcome to bitsandbytes. For bug reports, please run\n\npython -m bitsandbytes\n\n'),
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('and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues'))
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print('='*80)
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if not torch.cuda.is_available(): return 'libbitsandbytes_cpu.so', None, None, None, None
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cuda_setup = CUDASetup.get_instance()
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cudart_path = determine_cuda_runtime_lib_path()
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cuda = get_cuda_lib_handle()
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cc = get_compute_capability(cuda)
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cuda_version_string = get_cuda_version(cuda, cudart_path)
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failure = False
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if cudart_path is None:
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failure = True
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cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
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else:
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cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
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if cc == '' or cc is None:
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failure = True
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cuda_setup.add_log_entry("WARNING: No GPU detected! Check your CUDA paths. Proceeding to load CPU-only library...", is_warning=True)
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else:
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cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
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if cuda is None:
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failure = True
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else:
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cuda_setup.add_log_entry(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')
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# 7.5 is the minimum CC vor cublaslt
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has_cublaslt = is_cublasLt_compatible(cc)
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# TODO:
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# (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
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# (2) Multiple CUDA versions installed
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# we use ls -l instead of nvcc to determine the cuda version
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# since most installations will have the libcudart.so installed, but not the compiler
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if failure:
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binary_name = "libbitsandbytes_cpu.so"
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elif has_cublaslt:
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binary_name = f"libbitsandbytes_cuda{cuda_version_string}.so"
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else:
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"if not has_cublaslt (CC < 7.5), then we have to choose _nocublaslt.so"
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binary_name = f"libbitsandbytes_cuda{cuda_version_string}_nocublaslt.so"
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return binary_name, cudart_path, cuda, cc, cuda_version_string
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