Transformers Stability: Signal Propagation Theory for Language Models

Belangrijkste concepten

Developing a unified signal propagation theory to address issues of vanishing/exploding gradients and rank collapse in deep transformers, enabling training of very deep models with improved performance.

Samenvatting

The article introduces a signal propagation theory for transformer models to address stability issues associated with deep models. It proposes DeepScaleLM, an initialization and scaling scheme that enables training of very deep models with improved performance across various tasks. The theoretical framework explains signal propagation through transformer components and the entire model, providing insights into moments control and residual scaling. Empirical validation confirms the effectiveness of the proposed methods in stabilizing training and improving model performance.

Statistieken

Our deep models outperform shallow models in Language Modeling, Speech Translation, and Image Classification.
DeepScaleLM enables training of models with 100s of layers while reducing parameters.
The mean relative errors for model predictions are 6.8% and 5.2% for outputs and gradients respectively.
The R2 value for model predictions is 0.998.

Citaten

"No error (other than SHA gradient σ2) is larger than 10%, verifying our assumptions."
"Our formulae predict the observed gradient and forward/backward norms with remarkable accuracy."
"Our method can constrain the growth of moments in Vision Transformers."

Belangrijkste Inzichten Gedestilleerd Uit

Transformers Get Stable

by Akhil Kedia,... om arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09635.pdf

Diepere vragen

How can the proposed signal propagation theory be applied to other types of neural networks?

The signal propagation theory proposed in the context of transformer models can be extended and applied to various other types of neural networks. By deriving closed-form expressions for the moments of outputs and gradients through different components, this framework can help understand and mitigate issues like vanishing/exploding gradients, rank collapse, and instability associated with high attention scores in a wide range of neural network architectures. The key lies in adapting the derived equations for specific components such as embeddings, linear layers, activation functions, layer normalization, dropout, softmax layers, etc., based on the structure and characteristics of different neural network models. This approach provides a systematic way to analyze signal propagation dynamics across various layers and components within neural networks.

What are the potential ethical implications of using crawled web data for pre-training language models?

Using crawled web data for pre-training language models raises several ethical considerations. One major concern is privacy infringement as web data may contain personal information or sensitive content that individuals did not consent to share. There is also a risk of perpetuating biases present in online content which could lead to biased or discriminatory outcomes when using these language models. Additionally, there are concerns about misinformation being amplified through language models trained on unfiltered web data without proper fact-checking mechanisms in place. It is crucial to ensure transparency regarding data sources and processing methods while addressing these ethical implications by implementing robust data governance practices.

How does DeepScaleLM compare to other methods in terms of computational efficiency?

DeepScaleLM offers significant advantages in terms of computational efficiency compared to other methods for training deep transformers. By enabling very deep models with hundreds of layers while conserving unit activations and gradients throughout the model via appropriate initialization and scaling schemes, DeepScaleLM allows for improved performance without exponentially increasing computational requirements. The method ensures stable training even with increased depth by controlling variance growth during forward pass iterations efficiently.
This results in better utilization of compute resources as it enables training deeper-narrower models with consistent performance gains across various tasks while keeping parameters constant or slightly reducing them compared to shallower standard models at equal wall-clock time intervals.
Overall, DeepScaleLM stands out as an efficient solution that balances model complexity with computational demands effectively within deep learning frameworks like transformers.