SGLang is yet another fast serving framework for large language models and vision language models.
Project description
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
- [2024/07] 🔥 Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).
- [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).
More
Contents
- Install
- Backend: SGLang Runtime (SRT)
- Frontend: Structured Generation Language (SGLang)
- Benchmark And Performance
- Roadmap
- Citation And Acknowledgment
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/torch2.4/
Method 2: From source
# Use the last release branch
git clone -b v0.2.12 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/
Method 3: Using docker
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/Meta-Llama-3-8B-Instruct --host 0.0.0.0 --port 30000
Common Notes
- 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/Batch endpoints specified by the OpenAI API Reference.
Additional Server Arguments
- Add
--tp 2
to enable multi-GPU tensor parallelism. 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 --port 30000 --tp 2
- Add
--dp 2
to enable multi-GPU data parallelism. It can also be used together with tensor parallelism. 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, 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 --port 30000 --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 --port 30000 --chunked-prefill-size 4096
- 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, letsgl-dev-0
be the hostname of the first node and50000
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 experimental torch.compile support, you can add
--enable-torch-compile
. It accelerates small models on small batch sizes. - 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 / Llama 3.1
- Mistral / Mixtral / Mistral NeMo
- Gemma / Gemma 2
- Qwen / Qwen 2 / Qwen 2 MoE
- DeepSeek / DeepSeek 2
- 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
- see srt_example_yi_vl.py.
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- InternLM 2
Instructions for supporting a new model are here.
Use Models From ModelScope
To use model from ModelScope, setting 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
Run Llama 3.1 405B
## 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
# replace the `172.16.4.52:20000` with your own first node ip address and port, disable CUDA Graph temporarily
# on the first node
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 --mem-frac 0.75
# on the second
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 --mem-frac 0.75
Benchmark Performance
- 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, consider usingsglang.bench_serving
.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. It is an alternative to the OpenAI API. You may found it easier to use for complex prompting workflow.
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)
Roles
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()
Tips and Implementation Details
- The
choices
argument insgl.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 insgl.gen
is implemented through autoregressive decoding with logit bias masking, according to the constraints set by the regex. It is compatible withtemperature=0
andtemperature != 0
.
Benchmark And Performance
Learn more at this blog.
Roadmap
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.