解决bug： 多线程抢资源问题

mistral_model_predict_vllm.py

@@ -1,5 +1,5 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
 from transformers import pipeline
 import redis
 import uuid
@@ -14,20 +14,39 @@ import requests
 import socket
 
 
-pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50,db=5, password="zhicheng123*")
+pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50,db=4, password="zhicheng123*")
 redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
 
 db_key_query = 'query'
-db_key_query_articles_directory = 'query_articles_directory'
+db_key_querying = 'querying'
 db_key_result = 'result'
-batch_size = 512
+batch_size = 24
 
 # sampling_params = SamplingParams(temperature=0.95, top_p=0.7,presence_penalty=0.9,stop="</s>", max_tokens=4096)
-sampling_params = SamplingParams(temperature=0.95, top_p=0.7,stop="</s>", max_tokens=4096)
-models_path = "/home/majiahui/project/models-llm/openbuddy-mistral-7b-v13.1-finetune-90000"
+sampling_params = SamplingParams(temperature=0.95, top_p=0.7,stop="</s>", presence_penalty=1.1, max_tokens=4096)
+models_path = "/home/majiahui/project/LLaMA-Factory-main/lora_openbuddy_zephyr_paper_model_190000"
 llm = LLM(model=models_path, tokenizer_mode="slow")
 
 
+class log:
+    def __init__(self):
+        pass
+
+    def log(*args, **kwargs):
+        format = '%Y/%m/%d-%H:%M:%S'
+        format_h = '%Y-%m-%d'
+        value = time.localtime(int(time.time()))
+        dt = time.strftime(format, value)
+        dt_log_file = time.strftime(format_h, value)
+        log_file = 'log_file/access-%s' % dt_log_file + ".log"
+        if not os.path.exists(log_file):
+            with open(os.path.join(log_file), 'w', encoding='utf-8') as f:
+                print(dt, *args, file=f, **kwargs)
+        else:
+            with open(os.path.join(log_file), 'a+', encoding='utf-8') as f:
+                print(dt, *args, file=f, **kwargs)
+
+
 def classify(batch_size):  # 调用模型，设置最大batch_size
     while True:
         texts = []
@@ -35,10 +54,28 @@ def classify(batch_size):  # 调用模型，设置最大batch_size
         if redis_.llen(db_key_query) == 0:  # 若队列中没有元素就继续获取
             time.sleep(2)
             continue
-        for i in range(min(redis_.llen(db_key_query), batch_size)):
-            query = redis_.lpop(db_key_query).decode('UTF-8')  # 获取query的text
-            query_ids.append(json.loads(query)['id'])
-            texts.append(json.loads(query)['text'])  # 拼接若干text 为batch
+
+        # for i in range(min(redis_.llen(db_key_query), batch_size)):
+        while True:
+            query = redis_.lpop(db_key_query)  # 获取query的text
+            if query == None:
+                break
+
+            query = query.decode('UTF-8')
+            data_dict_path = json.loads(query)
+
+            path = data_dict_path['path']
+            with open(path, encoding='utf8') as f1:
+                # 加载文件的对象
+                data_dict = json.load(f1)
+            # query_ids.append(json.loads(query)['id'])
+            # texts.append(json.loads(query)['text'])  # 拼接若干text 为batch
+            query_id = data_dict['id']
+            text = data_dict["text"]
+            query_ids.append(query_id)
+            texts.append(text)
+            if len(texts) == batch_size:
+                break
         outputs = llm.generate(texts, sampling_params)  # 调用模型
 
         generated_text_list = [""] * len(texts)
@@ -48,10 +85,20 @@ def classify(batch_size):  # 调用模型，设置最大batch_size
             generated_text = output.outputs[0].text
             generated_text_list[int(index)] = generated_text
 
-
         for (id_, output) in zip(query_ids, generated_text_list):
-            res = output
-            redis_.set(id_, json.dumps(res))  # 将模型结果送回队列
+
+            return_text = {"texts": output, "probabilities": None, "status_code": 200}
+            load_result_path = "./new_data_logs/{}.json".format(id_)
+            with open(load_result_path, 'w', encoding='utf8') as f2:
+                # ensure_ascii=False才能输入中文，否则是Unicode字符
+                # indent=2 JSON数据的缩进，美观
+                json.dump(return_text, f2, ensure_ascii=False, indent=4)
+            redis_.set(id_, load_result_path, 86400)
+            # redis_.set(id_, load_result_path, 30)
+            redis_.srem(db_key_querying, id_)
+            log.log('start at',
+                    'query_id:{},load_result_path:{},return_text:{}'.format(
+                        id_, load_result_path, return_text))
 
 
 if __name__ == '__main__':