- Translated comments from Chinese to English for better accessibility.
- Improved clarity of comments related to request handling and scheduling logic.
- Ensured comments align with the code functionality and maintain a uniform style.
- Introduced `DiffusionScheduler` to handle scheduling for diffusion models, allowing immediate request completion.
- Added `OmniNewRequestData` to encapsulate additional request data, including prompt embeddings and extra information.
- Updated `OmniScheduler` to ensure compatibility with new request data structures.
- Created `output.py` to define new data classes for enriched request handling.
- Enhanced `__init__.py` in the engine module to support new payload structures for prompt embeddings and additional information.
- 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.
- Add complete directory structure for multimodal and non-autoregressive processing
- Include AsyncLLM, OmniEngine, and DiffusionEngine modules
- Add processors, schedulers, executors, and output processors
- Add API interfaces for Higress, Gradio, and ComfyUI
- Add configuration management and CLI interface
- Add comprehensive test structure and documentation
- Add pyproject.toml with dependencies and project configuration
- Add examples and documentation structure
Gao Han