HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
This addresses the cost-saving challenge for organizations training large language models on mixed GPU hardware, though it is an incremental improvement over existing heterogeneity-aware solutions.
The paper tackles the problem of inefficient training of Mixture-of-Experts models on heterogeneous GPU clusters by proposing HeterMoE, which disaggregates attention and expert computation to better utilize different GPU capabilities. The result is up to a 2.3x speed-up compared to existing systems and maintaining 95% training throughput with older GPUs.
The Mixture-of-Experts (MoE) architecture has become increasingly popular as a method to scale up large language models (LLMs). To save costs, heterogeneity-aware training solutions have been proposed to utilize GPU clusters made up of both newer and older-generation GPUs. However, existing solutions are agnostic to the performance characteristics of different MoE model components (i.e., attention and expert) and do not fully utilize each GPU's compute capability. In this paper, we introduce HeterMoE, a system to efficiently train MoE models on heterogeneous GPUs. Our key insight is that newer GPUs significantly outperform older generations on attention due to architectural advancements, while older GPUs are still relatively efficient for experts. HeterMoE disaggregates attention and expert computation, where older GPUs are only assigned with expert modules. Through the proposed zebra parallelism, HeterMoE overlaps the computation on different GPUs, in addition to employing an asymmetric expert assignment strategy for fine-grained load balancing to minimize GPU idle time. Our evaluation shows that HeterMoE achieves up to 2.3x speed-up compared to existing MoE training systems, and 1.4x compared to an optimally balanced heterogeneity-aware solution. HeterMoE efficiently utilizes older GPUs by maintaining 95% training throughput on average, even with half of the GPUs in a homogeneous A40 cluster replaced with V100.