don't initialize the output embeddings if we're going to tie them to input embeddings (#28192)

* test that tied output embeddings aren't initialized on load

* don't initialize the output embeddings if we're going to tie them to the input embeddings

tests/test_modeling_common.py

@@ -483,6 +483,40 @@ class ModelTesterMixin:
                 max_diff = torch.max(torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]))
                 self.assertLessEqual(max_diff.item(), 1e-3, msg=f"{key} not identical")
 
+    def test_fast_init_tied_embeddings(self):
+        class MyClass(PreTrainedModel):
+            config_class = PretrainedConfig
+            _tied_weights_keys = ["output_embeddings.weight"]
+
+            def __init__(self, config=None):
+                super().__init__(config if config is not None else PretrainedConfig())
+                self.input_embeddings = nn.Embedding(10, 10)
+                self.output_embeddings = nn.Linear(10, 10, bias=False)
+                self.tie_weights()
+
+            def get_output_embeddings(self):
+                return self.output_embeddings
+
+            def set_output_embeddings(self, output_embeddings):
+                self.output_embeddings = output_embeddings
+
+            def get_input_embeddings(self):
+                return self.input_embeddings
+
+            def set_input_embeddings(self, input_embeddings):
+                self.input_embeddings = input_embeddings
+
+            def _init_weights(self, module):
+                if module is self.output_embeddings:
+                    raise ValueError("unnecessarily initialized tied output embedding!")
+
+        model = MyClass()
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            # throws if it initializes the tied output_embeddings
+            MyClass.from_pretrained(tmpdirname)
+
     def test_save_load_fast_init_to_base(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         if config.__class__ not in MODEL_MAPPING: