PyTorch Internals 14 - AMP, Autocast, and Numerical Stability
A production-quality custom operator has to behave correctly under mixed precision, not just benchmark well in isolation
Why this matters
Real training runs usually rely on mixed precision. That means custom operators have to make good decisions about:
- execution dtype under autocast
- accumulation precision
- overflow and underflow risk
- stability of reductions and normalization logic
The next post uses profiling to connect these internal concerns back to real performance traces.