diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index 0bfe0f84..320481ec 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -21,7 +21,6 @@ import copy
 import gc
 import itertools
 import math
-import re
 import time
 from collections import defaultdict
 from collections.abc import Iterator
@@ -34,6 +33,7 @@ from typing import (TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional,
 
 import numpy as np
 import numpy.typing as npt
+import regex as re
 import torch
 import torch._dynamo.cache_size
 import torch.distributed as dist
@@ -92,6 +92,7 @@ from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
+from vllm.v1.structured_output.utils import apply_grammar_bitmask
 from vllm.v1.utils import CpuGpuBuffer
 from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorOutput
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
@@ -1699,70 +1700,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         )
         return metadata
 
-    def apply_grammar_bitmask(
-        self,
-        scheduler_output: "SchedulerOutput",
-        logits: torch.Tensor,
-    ) -> torch.Tensor:
-        grammar_bitmask = scheduler_output.grammar_bitmask
-
-        # We receive the structured output bitmask from the scheduler,
-        # compacted to contain bitmasks only for structured output requests.
-        # The order of the requests in the bitmask is not guaranteed to be the
-        # same as the order of the requests in the gpu runner's batch. We need
-        # to sort the bitmask to match the order of the requests used here.
-
-        # Get the batch indices of the structured output requests.
-        # Keep track of the number of speculative tokens scheduled for every
-        # request in the batch, as the logit indices are offset by this amount.
-        struct_out_req_batch_indices: dict[str, int] = {}
-        cumulative_offset = 0
-        seq = sorted(self.input_batch.req_id_to_index.items(),
-                     key=lambda x: x[1])
-        for req_id, batch_index in seq:
-            logit_index = batch_index + cumulative_offset
-            cumulative_offset += len(
-                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
-            if req_id in scheduler_output.structured_output_request_ids:
-                struct_out_req_batch_indices[req_id] = logit_index
-
-        out_indices = []
-
-        # Reorder the bitmask to match the order of the requests in the batch.
-        sorted_bitmask = np.zeros_like(grammar_bitmask,
-                                       shape=(logits.shape[0],
-                                              grammar_bitmask.shape[1]))
-        cumulative_index = 0
-        seq = sorted(scheduler_output.structured_output_request_ids.items(),
-                     key=lambda x: x[1])
-        for req_id, _ in seq:
-            logit_index = struct_out_req_batch_indices[req_id]
-            num_spec_tokens = len(
-                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
-            for i in range(1 + num_spec_tokens):
-                sorted_bitmask[logit_index + i] = \
-                    grammar_bitmask[cumulative_index + i]
-                out_indices.append(logit_index + i)
-            cumulative_index += 1 + num_spec_tokens
-        grammar_bitmask = sorted_bitmask
-
-        # Serialization of np.ndarray is much more efficient than a tensor,
-        # so we receive it in that format.
-        grammar_bitmask = torch.from_numpy(grammar_bitmask)
-
-        # NOTE:
-        # 1. XGrammar bitmask applying only supports CPU and GPU.
-        # 2. The logits and bitmask should be on the same device.
-        # 3. XGrammar logits on CPU only supports float32 dtype.
-        logits_dtype = logits.dtype
-        logits = logits.to("cpu").float()
-        xgr.apply_token_bitmask_inplace(
-            logits,
-            grammar_bitmask,
-            indices=out_indices,
-        )
-        return logits.to(self.device).to(logits_dtype)
-
     def propose_draft_token_ids(
         self,
         valid_sampled_token_ids: list[list[int]],
@@ -2011,7 +1948,16 @@ class NPUModelRunner(LoRAModelRunnerMixin):
 
             # Apply structured output bitmasks if present
             if scheduler_output.grammar_bitmask is not None:
-                logits = self.apply_grammar_bitmask(scheduler_output, logits)
+                assert logits is not None
+                # NOTE:
+                # 1. XGrammar bitmask applying only supports CPU and GPU.
+                # 2. The logits and bitmask should be on the same device.
+                # 3. XGrammar logits on CPU only supports float32 dtype.
+                logits_dtype = logits.dtype
+                logits = logits.to("cpu").float()
+                apply_grammar_bitmask(scheduler_output, self.input_batch,
+                                      logits, torch.device("cpu"))
+                logits = logits.to(self.device).to(logits_dtype)
 
             # Sample the next token and get logprobs if needed.
             sampling_metadata = self.input_batch.sampling_metadata