[MMS] Fix mms (#25267)

* [MMS] Fix mms

* [MMS] Fix mms

* fix mms loading

* Apply suggestions from code review

* make style

* Update tests/models/wav2vec2/test_modeling_wav2vec2.py

src/transformers/modeling_utils.py

@@ -3047,6 +3047,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 offload_state_dict = True
 
         is_sharded_safetensors = is_safetensors and sharded_metadata is not None
+
+        # tie the model weights before retrieving the state_dict
+        model.tie_weights()
+
         # Retrieve missing & unexpected_keys
         model_state_dict = model.state_dict()
         expected_keys = list(model_state_dict.keys())
@@ -3092,7 +3096,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             model_buffers = {".".join([prefix, key]) for key in model_buffers}
         unexpected_keys = list(unexpected_keys - model_buffers)
 
-        model.tie_weights()
         if device_map is None:
             ptrs = collections.defaultdict(list)
             for name, tensor in model.state_dict().items():

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -1151,6 +1151,43 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
 
+    # test that loading adapter weights with mismatched vocab sizes can be loaded
+    def test_load_target_lang_with_mismatched_size(self):
+        processor = Wav2Vec2Processor.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
+        )
+
+        def get_logits(model, input_features):
+            model = model.to(torch_device)
+            batch = processor(
+                input_features,
+                padding=True,
+                sampling_rate=processor.feature_extractor.sampling_rate,
+                return_tensors="pt",
+            )
+
+            with torch.no_grad():
+                logits = model(
+                    input_values=batch["input_values"].to(torch_device),
+                    attention_mask=batch["attention_mask"].to(torch_device),
+                ).logits
+            return logits
+
+        input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
+
+        model = Wav2Vec2ForCTC.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2-adapter", target_lang="fr", ignore_mismatched_sizes=True
+        )
+
+        logits = get_logits(model, input_features)
+
+        model_2 = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
+        model_2.load_adapter("fr")
+
+        logits_2 = get_logits(model_2, input_features)
+
+        self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
+
     def test_load_attn_adapter(self):
         processor = Wav2Vec2Processor.from_pretrained(
             "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True