Summary of Expertflow: Optimized Expert Activation and Token Allocation For Efficient Mixture-of-experts Inference, by Xin He et al.
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference
by Xin He, Shunkang Zhang, Yuxin Wang, Haiyan Yin, Zihao Zeng, Shaohuai Shi, Zhenheng Tang, Xiaowen Chu, Ivor Tsang, Ong Yew Soon
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The paper introduces ExpertFlow, a system designed to enhance inference efficiency in Sparse Mixture of Experts (MoE) models. Existing offloading techniques fail to adapt to dynamic routing, leading to inefficient cache utilization or prohibitive costs. ExpertFlow addresses these challenges by predicting routing paths using a lightweight predictor and implementing a dynamic token scheduling strategy. This reduces overhead and boosts system performance, achieving up to 93.72% GPU memory savings and enhancing inference speed by 2-10 times compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models are powerful but face deployment challenges due to high memory demands during inference. ExpertFlow is designed to solve these issues by predicting routing paths before computation begins, allowing for real-time error correction and reducing I/O overhead. The system also optimizes MoE inference by rearranging input tokens across different batches, reducing the number of activated experts per batch and improving computational efficiency. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Token
