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arXiv · 2107.07999

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers

Krzysztof Choromanski·Han Lin·Haoxian Chen·Tianyi Zhang·Arijit Sehanobish·Valerii Likhosherstov·Jack Parker-Holder·Tamas Sarlos·Adrian Weller·Thomas Weingarten
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Abstract

In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way. We show that recent results on linear causal attention (Choromanski et al., 2021) and log-linear RPE-attention (Luo et al., 2021) are special cases of this general mechanism. However by casting the problem as a topological (graph-based) modulation of unmasked attention, we obtain several results unknown before, including efficient d-dimensional RPE-masking and graph-kernel masking. We leverage many mathematical techniques ranging from spectral analysis through dynamic programming and random walks to new algorithms for solving Markov processes on graphs. We provide a corresponding empirical evaluation.

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Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten. 2021-07-16. From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. https://arxiv.org/abs/2107.07999

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