[Models] Add Qwen3-VL Model Support (#5763)

* support v1 loader

* remove useless code

* remove useless

* [Model] support Qwen3VL images success

* [Model] support Qwen3VL rope_3d

* [Model] support Qwen3VL remove log

* [Model] support Qwen3VL RL

* [Model] support Qwen3VL tp

* [Model] support Qwen3VL video

* [Model] support Qwen3VL fix ernievl

* [Model] support Qwen3VL fix get_image_boundaries.cc array out of bounds

* [Model] support Qwen3VL fix multi card

* [Model] support Qwen3VL file close

* [Model] support Qwen3VL fix ce

* [Model] support Qwen3VL fix unittest

* [Model] support Qwen3VL add unittest

---------

Co-authored-by: Ayakouji <yuhongh@qq.com>

fastdeploy/input/ernie4_5_vl_processor/process.py

@@ -23,6 +23,7 @@ from collections import defaultdict
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
+import paddle
 import zmq
 from paddleformers.transformers.image_utils import ChannelDimension
 from PIL import Image
@@ -30,6 +31,7 @@ from PIL import Image
 from fastdeploy.engine.request import ImagePosition
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
 from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
+from fastdeploy.input.mm_data_processor import MMBaseDataProcessor
 from fastdeploy.input.utils import IDS_TYPE_FLAG
 from fastdeploy.multimodal.hasher import MultimodalHasher
 from fastdeploy.utils import data_processor_logger
@@ -70,7 +72,7 @@ def fancy_print(input_ids, tokenizer, image_patch_id=None):
     return res
 
 
-class DataProcessor:
+class DataProcessor(MMBaseDataProcessor):
     """
     Processes multimodal chat messages into model-ready inputs,
     handling text, images, and videos with 3D positional embeddings.
@@ -102,6 +104,7 @@ class DataProcessor:
         video_fps: int = 2,
         **kwargs,
     ) -> None:
+        super().__init__()
         # Tokenizer and image preprocessor
         self.model_name_or_path = tokenizer_name
         self._load_tokenizer()
@@ -152,6 +155,29 @@ class DataProcessor:
             "tool": "Tool: ",
         }
 
+    @staticmethod
+    def mm_num_tokens(grid_thw: list | list[list[int]] | np.ndarray | paddle.Tensor) -> int | list[int]:
+        """
+        Calculate the number of tokens in the multimodal input.
+        """
+        if isinstance(grid_thw, paddle.Tensor):
+            grid_thw = grid_thw.numpy()
+
+        if len(grid_thw) == 0:
+            return 0
+
+        def calc_one(thw):
+            t, h, w = map(int, thw)
+            if t == 1:
+                return t * h * w // 4
+            else:
+                return t * h * w // 4 // 2
+
+        if isinstance(grid_thw[0], (list, tuple, np.ndarray)):
+            return [calc_one(x) for x in grid_thw]
+
+        return calc_one(grid_thw)
+
     def _build_token_type_mapping(self) -> Dict[Any, int]:
         mapping = defaultdict(lambda: IDS_TYPE_FLAG["text"])
         for token in (