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Co-authored-by: Lianmin Zheng <lianminzheng@gmail.com>
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@@ -6,23 +6,29 @@
| [**Blog**](https://lmsys.org/blog/2024-01-17-sglang/) | [**Paper**](https://arxiv.org/abs/2312.07104) |
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.
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, continuous batching, token attention (paged attention), tensor parallelism, flashinfer kernels, jump-forward constrained decoding, 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.
- **High-Performance Backend Runtime**: Features RadixAttention for accelerating complex LLM programs by reusing the KV cache across multiple calls. It can also serve as a standalone inference engine with all common techniques implemented (e.g., continuous batching and tensor parallelism).
## News
- [2024/04] 🔥 SGLang is used by the official **LLaVA-NeXT (video)** release ([blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)).
- [2024/02] 🔥 SGLang enables **3x faster JSON decoding** with compressed finite state machine ([blog](https://lmsys.org/blog/2024-02-05-compressed-fsm/)).
- [2024/01] 🔥 SGLang powers the serving of the official **LLaVA v1.6** release demo ([usage](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#demo)).
- [2024/01] SGLang provides up to **5x faster inference** with RadixAttention ([blog](https://lmsys.org/blog/2024-01-17-sglang/)).
<details>
<summary>More</summary>
- [2024/01] SGLang powers the serving of the official **LLaVA v1.6** release demo ([usage](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#demo)).
</details>
## Contents
- [Install](#install)
- [Quick Start](#quick-start)
- [Frontend: Structured Generation Language (SGLang)](#frontend-structured-generation-language-sglang)
- [Backend: SGLang Runtime (SRT)](#backend-sglang-runtime-srt)
- [Frontend: Structured Generation Language (SGLang)](#frontend-structured-generation-language-sglang)
- [Benchmark And Performance](#benchmark-and-performance)
- [Roadmap](#roadmap)
- [Citation And Acknowledgment](#citation-and-acknowledgment)
@@ -70,13 +76,118 @@ pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/
- If you cannot install FlashInfer, check out its [installation](https://docs.flashinfer.ai/installation.html#) 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]"`.
## Quick Start
## Backend: SGLang Runtime (SRT)
The SGLang Runtime (SRT) is an efficient serving engine.
### Launching a Server
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](docs/sampling_params.md).
### OpenAI Compatible API
In addition, the server supports OpenAI-compatible APIs.
```python
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 and most features of the Chat/Completions/Models endpoints specified by the [OpenAI API Reference](https://platform.openai.com/docs/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](docs/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-1` 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-1:50000 --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-1:50000 --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](docs/custom_chat_template.md).
### Supported Models
- Llama / Llama 2 / Llama 3
- Mistral / Mixtral
- Gemma / Gemma 2
- Qwen / Qwen 2 / Qwen 2 MoE
- LLaVA 1.5 / 1.6
- `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 30000`
- `python3 -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`
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 3000`
- LLaVA-NeXT-Video
- see [srt_example_llava_v.sh](examples/usage/llava_video/srt_example_llava_v.sh)
- Yi-VL
- see [srt_example_yi_vl.py](examples/quick_start/srt_example_yi_vl.py).
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- InternLM 2
Instructions for supporting a new model are [here](https://github.com/sgl-project/sglang/blob/main/docs/model_support.md).
## 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
#### Using Local Models
First, launch a server with
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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.
@@ -105,7 +216,7 @@ for m in state.messages():
print(state["answer_1"])
```
### Using OpenAI Models
#### Using OpenAI Models
Set the OpenAI API Key
```
export OPENAI_API_KEY=sk-******
@@ -136,13 +247,12 @@ for m in state.messages():
print(state["answer_1"])
```
### More Examples
#### More Examples
Anthropic and VertexAI (Gemini) models are also supported.
You can find more examples at [examples/quick_start](examples/quick_start).
## Frontend: Structured Generation Language (SGLang)
### Language Feature
To begin with, import sglang.
```python
import sglang as sgl
@@ -155,7 +265,7 @@ The system will manage the state, chat template, parallelism and batching for yo
The complete code for the examples below can be found at [readme_examples.py](examples/usage/readme_examples.py)
### Control Flow
#### Control Flow
You can use any Python code within the function body, including control flow, nested function calls, and external libraries.
```python
@@ -170,7 +280,7 @@ def tool_use(s, question):
s += "The key word to search is" + sgl.gen("word")
```
### Parallelism
#### Parallelism
Use `fork` to launch parallel prompts.
Because `sgl.gen` is non-blocking, the for loop below issues two generation calls in parallel.
@@ -192,7 +302,7 @@ def tip_suggestion(s):
s += "In summary" + sgl.gen("summary")
```
### Multi Modality
#### Multi Modality
Use `sgl.image` to pass an image as input.
```python
@@ -204,7 +314,7 @@ def image_qa(s, image_file, question):
See also [srt_example_llava.py](examples/quick_start/srt_example_llava.py).
### Constrained Decoding
#### Constrained Decoding
Use `regex` to specify a regular expression as a decoding constraint.
This is only supported for local models.
@@ -219,7 +329,7 @@ def regular_expression_gen(s):
)
```
### JSON Decoding
#### JSON Decoding
Use `regex` to specify a JSON schema with a regular expression.
```python
@@ -248,8 +358,7 @@ def character_gen(s, name):
See also [json_decode.py](examples/usage/json_decode.py) for an additional example on specifying formats with Pydantic models.
### Batching
#### Batching
Use `run_batch` to run a batch of requests with continuous batching.
```python
@@ -268,7 +377,7 @@ states = text_qa.run_batch(
)
```
### Streaming
#### Streaming
Add `stream=True` to enable streaming.
```python
@@ -287,139 +396,10 @@ for out in state.text_iter():
print(out, end="", flush=True)
```
### Tips and Implementation Details
#### Tips and Implementation Details
- The `choices` argument in `sgl.gen` is implemented by computing the [token-length normalized log probabilities](https://blog.eleuther.ai/multiple-choice-normalization/) 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`.
## Backend: SGLang Runtime (SRT)
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](https://arxiv.org/abs/2312.07104) can still greatly accelerate many use cases with automatic KV cache reuse.
### Usage
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](docs/sampling_params.md).
### OpenAI Compatible API
In addition, the server supports an experimental OpenAI-compatible API.
```python
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:
```json
{
"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
```
### Additional 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/Llama-2-7b-chat-hf --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/Llama-2-7b-chat-hf --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/Llama-2-7b-chat-hf --port 30000 --mem-fraction-static 0.7
```
- See [hyperparameter_tuning.md](docs/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-1` be the hostname of the first node and `50000` be an available port.
```
# Node 0
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 0
# Node 1
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 1
```
### Supported Models
- Llama
- Mistral
- Mixtral
- Qwen / Qwen 2 / Qwen 2 MoE
- Gemma / Gemma 2
- `python -m sglang.launch_server --model-path google/gemma-7b-it --port 30000 --attention-reduce-in-fp32`
- 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 30000`
- `python3 -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`
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 3000`
- LLaVA-NeXT-Video
- see [srt_example_llava_v.sh](examples/usage/llava_video/srt_example_llava_v.sh)
- Yi-VL
- see [srt_example_yi_vl.py](examples/quick_start/srt_example_yi_vl.py).
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- AWQ/GPTQ/Marlin quantization
Instructions for supporting a new model are [here](https://github.com/sgl-project/sglang/blob/main/docs/model_support.md).
## Benchmark And Performance
- Llama-7B on NVIDIA A10G, FP16, Tensor Parallelism=1
![llama_7b](assets/llama_7b.jpg)