Bugfix device map detr model (#26849)

* Fixed replace_batch_norm when on meta device

* lint fix

* Adding coauthor
Co-authored-by: Pi Esposito <piero.skywalker@gmail.com>

* Removed tests

* Remove unused deps

* Try to fix copy issue

* try fix copy one more time

* Reverted import changes

---------
Co-authored-by: Pi Esposito <piero.skywalker@gmail.com>

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -322,6 +322,7 @@ def replace_batch_norm(model):
         if isinstance(module, nn.BatchNorm2d):
             new_module = ConditionalDetrFrozenBatchNorm2d(module.num_features)
 
+            if not module.weight.device == torch.device("meta"):
                 new_module.weight.data.copy_(module.weight)
                 new_module.bias.data.copy_(module.bias)
                 new_module.running_mean.data.copy_(module.running_mean)
@@ -1145,6 +1146,7 @@ class ConditionalDetrPreTrainedModel(PreTrainedModel):
     config_class = ConditionalDetrConfig
     base_model_prefix = "model"
     main_input_name = "pixel_values"
+    _no_split_modules = [r"ConditionalDetrConvEncoder", r"ConditionalDetrEncoderLayer", r"ConditionalDetrDecoderLayer"]
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -369,6 +369,7 @@ def replace_batch_norm(model):
         if isinstance(module, nn.BatchNorm2d):
             new_module = DeformableDetrFrozenBatchNorm2d(module.num_features)
 
+            if not module.weight.device == torch.device("meta"):
                 new_module.weight.data.copy_(module.weight)
                 new_module.bias.data.copy_(module.bias)
                 new_module.running_mean.data.copy_(module.running_mean)
@@ -1061,6 +1062,7 @@ class DeformableDetrPreTrainedModel(PreTrainedModel):
     config_class = DeformableDetrConfig
     base_model_prefix = "model"
     main_input_name = "pixel_values"
+    _no_split_modules = [r"DeformableDetrConvEncoder", r"DeformableDetrEncoderLayer", r"DeformableDetrDecoderLayer"]
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/deta/modeling_deta.py

@@ -307,6 +307,7 @@ def replace_batch_norm(model):
         if isinstance(module, nn.BatchNorm2d):
             new_module = DetaFrozenBatchNorm2d(module.num_features)
 
+            if not module.weight.device == torch.device("meta"):
                 new_module.weight.data.copy_(module.weight)
                 new_module.bias.data.copy_(module.bias)
                 new_module.running_mean.data.copy_(module.running_mean)
@@ -947,11 +948,12 @@ class DetaClassificationHead(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrPreTrainedModel with DeformableDetr->Deta
+# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrPreTrainedModel with DeformableDetrConvEncoder->DetaBackboneWithPositionalEncodings,DeformableDetr->Deta
 class DetaPreTrainedModel(PreTrainedModel):
     config_class = DetaConfig
     base_model_prefix = "model"
     main_input_name = "pixel_values"
+    _no_split_modules = [r"DetaBackboneWithPositionalEncodings", r"DetaEncoderLayer", r"DetaDecoderLayer"]
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/detr/modeling_detr.py

@@ -316,6 +316,7 @@ def replace_batch_norm(model):
         if isinstance(module, nn.BatchNorm2d):
             new_module = DetrFrozenBatchNorm2d(module.num_features)
 
+            if not module.weight.device == torch.device("meta"):
                 new_module.weight.data.copy_(module.weight)
                 new_module.bias.data.copy_(module.bias)
                 new_module.running_mean.data.copy_(module.running_mean)
@@ -901,6 +902,7 @@ class DetrPreTrainedModel(PreTrainedModel):
     config_class = DetrConfig
     base_model_prefix = "model"
     main_input_name = "pixel_values"
+    _no_split_modules = [r"DetrConvEncoder", r"DetrEncoderLayer", r"DetrDecoderLayer"]
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/table_transformer/modeling_table_transformer.py

@@ -251,6 +251,7 @@ def replace_batch_norm(model):
         if isinstance(module, nn.BatchNorm2d):
             new_module = TableTransformerFrozenBatchNorm2d(module.num_features)
 
+            if not module.weight.device == torch.device("meta"):
                 new_module.weight.data.copy_(module.weight)
                 new_module.bias.data.copy_(module.bias)
                 new_module.running_mean.data.copy_(module.running_mean)
@@ -813,6 +814,11 @@ class TableTransformerPreTrainedModel(PreTrainedModel):
     config_class = TableTransformerConfig
     base_model_prefix = "model"
     main_input_name = "pixel_values"
+    _no_split_modules = [
+        r"TableTransformerConvEncoder",
+        r"TableTransformerEncoderLayer",
+        r"TableTransformerDecoderLayer",
+    ]
 
     def _init_weights(self, module):
         std = self.config.init_std