About This Architecture

The Transformer architecture implements the encoder-decoder pattern with multi-head self-attention and cross-attention mechanisms, processing source and target token sequences through 6 stacked layers each. Source tokens flow through input embedding and positional encoding into the encoder stack, where multi-head self-attention and feed-forward networks extract contextual representations; target tokens follow a parallel path through the decoder with masked self-attention to prevent future token leakage. The decoder's cross-attention layer fuses encoder outputs with decoder states, then projects through linear and softmax layers to generate output probability distributions over the vocabulary. This architecture eliminates recurrence entirely, enabling parallel processing and superior long-range dependency modeling compared to RNNs. Fork and customize this diagram on Diagrams.so to document your transformer implementation, training pipeline, or attention mechanism variants.

People also ask

How does the Transformer architecture use multi-head attention to process sequences in parallel?

The Transformer splits input sequences into source and target tokens, embeds them with positional encodings, then passes them through 6 encoder and decoder layers. Each layer applies multi-head self-attention (8 heads) using scaled dot-product attention on Query, Key, and Value projections, followed by feed-forward networks and layer normalization. The decoder's masked self-attention prevents atte