How does PyTorch autograd handle multiple backward passes on the same graph?

By default, PyTorch frees the computation graph after backward; multiple backward passes require retain_graph=True.

Autograd frees intermediate buffers after backward to save memory. If a second backward pass is needed on the same graph, retain_graph=True must be used to preserve saved tensors. Otherwise, PyTorch raises an error because required intermediate values no longer exist.