[Docs] optimize and supplement docs system (#86)
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# API Design Documentation
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This document contains comprehensive API design documentation for all core modules in vLLM-omni. These templates provide a standardized structure for designing and implementing the core, engine, executor, and worker modules.
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## 📋 Module API
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### Core Module API
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**Core module** provides fundamental scheduling, caching, and resource management functionality.
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**Key Components:**
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- Request scheduling and prioritization
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- DiT cache management for diffusion models
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- Resource allocation and coordination
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- Inter-module communication
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### Engine Module API
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**Engine module** handles model loading, inference execution, and output processing.
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**Key Components:**
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- Model loading and initialization
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- Inference execution for AR and diffusion models
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- Input preprocessing for various modalities
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- Output postprocessing and formatting
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### Executor Module API
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**Executor module** coordinates and manages request execution across different engines and workers.
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**Key Components:**
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- Request routing and load balancing
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- Execution pipeline coordination
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- Worker management and task distribution
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- Error handling and recovery
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### Worker Module API
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**Worker module** provides the actual execution environment for model inference.
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**Key Components:**
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- Model execution and inference
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- GPU resource management
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- Request batching and processing
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- Performance optimization
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## 🔧 Implementation Guidelines
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### Code Standards
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- Use type hints for all methods
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- Include comprehensive docstrings
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- Follow PEP 8 style guidelines
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- Use async/await for I/O operations
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### Error Handling
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- Define specific exception types
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- Provide meaningful error messages
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- Include error recovery strategies
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- Log errors appropriately
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### Configuration
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- Use dataclasses for configuration
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- Provide sensible defaults
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- Include validation methods
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- Support environment variables
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### Testing
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- Write unit tests for all public methods
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- Include integration tests
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- Test error conditions
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- Validate configuration options
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## 🚀 Getting Started
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### For Developers
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1. **Choose a Module**
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- Pick the module you want to work on
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- Read the corresponding API documentation
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- Understand the responsibilities and interfaces
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2. **Set Up Development Environment**
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```bash
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# Clone the repository
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git clone https://github.com/vllm-project/vllm-omni.git
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cd vllm-omni
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# Install dependencies
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pip install -e ".[dev]"
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# Set up pre-commit hooks
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pre-commit install
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```
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3. **Start Implementation**
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- Create the module directory
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- Implement base classes first
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- Add concrete implementations
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- Write tests as you go
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### For Contributors
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1. **Review API Documentation**
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- Read through all module APIs
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- Understand the overall architecture
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- Identify areas for improvement
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2. **Propose Changes**
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- Create issues for API changes
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- Discuss in pull requests
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- Update documentation accordingly
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3. **Submit Contributions**
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- Follow the coding standards
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- Include tests for new features
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- Update documentation
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- Submit pull requests
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## 🤝 Contributing
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We welcome contributions to improve the API documentation and implementation. Please:
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1. **Follow the Template Structure**
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- Use the [api template](https://github.com/vllm-project/vllm-omni/docs/contributing/design_documents/api_design_template.md) as a guide
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- Maintain consistency across modules
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- Include all required sections
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2. **Provide Working Examples**
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- Test all code examples
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- Include both basic and advanced usage
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- Show integration patterns
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3. **Keep Documentation Updated**
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- Update docs when APIs change
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- Version control documentation changes
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- Maintain backward compatibility notes
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## 📝 Notes
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- All API documentation should be kept up-to-date with implementation
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- Code examples should be tested and working
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- Configuration options should be validated
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- Error handling should be comprehensive
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- Performance considerations should be documented
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For questions or suggestions about the API documentation, please open an issue or start a discussion in the repository.
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# API Design Template for vLLM-omni Modules
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This template provides a standardized structure for designing APIs for core, engine, executor, and worker modules in vLLM-omni. Use this template to ensure consistency and completeness across all modules.
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## Template Structure
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### 1. Module Overview
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- **Purpose**: What this module does
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- **Responsibilities**: Key responsibilities of the module
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- **Dependencies**: What other modules this depends on
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- **Integration Points**: How it integrates with other modules
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### 2. Core Classes/Interfaces
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- **Base Classes**: Abstract base classes and interfaces
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- **Implementation Classes**: Concrete implementations
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- **Data Structures**: Key data structures and models
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### 3. Public API Methods
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- **Initialization**: Constructor and setup methods
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- **Core Operations**: Main functionality methods
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- **Configuration**: Configuration and parameter methods
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- **Lifecycle Management**: Start, stop, cleanup methods
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- **Monitoring**: Status, metrics, and debugging methods
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### 4. Configuration
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- **Configuration Classes**: Dataclasses or config objects
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- **Required Parameters**: Must-have configuration
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- **Optional Parameters**: Optional configuration with defaults
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- **Validation**: Parameter validation rules
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### 5. Error Handling
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- **Custom Exceptions**: Module-specific exceptions
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- **Error Codes**: Standardized error codes
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- **Recovery Strategies**: How to handle and recover from errors
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### 6. Examples
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- **Basic Usage**: Simple usage examples
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- **Advanced Usage**: Complex scenarios
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- **Integration Examples**: How to use with other modules
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---
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## Example: Core Module Template
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### 1. Module Overview
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**Purpose**: The core module provides fundamental scheduling and caching functionality for vLLM-omni.
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**Responsibilities**:
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- Request scheduling and prioritization
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- DiT cache management
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- Resource allocation and coordination
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- Inter-module communication
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**Dependencies**:
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- `vllm_omni.request` - Request handling
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- `vllm_omni.config` - Configuration management
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- `vllm_omni.utils` - Utility functions
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**Integration Points**:
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- Receives requests from entrypoints
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- Coordinates with engine modules
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- Manages worker allocation
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- Provides status to monitoring systems
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### 2. Core Classes/Interfaces
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```python
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from abc import ABC, abstractmethod
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from typing import List, Optional, Dict, Any
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from dataclasses import dataclass
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from enum import Enum
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class DiTCacheManager:
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"""Manages DiT cache for diffusion models."""
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def __init__(self, config: DiTCacheConfig):
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self.config = config
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self.cache = {}
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self.cache_stats = {}
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async def get_cache(self, cache_key: str) -> Optional[Any]:
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"""Retrieve cached data."""
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pass
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async def set_cache(self, cache_key: str, data: Any) -> None:
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"""Store data in cache."""
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pass
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async def invalidate_cache(self, cache_key: str) -> None:
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"""Invalidate cached data."""
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pass
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```
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### 3. Public API Methods
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#### Initialization
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```python
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class OmniScheduler():
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def __init__(self, config: SchedulerConfig):
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"""Initialize the core scheduler."""
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self.config = config
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self.scheduler = self._create_scheduler()
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self.cache_manager = DiTCacheManager(config.dit_cache_config)
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self._running = False
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def _create_scheduler(self) -> BaseScheduler:
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"""Factory method to create appropriate scheduler."""
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if self.config.scheduler_type == SchedulerType.FIFO:
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return FIFOScheduler(self.config)
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elif self.config.scheduler_type == SchedulerType.PRIORITY:
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return PriorityScheduler(self.config)
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# ... other scheduler types
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```
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#### Core Operations
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```python
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async def schedule(self, request: OmniRequest) -> bool:
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"""
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Schedule a request for processing.
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Args:
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request: The request to schedule
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Returns:
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bool: True if successfully scheduled, False otherwise
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Raises:
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SchedulerError: If scheduling fails
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QueueFullError: If queue is at capacity
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"""
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pass
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```
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#### Configuration
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```python
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def update_config(self, new_config: SchedulerConfig) -> None:
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"""Update scheduler configuration."""
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pass
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def get_config(self) -> SchedulerConfig:
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"""Get current configuration."""
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pass
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```
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#### Lifecycle Management
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```python
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async def start(self) -> None:
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"""Start the scheduler."""
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self._running = True
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# Start background tasks
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async def stop(self) -> None:
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"""Stop the scheduler gracefully."""
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self._running = False
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# Cleanup resources
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async def shutdown(self) -> None:
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"""Force shutdown the scheduler."""
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# Immediate cleanup
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```
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#### Monitoring
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```python
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def get_status(self) -> Dict[str, Any]:
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"""Get current scheduler status."""
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return {
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"running": self._running,
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"queue_size": self.scheduler.queue_size(),
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"processed_requests": self.scheduler.processed_count(),
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"cache_hit_rate": self.cache_manager.get_hit_rate()
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}
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def get_metrics(self) -> Dict[str, float]:
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"""Get performance metrics."""
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pass
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```
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### 4. Configuration
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```python
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@dataclass
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class OmniConfig:
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"""Configuration for the core module."""
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# Scheduler configuration
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scheduler: SchedulerConfig = field(default_factory=SchedulerConfig)
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# Cache configuration
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dit_cache: DiTCacheConfig = field(default_factory=DiTCacheConfig)
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# Resource limits
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max_memory_gb: float = 16.0
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max_gpu_utilization: float = 0.8
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# Timeouts
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request_timeout: int = 300
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worker_timeout: int = 60
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def validate(self) -> None:
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"""Validate configuration parameters."""
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if self.max_memory_gb <= 0:
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raise ValueError("max_memory_gb must be positive")
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if not 0 < self.max_gpu_utilization <= 1:
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raise ValueError("max_gpu_utilization must be between 0 and 1")
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```
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### 5. Error Handling
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```python
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class CoreModuleError(Exception):
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"""Base exception for core module errors."""
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pass
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class SchedulerError(CoreModuleError):
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"""Scheduler-related errors."""
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pass
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class QueueFullError(SchedulerError):
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"""Queue is at capacity."""
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pass
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class CacheError(CoreModuleError):
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"""Cache-related errors."""
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pass
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class ResourceError(CoreModuleError):
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"""Resource allocation errors."""
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pass
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```
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### 6. Examples
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#### Basic Usage
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```python
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from vllm_omni.core import CoreScheduler, CoreModuleConfig
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from vllm_omni.request import create_text_request
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# Create configuration
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config = CoreModuleConfig(
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scheduler=SchedulerConfig(scheduler_type=SchedulerType.FIFO),
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max_memory_gb=8.0
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)
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# Initialize scheduler
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scheduler = CoreScheduler(config)
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await scheduler.start()
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# Create and schedule a request
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request = create_text_request(
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request_id="req_001",
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prompt="Hello, world!",
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sampling_params=sampling_params
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)
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success = await scheduler.schedule_request(request)
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if success:
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result = await scheduler.process_request(request)
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print(f"Result: {result}")
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```
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#### Advanced Usage
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```python
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# Custom scheduler with priority handling
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config = CoreModuleConfig(
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scheduler=SchedulerConfig(
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scheduler_type=SchedulerType.PRIORITY,
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priority_weights={"high": 2.0, "normal": 1.0, "low": 0.5}
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)
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)
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scheduler = CoreScheduler(config)
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await scheduler.start()
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# Monitor scheduler status
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status = scheduler.get_status()
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print(f"Queue size: {status['queue_size']}")
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print(f"Cache hit rate: {status['cache_hit_rate']:.2%}")
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```
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---
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## Module-Specific Guidelines
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|
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### Core Module
|
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- Focus on scheduling, caching, and resource management
|
||||
- Provide clear interfaces for other modules
|
||||
- Handle concurrency and thread safety
|
||||
- Implement comprehensive monitoring
|
||||
|
||||
### Engine Module
|
||||
- Handle model loading and inference
|
||||
- Support both AR and diffusion models
|
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- Provide unified interface for different model types
|
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- Implement efficient memory management
|
||||
|
||||
### Executor Module
|
||||
- Coordinate between different engines
|
||||
- Handle request routing and load balancing
|
||||
- Manage execution pipelines
|
||||
- Provide error recovery mechanisms
|
||||
|
||||
### Worker Module
|
||||
- Handle actual model execution
|
||||
- Manage GPU resources
|
||||
- Implement batching strategies
|
||||
- Provide performance optimization
|
||||
|
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---
|
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|
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## Checklist for API Design
|
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- [ ] **Clear Purpose**: Module purpose is well-defined
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- [ ] **Complete Interface**: All public methods are documented
|
||||
- [ ] **Error Handling**: Comprehensive error handling strategy
|
||||
- [ ] **Configuration**: Flexible configuration system
|
||||
- [ ] **Examples**: Basic and advanced usage examples
|
||||
- [ ] **Type Hints**: All methods have proper type hints
|
||||
- [ ] **Documentation**: Comprehensive docstrings
|
||||
- [ ] **Testing**: Unit tests for all public methods
|
||||
- [ ] **Integration**: Clear integration points with other modules
|
||||
- [ ] **Performance**: Performance considerations documented
|
||||
|
||||
---
|
||||
|
||||
## Submission Guidelines
|
||||
|
||||
1. Create a new file: `docs/api/[module_name]_api.md`
|
||||
2. Follow the template structure exactly
|
||||
3. Include all required sections
|
||||
4. Provide working code examples
|
||||
5. Ensure all methods have proper docstrings
|
||||
6. Include error handling strategies
|
||||
7. Submit for review before implementation
|
||||
|
||||
This template ensures consistency and completeness across all vLLM-omni modules.
|
||||
@@ -0,0 +1,139 @@
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# Architecture Overview
|
||||
|
||||
# Introduction
|
||||
|
||||
This document outlines the architectural design for vLLM-omni, a new repository developed under the vLLM project. vLLM-omni supports multi-modality models with non-autoregressive structures and non-textual outputs, extending vLLM beyond traditional text-based, autoregressive generation.
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|
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# Goals
|
||||
|
||||
The primary goals for vLLM-omni are:
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|
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* **Support for Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
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* **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
|
||||
* **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
|
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* **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.
|
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|
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# Expected Gains
|
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|
||||
* It breaks through the limitation of vLLM that only supports autoregressive text generation models, and provides architectural support for popular model structures, especially multimodal generation.
|
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* By supporting new output formats and new model architectures, developers can easily connect to mature ecosystems such as Higress Gateway and Ray, improving the ease of deployment and efficiency of corresponding models.
|
||||
* Integrating efficient cache management, multiple parallel strategies, disaggregation from vLLM, to improve inference efficiency of corresponding models.
|
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|
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# Non-Autoregressive Structure
|
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|
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* **Diffusion Transformer:** initialized with a random latent noise, the transformer model iteratively predicts latent output for multiple steps, under control of sampling timestamps. Finally, it is decoded by the decoder of Variational AutoEncoder (VAE). It is not supported by vLLM now.
|
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* **Autoregressive:** dominant generation paradigm for text, now supported by vLLM. It generates tokens conditioned on previous tokens one by one. The efficient KV Cache management provided by vLLM can accelerate inference effectively.
|
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![][image1] ![][image2]
|
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|
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|
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| | Autoregressive | DiT |
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| :---- | ----- | ----- |
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||||
| Scenarios | Text generation | Multimodal generation |
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| Generation | Token-by-token, left to right | Continuous vector, iteratively refine |
|
||||
| Key advantages | Smooth, good local consistency | Global consistency, diversity and controlability |
|
||||
| Efficiency | More efficient with KV cache | Need generation repeatedly, slow |
|
||||
| Error accumulation | Easy to accumulate | Can fix possibly |
|
||||
| Key challenges | Limited global planning, long context generation | Large global computational cost |
|
||||
| Sequence length | Varied length | Fixed length |
|
||||
| Attention mask | Lower triangular | Full bi-directional matrix |
|
||||
| Parallel strategy | TP, DP, PP, .etc | DP, CP, .etc |
|
||||
| vLLM support | Supported | Unsupported |
|
||||
|
||||
# Targeted models to support
|
||||
|
||||
According to analysis for current popular open-source models, most of them have the combination of AR+DiT. Specifically, they can be further categorized into 3 types below:
|
||||
|
||||
* **DiT as a main structure, with AR as text encoder (e.g.: Qwen-Image)**
|
||||
A popular model for image generation and editing. Main visual generation models are similar to the structure, such as Flux.
|
||||
![][image3]
|
||||
* **AR as a main structure, with DiT as multi-modal generator (e.g. BAGEL)**
|
||||
A unified multimodal comprehension and generation model. Visual generation can utilize CoT text generation output. Main unified multimodal models are similar to the structure.
|
||||
![][image4]
|
||||
* **Extension, multiple AR+DiT (e.g. Qwen-omni)**
|
||||
A new model for multimodal input and output. It designs the Thinker-Talker-Codec structure, which is a double-AR+DiT format.
|
||||
![][image5]
|
||||
|
||||
|
||||
|
||||
# Architectural Overview
|
||||
|
||||
vLLM-omni will import core modules from existing vLLM framework and introduce several new components and modifications to achieve its goals. The core architecture will be based on a modular design, allowing for independent development and integration of different modality handlers and output generators.
|
||||
![][image6]
|
||||
|
||||
## General ideas
|
||||
|
||||
* It divides models in vLLM-omni into multiple stages, which are processed by different Engine Cores. Each stage can choose AR Engine Core for AR model part, and Diffusion Engine Core for diffusion model part.
|
||||
* Designed a specialized diffusion engine core for DiT or dLLM.
|
||||
* Plug-in with external DiT acceleration modules such as xDiT, Cache-DiT.
|
||||
* Output linked with Higress API, Gradio for online demo, ComfyUI for less-code process.
|
||||
|
||||
## Key Components
|
||||
|
||||
* **Processor:** Input processing, tokenization or encoding
|
||||
* **Scheduler:** for order and queue of incoming requests
|
||||
* **Executor:** Prepare model input and run
|
||||
* **Multimodal output processor:** support multimodal outputs
|
||||
* **Hidden State Output processor:** support intermediate hidden vectors as request output
|
||||
* **Engine Core:** accelerate AR modules, imported from vLLM main repo
|
||||
* **Diffusion Engine Core:** Wrap and accelerate DiT modules.
|
||||
* **Diffusion model:** Wrap up diffusion models with vLLM format
|
||||
|
||||
# Examples data flow of specific models
|
||||
|
||||
1. Qwen-omni
|
||||
![][image7]![][image8]
|
||||
2. Qwen-Image
|
||||
![][image9] ![][image10]
|
||||
|
||||
# Overview of function call dependency
|
||||
|
||||
![][image11]
|
||||
|
||||
# Roadmap
|
||||
|
||||
* AsyncLLM:
|
||||
- [ ] Initializer: resource allocation for different stages (2025 H2)
|
||||
- [ ] Hidden state Output Processor (2025 H2)
|
||||
- [ ] Multimodal Output Processor (2025 H2)
|
||||
- [ ] Interface for API, Gradio, ComfyUI
|
||||
* OmniEngine:
|
||||
- [ ] Basic Diffusion Engine core: Diffusion step manager, Cache manager (2025 H2)
|
||||
- [ ] Pipeline of different Engine Cores (2025 H2)
|
||||
- [ ] Communication between Engine Cores via AsyncLLM (2025 H2)
|
||||
* Model support (Included but not limited to):
|
||||
- [ ] DiT: Qwen-Image (2025 H2), Flux-Kontext
|
||||
- [ ] Unified-MLLM: BAGEL (2025 H2), OmniGen2, Janus-Pro
|
||||
- [ ] Extensions of AR+DiT: Qwen-omni (2025 H2)
|
||||
* Optimization
|
||||
- [ ] Integration of other DiT acceleration modules, such as xDiT, Cache-DiT
|
||||
- [ ] Disaggregation for AR and DiT
|
||||
- [ ] Integration with vLLM's existing KV cache for potential future hybrid models.
|
||||
- [ ] Support for streaming multi-modal inputs and outputs
|
||||
|
||||
# Contact
|
||||
|
||||
For any questions or feedback regarding this design document, please contact
|
||||
Han Gao: [hgaoaf@connect.ust.hk](mailto:hgaoaf@connect.ust.hk),
|
||||
Hongsheng Liu: [vllm-project@gmail.com](mailto:vllm-project@gmail.com)
|
||||
|
||||
[image1]: 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truncated
|
||||
|
||||
[image2]: 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[image3]: 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[image4]: 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[image6]: 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[image8]: 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[image9]: 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||||
[image10]: 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truncated
|
||||
|
||||
[image11]: 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truncated
|
||||
@@ -0,0 +1,12 @@
|
||||
# Design Documents
|
||||
|
||||
This section contains design documents and architecture specifications for vLLM-omni.
|
||||
|
||||
## Architecture Documents
|
||||
|
||||
- [vLLM-omni Software Design Document](vllm_omni_design.md)
|
||||
- [Architecture Overview](architecture_overview.md)
|
||||
|
||||
## API Design Documentation
|
||||
|
||||
- [vLLM-omni API Documentation](api_design_doc.md)
|
||||
@@ -0,0 +1,188 @@
|
||||
## Multi-Request Streaming (MRS) on a Single Machine
|
||||
|
||||
### 1. Background & Scope
|
||||
- All processing runs on a single physical machine with multi-process, per-stage workers. No proxy or network transport involved.
|
||||
- Current alignment with vllm-omni: `OmniLLM` supports multiple stages (`OmniStage`). GPU runners already expose streamable steps (prefill/decoding/diffusion), but the entry layer still collects lists and lacks intra-stage streaming and window scheduling.
|
||||
- Goal: implement multi-stage, multi-request streaming (MRS) locally. Each stage outputs segments; downstream stages stitch and trigger compute based on configured windows. Shared memory and zero-copy strategies reduce data movement overhead.
|
||||
|
||||
### 2. Key Constraints
|
||||
- Multi-process per stage: each stage is an independent process with a while loop; device visibility can be configured (`CUDA_VISIBLE_DEVICES`/`torch.cuda.set_device`).
|
||||
- Simple IPC (copy-based): use `multiprocessing.Queue`/Pipe for inter-process communication with CPU copies/serialization; do not rely on CUDA IPC/SHM zero-copy in this version.
|
||||
- Cross-stage pipeline: different stages can process different requests concurrently (e.g., stage A handles request 1 while stage B handles request 0).
|
||||
|
||||
### 3. Architecture Overview
|
||||
- Processes & IPC queues
|
||||
- Each "sub-stage" is an OS process (worker). The loop: take from input_queue → compute → put to output_queue.
|
||||
- Inter-stage connection via IPC: copy-based `multiprocessing.Queue` passing dict payloads; use shared memory for large objects.
|
||||
- Each link is SPSC (single-producer/single-consumer): the upstream is the orchestrator and the downstream is a single stage process; queues are unbounded (maxsize=0) on the orchestrator side.
|
||||
- Device visibility
|
||||
- Each stage sets `CUDA_VISIBLE_DEVICES` or calls `torch.cuda.set_device` to bind to GPU sets.
|
||||
- A stage may use multiple GPUs internally (TP/PP/DP) but presents as a single stage unit.
|
||||
- Simplified IPC: copy-based queues/pipes for data transfer; zero-copy is future work.
|
||||
- Pipeline progression: when a stage finishes a request, it enqueues outputs to the downstream stage; if downstream is idle, it starts immediately.
|
||||
- Scheduling
|
||||
- A downstream stage triggers only after the upstream completes the request.
|
||||
- Windowed segmentation/stitched triggering is not implemented; intra-stage streaming is not provided.
|
||||
|
||||
### 7. IPC Implementation (simplified: copy-based)
|
||||
- Use `multiprocessing.Queue`/Pipe for inter-process communication (control + data).
|
||||
- Data is serialized/copied via CPU; no CUDA IPC/SHM zero-copy in this version.
|
||||
- Backpressure: queues are unbounded; pressure manifests as compute-rate differences. Optional SHM reduces large-object transfer cost; RX/decoding overhead is recorded for observability.
|
||||
|
||||
### 8. Scheduling & Cancellation (simplified)
|
||||
- Pipeline: when a stage finishes a request, it enqueues to the next stage; that stage immediately pulls the next request from its input queue, enabling cross-stage concurrency.
|
||||
- Cancellation/timeout: explicit cancellation/timeouts are not provided; graceful shutdown uses a `None` sentinel sent to each stage input queue.
|
||||
|
||||
#### Short sequence example (req0/req1, stage A→B)
|
||||
1) t0: stage A handles req0
|
||||
2) t1: req0 completes on A → enters B; A immediately starts req1
|
||||
3) t2: B handles req0 while A handles req1 (parallel across stages)
|
||||
|
||||
### 9. Integration Points (by file)
|
||||
- `vllm_omni/entrypoints/omni_llm.py` (Orchestrator)
|
||||
- Class `OmniLLM` orchestrates multi-process stages; constructs `OmniStage` instances in parallel and spawns per-stage workers.
|
||||
- Spawns stage processes per config (set `CUDA_VISIBLE_DEVICES`/`torch.cuda.set_device`), creates control/data channels, builds simple full-trigger flow.
|
||||
- Stats/logging are disabled by default; per-stage and orchestrator stats are only written when explicitly enabled.
|
||||
- Manages process lifecycle: start/wait for readiness, graceful shutdown; forwards results between stages using copy-based IPC and optional SHM.
|
||||
- Stage readiness: each stage emits `{"type": "stage_ready"}` after initialization; the orchestrator waits for all stages or times out and logs diagnostic suggestions.
|
||||
|
||||
### 9.1 Process Device Visibility
|
||||
- Device binding: set `CUDA_VISIBLE_DEVICES` before process start, or call `torch.cuda.set_device` early in init; configs can be single/multi-GPU.
|
||||
- Cross-device transfer (simplified): via CPU copies; zero-copy paths are out of scope for now.
|
||||
|
||||
### 10. Worker Template (pseudo-code)
|
||||
```python
|
||||
def stage_worker(input_q, output_q, runtime, shm_threshold):
|
||||
# Device binding
|
||||
set_stage_gpu_devices(runtime.devices)
|
||||
engine = OmniStageLLM(model, **engine_args)
|
||||
batch_seq = 0
|
||||
while True:
|
||||
first = input_q.get()
|
||||
if first is None:
|
||||
break
|
||||
# Batch intake (up to runtime.max_batch_size)
|
||||
batch = [first]
|
||||
while len(batch) < int(runtime.max_batch_size or 1) and not input_q.empty():
|
||||
nxt = input_q.get()
|
||||
if nxt is None:
|
||||
input_q.put(None)
|
||||
break
|
||||
batch.append(nxt)
|
||||
# Decode IPC payload and sampling params
|
||||
request_ids = []
|
||||
engine_inputs = []
|
||||
rx_bytes = {}
|
||||
rx_decode_ms = {}
|
||||
for t in batch:
|
||||
ein, rx = maybe_load_from_ipc_with_metrics(t, "engine_inputs", "engine_inputs_shm")
|
||||
request_ids.append(t["request_id"])
|
||||
engine_inputs.extend(ein if isinstance(ein, list) else [ein])
|
||||
rx_bytes[t["request_id"]] = rx["rx_transfer_bytes"]
|
||||
rx_decode_ms[t["request_id"]] = rx["rx_decode_time_ms"]
|
||||
# Generate and dispatch (grouped by request_id)
|
||||
batch_seq += 1
|
||||
outputs = list(engine.generate(engine_inputs, batch[0]["sampling_params"], use_tqdm=False))
|
||||
grouped = group_by_request_id(outputs, request_ids)
|
||||
for rid in request_ids:
|
||||
r_out = grouped.get(rid, [])
|
||||
use_shm, payload = maybe_dump_to_shm(r_out, shm_threshold)
|
||||
msg = {
|
||||
"request_id": rid,
|
||||
"metrics": {
|
||||
"batch_id": batch_seq,
|
||||
"rx_transfer_bytes": int(rx_bytes.get(rid, 0)),
|
||||
"rx_decode_time_ms": float(rx_decode_ms.get(rid, 0.0)),
|
||||
# omitted: generation latency and token stats
|
||||
},
|
||||
}
|
||||
if use_shm:
|
||||
msg["engine_outputs_shm"] = payload
|
||||
else:
|
||||
msg["engine_outputs"] = payload
|
||||
output_q.put(msg)
|
||||
```
|
||||
|
||||
### 12. Observability
|
||||
- Metrics (as implemented):
|
||||
- per-request (emitted by stages): `num_tokens_out`, `stage_gen_time_ms`, `rx_transfer_bytes`, `rx_decode_time_ms`, `rx_in_flight_time_ms`, `batch_id`
|
||||
- orchestrator aggregates: E2E latency, tokens/s (written only when stats are enabled)
|
||||
- optional per-stage JSONL: `{log_file}.stage{stage_id}.stats.jsonl`
|
||||
- Logs/tracing:
|
||||
- optional per-stage log files: `{log_file}.stage{stage_id}.log`
|
||||
- the orchestrator can log readiness, forward size/time, and summary information
|
||||
|
||||
### 15. Risks & Mitigations
|
||||
— End-to-end latency: full-trigger sacrifices interactivity; start simple/correct, add windowed streaming later.
|
||||
— CPU copy overhead: copy-based IPC for maintainability; add SHM/zero-copy later as an optimization path.
|
||||
|
||||
### 16. Refactor: Sink LLM init and process into Stage (diagram)
|
||||
|
||||
Objective: without changing external call patterns, encapsulate per-stage LLM init, process creation, and worker logic inside `Stage`, so `PipelinedOmniLLM` focuses on orchestration (seeding, polling, forwarding, collecting) while keeping multi-process, shared memory, and device mapping capabilities.
|
||||
|
||||
Key changes (vs. current):
|
||||
- Before: Orchestrator created processes directly, passing `_stage_worker`; device setup, LLM init, batching, and SHM were implemented there.
|
||||
- After: enhanced `OmniStage` owns its subprocess (`init_stage_worker`), with `_stage_worker` as the entry; device setup, LLM init, batching, and SHM live in `OmniStage`.
|
||||
|
||||
Classes & responsibilities
|
||||
- OmniLLM (orchestrator)
|
||||
- Build `OmniStage` list in parallel (preserve `process_engine_inputs` wiring)
|
||||
- Connect adjacent `Stage` input/output queues; seed requests to stage 0
|
||||
- Poll stage outputs, decode results, call `process_engine_inputs`, then encode and forward to next stage
|
||||
- Termination & cleanup: distribute shutdown signals; join/terminate subprocesses
|
||||
- Stage (stage unit)
|
||||
- Members: `stage_config` (with `engine_args` and `runtime`), `in_q/out_q`, subprocess handle, stats
|
||||
- API:
|
||||
- `init_stage_worker(...)`: spawn subprocess
|
||||
- `stop_stage_worker()`: graceful exit
|
||||
- `submit(ipc_payload)`: submit to input queue (may use shared memory)
|
||||
- `try_collect() -> Optional[payload]`: non-blocking get from output queue
|
||||
- `process_engine_inputs(stages, prompts)`: reuse/delegate existing logic
|
||||
- Subprocess entry `worker_main()`:
|
||||
- `set_stage_gpu_devices` (`CUDA_VISIBLE_DEVICES` & logical index mapping)
|
||||
- build `OmniStageLLM(model, **engine_args)`
|
||||
- while-loop:
|
||||
- take first task + up to `runtime.max_batch_size-1` non-blocking; support `max_batch_size>=1`
|
||||
- `maybe_load_from_ipc_with_metrics` per `engine_inputs` in batch
|
||||
- `stage_engine.generate(batched_inputs, sampling_params)` (window `-1`)
|
||||
- write per-request aggregated outputs to output queue after `maybe_dump_to_shm`
|
||||
- log start/finish; report `error` per request on exceptions
|
||||
|
||||
Data flow (sketch)
|
||||
```
|
||||
OmniLLM
|
||||
├─ Stage[0].start_process(in0, out0)
|
||||
├─ Stage[1].start_process(in1, out1)
|
||||
├─ ...
|
||||
└─ Orchestrator loop:
|
||||
- stage0.submit(encode(engine_inputs))
|
||||
- for each stage:
|
||||
res = stage.try_collect()
|
||||
outputs = decode(res)
|
||||
stage.set_engine_outputs(outputs)
|
||||
if !final: next_inputs = next_stage.process_engine_inputs(stages, original_prompt)
|
||||
next_stage.submit(encode(next_inputs))
|
||||
```
|
||||
|
||||
Config (preserve & extend)
|
||||
- `runtime.devices`:
|
||||
- Comma-separated visibility list (e.g., "2,5,7"): set visible set; default logical index 0 (first GPU)
|
||||
- Number/string: treat as logical index (0/1-based per implementation) mapping to physical GPUs
|
||||
- `runtime.max_batch_size`: max requests taken per intake (default 1)
|
||||
- `shm_threshold_bytes`: objects larger than this go via shared memory (default 64KB; injected into stages)
|
||||
- Stage link and inputs:
|
||||
- `engine_input_source`: list of upstream stage_id(s) from which the downstream stage takes inputs (typically a single source)
|
||||
- `custom_process_input_func`: function to construct downstream engine_inputs from upstream outputs
|
||||
- Output flags:
|
||||
- `final_output`, `final_output_type`: marks that a stage produces the final external output and its type
|
||||
|
||||
Kept & strengthened
|
||||
- Multi-process: each stage in its own process; parallel across stages; serial/batching inside a stage controlled by `max_batch_size`
|
||||
- Shared memory: large objects via SHM with strict `memoryview -> close -> unlink`
|
||||
- Device mapping: unified `set_stage_gpu_devices`, support comma lists and logical indices
|
||||
|
||||
Migration (execution order)
|
||||
1) Enhanced `OmniStage` with `init_stage_worker/stop_stage_worker/submit/try_collect/_stage_worker`.
|
||||
2) `OmniLLM` holds `OmniStage` instances, replacing the older pipelined class; uses Stage APIs.
|
||||
3) Reuse `entrypoints/stage_utils`: device/SHM/encode-decode utilities live here.
|
||||
4) Regression: single/multi-request, various `max_batch_size`, SHM threshold, device mapping, multi-stage parallelism, error paths, and shutdown flows.
|
||||
@@ -0,0 +1,509 @@
|
||||
# Architecture Design Principles
|
||||
|
||||
## Overview
|
||||
|
||||
vLLM-omni is a multi-modality extension for vLLM that supports non-autoregressive structures and non-textual outputs. This document outlines the key software abstractions, APIs, and dependencies for the system, designed to maximize reuse of vLLM's proven architecture.
|
||||
|
||||
## Architecture Principles
|
||||
|
||||
1. **vLLM V1 Compatibility**: Built on vLLM's Engine V1 architecture with AsyncLLM and EngineCore patterns
|
||||
2. **Stage-based Processing**: Models are divided into multiple stages, each processed by different Engine Cores
|
||||
3. **Multiple Engine Core Support**: Each stage can use either AR Engine Core (reusing vLLM) or Diffusion Engine Core (new DiT support), or other new Cores
|
||||
4. **Worker Process Pattern**: Follows vLLM's multiprocess worker architecture for scalability
|
||||
5. **Extensibility**: Easy integration of new modalities, model architectures, and output formats
|
||||
|
||||
## Key Data Flow
|
||||
|
||||
API Server --> OmniLLM/AsyncOmniLLM (New, including multi engines) --> LLMEngine/AsyncLLM --> Engine Core
|
||||
--> Scheduler (New one for DiT) --> Executor (New one for diffusers) --> Worker (New one for DiT)
|
||||
--> ModelRunner (New one for AR hiddenstate, New one for DiT) --> RequestState --> OutputProcessoer (New one for final multimodal output)
|
||||
|
||||
## Core Components (Ordered by Data Flow)
|
||||
|
||||
### 1. Installation
|
||||
```bash
|
||||
pip install vllm
|
||||
pip install vllm-omni
|
||||
```
|
||||
|
||||
### 2. Online inference launch (Entry Point)
|
||||
|
||||
Keep consistency with the vllm main branch
|
||||
```bash
|
||||
vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8000
|
||||
```
|
||||
|
||||
#### Design Architecture
|
||||
```mermaid
|
||||
graph TD
|
||||
A[vllm serve model --omni] --> B[vLLM-omni CLI Wrapper]
|
||||
B --> C{Detect --omni flag}
|
||||
C -->|Yes| D[Parse OmniConfig]
|
||||
C -->|No| E[Forward to vLLM CLI]
|
||||
D --> F[Initialize AsyncOmniLLM]
|
||||
F --> G[Start omni Server]
|
||||
G --> H[Multi-stage Processing]
|
||||
E --> I[Standard vLLM Pipeline]
|
||||
|
||||
subgraph "vLLM-omni Components"
|
||||
F
|
||||
G
|
||||
H
|
||||
end
|
||||
|
||||
subgraph "vLLM Components"
|
||||
I
|
||||
end
|
||||
```
|
||||
|
||||
#### omni Serve Command Implementation
|
||||
File: vllm_omni/entrypoints/cli/main.py
|
||||
```python
|
||||
import sys
|
||||
import argparse
|
||||
from typing import List, Optional
|
||||
from vllm_omni.entrypoints.omni import OmniServeCommand
|
||||
|
||||
def main():
|
||||
"""Main CLI entry point that intercepts vLLM commands"""
|
||||
# Check if --omni flag is present
|
||||
if "--omni" in sys.argv:
|
||||
# Remove --omni flag and process with vLLM-omni
|
||||
omni_args = [arg for arg in sys.argv[1:] if arg != "--omni"]
|
||||
omni_serve = OmniServeCommand()
|
||||
omni_serve.run(omni_args)
|
||||
else:
|
||||
# Forward to original vLLM CLI
|
||||
from vllm.entrypoints.cli.main import main as vllm_main
|
||||
vllm_main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
```
|
||||
|
||||
#### vLLM Plugin Integration
|
||||
File: vllm_omni/pyproject.toml
|
||||
```python
|
||||
[project.scripts]
|
||||
# Override vLLM's CLI entry point
|
||||
vllm = "vllm_omni.cli:main"
|
||||
|
||||
# Add entry point for vLLM integration
|
||||
[project.entry-points."vllm.plugins"]
|
||||
omni = "vllm_omni.plugin:OmniPlugin"
|
||||
```
|
||||
|
||||
### 3. Offline inference launch (Entry Point)
|
||||
Design an upper level class to incorporate multi Engines, each engine has a engine call.
|
||||
```python
|
||||
from vllm.entrypoints.llm import LLM
|
||||
from vllm.v1.engine.llm_engine import LLMEngine
|
||||
|
||||
class OmniLLM(LLM):
|
||||
"""Extended LLM supporting multiple engines and stage-based processing"""
|
||||
|
||||
def __init__(self, stage_configs: List[StageConfig]):
|
||||
super().__init__()
|
||||
self.stage_configs = stage_configs
|
||||
self.engine_list = [] # List of AsyncLLM instances for each stage
|
||||
self.output_processor = MultimodalOutputProcessor()
|
||||
self._initialize_stage_engines()
|
||||
|
||||
def _initialize_stage_engines(self) -> None:
|
||||
"""Initialize LLMEngine instances for each stage"""
|
||||
for stage_config in self.stage_configs:
|
||||
stage_llm = LLMEngine.from_vllm_config(
|
||||
vllm_config=stage_config.vllm_config,
|
||||
executor_class=self.executor_class,
|
||||
log_stats=self.log_stats
|
||||
)
|
||||
self.engine_list.append(stage_llm)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
stage_args_list: List[stage_args],
|
||||
use_tqdm: Union[bool, Callable[..., tqdm]] = True,
|
||||
lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
|
||||
priority: Optional[list[int]] = None,
|
||||
) -> list[RequestOutput]:
|
||||
"""Main generation interface - orchestrates multi-stage processing"""
|
||||
|
||||
# Process through each stage sequentially
|
||||
for i, stage_config in enumerate(self.stage_configs):
|
||||
stage_engine = self.engine_list[i]
|
||||
|
||||
# Prepare input for this stage
|
||||
stage_args = stage_args_list[i]
|
||||
prompt_str, engine_request, tokenization_kwargs = self._process_stage_inputs(stage_config, **stage_args)
|
||||
|
||||
# Add inputs to Engine
|
||||
stage_engine.add_request(requesy_id, prompt_str, tokenization_kwargs)
|
||||
# Run Engine
|
||||
stage_output = stage_engine.step()
|
||||
|
||||
# Update stage input and output of each stage for later usage
|
||||
self._update_stage_io(stage_output, stage_config)
|
||||
|
||||
|
||||
# Process final output
|
||||
final_output = self.output_processor.process_output(
|
||||
self.stage_configs[-1].stage_output
|
||||
)
|
||||
return final_output
|
||||
|
||||
def _process_stage_inputs(stage_config, **stage_args) -> Any:
|
||||
"""Prepare input for specific stage"""
|
||||
if stage_config.engine_type == "AR":
|
||||
return self._process_ar_inputs(**stage_args)
|
||||
elif stage_config.engine_type == "DiT":
|
||||
return self._process_dit_inputs(**stage_args)
|
||||
else
|
||||
raise NotImplementedError
|
||||
|
||||
def _process_dit_inputs(**stage_args)-> Any:
|
||||
image_latent = self.vae.encode(**stage_args)
|
||||
|
||||
return image_latent
|
||||
```
|
||||
|
||||
### 4. Stage config for setting of different model stages
|
||||
```python
|
||||
@dataclass
|
||||
class StageConfig:
|
||||
"""Configuration for a processing stage"""
|
||||
stage_id: int
|
||||
engine_type: str # "AR" or "DiT"
|
||||
model_path: str
|
||||
input_modalities: List[str]
|
||||
output_modalities: List[str]
|
||||
vllm_config: Optional[VllmConfig] = None # For engine config of corresponding stage
|
||||
executor_class: type[Executor] # For execute class config of corresponding stage
|
||||
dit_config: Optional[DiTConfig] = None # For diffusion stages
|
||||
cache_config: Optional[DiTCacheConfig] = None
|
||||
```
|
||||
For AR stage, the setting is:
|
||||
```python
|
||||
from vllm.v1.core.sched.scheduler import Scheduler
|
||||
from vllm.v1.executor.multiproc_executor import MultiprocExecutor
|
||||
ar_stage_config = StageConfig()
|
||||
ar_stage_config.vllm_config.scheduler_config.scheduler_cls = Scheduler # original vllm scheduler for AR
|
||||
ar_stage_config.executor_class = MultiprocExecutor # original vllm executor for AR
|
||||
```
|
||||
For DiT stage, the setting is:
|
||||
```python
|
||||
from vllm.v1.executor.multiproc_executor import MultiprocExecutor
|
||||
dit_stage_config = StageConfig()
|
||||
dit_stage_config.vllm_config.scheduler_config.scheduler_cls = DiffusionScheduler # New scheduler for DiT
|
||||
|
||||
# For diffusion models without using diffusers
|
||||
dit_stage_config.executor_class = MultiprocExecutor
|
||||
|
||||
# For diffusion models using diffusers
|
||||
dit_stage_config.executor_class = DiffusersPipelineExecutor
|
||||
```
|
||||
|
||||
### 5. Online inference main class
|
||||
|
||||
Similar to OmniLLM in offline inference, add some asynchronous processing, referring to AsyncLLM
|
||||
```python
|
||||
from vllm.v1.engine.async_llm import AsyncLLM
|
||||
|
||||
|
||||
class AsyncOmniLLM(AsyncLLM):
|
||||
"""Extended AsyncLLM supporting multiple engines and stage-based processing"""
|
||||
|
||||
def __init__(self, stage_configs: List[StageConfig]):
|
||||
super().__init__()
|
||||
```
|
||||
|
||||
### 6. Engine Core
|
||||
No need to change. The specific executor, scheduler and output type can be transferred into it with new configs.
|
||||
```python
|
||||
# class: EngineCore
|
||||
# EngineCore.step (simplified)
|
||||
class EngineCore:
|
||||
def step(self):
|
||||
scheduler_output = scheduler.schedule()
|
||||
model_output = executor.execute_model(scheduler_output)
|
||||
engine_outputs = scheduler.update_from_output(
|
||||
scheduler_output, model_output
|
||||
)
|
||||
return engine_outputs
|
||||
```
|
||||
|
||||
### 7. Scheduler
|
||||
Create a new Diffusion Scheduler for DiT, which is inherited from original vllm AR scheduler. At first, no complicated strategy.
|
||||
Just process the request first in first out. Then create a class of DiT Cache Manager for optimization in the future.
|
||||
```python
|
||||
from vllm.v1.core.sched.scheduler import Scheduler
|
||||
class DiffusionScheduler(Scheduler):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vllm_config: VllmConfig,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
dit_cache_config: DiTCacheConfig,
|
||||
structured_output_manager: StructuredOutputManager,
|
||||
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
||||
include_finished_set: bool = False,
|
||||
log_stats: bool = False,
|
||||
) -> None:
|
||||
|
||||
self.dit_cache_manager = DiTCacheManager(dit_cache_config)
|
||||
```
|
||||
For DiT Cache Manager (Can refer to xDiT):
|
||||
```python
|
||||
class DiTCacheManager:
|
||||
"""Manages DiT-specific caching"""
|
||||
|
||||
def __init__(self, config: DiTCacheConfig):
|
||||
self.cache_tensors: Dict[str, torch.Tensor] = {}
|
||||
self.cache_groups: List[DiTCacheTensor] = config.dit_cache_tensors
|
||||
|
||||
def allocate_cache(self, request_id: str, size: int) -> torch.Tensor
|
||||
def get_cache(self, request_id: str) -> Optional[torch.Tensor]
|
||||
def release_cache(self, request_id: str) -> None
|
||||
def clear_expired_cache(self) -> None
|
||||
```
|
||||
|
||||
### 8. Executor for DiT without diffusers
|
||||
|
||||
No need to change. Just import original AR executor.
|
||||
|
||||
### 9. Diffusers Pipeline Executor (no worker)
|
||||
|
||||
A single-process executor that directly runs the Diffusers pipeline without spawning workers or using RPC. Interfaces remain identical to `Executor` so the EngineCore loop is unchanged.
|
||||
|
||||
#### Function map（Pipeline Executor）
|
||||
|
||||
##### 1) Inherited and overridden
|
||||
```python
|
||||
from concurrent.futures import Future
|
||||
from typing import Optional, Union, Callable, Any
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from vllm.v1.executor.abstract import Executor
|
||||
from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
|
||||
from vllm.v1.outputs import ModelRunnerOutput
|
||||
from vllm.tasks import SupportedTask
|
||||
from diffusers import DiffusionPipeline
|
||||
|
||||
class DiffusersPipelineExecutor(Executor):
|
||||
supports_pp: bool = False # Single-process, no TP/PP/DP
|
||||
|
||||
def _init_executor(self) -> None:
|
||||
# Called by ExecutorBase.__init__
|
||||
self._failure_callback: Optional[Callable[[], None]] = None
|
||||
self._device = self._resolve_device()
|
||||
self._dtype = self._resolve_dtype()
|
||||
self._pipeline = self._build_pipeline(device=self._device, dtype=self._dtype)
|
||||
self._profiler = None
|
||||
self._is_failed = False
|
||||
self.is_sleeping = False
|
||||
self.sleeping_tags: set[str] = set()
|
||||
|
||||
# major functions to build/run diffusers pipeline
|
||||
def _build_pipeline(self, device:torch.device, dtype:torch.dtype)->DiffusionPipeline:
|
||||
model_name = "Qwen/Qwen-Image"
|
||||
|
||||
self.pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=dtype)
|
||||
self.pipe = pipe.to(device)
|
||||
|
||||
def _run_pipeline(self, scheduler_output) -> ModelRunnerOutput: ...
|
||||
positive_magic = {
|
||||
"en": ", Ultra HD, 4K, cinematic composition.", # for english prompt
|
||||
"zh": ", 超清，4K，电影级构图." # for chinese prompt
|
||||
}
|
||||
|
||||
# Generate image
|
||||
prompt_embeds = self._get_and_process_prompt_embeds(scheduler_output, positive_magic)
|
||||
negative_prompt_embeds = self.pipe.embed_prompt(" ")
|
||||
|
||||
width, height = aspect_ratios["16:9"]
|
||||
|
||||
image = pipe(
|
||||
prompt_embeds=prompt_embeds,
|
||||
negative_prompt_embeds=negative_prompt_embeds,
|
||||
width=width,
|
||||
height=height,
|
||||
num_inference_steps=50,
|
||||
true_cfg_scale=4.0,
|
||||
generator=torch.Generator(device="cuda").manual_seed(42)
|
||||
).images[0]
|
||||
|
||||
output = self.wrap_image_as_ModelRunnerOutput(image)
|
||||
return output
|
||||
|
||||
# ---- Internal helpers (implementation-specific, not public API) ----
|
||||
def _resolve_device(self): ...
|
||||
def _resolve_dtype(self): ...
|
||||
def _get_model(self) -> nn.Module: ...
|
||||
def _get_and_process_prompt_embeds(self, scheduler_output, positive_magic):
|
||||
...
|
||||
#append the positive_magic to prompt and embed them to prompt embed tensors
|
||||
|
||||
# Functions related to workers should either raise NotImplementedError
|
||||
# or return empty defaults in this no-worker executor.
|
||||
|
||||
def collective_rpc(self, method, timeout=None, args=(), kwargs=None) -> list[Any]:
|
||||
# No workers in pipeline executor
|
||||
raise NotImplementedError("No workers in DiffusersPipelineExecutor")
|
||||
|
||||
def initialize_from_config(self, kv_cache_configs: list[KVCacheConfig]) -> None:
|
||||
return # no-op (pipeline already built in _init_executor)
|
||||
|
||||
def register_failure_callback(self, callback):
|
||||
self._failure_callback = callback
|
||||
|
||||
def determine_available_memory(self) -> list[int]: # bytes
|
||||
# Single device; return [available_bytes]. If CPU-only, return [0].
|
||||
return [self._determine_available_bytes(self._device)]
|
||||
|
||||
```
|
||||
|
||||
### 10. Worker
|
||||
|
||||
#### Inheritance strategy
|
||||
Prefer reusing the mature GPU Worker end-to-end. Worker class is selected by configuration (vllm_config). Do not add a new executor-specific worker binding. If customization is needed, override only `init_device` to construct the `DiffusionModelRunner`; all other behaviors (device init details, profiling, sleep/wake, PP/TP comms, execute path) remain from `vllm/v1/worker/gpu_worker.py::Worker`.
|
||||
|
||||
|
||||
Inherited and overridden
|
||||
```python
|
||||
# Optional: only if you need to plug a custom DiffusionModelRunner.
|
||||
from vllm.v1.worker.gpu_worker import Worker as GPUWorker
|
||||
from vllm.v1.worker.diffusion_model_runner import DiffusionModelRunner
|
||||
|
||||
class Worker(GPUWorker):
|
||||
def init_device(self) -> None:
|
||||
#those related to device check and init
|
||||
...
|
||||
|
||||
self.model_runner = DiffusionModelRunner(self.vllm_config, ...)
|
||||
```
|
||||
|
||||
### 11. Model Runner
|
||||
|
||||
### Function map (Model Runner)
|
||||
#### 1) Inherited and overridden
|
||||
Those parts relied to the KV Cache will be omitted if we do not register the model to the vllm config. The engine core will view it as do not require KV Cache, and handle it properly
|
||||
|
||||
Reuse `vllm/v1/outputs.py::ModelRunnerOutput`：
|
||||
- DiffusionModelRunner: Use the `pooler_output=[Tensor,...]` to return multi modal tensors
|
||||
- ARModelRunner: Use the `pooler_output=[Tensor,...]` to return hidden states.
|
||||
```python
|
||||
from typing import Optional, Union
|
||||
import torch
|
||||
from vllm.v1.worker.gpu_model_runner import GPUModelRunner
|
||||
from vllm.v1.outputs import ModelRunnerOutput
|
||||
|
||||
|
||||
class DiffusionModelRunner(GPUModelRunner):
|
||||
@torch.inference_mode()
|
||||
def execute_model(
|
||||
self,
|
||||
scheduler_output: "SchedulerOutput",
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
) -> Union[ModelRunnerOutput, IntermediateTensors]:
|
||||
...
|
||||
return ModelRunnerOutput(
|
||||
req_ids=[...],
|
||||
req_id_to_index={...},
|
||||
sampled_token_ids=[],
|
||||
spec_token_ids=None,
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[Tensor,...], # Return Hidden states
|
||||
kv_connector_output=None,
|
||||
num_nans_in_logits=None,
|
||||
)# return multi modal tensors via pooler_output=[Tensor,...]
|
||||
|
||||
|
||||
class ARModelRunner(GPUModelRunner):
|
||||
@torch.inference_mode()
|
||||
def execute_model(
|
||||
self,
|
||||
scheduler_output: "SchedulerOutput",
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
) -> Union[ModelRunnerOutput, IntermediateTensors]:
|
||||
...
|
||||
return ModelRunnerOutput(
|
||||
req_ids=[...],
|
||||
req_id_to_index={...},
|
||||
sampled_token_ids=[],
|
||||
spec_token_ids=None,
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[Tensor,...], # Return Hidden states
|
||||
kv_connector_output=None,
|
||||
num_nans_in_logits=None,
|
||||
)# return hidden states via pooler_output=[Tensor,...]
|
||||
```
|
||||
|
||||
### 12. RequestState Role and Usage
|
||||
```bash
|
||||
# RequestState serves as the per-request state tracker in OutputProcessor:
|
||||
# - Maintains request-specific state (tokens, logprobs, detokenizer, etc.)
|
||||
# - Converts EngineCoreOutput → RequestOutput/PoolingRequestOutput
|
||||
# - Manages request lifecycle from registration to completion
|
||||
AsyncLLM.add_request()
|
||||
↓
|
||||
OutputProcessor.add_request()
|
||||
↓
|
||||
RequestState.from_new_request() → 创建请求状态
|
||||
|
||||
AsyncLLM.__init__() / AsyncLLM.generate() / AsyncLLM.encode() → 创建一个Background loop 持续从EngineCore获取输出
|
||||
↓
|
||||
OutputProcessor.process_outputs() → 更新状态并处理输出
|
||||
↓
|
||||
RequestState.make_request_output() → 转换为最终输出,格式为RequestOutput或者PoolingRequestOutput
|
||||
↓
|
||||
RequestOutputCollector.put() → 推送到队列（AsyncLLM）
|
||||
```
|
||||
|
||||
Need to add implementation for an existing method
|
||||
```python
|
||||
class RequestState:
|
||||
def __init__(self, request_id: str, parent_req: Optional[ParentRequest],
|
||||
request_index: int, lora_name: Optional[str],
|
||||
output_kind: RequestOutputKind, prompt: Optional[str],
|
||||
prompt_token_ids: list[int], logprobs_processor: Optional[LogprobsProcessor],
|
||||
detokenizer: Optional[IncrementalDetokenizer],
|
||||
max_tokens_param: Optional[int], arrival_time: float,
|
||||
queue: Optional[RequestOutputCollector], log_stats: bool):
|
||||
|
||||
def _new_pooling_output(self, pooling_output: torch.Tensor) -> PoolingOutput:
|
||||
"""Create PoolingOutput for multimodal/pooling requests"""
|
||||
return PoolingOutput(data=pooling_output)
|
||||
```
|
||||
|
||||
|
||||
### 13. OutputProcessor
|
||||
|
||||
For hidden state output, original OutputProcessor already support.
|
||||
|
||||
We only need to add new one for final multimodal output.
|
||||
```python
|
||||
class MultimodalOutputProcessor(OutputProcessor):
|
||||
"""Handles multimodal output processing"""
|
||||
|
||||
def __init__(self):
|
||||
self.output_handlers: Dict[str, OutputProcessor] = {}
|
||||
|
||||
def process_outputs(self, engine_core_outputs: list[EngineCoreOutput], ...):
|
||||
for engine_core_output in engine_core_outputs:
|
||||
# Option 1: Use output_type field (if EngineCoreOutput is extended)
|
||||
if engine_core_output.output_type == "image":
|
||||
self._process_image_output(engine_core_output)
|
||||
elif engine_core_output.output_type == "text+image":
|
||||
self._process_text_image_output(engine_core_output)
|
||||
elif engine_core_output.output_type == "latents":
|
||||
self._process_latents_output(engine_core_output)
|
||||
elif engine_core_output.output_type == "text":
|
||||
self._process_text_output(engine_core_output)
|
||||
else:
|
||||
# Fallback: use existing pooling_output logic
|
||||
if engine_core_output.pooling_output is not None:
|
||||
self._process_pooling_output(engine_core_output)
|
||||
else:
|
||||
self._process_text_output(engine_core_output)
|
||||
```
|
||||
Reference in new issue
Block a user