[multiple models] skip saving/loading deterministic state_dict keys (#7878)

* make the save_load special key tests common

* handle mbart

* cleaner solution

* fix

* move test_save_load_missing_keys back into fstm for now

* restore

* style

* add marian

* add pegasus

* blenderbot

* revert - no static embed

tests/test_modeling_fsmt.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import tempfile
 import unittest
 
@@ -21,7 +20,7 @@ import timeout_decorator  # noqa
 
 from parameterized import parameterized
 from transformers import is_torch_available
-from transformers.file_utils import WEIGHTS_NAME, cached_property
+from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -203,8 +202,9 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
         )[0]
         _assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
 
-    def test_save_load_strict(self):
+    def test_save_load_missing_keys(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
+
         for model_class in self.all_model_classes:
             model = model_class(config)
 
@@ -213,27 +213,6 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
                 model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
             self.assertEqual(info["missing_keys"], [])
 
-    def test_save_load_no_save_keys(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
-        for model_class in self.all_model_classes:
-            model = model_class(config)
-
-            state_dict_no_save_keys = getattr(model, "state_dict_no_save_keys", None)
-            if state_dict_no_save_keys is None:
-                continue
-
-            # check the keys are in the original state_dict
-            for k in state_dict_no_save_keys:
-                self.assertIn(k, model.state_dict())
-
-            # check that certain keys didn't get saved with the model
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
-                state_dict_saved = torch.load(output_model_file)
-                for k in state_dict_no_save_keys:
-                    self.assertNotIn(k, state_dict_saved)
-
     @unittest.skip("can't be implemented for FSMT due to dual vocab.")
     def test_resize_tokens_embeddings(self):
         pass