@inproceedings{zeng-etal-2024-turn,
title = "Turn Waste into Worth: Rectifying Top-$k$ Router of {M}o{E}",
author = "Zeng, Zhiyuan and
Guo, Qipeng and
Fei, Zhaoye and
Yin, Zhangyue and
Zhou, Yunhua and
Li, Linyang and
Sun, Tianxiang and
Yan, Hang and
Lin, Dahua and
Qiu, Xipeng",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.739",
doi = "10.18653/v1/2024.emnlp-main.739",
pages = "13305--13320",
abstract = "Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top-$k$ routing mechanism suffers from redundancy computation and memory costs due to the unbalanced routing. Some experts are overflow, where the exceeding tokens are dropped. While some experts are empty, which are padded with zeros, negatively impacting model performance. To address the dropped tokens and padding, we propose the Rectify-Router, comprising the Intra-GPU Rectification and the Fill-in Rectification. The Intra-GPU Rectification handles dropped tokens, efficiently routing them to experts within the GPU where they are located to avoid inter-GPU communication. The Fill-in Rectification addresses padding by replacing padding tokens with the tokens that have high routing scores. Our experimental results demonstrate that the Intra-GPU Rectification and the Fill-in Rectification effectively handle dropped tokens and padding, respectively. Furthermore, the combination of them achieves superior performance, surpassing the accuracy of the vanilla top-1 router by 4.7{\%}.",
}
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<abstract>Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top-k routing mechanism suffers from redundancy computation and memory costs due to the unbalanced routing. Some experts are overflow, where the exceeding tokens are dropped. While some experts are empty, which are padded with zeros, negatively impacting model performance. To address the dropped tokens and padding, we propose the Rectify-Router, comprising the Intra-GPU Rectification and the Fill-in Rectification. The Intra-GPU Rectification handles dropped tokens, efficiently routing them to experts within the GPU where they are located to avoid inter-GPU communication. The Fill-in Rectification addresses padding by replacing padding tokens with the tokens that have high routing scores. Our experimental results demonstrate that the Intra-GPU Rectification and the Fill-in Rectification effectively handle dropped tokens and padding, respectively. Furthermore, the combination of them achieves superior performance, surpassing the accuracy of the vanilla top-1 router by 4.7%.</abstract>
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%0 Conference Proceedings
%T Turn Waste into Worth: Rectifying Top-k Router of MoE
%A Zeng, Zhiyuan
%A Guo, Qipeng
%A Fei, Zhaoye
%A Yin, Zhangyue
%A Zhou, Yunhua
%A Li, Linyang
%A Sun, Tianxiang
%A Yan, Hang
%A Lin, Dahua
%A Qiu, Xipeng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zeng-etal-2024-turn
%X Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top-k routing mechanism suffers from redundancy computation and memory costs due to the unbalanced routing. Some experts are overflow, where the exceeding tokens are dropped. While some experts are empty, which are padded with zeros, negatively impacting model performance. To address the dropped tokens and padding, we propose the Rectify-Router, comprising the Intra-GPU Rectification and the Fill-in Rectification. The Intra-GPU Rectification handles dropped tokens, efficiently routing them to experts within the GPU where they are located to avoid inter-GPU communication. The Fill-in Rectification addresses padding by replacing padding tokens with the tokens that have high routing scores. Our experimental results demonstrate that the Intra-GPU Rectification and the Fill-in Rectification effectively handle dropped tokens and padding, respectively. Furthermore, the combination of them achieves superior performance, surpassing the accuracy of the vanilla top-1 router by 4.7%.
%R 10.18653/v1/2024.emnlp-main.739
%U https://aclanthology.org/2024.emnlp-main.739
%U https://doi.org/10.18653/v1/2024.emnlp-main.739
%P 13305-13320
Markdown (Informal)
[Turn Waste into Worth: Rectifying Top-k Router of MoE](https://aclanthology.org/2024.emnlp-main.739) (Zeng et al., EMNLP 2024)
ACL
- Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin, Yunhua Zhou, Linyang Li, Tianxiang Sun, Hang Yan, Dahua Lin, and Xipeng Qiu. 2024. Turn Waste into Worth: Rectifying Top-k Router of MoE. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13305–13320, Miami, Florida, USA. Association for Computational Linguistics.