Summary of Pqcache: Product Quantization-based Kvcache For Long Context Llm Inference, by Hailin Zhang et al.
PQCache: Product Quantization-based KVCache for Long Context LLM Inference
by Hailin Zhang, Xiaodong Ji, Yilin Chen, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Weipeng Chen, Bin Cui
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PQCache method addresses the memory bottleneck issue in Large Language Models (LLMs) by employing Product Quantization (PQ) to manage Key-Value Cache (KVCache). This approach maintains model quality while ensuring low serving latency. The PQCache method consists of two phases: prefilling and autoregressive decoding. During prefilling, PQ is applied to tokens’ keys for each LLM layer and head. In the autoregressive decoding phase, important tokens are identified through Maximum Inner-Product Search (MIPS) using PQ codes and centroids, allowing for efficient self-attention computation. The method achieves both effectiveness and efficiency, maintaining model quality with only 1/5 of the tokens involved in attention while keeping system latency acceptable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PQCache is a new way to store and search Large Language Models’ Key-Value Cache (KVCache) without losing performance or speed. This helps solve the memory problem in LLMs, which are getting bigger. PQCache does this by using Product Quantization (PQ) to shrink the amount of information needed for KVCache. The method has two parts: preparing and generating new text. In preparation, PQ is used on tokens’ keys for each layer and head. When generating new text, important tokens are found quickly using a special search method. This way, the model can still work well even if only some parts of the text are being used in attention. |
Keywords
* Artificial intelligence * Attention * Autoregressive * Quantization * Self attention
