Few Heads are Enough
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: transformers, attention, moe, mixture of experts, efficient transformers, language modelling
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TL;DR: We show that with MoE value and output projections it is possible to use less heads, reducing resource usage of transformers.
Abstract: The costly self-attention layers in modern Transformers require memory and compute quadratic in sequence length. Existing approximation methods usually underperform and fail to obtain significant speedups in practice. The recently proposed Flash-Attention reduces both compute and memory through a *hardware*-aware implementation. Can we achieve this also through *algorithmic* improvements? Here we present Expert Projection Attention (EPA) - a novel method that reduces both compute and memory requirements, while matching the language modeling performance of baseline Transformers using the same parameter budget. EPA uses Mixture-of-Experts (MoE) layers for the value and output projections and requires 4 to 8 times fewer attention matrices than standard Transformers. Our novel attention can also be combined with MoE MLP layers, resulting in an efficient "Fast Transformer".
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Submission Number: 9177
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