Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models

• URL: http://arxiv.org/abs/2501.15255v1
• Date: Sat, 25 Jan 2025 16:03:58 GMT
• Title: Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models
• Authors: Zihuai Xu, Yang Xu, Hongli Xu, Yunming Liao, Zhiwei Yao, Zuan Xie,
• Abstract summary: We introduce COMP, a lightweight post-training structured pruning method that employs a hybrid-granularity pruning strategy.<n> COMP improves performance by 6.13% on the LLaMA-2-7B model with a 20% pruning ratio compared to LLM-Pruner.
• Score: 11.93284417365518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Considering the hardware-friendly characteristics and broad applicability, structured pruning has emerged as an efficient solution to reduce the resource demands of large language models (LLMs) on resource-constrained devices. Traditional structured pruning methods often need fine-tuning to recover performance loss, which incurs high memory overhead and substantial data requirements, rendering them unsuitable for on-device applications. Additionally, post-training structured pruning techniques typically necessitate specific activation functions or architectural modifications, thereby limiting their scope of applications. Herein, we introduce COMP, a lightweight post-training structured pruning method that employs a hybrid-granularity pruning strategy. COMP initially prunes selected model layers based on their importance at a coarse granularity, followed by fine-grained neuron pruning within the dense layers of each remaining model layer. To more accurately evaluate neuron importance, COMP introduces a new matrix condition-based metric. Subsequently, COMP utilizes mask tuning to recover accuracy without the need for fine-tuning, significantly reducing memory consumption. Experimental results demonstrate that COMP improves performance by 6.13\% on the LLaMA-2-7B model with a 20\% pruning ratio compared to LLM-Pruner, while simultaneously reducing memory overhead by 80\%.

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