Summary of Keyformer: Kv Cache Reduction Through Key Tokens Selection For Efficient Generative Inference, by Muhammad Adnan and Akhil Arunkumar and Gaurav Jain and Prashant J. Nair and Ilya Soloveychik and Purushotham Kamath
Keyformer: KV Cache Reduction through Key Tokens Selection for Efficient Generative Inference
by Muhammad Adnan, Akhil Arunkumar, Gaurav Jain, Prashant J. Nair, Ilya Soloveychik, Purushotham Kamath
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); 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 Transformers have become the foundation for Large Language Models (LLMs). The paper focuses on the token generation process, which accounts for most computational workload. This phase primarily involves vector-matrix multiplications and interactions with the Key-Value Cache. However, this process is limited by memory bandwidth due to the overhead of transferring weights and KV cache values from memory to computing units. This memory bottleneck becomes more significant in applications that require long-context and extensive text generation, which are increasingly important for LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers have become a key part of language models. The paper looks at how these models generate text. Most of the work is done during token generation, where the model does lots of calculations and talks to its memory cache. But this process uses up a lot of memory bandwidth because it needs to move weights and other information from memory to the parts that do the calculating. This can be a problem when we want language models to understand long pieces of text or generate lots of text, which is important for things like language translation. |
Keywords
* Artificial intelligence * Text generation * Token * Translation
