SGLang: Comprehensive Guide to Structured Generation Language

SGLang: Comprehensive Guide to Structured Generation Language

About This Guide

This comprehensive guide covers SGLang (Structured Generation Language), a revolutionary framework that transforms how developers interact with large language models (LLMs) and vision-language models. SGLang achieves unprecedented performance improvements while maintaining programming simplicity and flexibility.

Introduction

SGLang (Structured Generation Language) is a revolutionary framework that transforms how developers interact with large language models (LLMs) and vision-language models. By co-designing both the frontend programming interface and the backend runtime system, SGLang achieves unprecedented performance improvements while maintaining programming simplicity and flexibility.

What is SGLang?

SGLang is a fast serving framework for large language models and vision language models that makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. SGLang consists of a frontend language and a runtime, where the frontend simplifies programming with primitives for generation and parallelism control, and the runtime accelerates execution with novel optimizations.

Key Benefits

Performance

Up to 5x throughput improvements over traditional serving methods through advanced optimization techniques.

Controllability

Fine-grained control over generation processes with structured primitives and constraint handling.

Expressiveness

Rich primitives for complex LLM programming patterns including parallel execution and multi-step reasoning.

Efficiency

Advanced caching and optimization techniques including RadixAttention for automatic KV cache reuse.

Multimodal Support

Native support for both language and vision-language models with unified processing pipeline.

Key Features

Frontend Language Features

graph TD
    A[Frontend Language] --> B[Embedded DSL]
    A --> C[Generation Primitives]
    A --> D[Parallelism Control]
    A --> E[Structured Outputs]
    A --> F[Template System]
    
    B --> B1[Python Integration]
    C --> C1["gen()" function]
    C --> C2["select()" function]
    D --> D1["fork()" for Parallel]
    E --> E1[JSON/XML Support]
    F --> F1[Dynamic Prompts]

• Embedded DSL: Domain-specific language embedded in Python
• Generation Primitives: Built-in functions for text generation and control
• Parallelism Control: Native support for parallel generation calls
• Structured Outputs: Easy handling of JSON, XML, and custom formats
• Template System: Powerful templating for dynamic prompt construction

Backend Runtime Features

graph TD
    A[Backend Runtime] --> B[RadixAttention]
    A --> C[Zero-overhead Scheduler]
    A --> D[Continuous Batching]
    A --> E[Speculative Decoding]
    A --> F[Multi-modal Processing]
    A --> G[Quantization Support]
    A --> H[Parallel Execution]
    
    B --> B1[KV Cache Reuse]
    D --> D1[Dynamic Batching]
    G --> G1[FP4/FP8/INT4/AWQ/GPTQ]
    H --> H1[Tensor/Pipeline/Expert/Data]

Architecture Overview

SGLang’s architecture consists of two main components:

Architecture Details

1. Frontend Language

The frontend provides a Python-embedded DSL that simplifies LLM programming with:

• Intuitive syntax for generation tasks
• Built-in primitives for common patterns
• Automatic optimization of generation calls
• Type safety and error handling

2. Backend Runtime

The backend proposes RadixAttention, a technique for automatic and efficient KV cache reuse across multiple LLM generation calls. The runtime includes:

• High-performance serving engine
• Advanced memory management
• Automatic optimization passes
• Multi-GPU/multi-node support

Installation and Setup

Prerequisites

System Requirements

• Python 3.8 or higher
• CUDA 11.8+ (for GPU acceleration)
• PyTorch 2.0+

Basic Installation

install.sh

# Install from PyPI
pip install sglang

# Or install from source
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e .

GPU Support

# For CUDA support
pip install sglang[cuda]

# For ROCm/AMD GPU support
pip install sglang[rocm]

Docker Installation

# Pull official Docker image
docker pull lmsysorg/sglang:latest

# Run with GPU support
docker run --gpus all -p 30000:30000 lmsysorg/sglang:latest

Core Concepts

1. Generation Functions

The core abstraction in SGLang is the generation function, which encapsulates prompts and generation logic:

basic_generation.py

import sglang as sgl

@sgl.function
def simple_chat(s, user_message):
    s += sgl.user(user_message)
    s += sgl.assistant(sgl.gen("response", max_tokens=100))

2. State Management

SGLang uses a state object s to track conversation history and manage generation context:

state_management.py

@sgl.function
def multi_turn_chat(s, messages):
    for msg in messages:
        s += sgl.user(msg)
        s += sgl.assistant(sgl.gen("response", stop="\n"))

3. Control Primitives

gen()

Generate text with specified constraints and parameters.

select()

Choose from predefined options or multiple choice answers.

fork()

Create parallel execution branches for concurrent processing.

image()

Process image inputs for vision-language model tasks.

Frontend Language Features

Generation Primitives

Basic Text Generation

story_generator.py

@sgl.function
def story_writer(s, theme):
    s += f"Write a story about {theme}:\n"
    s += sgl.gen("story", max_tokens=500, temperature=0.7)

Structured Generation

json_generator.py

@sgl.function
def json_generator(s, query):
    s += f"Generate JSON for: {query}\n"
    s += sgl.gen("json", max_tokens=200, regex=r'\{.*\}')

Conditional Generation

conditional_response.py

@sgl.function
def conditional_response(s, question, context):
    s += f"Context: {context}\n"
    s += f"Question: {question}\n"
    
    # First, determine if answerable
    s += "Is this answerable? "
    s += sgl.gen("answerable", choices=["Yes", "No"])
    
    if s["answerable"] == "Yes":
        s += "\nAnswer: "
        s += sgl.gen("answer", max_tokens=100)
    else:
        s += "\nI don't have enough information to answer this question."

Parallel Execution

parallel_processing.py

@sgl.function
def parallel_summarization(s, documents):
    # Fork execution for parallel processing
    s += sgl.fork([
        lambda: summarize_doc(doc) for doc in documents
    ])
    
    # Combine results
    summaries = [s[f"summary_{i}"] for i in range(len(documents))]
    return summaries

Template System

email_template.py

@sgl.function
def email_generator(s, recipient, subject, tone="professional"):
    s += sgl.system(f"Write emails in a {tone} tone.")
    s += f"To: {recipient}\n"
    s += f"Subject: {subject}\n\n"
    s += sgl.gen("body", max_tokens=300)

Backend Runtime

RadixAttention

RadixAttention Innovation

RadixAttention structures and automates the reuse of Key-Value (KV) caches during runtime by storing them in a radix tree data structure.

This enables:

• Prefix Sharing: Common prompt prefixes are cached and reused
• Memory Efficiency: Reduced memory usage through intelligent caching
  
• Speed Improvements: Faster generation through cache hits

graph TD
    A[Input Prompts] --> B[Radix Tree]
    B --> C[Shared Prefixes]
    B --> D[Unique Suffixes]
    C --> E[KV Cache Reuse]
    D --> F[New Computation]
    E --> G[Performance Boost]
    F --> G

Continuous Batching

The runtime implements continuous batching to:

• Process multiple requests simultaneously
• Dynamically adjust batch sizes
• Optimize GPU utilization

Speculative Decoding

Acceleration technique that:

• Predicts multiple tokens ahead
• Verifies predictions in parallel
• Falls back to standard decoding when needed

Basic Usage Examples

1. Simple Text Generation

poem_generator.py

import sglang as sgl

# Set backend
sgl.set_default_backend(sgl.RuntimeEndpoint("http://localhost:30000"))

@sgl.function
def generate_poem(s, topic):
    s += f"Write a haiku about {topic}:\n"
    s += sgl.gen("poem", max_tokens=50)

# Execute
result = generate_poem("spring")
print(result["poem"])

2. Multi-step Reasoning

math_solver.py

@sgl.function
def math_solver(s, problem):
    s += f"Problem: {problem}\n"
    s += "Let me solve this step by step.\n"
    s += "Step 1: "
    s += sgl.gen("step1", max_tokens=50, stop="\n")
    s += "\nStep 2: "
    s += sgl.gen("step2", max_tokens=50, stop="\n")
    s += "\nTherefore, the answer is: "
    s += sgl.gen("answer", max_tokens=20)

result = math_solver("What is 15% of 240?")

3. JSON Structured Output

info_extractor.py

@sgl.function
def extract_info(s, text):
    s += f"Extract key information from this text:\n{text}\n"
    s += "Output as JSON:\n"
    s += sgl.gen(
        "info", 
        max_tokens=200, 
        regex=r'\{[^}]*"name"[^}]*"age"[^}]*"location"[^}]*\}'
    )

result = extract_info("John Smith is 30 years old and lives in New York.")

4. Role-playing Conversation

roleplay.py

@sgl.function
def roleplay_chat(s, character, user_input):
    s += sgl.system(f"You are {character}. Stay in character.")
    s += sgl.user(user_input)
    s += sgl.assistant(sgl.gen("response", max_tokens=150))

result = roleplay_chat("a wise old wizard", "How do I learn magic?")

Advanced Programming Patterns

1. Chain of Thought Reasoning

cot_reasoning.py

@sgl.function
def cot_reasoning(s, question):
    s += f"Question: {question}\n"
    s += "Let me think through this step by step:\n"
    
    for i in range(3):
        s += f"Step {i+1}: "
        s += sgl.gen(f"step_{i+1}", max_tokens=100, stop="\n")
        s += "\n"
    
    s += "Final Answer: "
    s += sgl.gen("answer", max_tokens=50)

2. Self-Correction Loop

self_correction.py

@sgl.function
def self_correct(s, task, max_iterations=3):
    s += f"Task: {task}\n"
    
    for i in range(max_iterations):
        s += f"Attempt {i+1}: "
        s += sgl.gen(f"attempt_{i+1}", max_tokens=200)
        
        s += "\nIs this correct? "
        s += sgl.gen("correct", choices=["Yes", "No"])
        
        if s["correct"] == "Yes":
            break
        else:
            s += "\nLet me try again.\n"

3. Tree Search Generation

tree_search.py

@sgl.function
def tree_search_story(s, prompt, branches=3, depth=2):
    s += prompt
    
    def explore_branch(state, current_depth):
        if current_depth >= depth:
            return
        
        candidates = []
        for i in range(branches):
            state += sgl.gen(f"branch_{current_depth}_{i}", max_tokens=50)
            candidates.append(state[f"branch_{current_depth}_{i}"])
        
        # Select best candidate (simplified selection)
        best_idx = 0  # In practice, use a scoring function
        state += candidates[best_idx]
        explore_branch(state, current_depth + 1)
    
    explore_branch(s, 0)

4. Parallel Agent Collaboration

multi_agent.py

@sgl.function
def multi_agent_discussion(s, topic, agents):
    s += f"Topic: {topic}\n"
    s += "Discussion:\n"
    
    # Initialize agents
    agent_states = {}
    for agent in agents:
        agent_states[agent] = sgl.fork(lambda: agent_response(agent, topic))
    
    # Simulate rounds of discussion
    for round in range(3):
        s += f"\nRound {round + 1}:\n"
        for agent in agents:
            s += f"{agent}: "
            s += sgl.gen(f"{agent}_round_{round}", max_tokens=100)
            s += "\n"

Performance Optimization

1. Batch Processing

Optimization Strategy

Process multiple inputs in a single batch for maximum throughput efficiency.

batch_processing.py

# Process multiple inputs in a single batch
@sgl.function
def batch_classification(s, texts):
    results = []
    for text in texts:
        s += f"Classify: {text}\nCategory: "
        s += sgl.gen("category", choices=["positive", "negative", "neutral"])
        results.append(s["category"])
    return results

# Execute with batching enabled
sgl.set_default_backend(
    sgl.RuntimeEndpoint("http://localhost:30000", batch_size=32)
)

2. Caching Strategies

caching.py

# Enable aggressive caching for repeated patterns
@sgl.function
def cached_qa(s, question, context):
    # Use consistent formatting for better cache hits
    s += f"Context: {context}\n"
    s += f"Question: {question}\n"
    s += "Answer: "
    s += sgl.gen("answer", max_tokens=100, temperature=0.0)  # Deterministic for caching

3. Memory Management

memory_management.py

# Optimize memory usage for long conversations
@sgl.function
def efficient_chat(s, messages, max_context_length=2000):
    # Truncate context to stay within limits
    total_length = sum(len(msg) for msg in messages)
    if total_length > max_context_length:
        messages = messages[-(max_context_length // 100):]
    
    for msg in messages:
        s += sgl.user(msg)
        s += sgl.assistant(sgl.gen("response", max_tokens=150))

Vision-Language Model Support

1. Image Understanding

image_description.py

@sgl.function
def describe_image(s, image_path, detail_level="medium"):
    s += sgl.image(image_path)
    s += f"Describe this image in {detail_level} detail:\n"
    s += sgl.gen("description", max_tokens=300)

# Usage
result = describe_image("/path/to/image.jpg", "high")

2. Visual Question Answering

visual_qa.py

@sgl.function
def visual_qa(s, image_path, question):
    s += sgl.image(image_path)
    s += f"Question: {question}\n"
    s += "Answer: "
    s += sgl.gen("answer", max_tokens=150)

result = visual_qa("/path/to/chart.png", "What is the highest value in this chart?")

3. Multi-modal Reasoning

multimodal_analysis.py

@sgl.function
def multimodal_analysis(s, image_path, context):
    s += f"Context: {context}\n"
    s += sgl.image(image_path)
    s += "Based on the context and image, analyze:\n"
    s += "1. Visual elements: "
    s += sgl.gen("visual", max_tokens=100, stop="\n")
    s += "\n2. Relationship to context: "
    s += sgl.gen("relationship", max_tokens=100, stop="\n")
    s += "\n3. Conclusion: "
    s += sgl.gen("conclusion", max_tokens=100)

Deployment and Serving

1. Starting a Server

start_server.sh

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

# With specific configurations
python -m sglang.launch_server \
    --model-path meta-llama/Llama-2-7b-chat-hf \
    --port 30000 \
    --host 0.0.0.0 \
    --tp-size 2 \
    --mem-fraction-static 0.8

2. Client Configuration

client_setup.py

import sglang as sgl

# Connect to local server
backend = sgl.RuntimeEndpoint("http://localhost:30000")
sgl.set_default_backend(backend)

# Connect to remote server with authentication
backend = sgl.RuntimeEndpoint(
    "https://api.example.com",
    headers={"Authorization": "Bearer your-token"}
)

3. Load Balancing

load_balancing.py

# Multiple endpoints for load distribution
endpoints = [
    "http://server1:30000",
    "http://server2:30000", 
    "http://server3:30000"
]

backend = sgl.LoadBalancedEndpoint(endpoints)
sgl.set_default_backend(backend)

4. Production Deployment

docker-compose.yml

# Docker Compose example
version: '3.8'
services:
  sglang-server:
    image: lmsysorg/sglang:latest
    ports:
      - "30000:30000"
    environment:
      - MODEL_PATH=meta-llama/Llama-2-7b-chat-hf
      - PORT=30000
      - TP_SIZE=2
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 2
              capabilities: [gpu]

Best Practices

1. Prompt Engineering

prompt_engineering.py

# Use clear, structured prompts
@sgl.function
def good_prompt(s, task, examples):
    s += "Task: " + task + "\n\n"
    
    # Provide examples
    for i, example in enumerate(examples):
        s += f"Example {i+1}:\n"
        s += f"Input: {example['input']}\n"
        s += f"Output: {example['output']}\n\n"
    
    s += "Now, complete this task:\n"
    s += "Input: " + sgl.gen("input") + "\n"
    s += "Output: " + sgl.gen("output", max_tokens=200)

2. Error Handling

error_handling.py

@sgl.function
def robust_generation(s, prompt):
    try:
        s += prompt
        s += sgl.gen("response", max_tokens=100, timeout=30)
        
        # Validate output
        if len(s["response"].strip()) == 0:
            s += "Please provide a more detailed response: "
            s += sgl.gen("retry", max_tokens=150)
            
    except sgl.GenerationError as e:
        s += f"Generation failed: {e}. Using fallback."
        s += "I apologize, but I cannot process this request."

3. Testing Strategies

testing.py

import unittest
import sglang as sgl

class TestSGLangFunctions(unittest.TestCase):
    def setUp(self):
        # Use mock backend for testing
        self.backend = sgl.MockBackend()
        sgl.set_default_backend(self.backend)
    
    def test_simple_generation(self):
        @sgl.function
        def test_func(s):
            s += "Hello"
            s += sgl.gen("response", max_tokens=10)
        
        result = test_func()
        self.assertIn("response", result)
    
    def test_structured_output(self):
        @sgl.function
        def json_test(s):
            s += "Generate JSON: "
            s += sgl.gen("json", regex=r'\{.*\}')
        
        result = json_test()
        self.assertTrue(result["json"].startswith("{"))

4. Monitoring and Logging

monitoring.py

import logging
import time

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@sgl.function
def monitored_generation(s, prompt):
    start_time = time.time()
    
    try:
        s += prompt
        s += sgl.gen("response", max_tokens=100)
        
        duration = time.time() - start_time
        logger.info(f"Generation completed in {duration:.2f}s")
        
    except Exception as e:
        logger.error(f"Generation failed: {e}")
        raise

Comparison with Other Frameworks

SGLang vs. LMQL

Feature	SGLang	LMQL
Performance	High (RadixAttention)	Medium
Python Integration	Native embedding	External DSL
Caching	Automatic	Manual
Parallelism	Built-in	Limited

SGLang vs. Guidance

Feature	SGLang	Guidance
Runtime Optimization	Yes	Limited
Structured Output	Advanced	Basic
Vision Support	Yes	No
Deployment	Production-ready	Research-focused

SGLang vs. LangChain

Feature	SGLang	LangChain
Level	Low-level control	High-level abstractions
Performance	Optimized runtime	Variable
Flexibility	High	Medium
Learning Curve	Moderate	Low

Troubleshooting

Common Issues

1. Connection Problems

debug_connection.py

# Debug connection issues
try:
    backend = sgl.RuntimeEndpoint("http://localhost:30000")
    backend.health_check()
    print("Server is healthy")
except ConnectionError:
    print("Cannot connect to server. Check if it's running.")

2. Memory Issues

memory_debug.sh

# Monitor memory usage
nvidia-smi

# Adjust memory settings
python -m sglang.launch_server \
    --model-path your-model \
    --mem-fraction-static 0.6  # Reduce if getting OOM

3. Generation Timeouts

timeout_handling.py

@sgl.function
def timeout_handling(s, prompt):
    try:
        s += prompt
        s += sgl.gen("response", max_tokens=100, timeout=30)
    except sgl.TimeoutError:
        s += "Request timed out. Please try again."

4. Performance Issues

performance_debug.py

# Enable performance profiling
sgl.set_debug_mode(True)

@sgl.function
def profiled_function(s, input):
    with sgl.profile("generation"):
        s += input
        s += sgl.gen("output", max_tokens=100)

Debugging Tips

Debugging Checklist

1. Enable Verbose Logging

   import logging
   logging.getLogger("sglang").setLevel(logging.DEBUG)

2. Check Server Logs

   # Server logs show detailed execution info
   tail -f sglang_server.log

3. Use Mock Backend for Testing

   # Test logic without actual model calls
   sgl.set_default_backend(sgl.MockBackend())

Contributing

Development Setup

dev_setup.sh

# Clone repository
git clone https://github.com/sgl-project/sglang.git
cd sglang

# Create development environment
conda create -n sglang-dev python=3.9
conda activate sglang-dev

# Install in development mode
pip install -e .
pip install -r requirements-dev.txt

Running Tests

run_tests.sh

# Run all tests
python -m pytest tests/

# Run specific test category
python -m pytest tests/test_frontend.py

# Run with coverage
python -m pytest --cov=sglang tests/

Code Style

code_style.sh

# Format code
black sglang/
isort sglang/

# Check style
flake8 sglang/
mypy sglang/

Submitting PRs

Pull Request Guidelines

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Update documentation
5. Submit pull request with clear description

Resources

Official Documentation

• SGLang Documentation
• GitHub Repository
• Paper: SGLang: Efficient Execution of Structured Language Model Programs

Community

• GitHub Discussions
• Discord Server
• Twitter Updates

Examples and Tutorials

• Official Examples
• Tutorial Notebooks
• Cookbook Recipes

Related Projects

• UC Berkeley Sky Computing Lab
• LMSYS Organization

---

Final Note

This guide covers the essential aspects of SGLang, from basic concepts to advanced usage patterns. As SGLang continues to evolve rapidly, always refer to the official documentation for the most current information and updates.

For questions and support, please visit the GitHub Discussions or check the official documentation.