OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2308.13137v3)

Abstract: LLMs have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, leading to low performance, especially in extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (\textbf{OmniQuant}) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family size 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4 (4-bit weight, 4-bit activation), W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes are available at \url{https://github.com/OpenGVLab/OmniQuant}.

Exploring Low-Bit Quantization in LLMs with OmniQuant

Introduction to OmniQuant

In an endeavor to address the computational and memory intensity of deploying LLMs, the paper introduces OmniQuant - a novel technique for omnidirectionally calibrated quantization. Highlighting the inefficiencies of previous post-training quantization (PTQ) methods, especially in extremely low-bit settings, OmniQuant presents a solution that efficiently optimizes various quantization parameters. This method revolves around two core components: Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET), both designed to modulate extreme values in weights and activations to facilitate efficient quantization.

Methodology

Block-wise Quantization Error Minimization

OmniQuant employs a block-wise quantization error minimization framework, which is distinct in its ability to efficiently manage the solution space for quantization parameters. This approach avoids the daunting task of optimizing the entire model at once, a common challenge in existing PTQ methods. The block-wise strategy allows for sequential optimization of one layer before proceeding to the next, ensuring both efficiency and practicality in resource utilization.

Key Components of OmniQuant

1. Learnable Weight Clipping (LWC): LWC introduces learnable clipping thresholds for modulating the weight values, ensuring that extreme values do not detrimentally affect the quantization process. Unlike previous methods that employ handcrafted parameters, LWC utilizes a differentiable framework that learns the optimal clipping range during training.
2. Learnable Equivalent Transformation (LET): LET addresses the quantization of activations by applying learnable transformations that make the distribution of activations more uniform. This is particularly beneficial for handling outlier activations, an area where previous methods have struggled. LET operates within both linear layers and attention mechanisms, showcasing its versatility.

Experimental Evaluation

Extensive experiments validate the superior performance of OmniQuant in a variety of quantization settings ranging from W4A4 (4-bit weights and activations) to W2A16, across different model sizes within the LLaMA-2 family. Remarkably, OmniQuant demonstrates its ability to compress models efficiently without sacrificing inference speed or memory consumption significantly. The method not only outperforms existing PTQ techniques but also shows potential in instruction-tuned models, providing a robust solution for deploying LLMs in memory-constrained environments.

Implications and Future Developments

The introduction of OmniQuant marks a significant step forward in the efficient quantization of LLMs. By mitigating the computational and memory demands of deploying these models, OmniQuant paves the way for broader application of LLMs across various platforms. The method's effectiveness in different quantization configurations and its application to instruction-tuned models opens up new avenues for research, particularly in optimizing LLM deployment without compromising performance. Future developments could explore further enhancements to the LWC and LET components, focusing on expanding the range of models and quantization settings where OmniQuant can be effectively applied.

Conclusion

OmniQuant's novel approach to low-bit quantization addresses the critical challenges of deploying LLMs efficiently. Through its innovative components, Learnable Weight Clipping, and Learnable Equivalent Transformation, it offers a compelling solution that marries the performance of Quantization-Aware Training with the efficiency of Post-Training Quantization methods. As LLMs continue to evolve, techniques like OmniQuant will play a crucial role in making these models more accessible and applicable in real-world scenarios.