Question: Through their observation, which model's loss decays is faster?
Evidence: The Transformer BIBREF0 is one of the most commonly used neural network architectures in natural language processing. Layer normalization BIBREF1 plays a key role in Transformer's success. The originally designed Transformer places the layer normalization between the residual blocks, which is usually referred to as the Transformer with Post-Layer Normalization (Post-LN) BIBREF2. This architecture has achieved state-of-the-art performance in many tasks including language modeling BIBREF3, BIBREF4 and machine translation BIBREF5, BIBREF6. Unsupervised pre-trained models based on the Post-LN Transformer architecture also show impressive performance in many downstream tasks BIBREF7, BIBREF8, BIBREF9.
Despite its great success, people usually need to deal with the optimization of the Post-LN Transformer more carefully than convolutional networks or other sequence-to-sequence models BIBREF10. In particular, to train the model from scratch, any gradient-based optimization approach requires a learning rate warm-up stage BIBREF0, BIBREF11: the optimization starts with an extremely small learning rate, and then gradually increases it to a pre-defined maximum value in a pre-defined number of iterations. Such a warm-up stage not only slows down the optimization process but also brings more hyperparameter tunings. BIBREF10 has shown that the final model performance is quite sensitive to the value of the maximum learning rate and the number of warm-up iterations. Tuning such sensitive hyper-parameters is costly in training large-scale models, e.g., BERT BIBREF8 or XLNet BIBREF9.
In this paper, we try to alleviate this problem by finding ways to safely remove the learning rate warm-up stage. As the warm-up stage happens in the first several iterations, we investigate the optimization behavior at initialization using mean field theory BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17. According to our theoretical analysis, when putting the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, without the warm-up stage, directly using a large learning rate to those parameters can make the optimization process unstable. Using a warm-up stage and training the model with small learning rates practically avoid this problem. Extensive experiments are provided to support our theoretical findings.
Our theory also shows that the layer normalization plays a crucial role in controlling the gradient scales. This motivates us to investigate whether there are some other ways of positioning the layer normalization that lead to well-behaved gradients. In particular, we study another variant, the Transformer with Pre-Layer Normalization (Pre-LN) BIBREF18, BIBREF19, BIBREF2. The Pre-LN Transformer puts the layer normalization inside the residual connection and equips with an additional final-layer normalization before prediction (Please see Figure SECREF1 for the differences between the two variants of the Transformer architectures). We show that at initialization, the gradients are well-behaved without any exploding or vanishing for the Pre-LN Transformer both theoretically and empirically.
Given the gradients are well-behaved in the Pre-LN Transformer, it is natural to consider removing the learning rate warm-up stage during training. We conduct a variety of experiments, including IWSLT14 German-English translation, WMT14 English-German translation, and BERT pre-training tasks. We show that, in all tasks, the learning rate warm-up stage can be safely removed, and thus, the number of hyper-parameter is reduced. Furthermore, we observe that the loss decays faster for the Pre-LN Transformer model. It can achieve comparable final performances but use much less training time. This is particularly important for training large-scale models on large-scale datasets.
Our contributions are summarized as follows:
$\bullet $ We investigate two Transformer variants, the Post-LN Transformer and the Pre-LN Transformer, using mean field theory. By studying the gradients at initialization, we provide evidence to show why the learning rate warm-up stage is essential in training the Post-LN Transformer.
$\bullet $ We are the first to show that the learning-rate warm-up stage can be removed for the Pre-LN Transformer, which eases the hyperparameter tuning. We further show that by using proper learning rate schedulers, the training time can be largely reduced on a wide range of applications.
Furthermore, we observe that the loss decays faster for the Pre-LN Transformer model.
Answer: The Pre-LN Transformer model.