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Compiling from source

Basic steps.

  1. CUDA_VERSION=XXX make [target] where [target] is among cuda92, cuda10x, cuda110, cuda11x, cuda12x, cpuonly
  2. python setup.py install

To run these steps you will need to have the nvcc compiler installed that comes with a CUDA installation. If you use anaconda (recommended) then you can figure out which version of CUDA you are using with PyTorch via the command conda list | grep cudatoolkit. Then you can install the nvcc compiler by downloading and installing the same CUDA version from the CUDA toolkit archive.

You can install CUDA locally without sudo by following the following steps:

wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
#   CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121}
#   EXPORT_TO_BASH in {0, 1} with 0=False and 1=True 

# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc
bash install_cuda.sh 117 ~/local 1

By default, the Makefile will look at your CUDA_HOME environmental variable to find your CUDA version for compiling the library. If this path is not set it is inferred from the path of your nvcc compiler.

Either nvcc needs to be in path for the CUDA_HOME variable needs to be set to the CUDA directory root (e.g. /usr/local/cuda) in order for compilation to succeed

If you type nvcc and it cannot be found, you might need to add to your path or set the CUDA_HOME variable. You can run python -m bitsandbytes to find the path to CUDA. For example if python -m bitsandbytes shows you the following:

++++++++++++++++++ /usr/local CUDA PATHS +++++++++++++++++++
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudart.so

You can set CUDA_HOME to /usr/local/cuda-11.7. For example, you might be able to compile like this.

CUDA_HOME=~/local/cuda-11.7 CUDA_VERSION=117 make cuda11x

If you have problems compiling the library with these instructions from source, please open an issue.

Compilation with Kepler

Since 0.39.1 bitsandbytes installed via pip no longer provides Kepler binaries and these need to be compiled from source. Follow the steps above and instead of cuda11x_nomatmul etc use cuda11x_nomatmul_kepler

Compilation with ROCm

Since this library requires hipblasLt this only supports ROCm 5.6+. Works well with these docker images:

For installation do:

make hip ROCM_TARGET=gfx1030
pip install .

see https://www.llvm.org/docs/AMDGPUUsage.html#processors for finding ROCM_TARGET (e.g. gfx1030 for 6800XT,6900XT) or do rocminfo | grep gfx.