This is awesome! Thank you very much.
I'll wait for the device then. 😎
mlajtos
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It seems, that this is indeed impossible due to MLIR dialect for Structured Control Flow.
https://llvm.discourse.group/t/scf-whileop-type-inference-with-dynamic-shapes/2529
The problem is with broadcasted template. Tensor with "static" shape works OK.
extension MPSDataType {
func description() -> String {
switch(self) {
case .float32:
return "f32"
case .int32:
return "i32"
// other...
default:
return "?"
}
}
}
Thank you, AncientCoder. I had a different use case in mind. You proposed using pre-made architectures that can be trained on the device (classifiers, regressors, etc.), but I meant something much lower-level than this.
For example, I would like to be able to create *.mlmodel that takes 2 inputs and just adds them together. Or any architecture using available ops. I can write MIL definition, but there is no way of turning it to the mlmodel/mlpackage in Swift, only using coremltools in Python.
It seems that MPSGraph is probably the best fit for my needs right now.