Merge pull request #348 from ev-br/test_py_scalars

ENH: test binary functions with python scalars

Author: ev-br

Diff for: array_api_tests/dtype_helpers.py

@@ -198,6 +198,7 @@ def get_scalar_type(dtype: DataType) -> ScalarType:
 def is_scalar(x):
     return isinstance(x, (int, float, complex, bool))
 
+
 def _make_dtype_mapping_from_names(mapping: Dict[str, Any]) -> EqualityMapping:
     dtype_value_pairs = []
     for name, value in mapping.items():

Diff for: array_api_tests/hypothesis_helpers.py

@@ -9,7 +9,7 @@
 
 from hypothesis import assume, reject
 from hypothesis.strategies import (SearchStrategy, booleans, composite, floats,
-                                   integers, just, lists, none, one_of,
+                                   integers, complex_numbers, just, lists, none, one_of,
                                    sampled_from, shared, builds, nothing)
 
 from . import _array_module as xp, api_version
@@ -19,7 +19,7 @@
 from . import xps
 from ._array_module import _UndefinedStub
 from ._array_module import bool as bool_dtype
-from ._array_module import broadcast_to, eye, float32, float64, full
+from ._array_module import broadcast_to, eye, float32, float64, full, complex64, complex128
 from .stubs import category_to_funcs
 from .pytest_helpers import nargs
 from .typing import Array, DataType, Scalar, Shape
@@ -462,6 +462,14 @@ def scalars(draw, dtypes, finite=False):
         if finite:
             return draw(floats(width=32, allow_nan=False, allow_infinity=False))
         return draw(floats(width=32))
+    elif dtype == complex64:
+        if finite:
+            return draw(complex_numbers(width=32, allow_nan=False, allow_infinity=False))
+        return draw(complex_numbers(width=32))
+    elif dtype == complex128:
+        if finite:
+            return draw(complex_numbers(allow_nan=False, allow_infinity=False))
+        return draw(complex_numbers())
     else:
         raise ValueError(f"Unrecognized dtype {dtype}")
 
@@ -571,6 +579,20 @@ def two_mutual_arrays(
     )
     return arrays1, arrays2
 
+
+@composite
+def array_and_py_scalar(draw, dtypes):
+    """Draw a pair: (array, scalar) or (scalar, array)."""
+    dtype = draw(sampled_from(dtypes))
+    scalar_var = draw(scalars(just(dtype), finite=True))
+    array_var = draw(arrays(dtype, shape=shapes(min_dims=1)))
+
+    if draw(booleans()):
+        return scalar_var, array_var
+    else:
+        return array_var, scalar_var
+
+
 @composite
 def kwargs(draw, **kw):
     """

Diff for: array_api_tests/test_operators_and_elementwise_functions.py

@@ -690,6 +690,40 @@ def binary_param_assert_against_refimpl(
         )
 
 
+def _convert_scalars_helper(x1, x2):
+    """Convert python scalar to arrays, record the shapes/dtypes of arrays.
+
+    For inputs being scalars or arrays, return the dtypes and shapes of array arguments,
+    and all arguments converted to arrays.
+
+    dtypes are separate to help distinguishing between 
+    `py_scalar + f32_array -> f32_array` and `f64_array + f32_array -> f64_array`
+    """
+    if dh.is_scalar(x1):
+        in_dtypes = [x2.dtype]
+        in_shapes = [x2.shape]
+        x1a, x2a = xp.asarray(x1), x2
+    elif dh.is_scalar(x2):
+        in_dtypes = [x1.dtype]
+        in_shapes = [x1.shape]
+        x1a, x2a = x1, xp.asarray(x2)
+    else:
+        in_dtypes = [x1.dtype, x2.dtype]
+        in_shapes = [x1.shape, x2.shape]
+        x1a, x2a = x1, x2
+
+    return in_dtypes, in_shapes, (x1a, x2a)
+
+
+def _assert_correctness_binary(
+    name, func, in_dtypes, in_shapes, in_arrs, out, expected_dtype=None, **kwargs
+):
+    x1a, x2a = in_arrs
+    ph.assert_dtype(name, in_dtype=in_dtypes, out_dtype=out.dtype, expected=expected_dtype)
+    ph.assert_result_shape(name, in_shapes=in_shapes, out_shape=out.shape)
+    binary_assert_against_refimpl(name, x1a, x2a, out, func, **kwargs)
+
+
 @pytest.mark.parametrize("ctx", make_unary_params("abs", dh.numeric_dtypes))
 @given(data=st.data())
 def test_abs(ctx, data):
@@ -789,10 +823,14 @@ def test_atan(x):
 @given(*hh.two_mutual_arrays(dh.real_float_dtypes))
 def test_atan2(x1, x2):
     out = xp.atan2(x1, x2)
-    ph.assert_dtype("atan2", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("atan2", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    refimpl = cmath.atan2 if x1.dtype in dh.complex_dtypes else math.atan2
-    binary_assert_against_refimpl("atan2", x1, x2, out, refimpl)
+    _assert_correctness_binary(
+        "atan",
+        cmath.atan2 if x1.dtype in dh.complex_dtypes else math.atan2,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out,
+    )
 
 
 @given(hh.arrays(dtype=hh.all_floating_dtypes(), shape=hh.shapes()))
@@ -1258,10 +1296,14 @@ def test_greater_equal(ctx, data):
 @given(*hh.two_mutual_arrays(dh.real_float_dtypes))
 def test_hypot(x1, x2):
     out = xp.hypot(x1, x2)
-    ph.assert_dtype("hypot", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("hypot", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl("hypot", x1, x2, out, math.hypot)
-
+    _assert_correctness_binary(
+        "hypot",
+        math.hypot,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out
+    )
 
 
 @pytest.mark.min_version("2022.12")
@@ -1411,21 +1453,17 @@ def logaddexp_refimpl(l: float, r: float) -> float:
         raise OverflowError
 
 
+@pytest.mark.min_version("2023.12")
 @given(*hh.two_mutual_arrays(dh.real_float_dtypes))
 def test_logaddexp(x1, x2):
     out = xp.logaddexp(x1, x2)
-    ph.assert_dtype("logaddexp", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("logaddexp", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl("logaddexp", x1, x2, out, logaddexp_refimpl)
-
-
-@given(*hh.two_mutual_arrays([xp.bool]))
-def test_logical_and(x1, x2):
-    out = xp.logical_and(x1, x2)
-    ph.assert_dtype("logical_and", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("logical_and", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl(
-        "logical_and", x1, x2, out, operator.and_, expr_template="({} and {})={}"
+    _assert_correctness_binary(
+        "logaddexp",
+        logaddexp_refimpl,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out
     )
 
 
@@ -1439,42 +1477,64 @@ def test_logical_not(x):
     )
 
 
+@given(*hh.two_mutual_arrays([xp.bool]))
+def test_logical_and(x1, x2):
+    out = xp.logical_and(x1, x2)
+    _assert_correctness_binary(
+        "logical_and",
+        operator.and_,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out,
+        expr_template="({} and {})={}"
+    )
+
+
 @given(*hh.two_mutual_arrays([xp.bool]))
 def test_logical_or(x1, x2):
     out = xp.logical_or(x1, x2)
-    ph.assert_dtype("logical_or", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("logical_or", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl(
-        "logical_or", x1, x2, out, operator.or_, expr_template="({} or {})={}"
+    _assert_correctness_binary(
+        "logical_or",
+        operator.or_,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out,
+        expr_template="({} or {})={}"
     )
 
 
 @given(*hh.two_mutual_arrays([xp.bool]))
 def test_logical_xor(x1, x2):
     out = xp.logical_xor(x1, x2)
-    ph.assert_dtype("logical_xor", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("logical_xor", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl(
-        "logical_xor", x1, x2, out, operator.xor, expr_template="({} ^ {})={}"
+    _assert_correctness_binary(
+        "logical_xor",
+        operator.xor,
+        in_dtypes=[x1.dtype, x2.dtype],
+        in_shapes=[x1.shape, x2.shape],
+        in_arrs=[x1, x2],
+        out=out,
+        expr_template="({} ^ {})={}"
     )
 
 
 @pytest.mark.min_version("2023.12")
 @given(*hh.two_mutual_arrays(dh.real_float_dtypes))
 def test_maximum(x1, x2):
     out = xp.maximum(x1, x2)
-    ph.assert_dtype("maximum", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("maximum", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl("maximum", x1, x2, out, max, strict_check=True)
+    _assert_correctness_binary(
+        "maximum", max, [x1.dtype, x2.dtype], [x1.shape, x2.shape], (x1, x2), out, strict_check=True
+    )
 
 
 @pytest.mark.min_version("2023.12")
 @given(*hh.two_mutual_arrays(dh.real_float_dtypes))
 def test_minimum(x1, x2):
     out = xp.minimum(x1, x2)
-    ph.assert_dtype("minimum", in_dtype=[x1.dtype, x2.dtype], out_dtype=out.dtype)
-    ph.assert_result_shape("minimum", in_shapes=[x1.shape, x2.shape], out_shape=out.shape)
-    binary_assert_against_refimpl("minimum", x1, x2, out, min, strict_check=True)
+    _assert_correctness_binary(
+        "minimum", min, [x1.dtype, x2.dtype], [x1.shape, x2.shape], (x1, x2), out, strict_check=True
+    )
 
 
 @pytest.mark.parametrize("ctx", make_binary_params("multiply", dh.numeric_dtypes))
@@ -1719,3 +1779,88 @@ def test_trunc(x):
     ph.assert_dtype("trunc", in_dtype=x.dtype, out_dtype=out.dtype)
     ph.assert_shape("trunc", out_shape=out.shape, expected=x.shape)
     unary_assert_against_refimpl("trunc", x, out, math.trunc, strict_check=True)
+
+
+def _check_binary_with_scalars(func_data, x1x2):
+    x1, x2 = x1x2
+    func, name, refimpl, kwds, expected_dtype = func_data
+    out = func(x1, x2)
+    in_dtypes, in_shapes, (x1a, x2a) = _convert_scalars_helper(x1, x2)
+    _assert_correctness_binary(
+        name, refimpl, in_dtypes, in_shapes, (x1a, x2a), out, expected_dtype, **kwds
+    )
+
+
+def _filter_zero(x):
+    return x != 0 if dh.is_scalar(x) else (not xp.any(x == 0))
+
+
+@pytest.mark.min_version("2024.12")
+@pytest.mark.parametrize('func_data',
+    # xp_func, name, refimpl, kwargs, expected_dtype
+    [
+        (xp.add, "add", operator.add, {}, None),
+        (xp.atan2, "atan2", math.atan2, {}, None),
+        (xp.copysign, "copysign", math.copysign, {}, None),
+        (xp.divide, "divide", operator.truediv, {"filter_": lambda s: s != 0}, None),
+        (xp.hypot, "hypot", math.hypot, {}, None),
+        (xp.logaddexp, "logaddexp", logaddexp_refimpl, {}, None),
+        (xp.maximum, "maximum", max, {'strict_check': True}, None),
+        (xp.minimum, "minimum", min, {'strict_check': True}, None),
+        (xp.multiply, "mul", operator.mul, {}, None),
+        (xp.subtract, "sub", operator.sub, {}, None),
+
+        (xp.equal, "equal", operator.eq, {}, xp.bool),
+        (xp.not_equal, "neq", operator.ne, {}, xp.bool),
+        (xp.less, "less", operator.lt, {}, xp.bool),
+        (xp.less_equal, "les_equal", operator.le, {}, xp.bool),
+        (xp.greater, "greater", operator.gt, {}, xp.bool),
+        (xp.greater_equal, "greater_equal", operator.ge, {}, xp.bool),
+        (xp.remainder, "remainder", operator.mod, {}, None),
+        (xp.floor_divide, "floor_divide", operator.floordiv, {}, None),
+    ],
+    ids=lambda func_data: func_data[1]  # use names for test IDs
+)
+@given(x1x2=hh.array_and_py_scalar(dh.real_float_dtypes))
+def test_binary_with_scalars_real(func_data, x1x2):
+
+    if func_data[1] == "remainder":
+        assume(_filter_zero(x1x2[1]))
+    if func_data[1] == "floor_divide":
+        assume(_filter_zero(x1x2[0]) and _filter_zero(x1x2[1]))
+
+    _check_binary_with_scalars(func_data, x1x2)
+
+
+@pytest.mark.min_version("2024.12")
+@pytest.mark.parametrize('func_data',
+    # xp_func, name, refimpl, kwargs, expected_dtype
+    [
+        (xp.logical_and, "logical_and", operator.and_, {"expr_template": "({} or {})={}"}, None),
+        (xp.logical_or, "logical_or", operator.or_, {"expr_template": "({} or {})={}"}, None),
+        (xp.logical_xor, "logical_xor", operator.xor, {"expr_template": "({} or {})={}"}, None),
+    ],
+    ids=lambda func_data: func_data[1]  # use names for test IDs
+)
+@given(x1x2=hh.array_and_py_scalar([xp.bool]))
+def test_binary_with_scalars_bool(func_data, x1x2):
+    _check_binary_with_scalars(func_data, x1x2)
+
+
+@pytest.mark.min_version("2024.12")
+@pytest.mark.parametrize('func_data',
+    # xp_func, name, refimpl, kwargs, expected_dtype
+    [
+        (xp.bitwise_and, "bitwise_and", operator.and_, {}, None),
+        (xp.bitwise_or, "bitwise_or", operator.or_, {}, None),
+        (xp.bitwise_xor, "bitwise_xor", operator.xor, {}, None),
+    ],
+    ids=lambda func_data: func_data[1]  # use names for test IDs
+)
+@given(x1x2=hh.array_and_py_scalar([xp.int32]))
+def test_binary_with_scalars_bitwise(func_data, x1x2):
+    xp_func, name, refimpl, kwargs, expected = func_data
+    # repack the refimpl
+    refimpl_ = lambda l, r: mock_int_dtype(refimpl(l, r), xp.int32 )
+    _check_binary_with_scalars((xp_func, name, refimpl_, kwargs,expected), x1x2)
+