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arXiv · 2505.11580

Flash Invariant Point Attention

Andrew Liu·Axel Elaldi·Nicholas T Franklin·Nathan Russell·Gurinder S Atwal·Yih-En A Ban·Olivia Viessmann
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Abstract

Invariant Point Attention (IPA) is a key algorithm for geometry-aware modeling in structural biology, central to many protein and RNA models. However, its quadratic complexity limits the input sequence length. We introduce FlashIPA, a factorized reformulation of IPA that leverages hardware-efficient FlashAttention to achieve linear scaling in GPU memory and wall-clock time with sequence length. FlashIPA matches or exceeds standard IPA performance while substantially reducing computational costs. FlashIPA extends training to previously unattainable lengths, and we demonstrate this by re-training generative models without length restrictions and generating structures of thousands of residues. FlashIPA is available at https://github.com/flagshippioneering/flash_ipa.

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BibTeXRIS

Andrew Liu, Axel Elaldi, Nicholas T Franklin, Nathan Russell, Gurinder S Atwal, Yih-En A Ban, Olivia Viessmann. 2025-05-16. Flash Invariant Point Attention. https://arxiv.org/abs/2505.11580

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