remove redundant tests for multiply

Author: ndgrigorian

dpctl/tests/elementwise/test_multiply.py

@@ -14,17 +14,14 @@
 #  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.tensor._type_utils import _can_cast
 from dpctl.tests.helper import get_queue_or_skip, skip_if_dtype_not_supported
 
-from .utils import _all_dtypes, _compare_dtypes, _usm_types
+from .utils import _all_dtypes, _compare_dtypes
 
 
 @pytest.mark.parametrize("op1_dtype", _all_dtypes)
@@ -61,100 +58,6 @@ def test_multiply_dtype_matrix(op1_dtype, op2_dtype):
     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_multiply_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.multiply(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_multiply_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.multiply(ar1, ar2, order="C")
-    assert r1.flags.c_contiguous
-    r2 = dpt.multiply(ar1, ar2, order="F")
-    assert r2.flags.f_contiguous
-    r3 = dpt.multiply(ar1, ar2, order="A")
-    assert r3.flags.c_contiguous
-    r4 = dpt.multiply(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.multiply(ar1, ar2, order="C")
-    assert r1.flags.c_contiguous
-    r2 = dpt.multiply(ar1, ar2, order="F")
-    assert r2.flags.f_contiguous
-    r3 = dpt.multiply(ar1, ar2, order="A")
-    assert r3.flags.f_contiguous
-    r4 = dpt.multiply(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.multiply(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.multiply(ar1, ar2, order="K")
-    assert r4.strides == (-1, 20)
-
-
-def test_multiply_broadcasting():
-    get_queue_or_skip()
-
-    m = dpt.ones((100, 5), dtype="i4")
-    v = dpt.arange(1, 6, dtype="i4")
-
-    r = dpt.multiply(m, v)
-
-    expected = np.multiply(
-        np.ones((100, 5), dtype="i4"), np.arange(1, 6, dtype="i4")
-    )
-    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()
-
-    r2 = dpt.multiply(v, m)
-    expected2 = np.multiply(
-        np.arange(1, 6, dtype="i4"), np.ones((100, 5), dtype="i4")
-    )
-    assert (dpt.asnumpy(r2) == expected2.astype(r2.dtype)).all()
-
-
-@pytest.mark.parametrize("arr_dt", _all_dtypes)
-def test_multiply_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.multiply(X, sc)
-        assert isinstance(R, dpt.usm_ndarray)
-        R = dpt.multiply(sc, X)
-        assert isinstance(R, dpt.usm_ndarray)
-
-
 @pytest.mark.parametrize("arr_dt", _all_dtypes)
 @pytest.mark.parametrize("sc", [bool(1), int(1), float(1), complex(1)])
 def test_multiply_python_scalar_gh1219(arr_dt, sc):
@@ -175,22 +78,6 @@ def test_multiply_python_scalar_gh1219(arr_dt, sc):
     assert _compare_dtypes(R.dtype, Rnp.dtype, sycl_queue=q)
 
 
-@pytest.mark.parametrize("dtype", _all_dtypes)
-def test_multiply_inplace_python_scalar(dtype):
-    q = get_queue_or_skip()
-    skip_if_dtype_not_supported(dtype, q)
-    X = dpt.ones((10, 10), dtype=dtype, sycl_queue=q)
-    dt_kind = X.dtype.kind
-    if dt_kind in "ui":
-        X *= int(1)
-    elif dt_kind == "f":
-        X *= float(1)
-    elif dt_kind == "c":
-        X *= complex(1)
-    elif dt_kind == "b":
-        X *= bool(1)
-
-
 @pytest.mark.parametrize("op1_dtype", _all_dtypes)
 @pytest.mark.parametrize("op2_dtype", _all_dtypes)
 def test_multiply_inplace_dtype_matrix(op1_dtype, op2_dtype):