Computer Science > Machine Learning
[Submitted on 13 Dec 2023 (v1), last revised 30 Sep 2024 (this version, v3)]
Title:SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
View PDF HTML (experimental)Abstract:Despite many recent works on Mixture of Experts (MoEs) for resource-efficient Transformer language models, existing methods mostly focus on MoEs for feedforward layers. Previous attempts at extending MoE to the self-attention layer fail to match the performance of the parameter-matched baseline. Our novel SwitchHead is an effective MoE method for the attention layer that successfully reduces both the compute and memory requirements, achieving wall-clock speedup, while matching the language modeling performance of the baseline Transformer. Our novel MoE mechanism allows SwitchHead to compute up to 8 times fewer attention matrices than the standard Transformer. SwitchHead can also be combined with MoE feedforward layers, resulting in fully-MoE "SwitchAll" Transformers. For our 262M parameter model trained on C4, SwitchHead matches the perplexity of standard models with only 44% compute and 27% memory usage. Zero-shot experiments on downstream tasks confirm the performance of SwitchHead, e.g., achieving more than 3.5% absolute improvements on BliMP compared to the baseline with an equal compute resource.
Submission history
From: Róbert Csordás [view email][v1] Wed, 13 Dec 2023 09:00:21 UTC (399 KB)
[v2] Thu, 14 Dec 2023 06:35:33 UTC (399 KB)
[v3] Mon, 30 Sep 2024 21:19:29 UTC (381 KB)
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