vLLM-Omni hosts developer-facing meetings for Chinese- and English-language audiences. See [Community Meetings](../community/meetings.md) for current schedules, access details, agendas, and past notes.
vLLM-Omni uses `uv` as the environment manager, to create and manage Python environments. Please follow the documentation to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
vLLM-Omni is quickly evolving, please see the [installation guide](../getting_started/installation/README.md) for details. It's recommended to build from source to provide the latest development environment.
vLLM-Omni is compatible with Python versions 3.10 to 3.12. However, we recommend developing with Python 3.12 to minimize the chance of your local environment clashing with our CI environment.
vLLM-Omni uses `pre-commit` to lint and format the codebase. See [pre-commit documentation](https://pre-commit.com/#usage) if `pre-commit` is new to you. Setting up `pre-commit` is as easy as:
Prefer fixing the call site. Growing an allowlist or raising a budget is a
policy change and needs review — do not edit these just to make the hook pass.
| Gate | File | What to edit |
| ---- | ---- | ------------ |
| pickle, `re`, `base64`, Hugging Face Hub, Triton/TileLang | [`tools/pre_commit/check_forbidden_imports.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_forbidden_imports.py) | `CHECK_IMPORTS[<rule>].allowed_files`. Use `allowed_dirs` only when a whole tree should stay exempt. |
| `torch.cuda` | [`tools/pre_commit/check_torch_cuda.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_torch_cuda.py) | `ALLOWED_FILES`, or `ALLOWED_PREFIXES` for a platform adapter tree. |
| TTS `_tts_model_type` branches | [`tools/pre_commit/check_tts_adapter.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_tts_adapter.py) | `MAX_MODEL_TYPE_BRANCHES` (down only, unless the PR justifies a raise). |
| Buildkite skip list | [`tools/pre_commit/check_buildkite.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_buildkite.py) | `SKIP_FILES` for pipelines that cannot be expanded. |
To skip one hook for a single commit (discouraged): `SKIP=<hook-id> git commit`.
To bypass every hook: `git commit --no-verify` (also discouraged).
MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, `mkdocs.yml`.
Get started with:
```bash
uv pip install -e ".[docs]"
```
MkDocs comes with a built-in dev-server that lets you preview your documentation as you work on it. From the root of the repository, run:
```bash
mkdocs serve # with API ref (~10 minutes)
API_AUTONAV_EXCLUDE=vllm_omni mkdocs serve # API ref off (~15 seconds)
```
Once you see `Serving on http://127.0.0.1:8000/` in the logs, the live preview is ready! Open <http://127.0.0.1:8000/> in your browser to see it.
For additional features and advanced configurations, refer to the:
- [MkDocs documentation](https://www.mkdocs.org/)
- [Material for MkDocs documentation](https://squidfunk.github.io/mkdocs-material/) (the MkDocs theme we use)
Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.
vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
| Task | Repository skill |
| --- | --- |
| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
Use the following workflow when collaborating with a coding agent:
1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
For example:
```text
Use add-diffusion-model and vllm-omni-test to implement the linked issue.
Read the relevant design documents first, keep the change within the issue,
and report every test command, result, and hardware validation gap.
```
```text
Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
regression test. Prefer L1 CPU coverage; if the failure requires real weights
or serving, explain the required L2/L3 environment and provide the exact command.
```
Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
AI tools are welcome in vLLM-Omni contributions. Contributors, including maintainers, remain responsible for everything they submit. These requirements apply to pull requests, issues, discussions, and review comments:
- **Disclose AI assistance.** Name the tools used and describe their role in the submission, including assistance with code, tests, documentation, or writing.
- **Understand your changes.** Review every generated change and be able to explain its behavior, design choices, and interaction with vLLM-Omni without relying on AI to answer for you.
- **Validate before submitting.** Run relevant checks and report actual results. For model, kernel, or performance changes, include the hardware, model, configuration, and commands needed to reproduce accuracy or benchmark evidence. Clearly identify checks you could not run; generated claims are not test results.
- **Keep communication useful.** Verify technical claims, search for existing reports, and edit AI-assisted text for accuracy and brevity. Submit only content you have personally reviewed, and take responsibility for responding to review feedback.
- **Label generated media.** Model-generated images, video, and audio are valid examples or test artifacts for this multimodal project. Identify them as generated output and provide the model, inputs, and reproduction settings. Do not present them as ground-truth references or independent evidence of correctness.
For example, a PR description could include:
```text
AI assistance: Used Codex to draft the regression test and PR description.
I reviewed the changes and verified the test reproduces the reported failure.
Validation: <commands, results, hardware, and any checks not run>.
```
Maintainer review time is limited. Contributions that are unverified, misleading, or beyond the author's understanding may be closed. Repeated violations may lead to contribution restrictions. New contributors are welcome to ask for help and submit small, well-understood changes.
This policy is adapted from [Ghostty's AI Usage Policy](https://github.com/ghostty-org/ghostty/blob/main/AI_POLICY.md) for vLLM-Omni's contribution and multimodal validation workflows. The [coding-agent workflow](#using-repository-skills-with-coding-agents) provides practical guidance for applying it.
If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.
Do not report suspected security vulnerabilities through a public issue, pull request, discussion, or Slack channel. Follow the [security disclosure instructions](../community/contact_us.md#security-disclosures) to arrange a private report.
Thank you for your contribution to vLLM-Omni! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM-Omni maintain the code quality and improve the efficiency of the review process.
To avoid agent spam, vLLM-Omni uses GitHub's [pull request limit](https://github.blog/open-source/maintainers/how-pull-request-limits-are-cutting-down-the-noise/) for contributors without write access. The current cap is 5 open PRs. If this blocks well-intentioned critical work, contact a committer to request bypass list access.
When contributing changes to this project, you must agree to the [DCO](https://developercertificate.org/). Commits must include a `Signed-off-by:` header which certifies agreement with the terms of the DCO.
Using `-s` with `git commit` will automatically add this header.
!!! tip
You can enable automatic sign-off via your IDE:
- **PyCharm**: Click on the `Show Commit Options` icon to the right of the `Commit and Push...` button in the `Commit` window. It will bring up a `git` window where you can modify the `Author` and enable `Sign-off commit`.
- **VSCode**: Open the Settings editor and enable the `Git: Always Sign Off` (`git.alwaysSignOff`) field.
### PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
- `[Bugfix]` for bug fixes.
- `[CI/Build]` for build or continuous integration improvements.
- `[Doc]` for documentation fixes and improvements.
- `[Model]` for adding a new model or improving an existing model. Model name should appear in the title.
Before submitting a PR, run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-review against project conventions:
The precheck covers five PR types: Bug Fix, Performance, New Model, Diffusion Model, and General. Each type has a tailored checklist that validates evidence quality (repro steps, A/B benchmarks, registry entries, etc.). See the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) for the full checklist.
Please run the L1 and L2 test cases locally first and attach the results before contacting us to add the "ready" label. Please refer to the [test instructions](./ci/test_execution_guide.md) for running the test cases.
On pull requests and `main` pushes, the bootstrap step in [`.buildkite/cuda/pipeline.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/pipeline.yml) runs [`.buildkite/common/scripts/upload_pipeline.py`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/common/scripts/upload_pipeline.py) against the git diff. When every changed file qualifies, **L2 (`ready`) and L3 (`merge-test`) pipelines are not uploaded**, so the default GPU CI jobs are skipped.
| Documentation | `docs/**`, any `*.md`, `mkdocs.yml` |
| Pytest skip marks only (under `tests/`) | Add/remove/edit `@pytest.mark.skip`, `@pytest.mark.skipif`, or `pytest.skip(...)`; reformat `pytestmark` only to add a skip/skipif alongside existing `pytest.mark.*` entries |
| New skipped test module | New `tests/**/*.py` whose `pytestmark` includes unconditional `pytest.mark.skip` |
These PR shapes all trigger skip-ci:
- Documentation only
- Qualifying skip-mark edits in `tests/**/*.py` only
- **A mix of documentation and qualifying skip-mark test edits**
Skip-ci does **not** apply when the diff also touches product code (for example `vllm_omni/`), or when test files change assertions, imports, fixtures, or other non-skip logic. If the diff cannot be resolved (non-PR branches outside `main`), CI runs as usual.
Skipping L2/L3 does **not** disable the Docker image build step. Nightly (L4) upload can still run when the PR has a `nightly-test` label or on scheduled `main` builds with `NIGHTLY=1`. Weekly (L5) upload can still run with `weekly-test`, `WEEKLY=1`, or `NON_CRITICAL=1`. Bootstrap child steps live in `bootstrap-upload-steps.yml`; the hook entry step runs `upload_pipeline.py --upload <platform>/bootstrap-upload-steps.yml`, which injects `if` by step `key` from skip-ci before upload. See [CI Settings — Diff-aware CI](./ci/ci_settings.md#diff-aware-ci) and [Test System Overview](./ci/test_system_overview.md).
- Please add documentation to `docs/` if the PR modifies the user-facing behaviors of vLLM-Omni. It helps vLLM-Omni users understand and utilize the new features or changes.
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with `rfc-required` and might not go through the PR.
The goal of the vLLM-Omni team is to be a _transparent reviewing machine_. We would like to make the review process transparent and efficient and make sure no contributor feels confused or frustrated. However, the vLLM-Omni team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
- After the PR is assigned, the reviewer will provide status updates every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM-Omni team.
- After the review, the reviewer will put an `action-required` label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.
- Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM-Omni. All of your contributions help make vLLM-Omni a great tool and community for everyone!
