A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models

• URL: http://arxiv.org/abs/2408.03728v1
• Date: Wed, 7 Aug 2024 12:33:46 GMT
• Title: A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
• Authors: Pengxiang Zhao, Hanyu Hu, Ping Li, Yi Zheng, Zhefeng Wang, Xiaoming Yuan,
• Abstract summary: We introduce FISTAPruner, the first post-training pruner based on convex optimization models and algorithms. FISTAPruner incorporates an intra-layer cumulative error correction mechanism and supports parallel pruning. We evaluate FISTAPruner on models such as OPT, LLaMA, LLaMA-2, and LLaMA-3 with 125M to 70B parameters under unstructured and 2:4 semi-structured sparsity.
• Score: 24.185245582500876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising performance. However, existing pruning methods often necessitate inefficient retraining for billion-scale LLMs or rely on heuristic methods such as the optimal brain surgeon framework, which degrade performance. In this paper, we introduce FISTAPruner, the first post-training pruner based on convex optimization models and algorithms. Specifically, we propose a convex optimization model incorporating $\ell_1$ norm to induce sparsity and utilize the FISTA solver for optimization. FISTAPruner incorporates an intra-layer cumulative error correction mechanism and supports parallel pruning. We comprehensively evaluate FISTAPruner on models such as OPT, LLaMA, LLaMA-2, and LLaMA-3 with 125M to 70B parameters under unstructured and 2:4 semi-structured sparsity, demonstrating superior performance over existing state-of-the-art methods across various language benchmarks.

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