"""Implementation of `array_api_extra.testing`.""" from __future__ import annotations import contextlib import enum import functools import inspect import math import typing import warnings from collections.abc import Callable, Generator, Iterator, Sequence from types import FunctionType, ModuleType from typing import Any, ParamSpec, TypeVar from .._lib import _compat, _helpers from .._lib._typing import ( Array, ArrayNamespace, Device, Graph, Key, SchedulerGetCallable, override, ) if typing.TYPE_CHECKING: import numpy as np import pytest __all__ = [ "assert_close", "assert_close_nulp", "assert_equal", "assert_less", "lazy_xp_function", "patch_lazy_xp_functions", ] __all__ = [ "assert_close", "assert_close_nulp", "assert_equal", "assert_less", "lazy_xp_function", "patch_lazy_xp_functions", ] P = ParamSpec("P") T = TypeVar("T") _ufuncs_tags: dict[object, dict[str, Any]] = {} class _Deprecated(enum.Enum): """Unique type for deprecated parameters.""" DEPRECATED = 1 DEPRECATED = _Deprecated.DEPRECATED def _clone_function( # numpydoc ignore=PR01,RT01 f: Callable[..., Any], ) -> Callable[..., Any]: """Return a clone of an existing function.""" f_new = FunctionType( f.__code__, f.__globals__, name=f.__name__, argdefs=f.__defaults__, closure=f.__closure__, ) f_new.__kwdefaults__ = f.__kwdefaults__ return functools.update_wrapper(f_new, f) def lazy_xp_function( func: Callable[..., Any] | tuple[type, str], *, allow_dask_compute: bool | int = False, jax_jit: bool = True, static_argnums: _Deprecated = DEPRECATED, static_argnames: _Deprecated = DEPRECATED, ) -> None: # numpydoc ignore=GL07 """ Tag a function to be tested on lazy backends. Tag a function so that when any tests are executed with ``xp=jax.numpy`` the function is replaced with a jitted version of itself, and when it is executed with ``xp=dask.array`` the function will raise if it attempts to materialize the graph. This will be later expanded to provide test coverage for other lazy backends. In order for the tag to be effective, the test or a fixture must call :func:`patch_lazy_xp_functions`. Parameters ---------- func : callable | tuple[type, str] Function to be tested, or a tuple containing an (uninstantiated) class and a method name to specify a class method to be tested. allow_dask_compute : bool | int, optional Whether `func` is allowed to internally materialize the Dask graph, or maximum number of times it is allowed to do so. This is typically triggered by ``bool()``, ``float()``, or ``np.asarray()``. Set to 1 if you are aware that `func` converts the input parameters to NumPy and want to let it do so at least for the time being, knowing that it is going to be extremely detrimental for performance. If a test needs values higher than 1 to pass, it is a canary that the conversion to NumPy/bool/float is happening multiple times, which translates to multiple computations of the whole graph. Short of making the function fully lazy, you should at least add explicit calls to ``np.asarray()`` early in the function. *Note:* the counter of `allow_dask_compute` resets after each call to `func`, so a test function that invokes `func` multiple times should still work with this parameter set to 1. Set to True to allow `func` to materialize the graph an unlimited number of times. Default: False, meaning that `func` must be fully lazy and never materialize the graph. jax_jit : bool, optional Set to True to replace `func` with a smart variant of ``jax.jit(func)`` after calling the :func:`patch_lazy_xp_functions` test helper with ``xp=jax.numpy``. This is the default behaviour. Set to False if `func` is only compatible with eager (non-jitted) JAX. Unlike with vanilla ``jax.jit``, all arguments and return types that are not JAX arrays are treated as static; the function can accept and return arbitrary wrappers around JAX arrays. This difference is because, in real life, most users won't wrap the function directly with ``jax.jit`` but rather they will use it within their own code, which is itself then wrapped by ``jax.jit``, and internally consume the function's outputs. In other words, the pattern that is being tested is:: @jax.jit def user_func(x): y = user_prepares_inputs(x) z = func(y, some_static_arg=True) return user_consumes(z) Default: True. static_argnums : Deprecated Deprecated; ignored. static_argnames : Deprecated Deprecated; ignored. See Also -------- patch_lazy_xp_functions : Companion function to call from the test or fixture. jax.jit : JAX function to compile a function for performance. Examples -------- In ``test_mymodule.py``:: from array_api_extra.testing import lazy_xp_function from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) # When xp=jax.numpy, this is similar to `b = jax.jit(myfunc)(a)` # When xp=dask.array, crash on compute() or persist() b = myfunc(a) Notes ----- In order for this tag to be effective, the test function must be imported into the test module globals without its namespace; alternatively its namespace must be declared in a ``lazy_xp_modules`` list in the test module globals. Example 1:: from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): x = myfunc(xp.asarray([1, 2])) Example 2:: import mymodule lazy_xp_modules = [mymodule] lazy_xp_function(mymodule.myfunc) def test_myfunc(xp): x = mymodule.myfunc(xp.asarray([1, 2])) A test function can circumvent this monkey-patching system by using a namespace outside of the two above patterns. You need to sanitize your code to make sure this only happens intentionally. Example 1:: import mymodule from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) b = myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array c = mymodule.myfunc(a) # This is not Example 2:: import mymodule class naked: myfunc = mymodule.myfunc lazy_xp_modules = [mymodule] lazy_xp_function(mymodule.myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) b = mymodule.myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array c = naked.myfunc(a) # This is not """ if static_argnums is not DEPRECATED or static_argnames is not DEPRECATED: warnings.warn( ( "The `static_argnums` and `static_argnames` parameters are deprecated " "and ignored. They will be removed in a future version." ), DeprecationWarning, stacklevel=2, ) tags: dict[str, bool | int | type] = { "allow_dask_compute": allow_dask_compute, "jax_jit": jax_jit, } if isinstance(func, tuple): # Replace the method with a clone before adding tags # to avoid adding unwanted tags to a parent method when # the method was inherited from a parent class. # Note: can't just accept an unbound method `cls.method_name` because in # case of inheritance it would be impossible to attribute it to the child class. # This also makes it so tagged methods will appear in their class's ``__dict__`` # and thus findable by ``iter_tagged_modules`` below. cls, method_name = func # The method might be a staticmethod or classmethod so we need to do a dance # to ensure that this is preserved. raw_attr = inspect.getattr_static(cls, method_name) method = getattr(cls, method_name) if isinstance(raw_attr, classmethod): method = method.__func__ cloned_method = _clone_function(method) method_to_set: Any if isinstance(raw_attr, staticmethod): method_to_set = staticmethod(cloned_method) elif isinstance(raw_attr, classmethod): method_to_set = classmethod(cloned_method) else: method_to_set = cloned_method setattr(cls, method_name, method_to_set) f = getattr(cls, method_name) if isinstance(raw_attr, classmethod): f = f.__func__ # Annotate that cls owns this method so we can check that later. tags["owner"] = cls else: f = func try: f._lazy_xp_function = tags # pylint: disable=protected-access # pyright: ignore[reportFunctionMemberAccess] # pyrefly: ignore[missing-attribute] except AttributeError: # @cython.vectorize _ufuncs_tags[f] = tags def patch_lazy_xp_functions( request: pytest.FixtureRequest, monkeypatch: pytest.MonkeyPatch | None = None, *, xp: ArrayNamespace, ) -> contextlib.AbstractContextManager[None]: """ Test lazy execution of functions tagged with :func:`lazy_xp_function`. If ``xp==jax.numpy``, search for all functions and methods which have been tagged with :func:`lazy_xp_function` in the globals of the module that defines the current test, as well as in the ``lazy_xp_modules`` list in the globals of the same module, and wrap them with :func:`jax.jit`. Unwrap them at the end of the test. If ``xp==dask.array``, wrap the functions with a decorator that disables ``compute()`` and ``persist()`` and ensures that exceptions and warnings are raised eagerly. This function should be typically called by your library's `xp` fixture that runs tests on multiple backends:: @pytest.fixture(params=[ numpy, array_api_strict, pytest.param(jax.numpy, marks=pytest.mark.thread_unsafe), pytest.param(dask.array, marks=pytest.mark.thread_unsafe), ]) def xp(request): with patch_lazy_xp_functions(request, xp=request.param): yield request.param but it can be otherwise be called by the test itself too. Parameters ---------- request : pytest.FixtureRequest Pytest fixture, as acquired by the test itself or by one of its fixtures. monkeypatch : pytest.MonkeyPatch Deprecated. xp : array_namespace Array namespace to be tested. Returns ------- contextlib.AbstractContextManager Testing context manager. See Also -------- lazy_xp_function : Tag a function to be tested on lazy backends. pytest.FixtureRequest : `request` test function parameter. Notes ----- This context manager monkey-patches modules and as such is thread unsafe on Dask and JAX. If you run your test suite with `pytest-run-parallel `_, you should mark these backends with ``@pytest.mark.thread_unsafe``, as shown in the example above. """ mod = typing.cast(ModuleType, request.module) search_targets: list[ModuleType | type] = [ mod, *typing.cast(list[ModuleType], getattr(mod, "lazy_xp_modules", [])), ] # Also search for classes within the above modules which have had lazy_xp_function # applied to methods through ``lazy_xp_function((cls, method_name))`` syntax. # We might end up adding classes incidentally imported into modules, so using a # set here to cut down on potential redundancy. classes: set[type] = set() for target in search_targets: for obj in target.__dict__.values(): if isinstance(obj, type): classes.add(obj) search_targets.extend(classes) to_revert: list[tuple[ModuleType | type, str, object]] = [] def temp_setattr( # numpydoc ignore=PR01 target: ModuleType | type, name: str, func: object ) -> None: """ Temporary setattr. Variant of monkeypatch.setattr, which allows monkey-patching only selected parameters of a test so that pytest-run-parallel can run on the remainder. """ assert hasattr(target, name) # Need inspect.getattr_static because the attr could be a staticmethod or other # descriptor and we don't want that to be stripped away. original = inspect.getattr_static(target, name) to_revert.append((target, name, original)) setattr(target, name, func) if monkeypatch is not None: warnings.warn( ( "The `monkeypatch` parameter is deprecated and will be removed in a " "future version. " "Use `patch_lazy_xp_function` as a context manager instead." ), DeprecationWarning, stacklevel=2, ) # Enable using patch_lazy_xp_function not as a context manager temp_setattr = monkeypatch.setattr # type: ignore[assignment] # pyright: ignore[reportAssignmentType] def iter_tagged() -> Iterator[ tuple[ModuleType | type, str, Any, Callable[..., Any], dict[str, Any]] ]: # numpydoc ignore=GL08 for target in search_targets: for name, attr in target.__dict__.items(): # attr might be a staticmethod or classmethod. If so we need # to peel it back and wrap the underlying function and later # make sure not to accidentally replace it with a regular # method. func: Any = ( attr.__func__ if isinstance(attr, (staticmethod, classmethod)) else attr ) tags: dict[str, Any] | None = None with contextlib.suppress(AttributeError): tags = func._lazy_xp_function # pylint: disable=protected-access if tags is None: with contextlib.suppress(KeyError, TypeError): tags = _ufuncs_tags[func] if tags is not None: if isinstance(target, type) and tags.get("owner") is not target: # There's a common pattern to wrap functions in namespace # classes to bypass lazy_xp_function like this: # # class naked: # myfunc = mymodule.myfunc # # To ensure this still works when checking for tags in # attributes of classes, ensure that target is the actual # owning class where func was defined. continue # put attr, and func in the outputs so we can later tell # if this was a staticmethod or classmethod. yield target, name, attr, func, tags wrapped: Any if _compat.is_dask_namespace(xp): for target, name, attr, func, tags in iter_tagged(): n = tags["allow_dask_compute"] if n is True: n = 1_000_000 elif n is False: n = 0 wrapped = _dask_wrap(func, n) # If we're dealing with a staticmethod or classmethod, make # sure things stay that way. if isinstance(attr, staticmethod): wrapped = staticmethod(wrapped) elif isinstance(attr, classmethod): wrapped = classmethod(wrapped) temp_setattr(target, name, wrapped) elif _compat.is_jax_namespace(xp): for target, name, attr, func, tags in iter_tagged(): if tags["jax_jit"]: wrapped = _helpers.jax_autojit(func) # If we're dealing with a staticmethod or classmethod, make # sure things stay that way. if isinstance(attr, staticmethod): wrapped = staticmethod(wrapped) elif isinstance(attr, classmethod): wrapped = classmethod(wrapped) temp_setattr(target, name, wrapped) # We can't just decorate patch_lazy_xp_functions with # @contextlib.contextmanager because it would not work with the # deprecated monkeypatch when not used as a context manager. @contextlib.contextmanager def revert_on_exit() -> Generator[None]: # numpydoc ignore=GL08 try: yield finally: for target, name, orig_func in to_revert: setattr(target, name, orig_func) return revert_on_exit() class _CountingDaskScheduler(SchedulerGetCallable): """ Dask scheduler that counts how many times `dask.compute` is called. If the number of times exceeds 'max_count', it raises an error. This is a wrapper around Dask's own 'synchronous' scheduler. Parameters ---------- max_count : int Maximum number of allowed calls to `dask.compute`. msg : str Assertion to raise when the count exceeds `max_count`. """ count: int max_count: int msg: str def __init__(self, max_count: int, msg: str) -> None: # numpydoc ignore=GL08 self.count = 0 self.max_count = max_count self.msg = msg @override def __call__( self, dsk: Graph, keys: Sequence[Key] | Key, **kwargs: Any ) -> Any: # numpydoc ignore=GL08 import dask self.count += 1 # This should yield a nice traceback to the # offending line in the user's code assert self.count <= self.max_count, self.msg return dask.get(dsk, keys, **kwargs) # type: ignore[attr-defined] # pyright: ignore[reportPrivateImportUsage] def _dask_wrap( func: Callable[P, T], n: int ) -> Callable[P, T]: # numpydoc ignore=PR01,RT01 """ Wrap `func` to raise if it attempts to call `dask.compute` more than `n` times. After the function returns, materialize the graph in order to re-raise exceptions. """ import dask import dask.array as da func_name = getattr(func, "__name__", str(func)) n_str = f"only up to {n}" if n else "no" msg = ( f"Called `dask.compute()` or `dask.persist()` {n + 1} times, " f"but {n_str} calls are allowed. Set " f"`lazy_xp_function({func_name}, allow_dask_compute={n + 1})` " "to allow for more (but note that this will harm performance). " ) @functools.wraps(func) def wrapper(*args: P.args, **kwargs: P.kwargs) -> T: # numpydoc ignore=GL08 scheduler = _CountingDaskScheduler(n, msg) with dask.config.set({"scheduler": scheduler}): # pyright: ignore[reportPrivateImportUsage] out = func(*args, **kwargs) # Block until the graph materializes and reraise exceptions. This allows # `pytest.raises` and `pytest.warns` to work as expected. Note that this would # not work on scheduler='distributed', as it would not block. arrays, rest = _helpers.pickle_flatten(out, da.Array) arrays = dask.persist(arrays, scheduler="threads")[0] # type: ignore[attr-defined,no-untyped-call] # pyright: ignore[reportPrivateImportUsage] return _helpers.pickle_unflatten(arrays, rest) # pyright: ignore[reportUnknownArgumentType] return wrapper def _require_numpy() -> ArrayNamespace: # numpydoc ignore=RT01 """ Import and return `numpy` if it is available, otherwise raise informative error. """ try: import numpy as np except ImportError as e: msg = ( "The assertion functions of `xpx.testing` require the numpy module " "to be importable in the Python environment." ) raise ImportError(msg) from e return np def _check_ns_shape_dtype_device( actual: Array, desired: Array, check_dtype: bool, check_shape: bool, check_scalar: bool, check_device: bool, xp: ArrayNamespace | None = None, ) -> tuple[Array, Array, ArrayNamespace, ArrayNamespace]: # numpydoc ignore=RT03 """ Assert that namespace, shape and dtype of the two arrays match. Parameters ---------- actual : Array The array produced by the tested function. desired : Array The expected array (typically hardcoded). check_dtype : bool, default: True Whether to check agreement between actual and desired dtypes. check_shape : bool, default: True Whether to check agreement between actual and desired shapes. check_scalar : bool, default: False NumPy only: whether to check agreement between actual and desired types - 0d array vs scalar. check_device : bool, default: True Whether to check agreement between actual and desired devices. xp : array_namespace, optional A standard-compatible namespace which `actual` and `desired` must match. Returns ------- Actual array, desired array, their array namespace, the numpy module. """ np = _require_numpy() actual_xp = _compat.array_namespace(actual) # Raises on Python scalars and lists if xp is not None: _msg = ( "Namespace of actual array does not match the `xp` argument.\n" f"Actual array's namespace: {actual_xp.__name__}\n" f"Expected namespace: {xp.__name__}." ) assert actual_xp == xp, _msg desired_xp = xp else: desired_xp = _compat.array_namespace(desired) _msg = ( "Namespaces of actual and desired arrays do not match.\n" f"Actual: {actual_xp.__name__}\n" f"Desired: {desired_xp.__name__}." ) assert actual_xp == desired_xp, _msg if _compat.is_numpy_namespace(actual_xp) and check_scalar: # only NumPy distinguishes between scalars and arrays; we do if check_scalar. _msg = ( "array-ness does not match:\n Actual: " f"{type(actual)}\n Desired: {type(desired)}" ) assert np.isscalar(actual) == np.isscalar(desired), _msg # Dask uses nan instead of None for unknown shapes actual_shape = typing.cast(tuple[float, ...], actual.shape) desired_shape = typing.cast(tuple[float, ...], desired.shape) assert None not in actual_shape # Requires explicit support assert None not in desired_shape if _compat.is_dask_namespace(desired_xp): if any(math.isnan(i) for i in actual_shape): actual.compute_chunk_sizes() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] actual_shape = typing.cast(tuple[float, ...], actual.shape) if any(math.isnan(i) for i in desired_shape): desired.compute_chunk_sizes() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] desired_shape = typing.cast(tuple[float, ...], desired.shape) if check_shape: msg = f"shapes do not match: {actual_shape} != {desired_shape}" assert actual_shape == desired_shape, msg elif desired.ndim > 0: # Ignore shape, but check flattened size. This is normally done by # np.testing.assert_array_equal etc even when strict=False, but not for # non-materializable arrays. # This check excludes 0d arrays as they are special-cased in NumPy. actual_size = math.prod(actual_shape) desired_size = math.prod(desired_shape) msg = f"sizes do not match: {actual_size} != {desired_size}" assert actual_size == desired_size, msg desired = desired_xp.asarray(desired) if check_dtype: msg = f"dtypes do not match: {actual.dtype} != {desired.dtype}" assert actual.dtype == desired.dtype, msg if check_device: msg = ( f"Devices do not match.\nActual: {_compat.device(actual)}\n" f"Desired: {_compat.device(desired)}" ) assert _compat.device(actual) == _compat.device(desired), msg desired = desired_xp.broadcast_to(desired, actual_shape) return actual, desired, desired_xp, np def _is_materializable(x: Array) -> bool: # numpydoc ignore=PR01,RT01 """ Return True if you can call `as_numpy_array(x)`; False otherwise. """ # Important: here we assume that we're not tracing - # e.g. we're not inside `jax.jit`` nor `cupy.cuda.Stream.begin_capture`. return not _compat.is_torch_array(x) or x.device.type != "meta" # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] def _as_numpy_array( # numpydoc ignore=PR01,RT01 array: Array, *, xp: ArrayNamespace ) -> np.typing.NDArray[Any]: """ Convert array to NumPy, bypassing GPU-CPU transfer guards and densification guards. """ np = _require_numpy() if _compat.is_cupy_namespace(xp): return xp.asnumpy(array) if _compat.is_pydata_sparse_namespace(xp): return array.todense() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] if _compat.is_torch_namespace(xp): array = typing.cast(Array, array.resolve_conj()) # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] array = _compat.to_device(array, "cpu") if _compat.is_array_api_strict_namespace(xp): cpu: Device = xp.Device("CPU_DEVICE") array = _compat.to_device(array, cpu) if _compat.is_jax_namespace(xp): import jax # Note: only needed if the transfer guard is enabled cpu = typing.cast(Device, jax.devices("cpu")[0]) array = _compat.to_device(array, cpu) if hasattr(array, "__dlpack__"): try: return np.from_dlpack(array) except (TypeError, BufferError): pass return np.asarray(array) def assert_close( actual: Array, desired: Array, *, rtol: float | Array | None = None, atol: float | Array = 0, equal_nan: bool = True, err_msg: str = "", verbose: bool = True, check_dtype: bool = True, check_shape: bool = True, check_scalar: bool = False, check_device: bool = True, xp: ArrayNamespace | None = None, ) -> None: """ Check that two arrays are close, up to tolerance ``atol + rtol * abs(desired)``. This is an interface to :func:`numpy.testing.assert_allclose` which accepts any standard-compatible array and performs additional array namespace, shape, and dtype checks. Parameters ---------- actual : Array The array produced by the tested function. desired : Array The expected array (typically hardcoded). rtol : float or Array, optional Relative tolerance. Default: dtype-dependent. atol : float or Array, optional Absolute tolerance. Default: 0. equal_nan : bool, default: True Whether to consider NaNs in corresponding locations as equal. err_msg : str, optional Error message to display on failure. verbose : bool, default: True Whether to include the conflicting arrays in the error message on failure. check_dtype : bool, default: True Whether to check agreement between actual and desired dtypes. check_shape : bool, default: True Whether to check agreement between actual and desired shapes. check_scalar : bool, default: False NumPy only: whether to check agreement between actual and desired types — 0-D :class:`numpy.ndarray` vs scalar (e.g. :class:`numpy.double`). check_device : bool, default: True Whether to check agreement between actual and desired devices. xp : array_namespace, optional A standard-compatible namespace which `actual` and `desired` must match. Raises ------ AssertionError If `actual` and `desired` are not equal up to the defined tolerance. ImportError If :mod:`numpy` is not importable in the Python environment. See Also -------- assert_equal : Similar function for exact equality checks. array_api_extra.isclose : Similar function checking closeness, returning a bool. numpy.testing.assert_allclose : Similar function for NumPy arrays. Notes ----- The default `atol` and `rtol` differ from ``xp.all(xpx.isclose(a, b))``. For inexact dtypes, the default `rtol` is ``xp.finfo(actual.dtype).eps ** 0.5 * 4``, which for ``float64`` is roughly halfway between :math:`\\sqrt{\\epsilon}` and the default for :func:`numpy.testing.assert_allclose`, ``1e-7``. This gives a more reasonable default for lower precision dtypes, for example approximately ``1e-3`` for ``float32``. For exact dtypes, the default ``1e-7`` is used. Array arguments to `atol` and `rtol` must be valid input to :class:`float`. """ __tracebackhide__ = True actual, desired, xp, np = _check_ns_shape_dtype_device( actual, desired, check_dtype, check_shape, check_scalar, check_device, xp ) if not _is_materializable(actual): return if rtol is None: if xp.isdtype(actual.dtype, ("real floating", "complex 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 else: rtol = 1e-7 else: rtol = float(rtol) atol = float(atol) actual_np = _as_numpy_array(actual, xp=xp) desired_np = _as_numpy_array(desired, xp=xp) np.testing.assert_allclose( actual_np, desired_np, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) def assert_equal( actual: Array, desired: Array, *, err_msg: str = "", verbose: bool = True, check_dtype: bool = True, check_shape: bool = True, check_scalar: bool = False, check_device: bool = True, xp: ArrayNamespace | None = None, ) -> None: """ Check that two arrays are equal. This is an interface to :func:`numpy.testing.assert_array_equal` which accepts any standard-compatible array and performs additional array namespace, shape, and dtype checks. Parameters ---------- actual : Array The array produced by the tested function. desired : Array The expected array (typically hardcoded). err_msg : str, optional Error message to display on failure. verbose : bool, default: True Whether to include the conflicting arrays in the error message on failure. check_dtype : bool, default: True Whether to check agreement between actual and desired dtypes. check_shape : bool, default: True Whether to check agreement between actual and desired shapes. check_scalar : bool, default: False NumPy only: whether to check agreement between actual and desired types — 0-D :class:`numpy.ndarray` vs scalar (e.g. :class:`numpy.double`). check_device : bool, default: True Whether to check agreement between actual and desired devices. xp : array_namespace, optional A standard-compatible namespace which `actual` and `desired` must match. Raises ------ AssertionError If `actual` and `desired` are not equal. ImportError If :mod:`numpy` is not importable in the Python environment. See Also -------- assert_close : Similar function for inexact equality checks. numpy.testing.assert_array_equal : Similar function for NumPy arrays. """ __tracebackhide__ = True actual, desired, xp, np = _check_ns_shape_dtype_device( actual, desired, check_dtype, check_shape, check_scalar, check_device, xp ) if not _is_materializable(actual): return actual_np = _as_numpy_array(actual, xp=xp) desired_np = _as_numpy_array(desired, xp=xp) np.testing.assert_array_equal( actual_np, desired_np, err_msg=err_msg, verbose=verbose ) def assert_less( x: Array, y: Array, *, err_msg: str = "", verbose: bool = True, check_dtype: bool = True, check_shape: bool = True, check_scalar: bool = False, check_device: bool = True, xp: ArrayNamespace | None = None, ) -> None: """ Check that two arrays are ordered by less than. This is an interface to :func:`numpy.testing.assert_array_less` which accepts any standard-compatible array and performs additional array namespace, shape, and dtype checks. Parameters ---------- x, y : Array Array to compare according to ``x < y`` (elementwise). err_msg : str, optional Error message to display on failure. verbose : bool, default: True Whether to include the conflicting arrays in the error message on failure. check_dtype : bool, default: True Whether to check agreement between the dtypes of `x` and `y`. check_shape : bool, default: True Whether to check agreement between the shapes of `x` and `y`. check_scalar : bool, default: False NumPy only: whether to check agreement between actual and desired types — 0-D :class:`numpy.ndarray` vs scalar (e.g. :class:`numpy.double`). check_device : bool, default: True Whether to check agreement between actual and desired devices. xp : array_namespace, optional A standard-compatible namespace which `x` and `y` must match. Raises ------ AssertionError If `x` is not strictly smaller than `y`, elementwise. ImportError If :mod:`numpy` is not importable in the Python environment. See Also -------- assert_close : Similar function for inexact equality checks. numpy.testing.assert_array_less : Similar function for NumPy arrays. """ __tracebackhide__ = True x, y, xp, np = _check_ns_shape_dtype_device( x, y, check_dtype, check_shape, check_scalar, check_device, xp ) if not _is_materializable(x): return x_np = _as_numpy_array(x, xp=xp) y_np = _as_numpy_array(y, xp=xp) np.testing.assert_array_less(x_np, y_np, err_msg=err_msg, verbose=verbose) def assert_close_nulp( actual: Array, desired: Array, *, nulp: int = 1, check_dtype: bool = True, check_shape: bool = True, check_scalar: bool = False, check_device: bool = True, xp: ArrayNamespace | None = None, ) -> None: """ Compare two arrays relatively to their spacing. This is an interface to :func:`numpy.testing.assert_array_almost_equal_nulp` which accepts any standard-compatible array and performs additional array namespace, shape, and dtype checks. Parameters ---------- actual : Array The array produced by the tested function. desired : Array The expected array (typically hardcoded). nulp : int, optional The maximum number of units in the last place for the tolerance check. Default: ``1``. check_dtype : bool, default: True Whether to check agreement between actual and desired dtypes. check_shape : bool, default: True Whether to check agreement between actual and desired shapes. check_scalar : bool, default: False NumPy only: whether to check agreement between actual and desired types — 0-D :class:`numpy.ndarray` vs scalar (e.g. :class:`numpy.double`). check_device : bool, default: True Whether to check agreement between actual and desired devices. xp : array_namespace, optional A standard-compatible namespace which `actual` and `desired` must match. Raises ------ AssertionError If the spacing between `actual` and `desired` for one or more elements is \ larger than `nulp`. ImportError If :mod:`numpy` is not importable in the Python environment. See Also -------- assert_close : Similar function for inexact equality checks. numpy.spacing : Spacing calculation for NumPy arrays. numpy.testing.assert_array_almost_equal_nulp : Similar function for NumPy arrays. Notes ----- This is a relatively robust method to compare two arrays whose amplitude is variable. An assertion is raised if the following condition is not met:: abs(actual - desired) <= nulp * spacing(maximum(abs(actual), abs(desired))) where ``spacing(x)`` is the distance between ``x`` and the nearest adjacent number representable by in the data type of ``x``. """ actual, desired, xp, np = _check_ns_shape_dtype_device( actual, desired, check_dtype, check_shape, check_scalar, check_device, xp ) if not _is_materializable(actual): return actual_np = _as_numpy_array(actual, xp=xp) desired_np = _as_numpy_array(desired, xp=xp) np.testing.assert_array_almost_equal_nulp(actual_np, desired_np, nulp=nulp)