mark expected failure test cases (#102)

Author: pinzhenx

tests/cpu/common_nn.py

@@ -63,7 +63,6 @@
 from torch.nn.functional import _Reduction
 from common_utils import TestCase, to_gpu, freeze_rng_state, is_iterable, \
     TEST_WITH_ROCM
-from common_cuda import TEST_CUDA
 from torch.autograd.gradcheck import get_numerical_jacobian, iter_tensors
 from torch.autograd import Variable
 import torch.backends.cudnn

tests/cpu/test_bf16_lazy_reorder.py

@@ -80,7 +80,6 @@ def test_batch_norm2d(self):
             self.assertEqual(res_bf16.dtype, torch.bfloat16)
 
             with AutoMixPrecision(True):
-                ipex.core.enable_mix_bf16_fp32()
                 self.assertEqual(x_auto_mix.dtype, torch.float)
                 self.assertFalse(ipex.core.is_bf16_dil_tensor(x_auto_mix))
                 res_auto_mix = bn_auto_mix(x_auto_mix)
@@ -108,7 +107,6 @@ def test_batch_norm3d(self):
             self.assertEqual(x_man_bf16.dtype, torch.bfloat16)
 
             with AutoMixPrecision(True):
-                ipex.core.enable_mix_bf16_fp32()
                 self.assertEqual(x_auto_mix.dtype, torch.float)
                 self.assertFalse(ipex.core.is_bf16_dil_tensor(x_auto_mix))
                 res_auto_mix = bn_auto_mix(x_auto_mix)

tests/cpu/test_rn50_cpu_ops.py

@@ -827,7 +827,7 @@ def test_avg_pool3d_with_zero_divisor(self):
         self.assertRaisesRegex(RuntimeError, "divisor must be not zero",
                                lambda: torch.nn.functional.avg_pool3d(torch.zeros(3, 3, 3, 3), (2, 2, 2), divisor_override=0))
 
-    @unittest.skip("oneDNN does not support this case")
+    @unittest.expectedFailure
     def test_max_pool_nan(self):
         for adaptive in ['', 'adaptive_']:
             for num_dim in [1, 2, 3]:

tests/cpu/test_torch.py

@@ -1581,6 +1581,7 @@ def _test_multinomial_invalid_probs(probs):
         except RuntimeError as e:
             return 'invalid multinomial distribution' in str(e)
 
+    @slowTest
     @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \
                      don't support multiprocessing with spawn start method")
     @unittest.skipIf(IS_WINDOWS, 'FIXME: CUDA OOM error on Windows')
@@ -10265,7 +10266,7 @@ def test_unfold_scalars(self, device):
         self.assertEqual(torch.empty(0, device=device), x.unfold(0, 0, 2))
         self.assertEqual(torch.tensor([0.5], device=device), x.unfold(0, 1, 1))
 
-    @unittest.skipIf(SKIP_TEST_CASE_FOR_DPCPP_STORAGE, "IPEX does not support copy")
+    @unittest.expectedFailure
     def test_copy_all_dtypes_and_devices(self, device):
         from copy import copy
         ipex.get_auto_optimization()
@@ -12835,7 +12836,7 @@ def transformation_fn(tensor, **kwargs):
             self._test_memory_format_transformations(
                 device, get_generator(mf, shape), transformation_fn, mf, default_is_preserve=True)
 
-    @unittest.skipIf(SKIP_TEST_CASE_FOR_DPCPP_STORAGE, "IPEX feature limitation")
+    @unittest.expectedFailure
     def test_memory_format_clone(self, device):
         def get_generator(memory_format, shape):
             def input_generator_fn(device):