On Mon, Apr 22, 2019 at 11:13 PM Stephan Hoyer <shoyer@gmail.com> wrote:
On Mon, Apr 22, 2019 at 9:26 PM Nathaniel Smith <njs@pobox.com> wrote:
Your last email didn't really clarify anything for me. I get that np.func.__numpy_implementation__ is intended to have the semantics of numpy's implementation of func, but that doesn't tell me much :-). And also, that's exactly the definition of np.func, isn't it?
My understanding of the protocol we came up with in NEP-18 is that every NumPy function (that takes array-like arguments) now has two parts to its implementation: 1. The NEP-18 part involving calling the dispatcher function, and checking for/calling __array_function__ attributes on array-like arguments. This part is documented in NEP-18. 2. The original function definition, which is called if either (a) no __array_function__ attributes exist, or (b) the only __array_function__ attribute is numpy.ndarray.__array_function__. This part is documented in the docstring of the NumPy function.
"__numpy_implementation__" provides a short-cut to (2) without (1). That's it.
OK, so the semantics are: the same as the normal function, except we pretend that none of the arguments have an __array_function__ attribute? That's much clearer to me than how you were phrasing it before :-). Though now the name "__numpy_implementation__" doesn't seem very evocative of what it does... numpy's dispatch sequence has changed a lot in the past (mostly adding new coercion rules), and will probably change in the future, and "__numpy_implementation__" doesn't give much guidance about which parts of the dispatch sequence should be skipped as "dispatch" and which should be included as "implementation". Maybe something like __skipping_array_function__? -n -- Nathaniel J. Smith -- https://vorpus.org