Array API Standard Compatibility

Note

The numpy.array_api module is still experimental. See NEP 47.

NumPy includes a reference implementation of the array API standard in numpy.array_api. NEP 47 describes the motivation and scope for implementing the array API standard in NumPy.

The numpy.array_api module serves as a minimal, reference implementation of the array API standard. In being minimal, the module only implements those things that are explicitly required by the specification. Certain things are allowed by the specification but are explicitly disallowed in numpy.array_api. This is so that the module can serve as a reference implementation for users of the array API standard. Any consumer of the array API can test their code against numpy.array_api and be sure that they aren’t using any features that aren’t guaranteed by the spec, and which may not be present in other conforming libraries.

The numpy.array_api module is not documented here. For a listing of the functions present in the array API specification, refer to the array API standard. The numpy.array_api implementation is functionally complete, so all functionality described in the standard is implemented.

Table of Differences between numpy.array_api and numpy

This table outlines the primary differences between numpy.array_api from the main numpy namespace. There are three types of differences:

1. Strictness. Things that are only done so that numpy.array_api is a strict, minimal implementation. They aren’t actually required by the spec, and other conforming libraries may not follow them. In most cases, spec does not specify or require any behavior outside of the given domain. The main numpy namespace would not need to change in any way to be spec-compatible for these.

2. Compatible. Things that could be added to the main numpy namespace without breaking backwards compatibility.

3. Breaking. Things that would break backwards compatibility if implemented in the main numpy namespace.

Name Differences

Many functions have been renamed in the spec from NumPy. These are otherwise identical in behavior, and are thus all compatible changes, unless otherwise noted.

Function Name Changes

The following functions are named differently in the array API

Array API name	NumPy namespace name	Notes
acos	arccos
acosh	arccosh
asin	arcsin
asinh	arcsinh
atan	arctan
atan2	arctan2
atanh	arctanh
bitwise_left_shift	left_shift
bitwise_invert	invert
bitwise_right_shift	right_shift
bool	bool_	This is breaking because np.bool is currently a deprecated alias for the built-in bool.
concat	concatenate
matrix_norm and vector_norm	norm	matrix_norm and vector_norm each do a limited subset of what np.norm does.
permute_dims	transpose	Unlike np.transpose, the axis keyword-argument to permute_dims is required.
pow	power
unique_all, unique_counts, unique_inverse, and unique_values	unique	Each is equivalent to np.unique with certain flags set.

Function instead of method

• astype is a function in the array API, whereas it is a method on ndarray in numpy.

linalg Namespace Differences

These functions are in the linalg sub-namespace in the array API, but are only in the top-level namespace in NumPy:

• cross

• diagonal

• matmul (*)

• outer

• tensordot (*)

• trace

(*): These functions are also in the top-level namespace in the array API.

Keyword Argument Renames

The following functions have keyword arguments that have been renamed. The functionality of the keyword argument is identical unless otherwise stated. Renamed keyword arguments with the same semantic definition may be considered either compatible or breaking, depending on how the change is implemented.

Note, this page does not list function keyword arguments that are in the main numpy namespace but not in the array API. Such keyword arguments are omitted from numpy.array_api for strictness, as the spec allows functions to include additional keyword arguments from those required.

Function	Array API keyword name	NumPy keyword name	Notes
argsort and sort	stable	kind	The definitions of stable and kind differ, as do the default values. The change of the default value makes this breaking. See Set Functions Differences.
matrix_rank	rtol	tol	The definitions of rtol and tol differ, as do the default values. The change of the default value makes this breaking. See Linear Algebra Differences.
pinv	rtol	rcond	The definitions of rtol and rcond are the same, but their default values differ, making this breaking. See Linear Algebra Differences.
std and var	correction	ddof
reshape	shape	newshape	The argument may be passed as a positional or keyword argument for both NumPy and the array API.

Type Promotion Differences

Type promotion is the biggest area where NumPy deviates from the spec. The most notable difference is that NumPy does value-based casting in many cases. The spec explicitly disallows value-based casting. In the array API, the result type of any operation is always determined entirely by the input types, independently of values or shapes.

Feature	Type	Notes
Limited set of dtypes.	Strictness	numpy.array_api only implements those dtypes that are required by the spec.
Operators (like +) with Python scalars only accept matching scalar types.	Strictness	For example, <int32 array> + 1.0 is not allowed. See the spec rules for mixing arrays and Python scalars.
Operators (like +) with Python scalars always return the same dtype as the array.	Breaking	For example, numpy.array_api.asarray(0., dtype=float32) + 1e64 is a float32 array.
In-place operators are disallowed when the left-hand side would be promoted.	Breaking	Example: a = np.array(1, dtype=np.int8); a += np.array(1, dtype=np.int16). The spec explicitly disallows this.
In-place operators are disallowed when the right-hand side operand cannot broadcast to the shape of the left-hand side operand.	Strictness	This so-called “reverse broadcasting” should not be allowed. Example: a = np.empty((2, 3, 4)); a += np.empty((3, 4)) should error. See https://github.com/numpy/numpy/issues/10404.
int promotion for operators is only specified for integers within the bounds of the dtype.	Strictness	numpy.array_api fallsback to np.ndarray behavior (either cast or raise OverflowError).
__pow__ and __rpow__ do not do value-based casting for 0-D arrays.	Breaking	For example, np.array(0., dtype=float32)**np.array(0., dtype=float64) is float32. Note that this is value-based casting on 0-D arrays, not scalars.
No cross-kind casting.	Strictness	Namely, boolean, integer, and floating-point data types do not cast to each other, except explicitly with astype (this is separate from the behavior with Python scalars).
No casting unsigned integer dtypes to floating dtypes (e.g., int64 + uint64 -> float64.	Strictness
can_cast and result_type are restricted.	Strictness	The numpy.array_api implementations disallow cross-kind casting.
sum and prod always upcast float32 to float64 when dtype=None.	Breaking

Indexing Differences

The spec requires only a subset of indexing, but all indexing rules in the spec are compatible with NumPy’s more broad indexing rules.

Feature	Type	Notes
No implicit ellipses (...).	Strictness	If an index does not include an ellipsis, all axes must be indexed.
The start and stop of a slice may not be out of bounds.	Strictness	For a slice i:j:k, only the following are allowed: i or j omitted (None). -n <= i <= max(0, n - 1). For k > 0 or k omitted (None), -n <= j <= n. For k < 0, -n - 1 <= j <= max(0, n - 1).
Boolean array indices are only allowed as the sole index.	Strictness
Integer array indices are not allowed at all.	Strictness	With the exception of 0-D arrays, which are treated like integers.

Type Strictness

Functions in numpy.array_api restrict their inputs to only those dtypes that are explicitly required by the spec, even when the wrapped corresponding NumPy function would allow a broader set. Here, we list each function and the dtypes that are allowed in numpy.array_api. These are strictness differences because the spec does not require that other dtypes result in an error. The categories here are defined as follows:

• Floating-point: float32 or float64.

• Integer: Any signed or unsigned integer dtype (int8, int16, int32, int64, uint8, uint16, uint32, or uint64).

• Boolean: bool.

• Integer or boolean: Any signed or unsigned integer dtype, or bool. For two-argument functions, both arguments must be integer or both must be bool.

• Numeric: Any integer or floating-point dtype. For two-argument functions, both arguments must be integer or both must be floating-point.

• All: Any of the above dtype categories. For two-argument functions, both arguments must be the same kind (integer, floating-point, or boolean).

In all cases, the return dtype is chosen according to the rules outlined in the spec, and does not differ from NumPy’s return dtype for any of the allowed input dtypes, except in the cases mentioned specifically in the subsections below.

Elementwise Functions

Function Name	Dtypes
abs	Numeric
acos	Floating-point
acosh	Floating-point
add	Numeric
asin (*)	Floating-point
asinh (*)	Floating-point
atan (*)	Floating-point
atan2 (*)	Floating-point
atanh (*)	Floating-point
bitwise_and	Integer or boolean
bitwise_invert	Integer or boolean
bitwise_left_shift (*)	Integer
bitwise_or	Integer or boolean
bitwise_right_shift (*)	Integer
bitwise_xor	Integer or boolean
ceil	Numeric
cos	Floating-point
cosh	Floating-point
divide	Floating-point
equal	All
exp	Floating-point
expm1	Floating-point
floor	Numeric
floor_divide	Numeric
greater	Numeric
greater_equal	Numeric
isfinite	Numeric
isinf	Numeric
isnan	Numeric
less	Numeric
less_equal	Numeric
log	Floating-point
logaddexp	Floating-point
log10	Floating-point
log1p	Floating-point
log2	Floating-point
logical_and	Boolean
logical_not	Boolean
logical_or	Boolean
logical_xor	Boolean
multiply	Numeric
negative	Numeric
not_equal	All
positive	Numeric
pow (*)	Numeric
remainder	Numeric
round	Numeric
sign	Numeric
sin	Floating-point
sinh	Floating-point
sqrt	Floating-point
square	Numeric
subtract	Numeric
tan	Floating-point
tanh	Floating-point
trunc	Numeric

(*) These functions have different names from the main numpy namespace. See Function Name Changes.

Creation Functions

Function Name	Dtypes
meshgrid	Any (all input dtypes must be the same)

Linear Algebra Functions

Function Name	Dtypes
cholesky	Floating-point
cross	Numeric
det	Floating-point
diagonal	Any
eigh	Floating-point
eighvals	Floating-point
inv	Floating-point
matmul	Numeric
matrix_norm (*)	Floating-point
matrix_power	Floating-point
matrix_rank	Floating-point
matrix_transpose (**)	Any
outer	Numeric
pinv	Floating-point
qr	Floating-point
slogdet	Floating-point
solve	Floating-point
svd	Floating-point
svdvals (**)	Floating-point
tensordot	Numeric
trace	Numeric
vecdot (**)	Numeric
vector_norm (*)	Floating-point

(*) These functions are split from norm from the main numpy namespace. See Function Name Changes.

(**) These functions are new in the array API and are not in the main numpy namespace.

Array Object

All the special __operator__ methods on the array object behave identically to their corresponding functions (see the spec for a list of which methods correspond to which functions). The exception is that operators explicitly allow Python scalars according to the rules outlined in the spec (see Type Promotion Differences).

Array Object Differences

Feature	Type	Notes
No array scalars	Strictness	The spec does not have array scalars, only 0-D arrays. However, other than the promotion differences outlined in Type Promotion Differences, scalars duck type as 0-D arrays for the purposes of the spec. The are immutable, but the spec does not require mutability.
bool(), int(), and float() only work on 0-D arrays.	Strictness	See https://github.com/numpy/numpy/issues/10404.
__imatmul__	Compatible	np.ndarray does not currently implement __imatmul. Note that a @= b should only defined when it does not change the shape of a.
The mT attribute for matrix transpose.	Compatible	See the spec definition for mT.
The T attribute should error if the input is not 2-dimensional.	Breaking	See the note in the spec.
New method to_device and attribute device	Compatible	The methods would effectively not do anything since NumPy is CPU only

Creation Functions Differences

Feature	Type	Notes
copy keyword argument to asarray	Compatible
New device keyword argument to all array creation functions (asarray, arange, empty, empty_like, eye, full, full_like, linspace, ones, ones_like, zeros, and zeros_like).	Compatible	device would effectively do nothing, since NumPy is CPU only.

Elementwise Functions Differences

Feature	Type	Notes
Various functions have been renamed.	Compatible	See Function Name Changes.
Elementwise functions are only defined for given input type combinations.	Strictness	See Type Strictness.
bitwise_left_shift and bitwise_right_shift are only defined for x2 nonnegative.	Strictness
ceil, floor, and trunc return an integer with integer input.	Breaking	np.ceil, np.floor, and np.trunc return a floating-point dtype on integer dtype input.

Linear Algebra Differences

Feature	Type	Notes
cholesky includes an upper keyword argument.	Compatible
cross does not allow size 2 vectors (only size 3).	Breaking
diagonal operates on the last two axes.	Breaking	Strictly speaking this can be compatible because diagonal is moved to the linalg namespace.
eigh, qr, slogdet and svd return a named tuple.	Compatible	The corresponding numpy functions return a tuple, with the resulting arrays in the same order.
New functions matrix_norm and vector_norm.	Compatible	The norm function has been omitted from the array API and split into matrix_norm for matrix norms and vector_norm for vector norms. Note that vector_norm supports any number of axes, whereas np.linalg.norm only supports a single axis for vector norms.
matrix_rank has an rtol keyword argument instead of tol.	Breaking	In the array API, rtol filters singular values smaller than rtol * largest_singular_value. In np.linalg.matrix_rank, tol filters singular values smaller than tol. Furthermore, the default value for rtol is max(M, N) * eps, whereas the default value of tol in np.linalg.matrix_rank is S.max() * max(M, N) * eps, where S is the singular values of the input. The new flag name is compatible but the default change is breaking
matrix_rank does not support 1-dimensional arrays.	Breaking
New function matrix_transpose.	Compatible	Unlike np.transpose, matrix_transpose only transposes the last two axes. See the spec definition
outer only supports 1-dimensional arrays.	Breaking	The spec currently only specifies behavior on 1-D arrays but future behavior will likely be to broadcast, rather than flatten, which is what np.outer does.
pinv has an rtol keyword argument instead of rcond	Breaking	The meaning of rtol and rcond is the same, but the default value for rtol is max(M, N) * eps, whereas the default value for rcond is 1e-15. The new flag name is compatible but the default change is breaking.
solve only accepts x2 as a vector when it is exactly 1-dimensional.	Breaking	The np.linalg.solve behavior is ambiguous. See this numpy issue and this array API specification issue for more details.
New function svdvals.	Compatible	Equivalent to np.linalg.svd(compute_uv=False).
The axis keyword to tensordot must be a tuple.	Compatible	In np.tensordot, it can also be an array or array-like.
trace operates on the last two axes.	Breaking	np.trace operates on the first two axes by default. Note that the array API trace does not allow specifying which axes to operate on.

Manipulation Functions Differences

Feature	Type	Notes
Various functions have been renamed	Compatible	See Function Name Changes.
concat has different default casting rules from np.concatenate	Strictness	No cross-kind casting. No value-based casting on scalars (when axis=None).
stack has different default casting rules from np.stack	Strictness	No cross-kind casting.
New function permute_dims.	Compatible	Unlike np.transpose, the axis keyword argument to permute_dims is required.
reshape function has a copy keyword argument	Compatible	See https://github.com/numpy/numpy/issues/9818.

Set Functions Differences

Feature	Type	Notes
New functions unique_all, unique_counts, unique_inverse, and unique_values.	Compatible	See Function Name Changes.
The four unique_* functions return a named tuple.	Compatible
unique_all and unique_indices return indices with the same shape as x.	Compatible	See https://github.com/numpy/numpy/issues/20638.

Set Functions Differences

Feature	Type	Notes
argsort and sort have a stable keyword argument instead of kind.	Breaking	stable is a boolean keyword argument, defaulting to True. kind takes a string, defaulting to "quicksort". stable=True is equivalent to kind="stable" and kind=False is equivalent to kind="quicksort", although any sorting algorithm is allowed by the spec when stable=False. The new flag name is compatible but the default change is breaking.
argsort and sort have a descending keyword argument.	Compatible

Statistical Functions Differences

Feature	Type	Notes
sum and prod always upcast float32 to float64 when dtype=None.	Breaking
The std and var functions have a correction keyword argument instead of ddof.	Compatible

Other Differences

Feature	Type	Notes
Dtypes can only be spelled as dtype objects.	Strictness	For example, numpy.array_api.asarray([0], dtype='int32') is not allowed.
asarray is not implicitly called in any function.	Strictness	The exception is Python operators, which accept Python scalars in certain cases (see Type Promotion Differences).
tril and triu require the input to be at least 2-D.	Strictness
finfo() return type uses float for the various attributes.	Strictness	The spec allows duck typing, so finfo returning dtype scalars is considered type compatible with float.
Positional arguments in every function are positional-only.	Breaking	See the spec for the exact signature of each function. Note that NumPy ufuncs already use positional-only arguments, but non-ufuncs like asarray generally do not.