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