xc-llm-ascend/vllm_ascend/patch/worker/patch_roberta.py

#
# 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, Union

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,
    inputs_embeds: Union[torch.Tensor, None] = None,
) -> torch.Tensor:

    token_type_ids = _decode_token_type_ids(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)
    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