SGLang

SGLang：让大模型”结构化输出”的推理利器

在与大语言模型（LLM）打交道时，你是否遇到过这样的困扰：明明只想让模型输出一个 JSON 格式的数据，它却天马行空地”自由发挥”？SGLang 正是为解决这类问题而生的新一代 LLM 推理框架。让我们一起来认识这个来自 UC Berkeley 的创新工具。

什么是 SGLang？

SGLang（Structured Generation Language）是由 UC Berkeley 团队开发的高性能 LLM 推理引擎和编程语言。它的核心目标是：让大模型的输出更加可控和结构化，同时保持极高的推理性能。

一句话概括： SGLang = 高性能推理 + 结构化输出控制

为什么需要 SGLang？

想象一下这个场景：

你让 ChatGPT 帮你提取一篇文章中的关键信息，并以 JSON 格式返回：

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请提取以下信息并以JSON格式返回：
- 作者姓名
- 发布日期
- 主要观点

模型可能返回：

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好的，我来帮你提取信息：

{
  "author": "张三",
  "date": "2024年1月1日",
  "main_points": ["观点1", "观点2"]
}

希望这对你有帮助！如果需要更多信息，请告诉我。

问题来了——模型在 JSON 前后加了”废话”，你的程序解析 JSON 时直接崩溃了。

SGLang 的解决方案： 通过约束生成，强制模型只输出符合预期格式的内容。

SGLang 的核心特性

1. 结构化生成（Constrained Decoding）

SGLang 最强大的功能是约束解码。你可以精确控制模型的输出格式：

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from sglang import gen, select

## 强制模型在固定选项中选择
answer = select("是", "否", "不确定")

## 强制输出符合正则表达式的内容
phone = gen(regex=r"\d{3}-\d{4}-\d{4}")

## 强制输出有效的 JSON
data = gen(json_schema={
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    }
})

类比： 这就像给学生发一张选择题试卷，而不是让他写作文。答案必须在 A、B、C、D 中选择，不能”自由发挥”。

2. RadixAttention：智能缓存复用

SGLang 引入了创新的 RadixAttention 机制：

  • 使用 Radix Tree（基数树）来管理 KV Cache
  • 自动识别和复用相同前缀的缓存
  • 多个请求共享公共部分，大幅提升效率

举个例子：

假设有 100 个用户都在使用同一个系统提示词（System Prompt），传统方法需要计算 100 次。而 RadixAttention 只计算一次，然后复用给所有用户。

3. 前端编程语言

SGLang 提供了一种直观的编程方式来编排复杂的 LLM 调用：

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@sgl.function
def multi_turn_qa(s, question1, question2):
    s += sgl.system("你是一个有帮助的助手。")
    s += sgl.user(question1)
    s += sgl.assistant(sgl.gen("answer1", max_tokens=100))
    s += sgl.user(question2)
    s += sgl.assistant(sgl.gen("answer2", max_tokens=100))

这种方式比传统的字符串拼接更清晰、更易维护。

4. 高性能推理后端

SGLang 的推理速度非常快，主要得益于：

  • 连续批处理（Continuous Batching）
  • 优化的 CUDA Kernel
  • 高效的内存管理
  • 推测解码（Speculative Decoding）支持

SGLang vs 其他框架

特性 SGLang vLLM TensorRT-LLM
结构化输出 ✅ 原生支持 ⚠️ 有限 ⚠️ 有限
约束解码 ✅ JSON/Regex ❌ ❌
前缀缓存 ✅ RadixAttention ✅ ✅
推理性能 优秀 优秀 极致
易用性 ✅ Python原生 ✅ ⚠️ 需要编译

实际应用场景

场景一：API 数据提取

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@sgl.function
def extract_info(s, text):
    s += f"从以下文本中提取结构化信息：\n{text}"
    s += sgl.gen("result", json_schema={
        "type": "object",
        "properties": {
            "entities": {"type": "array"},
            "sentiment": {"enum": ["positive", "negative", "neutral"]},
            "summary": {"type": "string"}
        }
    })

场景二：多选题问答

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@sgl.function  
def multiple_choice(s, question, options):
    s += f"问题：{question}\n选项：{options}\n请选择正确答案："
    s += sgl.select(["A", "B", "C", "D"], name="answer")

场景三：代码生成

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@sgl.function
def generate_function(s, description):
    s += f"根据以下描述生成Python函数：\n{description}"
    s += sgl.gen("code", regex=r"def \w+\([^)]*\):[\s\S]+")

快速上手

安装

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pip install sglang[all]

启动服务

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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000

使用示例

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import sglang as sgl

@sgl.function
def simple_qa(s, question):
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=256))

## 运行
state = simple_qa.run(question="什么是机器学习？")
print(state["answer"])

性能表现

根据官方基准测试，SGLang 在多种场景下表现出色：

  • JSON 模式生成： 比其他框架快 3-5 倍
  • 共享前缀场景： RadixAttention 带来 2-4 倍加速
  • 多轮对话： 缓存复用显著降低延迟

适用场景

SGLang 特别适合以下需求：

  • ✅ 需要结构化 JSON 输出的 API 服务
  • ✅ 需要约束模型在固定选项中选择
  • ✅ 多个请求共享相同前缀（如系统提示）
  • ✅ 复杂的多轮对话和推理链
  • ✅ 需要高性能的生产环境部署

总结

SGLang 是一个将结构化输出控制和高性能推理完美结合的 LLM 推理框架。通过约束解码技术，它让大模型的输出变得可控、可预测；通过 RadixAttention 等优化，它在保持灵活性的同时实现了极高的推理效率。如果你的应用需要可靠的结构化输出，SGLang 是一个非常值得尝试的选择。

SGLang: The Inference Powerhouse for “Structured Output” from Large Models

When working with Large Language Models (LLMs), have you ever encountered this frustration: you only want the model to output data in JSON format, but it goes off on a tangent with “creative freedom”? SGLang is a next-generation LLM inference framework designed precisely to solve such problems. Let’s get to know this innovative tool from UC Berkeley.

What is SGLang?

SGLang (Structured Generation Language) is a high-performance LLM inference engine and programming language developed by the UC Berkeley team. Its core goal is: to make LLM output more controllable and structured while maintaining extremely high inference performance.

In one sentence: SGLang = High-performance inference + Structured output control

Why Do We Need SGLang?

Imagine this scenario:

You ask ChatGPT to extract key information from an article and return it in JSON format:

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Please extract the following information and return in JSON format:
- Author name
- Publication date
- Main points

The model might return:

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Okay, let me help you extract the information:

{
  "author": "John Smith",
  "date": "January 1, 2024",
  "main_points": ["Point 1", "Point 2"]
}

Hope this helps! Let me know if you need more information.

The problem—the model added “fluff” before and after the JSON, and your program crashes when trying to parse the JSON.

SGLang’s Solution: Through constrained generation, force the model to output only content that matches the expected format.

Core Features of SGLang

1. Structured Generation (Constrained Decoding)

SGLang’s most powerful feature is constrained decoding. You can precisely control the model’s output format:

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from sglang import gen, select

## Force model to choose from fixed options
answer = select("Yes", "No", "Uncertain")

## Force output matching a regex pattern
phone = gen(regex=r"\d{3}-\d{4}-\d{4}")

## Force output of valid JSON
data = gen(json_schema={
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    }
})

Analogy: This is like giving students a multiple-choice test instead of asking them to write an essay. Answers must be chosen from A, B, C, D—no “creative freedom” allowed.

2. RadixAttention: Smart Cache Reuse

SGLang introduces the innovative RadixAttention mechanism:

  • Uses a Radix Tree to manage KV Cache
  • Automatically identifies and reuses caches with the same prefix
  • Multiple requests share common parts, dramatically improving efficiency

Example:

Suppose 100 users are all using the same system prompt. Traditional methods need to compute it 100 times. RadixAttention computes it once and reuses it for all users.

3. Frontend Programming Language

SGLang provides an intuitive programming approach to orchestrate complex LLM calls:

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@sgl.function
def multi_turn_qa(s, question1, question2):
    s += sgl.system("You are a helpful assistant.")
    s += sgl.user(question1)
    s += sgl.assistant(sgl.gen("answer1", max_tokens=100))
    s += sgl.user(question2)
    s += sgl.assistant(sgl.gen("answer2", max_tokens=100))

This approach is cleaner and more maintainable than traditional string concatenation.

4. High-Performance Inference Backend

SGLang’s inference is very fast, thanks to:

  • Continuous Batching
  • Optimized CUDA Kernels
  • Efficient memory management
  • Speculative Decoding support

SGLang vs Other Frameworks

Feature SGLang vLLM TensorRT-LLM
Structured Output ✅ Native support ⚠️ Limited ⚠️ Limited
Constrained Decoding ✅ JSON/Regex ❌ ❌
Prefix Caching ✅ RadixAttention ✅ ✅
Inference Performance Excellent Excellent Extreme
Ease of Use ✅ Python native ✅ ⚠️ Requires compilation

Practical Application Scenarios

Scenario 1: API Data Extraction

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@sgl.function
def extract_info(s, text):
    s += f"Extract structured information from the following text:\n{text}"
    s += sgl.gen("result", json_schema={
        "type": "object",
        "properties": {
            "entities": {"type": "array"},
            "sentiment": {"enum": ["positive", "negative", "neutral"]},
            "summary": {"type": "string"}
        }
    })

Scenario 2: Multiple Choice Q&A

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@sgl.function  
def multiple_choice(s, question, options):
    s += f"Question: {question}\nOptions: {options}\nPlease select the correct answer:"
    s += sgl.select(["A", "B", "C", "D"], name="answer")

Scenario 3: Code Generation

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@sgl.function
def generate_function(s, description):
    s += f"Generate a Python function based on the following description:\n{description}"
    s += sgl.gen("code", regex=r"def \w+\([^)]*\):[\s\S]+")

Getting Started

Installation

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pip install sglang[all]

Launch Server

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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000

Usage Example

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import sglang as sgl

@sgl.function
def simple_qa(s, question):
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=256))

## Run
state = simple_qa.run(question="What is machine learning?")
print(state["answer"])

Performance

According to official benchmarks, SGLang performs excellently in various scenarios:

  • JSON Mode Generation: 3-5x faster than other frameworks
  • Shared Prefix Scenarios: RadixAttention provides 2-4x speedup
  • Multi-turn Dialogue: Cache reuse significantly reduces latency

Use Cases

SGLang is particularly suitable for:

  • ✅ API services requiring structured JSON output
  • ✅ Constraining models to choose from fixed options
  • ✅ Multiple requests sharing the same prefix (like system prompts)
  • ✅ Complex multi-turn dialogues and reasoning chains
  • ✅ High-performance production deployments

Summary

SGLang is an LLM inference framework that perfectly combines structured output control with high-performance inference. Through constrained decoding technology, it makes LLM output controllable and predictable; through optimizations like RadixAttention, it achieves extremely high inference efficiency while maintaining flexibility. If your application requires reliable structured output, SGLang is definitely worth trying.