bitmat-tl 0.1.7

An efficent implementation for the paper: "The Era of 1-bit LLMs"

BitMat: Improving Matrix Multiplication with Triton

Introduction

BitMat is a Python package designed to optimize matrix multiplication operations by utilizing custom kernels written in Triton. Our package leverages the principles outlined in the "1bit-LLM Era" paper, specifically utilizing packed int8 data to enhance computational efficiency and performance in deep learning and numerical computing tasks.

Features

Custom Triton Kernels: Utilize highly optimized kernels for matrix multiplication, tailored for performance and efficiency. Packed int8 Operations: Follows the methodologies from the "1bit-LLM Era" to use packed int8 data, reducing memory usage and increasing throughput. Ease of Integration: BitMat is designed to be easily integrated into existing PyTorch workflows, providing a seamless user experience. Performance Boost: Significant performance improvements in matrix multiplication, especially beneficial for large-scale deep learning models and high-dimensional data.

Installation

pip install bitmat-tl

At the moment we only support Linux platforms. Windows installation is possible but is not tested.

Quick Start

High-level API (tranformers-compatible)

from transformers import AutoModelForCausalLM
from bitmat import convert_hf_model

# Initialize your model
model= AutoModelForCausalLM.from_pretrained("some-repo/some-model")
# Convert the model to use BitLinear layers
model = convert_hf_model(model)

Low-level API

import torch
from bitmat import BitLinear

layer = BitLinear(in_features=1024, out_features=512, bias=True, eps=1e-5)
# You can use the layer as a normal torch.nn.Linear layer

Contributing

We welcome contributions from the community, whether it's adding new features, improving documentation, or reporting bugs. Please refer to our contribution guidelines before making a pull request.

License

BitMat is open-sourced under the Apache-2.0 license.

Citation

If you use BitMat in your research, please cite it using the following Bibtex entry:

@article{bitmat2024,
  title={BitMat: Improving Matrix Multiplication with Custom Triton Kernels},
  author={AstraMind AI},
  journal={https://github.com/astramind-ai/BitMat},
  year={2024}
}

Support

For questions, issues, or support regarding BitMat, please open an issue on our GitHub repository.

Acknowledgments

Special thanks to the Triton community and the authors of the "1bit-LLM Era" paper for their groundbreaking work and inspiration.

Also thanks to the developer od BitDelta and UnSloth since part of the code is based on their work.