Skip to content

mark expected failure test cases #102

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jul 1, 2020
Merged

mark expected failure test cases #102

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion tests/cpu/common_nn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down
2 changes: 0 additions & 2 deletions tests/cpu/test_bf16_lazy_reorder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down
2 changes: 1 addition & 1 deletion tests/cpu/test_rn50_cpu_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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]:
Expand Down
5 changes: 3 additions & 2 deletions tests/cpu/test_torch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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')
Expand Down Expand Up @@ -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()
Expand Down Expand Up @@ -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):
Expand Down