What is the Layer Architecture of Transformers?

The transformer architecture revolutionized the field of deep learning when it was introduced in the seminal 2017 paper “Attention Is All You Need.” Understanding the layer architecture of transformers is essential for anyone working with modern natural language processing, computer vision, or any domain where these models have become dominant. At its core, the transformer’s layer architecture is built on a surprisingly elegant stack of repeating components that work together to process sequential information without relying on recurrence or convolution.

The Fundamental Building Block: The Transformer Layer

A transformer model consists of multiple identical layers stacked on top of each other, with each layer performing the same operations but learning different parameters. The original transformer architecture proposed using six layers for both the encoder and decoder, though modern implementations often use significantly more layers—sometimes exceeding one hundred in large language models.

Each transformer layer contains two primary sub-components that work in sequence: the multi-head self-attention mechanism and the position-wise feed-forward network. These components are connected through residual connections and layer normalization, creating a robust architecture that allows for effective gradient flow during training and enables the model to learn complex representations of the input data.

The information flow through a single transformer layer follows a consistent pattern. Input embeddings first pass through the multi-head self-attention mechanism, which allows each position to gather information from all other positions in the sequence. This output is then added to the original input through a residual connection and normalized using layer normalization. The normalized result passes through the position-wise feed-forward network, which applies the same transformation to each position independently. Finally, another residual connection and layer normalization produce the layer’s output, which becomes the input to the next layer in the stack.

Multi-Head Self-Attention: The Core Innovation

The multi-head self-attention mechanism represents the revolutionary heart of the transformer architecture. Unlike traditional recurrent neural networks that process sequences one element at a time, self-attention allows the model to consider all positions in the sequence simultaneously, computing relationships between every pair of positions regardless of their distance from each other.

Within each layer, the self-attention mechanism operates through three learned linear transformations that create Query (Q), Key (K), and Value (V) matrices from the input. The attention scores are computed by taking the dot product of queries with keys, scaling by the square root of the dimension, applying a softmax function, and finally multiplying by the values. This process determines how much attention each position should pay to every other position in the sequence.

The “multi-head” aspect means this attention operation is performed multiple times in parallel with different learned linear projections. Typically, transformers use eight or sixteen attention heads, each potentially learning to focus on different types of relationships in the data. Some heads might capture syntactic dependencies, while others might focus on semantic relationships or positional patterns. The outputs from all heads are concatenated and passed through another linear transformation to produce the final attention output.

The Mathematical Flow of Attention

For each attention head, the computation follows a precise mathematical formula. The attention weights are calculated as the softmax of scaled dot products between queries and keys. This scaling factor, which divides by the square root of the key dimension, prevents the dot products from becoming too large and pushing the softmax function into regions with extremely small gradients. After computing these attention weights, they’re multiplied by the value vectors to produce weighted combinations that represent the contextual information each position receives from all other positions.

The beauty of this mechanism lies in its ability to capture long-range dependencies without the sequential processing bottleneck of recurrent networks. Every position can directly attend to every other position in a single operation, making the transformer inherently parallelizable and capable of capturing relationships across vast distances in the input sequence. This parallel processing capability is one of the key reasons transformers can be trained efficiently on modern hardware and scaled to handle very long sequences.

Position-Wise Feed-Forward Networks

Following the multi-head attention sub-layer, each transformer layer contains a position-wise feed-forward network. This component applies the same fully connected feed-forward network to each position separately and identically. The feed-forward network consists of two linear transformations with a non-linear activation function in between, typically ReLU or GELU in modern implementations.

The feed-forward network serves several critical purposes in the transformer architecture. First, it introduces additional non-linearity beyond what the attention mechanism provides, allowing the model to learn more complex functions. Second, it operates as a form of feature transformation, projecting the attention output into a higher-dimensional space (typically four times the model dimension) before projecting it back down. This expansion and contraction allow the network to capture intricate patterns and interactions in the data.

The structure of the feed-forward network includes three key components:

• First linear transformation: Expands the dimensionality from the model dimension (e.g., 512) to a larger intermediate size (e.g., 2048), allowing for more expressive representations
• Activation function: Introduces non-linearity, enabling the network to model complex, non-linear relationships in the data
• Second linear transformation: Projects back down to the original model dimension, creating a bottleneck that forces the network to learn compressed, meaningful representations

This expansion and compression pattern is similar to the bottleneck architecture found in other deep learning models, but applied independently to each position in the sequence. The position-wise nature means that the same transformation is applied to each position, but there’s no mixing of information between positions at this stage—that’s handled exclusively by the attention mechanism.

Critical Architectural Components

Beyond the attention and feed-forward mechanisms, several other components play vital roles in the transformer layer architecture. Residual connections wrap around each sub-layer, adding the sub-layer’s input directly to its output. This architectural choice allows gradients to flow directly through the network during backpropagation, solving the vanishing gradient problem that plagued earlier attempts at training very deep neural networks. Without residual connections, training transformers with dozens or hundreds of layers would be virtually impossible.

Layer normalization is applied after each residual connection, stabilizing the learning process by normalizing activations across the feature dimension. This reduces internal covariate shift and allows the model to train more quickly and reliably. The specific placement of layer normalization has been the subject of much research, with some models using pre-normalization (applying normalization before sub-layers) instead of post-normalization, particularly for very deep architectures.

Dropout regularization is applied at multiple points within each layer: within the attention mechanism itself, in the feed-forward network, and to the residual connections. This random dropping of activations during training prevents overfitting by forcing the model to learn robust representations that don’t rely on any single pathway through the network.

Positional encoding deserves special mention, though it occurs before the first layer rather than within layers. Since the attention mechanism is inherently permutation-invariant—it treats the input as an unordered set—positional encodings are added to the input embeddings to inject information about the order of tokens in the sequence. These encodings use sine and cosine functions of different frequencies in the original paper, though learned positional embeddings are also common in modern implementations.

The Encoder-Decoder Structure

The original transformer architecture introduced a complete encoder-decoder structure, though many modern applications use only the encoder (like BERT) or only the decoder (like GPT). In the full architecture, the encoder consists of a stack of identical layers that process the input sequence, while the decoder contains a stack of layers that generate the output sequence.

The encoder layers are precisely as described above: multi-head self-attention followed by a feed-forward network, with residual connections and layer normalization around each. These layers can attend to all positions in the input sequence without any restrictions, allowing them to build rich, contextualized representations of the entire input.

The decoder layers add an additional component: a masked multi-head attention layer that prevents positions from attending to subsequent positions. This masking ensures the autoregressive property necessary for generation tasks—when generating the output sequence one token at a time, the model shouldn’t be able to “cheat” by looking at future tokens. Furthermore, decoder layers include a third sub-layer that performs multi-head attention over the encoder’s output, allowing the decoder to focus on relevant parts of the input sequence while generating each output token. This cross-attention mechanism creates the connection between the encoder and decoder stacks.

Layer Stacking and Information Flow

The power of transformer architecture emerges from stacking multiple identical layers. As information flows through successive layers, the model builds increasingly abstract and sophisticated representations of the input. Early layers often capture surface-level patterns and local dependencies, while deeper layers encode more abstract semantic relationships and global context.

Each layer refines the representations produced by the previous layer, with the multi-head attention allowing the model to integrate information from different positions and the feed-forward network transforming these integrated representations. The residual connections ensure that information from earlier layers can flow directly to later layers, preventing the degradation problem that affected earlier very deep neural networks. This architectural choice means that each layer only needs to learn the residual function—the difference between the input and desired output—rather than learning the entire transformation from scratch.

Research into transformer interpretability has revealed fascinating patterns in how different layers process information. Lower layers tend to focus on surface-level features and local contexts, similar to how convolutional neural networks process low-level visual features in early layers. Middle layers often capture syntactic structures and relationships between nearby tokens. Upper layers encode high-level semantic information and long-range dependencies, creating representations that capture the overall meaning and context of the input.

Parameter Sharing and Efficiency

While each layer performs the same operations, the parameters—the weights and biases that the model learns during training—are different for each layer. This means that a transformer with six layers doesn’t simply repeat the same computation six times; instead, each layer learns its own unique transformations that contribute to the overall model capacity. The number of parameters grows linearly with the number of layers, making depth a primary factor in model size and computational requirements.

The typical parameter distribution in a transformer layer allocates roughly equal amounts to the attention mechanism and the feed-forward network. For a model with hidden dimension 512 and feed-forward dimension 2048, the multi-head attention (including all projections) might contain around 2 million parameters per layer, while the feed-forward network contains a similar amount. This means a 12-layer transformer encoder could easily have over 100 million parameters, and modern large language models with dozens or hundreds of layers scale into the billions or even trillions of parameters.

Modern Variations and Optimizations

Since the original transformer was proposed, researchers have introduced numerous modifications to the layer architecture while maintaining its fundamental structure. Some models use pre-normalization (applying layer normalization before rather than after sub-layers), which can improve training stability for very deep networks. This seemingly small change has enabled the training of transformers with hundreds of layers that would otherwise suffer from optimization difficulties.

Others experiment with different attention mechanisms, such as sparse attention patterns that reduce the computational complexity of attending to very long sequences. Standard self-attention has quadratic complexity with respect to sequence length, making it computationally expensive for very long documents or high-resolution images. Sparse attention variants like local attention, strided attention, or learned sparse patterns can reduce this to linear or near-linear complexity while maintaining much of the modeling power.

Some recent architectures explore alternatives to the standard feed-forward network, such as mixture-of-experts layers where different sub-networks specialize in different types of inputs, or gated linear units that provide more expressive non-linearities. Others investigate different normalization schemes or alternative connection patterns between layers.

The basic principle, however, remains consistent across these variations: transformers achieve their remarkable performance through the careful composition of attention mechanisms and feed-forward networks, connected by residual pathways and stabilized by normalization. Understanding this layer architecture provides the foundation for working with and modifying transformer models for specific applications, whether in natural language processing, computer vision, or emerging domains like protein structure prediction and reinforcement learning.

Conclusion

The layer architecture of transformers represents an elegant solution to the challenge of modeling sequential data. By stacking layers that combine multi-head self-attention with position-wise feed-forward networks, transformers create powerful models capable of capturing complex patterns and long-range dependencies. The inclusion of residual connections and layer normalization makes these deep architectures trainable and effective, enabling the scaling to enormous model sizes that drive modern AI capabilities.

Understanding how these layers work together—from the parallel attention heads that capture different relationship types to the feed-forward networks that transform representations, all connected through residual pathways—provides crucial insight into why transformers have become the dominant architecture across diverse domains. This layered approach to building representations has proven remarkably flexible and scalable, driving breakthroughs in artificial intelligence that continue to shape the field and push the boundaries of what’s possible with machine learning.