Understanding the Transformer Decoder

In sequence-to-sequence (seq2seq) tasks, the Transformer model employs an encoder-decoder architecture. While the encoder processes the input sequence to create a context-rich representation, the decoder's role is to leverage this representation to generate the target sequence, typically one element (e.g., word) at a time.

For instance, in machine translation, translating "I am good" (English) to "Je vais bien" (French) involves the encoder processing the English sentence and the decoder generating the French sentence word by word.

Decoder Architecture and Inputs

The Transformer decoder is composed of a stack of identical decoder blocks. Each decoder block receives two primary inputs:

1. Output from the previous decoder block: For the first block, this is the initial token embedding.
2. Encoder's final representation: This provides the contextual information from the input sequence.

These inputs enable the decoder to generate the output sequence while continuously referring to the full context of the input.

Autoregressive Decoding Process

The decoder operates in an autoregressive manner, meaning it generates the output sequence step by step, with each new output element depending on the previously generated ones.

Here's an illustration of the process for translating "I am good" to "Je vais bien":

• Time Step 1:

  • Input: The <sos> (start of sentence) token.
  • Output: The decoder generates the first word: Je.

• Time Step 2:

  • Input: <sos> and the previously generated word Je.
  • Output: The decoder predicts the next word: vais.

• Time Step 3:

  • Input: <sos>, Je, and vais.
  • Output: The decoder predicts the next word: bien.

• Time Step 4:

  • Input: <sos>, Je, vais, and bien.
  • Output: The decoder predicts the <eos> (end of sentence) token, signaling the completion of the generated sequence.

This process continues until the <eos> token is produced.

Embeddings and Positional Encoding

Similar to the encoder, the decoder does not process raw tokens directly. Instead:

1. Output Embeddings: Each token in the target sequence (or the <sos> token at the beginning) is converted into an embedding vector.
2. Positional Encoding: A positional encoding vector is added to the token embedding. This is crucial for preserving the order of the generated tokens, as the Transformer's self-attention mechanism is permutation-invariant by itself.

The resulting matrix, combining embeddings and positional encodings, is then fed into the decoder blocks.

Components of a Decoder Block

Each decoder block comprises three main sublayers, each followed by Add & Norm (residual connection and layer normalization):

1. Masked Multi-Head Self-Attention:

   • Purpose: This layer allows the decoder to attend to all previous positions in the output sequence but prevents it from attending to future positions.
   • Mechanism: A mask is applied to the attention scores, setting scores for future tokens to negative infinity, effectively zeroing them out.
   • Benefit: This ensures that the prediction for each token is only based on previously generated tokens and the input context, preventing "cheating" during training.

2. Multi-Head Cross-Attention:

   • Purpose: This layer enables the decoder to attend to the output representation of the encoder.
   • Mechanism: The queries come from the output of the masked self-attention layer, while the keys and values come from the encoder's final output.
   • Benefit: This is how the decoder aligns the output generation with relevant parts of the input sentence, capturing dependencies between the source and target sequences.

3. Position-wise Feedforward Neural Network:

   • Purpose: This sublayer applies non-linear transformations to the output of the cross-attention layer, further refining the representation.
   • Structure: It consists of two linear transformations with a ReLU activation in between, identical to the feedforward network in the encoder block.

The Add & Norm operations help stabilize training by mitigating vanishing gradients and ensuring that information from earlier layers can propagate effectively.

Summary

The Transformer decoder is fundamental to sequence generation in seq2seq tasks. It reconstructs the target sequence token by token, leveraging the encoded representation of the input. Its key mechanisms include:

• Masked Self-Attention: To process the history of generated tokens without peeking ahead.
• Cross-Attention: To integrate information from the encoder's output and align with the input context.
• Feedforward Networks: For further transformation and refinement of the representations.

By stacking these components and employing positional encodings, the decoder effectively learns to generate coherent and contextually relevant output sequences.

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Potential Interview Questions:

• What is the primary role of the Transformer decoder in an encoder-decoder model?
• How does the decoder utilize the encoder's output during the generation process?
• Explain the necessity and functionality of masked multi-head attention in the decoder.
• Differentiate between masked self-attention and cross-attention within the decoder.
• Describe the step-by-step input processing of the decoder at each time step.
• What is the significance of positional encoding in the context of the decoder?
• What is the outcome when the decoder generates the <eos> token?
• How do residual connections and layer normalization contribute to the decoder's training stability?
• List and briefly describe the three core sublayers of a Transformer decoder block.
• How does the Transformer decoder prevent the model from accessing future tokens during training?