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arXiv · 2310.01082

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

Kwangjun Ahn·Xiang Cheng·Minhak Song·Chulhee Yun·Ali Jadbabaie·Suvrit Sra
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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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Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra. 2023-10-02. Linear attention is (maybe) all you need (to understand transformer optimization). https://arxiv.org/abs/2310.01082

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