Migrate elementwise tests to dpnp/tests/tensor/elementwise

Author: vlad-perevezentsev

dpnp/tests/tensor/elementwise/__init__.py

@@ -0,0 +1,32 @@
+# *****************************************************************************
+# Copyright (c) 2026, Intel Corporation
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# - Redistributions of source code must retain the above copyright notice,
+#   this list of conditions and the following disclaimer.
+# - Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# - Neither the name of the copyright holder nor the names of its contributors
+#   may be used to endorse or promote products derived from this software
+#   without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+"""
+Collection of test and utility files for testing elementwise operations
+over :class:`dpctl.tensor.usm_ndarray`.
+"""

dpnp/tests/tensor/elementwise/test_abs.py

@@ -0,0 +1,225 @@
+# *****************************************************************************
+# Copyright (c) 2026, Intel Corporation
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# - Redistributions of source code must retain the above copyright notice,
+#   this list of conditions and the following disclaimer.
+# - Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# - Neither the name of the copyright holder nor the names of its contributors
+#   may be used to endorse or promote products derived from this software
+#   without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+import itertools
+import warnings
+
+import numpy as np
+import pytest
+
+# TODO: revert to `import dpctl.tensor...`
+# when dpnp fully migrates dpctl/tensor
+import dpctl_ext.tensor as dpt
+from dpnp.tests.tensor.elementwise.utils import (
+    _all_dtypes,
+    _complex_fp_dtypes,
+    _real_fp_dtypes,
+    _usm_types,
+)
+from dpnp.tests.tensor.helper import (
+    get_queue_or_skip,
+    skip_if_dtype_not_supported,
+)
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes)
+def test_abs_out_type(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    arg_dt = np.dtype(dtype)
+    X = dpt.asarray(0, dtype=arg_dt, sycl_queue=q)
+    if np.issubdtype(arg_dt, np.complexfloating):
+        type_map = {
+            np.dtype("c8"): np.dtype("f4"),
+            np.dtype("c16"): np.dtype("f8"),
+        }
+        assert dpt.abs(X).dtype == type_map[arg_dt]
+
+        r = dpt.empty_like(X, dtype=type_map[arg_dt])
+        dpt.abs(X, out=r)
+        assert np.allclose(dpt.asnumpy(r), dpt.asnumpy(dpt.abs(X)))
+    else:
+        assert dpt.abs(X).dtype == arg_dt
+
+        r = dpt.empty_like(X, dtype=arg_dt)
+        dpt.abs(X, out=r)
+        assert np.allclose(dpt.asnumpy(r), dpt.asnumpy(dpt.abs(X)))
+
+
+@pytest.mark.parametrize("usm_type", _usm_types)
+def test_abs_usm_type(usm_type):
+    q = get_queue_or_skip()
+
+    arg_dt = np.dtype("i4")
+    input_shape = (10, 10, 10, 10)
+    X = dpt.empty(input_shape, dtype=arg_dt, usm_type=usm_type, sycl_queue=q)
+    X[..., 0::2] = 1
+    X[..., 1::2] = 0
+
+    Y = dpt.abs(X)
+    assert Y.usm_type == X.usm_type
+    assert Y.sycl_queue == X.sycl_queue
+    assert Y.flags.c_contiguous
+
+    expected_Y = dpt.asnumpy(X)
+    assert np.allclose(dpt.asnumpy(Y), expected_Y)
+
+
+def test_abs_types_property():
+    get_queue_or_skip()
+    types = dpt.abs.types
+    assert isinstance(types, list)
+    assert len(types) > 0
+    assert types == dpt.abs.types_
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes[1:])
+def test_abs_order(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    arg_dt = np.dtype(dtype)
+    exp_dt = np.abs(np.ones(tuple(), dtype=arg_dt)).dtype
+    input_shape = (10, 10, 10, 10)
+    X = dpt.empty(input_shape, dtype=arg_dt, sycl_queue=q)
+    X[..., 0::2] = 1
+    X[..., 1::2] = 0
+
+    for perms in itertools.permutations(range(4)):
+        U = dpt.permute_dims(X[:, ::-1, ::-1, :], perms)
+        expected_Y = np.ones(U.shape, dtype=exp_dt)
+        expected_Y[..., 1::2] = 0
+        expected_Y = np.transpose(expected_Y, perms)
+        for ord in ["C", "F", "A", "K"]:
+            Y = dpt.abs(U, order=ord)
+            assert np.allclose(dpt.asnumpy(Y), expected_Y)
+
+
+@pytest.mark.parametrize("dtype", ["c8", "c16"])
+def test_abs_complex(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    arg_dt = np.dtype(dtype)
+    input_shape = (10, 10, 10, 10)
+    X = dpt.empty(input_shape, dtype=arg_dt, sycl_queue=q)
+    Xnp = np.random.standard_normal(
+        size=input_shape
+    ) + 1j * np.random.standard_normal(size=input_shape)
+    Xnp = Xnp.astype(arg_dt)
+    X[...] = Xnp
+
+    for ord in ["C", "F", "A", "K"]:
+        for perms in itertools.permutations(range(4)):
+            U = dpt.permute_dims(X[:, ::-1, ::-1, :], perms)
+            Y = dpt.abs(U, order=ord)
+            expected_Y = np.abs(np.transpose(Xnp[:, ::-1, ::-1, :], perms))
+            tol = dpt.finfo(Y.dtype).resolution
+            np.testing.assert_allclose(
+                dpt.asnumpy(Y), expected_Y, atol=tol, rtol=tol
+            )
+
+
+def test_abs_out_overlap():
+    get_queue_or_skip()
+
+    X = dpt.arange(-3, 3, 1, dtype="i4")
+    expected = dpt.asarray([3, 2, 1, 0, 1, 2], dtype="i4")
+    Y = dpt.abs(X, out=X)
+
+    assert Y is X
+    assert dpt.all(expected == X)
+
+    X = dpt.arange(-3, 3, 1, dtype="i4")
+    expected = expected[::-1]
+    Y = dpt.abs(X, out=X[::-1])
+    assert Y is not X
+    assert dpt.all(expected == X)
+
+
+@pytest.mark.parametrize("dtype", _real_fp_dtypes)
+def test_abs_real_fp_special_values(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    nans_ = [dpt.nan, -dpt.nan]
+    infs_ = [dpt.inf, -dpt.inf]
+    finites_ = [-1.0, -0.0, 0.0, 1.0]
+    inps_ = nans_ + infs_ + finites_
+
+    x = dpt.asarray(inps_, dtype=dtype)
+    r = dpt.abs(x)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        expected_np = np.abs(np.asarray(inps_, dtype=dtype))
+
+    expected = dpt.asarray(expected_np, dtype=dtype)
+    tol = dpt.finfo(r.dtype).resolution
+
+    assert dpt.allclose(r, expected, atol=tol, rtol=tol, equal_nan=True)
+
+
+@pytest.mark.parametrize("dtype", _complex_fp_dtypes)
+def test_abs_complex_fp_special_values(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    nans_ = [dpt.nan, -dpt.nan]
+    infs_ = [dpt.inf, -dpt.inf]
+    finites_ = [-1.0, -0.0, 0.0, 1.0]
+    inps_ = nans_ + infs_ + finites_
+    c_ = [complex(*v) for v in itertools.product(inps_, repeat=2)]
+
+    z = dpt.asarray(c_, dtype=dtype)
+    r = dpt.abs(z)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        expected_np = np.abs(np.asarray(c_, dtype=dtype))
+
+    expected = dpt.asarray(expected_np, dtype=dtype)
+    tol = dpt.finfo(r.dtype).resolution
+
+    assert dpt.allclose(r, expected, atol=tol, rtol=tol, equal_nan=True)
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes)
+def test_abs_alignment(dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dtype, q)
+
+    x = dpt.ones(512, dtype=dtype)
+    r = dpt.abs(x)
+
+    r2 = dpt.abs(x[1:])
+    assert np.allclose(dpt.asnumpy(r[1:]), dpt.asnumpy(r2))
+
+    dpt.abs(x[:-1], out=r[1:])
+    assert np.allclose(dpt.asnumpy(r[1:]), dpt.asnumpy(r2))