Implementing Self Attention from scratch using Pytorch

Deep Learning
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Hi All,

I have written detailed blog post about implementing self attention from scratch using PyTorch. I have tried to keep things simple and jargon free with step by step explanation.

The primary objective of this article is to follow @jeremy advice write a blog post about your learning and explain a concept in simple words.

Here is a link, do read and let me know your feedback (both positive and negative).

2 Likes
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… loved reading your blog – especially the librarian analogy, that was an eye-opener. Thanks a lot! :+1:

This is a clear, beginner-friendly tutorial aimed at readers who already know basic PyTorch (tensors, nn.Linear, softmax) and want to understand the internals of self-attention – the heart of modern LLMs.

Tip: I like concepts and code – would invite you to do the same

You discussed: tokenization / embedding / cosine-similarity / attention.
The embedding and attention parts were very good. Tokenization and cosine-similarity could benefit from a bit more understanding

Since you like PyTorch as much as I do, maybe add a small note about F.scaled_dot_product_attention

Also, a quick note on the shape transition when introducing batches:
[context, attn_dim] → [batch, context, attn_dim]
(and when you introduce multi-head: [batch, context, heads, attn_dim])

Maybe your blog could end by: Next steps could be:

  1. Positional encoding
  2. Masking
  3. Multi-head attention
  4. Residual blocks / LayerNorm
  5. Encoder-Decoder
  6. Training / loss

So, well done – nice example – thx and keep blogging …

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Many thanks for your pointed feedback on what you liked it and what could be improved, I really like your approach. I have taken your feedback into consideration and I’ll update this post in next few days. In the meantime I have also started writing about Multi-Head Attention.