L o RAP rune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning (2024)

Abstract: Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to cheaply fine-tune these LLMs on downstream tasks, their deployment is still hindered by the vast model scale and computational costs. Post-training model pruning offers a way to compress LLMs. However, the current pruning methods designed for LLMs are not compatible with LoRA. This is due to their utilization of unstructured pruning on LLMs, impeding the merging of LoRA weights, or their dependence on the gradients of pre-trained weights to guide pruning, which can impose significant memory overhead.To this end, we propose LoRAPrune, a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner. Specifically, we first design a LoRA-guided pruning criterion, which uses the weights and gradients of LoRA, rather than the gradients of pre-trained weights for importance estimation. We subsequently integrate this criterion into an iterative pruning process, effectively removing redundant channels and heads. Extensive experimental results demonstrate the superior performance of our LoRAPrune over existing approaches on the LLaMA series models.At a 50% compression rate, LoRAPrune demonstrates superior performance over LLM-Pruner, achieving a reduction in perplexity by 4.81 on WikiText2 and 3.46 on PTB, while also decreasing memory usage by 52.6%.Besides, LoRAPrune also matches semi-structural pruning across multiple LLMs, proving its wide applicability. The code is available at https://github.com/aim-uofa/LoRAPrune.

## introduction large language models (llms) @xcite @xcite ) have * x. yu is the corresponding author. ## conclusion in this paper, we have proposed a method to effectively prune and fine-tune llms simultaneously, achieving state-of-the-art efficiency-accuracy tradeoffs. specifically, we have proposed a novel loraguided criterion, for evaluating the parameter importance by only computing the lora gradients, which greatly reduces the computational resources required for pruning llms. building upon the proposed criterion, we have presented loraprune, a technique that performs efficient joint pruning and fine-tuning without the need for computing gradients of the pre-trained weights. finally, comprehensive experiments on various llms and benchmarks have demonstrated the superiority of loraprune over other pruning methods. in terms of comparison with the vanilla criterion, the lora-guided criterion shows its efficiency and effectiveness. in the future, we aim to further enhance the pruning results of loraprune at higher compression rates. limitation. loraprune requires fine-tuning to restore model performance. this limitation can restrict the application of loraprune in scenarios where fine-tuning is unavailable.