9acb21ae27
Co-authored-by: liusy58 <liusy58@linux.alibaba.com> Co-authored-by: ZhengWG <zwg0606@gmail.com> Co-authored-by: Nicholas <45984215+liusy58@users.noreply.github.com> Co-authored-by: Shangming Cai <csmthu@gmail.com> Co-authored-by: Yuhao Yang <47235274+yhyang201@users.noreply.github.com>
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import re
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from typing import Dict, List, Union
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from sglang.srt.models.dots_ocr import DotsOCRForCausalLM
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from sglang.srt.models.dots_vlm import DotsVLMForCausalLM
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from sglang.srt.multimodal.processors.base_processor import (
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BaseMultimodalProcessor,
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MultimodalSpecialTokens,
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)
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class DotsVLMImageProcessor(BaseMultimodalProcessor):
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models = [DotsVLMForCausalLM, DotsOCRForCausalLM]
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def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
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super().__init__(hf_config, server_args, _processor, *args, **kwargs)
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# The single, pre-expanded image token.
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self.IMAGE_TOKEN = "<|img|><|imgpad|><|endofimg|>"
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# The regex that matches expanded image tokens.
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self.IMAGE_TOKEN_REGEX = re.compile(r"<\|img\|>(?:<\|imgpad\|>)+<\|endofimg\|>")
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assert len(_processor.tokenizer.encode("<|img|>")) == 1
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self.im_start_id = _processor.tokenizer.encode("<|img|>")[0]
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self.im_end_id = _processor.tokenizer.encode("<|endofimg|>")[0]
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self.image_token_id = _processor.tokenizer.encode("<|imgpad|>")[0]
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self.IM_TOKEN_ID = self.image_token_id
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self.IM_START_TOKEN_ID = self.im_start_id
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self.IM_END_TOKEN_ID = self.im_end_id
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vision_config = hf_config.vision_config
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patch_size = vision_config.patch_size
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merge_size = vision_config.spatial_merge_size
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self.IMAGE_FACTOR = patch_size * merge_size
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self.MIN_PIXELS = _processor.image_processor.min_pixels
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self.MAX_PIXELS = _processor.image_processor.max_pixels
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self.MAX_RATIO = 200
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self.mm_tokens = MultimodalSpecialTokens(
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image_token=self.IMAGE_TOKEN,
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image_token_id=self.image_token_id,
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image_token_regex=self.IMAGE_TOKEN_REGEX,
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).build(_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, 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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if isinstance(image_data, str):
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image_data = [image_data]
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if (
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isinstance(image_data, list)
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and image_data
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and isinstance(image_data[0], list)
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):
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image_data = sum(image_data, [])
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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=self.mm_tokens,
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)
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combined_mm_item, input_ids, _ = self.process_and_combine_mm_data(
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base_output, self.mm_tokens
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)
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if combined_mm_item is None:
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return None
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return {
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"input_ids": input_ids.tolist(),
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"mm_items": combined_mm_item,
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"im_start_id": self.im_start_id,
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"im_end_id": self.im_end_id,
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"im_token_id": self.image_token_id,
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}
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