custom backward pass

Build Your Own Transformer Autograd to See How Gradients Flow

Understanding how gradients actually flow through a Transformer's attention blocks is the kind of intuition that separates surface-level users from builders. This minimal, self-contained autograd implementation in PyTorch strips away the abstraction, letting you trace backward passes with your own eyes. It's a practical reference for writing custom gradients or debugging complex models. For a broader look at how hands-on implementation demystifies this architecture, our article on **Demystifying Transformer Architecture Through Hands-On Implementation** pairs well with this code.