Tree Attention Mechanism for Neural Network Efficiency
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Solution Overview
Problem
Conventional attention-based neural networks face significant computational resource challenges when processing long sequential data due to their quadratic dependency on sequence length, leading to high resource consumption and latency.
Innovation Solution
The implementation of tree attention layers using decision trees within attention mechanisms to reduce retrieval costs from linear to nearly logarithmic, allowing for more efficient processing and training of neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dot-product attention mechanism is used in conventional attention neural networks, then comprehensive attention computation is achieved, but computational cost increases quadratically with sequence length
Solution Approach 1:
The patent segments the sequence processing into hierarchical levels using a tree structure. Instead of computing attention across the entire sequence at once, the method divides the sequence into segments at different tree levels, computing attention locally at each node. This segmentation reduces the computational complexity from quadratic O(n²) to linear O(n) by processing smaller sub-sequences at each tree level rather than all pairwise comparisons.
Solution Approach 2:
The patent introduces a hierarchical tree dimension to organize the attention computation. By mapping the flat sequence onto a tree structure with multiple levels, the method adds a dimensional organization to the attention mechanism. This allows queries to attend to keys through hierarchical paths rather than direct pairwise comparisons, reducing computational burden while maintaining attention effectiveness.
2Loss of information
If conventional attention layers process long sequences, then complete information is captured, but processing time and latency increase significantly
Solution Approach 1:
The patent performs preliminary computation at higher tree levels by pre-computing aggregated representations and attention scores for sub-sequences. These preliminary results are then reused when computing attention at lower levels, avoiding redundant calculations. This preliminary action at the tree root and intermediate nodes significantly reduces the time required to process long sequences while capturing complete information.
Solution Approach 2:
The patent extracts and reuses computation results from higher tree levels. Instead of recomputing attention scores for all key-value pairs at each level, the method extracts relevant attention information from parent nodes and incorporates it into child node computations. This extraction and reuse of intermediate results reduces processing time while maintaining information completeness.
3Productivity
If decision tree based hierarchical navigation is implemented, then retrieval cost per query token is reduced to nearly logarithmic, but model complexity increases
Solution Approach 1:
The patent makes the tree structure universal by using it for multiple purposes: sequence segmentation, attention computation organization, and information aggregation. The same tree structure serves as the foundation for hierarchical navigation during both encoding and decoding phases. This multi-functionality justifies the added structural complexity by providing comprehensive benefits across different operational phases.
Solution Approach 2:
The patent implements nested computation within the tree structure, where computations at lower levels are nested within the context of higher levels. The tree nodes contain nested representations, with child nodes processing sub-sequences within the scope of parent nodes. This nesting allows efficient hierarchical navigation with nearly logarithmic retrieval cost while organizing model complexity in a structured, manageable way.
Data Source
AI summary
Systems and methods for processing inputs using attention neural networks with tree attention layers. Each tree attention layer includes one or more tree attention sub-layers that are each configured to: process query vectors using a decision tree model for the tree attention sub-layer to determine a respective tree path for each query vector; process key vectors using the decision tree model to determine a respective tree path for each key vector; and generate an attended input sequence comprising a respective attended input at each of the plurality of input positions, comprising: generating, for each particular input position, the respective attended input at the particular input position based on (i) the tree path for the query vector at the particular input position (ii) the respective tree paths for the key vectors at each of the plurality of input positions and (iii) the value vectors at a subset of the input positions.