Flash Invariant Point Attention
Andrew Liu, Axel Elaldi, Nicholas Franklin, Nathan Russell, Gurinder Atwal, Yih-En Ban, Olivia Viessmann
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://anonymous.4open.science/r/flash_ipa-07CE.