Revert "Fix gradient checkpointing + fp16 autocast for most models" (#24420)

Revert "Fix gradient checkpointing + fp16 autocast for most models (#24247)"

This reverts commit 285a48011d.

tests/test_modeling_common.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import collections
 import copy
 import gc
@@ -548,41 +549,6 @@ class ModelTesterMixin:
             loss = model(**inputs).loss
             loss.backward()
 
-    @slow
-    @require_torch_gpu
-    def test_training_gradient_checkpointing_autocast(self):
-        if not self.model_tester.is_training:
-            return
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            config.use_cache = False
-            config.return_dict = True
-
-            if (
-                model_class.__name__
-                in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)]
-                or not model_class.supports_gradient_checkpointing
-            ):
-                continue
-            model = model_class(config)
-            model.to(torch_device)
-
-            optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
-
-            model.gradient_checkpointing_enable()
-            model.train()
-            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
-            with torch.cuda.amp.autocast(True, dtype=torch.float16):
-                output = model(**inputs)[0]
-                loss = output.mean()
-
-                loss.backward()
-                optimizer.step()
-
-                for n, param in model.named_parameters():
-                    self.assertTrue(param.grad is not None, f"None gradient in param {n}")
-
     def test_attention_outputs(self):
         if not self.has_attentions:
             self.skipTest(reason="Model does not output attentions")