vllm-omni-contrib

name	vllm-omni-contrib
description	Contribute to vLLM-Omni by adding new model support, fixing bugs, or improving features. Use when integrating a new model into vllm-omni, setting up a development environment, writing tests, or submitting pull requests to the vllm-omni project.

Contributing to vLLM-Omni

Overview

vLLM-Omni welcomes contributions including new model integrations, bug fixes, performance improvements, and documentation. This skill covers the development workflow, model integration process, and testing practices.

Development Environment Setup

Step 1: Fork and Clone

git clone https://github.com/<your-username>/vllm-omni.git
cd vllm-omni

Step 2: Install in Development Mode

uv venv --python $PYTHON_VERSION --seed
source .venv/bin/activate
uv pip install vllm==$VLLM_VERSION --torch-backend=auto
uv pip install -e ".[dev]"

Step 3: Install Pre-commit Hooks

pre-commit install

Code Organization

vllm_omni/
├── entrypoints/          # API entry points (Omni, AsyncOmni, API server)
├── engine/               # OmniRouter, pipeline orchestration
├── stages/               # Stage implementations (AR, Diffusion)
├── models/               # Model-specific implementations
├── connectors/           # OmniConnector for disaggregation
├── worker/               # Worker processes for distributed execution
└── utils/                # Shared utilities

Adding a New Model

Step 1: Identify Model Architecture

Determine which category your model falls into:

• AR-only: Text generation models (use existing vLLM model support)
• Diffusion-only: Image/video generation (DiT architecture)
• Multi-stage: AR + Diffusion pipeline (e.g., Qwen-Image)
• Omni: Full multi-modal input/output (e.g., Qwen-Omni)

Step 2: Implement Model Class

Create a new file in vllm_omni/models/:

# vllm_omni/models/my_new_model.py

from vllm_omni.stages.base import BaseStage

class MyNewModelPipeline:
    """Pipeline for MyNewModel."""

    def __init__(self, model_config, ...):
        ...

    def generate(self, prompts, ...):
        ...

Step 3: Register the Model

Add your model to the model registry so vLLM-Omni can discover it:

# In the appropriate registry file
SUPPORTED_MODELS = {
    ...
    "MyNewModelPipeline": ("my_new_model", "MyNewModelPipeline"),
}

For out-of-tree plugins, use the public API instead:

from vllm_omni.diffusion.registry import register_diffusion_model

register_diffusion_model(
    model_arch="MyNewModel",
    module_name="my_plugin.models.my_new_model",
    class_name="MyNewModelPipeline",
    pre_process_func_name="pre_process",  # optional
    post_process_func_name="post_process",  # optional
)

This registers custom diffusion pipelines without modifying core source. For out-of-tree plugins, module_name should be the full import path of the module containing the pipeline class.

Step 4: Add Stage Configuration

Create a default stage config YAML:

# vllm_omni/configs/my_new_model.yaml
stages:
  - name: "main"
    stage_type: "diffusion"  # or "ar"
    stage_args:
      runtime:
        max_batch_size: 1

Step 5: Write Tests

# tests/models/test_my_new_model.py
import pytest
from vllm_omni.entrypoints.omni import Omni

@pytest.mark.parametrize("prompt", [
    "a simple test image",
    "a red circle on white background",
])
def test_basic_generation(prompt):
    omni = Omni(model="my-org/my-new-model")
    outputs = omni.generate(prompt)
    assert len(outputs) > 0
    assert outputs[0].request_output[0].images is not None

Step 6: Add Documentation

Add your model to docs/models/supported_models.md with:

• Architecture name
• Model name
• Example HF model ID
• Any special requirements

Testing

Run Unit Tests

pytest tests/ -v

Run Specific Test File

pytest tests/models/test_my_new_model.py -v

Run with Coverage

pytest tests/ --cov=vllm_omni --cov-report=html

Code Style

• Follow the existing code patterns in the repository
• Use type hints consistently
• Run pre-commit hooks before committing:

  pre-commit run --all-files
  

Pull Request Process

1. Create a feature branch: git checkout -b feat/add-my-model
2. Make changes and write tests
3. Run tests locally: pytest tests/
4. Run linting: pre-commit run --all-files
5. Push and open a PR against main
6. Fill in the PR template with description and test results
7. Address review feedback

Troubleshooting Development

Import errors after install: Reinstall with uv pip install -e .

Tests fail with GPU errors: Some tests require a GPU. Run with pytest -m "not gpu" to skip GPU tests.

Pre-commit hook fails: Run pre-commit run --all-files to see specific issues.

OmniDiffusionConfig field name collision with vLLM attention_config: Fixed in #3489. Use diffusion_attention_config (not attention_config) in deploy YAML and code for diffusion pipelines. The old key is silently dropped.

RMSNorm inductor KeyError under HSDP + torch.compile: Fixed in #3460. fused_rms_norm inductor tracing now avoids calling .data on DTensor objects.

References

• For detailed model integration patterns, see references/model-integration.md