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Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
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The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results.However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-k), resulting in significant computational overhead due to the extensive model training by searching over various hyper-parameter configurations. As a remedy, we introduce the Dynamic Mixture of Experts (DynMoE) technique. DynMoE incorporates (1) a novel gating method that enables each token to automatically determine the number of experts to activate. (2) An adaptive process automatically adjusts the number of experts during training. Extensive numerical results across Vision, Language, and Vision-Language tasks demonstrate the effectiveness of our approach to achieve competitive performance compared to GMoE for vision and language tasks, and MoE-LLaVA for vision-language tasks, while maintaining efficiency by activating fewer parameters. Our code is available at https://github.com/LINs-lab/DynMoE.
Forward citations
Cited by 4 Pith papers
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DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
A PI-controller-tuned, layerwise-normalized Top-p router trains sparse MoE models that beat Top-k at matched average activated-expert count.
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A global log for medical AI
MedLog defines a nine-field, syslog-style event log for clinical AI, intended to support real-world surveillance and auditing; the four-deployment validation claimed in the abstract is absent from the body.
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Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Grove MoE uses unequal-size adjugate experts with complexity-based activation to run 33B-parameter models at roughly 3.1 to 3.3B active parameters while matching larger open models in benchmarks.
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Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
An empirical study proposing a 'basic-refinement' split in MoE models, where shared experts generalize and routed experts specialize, with efficiency claims undermined by internally inconsistent numbers and an unvalid...
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