bitmat-tl 0.3.0

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

BitMat: Improving Ternary Matrix Multiplication with Triton

0️⃣1️⃣ 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: During inference the model uses packed int8 data to reduce memory usage and improve computational efficiency.

Ease of Integration: BitMat is designed to be easily integrated into existing PyTorch/transformers workflows, providing a seamless user experience.

💾 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 from an available hf model
model= AutoModelForCausalLM.from_pretrained("some-repo/some-model")
# Convert the model to use BitLinear layers
model = convert_hf_model(model)
# Save the converted model
model.save_pretrained('some_local_folder')

Loading the converted 1.58Bit Model

To utilize the converted 1.58Bit model, such as a customized version of Mistral in this exmaple, you will need to load the model from the AutoClass. Below is an example demonstrating how to load the model from a local directory:

from bitmat import Auto158ModelForCausalLM

# Replace 'path_to_your_model' with the actual path to your model's directory
model = Auto158ModelForCausalLM.from_pretrained('path_to_your_model')

Once loaded, the model operates in two distinct modes:

• Evaluation Mode: By default, the model employs quantized weights, optimizing performance for inference tasks. Activate this mode using model.eval().

• Training Mode: Switching to this mode, via model.train(), allows the model to leverage full-precision weights, which is essential for training and fine-tuning processes, ensuring accurate gradient calculations and effective model updates.

This API is fully compatible with the HuggingFace's Ecosystem

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 of BitDelta and UnSloth since part of the code is based on their work.