Fix test warnings in dpnp.tensor tests

dpnp/tensor/_elementwise_funcs.py

@@ -33,6 +33,7 @@
     _acceptance_fn_divide,
     _acceptance_fn_negative,
     _acceptance_fn_reciprocal,
+    _acceptance_fn_round,
     _acceptance_fn_subtract,
     _resolve_weak_types_all_py_ints,
 )
@@ -1723,7 +1724,11 @@
 """
 
 round = UnaryElementwiseFunc(
-    "round", ti._round_result_type, ti._round, _round_docstring
+    "round",
+    ti._round_result_type,
+    ti._round,
+    _round_docstring,
+    acceptance_fn=_acceptance_fn_round,
 )
 del _round_docstring
 

dpnp/tensor/_type_utils.py

@@ -133,6 +133,13 @@ def _acceptance_fn_reciprocal(arg_dtype, buf_dt, res_dt, sycl_dev):
         return True
 
 
+def _acceptance_fn_round(arg_dtype, buf_dt, res_dt, sycl_dev):
+    # for boolean input, prefer floating-point output over integral
+    if arg_dtype.kind == "b" and res_dt.kind != "f":
+        return False
+    return True
+
+
 def _acceptance_fn_subtract(
     arg1_dtype, arg2_dtype, buf1_dt, buf2_dt, res_dt, sycl_dev
 ):
@@ -970,6 +977,7 @@ def _default_accumulation_dtype_fp_types(inp_dt, q):
     "_find_buf_dtype2",
     "_to_device_supported_dtype",
     "_acceptance_fn_default_unary",
+    "_acceptance_fn_round",
     "_acceptance_fn_reciprocal",
     "_acceptance_fn_default_binary",
     "_acceptance_fn_divide",

dpnp/tensor/libtensor/include/kernels/elementwise_functions/round.hpp

@@ -116,7 +116,6 @@ template <typename T>
 struct RoundOutputType
 {
     using value_type = typename std::disjunction<
-        td_ns::TypeMapResultEntry<T, bool, sycl::half>,
         td_ns::TypeMapResultEntry<T, std::uint8_t>,
         td_ns::TypeMapResultEntry<T, std::uint16_t>,
         td_ns::TypeMapResultEntry<T, std::uint32_t>,

dpnp/tests/tensor/elementwise/test_exp.py

@@ -78,6 +78,7 @@ def test_exp_real_contig(dtype):
     assert_allclose(dpt.asnumpy(Z), np.repeat(Ynp, n_rep), atol=tol, rtol=tol)
 
 
+@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
 @pytest.mark.parametrize("dtype", ["c8", "c16"])
 def test_exp_complex_contig(dtype):
     q = get_queue_or_skip()

dpnp/tests/tensor/test_usm_ndarray_dlpack.py

@@ -783,7 +783,9 @@ def test_copy_via_host_gh_1789():
     get_queue_or_skip()
     x_np = np.ones((10, 10), dtype="i4")
     # strides are no longer multiple of itemsize
-    x_np.strides = (x_np.strides[0] - 1, x_np.strides[1])
+    x_np = np.lib.stride_tricks.as_strided(
+        x_np, shape=x_np.shape, strides=(x_np.strides[0] - 1, x_np.strides[1])
+    )
     with pytest.raises(BufferError):
         dpt.from_dlpack(x_np)
     with pytest.raises(BufferError):