PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling

TL;DR

This paper introduces PCDVQ, a novel vector quantization method that decouples direction and magnitude in polar coordinates to improve the accuracy of large language models at low-bit quantization levels.

Contribution

The paper proposes PCDVQ, a new VQ framework with polar coordinate decoupling and distribution-aligned codebooks, enhancing quantization accuracy for LLMs.

Findings

Outperforms baseline methods at 2-bit quantization by at least 1.5% zero-shot accuracy.

Decoupling direction and magnitude reduces quantization errors significantly.

Polar coordinate transformation improves the sensitivity of quantization to important vector features.

Abstract

Large Language Models (LLMs) face significant challenges in edge deployment due to their massive parameter scale. Vector Quantization (VQ), a clustering-based quantization method, serves as a prevalent solution to this issue for its extremely low-bit (even at 2-bit) and considerable accuracy. Since a vector is a quantity in mathematics and physics that has both direction and magnitude, existing VQ works typically quantize them in a coupled manner. However, we find that direction exhibits significantly greater sensitivity to quantization compared to the magnitude. For instance, when separately clustering the directions and magnitudes of weight vectors in LLaMA-2-7B, the accuracy drop of zero-shot tasks are 46.5\% and 2.3\%, respectively. This gap even increases with the reduction of clustering centers. Further, Euclidean distance, a common metric to access vector similarities in current VQ works, places greater emphasis on reducing the magnitude error. This property is contrary to the above finding, unavoidably leading to larger quantization errors. To these ends, this paper proposes Polar Coordinate Decoupled Vector Quantization (PCDVQ), an effective and efficient VQ framework consisting of two key modules: 1) Polar Coordinate Decoupling (PCD), which transforms vectors into their polar coordinate representations and perform independent quantization of the direction and magnitude parameters.2) Distribution Aligned Codebook Construction (DACC), which optimizes the direction and magnitude codebooks in accordance with the source distribution. Experimental results show that PCDVQ outperforms baseline methods at 2-bit level by at least 1.5\% zero-shot accuracy, establishing a novel paradigm for accurate and highly compressed LLMs.