Summary of Condense, Don’t Just Prune: Enhancing Efficiency and Performance in Moe Layer Pruning, by Mingyu Cao et al.
Condense, Don’t Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
by Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang, Xiaolong Ma, Shiwei Liu, Lu Yin
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposes ConDense-MoE (CD-MoE), a novel approach to reduce the memory requirements of Mixture-of-Experts (MoE) neural networks. MoE has gained popularity for its ability to scale up neural networks while utilizing fewer active parameters, but it still struggles with massive memory requirements. CD-MoE condenses large, sparse MoE layers into smaller, denser layers with a few activated experts for all tokens, maintaining hardware friendliness. The approach is designed for fine-grained MoE with shared experts, such as DeepSeekMoE and QwenMoE. Experiments demonstrate the effectiveness of CD-MoE, achieving 90% average accuracy while reducing memory usage by 27.5% and increasing inference speed by 1.26 times for the DeepSeekMoE-16B model. Additionally, lightweight expert fine-tuning recovers 98% of the original performance in just 5 hours on an A100 GPU. The paper’s code is available at https://github.com/duterscmy/CD-MoE/tree/main. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make big neural networks that are really good at learning, but use less memory and work faster. These networks are called Mixture-of-Experts (MoE). The problem is that they still need too much memory and take a long time to process. The new approach, called ConDense-MoE (CD-MoE), makes these big networks smaller and more efficient without losing their ability to learn well. It does this by making the parts of the network that are usually very large into smaller pieces that still work together. This helps make the network faster and use less memory, which is really important for using it in real-world applications. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Mixture of experts
