Layer Normalization (LN) is a fundamental component within each Transformer block, applied alongside the residual connections around the self-attention and feed-forward sub-layers. Its primary role is to stabilize the hidden state dynamics during training by normalizing the activations across the feature dimension for each position independently. This helps maintain consistent activation scales, smooths the loss surface, and generally improves gradient flow, making training deeper networks feasible.

However, the placement of the Layer Normalization step relative to the residual connection significantly impacts training dynamics and stability. The two predominant strategies are Post-Normalization (Post-LN), used in the original "Attention Is All You Need" paper, and Pre-Normalization (Pre-LN), which has gained popularity due to its enhanced stability. Let's examine each approach.

Post-Normalization (Post-LN)

In the original Transformer architecture, Layer Normalization is applied after the output of a sub-layer (like Multi-Head Attention or the Feed-Forward Network) is added back to the input via the residual connection.

The computation flow for a sub-layer in a Post-LN block looks like this:

Sub-layer Computation: $SubLayerOutput = SubLayer(x)$
Residual Addition: $Added = x + SubLayerOutput$
Layer Normalization: $Output = LayerNorm(Added)$

Data flow in a Post-Normalization Transformer block. Normalization occurs after the residual addition.

Characteristics:

Original Formulation: This is the setup described in Vaswani et al. (2017).
Potential Instability: The primary challenge with Post-LN is that the output of the residual branch (the sum $x + SubLayer(x)$ ) is not normalized before being passed to the next layer. In deep networks, the magnitude of activations can vary significantly from layer to layer, potentially leading to gradient explosion or vanishing early in training.
Warmup Requirement: Post-LN configurations often necessitate a careful learning rate warmup phase (starting with a small learning rate and gradually increasing it). Without warmup, the initial gradients can be excessively large due to the unnormalized additions, causing training to diverge.

Pre-Normalization (Pre-LN)

To address the stability issues of Post-LN, the Pre-Normalization approach was proposed. Here, Layer Normalization is applied to the input before it enters the sub-layer module, but within the residual branch. The residual connection then adds the original, unmodified input $x$ to the output of the sub-layer.

The computation flow for a sub-layer in a Pre-LN block is:

Layer Normalization: $Normalized\_x = LayerNorm(x)$
Sub-layer Computation: $SubLayerOutput = SubLayer(Normalized\_x)$
Residual Addition: $Output = x + SubLayerOutput$

Data flow in a Pre-Normalization Transformer block. Normalization occurs before the sub-layer computation.

Characteristics:

Improved Stability: By normalizing the input to each sub-layer, Pre-LN prevents the activations passed into these potentially complex functions from exploding. The output gradient path through the residual connection ( $x$ ) remains clean, allowing gradients to flow more easily through deep networks without being excessively scaled by the normalization layers.
Reduced Sensitivity to Warmup: Pre-LN configurations are generally much less sensitive to the learning rate schedule and often train stably even without a specific warmup phase, or with a much shorter one. This simplifies hyperparameter tuning.
Common Practice: Due to its stability benefits, Pre-LN has become the de facto standard in many modern large-scale Transformer implementations (e.g., GPT-2, GPT-3, ViT). It facilitates training significantly deeper models compared to Post-LN.

Comparison and Trade-offs

Feature	Post-Normalization (Post-LN)	Pre-Normalization (Pre-LN)
Placement	`LayerNorm(x + SubLayer(x))`	`x + SubLayer(LayerNorm(x))`
Stability	Less stable, especially in deep models	More stable, facilitates deeper model training
Warmup	Often requires careful LR warmup	Less sensitive to LR warmup, often trains without it
Gradient Flow	Gradients pass through LN after addition	Gradients through residual path bypass LN
Original Paper	Yes	No (later improvement)
Modern Usage	Less common in very large models	Widely adopted, especially for large models
Peak Performance	Can sometimes achieve slightly better peak results with extensive tuning	Generally easier to tune for good, stable results

Hypothetical training loss curves. Pre-LN often shows stable convergence. Post-LN without warmup might diverge, while Post-LN with proper warmup can converge well, sometimes achieving slightly lower final loss than Pre-LN but requiring careful tuning.

Conclusion

While the original Transformer used Post-Normalization, the Pre-Normalization variant offers significant practical advantages in terms of training stability and reduced sensitivity to hyperparameter choices like the learning rate schedule. By normalizing the input before it passes through the complex self-attention and feed-forward layers, Pre-LN ensures a smoother optimization process, particularly critical when scaling Transformers to dozens or even hundreds of layers. For these reasons, Pre-LN is frequently the preferred choice in contemporary Transformer architectures. Understanding both configurations, however, provides valuable insight into the design choices and training dynamics of these powerful models.