LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

• URL: http://arxiv.org/abs/2501.08582v1
• Date: Wed, 15 Jan 2025 05:07:06 GMT
• Title: LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model
• Authors: Yuxuan Hu, Jing Zhang, Xiaodong Chen, Zhe Zhao, Cuiping Li, Hong Chen,
• Abstract summary: Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. We introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs.
• Score: 21.98687961440789
• License:
• Abstract: Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an increased memory and computation overhead, which affects efficiency of LoRA methods. In response to this limitation, we introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs. To mitigate the substantial memory and computation demands associated with preserving sparsity, our approach incorporates strategies of weight recompute and computational graph rearrangement. In addition, we also improve the effectiveness of LoRS through better adapter initialization. These innovations lead to a notable reduction in memory and computation consumption during the fine-tuning phase, all while achieving performance levels that outperform existing LoRA approaches.

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