[Model][VLM] Support R-4B Model (#23246)

Signed-off-by: yannqi <yannqi@qq.com>
Signed-off-by: 杨奇(yann qi) <51905299+yannqi@users.noreply.github.com>
Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: yannqiyang <yannqiyang@tencent.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/models/supported_models.md

@@ -652,6 +652,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Qwen2VLForConditionalGeneration` | QVQ, Qwen2-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen2_5_VLForConditionalGeneration` | Qwen2.5-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen2.5-VL-3B-Instruct`, `Qwen/Qwen2.5-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-7B` | | ✅︎ | ✅︎ |
+| `RForConditionalGeneration` | R-VL-4B | T + I<sup>E+</sup> | `YannQi/R-4B` | | ✅︎ | ✅︎ |
 | `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ | ✅︎ |
 | `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | | ✅︎ |
 | `Step3VLForConditionalGeneration` | Step3-VL | T + I<sup>+</sup> | `stepfun-ai/step3` | | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -1436,6 +1436,28 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
     )
 
 
+# R-4B
+def run_r_vl(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+    model_name = "YannQi/R-4B"
+
+    prompts = [
+        f"<|im_start|>user <image>\n{question}<|im_end|><|im_start|>assistant\n"
+        for question in questions
+    ]
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=16384,
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # SkyworkR1V
 def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1622,6 +1644,7 @@ model_example_map = {
     "qwen2_vl": run_qwen2_vl,
     "qwen2_5_vl": run_qwen2_5_vl,
     "qwen2_5_omni": run_qwen2_5_omni,
+    "rvl": run_r_vl,
     "skywork_chat": run_skyworkr1v,
     "smolvlm": run_smolvlm,
     "step3": run_step3,

examples/offline_inference/vision_language_multi_image.py

@@ -992,6 +992,39 @@ def load_qwen2_5_vl(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_r_vl(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "YannQi/R-4B"
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=16384,
+        max_num_seqs=16,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                *placeholders,
+                {"type": "text", "text": question},
+            ],
+        }
+    ]
+
+    processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+    prompt = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_smolvlm(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
 
@@ -1193,6 +1226,7 @@ model_example_map = {
     "qwen_vl_chat": load_qwen_vl_chat,
     "qwen2_vl": load_qwen2_vl,
     "qwen2_5_vl": load_qwen2_5_vl,
+    "rvl": load_r_vl,
     "smolvlm": load_smolvlm,
     "step3": load_step3,
     "tarsier": load_tarsier,

tests/models/multimodal/processing/test_common.py

@@ -316,6 +316,7 @@ def _test_processing_correctness_one(
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
     "Qwen/Qwen2.5-Omni-3B",
+    "YannQi/R-4B",
     "Skywork/Skywork-R1V-38B",
     "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
     "stepfun-ai/step3",

tests/models/registry.py

@@ -489,6 +489,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                           max_model_len=4096),
     "Qwen2_5OmniModel": _HfExamplesInfo("Qwen/Qwen2.5-Omni-3B"),
     "Qwen2_5OmniForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-Omni-7B-AWQ"),  # noqa: E501
+    "RForConditionalGeneration": _HfExamplesInfo("YannQi/R-4B",
+                                                 trust_remote_code=True),
     "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B",
                                            trust_remote_code=True),
     "SmolVLMForConditionalGeneration": _HfExamplesInfo("HuggingFaceTB/SmolVLM2-2.2B-Instruct",  # noqa: E501

vllm/model_executor/models/registry.py

@@ -217,6 +217,7 @@ _MULTIMODAL_MODELS = {
     "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),
     "SmolVLMForConditionalGeneration": ("smolvlm","SmolVLMForConditionalGeneration"),  # noqa: E501
     "KeyeForConditionalGeneration": ("keye", "KeyeForConditionalGeneration"),
+    "RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
     "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
     "Llama_Nemotron_Nano_VL": ("nemotron_vl", "LlamaNemotronVLChatModel"),
     "LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"),

vllm/model_executor/models/rvl.py

@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Mapping
+
+import torch
+import torch.nn as nn
+from transformers.activations import GELUActivation
+
+from vllm.config import VllmConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict
+
+from .llava_next import (LlavaDummyInputsBuilder, LlavaNextMultiModalProcessor,
+                         LlavaNextProcessingInfo)
+from .llava_onevision import LlavaOnevisionForConditionalGeneration
+from .utils import WeightsMapper
+
+
+class RVLProcessingInfo(LlavaNextProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(**kwargs)
+
+
+class RVLDummyInputsBuilder(LlavaDummyInputsBuilder[RVLProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        image_token = "<image>"
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = (
+            self.info.get_image_size_with_most_features())
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+        }
+
+
+class RVLMultiModalProjector(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(config.vision_config.hidden_size,
+                                     eps=1e-06)
+        self.linear_1 = nn.Linear(
+            config.vision_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True,
+        )
+        self.act = GELUActivation()
+        self.linear_2 = nn.Linear(
+            config.text_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True,
+        )
+
+    def forward(self, image_feature: torch.Tensor) -> torch.Tensor:
+        image_feature = self.pre_norm(image_feature)
+        hidden_states = self.linear_1(image_feature)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+
+        return hidden_states
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    LlavaNextMultiModalProcessor,
+    info=RVLProcessingInfo,
+    dummy_inputs=RVLDummyInputsBuilder,
+)
+class RForConditionalGeneration(LlavaOnevisionForConditionalGeneration):
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers
+            # v4.52
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "model.image_newline": "image_newline",
+            "lm_head.": "language_model.lm_head.",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        config = vllm_config.model_config.hf_config
+        self.multi_modal_projector = RVLMultiModalProjector(config)