diff --git a/.gitignore b/.gitignore index ca43e1ccba..14f5212ece 100644 --- a/.gitignore +++ b/.gitignore @@ -166,6 +166,9 @@ cython_debug/ # Vim *.swp +# Documentation +docs/en/_build + # SGL benchmark/mmlu/data benchmark/mmlu/data.tar diff --git a/README.md b/README.md index eb3099cf7a..92c5c2ec39 100644 --- a/README.md +++ b/README.md @@ -15,10 +15,12 @@ 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. + +- **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 3, Gemma 2, 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, welcoming contributions to improve LLM and VLM serving. ## News - [2024/09] 🔥 SGLang v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision ([blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/)). @@ -44,6 +46,8 @@ The core features include: ## Install +You can install SGLang using any of the methods below. + ### Method 1: With pip ``` pip install --upgrade pip @@ -67,7 +71,7 @@ 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](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](docker). +The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker). Replace `` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens). ```bash @@ -218,6 +222,10 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct ``` python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --chunked-prefill-size 4096 ``` +- To enable torch.compile support, you can add `--enable-torch-compile`. It accelerates small models on small batch sizes. +- To enable fp8 weight quantization, you can add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments. +- To enable fp8 kv cache quanzation, you can add `--kv-cache-dtype fp8_e5m2`. +- If the model does not have a template in the Hugging Face tokenizer, you can specify a [custom chat template](docs/en/custom_chat_template.md). - 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-0` be the hostname of the first node and `50000` be an available port. ``` # Node 0 @@ -226,9 +234,6 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct # 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](docs/en/custom_chat_template.md). -- 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 diff --git a/docs/en/backend.md b/docs/en/backend.md new file mode 100644 index 0000000000..af874f5933 --- /dev/null +++ b/docs/en/backend.md @@ -0,0 +1,171 @@ +## 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](docs/en/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) + +# Text embedding +response = client.embeddings.create( + model="default", + input="How are you today", +) +print(response) +``` + +It supports streaming, vision, and most features of the Chat/Completions/Models/Batch endpoints specified by the [OpenAI API Reference](https://platform.openai.com/docs/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. 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 --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 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/en/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 +``` +- To enable torch.compile support, you can add `--enable-torch-compile`. It accelerates small models on small batch sizes. +- To enable fp8 weight quantization, you can add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments. +- To enable fp8 kv cache quanzation, you can add `--kv-cache-dtype fp8_e5m2`. +- If the model does not have a template in the Hugging Face tokenizer, you can specify a [custom chat template](docs/en/custom_chat_template.md). +- 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-0` 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-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 +``` + +### Supported Models + +**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 +- [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) + - `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` + - Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](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_3` + - `python -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](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](test/srt/test_vision_openai_server.py) +- Yi-VL +- StableLM +- Command-R +- DBRX +- Grok +- ChatGLM +- InternLM 2 +- Exaone 3 + +**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](https://github.com/sgl-project/sglang/blob/main/docs/en/model_support.md). + +#### Use Models From ModelScope +
+More + +To use a model from [ModelScope](https://www.modelscope.cn), set the environment variable SGLANG_USE_MODELSCOPE. +``` +export SGLANG_USE_MODELSCOPE=true +``` +Launch [Qwen2-7B-Instruct](https://www.modelscope.cn/models/qwen/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 +
+More + +```bash +# 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 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, please use `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 + ``` +- Benchmark online serving. Launch a server first and run the following command. + ``` + python3 -m sglang.bench_serving --backend sglang --num-prompt 10 + ``` \ No newline at end of file diff --git a/docs/en/frontend.md b/docs/en/frontend.md new file mode 100644 index 0000000000..4f18939b3a --- /dev/null +++ b/docs/en/frontend.md @@ -0,0 +1,239 @@ +## 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. + +```python +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. +```python +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](examples/frontend_language/quick_start). + +### Language Feature +To begin with, import sglang. +```python +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](examples/frontend_language/usage/readme_examples.py) + +#### Control Flow +You can use any Python code within the function body, including control flow, nested function calls, and external libraries. + +```python +@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. + +```python +@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. + +```python +@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](examples/frontend_language/quick_start/local_example_llava_next.py). + +#### Constrained Decoding +Use `regex` to specify a regular expression as a decoding constraint. +This is only supported for local models. + +```python +@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. + +```python +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](examples/frontend_language/usage/json_decode.py) for an additional example of specifying formats with Pydantic models. + +#### Batching +Use `run_batch` to run a batch of requests with continuous batching. + +```python +@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. + +```python +@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. + +```python +@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 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`. diff --git a/docs/en/index.rst b/docs/en/index.rst index d1a96e8cb0..1c3e947c0c 100644 --- a/docs/en/index.rst +++ b/docs/en/index.rst @@ -1,80 +1,32 @@ -Welcome to SGLang! +SGLang Documentation ==================================== -.. figure:: ./_static/image/logo.png - :width: 50% - :align: center - :alt: SGLang - :class: no-scaled-link +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: -.. raw:: html +- **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 3, Gemma 2, 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, welcoming contributions to improve LLM and VLM serving. -

- SGLang is yet another fast serving framework for large language models and vision language models. - -

-

- - Star - Watch - Fork -

- -SGLang has the following core features: - -* **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. - -* **Extensive Model Support**: SGLang supports a wide range of generative models including the Llama series (up to Llama 3.1), Mistral, Gemma, Qwen, DeepSeek, LLaVA, Yi-VL, StableLM, Command-R, DBRX, Grok, ChatGLM, InternLM 2 and Exaone 3. It also supports embedding models such as e5-mistral and gte-Qwen2. Easily extensible to support new models. - -* **Open Source Community**: SGLang is an open source project with a vibrant community of contributors. We welcome contributions from anyone interested in advancing the state of the art in LLM and VLM serving. - -Documentation -------------- - -.. In this documentation, we'll dive into these following areas to help you get the most out of SGLang. - -.. _installation: .. toctree:: :maxdepth: 1 - :caption: Installation + :caption: Getting Started install.md + backend.md + frontend.md -.. _hyperparameter_tuning: .. toctree:: :maxdepth: 1 - :caption: Hyperparameter Tuning + :caption: References + sampling_params.md hyperparameter_tuning.md - -.. _custom_chat_template: -.. toctree:: - :maxdepth: 1 - :caption: Custom Chat Template - - custom_chat_template.md - -.. _model_support: -.. toctree:: - :maxdepth: 1 - :caption: Model Support - model_support.md - -.. _sampling_params: -.. toctree:: - :maxdepth: 1 - :caption: Sampling Params - - sampling_params.md - - -.. _benchmark_and_profilling: -.. toctree:: - :maxdepth: 1 - :caption: Benchmark and Profilling - - benchmark_and_profiling.md \ No newline at end of file + contributor_guide.md + choices_methods.md + benchmark_and_profiling.md + troubleshooting.md diff --git a/docs/en/install.md b/docs/en/install.md index 877f69d680..656bc6840a 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -1,73 +1,56 @@ -# SGLang Installation Guide +## Install SGLang -SGLang consists of a frontend language (Structured Generation Language, SGLang) and a backend runtime (SGLang Runtime, SRT). The frontend can be used separately from the backend, allowing for a detached frontend-backend setup. +You can install SGLang using any of the methods below. -## Quick Installation Options - -### 1. Frontend Installation (Client-side, any platform) - -```bash -pip install --upgrade pip -pip install sglang +### Method 1: With pip ``` - -**Note: You can check [these examples](https://github.com/sgl-project/sglang/tree/main/examples/frontend_language/usage) for how to use frontend and backend separately.** - -### 2. Backend Installation (Server-side, Linux only) - -```bash pip install --upgrade pip pip install "sglang[all]" -pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ -``` - -**Note: The backend (SRT) is only needed on the server side and is only available for Linux right now.** - -**Important: Please check the [flashinfer installation guidance](https://docs.flashinfer.ai/installation.html) to install the proper version according to your PyTorch and CUDA versions.** -### 3. From Source (Latest version, Linux only for full installation) - -```bash -# Use the latest release branch -# As of this documentation, it's v0.2.15, but newer versions may be available -# Do not clone the main branch directly; always use a specific release version -# The main branch may contain unresolved bugs before a new release -git clone -b v0.2.15 https://github.com/sgl-project/sglang.git -cd sglang -pip install -e "python[all]" +# Install FlashInfer CUDA kernels pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ ``` -### 4. OpenAI Backend Only (Client-side, any platform) - -If you only need to use the OpenAI backend, you can avoid installing other dependencies by using: - -```bash -pip install "sglang[openai]" +### Method 2: From source ``` +# Use the last release branch +git clone -b v0.3.0 https://github.com/sgl-project/sglang.git +cd sglang -## Advanced Installation Options +pip install --upgrade pip +pip install -e "python[all]" -### 1. Using Docker (Server-side, Linux only) +# 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](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker). Replace `` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens). +### Method 3: Using docker +The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker). +Replace `` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens). ```bash -docker run --gpus all -p 30000:30000 \ +docker run --gpus all \ + -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ - --env "HF_TOKEN=" --ipc=host \ + --env "HF_TOKEN=" \ + --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000 ``` -### 2.Using docker compose +### Method 4: Using docker compose + +
+More -This method is recommended if you plan to serve it as a service. A better approach is to use the [k8s-sglang-service.yaml](https://github.com/sgl-project/sglang/blob/main/docker/k8s-sglang-service.yaml). +> 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/k8s-sglang-service.yaml). -1. Copy the [compose.yml](https://github.com/sgl-project/sglang/blob/main/docker/compose.yaml) to your local machine +1. Copy the [compose.yml](./docker/compose.yaml) to your local machine 2. Execute the command `docker compose up -d` in your terminal. +
-### 3.Run on Kubernetes or Clouds with SkyPilot +### Method 5: Run on Kubernetes or Clouds with SkyPilot
More @@ -108,9 +91,6 @@ sky status --endpoint 30000 sglang 3. To further scale up your deployment with autoscaling and failure recovery, check out the [SkyServe + SGLang guide](https://github.com/skypilot-org/skypilot/tree/master/llm/sglang#serving-llama-2-with-sglang-for-more-traffic-using-skyserve).
-## Troubleshooting - -- For FlashInfer issues on newer GPUs, use `--disable-flashinfer --disable-flashinfer-sampling` when launching the server. -- For out-of-memory errors, try `--mem-fraction-static 0.7` when launching the server. - -For more details and advanced usage, visit the [SGLang GitHub repository](https://github.com/sgl-project/sglang). \ No newline at end of file +### Common Notes +- [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is currently one of the dependencies that must be installed for SGLang. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), consider using Triton's kernel by `--disable-flashinfer --disable-flashinfer-sampling` and raise an issue. +- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.