arxiv:2503.10622

Transformers without Normalization

Published on Mar 13

· Submitted by

akhaliq on Mar 14

#1 Paper of the day

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Authors:

Jiachen Zhu ,

Xinlei Chen ,

Yann LeCun ,

Abstract

Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation DyT(x) = tanh(alpha x), as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, S-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks.

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akhaliq

Paper submitter 2 days ago

Sifal

1 day ago

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edited 1 day ago

As straight forward as it sounds (with caveats on the sensitivity of alpha), bruh

class DyT(nn.Module):
    def __init__(self, num_features, alpha_init_value=0.5):
        super().__init__()
        self.alpha = nn.Parameter(torch.ones(1) * alpha_init_value)
        self.weight = nn.Parameter(torch.ones(num_features))
        self.bias = nn.Parameter(torch.zeros(num_features))
    
    def forward(self, x):
        x = torch.tanh(self.alpha * x)
        return x * self.weight + self.bias