HMoE: Heterogeneous Mixture of Experts for Language Modeling

Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, which prevents homogeneous MoE from effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous HMoE framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages frequent activation of smaller experts so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Our codes are available at https://github.com/AnWang-AI/HMoE .