Transformer Recurrence With Lightweight RNN Gating for Faster Training
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Solution Overview
Problem
Transformers incorporating both attention and recurrence face challenges in achieving parity in performance with attention-only models, requiring significant computational resources and complex architectures like LSTM RNNs, which are not efficiently scalable on GPUs and TPUs.
Innovation Solution
A lightweight RNN block, such as SwishRNN, is integrated into the transformer architecture, utilizing element-wise operations and multiplicative gating functions to minimize computational steps, enabling efficient training and operation on GPUs and TPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a combined attention and recurrence model (e.g., LSTM RNN) is used to improve language model accuracy, then model performance improves, but computational resources and complexity increase significantly
Solution Approach 1:
The patent extracts only the essential recurrent processing capability needed for language modeling, discarding the complex LSTM architecture. The simplified recurrent block retains the core function of sequential processing while removing unnecessary computational complexity, achieving comparable accuracy with significantly reduced resources
Solution Approach 2:
The patent changes key parameters of the recurrent block, using element-wise operations instead of full matrix multiplications, and employing a simplified gating mechanism. These parameter changes reduce computational complexity from O(n²) to O(n), maintaining accuracy while reducing resource requirements
2Reliability
If a complex RNN architecture like LSTM is used to achieve better accuracy, then model performance improves, but training time and resource requirements increase
Solution Approach 1:
The patent extracts only the critical recurrent processing function needed for sequential language understanding, eliminating the multi-layer gating mechanisms of LSTM that contribute to training time without providing proportional accuracy benefits. This results in faster training with maintained performance
Solution Approach 2:
The patent employs a lightweight recurrent block that uses simpler, computationally cheaper operations compared to LSTM. The simplified architecture requires fewer floating-point operations per training step, reducing training time while maintaining adequate accuracy for language modeling tasks
3Adaptability or versatility
If recurrence is incorporated into transformer architecture to improve sequential processing, then model capability improves, but computational throughput decreases
Solution Approach 1:
The patent substitutes the traditional matrix-multiplication-based recurrent operations with element-wise operations. This mechanical substitution changes the computational paradigm from heavy linear algebra to lighter element-wise computations, maintaining sequential processing capability while improving computation throughput and enabling efficient GPU/TPU utilization
4Productivity
If a lightweight RNN block is used to reduce computational resources, then training efficiency improves, but model accuracy may decrease
Solution Approach 1:
The patent applies partial action by implementing only the essential recurrent processing needed for language modeling, rather than the full complexity of LSTM. This partial implementation achieves sufficient accuracy for the task while dramatically improving training efficiency, as the simplified block processes sequences with fewer operations
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for recurrence in a transformer architecture. In one aspect, a method includes receiving input embeddings representing a sequence of words as input; generating as output attention vectors for each of the words, the attention vectors for each word indicating an importance of the word in the sequence relative to other words in the sequence; generating first and second linear transformations X1 and X2 of the attention vectors; determining, in a recurrent neural network, a hidden state corresponding to each attention vector using only element wise operations on the first linear transformation of the attention vectors during a recurrent step; and generating a set of output vectors using a multiplicative gating function in combination with the second linear transformation.