Contributing to vLLM-Omni¶

Thank you for your interest in contributing to vLLM-Omni! This document provides guidelines and instructions for contributing.

Getting Started¶

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:

uv venv --python 3.12 --seed
source .venv/bin/activate

Development Environment for vLLM and vLLM-Omni¶

vLLM-Omni is quickly evolving, please see the installation guide for details. It's recommended to build from source to provide the latest development environment.

Adding a new model to vLLM-Omni¶

Please check model implementation for how to add diffusion and omni-modality models to vLLM-Omni.

Linting¶

vLLM-Omni uses pre-commit to lint and format the codebase. See pre-commit documentation if pre-commit is new to you. Setting up pre-commit is as easy as:

uv pip install pre-commit
pre-commit install

vLLM-Omni's pre-commit hooks will now run automatically every time you commit.

pre-commit run     # runs on staged files
pre-commit run --show-diff-on-failure --color=always --all-files  # runs on all files (short for --all-files)

Documentation¶

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:

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:

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
• Material for MkDocs documentation (the MkDocs theme we use)

Testing¶

vLLM-Omni uses pytest to test the codebase. Please refer to the test instructions for detailed testing information.

Using repository skills with coding agents¶

vLLM-Omni maintains repository-scale 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.

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:

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.

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.

Issues¶

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.

Pull Requests & Code Reviews¶

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.

DCO and Signed-off-by¶

When contributing changes to this project, you must agree to the DCO. 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.

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.
• [Frontend] For changes on the vLLM-Omni frontend (e.g., OpenAI API server, Omni/AsyncOmni, etc.)
• [Kernel] for changes affecting CUDA kernels or other compute kernels.
• [Core] for changes in the core vLLM-Omni logic (e.g., OmniProcessor, OmniARScheduler, etc.)
• [Hardware][Vendor] for hardware-specific changes. Vendor name should appear in the prefix, such as [Ascend] for Ascend NPUs.
• [Misc] for PRs that do not fit the above categories. Please use this sparingly.

Pre-Check Before Submitting¶

Before submitting a PR, run the precheck-pr skill with the code agent for a self-review against project conventions:

The skill offers two modes: - Quick (~3 min): catches showstoppers — PR title format, missing benchmark claims, rebase status - Full (~10 min): thorough maintainer-grade review — dead code scan, copy-paste detection, import hygiene

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 for the full checklist.

Local Test¶

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 for running the test cases.

Automatic skip-ci (docs and pytest skip marks)¶

On pull requests and main pushes, the bootstrap step in .buildkite/cuda/pipeline.yml runs .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.

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.

Code Quality¶

The PR needs to meet the following code quality standards:

• We adhere to Google Python style guide and Google C++ style guide.
• Pass all linter checks.
• The code needs to be well-documented to ensure future contributors can easily understand the code.
• Include sufficient tests to ensure the project stays correct and robust. This includes both unit tests and integration tests.
• 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.

Notes for Large 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.

What to Expect for the Reviews¶

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 submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.
• 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.

Additional Resources¶

• Design Documents - Architecture and design documentation

Thank You¶

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!