Move multimodal processors into a separate folder (#7581)

python/sglang/srt/multimodal/processors/kimi_vl.py

@@ -0,0 +1,53 @@
+import re
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.kimi_vl import KimiVLForConditionalGeneration
+from sglang.srt.multimodal.processors.base_processor import (
+    BaseMultimodalProcessor as SGLangBaseProcessor,
+)
+from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+
+
+# 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.IMAGE_TOKEN_REGEX = re.compile(r"(?:<\|media_pad\|>)+")
+        self.IM_TOKEN_ID = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes, Dict]],
+        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, image_token_regex=self.IMAGE_TOKEN_REGEX
+            ),
+            max_req_input_len=max_req_input_len,
+        )
+
+        combined_mm_item, input_ids = self.process_and_combine_mm_data(base_output)
+
+        return {
+            "input_ids": input_ids.tolist(),
+            "mm_items": [combined_mm_item] if combined_mm_item is not None else [],
+            "im_token_id": self.IM_TOKEN_ID,
+        }