Repository: huggingface/transformers
Pull request: Fixed loading LongT5 from legacy checkpoints

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Fixes #40635

The problem with loading legacy LongT5 checkpoints stems from the way the shared embedding tokens are defined in the model: 
https://github.com/huggingface/transformers/blob/96a5774f2e048c21b9c0408c4fddfcf23a8934c5/src/transformers/models/longt5/modeling_longt5.py#L1350-L1356
In most legacy checkpoints they are not tied via the tied keys, but were still trained from the same shared weight that is created here:
https://github.com/huggingface/transformers/blob/96a5774f2e048c21b9c0408c4fddfcf23a8934c5/src/transformers/models/longt5/modeling_longt5.py#L1901

The solution to this problem is to manually assign the weights in overriden `from_pretrained` if the model contains the shared weights.
This fix is somewhat dirty, it would be much better to just update them directly in the state_dict to avoid the missing keys warning, but there is currently no public interface of the `PretrainedModel` class that allows for that.

If the reviewer has a better idea of how this could be accounted for I'm ready to devote my time towards fixing this issue.

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Changed files:
filename: src/transformers/models/longt5/modeling_longt5.py
patch: @@ -1267,6 +1267,36 @@ def dummy_inputs(self):
         }
         return dummy_inputs
 
+    def _try_load_missing_tied_module(self, key):
+        module = self
+        if key.endswith(".weight"):
+            key = key[: -len(".weight")]
+        for sub_key in key.split("."):
+            if not hasattr(module, sub_key):
+                return
+            module = getattr(module, sub_key)
+
+        self._tie_or_clone_weights(module, self.shared)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requested_loading_info = kwargs.get("output_loading_info", False)
+        kwargs["output_loading_info"] = True
+        model, loading_info = super().from_pretrained(*args, **kwargs)
+        missing_keys = loading_info.get("missing_keys", [])
+
+        if hasattr(model, "shared") and hasattr(model, "_tied_weights_keys"):
+            for missing_key in missing_keys:
+                logger.warning(
+                    f"Recovering a missing tied weight {missing_key} from a legacy LongT5 checkpoint. "
+                    f"Consider saving {missing_key} in your checkpoint or updating the config (tie_word_embeddings=true)."
+                )
+                model._try_load_missing_tied_module(missing_key)
+
+        if requested_loading_info:
+            return model, loading_info
+        return model
+
     def _init_weights(self, module):
         """Initialize the weights"""
         factor = self.config.initializer_factor  # Used for testing weights initialization