The Transformer Architecture: Attention-Only, End to End
You have the parts — attention, heads, positions. Now assemble the machine: stacked attention + FFN with residuals and LayerNorm, the encoder/decoder/cross-attention wiring, and the three model families that split in 2018 — with decoder-only winning 2020-2026.
01.The Problem: Great Parts, No Machine
Inventory check. You now own:
- scaled dot-product attention (Topic 120): any token looks at any token,
- heads (Topic 122): eight parallel looks per layer,
- positions (Topic 123): order finally visible.
So... just stack them and go?
Try, and two failures bite immediately.
Failure one: one layer is one round of looks. After a single attention pass, "it" knows about "street" — but nothing has thought about what that mixture means, and no second-order facts ("who does what to whom") exist yet. Understanding needs many rounds.
Failure two: deep stacks break. Recall Topic 113 (in one sentence: gradients vanish through long chains of multiplied steps). Stack 96 attention blocks and the backward pass must thread 192 sublayers. Raw deep networks were famously un-trainable before residuals existed.
So the assembly problem is:
How do you repeat attention many times, add per-token thinking between rounds, and keep gradients alive through the whole depth?
The 2017 answer fits in one sentence:
A Transformer layer = mix information across positions, digest it per position, and stabilize both with a residual + norm — then do that N times.
That is the whole architecture. Everything else is wiring choices around it.
The Original Transformer: Two Stacks, One Contract 🏗️
The Original Transformer: Two Stacks, One Contract 🏗️
Every arrow is Topics 119-123. Encoder = understand fully; decoder = generate causally while re-reading the encoder’s states via cross-attention; add&norm and FFN make the stack trainable.
Unlock Topic #124: The Transformer Architecture: Attention-Only, End to End
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