[Feature] add support kimi vl model (#5383)

Co-authored-by: wenju.li <wenju.li@deepctr.cn>

python/sglang/srt/managers/multimodal_processors/kimi_vl.py

@@ -0,0 +1,73 @@
+import asyncio
+import math
+from typing import List, Union
+
+import torch
+from PIL import Image
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor as SGLangBaseProcessor,
+)
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.kimi_vl import KimiVLForConditionalGeneration
+
+
+# Compatible with KimiVLForConditionalGeneration
+class KimiVLImageProcessor(SGLangBaseProcessor):
+    models = [KimiVLForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.IMAGE_TOKEN = "<|media_pad|>"
+        self.im_token_id = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
+
+        self.im_start = "<|media_start|>"
+        self.im_start_id = _processor.tokenizer.convert_tokens_to_ids(self.im_start)
+
+        self.im_end = "<|media_end|>"
+        self.im_end_id = _processor.tokenizer.convert_tokens_to_ids(self.im_end)
+
+        self.im_content = "<|media_content|>"
+        self.im_content_id = _processor.tokenizer.convert_tokens_to_ids(self.im_content)
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        request_obj,
+        max_req_input_len,
+        *args,
+        **kwargs,
+    ):
+        if not image_data:
+            return None
+        if isinstance(image_data, str):
+            image_data = [image_data]
+
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            image_data=image_data,
+            multimodal_tokens=MultimodalSpecialTokens(image_token=self.IMAGE_TOKEN),
+            max_req_input_len=max_req_input_len,
+        )
+        ret = self.process_mm_data(
+            input_text=base_output.input_text,
+            images=base_output.images,
+        )
+        return {
+            "input_ids": ret["input_ids"].flatten().tolist(),
+            "mm_items": [
+                MultimodalDataItem(
+                    pixel_values=ret["pixel_values"],
+                    image_grid_thws=ret["image_grid_hws"],
+                    modality=Modality.IMAGE,
+                )
+            ],
+            "im_token_id": self.im_token_id,
+            "im_start_id": self.im_start_id,
+            "im_end_id": self.im_end_id,
+            "im_content_id": self.im_content_id,
+        }