Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
Authors: Yingtao Zhang,Haoli Bai,Haokun Lin,Jialin Zhao,Lu Hou,Carlo Vittorio Cannistraci
Presentation: ICLR 2024,Poster

Abstract: With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. 
In this paper, we present a plug-and-play solution for post-training pruning of LLMs.
The proposed solution has two innovative components: 1) **Relative Importance and Activations (RIA)**, a new pruning metric that jointly considers the weight and activations efficiently on LLMs, and 2) **Channel Permutation**, a new approach to maximally preserves important weights under N:M sparsity.
The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs.
Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware.
Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning