Translation Model Training With Pre- and Post-Layer Normalization
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Solution Overview
Problem
The transformer model based on Post-Layer Normalization (Post-LN) exhibits poorer training stability and is prone to collapse during the training process, especially with a large number of layers, limiting its performance and text translation quality.
Innovation Solution
A translation model is constructed by combining Pre-Layer Normalization (Pre-LN) and Post-LN, utilizing cascaded encoding and decoding sub-models with LayerNorm layers before sub-network layers and residual connections to stabilize gradient propagation, allowing successful training and convergence even in deep architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Post-Layer Normalization (Post-LN) is used in transformer models, then translation performance and generalization capabilities are improved, but training stability deteriorates and the model is prone to collapse during training, especially with large number of layers
Solution Approach 1:
The patent segments the normalization process into two distinct phases: pre-normalization (applying LayerNorm before the sub-network layer) and post-normalization (applying LayerNorm after the sub-network layer). This segmentation allows the model to benefit from both Pre-LN stability and Post-LN performance while mitigating their respective drawbacks through controlled combination at different stages of the network
2Manufacturing precision
If the number of model layers is increased to improve translation performance, then model capacity and representation ability are enhanced, but training stability deteriorates and collapse becomes more likely in Post-LN based models
Solution Approach 1:
The patent applies different normalization strategies to different parts of the network architecture. Specifically, it uses Pre-LN in the encoder portion and Post-LN in the decoder portion, or combines them in specific patterns within each layer. This local differentiation allows deep architectures to maintain stability while achieving high translation performance
Data Source
AI summary
In a method for training a translation model, sample text is obtained. Feature extraction is performed based on the sample text sequentially through n cascaded encoding sub-models to obtain encoding features. Feature extraction is performed based on the encoding features sequentially through m cascaded decoding sub-models to obtain decoding features. A sample translation result of the sample text is predicted based on the decoding features. A reference translation result of the sample text is obtained. An error between the reference translation result and the sample translation result is determined. A model parameter of the translation model according to the error is updated.