- Removed AsyncOmniLLM from the main entry points as it is not supported yet.
- Updated the constructor of MultimodalOutputProcessor to set a default value for engine_core_output_type.
- Adjusted output type handling in various methods to utilize the new default engine_core_output_type for consistency.
- Added logging for exceptions during tensor movement to CPU in OmniRequestState and MultimodalOutputProcessor.
- Improved robustness by ensuring the output pipeline continues without crashing on errors.
- Updated comments for clarity on error handling behavior.
- 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.
Gao Han