[Model] Add PaddleOCR-VL Model Support (#27758)

Signed-off-by: zhangyue <zhangyue66@baidu.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Signed-off-by: zhangyue66 <zhangyue66@baidu.com>
Co-authored-by: Roger Wang <hey@rogerw.io>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -675,6 +675,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ |
 | `Ovis` | Ovis2, Ovis1.6 | T + I<sup>+</sup> | `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc. | | ✅︎ |
 | `Ovis2_5` | Ovis2.5 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.5-9B`, etc. | | |
+| `PaddleOCRVLForConditionalGeneration` | Paddle-OCR | T + I<sup>+</sup> | `PaddlePaddle/PaddleOCR-VL`, etc. | | |
 | `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | | ✅︎ |
 | `Phi3VForCausalLM` | Phi-3-Vision, Phi-3.5-Vision | T + I<sup>E+</sup> | `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc. | | ✅︎ |
 | `Phi4MMForCausalLM` | Phi-4-multimodal | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct`, etc. | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -1242,6 +1242,32 @@ def run_ovis2_5(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# PaddleOCR-VL
+def run_paddleocr_vl(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "PaddlePaddle/PaddleOCR-VL"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=2,
+        limit_mm_per_prompt={modality: 1},
+        trust_remote_code=True,
+    )
+
+    placeholder = "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>"
+    prompts = [
+        (f"<|begin_of_sentence|>User: {question}{placeholder}\nAssistant: ")
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # PaliGemma
 def run_paligemma(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1817,6 +1843,7 @@ model_example_map = {
     "NVLM_D": run_nvlm_d,
     "ovis": run_ovis,
     "ovis2_5": run_ovis2_5,
+    "paddleocr_vl": run_paddleocr_vl,
     "paligemma": run_paligemma,
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,

examples/offline_inference/vision_language_multi_image.py

@@ -801,6 +801,27 @@ def load_ovis2_5(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_paddleocr_vl(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "PaddlePaddle/PaddleOCR-VL"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=8192,
+        max_num_seqs=2,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>" * len(image_urls)
+    prompt = f"<|begin_of_sentence|>User: {question}{placeholders}\nAssistant: "
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "mistral-community/pixtral-12b"
 
@@ -1312,6 +1333,7 @@ model_example_map = {
     "NVLM_D": load_nvlm_d,
     "ovis": load_ovis,
     "ovis2_5": load_ovis2_5,
+    "paddleocr_vl": load_paddleocr_vl,
     "phi3_v": load_phi3v,
     "phi4_mm": load_phi4mm,
     "phi4_multimodal": load_phi4_multimodal,

tests/models/registry.py

@@ -712,6 +712,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         },
     ),
     "Ovis2_5": _HfExamplesInfo("AIDC-AI/Ovis2.5-2B", trust_remote_code=True),
+    "PaddleOCRVLForConditionalGeneration": _HfExamplesInfo(
+        "PaddlePaddle/PaddleOCR-VL",
+        trust_remote_code=True,
+    ),
     "PaliGemmaForConditionalGeneration": _HfExamplesInfo(
         "google/paligemma-3b-mix-224",
         extras={"v2": "google/paligemma2-3b-ft-docci-448"},

vllm/model_executor/models/ernie45.py

@@ -23,12 +23,22 @@
 # limitations under the License.
 """Inference-only Erine model compatible with HuggingFace weights."""
 
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.model_executor.models.llama import LlamaForCausalLM
 
 from .utils import PPMissingLayer
 
 
+@support_torch_compile(
+    # set dynamic_arg_dims to support mrope
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    }
+)
 class Ernie4_5ForCausalLM(LlamaForCausalLM):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)

vllm/model_executor/models/paddleocr_vl.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 PaddlePaddle Authors. 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.
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Annotated, Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import BatchFeature, PretrainedConfig
+from transformers.activations import GELUActivation
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPooling,
+)
+from transformers.utils import torch_int
+
+from vllm.attention.backends.registry import _Backend
+from vllm.attention.layer import (
+    check_upstream_fa_availability,
+    maybe_get_vit_flash_attn_backend,
+)
+from vllm.attention.ops.vit_attn_wrappers import (
+    vit_flash_attn_wrapper,
+    vit_xformers_attn_wrapper,
+)
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding.common import (
+    dispatch_rotary_emb_function,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargs,
+)
+from vllm.multimodal.parse import (
+    ImageProcessorItems,
+    ImageSize,
+    MultiModalDataItems,
+)
+from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .ernie45 import Ernie4_5ForCausalLM
+from .interfaces import MultiModalEmbeddings, SupportsMRoPE, SupportsMultiModal
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    WeightsMapper,
+    is_pp_missing_parameter,
+    maybe_prefix,
+)
+from .vision import get_vit_attn_backend
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 28 * 28 * 130,
+    max_pixels: int = 28 * 28 * 1280,
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+
+    if height < factor:
+        width = round((width * factor) / height)
+        height = factor
+
+    if width < factor:
+        height = round((height * factor) / width)
+        width = factor
+
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            f"absolute aspect ratio must be smaller than 200, "
+            f"got {max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = math.floor(height / beta / factor) * factor
+        w_bar = math.floor(width / beta / factor) * factor
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    x1, x2 = x[..., ::2], x[..., 1::2]
+    return rearrange(torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2)
+
+
+def apply_rotary_emb_torch(
+    x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
+) -> torch.Tensor:
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos = repeat(
+        cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    sin = repeat(
+        sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
+            x[..., ro_dim:],
+        ],
+        dim=-1,
+    )
+
+
+def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
+    rotary_emb_function = dispatch_rotary_emb_function(default=apply_rotary_emb_torch)
+    t_ = t.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    output = rotary_emb_function(t_, cos, sin).type_as(t)
+    return output
+
+
+class PaddleOCRVLProcessingInfo(BaseProcessingInfo):
+    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)
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self):
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor,
+    ) -> int:
+        if image_processor is None:
+            image_processor = self.get_image_processor()
+
+        do_resize = True
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                min_pixels=image_processor.min_pixels,
+                max_pixels=image_processor.max_pixels,
+            )
+            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width, height=image_height)
+
+        grid_t = 1
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+
+        num_patches = grid_t * grid_h * grid_w
+        num_image_tokens = num_patches // (merge_size**2)
+
+        return num_image_tokens
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        hf_config = self.get_hf_config()
+        image_size = hf_config.vision_config.image_size
+        return ImageSize(height=image_size, width=image_size)
+
+
+class PaddleOCRVLDummyInputsBuilder(BaseDummyInputsBuilder[PaddleOCRVLProcessingInfo]):
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        max_image_size = self.info.get_image_size_with_most_features()
+        image_overrides = mm_options.get("image") if mm_options else None
+
+        return {
+            "image": self._get_dummy_images(
+                width=max_image_size.width,
+                height=max_image_size.height,
+                num_images=num_images,
+                overrides=image_overrides,
+            )
+        }
+
+
+class PaddleOCRVLMultiModalProcessor(
+    BaseMultiModalProcessor[PaddleOCRVLProcessingInfo]
+):
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            processed_outputs = self.info.ctx.call_hf_processor(
+                self.info.get_hf_processor(**mm_kwargs),
+                dict(text=prompt, **mm_data),
+                dict(**mm_kwargs, **tok_kwargs),
+            )
+            num_patches_per_image = processed_outputs["image_grid_thw"].prod(-1)
+            processed_outputs["pixel_values"] = processed_outputs["pixel_values"].split(
+                num_patches_per_image.tolist()
+            )
+        else:
+            tokenizer = self.info.get_tokenizer()
+            processed_outputs = tokenizer(
+                prompt, add_special_tokens=True, return_tensors="pt"
+            )
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            image_grid_thw=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
+        hf_config = self.info.get_hf_config()
+        image_token_id = hf_config.image_token_id
+
+        def get_replacement(item_idx: int, image_processor):
+            images = mm_items.get_items("image", ImageProcessorItems)
+
+            image_size = images.get_image_size(item_idx)
+            num_image_tokens = self.info.get_num_image_tokens(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                image_processor=image_processor,
+            )
+
+            return [image_token_id] * num_image_tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=partial(get_replacement, image_processor=image_processor),
+            ),
+        ]
+
+
+class Projector(nn.Module):
+    def __init__(
+        self,
+        text_config: PretrainedConfig,
+        vision_config: PretrainedConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.merge_kernel_size = (2, 2)
+
+        self.hidden_size = (
+            self.vision_config.hidden_size
+            * self.merge_kernel_size[0]
+            * self.merge_kernel_size[1]
+        )
+
+        self.pre_norm = torch.nn.LayerNorm(self.vision_config.hidden_size, eps=1e-05)
+        self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
+        self.act = GELUActivation()
+        self.linear_2 = nn.Linear(
+            self.hidden_size, self.text_config.hidden_size, bias=True
+        )
+
+    def forward(
+        self,
+        image_features: torch.Tensor,
+        image_grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        m1, m2 = self.merge_kernel_size
+        if isinstance(image_features, (list, tuple)):
+            processed_features = list()
+            for image_feature, image_grid in zip(image_features, image_grid_thw):
+                image_feature = self.pre_norm(image_feature)
+                t, h, w = image_grid
+
+                image_feature = rearrange(
+                    image_feature,
+                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
+                    t=t,
+                    h=h // m1,
+                    p1=m1,
+                    w=w // m2,
+                    p2=m2,
+                )
+                hidden_states = self.linear_1(image_feature)
+                hidden_states = self.act(hidden_states)
+                hidden_states = self.linear_2(hidden_states)
+                processed_features.append(hidden_states)
+
+            return processed_features
+
+        dims = image_features.shape[:-1]
+        dim = image_features.shape[-1]
+        image_features = image_features.view(np.prod(dims), dim)
+        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+
+        return hidden_states.view(*dims, -1)
+
+
+class PaddleOCRImagePixelInputs(TensorSchema):
+    type: Literal["pixel_values"]
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("bn", "p", 3, "patch_size", "patch_size", dynamic_dims={"p"}),
+    ]
+    image_grid_thw: Annotated[
+        torch.Tensor,
+        TensorShape("bn", 3),
+    ]
+
+
+class SiglipVisionEmbeddings(nn.Module):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.cache_position_embedding = dict()
+        self.cache_position_count = dict()
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)
+
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def interpolate_pos_encoding(
+        self,
+        embeddings: torch.Tensor,
+        height: int,
+        width: int,
+        is_after_patchify: bool = False,
+    ) -> torch.Tensor:
+        num_positions = self.position_embedding.weight.shape[0]
+
+        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+        dim = embeddings.shape[-1]
+
+        if is_after_patchify:
+            new_height = height
+            new_width = width
+        else:
+            new_height = height // self.patch_size
+            new_width = width // self.patch_size
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(
+            1, sqrt_num_positions, sqrt_num_positions, dim
+        )
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def fetch_position_embedding_lfu_cache(
+        self, embeddings: torch.Tensor, h: int, w: int, max_cache: int = 20
+    ):
+        grid = (h, w)
+        if grid in self.cache_position_embedding:
+            self.cache_position_count[grid] += 1
+            return self.cache_position_embedding[grid]
+
+        if len(self.cache_position_embedding) >= max_cache:
+            min_hit_grid = min(
+                self.cache_position_count,
+                key=self.cache_position_count.get,
+            )
+            self.cache_position_count.pop(min_hit_grid)
+            self.cache_position_embedding.pop(min_hit_grid)
+
+        position_embedding = self.interpolate_pos_encoding(embeddings, h, w, True)
+        self.cache_position_count[grid] = 1
+        self.cache_position_embedding[grid] = position_embedding
+        return position_embedding
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        position_ids: torch.Tensor | None = None,
+        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
+        | None = None,
+        interpolate_pos_encoding=False,
+    ) -> torch.Tensor:
+        if pixel_values.dim() == 4:
+            pixel_values = pixel_values.unsqueeze(0)
+        if pixel_values.dim() == 5:
+            if position_ids is None:
+                raise ValueError(
+                    "position_ids cannot be None when pixel_values.dim() is 5."
+                )
+            (
+                batch_size,
+                squence_len,
+                channel,
+                height,
+                width,
+            ) = pixel_values.shape
+            target_dtype = self.patch_embedding.weight.dtype
+            pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
+            patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
+            embeddings = patch_embeds.flatten(-2).squeeze(-1)
+
+            if interpolate_pos_encoding and image_grid_thw is not None:
+                start = 0
+                tmp_embeddings = list()
+                for image_grid in image_grid_thw:
+                    t, h, w = image_grid
+                    end = start + t * h * w
+                    image_embeddings = embeddings[start:end, :]
+                    position_embedding = (
+                        self.interpolate_pos_encoding(image_embeddings, h, w, True)
+                        .squeeze(0)
+                        .repeat(t, 1)
+                    )
+                    image_embeddings = image_embeddings + position_embedding
+                    tmp_embeddings.append(image_embeddings)
+                    start = end
+                embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0)
+            else:
+                embeddings = embeddings + self.packing_position_embedding(position_ids)
+            return embeddings
+        else:
+            raise ValueError(
+                "Unsupported pixel_values dimension:"
+                f" {pixel_values.dim()}. Expected 4 or 5."
+            )
+
+
+def all_gather_interleave(local_tensor: torch.Tensor, hidden_size: int, tp_size: int):
+    """All-gather the input tensor interleavely across model parallel group."""
+    import torch.distributed as dist
+
+    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
+    dist.all_gather(
+        gathered_tensors, local_tensor, group=parallel_state.get_tp_group().device_group
+    )
+
+    gathered_tensors_split = [
+        torch.split(tensor, hidden_size // tp_size, -1) for tensor in gathered_tensors
+    ]
+    ordered_tensors = [
+        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
+    ]
+    result_tensor = torch.cat(ordered_tensors, dim=-1)
+    return result_tensor
+
+
+class SiglipAttention(nn.Module):
+    """SigLIP vision attention adapted from Qwen2.5-VisionAttention."""
+
+    def __init__(
+        self,
+        *,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_backend: _Backend = _Backend.TORCH_SDPA,
+        attn_backend_override: _Backend | None = None,
+        use_upstream_fa: bool = False,
+    ) -> None:
+        super().__init__()
+
+        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads
+        )
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, self.tp_size
+        )
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            input_size=projection_size,
+            output_size=embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        self.attn_backend = attn_backend
+        self.use_upstream_fa = use_upstream_fa
+        self.attn_backend, self.flash_attn_varlen_func = (
+            maybe_get_vit_flash_attn_backend(
+                self.attn_backend,
+                self.use_upstream_fa,
+                attn_backend_override=attn_backend_override,
+            )
+        )
+        self.is_flash_attn_backend = self.attn_backend in {
+            _Backend.FLASH_ATTN,
+            _Backend.ROCM_AITER_FA,
+        }
+
+    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        seq_len, bs, _ = qkv.shape
+        if self.tp_size > 1:
+            qkv = all_gather_interleave(qkv, self.qkv_proj.hidden_size, self.tp_size)
+
+        q, k, v = qkv.chunk(3, dim=2)
+
+        if self.tp_size > 1:
+            splitter = partial(
+                dist_utils.split_tensor_along_last_dim, num_partitions=self.tp_size
+            )
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+            v = splitter(v)[self.tp_rank]
+
+        new_shape = (
+            seq_len,
+            bs,
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+        q, k, v = (x.view(*new_shape) for x in (q, k, v))
+        return q, k, v
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        *,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None,
+        max_seqlen: torch.Tensor | None,
+        seqlens: torch.Tensor | None,
+    ) -> torch.Tensor:
+        batch_size, _, _ = hidden_states.shape
+
+        x = rearrange(hidden_states, "b s d -> s b d")
+        x, _ = self.qkv_proj(x)
+        q, k, v = self.split_qkv(x)
+        q, k, v = (rearrange(t, "s b h d -> b s h d") for t in (q, k, v))
+
+        if rotary_pos_emb is not None:
+            qk_concat = torch.cat([q, k], dim=0)
+            qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
+            q, k = torch.chunk(qk_rotated, 2, dim=0)
+
+        if self.is_flash_attn_backend:
+            if max_seqlen is None:
+                raise ValueError("Flash attention backend requires max_seqlen.")
+            context_layer = vit_flash_attn_wrapper(
+                q,
+                k,
+                v,
+                cu_seqlens,
+                max_seqlen,
+                batch_size,
+                self.attn_backend == _Backend.ROCM_AITER_FA,
+                self.use_upstream_fa,
+            )
+        elif self.attn_backend == _Backend.TORCH_SDPA:
+            outputs = []
+            for i in range(1, len(cu_seqlens)):
+                start_idx = cu_seqlens[i - 1]
+                end_idx = cu_seqlens[i]
+                q_i = q[:, start_idx:end_idx]
+                k_i = k[:, start_idx:end_idx]
+                v_i = v[:, start_idx:end_idx]
+                q_i, k_i, v_i = (
+                    rearrange(tensor, "b s h d -> b h s d")
+                    for tensor in (q_i, k_i, v_i)
+                )
+                output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0)
+                output_i = rearrange(output_i, "b h s d -> b s h d")
+                outputs.append(output_i)
+            context_layer = torch.cat(outputs, dim=1)
+            context_layer = rearrange(
+                context_layer, "b s h d -> s b (h d)"
+            ).contiguous()
+        elif self.attn_backend == _Backend.XFORMERS:
+            if seqlens is None:
+                raise ValueError("xFormers attention backend requires seqlens tensor.")
+            context_layer = vit_xformers_attn_wrapper(q, k, v, seqlens)
+        else:
+            raise RuntimeError(
+                f"PaddleOCR-VL does not support {self.attn_backend} backend now."
+            )
+
+        output, _ = self.out_proj(context_layer)
+        output = rearrange(output, "s b d -> b s d")
+        return output
+
+
+class SigLIPRotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.rope_init()
+
+    def rope_init(self):
+        inv_freq = 1.0 / (
+            self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim)
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(
+            seqlen,
+            device=self.inv_freq.device,
+            dtype=self.inv_freq.dtype,
+        )
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class SiglipMLP(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        # Special handling for BNB and torchao quantization
+        if quant_config and quant_config.get_name() in ["bitsandbytes", "torchao"]:
+            quantizable = True
+        else:
+            # For other quantization, we require the hidden size to be a
+            # multiple of 64
+            quantizable = (
+                config.hidden_size % 64 == 0 and config.intermediate_size % 64 == 0
+            )
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            quant_config=quant_config if quantizable else None,
+            prefix=f"{prefix}.fc1",
+        )
+        self.fc2 = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            quant_config=quant_config if quantizable else None,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class SiglipEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        *,
+        attn_backend: _Backend = _Backend.TORCH_SDPA,
+        attn_backend_override: _Backend | None = None,
+        use_upstream_fa: bool = False,
+    ):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.self_attn = SiglipAttention(
+            embed_dim=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            projection_size=config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            attn_backend=attn_backend,
+            attn_backend_override=attn_backend_override,
+            use_upstream_fa=use_upstream_fa,
+        )
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        *,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None,
+        max_seqlen: torch.Tensor | None,
+        seqlens: torch.Tensor | None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            max_seqlen=max_seqlen,
+            seqlens=seqlens,
+        )
+
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class SiglipEncoder(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_backend_override: _Backend | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        num_heads = config.num_attention_heads
+        head_dim = embed_dim // num_heads
+        self.attn_backend = get_vit_attn_backend(
+            head_size=head_dim,
+            dtype=torch.get_default_dtype(),
+            attn_backend_override=attn_backend_override,
+        )
+        self.use_upstream_fa = False
+        if self.attn_backend not in {
+            _Backend.FLASH_ATTN,
+            _Backend.ROCM_AITER_FA,
+        } and check_upstream_fa_availability(torch.get_default_dtype()):
+            self.attn_backend = _Backend.FLASH_ATTN
+            self.use_upstream_fa = True
+        if self.attn_backend not in {
+            _Backend.FLASH_ATTN,
+            _Backend.TORCH_SDPA,
+            _Backend.XFORMERS,
+            _Backend.ROCM_AITER_FA,
+        }:
+            raise RuntimeError(
+                f"PaddleOCR-VL does not support {self.attn_backend} backend now."
+            )
+        self.layers = nn.ModuleList(
+            [
+                SiglipEncoderLayer(
+                    config,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                    attn_backend=self.attn_backend,
+                    attn_backend_override=attn_backend_override,
+                    use_upstream_fa=self.use_upstream_fa,
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)
+
+    @staticmethod
+    def flatten_list(image_grid_thw):
+        tmp_image_grid_thw = list()
+        for image_grid in image_grid_thw:
+            if isinstance(image_grid, list):
+                tmp_image_grid_thw.extend(image_grid)
+            else:
+                tmp_image_grid_thw.append(image_grid)
+        return tmp_image_grid_thw
+
+    def forward(
+        self,
+        inputs_embeds,
+        cu_seqlens: torch.Tensor | None = None,
+        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
+        | None = None,
+        height_position_ids: torch.Tensor | None = None,
+        width_position_ids: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = inputs_embeds.device
+        hidden_states = inputs_embeds
+
+        flatten_image_grid_thw = self.flatten_list(image_grid_thw)
+
+        if width_position_ids is None or height_position_ids is None:
+            split_hids = list()
+            split_wids = list()
+            for t, h, w in flatten_image_grid_thw:
+                image_pids = torch.arange(t * h * w, device=device) % (h * w)
+                sample_hids = image_pids // w
+                sample_wids = image_pids % w
+                split_hids.append(sample_hids)
+                split_wids.append(sample_wids)
+            width_position_ids = torch.concat(split_wids, dim=0)
+            height_position_ids = torch.concat(split_hids, dim=0)
+
+        pids = torch.stack(
+            [height_position_ids, width_position_ids],
+            dim=-1,
+        )
+        max_grid_size = pids.max() + 1
+        rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rope_emb_max_grid[pids].flatten(1)
+
+        if cu_seqlens is None:
+            raise ValueError("cu_seqlens cannot be None for SiglipEncoder.")
+        if not isinstance(cu_seqlens, torch.Tensor):
+            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32, device=device)
+        else:
+            cu_seqlens = cu_seqlens.to(device=device)
+
+        max_seqlen = None
+        seqlens = None
+        if self.attn_backend in {_Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA}:
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        elif self.attn_backend == _Backend.XFORMERS:
+            seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen,
+                seqlens=seqlens,
+            )
+        return hidden_states
+
+
+class SiglipVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_backend_override: _Backend | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder",
+            attn_backend_override=attn_backend_override,
+        )
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        interpolate_pos_encoding: bool | None = False,
+        position_ids: torch.Tensor | None = None,
+        height_position_ids: torch.Tensor | None = None,
+        width_position_ids: torch.Tensor | None = None,
+        cu_seqlens: torch.Tensor | None = None,
+        image_grid_thw: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embeddings(
+            pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            image_grid_thw=image_grid_thw,
+        )
+
+        last_hidden_state = self.encoder(
+            inputs_embeds=hidden_states,
+            cu_seqlens=cu_seqlens,
+            image_grid_thw=image_grid_thw,
+            height_position_ids=height_position_ids,
+            width_position_ids=width_position_ids,
+        )
+
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+        return last_hidden_state
+
+
+class SiglipVisionModel(nn.Module):
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_backend_override: _Backend | None = None,
+    ):
+        super().__init__()
+
+        self.vision_model = SiglipVisionTransformer(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.vision_model",
+            attn_backend_override=attn_backend_override,
+        )
+        self.quant_config = quant_config
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.vision_model.embeddings.patch_embedding.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.vision_model.embeddings.patch_embedding.weight.device
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.vision_model.embeddings.patch_embedding
+
+    def forward(
+        self,
+        pixel_values,
+        interpolate_pos_encoding: bool = False,
+        position_ids: torch.Tensor | None = None,
+        image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]]
+        | None = None,
+        cu_seqlens: torch.Tensor | None = None,
+    ) -> BaseModelOutputWithPooling:
+        return self.vision_model(
+            pixel_values=pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            image_grid_thw=image_grid_thw,
+            cu_seqlens=cu_seqlens,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "head.attention" in name or "head.layernorm" in name:
+                continue
+            if "head.mlp" in name or "head.probe" in name:
+                continue
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                param = params_dict[scale_name]
+                weight_loader = getattr(
+                    param,
+                    "weight_loader",
+                    default_weight_loader,
+                )
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (
+                param_name,
+                weight_name,
+                shard_id,
+            ) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(
+                    param,
+                    "weight_loader",
+                    default_weight_loader,
+                )
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    PaddleOCRVLMultiModalProcessor,
+    info=PaddleOCRVLProcessingInfo,
+    dummy_inputs=PaddleOCRVLDummyInputsBuilder,
+)
+class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsMRoPE):
+    merge_by_field_config = True
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.": "language_model.model.",
+            "lm_head.": "language_model.lm_head.",
+        }
+    )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        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
+
+        attn_backend_override = (
+            multimodal_config.mm_encoder_attn_backend
+            if multimodal_config is not None
+            else None
+        )
+
+        self.visual = SiglipVisionModel(
+            config=config.vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "visual"),
+            attn_backend_override=attn_backend_override,
+        )
+        self.mlp_AR = Projector(config, config.vision_config)
+
+        self.language_model = Ernie4_5ForCausalLM(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        for layer in self.language_model.model.layers:
+            if not isinstance(layer, PPMissingLayer):
+                layer.self_attn.rotary_emb.is_neox_style = True
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.language_model.compute_logits(hidden_states)
+
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: list[list[int]] | torch.Tensor,
+        video_grid_thw: list[list[int]] | torch.Tensor,
+        second_per_grid_ts: list[float],
+        context_len: int = 0,
+        seq_len: int | None = None,
+        audio_feature_lengths: torch.Tensor | None = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """Get mrope input positions and delta value."""
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(hf_config.vision_config, "tokens_per_second", 1.0)
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id
+        ).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            video_second_per_grid_t = 0.0
+            if remain_images > 0:
+                try:
+                    ed_image = input_tokens.index(image_token_id, st)
+                except ValueError:
+                    ed_image = len(input_tokens) + 1
+            else:
+                ed_image = len(input_tokens) + 1
+            if remain_videos > 0:
+                try:
+                    ed_video = input_tokens.index(video_token_id, st)
+                except ValueError:
+                    ed_video = len(input_tokens) + 1
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_second_per_grid_t = 1.0
+                if second_per_grid_ts:
+                    video_second_per_grid_t = second_per_grid_ts[video_index]
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = (
+                t,
+                h // spatial_merge_size,
+                w // spatial_merge_size,
+            )
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+            )
+
+            t_index = (
+                (
+                    torch.arange(llm_grid_t)
+                    .view(-1, 1)
+                    .expand(-1, llm_grid_h * llm_grid_w)
+                    * video_second_per_grid_t
+                    * tokens_per_second
+                )
+                .long()
+                .flatten()
+            )
+
+            h_index = (
+                torch.arange(llm_grid_h)
+                .view(1, -1, 1)
+                .expand(llm_grid_t, -1, llm_grid_w)
+                .flatten()
+            )
+            w_index = (
+                torch.arange(llm_grid_w)
+                .view(1, 1, -1)
+                .expand(llm_grid_t, llm_grid_h, -1)
+                .flatten()
+            )
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx
+            )
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+            )
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+
+        return llm_positions, mrope_position_delta
+
+    def get_language_model(self) -> nn.Module:
+        return self.language_model
+
+    def _parse_and_validate_image_input(
+        self, **kwargs: object
+    ) -> PaddleOCRImagePixelInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None:
+            return None
+
+        return PaddleOCRImagePixelInputs(
+            type="pixel_values",
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ):
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            is_multimodal = kwargs.pop("is_multimodal", None)
+            handle_oov_mm_token = kwargs.pop("handle_oov_mm_token", False)
+            inputs_embeds = self.get_input_embeddings(
+                input_ids,
+                vision_embeddings,
+                is_multimodal=is_multimodal,
+                handle_oov_mm_token=handle_oov_mm_token,
+            )
+            input_ids = None
+
+        return self.language_model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>"
+
+        raise ValueError("Only image modality is supported")
+
+    def encode_image(
+        self, pixel_values: torch.Tensor, image_grid_thw: torch.Tensor
+    ) -> torch.Tensor:
+        pixel_values = pixel_values.type(self.visual.dtype)
+        siglip_position_ids = list()
+        image_grid_hws = list()
+        cu_seqlens = [0]
+
+        thw_tuple = tuple(image_grid_thw.tolist())
+        numel = np.prod(thw_tuple)
+        image_grid_hws.append(thw_tuple)
+        image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
+        siglip_position_ids.append(image_position_ids)
+        cu_seqlens.append(cu_seqlens[-1] + numel)
+
+        siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
+            pixel_values.device
+        )
+        cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(pixel_values.device)
+
+        vision_outputs = self.visual(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_hws,
+            position_ids=siglip_position_ids,
+            interpolate_pos_encoding=True,
+            cu_seqlens=cu_seqlens,
+        )
+        return vision_outputs
+
+    def _process_image_input(
+        self, image_input: PaddleOCRImagePixelInputs
+    ) -> MultiModalEmbeddings:
+        pixel_values = image_input.pixel_values
+        image_grid_thw = image_input.image_grid_thw
+        vision_outputs = tuple(
+            self.encode_image(pixel, grid).squeeze(0)
+            for pixel, grid in zip(pixel_values, image_grid_thw)
+        )
+        image_embeds = self.mlp_AR(vision_outputs, image_grid_thw)
+        return image_embeds
+
+    def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return ()
+
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+        image_embeds = self._process_image_input(image_input)
+        multimodal_embeddings += tuple(image_embeds)
+
+        return multimodal_embeddings
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        return autoloaded_weights

vllm/model_executor/models/registry.py

@@ -340,6 +340,10 @@ _MULTIMODAL_MODELS = {
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "Ovis": ("ovis", "Ovis"),
     "Ovis2_5": ("ovis2_5", "Ovis2_5"),
+    "PaddleOCRVLForConditionalGeneration": (
+        "paddleocr_vl",
+        "PaddleOCRVLForConditionalGeneration",
+    ),
     "PaliGemmaForConditionalGeneration": (
         "paligemma",
         "PaliGemmaForConditionalGeneration",