Implementing a Mixture of Experts (MoE) layer is one part of the problem. Training it effectively, especially when dealing with models containing hundreds or thousands of experts, presents a distinct set of engineering challenges. Standard training procedures are often insufficient for these sparse architectures, which demand specialized techniques to manage immense parameter counts and maintain stability.
This chapter shifts from architectural theory to the practical mechanics of training and optimization. You will learn the methods required to successfully train large-scale MoE models from scratch and fine-tune existing ones.
The chapter concludes with a hands-on exercise where you will configure a distributed training job, applying these techniques to a large-scale MoE model.
3.1 Expert Parallelism for Distributed Training
3.2 Combining Model, Data, and Expert Parallelism
3.3 Capacity Factor and its Impact on Performance
3.4 Techniques for Mitigating Router Z-Loss Instability
3.5 Precision and its Effects: BFloat16 Training
3.6 Fine-tuning Strategies for Pre-trained MoE Models
3.7 Practice: Configuring a Distributed Training Job
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