78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
import logging
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from typing import List, Union
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from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
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from sglang.srt.models.phi4mm import Phi4MMForCausalLM
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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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logger = logging.getLogger(__name__)
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_IMAGE_SPECIAL_TOKEN = "<|endoftext10|>"
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_IMAGE_SPECIAL_TOKEN_ID = 200010
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class Phi4MMImageProcessor(BaseMultimodalProcessor):
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models = [Phi4MMForCausalLM]
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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.multimodal_tokens = MultimodalSpecialTokens(
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image_token=_IMAGE_SPECIAL_TOKEN,
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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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audio_data,
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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 audio_data:
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logger.warning(
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"Currently SGLang does not support audio data for Phi4MM. We are working on it. You can file an issue to help us prioritize."
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)
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audio_data = []
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base_output = self.load_mm_data(
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prompt=input_text,
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max_req_input_len=max_req_input_len,
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audio_data=audio_data,
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image_data=image_data,
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multimodal_tokens=self.multimodal_tokens,
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)
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if base_output is None:
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return None
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res = self.process_mm_data(
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input_text=base_output.input_text,
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images=base_output.images,
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audios=base_output.audios,
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)
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input_ids = res["input_ids"].flatten()
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image_offsets = self.get_mm_items_offset(
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input_ids=input_ids,
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mm_token_id=_IMAGE_SPECIAL_TOKEN_ID,
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)
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items = [
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MultimodalDataItem(
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feature=res["input_image_embeds"],
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image_sizes=res["image_sizes"],
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image_emb_mask=res["image_attention_mask"],
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offsets=image_offsets,
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modality=Modality.IMAGE,
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)
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]
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return {
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"mm_items": items,
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"input_ids": input_ids.tolist(),
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"im_token_id": _IMAGE_SPECIAL_TOKEN_ID,
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}
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