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57 lines
1.9 KiB
57 lines
1.9 KiB
import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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from transformers import pipeline
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import redis
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import uuid
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import json
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from threading import Thread
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from vllm import LLM, SamplingParams
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import time
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import threading
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import time
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import concurrent.futures
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import requests
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import socket
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pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50,db=11, password="zhicheng123*")
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redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
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db_key_query = 'query'
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db_key_query_articles_directory = 'query_articles_directory'
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db_key_result = 'result'
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batch_size = 512
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sampling_params = SamplingParams(temperature=0.95, top_p=0.7,presence_penalty=0.9,stop="</s>", max_tokens=4096)
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models_path = "/home/majiahui/project/models-llm/openbuddy-llama-7b-finetune"
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llm = LLM(model=models_path, tokenizer_mode="slow")
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def classify(batch_size): # 调用模型，设置最大batch_size
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while True:
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texts = []
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query_ids = []
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if redis_.llen(db_key_query) == 0: # 若队列中没有元素就继续获取
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time.sleep(2)
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continue
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for i in range(min(redis_.llen(db_key_query), batch_size)):
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query = redis_.lpop(db_key_query).decode('UTF-8') # 获取query的text
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query_ids.append(json.loads(query)['id'])
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texts.append(json.loads(query)['text']) # 拼接若干text 为batch
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outputs = llm.generate(texts, sampling_params) # 调用模型
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generated_text_list = [""] * len(texts)
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print("outputs", len(outputs))
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for i, output in enumerate(outputs):
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index = output.request_id
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generated_text = output.outputs[0].text
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generated_text_list[int(index)] = generated_text
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for (id_, output) in zip(query_ids, generated_text_list):
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res = output
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redis_.set(id_, json.dumps(res)) # 将模型结果送回队列
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if __name__ == '__main__':
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t = Thread(target=classify, args=(batch_size,))
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t.start()
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