hsliu
51d2570464
- 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.
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
"""
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vLLM-omni: Multi-modality models inference and serving with non-autoregressive structures.
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This package extends vLLM beyond traditional text-based, autoregressive generation
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to support multi-modality models with non-autoregressive structures and non-textual outputs.
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Architecture:
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- 🟡 Modified: vLLM components modified for multimodal support
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- 🔴 Added: New components for multimodal and non-autoregressive processing
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"""
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__version__ = "0.1.0"
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__author__ = "vLLM-omni Team"
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__email__ = "hsliuustc@gmail.com"
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# Main entry points
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from .core.omni_llm import OmniLLM, AsyncOmniLLM
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from .config import (
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OmniStageConfig,
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DiTConfig,
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DiTCacheConfig,
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create_ar_stage_config,
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create_dit_stage_config,
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)
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__all__ = [
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# Version info
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"__version__",
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"__author__",
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"__email__",
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# Main components
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"OmniLLM",
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"AsyncOmniLLM",
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# Configuration
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"OmniStageConfig",
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"DiTConfig",
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"DiTCacheConfig",
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"create_ar_stage_config",
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"create_dit_stage_config",
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# All other components are available through their respective modules
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# processors.*, schedulers.*, executors.*, etc.
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] |