61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
from typing import List, Union
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from sglang.srt.managers.multimodal_processors.base_processor import (
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BaseMultimodalProcessor,
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MultimodalSpecialTokens,
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)
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from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
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from sglang.srt.models.deepseek_janus_pro import MultiModalityCausalLM
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class JanusProImageProcessor(BaseMultimodalProcessor):
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models = [MultiModalityCausalLM]
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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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async def process_mm_data_async(
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self,
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image_data: List[Union[str, bytes]],
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input_text,
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request_obj,
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max_req_input_len,
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**kwargs,
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):
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if not image_data:
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return None
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if not isinstance(image_data, list):
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image_data = [image_data]
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processor = self._processor
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base_out = 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=processor.image_token
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),
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max_req_input_len=max_req_input_len,
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)
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images = base_out.images
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res = self.process_mm_data(
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input_text=base_out.input_text,
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prompt=base_out.input_text,
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images=images,
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)
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return {
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"mm_items": [
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MultimodalDataItem(
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pixel_values=res["pixel_values"],
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image_emb_mask=res["images_emb_mask"],
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modality=Modality.IMAGE,
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)
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],
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"input_ids": res["input_ids"].flatten().tolist(),
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"im_start_id": processor.image_start_id,
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"im_end_id": processor.image_end_id,
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"im_token_id": processor.image_id,
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}
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