LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models

• URL: http://arxiv.org/abs/2410.13299v2
• Date: Fri, 29 Nov 2024 11:21:29 GMT
• Title: LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models
• Authors: David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner,
• Abstract summary: We propose a novel pruning method utilising centrality measures from graph theory, reducing both the computational requirements and the memory footprint of these models.<n>We call this pruning method PageRankRank. Furthermore we introduce an extension to decoder-only transformer models and call it LLMRank.
• Score: 1.3108652488669736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques. We propose a novel pruning method utilising centrality measures from graph theory, reducing both the computational requirements and the memory footprint of these models. Specifically, we devise a method for creating a weighted directed acyclical graph representation of multilayer perceptrons to which we apply a modified version of the weighted PageRank centrality measure to compute node importance scores. In combination with uniform pruning this leads to structured sparsity. We call this pruning method MLPRank. Furthermore we introduce an extension to decoder-only transformer models and call it LLMRank. For both variants we demonstrate a strong performance. With MLPRank on average leading to 6.09 % higher accuracy retention than three popular baselines and 13.42 % with LLMRank compared to two popular baselines. Code is available at https://github.com/amazon-science/llm-rank-pruning.

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