Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
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Keywords: Post-Training Pruning, Combinatorial Optimization, Large Language Models, Inference Acceleration
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TL;DR: By integrating Relative Importance and Activations and Channel Permutation, we present a plug-and-play solution for post-training pruning of LLMs, which accelerates the inference speed of LLMs without performance degradation.
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
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 1198
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