DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912021248737280 |
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| author | Aghdam, Maryam Akhavan Jin, Hongpeng Wu, Yanzhao |
| author_facet | Aghdam, Maryam Akhavan Jin, Hongpeng Wu, Yanzhao |
| contents | Transformer-based Mixture-of-Experts (MoE) models have been driving several recent technological advancements in Natural Language Processing (NLP). These MoE models adopt a router mechanism to determine which experts to activate for routing input tokens. However, existing router mechanisms allocate a fixed number of experts to each token, which neglects the varying importance of different input tokens. In this study, we propose a novel dynamic router mechanism that Dynamically Allocates a variable number of experts for Mixture-of-Experts (DA-MoE) models based on an effective token importance measure. First, we show that the Transformer attention mechanism provides a natural and effective way of calculating token importance. Second, we propose a dynamic router mechanism that effectively decides the optimal number of experts (K) and allocates the top-K experts for each input token. Third, comprehensive experiments on several benchmark datasets demonstrate that our DA-MoE approach consistently outperforms the state-of-the-art Transformer based MoE model on the popular GLUE benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06669 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models Aghdam, Maryam Akhavan Jin, Hongpeng Wu, Yanzhao Machine Learning Transformer-based Mixture-of-Experts (MoE) models have been driving several recent technological advancements in Natural Language Processing (NLP). These MoE models adopt a router mechanism to determine which experts to activate for routing input tokens. However, existing router mechanisms allocate a fixed number of experts to each token, which neglects the varying importance of different input tokens. In this study, we propose a novel dynamic router mechanism that Dynamically Allocates a variable number of experts for Mixture-of-Experts (DA-MoE) models based on an effective token importance measure. First, we show that the Transformer attention mechanism provides a natural and effective way of calculating token importance. Second, we propose a dynamic router mechanism that effectively decides the optimal number of experts (K) and allocates the top-K experts for each input token. Third, comprehensive experiments on several benchmark datasets demonstrate that our DA-MoE approach consistently outperforms the state-of-the-art Transformer based MoE model on the popular GLUE benchmark. |
| title | DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.06669 |