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

Overview

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/dri

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