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
diff --git a/setup.py b/setup.py
index 67b655d..c425ca7 100644
--- a/setup.py
+++ b/setup.py
@@ -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.",