FAST: Factorizable Attention for Speeding up Transformers

• URL: http://arxiv.org/abs/2402.07901v1
• Date: Mon, 12 Feb 2024 18:59:39 GMT
• Title: FAST: Factorizable Attention for Speeding up Transformers
• Authors: Armin Gerami, Monte Hoover, Pranav S. Dulepet, Ramani Duraiswami
• Abstract summary: We present a linearly scaled attention mechanism that maintains the full representation of the attention matrix without compromising on sparsification. Results indicate that our attention mechanism has a robust performance and holds significant promise for diverse applications where self-attention is used.
• Score: 1.3637227185793512
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Motivated by the factorization inherent in the original fast multipole method and the improved fast Gauss transform we introduce a factorable form of attention that operates efficiently in high dimensions. This approach reduces the computational and memory complexity of the attention mechanism in transformers from $O(N^2)$ to $O(N)$. In comparison to previous attempts, our work presents a linearly scaled attention mechanism that maintains the full representation of the attention matrix without compromising on sparsification and incorporates the all-to-all relationship between tokens. We explore the properties of our new attention metric and conduct tests in various standard settings. Results indicate that our attention mechanism has a robust performance and holds significant promise for diverse applications where self-attention is used.

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