diff --git a/vllm/model_executor/models/bert.py b/vllm/model_executor/models/bert.py
index c984845204c4f..2ec3edc5a0a7a 100644
--- a/vllm/model_executor/models/bert.py
+++ b/vllm/model_executor/models/bert.py
@@ -61,11 +61,13 @@ class BertEmbedding(nn.Module):
         self,
         input_ids: torch.Tensor,
         position_ids: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-
         token_type_ids = _decode_token_type_ids(input_ids)
 
-        inputs_embeds = self.word_embeddings(input_ids)
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
         position_embeddings = self.position_embeddings(position_ids)
 
         token_type_embeddings = self.token_type_embeddings(token_type_ids)
@@ -358,11 +360,12 @@ class BertModel(nn.Module, SupportsQuant):
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        if inputs_embeds is not None:
-            hidden_states = inputs_embeds
-        else:
-            hidden_states = self.embeddings(input_ids=input_ids,
-                                            position_ids=positions)
+        hidden_states = self.embeddings(
+            input_ids=input_ids,
+            position_ids=positions,
+            inputs_embeds=inputs_embeds,
+        )
+
         return self.encoder(hidden_states)
 
     def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
diff --git a/vllm/model_executor/models/roberta.py b/vllm/model_executor/models/roberta.py
index 53e698c4fa806..a13042a6367cc 100644
--- a/vllm/model_executor/models/roberta.py
+++ b/vllm/model_executor/models/roberta.py
@@ -56,11 +56,13 @@ class RobertaEmbedding(nn.Module):
         self,
         input_ids: torch.Tensor,
         position_ids: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-
         token_type_ids = _decode_token_type_ids(input_ids)
 
-        inputs_embeds = self.word_embeddings(input_ids)
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
         position_embeddings = self.position_embeddings(position_ids)
 
         token_type_embeddings = self.token_type_embeddings(token_type_ids)