Fast and Effective Weight Update for Pruned Large Language Models

• URL: http://arxiv.org/abs/2401.02938v2
• Date: Mon, 22 Jul 2024 14:34:04 GMT
• Title: Fast and Effective Weight Update for Pruned Large Language Models
• Authors: Vladimír Boža,
• Abstract summary: Pruning large language models (LLMs) is a challenging task due to their enormous size. Recent approaches have either ignored fine-tuning entirely, or attempted layer-wise weight updates. We propose a fast and effective weight update algorithm for pruned layers based on the Alternating Direction Method of Multipliers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pruning large language models (LLMs) is a challenging task due to their enormous size. The primary difficulty is fine-tuning the model after pruning, which is needed to recover the lost performance caused by dropping weights. Recent approaches have either ignored fine-tuning entirely, focusing on efficient pruning criteria, or attempted layer-wise weight updates, preserving the behavior of each layer. However, even layer-wise weight updates can be costly for LLMs, and previous works have resorted to various approximations. In our paper, we propose a fast and effective weight update algorithm for pruned layers based on the Alternating Direction Method of Multipliers (ADMM). We further extend it with a simple gradual pruning mask selection and achieve state-of-the-art pruning performance across a wide range of LLMs. Code is available at https://github.com/fmfi-compbio/admm-pruning.

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