Large Language Model Compression with Global Rank and Sparsity Optimization

TL;DR

This paper introduces a two-stage global resource allocation method for compressing large language models by combining low-rank and sparse approximations, addressing layer redundancy and interaction challenges.

Contribution

It proposes a novel two-stage approach using PCA and probabilistic strategies for joint low-rank and sparse optimization with global resource management.

Findings

Significantly outperforms existing sparsification methods

Automatically detects layer redundancy

Effectively manages low-rank and sparse interactions

Abstract

Low-rank and sparse composite approximation is a natural idea to compress Large Language Models (LLMs). However, such an idea faces two primary challenges that adversely affect the performance of existing methods. The first challenge relates to the interaction and cooperation between low-rank and sparse matrices, while the second involves determining weight allocation across different layers, as redundancy varies considerably among them. To address these challenges, we propose a novel two-stage LLM compression method with the capability of global resource allocation for rank and sparsity. It is noteworthy that the overall optimization space is vast, making comprehensive optimization computationally prohibitive. Therefore, to reduce the optimization space, our first stage utilizes robust principal component analysis to decompose the weight matrices of LLMs into low-rank and sparse components, which span the low dimensional and sparse spaces containing the resultant low-rank and sparse matrices, respectively. In the second stage, we propose a probabilistic global allocation strategy to jointly identify the low-rank and sparse structures within the above two spaces. The appealing feature of our approach is its ability to automatically detect the redundancy across different layers and to manage the interaction between the sparse and low-rank components. Extensive experimental results indicate that our method significantly surpasses state-of-the-art techniques for sparsification and composite approximation.