qwen3_vl.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Copyright 2025 The vLLM team.
# Copyright 2025 The Qwen Team.
# Copyright 2025 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 Qwen3VL model compatible with HuggingFace weights."""
from collections.abc import Iterable, Mapping, Sequence
from functools import partial
from typing import Any, Callable, Optional, Union

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BatchFeature
from transformers.models.qwen2_vl import Qwen2VLImageProcessorFast
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
from transformers.models.qwen3_vl import (Qwen3VLProcessor,
                                          Qwen3VLVideoProcessor)
from transformers.models.qwen3_vl.configuration_qwen3_vl import (
    Qwen3VLConfig, Qwen3VLVisionConfig)
from transformers.video_utils import VideoMetadata

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,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
    GPTQMarlinConfig)
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.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargsItem,
                                    MultiModalKwargsItems, VideoItem)
from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        PromptReplacement, PromptUpdate,
                                        PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.platforms import _Backend
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.config import uses_mrope
from vllm.utils import is_list_of

from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                         SupportsMultiModal, SupportsPP)
from .qwen2_5_vl import (Qwen2_5_VisionAttention,
                         Qwen2_5_VisionRotaryEmbedding,
                         Qwen2_5_VLImageEmbeddingInputs, Qwen2_5_VLImageInputs,
                         Qwen2_5_VLImagePixelInputs,
                         Qwen2_5_VLVideoEmbeddingInputs, Qwen2_5_VLVideoInputs,
                         Qwen2_5_VLVideoPixelInputs)
from .qwen2_vl import Qwen2VLProcessingInfo
from .qwen3 import Qwen3ForCausalLM, Qwen3Model
from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
                    maybe_prefix, merge_multimodal_embeddings)
from .vision import get_vit_attn_backend

logger = init_logger(__name__)


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 = nn.Conv3d(in_channels,
                              hidden_size,
                              kernel_size=kernel_size,
                              stride=kernel_size,
                              bias=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        L, C = x.shape
        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
                   self.patch_size)
        x = self.proj(x).view(L, self.hidden_size)
        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: Optional[QuantizationConfig] = None,
                 prefix: str = "",
                 use_data_parallel: bool = False):
        super().__init__()
        self.linear_fc1 = ColumnParallelLinear(in_features,
                                               hidden_features,
                                               bias=bias,
                                               quant_config=quant_config,
                                               return_bias=False,
                                               prefix=f"{prefix}.linear_fc1",
                                               disable_tp=use_data_parallel)
        self.linear_fc2 = RowParallelLinear(hidden_features,
                                            in_features,
                                            bias=bias,
                                            quant_config=quant_config,
                                            return_bias=False,
                                            prefix=f"{prefix}.linear_fc2",
                                            disable_tp=use_data_parallel)
        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: Optional[Callable[[int], nn.Module]] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> 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",
            use_data_parallel=use_data_parallel)
        self.mlp = Qwen3_VisionMLP(dim,
                                   mlp_hidden_dim,
                                   act_fn=act_fn,
                                   bias=True,
                                   quant_config=quant_config,
                                   prefix=f"{prefix}.mlp",
                                   use_data_parallel=use_data_parallel)

    def forward(
            self,
            x: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor,
            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
            seqlens: Optional[list[int]] = 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: Optional[Callable[[int], nn.Module]] = None,
        spatial_merge_size: int = 2,
        use_postshuffle_norm: bool = False,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> 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.norm = norm_layer(context_dim)
        self.linear_fc1 = ColumnParallelLinear(self.hidden_size,
                                               self.hidden_size,
                                               bias=True,
                                               quant_config=quant_config,
                                               prefix=f"{prefix}.linear_fc1",
                                               disable_tp=use_data_parallel)
        self.act_fn = nn.GELU()
        self.linear_fc2 = RowParallelLinear(self.hidden_size,
                                            d_model,
                                            bias=True,
                                            quant_config=quant_config,
                                            prefix=f"{prefix}.linear_fc2",
                                            disable_tp=use_data_parallel)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.use_postshuffle_norm:
            x = self.norm(x.view(-1, self.hidden_size))
        else:
            x = self.norm(x).view(-1, self.hidden_size)

        x_parallel, _ = self.linear_fc1(x)
        x_parallel = self.act_fn(x_parallel)
        out, _ = self.linear_fc2(x_parallel)
        return out


class Qwen3_VisionTransformer(nn.Module):

    def __init__(
        self,
        vision_config: Qwen3VLVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> None:
        super().__init__()
        self.hidden_size = vision_config.hidden_size
        self.num_heads = vision_config.num_heads
        self.num_position_embeddings = vision_config.num_position_embeddings
        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.deepstack_visual_indexes = vision_config.deepstack_visual_indexes
        self.use_data_parallel = use_data_parallel
        self.num_grid_per_side = int(self.num_position_embeddings**0.5)

        # NOTE: This is used for creating empty tensor for all_gather for
        # DP ViT. Here out_hidden_size is enlarged due to deepstack
        self.out_hidden_size = (vision_config.out_hidden_size *
                                (1 + len(self.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,
        )

        self.pos_embed = nn.Embedding(self.num_position_embeddings,
                                      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}",
                use_data_parallel=use_data_parallel)
            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",
            use_data_parallel=use_data_parallel,
        )

        self.deepstack_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}.deepstack_merger_list.{layer_idx}",
                use_data_parallel=use_data_parallel)
            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())
        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 = []
        # Support both Tensor and list inputs for DP path
        if isinstance(grid_thw, list):
            grid_list = grid_thw
            max_grid_size = max(max(h, w) for _, h, w in grid_list)
        else:
            grid_list = grid_thw.tolist()
            max_grid_size = int(grid_thw[:, 1:].max().item())
        for t, h, w in grid_list:
            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)
        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

            w00 = ((1 - dh)[:, None] * (1 - dw)[None, :]).reshape(-1)
            w01 = ((1 - dh)[:, None] * dw[None, :]).reshape(-1)
            w10 = (dh[:, None] * (1 - dw)[None, :]).reshape(-1)
            w11 = (dh[:, None] * dw[None, :]).reshape(-1)

            idx00 = (h_floor[:, None] * num_grid_per_side +
                     w_floor[None, :]).reshape(-1)
            idx01 = (h_floor[:, None] * num_grid_per_side +
                     w_ceil[None, :]).reshape(-1)
            idx10 = (h_ceil[:, None] * num_grid_per_side +
                     w_floor[None, :]).reshape(-1)
            idx11 = (h_ceil[:, None] * num_grid_per_side +
                     w_ceil[None, :]).reshape(-1)

            indices = torch.stack([idx00, idx01, idx10, idx11], dim=0)
            weights = torch.stack([w00, w01, w10, w11],
                                  dim=0).to(dtype=self.dtype,
                                            device=self.device)
            weights = weights.unsqueeze(-1)

            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[Optional[int], Optional[list[int]]]:
        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)

        pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
        hidden_states = hidden_states + pos_embeds
        rotary_pos_emb = self.rot_pos_emb(grid_thw)

        grid_thw_tensor = torch.tensor(grid_thw,
                                       device=self.device,
                                       dtype=torch.int32)

        cu_seqlens = torch.repeat_interleave(
            grid_thw_tensor[:, 1] * grid_thw_tensor[:, 2],
            grid_thw_tensor[:, 0]).cumsum(
                dim=0,
                dtype=grid_thw_tensor.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)

        deepstack_feature_lists = []
        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 layer_num in self.deepstack_visual_indexes:
                deepstack_merger_idx = self.deepstack_visual_indexes.index(
                    layer_num)
                deepstack_feature = self.deepstack_merger_list[
                    deepstack_merger_idx](hidden_states)
                deepstack_feature_lists.append(deepstack_feature)
        hidden_states = self.merger(hidden_states)
        hidden_states = torch.cat(
            [hidden_states] + deepstack_feature_lists,
            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:
            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


class Qwen3VLProcessingInfo(Qwen2VLProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen3VLConfig)

    def get_hf_processor(self, **kwargs: object) -> Qwen3VLProcessor:
        return self.ctx.get_hf_processor(
            Qwen3VLProcessor,
            use_fast=kwargs.pop("use_fast", True),
            **kwargs,
        )

    def get_tokenizer(self):
        return self.ctx.tokenizer

    def get_image_processor(self,
                            **kwargs: object) -> Qwen2VLImageProcessorFast:
        return self.get_hf_processor(**kwargs).image_processor

    def get_video_processor(self, **kwargs: object) -> Qwen3VLVideoProcessor:
        return self.get_hf_processor(**kwargs).video_processor

    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 2,
        do_resize: bool = True,
        image_processor: Optional[Qwen2VLImageProcessorFast],
    ) -> tuple[ImageSize, int]:
        if image_processor is None:
            image_processor = self.get_image_processor()

        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = vision_config.temporal_patch_size

        if do_resize:
            resized_height, resized_width = smart_resize(
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                min_pixels=image_processor.size["shortest_edge"],
                max_pixels=image_processor.size["longest_edge"],
            )
            preprocessed_size = ImageSize(width=resized_width,
                                          height=resized_height)
        else:
            preprocessed_size = ImageSize(width=image_width,
                                          height=image_height)

        padded_num_frames = num_frames + num_frames % temporal_patch_size

        grid_t = max(padded_num_frames // temporal_patch_size, 1)
        grid_h = preprocessed_size.height // patch_size
        grid_w = preprocessed_size.width // patch_size

        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches // (merge_size**2)

        return preprocessed_size, num_vision_tokens

    def _calculate_timestamps(self, indices: list[int] | torch.Tensor,
                              video_fps: float, merge_size: int):
        if not isinstance(indices, list):
            indices = indices.tolist()
        if len(indices) % merge_size != 0:
            # don't update metadata's frames_indices directly
            indices = indices + [indices[-1]
                                 ] * (merge_size - len(indices) % merge_size)
        timestamps = [idx / video_fps for idx in indices]
        timestamps = [(timestamps[i] + timestamps[i + merge_size - 1]) / 2
                      for i in range(0, len(timestamps), merge_size)]
        return timestamps

    def _get_video_second_idx(
            self,
            metadata: dict[str, Any],
            out_item: MultiModalKwargsItem,
            do_sample_frames: Optional[bool] = None,
            sampled_fps: Optional[float] = None) -> list[int]:
        video_processor = self.get_video_processor()
        merge_size = video_processor.merge_size
        indices = metadata["frames_indices"]

        # metadata["fps"] refers to the true fps of the input video.
        video_fps = metadata["fps"]
        if do_sample_frames is None:
            do_sample_frames = metadata.get("do_sample_frames", False)

        # If video frames are sampled in HF processor (instead of vLLM
        # video loader), we need to re-calculate the indices from original
        # metadata.
        if do_sample_frames:
            # here video_fps is the fps of the sampled video, and
            # metadata["fps"] refers to the fps of the original video.
            video_fps = sampled_fps if sampled_fps else video_processor.fps
            total_num_frames = metadata["total_num_frames"]
            num_frames = int(total_num_frames / metadata["fps"] * video_fps)
            num_frames = min(
                min(max(num_frames, video_processor.min_frames),
                    video_processor.max_frames), total_num_frames)
            indices = np.linspace(0, total_num_frames - 1,
                                  num_frames).round().astype(int).tolist()
        timestamps = self._calculate_timestamps(indices, video_fps, merge_size)
        return timestamps


class Qwen3VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3VLProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        image_token = "<|vision_start|><|image_pad|><|vision_end|>"
        video_token = "<|vision_start|><|video_pad|><|vision_end|>"

        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        target_width, target_height = (
            self.info.get_image_size_with_most_features())
        target_num_frames = self.info.get_num_frames_with_most_features(
            seq_len, mm_counts)
        return {
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images),
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
            ),
        }

    def _get_dummy_videos(
        self,
        *,
        width: int,
        height: int,
        num_frames: int,
        num_videos: int,
    ) -> list[VideoItem]:
        num_frames = max(num_frames, 2)
        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
        video_items = []
        for i in range(num_videos):
            video_metadata = {
                "fps": 2.0,
                "duration": num_frames / 2.0,
                "total_num_frames": num_frames,
                "frames_indices": [i for i in range(num_frames)],
                "video_backend": "opencv",
                "do_sample_frames": False,
            }
            video_item = (video.copy(), video_metadata)
            video_items.append(video_item)
        return video_items


class Qwen3VLMultiModalProcessor(BaseMultiModalProcessor[Qwen3VLProcessingInfo]
                                 ):

    def _get_data_parser(self) -> MultiModalDataParser:
        return MultiModalDataParser(video_needs_metadata=True)

    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)
        processor = self.info.get_hf_processor(**mm_kwargs)

        # Separate video processing from image processing. Because the videos
        # are processed into serval image patches
        if ("videos" in mm_data and isinstance(mm_data["videos"], list)
                and len(mm_data["videos"]) > 0):
            video_grid_thw_lst = []
            pixel_values_videos_lst = []

            for item_idx, item in enumerate(mm_data.pop("videos", [])):
                video_array, metadata = item

                # NOTE: @JJJYmmm new attr metadata.frames_indices indicates
                # the sampled frames indices of pre-sampled videos, which is
                # used to calculate the timestamps. Make sure that
                # do_sample_frames in mm_kwargs is false for presampled videos.

                # NOTE: a copy of is created to update do_sample_frames,
                # otherwise mm_hash for the object will be incorrect.
                video_mm_kwargs = dict(**mm_kwargs)
                if "do_sample_frames" not in video_mm_kwargs:
                    # qwen_vl_utils already has "do_sample_frames" in
                    # mm_kwargs, don't overwrite it.
                    video_mm_kwargs["do_sample_frames"] = metadata.get(
                        "do_sample_frames", False)

                metadata = VideoMetadata(**{
                    k: metadata[k]
                    for k in metadata if k != "do_sample_frames"
                })

                video_mm_data = dict()
                video_mm_data["videos"] = [[video_array]]
                video_mm_data["video_metadata"] = [[metadata]]

                video_outputs = super()._call_hf_processor(
                    prompt="<|vision_start|><|video_pad|><|vision_end|>",
                    mm_data=video_mm_data,
                    mm_kwargs=video_mm_kwargs,
                    tok_kwargs=tok_kwargs,
                )
                input_ids = video_outputs.pop("input_ids")
                video_placeholder = processor.tokenizer.batch_decode(
                    input_ids)[0]
                prompt = prompt.replace(
                    "<|vision_start|><|video_pad|><|vision_end|>",
                    video_placeholder,
                    1,
                )

                video_grid_thw_lst.append(video_outputs["video_grid_thw"])
                pixel_values_videos_lst.append(
                    video_outputs["pixel_values_videos"])
            video_outputs = dict(
                pixel_values_videos=torch.cat(pixel_values_videos_lst),
                video_grid_thw=torch.cat(video_grid_thw_lst),
            )
        else:
            video_outputs = dict()

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        combined_outputs = dict(
            processed_outputs,
            **video_outputs,
        )
        return BatchFeature(combined_outputs)

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
        image_grid_sizes = image_grid_thw.prod(-1)

        video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
        video_grid_sizes = video_grid_thw.prod(-1)

        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes(
                "image", image_grid_sizes),
            image_embeds=MultiModalFieldConfig.flat_from_sizes(
                "image", image_grid_sizes),
            image_grid_thw=MultiModalFieldConfig.batched("image"),
            pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
                "video", video_grid_sizes),
            video_embeds=MultiModalFieldConfig.flat_from_sizes(
                "video", video_grid_sizes),
            video_grid_thw=MultiModalFieldConfig.batched("video"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_processor = self.info.get_image_processor(
            **hf_processor_mm_kwargs)
        tokenizer = self.info.get_tokenizer()
        hf_config = self.info.get_hf_config()

        video_token_id = hf_config.video_token_id
        vision_start_token_id = hf_config.vision_start_token_id
        vision_end_token_id = hf_config.vision_end_token_id

        merge_length = image_processor.merge_size**2

        def get_image_replacement_qwen3vl(item_idx: int):
            out_item = out_mm_kwargs["image"][item_idx]
            grid_thw = out_item["image_grid_thw"].data
            assert isinstance(grid_thw, torch.Tensor)

            num_tokens = int(grid_thw.prod()) // merge_length
            return [hf_processor.image_token_id] * num_tokens

        def get_video_replacement_qwen3vl(item_idx: int):
            out_item = out_mm_kwargs["video"][item_idx]
            grid_thw = out_item["video_grid_thw"].data
            assert isinstance(grid_thw, torch.Tensor)

            video, metadata = mm_items["video"][item_idx]
            do_sample_frames = hf_processor_mm_kwargs.get("do_sample_frames")
            sampled_fps = hf_processor_mm_kwargs.get("fps")
            if is_list_of(sampled_fps, float):
                sampled_fps = sampled_fps[item_idx]
            timestamps = self.info._get_video_second_idx(
                metadata, out_item, do_sample_frames, sampled_fps)

            assert len(timestamps) == grid_thw[0], (
                f"The timestamps length({len(timestamps)}) should be equal "
                f"video length ({grid_thw[0]}).")

            frames_idx_token = [
                tokenizer.encode(f"<{curr_time:.1f} seconds>",
                                 add_special_tokens=False)
                for curr_time in timestamps
            ]
            num_tokens_per_frame = int(grid_thw[1:].prod()) // merge_length
            placeholder = []
            for frame_idx in frames_idx_token:
                placeholder.extend(frame_idx)
                placeholder.extend([vision_start_token_id] +
                                   [video_token_id] * num_tokens_per_frame +
                                   [vision_end_token_id])
            return PromptUpdateDetails.select_token_id(placeholder,
                                                       video_token_id)

        return [
            PromptReplacement(
                modality="image",
                target=hf_processor.image_token,
                replacement=get_image_replacement_qwen3vl,
            ),

            # NOTE: We match string on purpose since searching sequence of
            # token ids takes more time.
            PromptReplacement(
                modality="video",
                target="<|vision_start|><|video_pad|><|vision_end|>",
                replacement=get_video_replacement_qwen3vl,
            ),
        ]


@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
        # the same shape as input_embeds
        "deepstack_input_embeds": 0
    })
class Qwen3LLMModel(Qwen3Model):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)
        if not get_pp_group().is_first_rank:
            assert self.start_layer >= len(
                vllm_config.model_config.hf_config.vision_config.
                deepstack_visual_indexes), (
                    "start_layer should be greater than or equal to "
                    "len(deepstack_visual_indexes)")

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        # args for deepstack
        deepstack_input_embeds: Optional[IntermediateTensors] = None,
    ) -> Union[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 Qwen3LLMForCausalLM(Qwen3ForCausalLM):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super(Qwen3ForCausalLM, self).__init__()
        config = vllm_config.model_config.hf_config.text_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config

        self.config = config
        self.lora_config = lora_config

        self.quant_config = quant_config
        self.model = Qwen3LLMModel(vllm_config=vllm_config, prefix=prefix)

        if get_pp_group().is_last_rank:
            if config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
                self.lm_head = ParallelLMHead(config.vocab_size,
                                              config.hidden_size,
                                              quant_config=quant_config,
                                              prefix="lm_head")
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)


@MULTIMODAL_REGISTRY.register_processor(Qwen3VLMultiModalProcessor,
                                        info=Qwen3VLProcessingInfo,
                                        dummy_inputs=Qwen3VLDummyInputsBuilder)
class Qwen3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
                                      SupportsLoRA, SupportsPP):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    supports_encoder_tp_data = True

    # To ensure correct weight loading and mapping.
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "model.visual.": "visual.",
            "lm_head.": "language_model.lm_head.",
            "model.language_model.": "language_model.model.",
        })

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<|vision_start|><|image_pad|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|video_pad|><|vision_end|>"

        raise ValueError("Only image or video modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
        super().__init__()
        config: Qwen3VLConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"

        self.visual = Qwen3_VisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
            quant_config=self._maybe_ignore_quant_config(quant_config),
            prefix=maybe_prefix(prefix, "visual"),
            use_data_parallel=self.use_data_parallel,
        )

        self.language_model = Qwen3LLMForCausalLM(vllm_config=vllm_config,
                                                  prefix=maybe_prefix(
                                                      prefix,
                                                      "language_model"))

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

        self.use_deepstack = hasattr(config.vision_config,
                                     'deepstack_visual_indexes')
        self.deepstack_num_level = len(
            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,
                        config.text_config.hidden_size)
            for _ in range(self.deepstack_num_level)
        ] if self.use_deepstack else None
        self.visual_dim = 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 _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
        # seems to avoid vision encoder sections for some models.
        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
            return None
        return quant_config

    def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                        name: str) -> torch.Tensor:
        if not isinstance(mm_input, (torch.Tensor, list)):
            raise ValueError(f"Incorrect type of {name}. "
                             f"Got type: {type(mm_input)}")
        if isinstance(mm_input, torch.Tensor):
            if mm_input.ndim == 2:
                return mm_input
            if mm_input.ndim != 3:
                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
                                 f"Got ndim: {mm_input.ndim} "
                                 f"(shape={mm_input.shape})")
            return torch.concat(list(mm_input))
        else:
            return torch.concat(mm_input)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[Qwen2_5_VLImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)
        image_grid_thw = kwargs.pop("image_grid_thw", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            pixel_values = self._validate_and_reshape_mm_tensor(
                pixel_values, "image pixel values")
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")

            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image pixel values. "
                                 f"Got type: {type(pixel_values)}")

            return Qwen2_5_VLImagePixelInputs(type="pixel_values",
                                              pixel_values=pixel_values,
                                              image_grid_thw=image_grid_thw)

        if image_embeds is not None:
            image_embeds = self._validate_and_reshape_mm_tensor(
                image_embeds, "image embeds")
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")

            if not isinstance(image_embeds, torch.Tensor):
                raise ValueError("Incorrect type of image embeddings. "
                                 f"Got type: {type(image_embeds)}")
            return Qwen2_5_VLImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw)

    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[Qwen2_5_VLVideoInputs]:
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_embeds = kwargs.pop("video_embeds", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)
        second_per_grid_ts = kwargs.pop("second_per_grid_ts", None)

        if pixel_values_videos is None and video_embeds is None:
            return None

        if pixel_values_videos is not None:
            pixel_values_videos = self._validate_and_reshape_mm_tensor(
                pixel_values_videos, "video pixel values")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            return Qwen2_5_VLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
            )

        if video_embeds is not None:
            video_embeds = self._validate_and_reshape_mm_tensor(
                video_embeds, "video embeds")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            if not isinstance(video_embeds, torch.Tensor):
                raise ValueError("Incorrect type of video embeddings. "
                                 f"Got type: {type(video_embeds)}")
            return Qwen2_5_VLVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw)

    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"].type(self.visual.dtype)
            if self.use_data_parallel:
                from vllm.multimodal.utils import (
                    run_dp_sharded_mrope_vision_model)
                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.
        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
        merge_size = self.visual.spatial_merge_size
        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).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"].type(
                self.visual.dtype)
            if self.use_data_parallel:
                from vllm.multimodal.utils import (
                    run_dp_sharded_mrope_vision_model)
                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)

        # Split concatenated embeddings for each video item.
        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
        merge_size = self.visual.spatial_merge_size
        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
                 (merge_size * merge_size)).tolist()
        return video_embeds.split(sizes)

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        mm_input_by_modality = {}
        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)
        return mm_input_by_modality

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_multimodal_embeddings(
            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:

        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
            **kwargs)
        if not mm_input_by_modality:
            return None

        # 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":
                vision_embeddings = self._process_image_input(multimodal_input)
                multimodal_embeddings += vision_embeddings
            if modality == "video":
                video_embeddings = self._process_video_input(multimodal_input)
                multimodal_embeddings += video_embeddings
        return multimodal_embeddings

    def _compute_deepstack_embeds(
            self, input_ids: torch.Tensor, inputs_embeds: torch.Tensor,
            multimodal_embeddings: MultiModalEmbeddings) -> torch.Tensor:
        visual_lens = [
            x.shape[0] if isinstance(x, torch.Tensor) else len(x)
            for x in multimodal_embeddings
        ]
        multimodal_embeddings_cat = torch.cat(multimodal_embeddings, dim=0)

        multimodal_embeddings_main, multimodal_embeddings_multiscale = torch.split(  # noqa:E501
            multimodal_embeddings_cat, [self.visual_dim, self.multiscale_dim],
            dim=-1)

        multimodal_embeddings = torch.split(multimodal_embeddings_main,
                                            visual_lens,
                                            dim=0)
        multimodal_embeddings_multiscale = torch.split(
            multimodal_embeddings_multiscale, visual_lens, dim=0)

        deepstack_input_embeds = inputs_embeds.new_zeros(
            inputs_embeds.size(0),
            self.deepstack_num_level * inputs_embeds.size(1))

        deepstack_input_embeds = merge_multimodal_embeddings(
            input_ids,
            deepstack_input_embeds,
            multimodal_embeddings_multiscale,
            placeholder_token_id=[
                self.config.image_token_id, self.config.video_token_id
            ],
        )
        deepstack_input_embeds = deepstack_input_embeds.view(
            inputs_embeds.shape[0], self.deepstack_num_level, self.visual_dim)
        deepstack_input_embeds = deepstack_input_embeds.permute(1, 0, 2)
        return deepstack_input_embeds, multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        deepstack_input_embeds = None
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None:
            if self.use_deepstack:
                deepstack_input_embeds, multimodal_embeddings = self._compute_deepstack_embeds(  # noqa:E501
                    input_ids, inputs_embeds, multimodal_embeddings)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                [self.config.image_token_id, self.config.video_token_id])

        if self.use_deepstack:
            if deepstack_input_embeds is None:
                deepstack_input_embeds = torch.zeros_like(
                    inputs_embeds).unsqueeze(0).repeat(
                        self.deepstack_num_level, 1, 1).contiguous()
            self._set_deepstack_input_embeds(deepstack_input_embeds)

        return inputs_embeds

    def get_input_embeddings_v0(
        self,
        input_ids: torch.Tensor,
        image_input: Optional[Qwen2_5_VLImageInputs] = None,
        video_input: Optional[Qwen2_5_VLVideoInputs] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.get_input_embeddings(input_ids)

        if self.use_deepstack:
            visual_dim = inputs_embeds.shape[-1]
            deepstack_input_embeds = None
            if image_input is not None or video_input is not None:
                deepstack_input_embeds = torch.zeros_like(
                    inputs_embeds).unsqueeze(1).repeat(
                        1, self.deepstack_num_level, 1).flatten(1)

        if image_input is not None:
            image_embeds = self._process_image_input(image_input)
            if self.use_deepstack:
                image_embeds = torch.cat(image_embeds)

                image_embeds, image_embeds_multiscale = image_embeds.split(
                    [visual_dim, visual_dim * self.deepstack_num_level],
                    dim=-1)

                deepstack_input_embeds = merge_multimodal_embeddings(
                    input_ids,
                    deepstack_input_embeds,
                    image_embeds_multiscale,
                    placeholder_token_id=self.config.image_token_id,
                )

            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                image_embeds,
                placeholder_token_id=self.config.image_token_id,
            )

        if video_input is not None:
            video_embeds = self._process_video_input(video_input)
            if self.use_deepstack:
                video_embeds = torch.cat(video_embeds)

                video_embeds, video_embeds_multiscale = video_embeds.split(
                    [visual_dim, visual_dim * self.deepstack_num_level],
                    dim=-1)

                deepstack_input_embeds = merge_multimodal_embeddings(
                    input_ids,
                    deepstack_input_embeds,
                    video_embeds_multiscale,
                    placeholder_token_id=self.config.video_token_id,
                )

            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                video_embeds,
                placeholder_token_id=self.config.video_token_id,
            )

        if self.use_deepstack and deepstack_input_embeds is not None:
            deepstack_input_embeds = deepstack_input_embeds.view(
                inputs_embeds.shape[0], self.deepstack_num_level,
                visual_dim).permute(1, 0, 2).contiguous()
            self._set_deepstack_input_embeds(deepstack_input_embeds)
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for Qwen3VL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen3VL
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
            pixel_values: Pixel values to be fed to a model.
                `None` if no images are passed.
            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
                `None` if no images are passed.
            pixel_values_videos: Pixel values of videos to be fed to a model.
                `None` if no videos are passed.
            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
                `None` if no videos are passed.
        """

        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner from
        # `get_multimodal_embeddings` and `get_input_embeddings`, this
        # condition is only for v0 compatibility.
        elif inputs_embeds is None:
            image_input = self._parse_and_validate_image_input(**kwargs)
            video_input = self._parse_and_validate_video_input(**kwargs)

            if image_input is None and video_input is None:
                inputs_embeds = None
            else:
                if uses_mrope(self.config):
                    assert positions.ndim == 2 and positions.size(0) == 3, (
                        "multimodal section rotary embedding requires "
                        f"(3, seq_len) positions, but got {positions.size()}")
                inputs_embeds = self.get_input_embeddings_v0(
                    input_ids,
                    image_input=image_input,
                    video_input=video_input)
                input_ids = 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=input_ids,
            positions=positions,
            intermediate_tensors=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,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="model.visual.merger",
            tower_model="model.visual.",
        )