Decoder-Only Self-Attention Networks for Long-Sequence Transduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sequence transduction systems struggle with processing very long input sequences due to sequential computation in recurrent neural networks, leading to high computational resource usage and long training and inference times, while also failing to effectively learn dependencies between distant positions.
Innovation Solution
A decoder-only, self-attention-based neural network architecture that uses masked self-attention and local or memory-compressed attention mechanisms to process long sequences efficiently, eliminating the need for an encoder network and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent neural networks are used for sequence transduction, then good performance on sequence transduction tasks is achieved, but sequential computation leads to long training and inference times and high computational resource usage
Solution Approach 1:
The patent replaces the sequential mechanical computation mechanism of recurrent neural networks with a parallel attention mechanism. The attention mechanism computes relationships between all positions in the sequence simultaneously through matrix operations, eliminating the sequential step-by-step processing inherent in RNNs while maintaining the ability to capture temporal dependencies.
Solution Approach 2:
The patent transitions from the sequential time-step dimension of RNNs to a parallel positional dimension in the attention mechanism. By representing sequence relationships as positional embeddings and attention scores across all positions simultaneously, the system achieves parallel computation while preserving the sequential nature of the data through positional information.
2Reliability
If recurrent neural networks are used for sequence transduction, then good performance on sequence transduction tasks is achieved, but a large amount of computational resources are consumed
Solution Approach 1:
The patent substitutes the computationally expensive sequential operations of RNNs with more efficient parallel matrix multiplications in the attention mechanism. The attention computation uses optimized linear algebra operations that can be executed simultaneously on GPU hardware, significantly reducing computational resource consumption while maintaining performance.
3Length of stationary object
If conventional sequence transduction systems are used, then processing of long sequences is attempted, but sequential computation causes long training and inference times
Solution Approach 1:
The patent handles long sequences by transforming the sequential processing problem into a parallel positional attention problem. The attention mechanism can attend to all positions in a long sequence simultaneously, and the parallel computation architecture allows processing of extended sequence lengths without the cumulative time penalty of sequential RNN computation.
4Reliability
If recurrent neural networks are used, then sequence transduction is performed, but the sequential nature precludes parallelization
Solution Approach 1:
The patent replaces the sequential computational architecture of RNNs with a parallel attention-based architecture. The attention mechanism inherently supports parallel computation across all sequence positions, and the transformer blocks can be executed simultaneously, enabling effective parallelization while maintaining sequence transduction capability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.