Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining

• URL: http://arxiv.org/abs/2407.19126v1
• Date: Fri, 26 Jul 2024 23:53:59 GMT
• Title: Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining
• Authors: Jianwei Li, Yijun Dong, Qi Lei,
• Abstract summary: We simplify the pruning process for Transformer-based large language models (LLMs) We propose two inference-aware pruning criteria derived from the optimization perspective of output approximation. We also introduce a two-step reconstruction technique to mitigate pruning errors without model retraining.
• Score: 16.026565606764954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To remove redundant components of large language models (LLMs) without incurring significant computational costs, this work focuses on single-shot pruning without a retraining phase. We simplify the pruning process for Transformer-based LLMs by identifying a depth-2 pruning structure that functions independently. Additionally, we propose two inference-aware pruning criteria derived from the optimization perspective of output approximation, which outperforms traditional training-aware metrics such as gradient and Hessian. We also introduce a two-step reconstruction technique to mitigate pruning errors without model retraining. Experimental results demonstrate that our approach significantly reduces computational costs and hardware requirements while maintaining superior performance across various datasets and models.

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