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- [2024/10] 🔥 The First SGLang Online Meetup (slides).
- [2024/09] SGLang v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
- [2024/07] Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).
More
- [2024/02] SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
- [2024/04] SGLang is used by the official LLaVA-NeXT (video) release (blog).
- [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
- [2024/01] SGLang powers the serving of the official LLaVA v1.6 release demo (usage).
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: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
- Flexible Frontend Language: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
- Extensive Model Support: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models.
- Active Community: SGLang is open-source and backed by an active community with industry adoption.
- Install
- Backend: SGLang Runtime (SRT)
- Frontend: Structured Generation Language (SGLang)
- Benchmark And Performance
- Roadmap
- Citation And Acknowledgment
You can install SGLang using any of the methods below.
pip install --upgrade pip
pip install "sglang[all]"
# Install FlashInfer accelerated kernels
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
Important: Please check the FlashInfer installation doc to install the proper version according to your PyTorch and CUDA versions.
# Use the last release branch
git clone -b v0.3.4.post1 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
pip install -e "python[all]"
# Install FlashInfer accelerated kernels
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
Important: Please check the FlashInfer installation doc to install the proper version according to your PyTorch and CUDA versions.
The docker images are available on Docker Hub as lmsysorg/sglang, built from Dockerfile.
Replace <secret> below with your huggingface hub token.
docker run --gpus all \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000More
This method is recommended if you plan to serve it as a service. A better approach is to use the k8s-sglang-service.yaml.
- Copy the compose.yml to your local machine
- Execute the command
docker compose up -din your terminal.
More
To deploy on Kubernetes or 12+ clouds, you can use SkyPilot.
- Install SkyPilot and set up Kubernetes cluster or cloud access: see SkyPilot's documentation.
- Deploy on your own infra with a single command and get the HTTP API endpoint:
SkyPilot YAML: sglang.yaml
# sglang.yaml
envs:
HF_TOKEN: null
resources:
image_id: docker:lmsysorg/sglang:latest
accelerators: A100
ports: 30000
run: |
conda deactivate
python3 -m sglang.launch_server \
--model-path meta-llama/Llama-3.1-8B-Instruct \
--host 0.0.0.0 \
--port 30000# Deploy on any cloud or Kubernetes cluster. Use --cloud <cloud> to select a specific cloud provider.
HF_TOKEN=<secret> sky launch -c sglang --env HF_TOKEN sglang.yaml
# Get the HTTP API endpoint
sky status --endpoint 30000 sglang- To further scale up your deployment with autoscaling and failure recovery, check out the SkyServe + SGLang guide.
- FlashInfer is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding
--attention-backend triton --sampling-backend pytorchand open an issue on GitHub. - If you only need to use the OpenAI backend, you can avoid installing other dependencies by using
pip install "sglang[openai]".
The SGLang Runtime (SRT) is an efficient serving engine.
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 specification, streaming, and multi-modal support here.
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)
# Text embedding
response = client.embeddings.create(
model="default",
input="How are you today",
)
print(response)It supports streaming, vision, and almost all features of the Chat/Completions/Models/Batch endpoints specified by the OpenAI API Reference.
- To enable multi-GPU tensor parallelism, add
--tp 2. If it reports the error "peer access is not supported between these two devices", add--enable-p2p-checkto the server launch command.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 2
- To enable multi-GPU data parallelism, add
--dp 2. Data parallelism is better for throughput if there is enough memory. It can also be used together with tensor parallelism. The following command uses 4 GPUs in total.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --dp 2 --tp 2
- If you see out-of-memory errors during serving, try to reduce the memory usage of the KV cache pool by setting a smaller value of
--mem-fraction-static. The default value is0.9.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7
- See hyperparameter_tuning.md on tuning hyperparameters for better performance.
- If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size.
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
- To enable the experimental overlapped scheduler, add
--enable-overlap-scheduler. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly. - To enable torch.compile acceleration, add
--enable-torch-compile. It accelerates small models on small batch sizes. This does not work for FP8 currenly. - To enable torchao quantization, add
--torchao-config int4wo-128. It supports various quantization strategies. - To enable fp8 weight quantization, add
--quantization fp8on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments. - To enable fp8 kv cache quantization, add
--kv-cache-dtype fp8_e5m2. - If the model does not have a chat template in the Hugging Face tokenizer, you can specify a custom chat template.
- To run tensor parallelism on multiple nodes, add
--nnodes 2. If you have two nodes with two GPUs on each node and want to run TP=4, letsgl-dev-0be the hostname of the first node and50000be an available port, you can use the following commands. If you meet deadlock, please try to add--disable-cuda-graph
# 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
We also provide an inference engine without a HTTP server. For example,
import sglang as sgl
def main():
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = {"temperature": 0.8, "top_p": 0.95}
llm = sgl.Engine(model_path="meta-llama/Meta-Llama-3.1-8B-Instruct")
outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
print("===============================")
print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
if __name__ == "__main__":
main()This can be used for offline batch inference and building custom servers. You can view the full example here.
Generative Models
- Llama / Llama 2 / Llama 3 / Llama 3.1
- Mistral / Mixtral / Mistral NeMo
- Gemma / Gemma 2
- Qwen / Qwen 2 / Qwen 2 MoE
- DeepSeek / DeepSeek 2
- OLMoE
- LLaVA-OneVision
python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --port=30000 --chat-template=chatml-llavapython3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8 --chat-template=chatml-llava- Query the server with the OpenAI Vision API. See examples at test/srt/test_vision_openai_server.py
- LLaVA 1.5 / 1.6 / NeXT
python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --tp-size=1 --chat-template=llava_llama_3python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 --chat-template=chatml-llava- Query the server with the OpenAI Vision API. See examples at test/srt/test_vision_openai_server.py
- Yi-VL
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- InternLM 2
- Exaone 3
- BaiChuan2
- MiniCPM / MiniCPM 3
- XVERSE / XVERSE MoE
- SmolLM
- GLM-4
Embedding Models
- e5-mistral
- gte-Qwen2
python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding
Instructions for supporting a new model are here.
More
To use a model from ModelScope, set the environment variable SGLANG_USE_MODELSCOPE.
export SGLANG_USE_MODELSCOPE=true
Launch Qwen2-7B-Instruct Server
SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server --model-path qwen/Qwen2-7B-Instruct --port 30000
Or start it by docker.
docker run --gpus all \
-p 30000:30000 \
-v ~/.cache/modelscope:/root/.cache/modelscope \
--env "SGLANG_USE_MODELSCOPE=true" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 30000More
# Run 405B (fp8) on a single node
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 --tp 8
# Run 405B (fp16) on two nodes
## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 0 --disable-cuda-graph
## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 1 --disable-cuda-graph- Benchmark a single static batch by running the following command without launching a server. The arguments are the same as for
launch_server.py. Note that this is not a dynamic batching server, so it may run out of memory for a batch size that a real server can handle. A real server truncates the prefill into several batches, while this unit test does not. For accurate large batch testing, please usesglang.bench_servinginstead.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
The frontend language can be used with local models or API models. It is an alternative to the OpenAI API. You may found it easier to use for complex prompting workflow.
The example below shows how to use sglang to answer a multi-turn question.
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"])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"])Anthropic and VertexAI (Gemini) models are also supported. You can find more examples at examples/quick_start.
To begin with, import sglang.
import sglang as sglsglang 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
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")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")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.
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?)",
)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 of specifying formats with Pydantic models.
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
)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)Use sgl.system， sgl.user and sgl.assistant to set roles when using Chat models. You can also define more complex role prompts using begin and end tokens.
@sgl.function
def chat_example(s):
s += sgl.system("You are a helpful assistant.")
# Same as: s += s.system("You are a helpful assistant.")
with s.user():
s += "Question: What is the capital of France?"
s += sgl.assistant_begin()
s += "Answer: " + sgl.gen(max_tokens=100, stop="\n")
s += sgl.assistant_end()- The
choicesargument insgl.genis implemented by computing the token-length normalized log probabilities of all choices and selecting the one with the highest probability. - The
regexargument insgl.genis implemented through autoregressive decoding with logit bias masking, according to the constraints set by the regex. It is compatible withtemperature=0andtemperature != 0.
Learn more in our release blogs: v0.2, v0.3.
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.