大改

vllm_npu/patch/worker/patch_roberta.py

@@ -0,0 +1,88 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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 vllm.model_executor.models.roberta import (
+    RobertaEmbedding, RobertaForSequenceClassification,
+    replace_roberta_positions)
+from vllm.sequence import IntermediateTensors
+
+# aclgraph does not support shift operator for now
+# TODO: revert me when aclgraph supports shift operator
+TOKEN_TYPE_SHIFT = 30
+TOKEN_TYPE_MULTIPLIER = 1 << 30
+TOKEN_MASK = TOKEN_TYPE_MULTIPLIER - 1
+
+
+def _encode_token_type_ids(input_ids: torch.Tensor,
+                           token_type_ids: torch.Tensor) -> None:
+    # input_ids can be padded to the right
+    input_ids[:token_type_ids.shape[0]].bitwise_or_(token_type_ids *
+                                                    TOKEN_TYPE_MULTIPLIER)
+
+
+def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
+
+    token_type_ids = input_ids // TOKEN_TYPE_MULTIPLIER
+
+    input_ids.bitwise_and_(TOKEN_MASK)
+
+    return token_type_ids
+
+
+def roberta_for_sequence_classification_forward(
+    self,
+    input_ids: Optional[torch.Tensor],
+    positions: torch.Tensor,
+    intermediate_tensors: Optional[IntermediateTensors] = None,
+    inputs_embeds: Optional[torch.Tensor] = None,
+    token_type_ids: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    replace_roberta_positions(input_ids=input_ids,
+                              position_ids=positions,
+                              padding_idx=self.padding_idx)
+    if token_type_ids is not None:
+        assert self.roberta.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
+        assert input_ids is not None
+        _encode_token_type_ids(input_ids, token_type_ids)
+    return self.roberta(input_ids=input_ids,
+                        positions=positions,
+                        inputs_embeds=inputs_embeds,
+                        intermediate_tensors=intermediate_tensors)
+
+
+def roberta_embedding_forward(
+    self,
+    input_ids: torch.Tensor,
+    position_ids: torch.Tensor,
+) -> torch.Tensor:
+
+    token_type_ids = _decode_token_type_ids(input_ids)
+
+    inputs_embeds = self.word_embeddings(input_ids)
+    position_embeddings = self.position_embeddings(position_ids)
+
+    token_type_embeddings = self.token_type_embeddings(token_type_ids)
+    embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+    embeddings = self.LayerNorm(embeddings)
+    return embeddings
+
+
+RobertaEmbedding.forward = roberta_embedding_forward
+RobertaForSequenceClassification.forward = roberta_for_sequence_classification_forward