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Add Paged/ adam8bit/lion8bit for Sdxl bitsandbytes 0.39.1 cuda118 on windows (#623)
* ADD libbitsandbytes.dll for 0.38.1 * Delete libbitsandbytes_cuda116.dll * Delete cextension.py * add main.py * Update requirements.txt for bitsandbytes 0.38.1 * Update README.md for bitsandbytes-windows * Update README-ja.md for bitsandbytes 0.38.1 * Update main.py for return cuda118 * Update train_util.py for lion8bit * Update train_README-ja.md for lion8bit * Update train_util.py for add DAdaptAdan and DAdaptSGD * Update train_util.py for DAdaptadam * Update train_network.py for dadapt * Update train_README-ja.md for DAdapt * Update train_util.py for DAdapt * Update train_network.py for DAdaptAdaGrad * Update train_db.py for DAdapt * Update fine_tune.py for DAdapt * Update train_textual_inversion.py for DAdapt * Update train_textual_inversion_XTI.py for DAdapt * Revert "Merge branch 'qinglong' into main" This reverts commit b65c023083d6d1e8a30eb42eddd603d1aac97650, reversing changes made to f6fda20caf5e773d56bcfb5c4575c650bb85362b. * Revert "Update requirements.txt for bitsandbytes 0.38.1" This reverts commit 83abc60dfaddb26845f54228425b98dd67997528. * Revert "Delete cextension.py" This reverts commit 3ba4dfe046874393f2a022a4cbef3628ada35391. * Revert "Update README.md for bitsandbytes-windows" This reverts commit 4642c52086b5e9791233007e2fdfd97f832cd897. * Revert "Update README-ja.md for bitsandbytes 0.38.1" This reverts commit fa6d7485ac067ebc49e6f381afdb8dd2f12caa8f. * Update train_util.py for DAdaptLion * Update train_README-zh.md for dadaptlion * Update train_README-ja.md for DAdaptLion * add DAdatpt V3 * Alignment * Update train_util.py for experimental * Update train_util.py V3 * Update train_util.py * Update requirements.txt * Update train_README-zh.md * Update train_README-ja.md * Update train_util.py fix * Update train_util.py * support Prodigy * add lower * Update main.py * support PagedAdamW8bit/PagedLion8bit * Update requirements.txt * update for PageAdamW8bit and PagedLion8bit * Revert * revert main * Update train_util.py * update for bitsandbytes 0.39.1 * Update requirements.txt * vram leak fix --------- Co-authored-by: Pam <pamhome21@gmail.com>
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青龍聖者@bdsqlsz
GitHub
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commit
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bitsandbytes_windows/libbitsandbytes_cuda118.dll
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bitsandbytes_windows/libbitsandbytes_cuda118.dll
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@ -1,166 +1,492 @@
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"""
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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 multiple)
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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
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from .paths import determine_cuda_runtime_lib_path
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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 = ctypes.c_char_p()
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cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
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print(f"CUDA exception! Error code: {error_str.value.decode()}")
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def get_cuda_version(cuda, cudart_path):
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# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
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try:
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cudart = ctypes.CDLL(cudart_path)
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except OSError:
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# TODO: shouldn't we error or at least warn here?
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print(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
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return None
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version = ctypes.c_int()
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check_cuda_result(cuda, cudart.cudaRuntimeGetVersion(ctypes.byref(version)))
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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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print('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 = ctypes.CDLL("libcuda.so")
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except OSError:
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# TODO: shouldn't we error or at least warn here?
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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!')
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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 = ctypes.c_int()
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cc_major = ctypes.c_int()
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cc_minor = ctypes.c_int()
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device = ctypes.c_int()
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check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.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(ctypes.byref(device), i))
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ref_major = ctypes.byref(cc_major)
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ref_minor = ctypes.byref(cc_minor)
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# 2. call extern C function to determine CC
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check_cuda_result(
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cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device)
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)
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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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ccs = get_compute_capabilities(cuda)
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if ccs is not None:
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# TODO: handle different compute capabilities; for now, take the max
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return ccs[-1]
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return None
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def evaluate_cuda_setup():
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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 submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues')
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print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')
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print('='*80)
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return "libbitsandbytes_cuda116.dll" # $$$
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binary_name = "libbitsandbytes_cpu.so"
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#if not torch.cuda.is_available():
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#print('No GPU detected. Loading CPU library...')
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#return binary_name
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cudart_path = determine_cuda_runtime_lib_path()
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if cudart_path is None:
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print(
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"WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!"
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)
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return binary_name
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print(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
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cuda = get_cuda_lib_handle()
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cc = get_compute_capability(cuda)
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print(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
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cuda_version_string = get_cuda_version(cuda, cudart_path)
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if cc == '':
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print(
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"WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..."
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)
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return binary_name
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# 7.5 is the minimum CC vor cublaslt
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has_cublaslt = cc in ["7.5", "8.0", "8.6"]
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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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print(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')
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def get_binary_name():
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"if not has_cublaslt (CC < 7.5), then we have to choose _nocublaslt.so"
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bin_base_name = "libbitsandbytes_cuda"
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if has_cublaslt:
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return f"{bin_base_name}{cuda_version_string}.so"
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else:
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return f"{bin_base_name}{cuda_version_string}_nocublaslt.so"
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binary_name = get_binary_name()
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return binary_name
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"""
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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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|
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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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import platform
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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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IS_WINDOWS_PLATFORM: bool = (platform.system()=="Windows")
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PATH_COLLECTION_SEPARATOR: str = ":" if not IS_WINDOWS_PLATFORM else ";"
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CUDA_RUNTIME_LIBS: list = ["libcudart.so", 'libcudart.so.11.0', 'libcudart.so.12.0'] if not IS_WINDOWS_PLATFORM else ["cudart64_110.dll", "cudart64_120.dll", "cudart64_12.dll"]
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backup_paths.append('$CONDA_PREFIX/lib/libcudart.so.11.0' if not IS_WINDOWS_PLATFORM else '%CONDA_PREFIX%\\lib\\cudart64_110.dll')
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CUDA_SHARED_LIB_NAME: str = "libcuda.so" if not IS_WINDOWS_PLATFORM else f"{os.environ['SystemRoot']}\\System32\\nvcuda.dll"
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SHARED_LIB_EXTENSION: str = ".so" if not IS_WINDOWS_PLATFORM else ".dll"
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class CUDASetup:
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_instance = None
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def __init__(self):
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raise RuntimeError("Call get_instance() instead")
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def generate_instructions(self):
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if getattr(self, 'error', False): return
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print(self.error)
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self.error = True
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if self.cuda is None:
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self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA library was not detected.')
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self.add_log_entry('CUDA SETUP: Solution 1): Your paths are probably not up-to-date. You can update them via: sudo ldconfig.')
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self.add_log_entry('CUDA SETUP: Solution 2): If you do not have sudo rights, you can do the following:')
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self.add_log_entry('CUDA SETUP: Solution 2a): Find the cuda library via: find / -name libcuda.so 2>/dev/null')
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self.add_log_entry('CUDA SETUP: Solution 2b): Once the library is found add it to the LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$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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self.error = 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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|
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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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|
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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" + SHARED_LIB_EXTENSION
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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:')
|
||||
self.add_log_entry('1. CUDA driver not installed')
|
||||
self.add_log_entry('2. CUDA not installed')
|
||||
self.add_log_entry('3. You have multiple conflicting CUDA libraries')
|
||||
self.add_log_entry('4. Required library not pre-compiled for this bitsandbytes release!')
|
||||
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.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!')
|
||||
self.lib = ct.cdll.LoadLibrary(str(binary_path))
|
||||
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(str(binary_path))
|
||||
except Exception as ex:
|
||||
self.add_log_entry(str(ex))
|
||||
|
||||
def add_log_entry(self, msg, is_warning=False):
|
||||
self.cuda_setup_log.append((msg, is_warning))
|
||||
|
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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):
|
||||
if cls._instance is None:
|
||||
cls._instance = cls.__new__(cls)
|
||||
cls._instance.initialize()
|
||||
return cls._instance
|
||||
|
||||
|
||||
def is_cublasLt_compatible(cc):
|
||||
has_cublaslt = False
|
||||
if cc is not None:
|
||||
cc_major, cc_minor = cc.split('.')
|
||||
if int(cc_major) < 7 or (int(cc_major) == 7 and int(cc_minor) < 5):
|
||||
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)
|
||||
else:
|
||||
has_cublaslt = True
|
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return has_cublaslt
|
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|
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def extract_candidate_paths(paths_list_candidate: str) -> Set[Path]:
|
||||
return {Path(ld_path) for ld_path in paths_list_candidate.split(PATH_COLLECTION_SEPARATOR) if ld_path}
|
||||
|
||||
|
||||
def remove_non_existent_dirs(candidate_paths: Set[Path]) -> Set[Path]:
|
||||
existent_directories: Set[Path] = set()
|
||||
for path in candidate_paths:
|
||||
try:
|
||||
if path.exists():
|
||||
existent_directories.add(path)
|
||||
except OSError as exc:
|
||||
if exc.errno != errno.ENAMETOOLONG:
|
||||
raise exc
|
||||
|
||||
non_existent_directories: Set[Path] = candidate_paths - existent_directories
|
||||
if 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
|
||||
|
||||
|
||||
def get_cuda_runtime_lib_paths(candidate_paths: Set[Path]) -> Set[Path]:
|
||||
paths = set()
|
||||
for libname in CUDA_RUNTIME_LIBS:
|
||||
for path in candidate_paths:
|
||||
if (path / libname).is_file():
|
||||
paths.add(path / libname)
|
||||
return paths
|
||||
|
||||
|
||||
def resolve_paths_list(paths_list_candidate: str) -> Set[Path]:
|
||||
"""
|
||||
Searches a given environmental var for the CUDA runtime library,
|
||||
i.e. `libcudart.so`.
|
||||
"""
|
||||
return remove_non_existent_dirs(extract_candidate_paths(paths_list_candidate))
|
||||
|
||||
|
||||
def find_cuda_lib_in(paths_list_candidate: str) -> Set[Path]:
|
||||
return get_cuda_runtime_lib_paths(
|
||||
resolve_paths_list(paths_list_candidate)
|
||||
)
|
||||
|
||||
|
||||
def warn_in_case_of_duplicates(results_paths: Set[Path]) -> None:
|
||||
if len(results_paths) > 1:
|
||||
warning_msg = (
|
||||
f"Found duplicate {CUDA_RUNTIME_LIBS} files: {results_paths}.. "
|
||||
"We'll flip a coin and try one of these, in order to fail forward.\n"
|
||||
"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_LIBS} 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]:
|
||||
"""
|
||||
Searches for a cuda installations, in the following order of priority:
|
||||
1. active conda env
|
||||
2. LD_LIBRARY_PATH
|
||||
3. any other env vars, while ignoring those that
|
||||
- are known to be unrelated (see `bnb.cuda_setup.env_vars.to_be_ignored`)
|
||||
- don't contain the path separator `/`
|
||||
|
||||
If multiple libraries are found in part 3, we optimistically try one,
|
||||
while giving a warning message.
|
||||
"""
|
||||
candidate_env_vars = get_potentially_lib_path_containing_env_vars()
|
||||
|
||||
if "CONDA_PREFIX" in candidate_env_vars:
|
||||
conda_libs_path = Path(candidate_env_vars["CONDA_PREFIX"]) / "bin"
|
||||
|
||||
conda_cuda_libs = find_cuda_lib_in(str(conda_libs_path))
|
||||
warn_in_case_of_duplicates(conda_cuda_libs)
|
||||
|
||||
if conda_cuda_libs:
|
||||
return next(iter(conda_cuda_libs))
|
||||
|
||||
conda_libs_path = Path(candidate_env_vars["CONDA_PREFIX"]) / "lib"
|
||||
|
||||
conda_cuda_libs = find_cuda_lib_in(str(conda_libs_path))
|
||||
warn_in_case_of_duplicates(conda_cuda_libs)
|
||||
|
||||
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 '
|
||||
f'{CUDA_RUNTIME_LIBS} as expected! Searching further paths...', is_warning=True)
|
||||
|
||||
if "CUDA_PATH" in candidate_env_vars:
|
||||
ld_cuda_libs_path = Path(candidate_env_vars["CUDA_PATH"]) / "bin"
|
||||
|
||||
lib_ld_cuda_libs = find_cuda_lib_in(str(ld_cuda_libs_path))
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
ld_cuda_libs_path = Path(candidate_env_vars["CUDA_PATH"]) / "lib"
|
||||
|
||||
lib_ld_cuda_libs = find_cuda_lib_in(str(ld_cuda_libs_path))
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["CUDA_PATH"]} did not contain '
|
||||
f'{CUDA_RUNTIME_LIBS} as expected! Searching further paths...', is_warning=True)
|
||||
|
||||
if "CUDA_HOME" in candidate_env_vars:
|
||||
ld_cuda_libs_path = Path(candidate_env_vars["CUDA_HOME"]) / "bin"
|
||||
|
||||
lib_ld_cuda_libs = find_cuda_lib_in(str(ld_cuda_libs_path))
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
ld_cuda_libs_path = Path(candidate_env_vars["CUDA_HOME"]) / "lib"
|
||||
|
||||
lib_ld_cuda_libs = find_cuda_lib_in(str(ld_cuda_libs_path))
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["CUDA_HOME"]} did not contain '
|
||||
f'{CUDA_RUNTIME_LIBS} 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"])
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["LD_LIBRARY_PATH"]} did not contain '
|
||||
f'{CUDA_RUNTIME_LIBS} as expected! Searching further paths...', is_warning=True)
|
||||
|
||||
if "PATH" in candidate_env_vars:
|
||||
lib_ld_cuda_libs = find_cuda_lib_in(candidate_env_vars["PATH"])
|
||||
warn_in_case_of_duplicates(lib_ld_cuda_libs)
|
||||
|
||||
if lib_ld_cuda_libs:
|
||||
return next(iter(lib_ld_cuda_libs))
|
||||
|
||||
CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["PATH"]} did not contain '
|
||||
f'{CUDA_RUNTIME_LIBS} as expected! Searching further paths...', is_warning=True)
|
||||
|
||||
remaining_candidate_env_vars = {
|
||||
env_var: value for env_var, value in candidate_env_vars.items()
|
||||
if env_var not in {"CONDA_PREFIX", "CUDA_HOME", "CUDA_PATH", "LD_LIBRARY_PATH", "PATH"}
|
||||
}
|
||||
|
||||
cuda_runtime_libs = set()
|
||||
for env_var, value in remaining_candidate_env_vars.items():
|
||||
cuda_runtime_libs.update(find_cuda_lib_in(value))
|
||||
|
||||
if len(cuda_runtime_libs) == 0:
|
||||
CUDASetup.get_instance().add_log_entry('CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...')
|
||||
cuda_runtime_libs.update(find_cuda_lib_in('/usr/local/cuda/lib64'))
|
||||
|
||||
warn_in_case_of_duplicates(cuda_runtime_libs)
|
||||
|
||||
return next(iter(cuda_runtime_libs)) if cuda_runtime_libs else None
|
||||
|
||||
|
||||
def check_cuda_result(cuda, result_val):
|
||||
# 3. Check for CUDA errors
|
||||
if result_val != 0:
|
||||
error_str = ct.c_char_p()
|
||||
cuda.cuGetErrorString(result_val, ct.byref(error_str))
|
||||
if error_str.value is not None:
|
||||
CUDASetup.get_instance().add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}")
|
||||
else:
|
||||
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.")
|
||||
|
||||
|
||||
# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
|
||||
def get_cuda_version(cuda, cudart_path):
|
||||
if cuda is None: return None
|
||||
|
||||
try:
|
||||
cudart = ct.CDLL(str(cudart_path))
|
||||
except OSError:
|
||||
CUDASetup.get_instance().add_log_entry(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
|
||||
return None
|
||||
|
||||
version = ct.c_int()
|
||||
try:
|
||||
check_cuda_result(cuda, cudart.cudaRuntimeGetVersion(ct.byref(version)))
|
||||
except AttributeError as e:
|
||||
CUDASetup.get_instance().add_log_entry(f'ERROR: {str(e)}')
|
||||
CUDASetup.get_instance().add_log_entry(f'CUDA SETUP: libcudart.so path is {cudart_path}')
|
||||
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.')
|
||||
version = int(version.value)
|
||||
major = version//1000
|
||||
minor = (version-(major*1000))//10
|
||||
|
||||
if major < 11:
|
||||
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!!')
|
||||
|
||||
return f'{major}{minor}'
|
||||
|
||||
|
||||
def get_cuda_lib_handle():
|
||||
# 1. find libcuda.so library (GPU driver) (/usr/lib)
|
||||
try:
|
||||
cuda = ct.CDLL(CUDA_SHARED_LIB_NAME)
|
||||
except OSError:
|
||||
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))
|
||||
|
||||
return cuda
|
||||
|
||||
|
||||
def get_compute_capabilities(cuda):
|
||||
"""
|
||||
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
|
||||
# bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
|
||||
"""
|
||||
|
||||
nGpus = ct.c_int()
|
||||
cc_major = ct.c_int()
|
||||
cc_minor = ct.c_int()
|
||||
|
||||
device = ct.c_int()
|
||||
|
||||
check_cuda_result(cuda, cuda.cuDeviceGetCount(ct.byref(nGpus)))
|
||||
ccs = []
|
||||
for i in range(nGpus.value):
|
||||
check_cuda_result(cuda, cuda.cuDeviceGet(ct.byref(device), i))
|
||||
ref_major = ct.byref(cc_major)
|
||||
ref_minor = ct.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}")
|
||||
|
||||
return ccs
|
||||
|
||||
|
||||
# def get_compute_capability()-> Union[List[str, ...], None]: # FIXME: error
|
||||
def get_compute_capability(cuda):
|
||||
"""
|
||||
Extracts the highest compute capbility from all available GPUs, as compute
|
||||
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: return ccs[-1]
|
||||
|
||||
|
||||
def evaluate_cuda_setup():
|
||||
if 'BITSANDBYTES_NOWELCOME' not in os.environ or str(os.environ['BITSANDBYTES_NOWELCOME']) == '0':
|
||||
print('')
|
||||
print('='*35 + 'BUG REPORT' + '='*35)
|
||||
print(('Welcome to bitsandbytes. For bug reports, please run\n\npython -m bitsandbytes\n\n'),
|
||||
('and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues'))
|
||||
print('='*80)
|
||||
return 'libbitsandbytes_cuda118.dll', None, None, None, None
|
||||
if not torch.cuda.is_available(): return 'libbitsandbytes_cpu'+SHARED_LIB_EXTENSION, None, None, None, None
|
||||
|
||||
cuda_setup = CUDASetup.get_instance()
|
||||
cudart_path = determine_cuda_runtime_lib_path()
|
||||
cuda = get_cuda_lib_handle()
|
||||
cc = get_compute_capability(cuda)
|
||||
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 == '' 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 = is_cublasLt_compatible(cc)
|
||||
|
||||
# TODO:
|
||||
# (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
|
||||
# (2) Multiple CUDA versions installed
|
||||
|
||||
# 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
|
||||
|
||||
if failure:
|
||||
binary_name = "libbitsandbytes_cpu" + SHARED_LIB_EXTENSION
|
||||
elif has_cublaslt:
|
||||
binary_name = f"libbitsandbytes_cuda{cuda_version_string}" + SHARED_LIB_EXTENSION
|
||||
else:
|
||||
"if not has_cublaslt (CC < 7.5), then we have to choose _nocublaslt"
|
||||
binary_name = f"libbitsandbytes_cuda{cuda_version_string}_nocublaslt" + SHARED_LIB_EXTENSION
|
||||
|
||||
return binary_name, cudart_path, cuda, cc, cuda_version_string
|
||||
|
||||
@ -2165,6 +2165,8 @@ def cache_batch_latents(
|
||||
info.latents = latent
|
||||
if flip_aug:
|
||||
info.latents_flipped = flipped_latent
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def cache_batch_text_encoder_outputs(
|
||||
|
||||
@ -6,7 +6,7 @@ albumentations==1.3.0
|
||||
opencv-python==4.7.0.68
|
||||
einops==0.6.0
|
||||
pytorch-lightning==1.9.0
|
||||
bitsandbytes==0.35.0
|
||||
bitsandbytes==0.39.1
|
||||
tensorboard==2.10.1
|
||||
safetensors==0.3.1
|
||||
# gradio==3.16.2
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user