If you are interested in writing backends or multimethods for unumpy, please look at the documentation for uarray, which explains how to do this.

unumpy is meant for three groups of individuals:

  • Those who write array-like objects, like developers of Dask, Xnd, PyData/Sparse, CuPy, and others.
  • Library authors or programmers that hope to target multiple array backends, listed above.
  • Users who wish to target their code to other backends.

For example, the following is currently possible:

>>> import uarray as ua
>>> import unumpy as np
>>> from unumpy.dask_backend import DaskBackend
>>> import unumpy.sparse_backend as SparseBackend
>>> import sparse, dask.array as da
>>> def main():
...     x = np.zeros(5)
...     return np.exp(x)
>>> with ua.set_backend(DaskBackend()):
...     isinstance(main(), da.core.Array)
>>> with ua.set_backend(SparseBackend):
...     isinstance(main(), sparse.SparseArray)

Now imagine some arbitrarily nested code, all for which the implementations can be switched out using a simple context manager.

unumpy is an in-progress mirror of the NumPy API which allows the user to dynamically switch out the backend that is used. It also allows auto-selection of the backend based on the arguments passed into a function. It does this by defining a collection of uarray multimethods that support dispatch. Although it currently provides a number of backends, the aspiration is that, with time, these back-ends will move into the respective libraries and it will be possible to use the library modules directly as backends.

Note that currently, our coverage is very incomplete. However, we have attempted to provide at least one of each kind of object in unumpy for reference. There are ufunc s and ndarray s, which are classes, methods on ufunc such as __call__, and reduce and also functions such as sum.

Where possible, we attempt to provide default implementations so that the whole API does not have to be reimplemented, however, it might be useful to gain speed or to re-implement it in terms of other functions which already exist in your library.

The idea is that once things are more mature, it will be possible to switch out your backend with a simple import statement switch:

import numpy as np  # Old method
import unumpy as np  # Once this project is mature

Currently, the following functions are supported:

For the full range of functions, use dir(unumpy).

You can use the uarray.set_backend decorator to set a backend and use the desired backend. Note that not every backend supports every method. For example, PyTorch does not have an exact ufunc equivalent, so we dispatch to actual methods using a dictionary lookup. The following backends are supported:

  • numpy_backend
  • torch_backend
  • xnd_backend
  • dask_backend
  • cupy_backend
  • sparse_backend

Writing Backends

Since unumpy is based on uarray, all overrides are done via the __ua_*__ protocols. We strongly recommend you read the uarray documentation for context.

All functions/methods in unumpy are uarray multimethods. This means you can override them using the __ua_function__ protocol.

In addition, unumpy allows dispatch on numpy.ndarray, numpy.ufunc and numpy.dtype via the __ua_convert__ protocol.

Dispatching on objects means one can intercept these, convert to an equivalent native format, or dispatch on their methods, including __call__.

We suggest you browse the source for example backends.

Differences between overriding numpy.ufunc objects and other multimethods

Of note here is that there are certain callable objects within NumPy, most prominently numpy.ufunc objects, that are not typical functions/methods, and so cannot be directly overridden, the key word here being directly.

In Python, when a method is called, i.e. x.method(*a, **kw) it is the same as writing type(x).method(x, *a, **kw) assuming that method was a regular method defined on the type. This allows some very interesting things to happen.

For instance, if we make method a multimethod, it allows us to override methods, provided we know that the first argument passed in will be x.

One other thing that is possible (and done in unumpy) is to override the __call__ method on a callable object. This is, in fact, exactly how to override a ufunc.

Other interesting things that can be done (but as of now, are not) are to replace ufunc objects entirely by native equivalents overriding the __get__ method. This technique can also be applied to dtype objects.

Meta-array support

Meta-arrays are arrays that can hold other arrays, such as Dask arrays and XArray datasets.

If meta-arrays and libraries depend on unumpy instead of NumPy, they can benefit from containerization and hold arbitrary arrays; not just numpy.ndarray objects.

Inside their __ua_function__ implementation, they might need to do something like the following:

>>> class Backend: pass
>>> meta_backend = Backend()
>>> meta_backend.__ua_domain__ = "numpy"
>>> def ua_func(f, a, kw):
...     # We do this to avoid infinite recursion
...     with ua.skip_backend(meta_backend):
...         # Actual implementation here
...         pass
>>> meta_backend.__ua_function__ = ua_func

In this form, one could do something like the following to use the meta-backend:

>>> with ua.set_backend(DaskBackend(inner=SparseBackend)):
...     x = np.zeros((2000, 2000))
...     isinstance(x, da.Array)
...     isinstance(x.compute(), sparse.SparseArray)