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

This commit is contained in:
Tim Dettmers 2022-08-16 10:57:10 -07:00
commit 111b876449
6 changed files with 29 additions and 12 deletions

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@ -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

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@ -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)

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@ -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:

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@ -65,7 +65,7 @@ if [[ -n "$CUDA_VERSION" ]]; then
echo $URL
echo $FILE
wget $URL
bash $FILE --no-drm --no-man-page --override --installpath=~/local --librarypath=$BASE_PATH/lib --toolkitpath=$BASE_PATH/$FOLDER/ --toolkit --silent
bash $FILE --no-drm --no-man-page --override --toolkitpath=$BASE_PATH/$FOLDER/ --toolkit --silent
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$BASE_PATH/$FOLDER/lib64/" >> ~/.bashrc
echo "export PATH=$PATH:$BASE_PATH/$FOLDER/bin/" >> ~/.bashrc
source ~/.bashrc

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@ -202,4 +202,4 @@ if [ ! -f "./bitsandbytes/libbitsandbytes_cuda117_nocublaslt.so" ]; then
fi
python -m build
python -m twine upload dist/* --verbose --repository testpypi
python -m twine upload dist/* --verbose

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@ -18,7 +18,7 @@ def read(fname):
setup(
name=f"bitsandbytes",
version=f"0.31.4",
version=f"0.31.8",
author="Tim Dettmers",
author_email="dettmers@cs.washington.edu",
description="8-bit optimizers and matrix multiplication routines.",