GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection

• URL: http://arxiv.org/abs/2412.19820v1
• Date: Sun, 15 Dec 2024 12:28:13 GMT
• Title: GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection
• Authors: Xutao Liao, Shaohui Li, Yuhui Xu, Zhi Li, Yu Liu, You He,
• Abstract summary: We propose GaLore$+$, which uses cross-head low-rank projection to reduce the substantial time consumption in estimating low-rank projections for multi-head attention. Our experiments demonstrate that GaLore$+$ delivers superior performance while achieving approximately $4times$ fine-tuning speed compared to vanilla GaLore.
• Score: 17.33732087380253
• License:
• Abstract: Recent low-rank training methods, such as GaLore, have significantly reduced the memory required to optimize large language models (LLMs). However, these methods often suffer from time-consuming low-rank projection estimations. In particular, the singular value decomposition (SVD) in GaLore can consume more than 80\% of the total training time. To address this issue, we propose GaLore$+$, which uses cross-head low-rank projection to reduce the substantial time consumption in estimating low-rank projections for multi-head attention. In addition, we employ randomized subspace iteration to achieve fast SVD. To further enhance performance, we propose sparsely coded residuals to reduce the errors caused by low-rank approximation on the first- and second-order moments of the optimizers and weight updates. We evaluate GaLore$+$ on arithmetic reasoning and natural language generation datasets. Our experiments demonstrate that GaLore$+$ delivers superior performance while achieving approximately $4\times$ fine-tuning speed compared to vanilla GaLore.

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