Root Mean Square Layer Normalization (1910.07467v1)

Abstract: Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. However, the computational overhead introduced by LayerNorm makes these improvements expensive and significantly slows the underlying network, e.g. RNN in particular. In this paper, we hypothesize that re-centering invariance in LayerNorm is dispensable and propose root mean square layer normalization, or RMSNorm. RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability. RMSNorm is computationally simpler and thus more efficient than LayerNorm. We also present partial RMSNorm, or pRMSNorm where the RMS is estimated from p% of the summed inputs without breaking the above properties. Extensive experiments on several tasks using diverse network architectures show that RMSNorm achieves comparable performance against LayerNorm but reduces the running time by 7%~64% on different models. Source code is available at https://github.com/bzhangGo/rmsnorm.

• The paper introduces RMSNorm, a novel method that replaces mean subtraction with root mean square scaling to improve training efficiency.
• It demonstrates comparable performance to traditional LayerNorm across tasks like machine translation and image classification with up to 64% faster training.
• The study challenges the necessity of centering in normalization, opening avenues for further simplification and efficiency in deep learning models.

An Overview of Root Mean Square Layer Normalization

The paper "Root Mean Square Layer Normalization" presents a novel approach to layer normalization in deep neural networks with the aim to enhance computational efficiency while maintaining model performance. The work introduces Root Mean Square Layer Normalization (RMSNorm) and presents a thorough investigation into its effectiveness and potential advantages compared to traditional LayerNorm.

Background and Motivation

Layer normalization, as proposed by Ba et al., has been widely used due to its ability to stabilize training by regularizing neuron dynamics within a layer through mean and variance statistics. This method has proven beneficial across various domains from natural language processing to computer vision. However, the computational cost associated with calculating these statistics can slow down training, especially in large, deep models like RNNs.

RMSNorm is introduced as an alternative that focuses solely on the re-scaling invariance by employing the root mean square (RMS) statistic for normalization, bypassing the re-centering invariance that is a haLLMark of LayerNorm. The authors propose that re-centering invariance is not essential for successful model convergence, thus allowing for a more simplified and computationally efficient normalization approach.

Methodology

RMSNorm normalizes the inputs to a neuron by the square root of the mean of the squares of its inputs, inherently providing a re-scaling invariance without the need for mean subtraction. A variant, partial RMSNorm ($p$RMSNorm), is also introduced, wherein RMS is estimated from a subset of summed inputs, further reducing computational overhead.

Experimental Results

Extensive experiments are conducted to evaluate RMSNorm using a diverse set of neural network architectures on tasks like machine translation, reading comprehension, image-caption retrieval, and image classification.

Notably, RMSNorm demonstrates comparable performance to LayerNorm across these tasks, while achieving significant computational speed-ups. For instance, in machine translation tasks, RMSNorm delivers comparable BLEU scores to LayerNorm but with reduced training time by 7% to 64%, depending on the architecture and framework. In particular, RMSNorm achieves up to 34% faster training times in some RNN models. These results indicate that the computational savings do not come at the cost of accuracy or convergence speed.

On the CIFAR-10 classification task, although RMSNorm's test accuracy is slightly lower than BatchNorm, it still outperforms LayerNorm, highlighting its suitability for non-sequential data as well.

The experiments suggest that while $p$RMSNorm theoretically offers further computational advantages, practical speed improvements are inconsistent, likely due to implementation inefficiencies.

Theoretical Implications and Future Directions

The paper makes a theoretical contribution by challenging the necessity of input mean normalization in the context of layer normalization. The findings open up potential avenues for further simplification and efficiency improvements in neural network training. Future work could involve exploring different norms as alternatives to RMS, and optimizing the implementation of $p$RMSNorm for practical speed advantages.

In conclusion, RMSNorm presents itself as an efficient and effective drop-in replacement for LayerNorm, offering a tangible speed advantage in various models without sacrificing performance. Its simplicity and computational benefits position it as a promising avenue for future research in optimizing deep learning algorithms.