SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system.
The core features of SGLang include:
- A Flexible Front-End Language: This allows for easy programming of LLM applications with multiple chained generation calls, advanced prompting techniques, control flow, multiple modalities, parallelism, and external interaction.
- A High-Performance Runtime with RadixAttention: This feature significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. It also supports other common techniques like continuous batching and tensor parallelism.
- [2024/02] 🔥 SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
- [2024/01] 🔥 SGLang powers the serving of the official LLaVA v1.6 release demo (usage).
- [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
- Install
- Quick Start
- Frontend: Structured Generation Language (SGLang)
- Backend: SGLang Runtime (SRT)
- Benchmark And Performance
- Roadmap
- Citation And Acknowledgment
pip install "sglang[all]"
git clone [email protected]:sgl-project/sglang.git
cd sglang
pip install --upgrade pip
pip install -e "python[all]"
- If you are using older GPUs (NVIDIA V100, T4), please pick the correct triton compiler version to avoid some known bugs.
- For NVIDIA T4, please use
pip install "triton>=2.2.0". - For NVIDIA V100, please install the nightly version.
- For NVIDIA T4, please use
- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using
pip install "sglang[openai]"
The example below shows how to use sglang to answer a mulit-turn question.
First, launch a server with
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --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 on 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)- The
choicesargument insgl.genis implemented by computing the 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.
The SGLang Runtime (SRT) is designed to work best with the SGLang frontend. However, it can also be used as a standalone API server. In this case, the RadixAttention can still greatly accelerate many use cases with automatic KV cache reuse.
Launch a server
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --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.
In addition, the server supports an experimental OpenAI-compatible API.
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)By default, the server uses the chat template specified in the model tokenizer from Hugging Face. It should just work for most official models such as Llama-2/Llama-3.
If needed, you can also override the chat template when launching the server:
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template llama-2
If the chat template you are looking for is missing, you are welcome to contribute it. Meanwhile, you can also temporarily register your chat template as follows:
{
"name": "my_model",
"system": "<|im_start|>system",
"user": "<|im_start|>user",
"assistant": "<|im_start|>assistant",
"sep_style": "CHATML",
"sep": "<|im_end|>",
"stop_str": ["<|im_end|>", "<|im_start|>"]
}python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.json
- Add
--tp 2to enable tensor parallelism.
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --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 is0.9
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --mem-fraction-static 0.7
- You can turn on flashinfer to accelerate the inference by using highly optimized CUDA kernels.
- Llama
- Mistral
- Mixtral
- Qwen / Qwen 2
- Gemma
- Please add a new flag
--attention-reduce-in-fp32to avoid some precision errors. python -m sglang.launch_server --model-path google/gemma-7b-it --port 30000 --attention-reduce-in-fp32
- Please add a new flag
- LLaVA
python3 -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 30000python3 -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 30000python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 3000
- Yi-VL
- see srt_example_yi_vl.py.
- StableLM
- Command-R
- DBRX
- AWQ/GPTQ/Marlin quantization
Instructions for supporting a new model are here.
-
Llama-7B on NVIDIA A10G, FP16, Tensor Parallelism=1
-
Mixtral-8x7B on NVIDIA A10G, FP16, Tensor Parallelism=8
Learn more here.
@misc{zheng2023efficiently,
title={Efficiently Programming Large Language Models using SGLang},
author={Lianmin Zheng and Liangsheng Yin and Zhiqiang Xie and Jeff Huang and Chuyue Sun and Cody Hao Yu and Shiyi Cao and Christos Kozyrakis and Ion Stoica and Joseph E. Gonzalez and Clark Barrett and Ying Sheng},
year={2023},
eprint={2312.07104},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
We learned from the design and reused some code of the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, LMQL.