2 files changed, 10 insertions, 3 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index a5446b7..01e7073 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -6,6 +6,7 @@ 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)
 
diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py
index b4409e4..1bddb52 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: