Fix skip of test_training_gradient_checkpointing

tests/test_modeling_common.py

@@ -849,29 +849,29 @@ def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=No
                 ):
                     self.skipTest(reason=f"`supports_gradient_checkpointing` is False for {model_class.__name__}.")
 
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            config.use_cache = False
-            config.return_dict = True
-            model = model_class(config)
+                config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+                config.use_cache = False
+                config.return_dict = True
+                model = model_class(config)
 
-            model.to(torch_device)
-            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
-            model.train()
+                model.to(torch_device)
+                model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
+                model.train()
 
-            # unfreeze additional layers
-            for p in model.parameters():
-                p.requires_grad_(True)
+                # unfreeze additional layers
+                for p in model.parameters():
+                    p.requires_grad_(True)
 
-            optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+                optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
-            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
-            loss = model(**inputs).loss
-            loss.backward()
-            optimizer.step()
+                inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+                loss = model(**inputs).loss
+                loss.backward()
+                optimizer.step()
 
-            for k, v in model.named_parameters():
-                if v.requires_grad:
-                    self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!")
+                for k, v in model.named_parameters():
+                    if v.requires_grad:
+                        self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!")
 
     def test_training(self):
         if not self.model_tester.is_training: