[FSMT] Fix non-shared weights (huggingface#26187)

* Fix non-shared weights

* Add tests

* Edit tied weights keys

src/transformers/models/fsmt/modeling_fsmt.py

@@ -1034,7 +1034,7 @@ def _get_shape(t):
     FSMT_START_DOCSTRING,
 )
 class FSMTModel(PretrainedFSMTModel):
-    _tied_weights_keys = ["decoder.embed_tokens.weight"]
+    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
 
     def __init__(self, config: FSMTConfig):
         super().__init__(config)
@@ -1055,6 +1055,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.decoder
 
+    def _tie_weights(self):
+        self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
+        self._tie_or_clone_weights(self.decoder.output_projection, self.get_input_embeddings())
+
     @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
@@ -1171,7 +1175,7 @@ def set_output_embeddings(self, value):
 )
 class FSMTForConditionalGeneration(PretrainedFSMTModel):
     base_model_prefix = "model"
-    _tied_weights_keys = ["model.decoder.embed_tokens.weight"]
+    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
 
     def __init__(self, config: FSMTConfig):
         super().__init__(config)

tests/models/fsmt/test_modeling_fsmt.py

@@ -271,6 +271,23 @@ def test_export_to_onnx(self):
                 input_names=["input_ids", "attention_mask"],
             )
 
+    def test_ensure_weights_are_shared(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
+        model = FSMTForConditionalGeneration(config)
+
+        # FSMT shares three weights.
+        # Not an issue to not have these correctly tied for torch.load, but it is an issue for safetensors.
+        self.assertEqual(
+            len(
+                {
+                    model.get_output_embeddings().weight.data_ptr(),
+                    model.get_input_embeddings().weight.data_ptr(),
+                    model.base_model.decoder.output_projection.weight.data_ptr(),
+                }
+            ),
+            1,
+        )
+
     @unittest.skip("can't be implemented for FSMT due to dual vocab.")
     def test_resize_tokens_embeddings(self):
         pass