Writing custom array containers

Writing custom array containers#

Numpy's dispatch mechanism, introduced in numpy version v1.16 is the recommended approach for writing custom N-dimensional array containers that are compatible with the numpy API and provide custom implementations of numpy functionality. Applications include dask arrays, an N-dimensional array distributed across multiple nodes, and cupy arrays, an N-dimensional array on a GPU.

To get a feel for writing custom array containers, we'll begin with a simple example that has rather narrow utility but illustrates the concepts involved.

>>> import numpy as np
>>> class DiagonalArray:
...     def __init__(self, N, value):
...         self._N = N
...         self._i = value
...     def __repr__(self):
...         return f"{self.__class__.__name__}(N={self._N}, value={self._i})"
...     def __array__(self, dtype=None, copy=None):
...         if copy is False:
...             raise ValueError(
...                 "`copy=False` isn't supported. A copy is always created."
...             )
...         return self._i * np.eye(self._N, dtype=dtype)

Our custom array can be instantiated like:

>>> arr = DiagonalArray(5, 1)
>>> arr
DiagonalArray(N=5, value=1)

We can convert to a numpy array using numpy.array() or numpy.asarray(), which will call its __array__ method to obtain a standard numpy.ndarray.

>>> np.asarray(arr)
array([[1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.]])

If we operate on arr with a numpy function, numpy will again use the __array__ interface to convert it to an array and then apply the function in the usual way.

>>> np.multiply(arr, 2)
array([[2., 0., 0., 0., 0.],
       [0., 2., 0., 0., 0.],
       [0., 0., 2., 0., 0.],
       [0., 0., 0., 2., 0.],
       [0., 0., 0., 0., 2.]])

Notice that the return type is a standard numpy.ndarray.

>>> type(np.multiply(arr, 2))
<class 'numpy.ndarray'>

How can we pass our custom array type through this function? Numpy allows a class to indicate that it would like to handle computations in a custom-defined way through the interfaces __array_ufunc__ and __array_function__. Let's take one at a time, starting with __array_ufunc__. This method covers Universal functions (ufunc), a class of functions that includes, for example, numpy.multiply() and numpy.sin().

The __array_ufunc__ receives:

  • ufunc, a function like numpy.multiply

  • method, a string, differentiating between numpy.multiply(...) and variants like numpy.multiply.outer, numpy.multiply.accumulate, and so on. For the common case, numpy.multiply(...), method == '__call__'.

  • inputs, which could be a mixture of different types

  • kwargs, keyword arguments passed to the function

For this example we will only handle the method __call__

>>> from numbers import Number
>>> class DiagonalArray:
...     def __init__(self, N, value):
...         self._N = N
...         self._i = value
...     def __repr__(self):
...         return f"{self.__class__.__name__}(N={self._N}, value={self._i})"
...     def __array__(self, dtype=None, copy=None):
...         if copy is False:
...             raise ValueError(
...                 "`copy=False` isn't supported. A copy is always created."
...             )
...         return self._i * np.eye(self._N, dtype=dtype)
...     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
...         if method == '__call__':
...             N = None
...             scalars = []
...             for input in inputs:
...                 if isinstance(input, Number):
...                     scalars.append(input)
...                 elif isinstance(input, self.__class__):
...                     scalars.append(input._i)
...                     if N is not None:
...                         if N != input._N:
...                             raise TypeError("inconsistent sizes")
...                     else:
...                         N = input._N
...                 else:
...                     return NotImplemented
...             return self.__class__(N, ufunc(*scalars, **kwargs))
...         else:
...             return NotImplemented

Now our custom array type passes through numpy functions.

>>> arr = DiagonalArray(5, 1)
>>> np.multiply(arr, 3)
DiagonalArray(N=5, value=3)
>>> np.add(arr, 3)
DiagonalArray(N=5, value=4)
>>> np.sin(arr)
DiagonalArray(N=5, value=0.8414709848078965)

At this point arr + 3 does not work.

>>> arr + 3
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'DiagonalArray' and 'int'

To support it, we need to define the Python interfaces __add__, __lt__, and so on to dispatch to the corresponding ufunc. We can achieve this conveniently by inheriting from the mixin NDArrayOperatorsMixin.

>>> import numpy.lib.mixins
>>> class DiagonalArray(numpy.lib.mixins.NDArrayOperatorsMixin):
...     def __init__(self, N, value):
...         self._N = N
...         self._i = value
...     def __repr__(self):
...         return f"{self.__class__.__name__}(N={self._N}, value={self._i})"
...     def __array__(self, dtype=None, copy=None):
...         if copy is False:
...             raise ValueError(
...                 "`copy=False` isn't supported. A copy is always created."
...             )
...         return self._i * np.eye(self._N, dtype=dtype)
...     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
...         if method == '__call__':
...             N = None
...             scalars = []
...             for input in inputs:
...                 if isinstance(input, Number):
...                     scalars.append(input)
...                 elif isinstance(input, self.__class__):
...                     scalars.append(input._i)
...                     if N is not None:
...                         if N != input._N:
...                             raise TypeError("inconsistent sizes")
...                     else:
...                         N = input._N
...                 else:
...                     return NotImplemented
...             return self.__class__(N, ufunc(*scalars, **kwargs))
...         else:
...             return NotImplemented

>>> arr = DiagonalArray(5, 1)
>>> arr + 3
DiagonalArray(N=5, value=4)
>>> arr > 0
DiagonalArray(N=5, value=True)

Now let's tackle __array_function__. We'll create dict that maps numpy functions to our custom variants.

>>> HANDLED_FUNCTIONS = {}
>>> class DiagonalArray(numpy.lib.mixins.NDArrayOperatorsMixin):
...     def __init__(self, N, value):
...         self._N = N
...         self._i = value
...     def __repr__(self):
...         return f"{self.__class__.__name__}(N={self._N}, value={self._i})"
...     def __array__(self, dtype=None, copy=None):
...         if copy is False:
...             raise ValueError(
...                 "`copy=False` isn't supported. A copy is always created."
...             )
...         return self._i * np.eye(self._N, dtype=dtype)
...     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
...         if method == '__call__':
...             N = None
...             scalars = []
...             for input in inputs:
...                 # In this case we accept only scalar numbers or DiagonalArrays.
...                 if isinstance(input, Number):
...                     scalars.append(input)
...                 elif isinstance(input, self.__class__):
...                     scalars.append(input._i)
...                     if N is not None:
...                         if N != input._N:
...                             raise TypeError("inconsistent sizes")
...                     else:
...                         N = input._N
...                 else:
...                     return NotImplemented
...             return self.__class__(N, ufunc(*scalars, **kwargs))
...         else:
...             return NotImplemented
...     def __array_function__(self, func, types, args, kwargs):
...         if func not in HANDLED_FUNCTIONS:
...             return NotImplemented
...         # Note: this allows subclasses that don't override
...         # __array_function__ to handle DiagonalArray objects.
...         if not all(issubclass(t, self.__class__) for t in types):
...             return NotImplemented
...         return HANDLED_FUNCTIONS[func](*args, **kwargs)
...

A convenient pattern is to define a decorator implements that can be used to add functions to HANDLED_FUNCTIONS.

>>> def implements(np_function):
...    "Register an __array_function__ implementation for DiagonalArray objects."
...    def decorator(func):
...        HANDLED_FUNCTIONS[np_function] = func
...        return func
...    return decorator
...

Now we write implementations of numpy functions for DiagonalArray. For completeness, to support the usage arr.sum() add a method sum that calls numpy.sum(self), and the same for mean.

>>> @implements(np.sum)
... def sum(arr):
...     "Implementation of np.sum for DiagonalArray objects"
...     return arr._i * arr._N
...
>>> @implements(np.mean)
... def mean(arr):
...     "Implementation of np.mean for DiagonalArray objects"
...     return arr._i / arr._N
...
>>> arr = DiagonalArray(5, 1)
>>> np.sum(arr)
5
>>> np.mean(arr)
0.2

If the user tries to use any numpy functions not included in HANDLED_FUNCTIONS, a TypeError will be raised by numpy, indicating that this operation is not supported. For example, concatenating two DiagonalArrays does not produce another diagonal array, so it is not supported.

>>> np.concatenate([arr, arr])
Traceback (most recent call last):
...
TypeError: no implementation found for 'numpy.concatenate' on types that implement __array_function__: [<class '__main__.DiagonalArray'>]

Additionally, our implementations of sum and mean do not accept the optional arguments that numpy's implementation does.

>>> np.sum(arr, axis=0)
Traceback (most recent call last):
...
TypeError: sum() got an unexpected keyword argument 'axis'

The user always has the option of converting to a normal numpy.ndarray with numpy.asarray() and using standard numpy from there.

>>> np.concatenate([np.asarray(arr), np.asarray(arr)])
array([[1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.],
       [1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.]])

The implementation of DiagonalArray in this example only handles the np.sum and np.mean functions for brevity. Many other functions in the Numpy API are also available to wrap and a full-fledged custom array container can explicitly support all functions that Numpy makes available to wrap.

Numpy provides some utilities to aid testing of custom array containers that implement the __array_ufunc__ and __array_function__ protocols in the numpy.testing.overrides namespace.

To check if a Numpy function can be overridden via __array_ufunc__, you can use allows_array_ufunc_override():

>>> from numpy.testing.overrides import allows_array_ufunc_override
>>> allows_array_ufunc_override(np.add)
True

Similarly, you can check if a function can be overridden via __array_function__ using allows_array_function_override().

Lists of every overridable function in the Numpy API are also available via get_overridable_numpy_array_functions() for functions that support the __array_function__ protocol and get_overridable_numpy_ufuncs() for functions that support the __array_ufunc__ protocol. Both functions return sets of functions that are present in the Numpy public API. User-defined ufuncs or ufuncs defined in other libraries that depend on Numpy are not present in these sets.

Refer to the dask source code and cupy source code for more fully-worked examples of custom array containers.

See also NEP 18.