931 lines
34 KiB
Python
931 lines
34 KiB
Python
"""Utility functions to use Python Array API compatible libraries.
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For the context about the Array API see:
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https://data-apis.org/array-api/latest/purpose_and_scope.html
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The SciPy use case of the Array API is described on the following page:
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https://data-apis.org/array-api/latest/use_cases.html#use-case-scipy
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"""
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import contextlib
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import dataclasses
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import functools
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import os
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import textwrap
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from collections.abc import Generator, Iterable, Iterator
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from contextlib import contextmanager
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from contextvars import ContextVar
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from types import ModuleType
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from typing import Any, Literal, TypeAlias
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import numpy as np
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import numpy.typing as npt
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from scipy._lib import array_api_compat
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from scipy._lib.array_api_compat import (
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is_array_api_obj,
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is_lazy_array,
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size as xp_size,
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numpy as np_compat,
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device as xp_device,
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is_numpy_namespace as is_numpy,
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is_cupy_namespace as is_cupy,
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is_torch_namespace as is_torch,
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is_jax_namespace as is_jax,
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is_dask_namespace as is_dask,
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is_array_api_strict_namespace as is_array_api_strict
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)
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from scipy._lib._sparse import issparse
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from scipy._lib._docscrape import FunctionDoc
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__all__ = [
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'_asarray', 'array_namespace', 'assert_almost_equal', 'assert_array_almost_equal',
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'default_xp', 'eager_warns', 'is_lazy_array', 'is_marray',
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'is_array_api_strict', 'is_complex', 'is_cupy', 'is_jax', 'is_numpy', 'is_torch',
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'SCIPY_ARRAY_API', 'SCIPY_DEVICE', 'scipy_namespace_for',
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'xp_assert_close', 'xp_assert_equal', 'xp_assert_less',
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'xp_copy', 'xp_device', 'xp_ravel', 'xp_size',
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'xp_unsupported_param_msg', 'xp_vector_norm', 'xp_capabilities',
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'xp_result_type', 'xp_promote'
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]
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# To enable array API and strict array-like input validation
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SCIPY_ARRAY_API: str | bool = os.environ.get("SCIPY_ARRAY_API", False)
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# To control the default device - for use in the test suite only
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SCIPY_DEVICE = os.environ.get("SCIPY_DEVICE", "cpu")
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_GLOBAL_CONFIG = {
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"SCIPY_ARRAY_API": SCIPY_ARRAY_API,
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"SCIPY_DEVICE": SCIPY_DEVICE,
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}
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Array: TypeAlias = Any # To be changed to a Protocol later (see array-api#589)
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ArrayLike: TypeAlias = Array | npt.ArrayLike
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def _compliance_scipy(arrays: Iterable[ArrayLike]) -> Iterator[Array]:
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"""Raise exceptions on known-bad subclasses. Discard 0-dimensional ArrayLikes
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and convert 1+-dimensional ArrayLikes to numpy.
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The following subclasses are not supported and raise and error:
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- `numpy.ma.MaskedArray`
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- `numpy.matrix`
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- NumPy arrays which do not have a boolean or numerical dtype
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- Any array-like which is neither array API compatible nor coercible by NumPy
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- Any array-like which is coerced by NumPy to an unsupported dtype
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"""
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for array in arrays:
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if array is None:
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continue
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# this comes from `_util._asarray_validated`
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if issparse(array):
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msg = ('Sparse arrays/matrices are not supported by this function. '
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'Perhaps one of the `scipy.sparse.linalg` functions '
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'would work instead.')
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raise ValueError(msg)
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if isinstance(array, np.ma.MaskedArray):
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raise TypeError("Inputs of type `numpy.ma.MaskedArray` are not supported.")
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if isinstance(array, np.matrix):
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raise TypeError("Inputs of type `numpy.matrix` are not supported.")
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if isinstance(array, np.ndarray | np.generic):
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dtype = array.dtype
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if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
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raise TypeError(f"An argument has dtype `{dtype!r}`; "
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f"only boolean and numerical dtypes are supported.")
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if is_array_api_obj(array):
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yield array
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else:
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try:
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array = np.asanyarray(array)
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except TypeError:
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raise TypeError("An argument is neither array API compatible nor "
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"coercible by NumPy.")
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dtype = array.dtype
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if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
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message = (
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f"An argument was coerced to an unsupported dtype `{dtype!r}`; "
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f"only boolean and numerical dtypes are supported."
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)
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raise TypeError(message)
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# Ignore 0-dimensional arrays, coherently with array-api-compat.
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# Raise if there are 1+-dimensional array-likes mixed with non-numpy
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# Array API objects.
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if array.ndim:
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yield array
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def _check_finite(array: Array, xp: ModuleType) -> None:
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"""Check for NaNs or Infs."""
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if not xp.all(xp.isfinite(array)):
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msg = "array must not contain infs or NaNs"
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raise ValueError(msg)
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def array_namespace(*arrays: Array) -> ModuleType:
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"""Get the array API compatible namespace for the arrays xs.
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Parameters
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----------
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*arrays : sequence of array_like
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Arrays used to infer the common namespace.
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Returns
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-------
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namespace : module
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Common namespace.
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Notes
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-----
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Thin wrapper around `array_api_compat.array_namespace`.
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1. Check for the global switch: SCIPY_ARRAY_API. This can also be accessed
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dynamically through ``_GLOBAL_CONFIG['SCIPY_ARRAY_API']``.
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2. `_compliance_scipy` raise exceptions on known-bad subclasses. See
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its definition for more details.
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When the global switch is False, it defaults to the `numpy` namespace.
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In that case, there is no compliance check. This is a convenience to
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ease the adoption. Otherwise, arrays must comply with the new rules.
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"""
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if not _GLOBAL_CONFIG["SCIPY_ARRAY_API"]:
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# here we could wrap the namespace if needed
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return np_compat
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api_arrays = list(_compliance_scipy(arrays))
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# In case of a mix of array API compliant arrays and scalars, return
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# the array API namespace. If there are only ArrayLikes (e.g. lists),
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# return NumPy (wrapped by array-api-compat).
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if api_arrays:
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return array_api_compat.array_namespace(*api_arrays)
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return np_compat
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def _asarray(
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array: ArrayLike,
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dtype: Any = None,
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order: Literal['K', 'A', 'C', 'F'] | None = None,
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copy: bool | None = None,
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*,
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xp: ModuleType | None = None,
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check_finite: bool = False,
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subok: bool = False,
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) -> Array:
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"""SciPy-specific replacement for `np.asarray` with `order`, `check_finite`, and
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`subok`.
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Memory layout parameter `order` is not exposed in the Array API standard.
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`order` is only enforced if the input array implementation
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is NumPy based, otherwise `order` is just silently ignored.
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`check_finite` is also not a keyword in the array API standard; included
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here for convenience rather than that having to be a separate function
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call inside SciPy functions.
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`subok` is included to allow this function to preserve the behaviour of
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`np.asanyarray` for NumPy based inputs.
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"""
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if xp is None:
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xp = array_namespace(array)
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if is_numpy(xp):
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# Use NumPy API to support order
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if copy is True:
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array = np.array(array, order=order, dtype=dtype, subok=subok)
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elif subok:
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array = np.asanyarray(array, order=order, dtype=dtype)
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else:
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array = np.asarray(array, order=order, dtype=dtype)
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else:
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try:
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array = xp.asarray(array, dtype=dtype, copy=copy)
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except TypeError:
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coerced_xp = array_namespace(xp.asarray(3))
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array = coerced_xp.asarray(array, dtype=dtype, copy=copy)
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if check_finite:
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_check_finite(array, xp)
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return array
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def xp_copy(x: Array, *, xp: ModuleType | None = None) -> Array:
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"""
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Copies an array.
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Parameters
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----------
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x : array
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xp : array_namespace
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Returns
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-------
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copy : array
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Copied array
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Notes
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-----
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This copy function does not offer all the semantics of `np.copy`, i.e. the
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`subok` and `order` keywords are not used.
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"""
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# Note: for older NumPy versions, `np.asarray` did not support the `copy` kwarg,
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# so this uses our other helper `_asarray`.
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if xp is None:
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xp = array_namespace(x)
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return _asarray(x, copy=True, xp=xp)
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_default_xp_ctxvar: ContextVar[ModuleType] = ContextVar("_default_xp")
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@contextmanager
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def default_xp(xp: ModuleType) -> Generator[None, None, None]:
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"""In all ``xp_assert_*`` and ``assert_*`` function calls executed within this
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context manager, test by default that the array namespace is
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the provided across all arrays, unless one explicitly passes the ``xp=``
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parameter or ``check_namespace=False``.
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Without this context manager, the default value for `xp` is the namespace
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for the desired array (the second parameter of the tests).
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"""
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token = _default_xp_ctxvar.set(xp)
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try:
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yield
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finally:
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_default_xp_ctxvar.reset(token)
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def eager_warns(x, warning_type, match=None):
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"""pytest.warns context manager, but only if x is not a lazy array."""
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import pytest
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# This attribute is interpreted by pytest-run-parallel, ensuring that tests that use
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# `eager_warns` aren't run in parallel (since pytest.warns isn't thread-safe).
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__thread_safe__ = False # noqa: F841
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if is_lazy_array(x):
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return contextlib.nullcontext()
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return pytest.warns(warning_type, match=match)
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def _strict_check(actual, desired, xp, *,
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check_namespace=True, check_dtype=True, check_shape=True,
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check_0d=True):
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__tracebackhide__ = True # Hide traceback for py.test
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if xp is None:
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try:
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xp = _default_xp_ctxvar.get()
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except LookupError:
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xp = array_namespace(desired)
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if check_namespace:
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_assert_matching_namespace(actual, desired, xp)
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# only NumPy distinguishes between scalars and arrays; we do if check_0d=True.
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# do this first so we can then cast to array (and thus use the array API) below.
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if is_numpy(xp) and check_0d:
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_msg = ("Array-ness does not match:\n Actual: "
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f"{type(actual)}\n Desired: {type(desired)}")
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assert ((xp.isscalar(actual) and xp.isscalar(desired))
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or (not xp.isscalar(actual) and not xp.isscalar(desired))), _msg
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actual = xp.asarray(actual)
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desired = xp.asarray(desired)
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if check_dtype:
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_msg = f"dtypes do not match.\nActual: {actual.dtype}\nDesired: {desired.dtype}"
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assert actual.dtype == desired.dtype, _msg
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if check_shape:
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if is_dask(xp):
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actual.compute_chunk_sizes()
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desired.compute_chunk_sizes()
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_msg = f"Shapes do not match.\nActual: {actual.shape}\nDesired: {desired.shape}"
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assert actual.shape == desired.shape, _msg
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desired = xp.broadcast_to(desired, actual.shape)
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return actual, desired, xp
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def _assert_matching_namespace(actual, desired, xp):
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__tracebackhide__ = True # Hide traceback for py.test
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desired_arr_space = array_namespace(desired)
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_msg = ("Namespace of desired array does not match expectations "
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"set by the `default_xp` context manager or by the `xp`"
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"pytest fixture.\n"
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f"Desired array's space: {desired_arr_space.__name__}\n"
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f"Expected namespace: {xp.__name__}")
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assert desired_arr_space == xp, _msg
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actual_arr_space = array_namespace(actual)
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_msg = ("Namespace of actual and desired arrays do not match.\n"
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f"Actual: {actual_arr_space.__name__}\n"
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f"Desired: {xp.__name__}")
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assert actual_arr_space == xp, _msg
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def xp_assert_equal(actual, desired, *, check_namespace=True, check_dtype=True,
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check_shape=True, check_0d=True, err_msg='', xp=None):
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__tracebackhide__ = True # Hide traceback for py.test
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actual, desired, xp = _strict_check(
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actual, desired, xp, check_namespace=check_namespace,
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check_dtype=check_dtype, check_shape=check_shape,
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check_0d=check_0d
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)
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if is_cupy(xp):
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return xp.testing.assert_array_equal(actual, desired, err_msg=err_msg)
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elif is_torch(xp):
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# PyTorch recommends using `rtol=0, atol=0` like this
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# to test for exact equality
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err_msg = None if err_msg == '' else err_msg
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return xp.testing.assert_close(actual, desired, rtol=0, atol=0, equal_nan=True,
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check_dtype=False, msg=err_msg)
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# JAX uses `np.testing`
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return np.testing.assert_array_equal(actual, desired, err_msg=err_msg)
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def xp_assert_close(actual, desired, *, rtol=None, atol=0, check_namespace=True,
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check_dtype=True, check_shape=True, check_0d=True,
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err_msg='', xp=None):
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__tracebackhide__ = True # Hide traceback for py.test
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actual, desired, xp = _strict_check(
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actual, desired, xp,
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check_namespace=check_namespace, check_dtype=check_dtype,
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check_shape=check_shape, check_0d=check_0d
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)
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floating = xp.isdtype(actual.dtype, ('real floating', 'complex floating'))
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if rtol is None and floating:
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# multiplier of 4 is used as for `np.float64` this puts the default `rtol`
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# roughly half way between sqrt(eps) and the default for
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# `numpy.testing.assert_allclose`, 1e-7
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rtol = xp.finfo(actual.dtype).eps**0.5 * 4
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elif rtol is None:
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rtol = 1e-7
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if is_cupy(xp):
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return xp.testing.assert_allclose(actual, desired, rtol=rtol,
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atol=atol, err_msg=err_msg)
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elif is_torch(xp):
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err_msg = None if err_msg == '' else err_msg
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return xp.testing.assert_close(actual, desired, rtol=rtol, atol=atol,
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equal_nan=True, check_dtype=False, msg=err_msg)
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# JAX uses `np.testing`
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return np.testing.assert_allclose(actual, desired, rtol=rtol,
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atol=atol, err_msg=err_msg)
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def xp_assert_less(actual, desired, *, check_namespace=True, check_dtype=True,
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check_shape=True, check_0d=True, err_msg='', verbose=True, xp=None):
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__tracebackhide__ = True # Hide traceback for py.test
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actual, desired, xp = _strict_check(
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actual, desired, xp, check_namespace=check_namespace,
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check_dtype=check_dtype, check_shape=check_shape,
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check_0d=check_0d
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)
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if is_cupy(xp):
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return xp.testing.assert_array_less(actual, desired,
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err_msg=err_msg, verbose=verbose)
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elif is_torch(xp):
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if actual.device.type != 'cpu':
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actual = actual.cpu()
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if desired.device.type != 'cpu':
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desired = desired.cpu()
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# JAX uses `np.testing`
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return np.testing.assert_array_less(actual, desired,
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err_msg=err_msg, verbose=verbose)
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def assert_array_almost_equal(actual, desired, decimal=6, *args, **kwds):
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"""Backwards compatible replacement. In new code, use xp_assert_close instead.
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"""
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rtol, atol = 0, 1.5*10**(-decimal)
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return xp_assert_close(actual, desired,
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atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
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*args, **kwds)
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def assert_almost_equal(actual, desired, decimal=7, *args, **kwds):
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"""Backwards compatible replacement. In new code, use xp_assert_close instead.
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"""
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rtol, atol = 0, 1.5*10**(-decimal)
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return xp_assert_close(actual, desired,
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atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
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*args, **kwds)
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def xp_unsupported_param_msg(param: Any) -> str:
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return f'Providing {param!r} is only supported for numpy arrays.'
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def is_complex(x: Array, xp: ModuleType) -> bool:
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return xp.isdtype(x.dtype, 'complex floating')
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|
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def scipy_namespace_for(xp: ModuleType) -> ModuleType | None:
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"""Return the `scipy`-like namespace of a non-NumPy backend
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That is, return the namespace corresponding with backend `xp` that contains
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`scipy` sub-namespaces like `linalg` and `special`. If no such namespace
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exists, return ``None``. Useful for dispatching.
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"""
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if is_cupy(xp):
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import cupyx # type: ignore[import-not-found,import-untyped]
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return cupyx.scipy
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if is_jax(xp):
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import jax # type: ignore[import-not-found]
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return jax.scipy
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if is_torch(xp):
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return xp
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return None
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# maybe use `scipy.linalg` if/when array API support is added
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def xp_vector_norm(x: Array, /, *,
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axis: int | tuple[int] | None = None,
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keepdims: bool = False,
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ord: int | float = 2,
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xp: ModuleType | None = None) -> Array:
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xp = array_namespace(x) if xp is None else xp
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if SCIPY_ARRAY_API:
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# check for optional `linalg` extension
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if hasattr(xp, 'linalg'):
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return xp.linalg.vector_norm(x, axis=axis, keepdims=keepdims, ord=ord)
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else:
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if ord != 2:
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raise ValueError(
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"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_swapaxes(a, axis1, axis2, xp=None):
|
|
# Equivalent of np.swapaxes written in terms of array API
|
|
xp = array_namespace(a) if xp is None else xp
|
|
axes = list(range(a.ndim))
|
|
axes[axis1], axes[axis2] = axes[axis2], axes[axis1]
|
|
a = xp.permute_dims(a, axes)
|
|
return a
|
|
|
|
|
|
# utility to find common dtype with option to force floating
|
|
def xp_result_type(*args, force_floating=False, xp):
|
|
"""
|
|
Returns the dtype that results from applying type promotion rules
|
|
(see Array API Standard Type Promotion Rules) to the arguments. Augments
|
|
standard `result_type` in a few ways:
|
|
|
|
- There is a `force_floating` argument that ensures that the result type
|
|
is floating point, even when all args are integer.
|
|
- When a TypeError is raised (e.g. due to an unsupported promotion)
|
|
and `force_floating=True`, we define a custom rule: use the result type
|
|
of the default float and any other floats passed. See
|
|
https://github.com/scipy/scipy/pull/22695/files#r1997905891
|
|
for rationale.
|
|
- This function accepts array-like iterables, which are immediately converted
|
|
to the namespace's arrays before result type calculation. Consequently, the
|
|
result dtype may be different when an argument is `1.` vs `[1.]`.
|
|
|
|
Typically, this function will be called shortly after `array_namespace`
|
|
on a subset of the arguments passed to `array_namespace`.
|
|
"""
|
|
args = [(_asarray(arg, subok=True, xp=xp) if np.iterable(arg) else arg)
|
|
for arg in args]
|
|
args_not_none = [arg for arg in args if arg is not None]
|
|
if force_floating:
|
|
args_not_none.append(1.0)
|
|
|
|
if is_numpy(xp) and xp.__version__ < '2.0':
|
|
# Follow NEP 50 promotion rules anyway
|
|
args_not_none = [arg.dtype if getattr(arg, 'size', 0) == 1 else arg
|
|
for arg in args_not_none]
|
|
return xp.result_type(*args_not_none)
|
|
|
|
try: # follow library's preferred promotion rules
|
|
return xp.result_type(*args_not_none)
|
|
except TypeError: # mixed type promotion isn't defined
|
|
if not force_floating:
|
|
raise
|
|
# use `result_type` of default floating point type and any floats present
|
|
# This can be revisited, but right now, the only backends that get here
|
|
# are array-api-strict (which is not for production use) and PyTorch
|
|
# (due to data-apis/array-api-compat#279).
|
|
float_args = []
|
|
for arg in args_not_none:
|
|
arg_array = xp.asarray(arg) if np.isscalar(arg) else arg
|
|
dtype = getattr(arg_array, 'dtype', arg)
|
|
if xp.isdtype(dtype, ('real floating', 'complex floating')):
|
|
float_args.append(arg)
|
|
return xp.result_type(*float_args, xp_default_dtype(xp))
|
|
|
|
|
|
def xp_promote(*args, broadcast=False, force_floating=False, xp):
|
|
"""
|
|
Promotes elements of *args to result dtype, ignoring `None`s.
|
|
Includes options for forcing promotion to floating point and
|
|
broadcasting the arrays, again ignoring `None`s.
|
|
Type promotion rules follow `xp_result_type` instead of `xp.result_type`.
|
|
|
|
Typically, this function will be called shortly after `array_namespace`
|
|
on a subset of the arguments passed to `array_namespace`.
|
|
|
|
This function accepts array-like iterables, which are immediately converted
|
|
to the namespace's arrays before result type calculation. Consequently, the
|
|
result dtype may be different when an argument is `1.` vs `[1.]`.
|
|
|
|
See Also
|
|
--------
|
|
xp_result_type
|
|
"""
|
|
args = [(_asarray(arg, subok=True, xp=xp) if np.iterable(arg) else arg)
|
|
for arg in args] # solely to prevent double conversion of iterable to array
|
|
|
|
dtype = xp_result_type(*args, force_floating=force_floating, xp=xp)
|
|
|
|
args = [(_asarray(arg, dtype=dtype, subok=True, xp=xp) if arg is not None else arg)
|
|
for arg in args]
|
|
|
|
if not broadcast:
|
|
return args[0] if len(args)==1 else tuple(args)
|
|
|
|
args_not_none = [arg for arg in args if arg is not None]
|
|
|
|
# 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)
|
|
|
|
# This is much faster than xp.astype(arg, dtype, copy=False)
|
|
if arg.dtype != dtype:
|
|
arg = xp.astype(arg, dtype)
|
|
|
|
out.append(arg)
|
|
|
|
return out[0] if len(out)==1 else tuple(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
|
|
|
|
|
|
def xp_default_dtype(xp):
|
|
"""Query the namespace-dependent default floating-point dtype.
|
|
"""
|
|
if is_torch(xp):
|
|
# historically, we allow pytorch to keep its default of float32
|
|
return xp.get_default_dtype()
|
|
else:
|
|
# we default to float64
|
|
return xp.float64
|
|
|
|
|
|
def xp_result_device(*args):
|
|
"""Return the device of an array in `args`, for the purpose of
|
|
input-output device propagation.
|
|
If there are multiple devices, return an arbitrary one.
|
|
If there are no arrays, return None (this typically happens only on NumPy).
|
|
"""
|
|
for arg in args:
|
|
# Do not do a duck-type test for the .device attribute, as many backends today
|
|
# don't have it yet. See workarouunds in array_api_compat.device().
|
|
if is_array_api_obj(arg):
|
|
return xp_device(arg)
|
|
return None
|
|
|
|
|
|
def is_marray(xp):
|
|
"""Returns True if `xp` is an MArray namespace; False otherwise."""
|
|
return "marray" in xp.__name__
|
|
|
|
|
|
@dataclasses.dataclass(repr=False)
|
|
class _XPSphinxCapability:
|
|
cpu: bool | None # None if not applicable
|
|
gpu: bool | None
|
|
warnings: list[str] = dataclasses.field(default_factory=list)
|
|
|
|
def _render(self, value):
|
|
if value is None:
|
|
return "n/a"
|
|
if not value:
|
|
return "⛔"
|
|
if self.warnings:
|
|
res = "⚠️ " + '; '.join(self.warnings)
|
|
assert len(res) <= 20, "Warnings too long"
|
|
return res
|
|
return "✅"
|
|
|
|
def __str__(self):
|
|
cpu = self._render(self.cpu)
|
|
gpu = self._render(self.gpu)
|
|
return f"{cpu:20} {gpu:20}"
|
|
|
|
|
|
def _make_sphinx_capabilities(
|
|
# lists of tuples [(module name, reason), ...]
|
|
skip_backends=(), xfail_backends=(),
|
|
# @pytest.mark.skip/xfail_xp_backends kwargs
|
|
cpu_only=False, np_only=False, exceptions=(),
|
|
# xpx.lazy_xp_backends kwargs
|
|
allow_dask_compute=False, jax_jit=True,
|
|
# list of tuples [(module name, reason), ...]
|
|
warnings = (),
|
|
# unused in documentation
|
|
reason=None,
|
|
):
|
|
exceptions = set(exceptions)
|
|
|
|
# Default capabilities
|
|
capabilities = {
|
|
"numpy": _XPSphinxCapability(cpu=True, gpu=None),
|
|
"array_api_strict": _XPSphinxCapability(cpu=True, gpu=None),
|
|
"cupy": _XPSphinxCapability(cpu=None, gpu=True),
|
|
"torch": _XPSphinxCapability(cpu=True, gpu=True),
|
|
"jax.numpy": _XPSphinxCapability(cpu=True, gpu=True,
|
|
warnings=[] if jax_jit else ["no JIT"]),
|
|
# Note: Dask+CuPy is currently untested and unsupported
|
|
"dask.array": _XPSphinxCapability(cpu=True, gpu=None,
|
|
warnings=["computes graph"] if allow_dask_compute else []),
|
|
}
|
|
|
|
# documentation doesn't display the reason
|
|
for module, _ in list(skip_backends) + list(xfail_backends):
|
|
backend = capabilities[module]
|
|
if backend.cpu is not None:
|
|
backend.cpu = False
|
|
if backend.gpu is not None:
|
|
backend.gpu = False
|
|
|
|
for module, backend in capabilities.items():
|
|
if np_only and module not in exceptions | {"numpy"}:
|
|
if backend.cpu is not None:
|
|
backend.cpu = False
|
|
if backend.gpu is not None:
|
|
backend.gpu = False
|
|
elif cpu_only and module not in exceptions and backend.gpu is not None:
|
|
backend.gpu = False
|
|
|
|
for module, warning in warnings:
|
|
backend = capabilities[module]
|
|
backend.warnings.append(warning)
|
|
|
|
return capabilities
|
|
|
|
|
|
def _make_capabilities_note(fun_name, capabilities):
|
|
# Note: deliberately not documenting array-api-strict
|
|
note = f"""
|
|
`{fun_name}` has experimental support for Python Array API Standard compatible
|
|
backends in addition to NumPy. Please consider testing these features
|
|
by setting an environment variable ``SCIPY_ARRAY_API=1`` and providing
|
|
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
|
combinations of backend and device (or other capability) are supported.
|
|
|
|
==================== ==================== ====================
|
|
Library CPU GPU
|
|
==================== ==================== ====================
|
|
NumPy {capabilities['numpy'] }
|
|
CuPy {capabilities['cupy'] }
|
|
PyTorch {capabilities['torch'] }
|
|
JAX {capabilities['jax.numpy'] }
|
|
Dask {capabilities['dask.array'] }
|
|
==================== ==================== ====================
|
|
|
|
See :ref:`dev-arrayapi` for more information.
|
|
"""
|
|
return textwrap.dedent(note)
|
|
|
|
|
|
def xp_capabilities(
|
|
*,
|
|
# Alternative capabilities table.
|
|
# Used only for testing this decorator.
|
|
capabilities_table=None,
|
|
# Generate pytest.mark.skip/xfail_xp_backends.
|
|
# See documentation in conftest.py.
|
|
# lists of tuples [(module name, reason), ...]
|
|
skip_backends=(), xfail_backends=(),
|
|
cpu_only=False, np_only=False, reason=None, exceptions=(),
|
|
# lists of tuples [(module name, reason), ...]
|
|
warnings=(),
|
|
# xpx.testing.lazy_xp_function kwargs.
|
|
# Refer to array-api-extra documentation.
|
|
allow_dask_compute=False, jax_jit=True,
|
|
):
|
|
"""Decorator for a function that states its support among various
|
|
Array API compatible backends.
|
|
|
|
This decorator has two effects:
|
|
1. It allows tagging tests with ``@make_xp_test_case`` or
|
|
``make_xp_pytest_param`` (see below) to automatically generate
|
|
SKIP/XFAIL markers and perform additional backend-specific
|
|
testing, such as extra validation for Dask and JAX;
|
|
2. It automatically adds a note to the function's docstring, containing
|
|
a table matching what has been tested.
|
|
|
|
See Also
|
|
--------
|
|
make_xp_test_case
|
|
make_xp_pytest_param
|
|
array_api_extra.testing.lazy_xp_function
|
|
"""
|
|
capabilities_table = (xp_capabilities_table if capabilities_table is None
|
|
else capabilities_table)
|
|
|
|
capabilities = dict(
|
|
skip_backends=skip_backends,
|
|
xfail_backends=xfail_backends,
|
|
cpu_only=cpu_only,
|
|
np_only=np_only,
|
|
reason=reason,
|
|
exceptions=exceptions,
|
|
allow_dask_compute=allow_dask_compute,
|
|
jax_jit=jax_jit,
|
|
warnings=warnings,
|
|
)
|
|
sphinx_capabilities = _make_sphinx_capabilities(**capabilities)
|
|
|
|
def decorator(f):
|
|
# Don't use a wrapper, as in some cases @xp_capabilities is
|
|
# applied to a ufunc
|
|
capabilities_table[f] = capabilities
|
|
note = _make_capabilities_note(f.__name__, sphinx_capabilities)
|
|
doc = FunctionDoc(f)
|
|
doc['Notes'].append(note)
|
|
doc = str(doc).split("\n", 1)[1] # remove signature
|
|
try:
|
|
f.__doc__ = doc
|
|
except AttributeError:
|
|
# Can't update __doc__ on ufuncs if SciPy
|
|
# was compiled against NumPy < 2.2.
|
|
pass
|
|
|
|
return f
|
|
return decorator
|
|
|
|
|
|
def _make_xp_pytest_marks(*funcs, capabilities_table=None):
|
|
capabilities_table = (xp_capabilities_table if capabilities_table is None
|
|
else capabilities_table)
|
|
import pytest
|
|
from scipy._lib.array_api_extra.testing import lazy_xp_function
|
|
|
|
marks = []
|
|
for func in funcs:
|
|
capabilities = capabilities_table[func]
|
|
exceptions = capabilities['exceptions']
|
|
reason = capabilities['reason']
|
|
|
|
if capabilities['cpu_only']:
|
|
marks.append(pytest.mark.skip_xp_backends(
|
|
cpu_only=True, exceptions=exceptions, reason=reason))
|
|
if capabilities['np_only']:
|
|
marks.append(pytest.mark.skip_xp_backends(
|
|
np_only=True, exceptions=exceptions, reason=reason))
|
|
|
|
for mod_name, reason in capabilities['skip_backends']:
|
|
marks.append(pytest.mark.skip_xp_backends(mod_name, reason=reason))
|
|
for mod_name, reason in capabilities['xfail_backends']:
|
|
marks.append(pytest.mark.xfail_xp_backends(mod_name, reason=reason))
|
|
|
|
lazy_kwargs = {k: capabilities[k]
|
|
for k in ('allow_dask_compute', 'jax_jit')}
|
|
lazy_xp_function(func, **lazy_kwargs)
|
|
|
|
return marks
|
|
|
|
|
|
def make_xp_test_case(*funcs, capabilities_table=None):
|
|
capabilities_table = (xp_capabilities_table if capabilities_table is None
|
|
else capabilities_table)
|
|
"""Generate pytest decorator for a test function that tests functionality
|
|
of one or more Array API compatible functions.
|
|
|
|
Read the parameters of the ``@xp_capabilities`` decorator applied to the
|
|
listed functions and:
|
|
|
|
- Generate the ``@pytest.mark.skip_xp_backends`` and
|
|
``@pytest.mark.xfail_xp_backends`` decorators
|
|
for the decorated test function
|
|
- Tag the function with `xpx.testing.lazy_xp_function`
|
|
|
|
See Also
|
|
--------
|
|
xp_capabilities
|
|
make_xp_pytest_param
|
|
array_api_extra.testing.lazy_xp_function
|
|
"""
|
|
marks = _make_xp_pytest_marks(*funcs, capabilities_table=capabilities_table)
|
|
return lambda func: functools.reduce(lambda f, g: g(f), marks, func)
|
|
|
|
|
|
def make_xp_pytest_param(func, *args, capabilities_table=None):
|
|
"""Variant of ``make_xp_test_case`` that returns a pytest.param for a function,
|
|
with all necessary skip_xp_backends and xfail_xp_backends marks applied::
|
|
|
|
@pytest.mark.parametrize(
|
|
"func", [make_xp_pytest_param(f1), make_xp_pytest_param(f2)]
|
|
)
|
|
def test(func, xp):
|
|
...
|
|
|
|
The above is equivalent to::
|
|
|
|
@pytest.mark.parametrize(
|
|
"func", [
|
|
pytest.param(f1, marks=[
|
|
pytest.mark.skip_xp_backends(...),
|
|
pytest.mark.xfail_xp_backends(...), ...]),
|
|
pytest.param(f2, marks=[
|
|
pytest.mark.skip_xp_backends(...),
|
|
pytest.mark.xfail_xp_backends(...), ...]),
|
|
)
|
|
def test(func, xp):
|
|
...
|
|
|
|
Parameters
|
|
----------
|
|
func : Callable
|
|
Function to be tested. It must be decorated with ``@xp_capabilities``.
|
|
*args : Any, optional
|
|
Extra pytest parameters for the use case, e.g.::
|
|
|
|
@pytest.mark.parametrize("func,verb", [
|
|
make_xp_pytest_param(f1, "hello"),
|
|
make_xp_pytest_param(f2, "world")])
|
|
def test(func, verb, xp):
|
|
# iterates on (func=f1, verb="hello")
|
|
# and (func=f2, verb="world")
|
|
|
|
See Also
|
|
--------
|
|
xp_capabilities
|
|
make_xp_test_case
|
|
array_api_extra.testing.lazy_xp_function
|
|
"""
|
|
import pytest
|
|
|
|
marks = _make_xp_pytest_marks(func, capabilities_table=capabilities_table)
|
|
return pytest.param(func, *args, marks=marks, id=func.__name__)
|
|
|
|
|
|
# Is it OK to have a dictionary that is mutated (once upon import) in many places?
|
|
xp_capabilities_table = {} # type: ignore[var-annotated]
|