[VLM] Qwen2.5-VL

Author: ywang96

docs/source/models/supported_models.md

@@ -846,6 +846,13 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
   * ✅︎
   * ✅︎
+- * `Qwen2_5_VLForConditionalGeneration`
+  * Qwen2.5-VL
+  * T + I<sup>E+</sup> + V<sup>E+</sup>
+  * `Qwen/Qwen2.5-VL-3B-Instruct`, `Qwen/Qwen2.5-VL-72B-Instruct`, etc.
+  *
+  * ✅︎
+  * ✅︎
 - * `UltravoxModel`
   * Ultravox
   * T + A<sup>E+</sup>
@@ -880,6 +887,10 @@ The chat template for Pixtral-HF is incorrect (see [discussion](https://huggingf
 A corrected version is available at <gh-file:examples/template_pixtral_hf.jinja>.
 :::
 
+:::{note}
+To use Qwen2.5-VL series models, you have to install Huggingface `transformers` library from source via `pip install git+https://github.com/huggingface/transformers`.
+:::
+
 ### Pooling Models
 
 See [this page](pooling-models) for more information on how to use pooling models.

examples/offline_inference/vision_language.py

@@ -531,6 +531,36 @@ def run_qwen2_vl(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# Qwen2.5-VL
+def run_qwen2_5_vl(question: str, modality: str):
+
+    model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 1280 * 28 * 28,
+            "fps": 1,
+        },
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+    )
+
+    if modality == "image":
+        placeholder = "<|image_pad|>"
+    elif modality == "video":
+        placeholder = "<|video_pad|>"
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 model_example_map = {
     "aria": run_aria,
     "blip-2": run_blip2,
@@ -557,6 +587,7 @@ def run_qwen2_vl(question: str, modality: str):
     "pixtral_hf": run_pixtral_hf,
     "qwen_vl": run_qwen_vl,
     "qwen2_vl": run_qwen2_vl,
+    "qwen2_5_vl": run_qwen2_5_vl,
 }
 
 

examples/offline_inference/vision_language_multi_image.py

@@ -392,6 +392,63 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
     )
 
 
+def load_qwen2_5_vl(question, image_urls: List[str]) -> ModelRequestData:
+    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/Qwen2.5-VL-3B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=32768 if process_vision_info is None else 4096,
+        max_num_seqs=5,
+        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_video_sample_fps=False)
+
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=stop_token_ids,
+        image_data=image_data,
+        chat_template=None,
+    )
+
+
 model_example_map = {
     "aria": load_aria,
     "deepseek_vl_v2": load_deepseek_vl2,
@@ -404,6 +461,7 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
     "pixtral_hf": load_pixtral_hf,
     "qwen_vl_chat": load_qwen_vl_chat,
     "qwen2_vl": load_qwen2_vl,
+    "qwen2_5_vl": load_qwen2_5_vl,
 }
 
 

tests/models/decoder_only/vision_language/test_models.py

@@ -121,6 +121,8 @@
                else ("half", "float")),
         marks=[pytest.mark.core_model],
     ),
+    # TODO(ywang96): Move Qwen2-VL out of core models in favor of Qwen2.5-VL
+    # once we upgraded to transformers>=4.49.0.
     "qwen2_vl": VLMTestInfo(
         models=["Qwen/Qwen2-VL-2B-Instruct"],
         test_type=(
@@ -138,6 +140,26 @@
         image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
+    "qwen2_5_vl": VLMTestInfo(
+        models=["Qwen/Qwen2.5-VL-3B-Instruct"],
+        test_type=(
+            VLMTestType.IMAGE,
+            VLMTestType.MULTI_IMAGE,
+            VLMTestType.VIDEO
+        ),
+        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
+        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
+        max_model_len=4096,
+        max_num_seqs=2,
+        auto_cls=AutoModelForVision2Seq,
+        vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
+        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        marks=[pytest.mark.skipif(
+                TRANSFORMERS_VERSION < "4.49.0",
+                reason="HF model requires transformers>=4.49.0",
+            ), pytest.mark.core_model, pytest.mark.cpu_model],
+    ),
     #### Extended model tests
     "aria": VLMTestInfo(
         models=["rhymes-ai/Aria"],

tests/models/multimodal/processing/test_common.py

@@ -161,6 +161,7 @@ def _test_processing_correctness(
     "nvidia/NVLM-D-72B",
     "Qwen/Qwen-VL-Chat",
     "Qwen/Qwen2-VL-2B-Instruct",
+    "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
     "fixie-ai/ultravox-v0_3",
 ])

tests/models/registry.py

@@ -264,6 +264,8 @@ def check_available_online(
                                        trust_remote_code=True),
     "Qwen2AudioForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-Audio-7B-Instruct"),  # noqa: E501
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
+    "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
+                                                          min_transformers_version="4.49"),  # noqa: E501
     "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_3",
                                      trust_remote_code=True),
     # [Encoder-decoder]

vllm/entrypoints/chat_utils.py

@@ -410,7 +410,7 @@ def _placeholder_str(self, modality: ModalityStr,
                 return "<image>"
             if model_type == "mllama":
                 return "<|image|>"
-            if model_type == "qwen2_vl":
+            if model_type in ("qwen2_vl", "qwen2_5_vl"):
                 return "<|vision_start|><|image_pad|><|vision_end|>"
             if model_type == "molmo":
                 return ""
@@ -430,7 +430,7 @@ def _placeholder_str(self, modality: ModalityStr,
                 return "(<audio>./</audio>)"
             raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "video":
-            if model_type == "qwen2_vl":
+            if model_type in ("qwen2_vl", "qwen2_5_vl"):
                 return "<|vision_start|><|video_pad|><|vision_end|>"
             if model_type in ("minicpmo", "minicpmv"):
                 return "(<video>./</video>)"

vllm/model_executor/layers/rotary_embedding.py

@@ -27,6 +27,7 @@
 
 import torch
 import torch.nn as nn
+from transformers import PretrainedConfig
 
 from vllm.model_executor.custom_op import CustomOp
 
@@ -772,8 +773,12 @@ def __init__(
         dtype: torch.dtype,
         mrope_section: Optional[List[int]] = None,
     ) -> None:
-        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
-                         is_neox_style, dtype)
+        # In Qwen2.5-VL, the maximum index value is related to the duration of
+        # the input video. We enlarge max_position_embeddings to 4 times to get
+        # a larger the cos and sin cache.
+        self.cache_max_position_num = max_position_embeddings * 4
+        super().__init__(head_size, rotary_dim, self.cache_max_position_num,
+                         base, is_neox_style, dtype)
 
         self.mrope_section = mrope_section
         if self.mrope_section:
@@ -831,49 +836,47 @@ def forward(
     @staticmethod
     def get_input_positions(
         input_tokens: List[int],
+        hf_config: PretrainedConfig,
         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,
+        second_per_grid_ts: Optional[List[float]] = None,
         context_len: int = 0,
         seq_len: Optional[int] = None,
     ) -> Tuple[List[List[int]], int]:
         """Get mrope input positions and delta value."""
 
         llm_positions, mrope_position_delta = \
             MRotaryEmbedding.get_input_positions_tensor(
-                input_tokens,
-                image_grid_thw,
-                video_grid_thw,
-                image_token_id,
-                video_token_id,
-                vision_start_token_id,
-                vision_end_token_id,
-                spatial_merge_size,
-                context_len,
-                seq_len,
+                input_tokens=input_tokens,
+                hf_config=hf_config,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                second_per_grid_ts=second_per_grid_ts,
+                context_len=context_len,
+                seq_len=seq_len,
             )
 
         return llm_positions.tolist(), mrope_position_delta
 
     @staticmethod
     def get_input_positions_tensor(
         input_tokens: List[int],
+        hf_config: PretrainedConfig,
         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,
+        second_per_grid_ts: Optional[List[float]] = None,
         context_len: int = 0,
         seq_len: Optional[int] = None,
     ) -> Tuple[torch.Tensor, int]:
         """Get mrope input positions and delta value."""
 
+        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
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(hf_config.vision_config,
+                                    "tokens_per_second", 1.0)
+
         if isinstance(image_grid_thw, torch.Tensor):
             image_grid_thw = image_grid_thw.tolist()
         if isinstance(video_grid_thw, torch.Tensor):
@@ -892,6 +895,7 @@ def get_input_positions_tensor(
 
         image_index, video_index = 0, 0
         for _ in range(image_nums + video_nums):
+            video_second_per_grid_t = 0.0
             if image_token_id in input_tokens and remain_images > 0:
                 ed_image = input_tokens.index(image_token_id, st)
             else:
@@ -915,9 +919,13 @@ def get_input_positions_tensor(
                     video_grid_thw[video_index][1],
                     video_grid_thw[video_index][2],
                 )
+                video_second_per_grid_t = 1.0
+                if second_per_grid_ts is not None:
+                    video_second_per_grid_t = second_per_grid_ts[video_index]
                 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
@@ -927,8 +935,10 @@ def get_input_positions_tensor(
             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()
+            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).long().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(