On Layer Normalization in the Transformer Architecture

Abstract: The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyper-parameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications.

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.
Gradient descent-based methods BIBREF20, BIBREF21, BIBREF22, BIBREF23 are popularly used in optimizing deep neural networks. For convolutional neural networks and recurrent neural networks, a relatively large learning rate is usually set in the beginning, and then decreased along with the optimization process BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28. The learning rate warm-up stage has only been shown essential in dealing with some very specific problems, e.g., the large-batch training. BIBREF29, BIBREF28, BIBREF30 showed that a learning rate warm-up stage is preferred when training neural networks with extremely large batch sizes.
However, the learning rate warm-up stage is essential and critical when optimizing the Transformer models in a majority of scenarios BIBREF0, BIBREF8, BIBREF3, BIBREF7, BIBREF31. BIBREF10 investigated the influence of different warm-up strategies for the optimization of the Post-LN Transformer model and found that without or with relatively less warm-up iterations, the optimization diverges. The Pre-LN Transformer has been proposed in several recent works BIBREF18, BIBREF19, BIBREF2 to alleviate some optimization issues when training deeper models, but the troublesome warm-up stage still remains in their training pipelines.
In a concurrent and independent work BIBREF11, the authors claimed that the benefit of the warm-up stage comes from reducing the variance for the adaptive learning rate in the Adam optimizer BIBREF20. They proposed to rectify the variance of adaptive learning rate by a new variant of Adam called RAdam. However, we find that not only for Adam, the learning rate warm-up stage also helps quite a lot for other optimizers. This may indicate that Adam is not the prerequisite for the necessity of the warm-up stage. Therefore, we re-identify the problem and find that it highly relates to the architecture, in particular, the position of layer normalization.
The Transformer architecture usually consists of stacked Transformer layers BIBREF0, BIBREF8, each of which takes a sequence of vectors as input and outputs a new sequence of vectors with the same shape. A Transformer layer has two sub-layers: the (multi-head) self-attention sub-layer and the position-wise feed-forward network sub-layer. Residual connection BIBREF24 and layer normalization BIBREF1 are applied for both sub-layers individually. We first introduce each component of the Transformer layer and then present the entire architecture.
An attention function can be formulated as querying an entry with key-value pairs BIBREF0. The self-attention sub-layer uses scaled dot-product attention, which is defined as: $\text{Attention}(Q,K,V)=\text{softmax}(\frac{QK^T}{\sqrt{d}})V$, where $d$ is the dimensionality of the hidden representations, and $Q$ (Query), $K$ (Key), $V$ (Value) are specified as the hidden representations of the previous layer. The multi-head variant of the self-attention sub-layer is popularly used which allows the model to jointly attend to information from different representation sub-spaces, and is defined as
where $W_k^Q\in \mathbb {R}^{d \times d_K}, W_k^K\in \mathbb {R}^{d \times d_K}, W_k^V\in \mathbb {R}^{d\times d_V},$ and $W^O\in \mathbb {R}^{Hd_V \times d}$ are project parameter matrices, $H$ is the number of heads. $d_K$ and $d_V$ are the dimensionalities of Key and Value. Without any confusion, given a sequence of vectors $(x_1,...,x_n)$, we use $\text{MultiHeadAtt}(x_{i},[ x_{ 1}, x_{2},\cdots , x_{n}])$ as the multi-head self-attention mechanism on position $i$ which considers the attention from $x_i$ to the entire sequence, i.e., $\text{MultiHeadAtt}(x_{i},[ x_{ 1}, x_{2},\cdots , x_{n}])=\text{Multi-head}(x_i, [x_1, \dots , x_n], [x_1, \dots , x_n])$.
In addition to the self-attention sub-layer, each Transformer layer contains a fully connected network, which is applied to each position separately and identically. This sub-layer is a two-layer feed-forward network with a ReLU activation function. Given a sequence of vectors $h_1, ..., h_n$, the computation of a position-wise FFN sub-layer on any $h_i$ is defined as:
where $W^1$, $W^2$, $b^1$ and $b^2$ are parameters.
Besides the two sub-layers described above, the residual connection and layer normalization are also key components to the Transformer. For any vector $v$, the layer normalization is computed as $\text{LayerNorm}(v) = \gamma \frac{v - \mu }{\sigma } + \beta $, in which $\mu , \sigma $ are the mean and standard deviation of the elements in $v$, i.e., $\mu = \frac{1}{d} \sum _{k=1}^d v_{k}$ and $\sigma ^2 = \frac{1}{d}\sum _{k=1}^d(v_{k} -\mu )^2$. Scale $\gamma $ and bias vector $\beta $ are parameters.
Different orders of the sub-layers, residual connection and layer normalization in a Transformer layer lead to variants of Transformer architectures. One of the original and most popularly used architecture for the Transformer and BERT BIBREF0, BIBREF8 follows “self-attention (FFN) sub-layer $\rightarrow $ residual connection $\rightarrow $ layer normalization”, which we call the Transformer with Post-Layer normalization (Post-LN Transformer), as illustrated in Figure SECREF1.
Denote $x_{l, i}$ as the input of the $l$-th Transformer layer at position $i$, where $x_{l,i}$ is a real-valued vector of dimension $d$, $i=1,2,...,n$, $l=1,2,...,L$. $n$ is the length of the sequence and $L$ is the number of layers. For completeness, we define $x_{0,i}$ as the input embedding at position $i$ which is usually a combination of word embedding and positional embedding. The computations inside the $l$-th layer are composed of several steps, and we use super-scripts on $x$ to present the input(output) of different steps as in Table 1 (left), where $W^{1,l}$, $W^{2,l}$, $b^{1,l}$ and $b^{2,l}$ are parameters of the FFN sub-layer in the $l$-th layer.
We are interested in the learning rate warm-up stage in the optimization of the Post-LN Transformer. Different from the optimization of many other architectures in which the learning rate starts from a relatively large value and then decays BIBREF32, BIBREF33, a learning rate warm-up stage for the Post-LN Transformer seems critical BIBREF10. We denote the learning rate of the $t$-th iteration as $\text{lr}(t)$ and the maximum learning rate during training as $\text{lr}_{max}$. Given a predefined time frame $T_{\text{warmup}}$, the learning rate scheduler for the first $T_{\text{warmup}}$ iterations BIBREF34 is defined as
After this warm-up stage, the learning rate will be set by classical learning rate schedulers, such as the linear decay, the inverse square-root decay, or forced decay at particular iterations. We conduct experiments to show that this learning rate warm-up stage is essential for training Post-LN Transformer models.
We conduct experiments on the IWSLT14 German-to-English (De-En) machine translation task. We mainly investigate two aspects: whether the learning rate warm-up stage is essential and whether the final model performance is sensitive to the value of $T_{\text{warmup}}$. To study the first aspect, we train the model with the Adam optimizer BIBREF20 and the vanilla SGD optimizer BIBREF35 respectively. For both optimziers, we check whether the warm-up stage can be removed. We follow BIBREF0 to set hyper-parameter $\beta $ to be $(0.9,0.98)$ in Adam. We also test different $\text{lr}_{max}$ for both optimizers. For Adam, we set $\text{lr}_{max}=5e^{-4}$ or $1e^{-3}$, and for SGD, we set $\text{lr}_{max}=5e^{-3}$ or $1e^{-3}$. When the warm-up stage is used, we set $T_{\text{warmup}}=4000$ as suggested by the original paper BIBREF0. To study the second aspect, we set $T_{\text{warmup}}$ to be 1/500/4000 (“1” refers to the no warm-up setting) and use $\text{lr}_{max}=5e^{-4}$ or $1e^{-3}$ with Adam. For all experiments, a same inverse square root learning rate scheduler is used after the warm-up stage. We use both validation loss and BLEU BIBREF36 as the evaluation measure of the model performance. All other details can be found in the supplementary material.
We record the model checkpoints for every epoch during training and calculate the validation loss and BLEU score. The performance of the models are plotted in Figure FIGREF13 and Figure FIGREF14. The x-axis is the epoch number and the y-axis is the BLEU score/validation loss. "w/o warm-up" indicates “without the warm-up stage” while "w/ warm-up" indicates “with the warm-up stage”.
First, we can see that for both optimizers, the learning rate warm-up stage is essential. Without the warm-up stage, the BLEU score of the model trained with Adam optimizer can only achieve 8.45. As a comparison, the model trained using the warm-up stage can achieve around 34 in terms of BLEU score. The same trend can also be observed on the validation loss curves. Although the performance of the model trained with SGD is significantly worse than Adam, we can still see similar phenomena as Adam. The BLEU score is just above zero in 15 epochs without using the warm-up stage.
Second, we can see that the optimization process is sensitive to the value of $T_{\text{warmup}}$, which means $T_{\text{warmup}}$ is an important hyper-parameter in training the Post-LN Transformer. For example, when setting $T_{\text{warmup}}=500$, the learned models with Adam achieve only 31.16 and 2.77 in term of BLEU score for $lr_{max}=5e^{-4}$ and $1e^{-3}$ respectively.
Such a warm-up stage has several disadvantages. First, its configuration significantly affects the final performance. The practitioners need a careful hyper-parameter tuning, which is computationally expensive for large-scale NLP tasks. Second, the warm-up stage could slow down the optimization. Standard optimization algorithms usually start with a large learning rate for fast convergence. However, when using the warm-up stage, the learning rate has to gradually increase from zero, which may make the training inefficient. BIBREF11 suggests that the warm-up stage plays a role in reducing the undesirably significant variance in Adam in the early stage of model training. However, according to our results, the warm-up stage also helps the training of SGD. This suggests that the benefit of the warm-up stage may be not for a particular optimizer.
We can see that the Post-LN Transformer cannot be trained with a large learning rate from scratch. This motivates us to investigate what happens at the model initialization. We first introduce the parameter initialization setting for our theoretical analysis and then present our theoretical findings.
We denote $\mathcal {L}(\cdot )$ as the loss function of one position, $\tilde{\mathcal {L}}(\cdot )$ as the loss function of the whole sequence, $\Vert \cdot \Vert _2$ and $\Vert \cdot \Vert _F$ as the $l_2$ norm (spectral norm) and the Frobenius norm, $\text{LN}(x)$ as the standard layer normalization with scale $\gamma =1$ and bias $\beta =0$, and $\mathbf {J}_{LN}(x)=\frac{\partial \text{LN}(x)}{\partial x}$ as the Jacobian matrix of $\text{LN}(x)$. Let $\mathcal {O}(\cdot )$ denote standard Big-O notation that suppress multiplicative constants.
The parameter matrices in each Transformer layer are usually initialized by the Xavier initialization BIBREF37. Given a matrix of size $n_{in}\times n_{out}$, the Xavier initialization sets the value of each element by independently sampling from Gaussian distribution $N(0, \frac{2}{n_{in}+n_{out}})$. The bias vectors are usually initialized as zero vectors. The scale $\gamma $ in the layer normalization is set to one.
For theoretical analysis, we study a simpler setting. First, we focus on single-head attention instead of the multi-head variant and for all layers, we set the shape of $W^{Q,l}$, $W^{K,l}$, $W^{V,l}$, $W^{1,l}$,$W^{2,l}$ to be $d\times d$. Second, we initialize the parameter matrices in the self-attention sub-layer $W^{Q,l}$ and $W^{K,l}$ to be zero matrices. In this setting, the attention is a uniform distribution at initialization and $\text{MultiHeadAtt}(x_{l, i}^1,[ x_{l, 1}^1, x_{l, 2}^1,\cdots , x_{l, n}^1])$ can be simplified as $\frac{1}{n}\sum _{j=1}^{n}x_{l, j} W^{V, l}$. Third, we assume the input vectors are also sampled from the same Gaussian distribution. This is reasonable since the inputs are linear combinations of word embeddings and learnable positional embeddings, both of which are initialized by Gaussian distributions.
We compare the Post-LN Transformer with another variant of the Transformer architecture, the Transformer with Pre-Layer Normalization (Pre-LN). The Pre-LN Transformer was implemented in several systems BIBREF34, BIBREF38, BIBREF39. BIBREF2 suggested that the Pre-LN Transformer outperforms the Post-LN Transformer when the number of layers increases. Different from the Post-LN Transformer that puts the layer normalization between the residual blocks, the Pre-LN Transformer puts the layer normalization inside the residual connection and places it before all other non-linear transformations. Additionally, the Pre-LN Transformer uses a final layer normalization right before the prediction. We provide the mathematical formulations and visualizations of the Post-LN/Pre-LN Transformer in Table TABREF9 and Figure SECREF1.
For both architectures, each $x_{L,i}$ passes through a softmax layer to produce a distribution over the dictionary $V$. The loss function is defined on the softmax distribution. For example, in sequence prediction, the loss function is defined as $\mathcal {L}(x_{L+1,i}^{post})=-\log (\text{softmax}_{y_i}(W^{emb}x_{L+1,i}^{post}))$ for the Post-LN Transformer and $\mathcal {L}(x_{Final,i}^{pre})=-\log (\text{softmax}_{y_i}(W^{emb}x_{Final,i}^{pre}))$ for the Pre-LN Transformer, where $\text{softmax}_{y_i}$ is the probability of ground truth token $y_i$ outputted by the softmax distribution and $W^{emb}$ is the word embedding matrix. The loss of the whole sequence is an average of the loss on each position. Without loss of generality, we assume that all the derivatives are bounded. We introduce the following concentration property of random variables which will be further used in the theorem.
Definition 1 A random variable $Z\ge 0$ is called $(\epsilon ,\delta )$-bounded if with probability at least $1-\delta $, $\frac{Z-\mathbb {E}Z}{\mathbb {E}Z}\le \epsilon $, where $\epsilon >0$ and $0<\delta <1$.
Intuitively, if the random variable $Z$ is $(\epsilon ,\delta )$-bounded, then with a high probability its realization will not get too far away from its expectation. For example, if $Y$ is a $d$-dimensional standard Gaussian random vector, then $Z=\Vert Y\Vert _2^2$ is $(\epsilon ,\delta )$-bounded with $\delta =\exp (-d\epsilon ^2/8)$, $0<\epsilon <1$ (see supplementary material for details). As parameter matrices in self-attention sub-layers and FFN sub-layers are initialized by Gaussian distributions, if the norm of the hidden states in the Transformer satisfies the concentrated condition above, we have the following theorem to characterize the scale of the gradients.
Theorem 1 (Gradients of the last layer in the Transformer) Assume that $\Vert x^{post,5}_{L,i}\Vert _2^2$ and $\Vert x^{pre}_{L+1,i}\Vert _2^2$ are $(\epsilon ,\delta )$-bounded for all $i$, where $\epsilon $ and $\delta =\delta (\epsilon )$ are small numbers. Then with probability at least $0.99-\delta -\frac{\epsilon }{0.9+\epsilon }$, for the Post-LN Transformer with $L$ layers, the gradient of the parameters of the last layer satisfies
and for the Pre-LN Transformer with $L$ layers,
From Theorem 1, we can see that for the Post-LN Transformer, the scale of the gradients to the last FFN layer is of order $\mathcal {O}(d\sqrt{\ln {d}})$ which is independent of $L$. For the Pre-LN Transformer, the scale of the gradients is much smaller. We first study the forward propagation of the Post-LN Transformer and the Pre-LN Transformer. Lemma 1 will be served as a basic tool to prove the main theorem and other lemmas.
Lemma 1 If $X\in \mathbb {R}^d$ is a Gaussian vector, $X\sim N(0,\sigma ^2 \mathbf {I}_d)$, then $\mathbb {E}(\Vert \text{ReLU}(X)\Vert _2^2)=\frac{1}{2}\sigma ^2 d$.
Based on Lemma 1, we have the following lemma to estimate the scale of the hidden states in different layers for the Post-LN Transformer and the Pre-LN Transformer.
Lemma 2 At initialization, for the Post-LN Transformer, $\mathbb {E}(\Vert x_{l,i}^{post,5}\Vert _2^2)=\frac{3}{2}d$ for all $l>0$ and $i$. For the Pre-LN Transformer, $(1+\frac{l}{2})d\le \mathbb {E}(\Vert x^{pre}_{l,i}\Vert _2^2)\le (1+\frac{3l}{2})d$ for all $l>0$ and $i$. Expectations are taken over the input and the randomness of initialization.
Lemma 2 studies the expected norm of the hidden states in both Post-LN/Pre-LN Transformer. It is obviously that in the Post-LN Transformer, the norm of $x_{l,i}^{post}$ is $\sqrt{d}$ and thus we study the norm of $x^{post,5}_{l,i}$ instead. As we can see from Lemma 2, the scale of the hidden states in the Post-LN Transformer keeps to be the same in expectation while the scale of the hidden states in the Pre-LN Transformer grows linearly along with the depth. The next lemma shows that the scale of the hidden states highly relates to the scale of the gradient in the architectures using layer normalization.
Lemma 3 For $x\in \mathbb {R}^d$, we have $\Vert \mathbf {J}_{LN}(x)\Vert _2=\mathcal {O}(\frac{\sqrt{d}}{\Vert x\Vert _2})$ in which $\mathbf {J}_{LN}(x)=\frac{\partial \text{LN}(x)}{\partial x}$.
The proof of Lemma 1, Lemma 2, Lemma 3, and Theorem 1 can be found in the supplementary material. The main idea is that the layer normalization will normalize the gradients. In the Post-LN Transformer, the scale of the inputs to the layer normalization is independent of $L$, and thus the gradients of parameters in the last layer are independent of $L$. While in the Pre-LN Transformer, the scale of the input to the final layer normalization is linear in $L$, and thus the gradients of all parameters will be normalized by $\sqrt{L}$.
We have provided a formal proof on the gradients of the last FFN sub-layer as above. In order to fully understand the optimization, we also make some preliminary analysis for other layers and other parameters. Our main result is that the gradient norm in the Post-LN Transformer is large for the parameters near the output and will be likely to decay as the layer index $l$ decreases. On the contrary, the gradient norm in the Pre- Transformer will be likely to stay the same for any layer $l$. All the preliminary theoretical results are provided in the supplementary material.
As our theory is derived based on several simplifications of the problem, we conduct experiments to study whether our theoretical insights are consistent with what we observe in real scenarios. The general model and training configuration exactly follow Section 3.2. The experiments are repeated ten times using different random seeds.
Given an initialized model, we record the hidden states in the Post-LN/Pre-LN Transformer across batches and find that the norm of the hidden states satisfies the property ((0.1,0.125)-bounded).
Theorem 1 suggests that for any sizes of the Post-LN Transformer, the scale of the gradient norm in the last FFN sub-layer remains the same. On the contrary, that of the Pre-LN Transformer decreases as the size of the model grows. We calculate and record the gradient norm in the last FFN sub-layer in 6-6/8-8/10-10/12-12/14-14 Post-LN/Pre-LN Transformer models at initialization. The results are plotted in Figure FIGREF27 and FIGREF28. The x-axis is the size of the model, and the y-axis is the value of the gradient norm of $W^2$ in the final FFN sub-layer. The figures show when the number of layers grows, the gradient norm remains in the Post-LN Transformer (around 1.6) and decreases in the Pre-LN Transformer. This observation is consistent with our theory.
We calculate the gradient norm of each paramter matrix in 6-6 Post-LN/Pre-LN Transformer. We record the gradient for each parameter for different mini-batches. For elements in a parameter matrix, we calculate their expected gradients and use the Frobenius norm of those values as the scale of the expected gradient of the matrix. Figure FIGREF25 and FIGREF26 shows those statistics for FFN sub-layers. The x-axis indexes different Transformer layers. It can be seen from the figure, the scale of the expected gradients grows along with the layer index for the Post-LN Transformer. On the contrary, the scale almost keeps the same for different layers in the Pre-LN Transformer. These observations are consistent with our theoretical findings.
Given the analysis above, we hypothesize that the gradient scale is one of the reasons that the Post-LN Transformer needs a careful learning rate scheduling. Since the gradients are large for some layers, using a large learning rate without warm-up may make the training unstable.
To verify this argument, first, we study the gradient statistics for the Post-LN Transformer after the warm-up stage with Adam. It can be seen from Figure FIGREF25 and FIGREF26 that the scale of the gradients are very small, and the model can be trained with large learning rates. Second, we conduct an experiment to train the Post-LN Transformer from scratch using a fixed small learning rate, i.e., $1e^{-4}$, to verify whether using small-step updates mitigates the issue. The details are provided in the supplementary material. In general, using a very small and fixed learning rate can mitigate the problem and optimize the Post-LN Transformer to a certain extent but the convergence is significantly slower. Both experiments above are supportive to our claim.
We find in the previous section that the gradients at initialization for Pre-LN Transformer are well-behaved. Given this observation, we deduce that the learning rate warm-up stage can be safely removed when training Pre-LN Transformer. In this section, we empirically verify it on two main tasks in NLP, machine translation and unsupervised pre-training.
We conduct our experiments on two widely used tasks: the IWSLT14 German-to-English (De-En) task and the WMT14 English-to-German (En-De) task. For the IWSLT14 De-En task, we use the same model configuration as in Section 3. For the WMT14 En-De task, we use the Transformer base setting. More details can be found in the supplementary material.
For training the Pre-LN Transformer, we remove the learning rate warm-up stage. On the IWSLT14 De-En task, we set the initial learning rate to be $5e^{-4}$ and decay the learning rate at the 8-th epoch by 0.1. On the WMT14 En-De task, we run two experiments in which the initial learning rates are set to be $7e^{-4}/1.5e^{-3}$ respectively. Both learning rates are decayed at the 6-th epoch followed by the inverse square root learning rate scheduler.
We train the Post-LN Transformer using the learning rate warm-up stage as the baseline. In both IWSLT14 De-En task and WMT14 En-De task, we set the number of the warm-up stage to be 4000 following BIBREF0 and then use the inverse square root learning rate scheduler. For all experiments above, we use the Adam optimizer and set the hyper-parameter $\beta $ to be $(0.9,0.98)$. We set $lr_{max}$ as same as the initial learning rates of the Pre-LN Transformer in each corresponding experiment. Since BIBREF11 suggests that the learning rate warm-up stage can be removed using RAdam, we try this optimizer on the IWSLT14 De-En task. We use linear learning rate decay suggested by BIBREF11 and keep all other hyper-parameters to be the same as in other experiments.
We follow BIBREF8 to use English Wikipedia corpus and BookCorpus for pre-training. As the dataset BookCorpus BIBREF40 is no longer freely distributed. We follow the suggestions from BIBREF8 to crawl and collect BookCorpus on our own. The concatenation of two datasets contains roughly 3.4B words in total, which is comparable with the data corpus used in BIBREF8. We randomly split documents into one training set and one validation set. The training-validation ratio for pre-training is 199:1.
We use base model configuration in our experiments. Similar to the translation task, we train the Pre-LN BERT without the warm-up stage and compare it with the Post-LN BERT. We follow the same hyper-parameter configuration in BIBREF8 to train the Post-LN BERT using 10k warm-up steps with $\text{lr}_{max}=1e^{-4}$. For the Pre-LN BERT, we use linear learning rate decay starting from $3e^{-4}$ without the warm-up stage. We have tried to use a larger learning rate (such as $3e^{-4}$) for the Post-LN BERT but found the optimization diverged.
We record the model checkpoints for every epoch during training and calculate the validation loss and BLEU score. The performance of the models at different checkpoints are plotted in Figure FIGREF41 - FIGREF44.
First, as we can see from the figure, the learning rate warm-up stage is not critical anymore for training the Pre-LN Transformer and the performance of the learned model is competitive. For example, on the IWSLT14 De-En task, the BLEU score and validation loss of the Pre-LN Transformer can achieve around 34 and 4, which are comparable with the performance of the Post-LN Transformer.
Second, the Pre-LN Transformer converges faster than the Post-LN Transformer using the same $\text{lr}_{max}$. On the IWSLT14 De-En task, the 9-th checkpoint of the Pre-LN Transformer achieves nearly the same performance (validation loss/BLEU score) as 15-th checkpoint of the Post-LN Transformer. Similar observations can be found in the WMT14 En-De task. Third, compared with RAdam, we find that the change of the position of layer normalization “dominates” the change of the optimizer. According to our experiments on the IWSLT14 De-En task, we can see that although RAdam trains the Post-LN Transformer well without the warm-up stage, it has little difference with Adam when training the Pre-LN Transformer.
We record validation loss of the model checkpoints and plot them in Figure FIGREF47. Similar to the machine translation tasks, the learning rate warm-up stage can be removed for the Pre-LN model. The Pre-LN model can be trained faster. For example, the Post-LN model achieves 1.69 validation loss at 500k updates while the Pre-LN model achieves similar validation loss at 700k updates, which suggests there is a 40% speed-up rate. Note that $T_{warmup}$ (10k) is far less than the acceleration (200k) which suggests the Pre-LN Transformer is easier to optimize using larger learning rates. We also evaluate different model checkpoints on the downstream task MRPC and RTE (more details can be found in the supplementary material). The experiments results are plotted in Figure FIGREF48 and FIGREF49. We can see that the Pre-LN model also converges faster on the downstream tasks.
As a summary, all the experiments on different tasks show that training the Pre-LN Transformer does not rely on the learning rate warm-up stage and can be trained much faster than the Post-LN Transformer.
In this paper, we study why the learning rate warm-up stage is important in training the Transformer and show that the location of layer normalization matters. We show that in the original Transformer, which locates the layer normalization outside the residual blocks, the expected gradients of the parameters near the output layer are large at initialization. This leads to an unstable training when using a large learning rate. We further show that the Transformer which locates the layer normalization inside the residual blocks, can be trained without the warm-up stage and converges much faster. In the future, we will investigate other strategies of positioning the layer normalization and understand the optimization of Transformer from a theoretical perspective.
The training/validation/test sets of the IWSLT14 German-to-English (De-En) task contain about 153K/7K/7K sentence pairs, respectively. We use a vocabulary of 10K tokens based on a joint source and target byte pair encoding (BPE) BIBREF44. All of our experiments use a Transformer architecture with a 6-layer encoder and 6-layer decoder. The size of embedding is set to 512, the size of hidden nodes in attention sub-layer and position-wise feed-forward network sub-layer are set to 512 and 1024, and the number of heads is set to 4. Label smoothed cross entropy is used as the objective function by setting $\epsilon = 0.1$ BIBREF46, and we apply dropout with a ratio 0.1. The batch size is set to be 4096 tokens. When we decode translation results from the model during inference, we set beam size as 5 and the length penalty as 1.2.
The configuration of IWLST14 De-En task is the same as in Section 3. For the WMT14 En-De task, we replicate the setup of BIBREF0, which consists of about 4.5M training parallel sentence pairs, and uses a 37K vocabulary based on a joint source and target BPE. Newstest2013 is used as the validation set, and Newstest2014 is used as the test set. One of the basic configurations of the Transformer architecture is the base setting, which consists of a 6-layer encoder and 6-layer decoder. The size of the hidden nodes and embeddings are set to 512. The number of heads is 8. Label smoothed cross entropy is used as the objective function by setting $\epsilon = 0.1$. The batch size is set to be 8192 tokens per GPU on 16 NVIDIA Tesla P40 GPUs.
We follow BIBREF8 to use English Wikipedia corpus and BookCorpus for the pre-training. As the dataset BookCorpus BIBREF40 is no longer freely distributed. We follow the suggestions from BIBREF8 to crawl and collect BookCorpus on our own. The concatenation of two datasets includes roughly 3.4B words in total, which is comparable with the data corpus used in BIBREF8. We first segment documents into sentences with Spacy; Then, we normalize, lower-case, and tokenize texts using Moses BIBREF43 and apply BPEBIBREF45. We randomly split documents into one training set and one validation set. The training-validation ratio for pre-training is 199:1. All experiments are conducted on 32 NVIDIA Tesla P40 GPUs.
The base model in BIBREF8 consists of 12 Transformer layers. The size of hidden nodes and embeddings are set to 768, and the number of heads is set to 12.
The Microsoft Research Paraphrase Corpus BIBREF42 is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent, and the task is to predict the equivalence. The performance is evaluated by the accuracy.
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges BIBREF41. The task is to predict whether sentences in a sentence pair are entailment. The performance is evaluated by the accuracy.
We use the validation set for evaluation. To fine-tune the models, following BIBREF8, BIBREF39, we search the optimization hyper-parameters in a search space including different batch sizes (16/32), learning rates ($1e^{-5}$ - $1e^{-4}$) and number of epochs (3-8). We find that the validation accuracy are sensitive to random seeds, so we repeat fine-tuning on each task for 6 times using different random seeds and compute the 95% confidence interval of validation accuracy.
Denote $X=(X_1,X_2,...,X_d)$ in which $X_i$ are i.i.d. Gaussian random variables with distribution $N(0,\sigma ^2)$. Denote $\rho _X(x)$ as the probability density function of $X_1$. Then $\mathbb {E}(\Vert \text{ReLU}(X)\Vert _2^2)=\sum _{i=1}^d \mathbb {E}[\text{ReLU}(X_i)^2] =\sum _{i=1}^d \mathbb {E}[\text{ReLU}(X_i)^2|X_i\ge 0]\mathbb {P}(X_i\ge 0) =\frac{d}{2}\mathbb {E}[\text{ReLU}(X_1)^2|X_1\ge 0]=\frac{d}{2}\mathbb {E}[X_1^2|X_1\ge 0] =\frac{d}{2}\int _{-\infty }^{+\infty } x^2 \rho _{X|X>0}(x)dx =\frac{d}{2}\int _{0}^{+\infty } x^2 2\rho _{X}(x)dx =\frac{1}{2}\sigma ^2 d$.
At initialization, the layer normalization is computed as $\text{LN}(v) = \frac{v - \mu }{\sigma }$. It is easy to see that layer normalization at initialization projects any vector $v$ onto the $d-1$-sphere of radius $\sqrt{d}$ since $\Vert \text{LN}(v)\Vert _2^2=\Vert \frac{v - \mu }{\sigma }\Vert _2^2=\frac{\sum _{k=1}^d(v_{k} -\mu )^2}{\sigma ^2}=d$.
We first estimate the expected $l_2$ norm of each intermediate output $x^{post,1}_{l,i},\cdots ,x^{post,5}_{l,i}$ for $l>0$. Using Xavier initialization, the elements in $W^{V,l}$ are i.i.d. Gaussian random variables sampled from $N(0,1/d)$. Since $\Vert x_{l,i}^{post}\Vert _2^2=d$ by the definition of Layer Normalization when $l>0$, we have
and $\mathbb {E}(\Vert x_{l,i}^{post,2}\Vert _2^2)=\mathbb {E}(\Vert x_{l,i}^{post}\Vert _2^2)+\mathbb {E}(\Vert x_{l,i}^{post,1}\Vert _2^2)=\mathbb {E}(\Vert x_{l,i}^{post}\Vert _2^2)+\mathbb {E}(\Vert \frac{1}{n}\sum _{i=1}^n x_{l,i}^{post}\Vert _2^2)\ge \mathbb {E}(\Vert x_{l,i}^{post}\Vert _2^2)=d$.
Similarly, we have $\Vert x_{l,i}^{post,3}\Vert _2^2=d$ by the definition of Layer Normalization. Again, for the ReLU activation function, the elements in $W^{1,l}$ and $W^{2,l}$ are i.i.d. Gaussian random variables sampled from $N(0,1/d)$. According to Lemma 1, we have
Based on this, we can estimate the scale of $\mathbb {E}(\Vert x_{l,i}^{post,5}\Vert _2^2)$ as follows.
Using similar technique we can bound $\mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2)$ for the Pre-LN Transformer.
It is easy to see that we have $\mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2) \le \mathbb {E}(\Vert x_{l,i}^{pre,3}\Vert _2^2) \le \mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2)+d$. And similar to (10)-(12),
Combining both, we have $\mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2) +\frac{1}{2}d\le \mathbb {E}(\Vert x_{l+1,i}^{pre}\Vert _2^2)\le \mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2)+\frac{3}{2}d$. Then we have $(1+\frac{l}{2})d\le \mathbb {E}(\Vert x_{l,i}^{pre}\Vert _2^2)\le (1+\frac{3l}{2})d$ by induction.
The proof of Lemma 3 is based on Lemma 4.1:
Lemma 4 Let $\alpha \in \mathbb {R}^d$ be a vector such that $\Vert \alpha \Vert _2=1$, then the eigenvalue of $I-\alpha ^{\top }\alpha $ is either 1 or 0.
Let $\lbrace e_1,...,e_d\rbrace $ be unit vectors such that $e_1=\alpha $ and $e_i\bot e_j$ for all $(i,j)$. Then we have $e_1(I-\alpha ^{\top }\alpha )=e_1-e_1\alpha ^{\top }\alpha =e_1-\alpha =0$ and $e_i(I-\alpha ^{\top }\alpha )=e_i-e_i\alpha ^{\top }\alpha =e_i$ for $i\ne 1$. So $e_i$ are all the eigenvectors of $I-\alpha ^{\top }\alpha $, and their corresponding eigenvalues are $(0,1,1,...,1)$. Hence we complete our proof.
[Proof of Lemma 3] Denote $y=x(I-\frac{1}{d}\textbf {1}^{\top }\textbf {1})$, where $\textbf {1}=(1,1,...,1)\in \mathbb {R}^d$, then the layer normalization can be rewritten as
We explicitly calculate the Jacobian of layer normalization as
where $\delta _{ij}=1$ when $i=j$ and $\delta _{ij}=0$ when $i\ne j$. In the matrix form,
and
Since the eigenvalue of the matrix $(I-\frac{y^{\top }y}{\Vert y\Vert _2^2})$ and $(I-\frac{1}{d}\textbf {1}^{\top }\textbf {1})$ are either 1 or 0 (by Lemma 4.1), we have $\Vert (I-\frac{y^{\top }y}{\Vert y\Vert _2^2})\Vert _2=\mathcal {O}(1)$ and $\Vert (I-\frac{1}{d}\textbf {1}^{\top }\textbf {1})\Vert _2=\mathcal {O}(1)$. So the spectral norm of $\mathbf {J}_{LN}(x)$ is
The proof of Theorem 1 is based on Lemma 4.2:
Lemma 5 Let $Y$ be a random variable that is never larger than B. Then for all $a<B$,
Let $X=B-Y$, then $X\ge 0$ and Markov's inequality tells us that
Hence
[Proof of Theorem 1] We prove Theorem 1 by estimating each element of the gradient matrix. Namely, we will analyze $\frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,L}_{pq}}$ for $p,q \in \lbrace 1,...,d\rbrace $. The loss of the post-LN Transformer can be written as
Through back propagation, for each $i\in \lbrace 1,2,...,n\rbrace $ the gradient of $\mathcal {L}(x_{L+1,i})$ with respect to the last layer's parameter $W^{2,L}$ in the post-LN setting can be written as:
Here $[\text{ReLU}(x_{L,i}^{post,3}W^{1,L})]_p$ means the $p$-th element of $\text{ReLU}(x_{L,i}^{post,3}W^{1,L})$. So the absolute value of $\frac{\partial \mathcal {L}(x_{L+1,i}^{post})}{\partial W^{2,L}_{pq}}$ can be bounded by
which implies
Since all the derivatives are bounded, we have $\Vert \frac{\partial \mathcal {L}(x_{L+1,i}^{post})}{\partial x_{L+1,i}^{post}}\Vert ^2_2=\mathcal {O}(1)$. So
Since $\Vert x_{L,i}^{post,3}\Vert _2^2=d$, $[x_{L,i}^{post,3}W^{1,L}]_p$ has distribution $N(0,1)$, using Chernoff bound we have
So
Thus with probability at least $0.99$, for all $p=1,2,...,d$ we have $\text{ReLU}([x_{L,i}^{post,3}W^{1,L}]_p)^2\le 2\ln {100d}$.
Since with probability $1-\delta (\epsilon )$, $\frac{|\Vert x^{post,5}_{L,i}\Vert _2^2-\mathbb {E}\Vert x^{post,5}_{L,i}\Vert _2^2|}{\mathbb {E}\Vert x^{post,5}_{L,i}\Vert _2^2}\le \epsilon $, we have $\Vert x^{post,5}_{L,i}\Vert _2^2\le (1+\epsilon )\mathbb {E}\Vert x^{post,5}_{L,i}\Vert _2^2$. Using Lemma 4.2, we have
for an arbitrary constant $\alpha _0>0$, which equals
So according to union bound, with probability at least $0.99-\delta (\epsilon )-\frac{\epsilon }{1+\epsilon -\alpha _0}$ we have $|\frac{\partial \mathcal {L}(x_{L+1,i}^{post})}{\partial W^{2,L}_{pq}}|^2=\mathcal {O}(\left[\Vert \mathbf {J}_{LN}(x_{L,i}^{post,5})\Vert ^2_2|[\text{ReLU}(x_{L,i}^{post,3}W^{1,L})]_p|^2\right])\le \mathcal {O}(\frac{2d\ln {100d}}{\Vert x_{L,i}^{post,5}\Vert _2^2})\le \mathcal {O}(\frac{d\ln {d}}{\alpha _0\mathbb {E}\Vert x_{L,i}^{post,5}\Vert _2^2})=\mathcal {O}(\frac{\ln {d}}{\alpha _0})$. So we have
and
.
The loss of the pre-LN Transformer can be written as
Using the same technique, in the pre-LN setting the gradient of $\mathcal {L}(x_{Final,i}^{pre})$ with respect to the last layer's parameter $W^{2,L}$ can be written as
So the absolute value of each component of the gradient is bounded by
Since $\Vert x_{L,i}^{pre,4}\Vert _2^2=d$ and $[ x_{L,i}^{pre,4}W^{1,L}]_p$ obeys distribution $N(0,1)$, using Chernoff bound we have
So
So with probability at least $0.99$, for all $p=1,2,...,d$ we have $\text{ReLU}([ x_{L,i}^{pre,4}W^{1,L}]_p)^2\le 2\ln {100d}$.
Since with probability $1-\delta (\epsilon )$, $\frac{|\Vert x^{pre}_{L+1,i}\Vert _2^2-\mathbb {E}\Vert x^{pre}_{L+1,i}\Vert _2^2|}{\mathbb {E}\Vert x^{pre}_{L+1,i}\Vert _2^2}\le \epsilon $, we have $\Vert x^{pre}_{L+1,i}\Vert _2^2\le (1+\epsilon )\mathbb {E}\Vert x^{pre}_{L+1,i}\Vert _2^2$. Using Lemma 5, we have
which equals
According to union bound, with probability $0.99-\delta (\epsilon )-\frac{\epsilon }{1+\epsilon -\alpha _0}$ we have $|\frac{\partial \mathcal {L}(x_{Final,i}^{pre})}{\partial W^{2,L}_{pq}}|^2=\mathcal {O}(\left[\Vert \mathbf {J}_{LN}(x_{L+1,i}^{pre})\Vert ^2_2|[\text{ReLU}(x_{L,i}^{pre,4}W^{1,L})]_p|^2\right])\le \mathcal {O}(\frac{2d\ln {100d}}{\Vert x_{L+1,i}^{pre}\Vert _2^2})\le \mathcal {O}(\frac{d\ln {d}}{\alpha _0\mathbb {E}\Vert x_{L+1,i}^{pre}\Vert _2^2})=\mathcal {O}(\frac{\ln {d}}{\alpha _0 L})$. So we have
Thus $\Vert \frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,L}}\Vert _F=\sqrt{\sum _{p,q=1}^d|\frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,L}_{pq}}|^2}\le \mathcal {O}(\sqrt{\frac{d^2\ln {d}}{\alpha _0L}})$.
Take $\alpha _0=\frac{1}{10}$, we have that with probability at least $0.99-\delta (\epsilon )-\frac{\epsilon }{0.9+\epsilon }$, for the Post-LN Transformer we have $\Vert \frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,L}}\Vert _F\le \mathcal {O}(d\sqrt{\ln {d}})$ and for the Pre-LN Transformer we have $\Vert \frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,L}}\Vert _F\le \mathcal {O}(d\sqrt{\frac{\ln {d}}{L}})$
For simplicity, we denote $x_l=\text{Concat}(x_{l,1},...,x_{l,n})\in \mathbb {R}^{nd}$ and $x_l^k=\text{Concat}(x_{l,1}^k,...,x_{l,n}^k)\in \mathbb {R}^{nd}$ for $k=\lbrace 1,2,3,4,5\rbrace $. Then in the Post-LN Transformer, the gradient of the parameters in the $l$-th layer (take $W^{2,l}$ as an example) can be written as
where
The Jacobian matrices of the Post-LN Transformer layers are:
where Jji = diag(( xj,ipost,3(w11,j)), ...,( xj,ipost,3(wd1,j))) Rd d
Using Hölder's inequality, we have
Since $\frac{\partial x_{j+1}}{\partial x_j^{post,5}}=diag(\mathbf {J}_{LN}(x_{j,1}^{post,5}),...,\mathbf {J}_{LN}(x_{j,n}^{post,5}))$, we have $\sqrt{\mathbb {E}\left[\Vert \frac{\partial x_{j+1}^{post}}{\partial x_j^{post,5}}\Vert _2^2\right]} \approx \sqrt{\mathbb {E}\frac{d}{\Vert x_{j,1}^{post,5}\Vert ^2_2}} \approx \sqrt{\frac{2}{3}}$ when $\Vert x_{j,1}^{post,5}\Vert ^2_2$ concentrates around its expectation $\mathbb {E}\Vert x_{j,1}^{post,5}\Vert ^2_2$ which equals $\frac{3}{2}d$ according to Lemma 2. Therefore, when we estimate the norm of $\frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,l}}$ for post-LN transformer, there exists a term $\mathcal {O}(\frac{2}{3}^{(L-l)/2})$, which exponentially decreases as $l$ goes smaller. Similarly, in the pre-LN Transformer, the gradient can be written as
where
The Jacobian matrices of the Pre-LN Transformer layers are:
If $l$ is sufficiently large, the norm of $\mathbf {J}_{LN}(x_{j,i}^{pre})$ and $\mathbf {J}_{LN}(x^{pre,3}_{j,i})$ are very small (of order $\mathcal {O}(\frac{1}{\sqrt{j}})$) as $j$ is between $l+1$ and $L$, which means the eigenvalues of matrix $\frac{\partial x_{j+1}^{pre}}{\partial x_j^{pre,3}}$ and $\frac{\partial x_{j}^{pre,3}}{\partial x_j^{pre}}$ are close to 1. Then we can see that $\mathbb {E}\Vert \frac{\partial x^{pre}_{j+1}}{\partial x_j^{pre,3}}\Vert _2$ and $\mathbb {E}\Vert \frac{\partial x_{j}^{pre,3}}{\partial x_j^{pre}}\Vert _2$ are nearly 1, and the norm of $\frac{\partial \tilde{\mathcal {L}}}{\partial W^{2,l}}$ for pre-LN transformer is independent of $l$ when $l$ is large.
In this section we give an example of $(\epsilon ,\delta )$-bounded random variable. This example comes from Example 2.5 in BIBREF47 and we give a short description below.
If $Z=(Z_1,...,Z_n)$ is a Gaussian vector with distribution $N(0,I_n)$, then $Y=\Vert Z\Vert ^2_2=\sum _{k=1}^n Z_k^2$ has distribution $\chi ^2_n$. And $\mathbb {E}Y=\sum _{k=1}^n \mathbb {E}Z_k^2=n$
A random variable $X$ with mean $\mu =\mathbb {E}[X]$ is called sub-exponential if there are non-negative parameters $(\nu ,\alpha )$ such that $\mathbb {E}[\exp (\lambda (X-\mu ))]\le \exp (\frac{\nu ^2\lambda ^2}{2})$ for all $|\lambda |<\frac{1}{\alpha }$. The next proposition comes from Proposition 2.2 in BIBREF47.
Proposition 1 (Sub-exponential tail bound) Suppose that $X$ is sub-exponential with parameters $(\nu ,\alpha )$. Then
and from Example 2.5 in BIBREF47, the $\chi ^2$ variable $Y$ is sub-exponential with parameters $(\nu , \alpha )=(2 \sqrt{n}, 4)$. So we can derive the one-sided bound
So $Y$ is $(\epsilon ,\delta )$-bounded with $\epsilon \in (0,1)$ and $\delta =\exp (-n\epsilon ^2/8)$.
Theoretically, we find that the gradients of the parameters near the output layers are very large for the Post-LN Transformer and suggest using large learning rates to those parameters makes the training unstable. To verify whether using small-step updates mitigates the issue, we use a very small but fixed learning rate and check whether it can optimize the Post-LN Transformer (without the learning rate warm-up step) to a certain extent. In detail, we use a fixed learning rate of $1e^{-4}$ at the beginning of the optimization, which is much smaller than the $\text{lr}_{max}= 1e^{-3}$ in the paper. Please note that as the learning rates during training are small, the training converges slowly, and this setting is not very practical in real large-scale tasks. We plot the validation curve together with other baseline approaches in Figure 6. We can see from the figure, the validation loss (pink curve) is around 4.3 in 27 epochs. This loss is much lower than that of the Post-LN Transformer trained using a large learning rate (blue curve). But it is still worse than the SOTA performance (green curve).

Figure and table captions:
Figure 1. (a) Post-LN Transformer layer; (b) Pre-LN Transformer layer.
Table 1. Post-LN Transformer v.s. Pre-LN Transformer
Figure 2. Performances of the models optimized by Adam and SGD on the IWSLT14 De-En task.
Figure 3. The norm of gradients of 1. different layers in the 6-6 Transformer (a,b). 2. W 2,L in different size of the Transformer (c,d).
Figure 4. Performances of the models on the IWSLT14 De-En task and WMT14 En-De task
Figure 5. Performances of the models on unsupervised pre-training (BERT) and downstream tasks
Figure 6. Performances of the models on the IWSLT14 De-En task.

Questions about this paper:
What experiments do they perform?

Answers:
whether the learning rate warm-up stage is essential, whether the final model performance is sensitive to the value of Twarmup.