Summary of Moe++: Accelerating Mixture-of-experts Methods with Zero-computation Experts, by Peng Jin et al.
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by Peng Jin, Bo Zhu, Li Yuan, Shuicheng Yan
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary MoE++, a novel framework for Mixture-of-Experts (MoE) methods, is designed to enhance effectiveness and efficiency. The proposed framework integrates Feed-Forward Network (FFN) and zero-computation experts, offering three key advantages: low computing overhead, high performance, and deployment-friendly. MoE++ allows each token to engage with a dynamic number of FFNs, be adjusted by constant vectors, or even skip the MoE layer entirely. The design leverages gating residuals, enabling tokens to consider previous layers when selecting experts. Experimental results demonstrate better performance and 1.1-2.1x expert forward throughput compared to vanilla MoE models of the same size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE++ is a new way to make Mixture-of-Experts (MoE) methods better. It combines two types of experts: ones that do lots of calculations and ones that don’t need to calculate anything. This makes MoE++ faster, more efficient, and easier to use. The framework also helps tokens in the model make decisions based on what happened earlier in the process. Overall, MoE++ does a great job at balancing speed, performance, and ease of use. |
Keywords
» Artificial intelligence » Mixture of experts » Token
