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AI Research
arXiv CS.AI·Jul 22·5 min read
CompilerKV: Risk-Adaptive KV Compression via Offline Experience Compilation
TL;DR
CompilerKV introduces a novel key-value (KV) retention policy that compiles corrective tables offline, significantly enhancing compression performance in machine learning models. It achieves state-of-the-art results, improving over existing methods by an average of 1.67 points across multiple architectures.
✦ Why It Matters
Implement CompilerKV to improve your model's KV compression and efficiency today.
Key Takeaways
- Adopt CompilerKV for better KV compression in your models.
- Utilize offline compiled tables to reduce online correction needs.
- Leverage high transferability of retention tables across datasets.
- Optimize performance in high-demand scenarios with minimal KV retention.
How It Works
CompilerKV compiles corrective tables from a calibration corpus, allowing for efficient O(1) lookups during inference. This method contrasts with traditional approaches that rely on noisy online signals, enabling more reliable retention decisions based on cross-prompt regularities.