Extend a number of supported types to all numeric dtypes in umath tests

dpnp/tests/helper.py

@@ -346,6 +346,14 @@ def is_cuda_device(device=None):
     return dev.backend == dpctl.backend_type.cuda
 
 
+def is_gpu_device(device=None):
+    """
+    Return True if a test is running on GPU device, False otherwise.
+    """
+    dev = dpctl.select_default_device() if device is None else device
+    return dev.has_aspect_gpu
+
+
 def is_win_platform():
     """
     Return True if a test is running on Windows OS, False otherwise.

dpnp/tests/skipped_tests.tbl

@@ -10,10 +10,3 @@ tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.
 tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)]
 
 tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpctl_memory
-
-tests/test_umath.py::test_umaths[('divmod', 'ii')]
-tests/test_umath.py::test_umaths[('divmod', 'll')]
-tests/test_umath.py::test_umaths[('divmod', 'ff')]
-tests/test_umath.py::test_umaths[('divmod', 'dd')]
-tests/test_umath.py::test_umaths[('frexp', 'f')]
-tests/test_umath.py::test_umaths[('frexp', 'd')]

dpnp/tests/skipped_tests_cuda.tbl

@@ -4,8 +4,6 @@
 tests/test_arithmetic.py::TestArithmetic::test_modf_part1
 tests/test_arithmetic.py::TestArithmetic::test_modf_part2
 tests/test_sycl_queue.py::test_modf[cuda:gpu:0]
-tests/test_umath.py::test_umaths[('modf', 'f')]
-tests/test_umath.py::test_umaths[('modf', 'd')]
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticModf::test_modf
 
 # random

dpnp/tests/skipped_tests_gpu.tbl

@@ -17,12 +17,4 @@ tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: (dpnp
 tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]]
 tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)]
 
-tests/test_umath.py::test_umaths[('divmod', 'ii')]
-tests/test_umath.py::test_umaths[('divmod', 'll')]
-tests/test_umath.py::test_umaths[('divmod', 'ff')]
-tests/test_umath.py::test_umaths[('divmod', 'dd')]
-tests/test_umath.py::test_umaths[('floor_divide', 'ff')]
-tests/test_umath.py::test_umaths[('frexp', 'f')]
-tests/test_umath.py::test_umaths[('frexp', 'd')]
-
 tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpctl_memory

dpnp/tests/test_random_state.py

@@ -15,7 +15,12 @@
 from dpnp.dpnp_array import dpnp_array
 from dpnp.random import RandomState
 
-from .helper import assert_dtype_allclose, get_array, is_cpu_device
+from .helper import (
+    assert_dtype_allclose,
+    get_array,
+    is_cpu_device,
+    is_gpu_device,
+)
 
 # aspects of default device:
 _def_device = dpctl.SyclQueue().sycl_device
@@ -688,7 +693,7 @@ def test_scalar(self, func):
         ],
     )
     def test_array_range(self, seed):
-        if not is_cpu_device():
+        if is_gpu_device():
             pytest.skip("seed as a scalar is only supported on GPU")
 
         size = 15

dpnp/tests/test_umath.py

@@ -9,6 +9,7 @@
 )
 
 import dpnp
+import dpnp.backend.extensions.vm._vm_impl as vmi
 from dpnp.dpnp_utils import map_dtype_to_device
 
 from .helper import (
@@ -20,21 +21,19 @@
     get_float_dtypes,
     has_support_aspect16,
     has_support_aspect64,
+    is_cuda_device,
+    is_gpu_device,
 )
 
 # full list of umaths
 umaths = [i for i in dir(numpy) if isinstance(getattr(numpy, i), numpy.ufunc)]
 
-types = {
-    "d": numpy.float64,
-    "f": numpy.float32,
-    "l": numpy.int64,
-    "i": numpy.int32,
-}
-
-supported_types = "fli"
+supported_types = "?bBhHiIlLkK"
+if has_support_aspect16():
+    supported_types += "e"
+supported_types += "fF"
 if has_support_aspect64():
-    supported_types += "d"
+    supported_types += "dD"
 
 
 def check_types(args_str):
@@ -55,7 +54,7 @@ def shaped_arange(shape, xp=numpy, dtype=numpy.float32):
 def get_args(args_str, sh, xp=numpy):
     args = []
     for s in args_str:
-        args.append(shaped_arange(shape=sh, xp=xp, dtype=types[s]))
+        args.append(shaped_arange(shape=sh, xp=xp, dtype=numpy.dtype(s)))
     return tuple(args)
 
 
@@ -75,6 +74,7 @@ def get_id(val):
     return val.__str__()
 
 
+@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
 @pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings")
 @pytest.mark.parametrize("test_cases", test_cases, ids=get_id)
 def test_umaths(test_cases):
@@ -91,7 +91,7 @@ def test_umaths(test_cases):
     iargs = get_args(args_str, sh, xp=dpnp)
 
     if umath == "reciprocal":
-        if args[0].dtype in [numpy.int32, numpy.int64]:
+        if numpy.issubdtype(args[0].dtype, numpy.integer):
             pytest.skip(
                 "For integer input array, numpy.reciprocal returns zero."
             )
@@ -102,11 +102,32 @@ def test_umaths(test_cases):
             and numpy.dtype("l") != numpy.int64
         ):
             pytest.skip("numpy.ldexp doesn't have a loop for the input types")
+    elif (
+        umath == "floor_divide"
+        and args[0].dtype in [dpnp.float16, dpnp.float32]
+        and is_gpu_device()
+    ):
+        pytest.skip("dpctl-1652")
+    elif umath in ["ceil", "floor", "trunc"] and args[0].dtype == dpnp.bool:
+        pytest.skip("dpctl-2030")
+    elif (
+        umath == "tan"
+        and dpnp.issubdtype(args[0].dtype, dpnp.complexfloating)
+        and not (vmi._is_available() and has_support_aspect64())
+    ):
+        pytest.skip("dpctl-2031")
+    elif umath in ["divmod", "frexp"]:
+        pytest.skip("Not implemented umath")
+    elif umath == "modf":
+        if args[0].dtype == dpnp.float16:
+            pytest.skip("dpnp.modf is not supported with dpnp.float16")
+        elif is_cuda_device():
+            pytest.skip("dpnp.modf is not supported on CUDA device")
 
     expected = getattr(numpy, umath)(*args)
     result = getattr(dpnp, umath)(*iargs)
-
-    assert_allclose(result, expected, rtol=1e-6)
+    for x, y in zip(result, expected):
+        assert_dtype_allclose(x, y)
 
 
 class TestArctan2: