Summary of Kvsharer: Efficient Inference Via Layer-wise Dissimilar Kv Cache Sharing, by Yifei Yang et al.
KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing
by Yifei Yang, Zouying Cao, Qiguang Chen, Libo Qin, Dongjie Yang, Hai Zhao, Zhi Chen
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel approach to large language models (LLMs) has been proposed in this paper, addressing the significant GPU memory requirements during inference. The key finding is that most memory consumption comes from storing attention maps in the key-value (KV) cache. Existing methods focus on compressing KV caches within a single Transformer layer, while this work explores layer-wise compression by sharing KV caches between layers. The proposed method, KVSharer, reduces KV cache computation by 30% without impacting model performance and achieves at least 1.3 times generation acceleration. Moreover, KVSharer is compatible with existing intra-layer KV cache compression methods, allowing for further memory savings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have gotten really big! This means they need a lot of computer power to run fast. The problem is that most of this power is used just storing information about how the model pays attention to different parts of what it’s processing. Scientists have been trying to make this process more efficient, but most of their efforts have focused on making individual layers (think of these like building blocks) work better. This new method, called KVSharer, takes a different approach by sharing information between these layers. Surprisingly, this actually helps the model perform better! By using KVSharer, scientists can make the model run 30% faster and use less computer power. |
Keywords
» Artificial intelligence » Attention » Inference » Transformer
