Make (TF) CI faster (test only a subset of model classes) (#24592)

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/test_modeling_tf_common.py

@@ -341,7 +341,7 @@ class TFModelTesterMixin:
 
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             model = model_class(config)
             model.build()
 
@@ -689,7 +689,7 @@ class TFModelTesterMixin:
     def test_compile_tf_model(self):
         config, _ = self.model_tester.prepare_config_and_inputs_for_common()
 
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             # Prepare our model
             model = model_class(config)
             # These are maximally general inputs for the model, with multiple None dimensions

tests/utils/test_modeling_tf_core.py

@@ -111,7 +111,7 @@ class TFCoreModelTesterMixin:
     @slow
     def test_graph_mode(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             inputs = self._prepare_for_class(inputs_dict, model_class)
             model = model_class(config)
 
@@ -125,7 +125,7 @@ class TFCoreModelTesterMixin:
     @slow
     def test_xla_mode(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             inputs = self._prepare_for_class(inputs_dict, model_class)
             model = model_class(config)
 
@@ -140,7 +140,7 @@ class TFCoreModelTesterMixin:
     def test_xla_fit(self):
         # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             model = model_class(config)
             if getattr(model, "hf_compute_loss", None):
                 # Test that model correctly compute the loss with kwargs
@@ -214,7 +214,7 @@ class TFCoreModelTesterMixin:
         encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
         encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
 
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
             model = model_class(config)
             model.build()
@@ -269,7 +269,7 @@ class TFCoreModelTesterMixin:
         # try/finally block to ensure subsequent tests run in float32
         try:
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            for model_class in self.all_model_classes:
+            for model_class in self.all_model_classes[:2]:
                 class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                 model = model_class(config)
                 outputs = model(class_inputs_dict)
@@ -352,7 +352,7 @@ class TFCoreModelTesterMixin:
     def test_graph_mode_with_inputs_embeds(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
-        for model_class in self.all_model_classes:
+        for model_class in self.all_model_classes[:2]:
             model = model_class(config)
 
             inputs = copy.deepcopy(inputs_dict)