Fixing bug with param count without embeddings (#12461)

* fixing bug with param count without embeddings

* Update src/transformers/modeling_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* style

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/modeling_utils.py

@@ -352,11 +352,16 @@ def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool
             :obj:`int`: The number of parameters.
         """
 
-        def parameter_filter(x):
-            return (x.requires_grad or not only_trainable) and not (isinstance(x, nn.Embedding) and exclude_embeddings)
-
-        params = filter(parameter_filter, self.parameters()) if only_trainable else self.parameters()
-        return sum(p.numel() for p in params)
+        if exclude_embeddings:
+            embedding_param_names = [
+                f"{name}.weight" for name, module_type in self.named_modules() if isinstance(module_type, nn.Embedding)
+            ]
+            non_embedding_parameters = [
+                parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
+            ]
+            return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
+        else:
+            return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)
 
     def estimate_tokens(self, input_dict: Dict[str, Union[torch.Tensor, Any]]) -> int:
         """