Summary of Baton: Enhancing Batch-wise Inference Efficiency For Large Language Models Via Dynamic Re-batching, by Peizhuang Cong et al.
BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching
by Peizhuang Cong, Qizhi Chen, Haochen Zhao, Tong Yang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 BATON scheme is an efficient approach for large language model inference in batch-wise settings. By dynamically adjusting processing batches and aligning vector dimensions, BATON achieves near-zero idle computations without increasing resource consumption. This method refines existing LLMs by leveraging prefilling and decoding separation mechanisms to embed new query keys and values into the KV cache of the processing batch. As a result, BATON improves query processing efficiency by up to 1.75 times compared to state-of-the-art Orca solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can be used for many different applications. One way they work is by having conversations with users. To make this more efficient, researchers have developed a new method called BATON. It helps computers process language models faster and use fewer resources. This is important because LLMs need to handle lots of information quickly. BATON does this by adjusting how it processes batches of information and making sure that all the necessary calculations are done correctly. This results in a big improvement in how fast and efficient language model inference can be. |
Keywords
» Artificial intelligence » Inference » Language model » Large language model
