PyTorch Internals 18 - Where Autograd Meets Distributed Runtime
DDP and FSDP are not external magic; they depend directly on autograd timing and tensor-state management inside the runtime
Why this connection matters
PyTorch internals and distributed training are not separate worlds. DDP and FSDP depend directly on:
- autograd hook timing
- gradient readiness
- parameter state transitions
- runtime dispatch and execution order
That is why understanding internals makes distributed behavior much easier to reason about.
The next post looks at packaging and testing custom extensions so they can survive beyond a local experiment.