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arXiv CS.AI·1d ago·5 min read
ConMoE: MoE Compression as Expert Selection and Functional Assignment
TL;DR
ConMoE introduces a novel approach to compressing Sparse Mixture-of-Experts (MoE) models by optimizing expert assignment after constructing a compact expert pool. This method significantly reduces GPU memory usage while improving model accuracy across various benchmarks.
✦ Why It Matters
Implement ConMoE to enhance model efficiency and reduce memory costs in your AI applications today.
Key Takeaways
- Use ConMoE for efficient expert assignment in MoE models.
- Optimize expert pools with Anchor Coverage for better performance.
- Achieve significant memory savings while maintaining accuracy.
- Test ConMoE across various architectures for best results.
How It Works
ConMoE separates the expert pool construction from workload assignment, allowing for independent optimization. It constructs a compact expert pool using Anchor Coverage, which identifies the most relevant experts for a given task.
The assignment of workloads is then optimized based on a router-conditioned functional objective, which enhances accuracy while keeping the router and expert weights fixed.