diff --git a/vllm_ascend/ops/mm_encoder_attention.py b/vllm_ascend/ops/mm_encoder_attention.py
index e6529f75..397e0ba3 100644
--- a/vllm_ascend/ops/mm_encoder_attention.py
+++ b/vllm_ascend/ops/mm_encoder_attention.py
@@ -104,6 +104,6 @@ class AscendMMEncoderAttention(MMEncoderAttention):
             context_layer = context_layer[..., :origin_shape]
 
         context_layer = einops.rearrange(context_layer,
-                                         "(b s) h d -> s b (h d)",
+                                         "(b s) h d -> b s h d",
                                          b=bsz).contiguous()
         return context_layer
diff --git a/vllm_ascend/ops/rotary_embedding.py b/vllm_ascend/ops/rotary_embedding.py
index 628b0dd2..12995575 100644
--- a/vllm_ascend/ops/rotary_embedding.py
+++ b/vllm_ascend/ops/rotary_embedding.py
@@ -18,12 +18,14 @@
 import math
 from typing import Optional, Tuple
 
+import einops
 import torch
 import torch_npu
 from vllm.config import CUDAGraphMode
 from vllm.model_executor.layers.rotary_embedding import (
     DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding,
     YaRNScalingRotaryEmbedding)
+from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb
 
 from vllm_ascend.platform import NPUPlatform
 from vllm_ascend.utils import (AscendDeviceType, enable_custom_op,
@@ -524,3 +526,59 @@ class AscendMRotaryEmbedding(MRotaryEmbedding):
                                          rotary_mode='half')
 
         return query, key
+
+
+class AscendApplyRotaryEmb(ApplyRotaryEmb):
+
+    def __init__(
+        self,
+        enforce_enable: bool = False,
+        is_neox_style: bool = True,
+        enable_fp32_compute: bool = False,
+    ) -> None:
+        super().__init__(
+            enforce_enable=enforce_enable,
+            is_neox_style=is_neox_style,
+            enable_fp32_compute=enable_fp32_compute,
+        )
+
+    def forward_oot(
+        self,
+        x: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> torch.Tensor:
+        head_dim = x.shape[-1]
+
+        origin_dtype = x.dtype
+        if self.enable_fp32_compute:
+            x = x.float()
+            cos = cos.float()
+            sin = sin.float()
+
+        # cos, sin: [seq_len, head_dim // 2]
+        cos = torch.cat((cos, cos), dim=-1)
+        sin = torch.cat((sin, sin), dim=-1)
+        # cos, sin: [1, seq_len, 1, head_dim]
+        cos = cos.reshape(1, -1, 1, head_dim)
+        sin = sin.reshape(1, -1, 1, head_dim)
+
+        if len(x.shape) == 3:
+            # x: [seq_len, num_heads, head_size]
+            x = x.unsqueeze(0)
+            # x: [1, seq_len, num_heads, head_size]
+            output = torch_npu.npu_rotary_mul(x, cos, sin).squeeze(0)
+        else:
+            assert len(x.shape) == 4
+            # x: [2 * b, s, head, head_dim]
+            qk = einops.rearrange(
+                x, "(two b) s head head_dim -> b s two head head_dim", two=2)
+            # q, k: [b, s, head, head_dim]
+            q, k = qk[:, :, 0], qk[:, :, 1]
+            q = torch_npu.npu_rotary_mul(q, cos, sin)
+            k = torch_npu.npu_rotary_mul(k, cos, sin)
+            output = torch.cat([q, k], dim=0)
+
+        if self.enable_fp32_compute:
+            output = output.to(origin_dtype)
+        return output
diff --git a/vllm_ascend/patch/__init__.py b/vllm_ascend/patch/__init__.py
index f751af3d..84c36a34 100644
--- a/vllm_ascend/patch/__init__.py
+++ b/vllm_ascend/patch/__init__.py
@@ -160,53 +160,7 @@
 #    Future Plan:
 #       Identify this pattern in torch-npu and remove this patch.
 #
-# ** 5. File: worker/patch_qwen2_5_omni.py**
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#   1. `vllm.model_executor.models.qwen2_5_omni_thinker.Qwen2_5OmniThinkerForConditionalGeneration`
-#    Why:
-#       we have ascend forward context which doesn't work with upstream.
-#    How：
-#       override forward_context in the model file
-#    Related PR (if no, explain why):
-#       This is a bug by Ascend only. we should drop set_ascend_forward_context
-#    Future Plan:
-#       Remove this patch once forward_context is refactor.
-#
-# ** 6. File: worker/patch_qwen2_5_vl.py**
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#   1. `vllm.model_executor.models.qwen2_5_vl.Qwen2_5_VLForConditionalGeneration`
-#    Why:
-#       we have ascend forward context which doesn't work with upstream.
-#    How：
-#       override forward_context in the model file
-#    Related PR (if no, explain why):
-#       This is a bug by Ascend only. we should drop set_ascend_forward_context
-#    Future Plan:
-#       Remove this patch once forward_context is refactor.
-#
-#   2. `vllm.model_executor.models.qwen2_vl.Qwen2VisionAttention.forward`
-#    Why:
-#       the attention is not custom ops
-#    How：
-#       make it to custom ops and pluggable
-#    Related PR (if no, explain why):
-#       https://github.com/vllm-project/vllm/pull/30125
-#    Future Plan:
-#       Remove this patch one the PR is merged into vLLM.
-#
-# ** 7. File: worker/patch_qwen3_vl.py**
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#   1. `vllm.model_executor.models.qwen3_vl.Qwen3_VisionTransformer.forward`
-#    Why:
-#       the attention is not custom ops
-#    How：
-#       make it to custom ops and pluggable
-#    Related PR (if no, explain why):
-#       https://github.com/vllm-project/vllm/pull/30125
-#    Future Plan:
-#       Remove this patch one the PR is merged into vLLM.
-#
-# ** 8. File: worker/patch_roberta.py **
+# ** 5. File: worker/patch_roberta.py **
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.model_executor.models.bert `
 #    Why:
@@ -218,7 +172,7 @@
 #    Future Plan:
 #       Revert this when CANN support shift aclnn operation
 #
-# ** 9. File: worker/patch_triton.py**
+# ** 6. File: worker/patch_triton.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.model_executor.layers.mamba.ops`, `vllm.model_executor.layers.fla.ops`
 #    Why:
@@ -230,7 +184,7 @@
 #    Future Plan:
 #       Remove this patch when vLLM support the dispatch function.
 #
-# ** 10. File: worker/patch_weight_loader.py**
+# ** 7. File: worker/patch_weight_loader.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.model_executor.layers.linear.UnquantizedLinearMethod`
 #    Why:
@@ -242,7 +196,7 @@
 #    Future Plan:
 #       Remove this patch when the bug is fixed.
 #
-# ** 11. File: worker/patch_qwen3_next_mtp.py**
+# ** 8. File: worker/patch_qwen3_next_mtp.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.v1.worker.utils.bind_kv_cache`
 #    Why:
@@ -255,7 +209,7 @@
 #    Future Plan:
 #       Remove this patch after discussing with vllm community and adapting bind_kv_cache to npu.
 #
-# ** 12. File: worker/patch_module.py**
+# ** 9. File: worker/patch_module.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.v1.attention.backends.gdn_attn.torch.argsort`
 #    Why:
@@ -271,7 +225,7 @@
 #       Remove this patch when bool is supported in 'torch.argsort' func of npu.
 #       Make 'torch.argsort' in `vllm.v1.attention.backends.gdn_attn` be stable.
 #
-# ** 13. File: worker/patch_rejection_sampler.py**
+# ** 10. File: worker/patch_rejection_sampler.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.v1.sample.rejection_sampler`
 #    Why:
@@ -287,7 +241,7 @@
 #           to override them, then delete the patch file `worker/patch_rejection_sampler.py`.
 #       2. make these functions as costom op, then remove AscendRejectionSampler
 #
-# ** 14.File: worker/patch_qwen3_next.py**
+# ** 11.File: worker/patch_qwen3_next.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet.forward`
 #    Why:
@@ -299,7 +253,7 @@
 #    Future Plan:
 #       Remove this patch when vLLM support these operators.
 #
-# ** 15. File: worker/patch_qwen3_next.py**
+# ** 12. File: worker/patch_qwen3_next.py**
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet._forward_core`
 #    Why:
diff --git a/vllm_ascend/patch/worker/__init__.py b/vllm_ascend/patch/worker/__init__.py
index ec135d2b..371dd99c 100644
--- a/vllm_ascend/patch/worker/__init__.py
+++ b/vllm_ascend/patch/worker/__init__.py
@@ -28,8 +28,6 @@ import vllm_ascend.patch.worker.patch_deepseek  # noqa
 import vllm_ascend.patch.worker.patch_weight_loader  # noqa
 import vllm_ascend.patch.worker.patch_multimodal_merge  # noqa
 import vllm_ascend.patch.worker.patch_minicpm  # noqa
-import vllm_ascend.patch.worker.patch_qwen2_5_vl  # noqa
-import vllm_ascend.patch.worker.patch_qwen2_5_omni  # noqa
 import vllm_ascend.patch.worker.patch_rope  # noqa
 import vllm_ascend.patch.worker.patch_qwen3_next  # noqa
 import vllm_ascend.patch.worker.patch_qwen3_next_mtp  # noqa
diff --git a/vllm_ascend/patch/worker/patch_qwen2_5_omni.py b/vllm_ascend/patch/worker/patch_qwen2_5_omni.py
deleted file mode 100644
index c272edb3..00000000
--- a/vllm_ascend/patch/worker/patch_qwen2_5_omni.py
+++ /dev/null
@@ -1,66 +0,0 @@
-#
-# 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.
-#
-
-import torch
-import torch.nn as nn
-from vllm.model_executor.models.qwen2_5_omni_thinker import (
-    Qwen2_5_VLImageInputs, Qwen2_5_VLVideoInputs)
-
-from vllm_ascend.ascend_forward_context import set_ascend_forward_context
-
-
-class AscendQwen2_5OmniThinkerForConditionalGeneration(nn.Module):
-
-    def _process_image_input(
-            self,
-            image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
-        if image_input["type"] == "image_embeds":
-            return image_input["image_embeds"].type(self.visual.dtype)
-
-        grid_thw = image_input["image_grid_thw"]
-        assert grid_thw.ndim == 2
-
-        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
-        with set_ascend_forward_context(None, self.vllm_config):
-            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
-        # Split concatenated embeddings for each image item.
-        merge_size = self.visual.spatial_merge_size
-        sizes = grid_thw.prod(-1) // merge_size // merge_size
-
-        return image_embeds.split(sizes.tolist())
-
-    def _process_video_input(
-        self,
-        video_input: Qwen2_5_VLVideoInputs,
-        video_hashes: list[str] | None = None,
-        cached_video_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        if video_input["type"] == "video_embeds":
-            return video_input["video_embeds"].type(self.visual.dtype)
-
-        grid_thw = video_input["video_grid_thw"]
-        assert grid_thw.ndim == 2
-
-        pixel_values_videos = video_input["pixel_values_videos"].type(
-            self.visual.dtype)
-        with set_ascend_forward_context(None, self.vllm_config):
-            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
-        # Split concatenated embeddings for each video item.
-        merge_size = self.visual.spatial_merge_size
-        sizes = grid_thw.prod(-1) // merge_size // merge_size
-
-        return video_embeds.split(sizes.tolist())
diff --git a/vllm_ascend/patch/worker/patch_qwen2_5_vl.py b/vllm_ascend/patch/worker/patch_qwen2_5_vl.py
deleted file mode 100644
index adfe2467..00000000
--- a/vllm_ascend/patch/worker/patch_qwen2_5_vl.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#
-# 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.
-#
-
-import einops
-import torch
-import torch.nn as nn
-import torch_npu
-from vllm.model_executor.models.qwen2_5_vl import (Qwen2_5_VisionAttention,
-                                                   Qwen2_5_VLImageInputs,
-                                                   Qwen2_5_VLVideoInputs)
-from vllm.model_executor.models.qwen2_vl import Qwen2VisionAttention
-from vllm.model_executor.models.vision import run_dp_sharded_mrope_vision_model
-
-from vllm_ascend.ascend_forward_context import set_ascend_forward_context
-
-MIN_PAD_SIZE = 64  # min_size to pad weight
-MAX_PAD_SIZE = 128  # max_size to pad weight
-
-
-class AscendQwen2_5_VisionAttention(nn.Module):
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        cu_seqlens: torch.Tensor,
-        rotary_pos_emb_cos: torch.Tensor,
-        rotary_pos_emb_sin: torch.Tensor,
-        max_seqlen: torch.Tensor,
-    ) -> torch.Tensor:
-        # [s, b, c] --> [s, b, head * 3 * head_dim]
-        x, _ = self.qkv(x)
-        seq_len, batch_size, _ = x.shape
-
-        qkv = einops.rearrange(
-            x,
-            "s b (three head head_dim) -> b s three head head_dim",
-            three=3,
-            head=self.num_attention_heads_per_partition,
-        )
-        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
-
-        cos = torch.cat((rotary_pos_emb_cos, rotary_pos_emb_cos), dim=-1)
-        sin = torch.cat((rotary_pos_emb_sin, rotary_pos_emb_sin), dim=-1)
-        cos = cos.reshape(1, -1, 1, self.hidden_size_per_attention_head)
-        sin = sin.reshape(1, -1, 1, self.hidden_size_per_attention_head)
-        q = torch_npu.npu_rotary_mul(q, cos, sin)
-        k = torch_npu.npu_rotary_mul(k, cos, sin)
-
-        context_layer = self.attn(
-            query=q,
-            key=k,
-            value=v,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-        )
-
-        output, _ = self.proj(context_layer)
-        return output
-
-
-class AscendQwen2_5_VLForConditionalGeneration(nn.Module):
-
-    def _process_image_input(
-            self,
-            image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
-        grid_thw = image_input["image_grid_thw"]
-        assert grid_thw.ndim == 2
-        grid_thw_list = grid_thw.tolist()
-
-        if image_input["type"] == "image_embeds":
-            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
-        else:
-            pixel_values = image_input["pixel_values"]
-            with set_ascend_forward_context(None, self.vllm_config):
-                if self.use_data_parallel:
-                    return run_dp_sharded_mrope_vision_model(
-                        self.visual,
-                        pixel_values,
-                        grid_thw_list,
-                        rope_type="rope_3d")
-                else:
-                    image_embeds = self.visual(pixel_values,
-                                               grid_thw=grid_thw_list)
-
-        # Split concatenated embeddings for each image item.
-        merge_size = self.visual.spatial_merge_size
-        sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
-        return image_embeds.split(sizes)
-
-    def _process_video_input(
-            self,
-            video_input: Qwen2_5_VLVideoInputs) -> tuple[torch.Tensor, ...]:
-        grid_thw = video_input["video_grid_thw"]
-        assert grid_thw.ndim == 2
-        grid_thw_list = grid_thw.tolist()
-
-        if video_input["type"] == "video_embeds":
-            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
-        else:
-            pixel_values_videos = video_input["pixel_values_videos"]
-            with set_ascend_forward_context(None, self.vllm_config):
-                if self.use_data_parallel:
-                    return run_dp_sharded_mrope_vision_model(
-                        self.visual,
-                        pixel_values_videos,
-                        grid_thw_list,
-                        rope_type="rope_3d",
-                    )
-                else:
-                    video_embeds = self.visual(pixel_values_videos,
-                                               grid_thw=grid_thw_list)
-
-        # Split concatenated embeddings for each video item.
-        merge_size = self.visual.spatial_merge_size
-        sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
-        return video_embeds.split(sizes)
-
-
-# NOTE: This will be removed after MMEncoderAttention has been extract as a CustomOp in vllm.
-Qwen2VisionAttention.forward = AscendQwen2_5_VisionAttention.forward
-Qwen2_5_VisionAttention.forward = AscendQwen2_5_VisionAttention.forward
diff --git a/vllm_ascend/utils.py b/vllm_ascend/utils.py
index e7e0484c..4246ee36 100644
--- a/vllm_ascend/utils.py
+++ b/vllm_ascend/utils.py
@@ -666,8 +666,9 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
     from vllm_ascend.ops.mla import AscendMultiHeadLatentAttention
     from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention
     from vllm_ascend.ops.rotary_embedding import (
-        AscendDeepseekScalingRotaryEmbedding, AscendMRotaryEmbedding,
-        AscendRotaryEmbedding, AscendYaRNRotaryEmbedding)
+        AscendApplyRotaryEmb, AscendDeepseekScalingRotaryEmbedding,
+        AscendMRotaryEmbedding, AscendRotaryEmbedding,
+        AscendYaRNRotaryEmbedding)
     from vllm_ascend.ops.vocab_parallel_embedding import (
         AscendLogitsProcessor, AscendParallelLMHead,
         AscendVocabParallelEmbedding)
@@ -694,6 +695,7 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
         "SharedFusedMoE": AscendSharedFusedMoE,
         "MultiHeadLatentAttentionWrapper": AscendMultiHeadLatentAttention,
         "MMEncoderAttention": AscendMMEncoderAttention,
+        "ApplyRotaryEmb": AscendApplyRotaryEmb,
     }
 
     for name, op_cls in REGISTERED_ASCEND_OPS.items():