Summary of Towards Efficient Mixture Of Experts: a Holistic Study Of Compression Techniques, by Shwai He et al.
Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques
by Shwai He, Daize Dong, Liang Ding, Ang Li
First submitted to arxiv on: 4 Jun 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 The paper presents a study on compression techniques for Mixture of Experts (MoE) models, aiming to enhance efficiency and scalability. MoE’s dynamic expert selection offers significant computational cost reductions while preserving high performance. However, this approach introduces new inefficiencies, such as excessive parameters and communication overhead. To address these challenges, the authors propose more aggressive pruning strategies, including Layer Drop and Block Drop, which eliminate entire MoE layers or transformer blocks. These techniques not only preserve model performance but also improve computation and memory efficiency. Additionally, Expert Slimming compresses individual experts to further boost performance and can be seamlessly integrated with Expert Trimming. The proposed methods achieve a 6.05x speedup with 77.1% reduced memory usage while maintaining over 92% of performance on Mixtral-8x7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making big language models smaller and more efficient for real-world use. Currently, these models are too big to work well in everyday applications. The authors suggest new ways to make them smaller without losing their ability to perform well. They propose three techniques: Layer Drop, Block Drop, and Expert Slimming. These methods can be used alone or together to make the model more efficient and fast while still keeping its good performance. |
Keywords
» Artificial intelligence » Mixture of experts » Pruning » Transformer
