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Sync tests with NumPy 2.6 behavior changes #3121

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7 changes: 7 additions & 0 deletions dpnp/tests/third_party/cupy/indexing_tests/test_indexing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,6 +208,13 @@ def test_choose(self, xp, dtype):
c = testing.shaped_arange((3, 4), xp, dtype)
return a.choose(c)

@testing.with_requires("numpy>=2.6.0.dev0")
@testing.numpy_cupy_array_equal()
def test_choose_uint64(self, xp):
a = xp.array([0, 2, 1, 2], dtype=xp.uint64)
choices = xp.arange(12).reshape(3, 4)
return xp.choose(a, choices)

@testing.for_all_dtypes()
@testing.numpy_cupy_array_equal()
def test_choose_broadcast(self, xp, dtype):
Expand Down
12 changes: 12 additions & 0 deletions dpnp/tests/third_party/cupy/indexing_tests/test_insert.py
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Expand Up @@ -126,6 +126,18 @@ def test_put_values_scalar(self, xp):
return a


class TestPutUint64(unittest.TestCase):

@testing.with_requires("numpy>=2.6.0.dev0")
@testing.numpy_cupy_array_equal()
def test_put_uint64(self, xp):
a = xp.arange(5)
indices = xp.array([0, 2, 4], dtype=xp.uint64)
# 'raise' mode is not supported, indices are in bounds anyway
xp.put(a, indices, [10, 20, 30], mode="clip")
return a


@testing.parameterize(
*testing.product(
{
Expand Down
19 changes: 11 additions & 8 deletions dpnp/tests/third_party/cupy/manipulation_tests/test_tiling.py
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Expand Up @@ -137,6 +137,7 @@ def test_repeat(self, xp):
numpy.uint8,
numpy.uint16,
numpy.uint32,
numpy.uint64,
]
]
)
Expand All @@ -146,7 +147,11 @@ class TestRepeatNdarrayRepsDtype:
@testing.numpy_cupy_array_equal()
def test_repeat(self, xp):
x = testing.shaped_arange((4,), xp)
return xp.repeat(x, xp.array([1, 2, 3, 4], dtype=self.rep_dtype), 0)
repeats = xp.array([1, 2, 3, 4], dtype=self.rep_dtype)
if xp is numpy and testing.numpy_satisfies("<2.6.0.dev0"):
# NumPy < 2.6 rejects uint64 repeats even when values fit intp.
repeats = repeats.astype(numpy.intp)
return xp.repeat(x, repeats, 0)


@testing.parameterize(
Expand Down Expand Up @@ -241,17 +246,15 @@ def test_negative(self):
cupy.repeat(cupy.arange(3), cupy.array([-1, 1, 2]))

def test_float_dtype_matches_numpy(self):
# Both NumPy and CuPy raise TypeError for unsafe cast
# Floating-point repeats cannot be cast to intp with same_kind.
for xp in (numpy, cupy):
with pytest.raises(TypeError):
xp.repeat(xp.arange(3), xp.array([1.0, 1.0, 1.0]), 0)

@pytest.mark.skip("dpnp accepts uint64 repeats, unlike numpy")
def test_uint64_matches_numpy(self):
# Both reject uint64 (unsigned → signed is unsafe)
for xp in (numpy, cupy):
with pytest.raises(TypeError):
xp.repeat(xp.arange(3), xp.array([1, 2, 3], dtype=numpy.uint64))
@pytest.mark.parametrize("repeats", [[2**64 - 1], [1, 2**64 - 1, 1]])
def test_uint64_overflow(self, repeats):
with pytest.raises(ValueError, match="int64 maximum"):
cupy.repeat(cupy.arange(3), cupy.array(repeats, dtype=numpy.uint64))

def test_ndim_gt1_matches_numpy(self):
for xp in (numpy, cupy):
Expand Down
29 changes: 20 additions & 9 deletions dpnp/tests/third_party/cupy/math_tests/test_trigonometric.py
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Expand Up @@ -5,6 +5,9 @@

from dpnp.tests.helper import has_support_aspect64
from dpnp.tests.third_party.cupy import testing
from dpnp.tests.third_party.cupy.testing._helper import (
installed_but_not_baseline,
)


class TestTrigonometric(unittest.TestCase):
Expand Down Expand Up @@ -67,16 +70,24 @@ def test_rad2deg(self):
@testing.with_requires("numpy>=1.21.0")
class TestUnwrap(unittest.TestCase):

def _make_input(self, shape, xp, dtype, period=2 * numpy.pi):
if (
installed_but_not_baseline(numpy="2.6.0.dev0")
and numpy.dtype(dtype).kind in "ub"
):
pytest.skip("NumPy 2.6 promotes inputs before differencing.")
return testing.shaped_random(shape, xp, dtype)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(type_check=has_support_aspect64())
def test_unwrap_1dim(self, xp, dtype):
a = testing.shaped_random((5,), xp, dtype)
a = self._make_input((5,), xp, dtype)
return xp.unwrap(a)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(type_check=has_support_aspect64())
def test_unwrap_1dim_with_discont(self, xp, dtype):
a = testing.shaped_random((5,), xp, dtype)
a = self._make_input((5,), xp, dtype)
return xp.unwrap(a, discont=1.0)

@testing.for_all_dtypes(no_complex=True)
Expand All @@ -89,7 +100,7 @@ def test_unwrap_1dim_with_period(self, xp, dtype):
# float64 and dpnp uses float32, especially with uint8/uint16
# dtypes where overflow occurs
pytest.skip("skipping due to large difference of result")
a = testing.shaped_random((5,), xp, dtype)
a = self._make_input((5,), xp, dtype, period=1.2)
return xp.unwrap(a, period=1.2)

@testing.for_all_dtypes(no_complex=True)
Expand All @@ -102,39 +113,39 @@ def test_unwrap_1dim_with_discont_and_period(self, xp, dtype):
# float64 and dpnp uses float32, especially with uint8/uint16
# dtypes where overflow occurs
pytest.skip("skipping due to large difference of result")
a = testing.shaped_random((5,), xp, dtype)
a = self._make_input((5,), xp, dtype, period=1.2)
return xp.unwrap(a, discont=1.0, period=1.2)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(
rtol=1e-06, atol=1e-06, type_check=has_support_aspect64()
)
def test_unwrap_2dim_without_axis(self, xp, dtype):
a = testing.shaped_random((4, 5), xp, dtype)
a = self._make_input((4, 5), xp, dtype)
return xp.unwrap(a)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(
rtol=1e-06, atol=1e-06, type_check=has_support_aspect64()
)
def test_unwrap_2dim_with_axis(self, xp, dtype):
a = testing.shaped_random((4, 5), xp, dtype)
a = self._make_input((4, 5), xp, dtype)
return xp.unwrap(a, axis=1)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(rtol=1e-06, type_check=has_support_aspect64())
def test_unwrap_2dim_with_discont(self, xp, dtype):
a = testing.shaped_random((4, 5), xp, dtype)
a = self._make_input((4, 5), xp, dtype)
return xp.unwrap(a, discont=5.0, axis=1)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(type_check=has_support_aspect64())
def test_unwrap_2dim_with_period(self, xp, dtype):
a = testing.shaped_random((4, 5), xp, dtype)
a = self._make_input((4, 5), xp, dtype, period=4.5)
return xp.unwrap(a, axis=1, period=4.5)

@testing.for_all_dtypes(no_complex=True)
@testing.numpy_cupy_allclose(type_check=has_support_aspect64())
def test_unwrap_2dim_with_discont_and_period(self, xp, dtype):
a = testing.shaped_random((4, 5), xp, dtype)
a = self._make_input((4, 5), xp, dtype, period=4.5)
return xp.unwrap(a, discont=5.0, axis=1, period=4.5)
7 changes: 7 additions & 0 deletions dpnp/tests/third_party/cupy/sorting_tests/test_search.py
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Expand Up @@ -849,6 +849,13 @@ def test_sorter(self, xp):
y = xp.searchsorted(bins, x, sorter=sorter)
return (y,)

@testing.with_requires("numpy>=2.6.0.dev0")
@testing.numpy_cupy_array_equal()
def test_sorter_uint64(self, xp):
a = xp.array([30, 10, 20])
sorter = xp.array([1, 2, 0], dtype=xp.uint64)
return xp.searchsorted(a, xp.array([15, 25]), sorter=sorter)

def test_invalid_sorter(self):
for xp in (numpy, cupy):
x = testing.shaped_arange((12,), xp, xp.float64)
Expand Down
76 changes: 46 additions & 30 deletions dpnp/tests/third_party/cupy/statistics_tests/test_histogram.py
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Expand Up @@ -543,48 +543,64 @@ def test_digitize_nan(self, xp):
y = xp.digitize(x, bins, right=self.right)
return (y,)

@testing.numpy_cupy_array_equal()
def test_digitize_nan_bins(self, xp):
x = testing.shaped_arange((14,), xp, xp.float32)
bins = xp.array([1.0, 3.0, 5.0, 8.0, float("nan")], xp.float32)
y = xp.digitize(x, bins, right=self.right)
return (y,)
def test_digitize_nan_bins(self):
# NumPy >= 2.6 rejects NaN bins during its monotonicity check.
# CuPy skips that check to avoid synchronization, so test it directly.
x = testing.shaped_arange((14,), cupy, cupy.float32)
bins = cupy.array([1.0, 3.0, 5.0, 8.0, float("nan")], cupy.float32)
y = cupy.digitize(x, bins, right=self.right)
if self.right:
expected = [0, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4]
else:
expected = [1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4]
testing.assert_array_equal(y, expected)

@testing.numpy_cupy_array_equal()
def test_digitize_nan_bins_repeated(self, xp):
x = testing.shaped_arange((14,), xp, xp.float32)
def test_digitize_nan_bins_repeated(self):
x = testing.shaped_arange((14,), cupy, cupy.float32)
x[5] = float("nan")
bins = [1.0, 3.0, 5.0, 8.0, float("nan"), float("nan")]
bins = xp.array(bins, xp.float32)
y = xp.digitize(x, bins, right=self.right)
return (y,)
bins = cupy.array(bins, cupy.float32)
y = cupy.digitize(x, bins, right=self.right)
if self.right:
expected = [0, 1, 1, 2, 2, 4, 3, 3, 4, 4, 4, 4, 4, 4]
else:
expected = [1, 1, 2, 2, 3, 6, 3, 4, 4, 4, 4, 4, 4, 4]
testing.assert_array_equal(y, expected)

@testing.numpy_cupy_array_equal()
def test_digitize_nan_bins_decreasing(self, xp):
x = testing.shaped_arange((14,), xp, xp.float32)
def test_digitize_nan_bins_decreasing(self):
x = testing.shaped_arange((14,), cupy, cupy.float32)
x[5] = float("nan")
bins = [float("nan"), 8.0, 5.0, 3.0, 1.0]
bins = xp.array(bins, xp.float32)
y = xp.digitize(x, bins, right=self.right)
return (y,)
bins = cupy.array(bins, cupy.float32)
y = cupy.digitize(x, bins, right=self.right)
if self.right:
expected = [5, 4, 4, 3, 3, 1, 2, 2, 1, 1, 1, 1, 1, 1]
else:
expected = [4, 4, 3, 3, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1]
testing.assert_array_equal(y, expected)

@testing.numpy_cupy_array_equal()
def test_digitize_nan_bins_decreasing_repeated(self, xp):
x = testing.shaped_arange((14,), xp, xp.float32)
def test_digitize_nan_bins_decreasing_repeated(self):
x = testing.shaped_arange((14,), cupy, cupy.float32)
x[5] = float("nan")
bins = [float("nan"), float("nan"), float("nan"), 5.0, 3.0, 1.0]
bins = xp.array(bins, xp.float32)
y = xp.digitize(x, bins, right=self.right)
return (y,)
bins = cupy.array(bins, cupy.float32)
y = cupy.digitize(x, bins, right=self.right)
if self.right:
expected = [6, 5, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3]
else:
expected = [5, 5, 4, 4, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3]
testing.assert_array_equal(y, expected)

@testing.numpy_cupy_array_equal()
def test_digitize_all_nan_bins(self, xp):
x = testing.shaped_arange((14,), xp, xp.float32)
def test_digitize_all_nan_bins(self):
x = testing.shaped_arange((14,), cupy, cupy.float32)
x[5] = float("nan")
bins = [float("nan"), float("nan"), float("nan"), float("nan")]
bins = xp.array(bins, xp.float32)
y = xp.digitize(x, bins, right=self.right)
return (y,)
bins = cupy.array(bins, cupy.float32)
y = cupy.digitize(x, bins, right=self.right)
expected = [4] * 14
if not self.right:
expected[5] = 0
testing.assert_array_equal(y, expected)

@testing.numpy_cupy_array_equal()
def test_searchsorted_inf(self, xp):
Expand Down
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