[Numpy-discussion] NEP 31 — Context-local and global overrides of the NumPy API

Nathaniel Smith njs at pobox.com
Fri Sep 6 19:50:46 EDT 2019

On Fri, Sep 6, 2019 at 2:45 PM Ralf Gommers <ralf.gommers at gmail.com> wrote:
> There may be another very concrete one (that's not yet in the NEP): allowing other libraries that consume ndarrays to use overrides. An example is numpy.fft: currently both mkl_fft and pyfftw monkeypatch NumPy, something we don't like all that much (in particular for mkl_fft, because it's the default in Anaconda). `__array_function__` isn't able to help here, because it will always choose NumPy's own implementation for ndarray input. With unumpy you can support multiple libraries that consume ndarrays.

unumpy doesn't help with this either though, does it? unumpy is
double-opt-in: the code using np.fft has to switch to using unumpy.fft
instead, and then someone has to enable the backend. But MKL/pyfftw
started out as opt-in – you could `import mkl_fft` or `import pyfftw`
– and the whole reason they switched to monkeypatching is that they
decided that opt-in wasn't good enough for them.

>> The import numpy.overridable part is meant to help garner adoption, and to prefer the unumpy module if it is available (which will continue to be developed separately). That way it isn't so tightly coupled to the release cycle. One alternative Sebastian Berg mentioned (and I am on board with) is just moving unumpy into the NumPy organisation. What we fear keeping it separate is that the simple act of a pip install unumpy will keep people from using it or trying it out.
> Note that this is not the most critical aspect. I pushed for vendoring as numpy.overridable because I want to not derail the comparison with NEP 30 et al. with a "should we add a dependency" discussion. The interesting part to decide on first is: do we need the unumpy override mechanism? Vendoring opt-in vs. making it default vs. adding a dependency is of secondary interest right now.

Wait, but I thought the only reason we would have a dependency is if
we're exporting it as part of the numpy namespace. If we keep the
import as `import unumpy`, then it works just as well, without any
dependency *or* vendoring in numpy, right?


Nathaniel J. Smith -- https://vorpus.org

More information about the NumPy-Discussion mailing list