Summary of Xgrammar: Flexible and Efficient Structured Generation Engine For Large Language Models, by Yixin Dong et al.
XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
by Yixin Dong, Charlie F. Ruan, Yaxing Cai, Ruihang Lai, Ziyi Xu, Yilong Zhao, Tianqi Chen
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
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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 paper proposes a new structured generation engine called XGrammar for large language models (LLMs). The increasing complexity and diversity of applications using LLM Agents require high-speed generation of structured outputs. Context-free grammar is a flexible approach, but it can be computationally expensive due to the need to traverse stack states over all tokens in vocabulary during runtime. To address this issue, XGrammar accelerates context-free grammar execution by dividing the vocabulary into context-independent and context-dependent tokens. The engine also incorporates transformations to expand the grammar context and reduce the number of context-independent tokens. Additionally, it features an efficient persistent stack to accelerate context-dependent token checks. The authors co-design XGrammar with LLM inference engine to overlap grammar computation with GPU executions. Evaluation results show that XGrammar achieves up to 100x speedup over existing solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a faster way for computers to understand and generate text from large language models. Right now, it takes a long time to generate structured outputs like code or commands for these models. The authors came up with a new engine called XGrammar that makes this process much quicker by organizing words into two groups: ones that don’t change based on context and ones that do. They also made some smart changes to how the engine works, which helped it run even faster. When combined with other technologies, XGrammar can generate structured outputs almost instantly. |
Keywords
» Artificial intelligence » Inference » Token
