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[Docs] refactor Contribution Guide #2690

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165 changes: 165 additions & 0 deletions docs/references/contribution_guide.md
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# Contribution Guide

Welcome to **SGLang**! We appreciate your interest in contributing. This guide provides a concise overview of how to set up your environment, run tests, build documentation, and open a Pull Request (PR). Whether you’re fixing a small bug or developing a major feature, we encourage following these steps for a smooth contribution process.

---

## 1. Setting Up & Building from Source

### 1.1 Fork and Clone the Repository

> **Note**: SGLang does **not** accept PRs on the main repo. Please fork the repository under your GitHub account, then clone your fork locally.
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delete >


```bash
git clone https://github.com/<your_user_name>/sglang.git
cd sglang
```

### 1.2 Install Dependencies & Build

Refer to our [Install SGLang](https://sgl-project.github.io/start/install.html) documentation for more details on setting up the necessary dependencies.
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delete our


Before installing packages like FlashInfer, ensure your local PyTorch and CUDA versions match:
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Install correct version of flashinfer according to your local xxx


```bash
python -c "import torch; print('PyTorch Version:', torch.__version__); print('CUDA Version:', torch.version.cuda)"
nvidia-smi
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delete this

```

Based on this information, choose the correct FlashInfer package (or any other CUDA-dependent library).
Below is an example assuming you have **PyTorch 2.4** with **CUDA 12.4**:
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Below is an example on PyTorch 2.4 with CUDA 12.4:


```bash
# Example: Installing FlashInfer (Pick the right version for your CUDA/PyTorch)
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delete

Example: Installing FlashInfer (Pick the right version for your CUDA/PyTorch)

pip install --upgrade pip
pip install -e "python[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/

# Now install the latest SGLang
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Install the lateset SGLang on your own fork repo

cd sglang/python
pip install .
```
---

## 2. Code Formatting with Pre-Commit

We use [pre-commit](https://pre-commit.com/) to maintain consistent code style checks. Before pushing your changes, please run:

```bash
pip3 install pre-commit
cd sglang
pre-commit run --all-files
```

- **`pre-commit run --all-files`** manually runs all configured checks, applying fixes if possible.
- If it fails the first time, re-run it to ensure lint errors are fully resolved.
- Make sure your code passes all checks **before** creating a Pull Request.
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Make them into one line.

- **Do not commit** directly to the `main` branch. Always create a new branch (e.g., `feature/my-new-feature`), push your changes, and open a PR from that branch.

---

## 3. Writing Documentation & Running Docs CI

Most documentation files are located under the `docs/` folder. We prefer **Jupyter Notebooks** over Markdown so that all examples can be executed and validated by our docs CI pipeline.

### 3.1 Docs Workflow

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```bash
# 1) Compile all Jupyter notebooks
make compile

# 2) Generate static HTML
make html

# 3) Preview documentation locally
bash serve.sh
# Open your browser at the displayed port to view the docs

# 4) Clean notebook outputs
pip install nbstripout
find . -name '*.ipynb' -exec nbstripout {} \;
# nbstripout removes notebook outputs so your PR stays clean

# 5) Pre-commit checks and create a PR
pre-commit run --all-files
# After these checks pass, push your changes and open a PR on your branch
```

- Add or update your Jupyter notebooks in the appropriate subdirectories under `docs/`.
- If you add new files, remember to update `index.rst` (or relevant `.rst` files) accordingly.

- For tasks that require a running SGLang backend, you might launch a server process or an engine instance. Once you finish testing or using it, **remember to shut it down**. Refer to:
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If you need to run and shut up a SGLang server or engine, following these examples:

  1. Launch and close Sever:
#Launch Sever

from sglang.utils import (
    execute_shell_command,
    wait_for_server,
    terminate_process,
    print_highlight,
)

server_process = execute_shell_command(
    "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --port 30000 --host 0.0.0.0"
)

wait_for_server("http://localhost:30000")

# Terminate Sever

terminate_process(server_process)
  1. Launch Engine
# Launch Engine

import sglang as sgl
import asyncio

llm = sgl.Engine(model_path="meta-llama/Meta-Llama-3.1-8B-Instruct")

# Terminalte Engine
llm.shutdown()


1. [OpenAI API Completions Docs](https://sgl-project.github.io/backend/openai_api_completions.html)
```python
from sglang.backend.utils import terminate_process
# ...
terminate_process(server_process)
```

2. [Offline Engine API Docs](https://sgl-project.github.io/backend/offline_engine_api.html)
```python
llm.shutdown()
```


---

## 4. Running Unit Tests & Adding to CI

SGLang uses Python’s built-in [unittest](https://docs.python.org/3/library/unittest.html) framework. You can run tests either individually or in suites.

### 4.1 Test Backend Runtime

```bash
cd sglang/test/srt

# Run a single file
python3 test_srt_endpoint.py

# Run a single test
python3 -m unittest test_srt_endpoint.TestSRTEndpoint.test_simple_decode

# Run a suite with multiple files
python3 run_suite.py --suite minimal
```

### 4.2 Test Frontend Language

```bash
cd sglang/test/lang
export OPENAI_API_KEY=sk-*****

# Run a single file
python3 test_openai_backend.py

# Run a single test
python3 -m unittest test_openai_backend.TestOpenAIBackend.test_few_shot_qa

# Run a suite with multiple files
python3 run_suite.py --suite minimal
```

### 4.3 Adding or Updating Tests in CI

- Create new test files under `test/srt` or `test/lang` depending on the type of test.
- Ensure they are referenced in the respective `run_suite.py` (e.g., `test/srt/run_suite.py` or `test/lang/run_suite.py`) so they’re picked up in CI.
- In CI, all tests run automatically. You may modify the workflows in [`.github/workflows/`](https://github.com/sgl-project/sglang/tree/main/.github/workflows) to add custom test groups or extra checks.

### 4.4 Writing Elegant Test Cases

- Examine existing tests in [sglang/test](https://github.com/sgl-project/sglang/tree/main/test) for practical examples.
- Keep each test function focused on a single scenario or piece of functionality.
- Give tests descriptive names reflecting their purpose.
- Use robust assertions (e.g., assert, unittest methods) to validate outcomes.
- Clean up resources to avoid side effects and preserve test independence.

---


## 5. Tips for Newcomers

If you want to contribute but don’t have a specific idea in mind, pick issues labeled [“good first issue” or “help wanted”](https://github.com/sgl-project/sglang/issues?q=is%3Aissue+label%3A%22good+first+issue%22%2C%22help+wanted%22). These tasks typically have lower complexity and provide an excellent introduction to the codebase. Also Check out this [code walk-through](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/tree/main/sglang/code-walk-through) for a deeper look into SGLang’s workflow.

If you have any questions or want to start a discussion, please feel free to ask in our [Slack channel](https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2um0ad92q-LkU19KQTxCGzlCgRiOiQEw).

Thank you for your interest in SGLang—**happy coding**!
22 changes: 0 additions & 22 deletions docs/references/contributor_guide.md

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