SHRP: Specialized Head Routing and Pruning for Efficient Encoder Compression
Analysis
This paper introduces SHRP, a novel approach to compress Transformer encoders by pruning redundant attention heads. The core idea of Expert Attention, treating each head as an independent expert, is promising. The unified Top-1 usage-driven mechanism for dynamic routing and deterministic pruning is a key contribution. The experimental results on BERT-base are compelling, showing a significant reduction in parameters with minimal accuracy loss. However, the paper could benefit from more detailed analysis of the computational cost reduction and a comparison with other compression techniques. Further investigation into the generalizability of SHRP to different Transformer architectures and datasets would also strengthen the findings.
Key Takeaways
- •SHRP is a novel structured pruning framework for Transformer encoders.
- •It uses Expert Attention and a Top-1 usage-driven mechanism for routing and pruning.
- •It achieves significant parameter reduction with minimal accuracy loss on BERT-base.
“SHRP achieves 93% of the original model accuracy while reducing parameters by 48 percent.”