[taliesinb]

Probably the main justification is that the analysis and transformation steps needed to compute the vjp and jvp pullbacks of a function (which correspond to reverse- and forward-mode automatic differentiation) require enough of the other machinery of a compiler that they are best done WITHIN a compiler. Then other things become quite natural, too, like producing the tangent vector versions of data structures like tuples and maps!

Moreover, a statically typed language like Swift is a much better starting point for this kind of effort than Python. Array shapes and dimensions are already a type system - you might as well go the whole distance and get all the other safety, readability, and efficiency benefits!

PS shout out for named array axes as the future of array-based (and hence differentiable) programming... see http://nlp.seas.harvard.edu/NamedTensor for a good rationale

[hsaliak]

jax does have a just in time compiler that lets you compile your python functions to XLA-optimized kernels (through llvmlite under the hood?).

The fact that you can jit compile and gain the benefits of "doing this within the compiler" is one of its main selling points.

[mlevental]

is there something in the proposal that addresses named tensors? ctrlf doesn't find anything.