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Encoder-Decoder Structure of Transformer

The Transformer model is built on a simple yet powerful Encoder-Decoder structure. Each part plays a distinct role in processing input data and generating the output sequence.

Encoder:

• The Encoder is responsible for processing the input sequence.
• It takes a sequence of tokens (e.g., words or image patches) and transforms them into a series of continuous representations that capture the contextual information of each token.
• The encoder is composed of N identical layers, each containing two main sublayers:
  1. Multi-Head Self-Attention Mechanism: This mechanism allows each token in the sequence to attend to every other token, ensuring the model captures both local and global context.
  2. Feed-Forward Neural Network (FFNN): A fully connected network applied to each token’s representation independently. It enhances the model's ability to learn complex relationships.

Decoder:

• The Decoder generates the output sequence from the encoded representations.
• Similar to the encoder, the decoder consists of N identical layers, but with an additional Masked Multi-Head Self-Attention mechanism to prevent the model from seeing future tokens (in tasks like language generation).
• Each layer in the decoder includes:
  1. Masked Multi-Head Self-Attention: Ensures that the decoder predicts one token at a time by attending only to previous tokens.
  2. Multi-Head Attention over the Encoder Output: Allows the decoder to attend to the encoder's output, effectively linking input and output sequences.
  3. Feed-Forward Neural Network: Just like the encoder, this helps refine the representations learned by the model.

Differences Between Encoder and Decoder

• Encoder:

  • Focuses on understanding the input sequence by creating contextual representations.
  • Utilizes Self-Attention to relate every token to every other token within the input.

• Decoder:

  • Generates the output sequence by attending to both the input representations and previously generated output tokens.
  • Utilizes Masked Self-Attention to prevent the model from accessing future tokens in the sequence.

The interaction between these two components allows the Transformer to perform tasks like translation, where the input (source language) is encoded, and the output (target language) is generated step-by-step.

Importance of Self-Attention and Multi-Head Attention

The Self-Attention mechanism is the backbone of the Transformer. It enables the model to capture dependencies between tokens, regardless of their distance from each other in the sequence. This is crucial in NLP tasks, where the meaning of a word often depends on the context provided by distant words.

How Self-Attention Works:

Each token in the sequence is transformed into three vectors: Query (Q), Key (K), and Value (V). The self-attention mechanism computes the relevance of one token to all other tokens by calculating a weighted sum of the value vectors, where the weights are derived from the dot products of queries and keys.

Multi-Head Attention:

Instead of computing attention only once, Multi-Head Attention allows the model to focus on different parts of the sequence simultaneously. Each "head" performs self-attention with different sets of learned parameters. The outputs from all heads are concatenated and passed through a linear layer, enriching the model’s representation by attending to multiple aspects of the sequence at once.

Positional Encoding: Why and How It’s Used

Unlike RNNs, Transformers have no inherent way of capturing the order of tokens in a sequence. To address this, the model adds Positional Encoding to the input embeddings. These encodings provide the model with information about the position of each token in the sequence, ensuring that the model understands the sequential relationships between tokens.

How Positional Encoding Works:

• The positional encoding is a set of vectors added to the input embeddings. These vectors are generated using a combination of sine and cosine functions of different frequencies.
• The formula for positional encoding is designed to ensure that the model can differentiate between the positions of tokens in sequences of varying lengths.

Positional encoding enables Transformers to excel at sequence-based tasks, making them effective in applications like language translation and text summarization, where the order of words is essential.

Conclusion

The basic Transformer architecture’s Encoder-Decoder structure, coupled with self-attention, multi-head attention, and positional encoding, forms the foundation of modern machine learning models. Its ability to process input sequences in parallel, capture long-range dependencies, and handle sequential information has made it a game-changer in the field of machine learning.

In the next post, we will dive deeper into the mathematics behind the Self-Attention Mechanism, including how Queries, Keys, and Values are calculated and used. Stay tuned!