MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks

• URL: http://arxiv.org/abs/2406.04801v1
• Date: Fri, 7 Jun 2024 10:05:42 GMT
• Title: MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
• Authors: Xingkui Zhu, Yiran Guan, Dingkang Liang, Yuchao Chen, Yuliang Liu, Xiang Bai,
• Abstract summary: Training MoE models from scratch requires extensive data and computational resources. We introduce MoE Jetpack, an effective method for fine-tuning dense checkpoints into MoE models. Our experiments on vision tasks demonstrate that MoE Jetpack significantly improves convergence speed and accuracy.
• Score: 58.075367597860044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sparsely activated mixture of experts (MoE) model presents a promising alternative to traditional densely activated (dense) models, enhancing both quality and computational efficiency. However, training MoE models from scratch demands extensive data and computational resources. Moreover, public repositories like timm mainly provide pre-trained dense checkpoints, lacking similar resources for MoE models, hindering their adoption. To bridge this gap, we introduce MoE Jetpack, an effective method for fine-tuning dense checkpoints into MoE models. MoE Jetpack incorporates two key techniques: (1) checkpoint recycling, which repurposes dense checkpoints as initial weights for MoE models, thereby accelerating convergence, enhancing accuracy, and alleviating the computational burden of pre-training; (2) hyperspherical adaptive MoE (SpheroMoE) layer, which optimizes the MoE architecture for better integration of dense checkpoints, enhancing fine-tuning performance. Our experiments on vision tasks demonstrate that MoE Jetpack significantly improves convergence speed and accuracy when fine-tuning dense checkpoints into MoE models. Our code will be publicly available at https://github.com/Adlith/MoE-Jetpack.

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