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The bitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and quantization functions.

Resources:

• 8-bit Optimizer Paper --Video --Docs

• LLM.int8() Paper --LLM.int8() Software Blog Post --LLM.int8() Emergent Features Blog Post

enviroment 6.0DP,dustynv/auto_gptq:r36.2.0

git clone https://github.com/to-aoki/bitsandbytescd bitsandbytesCUDA_VERSION=122 make cuda12xpip3 install scipypython3 -m bitsandbytes:++++++++++++++++++++++++++ OTHER +++++++++++++++++++++++++++COMPILED_WITH_CUDA = TrueCOMPUTE_CAPABILITIES_PER_GPU = ['8.7']++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ DEBUG INFO END ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Running a quick check that:    + library is importable    + CUDAfunctionis callableWARNING: Please be sure to sanitize sensible info from any such env vars!SUCCESS!Installation was successful!pip3 list| grep bitsandbytesbitsandbytes       0.41.2           /home/user/temp/bitsandbytes

RequirementsPython >=3.8. Linux distribution (Ubuntu, MacOS, etc.) + CUDA > 10.0.

(Deprecated: CUDA 10.0 is deprecated and only CUDA >= 11.0) will be supported with release 0.39.0)

Installation:

pip install bitsandbytes

In some cases it can happen that you need to compile from source. If this happens please consider submitting a bug report withpython -m bitsandbytes information. What now follows is some short instructions which might work out of the box ifnvcc is installed. If these do not work see further below.

Compilation quickstart:

git clone https://github.com/timdettmers/bitsandbytes.gitcd bitsandbytes# CUDA_VERSIONS in {110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 120}# make argument in {cuda110, cuda11x, cuda12x}# if you do not know what CUDA you have, try looking at the output of: python -m bitsandbytesCUDA_VERSION=117 make cuda11xpython setup.py install

Using Int8 inference with HuggingFace Transformers

fromtransformersimportAutoModelForCausalLMmodel=AutoModelForCausalLM.from_pretrained(  'decapoda-research/llama-7b-hf,device_map='auto',load_in_8bit=True,max_memory=f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB')

A more detailed example, can be found inexamples/int8_inference_huggingface.py.

Using 8-bit optimizer:

1. Comment out optimizer:#torch.optim.Adam(....)
2. Add 8-bit optimizer of your choicebnb.optim.Adam8bit(....) (arguments stay the same)
3. Replace embedding layer if necessary:torch.nn.Embedding(..) -> bnb.nn.Embedding(..)

Using 8-bit Inference:

1. Comment out torch.nn.Linear:#linear = torch.nn.Linear(...)
2. Add bnb 8-bit linear light module:linear = bnb.nn.Linear8bitLt(...) (base arguments stay the same)
3. There are two modes:
   • Mixed 8-bit training with 16-bit main weights. Pass the argumenthas_fp16_weights=True (default)
   • Int8 inference. Pass the argumenthas_fp16_weights=False
4. To use the full LLM.int8() method, use thethreshold=k argument. We recommendk=6.0.

# LLM.int8()linear=bnb.nn.Linear8bitLt(dim1,dim2,bias=True,has_fp16_weights=False,threshold=6.0)# inputs need to be fp16out=linear(x.to(torch.float16))

• 8-bit Matrix multiplication with mixed precision decomposition
• LLM.int8() inference
• 8-bit Optimizers: Adam, AdamW, RMSProp, LARS, LAMB, Lion (saves 75% memory)
• Stable Embedding Layer: Improved stability through better initialization, and normalization
• 8-bit quantization: Quantile, Linear, and Dynamic quantization
• Fast quantile estimation: Up to 100x faster than other algorithms

Requirements: anaconda, cudatoolkit, pytorch

Hardware requirements:

• LLM.int8(): NVIDIA Turing (RTX 20xx; T4) or Ampere GPU (RTX 30xx; A4-A100); (a GPU from 2018 or older).
• 8-bit optimizers and quantization: NVIDIA Kepler GPU or newer (>=GTX 78X).

Supported CUDA versions: 10.2 - 12.0

The bitsandbytes library is currently only supported on Linux distributions. Windows is not supported at the moment.

The requirements can best be fulfilled by installing pytorch via anaconda. You can install PyTorch by following the"Get Started" instructions on the official website.

To install run:

pip install bitsandbytes

For straight Int8 matrix multiplication with mixed precision decomposition you can usebnb.matmul(...). To enable mixed precision decomposition, use the threshold parameter:

bnb.matmul(...,threshold=6.0)

For instructions how to use LLM.int8() inference layers in your own code, see the TL;DR above or for extended instruction seethis blog post.

With bitsandbytes 8-bit optimizers can be used by changing a single line of code in your codebase. For NLP models we recommend also to use the StableEmbedding layers (see below) which improves results and helps with stable 8-bit optimization. To get started with 8-bit optimizers, it is sufficient to replace your old optimizer with the 8-bit optimizer in the following way:

importbitsandbytesasbnb# adam = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.995)) # comment out old optimizeradam=bnb.optim.Adam8bit(model.parameters(),lr=0.001,betas=(0.9,0.995))# add bnb optimizeradam=bnb.optim.Adam(model.parameters(),lr=0.001,betas=(0.9,0.995),optim_bits=8)# equivalenttorch.nn.Embedding(...)->bnb.nn.StableEmbedding(...)# recommended for NLP models

Note that by default all parameter tensors with less than 4096 elements are kept at 32-bit even if you initialize those parameters with 8-bit optimizers. This is done since such small tensors do not save much memory and often contain highly variable parameters (biases) or parameters that require high precision (batch norm, layer norm). You can change this behavior like so:

# parameter tensors with less than 16384 values are optimized in 32-bit# it is recommended to use multiplies of 4096adam = bnb.optim.Adam8bit(model.parameters(), min_8bit_size=16384)

If you want to optimize some unstable parameters with 32-bit Adam and others with 8-bit Adam, you can use theGlobalOptimManager. With this, we can also configure specific hyperparameters for particular layers, such as embedding layers. To do that, we need two things: (1) register the parameter while they are still on the CPU, (2) override the config with the new desired hyperparameters (anytime, anywhere). See ourguide for more details

To use the Stable Embedding Layer, override the respectivebuild_embedding(...) function of your model. Make sure to also use the--no-scale-embedding flag to disable scaling of the word embedding layer (nor replaced with layer norm). You can use the optimizers by replacing the optimizer in the respective file (adam.py etc.).

For upcoming features and changes and full history seePatch Notes.

1. RuntimeError: CUDA error: no kernel image is available for execution on the device.Solution
2. _fatbinwrap..Solution

To compile from source, you need an installation of CUDA. Ifnvcc is not installed, you can install the CUDA Toolkit with nvcc through the following commands.

wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/cuda_install.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 11.8 to ~/local/cuda-11.8 and exports the path to your .bashrcbash cuda install 118~/local 1

To use a specific CUDA version just for a single compile run, you can set the variableCUDA_HOME, for example the following command compileslibbitsandbytes_cuda117.so using compiler flags for cuda11x with the cuda version at~/local/cuda-11.7:

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

For more detailed instruction, please follow thecompile_from_source.md instructions.

The majority of bitsandbytes is licensed under MIT, however portions of the project are available under separate license terms: Pytorch is licensed under the BSD license.

We thank Fabio Cannizzo for his work onFastBinarySearch which we use for CPU quantization.

If you found this library and found LLM.int8() useful, please consider citing our work:

@article{dettmers2022llmint8,title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},journal={arXiv preprint arXiv:2208.07339},year={2022}}

For 8-bit optimizers or quantization routines, please consider citing the following work:

@article{dettmers2022optimizers,title={8-bit Optimizers via Block-wise Quantization},author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},journal={9th International Conference on Learning Representations, ICLR},year={2022}}