From f9cbe2fe99c805dcca934c66677951f428d3b3e2 Mon Sep 17 00:00:00 2001 From: Tim Dettmers Date: Mon, 8 Aug 2022 09:13:22 -0700 Subject: Fixed prod Python < 3.7 compatibility in function.py. --- bitsandbytes/autograd/_functions.py | 1 + bitsandbytes/functional.py | 12 +++++++++--- 2 files changed, 10 insertions(+), 3 deletions(-) (limited to 'bitsandbytes') 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: -- cgit v1.2.3