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Computer Science > Machine Learning

arXiv:2404.02852 (cs)
[Submitted on 3 Apr 2024]

Title:Toward Inference-optimal Mixture-of-Expert Large Language
Models

Authors:Longfei Yun, Yonghao Zhuang, Yao Fu, Eric P Xing, Hao Zhang
View a PDF of the paper titled Toward Inference-optimal
Mixture-of-Expert Large Language Models, by Longfei Yun and 4 other
authors
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    Abstract:Mixture-of-Expert (MoE) based large language models
    (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown
    great promise in scaling model size without suffering from the
    quadratic growth of training cost of dense transformers. Like
    dense models, training MoEs requires answering the same question:
    given a training budget, what is the optimal allocation on the
    model size and number of tokens? We study the scaling law of
    MoE-based LLMs regarding the relations between the model
    performance, model size, dataset size, and the expert degree.
    Echoing previous research studying MoE in different contexts, we
    observe the diminishing return of increasing the number of
    experts, but this seems to suggest we should scale the number of
    experts until saturation, as the training cost would remain
    constant, which is problematic during inference time. We propose
    to amend the scaling law of MoE by introducing inference
    efficiency as another metric besides the validation loss. We find
    that MoEs with a few (4/8) experts are the most serving efficient
    solution under the same performance, but costs 2.5-3.5x more in
    training. On the other hand, training a (16/32) expert MoE much
    smaller (70-85%) than the loss-optimal solution, but with a
    larger training dataset is a promising setup under a training
    budget.

Comments: 15 pages, 8 figures
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2404.02852 [cs.LG]
          (or arXiv:2404.02852v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2404.02852
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Longfei Yun [view email]
[v1] Wed, 3 Apr 2024 16:33:42 UTC (1,107 KB)
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