Merge pull request #275 from asmeurer/more-2023

More 2023.12 test fixes

Author: asmeurer

.github/workflows/test.yml

@@ -27,6 +27,7 @@ jobs:
     - name: Run the test suite
       env:
         ARRAY_API_TESTS_MODULE: array_api_strict
+        ARRAY_API_STRICT_API_VERSION: 2023.12
       run: |
         pytest -v -rxXfE --skips-file array-api-strict-skips.txt array_api_tests/
         # We also have internal tests that isn't really necessary for adopters

array_api_tests/array_helpers.py

@@ -1,5 +1,5 @@
 from ._array_module import (isnan, all, any, equal, not_equal, logical_and,
-                            logical_or, isfinite, greater, less, less_equal,
+                            logical_or, isfinite, greater, less_equal,
                             zeros, ones, full, bool, int8, int16, int32,
                             int64, uint8, uint16, uint32, uint64, float32,
                             float64, nan, inf, pi, remainder, divide, isinf,
@@ -164,6 +164,16 @@ def notequal(x, y):
 
     return not_equal(x, y)
 
+def less(x, y):
+    """
+    Same as less(x, y) except it allows comparing uint64 with signed int dtypes
+    """
+    if x.dtype == uint64 and dh.dtype_signed[y.dtype]:
+        return xp.where(y < 0, xp.asarray(False), xp.less(x, xp.astype(y, uint64)))
+    if y.dtype == uint64 and dh.dtype_signed[x.dtype]:
+        return xp.where(x < 0, xp.asarray(True), xp.less(xp.astype(x, uint64), y))
+    return xp.less(x, y)
+
 def assert_exactly_equal(x, y, msg_extra=None):
     """
     Test that the arrays x and y are exactly equal.

array_api_tests/dtype_helpers.py

@@ -209,6 +209,9 @@ class MinMax(NamedTuple):
     min: Union[int, float]
     max: Union[int, float]
 
+    def __contains__(self, other):
+        assert isinstance(other, (int, float))
+        return self.min <= other <= self.max
 
 dtype_ranges = _make_dtype_mapping_from_names(
     {

array_api_tests/hypothesis_helpers.py

@@ -177,7 +177,7 @@ def oneway_broadcastable_shapes(draw) -> OnewayBroadcastableShapes:
 # Use these instead of xps.scalar_dtypes, etc. because it skips dtypes from
 # ARRAY_API_TESTS_SKIP_DTYPES
 all_dtypes = sampled_from(_sorted_dtypes)
-int_dtypes = sampled_from(dh.int_dtypes)
+int_dtypes = sampled_from(dh.all_int_dtypes)
 uint_dtypes = sampled_from(dh.uint_dtypes)
 real_dtypes = sampled_from(dh.real_dtypes)
 # Warning: The hypothesis "floating_dtypes" is what we call

array_api_tests/shape_helpers.py

@@ -8,7 +8,7 @@
 
 __all__ = [
     "broadcast_shapes",
-    "normalise_axis",
+    "normalize_axis",
     "ndindex",
     "axis_ndindex",
     "axes_ndindex",
@@ -65,7 +65,7 @@ def broadcast_shapes(*shapes: Shape):
     return result
 
 
-def normalise_axis(
+def normalize_axis(
     axis: Optional[Union[int, Sequence[int]]], ndim: int
 ) -> Tuple[int, ...]:
     if axis is None:

array_api_tests/test_array_object.py

@@ -26,9 +26,9 @@ def scalar_objects(
     )
 
 
-def normalise_key(key: Index, shape: Shape) -> Tuple[Union[int, slice], ...]:
+def normalize_key(key: Index, shape: Shape) -> Tuple[Union[int, slice], ...]:
     """
-    Normalise an indexing key.
+    Normalize an indexing key.
 
     * If a non-tuple index, wrap as a tuple.
     * Represent ellipsis as equivalent slices.
@@ -48,7 +48,7 @@ def get_indexed_axes_and_out_shape(
     key: Tuple[Union[int, slice, None], ...], shape: Shape
 ) -> Tuple[Tuple[Sequence[int], ...], Shape]:
     """
-    From the (normalised) key and input shape, calculates:
+    From the (normalized) key and input shape, calculates:
 
     * indexed_axes: For each dimension, the axes which the key indexes.
     * out_shape: The resulting shape of indexing an array (of the input shape)
@@ -88,7 +88,7 @@ def test_getitem(shape, dtype, data):
     out = x[key]
 
     ph.assert_dtype("__getitem__", in_dtype=x.dtype, out_dtype=out.dtype)
-    _key = normalise_key(key, shape)
+    _key = normalize_key(key, shape)
     axes_indices, expected_shape = get_indexed_axes_and_out_shape(_key, shape)
     ph.assert_shape("__getitem__", out_shape=out.shape, expected=expected_shape)
     out_zero_sided = any(side == 0 for side in expected_shape)
@@ -119,7 +119,7 @@ def test_setitem(shape, dtypes, data):
         x = xp.asarray(obj, dtype=dtypes.result_dtype)
     note(f"{x=}")
     key = data.draw(xps.indices(shape=shape), label="key")
-    _key = normalise_key(key, shape)
+    _key = normalize_key(key, shape)
     axes_indices, out_shape = get_indexed_axes_and_out_shape(_key, shape)
     value_strat = hh.arrays(dtype=dtypes.result_dtype, shape=out_shape)
     if out_shape == ():

array_api_tests/test_fft.py

@@ -94,7 +94,7 @@ def assert_s_axes_shape(
     axes: Optional[List[int]],
     out: Array,
 ):
-    _axes = sh.normalise_axis(axes, x.ndim)
+    _axes = sh.normalize_axis(axes, x.ndim)
     _s = x.shape if s is None else s
     expected = []
     for i in range(x.ndim):
@@ -193,7 +193,7 @@ def test_rfftn(x, data):
 
     ph.assert_float_to_complex_dtype("rfftn", in_dtype=x.dtype, out_dtype=out.dtype)
 
-    _axes = sh.normalise_axis(axes, x.ndim)
+    _axes = sh.normalize_axis(axes, x.ndim)
     _s = x.shape if s is None else s
     expected = []
     for i in range(x.ndim):
@@ -225,7 +225,7 @@ def test_irfftn(x, data):
     )
 
     # TODO: assert shape correctly
-    # _axes = sh.normalise_axis(axes, x.ndim)
+    # _axes = sh.normalize_axis(axes, x.ndim)
     # _s = x.shape if s is None else s
     # expected = []
     # for i in range(x.ndim):

array_api_tests/test_linalg.py

@@ -980,7 +980,7 @@ def test_vector_norm(x, data):
     # TODO: Check that the ord values give the correct norms.
     # ord = kw.get('ord', 2)
 
-    _axes = sh.normalise_axis(axis, x.ndim)
+    _axes = sh.normalize_axis(axis, x.ndim)
 
     ph.assert_keepdimable_shape('linalg.vector_norm', out_shape=res.shape,
                                 in_shape=x.shape, axes=_axes,

array_api_tests/test_manipulation_functions.py

@@ -172,7 +172,27 @@ def test_moveaxis(x, data):
     out = xp.moveaxis(x, source, destination)
 
     ph.assert_dtype("moveaxis", in_dtype=x.dtype, out_dtype=out.dtype)
-    # TODO: shape and values testing
+
+
+    _source = sh.normalize_axis(source, x.ndim)
+    _destination = sh.normalize_axis(destination, x.ndim)
+
+    new_axes = [n for n in range(x.ndim) if n not in _source]
+
+    for dest, src in sorted(zip(_destination, _source)):
+        new_axes.insert(dest, src)
+
+    expected_shape = tuple(x.shape[i] for i in new_axes)
+
+    ph.assert_result_shape("moveaxis", in_shapes=[x.shape],
+                           out_shape=out.shape, expected=expected_shape,
+                           kw={"source": source, "destination": destination})
+
+    indices = list(sh.ndindex(x.shape))
+    permuted_indices = [tuple(idx[axis] for axis in new_axes) for idx in indices]
+    assert_array_ndindex(
+        "moveaxis", x, x_indices=sh.ndindex(x.shape), out=out, out_indices=permuted_indices
+    )
 
 @pytest.mark.unvectorized
 @given(
@@ -190,7 +210,7 @@ def test_squeeze(x, data):
     )
 
     axes = (axis,) if isinstance(axis, int) else axis
-    axes = sh.normalise_axis(axes, x.ndim)
+    axes = sh.normalize_axis(axes, x.ndim)
 
     squeezable_axes = [i for i, side in enumerate(x.shape) if side == 1]
     if any(i not in squeezable_axes for i in axes):
@@ -230,7 +250,7 @@ def test_flip(x, data):
 
     ph.assert_dtype("flip", in_dtype=x.dtype, out_dtype=out.dtype)
 
-    _axes = sh.normalise_axis(kw.get("axis", None), x.ndim)
+    _axes = sh.normalize_axis(kw.get("axis", None), x.ndim)
     for indices in sh.axes_ndindex(x.shape, _axes):
         reverse_indices = indices[::-1]
         assert_array_ndindex("flip", x, x_indices=indices, out=out,
@@ -360,7 +380,7 @@ def test_roll(x, data):
         assert_array_ndindex("roll", x, x_indices=indices, out=out, out_indices=shifted_indices, kw=kw)
     else:
         shifts = (shift,) if isinstance(shift, int) else shift
-        axes = sh.normalise_axis(kw["axis"], x.ndim)
+        axes = sh.normalize_axis(kw["axis"], x.ndim)
         shifted_indices = roll_ndindex(x.shape, shifts, axes)
         assert_array_ndindex("roll", x, x_indices=sh.ndindex(x.shape), out=out, out_indices=shifted_indices, kw=kw)
 

array_api_tests/test_operators_and_elementwise_functions.py

@@ -929,8 +929,6 @@ def test_ceil(x):
 @pytest.mark.min_version("2023.12")
 @given(x=hh.arrays(dtype=hh.real_dtypes, shape=hh.shapes()), data=st.data())
 def test_clip(x, data):
-    # TODO: test min/max kwargs, adjust values testing accordingly
-
     # Ensure that if both min and max are arrays that all three of x, min, max
     # are broadcast compatible.
     shape1, shape2 = data.draw(hh.mutually_broadcastable_shapes(2,
@@ -951,7 +949,7 @@ def test_clip(x, data):
     ), label="max")
 
     # min > max is undefined (but allow nans)
-    assume(min is None or max is None or not xp.any(xp.asarray(min) > xp.asarray(max)))
+    assume(min is None or max is None or not xp.any(ah.less(xp.asarray(max), xp.asarray(min))))
 
     kw = data.draw(
         hh.specified_kwargs(
@@ -972,80 +970,86 @@ def test_clip(x, data):
     expected_shape = sh.broadcast_shapes(*shapes)
     ph.assert_shape("clip", out_shape=out.shape, expected=expected_shape)
 
-    if min is max is None:
-        ph.assert_array_elements("clip", out=out, expected=x)
-    elif max is None:
-        # If one operand is nan, the result is nan. See
-        # https://github.com/data-apis/array-api/pull/813.
-        def refimpl(_x, _min):
-            if math.isnan(_x) or math.isnan(_min):
-                return math.nan
+    # This is based on right_scalar_assert_against_refimpl and
+    # binary_assert_against_refimpl. clip() is currently the only ternary
+    # elementwise function and the only function that supports arrays and
+    # scalars. However, where() (in test_searching_functions) is similar
+    # and if scalar support is added to it, we may want to factor out and
+    # reuse this logic.
+
+    def refimpl(_x, _min, _max):
+        # Skip cases where _min and _max are integers whose values do not
+        # fit in the dtype of _x, since this behavior is unspecified.
+        if dh.is_int_dtype(x.dtype):
+            if _min is not None and _min not in dh.dtype_ranges[x.dtype]:
+                return None
+            if _max is not None and _max not in dh.dtype_ranges[x.dtype]:
+                return None
+
+        # If min or max are float64 and x is float32, they will need to be
+        # downcast to float32. This could result in a round in the wrong
+        # direction meaning the resulting clipped value might not actually be
+        # between min and max. This behavior is unspecified, so skip any cases
+        # where x is within the rounding error of downcasting min or max.
+        if x.dtype == xp.float32:
+            if min is not None and not dh.is_scalar(min) and min.dtype == xp.float64 and math.isfinite(_min):
+                _min_float32 = float(xp.asarray(_min, dtype=xp.float32))
+                if math.isinf(_min_float32):
+                    return None
+                tol = abs(_min - _min_float32)
+                if math.isclose(_min, _min_float32, abs_tol=tol):
+                    return None
+            if max is not None and not dh.is_scalar(max) and max.dtype == xp.float64 and math.isfinite(_max):
+                _max_float32 = float(xp.asarray(_max, dtype=xp.float32))
+                if math.isinf(_max_float32):
+                    return None
+                tol = abs(_max - _max_float32)
+                if math.isclose(_max, _max_float32, abs_tol=tol):
+                    return None
+
+        if (math.isnan(_x)
+            or (_min is not None and math.isnan(_min))
+            or (_max is not None and math.isnan(_max))):
+            return math.nan
+        if _min is _max is None:
+            return _x
+        if _max is None:
             return builtins.max(_x, _min)
-        if dh.is_scalar(min):
-            right_scalar_assert_against_refimpl(
-                "clip", x, min, out, refimpl,
-                left_sym="x",
-                expr_template="clip({}, min={})",
-            )
-        else:
-            binary_assert_against_refimpl(
-                "clip", x, min, out, refimpl,
-                left_sym="x", right_sym="min",
-                expr_template="clip({}, min={})",
-            )
-    elif min is None:
-        def refimpl(_x, _max):
-            if math.isnan(_x) or math.isnan(_max):
-                return math.nan
+        if _min is None:
             return builtins.min(_x, _max)
-        if dh.is_scalar(max):
-            right_scalar_assert_against_refimpl(
-                "clip", x, max, out, refimpl,
-                left_sym="x",
-                expr_template="clip({}, max={})",
+        return builtins.min(builtins.max(_x, _min), _max)
+
+    stype = dh.get_scalar_type(x.dtype)
+    min_shape = () if min is None or dh.is_scalar(min) else min.shape
+    max_shape = () if max is None or dh.is_scalar(max) else max.shape
+
+    for x_idx, min_idx, max_idx, o_idx in sh.iter_indices(
+            x.shape, min_shape, max_shape, out.shape):
+        x_val = stype(x[x_idx])
+        if min is None or dh.is_scalar(min):
+            min_val = min
+        else:
+            min_val = stype(min[min_idx])
+        if max is None or dh.is_scalar(max):
+            max_val = max
+        else:
+            max_val = stype(max[max_idx])
+        expected = refimpl(x_val, min_val, max_val)
+        if expected is None:
+            continue
+        out_val = stype(out[o_idx])
+        if math.isnan(expected):
+            assert math.isnan(out_val), (
+                f"out[{o_idx}]={out[o_idx]} but should be nan [clip()]\n"
+                f"x[{x_idx}]={x_val}, min[{min_idx}]={min_val}, max[{max_idx}]={max_val}"
             )
         else:
-            binary_assert_against_refimpl(
-                "clip", x, max, out, refimpl,
-                left_sym="x", right_sym="max",
-                expr_template="clip({}, max={})",
+            assert out_val == expected, (
+                f"out[{o_idx}]={out[o_idx]} but should be {expected} [clip()]\n"
+                f"x[{x_idx}]={x_val}, min[{min_idx}]={min_val}, max[{max_idx}]={max_val}"
             )
-    else:
-        def refimpl(_x, _min, _max):
-            if math.isnan(_x) or math.isnan(_min) or math.isnan(_max):
-                return math.nan
-            return builtins.min(builtins.max(_x, _min), _max)
-
-        # This is based on right_scalar_assert_against_refimpl and
-        # binary_assert_against_refimpl. clip() is currently the only ternary
-        # elementwise function and the only function that supports arrays and
-        # scalars. However, where() (in test_searching_functions) is similar
-        # and if scalar support is added to it, we may want to factor out and
-        # reuse this logic.
-
-        stype = dh.get_scalar_type(x.dtype)
-        min_shape = () if dh.is_scalar(min) else min.shape
-        max_shape = () if dh.is_scalar(max) else max.shape
-
-        for x_idx, min_idx, max_idx, o_idx in sh.iter_indices(
-                x.shape, min_shape, max_shape, out.shape):
-            x_val = stype(x[x_idx])
-            min_val = min if dh.is_scalar(min) else min[min_idx]
-            min_val = stype(min_val)
-            max_val = max if dh.is_scalar(max) else max[max_idx]
-            max_val = stype(max_val)
-            expected = refimpl(x_val, min_val, max_val)
-            out_val = stype(out[o_idx])
-            if math.isnan(expected):
-                assert math.isnan(out_val), (
-                    f"out[{o_idx}]={out[o_idx]} but should be nan [clip()]\n"
-                    f"x[{x_idx}]={x_val}, min[{min_idx}]={min_val}, max[{max_idx}]={max_val}"
-                )
-            else:
-                assert out_val == expected, (
-                    f"out[{o_idx}]={out[o_idx]} but should be {expected} [clip()]\n"
-                    f"x[{x_idx}]={x_val}, min[{min_idx}]={min_val}, max[{max_idx}]={max_val}"
-)
+
+
 if api_version >= "2022.12":
 
     @given(hh.arrays(dtype=hh.complex_dtypes, shape=hh.shapes()))
@@ -1062,7 +1066,7 @@ def test_copysign(x1, x2):
     out = xp.copysign(x1, x2)
     ph.assert_dtype("copysign", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
     ph.assert_result_shape("copysign", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    # TODO: values testing
+    binary_assert_against_refimpl("copysign", x1, x2, out, math.copysign)
 
 
 @given(hh.arrays(dtype=hh.all_floating_dtypes(), shape=hh.shapes()))
@@ -1535,7 +1539,8 @@ def test_signbit(x):
     out = xp.signbit(x)
     ph.assert_dtype("signbit", in_dtype=x.dtype, out_dtype=out.dtype, expected=xp.bool)
     ph.assert_shape("signbit", out_shape=out.shape, expected=x.shape)
-    # TODO: values testing
+    refimpl = lambda x: math.copysign(1.0, x) < 0
+    unary_assert_against_refimpl("round", x, out, refimpl, strict_check=True)
 
 
 @given(hh.arrays(dtype=hh.numeric_dtypes, shape=hh.shapes(), elements=finite_kw))