PyTorch Internals 04 - What the Dispatcher and Operator Registry Actually Do
A single operator name in PyTorch may map to many implementations, and the dispatcher is the runtime layer that decides which one runs
Why the dispatcher matters
An operator such as add or layer_norm is not just one function body. There may be:
- a CPU implementation
- a CUDA implementation
- autograd-related behavior
- meta or shape-inference behavior
- special backend variants
The dispatcher is the runtime layer that chooses among those possibilities.
Why this matters for custom work
If you want to add a custom operator, you need more than kernel code. You need to understand:
- how operator schema is defined
- how backend-specific kernels are registered
- how autograd interacts with dispatch
The next post moves to autograd, which is the execution layer behind loss.backward().