trunk/56c0c73c2937d025a9c6551dc949b54383da3e62: [autogradable leaf module] add effect token support (#174122)
A subtle but powerful change to PyTorch's core autograd engine just landed.
Deep Dive
A new commit to PyTorch's main development branch (trunk) adds support for 'effect tokens' to autogradable leaf modules via `invoke_leaf_function`. The change, approved by core maintainers, specifically handles cases where a forward pass has no output, ensuring backward propagation behaves as expected. This is a low-level but foundational update to the framework's automatic differentiation system, which underpins nearly all modern deep learning training loops.
Why It Matters
This core engine tweak could enable more complex, stateful model architectures and improve training stability for advanced AI research.