> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.

## Install SGLang from Source

### Prepare Environment

Before contributing, please ensure that your environment is set up correctly. Follow the steps in the [Installation Guide](./ascend_npu) to install the necessary dependencies. We recommend [using docker](./ascend_npu#method-2-using-docker-image) to build the environment.

### Fork and clone the repository

**Note**: New contributors do **not** have the write permission to push to the official SGLang repo. Please fork the repository under your GitHub account, then clone your fork locally.

```bash theme={null}
git clone https://github.com/<your_user_name>/sglang.git
# if you are using docker, the environment is already set up.
cd sglang
export PYTHONPATH=$PWD/python:$PYTHONPATH
```

## Format code with pre-commit

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

```bash theme={null}
pip3 install pre-commit
pre-commit install
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.
* **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.
* Link checking with lychee is **enforced in CI**. By default, it is not blocking local commits.
* To run local link checks manually, use: `pre-commit run --hook-stage manual lychee --all-files`.

## Run and add tests

All NPU tests are end-to-end (E2E) and require launching a server with real model weights.
Tests live under [`test/registered/ascend/`](https://github.com/sgl-project/sglang/tree/main/test/registered/ascend), organized by model type and functionality:

```
ascend/
├── llm_models/          # Per-model inference accuracy
├── vlm_models/          # Vision-language models
├── embedding_models/    # Embedding model tests
├── rerank_models/       # Reranker model tests
├── reward_models/       # Reward model tests
├── interface/           # API correctness, function calling
├── basic_function/      # Cache, sampling, quantization, etc.
└── test_npu_memory_consumption.py
```

### Adding a test

See [`test_npu_sampling_backend.py`](https://github.com/sgl-project/sglang/tree/main/test/registered/ascend/basic_function/backends/test_npu_sampling_backend.py) for a complete example. Key steps:

1. Place your test file in the appropriate directory under `test/registered/ascend/`.
2. Extend `CustomTestCase` (from `sglang.test.test_utils`) for CI retry support.
3. Launch server with `popen_launch_server()` in `setUpClass` and clean up with `kill_process_tree()` in `tearDownClass`.
4. Register your test with `register_npu_ci()`:
   ```python theme={null}
   from sglang.test.ci.ci_register import register_npu_ci
   register_npu_ci(est_time=400, suite="stage-b-test-1-npu-a2", nightly=False)
   register_npu_ci(est_time=400, suite="nightly-1-npu-a3", nightly=True)
   ```

### Running tests locally

```bash theme={null}
pytest test/registered/ascend/llm_models/test_npu_qwen3_0_6b.py -v
```

For detailed instructions, refer to [test/README.md](https://github.com/sgl-project/sglang/tree/main/test/README.md).

### Register models for CI

If you need to use model which is not in `python/sglang/test/ascend/test_ascend_utils.py` list. Follow these steps:

1. Register account and upload your model to [modelscope](https://modelscope.cn/models).
2. Make sure your model is pre-cached on the CI server and is at the path "/data/ascend-ci-share-pkking-sglang/modelscope/hub/models/{your_model_repo}/{your_model}".
   If this is not the case, use following command on CI server:

   ```bash theme={null}
   modelscope download \
     --model {your_model_repo}/{your_model} \
     --local_dir /data/ascend-ci-share-pkking-sglang/modelscope/hub/models/{your_model_repo}/{your_model}
   ```

> Note: If you don’t have access to CI server, please ask maintainers ([zl19940307@163.com](mailto:zl19940307@163.com)) to download your model.

3. Add model to `python/sglang/test/ascend/test_ascend_utils.py` (use docker `"/root/.cache/modelscope/hub/models/{your_model_repo}/{your_model}"` path).

## Write documentation

We recommend new contributors start by writing documentation, which helps you quickly understand SGLang codebase.
For more details, please refer to [docs/README.md](https://github.com/sgl-project/sglang/tree/main/docs/README.md).

## Test the accuracy

If your code changes the model output, please run the accuracy tests. A quick sanity check is the few-shot GSM8K.

```bash theme={null}
# Launch a server
python3 -m sglang.launch_server --model Qwen/Qwen2-7B-Instruct

# Evaluate. --base-url must point at the server launched above.
# The default SGLang server port is 30000; change it if you launched
# the server with a different --port.
python3 -m sglang.test.run_eval --base-url http://localhost:30000 --eval-name gsm8k --num-examples 200
```

Please note that the above script is primarily a sanity check, not a rigorous accuracy or speed test.
This test can have significant variance (1%–5%) in accuracy due to batching and the non-deterministic nature of the inference engine.
Also, do not rely on the "Latency/Output throughput" from this script, as it is not a proper speed test.

GSM8K is too easy for state-of-the-art models nowadays. Please try your own more challenging accuracy tests.
You can find additional accuracy eval examples in:

* [test\_eval\_accuracy\_large.py](https://github.com/sgl-project/sglang/blob/main/test/manual/eval/test_eval_accuracy_large.py)
* [test\_gpt\_oss\_1gpu.py](https://github.com/sgl-project/sglang/blob/main/test/manual/core/test_gpt_oss_1gpu.py)

## Benchmark the speed

Refer to [Benchmark and Profiling](../../developer_guide/benchmark_and_profiling).

## Requesting a review for merge

You can follow the pull request merge process described in [MAINTAINER.md](https://github.com/sgl-project/sglang/blob/main/.github/MAINTAINER.md).
You will need to work with the Merge Oncall, Codeowner, and other reviewers to get their approvals.
Then your PR can be merged.

## How to Trigger CI Tests

We have a lot of open PRs but limited CI machines, so only top and trusted contributors have permission to trigger CI tests.
Users with permission are listed in the [CI\_PERMISSIONS.json](https://github.com/sgl-project/sglang/blob/main/.github/CI_PERMISSIONS.json)

**PR authors** can always use `/rerun-failed-ci` on their own PRs, even if they are not listed in `CI_PERMISSIONS.json`.

For CI to run on a pull request, it must have the "run-ci" label. Authorized users can add the label or rerun failed tests by commenting on the PR with one of these commands:

* `/tag-run-ci-label`: Adds the "run-ci" label. Only **future** commits trigger CI; the current commit is unaffected. Add the `extra` argument (`/tag-run-ci-label extra`) to additionally apply the "run-ci-extra" label, opting the PR into the extra test workflow (`pr-test-extra.yml`).
* `/rerun-failed-ci`: Reruns workflows from the latest commit with conclusion **failed, flaky, or skipped**.
* `/tag-and-rerun-ci`: Runs both. Use this on a fresh PR to kick off CI on the current commit — `/tag-run-ci-label` alone won't. Accepts the same `extra` argument (`/tag-and-rerun-ci extra`).
* `/rerun-stage <stage-name>`: Reruns a single test stage without waiting for its dependencies. Useful for quickly validating a specific test fix instead of waiting \~30 minutes for preceding stages.
* `/rerun-test <test-spec> [<test-spec> ...]`: Reruns one or more specific tests directly, bypassing stage boundaries. Each `<test-spec>` is pytest-style `<file>::<TestClass>[.<test_method>]` (the `::TestClass` and `.<test_method>` parts are optional). The handler resolves each spec, groups specs by their registered runner-label, and dispatches one [Rerun Test workflow](https://github.com/sgl-project/sglang/actions/workflows/rerun-test.yml) per group. Examples: `/rerun-test test_srt_endpoint.py`, `/rerun-test registered/core/test_srt_endpoint.py::TestSRTEndpoint.test_simple_decode`, `/rerun-test test_a.py test_b.py` (multiple at once).

If you have permission, the [Slash Command Handler](https://github.com/sgl-project/sglang/actions/workflows/slash-command-handler.yml) will run your command and react with a 👍 to your comment. It may take up to a few minutes for the reaction to appear. Here’s a usage [example](https://github.com/sgl-project/sglang/pull/14253#issuecomment-3599509302).

To avoid spamming a PR with too many `/rerun-failed-ci` comments, you can also trigger the command by editing an existing comment and adding any suffix (e.g., `/rerun-failed-ci try again`).

Example of rerunning a single test stage: `/rerun-stage unit-test-backend-4-gpu`.

If you don’t have permission, please ask maintainers to trigger CI for you.

### CI rate limits

Due to CI scheduling and limited resources, higher-priority PRs may preempt running jobs. In such cases, you may need to rerun the tests.
We apply CI rate limits to prevent abuse and ensure fair usage of our CI resources.

Each CI workflow has a default limit defined in its workflow configuration file. For example, in [pr-gate.yml](https://github.com/sgl-project/sglang/blob/main/.github/workflows/pr-gate.yml), the default cooldown period is 120 minutes, and each workflow can override it via the `cool-down-minutes` input parameter:

```yaml theme={null}
cool-down-minutes:
  description: "Cooldown period in minutes for low-permission users; 0 disables rate limiting"
  type: number
  default: 120
```

Users listed in [CI\_PERMISSIONS.json](https://github.com/sgl-project/sglang/blob/main/.github/CI_PERMISSIONS.json) may have a per-user cooldown interval. In practice, we use the minimum of the workflow’s default window and the user-specific interval.

## Code style guidance

* Avoid code duplication. If the same code snippet (more than five lines) appears multiple times, extract it into a shared function.
* Minimize device synchronization. Reduce expensive CPU-NPU synchronization operations, such as `tensor.item()` or `tensor.cpu()`, whenever possible. Use vectorized code.
* Prioritize extreme efficiency. SGLang is a runtime, and most of your code runs on the critical path for every request. Optimize all minor overheads as much as possible, especially in the model forward code.
  * A common pattern is some runtime checks in the model forward pass (e.g., [this](https://github.com/sgl-project/sglang/blob/f1b0eda55c2c4838e8ab90a0fac7fb1e3d7064ab/python/sglang/srt/models/deepseek_v2.py#L486-L491)). These are very likely the same for every layer. Please cache the result as a single boolean value in `__init__` whenever possible.
* Make functions as pure as possible. Avoid in-place modification of arguments.
* Keep files concise. If a file exceeds 2,000 lines of code, split it into multiple smaller files. (e.g., `scheduler.py`, `scheduler_pp_mixin.py`)
* In a file, put core data structures at the top of the file. Put utility functions at the bottom of the file.
* Keep tests run fast.
  * If a single test file run longer than 500 seconds, split it into multiple smaller files (e.g., `test_eagle_infer_a.py`, `test_eagle_infer_b.py`).
  * If a single job in a github workflow runs longer than 30 min, split it into smaller jobs/steps.
  * Reuse server launches in your unit tests to make tests run faster.
* Never use `pickle.loads()`, `pickle.load()`, or `recv_pyobj()` to deserialize untrusted or network-received data. Python’s [pickle module is not secure](https://docs.python.org/3/library/pickle.html) — it can execute arbitrary code during deserialization. Use safe serialization formats such as [msgpack](https://github.com/jcrist/msgspec) or JSON instead.
* When supporting new hardware or features, follow these guidelines:
  * Do not drastically change existing code.
  * Always prefer new files to introduce specific components for your new hardware (e.g., `allocator_npu.py`).
  * If you write multiple if/else blocks for new features, ensure the common path (e.g., NVIDIA hardware or the existing code path) is the first branch.

## How to update sgl-kernel-npu

Sgl-kernel-npu is the separate kernel package for Ascend NPU, containing both Ascend C and Triton operators. It is maintained in the [sgl-kernel-npu](https://github.com/sgl-project/sgl-kernel-npu) repository.

For detailed guidance on developing and integrating operators (Ascend C directory structure, PyTorch op registration, build, test, and code style), see the [Ascend NPU Operator Development Guide](./ascend_npu_operator_development).

### Multi-PR workflow

Since SGLang and sgl-kernel-npu are separate Python packages, dependency updates require a multi-PR workflow:

1. **Submit sgl-kernel-npu PR**: Add or modify operators in the sgl-kernel-npu repository following the operator development guide. Ensure all tests pass.
2. **Bump sgl-kernel-npu version**: Update the version number. Merging triggers an automatic PyPI release. If not urgent, wait for a regular release (typically within one week).
3. **Reference the new version in SGLang**:
   * Update the `SGLANG_KERNEL_NPU_TAG` argument in [`docker/npu.Dockerfile`](https://github.com/sgl-project/sglang/blob/main/docker/npu.Dockerfile) to the new sgl-kernel-npu release tag.
   * Use the new operator in SGLang code.

## 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 the following materials as startup guide:

* [Mini-SGLang](https://github.com/sgl-project/mini-sglang) for a quick overview on the structure of sglang.
* [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.
* [GTC-2026 Training Lab](https://drive.google.com/file/d/1mwOZEtipNLJzrflCTodj34KhuOZEoEw5/view?usp=drive_link) for hands-on practices of how to do optimization, benchmarking, or profiling on a launched SGLang instance.

If you have any questions or want to start a discussion, please feel free to ask in our [Slack channel](https://slack.sglang.io).

Thank you for your interest in SGLang. Happy coding!