Other emerging architectures
Explore emerging architectures such as RWKV, RetNet, and Griffin, and learn how they attempt to address the limitations of Transformer and drive continuous innovation in AI architecture.
RWKV: RNN with linear attention
RWKV (Receptance Weighted Key Value) combines the advantages of RNNs and Transformers to achieve linear-complexity attention mechanisms.
Core innovation
- • Linear attention: Reduce attention computation complexity from O(n²) to O(n) through mathematical transformation
- • RNN form: can be represented as an RNN and supports efficient autoregressive generation
- • State mechanism: maintains internal state and supports long-sequence modeling
- • Parallel training: can be parallelized during training and is in RNN form during inference
Advantages
- • Linear complexity: O(n) complexity, suitable for long sequences
- • Efficient inference: Fast inference speed, low memory usage
- • Long-sequence capability: can handle ultra-long sequences
- • Open-source ecosystem: Fully open source, active community
Application scenarios
- • Long-form generation: long-form text generation such as novels and scripts
- • Code generation: Generation and understanding of long code files
- • Dialogue systems: systems that need to maintain long conversation history
- • Resource-constrained environments: Edge devices, mobile apps
RetNet: a breakthrough in retention mechanisms
RetNet (Retention Network) achieves the unification of parallel training and efficient inference through a retention mechanism.
Core innovation
- • Retention mechanism: unify parallel training and sequence inference through mathematical design
- • Parallel training: Can be computed in parallel during training, making full use of the GPU
- • Efficient inference: during inference it is recursive, with high memory and computational efficiency
- • Linear complexity: O(n) complexity, suitable for long sequences
Advantages
- • Training efficiency: parallel training, faster training speed
- • Inference efficiency: recursive reasoning, fast inference speed
- • Performance: Performance close to Transformer on multiple tasks
- • Scalability: Can scale to large-scale models
Technical characteristics
- • Mathematical elegance: Unifying parallelism and recursion through mathematical transformations
- • Hardware-friendly: Hardware-friendly and easy to optimize
- • Backward compatibility: Can replace Transformer while maintaining interface compatibility
- • Actively researched: Institutions such as Microsoft continue to research
Griffin: Exploring Hybrid Architectures
Griffin combines local attention and global attention, aiming to strike a balance between efficiency and performance.
Core innovation
- • Hybrid attention: combine local attention and global attention
- • Local window: Local window attention handles local dependencies
- • global mechanism: A global mechanism handles long-range dependencies
- • Flexible design: The local and global proportions can be adjusted according to the task
Advantages
- • Improved efficiency: Local attention reduces computational complexity
- • Performance 유지: global mechanisms maintain long-range dependency capability
- • Flexibility: can adjust the architecture according to task requirements
- • Practicality: performs well on multiple tasks
Design approach
Griffin’s design philosophy is that most dependencies are local, and only a few require global attention. Through a hybrid architecture, it improves efficiency while maintaining performance.
Common characteristics of emerging architectures
These emerging architectures all attempt to address the limitations of Transformer and share some common characteristics.
Common goal
- • Reduce complexity: reduced from O(n²) to O(n) or near O(n)
- • Improve efficiency: Improve training and inference efficiency
- • Maintain performance: Improve efficiency while maintaining performance
- • Long-sequence capability: Enhance long-sequence processing capabilities
Technical path
Linearization
Linear complexity through mathematical transformations
Hybrid architecture
Combine the strengths of different mechanisms
State mechanism
Handle long-distance dependencies through state management
Current status and prospects
Understand the current status and future development of these emerging architectures.
Current status
- • Research phase: Most architectures are still in the research stage
- • Performance validation: validated as effective on small-scale tasks
- • Large-scale validation: Large-scale validation is still in progress
- • Ecosystem building: Tools and ecosystem are still under construction
Challenge
- • Performance gap: on some tasks, performance is still worse than Transformer
- • Training difficulty: training may be more complex or unstable
- • The ecosystem is immature: fewer tools and pre-trained models
- • Theoretical understanding: Theoretical understanding is still deepening
Future prospects
- • Continuous optimization: The architecture will continue to be optimized and improved
- • Application extension: Application scenarios will continue to expand
- • Mature ecosystem: Tools and ecosystems will gradually mature
- • Potential breakthrough: May outperform Transformer in specific scenarios
Learning outcomes
After completing this chapter, you will:
- 1Understand the core innovations and characteristics of emerging architectures such as RWKV, RetNet, and Griffin
- 2Understand how these architectures attempt to address the limitations of Transformers
- 3Master the applicable scenarios and advantages of different architectures
- 4Be able to track the latest developments in the architecture field and understand the trends in architectural evolution