Model: Support Qwen 2.5 vl (#3258)

docs/references/supported_models.md

@@ -4,7 +4,7 @@
 - Llama / Llama 2 / Llama 3 / Llama 3.1 / Llama 3.2
 - Mistral / Mixtral / Mistral NeMo / Mistral Small 3
 - Gemma / Gemma 2
-- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL
+- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL / Qwen 2.5 VL
 - DeepSeek / DeepSeek 2 / [DeepSeek 3](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3)
 - OLMoE
 - [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/)
@@ -54,7 +54,7 @@ To support a new model in SGLang, you only need to add a single file under [SGLa
 You can learn from existing model implementations and create new files for the new models.
 For most models, you should be able to find a similar model to start with (e.g., starting from Llama).
 
-## How to Support a New vision LLM
+## How to Support a New vLM
 
 To support a new vision-language model (vLM) in SGLang, there are several key components in addition to the standard LLM.
 

python/sglang/lang/chat_template.py

@@ -427,6 +427,8 @@ def match_chat_ml(model_path: str):
     if "tinyllama" in model_path:
         return get_chat_template("chatml")
     # Now the suffix for qwen2 chat model is "instruct"
+    if "qwen" in model_path and "vl" in model_path:
+        return get_chat_template("qwen2-vl")
     if "qwen" in model_path:
         if "vl" in model_path:
             return get_chat_template("qwen2-vl")
@@ -443,6 +445,12 @@ def match_chat_ml(model_path: str):
         return get_chat_template("chatml-llava")
 
 
+@register_chat_template_matching_function
+def match_chat_minicpm(model_path: str):
+    if "minicpm" in model_path:
+        return get_chat_template("minicpmv")
+
+
 @register_chat_template_matching_function
 def match_chat_yi(model_path: str):
     model_path = model_path.lower()

python/sglang/srt/configs/__init__.py

 from sglang.srt.configs.chatglm import ChatGLMConfig
 from sglang.srt.configs.dbrx import DbrxConfig
 from sglang.srt.configs.exaone import ExaoneConfig
-from sglang.srt.configs.qwen2vl import Qwen2VLConfig, Qwen2VLVisionConfig
+from sglang.srt.configs.qwen2_5_vl_config import (
+    Qwen2_5_VLConfig,
+    Qwen2_5_VLVisionConfig,
+)
 
 __all__ = [
     "ExaoneConfig",
-    "Qwen2VLConfig",
-    "Qwen2VLVisionConfig",
     "ChatGLMConfig",
     "DbrxConfig",
+    "Qwen2_5_VLConfig",
+    "Qwen2_5_VLVisionConfig",
 ]

python/sglang/srt/configs/model_config.py

@@ -403,6 +403,7 @@ def is_multimodal_model(model_architectures: List[str]):
         or "LlavaVidForCausalLM" in model_architectures
         or "MllamaForConditionalGeneration" in model_architectures
         or "Qwen2VLForConditionalGeneration" in model_architectures
+        or "Qwen2_5_VLForConditionalGeneration" in model_architectures
         or "MiniCPMV" in model_architectures
     ):
         return True

python/sglang/srt/configs/qwen2_5_vl_config.py

+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
+# All rights reserved.
+#
+# 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.
+"""Qwen2VL model configuration"""
+from typing import Dict, Iterable, List, Optional, Union
+
+import numpy as np
+from transformers import (
+    AutoImageProcessor,
+    AutoProcessor,
+    BaseImageProcessor,
+    BatchFeature,
+    PretrainedConfig,
+    ProcessorMixin,
+    TensorType,
+)
+from transformers.image_transforms import (
+    convert_to_rgb,
+    normalize,
+    rescale,
+    resize,
+    to_channel_dimension_format,
+)
+from transformers.image_utils import (
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    VideoInput,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_pil_image,
+    is_valid_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+    validate_preprocess_arguments,
+)
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.models.mllama.image_processing_mllama import is_valid_list_of_images
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+from transformers.processing_utils import ProcessingKwargs, Unpack, VideosKwargs
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+
+
+class Qwen2_5_VLVisionConfig(PretrainedConfig):
+    model_type = "qwen2_5_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=32,
+        hidden_size=3584,
+        hidden_act="silu",
+        intermediate_size=3420,
+        num_heads=16,
+        in_channels=3,
+        patch_size=14,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=4,
+        window_size=112,
+        out_hidden_size=3584,
+        fullatt_block_indexes=[7, 15, 23, 31],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+        self.window_size = window_size
+        self.fullatt_block_indexes = fullatt_block_indexes
+        self.out_hidden_size = out_hidden_size
+
+
+class Qwen2_5_VLConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2_5_VLModel`]. It is used to instantiate a
+    Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 152064):
+            Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2_5_VLModel`]
+        hidden_size (`int`, *optional*, defaults to 8192):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 29568):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 80):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 80):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        vision_config (`Dict`, *optional*):
+            The config for the visual encoder initialization.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+
+    ```python
+    >>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig
+
+    >>> # Initializing a Qwen2_5_VL style configuration
+    >>> configuration = Qwen2_5_VLConfig()
+
+    >>> # Initializing a model from the Qwen2-VL-7B style configuration
+    >>> model = Qwen2_5_VLForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2_5_vl"
+    sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
+    keys_to_ignore_at_inference = ["past_key_values"]
+    # Default tensor parallel plan for base model `Qwen2_5_VL`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+
+    def __init__(
+        self,
+        vocab_size=152064,
+        hidden_size=8192,
+        intermediate_size=29568,
+        num_hidden_layers=80,
+        num_attention_heads=64,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=1000000.0,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=80,
+        attention_dropout=0.0,
+        vision_config=None,
+        rope_scaling=None,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.rope_scaling = rope_scaling
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        # and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations
+        # one can set it to "linear"/"dynamic" etc. to have scaled RoPE
+        # TODO: @raushan update config in the hub
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            if self.rope_scaling["type"] == "mrope":
+                self.rope_scaling["type"] = "default"
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self, ignore_keys={"mrope_section"})
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+# FIXME: workaround of obsolete transformers version
+
+
+class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
+    fps: Union[List[float], float]
+
+
+class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "videos_kwargs": {"fps": 2.0},
+    }
+
+
+class Qwen2_5_VLProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
+    [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(
+        self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
+    ):
+        self.image_token = (
+            "<|image_pad|>"
+            if not hasattr(tokenizer, "image_token")
+            else tokenizer.image_token
+        )
+        self.video_token = (
+            "<|video_pad|>"
+            if not hasattr(tokenizer, "video_token")
+            else tokenizer.video_token
+        )
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Union[
+            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
+        ] = None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Qwen2_5_VLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if images is not None:
+            image_inputs = self.image_processor(
+                images=images, videos=None, **output_kwargs["images_kwargs"]
+            )
+            image_grid_thw = image_inputs["image_grid_thw"]
+        else:
+            image_inputs = {}
+            image_grid_thw = None
+
+        if videos is not None:
+            videos_inputs = self.image_processor(
+                images=None, videos=videos, **output_kwargs["images_kwargs"]
+            )
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [
+                    self.image_processor.temporal_patch_size / fps
+                ] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [
+                    self.image_processor.temporal_patch_size / tmp for tmp in fps
+                ]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
+
+        else:
+            videos_inputs = {}
+            video_grid_thw = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        if image_grid_thw is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.image_token,
+                        "<|placeholder|>"
+                        * (image_grid_thw[index].prod() // merge_length),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if video_grid_thw is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.video_token,
+                        "<|placeholder|>"
+                        * (video_grid_thw[index].prod() // merge_length),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(self, generated_outputs):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(
+            dict.fromkeys(tokenizer_input_names + image_processor_input_names)
+        )
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+class Qwen2_5_VLImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Qwen2.5-VL image processor that dynamically resizes images based on the original images.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Resampling filter to use when resizing the image.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+            Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+        min_pixels (`int`, *optional*, defaults to `56 * 56`):
+            The min pixels of the image to resize the image.
+        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
+            The max pixels of the image to resize the image.
+        patch_size (`int`, *optional*, defaults to 14):
+            The spacial patch size of the vision encoder.
+        temporal_patch_size (`int`, *optional*, defaults to 2):
+            The temporal patch size of the vision encoder.
+        merge_size (`int`, *optional*, defaults to 2):
+            The merge size of the vision encoder to llm encoder.
+    """
+
+    model_input_names = [
+        "pixel_values",
+        "image_grid_thw",
+        "pixel_values_videos",
+        "video_grid_thw",
+        "second_per_grid_ts",
+    ]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = True,
+        min_pixels: int = 56 * 56,
+        max_pixels: int = 28 * 28 * 1280,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        merge_size: int = 2,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.do_resize = do_resize
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+        self.min_pixels = min_pixels
+        self.max_pixels = max_pixels
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+        self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
+        self.do_convert_rgb = do_convert_rgb
+
+    def rescale(
+        self,
+        image: np.ndarray,
+        scale: float,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Rescale an image by a scale factor. image = image * scale.
+
+        Args:
+            image (`np.ndarray`):
+                Image to rescale.
+            scale (`float`):
+                The scaling factor to rescale pixel values by.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The rescaled image.
+        """
+        return rescale(
+            image,
+            scale=scale,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def normalize(
+        self,
+        image: np.ndarray,
+        mean: Union[float, Iterable[float]],
+        std: Union[float, Iterable[float]],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Normalize an image. image = (image - image_mean) / image_std.
+
+        Args:
+            image (`np.ndarray`):
+                Image to normalize.
+            mean (`float` or `Iterable[float]`):
+                Image mean to use for normalization.
+            std (`float` or `Iterable[float]`):
+                Image standard deviation to use for normalization.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The normalized image.
+        """
+        return normalize(
+            image,
+            mean=mean,
+            std=std,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def _preprocess(
+        self,
+        images: Union[ImageInput, VideoInput],
+        do_resize: bool = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        """
+        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+        Args:
+            images (`ImageInput`):
+                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
+            vision_info (`List[Dict]`, *optional*):
+                Optional list of dictionaries containing additional information about vision inputs.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Scale factor to use if rescaling the image.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        images = make_list_of_images(images)
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if input_data_format is None:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(images[0])
+
+        height, width = get_image_size(images[0], channel_dim=input_data_format)
+        resized_height, resized_width = height, width
+        processed_images = []
+        for image in images:
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height,
+                    width,
+                    factor=self.patch_size * self.merge_size,
+                    min_pixels=self.min_pixels,
+                    max_pixels=self.max_pixels,
+                )
+                image = resize(
+                    image,
+                    size=(resized_height, resized_width),
+                    resample=resample,
+                    input_data_format=input_data_format,
+                )
+
+            if do_rescale:
+                image = self.rescale(
+                    image, scale=rescale_factor, input_data_format=input_data_format
+                )
+
+            if do_normalize:
+                image = self.normalize(
+                    image=image,
+                    mean=image_mean,
+                    std=image_std,
+                    input_data_format=input_data_format,
+                )
+
+            image = to_channel_dimension_format(
+                image, data_format, input_channel_dim=input_data_format
+            )
+            processed_images.append(image)
+
+        patches = np.array(processed_images)
+        if data_format == ChannelDimension.LAST:
+            patches = patches.transpose(0, 3, 1, 2)
+        if patches.shape[0] % self.temporal_patch_size != 0:
+            repeats = np.repeat(
+                patches[-1][np.newaxis], self.temporal_patch_size - 1, axis=0
+            )
+            patches = np.concatenate([patches, repeats], axis=0)
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // self.temporal_patch_size
+        grid_h, grid_w = (
+            resized_height // self.patch_size,
+            resized_width // self.patch_size,
+        )
+        patches = patches.reshape(
+            grid_t,
+            self.temporal_patch_size,
+            channel,
+            grid_h // self.merge_size,
+            self.merge_size,
+            self.patch_size,
+            grid_w // self.merge_size,
+            self.merge_size,
+            self.patch_size,
+        )
+        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
+        flatten_patches = patches.reshape(
+            grid_t * grid_h * grid_w,
+            channel * self.temporal_patch_size * self.patch_size * self.patch_size,
+        )
+
+        return flatten_patches, (grid_t, grid_h, grid_w)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        videos: VideoInput = None,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            videos (`VideoInput`):
+                Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
+                passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+                the longest edge resized to keep the input aspect ratio.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = (
+            do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+        )
+
+        def make_flat_list_of_images(
+            images: Union[List[ImageInput], ImageInput],
+        ) -> ImageInput:
+            """
+            Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1.
+            If the input is a nested list of images, it is converted to a flat list of images.
+            Args:
+                images (`Union[List[ImageInput], ImageInput]`):
+                    The input image.
+            Returns:
+                list: A list of images or a 4d array of images.
+            """
+            # If the input is a nested list of images, we flatten it
+            if (
+                isinstance(images, (list, tuple))
+                and all(isinstance(images_i, (list, tuple)) for images_i in images)
+                and all(is_valid_list_of_images(images_i) for images_i in images)
+            ):
+                return [img for img_list in images for img in img_list]
+
+            if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
+                if is_pil_image(images[0]) or images[0].ndim == 3:
+                    return images
+                if images[0].ndim == 4:
+                    return [img for img_list in images for img in img_list]
+
+            if is_valid_image(images):
+                if is_pil_image(images) or images.ndim == 3:
+                    return [images]
+                if images.ndim == 4:
+                    return list(images)
+
+            raise ValueError(f"Could not make a flat list of images from {images}")
+
+        def make_batched_videos(videos) -> VideoInput:
+            """
+            Ensure that the input is a list of videos.
+            Args:
+                videos (`VideoInput`):
+                    Video or videos to turn into a list of videos.
+            Returns:
+                list: A list of videos.
+            """
+            if (
+                isinstance(videos, (list, tuple))
+                and isinstance(videos[0], (list, tuple))
+                and is_valid_image(videos[0][0])
+            ):
+                # case 1: nested batch of videos so we flatten it
+                if not is_pil_image(videos[0][0]) and videos[0][0].ndim == 4:
+                    videos = [
+                        [video for batch_list in batched_videos for video in batch_list]
+                        for batched_videos in videos
+                    ]
+                # case 2: list of videos represented as list of video frames
+                return videos
+
+            elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
+                if is_pil_image(videos[0]) or videos[0].ndim == 3:
+                    return [videos]
+                elif videos[0].ndim == 4:
+                    return [list(video) for video in videos]
+
+            elif is_valid_image(videos):
+                if is_pil_image(videos) or videos.ndim == 3:
+                    return [[videos]]
+                elif videos.ndim == 4:
+                    return [list(videos)]
+
+            raise ValueError(f"Could not make batched video from {videos}")
+
+        if images is not None:
+            images = make_flat_list_of_images(images)
+        if videos is not None:
+            videos = make_batched_videos(videos)
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        validate_preprocess_arguments(
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        if images is not None:
+            pixel_values, vision_grid_thws = [], []
+            for image in images:
+                patches, image_grid_thw = self._preprocess(
+                    image,
+                    do_resize=do_resize,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values.extend(patches)
+                vision_grid_thws.append(image_grid_thw)
+            pixel_values = np.array(pixel_values)
+            vision_grid_thws = np.array(vision_grid_thws)
+            data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
+
+        if videos is not None:
+            pixel_values, vision_grid_thws = [], []
+            for images in videos:
+                patches, video_grid_thw = self._preprocess(
+                    images,
+                    do_resize=do_resize,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values.extend(patches)
+                vision_grid_thws.append(video_grid_thw)
+            pixel_values = np.array(pixel_values)
+            vision_grid_thws = np.array(vision_grid_thws)
+            data = {
+                "pixel_values_videos": pixel_values,
+                "video_grid_thw": vision_grid_thws,
+            }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+AutoImageProcessor.register(Qwen2_5_VLConfig, Qwen2_5_VLImageProcessor)
+AutoProcessor.register(Qwen2_5_VLConfig, Qwen2_5_VLProcessor)

python/sglang/srt/configs/qwen2vl.py

-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
-# All rights reserved.
-#
-# 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.
-"""Qwen2VL model configuration"""
-
-import os
-from typing import Union
-
-from transformers import PretrainedConfig
-
-
-class Qwen2VLVisionConfig(PretrainedConfig):
-    model_type = "qwen2_vl"
-
-    def __init__(
-        self,
-        depth=32,
-        embed_dim=1280,
-        hidden_size=3584,
-        hidden_act="quick_gelu",
-        mlp_ratio=4,
-        num_heads=16,
-        in_channels=3,
-        patch_size=14,
-        spatial_merge_size=2,
-        temporal_patch_size=2,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.depth = depth
-        self.embed_dim = embed_dim
-        self.hidden_size = hidden_size
-        self.hidden_act = hidden_act
-        self.mlp_ratio = mlp_ratio
-        self.num_heads = num_heads
-        self.in_channels = in_channels
-        self.patch_size = patch_size
-        self.spatial_merge_size = spatial_merge_size
-        self.temporal_patch_size = temporal_patch_size
-
-    @classmethod
-    def from_pretrained(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(
-            pretrained_model_name_or_path, **kwargs
-        )
-
-        if config_dict.get("model_type") == "qwen2_vl":
-            config_dict = config_dict["vision_config"]
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class Qwen2VLConfig(PretrainedConfig):
-    model_type = "qwen2_vl"
-
-    def __init__(
-        self,
-        vocab_size=152064,
-        hidden_size=8192,
-        intermediate_size=29568,
-        num_hidden_layers=80,
-        num_attention_heads=64,
-        num_key_value_heads=8,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-05,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=1000000.0,
-        use_sliding_window=False,
-        sliding_window=4096,
-        max_window_layers=80,
-        attention_dropout=0.0,
-        vision_config=None,
-        rope_scaling=None,
-        **kwargs,
-    ):
-        if isinstance(vision_config, dict):
-            self.vision_config = Qwen2VLVisionConfig(**vision_config)
-        elif vision_config is None:
-            self.vision_config = Qwen2VLVisionConfig()
-
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window
-        self.max_window_layers = max_window_layers
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_dropout = attention_dropout
-        self.rope_scaling = rope_scaling
-
-        # NOTE(HandH1998): This is necessary for configuring the `rope_type`` of qwen2vl models after removing dependencies on vllm.
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            if self.rope_scaling["type"] == "mrope":
-                self.rope_scaling["type"] = "default"
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

python/sglang/srt/hf_transformers_utils.py

@@ -30,16 +30,15 @@ from transformers import (
 )
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 
-from sglang.srt.configs import ChatGLMConfig, DbrxConfig, ExaoneConfig, Qwen2VLConfig
+from sglang.srt.configs import ChatGLMConfig, DbrxConfig, ExaoneConfig, Qwen2_5_VLConfig
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     ChatGLMConfig.model_type: ChatGLMConfig,
     DbrxConfig.model_type: DbrxConfig,
     ExaoneConfig.model_type: ExaoneConfig,
-    Qwen2VLConfig.model_type: Qwen2VLConfig,
+    Qwen2_5_VLConfig.model_type: Qwen2_5_VLConfig,
 }
 
-
 for name, cls in _CONFIG_REGISTRY.items():
     with contextlib.suppress(ValueError):
         AutoConfig.register(name, cls)

python/sglang/srt/managers/image_processor.py

 # TODO: also move pad_input_ids into this module
 import asyncio
 import concurrent.futures
+import dataclasses
 import logging
 import multiprocessing as mp
 import os
@@ -8,6 +9,7 @@ from abc import ABC, abstractmethod
 from typing import List, Optional, Union
 
 import numpy as np
+import PIL
 import transformers
 from decord import VideoReader, cpu
 from PIL import Image
@@ -34,11 +36,22 @@ def init_global_processor(server_args: ServerArgs):
     )
 
 
+@dataclasses.dataclass
+class BaseImageProcessorOutput:
+    image_hashes: list[int]
+    image_sizes: list[int]
+    all_frames: [PIL.Image]
+    # input_text, with each frame of video/image represented with a image_token
+    input_text: str
+
+
 class BaseImageProcessor(ABC):
     def __init__(self, hf_config, server_args, _processor):
         self.hf_config = hf_config
         self._processor = _processor
         self.server_args = server_args
+        # FIXME: not accurate, model and image specific
+        self.NUM_TOKEN_PER_FRAME = 330
 
         self.executor = concurrent.futures.ProcessPoolExecutor(
             initializer=init_global_processor,
@@ -48,9 +61,128 @@ class BaseImageProcessor(ABC):
         )
 
     @abstractmethod
-    async def process_images_async(self, image_data, input_text, **kwargs):
+    async def process_images_async(
+        self, image_data, input_text, max_req_input_len, **kwargs
+    ):
         pass
 
+    def get_estimated_frames_list(self, image_data):
+        """
+        estimate the total frame count from all visual input
+        """
+        # Before processing inputs
+        estimated_frames_list = []
+        for image in image_data:
+            if isinstance(image, str) and image.startswith("video:"):
+                path = image[len("video:") :]
+                # Estimate frames for the video
+                vr = VideoReader(path, ctx=cpu(0))
+                num_frames = len(vr)
+            else:
+                # For images, each contributes one frame
+                num_frames = 1
+            estimated_frames_list.append(num_frames)
+
+        return estimated_frames_list
+
+    def encode_video(self, video_path, frame_count_limit=None):
+        if not os.path.exists(video_path):
+            logger.error(f"Video {video_path} does not exist")
+            return []
+
+        if frame_count_limit == 0:
+            return []
+
+        def uniform_sample(l, n):
+            gap = len(l) / n
+            idxs = [int(i * gap + gap / 2) for i in range(n)]
+            return [l[i] for i in idxs]
+
+        vr = VideoReader(video_path, ctx=cpu(0))
+        sample_fps = round(vr.get_avg_fps() / 1)  # FPS
+        frame_idx = [i for i in range(0, len(vr), sample_fps)]
+        if frame_count_limit is not None and len(frame_idx) > frame_count_limit:
+            frame_idx = uniform_sample(frame_idx, frame_count_limit)
+        frames = vr.get_batch(frame_idx).asnumpy()
+        frames = [Image.fromarray(v.astype("uint8")) for v in frames]
+        return frames
+
+    def load_images(
+        self,
+        max_req_input_len: int,
+        input_ids: list,
+        image_data,
+        image_token: str,
+    ) -> BaseImageProcessorOutput:
+        """
+        Each frame of video/image will be replaced by a single image token
+        """
+        image_hashes, image_sizes = [], []
+        all_frames = []
+        new_text_parts = []
+
+        if isinstance(input_ids, list):
+            assert len(input_ids) and isinstance(input_ids[0], int)
+            input_text = self._processor.tokenizer.decode(input_ids)
+        else:
+            input_text = input_ids
+
+        text_parts = input_text.split(image_token)
+
+        # roughly calculate the max number of frames under the max_req_input_len limit
+        def calculate_max_num_frames() -> int:
+            ret = (max_req_input_len - len(input_ids)) // self.NUM_TOKEN_PER_FRAME
+            return min(ret, 100)
+
+        MAX_NUM_FRAMES = calculate_max_num_frames()
+        estimated_frames_list = self.get_estimated_frames_list(image_data=image_data)
+        total_frame_count = sum(estimated_frames_list)
+        # a heuristic value, suggesting the maximum fraction of frames to embed from all visual inputs.
+        # e.g., 0.1 suggests that 1 frame out of 10 input frames should be used
+        scaling_factor = min(1.0, MAX_NUM_FRAMES / total_frame_count)
+
+        # Process each input with allocated frames
+        for image_index, (image, estimated_frames) in enumerate(
+            zip(image_data, estimated_frames_list)
+        ):
+            if len(all_frames) >= MAX_NUM_FRAMES:
+                frames_to_process = 0
+            else:
+                frames_to_process = max(1, int(estimated_frames * scaling_factor))
+
+            if frames_to_process == 0:
+                frames = []
+            else:
+                try:
+                    if isinstance(image, str) and image.startswith("video:"):
+                        path = image[len("video:") :]
+                        frames = self.encode_video(
+                            path, frame_count_limit=frames_to_process
+                        )
+                    else:
+                        raw_image, _size = load_image(image)
+                        frames = [raw_image]
+                    if len(frames) == 0:
+                        continue
+                except FileNotFoundError as e:
+                    print(e)
+                    return None
+                image_sizes += frames[0].size * len(frames)
+                image_hashes += [hash(image)] * len(frames)
+                all_frames += frames
+
+            new_text_parts.append(text_parts[image_index])
+            if frames_to_process != 0:
+                new_text_parts.append(image_token * len(frames))
+            assert frames_to_process == len(frames)
+
+        new_text_parts.append(text_parts[-1])
+
+        input_text = "".join(new_text_parts)
+        return BaseImageProcessorOutput(
+            image_hashes, image_sizes, all_frames, input_text
+        )
+
 
 class DummyImageProcessor(BaseImageProcessor):
     def __init__(self):
@@ -248,9 +380,9 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
             text=input_text, images=images, return_tensors="pt"
         )
         return {
-            "input_ids": result["input_ids"],
-            "pixel_values": result["pixel_values"],
-            "tgt_sizes": result["tgt_sizes"],
+            "input_ids": result.input_ids,
+            "pixel_values": result.pixel_values,
+            "tgt_sizes": result.tgt_sizes,
         }
 
     async def _process_images(self, images, input_text):
@@ -278,124 +410,20 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
     ):
         if not image_data:
             return None
-
         if not isinstance(image_data, list):
             image_data = [image_data]
 
-        image_hashes, image_sizes = [], []
-        all_frames = []
-
-        # roughly calculate the max number of frames under the max_req_input_len limit
-        def calculate_max_num_frames() -> int:
-            # Model-specific
-            NUM_TOKEN_PER_FRAME = 330
-
-            ret = (max_req_input_len - len(input_ids)) // NUM_TOKEN_PER_FRAME
-            return min(ret, 100)
-
-        MAX_NUM_FRAMES = calculate_max_num_frames()
-
-        # print(f"MAX_NUM_FRAMES: {MAX_NUM_FRAMES}")
-
-        def get_estimated_frames_list():
-            """
-            estimate the total frame count from all visual input
-            """
-            # Before processing inputs
-            estimated_frames_list = []
-            for image in image_data:
-                if isinstance(image, str) and image.startswith("video:"):
-                    path = image[len("video:") :]
-                    # Estimate frames for the video
-                    vr = VideoReader(path, ctx=cpu(0))
-                    num_frames = len(vr)
-                else:
-                    # For images, each contributes one frame
-                    num_frames = 1
-                estimated_frames_list.append(num_frames)
-
-            return estimated_frames_list
-
-        estimated_frames_list = get_estimated_frames_list()
-        total_frame_count = sum(estimated_frames_list)
-        scaling_factor = min(1.0, MAX_NUM_FRAMES / total_frame_count)
-
-        def encode_video(video_path, frame_count_limit=None):
-            if not os.path.exists(video_path):
-                logger.error(f"Video {video_path} does not exist")
-                return []
-
-            if frame_count_limit == 0:
-                return []
-
-            def uniform_sample(l, n):
-                gap = len(l) / n
-                idxs = [int(i * gap + gap / 2) for i in range(n)]
-                return [l[i] for i in idxs]
-
-            vr = VideoReader(video_path, ctx=cpu(0))
-            sample_fps = round(vr.get_avg_fps() / 1)  # FPS
-            frame_idx = [i for i in range(0, len(vr), sample_fps)]
-            if frame_count_limit is not None and len(frame_idx) > frame_count_limit:
-                frame_idx = uniform_sample(frame_idx, frame_count_limit)
-            frames = vr.get_batch(frame_idx).asnumpy()
-            frames = [Image.fromarray(v.astype("uint8")) for v in frames]
-            return frames
-
-        if isinstance(input_ids, list):
-            assert len(input_ids) and isinstance(input_ids[0], int)
-            input_text = self._processor.tokenizer.decode(input_ids)
-        else:
-            input_text = input_ids
-        # MiniCPMV requires each frame of video as a single image token
-        text_parts = input_text.split(self.IMAGE_TOKEN)
-        new_text_parts = []
-
-        # Process each input with allocated frames
-        for image_index, (image, estimated_frames) in enumerate(
-            zip(image_data, estimated_frames_list)
-        ):
-            if len(all_frames) >= MAX_NUM_FRAMES:
-                frames_to_process = 0
-            else:
-                frames_to_process = max(1, int(estimated_frames * scaling_factor))
-
-            if frames_to_process == 0:
-                frames = []
-            else:
-                try:
-                    if isinstance(image, str) and image.startswith("video:"):
-                        path = image[len("video:") :]
-                        frames = encode_video(path, frame_count_limit=frames_to_process)
-                    else:
-                        raw_image, _size = load_image(image)
-                        frames = [raw_image]
-                    if len(frames) == 0:
-                        continue
-                except FileNotFoundError as e:
-                    print(e)
-                    return None
-                image_sizes += frames[0].size * len(frames)
-                image_hashes += [hash(image)] * len(frames)
-                all_frames += frames
-
-            assert frames_to_process == len(frames)
-
-            new_text_parts.append(text_parts[image_index])
-
-            if frames_to_process != 0:
-                new_text_parts.append(self.IMAGE_TOKEN * len(frames))
-
-        new_text_parts.append(text_parts[-1])
-
-        input_text = "".join(new_text_parts)
+        base_output = self.load_images(
+            max_req_input_len, input_ids, image_data, self.IMAGE_TOKEN
+        )
+        if base_output is None:
+            return None
 
-        if len(all_frames) == 0:
+        if len(base_output.all_frames) == 0:
             return None
-        res = await self._process_images(images=all_frames, input_text=input_text)
-        pixel_values = res["pixel_values"]
-        tgt_sizes = res["tgt_sizes"]
-        input_ids = res["input_ids"]
+        res = await self._process_images(
+            images=base_output.all_frames, input_text=base_output.input_text
+        )
 
         # Collect special token ids
         tokenizer = self._processor.tokenizer
@@ -405,10 +433,10 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
             slice_start_id = [tokenizer.slice_start_id]
             slice_end_id = [tokenizer.slice_end_id]
         return {
-            "input_ids": input_ids.flatten().tolist(),
-            "pixel_values": pixel_values,
-            "tgt_sizes": tgt_sizes,
-            "image_hashes": image_hashes,
+            "input_ids": res["input_ids"].flatten().tolist(),
+            "pixel_values": res["pixel_values"],
+            "tgt_sizes": res["tgt_sizes"],
+            "image_hashes": base_output.image_hashes,
             "modalities": request_obj.modalities or ["image"],
             "im_start_id": im_start_id,
             "im_end_id": im_end_id,
@@ -536,13 +564,80 @@ class Qwen2VLImageProcessor(BaseImageProcessor):
         }
 
 
+class Qwen2_5VLImageProcessor(BaseImageProcessor):
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.IMAGE_TOKEN = "<|vision_start|><|image_pad|><|vision_end|>"
+        self.IM_START_TOKEN_ID = hf_config.vision_start_token_id
+        self.IM_END_TOKEN_ID = hf_config.vision_end_token_id
+        self.NUM_TOKEN_PER_FRAME = 770
+
+    @staticmethod
+    def _process_images_task(images, input_text):
+        result = global_processor.__call__(
+            text=input_text, images=images, return_tensors="pt"
+        )
+        return {
+            "input_ids": result.input_ids,
+            "pixel_values": result.pixel_values,
+            "image_grid_thws": result.image_grid_thw,
+        }
+
+    async def _process_images(self, images, input_text) -> dict:
+        if self.executor is not None:
+            loop = asyncio.get_event_loop()
+            return await loop.run_in_executor(
+                self.executor,
+                Qwen2_5VLImageProcessor._process_images_task,
+                images,
+                input_text,
+            )
+        else:
+            return self._process_images_task(images, input_text)
+
+    async def process_images_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_ids,
+        request_obj,
+        max_req_input_len,
+        *args,
+        **kwargs,
+    ):
+        if not image_data:
+            return None
+        if isinstance(image_data, str):
+            image_data = [image_data]
+
+        image_token = self.IMAGE_TOKEN
+        base_output = self.load_images(
+            max_req_input_len, input_ids, image_data, image_token
+        )
+
+        ret = await self._process_images(base_output.all_frames, base_output.input_text)
+
+        return {
+            "input_ids": ret["input_ids"].flatten().tolist(),
+            "pixel_values": ret["pixel_values"],
+            "image_hashes": base_output.image_hashes,
+            "modalities": request_obj.modalities or ["image"],
+            "image_grid_thws": ret["image_grid_thws"],
+            "im_start_id": self.IM_START_TOKEN_ID,
+            "im_end_id": self.IM_END_TOKEN_ID,
+        }
+
+
 def get_image_processor(
     hf_config, server_args: ServerArgs, processor
 ) -> BaseImageProcessor:
     if "MllamaForConditionalGeneration" in hf_config.architectures:
         return MllamaImageProcessor(hf_config, server_args, processor)
     elif "Qwen2VLForConditionalGeneration" in hf_config.architectures:
-        return Qwen2VLImageProcessor(hf_config, server_args, processor.image_processor)
+
+        return Qwen2VLImageProcessor(hf_config, server_args, processor)
+    elif "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures:
+        return Qwen2_5VLImageProcessor(hf_config, server_args, processor)
+
     elif "MiniCPMV" in hf_config.architectures:
         return MiniCPMVImageProcessor(hf_config, server_args, processor)
     else:

python/sglang/srt/models/qwen2_5_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."""
+import logging
+from functools import lru_cache, partial
+from typing import Iterable, List, Optional, Tuple, Type
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import AutoModel, Qwen2VLConfig
+from transformers.activations import ACT2FN
+from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm
+
+from sglang.srt.configs import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.schedule_batch import ImageInputs
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen2 import Qwen2Model
+from sglang.srt.models.qwen2_vl import Qwen2VLImageInputs, Qwen2VLVideoInputs
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen2_5_VLMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        bias: bool = True,
+        hidden_act="silu",
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.gate_proj = ColumnParallelLinear(
+            in_features, hidden_features, bias=bias, quant_config=quant_config
+        )
+        self.up_proj = ColumnParallelLinear(
+            in_features, hidden_features, bias=bias, quant_config=quant_config
+        )
+        self.down_proj = RowParallelLinear(
+            hidden_features, in_features, bias=bias, quant_config=quant_config
+        )
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x_parallel_gate, _ = self.gate_proj(x)
+        x_parallel_gate = self.act(x_parallel_gate)
+        x_parallel_up, _ = self.up_proj(x)
+        x_parallel = x_parallel_gate * x_parallel_up
+        x, _ = self.down_proj(x_parallel)
+        return x
+
+
+class Qwen2_5_VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        num_heads: int,
+        hidden_act="silu",
+        norm_layer: Type[nn.Module] = None,
+        attn_implementation: Optional[str] = "sdpa",
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = Qwen2RMSNorm(dim, eps=1e-6)
+        self.norm2 = Qwen2RMSNorm(dim, eps=1e-6)
+        if attn_implementation == "sdpa":
+            use_context_forward = False
+            use_full_precision_softmax = False
+        elif attn_implementation == "flash_attention_2":
+            use_full_precision_softmax = False
+            use_context_forward = True
+        elif attn_implementation == "eager":
+            use_full_precision_softmax = True
+            use_context_forward = False
+
+        self.attn = VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            use_qkv_parallel=False,
+            use_context_forward=use_context_forward,
+            use_full_precision_softmax=use_full_precision_softmax,
+            flatten_batch=True,
+            quant_config=quant_config,
+        )
+        self.mlp = Qwen2_5_VLMLP(
+            dim, intermediate_dim, hidden_act=hidden_act, quant_config=quant_config
+        )
+
+    def forward(
+        self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states = self.norm1(x)
+        hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
+        attn = self.attn(
+            hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
+        )
+        attn = rearrange(attn, "b s ... -> s b ...")
+        x = x + attn
+        norm2 = self.norm2(x)
+        mlp = self.mlp(norm2)
+        x = x + mlp
+        return x
+
+
+class Qwen2_5_VisionPatchEmbed(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 Qwen2_5_VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        context_dim: int,
+        spatial_merge_size: int = 2,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
+        self.mlp = nn.ModuleList(
+            [
+                ColumnParallelLinear(
+                    self.hidden_size,
+                    self.hidden_size,
+                    bias=True,
+                    quant_config=quant_config,
+                ),
+                nn.GELU(),
+                RowParallelLinear(
+                    self.hidden_size, dim, 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 Qwen2_5_VisionRotaryEmbedding(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 Qwen2_5_VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen2_5_VLVisionConfig,
+        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
+        self.spatial_merge_size = spatial_merge_size
+        self.spatial_merge_unit: int = spatial_merge_size * spatial_merge_size
+        in_chans: int = vision_config.in_chans
+        hidden_size: int = vision_config.hidden_size
+        depth: int = vision_config.depth
+        num_heads: int = vision_config.num_heads
+        self.fullatt_block_indexes = vision_config.fullatt_block_indexes
+        self.window_size = vision_config.window_size
+        self.patch_size = vision_config.patch_size
+        mlp_hidden_size: int = vision_config.intermediate_size
+        self.patch_embed = Qwen2_5_VisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_chans=in_chans,
+            embed_dim=hidden_size,
+        )
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = hidden_size // num_heads
+        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+        self.blocks = nn.ModuleList(
+            [
+                Qwen2_5_VisionBlock(
+                    dim=hidden_size,
+                    intermediate_dim=mlp_hidden_size,
+                    num_heads=num_heads,
+                    hidden_act=vision_config.hidden_act,
+                    norm_layer=norm_layer,
+                    attn_implementation="sdpa",
+                    quant_config=quant_config,
+                )
+                for _ in range(depth)
+            ]
+        )
+        self.merger = Qwen2_5_VisionPatchMerger(
+            dim=vision_config.out_hidden_size,
+            context_dim=hidden_size,
+            spatial_merge_size=spatial_merge_size,
+            quant_config=quant_config,
+        )
+
+    def get_window_index(self, grid_thw):
+        window_index: list = []
+        cu_window_seqlens: list = [0]
+        window_index_id = 0
+        vit_merger_window_size = (
+            self.window_size // self.spatial_merge_size // self.patch_size
+        )
+
+        for grid_t, grid_h, grid_w in grid_thw:
+            llm_grid_h, llm_grid_w = (
+                grid_h // self.spatial_merge_size,
+                grid_w // self.spatial_merge_size,
+            )
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
+                grid_t, llm_grid_h, llm_grid_w
+            )
+            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
+            index_padded = index_padded.reshape(
+                grid_t,
+                num_windows_h,
+                vit_merger_window_size,
+                num_windows_w,
+                vit_merger_window_size,
+            )
+            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+                grid_t,
+                num_windows_h * num_windows_w,
+                vit_merger_window_size,
+                vit_merger_window_size,
+            )
+            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+            index_padded = index_padded.reshape(-1)
+            index_new = index_padded[index_padded != -100]
+            window_index.append(index_new + window_index_id)
+            cu_seqlens_tmp = (
+                seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+            )
+            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
+        window_index = torch.cat(window_index, dim=0)
+
+        return window_index, cu_window_seqlens
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.blocks[0].mlp.gate_proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.blocks[0].mlp.gate_proj.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)
+
+        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+        cu_window_seqlens = torch.tensor(
+            cu_window_seqlens,
+            device=x.device,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+
+        seq_len, _ = x.size()
+
+        x = x.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        x = x[window_index, :, :]
+        x = x.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(
+            seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1
+        )
+        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+
+        # 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 layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+            else:
+                cu_seqlens_now = cu_window_seqlens
+            x = blk(x, cu_seqlens=cu_seqlens_now, rotary_pos_emb=rotary_pos_emb)
+
+        # adapter
+        x = self.merger(x)
+
+        reverse_indices = torch.argsort(window_index)
+        x = x[reverse_indices, :]
+
+        return x
+
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Qwen2_5_VLForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: Qwen2VLConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.visual = Qwen2_5_VisionTransformer(
+            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, 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)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+    def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
+        processor = cached_get_processor(self.config._name_or_path)
+        grid_t, grid_h, grid_w = image_grid_thw
+        num_image_tokens = (
+            grid_t
+            * grid_h
+            * grid_w
+            // processor.image_processor.merge_size
+            // processor.image_processor.merge_size
+        )
+        return num_image_tokens
+
+    def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
+        new_input_ids = []
+        last_idx = 0
+        image_idx = -1
+        image_inputs.image_offsets = []
+
+        # Get all special token IDs
+        im_start_id = image_inputs.im_start_id
+        im_end_id = image_inputs.im_end_id
+
+        # Find all start and end positions for both types
+        start_indices = [i for i, x in enumerate(input_ids) if x == im_start_id]
+        end_indices = [i for i, x in enumerate(input_ids) if x == im_end_id]
+
+        if len(start_indices) != len(end_indices):
+            return input_ids
+        # Process each region (both image and slice)
+        for start_idx, end_idx in zip(start_indices, end_indices):
+            # Add non-image tokens before this region
+            new_input_ids.extend(input_ids[last_idx : start_idx + 1])
+
+            is_image_start = input_ids[start_idx] == im_start_id
+
+            if is_image_start:
+                image_inputs.image_offsets += [start_idx]
+                image_idx += 1
+
+            num_tokens = end_idx - start_idx - 1  # exclude start and end tokens
+
+            # Generate pad_ids
+            pad_values = [image_inputs.pad_values[image_idx]]
+
+            pad_ids = pad_values * ((num_tokens + len(pad_values)) // len(pad_values))
+            pad_ids = pad_ids[:num_tokens]
+
+            # Add pad_ids
+            new_input_ids.extend(pad_ids)
+
+            # Update last_idx to after end token
+            last_idx = end_idx
+
+        # Add remaining tokens after last region
+        new_input_ids.extend(input_ids[last_idx:])
+        assert len(input_ids) == len(new_input_ids)
+        return new_input_ids
+
+    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 forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        """Run forward pass for Qwen2_5-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,).
+                (Use input_metadata.mrope_positions to replace it)
+        """
+        if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
+            positions = forward_batch.mrope_positions
+
+        image_inputs = None
+        if forward_batch.image_inputs is not None:
+            image_inputs = [
+                img for img in forward_batch.image_inputs if img is not None
+            ]
+
+        if (
+            forward_batch.forward_mode.is_decode()
+            or image_inputs is None
+            or len(image_inputs) == 0
+        ):
+            inputs_embeds = self.model.embed_tokens(input_ids)
+        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()}"
+                )
+
+            # Clamp input ids. This is because the input_ids for the image tokens are
+            # filled with the hash values of the image for the prefix matching in the radix attention.
+            # There values are useless because their embeddings will be replaced by vision embeddings anyway.
+            input_ids.clamp_(min=0, max=self.config.vocab_size - 1)
+            # [B, s, hidden_size]
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
+            prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
+            for i, image in enumerate(forward_batch.image_inputs):
+                if image is None:
+                    continue
+                start_idx = extend_start_loc_cpu[i]
+                prefix_len = prefix_lens_cpu[i]
+
+                pixel_values = image.pixel_values.clone().detach().requires_grad_(False)
+                image_grid_thws = torch.tensor(
+                    np.array(image.image_grid_thws), device="cuda"
+                )
+                image_offsets = image.image_offsets
+                image_input = Qwen2VLImageInputs(
+                    pixel_values=pixel_values, image_grid_thw=image_grid_thws
+                )
+                image_embeds = self._process_image_input(image_input)
+
+                image_embeds_offset = 0
+                for idx, image_offset in enumerate(image_offsets):
+                    if image_offset < prefix_len:
+                        continue
+                    num_image_tokens = self.calculate_num_image_tokens(
+                        image_grid_thws[idx]
+                    )
+
+                    left_idx = start_idx + (image_offset - prefix_len)
+                    right_idx = left_idx + num_image_tokens
+
+                    tp_size = get_tensor_model_parallel_world_size()
+
+                    hidden_size = image_embeds.shape[-1]
+
+                    if hidden_size % tp_size != 0:
+                        padding_size = tp_size - (hidden_size % tp_size)
+                        image_embeds = F.pad(image_embeds, (0, padding_size))
+                        inputs_embeds = F.pad(inputs_embeds, (0, padding_size))
+
+                    hidden_chunk_size = image_embeds.shape[-1] // tp_size
+                    rank = get_tensor_model_parallel_rank()
+                    start_dim = rank * hidden_chunk_size
+                    end_dim = (rank + 1) * hidden_chunk_size
+                    inputs_embeds[left_idx:right_idx, ..., start_dim:end_dim] = (
+                        image_embeds[
+                            image_embeds_offset : image_embeds_offset
+                            + num_image_tokens,
+                            ...,
+                            start_dim:end_dim,
+                        ]
+                    )
+                    image_embeds_offset += num_image_tokens
+
+        input_ids = None
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            forward_batch=forward_batch,
+            input_embeds=inputs_embeds,
+        )
+
+        if not get_embedding:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            return self.pooler(hidden_states, forward_batch)
+
+    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
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "visual" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                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.hidden_size
+                    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.hidden_size
+                    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)
+
+                if "visual" in name:
+                    # adapt to VisionAttention
+                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+                try:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    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)
+
+
+EntryClass = [Qwen2_5_VLForConditionalGeneration]
+AutoModel.register(Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration)

python/sglang/srt/models/qwen2_vl.py

@@ -31,8 +31,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
+from transformers import Qwen2VLConfig
+from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
 
-from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig
 from sglang.srt.hf_transformers_utils import get_processor
 from sglang.srt.layers.activation import QuickGELU
 from sglang.srt.layers.attention.vision import VisionAttention

test/srt/test_vision_openai_server.py

@@ -252,6 +252,18 @@ class TestOpenAIVisionServer(unittest.TestCase):
         print("-" * 30)
 
         # Add assertions to validate the video response
+        assert "iPod" in video_response or "device" in video_response, video_response
+        assert (
+            "man" in video_response
+            or "person" in video_response
+            or "individual" in video_response
+        ), video_response
+        assert (
+            "present" in video_response
+            or "examine" in video_response
+            or "display" in video_response
+        )
+        assert "black" in video_response or "dark" in video_response
         self.assertIsNotNone(video_response)
         self.assertGreater(len(video_response), 0)
 
@@ -366,6 +378,30 @@ class TestQWen2VLServer(TestOpenAIVisionServer):
         cls.base_url += "/v1"
 
 
+class TestQWen2_5_VLServer(TestOpenAIVisionServer):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "Qwen/Qwen2.5-VL-7B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            api_key=cls.api_key,
+            other_args=[
+                "--chat-template",
+                "qwen2-vl",
+                # FIXME: workaround to chunked prefill within image embeds
+                "--chunked-prefill-size",
+                "10000",
+                "--mem-fraction-static",
+                "0.4",
+            ],
+        )
+        cls.base_url += "/v1"
+
+
 class TestQWen2VLServerContextLengthIssue(unittest.TestCase):
     @classmethod
     def setUpClass(cls):