Fix generation config for empty state dict (#21630)

src/transformers/modeling_utils.py

@@ -2648,7 +2648,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                     _from_pipeline=from_pipeline,
                     **kwargs,
                 )
-            except OSError:
+            except (OSError, TypeError):
                 logger.info(
                     "Generation config file not found, using a generation config created from the model config."
                 )

tests/test_modeling_common.py

@@ -325,6 +325,18 @@ class ModelTesterMixin:
             else:
                 check_save_load(first, second)
 
+    def test_from_pretrained_no_checkpoint(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            state_dict = model.state_dict()
+
+            new_model = model_class.from_pretrained(
+                pretrained_model_name_or_path=None, config=config, state_dict=state_dict
+            )
+            for p1, p2 in zip(model.parameters(), new_model.parameters()):
+                self.assertTrue(torch.equal(p1, p2))
+
     def test_save_load_keys_to_ignore_on_save(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -2776,15 +2788,6 @@ class ModelUtilsTest(TestCasePlus):
             BertModel.from_pretrained(TINY_T5)
         self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
 
-    def test_model_from_pretrained_no_checkpoint(self):
-        config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
-        model = BertModel(config)
-        state_dict = model.state_dict()
-
-        new_model = BertModel.from_pretrained(pretrained_model_name_or_path=None, config=config, state_dict=state_dict)
-        for p1, p2 in zip(model.parameters(), new_model.parameters()):
-            self.assertTrue(torch.equal(p1, p2))
-
     def test_model_from_config_torch_dtype(self):
         # test that the model can be instantiated with dtype of user's choice - as long as it's a
         # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the