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cloud.google.com·6h ago
AI Research
arXiv CS.AI·10d ago·5 min read
ITC-MoE: Importance-guided Token-aware Compression for MoE Diffusion Language Models
TL;DR
ITC-MoE introduces a novel framework for compressing Mixture-of-Experts (MoE) Diffusion Language Models, significantly reducing computation and storage costs while maintaining high accuracy. It achieves up to 7.22x speedup and 96.33% accuracy under a 30% compression budget.
✦ Why It Matters
Implement ITC-MoE to enhance the efficiency of your MoE DLMs without sacrificing performance.
Key Takeaways
- Explore ITC-MoE for efficient MoE DLM compression.
- Utilize IATC for adaptive expert weight transformation.
- Apply TCR for optimized token routing and compensation.
- Achieve significant speedup while maintaining model accuracy.
How It Works
ITC-MoE combines two innovative techniques: IATC, which uses activation and gradient importance to adaptively compress expert weights, and TCR, which focuses on optimizing the routing of tokens based on their usage patterns. This allows the model to dynamically adjust its compression strategy based on the specific characteristics of the input data.