Linear attention is (maybe) all you need (to understand transformer optimization)

• URL: http://arxiv.org/abs/2310.01082v2
• Date: Wed, 13 Mar 2024 16:48:27 GMT
• Title: Linear attention is (maybe) all you need (to understand transformer optimization)
• Authors: Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
• Abstract summary: We make progress towards understanding the subtleties of training Transformers by studying a simple yet canonicalized shallow Transformer model. Most importantly, we observe that our proposed linearized models can reproduce several prominent aspects of Transformer training dynamics.
• Score: 55.81555204646486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics. We make progress towards understanding the subtleties of training Transformers by carefully studying a simple yet canonical linearized shallow Transformer model. Specifically, we train linear Transformers to solve regression tasks, inspired by J.~von Oswald et al.~(ICML 2023), and K.~Ahn et al.~(NeurIPS 2023). Most importantly, we observe that our proposed linearized models can reproduce several prominent aspects of Transformer training dynamics. Consequently, the results obtained in this paper suggest that a simple linearized Transformer model could actually be a valuable, realistic abstraction for understanding Transformer optimization.

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