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vllm_br/model_executor/models/bert.py

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+################################################################################
+# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+from typing import Optional
+
+import torch
+from fastcore.basics import patch_to
+
+from vllm.model_executor.models.bert import BertModel
+from vllm.sequence import IntermediateTensors
+
+
+@patch_to(BertModel)
+def forward(
+    self,
+    input_ids: torch.Tensor,
+    positions: torch.Tensor,
+    intermediate_tensors: Optional[IntermediateTensors] = None,
+    inputs_embeds: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    if inputs_embeds is not None:
+        hidden_states = inputs_embeds
+    else:
+        input_ids = input_ids.unsqueeze(
+            0
+        )  # Note: set input batch size (bs) to 1 here; otherwise attention module will raise an error.
+        hidden_states = self.embeddings(input_ids=input_ids,
+                                        position_ids=positions)
+        hidden_states = self.encoder(hidden_states).squeeze(0)
+    return hidden_states