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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Do Transformers Need Three Projections? Systematic Study of QKV Variants
TL;DR
A systematic study of query, key, and value (QKV) projections in transformers reveals that sharing projections can maintain performance while reducing memory usage. Specifically, the Q-K=V variant achieves significant cache reductions with minimal impact on language modeling perplexity.
✦ Why It Matters
Consider implementing Q-K=V projection sharing to optimize memory usage in your transformer models today.
Key Takeaways
- Explore Q-K=V projection sharing for memory efficiency.
- Combine Q-K=V with head sharing for optimal performance.
- Evaluate the impact of projection sharing on your models.
- Utilize findings for edge deployment of transformer models.
How It Works
The study investigates the effects of sharing projections in transformers, specifically focusing on the Q-K=V variant. By sharing keys and values, the model reduces memory usage while maintaining performance.
This approach allows keys and values to occupy similar representational spaces, which is effective in low-rank attention regimes. The introduction of asymmetric attention through 2D positional encodings further enhances the model's capabilities.