Skipping more high mem tests - Wav2Vec2 Hubert (#21647)
Skipping more tests
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@@ -321,19 +321,15 @@ class TFHubertModelTest(TFModelTesterMixin, unittest.TestCase):
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model = TFHubertModel.from_pretrained("facebook/hubert-base-ls960")
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model = TFHubertModel.from_pretrained("facebook/hubert-base-ls960")
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self.assertIsNotNone(model)
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self.assertIsNotNone(model)
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# We override here as passing a full batch of 13 samples results in OOM errors for CTC
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@unittest.skip(reason="Fix me! Hubert hits OOM errors when loss is computed on full batch")
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def test_dataset_conversion(self):
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def test_dataset_conversion(self):
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default_batch_size = self.model_tester.batch_size
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# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
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self.model_tester.batch_size = 2
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pass
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super().test_dataset_conversion()
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self.model_tester.batch_size = default_batch_size
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# We override here as passing a full batch of 13 samples results in OOM errors for CTC
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@unittest.skip(reason="Fix me! Hubert hits OOM errors when loss is computed on full batch")
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def test_keras_fit(self):
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def test_keras_fit(self):
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default_batch_size = self.model_tester.batch_size
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# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
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self.model_tester.batch_size = 2
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pass
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super().test_keras_fit()
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self.model_tester.batch_size = default_batch_size
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@require_tf
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@require_tf
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@@ -512,20 +512,15 @@ class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
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model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsNotNone(model)
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self.assertIsNotNone(model)
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# We override here as passing a full batch of 13 samples results in OOM errors for CTC
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@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
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@unittest.skip("Fix me!")
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def test_dataset_conversion(self):
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def test_dataset_conversion(self):
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default_batch_size = self.model_tester.batch_size
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# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
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self.model_tester.batch_size = 2
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pass
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super().test_dataset_conversion()
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self.model_tester.batch_size = default_batch_size
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# We override here as passing a full batch of 13 samples results in OOM errors for CTC
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@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
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def test_keras_fit(self):
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def test_keras_fit(self):
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default_batch_size = self.model_tester.batch_size
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# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
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self.model_tester.batch_size = 2
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pass
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super().test_keras_fit()
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self.model_tester.batch_size = default_batch_size
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@require_tf
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@require_tf
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