MoreauPruner: Robust Structured Pruning of Large Language Models against Weight Perturbations

Zixiao Wang, Jingwei Zhang, Wenqian Zhao, Farzan Farnia, Bei Yu

Keywords: Structured Pruning, Large Language Model, Robustness, Moreau Envelop

Abstract: In the existing model pruning literature, the weight gradient has been extensively utilized to measure the importance of weight, where the gradient is well-known to be sensitive to perturbations. On the other hand, the widely used large language models (LLMs) have several billion model parameters, which could increase the fragility of few-shot gradient pruning. In this work, we experimentally show that one-shot gradient pruning algorithms could lead to unstable results under perturbations to model weights. Even the minor error of switching between data formats bfloat16 and float16 could result in obviously different outcomes. To address such instabilities, we leverage optimization analysis and propose an LLM structural pruning method, called MoreauPruner, with provable robustness against weight perturbations. In MoreauPruner, the model weight importance is estimated based on the neural network's Moreau envelope, which can be flexibly combined with $\ell_1$-norm regularization techniques to induce the sparsity required in the pruning task. We extensively evaluate the MoreauPruner algorithm on several well-known LLMs, including LLaMA-7B, LLaMA-13B, LLaMA3-8B, and Vicuna-7B. Our numerical results suggest the robustness of MoreauPruner against weight perturbation and how robust importance estimation in MoreauPruner contributes to successful accuracy-based scores compared to several existing pruning methods.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 650