Attention Is All You Need

Transformer (Vaswani et al., 2017) replaces recurrence and convolution with self-attention, making sequence modeling highly parallel, scalable, and better at capturing long-range dependencies.

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Core ideas (quick read)

Attention Is All You Need introduces the Transformer: a sequence model built entirely on attention. It avoids the step-by-step recursion of RNNs and the stacked local convolutions of CNNs, enabling efficient parallel training and strong results on tasks like machine translation.

Key contributions at a glance

Self-Attention: each token can directly attend to any other token in the sequence. This shortens the interaction path and makes long-range information flow easier.
Multi-Head Attention: learn multiple alignment patterns in parallel (semantic, syntactic, positional, etc.), then concatenate and mix them.
Positional Encoding: inject order information without recurrence or convolution.
Stacked encoder/decoder: compose attention, feed-forward networks, residual connections, and LayerNorm into a scalable deep architecture.

Transformer architecture (simplified)

Fig. 1 — Encoder/decoder stack (schematic; not to scale)

How self-attention is computed (flow)

Fig. 2 — Main steps from inputs to attention output

Multi-Head Attention (split → parallel → concat)

Fig. 3 — Split into heads, run attention in parallel, then concatenate and project

Attention heatmap (example visualization)

Attention (simplified equation)

Attention(Q, K, V) = softmax(QKᵀ / √dₖ) · V

Why it scales: parallelism and long-range interaction

Approach	Interaction	Training parallelism	Long-range dependencies
RNN/LSTM	Step-by-step recurrence over time	Weak (inherently sequential)	Long path; signal can decay
CNN	Stacked local convolutions expand receptive field	Strong (parallel within a layer)	Needs more layers to cover long context
Self-Attention	Global token-to-token interaction + weighted aggregation	Strong (matrix-friendly)	Direct interaction; short path

Details to watch for while reading

Masking: decoder self-attention masks future tokens to preserve autoregressive causality.
Cross-Attention: the decoder attends over encoder outputs to selectively read the source sequence.
Head split + concat: split the hidden dimension into heads, run attention per head, then concatenate and apply a linear projection.
Residual + LayerNorm: crucial for stable training when stacking many layers.