- Introduced `OmniModelRunnerOutput` and `OmniRequestOutput` classes for handling multimodal outputs.
- Implemented `OmniEngineCoreRequest` and `OmniInputPreprocessor` to manage prompt embeddings and additional information.
- Created `OmniTokensPrompt`, `OmniTokenInputs`, and `OmniEmbedsPrompt` for enhanced token handling.
- Added parsing and preprocessing functions to support new input structures.
- Removed the obsolete `diffusion_engine.py` file as part of the refactor.
- Introduced OmniRequestState to manage multimodal request states.
- Enhanced MultimodalOutputProcessor to handle various output types including images, text, and latents.
- Implemented methods for accumulating multimodal tensors and processing outputs.
- Updated output handling to ensure compatibility with vLLM's base processor while allowing custom modality handlers.
- Add comprehensive PRD, architecture design, and test design documents
- Implement core modules: OmniLLM, AsyncOmniLLM, stage configurations
- Add DiT scheduler and cache manager for diffusion models
- Implement CLI integration with --omni flag support
- Add API server and plugin system for vLLM integration
- Create comprehensive test suite with fixtures
- Update dependencies to vLLM 0.10.2 and PyTorch 2.8.0
- Add conda environment setup and package installation
- Implement stage-based processing architecture
- Add multimodal output processing capabilities
This commit establishes the foundation for multi-modality models
inference and serving with non-autoregressive structures.
tzhouam