9b00990bea
Signed-off-by: Xinyuan Tong <justinning0323@outlook.com> Co-authored-by: yhyang201 <yhyang201@gmail.com> Co-authored-by: Mick <mickjagger19@icloud.com>
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
# Copyright 2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import re
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from typing import Dict, List, Optional, Union
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from sglang.srt.managers.multimodal_processor import (
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BaseMultimodalProcessor as SGLangBaseProcessor,
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)
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from sglang.srt.managers.multimodal_processors.base_processor import (
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MultimodalSpecialTokens,
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)
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from sglang.srt.models.gemma3n_mm import Gemma3nForConditionalGeneration
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class Gemma3nSGLangProcessor(SGLangBaseProcessor):
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"""Multimodal processor for Gemma3n supporting image and audio inputs."""
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models = [Gemma3nForConditionalGeneration]
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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 = "<image_soft_token>"
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self.IMAGE_TOKEN_REGEX = re.compile(
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r"<start_of_image>(?:(?:<image_soft_token>)*<end_of_image>)?"
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)
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self.AUDIO_TOKEN = "<audio_soft_token>"
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self.AUDIO_TOKEN_REGEX = re.compile(
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r"<start_of_audio>(?:(?:<audio_soft_token>)*<end_of_audio>)?"
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)
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self.IM_TOKEN_ID = hf_config.image_token_id
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self.IM_START_TOKEN_ID = hf_config.boi_token_id
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self.IM_END_TOKEN_ID = hf_config.eoi_token_id
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self.AUDIO_TOKEN_ID = hf_config.audio_token_id
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self.AUDIO_START_TOKEN_ID = hf_config.boa_token_id
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self.AUDIO_END_TOKEN_ID = hf_config.eoa_token_id
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async def process_mm_data_async(
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self,
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image_data: Optional[List[Union[str, bytes, Dict]]] = None,
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audio_data: Optional[List[Union[str, bytes, Dict]]] = None,
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input_text: str = "",
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request_obj=None,
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max_req_input_len: int = 0,
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*args,
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**kwargs,
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):
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"""Process multimodal data including images and audio."""
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audio_data = request_obj.audio_data
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if not image_data and not audio_data:
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return None
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if isinstance(image_data, str):
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image_data = [image_data]
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if isinstance(audio_data, str):
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audio_data = [audio_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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audio_data=audio_data,
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max_req_input_len=max_req_input_len,
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multimodal_tokens=MultimodalSpecialTokens(
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image_token=self.IMAGE_TOKEN,
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image_token_regex=self.IMAGE_TOKEN_REGEX,
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audio_token=self.AUDIO_TOKEN,
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audio_token_regex=self.AUDIO_TOKEN_REGEX,
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),
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)
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combined_mm_item, 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": [combined_mm_item] if combined_mm_item is not None else [],
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"im_start_id": self.IM_START_TOKEN_ID,
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"im_end_id": self.IM_END_TOKEN_ID,
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"audio_start_id": self.AUDIO_START_TOKEN_ID,
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"audio_end_id": self.AUDIO_END_TOKEN_ID,
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}
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