Building a Transformer from Scratch in PyTorch

Short summary

A detailed walkthrough of building a transformer model from scratch in pure PyTorch without HuggingFace abstractions. Covers self-attention math, multi-head attention implementation, sinusoidal positional encoding, pre-norm vs post-norm architecture choices, and training decisions like AdamW with weight decay. The author shares practical mistakes (forgetting √d_k scaling) and their fixes, making it valuable for understanding transformer internals.

• •Build multi-head attention, positional encoding, and full transformer blocks in pure PyTorch
• •Key training decisions: pre-norm architecture, AdamW with 0.01 weight decay, 8 heads with d_model=512
• •Practical debugging insights like √d_k scaling preventing softmax saturation and training divergence

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