Tests for negative, positive, pow, and square

Author: ndgrigorian

dpctl/tests/elementwise/test_negative.py

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+#                       Data Parallel Control (dpctl)
+#
+#  Copyright 2020-2023 Intel Corporation
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import itertools
+
+import numpy as np
+import pytest
+
+import dpctl.tensor as dpt
+from dpctl.tests.helper import get_queue_or_skip, skip_if_dtype_not_supported
+
+from .utils import _all_dtypes, _usm_types
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes[1:])
+def test_negative_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)
+    assert dpt.negative(X).dtype == arg_dt
+
+    r = dpt.empty_like(X, dtype=arg_dt)
+    dpt.negative(X, out=r)
+    assert np.allclose(dpt.asnumpy(r), dpt.asnumpy(dpt.negative(X)))
+
+
+@pytest.mark.parametrize("usm_type", _usm_types)
+def test_negative_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.negative(X)
+    assert Y.usm_type == X.usm_type
+    assert Y.sycl_queue == X.sycl_queue
+    assert Y.flags.c_contiguous
+
+    expected_Y = np.negative(dpt.asnumpy(X))
+    assert np.allclose(dpt.asnumpy(Y), expected_Y)
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes[1:])
+def test_negative_order(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)
+    X[..., 0::2] = 1
+    X[..., 1::2] = 0
+
+    for ord in ["C", "F", "A", "K"]:
+        for perms in itertools.permutations(range(4)):
+            U = dpt.permute_dims(X[:, ::-1, ::-1, :], perms)
+            Y = dpt.negative(U, order=ord)
+            expected_Y = np.negative(np.ones(Y.shape, dtype=Y.dtype))
+            expected_Y[..., 1::2] = 0
+            expected_Y = np.transpose(expected_Y, perms)
+            assert np.allclose(dpt.asnumpy(Y), expected_Y)

dpctl/tests/elementwise/test_positive.py

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+#                       Data Parallel Control (dpctl)
+#
+#  Copyright 2020-2023 Intel Corporation
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import itertools
+
+import numpy as np
+import pytest
+
+import dpctl.tensor as dpt
+from dpctl.tests.helper import get_queue_or_skip, skip_if_dtype_not_supported
+
+from .utils import _all_dtypes, _usm_types
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes[1:])
+def test_positive_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)
+    assert dpt.positive(X).dtype == arg_dt
+
+    r = dpt.empty_like(X, dtype=arg_dt)
+    dpt.positive(X, out=r)
+    assert np.allclose(dpt.asnumpy(r), dpt.asnumpy(dpt.positive(X)))
+
+
+@pytest.mark.parametrize("usm_type", _usm_types)
+def test_positive_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.positive(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)
+
+
+@pytest.mark.parametrize("dtype", _all_dtypes[1:])
+def test_positive_order(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)
+    X[..., 0::2] = 1
+    X[..., 1::2] = 0
+
+    for ord in ["C", "F", "A", "K"]:
+        for perms in itertools.permutations(range(4)):
+            U = dpt.permute_dims(X[:, ::-1, ::-1, :], perms)
+            Y = dpt.positive(U, order=ord)
+            expected_Y = np.ones(Y.shape, dtype=Y.dtype)
+            expected_Y[..., 1::2] = 0
+            expected_Y = np.transpose(expected_Y, perms)
+            assert np.allclose(dpt.asnumpy(Y), expected_Y)

dpctl/tests/elementwise/test_pow.py

@@ -0,0 +1,154 @@
+#                       Data Parallel Control (dpctl)
+#
+#  Copyright 2020-2023 Intel Corporation
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import ctypes
+
+import numpy as np
+import pytest
+
+import dpctl
+import dpctl.tensor as dpt
+from dpctl.tests.helper import get_queue_or_skip, skip_if_dtype_not_supported
+
+from .utils import _all_dtypes, _compare_dtypes, _usm_types
+
+
+@pytest.mark.parametrize("op1_dtype", _all_dtypes[1:])
+@pytest.mark.parametrize("op2_dtype", _all_dtypes[1:])
+def test_power_dtype_matrix(op1_dtype, op2_dtype):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(op1_dtype, q)
+    skip_if_dtype_not_supported(op2_dtype, q)
+
+    sz = 127
+    ar1 = dpt.ones(sz, dtype=op1_dtype)
+    ar2 = dpt.ones_like(ar1, dtype=op2_dtype)
+
+    r = dpt.pow(ar1, ar2)
+    assert isinstance(r, dpt.usm_ndarray)
+    expected = np.power(
+        np.ones(1, dtype=op1_dtype), np.ones(1, dtype=op2_dtype)
+    )
+    assert _compare_dtypes(r.dtype, expected.dtype, sycl_queue=q)
+    assert r.shape == ar1.shape
+    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()
+    assert r.sycl_queue == ar1.sycl_queue
+
+    ar3 = dpt.ones(sz, dtype=op1_dtype)
+    ar4 = dpt.ones(2 * sz, dtype=op2_dtype)
+
+    r = dpt.pow(ar3[::-1], ar4[::2])
+    assert isinstance(r, dpt.usm_ndarray)
+    expected = np.power(
+        np.ones(1, dtype=op1_dtype), np.ones(1, dtype=op2_dtype)
+    )
+    assert _compare_dtypes(r.dtype, expected.dtype, sycl_queue=q)
+    assert r.shape == ar3.shape
+    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()
+
+
+@pytest.mark.parametrize("op1_usm_type", _usm_types)
+@pytest.mark.parametrize("op2_usm_type", _usm_types)
+def test_power_usm_type_matrix(op1_usm_type, op2_usm_type):
+    get_queue_or_skip()
+
+    sz = 128
+    ar1 = dpt.ones(sz, dtype="i4", usm_type=op1_usm_type)
+    ar2 = dpt.ones_like(ar1, dtype="i4", usm_type=op2_usm_type)
+
+    r = dpt.pow(ar1, ar2)
+    assert isinstance(r, dpt.usm_ndarray)
+    expected_usm_type = dpctl.utils.get_coerced_usm_type(
+        (op1_usm_type, op2_usm_type)
+    )
+    assert r.usm_type == expected_usm_type
+
+
+def test_pow_order():
+    get_queue_or_skip()
+
+    ar1 = dpt.ones((20, 20), dtype="i4", order="C")
+    ar2 = dpt.ones((20, 20), dtype="i4", order="C")
+    r1 = dpt.pow(ar1, ar2, order="C")
+    assert r1.flags.c_contiguous
+    r2 = dpt.pow(ar1, ar2, order="F")
+    assert r2.flags.f_contiguous
+    r3 = dpt.pow(ar1, ar2, order="A")
+    assert r3.flags.c_contiguous
+    r4 = dpt.pow(ar1, ar2, order="K")
+    assert r4.flags.c_contiguous
+
+    ar1 = dpt.ones((20, 20), dtype="i4", order="F")
+    ar2 = dpt.ones((20, 20), dtype="i4", order="F")
+    r1 = dpt.pow(ar1, ar2, order="C")
+    assert r1.flags.c_contiguous
+    r2 = dpt.pow(ar1, ar2, order="F")
+    assert r2.flags.f_contiguous
+    r3 = dpt.pow(ar1, ar2, order="A")
+    assert r3.flags.f_contiguous
+    r4 = dpt.pow(ar1, ar2, order="K")
+    assert r4.flags.f_contiguous
+
+    ar1 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2]
+    ar2 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2]
+    r4 = dpt.pow(ar1, ar2, order="K")
+    assert r4.strides == (20, -1)
+
+    ar1 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2].mT
+    ar2 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2].mT
+    r4 = dpt.pow(ar1, ar2, order="K")
+    assert r4.strides == (-1, 20)
+
+
+def test_pow_broadcasting():
+    get_queue_or_skip()
+
+    v = dpt.arange(1, 6, dtype="i4")
+    m = dpt.full((100, 5), 2, dtype="i4")
+
+    r = dpt.pow(m, v)
+
+    expected = np.power(
+        np.full((100, 5), 2, dtype="i4"), np.arange(1, 6, dtype="i4")
+    )
+    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()
+
+    r2 = dpt.pow(v, m)
+    expected2 = np.power(
+        np.arange(1, 6, dtype="i4"), np.full((100, 5), 2, dtype="i4")
+    )
+    assert (dpt.asnumpy(r2) == expected2.astype(r2.dtype)).all()
+
+
+@pytest.mark.parametrize("arr_dt", _all_dtypes)
+def test_pow_python_scalar(arr_dt):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(arr_dt, q)
+
+    X = dpt.ones((10, 10), dtype=arr_dt, sycl_queue=q)
+    py_ones = (
+        bool(1),
+        int(1),
+        float(1),
+        complex(1),
+        np.float32(1),
+        ctypes.c_int(1),
+    )
+    for sc in py_ones:
+        R = dpt.pow(X, sc)
+        assert isinstance(R, dpt.usm_ndarray)
+        R = dpt.pow(sc, X)
+        assert isinstance(R, dpt.usm_ndarray)