Add Qwen3-Omni moe thinker

docs/models/supported_models.md

@@ -689,6 +689,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-3B`, `Qwen/Qwen2.5-Omni-7B` | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3VLForConditionalGeneration` | Qwen3-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-4B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3VLMoeForConditionalGeneration` | Qwen3-VL-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-30B-A3B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, `Qwen/Qwen3-Omni-30B-A3B-Thinking` | ✅︎ | ✅︎ | ✅︎ |
 | `RForConditionalGeneration` | R-VL-4B | T + I<sup>E+</sup> | `YannQi/R-4B` | | ✅︎ | ✅︎ |
 | `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ | ✅︎ |
 | `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | | ✅︎ |
@@ -779,7 +780,7 @@ Some models are supported only via the [Transformers backend](#transformers). Th
     Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
 
 !!! note
-    For Qwen2.5-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`)
+    For Qwen2.5-Omni and Qwen3-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`)
     is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1.
 
 #### Transcription

tests/models/multimodal/processing/test_common.py

@@ -362,6 +362,7 @@ def _test_processing_correctness_one(
     os.path.join(models_path_prefix, "Qwen/Qwen2.5-Omni-3B"),
     os.path.join(models_path_prefix, "Qwen/Qwen3-VL-4B-Instruct"),
     os.path.join(models_path_prefix, "Qwen/Qwen3-VL-30B-A3B-Instruct"),
+    os.path.join(models_path_prefix, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
     os.path.join(models_path_prefix, "YannQi/R-4B"),
     os.path.join(models_path_prefix, "Skywork/Skywork-R1V-38B"),
     os.path.join(models_path_prefix, "HuggingFaceTB/SmolVLM2-2.2B-Instruct"),

tests/models/registry.py

@@ -580,6 +580,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                           max_model_len=4096,
                                                           min_transformers_version="4.57",
                                                           is_available_online=False),
+    "Qwen3OmniMoeForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
+                                                            max_model_len=4096,
+                                                            min_transformers_version="4.57"),
     "RForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "YannQi/R-4B"),
                                                  trust_remote_code=True),
     "SkyworkR1VChatModel": _HfExamplesInfo(os.path.join(models_path_prefix, "Skywork/Skywork-R1V-38B"),

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -429,7 +429,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         use_audio_in_video: bool = False,
     ) -> tuple[torch.Tensor, int]:
         from vllm.transformers_utils.config import thinker_uses_mrope
-        if thinker_uses_mrope(hf_config):
+        if thinker_uses_mrope(hf_config) and hf_config.model_type == "qwen2_5_omni":
             return cls._omni_get_input_positions_tensor(
                 input_tokens=input_tokens,
                 hf_config=hf_config,
@@ -899,7 +899,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                 torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
 
             t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
-                -1, llm_grid_h * llm_grid_w)).long().flatten()
+                -1, llm_grid_h * llm_grid_w)).flatten()
 
             h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
                 llm_grid_t, -1, llm_grid_w).flatten()
@@ -1003,8 +1003,8 @@ class MRotaryEmbedding(RotaryEmbedding):
                 torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
 
             t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
-                -1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
-                       tokens_per_second).long().flatten()
+                -1, llm_grid_h * llm_grid_w)* video_second_per_grid_t* 
+                       tokens_per_second).flatten()
 
             h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
                 llm_grid_t, -1, llm_grid_w).flatten()
@@ -1027,7 +1027,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         llm_positions = llm_positions[:, context_len:seq_len]
 
         return llm_positions, mrope_position_delta
-
+    
     @classmethod
     def _omni_get_input_positions_tensor(
         cls,
@@ -1061,6 +1061,11 @@ class MRotaryEmbedding(RotaryEmbedding):
         #  _vl_get_input_positions_tensor.
 
         thinker_config = hf_config.thinker_config
+        if isinstance(image_grid_thw, list):
+            image_grid_thw = torch.tensor(image_grid_thw)
+        if isinstance(video_grid_thw, list):
+            video_grid_thw = torch.tensor(video_grid_thw)
+
         audio_token_id = thinker_config.audio_token_index
         image_token_id = thinker_config.image_token_index
         video_token_id = thinker_config.video_token_index
@@ -1073,11 +1078,6 @@ class MRotaryEmbedding(RotaryEmbedding):
         tokens_per_second = getattr(thinker_config.vision_config,
                                     "tokens_per_second", 25)
 
-        if isinstance(image_grid_thw, list):
-            image_grid_thw = torch.tensor(image_grid_thw)
-        if isinstance(video_grid_thw, list):
-            video_grid_thw = torch.tensor(video_grid_thw)
-
         src_item = input_tokens
         audio_seqlens = audio_feature_lengths
         if not second_per_grid_ts:
@@ -1121,7 +1121,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                 grid_t = image_grid_thw[image_idx][0]
                 grid_hs = image_grid_thw[:, 1]
                 grid_ws = image_grid_thw[:, 2]
-                t_index = (torch.arange(grid_t) * 1 * tokens_per_second).long()
+                t_index = torch.arange(grid_t) * 1 * tokens_per_second
                 llm_pos_ids = cls._get_llm_pos_ids_for_vision(
                     start_idx, image_idx, spatial_merge_size, t_index, grid_hs,
                     grid_ws)
@@ -1136,7 +1136,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                 grid_ws = video_grid_thw[:, 2]
                 t_index = (torch.arange(grid_t) *
                            second_per_grid_ts[video_idx] *
-                           tokens_per_second).long()
+                           tokens_per_second)
                 llm_pos_ids = cls._get_llm_pos_ids_for_vision(
                     start_idx, video_idx, spatial_merge_size, t_index, grid_hs,
                     grid_ws)
@@ -1159,7 +1159,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                 t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
                 t_index = (torch.arange(grid_t) *
                            second_per_grid_ts[video_idx] *
-                           tokens_per_second).long()
+                           tokens_per_second)
                 t_index_split_chunk = cls._split_list_into_ranges(
                     t_index, t_ntoken_per_chunk)
                 place_num = (((audio_seqlen - 1) // 2 + 1 - 2) // 2 + 1) + 2
@@ -1299,7 +1299,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         grid_w = video_grid_thw[2]
         t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
         t_index = (torch.arange(grid_t) * video_second_per_grid_t *
-                   tokens_per_second).long()
+                   tokens_per_second)
         t_index_split_chunk = cls._split_list_into_ranges(
             t_index, t_ntoken_per_chunk)
 

vllm/model_executor/models/qwen3_omni_moe_thinker.py

+
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Inference-only Qwen3-Omni-Moe model (thinker part)."""
+
+import os
+import math
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from functools import partial
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from packaging.version import Version
+from transformers import PretrainedConfig
+from transformers import __version__ as TRANSFORMERS_VERSION
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import (
+    Qwen3OmniMoeConfig,
+    Qwen3OmniMoeThinkerConfig,
+)
+from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import (
+    Qwen3OmniMoeAudioEncoder,
+)
+from transformers.models.qwen3_omni_moe.processing_qwen3_omni_moe import (
+    Qwen3OmniMoeProcessor,
+)
+from transformers.models.whisper import WhisperFeatureExtractor
+
+from vllm.attention.backends.registry import _Backend
+from vllm.attention.layer import check_upstream_fa_availability
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.qwen2_audio import (
+    Qwen2AudioFeatureInputs,
+    Qwen2AudioProcessingInfo,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalKwargsItems
+from vllm.multimodal.parse import AudioProcessorItems, MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    MultiModalPromptUpdates,
+    PlaceholderFeaturesInfo,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMRoPE,
+    SupportsMultiModal,
+    SupportsPP,
+)
+
+from .qwen2_5_omni_thinker import (
+    Qwen2_5OmniConditionalGenerationMixin,
+    Qwen2_5OmniThinkerDummyInputsBuilder,
+    Qwen2_5OmniThinkerMultiModalProcessor,
+    Qwen2_5OmniThinkerProcessingInfo,
+)
+
+from .qwen2_5_vl import (
+    Qwen2_5_VisionAttention,
+    Qwen2_5_VisionRotaryEmbedding,
+    Qwen2_5_VLProcessingInfo,
+)
+from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from .utils import (
+    AutoWeightsLoader,
+    WeightsMapper,
+    _merge_multimodal_embeddings,
+    maybe_prefix,
+)
+from .vision import (
+    conv3d_to_linear_weight,
+    get_llm_pos_ids_for_vision,
+    get_vit_attn_backend,
+)
+
+try:
+    import flash_attn
+except (ImportError, ModuleNotFoundError):
+    flash_attn = None
+
+logger = init_logger(__name__)
+
+
+def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
+    input_lengths_leave = input_lengths % 100
+    feat_lengths = (input_lengths_leave - 1) // 2 + 1
+    output_lengths = (
+        ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+    )
+    return feat_lengths, output_lengths
+
+
+class Qwen3_VisionPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        hidden_size: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = ReplicatedLinear(
+            in_channels * math.prod(kernel_size),
+            hidden_size,
+            bias=True,
+            return_bias=False,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        L, C = x.shape
+        if os.environ.get('PYTORCH_MIOPEN_SUGGEST_NDHWC') == '1':
+            x = x.to(memory_format=torch.channels_last_3d)
+        x = self.proj(x)
+        return x
+
+
+class Qwen3_VisionMLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        bias: bool = False,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.linear_fc1 = ColumnParallelLinear(
+            in_features,
+            hidden_features,
+            bias=bias,
+            quant_config=quant_config,
+            return_bias=False,
+            prefix=f"{prefix}.linear_fc1",
+        )
+        self.linear_fc2 = RowParallelLinear(
+            hidden_features,
+            in_features,
+            bias=bias,
+            quant_config=quant_config,
+            return_bias=False,
+            prefix=f"{prefix}.linear_fc2",
+        )
+        self.act_fn = act_fn
+
+    def forward(self, x: torch.Tensor):
+        mlp_output = self.linear_fc2(self.act_fn(self.linear_fc1(x)))
+        return mlp_output
+
+
+class Qwen3_VisionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        norm_layer: Callable[[int], nn.Module] | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        self.attn = Qwen2_5_VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.mlp = Qwen3_VisionMLP(
+            dim,
+            mlp_hidden_dim,
+            act_fn=act_fn,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor,
+        max_seqlen: int | None = None,  # Only used for Flash Attention
+        seqlens: list[int] | None = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        x = x + self.attn(
+            self.norm1(x),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            max_seqlen=max_seqlen,
+            seqlens=seqlens,
+        )
+
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class Qwen3_VisionPatchMerger(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Callable[[int], nn.Module] | None = None,
+        spatial_merge_size: int = 2,
+        use_postshuffle_norm: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+
+        self.use_postshuffle_norm = use_postshuffle_norm
+        if self.use_postshuffle_norm:
+            context_dim = self.hidden_size
+
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.use_postshuffle_norm = use_postshuffle_norm
+        self.ln_q = norm_layer(
+            self.hidden_size if use_postshuffle_norm else context_dim
+        )
+        self.mlp = nn.ModuleList(
+            [
+                ColumnParallelLinear(
+                    self.hidden_size,
+                    self.hidden_size,
+                    bias=True,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp.0",
+                ),
+                nn.GELU(),
+                RowParallelLinear(
+                    self.hidden_size,
+                    d_model,
+                    bias=True,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp.2",
+                ),
+            ]
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.use_postshuffle_norm:
+            x = self.ln_q(x.view(-1, self.hidden_size))
+        else:
+            x = self.ln_q(x).view(-1, self.hidden_size)
+
+        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
+        x_parallel, _ = mlp_fc1(x)
+        x_parallel = mlp_act(x_parallel)
+        out, _ = mlp_fc2(x_parallel)
+        return out
+
+
+class Qwen3Omni_VisionTransformer(nn.Module):
+    def __init__(
+        self,
+        vision_config,
+        norm_eps: float = 1e-6,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_backend_override: _Backend | None = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+        self.image_size = vision_config.image_size
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.spatial_merge_unit = self.spatial_merge_size**2
+        self.temporal_patch_size = vision_config.temporal_patch_size
+        self.num_grid_per_side = self.image_size // self.patch_size
+        self.apply_vit_abs_pos_embed = vision_config.apply_vit_abs_pos_embed
+        self.deepstack_visual_indexes = vision_config.deepstack_visual_indexes
+
+        self.patch_embed = Qwen3_VisionPatchEmbed(
+            patch_size=self.patch_size,
+            temporal_patch_size=self.temporal_patch_size,
+            in_channels=vision_config.in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        # vit pos embeding, TODO: spatial_patch_size vs patch_size
+        if self.apply_vit_abs_pos_embed:
+            self.pos_embed = nn.Embedding(self.num_grid_per_side**2, self.hidden_size)
+        else:
+            self.pos_embed = nn.Parameter(
+                torch.empty([1, self.num_grid_per_side**2, self.hidden_size])
+            )
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [
+                Qwen3_VisionBlock(
+                    dim=self.hidden_size,
+                    num_heads=self.num_heads,
+                    mlp_hidden_dim=vision_config.intermediate_size,
+                    act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act],
+                    norm_layer=norm_layer,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.blocks.{layer_idx}",
+                )
+                for layer_idx in range(vision_config.depth)
+            ]
+        )
+        self.merger = Qwen3_VisionPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=self.hidden_size,
+            norm_layer=norm_layer,
+            spatial_merge_size=self.spatial_merge_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.merger",
+        )
+        if self.deepstack_visual_indexes is not None:
+            self.merger_list = nn.ModuleList(
+                [
+                    Qwen3_VisionPatchMerger(
+                        d_model=vision_config.out_hidden_size,
+                        context_dim=self.hidden_size,
+                        spatial_merge_size=self.spatial_merge_size,
+                        use_postshuffle_norm=True,
+                        norm_layer=norm_layer,
+                        quant_config=quant_config,
+                        prefix=f"{prefix}.merger_list.{layer_idx}",
+                    )
+                    for layer_idx in range(len(self.deepstack_visual_indexes))
+                ]
+            )
+
+        self.attn_backend = get_vit_attn_backend(
+            head_size=head_dim,
+            dtype=torch.get_default_dtype(),
+            attn_backend_override=attn_backend_override,
+        )
+        if self.attn_backend != _Backend.FLASH_ATTN and check_upstream_fa_availability(
+            torch.get_default_dtype()
+        ):
+            self.attn_backend = _Backend.FLASH_ATTN
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(self, grid_thw):
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
+        num_grid_per_side = self.num_grid_per_side
+        m_size = self.spatial_merge_size
+        hidden_dim = self.pos_embed.embedding_dim
+
+        outputs = []
+        for t, h, w in grid_thw:
+            h_idxs = torch.linspace(
+                0, num_grid_per_side - 1, h, dtype=torch.float32, device=self.device
+            )
+            w_idxs = torch.linspace(
+                0, num_grid_per_side - 1, w, dtype=torch.float32, device=self.device
+            )
+
+            h_floor = h_idxs.to(torch.long)
+            w_floor = w_idxs.to(torch.long)
+            h_ceil = torch.clamp(h_floor + 1, max=num_grid_per_side - 1)
+            w_ceil = torch.clamp(w_floor + 1, max=num_grid_per_side - 1)
+
+            dh = h_idxs - h_floor
+            dw = w_idxs - w_floor
+
+            # Create meshgrid view for all h, w vars
+            dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing="ij")
+            h_floor_grid, w_floor_grid = torch.meshgrid(h_floor, w_floor, indexing="ij")
+            h_ceil_grid, w_ceil_grid = torch.meshgrid(h_ceil, w_ceil, indexing="ij")
+            h_floor_grid_idx = h_floor_grid * num_grid_per_side
+            h_ceil_grid_idx = h_ceil_grid * num_grid_per_side
+
+            # original computation of weights
+            # w00 = (1 - dh_grid) * (1 - dw_grid)
+            # w01 = (1 - dh_grid) * dw_grid
+            # w10 = dh_grid * (1 - dw_grid)
+            # w11 = dh_grid * dw_grid
+            # we reuse w11 here to avoid duplicate
+            # dh_grid * dw_grid computation
+            w11 = dh_grid * dw_grid
+            w10 = dh_grid - w11
+            w01 = dw_grid - w11
+            w00 = 1 - dh_grid - dw_grid + w11
+
+            idx00 = h_floor_grid_idx + w_floor_grid
+            idx01 = h_floor_grid_idx + w_ceil_grid
+            idx10 = h_ceil_grid_idx + w_floor_grid
+            idx11 = h_ceil_grid_idx + w_ceil_grid
+
+            indices = torch.stack([idx00, idx01, idx10, idx11], dim=0).reshape(4, -1)
+            weights = torch.stack([w00, w01, w10, w11], dim=0).reshape(4, -1, 1)
+            weights = weights.to(dtype=self.dtype, device=self.device)
+
+            embeds = self.pos_embed(indices)
+            weighted_embeds = embeds * weights
+            p0, p1, p2, p3 = weighted_embeds.unbind(dim=0)
+            combined = p0 + p1 + p2 + p3
+
+            combined = combined.view(h * w, hidden_dim)
+            repeated = combined.unsqueeze(0).expand(t, -1, -1).contiguous()
+            repeated = repeated.view(
+                t, h // m_size, m_size, w // m_size, m_size, hidden_dim
+            )
+            repeated = repeated.permute(0, 1, 3, 2, 4, 5).reshape(-1, hidden_dim)
+            outputs.append(repeated)
+
+        return torch.cat(outputs, dim=0)
+
+    def compute_attn_mask_seqlen(
+        self,
+        cu_seqlens: torch.Tensor,
+    ) -> tuple[int | None, list[int] | None]:
+        max_seqlen, seqlens = None, None
+        if self.attn_backend == _Backend.FLASH_ATTN:
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        elif self.attn_backend == _Backend.XFORMERS:
+            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        return max_seqlen, seqlens
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: list[list[int]],
+    ) -> torch.Tensor:
+        hidden_states = x.to(device=self.device, dtype=self.dtype)
+        hidden_states = self.patch_embed(hidden_states)
+
+        if self.apply_vit_abs_pos_embed:
+            pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
+            hidden_states = hidden_states + pos_embeds
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(
+            dim=0,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        hidden_states = hidden_states.unsqueeze(1)
+        rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
+        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+
+        hidden_states_list = []
+        deepstack_visual_indexes = self.deepstack_visual_indexes
+
+        for layer_num, blk in enumerate(self.blocks):
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen,
+                seqlens=seqlens,
+            )
+            if (
+                deepstack_visual_indexes is not None
+                and layer_num in deepstack_visual_indexes
+            ):
+                hidden_states_list.append(hidden_states)
+
+        hidden_states = self.merger(hidden_states)
+
+        # processing deepstack
+        if deepstack_visual_indexes is not None:
+            processed_hidden_states_list = [hidden_states]
+            for idx, x in enumerate(hidden_states_list):
+                x = self.merger_list[idx](x)
+                processed_hidden_states_list.append(x)
+            # we cat the original visual features and deepstack features
+            # along the feature dim
+            hidden_states = torch.cat(
+                processed_hidden_states_list, dim=1
+            )  # [seq_len, hidden_size * (1 + depth_of_deepstack)]
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("attn.qkv.", "attn.q.", "q"),
+            ("attn.qkv.", "attn.k.", "k"),
+            ("attn.qkv.", "attn.v.", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if name.endswith("patch_embed.proj.weight"):
+                loaded_weight = conv3d_to_linear_weight(loaded_weight)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        "deepstack_input_embeds": 0,
+    }
+)
+class Qwen3MoeLLMModel(Qwen3MoeModel):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        self.deepstack_multiscale_layer_start = 1
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        deepstack_input_embeds: IntermediateTensors | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer_idx, layer in enumerate(
+            self.layers[self.start_layer : self.end_layer]
+        ):
+            layer_idx = layer_idx + self.start_layer
+
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+            if deepstack_input_embeds is not None and layer_idx in range(
+                0, len(deepstack_input_embeds)
+            ):
+                hidden_states = (
+                    hidden_states
+                    + deepstack_input_embeds[f"deepstack_input_embeds_{layer_idx}"]
+                )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super(Qwen3MoeForCausalLM, self).__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen3MoeLLMModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size, config.hidden_size, quant_config=quant_config
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+
+class Qwen3OmniMoeThinkerProcessingInfo(
+    Qwen2AudioProcessingInfo, Qwen2_5_VLProcessingInfo
+):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen3OmniMoeConfig).thinker_config
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen3OmniMoeProcessor:
+        processor = self.ctx.get_hf_processor(
+            Qwen3OmniMoeProcessor,
+            use_fast=kwargs.pop("use_fast", True),
+            **kwargs,
+        )
+        if not hasattr(processor, "audio_token"):
+            processor.audio_token = "<|audio_pad|>"
+        if not hasattr(processor, "image_token"):
+            processor.image_token = "<|image_pad|>"
+        if not hasattr(processor, "video_token"):
+            processor.video_token = "<|video_pad|>"
+        return processor
+
+    def get_feature_extractor(self, **kwargs: object):
+        hf_processor = self.get_hf_processor(**kwargs)
+        feature_extractor = hf_processor.feature_extractor  # type: ignore
+        assert isinstance(feature_extractor, WhisperFeatureExtractor)
+        return feature_extractor
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"audio": None, "image": None, "video": None}
+
+
+Qwen3OmniMoeThinkerDummyInputsBuilder = Qwen2_5OmniThinkerDummyInputsBuilder
+
+
+class Qwen3OmniMoeThinkerMultiModalProcessor(
+    Qwen2_5OmniThinkerMultiModalProcessor,
+):
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        mm_data = dict(mm_data)
+        audios = mm_data.pop("audios", [])
+
+        def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
+            length = x.shape[-1]
+            if length % hop_length != 0:
+                pad_length = hop_length - (length % hop_length)
+                x = np.pad(x, (0, pad_length), mode="constant", constant_values=0)
+            return x
+
+        # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
+        feature_extractor = self.info.get_feature_extractor()
+        hop_length = feature_extractor.hop_length
+        if audios:
+            # NOTE: Qwen3-Omni processor accept "audio"
+            # To make sure the cache works with padding=True, we pre-padded
+            # the audio to multiple of hop_length.
+            mm_data["audio"] = [
+                pad_to_hop_length(audio, hop_length)
+                if isinstance(audio, np.ndarray)
+                else (pad_to_hop_length(audio[0], hop_length), audio[1])
+                for audio in audios
+            ]
+            mm_kwargs = dict(
+                **mm_kwargs,
+            )
+            # TODO(Isotr0py): Remove this patch after upstream fix PR
+            # released and Transformers version update:
+            # https://github.com/huggingface/transformers/pull/41473
+            if (
+                Version(TRANSFORMERS_VERSION) < Version("4.58.0")
+                and "truncation" not in mm_kwargs
+            ):
+                mm_kwargs["truncation"] = False
+
+        hf_inputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        if (
+            "audio_feature_lengths" in hf_inputs
+            and "feature_attention_mask" in hf_inputs
+            and (audios := mm_data.get("audio", []))
+        ):
+            audio_num_frames = []
+            for _, audio in enumerate(audios):
+                audio_length = len(audio[0]) if isinstance(audio, tuple) else len(audio)
+                num_frame = (
+                    (audio_length // hop_length)
+                    if audio_length % hop_length == 0
+                    else (audio_length // hop_length - 1)
+                )
+                if mm_kwargs.get("truncation", False):
+                    num_frame = min(
+                        num_frame, feature_extractor.n_samples // hop_length
+                    )
+                audio_num_frames.append(num_frame)
+            hf_inputs["feature_attention_mask"] = [
+                torch.ones(num_frame) for num_frame in audio_num_frames
+            ]
+            hf_inputs["audio_feature_lengths"] = torch.tensor(audio_num_frames)
+        return hf_inputs
+
+    def _maybe_apply_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        prompt_ids: list[int],
+        mm_kwargs: MultiModalKwargsItems,
+        mm_prompt_updates: MultiModalPromptUpdates,
+        is_update_applied: bool,
+    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
+        """
+        Qwen3-Omni reimplements this function to handle `use_audio_in_video`.
+        """
+        mm_item_counts = mm_items.get_all_counts()
+        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
+
+        use_audio_in_video = False
+        if "video" in mm_kwargs:
+            for item in mm_kwargs["video"]:
+                if item and item["use_audio_in_video"].data:
+                    use_audio_in_video = True
+                else:
+                    use_audio_in_video = False
+
+        if use_audio_in_video and "video" in mm_item_counts:
+            assert "audio" in mm_item_counts
+            mm_item_counts["audio"] -= mm_item_counts["video"]
+
+        # Special case with `use_audio_in_video=True`
+        if use_audio_in_video:
+            if is_update_applied:
+                prompt_ids = self._get_raw_input_ids(prompt_ids, use_audio_in_video)
+            (
+                prompt_ids,
+                mm_placeholders,
+            ) = self._apply_prompt_updates(
+                prompt_ids,
+                mm_prompt_updates,
+            )
+            self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
+        # normal case with `use_audio_in_video=False`
+        elif is_update_applied:
+            mm_placeholders = self._find_mm_placeholders(
+                prompt_ids,
+                mm_prompt_updates,
+            )
+            self._validate_mm_placeholders(
+                mm_placeholders,
+                mm_item_counts,
+            )
+        else:
+            prompt_ids, mm_placeholders = self._apply_prompt_updates(
+                prompt_ids,
+                mm_prompt_updates,
+            )
+            self._validate_mm_placeholders(
+                mm_placeholders,
+                mm_item_counts,
+            )
+
+        return prompt_ids, mm_placeholders
+
+    def get_updates_use_audio_in_video(
+        self,
+        thinker_config: PretrainedConfig,
+        audio_len: int,
+        video_grid_thw: list[int] | torch.Tensor,
+        video_second_per_grid_t: float,
+    ) -> list[int]:
+        shift = 0
+        audio_token_id = thinker_config.audio_token_id
+        video_token_id = thinker_config.video_token_id
+        audio_start_token_id = thinker_config.audio_start_token_id
+        audio_end_token_id = thinker_config.audio_end_token_id
+        spatial_merge_size = thinker_config.vision_config.spatial_merge_size
+        position_id_per_seconds = thinker_config.position_id_per_seconds
+        audio_token_indices = np.arange(next(iter([audio_len])))
+        curr_video_grid_thw = next(iter([video_grid_thw]))
+        height = curr_video_grid_thw[1] // spatial_merge_size
+        width = curr_video_grid_thw[2] // spatial_merge_size
+        video_token_indices = np.arange(curr_video_grid_thw[0]).reshape(-1, 1, 1)
+        video_token_indices = np.broadcast_to(
+            video_token_indices, (video_token_indices.shape[0], height, width)
+        ).reshape(-1)
+        video_token_indices = (
+            (video_token_indices + shift)
+            * next(iter([video_second_per_grid_t]))
+            * position_id_per_seconds
+        )
+        video_data_index, audio_data_index = 0, 0
+        updates = [audio_start_token_id]
+        while video_data_index < len(video_token_indices) and audio_data_index < len(
+            audio_token_indices
+        ):
+            if (
+                video_token_indices[video_data_index]
+                <= audio_token_indices[audio_data_index]
+            ):
+                updates += [video_token_id]
+                video_data_index += 1
+            else:
+                updates += [audio_token_id]
+                audio_data_index += 1
+        if video_data_index < len(video_token_indices):
+            updates += [video_token_id] * (len(video_token_indices) - video_data_index)
+        if audio_data_index < len(audio_token_indices):
+            updates += [audio_token_id] * (len(audio_token_indices) - audio_data_index)
+        updates += [audio_end_token_id]
+        return updates
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
+        vocab = tokenizer.get_vocab()
+
+        audio_token = processor.audio_token
+        image_token = processor.image_token
+        video_token = processor.video_token
+        audio_token_id = vocab[audio_token]
+        image_token_id = vocab[image_token]
+        video_token_id = vocab[video_token]
+
+        out_mm_data = out_mm_kwargs.get_data()
+        audio_feature_lengths = out_mm_data.get("audio_feature_lengths")
+        feature_attention_mask = out_mm_data.get("feature_attention_mask")
+        if audio_feature_lengths is None and feature_attention_mask is None:
+            audio_output_lengths = []
+        elif audio_feature_lengths is not None:
+            _, audio_output_lens = _get_feat_extract_output_lengths(
+                audio_feature_lengths
+            )
+            audio_output_lengths = audio_output_lens.tolist()
+        elif feature_attention_mask is not None:
+            assert isinstance(feature_attention_mask, torch.Tensor)
+            _, audio_output_lens = _get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1)
+            )
+            audio_output_lengths = audio_output_lens.tolist()
+
+        # number of audios read from video.
+        audio_in_video_item_idx = 0
+        audio_item_idx = 0
+
+        def get_replacement_qwen2_audio(item_idx: int):
+            nonlocal audio_item_idx
+            item_idx += audio_in_video_item_idx
+
+            audio_item_idx += 1
+
+            num_features = audio_output_lengths[item_idx]
+            if num_features == 0:
+                audios = mm_items.get_items("audio", AudioProcessorItems)
+                audio = audios.get(item_idx)
+                raise ValueError(
+                    f"The audio {audio} (len={len(audio)}) is too short "
+                    "to be represented inside the model"
+                )
+
+            return [audio_token_id] * num_features
+
+        def get_replacement_qwen2_vision(item_idx: int, modality: str):
+            grid_thw = out_mm_data[f"{modality}_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+            merge_length = image_processor.merge_size**2
+
+            token_id = image_token_id if modality == "image" else video_token_id
+            return [token_id] * (int(grid_thw.prod()) // merge_length)
+
+        use_audio_in_video = hf_processor_mm_kwargs.get("use_audio_in_video", False)
+        thinker_config = self.info.get_hf_config()
+
+        def get_replacement_qwen2_use_audio_in_video(item_idx: int):
+            nonlocal audio_in_video_item_idx
+            audio_num_features = audio_output_lengths[audio_item_idx + item_idx]
+            video_grid_thw = out_mm_data["video_grid_thw"][item_idx]
+
+            audio_in_video_item_idx += 1
+
+            second_per_grid_ts = hf_processor_mm_kwargs.get("second_per_grid_ts", None)
+            if second_per_grid_ts:
+                video_second_per_grid_t = second_per_grid_ts[item_idx]
+            else:
+                video_second_per_grid_t = 1.0
+
+            return self.get_updates_use_audio_in_video(
+                thinker_config=thinker_config,
+                audio_len=audio_num_features,
+                video_grid_thw=video_grid_thw,
+                video_second_per_grid_t=video_second_per_grid_t,
+            )
+
+        video_replacement_fn = (
+            get_replacement_qwen2_use_audio_in_video
+            if use_audio_in_video
+            else partial(get_replacement_qwen2_vision, modality="video")
+        )
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=audio_token,
+                replacement=get_replacement_qwen2_audio,
+            ),
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=partial(get_replacement_qwen2_vision, modality="image"),
+            ),
+            PromptReplacement(
+                modality="video",
+                target=video_token,
+                replacement=video_replacement_fn,
+            ),
+        ]
+
+    def _validate_mm_placeholders(
+        self,
+        mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
+        mm_item_counts: Mapping[str, int],
+    ) -> None:
+        BaseMultiModalProcessor[
+            Qwen2_5OmniThinkerProcessingInfo
+        ]._validate_mm_placeholders(self, mm_placeholders, mm_item_counts)
+
+    def _get_raw_input_ids(
+        self,
+        token_ids: list[int],
+        use_audio_in_video: bool = False,
+    ) -> list[int]:
+        tokenizer = self.info.get_tokenizer()
+        vision_bos_token = tokenizer.encode(tokenizer.vision_bos_token)[0]
+        vision_eos_token = tokenizer.encode(tokenizer.vision_eos_token)[0]
+        audio_bos_token = tokenizer.encode(tokenizer.audio_bos_token)[0]
+        audio_eos_token = tokenizer.encode(tokenizer.audio_eos_token)[0]
+        audio_token = tokenizer.encode("<|audio_pad|>")[0]
+        image_token = tokenizer.encode("<|image_pad|>")[0]
+        video_token = tokenizer.encode("<|video_pad|>")[0]
+
+        result = token_ids[:]
+        if use_audio_in_video:
+            while True:
+                start = None
+                for i in range(len(result) - 1):
+                    if result[i : i + 2] == [vision_bos_token, audio_bos_token]:
+                        start = i
+                        break
+                if start is not None:
+                    end = None
+                    for i in range(start + 2, len(result) - 1):
+                        if result[i : i + 2] == [audio_eos_token, vision_eos_token]:
+                            end = i
+                            break
+                    if end is not None:
+                        result = (
+                            result[:start]
+                            + [vision_bos_token, video_token, vision_eos_token]
+                            + result[end + 2 :]
+                        )
+                else:
+                    break
+
+        for mm_token in [audio_token, image_token, video_token]:
+            compressed = []
+            for x in result:
+                if x != mm_token or (not compressed or compressed[-1] != mm_token):
+                    compressed.append(x)
+            result = compressed
+
+        return result
+
+
+class Qwen3OmniMoeConditionalGenerationMixin(Qwen2_5OmniConditionalGenerationMixin):
+    def _validate_and_reshape_mm_tensor(
+        self, mm_input: object, name: str, dim: int = 0
+    ) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. Got type: {type(mm_input)}")
+        if name == "feature_attention_mask":
+            dim = -1
+        if isinstance(mm_input, torch.Tensor):
+            return torch.concat(list(mm_input), dim=dim)
+        else:
+            if isinstance(mm_input[0], list):
+                return torch.concat(
+                    [torch.concat(mm_input[i], dim=dim) for i in range(len(mm_input))],
+                    dim=dim,
+                )
+            else:
+                return torch.concat(mm_input, dim=dim)
+
+    def _process_audio_input(
+        self,
+        audio_input: Qwen2AudioFeatureInputs,
+        audio_hashes: list[str] = None,
+        cached_audio_features: torch.Tensor = None,
+    ) -> torch.Tensor:
+        input_features = audio_input["input_features"]
+        audio_feature_lengths = audio_input["audio_feature_lengths"]
+
+        if input_features.ndim == 3:
+            assert input_features.shape[0] == 1
+            input_features = input_features.squeeze(0)
+
+        if not isinstance(audio_feature_lengths, torch.Tensor):
+            audio_feature_lengths = torch.cat(audio_feature_lengths)
+        if audio_feature_lengths.ndim == 2:
+            audio_feature_lengths = audio_feature_lengths.reshape(-1)
+
+        audio_feat_lengths, audio_output_lengths = (
+            _get_feat_extract_output_lengths(audio_feature_lengths)
+        )
+
+        audio_outputs = self.audio_tower(
+            input_features.to(self.audio_tower.dtype),
+            feature_lens=audio_feature_lengths,
+            aftercnn_lens=audio_feat_lengths,
+        )
+        audio_features = audio_outputs.last_hidden_state
+        return audio_features.split(audio_output_lengths.tolist())
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen3OmniMoeThinkerMultiModalProcessor,
+    info=Qwen3OmniMoeThinkerProcessingInfo,
+    dummy_inputs=Qwen3OmniMoeThinkerDummyInputsBuilder,
+)
+class Qwen3OmniMoeThinkerForConditionalGeneration(
+    nn.Module,
+    SupportsMultiModal,
+    SupportsPP,
+    SupportsMRoPE,
+    Qwen3OmniMoeConditionalGenerationMixin,
+):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "thinker.lm_head.": "language_model.lm_head.",
+            "thinker.model.": "language_model.model.",
+            "thinker.": "",
+        }
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+        if modality.startswith("audio"):
+            return "<|audio_start|><|audio_pad|><|audio_end|>"
+
+        raise ValueError("Only image, video or audio modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        thinker_config: Qwen3OmniMoeThinkerConfig = (
+            vllm_config.model_config.hf_config.thinker_config
+        )
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = thinker_config
+        self.multimodal_config = multimodal_config
+
+        # force "use_flash_attention_2=True" to audio tower to align
+        # the results.
+        if flash_attn is not None:
+            audio_config = thinker_config.audio_config
+            audio_config._attn_implementation_autoset = True
+            audio_config._attn_implementation = "flash_attention_2"
+        else:
+            logger.warning(
+                "flash_attn is not available, the model may not yield the "
+                "exactly same result as the transformers implementation "
+                "in the audio tower part."
+            )
+
+        self.audio_tower = Qwen3OmniMoeAudioEncoder(thinker_config.audio_config)
+        
+        attn_backend_override = (
+            multimodal_config.mm_encoder_attn_backend
+            if multimodal_config is not None
+            else None
+        )
+        self.visual = Qwen3Omni_VisionTransformer(
+            vision_config=thinker_config.vision_config,
+            norm_eps=getattr(thinker_config.text_config, "rms_norm_eps", 1e-6),
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "visual"),
+            attn_backend_override=attn_backend_override,
+        )
+        self.quant_config = quant_config
+
+        self.language_model = Qwen3MoeLLMForCausalLM(
+            vllm_config=vllm_config.with_hf_config(
+                thinker_config.text_config, architectures=["Qwen3MoeForCausalLM"]
+            ),
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+        self.use_deepstack = hasattr(
+            thinker_config.vision_config, "deepstack_visual_indexes"
+        )
+        self.deepstack_num_level = (
+            len(thinker_config.vision_config.deepstack_visual_indexes)
+            if self.use_deepstack
+            else 0
+        )
+        # register buffer for deepstack
+        self.deepstack_input_embeds = (
+            [
+                torch.zeros(
+                    vllm_config.scheduler_config.max_num_batched_tokens,
+                    thinker_config.text_config.hidden_size,
+                )
+                for _ in range(self.deepstack_num_level)
+            ]
+            if self.use_deepstack
+            else None
+        )
+        self.visual_dim = thinker_config.vision_config.out_hidden_size
+        self.multiscale_dim = self.visual_dim * self.deepstack_num_level
+
+    def _get_deepstack_input_embeds(self, num_tokens: int) -> IntermediateTensors:
+        # get deepstack_input_embeds from buffer, and clear the buffer
+        return IntermediateTensors(
+            {
+                f"deepstack_input_embeds_{idx}": self.deepstack_input_embeds[idx][
+                    :num_tokens
+                ]
+                for idx in range(self.deepstack_num_level)
+            }
+        )
+
+    def _set_deepstack_input_embeds(self, deepstack_input_embeds: torch.Tensor) -> None:
+        # set deepstack_input_embeds to buffer
+        num_tokens = deepstack_input_embeds.size(1)
+        if num_tokens > self.deepstack_input_embeds[0].size(0):
+            self.deepstack_input_embeds = [
+                torch.zeros(
+                    num_tokens,
+                    self.config.text_config.hidden_size,
+                    device=self.deepstack_input_embeds[0].device,
+                    dtype=self.deepstack_input_embeds[0].dtype,
+                )
+                for _ in range(self.deepstack_num_level)
+            ]
+        for idx in range(self.deepstack_num_level):
+            self.deepstack_input_embeds[idx][:num_tokens].copy_(
+                deepstack_input_embeds[idx]
+            )
+
+    def _clear_deepstack_input_embeds(self, num_tokens: int) -> None:
+        # clear deepstack_input_embeds in buffer
+        if num_tokens > 0:
+            for idx in range(self.deepstack_num_level):
+                self.deepstack_input_embeds[idx][:num_tokens].zero_()
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if (
+                input_key in ("pixel_values", "image_embeds")
+                and "image" not in mm_input_by_modality
+            ):
+                mm_input_by_modality["image"] = self._parse_and_validate_image_input(
+                    **kwargs
+                )
+            if (
+                input_key in ("pixel_values_videos", "video_embeds")
+                and "video" not in mm_input_by_modality
+            ):
+                mm_input_by_modality["video"] = self._parse_and_validate_video_input(
+                    **kwargs
+                )
+            if (
+                input_key in ("input_audio_features")
+                and "audio" not in mm_input_by_modality
+            ):
+                mm_input_by_modality["audio"] = self._parse_and_validate_audio_input(
+                    **kwargs
+                )
+        return mm_input_by_modality
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+        self, **kwargs: object
+    ) -> MultiModalEmbeddings | None:
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not mm_input_by_modality:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                image_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings += tuple(image_embeddings)
+            if modality == "video":
+                video_embeddings = self._process_video_input(multimodal_input)
+                multimodal_embeddings += tuple(video_embeddings)
+            if modality == "audio":
+                audio_embeddings = self._process_audio_input(multimodal_input)
+                multimodal_embeddings += tuple(audio_embeddings)
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
+    ) -> torch.Tensor:
+        inputs_embeds = self._get_text_embeddings(
+            input_ids,
+            self.language_model.get_input_embeddings,
+            is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        deepstack_input_embeds = None
+        # TODO (ywang96): support overlapping modalitiy embeddings so that
+        # `use_audio_in_video` will work on V1.
+        # split the feat dim to obtain multi-scale visual feature
+        has_vision_embeddings = [
+            embeddings.shape[-1] != self.config.text_config.hidden_size
+            for embeddings in multimodal_embeddings
+        ]
+        if self.visual.deepstack_visual_indexes is not None and any(
+            has_vision_embeddings
+        ):
+            multiscale_len = len(self.visual.deepstack_visual_indexes)
+            multimodal_embeddings_multiscale = []
+            is_vision = torch.zeros_like(is_multimodal)
+            mm_positions = torch.nonzero(is_multimodal, as_tuple=True)[0]
+            mm_position_idx = 0
+            for index, embeddings in enumerate(multimodal_embeddings):
+                num_tokens = embeddings.shape[0]
+                current_positions = mm_positions[
+                    mm_position_idx : mm_position_idx + num_tokens
+                ]
+
+                # Vision embeddings
+                if embeddings.shape[-1] != self.config.text_config.hidden_size:
+                    visual_dim = embeddings.shape[-1] // (multiscale_len + 1)
+                    multi_dim = visual_dim * multiscale_len
+                    embeddings_main, embeddings_multiscale = torch.split(
+                        embeddings, [visual_dim, multi_dim], dim=-1
+                    )
+                    multimodal_embeddings[index] = embeddings_main
+                    multimodal_embeddings_multiscale.append(embeddings_multiscale)
+                    is_vision[current_positions] = True
+
+                # Audio embeddings
+                else:
+                    is_vision[current_positions] = False
+
+                mm_position_idx += num_tokens
+
+            deepstack_input_embeds = inputs_embeds.new_zeros(
+                inputs_embeds.size(0), multiscale_len * inputs_embeds.size(1)
+            )
+            deepstack_input_embeds = _merge_multimodal_embeddings(
+                inputs_embeds=deepstack_input_embeds,
+                multimodal_embeddings=multimodal_embeddings_multiscale,
+                is_multimodal=is_vision,
+            )
+            deepstack_input_embeds = (
+                deepstack_input_embeds.view(
+                    inputs_embeds.shape[0], multiscale_len, visual_dim
+                )
+                .permute(1, 0, 2)
+                .contiguous()
+            )
+            self._set_deepstack_input_embeds(deepstack_input_embeds)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor | IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        if (
+            self.use_deepstack
+            and inputs_embeds is not None
+            and get_pp_group().is_first_rank
+        ):
+            deepstack_input_embeds = self._get_deepstack_input_embeds(
+                inputs_embeds.size(0)
+            )
+        else:
+            deepstack_input_embeds = None
+
+        hidden_states = self.language_model.model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            # args for deepstack
+            deepstack_input_embeds=deepstack_input_embeds,
+        )
+
+        if inputs_embeds is not None and get_pp_group().is_first_rank:
+            self._clear_deepstack_input_embeds(inputs_embeds.size(0))
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.language_model.compute_logits(hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["talker.", "code2wav."],
+        )
+        loaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+        return loaded_weights
+    
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: list[list[int]] | torch.Tensor | None,
+        video_grid_thw: list[list[int]] | torch.Tensor | None,
+        second_per_grid_ts: list[float] | None = None,
+        context_len: int = 0,
+        seq_len: int | None = None,
+        audio_feature_lengths: torch.Tensor | None = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        config = hf_config.thinker_config
+        if isinstance(image_grid_thw, list):
+            image_grid_thw = torch.tensor(image_grid_thw)
+        if isinstance(video_grid_thw, list):
+            video_grid_thw = torch.tensor(video_grid_thw)
+        input_ids = torch.tensor(input_tokens)
+        if input_ids is None or input_ids.ndim != 1:
+            raise ValueError("_omni3_get_input_positions_tensor expects 1D input_ids")
+
+        seq_len = input_ids.shape[0]
+        if audio_feature_lengths is not None and not isinstance(
+            audio_feature_lengths, torch.Tensor
+        ):
+            audio_feature_lengths = torch.as_tensor(
+                audio_feature_lengths, dtype=torch.long
+            )
+        if second_per_grid_ts is None:
+            if video_grid_thw is not None and video_grid_thw.numel() > 0:
+                second_per_grids = torch.ones(
+                    video_grid_thw.shape[0], dtype=torch.float32
+                )
+            else:
+                second_per_grids = torch.tensor([], dtype=torch.float32)
+        else:
+            second_per_grids = torch.tensor(second_per_grid_ts, dtype=torch.float32)
+
+        spatial_merge_size = config.vision_config.spatial_merge_size
+        image_token_id = config.image_token_id
+        video_token_id = config.video_token_id
+        audio_token_id = config.audio_token_id
+        vision_start_token_id = config.vision_start_token_id
+        audio_start_token_id = config.audio_start_token_id
+        position_id_per_seconds = config.position_id_per_seconds
+
+        vision_start_indices = torch.argwhere(
+            input_ids == vision_start_token_id
+        ).squeeze(1)
+        if vision_start_indices.numel() > 0:
+            vision_tokens = input_ids[vision_start_indices + 1]
+        else:
+            vision_tokens = input_ids.new_empty((0,), dtype=input_ids.dtype)
+        audio_nums = torch.sum(input_ids == audio_start_token_id)
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (
+            (vision_tokens == audio_start_token_id).sum()
+            if use_audio_in_video
+            else (vision_tokens == video_token_id).sum()
+        )
+
+        llm_pos_ids_list: list[torch.Tensor] = []
+        st = 0
+        image_idx = 0
+        video_idx = 0
+        audio_idx = 0
+        remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums  # noqa: E501
+        multimodal_nums = (
+            image_nums + audio_nums
+            if use_audio_in_video
+            else image_nums + video_nums + audio_nums
+        )  # noqa: E501
+
+        for _ in range(multimodal_nums):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+            if (image_token_id in input_tokens or video_token_id in input_tokens) and (
+                remain_videos > 0 or remain_images > 0
+            ):
+                ed_vision_start = input_tokens.index(vision_start_token_id, st)
+            else:
+                ed_vision_start = len(input_tokens) + 1
+            if audio_token_id in input_tokens and remain_audios > 0:
+                ed_audio_start = input_tokens.index(audio_start_token_id, st)
+            else:
+                ed_audio_start = len(input_tokens) + 1
+            min_ed = min(ed_vision_start, ed_audio_start)
+
+            if min_ed == ed_audio_start:
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                _, audio_len = _get_feat_extract_output_lengths(
+                    audio_feature_lengths[audio_idx]
+                )
+                llm_pos_ids = (
+                    torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + audio_len + eos_len
+                audio_idx += 1
+                remain_audios -= 1
+            elif (
+                min_ed == ed_vision_start
+                and input_ids[ed_vision_start + 1] == image_token_id
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                grid_t = image_grid_thw[image_idx][0]
+                grid_hs = image_grid_thw[:, 1]
+                grid_ws = image_grid_thw[:, 2]
+                t_index = torch.arange(grid_t) * position_id_per_seconds
+                llm_pos_ids = get_llm_pos_ids_for_vision(
+                    st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + image_len + eos_len
+                image_idx += 1
+                remain_images -= 1
+            elif (
+                min_ed == ed_vision_start
+                and input_ids[ed_vision_start + 1] == video_token_id
+                and not use_audio_in_video
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                grid_t = video_grid_thw[video_idx][0]
+                grid_hs = video_grid_thw[:, 1]
+                grid_ws = video_grid_thw[:, 2]
+                t_index = (
+                    torch.arange(grid_t)
+                    * float(second_per_grids[video_idx].item())
+                    * position_id_per_seconds
+                )
+                llm_pos_ids = get_llm_pos_ids_for_vision(
+                    st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + video_len + eos_len
+                video_idx += 1
+                remain_videos -= 1
+            elif (
+                min_ed == ed_vision_start
+                and ed_vision_start + 1 == ed_audio_start
+                and use_audio_in_video
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                bos_block = (
+                    torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(bos_block)
+                llm_pos_ids_list.append(bos_block)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                _, audio_len = _get_feat_extract_output_lengths(
+                    audio_feature_lengths[audio_idx]
+                )
+                audio_llm_pos_ids = (
+                    torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                grid_t = video_grid_thw[video_idx][0]
+                grid_hs = video_grid_thw[:, 1]
+                grid_ws = video_grid_thw[:, 2]
+                t_index = (
+                    torch.arange(grid_t)
+                    * float(second_per_grids[video_idx].item())
+                    * position_id_per_seconds
+                )
+                video_llm_pos_ids = get_llm_pos_ids_for_vision(
+                    st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                video_data_index, audio_data_index = 0, 0
+                while (
+                    video_data_index < video_llm_pos_ids.shape[-1]
+                    and audio_data_index < audio_llm_pos_ids.shape[-1]
+                ):
+                    if (
+                        video_llm_pos_ids[0][video_data_index]
+                        <= audio_llm_pos_ids[0][audio_data_index]
+                    ):
+                        llm_pos_ids_list.append(
+                            video_llm_pos_ids[
+                                :, video_data_index : video_data_index + 1
+                            ]
+                        )
+                        video_data_index += 1
+                    else:
+                        llm_pos_ids_list.append(
+                            audio_llm_pos_ids[
+                                :, audio_data_index : audio_data_index + 1
+                            ]
+                        )
+                        audio_data_index += 1
+                if video_data_index < video_llm_pos_ids.shape[-1]:
+                    llm_pos_ids_list.append(
+                        video_llm_pos_ids[
+                            :, video_data_index : video_llm_pos_ids.shape[-1]
+                        ]
+                    )
+                if audio_data_index < audio_llm_pos_ids.shape[-1]:
+                    llm_pos_ids_list.append(
+                        audio_llm_pos_ids[
+                            :, audio_data_index : audio_llm_pos_ids.shape[-1]
+                        ]
+                    )
+                video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                eos_block = (
+                    torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(eos_block)
+                llm_pos_ids_list.append(eos_block)
+                st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2  # noqa: E501
+                audio_idx += 1
+                video_idx += 1
+                remain_videos -= 1
+                remain_audios -= 1
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
+                + st_idx
+            )
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        if llm_positions.shape[1] != seq_len:
+            raise RuntimeError("Position ids length mismatch with input ids length")
+
+        mrope_position_delta = llm_positions.max() + 1 - seq_len
+        return llm_positions, mrope_position_delta
\ No newline at end of file

vllm/model_executor/models/registry.py

@@ -270,6 +270,7 @@ _MULTIMODAL_MODELS = {
     "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
     "Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
     "Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
+    "Qwen3OmniMoeForConditionalGeneration": ("qwen3_omni_moe_thinker", "Qwen3OmniMoeThinkerForConditionalGeneration"),
     "Qwen3VLForConditionalGeneration": ("qwen3_vl", "Qwen3VLForConditionalGeneration"),  # noqa: E501
     "Qwen3VLMoeForConditionalGeneration": ("qwen3_vl_moe", "Qwen3VLMoeForConditionalGeneration"),  # noqa: E501
     "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),

vllm/model_executor/models/vision.py

@@ -72,10 +72,18 @@ def get_vision_encoder_info(
     raise NotImplementedError(msg)
 
 
-def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend:
+def get_vit_attn_backend(
+    head_size: int,
+    dtype: torch.dtype,
+    *,
+    attn_backend_override: _Backend | None = None,
+) -> _Backend:
     """
     Get the available attention backend for Vision Transformer.
     """
+    if attn_backend_override is not None:
+        return attn_backend_override
+    
     # Lazy import to avoid circular dependency
     from vllm.attention.selector import get_env_variable_attn_backend
 
@@ -402,3 +410,56 @@ def run_dp_sharded_mrope_vision_model(
     assert len(out_embeddings) == len(
         original_order_embeddings), "Found unassigned embeddings"
     return out_embeddings
+
+
+def get_llm_pos_ids_for_vision(
+    start_idx: int,
+    vision_idx: int,
+    spatial_merge_size: int,
+    t_index: list[int],
+    grid_hs: torch.Tensor,
+    grid_ws: torch.Tensor,
+) -> torch.Tensor:
+    llm_pos_ids_list = []
+    llm_grid_h = grid_hs[vision_idx] // spatial_merge_size
+    llm_grid_w = grid_ws[vision_idx] // spatial_merge_size
+    h_index = (
+        torch.arange(llm_grid_h)
+        .view(1, -1, 1)
+        .expand(len(t_index), -1, llm_grid_w)
+        .flatten()
+    )
+    w_index = (
+        torch.arange(llm_grid_w)
+        .view(1, 1, -1)
+        .expand(len(t_index), llm_grid_h, -1)
+        .flatten()
+    )
+    t_index_tensor = (
+        torch.Tensor(t_index)
+        .to(llm_grid_h.device)
+        .view(-1, 1)
+        .expand(-1, llm_grid_h * llm_grid_w)
+        .long()
+        .flatten()
+    )
+    _llm_pos_ids = torch.stack([t_index_tensor, h_index, w_index])
+    llm_pos_ids_list.append(_llm_pos_ids + start_idx)
+    llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1)
+    return llm_pos_ids
+
+
+# Due to a performance regression with Conv3D in PyTorch2.9, we reshape
+# Conv3D weights to Linear weights for better performance.
+# See: https://github.com/vllm-project/vllm/issues/27406
+# and https://github.com/pytorch/pytorch/issues/166122
+# FIXME(Isotr0py): Revert the PR introduces this workaround
+# (https://github.com/vllm-project/vllm/pull/27418),
+# once the performance issue is resolved in PyTorch.
+def conv3d_to_linear_weight(conv3d_weight: torch.Tensor) -> torch.Tensor:
+    """
+    Reshape Conv3D weight to Linear weight. Only work when kernel_size==stride.
+    """
+    out_channels, in_channels, kt, kh, kw = conv3d_weight.shape
+    linear_weight = conv3d_weight.reshape(out_channels, in_channels * kt * kh * kw)
+    return linear_weight

vllm/v1/worker/gpu_model_runner.py

@@ -752,7 +752,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             if mm_input.get("use_audio_in_video") is True:
                 use_audio_in_video = True
 
-        if supports_mrope(self.model):
+        if supports_mrope(self.get_model()):
             req_state.mrope_positions, req_state.mrope_position_delta = \
                 self.model.get_mrope_input_positions(
                     req_state.prompt_token_ids,