SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.
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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:
You can install SGLang using any of the methods below.
pip install --upgrade pip
pip install "sglang[all]"
# Install FlashInfer CUDA kernels
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
# Use the last release branch
git clone -b v0.3.3.post1 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/torch2.4/
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 30000
This method is recommended if you plan to serve it as a service. A better approach is to use the k8s-sglang-service.yaml.
docker compose up -d
in your terminal.To deploy on Kubernetes or 12+ clouds, you can use SkyPilot.
# 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
--attention-backend triton --sampling-backend pytorch
and open an issue on GitHub.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.
--tp 2
. If it reports the error "peer access is not supported between these two devices", add --enable-p2p-check
to the server launch command.python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 2
--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
--mem-fraction-static
. The default value is 0.9
.python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
--enable-torch-compile
. It accelerates small models on small batch sizes.--torchao-config int4wo-128
. It supports various quantization strategies.--quantization fp8
on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.--kv-cache-dtype fp8_e5m2
.--nnodes 2
. 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, 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
Generative Models
python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --port=30000 --chat-template=chatml-llava
python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8 --chat-template=chatml-llava
python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --tp-size=1 --chat-template=llava_llama_3
python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 --chat-template=chatml-llava
Embedding Models
python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding
Instructions for supporting a new model are here.
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 30000
# 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
launch_server.py
.sglang.bench_serving
instead.
python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --batch 32 --input-len 256 --output-len 32
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 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
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()
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.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
.Learn more at this blog.
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.