Hierarchical Attention Mechanism for Sub-Quadratic Sequence Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current attention-based models, such as Transformers, face limitations due to their quadratic memory and time complexity with respect to sequence length, making them prohibitive for applications involving long sequences, as they often require sparsity or low-rank assumptions that sacrifice expressiveness.
Innovation Solution
A machine-learned attention mechanism that uses a structured attention pattern with hierarchical partition levels, allowing for full attention capability while maintaining sub-quadratic computational and memory costs, achieved through a combination of direct and local expectations, enabling efficient processing of long sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vanilla Transformer uses full pairwise attention, then expressiveness and modeling capability are improved, but computational cost and memory consumption increase quadratically with sequence length
Solution Approach 1:
The patent segments the attention computation by introducing a hierarchical partition of the sequence into multiple levels. Instead of computing full pairwise attention, the method partitions the sequence into blocks and computes attention hierarchically, reducing the computational complexity from O(L^2) to O(L log L) while maintaining full attention capability through the hierarchical structure.
Solution Approach 2:
The patent transforms the attention computation from a two-dimensional matrix operation to a hierarchical multi-level structure. By adding a temporal dimension to the partitioning and computing attention across hierarchical levels, the method achieves sub-quadratic complexity while preserving the full-rank attention matrix properties.
2Productivity
If sparse attention patterns are used, then computational cost is reduced to sub-quadratic, but expressiveness and capture of all-pair relationships are compromised
Solution Approach 1:
The patent segments the sequence into hierarchical partitions and computes attention at multiple levels. This segmentation allows the method to achieve sub-quadratic complexity by computing attention only within partitions and combining results hierarchically, while still capturing all-pair relationships through the complete hierarchical decomposition.
Solution Approach 2:
The patent implements a nested hierarchical structure where attention is computed within local partitions and then combined across partitions. This nesting allows the method to maintain full attention capability while reducing computational cost, as the hierarchical structure reuses computations across different levels.
3Productivity
If low-rank approximation methods are used, then computational cost is reduced, but accuracy and performance on sequence modeling tasks deteriorate
Solution Approach 1:
The patent avoids low-rank approximation by introducing a hierarchical temporal dimension to the attention computation. This dimensional change allows the method to compute full-rank attention with reduced complexity, achieving both sub-quadratic cost and high accuracy on sequence modeling tasks without the restrictions of explicit low-dimensional approximations.
Data Source
AI summary
The present disclosure is directed to machine learning model architectures which provide full attention capability in each attention head while maintaining low computation and memory complexity. Specifically, according to one aspect of the present disclosure, example attention models provided herein can treat the self-attention mechanism as a conditional expectation over embeddings at each location and approximate the conditional distribution with a structured factorization. Each location can attend to all other locations, either via direct attention, or through indirect attention to group representations, which are again conditional expectations of embeddings from corresponding local regions.