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arXiv · 2502.00258

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs

Hongyi Liu·Rajarshi Saha·Zhen Jia·Youngsuk Park·Jiaji Huang·Shoham Sabach·Yu-Xiang Wang·George Karypis
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Abstract

Large Language Models (LLMs) have demonstrated exceptional performance in natural language processing tasks, yet their massive size makes serving them inefficient and costly. Semi-structured pruning has emerged as an effective method for model acceleration, but existing approaches are suboptimal because they focus on local, layer-wise optimizations using heuristic rules, failing to leverage global feedback. We present ProxSparse, a learning-based framework for mask selection enabled by regularized optimization. ProxSparse transforms the rigid, non-differentiable mask selection process into a smoother optimization procedure, allowing gradual mask exploration with flexibility. ProxSparse does not involve additional weight updates once the mask is determined. Our extensive evaluations on 7 widely used models show that ProxSparse consistently outperforms previously proposed semi-structured mask selection methods with significant improvement, demonstrating the effectiveness of our learned approach towards semi-structured pruning.

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Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis. 2025-02-01. ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs. https://arxiv.org/abs/2502.00258

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