# 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 Callable, Iterable, Iterator, Mapping, Sequence from functools import lru_cache, partial from itertools import islice from typing import Any 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 as image_smart_resize, ) from transformers.models.qwen3_vl import Qwen3VLProcessor, Qwen3VLVideoProcessor from transformers.models.qwen3_vl.configuration_qwen3_vl import ( Qwen3VLConfig, Qwen3VLVisionConfig, ) from transformers.models.qwen3_vl.video_processing_qwen3_vl import ( smart_resize as video_smart_resize, ) from transformers.video_utils import VideoMetadata from vllm.attention.backends.registry import AttentionBackendEnum from vllm.attention.layer import check_upstream_fa_availability from vllm.compilation.decorators import support_torch_compile from vllm.config import VllmConfig from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions 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.conv import Conv3dLayer 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.rotary_embedding import get_rope 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, MultiModalFeatureSpec, 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.sequence import IntermediateTensors from vllm.utils.collection_utils import is_list_of from .interfaces import ( MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE, SupportsMultiModal, SupportsPP, ) from .qwen2_5_vl import ( Qwen2_5_VisionAttention, 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, _merge_multimodal_embeddings, maybe_prefix, ) from .vision import ( get_vit_attn_backend, run_dp_sharded_mrope_vision_model, ) logger = init_logger(__name__) # Official recommended max pixels is 24576 * 32 * 32 _MAX_FRAMES_PER_VIDEO = 24576 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 = Conv3dLayer( 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: QuantizationConfig | None = 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: Callable[[int], nn.Module] | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", use_data_parallel: bool = False, attn_backend: AttentionBackendEnum = AttentionBackendEnum.TORCH_SDPA, use_upstream_fa: 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, attn_backend=attn_backend, use_upstream_fa=use_upstream_fa, ) 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_cos: torch.Tensor, rotary_pos_emb_sin: torch.Tensor, max_seqlen: torch.Tensor, # Only used for Flash Attention seqlens: torch.Tensor, # Only used for xFormers ) -> torch.Tensor: x = x + self.attn( self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb_cos=rotary_pos_emb_cos, rotary_pos_emb_sin=rotary_pos_emb_sin, 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 = "", 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: QuantizationConfig | None = None, prefix: str = "", use_data_parallel: bool = False, attn_backend_override: AttentionBackendEnum | None = None, ) -> 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 = get_rope( head_size=head_dim, rotary_dim=head_dim // 2, max_position=8192, base=10000.0, is_neox_style=True, ) 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(), attn_backend_override=attn_backend_override, ) use_upstream_fa = False if ( self.attn_backend != AttentionBackendEnum.FLASH_ATTN and self.attn_backend != AttentionBackendEnum.ROCM_AITER_FA and check_upstream_fa_availability(torch.get_default_dtype()) ): self.attn_backend = AttentionBackendEnum.FLASH_ATTN use_upstream_fa = True if self.attn_backend not in { AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA, AttentionBackendEnum.XFORMERS, AttentionBackendEnum.ROCM_AITER_FA, }: raise RuntimeError( f"Qwen3-VL does not support {self.attn_backend} backend now." ) 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, attn_backend=self.attn_backend, use_upstream_fa=use_upstream_fa, ) for layer_idx in range(vision_config.depth) ] ) @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 @staticmethod @lru_cache(maxsize=1024) def rot_pos_ids(h: int, w: int, spatial_merge_size: int) -> torch.Tensor: hpos_ids = np.broadcast_to(np.arange(h).reshape(h, 1), (h, w)) h_div = h // spatial_merge_size w_div = w // spatial_merge_size hpos_ids = hpos_ids.reshape( h_div, spatial_merge_size, w_div, spatial_merge_size, ) hpos_ids = hpos_ids.transpose(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = np.broadcast_to(np.arange(w).reshape(1, w), (h, w)) wpos_ids = wpos_ids.reshape( h_div, spatial_merge_size, w_div, spatial_merge_size, ) wpos_ids = wpos_ids.transpose(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() return torch.from_numpy(np.stack([hpos_ids, wpos_ids], axis=-1)) def rot_pos_emb(self, grid_thw: list[list[int]]): max_grid_size = max(max(h, w) for _, h, w in grid_thw) pos_ids = [ self.rot_pos_ids(h, w, self.spatial_merge_size) if t == 1 else self.rot_pos_ids(h, w, self.spatial_merge_size).repeat(t, 1) for t, h, w in grid_thw ] pos_ids = torch.cat(pos_ids, dim=0).to(self.device, non_blocking=True) # Use pre-computed cos_sin_cache from RotaryEmbedding cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size) cos_combined = cos[pos_ids].flatten(1) sin_combined = sin[pos_ids].flatten(1) return cos_combined, sin_combined 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") # 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 - w01 h_grid = torch.stack([h_floor_grid, h_floor_grid, h_ceil_grid, h_ceil_grid]) w_grid = torch.stack([w_floor_grid, w_ceil_grid, w_floor_grid, w_ceil_grid]) h_grid_idx = h_grid * num_grid_per_side indices = (h_grid_idx + w_grid).reshape(4, -1) weights = torch.stack([w00, w01, w10, w11], dim=0).reshape(4, -1, 1) weights = weights.to(dtype=self.dtype) embeds = self.pos_embed(indices) embeds *= weights combined = embeds.sum(dim=0) combined = combined.reshape( h // m_size, m_size, w // m_size, m_size, hidden_dim ) combined = combined.permute(0, 2, 1, 3, 4).reshape(1, -1, hidden_dim) repeated = combined.expand(t, -1, -1).reshape(-1, hidden_dim) outputs.append(repeated) return torch.cat(outputs, dim=0) def compute_attn_mask_seqlen( self, cu_seqlens: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: max_seqlen = torch.zeros([], device=cu_seqlens.device) seqlens = torch.zeros(1, device=cu_seqlens.device) if ( self.attn_backend == AttentionBackendEnum.FLASH_ATTN or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA ): max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() elif self.attn_backend == AttentionBackendEnum.XFORMERS: seqlens = cu_seqlens[1:] - cu_seqlens[:-1] return max_seqlen, seqlens def forward( self, x: torch.Tensor, grid_thw: torch.Tensor | list[list[int]], ) -> torch.Tensor: hidden_states = x.to(device=self.device, dtype=self.dtype, non_blocking=True) hidden_states = self.patch_embed(hidden_states) if isinstance(grid_thw, list): grid_thw_list = grid_thw grid_thw = torch.tensor(grid_thw, dtype=torch.int32) else: grid_thw_list = grid_thw.tolist() pos_embeds = self.fast_pos_embed_interpolate(grid_thw_list) hidden_states = hidden_states + pos_embeds rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list) 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 = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens]) hidden_states = hidden_states.unsqueeze(1) max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens) cu_seqlens = cu_seqlens.to(self.device, non_blocking=True) deepstack_feature_lists = [] for layer_num, blk in enumerate(self.blocks): hidden_states = blk( hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb_cos=rotary_pos_emb_cos, rotary_pos_emb_sin=rotary_pos_emb_sin, 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: Qwen2VLImageProcessorFast | Qwen3VLVideoProcessor | None, ) -> tuple[ImageSize, int]: if image_processor is None and num_frames > 1: image_processor = self.get_video_processor() elif image_processor is None: image_processor = self.get_image_processor() is_video = isinstance(image_processor, Qwen3VLVideoProcessor) 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: if is_video: smart_resize = video_smart_resize extra_kwargs = { "num_frames": num_frames, "temporal_factor": temporal_patch_size, } else: smart_resize = image_smart_resize extra_kwargs = {} 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"], **extra_kwargs, ) 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 _get_max_video_frames(self, max_tokens: int, start_num_frames: int = 2) -> int: return super()._get_max_video_frames( max_tokens, start_num_frames=start_num_frames ) def get_num_frames_with_most_features( self, seq_len: int, mm_counts: Mapping[str, int], ) -> int: return super().get_num_frames_with_most_features( seq_len, mm_counts, max_frames_per_video=_MAX_FRAMES_PER_VIDEO ) def get_max_video_tokens( self, seq_len: int, mm_counts: Mapping[str, int], ) -> int: target_width, target_height = self.get_image_size_with_most_features() video_soft_tokens = self.get_num_video_tokens( image_width=target_width, image_height=target_height, num_frames=self.get_num_frames_with_most_features(seq_len, mm_counts), image_processor=None, ) # NOTE: By default in Qwen3-VL, one video token is converted to # "<{timestamp} seconds>" (on average 9.5 tokens) + vision_start_token + video_token + vision_end_token # noqa: E501 formatted_video_soft_tokens = video_soft_tokens * 12.5 return int(formatted_video_soft_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: bool | None = None, sampled_fps: float | None = 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. sampled_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"] * sampled_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], mm_options: Mapping[str, BaseDummyOptions] | None = None, ) -> MultiModalDataDict: num_images = mm_counts.get("image", 0) num_videos = mm_counts.get("video", 0) image_overrides = mm_options.get("image") if mm_options else None video_overrides = mm_options.get("video") if mm_options else None 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 ) if video_overrides: assert isinstance(video_overrides, VideoDummyOptions) num_frames_override = video_overrides.num_frames if num_frames_override: if num_frames_override > target_num_frames: logger.warning( "video.num_frames override (%d) exceeds model's " "maximum number of frames (%d), will be ignored", num_frames_override, target_num_frames, ) if num_frames_override < 2: logger.warning( "video.num_frames override (%d) cannot be less " "than 2, will be ignored", num_frames_override, ) target_num_frames = min(target_num_frames, num_frames_override) target_num_frames = max(target_num_frames, 2) target_video_size, _ = self.info._get_vision_info( image_width=target_width, image_height=target_height, num_frames=target_num_frames, image_processor=self.info.get_video_processor(), ) # NOTE: we need to do this check here since Qwen3-VL resizes video # frames depending on how many frames there are. width, height = target_video_size.width, target_video_size.height if video_overrides: assert isinstance(video_overrides, VideoDummyOptions) width_override = video_overrides.width if width_override: if width_override > width: logger.warning( "video.width override (%d) exceeds model's " "maximum width (%d), will be ignored", width_override, width, ) width = min(width, width_override) height_override = video_overrides.height if height_override: if height_override > height: logger.warning( "video.height override (%d) exceeds model's " "maximum height (%d), will be ignored", height_override, height, ) height = min(height, height_override) return { "image": self._get_dummy_images( width=target_width, height=target_height, num_images=num_images, overrides=image_overrides, ), "video": self._get_dummy_videos( width=width, height=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]: 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 several image patches if videos := mm_data.pop("videos", []): video_grid_thw_lst = [] pixel_values_videos_lst = [] for item in 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: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, # args for deepstack 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.embed_input_ids(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 islice( enumerate(self.layers), self.start_layer, self.end_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 self.config = config self.quant_config = quant_config self.model = Qwen3LLMModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) 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, SupportsMRoPE ): merge_by_field_config = True multimodal_cpu_fields = {"image_grid_thw", "video_grid_thw"} 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) -> str | None: 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" if not multimodal_config.get_limit_per_prompt( "image" ) and not multimodal_config.get_limit_per_prompt("video"): self.visual = None else: attn_backend_override = ( multimodal_config.mm_encoder_attn_backend if multimodal_config is not None else None ) self.visual = Qwen3_VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), quant_config=quant_config, prefix=maybe_prefix(prefix, "visual"), use_data_parallel=self.use_data_parallel, attn_backend_override=attn_backend_override, ) 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 if self.use_deepstack and self.visual is not None: 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) ] else: self.deepstack_input_embeds = 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 _parse_and_validate_image_input( self, **kwargs: object ) -> Qwen2_5_VLImageInputs | None: 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: return Qwen2_5_VLImagePixelInputs( type="pixel_values", pixel_values=pixel_values, image_grid_thw=image_grid_thw, ) if image_embeds is not None: 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 ) -> Qwen2_5_VLVideoInputs | None: 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: 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: 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 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: return run_dp_sharded_mrope_vision_model( self.visual, pixel_values, grid_thw.tolist(), rope_type="rope_3d" ) else: image_embeds = self.visual(pixel_values, grid_thw=grid_thw) # Split concatenated embeddings for each image item. merge_size = self.visual.spatial_merge_size sizes = (grid_thw.prod(-1) // merge_size // merge_size).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 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: grid_thw_list = grid_thw.tolist() 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. merge_size = self.visual.spatial_merge_size sizes = (grid_thw.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 iter_mm_grid_hw( self, input_tokens: list[int], mm_features: list[MultiModalFeatureSpec] ) -> Iterator[tuple[int, int, int]]: video_token_id = self.config.video_token_id spatial_merge_size = self.config.vision_config.spatial_merge_size for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset): offset = mm_feature.mm_position.offset if mm_feature.modality == "image": t, h, w = mm_feature.data["image_grid_thw"].data.tolist() assert t == 1, f"Image must have 1 frame, got {t}" yield offset, h // spatial_merge_size, w // spatial_merge_size elif mm_feature.modality == "video": t, h, w = mm_feature.data["video_grid_thw"].data.tolist() llm_grid_h = h // spatial_merge_size llm_grid_w = w // spatial_merge_size for _ in range(t): offset = input_tokens.index(video_token_id, offset) yield offset, llm_grid_h, llm_grid_w offset += llm_grid_h * llm_grid_w else: raise ValueError(f"Unsupported modality: {mm_feature.modality}") def get_mrope_input_positions( self, input_tokens: list[int], mm_features: list[MultiModalFeatureSpec], ) -> tuple[torch.Tensor, int]: llm_pos_ids_list = [] st = 0 for offset, llm_grid_h, llm_grid_w in self.iter_mm_grid_hw( input_tokens, mm_features ): text_len = offset - st st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append( np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx ) grid_indices = np.indices((1, llm_grid_h, llm_grid_w)).reshape(3, -1) llm_pos_ids_list.append(grid_indices + text_len + st_idx) st = offset + llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append( np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx ) llm_positions = np.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1) mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item() return torch.from_numpy(llm_positions), mrope_position_delta def get_language_model(self) -> torch.nn.Module: return self.language_model def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None: 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": 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) return multimodal_embeddings def _compute_deepstack_embeds( self, inputs_embeds: torch.Tensor, multimodal_embeddings: MultiModalEmbeddings, is_multimodal: torch.Tensor, ) -> tuple[torch.Tensor, MultiModalEmbeddings]: visual_lens = [len(x) for x in multimodal_embeddings] multimodal_embeddings_cat = torch.cat(multimodal_embeddings, dim=0) ( multimodal_embeddings_main, multimodal_embeddings_multiscale, ) = torch.split( 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( inputs_embeds=deepstack_input_embeds, multimodal_embeddings=multimodal_embeddings_multiscale, is_multimodal=is_multimodal, ) 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 embed_input_ids( 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._embed_text_input_ids( input_ids, self.language_model.embed_input_ids, 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 if is_multimodal is None: raise ValueError( "`embed_input_ids` now requires `is_multimodal` arg, " "please update your model runner according to " "https://github.com/vllm-project/vllm/pull/16229." ) if self.use_deepstack: ( deepstack_input_embeds, multimodal_embeddings, ) = self._compute_deepstack_embeds( inputs_embeds=inputs_embeds, multimodal_embeddings=multimodal_embeddings, is_multimodal=is_multimodal, ) else: deepstack_input_embeds = None inputs_embeds = _merge_multimodal_embeddings( inputs_embeds=inputs_embeds, multimodal_embeddings=multimodal_embeddings, is_multimodal=is_multimodal, ) if deepstack_input_embeds is not None: self._set_deepstack_input_embeds(deepstack_input_embeds) 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: """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,). intermediate_tensors: Intermediate tensors from previous pipeline stages. inputs_embeds: Pre-computed input embeddings. **kwargs: Additional keyword arguments including: - 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 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, ) -> torch.Tensor | None: return self.language_model.compute_logits(hidden_states) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: skip_prefixes = [] if self.visual is None: skip_prefixes.extend(["visual."]) loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes) 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="visual.merger", tower_model="visual.", )