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Skip tests properly (#31308)

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* Skip tests properly

* [test_all]

* Add 'reason' as kwarg for skipTest

* [test_all] Fix up

* [test_all]
This commit is contained in:
amyeroberts
2024-06-26 21:59:08 +01:00
committed by GitHub
parent 1f9f57ab4c
commit 1de7dc7403
254 changed files with 1721 additions and 1298 deletions
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14
tests/models/fsmt/test_modeling_fsmt.py
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@@ -263,7 +263,7 @@ class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
@unittest.skip("Test has a segmentation fault on torch 1.8.0")
@unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
def test_export_to_onnx(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
model = FSMTModel(config).to(torch_device)
@@ -312,23 +312,23 @@ class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
2,
)
@unittest.skip("can't be implemented for FSMT due to dual vocab.")
@unittest.skip(reason="can't be implemented for FSMT due to dual vocab.")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip("Passing inputs_embeds not implemented for FSMT.")
@unittest.skip(reason="Passing inputs_embeds not implemented for FSMT.")
def test_inputs_embeds(self):
pass
@unittest.skip("Input ids is required for FSMT.")
@unittest.skip(reason="Input ids is required for FSMT.")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip("model weights aren't tied in FSMT.")
@unittest.skip(reason="model weights aren't tied in FSMT.")
def test_tie_model_weights(self):
pass
@unittest.skip("TODO: Decoder embeddings cannot be resized at the moment")
@unittest.skip(reason="TODO: Decoder embeddings cannot be resized at the moment")
def test_resize_embeddings_untied(self):
pass
@@ -582,7 +582,7 @@ class TestSinusoidalPositionalEmbeddings(unittest.TestCase):
# odd num_embeddings is allowed
SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device)
@unittest.skip("different from marian (needs more research)")
@unittest.skip(reason="different from marian (needs more research)")
def test_positional_emb_weights_against_marian(self):
desired_weights = torch.tensor(
[