[modeling_utils] respect original dtype in _get_resized_lm_head (#14181)

* respect dtype in _get_resized_lm_head

* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

src/transformers/modeling_utils.py

@@ -789,9 +789,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             )
 
         # Build new embeddings
-        new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim).to(
-            self.device, dtype=old_embeddings.weight.dtype
-        )
+        new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
+        new_embeddings.to(self.device, dtype=old_embeddings.weight.dtype)
 
         # initialize all new embeddings (in particular added tokens)
         self._init_weights(new_embeddings)
@@ -862,7 +861,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         # Build new lm head
         new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (new_num_tokens, old_lm_head_dim)
         has_new_lm_head_bias = old_lm_head.bias is not None
-        new_lm_head = nn.Linear(*new_lm_head_shape, bias=has_new_lm_head_bias).to(self.device)
+        new_lm_head = nn.Linear(*new_lm_head_shape, bias=has_new_lm_head_bias)
+        new_lm_head = new_lm_head.to(self.device, dtype=old_lm_head.weight.dtype)
 
         # initialize new lm head (in particular added tokens)
         self._init_weights(new_lm_head)