Summary of Inference-friendly Models with Mixattention, by Shashank Rajput et al.
Inference-Friendly Models With MixAttention
by Shashank Rajput, Ying Sheng, Sean Owen, Vitaliy Chiley
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper investigates the impact of key-value (KV) cache size on modern language models during inference. The KV cache size is directly proportional to the number of attention heads and tokens processed, leading to increased memory consumption and slower inference for longer inputs. To address this issue, the authors propose MixAttention, a model architecture modification that combines sliding window attention with KV cache sharing across layers. Experiments demonstrate that MixAttention reduces memory usage and improves inference speed without sacrificing model performance in both short- and long-context tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make language models more efficient by modifying their architecture. It shows that the size of a special part called the key-value (KV) cache affects how well the model works, especially when it’s processing longer inputs. To solve this problem, the researchers created something new called MixAttention, which combines two techniques: storing only recent tokens in the KV cache and sharing the cache across different layers. By doing this, they were able to make the model use less memory and work faster without losing its ability to understand language. |
Keywords
» Artificial intelligence » Attention » Inference
