Automatically create/update tiny models (#22275)
* Automatically create or update tiny models * Skip failed tests * update workflow file * use revision --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
@@ -402,6 +402,15 @@ class AltCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
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test_resize_embeddings = False
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test_attention_outputs = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "FeatureExtractionPipelineTests":
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return True
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return False
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def setUp(self):
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self.model_tester = AltCLIPModelTester(self)
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@@ -165,6 +165,15 @@ class ASTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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test_resize_embeddings = False
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test_head_masking = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "AudioClassificationPipelineTests":
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return True
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return False
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def setUp(self):
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self.model_tester = ASTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ASTConfig, has_text_modality=False, hidden_size=37)
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@@ -237,6 +237,15 @@ class BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, Pipeline
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test_pruning = False
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test_missing_keys = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "TextGenerationPipelineTests":
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return True
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return False
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def setUp(self):
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self.model_tester = BlenderbotSmallModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
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@@ -183,6 +183,15 @@ class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
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test_head_masking = False
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test_missing_keys = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "ObjectDetectionPipelineTests":
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return True
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return False
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# special case for head models
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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@@ -250,6 +250,15 @@ class ErnieMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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)
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test_torchscript = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "QAPipelineTests":
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return True
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return False
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def setUp(self):
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self.model_tester = ErnieMModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ErnieMConfig, hidden_size=37)
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@@ -231,6 +231,15 @@ class OneFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
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test_head_masking = False
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test_missing_keys = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "FeatureExtractionPipelineTests":
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return True
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return False
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def setUp(self):
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self.model_tester = OneFormerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=OneFormerConfig, has_text_modality=False)
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@@ -224,6 +224,15 @@ class SplinterModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
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else {}
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)
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "QAPipelineTests":
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return True
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return False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if return_labels:
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