Self-Attention Encoder Networks for Parallel Sequence Transduction
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Solution Overview
Problem
Existing sequence transduction neural networks using recurrent neural networks face challenges with long training and inference times due to their sequential nature, leading to high computational resource usage and difficulty in learning dependencies between distant positions.
Innovation Solution
Implementing an attention-based encoder and decoder neural network architecture that eliminates recurrent layers, allowing for parallelization and efficient learning of global dependencies, thereby reducing operation complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent neural networks are used for sequence transduction, then the model can achieve good performance on sequence transduction tasks, but the computation becomes sequential resulting in long training and inference times and high computational resource usage
Solution Approach 1:
The patent replaces the sequential recurrent neural network mechanism with a parallel attention-based mechanism. The attention mechanism allows the model to process all positions in the sequence simultaneously rather than sequentially, substituting the time-dependent recurrent computation with a position-independent attention computation that enables parallel processing across multiple time steps.
Solution Approach 2:
The patent introduces a new computational dimension by using attention mechanisms that operate across all sequence positions simultaneously. Instead of processing sequences step-by-step through time, the attention mechanism creates a multi-dimensional representation where all positions can be processed in parallel, effectively adding a temporal dimension to the computation.
2Productivity
If recurrent neural networks are used for sequence transduction, then the model can process sequences, but the sequential nature precludes parallelization resulting in high computational resource usage
Solution Approach 1:
The patent replaces the sequential recurrent computation with parallel attention-based computation. The attention mechanism computes relationships between all pairs of positions simultaneously using matrix operations, substituting the energy-intensive sequential recurrent steps with more efficient parallel matrix multiplications that consume fewer computational resources.
Solution Approach 2:
The patent merges multiple sequential computation steps into a single parallel attention mechanism. By combining the processing of multiple time steps and positions into unified attention computations, the model achieves the same productivity with significantly reduced computational resource consumption through consolidation of operations.
3Device complexity
If conventional models are used, then the number of operations required to relate signals from two arbitrary input or output positions grows with the distance between positions, but this makes it more difficult to learn dependencies between distant positions during training
Solution Approach 1:
The patent replaces the cumulative operation mechanism with a constant-time attention mechanism. The attention mechanism computes relationships between any two positions in constant time regardless of their distance, substituting the linearly growing number of operations with a fixed computational cost that enables effective learning of long-range dependencies.
Solution Approach 2:
The patent changes the computational parameter from variable operation count to constant operation count. By using attention mechanisms that compute all pairwise relationships simultaneously with fixed computational complexity, the model maintains consistent performance regardless of sequence length, enabling accurate learning of dependencies between distant positions.
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. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.