"""Utility functions to use Python Array API compatible libraries.
For the context about the Array API see:
https://data-apis.org/array-api/latest/purpose_and_scope.html
The SciPy use case of the Array API is described on the following page:
https://data-apis.org/array-api/latest/use_cases.html#use-case-scipy
"""
import os
from types import ModuleType
from typing import Any, Literal, TypeAlias
import numpy as np
import numpy.typing as npt
from scipy._lib import array_api_compat
from scipy._lib.array_api_compat import (
is_array_api_obj,
size as xp_size,
numpy as np_compat,
device as xp_device,
is_numpy_namespace as is_numpy,
is_cupy_namespace as is_cupy,
is_torch_namespace as is_torch,
is_jax_namespace as is_jax,
is_array_api_strict_namespace as is_array_api_strict
)
__all__ = [
'_asarray', 'array_namespace', 'assert_almost_equal', 'assert_array_almost_equal',
'get_xp_devices',
'is_array_api_strict', 'is_complex', 'is_cupy', 'is_jax', 'is_numpy', 'is_torch',
'SCIPY_ARRAY_API', 'SCIPY_DEVICE', 'scipy_namespace_for',
'xp_assert_close', 'xp_assert_equal', 'xp_assert_less',
'xp_copy', 'xp_copysign', 'xp_device',
'xp_moveaxis_to_end', 'xp_ravel', 'xp_real', 'xp_sign', 'xp_size',
'xp_take_along_axis', 'xp_unsupported_param_msg', 'xp_vector_norm',
]
# To enable array API and strict array-like input validation
SCIPY_ARRAY_API: str | bool = os.environ.get("SCIPY_ARRAY_API", False)
# To control the default device - for use in the test suite only
SCIPY_DEVICE = os.environ.get("SCIPY_DEVICE", "cpu")
_GLOBAL_CONFIG = {
"SCIPY_ARRAY_API": SCIPY_ARRAY_API,
"SCIPY_DEVICE": SCIPY_DEVICE,
}
Array: TypeAlias = Any # To be changed to a Protocol later (see array-api#589)
ArrayLike: TypeAlias = Array | npt.ArrayLike
def _compliance_scipy(arrays):
"""Raise exceptions on known-bad subclasses.
The following subclasses are not supported and raise and error:
- `numpy.ma.MaskedArray`
- `numpy.matrix`
- NumPy arrays which do not have a boolean or numerical dtype
- Any array-like which is neither array API compatible nor coercible by NumPy
- Any array-like which is coerced by NumPy to an unsupported dtype
"""
for i in range(len(arrays)):
array = arrays[i]
from scipy.sparse import issparse
# this comes from `_util._asarray_validated`
if issparse(array):
msg = ('Sparse arrays/matrices are not supported by this function. '
'Perhaps one of the `scipy.sparse.linalg` functions '
'would work instead.')
raise ValueError(msg)
if isinstance(array, np.ma.MaskedArray):
raise TypeError("Inputs of type `numpy.ma.MaskedArray` are not supported.")
elif isinstance(array, np.matrix):
raise TypeError("Inputs of type `numpy.matrix` are not supported.")
if isinstance(array, np.ndarray | np.generic):
dtype = array.dtype
if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
raise TypeError(f"An argument has dtype `{dtype!r}`; "
f"only boolean and numerical dtypes are supported.")
elif not is_array_api_obj(array):
try:
array = np.asanyarray(array)
except TypeError:
raise TypeError("An argument is neither array API compatible nor "
"coercible by NumPy.")
dtype = array.dtype
if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
message = (
f"An argument was coerced to an unsupported dtype `{dtype!r}`; "
f"only boolean and numerical dtypes are supported."
)
raise TypeError(message)
arrays[i] = array
return arrays
def _check_finite(array: Array, xp: ModuleType) -> None:
"""Check for NaNs or Infs."""
msg = "array must not contain infs or NaNs"
try:
if not xp.all(xp.isfinite(array)):
raise ValueError(msg)
except TypeError:
raise ValueError(msg)
def array_namespace(*arrays: Array) -> ModuleType:
"""Get the array API compatible namespace for the arrays xs.
Parameters
----------
*arrays : sequence of array_like
Arrays used to infer the common namespace.
Returns
-------
namespace : module
Common namespace.
Notes
-----
Thin wrapper around `array_api_compat.array_namespace`.
1. Check for the global switch: SCIPY_ARRAY_API. This can also be accessed
dynamically through ``_GLOBAL_CONFIG['SCIPY_ARRAY_API']``.
2. `_compliance_scipy` raise exceptions on known-bad subclasses. See
its definition for more details.
When the global switch is False, it defaults to the `numpy` namespace.
In that case, there is no compliance check. This is a convenience to
ease the adoption. Otherwise, arrays must comply with the new rules.
"""
if not _GLOBAL_CONFIG["SCIPY_ARRAY_API"]:
# here we could wrap the namespace if needed
return np_compat
_arrays = [array for array in arrays if array is not None]
_arrays = _compliance_scipy(_arrays)
return array_api_compat.array_namespace(*_arrays)
def _asarray(
array: ArrayLike,
dtype: Any = None,
order: Literal['K', 'A', 'C', 'F'] | None = None,
copy: bool | None = None,
*,
xp: ModuleType | None = None,
check_finite: bool = False,
subok: bool = False,
) -> Array:
"""SciPy-specific replacement for `np.asarray` with `order`, `check_finite`, and
`subok`.
Memory layout parameter `order` is not exposed in the Array API standard.
`order` is only enforced if the input array implementation
is NumPy based, otherwise `order` is just silently ignored.
`check_finite` is also not a keyword in the array API standard; included
here for convenience rather than that having to be a separate function
call inside SciPy functions.
`subok` is included to allow this function to preserve the behaviour of
`np.asanyarray` for NumPy based inputs.
"""
if xp is None:
xp = array_namespace(array)
if is_numpy(xp):
# Use NumPy API to support order
if copy is True:
array = np.array(array, order=order, dtype=dtype, subok=subok)
elif subok:
array = np.asanyarray(array, order=order, dtype=dtype)
else:
array = np.asarray(array, order=order, dtype=dtype)
else:
try:
array = xp.asarray(array, dtype=dtype, copy=copy)
except TypeError:
coerced_xp = array_namespace(xp.asarray(3))
array = coerced_xp.asarray(array, dtype=dtype, copy=copy)
if check_finite:
_check_finite(array, xp)
return array
def xp_copy(x: Array, *, xp: ModuleType | None = None) -> Array:
"""
Copies an array.
Parameters
----------
x : array
xp : array_namespace
Returns
-------
copy : array
Copied array
Notes
-----
This copy function does not offer all the semantics of `np.copy`, i.e. the
`subok` and `order` keywords are not used.
"""
# Note: for older NumPy versions, `np.asarray` did not support the `copy` kwarg,
# so this uses our other helper `_asarray`.
if xp is None:
xp = array_namespace(x)
return _asarray(x, copy=True, xp=xp)
def _strict_check(actual, desired, xp, *,
check_namespace=True, check_dtype=True, check_shape=True,
check_0d=True):
__tracebackhide__ = True # Hide traceback for py.test
if check_namespace:
_assert_matching_namespace(actual, desired)
# only NumPy distinguishes between scalars and arrays; we do if check_0d=True.
# do this first so we can then cast to array (and thus use the array API) below.
if is_numpy(xp) and check_0d:
_msg = ("Array-ness does not match:\n Actual: "
f"{type(actual)}\n Desired: {type(desired)}")
assert ((xp.isscalar(actual) and xp.isscalar(desired))
or (not xp.isscalar(actual) and not xp.isscalar(desired))), _msg
actual = xp.asarray(actual)
desired = xp.asarray(desired)
if check_dtype:
_msg = f"dtypes do not match.\nActual: {actual.dtype}\nDesired: {desired.dtype}"
assert actual.dtype == desired.dtype, _msg
if check_shape:
_msg = f"Shapes do not match.\nActual: {actual.shape}\nDesired: {desired.shape}"
assert actual.shape == desired.shape, _msg
desired = xp.broadcast_to(desired, actual.shape)
return actual, desired
def _assert_matching_namespace(actual, desired):
__tracebackhide__ = True # Hide traceback for py.test
actual = actual if isinstance(actual, tuple) else (actual,)
desired_space = array_namespace(desired)
for arr in actual:
arr_space = array_namespace(arr)
_msg = (f"Namespaces do not match.\n"
f"Actual: {arr_space.__name__}\n"
f"Desired: {desired_space.__name__}")
assert arr_space == desired_space, _msg
def xp_assert_equal(actual, desired, *, check_namespace=True, check_dtype=True,
check_shape=True, check_0d=True, err_msg='', xp=None):
__tracebackhide__ = True # Hide traceback for py.test
if xp is None:
xp = array_namespace(actual)
actual, desired = _strict_check(
actual, desired, xp, check_namespace=check_namespace,
check_dtype=check_dtype, check_shape=check_shape,
check_0d=check_0d
)
if is_cupy(xp):
return xp.testing.assert_array_equal(actual, desired, err_msg=err_msg)
elif is_torch(xp):
# PyTorch recommends using `rtol=0, atol=0` like this
# to test for exact equality
err_msg = None if err_msg == '' else err_msg
return xp.testing.assert_close(actual, desired, rtol=0, atol=0, equal_nan=True,
check_dtype=False, msg=err_msg)
# JAX uses `np.testing`
return np.testing.assert_array_equal(actual, desired, err_msg=err_msg)
def xp_assert_close(actual, desired, *, rtol=None, atol=0, check_namespace=True,
check_dtype=True, check_shape=True, check_0d=True,
err_msg='', xp=None):
__tracebackhide__ = True # Hide traceback for py.test
if xp is None:
xp = array_namespace(actual)
actual, desired = _strict_check(
actual, desired, xp,
check_namespace=check_namespace, check_dtype=check_dtype,
check_shape=check_shape, check_0d=check_0d
)
floating = xp.isdtype(actual.dtype, ('real floating', 'complex floating'))
if rtol is None and floating:
# multiplier of 4 is used as for `np.float64` this puts the default `rtol`
# roughly half way between sqrt(eps) and the default for
# `numpy.testing.assert_allclose`, 1e-7
rtol = xp.finfo(actual.dtype).eps**0.5 * 4
elif rtol is None:
rtol = 1e-7
if is_cupy(xp):
return xp.testing.assert_allclose(actual, desired, rtol=rtol,
atol=atol, err_msg=err_msg)
elif is_torch(xp):
err_msg = None if err_msg == '' else err_msg
return xp.testing.assert_close(actual, desired, rtol=rtol, atol=atol,
equal_nan=True, check_dtype=False, msg=err_msg)
# JAX uses `np.testing`
return np.testing.assert_allclose(actual, desired, rtol=rtol,
atol=atol, err_msg=err_msg)
def xp_assert_less(actual, desired, *, check_namespace=True, check_dtype=True,
check_shape=True, check_0d=True, err_msg='', verbose=True, xp=None):
__tracebackhide__ = True # Hide traceback for py.test
if xp is None:
xp = array_namespace(actual)
actual, desired = _strict_check(
actual, desired, xp, check_namespace=check_namespace,
check_dtype=check_dtype, check_shape=check_shape,
check_0d=check_0d
)
if is_cupy(xp):
return xp.testing.assert_array_less(actual, desired,
err_msg=err_msg, verbose=verbose)
elif is_torch(xp):
if actual.device.type != 'cpu':
actual = actual.cpu()
if desired.device.type != 'cpu':
desired = desired.cpu()
# JAX uses `np.testing`
return np.testing.assert_array_less(actual, desired,
err_msg=err_msg, verbose=verbose)
def assert_array_almost_equal(actual, desired, decimal=6, *args, **kwds):
"""Backwards compatible replacement. In new code, use xp_assert_close instead.
"""
rtol, atol = 0, 1.5*10**(-decimal)
return xp_assert_close(actual, desired,
atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
*args, **kwds)
def assert_almost_equal(actual, desired, decimal=7, *args, **kwds):
"""Backwards compatible replacement. In new code, use xp_assert_close instead.
"""
rtol, atol = 0, 1.5*10**(-decimal)
return xp_assert_close(actual, desired,
atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
*args, **kwds)
def xp_unsupported_param_msg(param: Any) -> str:
return f'Providing {param!r} is only supported for numpy arrays.'
def is_complex(x: Array, xp: ModuleType) -> bool:
return xp.isdtype(x.dtype, 'complex floating')
def get_xp_devices(xp: ModuleType) -> list[str] | list[None]:
"""Returns a list of available devices for the given namespace."""
devices: list[str] = []
if is_torch(xp):
devices += ['cpu']
import torch # type: ignore[import]
num_cuda = torch.cuda.device_count()
for i in range(0, num_cuda):
devices += [f'cuda:{i}']
if torch.backends.mps.is_available():
devices += ['mps']
return devices
elif is_cupy(xp):
import cupy # type: ignore[import]
num_cuda = cupy.cuda.runtime.getDeviceCount()
for i in range(0, num_cuda):
devices += [f'cuda:{i}']
return devices
elif is_jax(xp):
import jax # type: ignore[import]
num_cpu = jax.device_count(backend='cpu')
for i in range(0, num_cpu):
devices += [f'cpu:{i}']
num_gpu = jax.device_count(backend='gpu')
for i in range(0, num_gpu):
devices += [f'gpu:{i}']
num_tpu = jax.device_count(backend='tpu')
for i in range(0, num_tpu):
devices += [f'tpu:{i}']
return devices
# given namespace is not known to have a list of available devices;
# return `[None]` so that one can use this in tests for `device=None`.
return [None]
def scipy_namespace_for(xp: ModuleType) -> ModuleType | None:
"""Return the `scipy`-like namespace of a non-NumPy backend
That is, return the namespace corresponding with backend `xp` that contains
`scipy` sub-namespaces like `linalg` and `special`. If no such namespace
exists, return ``None``. Useful for dispatching.
"""
if is_cupy(xp):
import cupyx # type: ignore[import-not-found,import-untyped]
return cupyx.scipy
if is_jax(xp):
import jax # type: ignore[import-not-found]
return jax.scipy
if is_torch(xp):
return xp
return None
# temporary substitute for xp.moveaxis, which is not yet in all backends
# or covered by array_api_compat.
def xp_moveaxis_to_end(
x: Array,
source: int,
/, *,
xp: ModuleType | None = None) -> Array:
xp = array_namespace(xp) if xp is None else xp
axes = list(range(x.ndim))
temp = axes.pop(source)
axes = axes + [temp]
return xp.permute_dims(x, axes)
# temporary substitute for xp.copysign, which is not yet in all backends
# or covered by array_api_compat.
def xp_copysign(x1: Array, x2: Array, /, *, xp: ModuleType | None = None) -> Array:
# no attempt to account for special cases
xp = array_namespace(x1, x2) if xp is None else xp
abs_x1 = xp.abs(x1)
return xp.where(x2 >= 0, abs_x1, -abs_x1)
# partial substitute for xp.sign, which does not cover the NaN special case
# that I need. (https://github.com/data-apis/array-api-compat/issues/136)
def xp_sign(x: Array, /, *, xp: ModuleType | None = None) -> Array:
xp = array_namespace(x) if xp is None else xp
if is_numpy(xp): # only NumPy implements the special cases correctly
return xp.sign(x)
sign = xp.zeros_like(x)
one = xp.asarray(1, dtype=x.dtype)
sign = xp.where(x > 0, one, sign)
sign = xp.where(x < 0, -one, sign)
sign = xp.where(xp.isnan(x), xp.nan*one, sign)
return sign
# maybe use `scipy.linalg` if/when array API support is added
def xp_vector_norm(x: Array, /, *,
axis: int | tuple[int] | None = None,
keepdims: bool = False,
ord: int | float = 2,
xp: ModuleType | None = None) -> Array:
xp = array_namespace(x) if xp is None else xp
if SCIPY_ARRAY_API:
# check for optional `linalg` extension
if hasattr(xp, 'linalg'):
return xp.linalg.vector_norm(x, axis=axis, keepdims=keepdims, ord=ord)
else:
if ord != 2:
raise ValueError(
"only the Euclidean norm (`ord=2`) is currently supported in "
"`xp_vector_norm` for backends not implementing the `linalg` "
"extension."
)
# return (x @ x)**0.5
# or to get the right behavior with nd, complex arrays
return xp.sum(xp.conj(x) * x, axis=axis, keepdims=keepdims)**0.5
else:
# to maintain backwards compatibility
return np.linalg.norm(x, ord=ord, axis=axis, keepdims=keepdims)
def xp_ravel(x: Array, /, *, xp: ModuleType | None = None) -> Array:
# Equivalent of np.ravel written in terms of array API
# Even though it's one line, it comes up so often that it's worth having
# this function for readability
xp = array_namespace(x) if xp is None else xp
return xp.reshape(x, (-1,))
def xp_real(x: Array, /, *, xp: ModuleType | None = None) -> Array:
# Convenience wrapper of xp.real that allows non-complex input;
# see data-apis/array-api#824
xp = array_namespace(x) if xp is None else xp
return xp.real(x) if xp.isdtype(x.dtype, 'complex floating') else x
def xp_take_along_axis(arr: Array,
indices: Array, /, *,
axis: int = -1,
xp: ModuleType | None = None) -> Array:
# Dispatcher for np.take_along_axis for backends that support it;
# see data-apis/array-api/pull#816
xp = array_namespace(arr) if xp is None else xp
if is_torch(xp):
return xp.take_along_dim(arr, indices, dim=axis)
elif is_array_api_strict(xp):
raise NotImplementedError("Array API standard does not define take_along_axis")
else:
return xp.take_along_axis(arr, indices, axis)
# utility to broadcast arrays and promote to common dtype
def xp_broadcast_promote(*args, ensure_writeable=False, force_floating=False, xp=None):
xp = array_namespace(*args) if xp is None else xp
args = [(_asarray(arg, subok=True) if arg is not None else arg) for arg in args]
args_not_none = [arg for arg in args if arg is not None]
# determine minimum dtype
default_float = xp.asarray(1.).dtype
dtypes = [arg.dtype for arg in args_not_none]
try: # follow library's prefered mixed promotion rules
dtype = xp.result_type(*dtypes)
if force_floating and xp.isdtype(dtype, 'integral'):
# If we were to add `default_float` before checking whether the result
# type is otherwise integral, we risk promotion from lower float.
dtype = xp.result_type(dtype, default_float)
except TypeError: # mixed type promotion isn't defined
float_dtypes = [dtype for dtype in dtypes
if not xp.isdtype(dtype, 'integral')]
if float_dtypes:
dtype = xp.result_type(*float_dtypes, default_float)
elif force_floating:
dtype = default_float
else:
dtype = xp.result_type(*dtypes)
# determine result shape
shapes = {arg.shape for arg in args_not_none}
try:
shape = (np.broadcast_shapes(*shapes) if len(shapes) != 1
else args_not_none[0].shape)
except ValueError as e:
message = "Array shapes are incompatible for broadcasting."
raise ValueError(message) from e
out = []
for arg in args:
if arg is None:
out.append(arg)
continue
# broadcast only if needed
# Even if two arguments need broadcasting, this is faster than
# `broadcast_arrays`, especially since we've already determined `shape`
if arg.shape != shape:
kwargs = {'subok': True} if is_numpy(xp) else {}
arg = xp.broadcast_to(arg, shape, **kwargs)
# convert dtype/copy only if needed
if (arg.dtype != dtype) or ensure_writeable:
arg = xp.astype(arg, dtype, copy=True)
out.append(arg)
return out
def xp_float_to_complex(arr: Array, xp: ModuleType | None = None) -> Array:
xp = array_namespace(arr) if xp is None else xp
arr_dtype = arr.dtype
# The standard float dtypes are float32 and float64.
# Convert float32 to complex64,
# and float64 (and non-standard real dtypes) to complex128
if xp.isdtype(arr_dtype, xp.float32):
arr = xp.astype(arr, xp.complex64)
elif xp.isdtype(arr_dtype, 'real floating'):
arr = xp.astype(arr, xp.complex128)
return arr