Fix DETA save_pretrained

src/transformers/models/deta/modeling_deta.py

@@ -1888,7 +1888,7 @@ def forward(
 )
 class DetaForObjectDetection(DetaPreTrainedModel):
     # When using clones, all layers > 0 will be clones, but layer 0 *is* required
-    _tied_weights_keys = [r"bbox_embed\.\d+"]
+    _tied_weights_keys = [r"bbox_embed\.\d+", r"class_embed\.\d+"]
     # We can't initialize the model on meta device as some weights are modified during the initialization
     _no_split_modules = None
 

tests/models/deta/test_modeling_deta.py

@@ -15,8 +15,10 @@
 """ Testing suite for the PyTorch DETA model. """
 
 
+import collections
 import inspect
 import math
+import re
 import unittest
 
 from transformers import DetaConfig, ResNetConfig, is_torch_available, is_torchvision_available, is_vision_available
@@ -32,6 +34,8 @@
 if is_torch_available():
     import torch
 
+    from transformers.pytorch_utils import id_tensor_storage
+
 if is_torchvision_available():
     from transformers import DetaForObjectDetection, DetaModel
 
@@ -520,6 +524,43 @@ def test_initialization(self):
                         msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                     )
 
+    # Inspired by tests.test_modeling_common.ModelTesterMixin.test_tied_weights_keys
+    def test_tied_weights_keys(self):
+        for model_class in self.all_model_classes:
+            # We need to pass model class name to correctly initialize the config.
+            # If we don't pass it, the config for `DetaForObjectDetection`` will be initialized
+            # with `two_stage=False` and the test will fail because for that case `class_embed`
+            # weights are not tied.
+            config, _ = self.model_tester.prepare_config_and_inputs_for_common(model_class_name=model_class.__name__)
+            config.tie_word_embeddings = True
+
+            model_tied = model_class(config)
+
+            ptrs = collections.defaultdict(list)
+            for name, tensor in model_tied.state_dict().items():
+                ptrs[id_tensor_storage(tensor)].append(name)
+
+            # These are all the pointers of shared tensors.
+            tied_params = [names for _, names in ptrs.items() if len(names) > 1]
+
+            tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
+            # Detect we get a hit for each key
+            for key in tied_weight_keys:
+                if not any(re.search(key, p) for group in tied_params for p in group):
+                    raise ValueError(f"{key} is not a tied weight key for {model_class}.")
+
+            # Removed tied weights found from tied params -> there should only be one left after
+            for key in tied_weight_keys:
+                for i in range(len(tied_params)):
+                    tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
+
+            tied_params = [group for group in tied_params if len(group) > 1]
+            self.assertListEqual(
+                tied_params,
+                [],
+                f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
+            )
+
 
 TOLERANCE = 1e-4