How does backpropagation work in PyTorch at a low level?

Backpropagation computes gradients by traversing the dynamic computation graph in reverse.

PyTorch builds a DAG during forward pass where each node stores function metadata. During backward(), gradients are propagated using chain rule from output to inputs. Each tensor stores grad_fn linking to its creator operation.