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Align with new array api version in dpctl #1774

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merged 4 commits into from
Apr 5, 2024
Merged

Align with new array api version in dpctl #1774

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0ca4e10
Use utility function from dpctl.tensor._type_utils
antonwolfy Apr 4, 2024
9e597fb
Align sum and prod tests
antonwolfy Apr 4, 2024
ea30077
Align logsumexp and reduce_hypot tests
antonwolfy Apr 4, 2024
37d4b0e
Updated docstring of impacted functions
antonwolfy Apr 4, 2024
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5 changes: 2 additions & 3 deletions dpnp/dpnp_iface_mathematical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@


import dpctl.tensor as dpt
import dpctl.tensor._type_utils as dtu
import numpy
from numpy.core.numeric import (
normalize_axis_index,
Expand Down Expand Up @@ -2905,8 +2906,6 @@ def sum(
)
)
):
from dpctl.tensor._reduction import _default_reduction_dtype

from dpnp.backend.extensions.sycl_ext import _sycl_ext_impl

input = a
Expand All @@ -2916,7 +2915,7 @@ def sum(

queue = input.sycl_queue
out_dtype = (
_default_reduction_dtype(input.dtype, queue)
dtu._default_accumulation_dtype(input.dtype, queue)
if dtype is None
else dtype
)
Expand Down
11 changes: 1 addition & 10 deletions tests/test_arithmetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,15 +6,6 @@
from tests.third_party.cupy import testing


# Note: numpy.sum() always upcast integers to (u)int64 and float32 to
# float64 for dtype=None. `np.sum` does that too for integers, but not for
# float32, so we need to special-case it for these tests
def _get_dtype_kwargs(xp, dtype):
if xp is numpy and dtype == numpy.float32 and has_support_aspect64():
return {"dtype": numpy.float64}
return {}


class TestArithmetic(unittest.TestCase):
@testing.for_float_dtypes()
@testing.numpy_cupy_allclose()
Expand Down Expand Up @@ -42,7 +33,7 @@ def test_nanprod(self, xp, dtype):
@testing.numpy_cupy_allclose()
def test_nansum(self, xp, dtype):
a = xp.array([-2.5, -1.5, xp.nan, 10.5, 1.5, xp.nan], dtype=dtype)
return xp.nansum(a, **_get_dtype_kwargs(xp, a.dtype))
return xp.nansum(a)

@testing.for_float_dtypes()
@testing.numpy_cupy_allclose()
Expand Down
28 changes: 22 additions & 6 deletions tests/test_mathematical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1884,7 +1884,9 @@ class TestLogSumExp:
def test_logsumexp(self, dtype, axis, keepdims):
a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
res = dpnp.logsumexp(a, axis=axis, keepdims=keepdims)
exp_dtype = dpnp.default_float_type(a.device)
exp_dtype = (
dpnp.default_float_type(a.device) if dtype == dpnp.bool else None
)
exp = numpy.logaddexp.reduce(
dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
)
Expand All @@ -1896,11 +1898,17 @@ def test_logsumexp(self, dtype, axis, keepdims):
@pytest.mark.parametrize("keepdims", [True, False])
def test_logsumexp_out(self, dtype, axis, keepdims):
a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
exp_dtype = dpnp.default_float_type(a.device)
exp_dtype = (
dpnp.default_float_type(a.device) if dtype == dpnp.bool else None
)
exp = numpy.logaddexp.reduce(
dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
)
dpnp_out = dpnp.empty(exp.shape, dtype=exp_dtype)

exp_dtype = exp.dtype
if exp_dtype == numpy.float64 and not has_support_aspect64():
exp_dtype = numpy.float32
dpnp_out = dpnp.empty_like(a, shape=exp.shape, dtype=exp_dtype)
res = dpnp.logsumexp(a, axis=axis, out=dpnp_out, keepdims=keepdims)

assert res is dpnp_out
Expand All @@ -1926,7 +1934,9 @@ class TestReduceHypot:
def test_reduce_hypot(self, dtype, axis, keepdims):
a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
res = dpnp.reduce_hypot(a, axis=axis, keepdims=keepdims)
exp_dtype = dpnp.default_float_type(a.device)
exp_dtype = (
dpnp.default_float_type(a.device) if dtype == dpnp.bool else None
)
exp = numpy.hypot.reduce(
dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
)
Expand All @@ -1938,11 +1948,17 @@ def test_reduce_hypot(self, dtype, axis, keepdims):
@pytest.mark.parametrize("keepdims", [True, False])
def test_reduce_hypot_out(self, dtype, axis, keepdims):
a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
exp_dtype = dpnp.default_float_type(a.device)
exp_dtype = (
dpnp.default_float_type(a.device) if dtype == dpnp.bool else None
)
exp = numpy.hypot.reduce(
dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
)
dpnp_out = dpnp.empty(exp.shape, dtype=exp_dtype)

exp_dtype = exp.dtype
if exp_dtype == numpy.float64 and not has_support_aspect64():
exp_dtype = numpy.float32
dpnp_out = dpnp.empty_like(a, shape=exp.shape, dtype=exp_dtype)
res = dpnp.reduce_hypot(a, axis=axis, out=dpnp_out, keepdims=keepdims)

assert res is dpnp_out
Expand Down
5 changes: 1 addition & 4 deletions tests/test_sum.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,10 +61,7 @@ def test_sum_axis():
ia = dpnp.array(a)

result = dpnp.sum(ia, axis=1)
if has_support_aspect64():
expected = numpy.sum(a, axis=1, dtype=numpy.float64)
else:
expected = numpy.sum(a, axis=1)
expected = numpy.sum(a, axis=1)
assert_array_equal(expected, result)


Expand Down
62 changes: 24 additions & 38 deletions tests/third_party/cupy/math_tests/test_sumprod.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,15 +6,6 @@
from tests.third_party.cupy import testing


# Note: numpy.sum() always upcast integers to (u)int64 and float32 to
# float64 for dtype=None. `np.sum` does that too for integers, but not for
# float32, so we need to special-case it for these tests
def _get_dtype_kwargs(xp, dtype):
if xp is numpy and dtype == numpy.float32 and has_support_aspect64():
return {"dtype": numpy.float64}
return {}


class TestSumprod:
def tearDown(self):
# Free huge memory for slow test
Expand All @@ -26,43 +17,43 @@ def tearDown(self):
@testing.numpy_cupy_allclose()
def test_sum_all(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype))
return a.sum()

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_sum_all_keepdims(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), keepdims=True)
return a.sum(keepdims=True)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_external_sum_all(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return xp.sum(a, **_get_dtype_kwargs(xp, dtype))
return xp.sum(a)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(rtol=1e-06)
def test_sum_all2(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype))
return a.sum()

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_sum_all_transposed(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(2, 0, 1)
return a.sum(**_get_dtype_kwargs(xp, dtype))
return a.sum()

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(rtol=1e-06)
def test_sum_all_transposed2(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40), xp, dtype).transpose(2, 0, 1)
return a.sum(**_get_dtype_kwargs(xp, dtype))
return a.sum()

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_sum_axis(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=1)
return a.sum(axis=1)

@testing.slow
@testing.numpy_cupy_allclose()
Expand All @@ -74,57 +65,57 @@ def test_sum_axis_huge(self, xp):
@testing.numpy_cupy_allclose()
def test_external_sum_axis(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return xp.sum(a, **_get_dtype_kwargs(xp, dtype), axis=1)
return xp.sum(a, axis=1)

# float16 is omitted, since NumPy's sum on float16 arrays has more error
# than CuPy's.
@testing.for_all_dtypes(no_float16=True)
@testing.numpy_cupy_allclose()
def test_sum_axis2(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=1)
return a.sum(axis=1)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(contiguous_check=False)
def test_sum_axis_transposed(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(2, 0, 1)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=1)
return a.sum(axis=1)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(contiguous_check=False)
def test_sum_axis_transposed2(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40), xp, dtype).transpose(2, 0, 1)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=1)
return a.sum(axis=1)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_sum_axes(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4, 5), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=(1, 3))
return a.sum(axis=(1, 3))

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(rtol=1e-4)
def test_sum_axes2(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40, 50), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=(1, 3))
return a.sum(axis=(1, 3))

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(rtol=1e-6)
def test_sum_axes3(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4, 5), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=(0, 2, 3))
return a.sum(axis=(0, 2, 3))

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose(rtol=1e-6)
def test_sum_axes4(self, xp, dtype):
a = testing.shaped_arange((20, 30, 40, 50), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=(0, 2, 3))
return a.sum(axis=(0, 2, 3))

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_sum_empty_axis(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4, 5), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=())
return a.sum(axis=())

@testing.for_all_dtypes_combination(names=["src_dtype", "dst_dtype"])
@testing.numpy_cupy_allclose()
Expand All @@ -142,7 +133,7 @@ def test_sum_keepdims_and_dtype(self, xp, src_dtype, dst_dtype):
@testing.numpy_cupy_allclose()
def test_sum_keepdims_multiple_axes(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return a.sum(**_get_dtype_kwargs(xp, dtype), axis=(1, 2), keepdims=True)
return a.sum(axis=(1, 2), keepdims=True)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
Expand All @@ -162,25 +153,25 @@ def test_sum_out_wrong_shape(self):
@testing.numpy_cupy_allclose()
def test_prod_all(self, xp, dtype):
a = testing.shaped_arange((2, 3), xp, dtype)
return a.prod(**_get_dtype_kwargs(xp, dtype))
return a.prod()

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_external_prod_all(self, xp, dtype):
a = testing.shaped_arange((2, 3), xp, dtype)
return xp.prod(a, **_get_dtype_kwargs(xp, dtype))
return xp.prod(a)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_prod_axis(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return a.prod(axis=1, **_get_dtype_kwargs(xp, dtype))
return a.prod(axis=1)

@testing.for_all_dtypes()
@testing.numpy_cupy_allclose()
def test_external_prod_axis(self, xp, dtype):
a = testing.shaped_arange((2, 3, 4), xp, dtype)
return xp.prod(a, axis=1, **_get_dtype_kwargs(xp, dtype))
return xp.prod(a, axis=1)

@testing.for_all_dtypes_combination(names=["src_dtype", "dst_dtype"])
@testing.numpy_cupy_allclose()
Expand Down Expand Up @@ -228,12 +219,7 @@ def _test(self, xp, dtype):
if not issubclass(dtype, xp.integer):
a[:, 1] = xp.nan
func = getattr(xp, self.func)
return func(
a,
**_get_dtype_kwargs(xp, dtype),
axis=self.axis,
keepdims=self.keepdims,
)
return func(a, axis=self.axis, keepdims=self.keepdims)

@testing.for_all_dtypes(no_bool=True, no_float16=True)
@testing.numpy_cupy_allclose(type_check=has_support_aspect64())
Expand Down Expand Up @@ -299,15 +285,15 @@ def test_nansum_axes(self, xp, dtype):
a = testing.shaped_arange(self.shape, xp, dtype)
if not issubclass(dtype, xp.integer):
a[:, 1] = xp.nan
return xp.nansum(a, **_get_dtype_kwargs(xp, dtype), axis=self.axis)
return xp.nansum(a, axis=self.axis)


class TestNansumNanprodHuge:
def _test(self, xp, nan_slice):
a = testing.shaped_random((2048, 1, 1024), xp, "f")
a[nan_slice] = xp.nan
a = xp.broadcast_to(a, (2048, 256, 1024))
return xp.nansum(a, **_get_dtype_kwargs(xp, a.dtype), axis=2)
return xp.nansum(a, axis=2)

@testing.slow
@testing.numpy_cupy_allclose(atol=1e-1)
Expand Down
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