diff options
Diffstat (limited to 'bitsandbytes')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 15 | ||||
| -rw-r--r-- | bitsandbytes/cuda_setup/main.py | 8 | ||||
| -rw-r--r-- | bitsandbytes/functional.py | 12 |
3 files changed, 26 insertions, 9 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 14f2660..01e7073 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -1,10 +1,15 @@ -from dataclasses import dataclass - +import operator import torch -import math import bitsandbytes as bnb import bitsandbytes.functional as F +from dataclasses import dataclass +from functools import reduce # Required in Python 3 + +# math.prod not compatible with python < 3.8 +def prod(iterable): + return reduce(operator.mul, iterable, 1) + tensor = torch.Tensor """ @@ -12,8 +17,6 @@ tensor = torch.Tensor This is particularly important for small models where outlier features are less systematic and occur with low frequency. """ - - class GlobalOutlierPooler(object): _instance = None @@ -201,7 +204,7 @@ class MatMul8bitLt(torch.autograd.Function): def forward(ctx, A, B, out=None, state=MatmulLtState()): # default to pytorch behavior if inputs are empty ctx.is_empty = False - if math.prod(A.shape) == 0: + if prod(A.shape) == 0: ctx.is_empty = True ctx.A = A ctx.B = B diff --git a/bitsandbytes/cuda_setup/main.py b/bitsandbytes/cuda_setup/main.py index f1c845c..1f2ceb4 100644 --- a/bitsandbytes/cuda_setup/main.py +++ b/bitsandbytes/cuda_setup/main.py @@ -45,6 +45,9 @@ def get_cuda_version(cuda, cudart_path): major = version//1000 minor = (version-(major*1000))//10 + if major < 11: + print('CUDA SETUP: CUDA version lower than 11 are currenlty not supported!') + return f'{major}{minor}' @@ -110,6 +113,10 @@ def get_compute_capability(cuda): def evaluate_cuda_setup(): + print('') + print('='*35 + 'BUG REPORT' + '='*35) + print('Welcome to bitsandbytes. For bug reports, please use this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link') + print('='*80) binary_name = "libbitsandbytes_cpu.so" cudart_path = determine_cuda_runtime_lib_path() if cudart_path is None: @@ -121,6 +128,7 @@ def evaluate_cuda_setup(): print(f"CUDA SETUP: CUDA 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_version_string = get_cuda_version(cuda, cudart_path) diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py index 23e5464..6637554 100644 --- a/bitsandbytes/functional.py +++ b/bitsandbytes/functional.py @@ -3,6 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import ctypes as ct +import operator import random import math import torch @@ -11,6 +12,11 @@ from typing import Tuple from torch import Tensor from .cextension import COMPILED_WITH_CUDA, lib +from functools import reduce # Required in Python 3 + +# math.prod not compatible with python < 3.8 +def prod(iterable): + return reduce(operator.mul, iterable, 1) name2qmap = {} @@ -326,8 +332,8 @@ def nvidia_transform( dim1 = ct.c_int32(shape[0]) dim2 = ct.c_int32(shape[1]) elif ld is not None: - n = math.prod(shape) - dim1 = math.prod([shape[i] for i in ld]) + n = prod(shape) + dim1 = prod([shape[i] for i in ld]) dim2 = ct.c_int32(n // dim1) dim1 = ct.c_int32(dim1) else: @@ -1314,7 +1320,7 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32): m = shapeA[0] * shapeA[1] rows = n = shapeB[0] - assert math.prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}' + assert prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}' # if the tensor is empty, return a transformed empty tensor with the right dimensions if shapeA[0] == 0 and dimsA == 2: |