Transformer Model Architecture Overview

The original Transformer model architecture is divided into an encoder and a decoder, each composed of a stack of 6 identical layers. Here are the key components:

Encoder:

• Multi-Head Self-Attention: Allows the encoder to consider other words in the input when encoding a particular word.
• Position-wise Feed-Forward Networks: A simple fully connected neural network applied to each position separately and identically.
• Residual Connection & Layer Normalization: Around each of the two sub-layers.

Decoder:

• Multi-Head Self-Attention: Similar to the encoder but also includes a masking mechanism to prevent future information from affecting the current and past states.
• Encoder-Decoder Attention: Allows every position in the decoder to attend over all positions in the encoder.
• Position-wise Feed-Forward Networks: Same as in the encoder.
• Residual Connection & Layer Normalization: Same as in the encoder.

Both the encoder and decoder use multi-head attention mechanisms, where the attention function is run in parallel multiple times and the results are concatenated.

Dimensions:

• Output of each sub-layer and the embedding layers: $d_{\text{model}} = 512$
• Inner-layer of the feed-forward networks: $d_{\text{ff}} = 2048$

Additional Features:

• Positional Encoding: Added to the input embeddings to give the model information about the position of each word.

This architecture leverages solely attention mechanisms, dispensing with recurrence and convolutions entirely. It's designed for high parallelization and low training time while achieving state-of-the-art performance on sequence transduction tasks.

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