LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning

• URL: http://arxiv.org/abs/2308.03303v1
• Date: Mon, 7 Aug 2023 05:12:27 GMT
• Title: LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning
• Authors: Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, Bo Li
• Abstract summary: We present LoRA-FA, a memory-efficient fine-tuning method that reduces the activation memory without performance degradation and expensive recomputation. Our results show that LoRA-FA can always achieve close fine-tuning accuracy across different tasks compared to full parameter fine-tuning and LoRA.
• Score: 19.08716369943138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The low-rank adaptation (LoRA) method can largely reduce the amount of trainable parameters for fine-tuning large language models (LLMs), however, it still requires expensive activation memory to update low-rank weights. Reducing the number of LoRA layers or using activation recomputation could harm the fine-tuning performance or increase the computational overhead. In this work, we present LoRA-FA, a memory-efficient fine-tuning method that reduces the activation memory without performance degradation and expensive recomputation. LoRA-FA chooses to freeze the projection-down weight of $A$ and update the projection-up weight of $B$ in each LoRA layer. It ensures the change of model weight reside in a low-rank space during LLMs fine-tuning, while eliminating the requirement to store full-rank input activations. We conduct extensive experiments across multiple model types (RoBERTa, T5, LLaMA) and model scales. Our results show that LoRA-FA can always achieve close fine-tuning accuracy across different tasks compared to full parameter fine-tuning and LoRA. Furthermore, LoRA-FA can reduce the overall memory cost by up to 1.4$\times$ compared to LoRA.

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