SGLang: Efficient Execution of Structured Language Model Programs
xAI · Shanghai Jiao Tong University, UC Berkeley · Stanford University · , Texas A&M University - College Station · Anyscale, Inc · University of California, Berkeley · Computer Science Department, Stanford University · UC Berkeley
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。
摘要
Large language models (LLMs) are increasingly used for complex tasks that require multiple generation calls, advanced prompting techniques, control flow, and structured inputs/outputs. However, efficient systems are lacking for programming and executing these applications. We introduce SGLang, a system for efficient execution of complex language model programs. SGLang consists of a frontend language and a runtime. The frontend simplifies programming with primitives for generation and parallelism control. The runtime accelerates execution with novel optimizations like RadixAttention for KV cache reuse and compressed finite state machines for faster structured output decoding. Experiments show that SGLang achieves up to $6.4\times$ higher throughput compared to state-of-the-art inference systems on various large language and multi-modal models on tasks including agent control, logical reasoning, few-shot learning benchmarks, JSON decoding, retrieval-augmented generation pipelines, and multi-turn chat. The code is publicly available at https://github.com/sgl-project/sglang.