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Merged
merged 4 commits into from
Apr 25, 2025
Merged

Rework prepare_for_test #2

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ae8d83b
Merge branch 'main' into xp_assert_enhancements
crusaderky Apr 25, 2025
483a16b
Rework prepare_for_test
crusaderky Apr 25, 2025
f15e628
nit
crusaderky Apr 25, 2025
e9a74a8
Merge remote-tracking branch 'mdhaber/xp_assert_enhancements' into pr…
crusaderky Apr 25, 2025
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216 changes: 132 additions & 84 deletions src/array_api_extra/_lib/_testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,25 +7,33 @@

import math
from types import ModuleType
from typing import cast
from typing import Any, cast

import numpy as np
import pytest

from ._utils._compat import (
array_namespace,
is_array_api_strict_namespace,
is_cupy_namespace,
is_dask_namespace,
is_jax_namespace,
is_numpy_namespace,
is_pydata_sparse_namespace,
is_torch_namespace,
to_device,
)
from ._utils._typing import Array
from ._utils._typing import Array, Device

__all__ = ["xp_assert_close", "xp_assert_equal"]
__all__ = ["as_numpy_array", "xp_assert_close", "xp_assert_equal", "xp_assert_less"]


def _check_ns_shape_dtype(
actual: Array, desired: Array
actual: Array,
desired: Array,
check_dtype: bool,
check_shape: bool,
check_scalar: bool,
) -> ModuleType: # numpydoc ignore=RT03
"""
Assert that namespace, shape and dtype of the two arrays match.
Expand All @@ -47,43 +55,67 @@ def _check_ns_shape_dtype(
msg = f"namespaces do not match: {actual_xp} != f{desired_xp}"
assert actual_xp == desired_xp, msg

actual_shape = actual.shape
desired_shape = desired.shape
if is_dask_namespace(desired_xp):
# Dask uses nan instead of None for unknown shapes
if any(math.isnan(i) for i in cast(tuple[float, ...], actual_shape)):
actual_shape = actual.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]
if any(math.isnan(i) for i in cast(tuple[float, ...], desired_shape)):
desired_shape = desired.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]

msg = f"shapes do not match: {actual_shape} != f{desired_shape}"
assert actual_shape == desired_shape, msg

msg = f"dtypes do not match: {actual.dtype} != {desired.dtype}"
assert actual.dtype == desired.dtype, msg
if check_shape:
actual_shape = actual.shape
desired_shape = desired.shape
if is_dask_namespace(desired_xp):
# Dask uses nan instead of None for unknown shapes
if any(math.isnan(i) for i in cast(tuple[float, ...], actual_shape)):
actual_shape = actual.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]
if any(math.isnan(i) for i in cast(tuple[float, ...], desired_shape)):
desired_shape = desired.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]

msg = f"shapes do not match: {actual_shape} != f{desired_shape}"
assert actual_shape == desired_shape, msg

if check_dtype:
msg = f"dtypes do not match: {actual.dtype} != {desired.dtype}"
assert actual.dtype == desired.dtype, msg

if 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

return desired_xp


def _prepare_for_test(array: Array, xp: ModuleType) -> Array:
def as_numpy_array(array: Array, *, xp: ModuleType) -> np.typing.NDArray[Any]: # type: ignore[explicit-any]
"""
Ensure that the array can be compared with xp.testing or np.testing.

This involves transferring it from GPU to CPU memory, densifying it, etc.
Convert array to NumPy, bypassing GPU-CPU transfer guards and densification guards.
"""
if is_torch_namespace(xp):
return array.cpu() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]
if is_cupy_namespace(xp):
return xp.asnumpy(array)
if is_pydata_sparse_namespace(xp):
return array.todense() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]

if is_torch_namespace(xp):
array = to_device(array, "cpu")
if is_array_api_strict_namespace(xp):
# Note: we deliberately did not add a `.to_device` method in _typing.pyi
# even if it is required by the standard as many backends don't support it
return array.to_device(xp.Device("CPU_DEVICE")) # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]
# Note: nothing to do for CuPy, because it uses a bespoke test function
return array
cpu: Device = xp.Device("CPU_DEVICE")
array = to_device(array, cpu)
if is_jax_namespace(xp):
import jax

# Note: only needed if the transfer guard is enabled
cpu = cast(Device, jax.devices("cpu")[0])
array = to_device(array, cpu)

return np.asarray(array)

def xp_assert_equal(actual: Array, desired: Array, err_msg: str = "") -> None:

def xp_assert_equal(
actual: Array,
desired: Array,
*,
err_msg: str = "",
check_dtype: bool = True,
check_shape: bool = True,
check_scalar: bool = False,
) -> None:
"""
Array-API compatible version of `np.testing.assert_array_equal`.

Expand All @@ -95,34 +127,56 @@ def xp_assert_equal(actual: Array, desired: Array, err_msg: str = "") -> None:
The expected array (typically hardcoded).
err_msg : str, optional
Error message to display on failure.
check_dtype, check_shape : bool, default: True
Whether to check agreement between actual and desired dtypes and shapes
check_scalar : bool, default: False
NumPy only: whether to check agreement between actual and desired types -
0d array vs scalar.

See Also
--------
xp_assert_close : Similar function for inexact equality checks.
numpy.testing.assert_array_equal : Similar function for NumPy arrays.
"""
xp = _check_ns_shape_dtype(actual, desired)
actual = _prepare_for_test(actual, xp)
desired = _prepare_for_test(desired, xp)
xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)
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)

if is_cupy_namespace(xp):
xp.testing.assert_array_equal(actual, desired, err_msg=err_msg)
elif is_torch_namespace(xp):
# PyTorch recommends using `rtol=0, atol=0` like this
# to test for exact equality
xp.testing.assert_close(
actual,
desired,
rtol=0,
atol=0,
equal_nan=True,
check_dtype=False,
msg=err_msg or None,
)
else:
import numpy as np # pylint: disable=import-outside-toplevel

np.testing.assert_array_equal(actual, desired, err_msg=err_msg)
def xp_assert_less(
x: Array,
y: Array,
*,
err_msg: str = "",
check_dtype: bool = True,
check_shape: bool = True,
check_scalar: bool = False,
) -> None:
"""
Array-API compatible version of `np.testing.assert_array_less`.

Parameters
----------
x, y : Array
The arrays to compare according to ``x < y`` (elementwise).
err_msg : str, optional
Error message to display on failure.
check_dtype, check_shape : bool, default: True
Whether to check agreement between actual and desired dtypes and shapes
check_scalar : bool, default: False
NumPy only: whether to check agreement between actual and desired types -
0d array vs scalar.

See Also
--------
xp_assert_close : Similar function for inexact equality checks.
numpy.testing.assert_array_equal : Similar function for NumPy arrays.
"""
xp = _check_ns_shape_dtype(x, y, check_dtype, check_shape, check_scalar)
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)


def xp_assert_close(
Expand All @@ -132,6 +186,9 @@ def xp_assert_close(
rtol: float | None = None,
atol: float = 0,
err_msg: str = "",
check_dtype: bool = True,
check_shape: bool = True,
check_scalar: bool = False,
) -> None:
"""
Array-API compatible version of `np.testing.assert_allclose`.
Expand All @@ -148,6 +205,11 @@ def xp_assert_close(
Absolute tolerance. Default: 0.
err_msg : str, optional
Error message to display on failure.
check_dtype, check_shape : bool, default: True
Whether to check agreement between actual and desired dtypes and shapes
check_scalar : bool, default: False
NumPy only: whether to check agreement between actual and desired types -
0d array vs scalar.

See Also
--------
Expand All @@ -159,40 +221,26 @@ def xp_assert_close(
-----
The default `atol` and `rtol` differ from `xp.all(xpx.isclose(a, b))`.
"""
xp = _check_ns_shape_dtype(actual, desired)

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

actual = _prepare_for_test(actual, xp)
desired = _prepare_for_test(desired, xp)

if is_cupy_namespace(xp):
xp.testing.assert_allclose(
actual, desired, rtol=rtol, atol=atol, err_msg=err_msg
)
elif is_torch_namespace(xp):
xp.testing.assert_close(
actual, desired, rtol=rtol, atol=atol, equal_nan=True, msg=err_msg or None
)
else:
import numpy as np # pylint: disable=import-outside-toplevel

# JAX/Dask arrays work directly with `np.testing`
assert isinstance(rtol, float)
np.testing.assert_allclose( # type: ignore[call-overload] # pyright: ignore[reportCallIssue]
actual, # pyright: ignore[reportArgumentType]
desired, # pyright: ignore[reportArgumentType]
rtol=rtol,
atol=atol,
err_msg=err_msg,
)
xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)

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
Comment on lines +226 to +233
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To save microseconds in a test?

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No, it was an attempt to convince pyright. Regardless, I think it's slightly cleaner this way.


actual_np = as_numpy_array(actual, xp=xp)
desired_np = as_numpy_array(desired, xp=xp)
Comment on lines +235 to +236
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Is this fixing a pyright issue?

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In data-apis#267 all testing becomes done exclusively by numpy.
So it makes sense to push the conversion into this function.
As a cascade effect, this makes type validation on behalf of pyright a lot easier.

np.testing.assert_allclose( # pyright: ignore[reportCallIssue]
actual_np,
desired_np,
rtol=rtol, # pyright: ignore[reportArgumentType]
atol=atol,
err_msg=err_msg,
)


def xfail(
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