Transformer Architecture: From Token Embeddings to the Training Loop

Mon, 14 Sep 2026 10:25:02 +0200

Transformer appears to be made up of many components, but a GPT-style forward path can be summarized as:

Token IDs
   ↓
Token Embedding + Position Embedding
   ↓
[LayerNorm → Self-Attention → Residual]
   ↓
[LayerNorm → FFN → Residual]
   ↓
Repeat for multiple Transformer blocks
   ↓
Final LayerNorm → LM Head
   ↓
Next-token logits → Cross-Entropy Loss

This article starts from a simple GPT implementation and explains the role and tensor shape of each step along the data flow. The complete source code is placed at the end of the article. After reading the previous principles, you can compare and understand it with the code.

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Transformer Architecture: From Token Embeddings to the Training Loop