66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
# Copyright (c) 2023-2024 DeepSeek.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy of
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# this software and associated documentation files (the "Software"), to deal in
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# the Software without restriction, including without limitation the rights to
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# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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# the Software, and to permit persons to whom the Software is furnished to do so,
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# subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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from typing import List, 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.deepseek_vl2 import DeepseekVL2ForCausalLM
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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 DeepseekVL2ImageProcessor(BaseMultimodalProcessor):
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models = [DeepseekVL2ForCausalLM]
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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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self.mm_tokens = MultimodalSpecialTokens(
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image_token="<image>", image_token_id=self._processor.image_token_id
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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]],
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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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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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mm_items, input_ids, _ = self.process_and_combine_mm_data(
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base_output,
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self.mm_tokens,
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max_req_input_len=max_req_input_len,
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conversations=base_output.input_text,
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)
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return {
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"mm_items": mm_items,
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"input_ids": input_ids.tolist(),
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"im_token_id": self._processor.image_token_id,
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}
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