[Model][VLM] Add Qwen2-VL model support (#7905)

Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/models/supported_models.rst

@@ -252,6 +252,11 @@ Multimodal Language Models
     - Image\ :sup:`E`
     - :code:`Qwen/Qwen-VL`, :code:`Qwen/Qwen-VL-Chat`, etc.
     -
+  * - :code:`Qwen2VLForConditionalGeneration`
+    - Qwen2-VL (see note)
+    - Image\ :sup:`+` / Video\ :sup:`+`
+    - :code:`Qwen/Qwen2-VL-2B-Instruct`, :code:`Qwen/Qwen2-VL-7B-Instruct`, :code:`Qwen/Qwen2-VL-72B-Instruct`, etc.
+    -
   * - :code:`UltravoxModel`
     - Ultravox
     - Audio\ :sup:`E+`
@@ -265,15 +270,14 @@ Multimodal Language Models
   For :code:`openbmb/MiniCPM-V-2`, the official repo doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
   For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
 
-  For :code:`LLaVA-NeXT-Video`, the latest release of :code:`huggingface/transformers` doesn't work yet, so we need to use a developer version (:code:`21fac7abba2a37fae86106f87fcf9974fd1e3830`) for now.
+.. note::
+  For :code:`LLaVA-NeXT-Video` and :code:`Qwen2-VL`, the latest release of :code:`huggingface/transformers` doesn't work yet, so we need to use a developer version (:code:`21fac7abba2a37fae86106f87fcf9974fd1e3830`) for now.
   This can be installed by running the following command: 
 
-
   .. code-block:: bash
     
     pip install git+https://github.com/huggingface/transformers.git@21fac7abba2a37fae86106f87fcf9974fd1e3830
 
-
 ----
 
 If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.

examples/offline_inference_vision_language.py

@@ -179,6 +179,23 @@ def run_qwen_vl(question):
     return llm, prompt, stop_token_ids
 
 
+# Qwen2-VL
+def run_qwen2_vl(question):
+    model_name = "Qwen/Qwen2-VL-7B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_num_seqs=5,
+    )
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 model_example_map = {
     "llava": run_llava,
     "llava-next": run_llava_next,
@@ -191,6 +208,7 @@ model_example_map = {
     "blip-2": run_blip2,
     "internvl_chat": run_internvl,
     "qwen_vl": run_qwen_vl,
+    "qwen2_vl": run_qwen2_vl,
 }
 
 

examples/offline_inference_vision_language_multi_image.py

@@ -6,7 +6,7 @@ by the model.
 from argparse import Namespace
 from typing import List
 
-from transformers import AutoTokenizer
+from transformers import AutoProcessor, AutoTokenizer
 
 from vllm import LLM, SamplingParams
 from vllm.multimodal.utils import fetch_image
@@ -30,7 +30,7 @@ def load_phi3v(question, image_urls: List[str]):
                              for i, _ in enumerate(image_urls, start=1))
     prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n"
     stop_token_ids = None
-    return llm, prompt, stop_token_ids
+    return llm, prompt, stop_token_ids, None
 
 
 def load_internvl(question, image_urls: List[str]):
@@ -60,18 +60,72 @@ def load_internvl(question, image_urls: List[str]):
     # https://huggingface.co/OpenGVLab/InternVL2-2B#service
     stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"]
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
-    return llm, prompt, stop_token_ids
+
+    return llm, prompt, stop_token_ids, None
+
+
+def load_qwen2_vl(question, image_urls: List[str]):
+    try:
+        from qwen_vl_utils import process_vision_info
+    except ModuleNotFoundError:
+        print('WARNING: `qwen-vl-utils` not installed, input images will not '
+              'be automatically resized. You can enable this functionality by '
+              '`pip install qwen-vl-utils`.')
+        process_vision_info = None
+
+    model_name = "Qwen/Qwen2-VL-7B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_num_seqs=5,
+        max_model_len=32768 if process_vision_info is None else 4096,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [{
+        "role": "system",
+        "content": "You are a helpful assistant."
+    }, {
+        "role":
+        "user",
+        "content": [
+            *placeholders,
+            {
+                "type": "text",
+                "text": question
+            },
+        ],
+    }]
+
+    processor = AutoProcessor.from_pretrained(model_name)
+
+    prompt = processor.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    stop_token_ids = None
+
+    if process_vision_info is None:
+        image_data = [fetch_image(url) for url in image_urls]
+    else:
+        image_data, _ = process_vision_info(messages)
+
+    return llm, prompt, stop_token_ids, image_data
 
 
 model_example_map = {
     "phi3_v": load_phi3v,
     "internvl_chat": load_internvl,
+    "qwen2_vl": load_qwen2_vl,
 }
 
 
 def run_generate(model, question: str, image_urls: List[str]):
-    llm, prompt, stop_token_ids = model_example_map[model](question,
-                                                           image_urls)
+    llm, prompt, stop_token_ids, image_data = model_example_map[model](
+        question, image_urls)
+    if image_data is None:
+        image_data = [fetch_image(url) for url in image_urls]
 
     sampling_params = SamplingParams(temperature=0.0,
                                      max_tokens=128,
@@ -81,7 +135,7 @@ def run_generate(model, question: str, image_urls: List[str]):
         {
             "prompt": prompt,
             "multi_modal_data": {
-                "image": [fetch_image(url) for url in image_urls]
+                "image": image_data
             },
         },
         sampling_params=sampling_params)
@@ -92,7 +146,7 @@ def run_generate(model, question: str, image_urls: List[str]):
 
 
 def run_chat(model: str, question: str, image_urls: List[str]):
-    llm, _, stop_token_ids = model_example_map[model](question, image_urls)
+    llm, _, stop_token_ids, _ = model_example_map[model](question, image_urls)
 
     sampling_params = SamplingParams(temperature=0.0,
                                      max_tokens=128,

requirements-common.txt

@@ -28,3 +28,4 @@ importlib_metadata
 mistral_common >= 1.3.4
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
+einops # Required for Qwen2-VL.

tests/models/test_registry.py

 import pytest
+import transformers
 
 from vllm.model_executor.models import _MODELS, ModelRegistry
 
 
 @pytest.mark.parametrize("model_cls", _MODELS)
 def test_registry_imports(model_cls):
+    if (model_cls == "Qwen2VLForConditionalGeneration"
+            and transformers.__version__ < "4.45"):
+        pytest.skip("Waiting for next transformers release")
+
     # Ensure all model classes can be imported successfully
     ModelRegistry.resolve_model_cls([model_cls])

vllm/config.py

@@ -1733,6 +1733,9 @@ def _get_and_verify_max_len(
                     "with rope_scaling. Please raise an issue so we can "
                     "investigate.")
 
+            if rope_type == "mrope":
+                scaling_factor = 1
+            else:
                 assert "factor" in rope_scaling
                 scaling_factor = rope_scaling["factor"]
             if rope_type == "yarn":

vllm/entrypoints/chat_utils.py

@@ -108,7 +108,7 @@ class ConversationMessage(TypedDict, total=False):
     """The tool calls generated by the model, such as function calls."""
 
 
-ModalityStr = Literal["image", "audio"]
+ModalityStr = Literal["image", "audio", "video"]
 _T = TypeVar("_T")
 
 
@@ -158,12 +158,18 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                                               hf_config.image_token_index)
             if model_type in ("chameleon", "internvl_chat"):
                 return "<image>"
+            if model_type == "qwen2_vl":
+                return "<|vision_start|><|image_pad|><|vision_end|>"
 
             raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "audio":
             if model_type == "ultravox":
                 return "<|reserved_special_token_0|>"
             raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "video":
+            if model_type == "qwen2_vl":
+                return "<|vision_start|><|video_pad|><|vision_end|>"
+            raise TypeError(f"Unknown model type: {model_type}")
         else:
             raise TypeError(f"Unknown modality: {modality}")
 

vllm/model_executor/layers/rotary_embedding.py

@@ -712,6 +712,179 @@ class Llama3RotaryEmbedding(RotaryEmbedding):
         return new_freqs
 
 
+class MRotaryEmbedding(RotaryEmbedding):
+    """Rotary Embedding with Multimodal Sections."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        mrope_section: Optional[List[int]] = None,
+    ) -> None:
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+
+        self.mrope_section = mrope_section
+        if self.mrope_section:
+            assert sum(self.mrope_section) == rotary_dim // 2
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """PyTorch-native implementation equivalent to forward().
+
+        Args:
+            positions:
+                [num_tokens,] (text only) or
+                [3, num_tokens] (T/H/W positions with multimodal inputs)
+            query: [num_tokens, num_heads * head_size]
+            key: [num_tokens, num_kv_heads * head_size]
+        """
+        assert positions.ndim == 1 or positions.ndim == 2
+
+        num_tokens = positions.shape[-1]
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if positions.ndim == 2:
+            assert self.mrope_section
+
+            cos = torch.cat([
+                m[i]
+                for i, m in enumerate(cos.split(self.mrope_section, dim=-1))
+            ],
+                            dim=-1)
+            sin = torch.cat([
+                m[i]
+                for i, m in enumerate(sin.split(self.mrope_section, dim=-1))
+            ],
+                            dim=-1)
+
+        query_shape = query.shape
+        query = query.view(num_tokens, -1, self.head_size)
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
+        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        key_shape = key.shape
+        key = key.view(num_tokens, -1, self.head_size)
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
+        key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
+    @staticmethod
+    def get_input_positions(
+        input_tokens: List[int],
+        image_grid_thw: Union[List[List[int]], torch.Tensor],
+        video_grid_thw: Union[List[List[int]], torch.Tensor],
+        image_token_id: int,
+        video_token_id: int,
+        vision_start_token_id: int,
+        vision_end_token_id: int,
+        spatial_merge_size: int,
+        context_len: int = 0,
+    ) -> Tuple[List[List[int]], int]:
+        """Get mrope input positions and delta value."""
+
+        if isinstance(image_grid_thw, torch.Tensor):
+            image_grid_thw = image_grid_thw.tolist()
+        if isinstance(video_grid_thw, torch.Tensor):
+            video_grid_thw = video_grid_thw.tolist()
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            if image_token_id in input_tokens and remain_images > 0:
+                ed_image = input_tokens.index(image_token_id, st)
+            else:
+                ed_image = len(input_tokens) + 1
+            if video_token_id in input_tokens and remain_videos > 0:
+                ed_video = input_tokens.index(video_token_id, st)
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                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()
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * 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(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        llm_positions = llm_positions[:, context_len:]
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+
+        return llm_positions.tolist(), mrope_position_delta
+
+    @staticmethod
+    def get_next_input_positions(
+        mrope_position_delta: int,
+        context_len: int,
+        seq_len: int,
+    ) -> List[List[int]]:
+        return [
+            list(
+                range(context_len + mrope_position_delta,
+                      seq_len + mrope_position_delta)) for _ in range(3)
+        ]
+
+
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 
 
@@ -752,7 +925,7 @@ def get_rope(
         # The correct one should be "longrope" but keep "su" here
         # for backward compatible
         if scaling_type not in {"su", "longrope"}:
-            scaling_factor = rope_scaling["factor"]
+            scaling_factor = rope_scaling.get("factor", 1.0)
         if scaling_type == "llama3":
             low_freq_factor = rope_scaling["low_freq_factor"]
             high_freq_factor = rope_scaling["high_freq_factor"]
@@ -816,6 +989,16 @@ def get_rope(
                 head_size, rotary_dim, max_position, original_max_position,
                 base, is_neox_style, dtype, short_factor, long_factor,
                 **extra_kwargs)
+        elif scaling_type == "mrope":
+            return MRotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                dtype,
+                mrope_section=rope_scaling["mrope_section"],
+            )
         else:
             raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
     _ROPE_DICT[key] = rotary_emb

vllm/model_executor/models/__init__.py

@@ -53,6 +53,8 @@ _GENERATION_MODELS = {
     "PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"),
     "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
     "Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"),
+    "Qwen2VLForConditionalGeneration":
+    ("qwen2_vl", "Qwen2VLForConditionalGeneration"),
     "RWForCausalLM": ("falcon", "FalconForCausalLM"),
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
@@ -90,6 +92,8 @@ _MULTIMODAL_MODELS = {
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "UltravoxModel": ("ultravox", "UltravoxModel"),
     "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
+    "Qwen2VLForConditionalGeneration": ("qwen2_vl",
+                                        "Qwen2VLForConditionalGeneration"),
 }
 _CONDITIONAL_GENERATION_MODELS = {
     "BartModel": ("bart", "BartForConditionalGeneration"),

vllm/model_executor/models/qwen2_vl.py

+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
+# Copyright 2024 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 Qwen2-VL model compatible with HuggingFace weights."""
+from array import array
+from functools import lru_cache, partial
+from typing import (Iterable, List, Mapping, Optional, Tuple, Type, TypedDict,
+                    Union)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from PIL import Image
+from transformers import Qwen2VLConfig
+from transformers.image_utils import (get_image_size,
+                                      infer_channel_dimension_format,
+                                      to_numpy_array)
+from transformers.models.qwen2_vl.configuration_qwen2_vl import (
+    Qwen2VLVisionConfig)
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import (
+    make_batched_images, make_batched_videos, smart_resize)
+
+import vllm.envs as envs
+from vllm.attention import AttentionMetadata
+from vllm.attention.selector import (_Backend, backend_name_to_enum,
+                                     get_global_forced_attn_backend)
+from vllm.config import CacheConfig, MultiModalConfig
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import QuickGELU
+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.sampler import Sampler, SamplerOutput
+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.interfaces import SupportsMultiModal
+from vllm.model_executor.models.qwen2 import Qwen2Model
+from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
+                             MultiModalInputs)
+from vllm.multimodal.base import MultiModalData
+from vllm.multimodal.image import cached_get_image_processor
+from vllm.platforms import current_platform
+from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+                           SequenceData)
+from vllm.transformers_utils.processor import get_processor
+
+logger = init_logger(__name__)
+
+# === Vision Inputs === #
+
+
+class Qwen2VLImageInputs(TypedDict):
+    pixel_values: torch.Tensor
+    """Shape: 
+    `(num_patches, num_channels * patch_size * patch_size)`
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2VLVideoInputs(TypedDict):
+    pixel_values_videos: torch.Tensor
+    """Shape: 
+    `(num_patches, 
+      num_channels * temporal_patch_size * patch_size * patch_size)`
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+    
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+# === Vision Encoder === #
+
+
+class Qwen2VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        act_layer: Type[nn.Module] = QuickGELU,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(in_features,
+                                        hidden_features,
+                                        quant_config=quant_config)
+        self.act = act_layer()
+        self.fc2 = RowParallelLinear(hidden_features,
+                                     in_features,
+                                     quant_config=quant_config)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x_parallel, _ = self.fc1(x)
+        x_parallel = self.act(x_parallel)
+        x, _ = self.fc2(x_parallel)
+        return x
+
+
+def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(torch.stack((-x2, x1), dim=-1),
+                         "... d two -> ... (d two)",
+                         two=2)
+
+
+def apply_rotary_emb_torch(x: torch.Tensor,
+                           cos: torch.Tensor,
+                           sin: torch.Tensor,
+                           interleaved: bool = False) -> torch.Tensor:
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos = repeat(
+        cos,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    sin = repeat(
+        sin,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos +
+            rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]
+        ],
+        dim=-1,
+    )
+
+
+def apply_rotary_pos_emb_vision(t: torch.Tensor,
+                                freqs: torch.Tensor) -> torch.Tensor:
+    t_ = t.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    output = apply_rotary_emb_torch(t_, cos, sin).type_as(t)
+    return output
+
+
+class Qwen2VisionAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: Optional[int] = None,
+        num_heads: Optional[int] = None,
+        projection_size: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        # Per attention head and per partition values.
+        world_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, world_size)
+
+        self.qkv = ColumnParallelLinear(input_size=embed_dim,
+                                        output_size=3 * projection_size,
+                                        quant_config=quant_config)
+        self.proj = RowParallelLinear(input_size=projection_size,
+                                      output_size=embed_dim,
+                                      quant_config=quant_config)
+
+        # Detect attention implementation.
+        selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
+        if selected_backend is None:
+            backend_by_env_var: Optional[str] = envs.VLLM_ATTENTION_BACKEND
+            if backend_by_env_var is not None:
+                selected_backend = backend_name_to_enum(backend_by_env_var)
+        if selected_backend is None:
+            # For Volta and Turing GPUs, use xformers instead.
+            device_available = current_platform.get_device_capability()[0] >= 8
+            if device_available:
+                from transformers.utils import is_flash_attn_2_available
+
+                if is_flash_attn_2_available():
+                    self._use_flash_attn = True
+                else:
+                    logger.warning(
+                        "Current Qwen2-VL implementation has a bug with "
+                        "`vllm-flash-attn` inside vision module, so we use "
+                        "xformers backend instead. You can run `pip install "
+                        "flash-attn to use flash-attention backend.")
+                    self._use_flash_attn = False
+            else:
+                self._use_flash_attn = False
+        else:
+            if selected_backend == _Backend.FLASH_ATTN:
+                self._use_flash_attn = True
+            elif selected_backend == _Backend.XFORMERS:
+                self._use_flash_attn = False
+            else:
+                raise RuntimeError(
+                    f"Qwen2-VL does not support {selected_backend} backend now."
+                )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor = None,
+    ) -> torch.Tensor:
+        # [s, b, c] --> [s, b, head * 3 * head_dim]
+        x, _ = self.qkv(x)
+
+        # [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads_per_partition,
+            3 * self.hidden_size_per_attention_head,
+        )
+        x = x.view(*new_x_shape)
+
+        # [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
+        q, k, v = dist_utils.split_tensor_along_last_dim(x, 3)
+        batch_size = q.shape[1]
+
+        q, k, v = [
+            rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
+        ]
+        if rotary_pos_emb is not None:
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
+
+        if self._use_flash_attn:
+            # from vllm_flash_attn.flash_attn_interface import (
+            #   flash_attn_varlen_func)
+            from flash_attn import flash_attn_varlen_func
+
+            q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
+
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+            output = flash_attn_varlen_func(q,
+                                            k,
+                                            v,
+                                            cu_seqlens_q=cu_seqlens,
+                                            cu_seqlens_k=cu_seqlens,
+                                            max_seqlen_q=max_seqlen,
+                                            max_seqlen_k=max_seqlen,
+                                            dropout_p=0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        else:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None)
+
+            context_layer = xops.memory_efficient_attention_forward(
+                q, k, v, attn_bias=attn_bias, p=0, scale=None)
+        context_layer = rearrange(context_layer,
+                                  "b s h d -> s b (h d)").contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class Qwen2VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float,
+        act_layer: Type[nn.Module] = QuickGELU,
+        norm_layer: Type[nn.Module] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> 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)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+
+        self.attn = Qwen2VisionAttention(embed_dim=dim,
+                                         num_heads=num_heads,
+                                         projection_size=dim,
+                                         quant_config=quant_config)
+        self.mlp = Qwen2VisionMLP(dim,
+                                  mlp_hidden_dim,
+                                  act_layer=act_layer,
+                                  quant_config=quant_config)
+
+    def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
+                rotary_pos_emb: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(self.norm1(x),
+                          cu_seqlens=cu_seqlens,
+                          rotary_pos_emb=rotary_pos_emb)
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class Qwen2VisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_chans: int = 3,
+        embed_dim: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.embed_dim = embed_dim
+
+        kernel_size = [temporal_patch_size, patch_size, patch_size]
+        self.proj = nn.Conv3d(in_chans,
+                              embed_dim,
+                              kernel_size=kernel_size,
+                              stride=kernel_size,
+                              bias=False)
+
+    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.embed_dim)
+        return x
+
+
+class Qwen2VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Type[nn.Module] = None,
+        spatial_merge_size: int = 2,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.ln_q = norm_layer(context_dim)
+        self.mlp = nn.ModuleList([
+            ColumnParallelLinear(self.hidden_size,
+                                 self.hidden_size,
+                                 bias=True,
+                                 quant_config=quant_config),
+            nn.GELU(),
+            RowParallelLinear(self.hidden_size,
+                              d_model,
+                              bias=True,
+                              quant_config=quant_config),
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ln_q(x)
+        x = 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 Qwen2VisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta
+                          **(torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._freqs_cached = None
+
+    def update_freqs_cache(self, seqlen: int) -> None:
+        if seqlen > self._seq_len_cached:
+            seqlen *= 2
+            self._seq_len_cached = seqlen
+            self.inv_freq = 1.0 / (self.theta**(torch.arange(
+                0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device)
+                                                / self.dim))
+            seq = torch.arange(seqlen,
+                               device=self.inv_freq.device,
+                               dtype=self.inv_freq.dtype)
+            freqs = torch.outer(seq, self.inv_freq)
+            self._freqs_cached = freqs
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        self.update_freqs_cache(seqlen)
+        return self._freqs_cached[:seqlen]
+
+
+class Qwen2VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen2VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        patch_size: int = vision_config.patch_size
+        temporal_patch_size: int = vision_config.temporal_patch_size
+        spatial_merge_size: int = vision_config.spatial_merge_size
+        in_chans: int = vision_config.in_chans
+        hidden_size: int = vision_config.hidden_size
+        embed_dim: int = vision_config.embed_dim
+        depth: int = vision_config.depth
+        num_heads: int = vision_config.num_heads
+        mlp_ratio: float = vision_config.mlp_ratio
+
+        self.spatial_merge_size = spatial_merge_size
+
+        self.patch_embed = Qwen2VisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = embed_dim // num_heads
+        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([
+            Qwen2VisionBlock(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+            ) for _ in range(depth)
+        ])
+        self.merger = Qwen2VisionPatchMerger(
+            d_model=hidden_size,
+            context_dim=embed_dim,
+            norm_layer=norm_layer,
+            quant_config=quant_config,
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.blocks[0].mlp.fc2.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.blocks[0].mlp.fc2.weight.device
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).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 forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+
+        # compute position embedding
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:, 0]).cumsum(
+                                                 dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        # transformers
+        x = x.unsqueeze(1)
+        for blk in self.blocks:
+            x = blk(x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
+
+        # adapter
+        x = self.merger(x)
+        return x
+
+
+# === Vision input helpers === #
+
+cached_get_processor = lru_cache(get_processor)
+
+
+def mm_input_mapper_for_qwen2_vl(
+    ctx: InputContext,
+    data: MultiModalData[object],
+    data_type_key: str,
+) -> MultiModalInputs:
+    """Input mapper for Qwen2-VL."""
+    model_config = ctx.model_config
+    image_processor = cached_get_image_processor(
+        model_config.model, trust_remote_code=model_config.trust_remote_code)
+    if image_processor is None:
+        raise RuntimeError("No HuggingFace processor is available "
+                           "to process the image object")
+
+    images = None
+    videos = None
+    if data_type_key == "image":
+        images = data
+    else:
+        assert data_type_key == "video"
+        videos = data
+
+    try:
+        batch_data = image_processor \
+            .preprocess(images=images, videos=videos, return_tensors="pt") \
+            .data
+    except Exception:
+        logger.error("Failed to process image (%s)", data)
+        raise
+
+    return MultiModalInputs(batch_data)
+
+
+image_input_mapper_for_qwen2_vl = partial(mm_input_mapper_for_qwen2_vl,
+                                          data_type_key="image")
+video_input_mapper_for_qwen2_vl = partial(mm_input_mapper_for_qwen2_vl,
+                                          data_type_key="video")
+
+
+def _get_vision_info(
+    image_processor,
+    height: int,
+    width: int,
+    min_pixels: int,
+    max_pixels: int,
+    do_resize: bool = True,
+    data_type_key: str = "image",
+    mm_count: int = 1,
+):
+    """Get information (resized height / width and number of vision tokens)
+    of input image / video frame."""
+
+    if do_resize:
+        resized_height, resized_width = smart_resize(
+            height=height,
+            width=width,
+            factor=image_processor.patch_size * image_processor.merge_size,
+            min_pixels=min_pixels,
+            max_pixels=max_pixels,
+        )
+    else:
+        resized_height, resized_width = height, width
+
+    if data_type_key == "image":
+        grid_t = mm_count
+    else:
+        assert data_type_key == "video"
+        grid_t = max(mm_count // image_processor.temporal_patch_size, 1)
+
+    grid_h = resized_height // image_processor.patch_size
+    grid_w = resized_width // image_processor.patch_size
+    vision_tokens = grid_t * grid_h * grid_w
+    llm_num_vision_tokens = (vision_tokens // image_processor.merge_size //
+                             image_processor.merge_size)
+
+    return resized_height, resized_width, llm_num_vision_tokens
+
+
+def _get_max_image_info(
+    image_processor,
+    data_type_key: str = "image",
+    mm_count: int = 1,
+):
+    return _get_vision_info(
+        image_processor,
+        height=9999999,
+        width=9999999,
+
+        # Limit min / max pixels.
+        min_pixels=max(image_processor.min_pixels, 28 * 28),
+        max_pixels=min(image_processor.max_pixels, 1280 * 28 * 28),
+        data_type_key=data_type_key,
+        mm_count=mm_count,
+    )
+
+
+def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int:
+    image_processor = cached_get_image_processor(ctx.model_config.model)
+    max_resized_height, max_resized_width, max_llm_image_tokens = \
+        _get_max_image_info(image_processor, data_type_key=data_type_key,
+                            mm_count=1)
+    return max_llm_image_tokens
+
+
+get_max_qwen2_vl_image_tokens = partial(get_max_qwen2_vl_mm_tokens,
+                                        data_type_key="image")
+get_max_qwen2_vl_video_tokens = partial(get_max_qwen2_vl_mm_tokens,
+                                        data_type_key="video")
+
+
+def dummy_data_for_qwen2_vl(
+    ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]
+) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
+    image_processor = cached_get_image_processor(ctx.model_config.model)
+
+    num_images = mm_counts["image"]
+    max_resized_height, max_resized_width, max_llm_image_tokens = \
+        _get_max_image_info(image_processor, data_type_key="image",
+                            mm_count=num_images)
+    if seq_len - max_llm_image_tokens - 2 < 0:
+        raise RuntimeError(
+            f"Qwen2-VL cannot process {num_images} images in a prompt, "
+            "please increase max_model_len or reduce image limit by "
+            "--limit-mm-per-prompt.")
+
+    # Check video counts.
+    num_videos = mm_counts["video"]
+    max_resized_height, max_resized_width, max_llm_video_tokens = \
+        _get_max_image_info(image_processor, data_type_key="video",
+                            mm_count=num_videos)
+    if seq_len - max_llm_video_tokens - 2 < 0:
+        raise RuntimeError(
+            f"Qwen2-VL cannot process {num_images} videos in a prompt, "
+            "please increase max_model_len or reduce video limit by "
+            "--limit-mm-per-prompt.")
+
+    hf_config = ctx.get_hf_config(Qwen2VLConfig)
+    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                      [hf_config.vision_start_token_id])
+    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                       [hf_config.image_token_id]) * max_llm_image_tokens
+    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                       [hf_config.vision_end_token_id])
+    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                       [0]) * (seq_len - max_llm_image_tokens - 2)
+    dummy_seqdata = SequenceData(token_ids)
+    dummy_image = Image.new("RGB", (max_resized_width, max_resized_height),
+                            color=0)
+
+    return dummy_seqdata, {
+        "image": dummy_image if num_images == 1 else [dummy_image] * num_images
+    }
+
+
+def _get_llm_num_vision_tokens(
+    mm_inputs: list,
+    data_type_key: str,
+    image_processor,
+):
+    """Get number of vision tokens of multimodal inputs.
+
+    This method is derived from `transformers.models.qwen2_vl.
+    image_processing_qwen2_vl.Qwen2VLImageProcessor._preprocess`.
+    """
+    image = to_numpy_array(mm_inputs[0])
+    input_data_format = infer_channel_dimension_format(image)
+    height, width = get_image_size(image, channel_dim=input_data_format)
+    _, _, llm_num_vision_tokens = _get_vision_info(
+        image_processor,
+        height=height,
+        width=width,
+        min_pixels=image_processor.min_pixels,
+        max_pixels=image_processor.max_pixels,
+        do_resize=image_processor.do_resize,
+        data_type_key=data_type_key,
+        mm_count=len(mm_inputs),
+    )
+    return llm_num_vision_tokens
+
+
+def input_processor_for_qwen2_vl(ctx: InputContext,
+                                 llm_inputs: LLMInputs) -> LLMInputs:
+    multi_modal_data = llm_inputs.get("multi_modal_data", None)
+    if multi_modal_data is None:
+        return llm_inputs
+
+    image_inputs = multi_modal_data.get("image", None)
+    video_inputs = multi_modal_data.get("video", None)
+
+    processor = cached_get_processor(ctx.model_config.model)
+    image_processor = processor.image_processor
+    hf_config = ctx.get_hf_config(Qwen2VLConfig)
+
+    # To avoid redundant processing of vision objects (resize, rescale, etc.),
+    # we extract code of calculating number of vision tokens from
+    # `transformers.models.qwen2_vl.processing_qwen2_vl.Qwen2VLProcessor`.
+    #
+    # The following code is equivalent to:
+    #    prompt = llm_inputs["prompt"]
+    #    inputs = processor(text=[prompt],
+    #                       images=image_inputs,
+    #                       videos=video_inputs,
+    #                       padding=True,
+    #                       return_tensors="pt")
+    #    prompt_token_ids = inputs["input_ids"][0].tolist()
+
+    prompt_token_ids = llm_inputs.get("prompt_token_ids", None)
+    if prompt_token_ids is None:
+        prompt = llm_inputs["prompt"]
+        prompt_token_ids = processor.tokenizer(
+            prompt,
+            padding=True,
+            return_tensors=None,
+        )["input_ids"]
+
+    # Expand image pad tokens.
+    if image_inputs is not None:
+        image_indices = [
+            idx for idx, token in enumerate(prompt_token_ids)
+            if token == hf_config.image_token_id
+        ]
+        image_inputs = make_batched_images(image_inputs)
+        assert len(image_indices) == len(image_inputs)
+
+        prompt_token_ids_with_image = []
+        for image_cnt, image in enumerate(image_inputs):
+            num_image_tokens = _get_llm_num_vision_tokens(
+                [image],
+                data_type_key="image",
+                image_processor=image_processor,
+            )
+            if image_cnt == 0:
+                non_image_tokens = prompt_token_ids[:image_indices[image_cnt]]
+            else:
+                non_image_tokens = prompt_token_ids[image_indices[image_cnt -
+                                                                  1] +
+                                                    1:image_indices[image_cnt]]
+            prompt_token_ids_with_image.extend(non_image_tokens)
+            prompt_token_ids_with_image.extend(
+                hf_config.image_token_id for _ in range(num_image_tokens))
+        prompt_token_ids_with_image.extend(prompt_token_ids[image_indices[-1] +
+                                                            1:])
+        prompt_token_ids = prompt_token_ids_with_image
+
+    # Expand video pad tokens.
+    if video_inputs is not None:
+        video_indices = [
+            idx for idx, token in enumerate(prompt_token_ids)
+            if token == hf_config.video_token_id
+        ]
+        video_inputs = make_batched_videos(video_inputs)
+        assert len(video_indices) == len(video_inputs)
+
+        prompt_token_ids_with_video = []
+        for video_cnt, video in enumerate(video_inputs):
+            num_video_tokens = _get_llm_num_vision_tokens(
+                video,
+                data_type_key="video",
+                image_processor=image_processor,
+            )
+            if video_cnt == 0:
+                non_video_tokens = prompt_token_ids[:video_indices[video_cnt]]
+            else:
+                non_video_tokens = prompt_token_ids[video_indices[video_cnt -
+                                                                  1] +
+                                                    1:video_indices[video_cnt]]
+            prompt_token_ids_with_video.extend(non_video_tokens)
+            prompt_token_ids_with_video.extend(
+                hf_config.video_token_id for _ in range(num_video_tokens))
+        prompt_token_ids_with_video.extend(prompt_token_ids[video_indices[-1] +
+                                                            1:])
+        prompt_token_ids = prompt_token_ids_with_video
+
+    return LLMInputs(
+        prompt_token_ids=prompt_token_ids,
+        prompt=llm_inputs["prompt"],
+        multi_modal_data=multi_modal_data,
+    )
+
+
+@MULTIMODAL_REGISTRY.register_image_input_mapper(
+    image_input_mapper_for_qwen2_vl)
+@MULTIMODAL_REGISTRY.register_input_mapper("video",
+                                           video_input_mapper_for_qwen2_vl)
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_qwen2_vl_image_tokens)
+@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
+    "video", get_max_qwen2_vl_video_tokens)
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_vl)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen2_vl)
+class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
+
+    def __init__(self,
+                 config: Qwen2VLConfig,
+                 multimodal_config: MultiModalConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
+        super().__init__()
+
+        assert not cache_config.enable_prefix_caching, \
+            "Qwen2-VL currently does not support prefix caching"
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.visual = Qwen2VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+
+            # NOTE: Qwen2-VL vision encoder does not support any
+            # quantization method now.
+            quant_config=None,
+        )
+
+        self.model = Qwen2Model(config, cache_config, quant_config)
+
+        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)
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.sampler = Sampler()
+
+    def _validate_and_reshape_mm_tensor(self,
+                                        mm_input: Union[torch.Tensor,
+                                                        List[torch.Tensor]],
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim}")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Qwen2VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None:
+            return None
+
+        pixel_values = self._validate_and_reshape_mm_tensor(
+            pixel_values, "image pixel values")
+        image_grid_thw = self._validate_and_reshape_mm_tensor(
+            image_grid_thw, "image grid_thw")
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of image pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        return Qwen2VLImageInputs(pixel_values=pixel_values,
+                                  image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Qwen2VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None:
+            return None
+
+        pixel_values_videos = self._validate_and_reshape_mm_tensor(
+            pixel_values_videos, "video pixel values")
+        video_grid_thw = self._validate_and_reshape_mm_tensor(
+            video_grid_thw, "video grid_thw")
+
+        return Qwen2VLVideoInputs(
+            pixel_values_videos=pixel_values_videos,
+            video_grid_thw=video_grid_thw,
+        )
+
+    def _process_image_input(self,
+                             image_input: Qwen2VLImageInputs) -> torch.Tensor:
+        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+        image_embeds = self.visual(pixel_values,
+                                   grid_thw=image_input["image_grid_thw"])
+        return image_embeds
+
+    def _process_video_input(self,
+                             video_input: Qwen2VLVideoInputs) -> torch.Tensor:
+        pixel_values_videos = video_input["pixel_values_videos"].type(
+            self.visual.dtype)
+        video_embeds = self.visual(pixel_values_videos,
+                                   grid_thw=video_input["video_grid_thw"])
+        return video_embeds
+
+    def _merge_multimodal_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        inputs_embeds: torch.Tensor,
+        multimodal_embeddings: torch.Tensor,
+        placeholder_token_id: int,
+    ) -> torch.Tensor:
+        mask = (input_ids == placeholder_token_id)
+        inputs_embeds[mask, :] = multimodal_embeddings
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        **kwargs: object,
+    ) -> SamplerOutput:
+        """Run forward pass for Qwen2-VL.
+
+        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 Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+        """
+
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        video_input = self._parse_and_validate_video_input(**kwargs)
+
+        if image_input is None and video_input is None:
+            inputs_embeds = None
+        else:
+            if getattr(self.config, "rope_scaling", {}).get("type",
+                                                            None) == "mrope":
+                assert positions.ndim == 2 and positions.size(0) == 3, (
+                    "multimodal section rotary embedding requires "
+                    f"(3, seq_len) positions, but got {positions.size()}")
+
+            inputs_embeds = self.model.embed_tokens(input_ids)
+
+            if image_input is not None:
+                image_embeds = self._process_image_input(image_input)
+                inputs_embeds = self._merge_multimodal_embeddings(
+                    input_ids,
+                    inputs_embeds,
+                    image_embeds,
+                    placeholder_token_id=self.config.image_token_id,
+                )
+
+            if video_input is not None:
+                video_embeds = self._process_video_input(video_input)
+                inputs_embeds = self._merge_multimodal_embeddings(
+                    input_ids,
+                    inputs_embeds,
+                    video_embeds,
+                    placeholder_token_id=self.config.video_token_id,
+                )
+
+            input_ids = None
+
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "up_proj", 1),
+            ("gate_up_proj", "gate_proj", 0),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            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:
+                if "visual" in name and "qkv.weight" in name:
+                    visual_num_heads = self.config.vision_config.num_heads
+                    visual_embed_dim = self.config.vision_config.embed_dim
+                    head_size = visual_embed_dim // visual_num_heads
+                    loaded_weight = loaded_weight.view(3, visual_num_heads,
+                                                       head_size,
+                                                       visual_embed_dim)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
+                elif "visual" in name and "qkv.bias" in name:
+                    visual_num_heads = self.config.vision_config.num_heads
+                    visual_embed_dim = self.config.vision_config.embed_dim
+                    head_size = visual_embed_dim // visual_num_heads
+                    loaded_weight = loaded_weight.view(3, visual_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1)
+                try:
+                    param = params_dict[name]
+                except KeyError:
+                    print(params_dict.keys())
+                    raise
+
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/multimodal/base.py

@@ -79,14 +79,12 @@ class MultiModalInputs(_MultiModalInputsBase):
         if len(inputs_list) == 0:
             return {}
 
-        keys = inputs_list[0].keys()
-
         item_lists: Dict[str, List[NestedTensors]] = defaultdict(list)
 
         for inputs in inputs_list:
-            if inputs.keys() != keys:
-                msg = f"Inputs do not share the same keys ({keys})"
-                raise ValueError(msg)
+            # For models that supports multiple modalities (e.g. Qwen2-VL),
+            # different modalities will return different data keys,
+            # so batch() should skip the same key check.
 
             for k, v in inputs.items():
                 item_lists[k].append(v)

vllm/sequence.py

@@ -165,6 +165,9 @@ class SequenceData(msgspec.Struct,
     # is called.
     _new_appended_tokens: List[int] = msgspec.field(default_factory=list)
 
+    # It is used to compute mrope_position_ids.
+    _mrope_position_delta: Optional[int] = None
+
     def __post_init__(self) -> None:
         assert self._prompt_token_ids.typecode == "l"
         assert self._output_token_ids.typecode == "l"
@@ -219,6 +222,14 @@ class SequenceData(msgspec.Struct,
         assert isinstance(self._output_token_ids, array)
         return self._output_token_ids
 
+    @property
+    def mrope_position_delta(self) -> Optional[int]:
+        return self._mrope_position_delta
+
+    @mrope_position_delta.setter
+    def mrope_position_delta(self, new_mrope_position_delta):
+        self._mrope_position_delta = new_mrope_position_delta
+
     def append_token_id(self, token_id: int, logprob: float) -> None:
         self._output_token_ids.append(token_id)
         self._new_appended_tokens.append(token_id)

vllm/transformers_utils/processor.py

+from typing import cast
+
+
+def get_processor(
+    processor_name: str,
+    *args,
+    trust_remote_code: bool = False,
+    **kwargs,
+):
+    """Gets a processor for the given model name via HuggingFace."""
+    # don't put this import at the top level
+    # it will call torch.cuda.device_count()
+    from transformers import AutoProcessor
+    from transformers.processing_utils import ProcessorMixin
+
+    try:
+        processor = AutoProcessor.from_pretrained(
+            processor_name,
+            *args,
+            trust_remote_code=trust_remote_code,
+            **kwargs)
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the processor. If the processor is "
+                "a custom processor not yet available in the HuggingFace "
+                "transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI.")
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+
+    return cast(ProcessorMixin, processor)

vllm/worker/model_runner.py

@@ -30,6 +30,7 @@ from vllm.lora.layers import LoRAMapping
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
 from vllm.model_executor import SamplingMetadata, SamplingMetadataCache
+from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
@@ -181,6 +182,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         def simple_reinit(self):
             self.input_tokens[0].clear()  # type: ignore
             self.input_positions[0].clear()  # type: ignore
+            self.mrope_input_positions = None  # type: ignore
             self.seq_lens[0] = 0  # type: ignore
             self.orig_seq_lens[0] = 0  # type: ignore
             self.query_lens[0] = 0  # type: ignore
@@ -206,6 +208,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             # Input tokens and positions.
             input_tokens: Optional[List[List[int]]] = None,
             input_positions: Optional[List[List[int]]] = None,
+            mrope_input_positions: Optional[List[List[List[int]]]] = None,
 
             # The sequence length (may be capped to the sliding window).
             seq_lens: Optional[List[int]] = None,
@@ -266,6 +269,8 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                         for seq_id in range(len(self.seq_ids)):
                             self.input_positions[seq_id].clear()
 
+                    self.mrope_input_positions = None
+
                     if seq_lens:
                         self.seq_lens = seq_lens
                     else:
@@ -327,6 +332,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             else:
                 self.input_tokens = input_tokens or []
                 self.input_positions = input_positions or []
+                self.mrope_input_positions = mrope_input_positions or None
                 self.seq_lens = seq_lens or []
                 self.orig_seq_lens = orig_seq_lens or []
                 self.query_lens = query_lens or []
@@ -357,6 +363,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
 
             self.input_tokens = [[] for _ in range(self.n_seqs)]
             self.input_positions = [[] for _ in range(self.n_seqs)]
+            self.mrope_input_positions = None
             self.seq_lens = [0] * self.n_seqs
             self.orig_seq_lens = [0] * self.n_seqs
             self.query_lens = [0] * self.n_seqs
@@ -493,6 +500,17 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         inter_data.query_lens[
             seq_idx] = seq_len - context_len if inter_data.is_prompt else 1
 
+        if seq_data.mrope_position_delta is not None:
+            if inter_data.mrope_input_positions is None:
+                inter_data.mrope_input_positions = [None] * inter_data.n_seqs
+
+            inter_data.mrope_input_positions[
+                seq_idx] = MRotaryEmbedding.get_next_input_positions(
+                    seq_data.mrope_position_delta,
+                    context_len,
+                    seq_len,
+                )
+
     def _compute_for_prefix_cache_hit(
             self, inter_data: InterDataForSeqGroup, seq_idx: int,
             seq_group_metadata: SequenceGroupMetadata):
@@ -636,6 +654,40 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         mm_kwargs = self.multi_modal_input_mapper(mm_data)
         inter_data.multi_modal_inputs = mm_kwargs
 
+        # special processing for mrope position deltas.
+        if self.runner.model_is_mrope:
+            image_grid_thw = mm_kwargs.get("image_grid_thw", None)
+            video_grid_thw = mm_kwargs.get("video_grid_thw", None)
+            assert image_grid_thw is not None or video_grid_thw is not None, (
+                "mrope embedding type requires multi-modal input mapper "
+                "returns 'image_grid_thw' or 'video_grid_thw'.")
+
+            hf_config = self.runner.model_config.hf_config
+
+            inter_data.mrope_input_positions = [None] * inter_data.n_seqs
+            for seq_idx in range(inter_data.n_seqs):
+                seq_data = seq_group_metadata.seq_data[
+                    inter_data.seq_ids[seq_idx]]
+                token_ids = seq_data.get_token_ids()
+
+                mrope_input_positions, mrope_position_delta = \
+                    MRotaryEmbedding.get_input_positions(
+                        token_ids,
+                        image_grid_thw=image_grid_thw,
+                        video_grid_thw=video_grid_thw,
+                        image_token_id=hf_config.image_token_id,
+                        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,
+                        spatial_merge_size=hf_config.vision_config.
+                        spatial_merge_size,
+                        context_len=inter_data.context_lens[seq_idx],
+                    )
+
+                seq_data.mrope_position_delta = mrope_position_delta
+                inter_data.mrope_input_positions[
+                    seq_idx] = mrope_input_positions
+
     def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
         """Add a sequence group to the builder."""
         seq_ids = seq_group_metadata.seq_data.keys()
@@ -684,6 +736,23 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             # prefix caching and there is no decode request.
             return self.model_input_cls()
 
+        mrope_input_positions: Optional[List[List[int]]] = None
+        if any(inter_data.mrope_input_positions is not None
+               for inter_data in self.inter_data_list):
+            mrope_input_positions = [[] for _ in range(3)]
+            for idx in range(3):
+                for inter_data in self.inter_data_list:
+                    msections = inter_data.mrope_input_positions
+                    if msections is None:
+                        for _seq_input_positions in inter_data.input_positions:
+                            mrope_input_positions[idx].extend(
+                                _seq_input_positions)
+                    else:
+                        for _seq_mrope_input_positions in msections:
+                            mrope_input_positions[idx].extend(
+                                _seq_mrope_input_positions[idx])
+            input_positions = None
+        else:
             input_positions = []
             for inter_data in self.inter_data_list:
                 for cur_input_positions in inter_data.input_positions:
@@ -724,15 +793,24 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         # Tokens and positions.
         if cuda_graph_pad_size:
             input_tokens.extend(itertools.repeat(0, cuda_graph_pad_size))
-            input_positions.extend(itertools.repeat(0, cuda_graph_pad_size))
         assert self.runner.device is not None
         input_tokens_tensor = async_tensor_h2d(input_tokens, torch.long,
                                                self.runner.device,
                                                self.runner.pin_memory)
-        input_positions_tensor = async_tensor_h2d(input_positions, torch.long,
+        if mrope_input_positions is not None:
+            for idx in range(3):
+                mrope_input_positions[idx].extend(
+                    itertools.repeat(0, cuda_graph_pad_size))
+            input_positions_tensor = async_tensor_h2d(mrope_input_positions,
+                                                      torch.long,
+                                                      self.runner.device,
+                                                      self.runner.pin_memory)
+        else:
+            input_positions.extend(itertools.repeat(0, cuda_graph_pad_size))
+            input_positions_tensor = async_tensor_h2d(input_positions,
+                                                      torch.long,
                                                       self.runner.device,
                                                       self.runner.pin_memory)
-
         # Sequence and query lengths.
         if cuda_graph_pad_size:
             seq_lens.extend(itertools.repeat(1, cuda_graph_pad_size))
@@ -1199,6 +1277,15 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
             raise RuntimeError("PromptAdapter is not enabled.")
         return self.prompt_adapter_manager.list_adapters()
 
+    @property
+    def model_is_mrope(self) -> bool:
+        """Detect if the model has "mrope" rope_scaling type.
+        mrope requires keep "rope_deltas" between prompt and decoding phases."""
+        rope_scaling = getattr(self.model_config.hf_config, "rope_scaling", {})
+        if rope_scaling is None:
+            return False
+        return rope_scaling.get("type", None) == "mrope"
+
     @torch.inference_mode()
     def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
         """Cuda graph capture a model.
@@ -1229,7 +1316,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         max_batch_size = self.max_batchsize_to_capture
         input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda()
         input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda()
-
+        if self.model_is_mrope:
+            input_positions = torch.tile(input_positions, (3, 1))
         # Prepare dummy previous_hidden_states only if needed by the model.
         # This is used by draft models such as EAGLE.
         previous_hidden_states = None
@@ -1293,7 +1381,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                         "input_ids":
                         input_tokens[:batch_size],
                         "positions":
-                        input_positions[:batch_size],
+                        input_positions[..., :batch_size],
                         "hidden_or_intermediate_states":
                         hidden_or_intermediate_states[
                             virtual_engine]  # type: ignore