[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/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,
+                                             SkyworkR1VChatConfig, SolarConfig,
-                                             UltravoxConfig)
+                                             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

@@ -0,0 +1,53 @@
+# 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