What's the difference between

np.concatenate.__numpy_implementation__(...)

and

np.ndarray.__array_function__(np.concatenate, ...)

?

More generally, I guess I'm not quite clear on how to think about what the "no dispatch" version does, because obviously it doesn't make sense to have *no* dispatch. Instead it's something like "the legacy hard-coded dispatch"?

On Mon, Apr 15, 2019, 08:30 Stephan Hoyer <shoyer@gmail.com> wrote:

Hi everyone,_______________________________________________We have a proposed a revision to NEP-18 (__array_function__). The proposal is for an adding an alias to the non-dispatched version of NumPy array functions in the __numpy_implementaiton__ function attribute:I believe this attribute improves the protocol in three ways:1. It provides a hook that __array_function__ methods can use to call implementation intended for NumPy arrays. This allows for "partial implementations" of NumPy's API, which turns out to useful even for some array libraries that reimplement nearly everything (namely, for CuPy and JAX).2. It allows for fast access to the non-dispatching version of NumPy functions, e.g., np.concatenate.__numpy_implementation__(list_of_all_numpy_arrays).3. Internally, the implementation of numpy.ndarray.__array_function__ now looks identical to how we encourage outside developers to write their own __array_function__ methods. The dispatching logic no longer includes a special case for NumPy arrays.Feedback would be greatly welcomed!Best,Stephan

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