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Mixture-of-Depths Attention
arXiv – CS AI|Lianghui Zhu, Yuxin Fang, Bencheng Liao, Shijie Wang, Tianheng Cheng, Zilong Huang, Chen Chen, Lai Wei, Yutao Zeng, Ya Wang, Yi Lin, Yu Li, Xinggang Wang|
🤖AI Summary
Researchers introduce Mixture-of-Depths Attention (MoDA), a new mechanism for large language models that allows attention heads to access key-value pairs from both current and preceding layers to combat signal degradation in deeper models. Testing on 1.5B-parameter models shows MoDA improves perplexity by 0.2 and downstream task performance by 2.11% with only 3.7% computational overhead while maintaining 97.3% of FlashAttention-2's efficiency.
Key Takeaways
- →MoDA addresses signal degradation in deep LLMs by allowing attention heads to access KV pairs from multiple layers rather than just the current layer.
- →The mechanism achieves 97.3% of FlashAttention-2's efficiency at 64K sequence length through hardware-efficient algorithms.
- →Testing on 1.5B-parameter models shows 0.2 perplexity improvement across 10 benchmarks and 2.11% better performance on downstream tasks.
- →The computational overhead is minimal at only 3.7% additional FLOPs compared to standard attention mechanisms.
- →Combining MoDA with post-norm architecture yields better results than using pre-norm configurations.
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#mixture-of-depths#attention-mechanism#llm#transformer#deep-learning#model-architecture#signal-degradation#flashattention#perplexity#computational-efficiency
Read Original →via arXiv – CS AI
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