Sparse Mixture of Experts (SMoEs) offers efficient model scaling, pivotal in Switch Transformer and Universal Transformers. Challenges in its implementation are addressed by ScatterMoE, showcasing enhanced GPU performance, reduced memory footprint, and improved throughput compared to Megablocks. ParallelLinear enables easy extension to other expert modules, boosting efficient deep learning model training and inference.
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ScatterMoE: Enhancing SMoE Implementations on GPUs
Introduction
A sparse Mixture of Experts (SMoEs) has gained traction for scaling models, especially useful in memory-constrained setups. They are pivotal in Switch Transformer and Universal Transformers, offering efficient training and inference. However, implementing SMoEs efficiently poses challenges.
Challenges and Solutions
Megablocks and PIT propose framing SMoE computation as a sparse matrix multiplication problem to address challenges, allowing for more efficient GPU-based implementations. Researchers from IBM, Mila, and the University of Montreal present ScatterMoE, an efficient SMoE implementation that minimizes memory footprint via ParallelLinear, which conducts grouped matrix operations on scattered groups.
Benefits of ScatterMoE
ScatterMoE outperforms Megablocks by a staggering 38.1% overall throughput, demonstrating superior throughput and reduced memory usage. It also facilitates the extension of Mixture-of-Experts concepts, exemplified by its implementation of Mixture of Attention, significantly advancing efficient deep learning model training and inference.
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