Understanding Transformer Autograd by Building It Manually in PyTorch [P]

I’ve uploaded a minimal, self-contained implementation of manual autograd for a transformer-based classifier in PyTorch.

It can help build intuition for what autograd is doing under the hood and is a useful hands-on reference for low-level differentiation in Transformer models, such as writing custom backward passes and tracing how gradients flow through attention blocks.

• 🐙 GitHub: https://github.com/ifiaposto/transformer_custom_autograd/tree/main
• 📓 Colab: https://colab.research.google.com/drive/1Lt7JDYG44p7YHJ76eRH_8QFOPkkoIwhn