102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
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import torch
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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from appPublic.worker import awaitify
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from appPublic.jsonConfig import getConfig
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from ahserver.serverenv import ServerEnv
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from ahserver.webapp import webapp
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class Qwen2VLClass:
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def __init__(self, modelname):
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# default: Load the model on the available device(s)
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self.model = Qwen2VLForConditionalGeneration.from_pretrained(
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modelname,
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torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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device_map="auto"
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen2VLForConditionalGeneration.from_pretrained(
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# "Qwen/Qwen2-VL-7B-Instruct",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# default processer
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self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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# The default range for the number of visual tokens per image in the model is 4-16384.
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# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
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# min_pixels = 256*28*28
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# max_pixels = 1280*28*28
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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def inference(self, prompt, image=None, videofile=None):
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content = [
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{
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"type":"text",
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"text":prompt
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}
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]
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if image:
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if not image.startswith('file:///') \
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and not image.startswith('http://') \
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and not image.startswith('https://'):
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image = f'data:image;base64,{image}'
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content.append({
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"type":"image",
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"image":image
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})
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if videofile:
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if not videofile.startswith('file:///'):
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return 'only local video file support'
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content.append({
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"type":"video",
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"video":videofile
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})
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messages = [
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{
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"role": "user",
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"content": content
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}
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]
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# Preparation for inference
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text = self.processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = self.processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = self.model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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return output_text
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def main():
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config = getConfig()
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modelname = config.modelname
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m = Qwen2VLClass(modelname)
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g = ServerEnv()
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g.inference = awaitify(m.inference)
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if __name__ == '__main__':
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webapp(main)
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