Transformer Model Architecture: Encoder-Decoder Structure with Attention Mechanisms

This image depicts a visual representation of a Transformer model architecture, commonly used in natural language processing (NLP) tasks. Here’s a breakdown of the words in the image, followed by an explanation of what is happening in this architecture:

Words in the Image (organized by sections and labels):

1. Overall Labels:

- Encoder

- Decoder

- Input Embedding

- Output Embedding

- Positional Encoding

- Feed-forward network: after taking information from other tokens, take a moment to think and process this information

- Encoder self-attention: tokens look at each other

- Decoder-encoder attention: target token looks at the source

- Decoder self-attention (masked): tokens look at the previous tokens

- Residual connections and layer normalization

- Output Probabilities

- Softmax

- Linear

2. Components in Encoder and Decoder:

- Add & Norm

- Multi-Head Attention

- Masked Multi-Head Attention

- Feed Forward

- Nx (indicating repeating layers in both encoder and decoder stacks)

3. Descriptive Texts:

- Encoder self-attention: tokens look at each other

- Queries, keys, values are computed from encoder states

- Decoder-encoder attention: target token looks at the source

- Queries – from decoder states; keys and values from encoder states

- Decoder self-attention (masked): tokens look at the previous tokens

- Queries, keys, values are computed from decoder states

- Feed-forward network: after taking information from other tokens, take a moment to think and process this information

---

### What Happens in This Transformer Model:

1. Input Embedding and Positional Encoding:

- The input tokens (words or characters) are first converted into embeddings, which are vector representations of the tokens. Positional encoding is then added to these embeddings to incorporate information about the position of each token, as Transformers do not have a built-in sequence order mechanism.

2. Encoder Stack:

- The encoder stack consists of multiple layers (represented by Nx, where each layer repeats the same structure). Each layer has:

- Multi-Head Attention: This mechanism allows each token to focus on other tokens in the sequence, capturing relationships between words (like how words in a sentence relate to each other).

- Add & Norm: Residual connections and layer normalization are applied after the attention mechanism and feed-forward network to stabilize training.

- Feed-Forward Network: After attention processing, this network further processes each token independently, helping the model refine and understand the input.

3. Decoder Stack:

- The decoder also consists of multiple layers (denoted by Nx), with three main sub-layers in each layer:

- Masked Multi-Head Attention: This layer processes the output sequence generated so far, attending only to previous tokens to prevent the model from looking ahead (hence, "masked").

- Decoder-Encoder Attention: This layer enables the decoder to focus on relevant parts of the encoder’s output, helping the model align the target and source sentences.

- Feed-Forward Network: Like in the encoder, this network processes each token independently to refine the output.

4. Final Output:

- The processed tokens are passed through a linear layer and softmax function to generate output probabilities for each token in the target vocabulary, predicting the next word or token in the sequence.

• In essence, the Transformer model processes an input sequence through an encoder, attends to relevant information in both the encoder and decoder, and generates a corresponding output sequence step-by-step, a mechanism crucial in tasks like translation and text generation.