Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models

摘要

How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (BCQ) and uniform quantization (UQ) are promising quantization schemes that have strong expressiveness and optimizability, respectively. However, neither scheme leverages both advantages. In this paper, we propose UniQuan_F (Unified Quantization with Flexible Mapping), an accurate quantization method for LLMs. UniQuan_F harnesses both strong expressiveness and optimizability by unifying the flexible mapping technique in UQ and BCQ’s non-uniform quantization levels. We propose unified initialization, and local and periodic mapping techniques to optimize the parameters in UniQuan_F precisely. After optimization, our unification theorem removes computational and memory overhead, allowing us to utilize the superior accuracy of UniQuan_F without extra deployment costs induced by the unification. Experimental results demonstrate that UniQuan_F outperforms existing UQ and BCQ methods, achieving up to 4.60% higher accuracy on GSM8K benchmark.