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Commit 58117664 authored by zhuwenwen's avatar zhuwenwen
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Revert "Add Qwen3-Omni moe thinker" 

This reverts commit e9cb2241.
parent c16e075a
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docs/models/supported_models.md
View file @ 58117664
......@@ -689,7 +689,6 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-3B`, `Qwen/Qwen2.5-Omni-7B` | ✅︎ | ✅︎ | ✅︎ |
| `Qwen3VLForConditionalGeneration` | Qwen3-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-4B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Qwen3VLMoeForConditionalGeneration` | Qwen3-VL-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-30B-A3B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, `Qwen/Qwen3-Omni-30B-A3B-Thinking` | ✅︎ | ✅︎ | ✅︎ |
| `RForConditionalGeneration` | R-VL-4B | T + I<sup>E+</sup> | `YannQi/R-4B` | | ✅︎ | ✅︎ |
| `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ | ✅︎ |
| `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | | ✅︎ |
......@@ -780,7 +779,7 @@ Some models are supported only via the [Transformers backend](#transformers). Th
Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
!!! note
For Qwen2.5-Omni and Qwen3-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`)
For Qwen2.5-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`)
is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1.
#### Transcription
......
tests/models/multimodal/processing/test_common.py
View file @ 58117664
......@@ -362,7 +362,6 @@ def _test_processing_correctness_one(
os.path.join(models_path_prefix, "Qwen/Qwen2.5-Omni-3B"),
os.path.join(models_path_prefix, "Qwen/Qwen3-VL-4B-Instruct"),
os.path.join(models_path_prefix, "Qwen/Qwen3-VL-30B-A3B-Instruct"),
os.path.join(models_path_prefix, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
os.path.join(models_path_prefix, "YannQi/R-4B"),
os.path.join(models_path_prefix, "Skywork/Skywork-R1V-38B"),
os.path.join(models_path_prefix, "HuggingFaceTB/SmolVLM2-2.2B-Instruct"),
......
tests/models/registry.py
View file @ 58117664
......@@ -580,9 +580,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
max_model_len=4096,
min_transformers_version="4.57",
is_available_online=False),
"Qwen3OmniMoeForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
max_model_len=4096,
min_transformers_version="4.57"),
"RForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "YannQi/R-4B"),
trust_remote_code=True),
"SkyworkR1VChatModel": _HfExamplesInfo(os.path.join(models_path_prefix, "Skywork/Skywork-R1V-38B"),
......
vllm/model_executor/layers/rotary_embedding/mrope.py
View file @ 58117664
......@@ -429,7 +429,7 @@ class MRotaryEmbedding(RotaryEmbedding):
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
from vllm.transformers_utils.config import thinker_uses_mrope
if thinker_uses_mrope(hf_config) and hf_config.model_type == "qwen2_5_omni":
if thinker_uses_mrope(hf_config):
return cls._omni_get_input_positions_tensor(
input_tokens=input_tokens,
hf_config=hf_config,
......@@ -899,7 +899,7 @@ class MRotaryEmbedding(RotaryEmbedding):
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
-1, llm_grid_h * llm_grid_w)).flatten()
-1, llm_grid_h * llm_grid_w)).long().flatten()
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
llm_grid_t, -1, llm_grid_w).flatten()
......@@ -1003,8 +1003,8 @@ class MRotaryEmbedding(RotaryEmbedding):
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
-1, llm_grid_h * llm_grid_w)* video_second_per_grid_t*
tokens_per_second).flatten()
-1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
tokens_per_second).long().flatten()
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
llm_grid_t, -1, llm_grid_w).flatten()
......@@ -1027,7 +1027,7 @@ class MRotaryEmbedding(RotaryEmbedding):
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta
@classmethod
def _omni_get_input_positions_tensor(
cls,
......@@ -1061,11 +1061,6 @@ class MRotaryEmbedding(RotaryEmbedding):
# _vl_get_input_positions_tensor.
thinker_config = hf_config.thinker_config
if isinstance(image_grid_thw, list):
image_grid_thw = torch.tensor(image_grid_thw)
if isinstance(video_grid_thw, list):
video_grid_thw = torch.tensor(video_grid_thw)
audio_token_id = thinker_config.audio_token_index
image_token_id = thinker_config.image_token_index
video_token_id = thinker_config.video_token_index
......@@ -1078,6 +1073,11 @@ class MRotaryEmbedding(RotaryEmbedding):
tokens_per_second = getattr(thinker_config.vision_config,
"tokens_per_second", 25)
if isinstance(image_grid_thw, list):
image_grid_thw = torch.tensor(image_grid_thw)
if isinstance(video_grid_thw, list):
video_grid_thw = torch.tensor(video_grid_thw)
src_item = input_tokens
audio_seqlens = audio_feature_lengths
if not second_per_grid_ts:
......@@ -1121,7 +1121,7 @@ class MRotaryEmbedding(RotaryEmbedding):
grid_t = image_grid_thw[image_idx][0]
grid_hs = image_grid_thw[:, 1]
grid_ws = image_grid_thw[:, 2]
t_index = torch.arange(grid_t) * 1 * tokens_per_second
t_index = (torch.arange(grid_t) * 1 * tokens_per_second).long()
llm_pos_ids = cls._get_llm_pos_ids_for_vision(
start_idx, image_idx, spatial_merge_size, t_index, grid_hs,
grid_ws)
......@@ -1136,7 +1136,7 @@ class MRotaryEmbedding(RotaryEmbedding):
grid_ws = video_grid_thw[:, 2]
t_index = (torch.arange(grid_t) *
second_per_grid_ts[video_idx] *
tokens_per_second)
tokens_per_second).long()
llm_pos_ids = cls._get_llm_pos_ids_for_vision(
start_idx, video_idx, spatial_merge_size, t_index, grid_hs,
grid_ws)
......@@ -1159,7 +1159,7 @@ class MRotaryEmbedding(RotaryEmbedding):
t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
t_index = (torch.arange(grid_t) *
second_per_grid_ts[video_idx] *
tokens_per_second)
tokens_per_second).long()
t_index_split_chunk = cls._split_list_into_ranges(
t_index, t_ntoken_per_chunk)
place_num = (((audio_seqlen - 1) // 2 + 1 - 2) // 2 + 1) + 2
......@@ -1299,7 +1299,7 @@ class MRotaryEmbedding(RotaryEmbedding):
grid_w = video_grid_thw[2]
t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
t_index = (torch.arange(grid_t) * video_second_per_grid_t *
tokens_per_second)
tokens_per_second).long()
t_index_split_chunk = cls._split_list_into_ranges(
t_index, t_ntoken_per_chunk)
......
vllm/model_executor/models/qwen3_omni_moe_thinker.py deleted 100644 → 0
View file @ c16e075a
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen3-Omni-Moe model (thinker part)."""
import os
import math
from collections.abc import Callable, Iterable, Mapping, Sequence
from functools import partial
from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from packaging.version import Version
from transformers import PretrainedConfig
from transformers import __version__ as TRANSFORMERS_VERSION
from transformers.feature_extraction_utils import BatchFeature
from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import (
Qwen3OmniMoeConfig,
Qwen3OmniMoeThinkerConfig,
)
from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import (
Qwen3OmniMoeAudioEncoder,
)
from transformers.models.qwen3_omni_moe.processing_qwen3_omni_moe import (
Qwen3OmniMoeProcessor,
)
from transformers.models.whisper import WhisperFeatureExtractor
from vllm.attention.backends.registry import _Backend
from vllm.attention.layer import check_upstream_fa_availability
from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
from vllm.model_executor.layers.linear import (
ColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.qwen2_audio import (
Qwen2AudioFeatureInputs,
Qwen2AudioProcessingInfo,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargsItems
from vllm.multimodal.parse import AudioProcessorItems, MultiModalDataItems
from vllm.multimodal.processing import (
BaseMultiModalProcessor,
MultiModalPromptUpdates,
PlaceholderFeaturesInfo,
PromptReplacement,
PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from .interfaces import (
MultiModalEmbeddings,
SupportsMRoPE,
SupportsMultiModal,
SupportsPP,
)
from .qwen2_5_omni_thinker import (
Qwen2_5OmniConditionalGenerationMixin,
Qwen2_5OmniThinkerDummyInputsBuilder,
Qwen2_5OmniThinkerMultiModalProcessor,
Qwen2_5OmniThinkerProcessingInfo,
)
from .qwen2_5_vl import (
Qwen2_5_VisionAttention,
Qwen2_5_VisionRotaryEmbedding,
Qwen2_5_VLProcessingInfo,
)
from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
from .utils import (
AutoWeightsLoader,
WeightsMapper,
_merge_multimodal_embeddings,
maybe_prefix,
)
from .vision import (
conv3d_to_linear_weight,
get_llm_pos_ids_for_vision,
get_vit_attn_backend,
)
try:
import flash_attn
except (ImportError, ModuleNotFoundError):
flash_attn = None
logger = init_logger(__name__)
def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
input_lengths_leave = input_lengths % 100
feat_lengths = (input_lengths_leave - 1) // 2 + 1
output_lengths = (
((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
)
return feat_lengths, output_lengths
class Qwen3_VisionPatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 14,
temporal_patch_size: int = 2,
in_channels: int = 3,
hidden_size: int = 1152,
) -> None:
super().__init__()
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.hidden_size = hidden_size
kernel_size = (temporal_patch_size, patch_size, patch_size)
self.proj = ReplicatedLinear(
in_channels * math.prod(kernel_size),
hidden_size,
bias=True,
return_bias=False,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
L, C = x.shape
if os.environ.get('PYTORCH_MIOPEN_SUGGEST_NDHWC') == '1':
x = x.to(memory_format=torch.channels_last_3d)
x = self.proj(x)
return x
class Qwen3_VisionMLP(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int,
bias: bool = False,
act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
self.linear_fc1 = ColumnParallelLinear(
in_features,
hidden_features,
bias=bias,
quant_config=quant_config,
return_bias=False,
prefix=f"{prefix}.linear_fc1",
)
self.linear_fc2 = RowParallelLinear(
hidden_features,
in_features,
bias=bias,
quant_config=quant_config,
return_bias=False,
prefix=f"{prefix}.linear_fc2",
)
self.act_fn = act_fn
def forward(self, x: torch.Tensor):
mlp_output = self.linear_fc2(self.act_fn(self.linear_fc1(x)))
return mlp_output
class Qwen3_VisionBlock(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_hidden_dim: int,
act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
norm_layer: Callable[[int], nn.Module] | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = partial(nn.LayerNorm, eps=1e-6)
self.norm1 = norm_layer(dim)
self.norm2 = norm_layer(dim)
self.attn = Qwen2_5_VisionAttention(
embed_dim=dim,
num_heads=num_heads,
projection_size=dim,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
self.mlp = Qwen3_VisionMLP(
dim,
mlp_hidden_dim,
act_fn=act_fn,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
def forward(
self,
x: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor,
max_seqlen: int | None = None, # Only used for Flash Attention
seqlens: list[int] | None = None, # Only used for xFormers
) -> torch.Tensor:
x = x + self.attn(
self.norm1(x),
cu_seqlens=cu_seqlens,
rotary_pos_emb=rotary_pos_emb,
max_seqlen=max_seqlen,
seqlens=seqlens,
)
x = x + self.mlp(self.norm2(x))
return x
class Qwen3_VisionPatchMerger(nn.Module):
def __init__(
self,
d_model: int,
context_dim: int,
norm_layer: Callable[[int], nn.Module] | None = None,
spatial_merge_size: int = 2,
use_postshuffle_norm: bool = False,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = context_dim * (spatial_merge_size**2)
self.use_postshuffle_norm = use_postshuffle_norm
if self.use_postshuffle_norm:
context_dim = self.hidden_size
if norm_layer is None:
norm_layer = partial(nn.LayerNorm, eps=1e-6)
self.use_postshuffle_norm = use_postshuffle_norm
self.ln_q = norm_layer(
self.hidden_size if use_postshuffle_norm else context_dim
)
self.mlp = nn.ModuleList(
[
ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.mlp.0",
),
nn.GELU(),
RowParallelLinear(
self.hidden_size,
d_model,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.mlp.2",
),
]
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.use_postshuffle_norm:
x = self.ln_q(x.view(-1, self.hidden_size))
else:
x = self.ln_q(x).view(-1, self.hidden_size)
mlp_fc1, mlp_act, mlp_fc2 = self.mlp
x_parallel, _ = mlp_fc1(x)
x_parallel = mlp_act(x_parallel)
out, _ = mlp_fc2(x_parallel)
return out
class Qwen3Omni_VisionTransformer(nn.Module):
def __init__(
self,
vision_config,
norm_eps: float = 1e-6,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
attn_backend_override: _Backend | None = None,
) -> None:
super().__init__()
self.hidden_size = vision_config.hidden_size
self.num_heads = vision_config.num_heads
self.image_size = vision_config.image_size
self.patch_size = vision_config.patch_size
self.spatial_merge_size = vision_config.spatial_merge_size
self.spatial_merge_unit = self.spatial_merge_size**2
self.temporal_patch_size = vision_config.temporal_patch_size
self.num_grid_per_side = self.image_size // self.patch_size
self.apply_vit_abs_pos_embed = vision_config.apply_vit_abs_pos_embed
self.deepstack_visual_indexes = vision_config.deepstack_visual_indexes
self.patch_embed = Qwen3_VisionPatchEmbed(
patch_size=self.patch_size,
temporal_patch_size=self.temporal_patch_size,
in_channels=vision_config.in_channels,
hidden_size=self.hidden_size,
)
# vit pos embeding, TODO: spatial_patch_size vs patch_size
if self.apply_vit_abs_pos_embed:
self.pos_embed = nn.Embedding(self.num_grid_per_side**2, self.hidden_size)
else:
self.pos_embed = nn.Parameter(
torch.empty([1, self.num_grid_per_side**2, self.hidden_size])
)
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
head_dim = self.hidden_size // self.num_heads
self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
self.blocks = nn.ModuleList(
[
Qwen3_VisionBlock(
dim=self.hidden_size,
num_heads=self.num_heads,
mlp_hidden_dim=vision_config.intermediate_size,
act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act],
norm_layer=norm_layer,
quant_config=quant_config,
prefix=f"{prefix}.blocks.{layer_idx}",
)
for layer_idx in range(vision_config.depth)
]
)
self.merger = Qwen3_VisionPatchMerger(
d_model=vision_config.out_hidden_size,
context_dim=self.hidden_size,
norm_layer=norm_layer,
spatial_merge_size=self.spatial_merge_size,
quant_config=quant_config,
prefix=f"{prefix}.merger",
)
if self.deepstack_visual_indexes is not None:
self.merger_list = nn.ModuleList(
[
Qwen3_VisionPatchMerger(
d_model=vision_config.out_hidden_size,
context_dim=self.hidden_size,
spatial_merge_size=self.spatial_merge_size,
use_postshuffle_norm=True,
norm_layer=norm_layer,
quant_config=quant_config,
prefix=f"{prefix}.merger_list.{layer_idx}",
)
for layer_idx in range(len(self.deepstack_visual_indexes))
]
)
self.attn_backend = get_vit_attn_backend(
head_size=head_dim,
dtype=torch.get_default_dtype(),
attn_backend_override=attn_backend_override,
)
if self.attn_backend != _Backend.FLASH_ATTN and check_upstream_fa_availability(
torch.get_default_dtype()
):
self.attn_backend = _Backend.FLASH_ATTN
@property
def dtype(self) -> torch.dtype:
return self.patch_embed.proj.weight.dtype
@property
def device(self) -> torch.device:
return self.patch_embed.proj.weight.device
def rot_pos_emb(self, grid_thw):
pos_ids = []
for t, h, w in grid_thw:
hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
hpos_ids = hpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
)
hpos_ids = hpos_ids.permute(0, 2, 1, 3)
hpos_ids = hpos_ids.flatten()
wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
wpos_ids = wpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
)
wpos_ids = wpos_ids.permute(0, 2, 1, 3)
wpos_ids = wpos_ids.flatten()
pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
pos_ids = torch.cat(pos_ids, dim=0)
max_grid_size = grid_thw[:, 1:].max()
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
return rotary_pos_emb
def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
num_grid_per_side = self.num_grid_per_side
m_size = self.spatial_merge_size
hidden_dim = self.pos_embed.embedding_dim
outputs = []
for t, h, w in grid_thw:
h_idxs = torch.linspace(
0, num_grid_per_side - 1, h, dtype=torch.float32, device=self.device
)
w_idxs = torch.linspace(
0, num_grid_per_side - 1, w, dtype=torch.float32, device=self.device
)
h_floor = h_idxs.to(torch.long)
w_floor = w_idxs.to(torch.long)
h_ceil = torch.clamp(h_floor + 1, max=num_grid_per_side - 1)
w_ceil = torch.clamp(w_floor + 1, max=num_grid_per_side - 1)
dh = h_idxs - h_floor
dw = w_idxs - w_floor
# Create meshgrid view for all h, w vars
dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing="ij")
h_floor_grid, w_floor_grid = torch.meshgrid(h_floor, w_floor, indexing="ij")
h_ceil_grid, w_ceil_grid = torch.meshgrid(h_ceil, w_ceil, indexing="ij")
h_floor_grid_idx = h_floor_grid * num_grid_per_side
h_ceil_grid_idx = h_ceil_grid * num_grid_per_side
# original computation of weights
# w00 = (1 - dh_grid) * (1 - dw_grid)
# w01 = (1 - dh_grid) * dw_grid
# w10 = dh_grid * (1 - dw_grid)
# w11 = dh_grid * dw_grid
# we reuse w11 here to avoid duplicate
# dh_grid * dw_grid computation
w11 = dh_grid * dw_grid
w10 = dh_grid - w11
w01 = dw_grid - w11
w00 = 1 - dh_grid - dw_grid + w11
idx00 = h_floor_grid_idx + w_floor_grid
idx01 = h_floor_grid_idx + w_ceil_grid
idx10 = h_ceil_grid_idx + w_floor_grid
idx11 = h_ceil_grid_idx + w_ceil_grid
indices = torch.stack([idx00, idx01, idx10, idx11], dim=0).reshape(4, -1)
weights = torch.stack([w00, w01, w10, w11], dim=0).reshape(4, -1, 1)
weights = weights.to(dtype=self.dtype, device=self.device)
embeds = self.pos_embed(indices)
weighted_embeds = embeds * weights
p0, p1, p2, p3 = weighted_embeds.unbind(dim=0)
combined = p0 + p1 + p2 + p3
combined = combined.view(h * w, hidden_dim)
repeated = combined.unsqueeze(0).expand(t, -1, -1).contiguous()
repeated = repeated.view(
t, h // m_size, m_size, w // m_size, m_size, hidden_dim
)
repeated = repeated.permute(0, 1, 3, 2, 4, 5).reshape(-1, hidden_dim)
outputs.append(repeated)
return torch.cat(outputs, dim=0)
def compute_attn_mask_seqlen(
self,
cu_seqlens: torch.Tensor,
) -> tuple[int | None, list[int] | None]:
max_seqlen, seqlens = None, None
if self.attn_backend == _Backend.FLASH_ATTN:
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
elif self.attn_backend == _Backend.XFORMERS:
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
return max_seqlen, seqlens
def forward(
self,
x: torch.Tensor,
grid_thw: list[list[int]],
) -> torch.Tensor:
hidden_states = x.to(device=self.device, dtype=self.dtype)
hidden_states = self.patch_embed(hidden_states)
if self.apply_vit_abs_pos_embed:
pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
hidden_states = hidden_states + pos_embeds
rotary_pos_emb = self.rot_pos_emb(grid_thw)
cu_seqlens = torch.repeat_interleave(
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
).cumsum(
dim=0,
dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
)
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
hidden_states = hidden_states.unsqueeze(1)
rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
hidden_states_list = []
deepstack_visual_indexes = self.deepstack_visual_indexes
for layer_num, blk in enumerate(self.blocks):
hidden_states = blk(
hidden_states,
cu_seqlens=cu_seqlens,
rotary_pos_emb=rotary_pos_emb,
max_seqlen=max_seqlen,
seqlens=seqlens,
)
if (
deepstack_visual_indexes is not None
and layer_num in deepstack_visual_indexes
):
hidden_states_list.append(hidden_states)
hidden_states = self.merger(hidden_states)
# processing deepstack
if deepstack_visual_indexes is not None:
processed_hidden_states_list = [hidden_states]
for idx, x in enumerate(hidden_states_list):
x = self.merger_list[idx](x)
processed_hidden_states_list.append(x)
# we cat the original visual features and deepstack features
# along the feature dim
hidden_states = torch.cat(
processed_hidden_states_list, dim=1
) # [seq_len, hidden_size * (1 + depth_of_deepstack)]
return hidden_states
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("attn.qkv.", "attn.q.", "q"),
("attn.qkv.", "attn.k.", "k"),
("attn.qkv.", "attn.v.", "v"),
]
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if name.endswith("patch_embed.proj.weight"):
loaded_weight = conv3d_to_linear_weight(loaded_weight)
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
@support_torch_compile(
dynamic_arg_dims={
"input_ids": 0,
"positions": -1,
"intermediate_tensors": 0,
"inputs_embeds": 0,
"deepstack_input_embeds": 0,
}
)
class Qwen3MoeLLMModel(Qwen3MoeModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config, prefix=prefix)
self.deepstack_multiscale_layer_start = 1
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
deepstack_input_embeds: IntermediateTensors | None = None,
) -> torch.Tensor | IntermediateTensors:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for layer_idx, layer in enumerate(
self.layers[self.start_layer : self.end_layer]
):
layer_idx = layer_idx + self.start_layer
hidden_states, residual = layer(
positions,
hidden_states,
residual,
)
if deepstack_input_embeds is not None and layer_idx in range(
0, len(deepstack_input_embeds)
):
hidden_states = (
hidden_states
+ deepstack_input_embeds[f"deepstack_input_embeds_{layer_idx}"]
)
if not get_pp_group().is_last_rank:
return IntermediateTensors(
{"hidden_states": hidden_states, "residual": residual}
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super(Qwen3MoeForCausalLM, self).__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.model = Qwen3MoeLLMModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
self.lm_head = ParallelLMHead(
config.vocab_size, config.hidden_size, quant_config=quant_config
)
if self.config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(config.vocab_size)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors
)
class Qwen3OmniMoeThinkerProcessingInfo(
Qwen2AudioProcessingInfo, Qwen2_5_VLProcessingInfo
):
def get_hf_config(self):
return self.ctx.get_hf_config(Qwen3OmniMoeConfig).thinker_config
def get_hf_processor(self, **kwargs: object) -> Qwen3OmniMoeProcessor:
processor = self.ctx.get_hf_processor(
Qwen3OmniMoeProcessor,
use_fast=kwargs.pop("use_fast", True),
**kwargs,
)
if not hasattr(processor, "audio_token"):
processor.audio_token = "<|audio_pad|>"
if not hasattr(processor, "image_token"):
processor.image_token = "<|image_pad|>"
if not hasattr(processor, "video_token"):
processor.video_token = "<|video_pad|>"
return processor
def get_feature_extractor(self, **kwargs: object):
hf_processor = self.get_hf_processor(**kwargs)
feature_extractor = hf_processor.feature_extractor # type: ignore
assert isinstance(feature_extractor, WhisperFeatureExtractor)
return feature_extractor
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
return {"audio": None, "image": None, "video": None}
Qwen3OmniMoeThinkerDummyInputsBuilder = Qwen2_5OmniThinkerDummyInputsBuilder
class Qwen3OmniMoeThinkerMultiModalProcessor(
Qwen2_5OmniThinkerMultiModalProcessor,
):
def _call_hf_processor(
self,
prompt: str,
mm_data: Mapping[str, object],
mm_kwargs: Mapping[str, object],
tok_kwargs: Mapping[str, object],
) -> BatchFeature:
mm_data = dict(mm_data)
audios = mm_data.pop("audios", [])
def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
length = x.shape[-1]
if length % hop_length != 0:
pad_length = hop_length - (length % hop_length)
x = np.pad(x, (0, pad_length), mode="constant", constant_values=0)
return x
# NOTE: WhisperFeatureExtractor cannot handle empty list of audios
feature_extractor = self.info.get_feature_extractor()
hop_length = feature_extractor.hop_length
if audios:
# NOTE: Qwen3-Omni processor accept "audio"
# To make sure the cache works with padding=True, we pre-padded
# the audio to multiple of hop_length.
mm_data["audio"] = [
pad_to_hop_length(audio, hop_length)
if isinstance(audio, np.ndarray)
else (pad_to_hop_length(audio[0], hop_length), audio[1])
for audio in audios
]
mm_kwargs = dict(
**mm_kwargs,
)
# TODO(Isotr0py): Remove this patch after upstream fix PR
# released and Transformers version update:
# https://github.com/huggingface/transformers/pull/41473
if (
Version(TRANSFORMERS_VERSION) < Version("4.58.0")
and "truncation" not in mm_kwargs
):
mm_kwargs["truncation"] = False
hf_inputs = super()._call_hf_processor(
prompt=prompt,
mm_data=mm_data,
mm_kwargs=mm_kwargs,
tok_kwargs=tok_kwargs,
)
if (
"audio_feature_lengths" in hf_inputs
and "feature_attention_mask" in hf_inputs
and (audios := mm_data.get("audio", []))
):
audio_num_frames = []
for _, audio in enumerate(audios):
audio_length = len(audio[0]) if isinstance(audio, tuple) else len(audio)
num_frame = (
(audio_length // hop_length)
if audio_length % hop_length == 0
else (audio_length // hop_length - 1)
)
if mm_kwargs.get("truncation", False):
num_frame = min(
num_frame, feature_extractor.n_samples // hop_length
)
audio_num_frames.append(num_frame)
hf_inputs["feature_attention_mask"] = [
torch.ones(num_frame) for num_frame in audio_num_frames
]
hf_inputs["audio_feature_lengths"] = torch.tensor(audio_num_frames)
return hf_inputs
def _maybe_apply_prompt_updates(
self,
mm_items: MultiModalDataItems,
prompt_ids: list[int],
mm_kwargs: MultiModalKwargsItems,
mm_prompt_updates: MultiModalPromptUpdates,
is_update_applied: bool,
) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
"""
Qwen3-Omni reimplements this function to handle `use_audio_in_video`.
"""
mm_item_counts = mm_items.get_all_counts()
self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
use_audio_in_video = False
if "video" in mm_kwargs:
for item in mm_kwargs["video"]:
if item and item["use_audio_in_video"].data:
use_audio_in_video = True
else:
use_audio_in_video = False
if use_audio_in_video and "video" in mm_item_counts:
assert "audio" in mm_item_counts
mm_item_counts["audio"] -= mm_item_counts["video"]
# Special case with `use_audio_in_video=True`
if use_audio_in_video:
if is_update_applied:
prompt_ids = self._get_raw_input_ids(prompt_ids, use_audio_in_video)
(
prompt_ids,
mm_placeholders,
) = self._apply_prompt_updates(
prompt_ids,
mm_prompt_updates,
)
self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
# normal case with `use_audio_in_video=False`
elif is_update_applied:
mm_placeholders = self._find_mm_placeholders(
prompt_ids,
mm_prompt_updates,
)
self._validate_mm_placeholders(
mm_placeholders,
mm_item_counts,
)
else:
prompt_ids, mm_placeholders = self._apply_prompt_updates(
prompt_ids,
mm_prompt_updates,
)
self._validate_mm_placeholders(
mm_placeholders,
mm_item_counts,
)
return prompt_ids, mm_placeholders
def get_updates_use_audio_in_video(
self,
thinker_config: PretrainedConfig,
audio_len: int,
video_grid_thw: list[int] | torch.Tensor,
video_second_per_grid_t: float,
) -> list[int]:
shift = 0
audio_token_id = thinker_config.audio_token_id
video_token_id = thinker_config.video_token_id
audio_start_token_id = thinker_config.audio_start_token_id
audio_end_token_id = thinker_config.audio_end_token_id
spatial_merge_size = thinker_config.vision_config.spatial_merge_size
position_id_per_seconds = thinker_config.position_id_per_seconds
audio_token_indices = np.arange(next(iter([audio_len])))
curr_video_grid_thw = next(iter([video_grid_thw]))
height = curr_video_grid_thw[1] // spatial_merge_size
width = curr_video_grid_thw[2] // spatial_merge_size
video_token_indices = np.arange(curr_video_grid_thw[0]).reshape(-1, 1, 1)
video_token_indices = np.broadcast_to(
video_token_indices, (video_token_indices.shape[0], height, width)
).reshape(-1)
video_token_indices = (
(video_token_indices + shift)
* next(iter([video_second_per_grid_t]))
* position_id_per_seconds
)
video_data_index, audio_data_index = 0, 0
updates = [audio_start_token_id]
while video_data_index < len(video_token_indices) and audio_data_index < len(
audio_token_indices
):
if (
video_token_indices[video_data_index]
<= audio_token_indices[audio_data_index]
):
updates += [video_token_id]
video_data_index += 1
else:
updates += [audio_token_id]
audio_data_index += 1
if video_data_index < len(video_token_indices):
updates += [video_token_id] * (len(video_token_indices) - video_data_index)
if audio_data_index < len(audio_token_indices):
updates += [audio_token_id] * (len(audio_token_indices) - audio_data_index)
updates += [audio_end_token_id]
return updates
def _get_prompt_updates(
self,
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, Any],
out_mm_kwargs: MultiModalKwargsItems,
) -> Sequence[PromptUpdate]:
processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
tokenizer = self.info.get_tokenizer()
image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
vocab = tokenizer.get_vocab()
audio_token = processor.audio_token
image_token = processor.image_token
video_token = processor.video_token
audio_token_id = vocab[audio_token]
image_token_id = vocab[image_token]
video_token_id = vocab[video_token]
out_mm_data = out_mm_kwargs.get_data()
audio_feature_lengths = out_mm_data.get("audio_feature_lengths")
feature_attention_mask = out_mm_data.get("feature_attention_mask")
if audio_feature_lengths is None and feature_attention_mask is None:
audio_output_lengths = []
elif audio_feature_lengths is not None:
_, audio_output_lens = _get_feat_extract_output_lengths(
audio_feature_lengths
)
audio_output_lengths = audio_output_lens.tolist()
elif feature_attention_mask is not None:
assert isinstance(feature_attention_mask, torch.Tensor)
_, audio_output_lens = _get_feat_extract_output_lengths(
feature_attention_mask.sum(-1)
)
audio_output_lengths = audio_output_lens.tolist()
# number of audios read from video.
audio_in_video_item_idx = 0
audio_item_idx = 0
def get_replacement_qwen2_audio(item_idx: int):
nonlocal audio_item_idx
item_idx += audio_in_video_item_idx
audio_item_idx += 1
num_features = audio_output_lengths[item_idx]
if num_features == 0:
audios = mm_items.get_items("audio", AudioProcessorItems)
audio = audios.get(item_idx)
raise ValueError(
f"The audio {audio} (len={len(audio)}) is too short "
"to be represented inside the model"
)
return [audio_token_id] * num_features
def get_replacement_qwen2_vision(item_idx: int, modality: str):
grid_thw = out_mm_data[f"{modality}_grid_thw"][item_idx]
assert isinstance(grid_thw, torch.Tensor)
merge_length = image_processor.merge_size**2
token_id = image_token_id if modality == "image" else video_token_id
return [token_id] * (int(grid_thw.prod()) // merge_length)
use_audio_in_video = hf_processor_mm_kwargs.get("use_audio_in_video", False)
thinker_config = self.info.get_hf_config()
def get_replacement_qwen2_use_audio_in_video(item_idx: int):
nonlocal audio_in_video_item_idx
audio_num_features = audio_output_lengths[audio_item_idx + item_idx]
video_grid_thw = out_mm_data["video_grid_thw"][item_idx]
audio_in_video_item_idx += 1
second_per_grid_ts = hf_processor_mm_kwargs.get("second_per_grid_ts", None)
if second_per_grid_ts:
video_second_per_grid_t = second_per_grid_ts[item_idx]
else:
video_second_per_grid_t = 1.0
return self.get_updates_use_audio_in_video(
thinker_config=thinker_config,
audio_len=audio_num_features,
video_grid_thw=video_grid_thw,
video_second_per_grid_t=video_second_per_grid_t,
)
video_replacement_fn = (
get_replacement_qwen2_use_audio_in_video
if use_audio_in_video
else partial(get_replacement_qwen2_vision, modality="video")
)
return [
PromptReplacement(
modality="audio",
target=audio_token,
replacement=get_replacement_qwen2_audio,
),
PromptReplacement(
modality="image",
target=image_token,
replacement=partial(get_replacement_qwen2_vision, modality="image"),
),
PromptReplacement(
modality="video",
target=video_token,
replacement=video_replacement_fn,
),
]
def _validate_mm_placeholders(
self,
mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
mm_item_counts: Mapping[str, int],
) -> None:
BaseMultiModalProcessor[
Qwen2_5OmniThinkerProcessingInfo
]._validate_mm_placeholders(self, mm_placeholders, mm_item_counts)
def _get_raw_input_ids(
self,
token_ids: list[int],
use_audio_in_video: bool = False,
) -> list[int]:
tokenizer = self.info.get_tokenizer()
vision_bos_token = tokenizer.encode(tokenizer.vision_bos_token)[0]
vision_eos_token = tokenizer.encode(tokenizer.vision_eos_token)[0]
audio_bos_token = tokenizer.encode(tokenizer.audio_bos_token)[0]
audio_eos_token = tokenizer.encode(tokenizer.audio_eos_token)[0]
audio_token = tokenizer.encode("<|audio_pad|>")[0]
image_token = tokenizer.encode("<|image_pad|>")[0]
video_token = tokenizer.encode("<|video_pad|>")[0]
result = token_ids[:]
if use_audio_in_video:
while True:
start = None
for i in range(len(result) - 1):
if result[i : i + 2] == [vision_bos_token, audio_bos_token]:
start = i
break
if start is not None:
end = None
for i in range(start + 2, len(result) - 1):
if result[i : i + 2] == [audio_eos_token, vision_eos_token]:
end = i
break
if end is not None:
result = (
result[:start]
+ [vision_bos_token, video_token, vision_eos_token]
+ result[end + 2 :]
)
else:
break
for mm_token in [audio_token, image_token, video_token]:
compressed = []
for x in result:
if x != mm_token or (not compressed or compressed[-1] != mm_token):
compressed.append(x)
result = compressed
return result
class Qwen3OmniMoeConditionalGenerationMixin(Qwen2_5OmniConditionalGenerationMixin):
def _validate_and_reshape_mm_tensor(
self, mm_input: object, name: str, dim: int = 0
) -> torch.Tensor:
if not isinstance(mm_input, (torch.Tensor, list)):
raise ValueError(f"Incorrect type of {name}. Got type: {type(mm_input)}")
if name == "feature_attention_mask":
dim = -1
if isinstance(mm_input, torch.Tensor):
return torch.concat(list(mm_input), dim=dim)
else:
if isinstance(mm_input[0], list):
return torch.concat(
[torch.concat(mm_input[i], dim=dim) for i in range(len(mm_input))],
dim=dim,
)
else:
return torch.concat(mm_input, dim=dim)
def _process_audio_input(
self,
audio_input: Qwen2AudioFeatureInputs,
audio_hashes: list[str] = None,
cached_audio_features: torch.Tensor = None,
) -> torch.Tensor:
input_features = audio_input["input_features"]
audio_feature_lengths = audio_input["audio_feature_lengths"]
if input_features.ndim == 3:
assert input_features.shape[0] == 1
input_features = input_features.squeeze(0)
if not isinstance(audio_feature_lengths, torch.Tensor):
audio_feature_lengths = torch.cat(audio_feature_lengths)
if audio_feature_lengths.ndim == 2:
audio_feature_lengths = audio_feature_lengths.reshape(-1)
audio_feat_lengths, audio_output_lengths = (
_get_feat_extract_output_lengths(audio_feature_lengths)
)
audio_outputs = self.audio_tower(
input_features.to(self.audio_tower.dtype),
feature_lens=audio_feature_lengths,
aftercnn_lens=audio_feat_lengths,
)
audio_features = audio_outputs.last_hidden_state
return audio_features.split(audio_output_lengths.tolist())
@MULTIMODAL_REGISTRY.register_processor(
Qwen3OmniMoeThinkerMultiModalProcessor,
info=Qwen3OmniMoeThinkerProcessingInfo,
dummy_inputs=Qwen3OmniMoeThinkerDummyInputsBuilder,
)
class Qwen3OmniMoeThinkerForConditionalGeneration(
nn.Module,
SupportsMultiModal,
SupportsPP,
SupportsMRoPE,
Qwen3OmniMoeConditionalGenerationMixin,
):
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_prefix={
"thinker.lm_head.": "language_model.lm_head.",
"thinker.model.": "language_model.model.",
"thinker.": "",
}
)
@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
if modality.startswith("image"):
return "<|vision_start|><|image_pad|><|vision_end|>"
if modality.startswith("video"):
return "<|vision_start|><|video_pad|><|vision_end|>"
if modality.startswith("audio"):
return "<|audio_start|><|audio_pad|><|audio_end|>"
raise ValueError("Only image, video or audio modality is supported")
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
thinker_config: Qwen3OmniMoeThinkerConfig = (
vllm_config.model_config.hf_config.thinker_config
)
quant_config = vllm_config.quant_config
multimodal_config = vllm_config.model_config.multimodal_config
self.config = thinker_config
self.multimodal_config = multimodal_config
# force "use_flash_attention_2=True" to audio tower to align
# the results.
if flash_attn is not None:
audio_config = thinker_config.audio_config
audio_config._attn_implementation_autoset = True
audio_config._attn_implementation = "flash_attention_2"
else:
logger.warning(
"flash_attn is not available, the model may not yield the "
"exactly same result as the transformers implementation "
"in the audio tower part."
)
self.audio_tower = Qwen3OmniMoeAudioEncoder(thinker_config.audio_config)
attn_backend_override = (
multimodal_config.mm_encoder_attn_backend
if multimodal_config is not None
else None
)
self.visual = Qwen3Omni_VisionTransformer(
vision_config=thinker_config.vision_config,
norm_eps=getattr(thinker_config.text_config, "rms_norm_eps", 1e-6),
quant_config=quant_config,
prefix=maybe_prefix(prefix, "visual"),
attn_backend_override=attn_backend_override,
)
self.quant_config = quant_config
self.language_model = Qwen3MoeLLMForCausalLM(
vllm_config=vllm_config.with_hf_config(
thinker_config.text_config, architectures=["Qwen3MoeForCausalLM"]
),
prefix=maybe_prefix(prefix, "language_model"),
)
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors
)
self.use_deepstack = hasattr(
thinker_config.vision_config, "deepstack_visual_indexes"
)
self.deepstack_num_level = (
len(thinker_config.vision_config.deepstack_visual_indexes)
if self.use_deepstack
else 0
)
# register buffer for deepstack
self.deepstack_input_embeds = (
[
torch.zeros(
vllm_config.scheduler_config.max_num_batched_tokens,
thinker_config.text_config.hidden_size,
)
for _ in range(self.deepstack_num_level)
]
if self.use_deepstack
else None
)
self.visual_dim = thinker_config.vision_config.out_hidden_size
self.multiscale_dim = self.visual_dim * self.deepstack_num_level
def _get_deepstack_input_embeds(self, num_tokens: int) -> IntermediateTensors:
# get deepstack_input_embeds from buffer, and clear the buffer
return IntermediateTensors(
{
f"deepstack_input_embeds_{idx}": self.deepstack_input_embeds[idx][
:num_tokens
]
for idx in range(self.deepstack_num_level)
}
)
def _set_deepstack_input_embeds(self, deepstack_input_embeds: torch.Tensor) -> None:
# set deepstack_input_embeds to buffer
num_tokens = deepstack_input_embeds.size(1)
if num_tokens > self.deepstack_input_embeds[0].size(0):
self.deepstack_input_embeds = [
torch.zeros(
num_tokens,
self.config.text_config.hidden_size,
device=self.deepstack_input_embeds[0].device,
dtype=self.deepstack_input_embeds[0].dtype,
)
for _ in range(self.deepstack_num_level)
]
for idx in range(self.deepstack_num_level):
self.deepstack_input_embeds[idx][:num_tokens].copy_(
deepstack_input_embeds[idx]
)
def _clear_deepstack_input_embeds(self, num_tokens: int) -> None:
# clear deepstack_input_embeds in buffer
if num_tokens > 0:
for idx in range(self.deepstack_num_level):
self.deepstack_input_embeds[idx][:num_tokens].zero_()
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
mm_input_by_modality = {}
# Preserve the order of modalities if there are multiple of them
# from the order of kwargs.
for input_key in kwargs:
if (
input_key in ("pixel_values", "image_embeds")
and "image" not in mm_input_by_modality
):
mm_input_by_modality["image"] = self._parse_and_validate_image_input(
**kwargs
)
if (
input_key in ("pixel_values_videos", "video_embeds")
and "video" not in mm_input_by_modality
):
mm_input_by_modality["video"] = self._parse_and_validate_video_input(
**kwargs
)
if (
input_key in ("input_audio_features")
and "audio" not in mm_input_by_modality
):
mm_input_by_modality["audio"] = self._parse_and_validate_audio_input(
**kwargs
)
return mm_input_by_modality
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def get_multimodal_embeddings(
self, **kwargs: object
) -> MultiModalEmbeddings | None:
mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
if not mm_input_by_modality:
return []
# The result multimodal_embeddings is tuple of tensors, with each
# tensor correspoending to a multimodal data item (image or video).
multimodal_embeddings: tuple[torch.Tensor, ...] = ()
# NOTE: It is important to iterate over the keys in this dictionary
# to preserve the order of the modalities.
for modality in mm_input_by_modality:
multimodal_input = mm_input_by_modality[modality]
if modality == "image":
image_embeddings = self._process_image_input(multimodal_input)
multimodal_embeddings += tuple(image_embeddings)
if modality == "video":
video_embeddings = self._process_video_input(multimodal_input)
multimodal_embeddings += tuple(video_embeddings)
if modality == "audio":
audio_embeddings = self._process_audio_input(multimodal_input)
multimodal_embeddings += tuple(audio_embeddings)
return multimodal_embeddings
def get_input_embeddings(
self,
input_ids: torch.Tensor,
multimodal_embeddings: MultiModalEmbeddings | None = None,
*,
is_multimodal: torch.Tensor | None = None,
handle_oov_mm_token: bool = False,
) -> torch.Tensor:
inputs_embeds = self._get_text_embeddings(
input_ids,
self.language_model.get_input_embeddings,
is_multimodal=is_multimodal,
handle_oov_mm_token=handle_oov_mm_token,
)
if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
return inputs_embeds
deepstack_input_embeds = None
# TODO (ywang96): support overlapping modalitiy embeddings so that
# `use_audio_in_video` will work on V1.
# split the feat dim to obtain multi-scale visual feature
has_vision_embeddings = [
embeddings.shape[-1] != self.config.text_config.hidden_size
for embeddings in multimodal_embeddings
]
if self.visual.deepstack_visual_indexes is not None and any(
has_vision_embeddings
):
multiscale_len = len(self.visual.deepstack_visual_indexes)
multimodal_embeddings_multiscale = []
is_vision = torch.zeros_like(is_multimodal)
mm_positions = torch.nonzero(is_multimodal, as_tuple=True)[0]
mm_position_idx = 0
for index, embeddings in enumerate(multimodal_embeddings):
num_tokens = embeddings.shape[0]
current_positions = mm_positions[
mm_position_idx : mm_position_idx + num_tokens
]
# Vision embeddings
if embeddings.shape[-1] != self.config.text_config.hidden_size:
visual_dim = embeddings.shape[-1] // (multiscale_len + 1)
multi_dim = visual_dim * multiscale_len
embeddings_main, embeddings_multiscale = torch.split(
embeddings, [visual_dim, multi_dim], dim=-1
)
multimodal_embeddings[index] = embeddings_main
multimodal_embeddings_multiscale.append(embeddings_multiscale)
is_vision[current_positions] = True
# Audio embeddings
else:
is_vision[current_positions] = False
mm_position_idx += num_tokens
deepstack_input_embeds = inputs_embeds.new_zeros(
inputs_embeds.size(0), multiscale_len * inputs_embeds.size(1)
)
deepstack_input_embeds = _merge_multimodal_embeddings(
inputs_embeds=deepstack_input_embeds,
multimodal_embeddings=multimodal_embeddings_multiscale,
is_multimodal=is_vision,
)
deepstack_input_embeds = (
deepstack_input_embeds.view(
inputs_embeds.shape[0], multiscale_len, visual_dim
)
.permute(1, 0, 2)
.contiguous()
)
self._set_deepstack_input_embeds(deepstack_input_embeds)
inputs_embeds = _merge_multimodal_embeddings(
inputs_embeds=inputs_embeds,
multimodal_embeddings=multimodal_embeddings,
is_multimodal=is_multimodal,
)
return inputs_embeds
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
**kwargs: object,
) -> torch.Tensor | IntermediateTensors:
if intermediate_tensors is not None:
inputs_embeds = None
if (
self.use_deepstack
and inputs_embeds is not None
and get_pp_group().is_first_rank
):
deepstack_input_embeds = self._get_deepstack_input_embeds(
inputs_embeds.size(0)
)
else:
deepstack_input_embeds = None
hidden_states = self.language_model.model(
input_ids,
positions,
intermediate_tensors,
inputs_embeds=inputs_embeds,
# args for deepstack
deepstack_input_embeds=deepstack_input_embeds,
)
if inputs_embeds is not None and get_pp_group().is_first_rank:
self._clear_deepstack_input_embeds(inputs_embeds.size(0))
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor | None:
return self.language_model.compute_logits(hidden_states)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(
self,
skip_prefixes=["talker.", "code2wav."],
)
loaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
return loaded_weights
def get_mrope_input_positions(
self,
input_tokens: list[int],
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
config = hf_config.thinker_config
if isinstance(image_grid_thw, list):
image_grid_thw = torch.tensor(image_grid_thw)
if isinstance(video_grid_thw, list):
video_grid_thw = torch.tensor(video_grid_thw)
input_ids = torch.tensor(input_tokens)
if input_ids is None or input_ids.ndim != 1:
raise ValueError("_omni3_get_input_positions_tensor expects 1D input_ids")
seq_len = input_ids.shape[0]
if audio_feature_lengths is not None and not isinstance(
audio_feature_lengths, torch.Tensor
):
audio_feature_lengths = torch.as_tensor(
audio_feature_lengths, dtype=torch.long
)
if second_per_grid_ts is None:
if video_grid_thw is not None and video_grid_thw.numel() > 0:
second_per_grids = torch.ones(
video_grid_thw.shape[0], dtype=torch.float32
)
else:
second_per_grids = torch.tensor([], dtype=torch.float32)
else:
second_per_grids = torch.tensor(second_per_grid_ts, dtype=torch.float32)
spatial_merge_size = config.vision_config.spatial_merge_size
image_token_id = config.image_token_id
video_token_id = config.video_token_id
audio_token_id = config.audio_token_id
vision_start_token_id = config.vision_start_token_id
audio_start_token_id = config.audio_start_token_id
position_id_per_seconds = config.position_id_per_seconds
vision_start_indices = torch.argwhere(
input_ids == vision_start_token_id
).squeeze(1)
if vision_start_indices.numel() > 0:
vision_tokens = input_ids[vision_start_indices + 1]
else:
vision_tokens = input_ids.new_empty((0,), dtype=input_ids.dtype)
audio_nums = torch.sum(input_ids == audio_start_token_id)
image_nums = (vision_tokens == image_token_id).sum()
video_nums = (
(vision_tokens == audio_start_token_id).sum()
if use_audio_in_video
else (vision_tokens == video_token_id).sum()
)
llm_pos_ids_list: list[torch.Tensor] = []
st = 0
image_idx = 0
video_idx = 0
audio_idx = 0
remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums # noqa: E501
multimodal_nums = (
image_nums + audio_nums
if use_audio_in_video
else image_nums + video_nums + audio_nums
) # noqa: E501
for _ in range(multimodal_nums):
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
if (image_token_id in input_tokens or video_token_id in input_tokens) and (
remain_videos > 0 or remain_images > 0
):
ed_vision_start = input_tokens.index(vision_start_token_id, st)
else:
ed_vision_start = len(input_tokens) + 1
if audio_token_id in input_tokens and remain_audios > 0:
ed_audio_start = input_tokens.index(audio_start_token_id, st)
else:
ed_audio_start = len(input_tokens) + 1
min_ed = min(ed_vision_start, ed_audio_start)
if min_ed == ed_audio_start:
text_len = min_ed - st
if text_len != 0:
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
llm_pos_ids_list.append(
torch.arange(text_len, dtype=torch.long)
.view(1, -1)
.expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
bos_len = 1
llm_pos_ids_list.append(
torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
_, audio_len = _get_feat_extract_output_lengths(
audio_feature_lengths[audio_idx]
)
llm_pos_ids = (
torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
llm_pos_ids_list.append(llm_pos_ids)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
eos_len = 1
llm_pos_ids_list.append(
torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st += text_len + bos_len + audio_len + eos_len
audio_idx += 1
remain_audios -= 1
elif (
min_ed == ed_vision_start
and input_ids[ed_vision_start + 1] == image_token_id
):
text_len = min_ed - st
if text_len != 0:
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
llm_pos_ids_list.append(
torch.arange(text_len, dtype=torch.long)
.view(1, -1)
.expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
bos_len = 1
llm_pos_ids_list.append(
torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
grid_t = image_grid_thw[image_idx][0]
grid_hs = image_grid_thw[:, 1]
grid_ws = image_grid_thw[:, 2]
t_index = torch.arange(grid_t) * position_id_per_seconds
llm_pos_ids = get_llm_pos_ids_for_vision(
st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
)
image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
llm_pos_ids_list.append(llm_pos_ids)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
eos_len = 1
llm_pos_ids_list.append(
torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st += text_len + bos_len + image_len + eos_len
image_idx += 1
remain_images -= 1
elif (
min_ed == ed_vision_start
and input_ids[ed_vision_start + 1] == video_token_id
and not use_audio_in_video
):
text_len = min_ed - st
if text_len != 0:
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
llm_pos_ids_list.append(
torch.arange(text_len, dtype=torch.long)
.view(1, -1)
.expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
bos_len = 1
llm_pos_ids_list.append(
torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
grid_t = video_grid_thw[video_idx][0]
grid_hs = video_grid_thw[:, 1]
grid_ws = video_grid_thw[:, 2]
t_index = (
torch.arange(grid_t)
* float(second_per_grids[video_idx].item())
* position_id_per_seconds
)
llm_pos_ids = get_llm_pos_ids_for_vision(
st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
)
video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
llm_pos_ids_list.append(llm_pos_ids)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
eos_len = 1
llm_pos_ids_list.append(
torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
st += text_len + bos_len + video_len + eos_len
video_idx += 1
remain_videos -= 1
elif (
min_ed == ed_vision_start
and ed_vision_start + 1 == ed_audio_start
and use_audio_in_video
):
text_len = min_ed - st
if text_len != 0:
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
llm_pos_ids_list.append(
torch.arange(text_len, dtype=torch.long)
.view(1, -1)
.expand(3, -1)
+ st_idx
)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
bos_len = 1
bos_block = (
torch.arange(bos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
llm_pos_ids_list.append(bos_block)
llm_pos_ids_list.append(bos_block)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
_, audio_len = _get_feat_extract_output_lengths(
audio_feature_lengths[audio_idx]
)
audio_llm_pos_ids = (
torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
grid_t = video_grid_thw[video_idx][0]
grid_hs = video_grid_thw[:, 1]
grid_ws = video_grid_thw[:, 2]
t_index = (
torch.arange(grid_t)
* float(second_per_grids[video_idx].item())
* position_id_per_seconds
)
video_llm_pos_ids = get_llm_pos_ids_for_vision(
st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
)
video_data_index, audio_data_index = 0, 0
while (
video_data_index < video_llm_pos_ids.shape[-1]
and audio_data_index < audio_llm_pos_ids.shape[-1]
):
if (
video_llm_pos_ids[0][video_data_index]
<= audio_llm_pos_ids[0][audio_data_index]
):
llm_pos_ids_list.append(
video_llm_pos_ids[
:, video_data_index : video_data_index + 1
]
)
video_data_index += 1
else:
llm_pos_ids_list.append(
audio_llm_pos_ids[
:, audio_data_index : audio_data_index + 1
]
)
audio_data_index += 1
if video_data_index < video_llm_pos_ids.shape[-1]:
llm_pos_ids_list.append(
video_llm_pos_ids[
:, video_data_index : video_llm_pos_ids.shape[-1]
]
)
if audio_data_index < audio_llm_pos_ids.shape[-1]:
llm_pos_ids_list.append(
audio_llm_pos_ids[
:, audio_data_index : audio_llm_pos_ids.shape[-1]
]
)
video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
eos_len = 1
eos_block = (
torch.arange(eos_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
llm_pos_ids_list.append(eos_block)
llm_pos_ids_list.append(eos_block)
st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2 # noqa: E501
audio_idx += 1
video_idx += 1
remain_videos -= 1
remain_audios -= 1
if st < len(input_tokens):
st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
text_len = len(input_tokens) - st
llm_pos_ids_list.append(
torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
+ st_idx
)
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
if llm_positions.shape[1] != seq_len:
raise RuntimeError("Position ids length mismatch with input ids length")
mrope_position_delta = llm_positions.max() + 1 - seq_len
return llm_positions, mrope_position_delta
\ No newline at end of file
vllm/model_executor/models/registry.py
View file @ 58117664
......@@ -270,7 +270,6 @@ _MULTIMODAL_MODELS = {
"Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"), # noqa: E501
"Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
"Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
"Qwen3OmniMoeForConditionalGeneration": ("qwen3_omni_moe_thinker", "Qwen3OmniMoeThinkerForConditionalGeneration"),
"Qwen3VLForConditionalGeneration": ("qwen3_vl", "Qwen3VLForConditionalGeneration"), # noqa: E501
"Qwen3VLMoeForConditionalGeneration": ("qwen3_vl_moe", "Qwen3VLMoeForConditionalGeneration"), # noqa: E501
"SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
......
vllm/model_executor/models/vision.py
View file @ 58117664
......@@ -72,18 +72,10 @@ def get_vision_encoder_info(
raise NotImplementedError(msg)
def get_vit_attn_backend(
head_size: int,
dtype: torch.dtype,
*,
attn_backend_override: _Backend | None = None,
) -> _Backend:
def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend:
"""
Get the available attention backend for Vision Transformer.
"""
if attn_backend_override is not None:
return attn_backend_override
# Lazy import to avoid circular dependency
from vllm.attention.selector import get_env_variable_attn_backend
......@@ -410,56 +402,3 @@ def run_dp_sharded_mrope_vision_model(
assert len(out_embeddings) == len(
original_order_embeddings), "Found unassigned embeddings"
return out_embeddings
def get_llm_pos_ids_for_vision(
start_idx: int,
vision_idx: int,
spatial_merge_size: int,
t_index: list[int],
grid_hs: torch.Tensor,
grid_ws: torch.Tensor,
) -> torch.Tensor:
llm_pos_ids_list = []
llm_grid_h = grid_hs[vision_idx] // spatial_merge_size
llm_grid_w = grid_ws[vision_idx] // spatial_merge_size
h_index = (
torch.arange(llm_grid_h)
.view(1, -1, 1)
.expand(len(t_index), -1, llm_grid_w)
.flatten()
)
w_index = (
torch.arange(llm_grid_w)
.view(1, 1, -1)
.expand(len(t_index), llm_grid_h, -1)
.flatten()
)
t_index_tensor = (
torch.Tensor(t_index)
.to(llm_grid_h.device)
.view(-1, 1)
.expand(-1, llm_grid_h * llm_grid_w)
.long()
.flatten()
)
_llm_pos_ids = torch.stack([t_index_tensor, h_index, w_index])
llm_pos_ids_list.append(_llm_pos_ids + start_idx)
llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1)
return llm_pos_ids
# Due to a performance regression with Conv3D in PyTorch2.9, we reshape
# Conv3D weights to Linear weights for better performance.
# See: https://github.com/vllm-project/vllm/issues/27406
# and https://github.com/pytorch/pytorch/issues/166122
# FIXME(Isotr0py): Revert the PR introduces this workaround
# (https://github.com/vllm-project/vllm/pull/27418),
# once the performance issue is resolved in PyTorch.
def conv3d_to_linear_weight(conv3d_weight: torch.Tensor) -> torch.Tensor:
"""
Reshape Conv3D weight to Linear weight. Only work when kernel_size==stride.
"""
out_channels, in_channels, kt, kh, kw = conv3d_weight.shape
linear_weight = conv3d_weight.reshape(out_channels, in_channels * kt * kh * kw)
return linear_weight
vllm/v1/worker/gpu_model_runner.py
View file @ 58117664
......@@ -752,7 +752,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
if mm_input.get("use_audio_in_video") is True:
use_audio_in_video = True
if supports_mrope(self.get_model()):
if supports_mrope(self.model):
req_state.mrope_positions, req_state.mrope_position_delta = \
self.model.get_mrope_input_positions(
req_state.prompt_token_ids,
......
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