[Model] Support Skywork-R1V (#15397)

Signed-off-by: jiacai.liu <932997367@qq.com>
Co-authored-by: jiacai.liu <932997367@qq.com>

docs/source/models/supported_models.md

@@ -921,6 +921,13 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
   * ✅︎
   * ✅︎
+- * `SkyworkR1VChatModel`
+  * Skywork-R1V-38B
+  * T + I
+  * `Skywork/Skywork-R1V-38B`
+  *
+  * ✅︎
+  * ✅︎
 - * `UltravoxModel`
   * Ultravox
   * T + A<sup>E+</sup>

examples/offline_inference/vision_language.py

@@ -804,6 +804,41 @@ def run_qwen2_5_vl(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# SkyworkR1V
+def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "Skywork/Skywork-R1V-38B"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=4096,
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    messages = [[{
+        'role': 'user',
+        'content': f"<image>\n{question}"
+    }] for question in questions]
+    prompts = tokenizer.apply_chat_template(messages,
+                                            tokenize=False,
+                                            add_generation_prompt=True)
+
+    # Stop tokens for SkyworkR1V
+    # https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/conversation.py
+    stop_tokens = ["<｜end▁of▁sentence｜>", "<|endoftext|>"]
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+        stop_token_ids=stop_token_ids,
+    )
+
+
 model_example_map = {
     "aria": run_aria,
     "blip-2": run_blip2,
@@ -834,6 +869,7 @@ model_example_map = {
     "qwen_vl": run_qwen_vl,
     "qwen2_vl": run_qwen2_vl,
     "qwen2_5_vl": run_qwen2_5_vl,
+    "skywork_chat": run_skyworkr1v,
 }
 
 

tests/models/decoder_only/vision_language/test_models.py

@@ -474,6 +474,20 @@ VLM_TEST_SETTINGS = {
         vllm_output_post_proc=model_utils.qwen_vllm_to_hf_output,
         prompt_path_encoder=model_utils.qwen_prompt_path_encoder,
     ),
+    "skywork_r1v": VLMTestInfo(
+        models=["Skywork/Skywork-R1V-38B"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<｜begin▁of▁sentence｜><｜User｜>\n{img_prompt}<｜Assistant｜><think>\n", # noqa: E501
+        single_image_prompts=IMAGE_ASSETS.prompts({
+            "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
+            "cherry_blossom": "<image>\nWhat is the season?",
+        }),
+        multi_image_prompt="<image>\n<image>\nDescribe the two images in short.",  # noqa: E501
+        max_model_len=4096,
+        use_tokenizer_eos=True,
+        patch_hf_runner=model_utils.skyworkr1v_patch_hf_runner,
+        marks=[large_gpu_mark(min_gb=80)],
+    ),
     ### Tensor parallel / multi-gpu broadcast tests
     "chameleon-broadcast": VLMTestInfo(
         models=["facebook/chameleon-7b"],

tests/models/decoder_only/vision_language/vlm_utils/model_utils.py

@@ -376,6 +376,63 @@ def h2ovl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     return hf_model
 
 
+def skyworkr1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patches and returns an instance of the HfRunner to use for SkyworkR1V."""
+
+    class SkyworkR1VProcessor:
+        """A simple processor for SkyworkR1V."""
+
+        def __init__(self, hf_runner: HfRunner):
+            self.num_image_token = hf_runner.model.num_image_token
+            self.tokenizer = hf_runner.tokenizer
+
+            self.config = AutoConfig.from_pretrained(hf_runner.model_name,
+                                                     trust_remote_code=True)
+            self.vision_config = self.config.vision_config
+            self.use_thumbnail = self.config.use_thumbnail
+            self.min_num = self.config.min_dynamic_patch
+            self.max_num = self.config.max_dynamic_patch
+            self.image_size = self.vision_config.image_size
+
+        def __call__(self, text: str, images: Union[Image, list[Image]],
+                     **kwargs):
+            from vllm.model_executor.models.skyworkr1v import (
+                IMG_CONTEXT, IMG_END, IMG_START,
+                image_to_pixel_values_skyworkr1v)
+            images = [images] if isinstance(images, Image) else images
+            pixel_values = [
+                image_to_pixel_values_skyworkr1v(
+                    image,
+                    input_size=self.image_size,
+                    min_num=self.min_num,
+                    max_num=self.max_num,
+                    use_thumbnail=self.use_thumbnail,
+                ) for image in images
+            ]
+            num_patches_list = [
+                pixel_value.shape[0] for pixel_value in pixel_values
+            ]
+            pixel_values = torch.cat(pixel_values, dim=0)
+            for num_patches in num_patches_list:
+                context_tokens = IMG_CONTEXT * self.num_image_token \
+                    * num_patches
+                image_tokens = IMG_START + context_tokens + IMG_END
+                text = text.replace('<image>', image_tokens, 1)
+            prompt = self.tokenizer(text, return_tensors="pt")
+            prompt.update({"pixel_values": pixel_values})
+            return prompt
+
+    img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids(
+        "<IMG_CONTEXT>")
+    hf_model.model.img_context_token_id = img_context_token_id
+    hf_model.processor = SkyworkR1VProcessor(hf_model)
+    hf_model.model.get_output_embeddings = lambda: \
+        hf_model.model.language_model.get_output_embeddings()
+    hf_model.model.generate = types.MethodType(_internvl_generate,
+                                               hf_model.model)
+    return hf_model
+
+
 def internvl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     """Patches and returns an instance of the HfRunner to use for InternVL."""
 

tests/models/multimodal/processing/test_common.py

@@ -262,22 +262,23 @@ def _test_processing_correctness_mistral(
     "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
     "meta-llama/Llama-3.2-11B-Vision-Instruct",
     "TIGER-Lab/Mantis-8B-siglip-llama3",
-    "mistralai/Pixtral-12B-2409",
-    "mistral-community/pixtral-12b",
     "openbmb/MiniCPM-Llama3-V-2_5",
     "openbmb/MiniCPM-o-2_6",
     "openbmb/MiniCPM-V-2_6",
     "allenai/Molmo-7B-D-0924",
     "allenai/Molmo-7B-O-0924",
     "nvidia/NVLM-D-72B",
+    "google/paligemma-3b-mix-224",
+    "google/paligemma2-3b-ft-docci-448",
+    "mistralai/Pixtral-12B-2409",
+    "mistral-community/pixtral-12b",
     "Qwen/Qwen-VL-Chat",
     "Qwen/Qwen2-VL-2B-Instruct",
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
+    "Skywork/Skywork-R1V-38B",
     "fixie-ai/ultravox-v0_5-llama-3_2-1b",
     "openai/whisper-large-v3",
-    "google/paligemma-3b-mix-224",
-    "google/paligemma2-3b-ft-docci-448",
 ])
 @pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
 @pytest.mark.parametrize("num_batches", [32])

tests/models/registry.py

@@ -294,6 +294,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
     "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
                                                           min_transformers_version="4.49"),  # noqa: E501
+    "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B"),
     "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b",  # noqa: E501
                                      trust_remote_code=True),
     # [Encoder-decoder]

vllm/entrypoints/chat_utils.py

@@ -496,7 +496,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                 return self._cached_token_str(self._tokenizer,
                                               hf_config.image_token_index)
             if model_type in ("chameleon", "deepseek_vl_v2", "internvl_chat",
-                              "NVLM_D", "h2ovl_chat"):
+                              "skywork_chat", "NVLM_D", "h2ovl_chat"):
                 return "<image>"
             if model_type == "mllama":
                 return "<|image|>"

vllm/model_executor/models/registry.py

@@ -190,6 +190,7 @@ _MULTIMODAL_MODELS = {
     # [Encoder-decoder]
     "Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"),  # noqa: E501
     "MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"),  # noqa: E501
+    "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
     "WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"),  # noqa: E501
 }
 

vllm/model_executor/models/skyworkr1v.py

+# SPDX-License-Identifier: Apache-2.0
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py
+# --------------------------------------------------------
+# SkyworkR1V
+# Copyright (c) 2025 Skywork
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from abc import ABC, abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
+from functools import cached_property
+from typing import Literal, Optional, Set, Tuple, TypedDict, TypeVar, Union
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from PIL import Image
+from transformers import BatchEncoding, PretrainedConfig, TensorType
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.models.intern_vit import (InternVisionModel,
+                                                   InternVisionPatchModel)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
+                                    NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+from .vision import scatter_patch_features, select_patch_features
+
+IMG_START = '<img>'
+IMG_END = '</img>'
+IMG_CONTEXT = '<IMG_CONTEXT>'
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+class SkyworkR1VImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values_flat: torch.Tensor
+    """
+    Shape:
+    `(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
+    """
+
+    num_patches: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+    embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    A boolean mask indicating which image embeddings correspond
+    to patch tokens.
+
+    Shape: `(batch_size * num_images, num_embeds)`
+    """
+
+
+class SkyworkR1VImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """ 
+    A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
+    or a list of tensors of shape `(total_image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+SkyworkR1VImageInputs = Union[SkyworkR1VImagePixelInputs,
+                              SkyworkR1VImageEmbeddingInputs]
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
+def build_transform(input_size: int):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    return T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size),
+                 interpolation=T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    *,
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def resolve_skyworkr1v_min_max_num(
+    *,
+    min_dynamic_patch: int,
+    max_dynamic_patch: int,
+    dynamic_image_size: bool,
+    use_thumbnail: bool,
+) -> tuple[int, int]:
+    min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
+
+    if use_thumbnail and max_dynamic_patch != 1:
+        max_dynamic_patch += 1
+
+    return min_dynamic_patch, max_dynamic_patch
+
+
+def get_skyworkr1v_target_ratios(
+    min_num: int,
+    max_num: int,
+) -> list[tuple[int, int]]:
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1)
+                     for j in range(1, n + 1) if min_num <= i * j <= max_num}
+    return sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+
+def calculate_skyworkr1v_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height
+
+
+def dynamic_preprocess_skyworkr1v(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> list[Image.Image]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    blocks, target_width, target_height = calculate_skyworkr1v_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B
+def image_to_pixel_values_skyworkr1v(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_skyworkr1v_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess_skyworkr1v(
+        image,
+        target_ratios=target_ratios,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values
+
+
+class BaseSkyworkR1VProcessor(ABC):
+    """
+    This model doesn't define its own HF processor,
+    so we implement our own one here.
+
+    The code to insert image tokens is based on:
+    https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py#L252
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tokenizer = tokenizer
+
+        image_size: int = config.vision_config.image_size
+        patch_size: int = config.vision_config.patch_size
+
+        if min_dynamic_patch is None:
+            min_dynamic_patch = config.min_dynamic_patch
+        assert isinstance(min_dynamic_patch, int)
+
+        if max_dynamic_patch is None:
+            max_dynamic_patch = config.max_dynamic_patch
+        assert isinstance(max_dynamic_patch, int)
+
+        if dynamic_image_size is None:
+            dynamic_image_size = config.dynamic_image_size
+        assert isinstance(dynamic_image_size, bool)
+
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.image_size = image_size
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail: bool = config.use_thumbnail
+
+    @property
+    @abstractmethod
+    def image_token_id(self) -> int:
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        raise NotImplementedError
+
+    def resolve_min_max_num(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> tuple[int, int]:
+        min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
+                             is None else min_dynamic_patch)
+        max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
+                             is None else max_dynamic_patch)
+        dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
+                              is None else dynamic_image_size)
+        use_thumbnail = (self.use_thumbnail
+                         if use_thumbnail is None else use_thumbnail)
+
+        return resolve_skyworkr1v_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+    def resolve_target_ratios(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> list[tuple[int, int]]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+        return get_skyworkr1v_target_ratios(min_num, max_num)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        target_ratios = self.resolve_target_ratios(
+            use_thumbnail=False,  # Applied in calculate_targets
+        )
+
+        num_patches, _, _ = calculate_skyworkr1v_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            image_size=self.image_size,
+            target_ratios=target_ratios,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_skyworkr1v(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+            ) for image in images
+        ]
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> Mapping[str, NestedTensors]:
+        if text is None:
+            text = []
+        if not isinstance(text, list):
+            text = [text]
+        if images is None:
+            images = []
+        if not isinstance(images, list):
+            images = [images]
+
+        if len(images) == 0:
+            image_inputs = {}
+        else:
+            pixel_values_lst = self._images_to_pixel_values_lst(
+                images,
+                min_dynamic_patch=min_dynamic_patch,
+                max_dynamic_patch=max_dynamic_patch,
+                dynamic_image_size=dynamic_image_size,
+            )
+            image_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat":
+                torch.cat(pixel_values_lst),
+                "image_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst]),
+            }
+
+            tokenizer = self.tokenizer
+            image_token_id = self.image_token_id
+
+            embed_is_patch = list[torch.Tensor]()
+
+            for pixel_values in pixel_values_lst:
+                num_patches = pixel_values.shape[0]
+                feature_size = num_patches * self.num_image_token
+
+                image_repl = self.get_image_repl(feature_size, num_patches)
+                feature_tokens = tokenizer.encode(image_repl.features,
+                                                  add_special_tokens=False)
+
+                text = [t.replace('<image>', image_repl.full, 1) for t in text]
+                embed_is_patch.append(
+                    torch.tensor(feature_tokens) == image_token_id)
+
+            image_inputs["embed_is_patch"] = embed_is_patch
+
+        text_inputs = self.tokenizer(text)
+
+        return {
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+        }
+
+
+class SkyworkR1VProcessor(BaseSkyworkR1VProcessor):
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_CONTEXT]
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails(full=repl_full, features=repl_features)
+
+
+class BaseSkyworkR1VProcessingInfo(BaseProcessingInfo):
+
+    @abstractmethod
+    def get_hf_processor(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        **kwargs: object,
+    ) -> BaseSkyworkR1VProcessor:
+        raise NotImplementedError
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {"image": self.get_max_image_tokens()}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[BaseSkyworkR1VProcessor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+        )
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            processor=None,
+        )
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        base_size = processor.image_size
+        target_ratios = processor.resolve_target_ratios()
+
+        largest_feature_size, largest_feature_pinpoint = 0, None
+        for wr, hr in target_ratios:
+            width, height = base_size * wr, base_size * hr
+
+            feat_size = self.get_num_image_tokens(
+                image_width=width,
+                image_height=height,
+                processor=processor,
+            )
+            if feat_size > largest_feature_size:
+                largest_feature_size = feat_size
+                largest_feature_pinpoint = ImageSize(width=width,
+                                                     height=height)
+
+        if largest_feature_size == 0 or largest_feature_pinpoint is None:
+            raise ValueError("Cannot have a largest feature size of 0!")
+
+        return largest_feature_pinpoint
+
+
+_I = TypeVar("_I", bound=BaseSkyworkR1VProcessingInfo)
+
+
+class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+        return ProcessorInputs(
+            prompt_text="<image>" * num_images,
+            mm_data=mm_data,
+        )
+
+
+class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[_I]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+        )
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_token_id = hf_processor.image_token_id
+
+        # Since there may be extra tokens in the feature placeholders,
+        # we need to pass the image token ID to the model to select the
+        # tokens to merge from the vision encoder outputs
+        processed_outputs["image_token_id"] = torch.tensor(image_token_id)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
+        num_images = len(image_num_patches)
+
+        return dict(
+            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_num_patches),
+            image_num_patches=MultiModalFieldConfig.batched("image"),
+            embed_is_patch=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+            image_token_id=MultiModalFieldConfig.shared("image", num_images),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # TODO: Use image size information in dictionary embedding inputs
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        def get_replacement_skyworkr1v(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            num_patches = image_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_image_repl(feature_size, num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_skyworkr1v,
+            )
+        ]
+
+
+class SkyworkR1VProcessingInfo(BaseSkyworkR1VProcessingInfo):
+
+    def get_hf_processor(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        **kwargs: object,
+    ) -> SkyworkR1VProcessor:
+        if min_dynamic_patch is not None:
+            kwargs["min_dynamic_patch"] = min_dynamic_patch
+        if max_dynamic_patch is not None:
+            kwargs["max_dynamic_patch"] = max_dynamic_patch
+        if dynamic_image_size is not None:
+            kwargs["dynamic_image_size"] = dynamic_image_size
+
+        return self.ctx.init_processor(
+            SkyworkR1VProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    SkyworkR1VMultiModalProcessor,
+    info=SkyworkR1VProcessingInfo,
+    dummy_inputs=SkyworkR1VDummyInputsBuilder)
+class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self._patch_quant_config(config, quant_config)
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+
+        self.llm_arch_name = config.text_config.architectures[0]
+        self.is_mono = self.llm_arch_name == 'SkyworkLM2VEForCausalLM'
+        self.vision_model = self._init_vision_model(
+            config,
+            quant_config=quant_config,
+            is_mono=self.is_mono,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.mlp1 = self._init_mlp1(config)
+
+        self.img_context_token_id = None
+        self.visual_token_mask = None
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _patch_quant_config(self, config: PretrainedConfig,
+                            quant_config: QuantizationConfig):
+        # the awq models from OpenGVLab missing `modules_to_not_convert`
+        # patch the quant_config to add `modules_to_not_convert` back
+        if isinstance(quant_config, AWQConfig):
+            text_config = config.text_config
+            llm_quant_config = getattr(text_config, "quantization_config",
+                                       None)
+            if (not quant_config.modules_to_not_convert) and \
+                (llm_quant_config is not None):
+                quant_config.modules_to_not_convert.append("vision_model")
+
+    @cached_property
+    def sampler(self):
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+
+        return get_sampler()
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        is_mono: bool,
+        prefix: str,
+    ):
+        if not is_mono:
+            vision_feature_layer = config.select_layer
+            if vision_feature_layer < 0:
+                num_hidden_layers = config.vision_config.num_hidden_layers \
+                    + vision_feature_layer + 1
+            else:
+                num_hidden_layers = vision_feature_layer + 1
+
+            return InternVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                num_hidden_layers_override=num_hidden_layers,
+                prefix=prefix,
+            )
+        else:
+            return InternVisionPatchModel(config.vision_config)
+
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        return nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
+            ReplicatedLinear(vit_hidden_size *
+                             int(1 / self.downsample_ratio)**2,
+                             llm_hidden_size,
+                             return_bias=False),
+            nn.GELU(),
+            ReplicatedLinear(llm_hidden_size,
+                             llm_hidden_size,
+                             return_bias=False),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            pass
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        vit_embeds = self.vision_model(pixel_values=pixel_values)
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1]**0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds,
+                                        scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
+                                        vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape)
+
+            if actual_dims != expected_dims:
+                expected_expr = str(expected_dims)
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f" per patch is {expected_expr}. "
+                    f"You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[SkyworkR1VImageInputs]:
+        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
+        image_num_patches = kwargs.pop("image_num_patches", None)
+        embed_is_patch = kwargs.pop("embed_is_patch", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return SkyworkR1VImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        image_token_id = kwargs["image_token_id"]
+        assert isinstance(image_token_id, torch.Tensor)
+        self.img_context_token_id = image_token_id.flatten().unique().item()
+
+        if pixel_values_flat is not None:
+            if not isinstance(pixel_values_flat, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat)}")
+
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(image_num_patches)}")
+
+            if not isinstance(embed_is_patch, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of embed_is_patch. "
+                                 f"Got type: {type(embed_is_patch)}")
+
+            pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
+            image_num_patches = flatten_bn(image_num_patches, concat=True)
+            embed_is_patch = flatten_bn(embed_is_patch)
+
+            return SkyworkR1VImagePixelInputs(
+                type="pixel_values",
+                pixel_values_flat=self._validate_pixel_values(
+                    pixel_values_flat),
+                num_patches=image_num_patches,
+                embed_is_patch=embed_is_patch,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self,
+        image_input: SkyworkR1VImageInputs,
+    ) -> Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor, ...]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+
+        image_embeds = self.extract_feature(image_input["pixel_values_flat"])
+
+        num_patches = image_input["num_patches"]
+
+        # Only one image in the current batch
+        if len(num_patches) == 1:
+            return image_embeds.view(
+                -1, self.config.text_config.hidden_size).unsqueeze(0)
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in num_patches
+        ]
+        return image_embeds.split(image_feature_sizes)
+
+    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
+        if self.is_mono:
+            self.visual_token_mask = (
+                input_ids == self.img_context_token_id).reshape(-1, 1)
+        else:
+            self.visual_token_mask = None
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+
+        image_features = self._process_image_input(image_input)
+
+        if image_input["type"] != "pixel_values":
+            return image_features
+
+        return scatter_patch_features(
+            image_features,
+            image_input["embed_is_patch"],
+        )
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            assert self.img_context_token_id is not None
+            self._set_visual_token_mask(input_ids)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                select_patch_features(multimodal_embeddings),
+                self.img_context_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[SamplerOutput, IntermediateTensors]:
+
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        forward_kwargs = {
+            "input_ids": input_ids,
+            "positions": positions,
+            "intermediate_tensors": intermediate_tensors,
+            "inputs_embeds": inputs_embeds,
+        }
+
+        # Only required if the model is mono-architecture
+        if self.visual_token_mask is not None:
+            forward_kwargs.update(
+                {"visual_token_mask": self.visual_token_mask})
+            self.visual_token_mask = None
+
+        hidden_states = self.language_model.model(**forward_kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        skip_prefixes = [
+            "action_embed", "temporal_embed", "track_embed",
+            "track_embed_decoder", "box_token", "cg_criterion", "cg_model",
+            "loc_encoder", "loc_decoder", "sam", "temporal_token",
+            "track_token"
+        ]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights)

vllm/transformers_utils/config.py

@@ -37,8 +37,8 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, NVLM_D_Config,
                                              Olmo2Config, RWConfig,
-                                             SolarConfig, Telechat2Config,
-                                             UltravoxConfig)
+                                             SkyworkR1VChatConfig, SolarConfig,
+                                             Telechat2Config, UltravoxConfig)
 # yapf: enable
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.utils import resolve_obj_by_qualname
@@ -76,6 +76,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "NVLM_D": NVLM_D_Config,
     "olmo2": Olmo2Config,
     "solar": SolarConfig,
+    "skywork_chat": SkyworkR1VChatConfig,
     "telechat": Telechat2Config,
     "ultravox": UltravoxConfig,
     **_CONFIG_REGISTRY_OVERRIDE_HF

vllm/transformers_utils/configs/__init__.py

@@ -20,6 +20,7 @@ from vllm.transformers_utils.configs.mpt import MPTConfig
 from vllm.transformers_utils.configs.nemotron import NemotronConfig
 from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
 from vllm.transformers_utils.configs.olmo2 import Olmo2Config
+from vllm.transformers_utils.configs.skyworkr1v import SkyworkR1VChatConfig
 from vllm.transformers_utils.configs.solar import SolarConfig
 from vllm.transformers_utils.configs.telechat2 import Telechat2Config
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
@@ -42,6 +43,7 @@ __all__ = [
     "NemotronConfig",
     "NVLM_D_Config",
     "Olmo2Config",
+    "SkyworkR1VChatConfig",
     "SolarConfig",
     "Telechat2Config",
     "UltravoxConfig",

vllm/transformers_utils/configs/skyworkr1v.py

+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/configuration_skywork_chat.py
+# --------------------------------------------------------
+# SkyworkR1V
+# Copyright (c) 2025 Skywork
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from transformers.configuration_utils import PretrainedConfig
+
+
+class SkyworkR1VChatConfig(PretrainedConfig):
+    model_type = 'internvl_chat'
+    is_composition = True
+
+    def __init__(self,
+                 vision_config=None,
+                 llm_config=None,
+                 use_backbone_lora=0,
+                 use_llm_lora=0,
+                 select_layer=-1,
+                 force_image_size=None,
+                 downsample_ratio=0.5,
+                 template=None,
+                 dynamic_image_size=False,
+                 use_thumbnail=False,
+                 ps_version='v1',
+                 min_dynamic_patch=1,
+                 max_dynamic_patch=6,
+                 **kwargs):
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {}
+
+        if llm_config is None:
+            llm_config = {}
+
+        self.vision_config = PretrainedConfig(**vision_config)
+        self.text_config = PretrainedConfig(**llm_config)
+
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch