Summary of Lorc: Low-rank Compression For Llms Kv Cache with a Progressive Compression Strategy, by Rongzhi Zhang et al.
LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy
by Rongzhi Zhang, Kuang Wang, Liyuan Liu, Shuohang Wang, Hao Cheng, Chao Zhang, Yelong Shen
First submitted to arxiv on: 4 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 In this paper, researchers tackle the issue of memory consumption in transformer-based language models. Specifically, they focus on the Key-Value (KV) cache, a crucial component that enables faster inference by storing previously computed KV vectors. However, as sequence length and batch size increase, the KV cache’s memory consumption grows linearly, posing a significant bottleneck in model deployment. To mitigate this issue, existing approaches either require extensive parameter tuning or overlook inter-layer dependencies. This paper proposes new methods to optimize KV cache compression at test time, leveraging task-specific dependencies for improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Key-Value (KV) cache is an important part of transformer-based language models that helps speed up predictions by storing information from previous computations. However, this cache uses a lot of memory and gets bigger as the model processes longer sentences or more data at once. This makes it hard to use these powerful models in real-world applications. Researchers have tried to solve this problem before, but their methods either require tweaking many settings or ignore important connections between different parts of the model. This paper explores new ways to make the KV cache more efficient without sacrificing performance. |
Keywords
» Artificial intelligence » Inference » Transformer
