Add exception to float->int conversion test case (#2105)

Downcasting float32 values beyond target type value range is an
undefined behaviour according to torch documentation.

---------

Co-authored-by: Piotr Papierkowski <ppapierkowski@habana.ai>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Tadeusz Krawiec <72737249+tadkrawiec@users.noreply.github.com>
Co-authored-by: mengfei25 <mengfei.li@Intel.com>

Author: 5 people

test/xpu/skip_list_common.py

@@ -658,11 +658,7 @@
     "nn/test_pooling_xpu.py": None,
     "nn/test_dropout_xpu.py": None,
     "test_dataloader_xpu.py": None,
-    "test_tensor_creation_ops_xpu.py": (
-        # CPU only (vs Numpy). CUDA skips these cases since non-deterministic results are outputed for inf and nan.
-        "test_float_to_int_conversion_finite_xpu_int8",
-        "test_float_to_int_conversion_finite_xpu_int16",
-    ),
+    "test_tensor_creation_ops_xpu.py": None,
     "test_autocast_xpu.py": None,
     "test_autograd_xpu.py": (
         # AttributeError: module 'torch.xpu' has no attribute

test/xpu/test_tensor_creation_ops_xpu.py

@@ -1226,8 +1226,10 @@ def test_float_to_int_conversion_finite(self, device, dtype):
         vals = (min, -2, -1.5, -0.5, 0, 0.5, 1.5, 2, max)
         refs = None
         if self.device_type == "cuda" or self.device_type == "xpu":
-            if torch.version.hip:
+            if torch.version.hip or torch.version.xpu:
                 # HIP min float -> int64 conversion is divergent
+                # XPU min float -> int8 conversion is divergent
+                # XPU min float -> int16 conversion is divergent
                 vals = (-2, -1.5, -0.5, 0, 0.5, 1.5, 2)
             else:
                 vals = (min, -2, -1.5, -0.5, 0, 0.5, 1.5, 2)