commit 4e65ce94b0ac0b8b2b8c461ad284a807902b805d
Author: majiahui@haimaqingfan.com <majiahui@haimaqingfan.com>
Date:   Fri Sep 27 17:59:51 2024 +0800

    首次提交

diff --git a/gunicorn_config.py b/gunicorn_config.py
new file mode 100644
index 0000000..c7216e5
--- /dev/null
+++ b/gunicorn_config.py
@@ -0,0 +1,21 @@
+# 并行工作线程数
+workers = 8
+# 监听内网端口5000【按需要更改】
+bind = '0.0.0.0:12003'
+
+loglevel = 'debug'
+
+worker_class = "gevent"
+# 设置守护进程【关闭连接时，程序仍在运行】
+daemon = True
+# 设置超时时间120s，默认为30s。按自己的需求进行设置
+timeout = 120
+# 设置访问日志和错误信息日志路径
+accesslog = './logs/acess1.log'
+errorlog = './logs/error1.log'
+# access_log_format = '%(h) -  %(t)s - %(u)s - %(s)s %(H)s'
+# errorlog = '-' # 记录到标准输出
+
+
+# 设置最大并发量
+worker_connections = 20000
diff --git a/mistral_api.py b/mistral_api.py
new file mode 100644
index 0000000..5671788
--- /dev/null
+++ b/mistral_api.py
@@ -0,0 +1,131 @@
+from flask import Flask, jsonify
+from flask import request
+from transformers import pipeline
+import redis
+import uuid
+import json
+from threading import Thread
+from vllm import LLM, SamplingParams
+import time
+import threading
+import time
+import concurrent.futures
+import requests
+import socket
+
+app = Flask(__name__)
+app.config["JSON_AS_ASCII"] = False
+pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50,db=2, password="zhicheng123*")
+redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
+
+db_key_query = 'query'
+db_key_querying = 'querying'
+db_key_queryset = 'queryset'
+db_key_result = 'result'
+db_key_error = 'error'
+
+def smtp_f(name):
+    # 在下面的代码行中使用断点来调试脚本。
+    import smtplib
+    from email.mime.text import MIMEText
+    from email.header import Header
+
+    sender = '838878981@qq.com'  # 发送邮箱
+    receivers = ['838878981@qq.com']  # 接收邮箱
+    auth_code = "jfqtutaiwrtdbcge"  # 授权码
+
+    message = MIMEText('基础大模型出现错误，紧急', 'plain', 'utf-8')
+    message['From'] = Header("Sender<%s>" % sender)  # 发送者
+    message['To'] = Header("Receiver<%s>" % receivers[0])  # 接收者
+
+    subject = name
+    message['Subject'] = Header(subject, 'utf-8')
+
+    try:
+        server = smtplib.SMTP_SSL('smtp.qq.com', 465)
+        server.login(sender, auth_code)
+        server.sendmail(sender, receivers, message.as_string())
+        print("邮件发送成功")
+        server.close()
+    except smtplib.SMTPException:
+        print("Error: 无法发送邮件")
+
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    text = request.json["texts"]  # 获取用户query中的文本 例如"I love you"
+    id_ = str(uuid.uuid1())  # 为query生成唯一标识
+    print("uuid: ", uuid)
+    d = {'id': id_, 'text': text}  # 绑定文本和query id
+    try:
+        load_request_path = './request_data_logs/{}.json'.format(id_)
+        with open(load_request_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(d, f2, ensure_ascii=False, indent=4)
+        redis_.rpush(db_key_query, json.dumps({"id": id_, "path": load_request_path}))  # 加入redis
+        redis_.sadd(db_key_querying, id_)
+        redis_.sadd(db_key_queryset, id_)
+        return_text = {"texts": {'id': id_, }, "probabilities": None, "status_code": 200}
+    except:
+        return_text = {"texts": {'id': id_, }, "probabilities": None, "status_code": 400}
+        smtp_f("vllm-main-drop")
+    return jsonify(return_text)  # 返回结果
+
+
+@app.route("/search", methods=["POST"])
+def search():
+    id_ = request.json['id']  # 获取用户query中的文本 例如"I love you"
+    result = redis_.get(id_)  # 获取该query的模型结果
+    try:
+        if result is not None:
+            result_path = result.decode('UTF-8')
+            with open(result_path, encoding='utf8') as f1:
+                # 加载文件的对象
+                result_dict = json.load(f1)
+            code = result_dict["status_code"]
+            texts = result_dict["texts"]
+            probabilities = result_dict["probabilities"]
+            if str(code) == 400:
+                redis_.rpush(db_key_error, json.dumps({"id": id_}))
+                return False
+            result_text = {'code': code, 'text': texts, 'probabilities': probabilities}
+        else:
+            querying_list = list(redis_.smembers(db_key_querying))
+            querying_set = set()
+            for i in querying_list:
+                querying_set.add(i.decode())
+
+            querying_bool = False
+            if id_ in querying_set:
+                querying_bool = True
+
+            query_list_json = redis_.lrange(db_key_query, 0, -1)
+            query_set_ids = set()
+            for i in query_list_json:
+                data_dict = json.loads(i)
+                query_id = data_dict['id']
+                query_set_ids.add(query_id)
+
+            query_bool = False
+            if id_ in query_set_ids:
+                query_bool = True
+
+            if querying_bool == True and query_bool == True:
+                result_text = {'code': "201", 'text': "", 'probabilities': None}
+            elif querying_bool == True and query_bool == False:
+                result_text = {'code': "202", 'text': "", 'probabilities': None}
+            else:
+                result_text = {'code': "203", 'text': "", 'probabilities': None}
+                load_request_path = './request_data_logs_203/{}.json'.format(id_)
+                with open(load_request_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(result_text, f2, ensure_ascii=False, indent=4)
+    except:
+        smtp_f("vllm-main")
+        result_text = {'code': "400", 'text': "", 'probabilities': None}
+    return jsonify(result_text)  # 返回结果
+
+if __name__ == "__main__":
+    app.run(debug=False, host='0.0.0.0', port=12006)
diff --git a/qwen_model_perdict_vllm_4.py b/qwen_model_perdict_vllm_4.py
new file mode 100644
index 0000000..d1da47f
--- /dev/null
+++ b/qwen_model_perdict_vllm_4.py
@@ -0,0 +1,181 @@
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+import argparse
+from typing import List, Tuple
+from threading import Thread
+from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
+# from vllm.utils import FlexibleArgumentParser
+from flask import Flask, jsonify
+from flask import request
+import redis
+import time
+import json
+
+# http接口服务
+# app = FastAPI()
+app = Flask(__name__)
+app.config["JSON_AS_ASCII"] = False
+
+pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50,db=2, password="zhicheng123*")
+redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
+
+db_key_query = 'query'
+db_key_querying = 'querying'
+db_key_result = 'result'
+batch_size = 32
+
+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 initialize_engine() -> LLMEngine:
+    """Initialize the LLMEngine from the command line arguments."""
+    # model_dir = "/home/majiahui/project/models-llm/Qwen-0_5B-Chat"
+    model_dir = "/home/majiahui/project/models-llm/qwen2_0_5B_rewrite_lora_hebing"
+    args = EngineArgs(model_dir)
+    args.max_num_seqs = 16  # batch最大20条样本
+    args.gpu_memory_utilization = 0.3
+    # 加载模型
+    return LLMEngine.from_engine_args(args)
+
+engine = initialize_engine()
+
+
+def create_test_prompts(prompt_texts, query_ids, sampling_params) -> List[Tuple[str,str, SamplingParams]]:
+    """Create a list of test prompts with their sampling parameters."""
+
+    return_list = []
+
+    for i,j in zip(prompt_texts, query_ids):
+        return_list.append((i, j, sampling_params))
+    return return_list
+
+
+def process_requests(engine: LLMEngine,
+                     test_prompts: List[Tuple[str, str, SamplingParams]]):
+    """Continuously process a list of prompts and handle the outputs."""
+
+    return_list = []
+    while test_prompts or engine.has_unfinished_requests():
+        if test_prompts:
+            prompt,  query_id, sampling_params = test_prompts.pop(0)
+            engine.add_request(str(query_id), prompt, sampling_params)
+
+        request_outputs: List[RequestOutput] = engine.step()
+
+        for request_output in request_outputs:
+            if request_output.finished:
+                return_list.append(request_output)
+    return return_list
+
+
+def main(prompt_texts, query_ids, sampling_params):
+    """Main function that sets up and runs the prompt processing."""
+
+    test_prompts = create_test_prompts(prompt_texts, query_ids,sampling_params)
+    return process_requests(engine, test_prompts)
+
+
+# chat对话接口
+# @app.route("/predict/", methods=["POST"])
+# def chat():
+#     # request = request.json()
+#     # query = request.get('query', None)
+#     # history = request.get('history', [])
+#     # system = request.get('system', 'You are a helpful assistant.')
+#     # stream = request.get("stream", False)
+#     # user_stop_words = request.get("user_stop_words",
+#     #                               [])  # list[str]，用户自定义停止句，例如：['Observation: ', 'Action: ']定义了2个停止句，遇到任何一个都会停止
+#
+#     query = request.json['query']
+#
+#
+#     # 构造prompt
+#     # prompt_text, prompt_tokens = _build_prompt(generation_config, tokenizer, query, history=history, system=system)
+#
+#     prompt_text = f"<|im_start|>user\n{query}\n<|im_end|>\n<|im_start|>assistant\n"
+#
+#
+#     return_output = main(prompt_text, sampling_params)
+#     return_info = {
+#         "request_id": return_output.request_id,
+#         "text": return_output.outputs[0].text
+#     }
+#
+#     return jsonify(return_info)
+
+def classify(batch_size):  # 调用模型，设置最大batch_size
+    while True:
+        texts = []
+        query_ids = []
+        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)):
+        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
+
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95, frequency_penalty=0.5, max_tokens=8192)
+        outputs = main(texts, query_ids, sampling_params)
+
+        print("预测完成")
+        generated_text_dict = {}
+        print("outputs", len(outputs))
+        for i, output in enumerate(outputs):
+            index = output.request_id
+            generated_text = output.outputs[0].text
+            generated_text_dict[index] = generated_text
+
+        for id_, output in generated_text_dict.items():
+
+            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__':
+    t = Thread(target=classify, args=(batch_size,))
+    t.start()
diff --git a/run_api_gunicorn.sh b/run_api_gunicorn.sh
new file mode 100644
index 0000000..4060eb5
--- /dev/null
+++ b/run_api_gunicorn.sh
@@ -0,0 +1 @@
+gunicorn mistral_api:app -c gunicorn_config.py
diff --git a/run_model.sh b/run_model.sh
new file mode 100644
index 0000000..1629894
--- /dev/null
+++ b/run_model.sh
@@ -0,0 +1 @@
+nohup python qwen_model_perdict_vllm_4.py > myout_model_qwen.file 2>&1 &