SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

• URL: http://arxiv.org/abs/2312.07987v2
• Date: Thu, 14 Dec 2023 06:35:33 GMT
• Title: SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
• Authors: R\'obert Csord\'as, Piotr Pi\k{e}kos, Kazuki Irie, J\"urgen Schmidhuber
• Abstract summary: We present SwitchHead - a novel method that reduces both compute and memory requirements and achieves wall-clock speedup. SwitchHead uses Mixture-of-Experts (MoE) layers for the value and output projections and requires 4 to 8 times fewer attention than standard Transformers.
• Score: 14.238528956017005
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Here we present SwitchHead - a novel method that reduces both compute and memory requirements and achieves wall-clock speedup, while matching the language modeling performance of baseline Transformers with the same parameter budget. SwitchHead 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 fully-MoE "SwitchAll" Transformer model. Our code is public.

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