49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import re
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from typing import Any, Dict, List, Optional, Union
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import torch
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from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
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from sglang.srt.models.kimi_vl import KimiVLForConditionalGeneration
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from sglang.srt.multimodal.processors.base_processor import (
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BaseMultimodalProcessor as SGLangBaseProcessor,
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)
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from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
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# Compatible with KimiVLForConditionalGeneration
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class KimiVLImageProcessor(SGLangBaseProcessor):
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models = [KimiVLForConditionalGeneration]
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def __init__(self, hf_config, server_args, _processor):
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super().__init__(hf_config, server_args, _processor)
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self.IMAGE_TOKEN = "<|media_pad|>"
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self.IMAGE_TOKEN_REGEX = re.compile(r"(?:<\|media_pad\|>)+")
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self.IM_TOKEN_ID = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
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async def process_mm_data_async(
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self,
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image_data: List[Union[str, bytes, Dict]],
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input_text,
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request_obj,
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max_req_input_len,
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*args,
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**kwargs,
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):
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base_output = self.load_mm_data(
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prompt=input_text,
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image_data=image_data,
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multimodal_tokens=MultimodalSpecialTokens(
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image_token=self.IMAGE_TOKEN, image_token_regex=self.IMAGE_TOKEN_REGEX
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),
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max_req_input_len=max_req_input_len,
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)
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mm_items, input_ids, _ = self.process_and_combine_mm_data(base_output)
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return {
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"input_ids": input_ids.tolist(),
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"mm_items": mm_items,
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"im_token_id": self.IM_TOKEN_ID,
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}
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