Combining All Decoder Components in the Transformer Architecture

This document outlines the process of integrating all the components of the Transformer's decoder, explaining how they work together within the stacked architecture to generate output sequences.

Understanding the Full Decoder Stack

The Transformer decoder is designed as a stack of identical blocks. Each block meticulously processes the target sequence in stages, enabling the model to predict one word at a time while maintaining a comprehensive understanding of the context. The following sections detail the step-by-step flow of information through these stacked decoder blocks.

Step-by-Step Decoder Process

The decoder's operation can be understood by following the data flow through its sublayers and subsequent blocks.

1. Input Embedding and Positional Encoding

The process begins with the target sequence, which is typically prefixed with a start-of-sequence token (e.g., <sos>).

• Input Embedding: The target sequence (e.g., <sos> Je vais) is first transformed into a matrix of dense vector representations, known as embeddings. Each word is mapped to a high-dimensional vector that captures its semantic meaning.
• Positional Encoding: To inject information about the order of words in the sequence, positional encodings are added to the input embeddings. This ensures the model understands word order, which is crucial for sequence generation.

This combined embedding and positional encoding matrix is then fed into the first decoder block (Decoder 1).

2. Masked Multi-Head Attention

The first sublayer within a decoder block is the Masked Multi-Head Attention mechanism.

• Purpose: This layer is responsible for performing self-attention on the target sequence. The "masked" aspect is critical: it prevents the model from attending to subsequent tokens in the target sequence during training. This is essential for maintaining the autoregressive property, where the prediction of the current word depends only on previously generated words.
• Mechanism: It computes attention scores between each token in the target sequence and all preceding tokens.
• Output: The result of this sublayer is an attention matrix (let's call it A) that represents the context derived solely from the preceding parts of the target sequence.

3. Encoder-Decoder Multi-Head Attention

Following the masked self-attention, the decoder incorporates context from the source sequence via the Encoder-Decoder Multi-Head Attention sublayer.

• Inputs: This sublayer takes two primary inputs:
  • The refined target sequence representation from the previous masked multi-head attention step (A).
  • The output representations from the encoder stack (often referred to as encoder outputs or memory, R).
• Purpose: This mechanism allows the decoder to attend to relevant parts of the source sentence while generating the target sequence. It effectively bridges the gap between the source and target modalities.
• Mechanism: It computes attention scores between the current decoder state (queries) and the encoder's output (keys and values).
• Output: The output is a new, refined attention matrix that incorporates context from both the source and target sequences.

4. Feedforward Network

The output from the encoder-decoder attention sublayer is then passed through a Position-wise Feedforward Network.

• Purpose: This network applies non-linear transformations independently to each position in the sequence. It helps to further process and enrich the representations learned through the attention mechanisms.
• Structure: It typically consists of two linear transformations with a ReLU activation function in between.
• Output: The output of the feedforward network is the decoder representation for the current layer, after processing the target sequence with source context.

5. Passing to the Next Decoder Block

The output from Decoder Block 1 is not the final output. Instead, it is passed on to the next decoder block (Decoder 2) in the stack.

• Iteration: Decoder 2 receives the output from Decoder 1 and repeats the same three-step process: masked multi-head attention, encoder-decoder multi-head attention, and the feedforward network.
• Stacking: This process continues for all N decoder layers in the stack. Each layer refines the representation of the target sequence, building upon the context learned by the previous layers.

6. Final Output Representation

After passing through the topmost decoder block, the final decoder representation is produced.

• Linear Layer: This final representation is fed into a linear layer. This layer maps the high-dimensional decoder representation to a vector whose dimension is equal to the size of the target vocabulary. These output values are often referred to as "logits."
• Softmax Function: A softmax function is then applied to the logits. This converts the raw scores into a probability distribution over the entire target vocabulary.
• Word Selection: The word with the highest probability in this distribution is selected as the next predicted word in the output sequence. This predicted word is then appended to the target sequence and fed back into the decoder for the next prediction step, completing the autoregressive generation loop.

Conclusion

The Transformer decoder's efficacy stems from the synergistic integration of its components:

• Masked Self-Attention: Enables context awareness within the target sequence while preserving the autoregressive property.
• Encoder-Decoder Attention: Effectively aligns and incorporates relevant information from the source sequence.
• Feedforward Networks: Introduce non-linear transformations for richer feature extraction.
• Stacked Architecture: Allows for hierarchical learning and increasingly abstract representations.

By processing the target sequence autoregressively and leveraging the full source context provided by the encoder, the decoder generates context-aware and highly accurate predictions.

In the next section, we will explore how the encoder and decoder components are integrated to form the complete Transformer model, enabling end-to-end sequence-to-sequence transduction.

SEO Keywords

Transformer decoder architecture, Masked multi-head attention, Encoder-decoder attention mechanism, Transformer feedforward network, Positional encoding in Transformers, Autoregressive text generation, Add and Norm layer in Transformer, Linear and softmax layers in decoder

Interview Questions

• What is the role of the Transformer decoder in sequence-to-sequence models?
• How does masked multi-head attention work and why is masking necessary?
• Can you explain how encoder-decoder attention helps in the decoding process?
• What is the purpose of positional encoding in the decoder?
• Describe the feedforward network sublayer in the Transformer decoder.
• Why are residual connections and layer normalization important in the decoder?
• How does the decoder generate words autoregressively?
• What happens in the linear and softmax layers at the end of the decoder?
• How are query, key, and value matrices computed in encoder-decoder attention?
• What differences exist between self-attention in the encoder and masked self-attention in the decoder?