@inproceedings{zhu-etal-2025-foldmoe,
title = "{F}old{M}o{E}: Efficient Long Sequence {M}o{E} Training via Attention-{M}o{E} Pipelining",
author = "Zhu, Guichao and
Lei, Lintian and
Qing, Yuhao and
Fu, Yichao and
Li, Fanxin and
Huang, Dong and
Sun, Zekai and
Cui, Heming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.186/",
doi = "10.18653/v1/2025.acl-long.186",
pages = "3705--3717",
ISBN = "979-8-89176-251-0",
abstract = "Training LLMs with Mixture-of-Experts (MoE) architecture on long sequences poses significant challenges due to the all-to-all communication bottleneck of expert parallelism. While existing approaches attempt to hide the communication costs in computation through token-level pipelining within MoE layers, their effectiveness is limited by the insufficient computation. We present FoldMoE, a high-performance MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. We propose an efficient pipeline schedule, and a novel token buffering design to decouple attention and MoE layer partitioning, along with a time-uniform micro-batching strategy for enhanced efficiency. Evaluations on GPT-MoE models with sequences up to 32K tokens show that FoldMoE achieves up to 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines respectively."
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<abstract>Training LLMs with Mixture-of-Experts (MoE) architecture on long sequences poses significant challenges due to the all-to-all communication bottleneck of expert parallelism. While existing approaches attempt to hide the communication costs in computation through token-level pipelining within MoE layers, their effectiveness is limited by the insufficient computation. We present FoldMoE, a high-performance MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. We propose an efficient pipeline schedule, and a novel token buffering design to decouple attention and MoE layer partitioning, along with a time-uniform micro-batching strategy for enhanced efficiency. Evaluations on GPT-MoE models with sequences up to 32K tokens show that FoldMoE achieves up to 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines respectively.</abstract>
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%0 Conference Proceedings
%T FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining
%A Zhu, Guichao
%A Lei, Lintian
%A Qing, Yuhao
%A Fu, Yichao
%A Li, Fanxin
%A Huang, Dong
%A Sun, Zekai
%A Cui, Heming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhu-etal-2025-foldmoe
%X Training LLMs with Mixture-of-Experts (MoE) architecture on long sequences poses significant challenges due to the all-to-all communication bottleneck of expert parallelism. While existing approaches attempt to hide the communication costs in computation through token-level pipelining within MoE layers, their effectiveness is limited by the insufficient computation. We present FoldMoE, a high-performance MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. We propose an efficient pipeline schedule, and a novel token buffering design to decouple attention and MoE layer partitioning, along with a time-uniform micro-batching strategy for enhanced efficiency. Evaluations on GPT-MoE models with sequences up to 32K tokens show that FoldMoE achieves up to 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines respectively.
%R 10.18653/v1/2025.acl-long.186
%U https://aclanthology.org/2025.acl-long.186/
%U https://doi.org/10.18653/v1/2025.acl-long.186
%P 3705-3717
Markdown (Informal)
[FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining](https://aclanthology.org/2025.acl-long.186/) (Zhu et al., ACL 2025)
ACL
- Guichao Zhu, Lintian Lei, Yuhao Qing, Yichao Fu, Fanxin Li, Dong Huang, Zekai Sun, and Heming Cui. 2025. FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3705–3717, Vienna, Austria. Association for Computational Linguistics.