Today, things like PyTorch are AD systems, optimization systems, libraries of high performance interoperable common blocks, and more. This massive complexity is why there is no standard. If you have a compiler front end that can AD arbitrary code, then the framework no longer has to worry about that. If you have a compiler backend that can target multiple devices, then the framework doesn’t have to do that, either. The size and complexity of PyTorch/TensorFlow is exactly why there isn’t a standard hardware portable NN API. A sane API of hardware independent common blocks can’t happen without all the front and backend compiler stuff first.

A PyTorch/TensorFlow-like API is always going to be massive, complicated, and hard to port to new hardware targets. Additionally, the complexity and reliance on C alone will make integrating exotic new concepts like say, differential equation solvers, extremely laborious.