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SGLang is yet another fast serving framework for large language models and vision language models.

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SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.

The core features include:

  • Fast Backend Runtime: Efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, flashinfer kernels, and quantization (AWQ/FP8/GPTQ/Marlin).
  • Flexible Frontend Language: Enables easy programming of LLM applications with chained generation calls, advanced prompting, control flow, multiple modalities, parallelism, and external interactions.

News

  • [2024/04] 🔥 SGLang is used by the official LLaVA-NeXT (video) release (blog).
  • [2024/02] 🔥 SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
  • [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
More
  • [2024/01] SGLang powers the serving of the official LLaVA v1.6 release demo (usage).

Contents

Install

Method 1: With pip

pip install --upgrade pip
pip install "sglang[all]"

# Install FlashInfer CUDA kernels
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/

Method 2: From source

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

pip install --upgrade pip
pip install -e "python[all]"

# Install FlashInfer CUDA kernels
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/

Method 3: Using docker

The docker images are available on Docker Hub as lmsysorg/sglang.

docker run --gpus all \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B --host 0.0.0.0 --port 30000

Common Notes

  • If you see errors from the Triton compiler, please install the Triton Nightly by
pip uninstall -y triton triton-nightly
pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly
  • If you cannot install FlashInfer, check out its installation page. If you still cannot install it, you can use the slower Triton kernels by adding --disable-flashinfer when launching the server.
  • If you only need to use the OpenAI backend, you can avoid installing other dependencies by using pip install "sglang[openai]".

Backend: SGLang Runtime (SRT)

The SGLang Runtime (SRT) is an efficient serving engine.

Quick Start

Launch a server

python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000

Send a request

curl http://localhost:30000/generate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Once upon a time,",
    "sampling_params": {
      "max_new_tokens": 16,
      "temperature": 0
    }
  }'

Learn more about the argument format here.

OpenAI Compatible API

In addition, the server supports OpenAI-compatible APIs.

import openai
client = openai.Client(
    base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")

# Text completion
response = client.completions.create(
	model="default",
	prompt="The capital of France is",
	temperature=0,
	max_tokens=32,
)
print(response)

# Chat completion
response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant"},
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)
print(response)

It supports streaming, vision, and most features of the Chat/Completions/Models endpoints specified by the OpenAI API Reference.

Additional Server Arguments

  • Add --tp 2 to enable tensor parallelism. If it indicates peer access is not supported between these two devices, add --enable-p2p-check option.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --tp 2
  • Add --dp 2 to enable data parallelism. It can also be used together with tp. Data parallelism is better for throughput if there is enough memory.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --dp 2 --tp 2
  • If you see out-of-memory errors during serving, please try to reduce the memory usage of the KV cache pool by setting a smaller value of --mem-fraction-static. The default value is 0.9
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --mem-fraction-static 0.7
  • See hyperparameter_tuning.md on tuning hyperparameters for better performance.
  • Add --nnodes 2 to run tensor parallelism on multiple nodes. If you have two nodes with two GPUs on each node and want to run TP=4, let sgl-dev-0 be the hostname of the first node and 50000 be an available port.
# Node 0
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-0:50000 --nnodes 2 --node-rank 0

# Node 1
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-0:50000 --nnodes 2 --node-rank 1
  • If the model does not have a template in the Hugging Face tokenizer, you can specify a custom chat template.
  • To enable fp8 quantization, you can add --quantization fp8 on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.

Supported Models

  • Llama / Llama 2 / Llama 3
  • Mistral / Mixtral
  • Gemma / Gemma 2
  • Qwen / Qwen 2 / Qwen 2 MoE
  • LLaVA 1.5 / 1.6
    • python -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000
    • python -m sglang.launch_server --model-path liuhaotian/llava-v1.6-vicuna-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000
    • python -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 30000
  • LLaVA-NeXT-Video
  • Yi-VL
  • StableLM
  • Command-R
  • DBRX
  • Grok
  • ChatGLM
  • InternLM 2
  • Mistral NeMo

Instructions for supporting a new model are here.

Benchmark Performance

  • Benchmark a single static batch. Run the following command without launching a server. The arguments are the same as those for launch_server.py. 
    python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --batch 32 --input-len 256 --output-len 32
  • Benchmark online serving. Launch a server first and run the following command. 
    python3 -m sglang.bench_serving --backend sglang --num-prompt 10

Frontend: Structured Generation Language (SGLang)

The frontend language can be used with local models or API models.

Quick Start

The example below shows how to use sglang to answer a mulit-turn question.

Using Local Models

First, launch a server with

python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000

Then, connect to the server and answer a multi-turn question.

from sglang import function, system, user, assistant, gen, set_default_backend, RuntimeEndpoint

@function
def multi_turn_question(s, question_1, question_2):
    s += system("You are a helpful assistant.")
    s += user(question_1)
    s += assistant(gen("answer_1", max_tokens=256))
    s += user(question_2)
    s += assistant(gen("answer_2", max_tokens=256))

set_default_backend(RuntimeEndpoint("http://localhost:30000"))

state = multi_turn_question.run(
    question_1="What is the capital of the United States?",
    question_2="List two local attractions.",
)

for m in state.messages():
    print(m["role"], ":", m["content"])

print(state["answer_1"])

Using OpenAI Models

Set the OpenAI API Key

export OPENAI_API_KEY=sk-******

Then, answer a multi-turn question.

from sglang import function, system, user, assistant, gen, set_default_backend, OpenAI

@function
def multi_turn_question(s, question_1, question_2):
    s += system("You are a helpful assistant.")
    s += user(question_1)
    s += assistant(gen("answer_1", max_tokens=256))
    s += user(question_2)
    s += assistant(gen("answer_2", max_tokens=256))

set_default_backend(OpenAI("gpt-3.5-turbo"))

state = multi_turn_question.run(
    question_1="What is the capital of the United States?",
    question_2="List two local attractions.",
)

for m in state.messages():
    print(m["role"], ":", m["content"])

print(state["answer_1"])

More Examples

Anthropic and VertexAI (Gemini) models are also supported. You can find more examples at examples/quick_start.

Language Feature

To begin with, import sglang.

import sglang as sgl

sglang provides some simple primitives such as gen, select, fork, image. You can implement your prompt flow in a function decorated by sgl.function. You can then invoke the function with run or run_batch. The system will manage the state, chat template, parallelism and batching for you.

The complete code for the examples below can be found at readme_examples.py

Control Flow

You can use any Python code within the function body, including control flow, nested function calls, and external libraries.

@sgl.function
def tool_use(s, question):
    s += "To answer this question: " + question + ". "
    s += "I need to use a " + sgl.gen("tool", choices=["calculator", "search engine"]) + ". "

    if s["tool"] == "calculator":
        s += "The math expression is" + sgl.gen("expression")
    elif s["tool"] == "search engine":
        s += "The key word to search is" + sgl.gen("word")

Parallelism

Use fork to launch parallel prompts. Because sgl.gen is non-blocking, the for loop below issues two generation calls in parallel.

@sgl.function
def tip_suggestion(s):
    s += (
        "Here are two tips for staying healthy: "
        "1. Balanced Diet. 2. Regular Exercise.\n\n"
    )

    forks = s.fork(2)
    for i, f in enumerate(forks):
        f += f"Now, expand tip {i+1} into a paragraph:\n"
        f += sgl.gen(f"detailed_tip", max_tokens=256, stop="\n\n")

    s += "Tip 1:" + forks[0]["detailed_tip"] + "\n"
    s += "Tip 2:" + forks[1]["detailed_tip"] + "\n"
    s += "In summary" + sgl.gen("summary")

Multi Modality

Use sgl.image to pass an image as input.

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

See also srt_example_llava.py.

Constrained Decoding

Use regex to specify a regular expression as a decoding constraint. This is only supported for local models.

@sgl.function
def regular_expression_gen(s):
    s += "Q: What is the IP address of the Google DNS servers?\n"
    s += "A: " + sgl.gen(
        "answer",
        temperature=0,
        regex=r"((25[0-5]|2[0-4]\d|[01]?\d\d?).){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)",
    )

JSON Decoding

Use regex to specify a JSON schema with a regular expression.

character_regex = (
    r"""\{\n"""
    + r"""    "name": "[\w\d\s]{1,16}",\n"""
    + r"""    "house": "(Gryffindor|Slytherin|Ravenclaw|Hufflepuff)",\n"""
    + r"""    "blood status": "(Pure-blood|Half-blood|Muggle-born)",\n"""
    + r"""    "occupation": "(student|teacher|auror|ministry of magic|death eater|order of the phoenix)",\n"""
    + r"""    "wand": \{\n"""
    + r"""        "wood": "[\w\d\s]{1,16}",\n"""
    + r"""        "core": "[\w\d\s]{1,16}",\n"""
    + r"""        "length": [0-9]{1,2}\.[0-9]{0,2}\n"""
    + r"""    \},\n"""
    + r"""    "alive": "(Alive|Deceased)",\n"""
    + r"""    "patronus": "[\w\d\s]{1,16}",\n"""
    + r"""    "bogart": "[\w\d\s]{1,16}"\n"""
    + r"""\}"""
)

@sgl.function
def character_gen(s, name):
    s += name + " is a character in Harry Potter. Please fill in the following information about this character.\n"
    s += sgl.gen("json_output", max_tokens=256, regex=character_regex)

See also json_decode.py for an additional example on specifying formats with Pydantic models.

Batching

Use run_batch to run a batch of requests with continuous batching.

@sgl.function
def text_qa(s, question):
    s += "Q: " + question + "\n"
    s += "A:" + sgl.gen("answer", stop="\n")

states = text_qa.run_batch(
    [
        {"question": "What is the capital of the United Kingdom?"},
        {"question": "What is the capital of France?"},
        {"question": "What is the capital of Japan?"},
    ],
    progress_bar=True
)

Streaming

Add stream=True to enable streaming.

@sgl.function
def text_qa(s, question):
    s += "Q: " + question + "\n"
    s += "A:" + sgl.gen("answer", stop="\n")

state = text_qa.run(
    question="What is the capital of France?",
    temperature=0.1,
    stream=True
)

for out in state.text_iter():
    print(out, end="", flush=True)

Tips and Implementation Details

  • The choices argument in sgl.gen is implemented by computing the token-length normalized log probabilities of all choices and selecting the one with the highest probability.
  • The regex argument in sgl.gen is implemented through autoregressive decoding with logit bias masking, according to the constraints set by the regex. It is compatible with temperature=0 and temperature != 0.

Benchmark And Performance

  • Llama-7B on NVIDIA A10G, FP16, Tensor Parallelism=1 llama_7b

  • Mixtral-8x7B on NVIDIA A10G, FP16, Tensor Parallelism=8 mixtral_8x7b

  • Learn more about the above results.

  • Synthetic latency and throughput benchmark scripts.

Roadmap

Development Roadmap (2024 Q3)

Citation And Acknowledgment

Please cite our paper, SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful. We also learned from the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL.

About

SGLang is yet another fast serving framework for large language models and vision language models.

License:Apache License 2.0

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