PyTorch Internals 10 - Connecting a Custom CUDA Kernel Through an Extension
A CUDA kernel becomes a real PyTorch operator only when tensor contracts, runtime semantics, and integration details are handled correctly
Why kernel speed is not enough
A fast CUDA kernel still needs to become a correct PyTorch operator. That means:
- validating tensor shape, dtype, and device
- handling stream semantics properly
- matching output and error behavior
- preparing for backward integration if needed
The next post focuses on schema, dispatch keys, and meta functions, which are central to making custom operators fit well into modern PyTorch.