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

Co-authored-by: Roger Wang <[email protected]>
Co-authored-by: DarkLight1337 <[email protected]>
Signed-off-by: Alvant <[email protected]>

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 @@ def run_qwen_vl(question):
     "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 @@
 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

@@ -1,9 +1,14 @@
 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

@@ -773,7 +773,7 @@ class LoadConfig:
         ignore_patterns: The list of patterns to ignore when loading the model.
             Default to "original/**/*" to avoid repeated loading of llama's 
             checkpoints.
-            
+
     """
 
     load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
@@ -1733,8 +1733,11 @@ def _get_and_verify_max_len(
                     "with rope_scaling. Please raise an issue so we can "
                     "investigate.")
 
-            assert "factor" in rope_scaling
-            scaling_factor = rope_scaling["factor"]
+            if rope_type == "mrope":
+                scaling_factor = 1
+            else:
+                assert "factor" in rope_scaling
+                scaling_factor = rope_scaling["factor"]
             if rope_type == "yarn":
                 derived_max_model_len = rope_scaling[
                     "original_max_position_embeddings"]

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 @@ def _placeholder_str(self, modality: ModalityStr,
                                               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}")