diff options
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 14 | ||||
| -rw-r--r-- | setup.py | 2 |
2 files changed, 9 insertions, 7 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 14f2660..a5446b7 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -1,10 +1,14 @@ -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 + +def prod(iterable): + return reduce(operator.mul, iterable, 1) + tensor = torch.Tensor """ @@ -12,8 +16,6 @@ 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 +203,7 @@ 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 @@ -18,7 +18,7 @@ def read(fname): setup( name=f"bitsandbytes", - version=f"0.31.4", + version=f"0.31.5", author="Tim Dettmers", author_email="dettmers@cs.washington.edu", description="8-bit optimizers and matrix multiplication routines.", |