OmniQuant - Precision Quantization Techniques for Large Language Models like LLaMa and Falcon

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

OmniQuant is an advanced quantization technique designed specifically for Large Language Models (LLMs). It stands out for its simplicity and effectiveness, offering various quantization options that improve model efficiency without compromising performance. This technology is particularly valuable for those working with large-scale AI models and seeking ways to optimize both memory usage and processing speed.

Key Features

OmniQuant Algorithm: A robust tool for precise weight-only and weight-activation quantization. It supports configurations like W4A16, W3A16, W2A16 for weight-only, and W6A6, W4A4 for combined quantization.
Pre-Trained Model Availability: OmniQuant provides a repository of pre-trained models from different families, including LLaMA, OPT, and Falcon, that are ready to be used with quantized weights.
Versatile Deployment: An out-of-the-box solution allows the deployment of optimized models on GPUs and mobile devices using W3A16g128 quantization.

Recent Updates

PrefixQuant Algorithm: A new release improves activation quantization, surpassing previous dynamic methods.
EfficientQAT Algorithm: Integrates quantization-aware training methods, providing the current state-of-the-art in uniform quantization efficiently.
OmniQuant paper recognition at ICLR 2024, highlighting its significance and impact in the field.

Getting Started

Installation

Setting up OmniQuant on your machine is straightforward. You'll need Python 3.10, and the project is hosted on GitHub for easy access and implementation. Additionally, integrating the AutoGPTQ tool enhances the quantization process.

conda create -n omniquant python=3.10 -y
conda activate omniquant
git clone https://github.com/OpenGVLab/OmniQuant.git
cd OmniQuant
pip install --upgrade pip 
pip install -e .

Usage

OmniQuant provides scripts for both weight-only and weight-activation quantization. Users can customize parameters like weight bits, activation bits, and epochs to suit their specific model needs.

Example command for weight-only quantization:

CUDA_VISIBLE_DEVICES=0 python main.py --model /PATH/TO/LLaMA/llama-7b --epochs 20 --output_dir ./log/llama-7b-w3a16 --eval_ppl --wbits 3 --abits 16 --lwc

Performance and Results

OmniQuant demonstrates state-of-the-art performance in its category, achieving exceptional results in compressing models such as Falcon-180b with significant reductions in memory requirements. A notable achievement is enabling these large models to run on single GPUs and even on mobile devices, offering flexibility and accessibility in real-world applications.

Deploying Quantized Models with MLC-LLM

OmniQuant models can also be deployed on a variety of hardware platforms through MLC-LLM. This includes deployment on mobile phones using an Android app, demonstrating its versatility and the potential for wide-scale usage. Detailed user reviews on both Android and iOS underscore its practical utility.

Conclusion

OmniQuant presents itself as a powerful tool for optimizing large language models, providing solutions that span from cloud-based GPUs to mobile devices. It integrates advances in quantization techniques offering efficient, scalable, and versatile deployment options.

Citation

For those utilizing OmniQuant in research, the creators have provided a citation format to acknowledge the foundational work of the developers:

@article{OmniQuant,
  title={OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models},
  author={Shao, Wenqi and Chen, Mengzhao and Zhang, Zhaoyang and Xu, Peng and Zhao, Lirui and Li, Zhiqian and Zhang, Kaipeng Zhang, and Gao, Peng, and Qiao, Yu, and Luo, Ping},
  journal={arXiv preprint arXiv:2308.13137},
  year={2023}
}

OmniQuant not only advances the field of model optimization but also serves as a testament to the continuous progress in AI technology, bringing high-performance solutions to broader applications.