Latte: Latent Attention for Linear Time Transformers

Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber

Keywords: Sequence Modelling, Long Sequences, Linear Attention, Latent Variable Model

TL;DR: A latent variable model for linear time attention

Abstract: The time complexity of the standard attention mechanism in transformers scales quadratically with sequence length. We propose a probabilistic framework for attention, enabling us to derive a novel low-rank linear re-parameterisation of both bidirectional and causal cases, based on defining a latent variable model. Our method can be seamlessly integrated as a drop-in replacement for the standard attention mechanism. Additionally, this framework provides a natural extension for combining local standard attention with our global linear attention. This approach allows us to extend the context length of existing large pre-trained models with only a few additional training steps. The resulting ``Latte Transformer'' achieves performance comparable to standard attention and other state-of-the-art models, while maintaining linear time and memory complexity, along with constant-time next-token prediction during inference.

Primary Area: generative models

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Submission Number: 11402