Encoder and Decoder Stacks

Having explored the attention mechanisms and positional encodings that form the heart of the Transformer, we now assemble these components into the larger structures: the encoder and decoder stacks. The original Transformer model proposed a stack of N=6 identical layers for both the encoder and the decoder, although modern large language models often employ significantly more layers.

The Encoder Stack

The encoder's role is to process the input sequence and generate a sequence of context-rich representations. Each layer in the encoder stack receives a sequence of embeddings (or the output from the previous layer) and transforms it. A single encoder layer consists of two main sub-layers:

1. Multi-Head Self-Attention: This mechanism allows each position in the input sequence to attend to all positions (including itself) in the previous layer's output sequence. It calculates weighted representations based on the relevance between different parts of the sequence.
2. Position-wise Feed-Forward Network (FFN): This is a simple, fully connected feed-forward network applied independently to each position. It typically consists of two linear transformations with a non-linear activation function in between (commonly ReLU or GeLU). Its purpose is to further process the representations produced by the attention mechanism.

Crucially, residual connections are employed around each of the two sub-layers, followed by layer normalization. If $x$ is the input to a sub-layer (e.g., Multi-Head Attention or FFN) and $\text{Sublayer}(x)$ is the function implemented by the sub-layer itself, the output of the sub-layer block is $\text{LayerNorm}(x + \text{Sublayer}(x))$. This structure aids in training deeper models by facilitating gradient flow and stabilizing activations.

Flow within a single Transformer Encoder layer.

The output of one encoder layer serves as the input to the next identical encoder layer. The stacking allows the model to progressively build more complex representations of the input sequence, capturing dependencies at various levels.

Here's a simplified PyTorch representation of an encoder layer's structure:

import torch
import torch.nn as nn

class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(
            d_model,
            num_heads,
            dropout=dropout,
            batch_first=True
        )
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(), # Or GeLU, SwiGLU etc.
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask=None):
        # --- Self-Attention Block ---
        # Q, K, V are all from the input 'src'
        attn_output, _ = self.self_attn(
            src,
            src,
            src,
            key_padding_mask=src_mask,
            need_weights=False
        )
        # Residual connection and Layer Normalization
        src = self.norm1(src + self.dropout(attn_output))

        # --- Feed-Forward Block ---
        ff_output = self.feed_forward(src)
        # Residual connection and Layer Normalization
        src = self.norm2(src + self.dropout(ff_output))

        return src

class Encoder(nn.Module):
    def __init__(self, num_layers, d_model, num_heads, d_ff, dropout=0.1):
        super().__init__()
        self.layers = nn.ModuleList([
            EncoderLayer(d_model, num_heads, d_ff, dropout)
            for _ in range(num_layers)
        ])
        # Assuming input embeddings (incl. positional)
        # are handled outside

    def forward(self, src, src_mask=None):
        for layer in self.layers:
            src = layer(src, src_mask)
        return src # Final output representation

The final output of the entire encoder stack is a sequence of vectors, one for each input token, intended to capture the contextual meaning of that token within the sequence. This output is then typically used by the decoder.

The Decoder Stack

The decoder's role is often to generate an output sequence, one token at a time, based on the encoded input sequence and the tokens generated so far. Like the encoder, the decoder is also composed of a stack of N identical layers. Each decoder layer, however, has three main sub-layers:

1. Masked Multi-Head Self-Attention: This operates similarly to the encoder's self-attention, but with a crucial difference: it incorporates masking. The mask ensures that when predicting the output token at position $i$, the self-attention mechanism can only attend to positions less than or equal to $i$ in the decoder's input sequence. It cannot "look ahead" at future tokens, preserving the autoregressive property needed for generation.
2. Multi-Head Encoder-Decoder Attention: This is where the decoder interacts with the encoder's output. The queries ($Q$) come from the output of the previous decoder sub-layer (the masked self-attention). The keys ($K$) and values ($V$) come from the final output of the encoder stack. This allows every position in the decoder to attend to all positions in the input sequence, incorporating relevant context from the source.
3. Position-wise Feed-Forward Network (FFN): Identical in structure to the one used in the encoder layers, applied independently to each position after the encoder-decoder attention.

Similar to the encoder, residual connections and layer normalization are applied after each of these three sub-layers: $\text{LayerNorm}(x + \text{Sublayer}(x))$.

Flow within a single Transformer Decoder layer, highlighting the three sub-layers and inputs.

A simplified PyTorch structure for a decoder layer illustrates this:

import torch
import torch.nn as nn

class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(
            d_model,
            num_heads,
            dropout=dropout,
            batch_first=True
        )
        self.encoder_attn = nn.MultiheadAttention(
            d_model,
            num_heads,
            dropout=dropout,
            batch_first=True
        )
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(), # Or GeLU, SwiGLU etc.
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):
        # --- Masked Self-Attention Block ---
        # Q, K, V are all from the target input 'tgt'
        # 'tgt_mask' prevents attending to future positions
        attn_output, _ = self.self_attn(
            tgt,
            tgt,
            tgt,
            attn_mask=tgt_mask,
            need_weights=False
        )
        # Residual connection and Layer Normalization
        tgt = self.norm1(tgt + self.dropout(attn_output))

        # --- Encoder-Decoder Attention Block ---
        # Q from previous decoder layer ('tgt'), K & V from encoder output ('memory')
        # 'memory_mask' (optional) masks padding in the source sequence
        attn_output, _ = self.encoder_attn(
            tgt,
            memory,
            memory,
            key_padding_mask=memory_mask,
            need_weights=False
        )
        # Residual connection and Layer Normalization
        tgt = self.norm2(tgt + self.dropout(attn_output))

        # --- Feed-Forward Block ---
        ff_output = self.feed_forward(tgt)
        # Residual connection and Layer Normalization
        tgt = self.norm3(tgt + self.dropout(ff_output))

        return tgt

class Decoder(nn.Module):
    def __init__(self, num_layers, d_model, num_heads, d_ff, dropout=0.1):
        super().__init__()
        self.layers = nn.ModuleList([
            DecoderLayer(d_model, num_heads, d_ff, dropout)
            for _ in range(num_layers)
        ])
        # Assuming target embeddings (incl. positional) are handled outside

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):
        for layer in self.layers:
            tgt = layer(tgt, memory, tgt_mask, memory_mask)
        return tgt # Final output representation before linear/softmax

Final Output Generation

After the input passes through the entire decoder stack, the resulting sequence of vectors represents the predicted output tokens. To convert these vectors into probabilities over the target vocabulary, a final linear layer is typically applied, followed by a softmax function. The linear layer projects the decoder output vector (of dimension $d_{model}$) to the size of the vocabulary ($V$). The softmax function then converts these scores (logits) into probabilities, indicating the likelihood of each word in the vocabulary being the next token in the sequence.

$$P(y_i | y_{<i}, x) = \text{softmax}(\text{Linear}(\text{DecoderOutput}_i))$$

The combination of the encoder stack (processing the input) and the decoder stack (generating the output conditioned on the input and previous outputs) forms the complete Transformer architecture, capable of handling a wide range of sequence-to-sequence tasks. Understanding how these stacks are constructed from attention and feed-forward layers, along with normalization and residuals, is fundamental to building and modifying these powerful models.