Interoperability with NumPy#
NumPy’s ndarray objects provide both a high-level API for operations on
array-structured data and a concrete implementation of the API based on
strided in-RAM storage. While this API is powerful and fairly
general, its concrete implementation has limitations. As datasets grow and NumPy
becomes used in a variety of new environments and architectures, there are cases
where the strided in-RAM storage strategy is inappropriate, which has caused
different libraries to reimplement this API for their own uses. This includes
GPU arrays (CuPy), Sparse arrays (scipy.sparse, PyData/Sparse)
and parallel arrays (Dask arrays) as well as various NumPy-like implementations
in deep learning frameworks, like TensorFlow and PyTorch. Similarly, there are
many projects that build on top of the NumPy API for labeled and indexed arrays
(XArray), automatic differentiation (JAX), masked arrays (numpy.ma),
physical units (astropy.units, pint, unyt), among others that add additional
functionality on top of the NumPy API.
Yet, users still want to work with these arrays using the familiar NumPy API and reuse existing code with minimal (ideally zero) porting overhead. With this goal in mind, various protocols are defined for implementations of multi-dimensional arrays with high-level APIs matching NumPy.
Broadly speaking, there are three groups of features used for interoperability with NumPy:
Methods of turning a foreign object into an ndarray;
Methods of deferring execution from a NumPy function to another array library;
Methods that use NumPy functions and return an instance of a foreign object.
We describe these features below.
1. Using arbitrary objects in NumPy#
The first set of interoperability features from the NumPy API allows foreign objects to be treated as NumPy arrays whenever possible. When NumPy functions encounter a foreign object, they will try (in order):
The buffer protocol, described in the Python C-API documentation.
The
__array_interface__protocol, described in this page. A precursor to Python’s buffer protocol, it defines a way to access the contents of a NumPy array from other C extensions.The
__array__()method, which asks an arbitrary object to convert itself into an array.
For both the buffer and the __array_interface__ protocols, the object
describes its memory layout and NumPy does everything else (zero-copy if
possible). If that’s not possible, the object itself is responsible for
returning a ndarray from __array__().
DLPack is yet another protocol to convert foreign objects
to NumPy arrays in a language and device agnostic manner. NumPy doesn’t implicitly
convert objects to ndarrays using DLPack. It provides the function
numpy.from_dlpack that accepts any object implementing the __dlpack__ method
and outputs a NumPy ndarray (which is generally a view of the input object’s data
buffer). The Python Specification for DLPack page explains the __dlpack__ protocol
in detail.
The array interface protocol#
The array interface protocol defines a way for array-like objects to reuse each other’s data buffers. Its implementation relies on the existence of the following attributes or methods:
__array_interface__: a Python dictionary containing the shape, the element type, and optionally, the data buffer address and the strides of an array-like object;__array__(): a method returning the NumPy ndarray copy or a view of an array-like object;
The __array_interface__ attribute can be inspected directly:
>>> import numpy as np
>>> x = np.array([1, 2, 5.0, 8])
>>> x.__array_interface__
{'data': (94708397920832, False), 'strides': None, 'descr': [('', '<f8')], 'typestr': '<f8', 'shape': (4,), 'version': 3}
The __array_interface__ attribute can also be used to manipulate the object
data in place:
>>> class wrapper():
... pass
...
>>> arr = np.array([1, 2, 3, 4])
>>> buf = arr.__array_interface__
>>> buf
{'data': (140497590272032, False), 'strides': None, 'descr': [('', '<i8')], 'typestr': '<i8', 'shape': (4,), 'version': 3}
>>> buf['shape'] = (2, 2)
>>> w = wrapper()
>>> w.__array_interface__ = buf
>>> new_arr = np.array(w, copy=False)
>>> new_arr
array([[1, 2],
[3, 4]])
We can check that arr and new_arr share the same data buffer:
>>> new_arr[0, 0] = 1000
>>> new_arr
array([[1000, 2],
[ 3, 4]])
>>> arr
array([1000, 2, 3, 4])
The __array__() method#
The __array__() method ensures that any NumPy-like object (an array, any
object exposing the array interface, an object whose __array__() method
returns an array or any nested sequence) that implements it can be used as a
NumPy array. If possible, this will mean using __array__() to create a NumPy
ndarray view of the array-like object. Otherwise, this copies the data into a
new ndarray object. This is not optimal, as coercing arrays into ndarrays may
cause performance problems or create the need for copies and loss of metadata,
as the original object and any attributes/behavior it may have had, is lost.
The signature of the method should be __array__(self, dtype=None, copy=None).
If a passed dtype isn’t None and different than the object’s data type,
a casting should happen to a specified type. If copy is None, a copy
should be made only if dtype argument enforces it. For copy=True, a copy
should always be made, where copy=False should raise an exception if a copy
is needed.
If a class implements the old signature __array__(self), for np.array(a)
a warning will be raised saying that dtype and copy arguments are missing.
To see an example of a custom array implementation including the use of
__array__(), see Writing custom array containers.
The DLPack Protocol#
The DLPack protocol defines a memory-layout of strided n-dimensional array objects. It offers the following syntax for data exchange:
A
numpy.from_dlpackfunction, which accepts (array) objects with a__dlpack__method and uses that method to construct a new array containing the data fromx.__dlpack__(self, stream=None)and__dlpack_device__methods on the array object, which will be called from withinfrom_dlpack, to query what device the array is on (may be needed to pass in the correct stream, e.g. in the case of multiple GPUs) and to access the data.
Unlike the buffer protocol, DLPack allows exchanging arrays containing data on devices other than the CPU (e.g. Vulkan or GPU). Since NumPy only supports CPU, it can only convert objects whose data exists on the CPU. But other libraries, like PyTorch and CuPy, may exchange data on GPU using this protocol.
2. Operating on foreign objects without converting#
A second set of methods defined by the NumPy API allows us to defer the execution from a NumPy function to another array library.
Consider the following function.
>>> import numpy as np
>>> def f(x):
... return np.mean(np.exp(x))
Note that np.exp is a ufunc, which means
that it operates on ndarrays in an element-by-element fashion. On the other
hand, np.mean operates along one of the array’s axes.
We can apply f to a NumPy ndarray object directly:
>>> x = np.array([1, 2, 3, 4])
>>> f(x)
21.1977562209304
We would like this function to work equally well with any NumPy-like array object.
NumPy allows a class to indicate that it would like to handle computations in a custom-defined way through the following interfaces:
__array_ufunc__: allows third-party objects to support and override ufuncs.__array_function__: a catch-all for NumPy functionality that is not covered by the__array_ufunc__protocol for universal functions.
As long as foreign objects implement the __array_ufunc__ or
__array_function__ protocols, it is possible to operate on them without the
need for explicit conversion.
The __array_ufunc__ protocol#
A universal function (or ufunc for short) is a
“vectorized” wrapper for a function that takes a fixed number of specific inputs
and produces a fixed number of specific outputs. The output of the ufunc (and
its methods) is not necessarily a ndarray, if not all input arguments are
ndarrays. Indeed, if any input defines an __array_ufunc__ method, control
will be passed completely to that function, i.e., the ufunc is overridden. The
__array_ufunc__ method defined on that (non-ndarray) object has access to
the NumPy ufunc. Because ufuncs have a well-defined structure, the foreign
__array_ufunc__ method may rely on ufunc attributes like .at(),
.reduce(), and others.
A subclass can override what happens when executing NumPy ufuncs on it by
overriding the default ndarray.__array_ufunc__ method. This method is
executed instead of the ufunc and should return either the result of the
operation, or NotImplemented if the operation requested is not implemented.
The __array_function__ protocol#
To achieve enough coverage of the NumPy API to support downstream projects,
there is a need to go beyond __array_ufunc__ and implement a protocol that
allows arguments of a NumPy function to take control and divert execution to
another function (for example, a GPU or parallel implementation) in a way that
is safe and consistent across projects.
The semantics of __array_function__ are very similar to __array_ufunc__,
except the operation is specified by an arbitrary callable object rather than a
ufunc instance and method. For more details, see NEP 18 — A dispatch mechanism for NumPy’s high level array functions.
3. Returning foreign objects#
A third type of feature set is meant to use the NumPy function implementation
and then convert the return value back into an instance of the foreign object.
The __array_finalize__ and __array_wrap__ methods act behind the scenes
to ensure that the return type of a NumPy function can be specified as needed.
The __array_finalize__ method is the mechanism that NumPy provides to allow
subclasses to handle the various ways that new instances get created. This
method is called whenever the system internally allocates a new array from an
object which is a subclass (subtype) of the ndarray. It can be used to change
attributes after construction, or to update meta-information from the “parent.”
The __array_wrap__ method “wraps up the action” in the sense of allowing any
object (such as user-defined functions) to set the type of its return value and
update attributes and metadata. This can be seen as the opposite of the
__array__ method. At the end of every object that implements
__array_wrap__, this method is called on the input object with the highest
array priority, or the output object if one was specified. The
__array_priority__ attribute is used to determine what type of object to
return in situations where there is more than one possibility for the Python
type of the returned object. For example, subclasses may opt to use this method
to transform the output array into an instance of the subclass and update
metadata before returning the array to the user.
For more information on these methods, see Subclassing ndarray and Specific features of ndarray sub-typing.
Interoperability examples#
Example: Pandas Series objects#
Consider the following:
>>> import pandas as pd
>>> ser = pd.Series([1, 2, 3, 4])
>>> type(ser)
pandas.core.series.Series
Now, ser is not a ndarray, but because it
implements the __array_ufunc__ protocol,
we can apply ufuncs to it as if it were a ndarray:
>>> np.exp(ser)
0 2.718282
1 7.389056
2 20.085537
3 54.598150
dtype: float64
>>> np.sin(ser)
0 0.841471
1 0.909297
2 0.141120
3 -0.756802
dtype: float64
We can even do operations with other ndarrays:
>>> np.add(ser, np.array([5, 6, 7, 8]))
0 6
1 8
2 10
3 12
dtype: int64
>>> f(ser)
21.1977562209304
>>> result = ser.__array__()
>>> type(result)
numpy.ndarray
Example: PyTorch tensors#
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. PyTorch arrays are commonly called tensors. Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. In fact, tensors and NumPy arrays can often share the same underlying memory, eliminating the need to copy data.
>>> import torch
>>> data = [[1, 2],[3, 4]]
>>> x_np = np.array(data)
>>> x_tensor = torch.tensor(data)
Note that x_np and x_tensor are different kinds of objects:
>>> x_np
array([[1, 2],
[3, 4]])
>>> x_tensor
tensor([[1, 2],
[3, 4]])
However, we can treat PyTorch tensors as NumPy arrays without the need for explicit conversion:
>>> np.exp(x_tensor)
tensor([[ 2.7183, 7.3891],
[20.0855, 54.5982]], dtype=torch.float64)
Also, note that the return type of this function is compatible with the initial data type.
Warning
While this mixing of ndarrays and tensors may be convenient, it is not recommended. It will not work for non-CPU tensors, and will have unexpected behavior in corner cases. Users should prefer explicitly converting the ndarray to a tensor.
Note
PyTorch does not implement __array_function__ or __array_ufunc__.
Under the hood, the Tensor.__array__() method returns a NumPy ndarray as
a view of the tensor data buffer. See this issue and the
__torch_function__ implementation
for details.
Note also that we can see __array_wrap__ in action here, even though
torch.Tensor is not a subclass of ndarray:
>>> import torch
>>> t = torch.arange(4)
>>> np.abs(t)
tensor([0, 1, 2, 3])
PyTorch implements __array_wrap__ to be able to get tensors back from NumPy
functions, and we can modify it directly to control which type of objects are
returned from these functions.
Example: CuPy arrays#
CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing
with Python. CuPy implements a subset of the NumPy interface by implementing
cupy.ndarray, a counterpart to NumPy ndarrays.
>>> import cupy as cp
>>> x_gpu = cp.array([1, 2, 3, 4])
The cupy.ndarray object implements the __array_ufunc__ interface. This
enables NumPy ufuncs to be applied to CuPy arrays (this will defer operation to
the matching CuPy CUDA/ROCm implementation of the ufunc):
>>> np.mean(np.exp(x_gpu))
array(21.19775622)
Note that the return type of these operations is still consistent with the initial type:
>>> arr = cp.random.randn(1, 2, 3, 4).astype(cp.float32)
>>> result = np.sum(arr)
>>> print(type(result))
<class 'cupy._core.core.ndarray'>
See this page in the CuPy documentation for details.
cupy.ndarray also implements the __array_function__ interface, meaning
it is possible to do operations such as
>>> a = np.random.randn(100, 100)
>>> a_gpu = cp.asarray(a)
>>> qr_gpu = np.linalg.qr(a_gpu)
CuPy implements many NumPy functions on cupy.ndarray objects, but not all.
See the CuPy documentation
for details.
Example: Dask arrays#
Dask is a flexible library for parallel computing in Python. Dask Array implements a subset of the NumPy ndarray interface using blocked algorithms, cutting up the large array into many small arrays. This allows computations on larger-than-memory arrays using multiple cores.
Dask supports __array__() and __array_ufunc__.
>>> import dask.array as da
>>> x = da.random.normal(1, 0.1, size=(20, 20), chunks=(10, 10))
>>> np.mean(np.exp(x))
dask.array<mean_agg-aggregate, shape=(), dtype=float64, chunksize=(), chunktype=numpy.ndarray>
>>> np.mean(np.exp(x)).compute()
5.090097550553843
Note
Dask is lazily evaluated, and the result from a computation isn’t computed
until you ask for it by invoking compute().
See the Dask array documentation and the scope of Dask arrays interoperability with NumPy arrays for details.
Example: DLPack#
Several Python data science libraries implement the __dlpack__ protocol.
Among them are PyTorch and CuPy. A full list of libraries that implement
this protocol can be found on
this page of DLPack documentation.
Convert a PyTorch CPU tensor to NumPy array:
>>> import torch
>>> x_torch = torch.arange(5)
>>> x_torch
tensor([0, 1, 2, 3, 4])
>>> x_np = np.from_dlpack(x_torch)
>>> x_np
array([0, 1, 2, 3, 4])
>>> # note that x_np is a view of x_torch
>>> x_torch[1] = 100
>>> x_torch
tensor([ 0, 100, 2, 3, 4])
>>> x_np
array([ 0, 100, 2, 3, 4])
The imported arrays are read-only so writing or operating in-place will fail:
>>> x.flags.writeable
False
>>> x_np[1] = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: assignment destination is read-only
A copy must be created in order to operate on the imported arrays in-place, but will mean duplicating the memory. Do not do this for very large arrays:
>>> x_np_copy = x_np.copy()
>>> x_np_copy.sort() # works
Note
Note that GPU tensors can’t be converted to NumPy arrays since NumPy doesn’t support GPU devices:
>>> x_torch = torch.arange(5, device='cuda')
>>> np.from_dlpack(x_torch)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: Unsupported device in DLTensor.
But, if both libraries support the device the data buffer is on, it is
possible to use the __dlpack__ protocol (e.g. PyTorch and CuPy):
>>> x_torch = torch.arange(5, device='cuda')
>>> x_cupy = cupy.from_dlpack(x_torch)
Similarly, a NumPy array can be converted to a PyTorch tensor:
>>> x_np = np.arange(5)
>>> x_torch = torch.from_dlpack(x_np)
Read-only arrays cannot be exported:
>>> x_np = np.arange(5)
>>> x_np.flags.writeable = False
>>> torch.from_dlpack(x_np)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../site-packages/torch/utils/dlpack.py", line 63, in from_dlpack
dlpack = ext_tensor.__dlpack__()
TypeError: NumPy currently only supports dlpack for writeable arrays
Further reading#
Special attributes and methods (details on the
__array_ufunc__and__array_function__protocols)Subclassing ndarray (details on the
__array_wrap__and__array_finalize__methods)Specific features of ndarray sub-typing (more details on the implementation of
__array_finalize__,__array_wrap__and__array_priority__)