Merge branch 'cuda-bin-switch-and-cli' of github.com:TimDettmers/bitsandbytes into cuda-bin-switch-and-cli

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: