Binder Multi-Head Attention Mechanism for Linear Complexity
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Solution Overview
Problem
Existing natural language processing (NLP) models employing Transformer architectures with self-attention mechanisms face computational inefficiencies due to quadratic time complexity, making them resource-intensive for long input sequences.
Innovation Solution
The introduction of a binder multi-head attention mechanism that uses learnable binder vectors to compute attention weights, reducing computational complexity from quadratic to linear with respect to the input sequence length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-attention mechanism is used to compute attention weights pair-wisely between all positions, then the importance or relevance of different tokens can be captured, but the time complexity becomes quadratic with respect to input sequence length
Solution Approach 1:
The patent segments the self-attention computation by introducing a query vector that is shared across all positions, dividing the quadratic attention computation into linear position-specific key-value computations. Instead of computing attention between all position pairs, the system computes attention between a single query vector and multiple key vectors, reducing complexity from O(n²) to O(n).
Solution Approach 2:
The query vector serves as a universal attention mechanism that can attend to all positions in the sequence simultaneously. This single query vector performs the function of multiple position-specific queries, enabling the model to capture global dependencies without requiring separate computations for each position pair.
2Reliability
If self-attention mechanism computes attention weights for all position pairs, then comprehensive token relevance can be captured, but resource consumption increases significantly
Solution Approach 1:
The computation is segmented into a universal query component and position-specific key components. This segmentation allows the system to process token relevance information more efficiently by avoiding redundant computations between all position pairs, thereby reducing resource consumption while maintaining reliability.
Solution Approach 2:
The query vector is copied and applied to all positions in the sequence, allowing the same attention mechanism to be reused across different positions. This copying approach eliminates the need for unique query vectors at each position, reducing computational overhead and resource consumption.
3Measurement precision
If quadratic time complexity is accepted for accurate attention computation, then processing accuracy is maintained, but processing speed decreases
Solution Approach 1:
The patent changes the parameter structure by introducing a shared query vector that transforms the attention computation from O(n²) to O(n). This parameter change maintains the ability to compute accurate attention weights while significantly improving processing speed by reducing the number of computational operations required.
Solution Approach 2:
The attention mechanism is segmented into a global query computation and local key-value computations. This segmentation enables parallel processing of position-specific operations while maintaining a single query vector, thereby improving processing speed without sacrificing computation accuracy.
Data Source
AI summary
Embodiments provide an attention mechanism that computes attention weights for an input sequence by employing a set of multi-head learnable vectors (referred to as “binder vectors”) to attend to the input sequence.