Hello all,

It was recently brought to my attention that my mails to NumPy-discussion were probably going into the spam folder for many people, so here I am trying from another email. Probably Google trying
to force people onto their products as usual. 😉

Me, Ralf Gommers and Peter Bell (both cc’d) have come up with a proposal on how to solve the array creation and duck array problems. The solution is outlined
in NEP-31, currently in the form of a PR, [1]

Following the high level discussion in NEP-22. [2]

It would be nice to get some feedback.

Full-text of the NEP:

============================================================

NEP 31 — Context-local and global overrides of the NumPy API

============================================================

:Author: Hameer Abbasi <habbasi@quansight.com>

:Author: Ralf Gommers <rgommers@quansight.com>

:Author: Peter Bell <peterbell10@live.co.uk>

:Status: Draft

:Type: Standards Track

:Created: 2019-08-22

Abstract

--------

This NEP proposes to make all of NumPy's public API overridable via an

extensible backend mechanism, using a library called ``uarray`` `[1]`_

``uarray`` provides global and context-local overrides, as well as a dispatch

mechanism similar to NEP-18 `[2]`_. First experiences with ``__array_function__``

show that it is necessary to be able to override NumPy functions that

*do not take an array-like argument*, and hence aren't overridable via

``__array_function__``. The most pressing need is array creation and coercion

functions - see e.g. NEP-30 `[9]`_.

This NEP proposes to allow, in an opt-in fashion, overriding any part of the NumPy API.

It is intended as a comprehensive resolution to NEP-22 `[3]`_, and obviates the need to

add an ever-growing list of new protocols for each new type of function or object that needs

to become overridable.

Motivation and Scope

--------------------

The motivation behind ``uarray`` is manyfold: First, there have been several attempts to allow

dispatch of parts of the NumPy API, including (most prominently), the ``__array_ufunc__`` protocol

in NEP-13 `[4]`_, and the ``__array_function__`` protocol in NEP-18 `[2]`_, but this has shown the

need for further protocols to be developed, including a protocol for coercion (see `[5]`_). The reasons

these overrides are needed have been extensively discussed in the references, and this NEP will not

attempt to go into the details of why these are needed. Another pain point requiring yet another

protocol is the duck-array protocol (see `[9]`_).

This NEP takes a more holistic approach: It assumes that there are parts of the API that need to be

overridable, and that these will grow over time. It provides a general framework and a mechanism to

avoid a design of a new protocol each time this is required.

This NEP proposes the following: That ``unumpy`` `[8]`_ becomes the recommended override mechanism

for the parts of the NumPy API not yet covered by ``__array_function__`` or ``__array_ufunc__``,

and that ``uarray`` is vendored into a new namespace within NumPy to give users and downstream dependencies

access to these overrides. This vendoring mechanism is similar to what SciPy decided to do for

making ``scipy.fft`` overridable (see `[10]`_).

Detailed description

--------------------

**Note:** *This section will not attempt to explain the specifics or the mechanism of ``uarray``,

that is explained in the ``uarray`` documentation.* `[1]`_ *However, the NumPy community

will have input into the design of ``uarray``, and any backward-incompatible changes

will be discussed on the mailing list.*

The way we propose the overrides will be used by end users is::

import numpy.overridable as np

with np.set_backend(backend):

x = np.asarray(my_array, dtype=dtype)

And a library that implements a NumPy-like API will use it in the following manner (as an example)::

import numpy.overridable as np

_ua_implementations = {}

__ua_domain__ = "numpy"

def __ua_function__(func, args, kwargs):

fn = _ua_implementations.get(func, None)

return fn(*args, **kwargs) if fn is not None else NotImplemented

def implements(ua_func):

def inner(func):

_ua_implementations[ua_func] = func

return func

return inner

@implements(np.asarray)

def asarray(a, dtype=None, order=None):

# Code here

# Either this method or __ua_convert__ must

# return NotImplemented for unsupported types,

# Or they shouldn't be marked as dispatchable.

# Provides a default implementation for ones and zeros.

@implements(np.full)

def full(shape, fill_value, dtype=None, order='C'):

# Code here

The only change this NEP proposes at its acceptance, is to make ``unumpy`` the officially recommended

way to override NumPy. ``unumpy`` will remain a separate repository/package (which we propose to vendor

to avoid a hard dependency, and use the separate ``unumpy`` package only if it is installed)

rather than depend on for the time being), and will be developed

primarily with the input of duck-array authors and secondarily, custom dtype authors, via the usual

GitHub workflow. There are a few reasons for this:

* Faster iteration in the case of bugs or issues.

* Faster design changes, in the case of needed functionality.

* ``unumpy`` will work with older versions of NumPy as well.

* The user and library author opt-in to the override process,

rather than breakages happening when it is least expected.

In simple terms, bugs in ``unumpy`` mean that ``numpy`` remains

unaffected.

Advantanges of ``unumpy`` over other solutions

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``unumpy`` offers a number of advantanges over the approach of defining a new protocol for every

problem encountered: Whenever there is something requiring an override, ``unumpy`` will be able to

offer a unified API with very minor changes. For example:

* ``ufunc`` objects can be overridden via their ``__call__``, ``reduce`` and other methods.

* ``dtype`` objects can be overridden via the dispatch/backend mechanism, going as far as to allow

``np.float32`` et. al. to be overridden by overriding ``__get__``.

* Other functions can be overridden in a similar fashion.

* ``np.asduckarray`` goes away, and becomes ``np.asarray`` with a backend set.

* The same holds for array creation functions such as ``np.zeros``, ``np.empty`` and so on.

This also holds for the future: Making something overridable would require only minor changes to ``unumpy``.

Another promise ``unumpy`` holds is one of default implementations. Default implementations can be provided for

any multimethod, in terms of others. This allows one to override a large part of the NumPy API by defining

only a small part of it. This is to ease the creation of new duck-arrays, by providing default implementations of many

functions that can be easily expressed in terms of others, as well as a repository of utility functions

that help in the implementation of duck-arrays that most duck-arrays would require.

The last benefit is a clear way to coerce to a given backend, and a protocol for coercing not only arrays,

but also ``dtype`` objects and ``ufunc`` objects with similar ones from other libraries. This is due to the existence of

actual, third party dtype packages, and their desire to blend into the NumPy ecosystem (see `[6]`_). This is a separate

issue compared to the C-level dtype redesign proposed in `[7]`_, it's about allowing third-party dtype implementations to

work with NumPy, much like third-party array implementations.

Mixing NumPy and ``unumpy`` in the same file

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Normally, one would only want to import only one of ``unumpy`` or ``numpy``, you would import it as ``np`` for

familiarity. However, there may be situations where one wishes to mix NumPy and the overrides, and there are

a few ways to do this, depending on the user's style::

import numpy.overridable as unumpy

import numpy as np

or::

import numpy as np

# Use unumpy via np.overridable

Related Work

------------

Previous override mechanisms

^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* NEP-18, the ``__array_function__`` protocol. `[2]`_

* NEP-13, the ``__array_ufunc__`` protocol. `[3]`_

Existing NumPy-like array implementations

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* Dask: https://dask.org/

* CuPy: https://cupy.chainer.org/

* PyData/Sparse: https://sparse.pydata.org/

* Xnd: https://xnd.readthedocs.io/

* Astropy's Quantity: https://docs.astropy.org/en/stable/units/

Existing and potential consumers of alternative arrays

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* Dask: https://dask.org/

* scikit-learn: https://scikit-learn.org/

* Xarray: https://xarray.pydata.org/

* TensorLy: http://tensorly.org/

Existing alternate dtype implementations

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* ``ndtypes``: https://ndtypes.readthedocs.io/en/latest/

* Datashape: https://datashape.readthedocs.io

* Plum: https://plum-py.readthedocs.io/

Implementation

--------------

The implementation of this NEP will require the following steps:

* Implementation of ``uarray`` multimethods corresponding to the

NumPy API, including classes for overriding ``dtype``, ``ufunc``

and ``array`` objects, in the ``unumpy`` repository.

* Moving backends from ``unumpy`` into the respective array libraries.

Backward compatibility

----------------------

There are no backward incompatible changes proposed in this NEP.

Alternatives

------------

The current alternative to this problem is NEP-30 plus adding more protocols

(not yet specified) in addition to it. Even then, some parts of the NumPy

API will remain non-overridable, so it's a partial alternative.

The main alternative to vendoring ``unumpy`` is to simply move it into NumPy

completely and not distribute it as a separate package. This would also achieve

the proposed goals, however we prefer to keep it a separate package for now,

for reasons already stated above.

Discussion

----------

* ``uarray`` blogpost: https://labs.quansight.org/blog/2019/07/uarray-update-api-changes-overhead-and-comparison-to-__array_function__/

* The discussion section of NEP-18: https://numpy.org/neps/nep-0018-array-function-protocol.html#discussion

* NEP-22: https://numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html

* Dask issue #4462: https://github.com/dask/dask/issues/4462

* PR #13046: https://github.com/numpy/numpy/pull/13046

* Dask issue #4883: https://github.com/dask/dask/issues/4883

* Issue #13831: https://github.com/numpy/numpy/issues/13831

* Discussion PR 1: https://github.com/hameerabbasi/numpy/pull/3

* Discussion PR 2: https://github.com/hameerabbasi/numpy/pull/4

References and Footnotes

------------------------

.. _[1]:

[1] uarray, A general dispatch mechanism for Python: https://uarray.readthedocs.io

.. _[2]:

[2] NEP 18 — A dispatch mechanism for NumPy’s high level array functions: https://numpy.org/neps/nep-0018-array-function-protocol.html

.. _[3]:

[3] NEP 22 — Duck typing for NumPy arrays – high level overview: https://numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html

.. _[4]:

[4] NEP 13 — A Mechanism for Overriding Ufuncs: https://numpy.org/neps/nep-0013-ufunc-overrides.html

.. _[5]:

[5] Reply to Adding to the non-dispatched implementation of NumPy methods: http://numpy-discussion.10968.n7.nabble.com/Adding-to-the-non-dispatched-implementation-of-NumPy-methods-tp46816p46874.html

.. _[6]:

[6] Custom Dtype/Units discussion: http://numpy-discussion.10968.n7.nabble.com/Custom-Dtype-Units-discussion-td43262.html

.. _[7]:

[7] The epic dtype cleanup plan: https://github.com/numpy/numpy/issues/2899

.. _[8]:

[8] unumpy: NumPy, but implementation-independent: https://unumpy.readthedocs.io

.. _[9]:

[9] NEP 30 — Duck Typing for NumPy Arrays - Implementation: https://www.numpy.org/neps/nep-0030-duck-array-protocol.html

.. _[10]:

[10] http://scipy.github.io/devdocs/fft.html#backend-control

Copyright

---------

This document has been placed in the public domain.

Best regards,

Hameer Abbasi

[1] https://github.com/numpy/numpy/pull/14389

[2] https://numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html