ENH/TST: xp_assert_ enhancements

src/array_api_extra/_lib/_testing.py

@@ -9,13 +9,15 @@
 from types import ModuleType
 from typing import 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_numpy_namespace,
     is_pydata_sparse_namespace,
     is_torch_namespace,
 )
@@ -25,7 +27,11 @@
 
 
 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.
@@ -47,43 +53,62 @@
     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:
     """
-    Ensure that the array can be compared with xp.testing or np.testing.
+    Ensure that the array can be compared with np.testing.
 
     This involves transferring it from GPU to CPU memory, densifying it, etc.
     """
     if is_torch_namespace(xp):
-        return array.cpu()  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+        return np.asarray(array.cpu())  # type: ignore[attr-defined, return-value]  # pyright: ignore[reportAttributeAccessIssue, reportUnknownArgumentType, reportReturnType]
     if is_pydata_sparse_namespace(xp):
         return array.todense()  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
     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
+    if is_cupy_namespace(xp):
+        return xp.asnumpy(array)
     return 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`.
 
@@ -95,34 +120,56 @@
         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)
+    xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)
     actual = _prepare_for_test(actual, xp)
     desired = _prepare_for_test(desired, xp)
+    np.testing.assert_array_equal(actual, desired, 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 = _prepare_for_test(x, xp)
+    y = _prepare_for_test(y, xp)
+    np.testing.assert_array_less(x, y, err_msg=err_msg)  # type: ignore[call-overload]
 
 
 def xp_assert_close(
@@ -132,6 +179,9 @@
     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`.
@@ -148,6 +198,11 @@
         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
     --------
@@ -159,7 +214,7 @@
     -----
     The default `atol` and `rtol` differ from `xp.all(xpx.isclose(a, b))`.
     """
-    xp = _check_ns_shape_dtype(actual, desired)
+    xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)
 
     floating = xp.isdtype(actual.dtype, ("real floating", "complex floating"))
     if rtol is None and floating:
@@ -173,26 +228,14 @@
     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,
-        )
+    # JAX/Dask arrays work directly with `np.testing`
+    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,
+    )
 
 
 def xfail(

tests/test_testing.py

@@ -1,12 +1,17 @@
 from collections.abc import Callable
+from contextlib import nullcontext
 from types import ModuleType
 from typing import cast
 
 import numpy as np
 import pytest
 
 from array_api_extra._lib._backends import Backend
-from array_api_extra._lib._testing import xp_assert_close, xp_assert_equal
+from array_api_extra._lib._testing import (
+    xp_assert_close,
+    xp_assert_equal,
+    xp_assert_less,
+)
 from array_api_extra._lib._utils._compat import (
     array_namespace,
     is_dask_namespace,
@@ -22,15 +27,19 @@
     "func",
     [
         xp_assert_equal,
+        xp_assert_less,
         pytest.param(
             xp_assert_close,
-            marks=pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no isdtype"),
+            marks=pytest.mark.xfail_xp_backend(
+                Backend.SPARSE, reason="no isdtype", strict=False
+            ),
         ),
     ],
 )
 
 
-@param_assert_equal_close
+@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no isdtype", strict=False)
+@pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close])
 def test_assert_close_equal_basic(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     func(xp.asarray(0), xp.asarray(0))
     func(xp.asarray([1, 2]), xp.asarray([1, 2]))
@@ -50,8 +59,8 @@ def test_assert_close_equal_basic(xp: ModuleType, func: Callable[..., None]):  #
 
 @pytest.mark.skip_xp_backend(Backend.NUMPY, reason="test other ns vs. numpy")
 @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="test other ns vs. numpy")
-@pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close])
-def test_assert_close_equal_namespace(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
+@pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less])
+def test_assert_close_equal_less_namespace(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     with pytest.raises(AssertionError, match="namespaces do not match"):
         func(xp.asarray(0), np.asarray(0))
     with pytest.raises(TypeError, match="Unrecognized array input"):
@@ -60,6 +69,58 @@ def test_assert_close_equal_namespace(xp: ModuleType, func: Callable[..., None])
         func(xp.asarray([0]), [0])
 
 
+@param_assert_equal_close
+@pytest.mark.parametrize("check_shape", [False, True])
+def test_assert_close_equal_less_shape(  # type: ignore[explicit-any]
+    xp: ModuleType,
+    func: Callable[..., None],
+    check_shape: bool,
+):
+    context = (
+        pytest.raises(AssertionError, match="shapes do not match")
+        if check_shape
+        else nullcontext()
+    )
+    with context:
+        func(xp.asarray([xp.nan, xp.nan]), xp.asarray(xp.nan), check_shape=check_shape)
+
+
+@param_assert_equal_close
+@pytest.mark.parametrize("check_dtype", [False, True])
+def test_assert_close_equal_less_dtype(  # type: ignore[explicit-any]
+    xp: ModuleType,
+    func: Callable[..., None],
+    check_dtype: bool,
+):
+    context = (
+        pytest.raises(AssertionError, match="dtypes do not match")
+        if check_dtype
+        else nullcontext()
+    )
+    with context:
+        func(
+            xp.asarray(xp.nan, dtype=xp.float32),
+            xp.asarray(xp.nan, dtype=xp.float64),
+            check_dtype=check_dtype,
+        )
+
+
+@pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less])
+@pytest.mark.parametrize("check_scalar", [False, True])
+def test_assert_close_equal_less_scalar(  # type: ignore[explicit-any]
+    xp: ModuleType,
+    func: Callable[..., None],
+    check_scalar: bool,
+):
+    context = (
+        pytest.raises(AssertionError, match="array-ness does not match")
+        if check_scalar
+        else nullcontext()
+    )
+    with context:
+        func(np.asarray(xp.nan), np.asarray(xp.nan)[()], check_scalar=check_scalar)
+
+
 @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no isdtype")
 def test_assert_close_tolerance(xp: ModuleType):
     xp_assert_close(xp.asarray([100.0]), xp.asarray([102.0]), rtol=0.03)
@@ -71,9 +132,18 @@ def test_assert_close_tolerance(xp: ModuleType):
         xp_assert_close(xp.asarray([100.0]), xp.asarray([102.0]), atol=1)
 
 
-@param_assert_equal_close
+def test_assert_less_basic(xp: ModuleType):
+    xp_assert_less(xp.asarray(-1), xp.asarray(0))
+    xp_assert_less(xp.asarray([1, 2]), xp.asarray([2, 3]))
+    with pytest.raises(AssertionError):
+        xp_assert_less(xp.asarray([1, 1]), xp.asarray([2, 1]))
+    with pytest.raises(AssertionError, match="hello"):
+        xp_assert_less(xp.asarray([1, 1]), xp.asarray([2, 1]), err_msg="hello")
+
+
 @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array")
 @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing")
+@pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close])
 def test_assert_close_equal_none_shape(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     """On Dask and other lazy backends, test that a shape with NaN's or None's
     can be compared to a real shape.