@inproceedings{jawahar-etal-2023-automoe,
    title = "{A}uto{M}o{E}: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation",
    author = "Jawahar, Ganesh  and
      Mukherjee, Subhabrata  and
      Liu, Xiaodong  and
      Kim, Young Jin  and
      Abdul-Mageed, Muhammad  and
      Lakshmanan, V.S., Laks  and
      Awadallah, Ahmed Hassan  and
      Bubeck, Sebastien  and
      Gao, Jianfeng",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.580",
    doi = "10.18653/v1/2023.findings-acl.580",
    pages = "9116--9132",
    abstract = "Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. Furthermore, existing MoE works do not consider computational constraints (e.g., FLOPs, latency) to guide their design. To this end, we develop AutoMoE {--} a framework for designing heterogeneous MoE{'}s under computational constraints. AutoMoE leverages Neural Architecture Search (NAS) to obtain efficient sparse MoE sub-transformers with 4x inference speedup (CPU) and FLOPs reduction over manually designed Transformers, with parity in BLEU score over dense Transformer and within 1 BLEU point of MoE SwitchTransformer, on aggregate over benchmark datasets for NMT.Heterogeneous search space with dense and sparsely activated Transformer modules (e.g., how many experts? where to place them? what should be their sizes?) allows for adaptive compute {--} where different amounts of computations are used for different tokens in the input. Adaptivity comes naturally from routing decisions which send tokens to experts of different sizes. AutoMoE code, data, and trained models are available at \url{https://aka.ms/AutoMoE}.",
}

Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. Furthermore, existing MoE works do not consider computational constraints (e.g., FLOPs, latency) to guide their design. To this end, we develop AutoMoE {--} a framework for designing heterogeneous MoE{'}s under computational constraints. AutoMoE leverages Neural Architecture Search (NAS) to obtain efficient sparse MoE sub-transformers with 4x inference speedup (CPU) and FLOPs reduction over manually designed Transformers, with parity in BLEU score over dense Transformer and within 1 BLEU point of MoE SwitchTransformer, on aggregate over benchmark datasets for NMT.Heterogeneous search space with dense and sparsely activated Transformer modules (e.g., how many experts? where to place them? what should be their sizes?) allows for adaptive compute {--} where different amounts of computations are used for different tokens in the input. Adaptivity comes naturally from routing decisions which send tokens to experts of different sizes. AutoMoE code, data, and trained models are available at \url{https://aka.ms/AutoMoE}.
Jawahar, Ganesh Mukherjee, Subhabrata Liu, Xiaodong Kim, Young Jin Abdul-Mageed, Muhammad Lakshmanan, V.S., Laks Awadallah, Ahmed Hassan Bubeck, Sebastien Gao, Jianfeng
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation