PyTorch Internals 06 - When and How to Use a Custom Autograd Function
Custom autograd functions are a practical place to define forward-backward contracts before dropping to lower-level extensions
Why this layer matters
torch.autograd.Function is often the first place where you explicitly define the contract of a new operation.
It is useful because it lets you:
- prototype new semantics quickly
- define backward behavior directly
- validate an interface before writing lower-level code
But it also makes you responsible for saved state, gradient shape, dtype/device rules, and performance tradeoffs.
The next post shifts to memory lifetime and the caching allocator.