commit 43527d41a255322003fd806eaefce56f0de7bfb0
author PyTorch MergeBot <pytorchmergebot@users.noreply.github.com> Thu Sep 07 00:56:45 2023 +0000
committer PyTorch MergeBot <pytorchmergebot@users.noreply.github.com> Thu Sep 07 00:56:46 2023 +0000
tree f4d3671342811c386d983e4c8816efb48d2e3f36
parent 27fe45eaf60548f4919b398f374b031185fb19f0

Revert "Remove fixed skips (#108674)"

This reverts commit 518cfda2dd0e940603c74717b4cb33493a9ec908.

Reverted https://github.com/pytorch/pytorch/pull/108674 on behalf of https://github.com/huydhn due to Sorry for reverting this, but one test is failing on inductor https://hud.pytorch.org/pytorch/pytorch/commit/518cfda2dd0e940603c74717b4cb33493a9ec908, and it seems easier to revert this than disabling the test ([comment](https://github.com/pytorch/pytorch/pull/108674#issuecomment-1709310192))

test/test_ops.py

diff --git a/test/test_ops.py b/test/test_ops.py
index 1680615..8830a52 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -648,6 +648,7 @@
     # Cases test here:
     #   - out= with the correct dtype and device, but the wrong shape
     @ops(_ops_and_refs, dtypes=OpDTypes.none)
+    @skipIfTorchInductor("Inductor does not support complex dtype yet")
     def test_out_warning(self, device, op):
         # Prefers running in float32 but has a fallback for the first listed supported dtype
         supported_dtypes = op.supported_dtypes(self.device_type)
@@ -776,6 +777,7 @@
     #   - if device, dtype are NOT passed, any combination of dtype/device should be OK for out
     #   - if device, dtype are passed, device and dtype should match
     @ops(_ops_and_refs, dtypes=OpDTypes.any_one)
+    @skipIfTorchInductor("Inductor does not support complex dtype yet")
     def test_out(self, device, dtype, op):
         # Prefers running in float32 but has a fallback for the first listed supported dtype
         samples = op.sample_inputs(device, dtype)
@@ -995,6 +997,7 @@
     #   same values for the cross-product of op variants (method, inplace)
     #   against eager's gold standard op function variant
     @_variant_ops(op_db)
+    @skipIfTorchInductor("Inductor does not support complex dtype yet")
     def test_variant_consistency_eager(self, device, dtype, op):
         # Acquires variants (method variant, inplace variant, operator variant, inplace_operator variant, aliases)
 
@@ -1176,6 +1179,7 @@
     # Reference testing for operations in complex32 against complex64.
     # NOTE: We test against complex64 as NumPy doesn't have a complex32 equivalent dtype.
     @ops(op_db, allowed_dtypes=(torch.complex32,))
+    @skipIfTorchInductor("Inductor does not support complex dtype yet")
     def test_complex_half_reference_testing(self, device, dtype, op):
         if not op.supports_dtype(torch.complex32, device):
             unittest.skip("Does not support complex32")
@@ -1206,6 +1210,7 @@
 
     @ops(op_db, allowed_dtypes=(torch.bool,))
     @unittest.skipIf(TEST_WITH_UBSAN, "Test uses undefined behavior")
+    @skipIfTorchInductor("Inductor does not support view with dtype yet")
     def test_non_standard_bool_values(self, device, dtype, op):
         # Test boolean values other than 0x00 and 0x01 (gh-54789)
         def convert_boolean_tensors(x):

test/test_torch.py

diff --git a/test/test_torch.py b/test/test_torch.py
index c8d18c7..ffcf3bf 100644
--- a/test/test_torch.py
+++ b/test/test_torch.py
@@ -8383,6 +8383,7 @@
                     self.assertIs(torch.int32, b.to(dtype=torch.int32).dtype)
                     self.assertEqual(b.device, b.to(dtype=torch.int32).device)
 
+    @skipIfTorchInductor("FIXME")
     def test_to(self):
         self._test_to_with_layout(torch.strided)
         is_cuda10_2_or_higher = (