Add codes

llm_service/client.py

+import json
+import argparse
+import requests
+
+parse = argparse.ArgumentParser()
+parse.add_argument('--query', default='请写一首诗')
+args = parse.parse_args()
+
+print(args.query)
+headers = {"Content-Type": "application/json"}
+data = {
+        "query": args.query,
+        "history": []
+        }
+
+json_str = json.dumps(data)
+
+response = requests.post("http://localhost:8888/inference", headers=headers, data=json_str.encode("utf-8"), verify=False)
+
+str_response = response.content.decode("utf-8")
+print(json.loads(str_response))

llm_service/inferencer.py

@@ -2,11 +2,12 @@ import time
 import os
 import configparser
 import argparse
-import torch
+# import torch
+import asyncio
 
 from loguru import logger
 from aiohttp import web
-from multiprocessing import Value
+# from multiprocessing import Value
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -72,7 +73,6 @@ class LLMInference:
                  sampling_params,
                  device: str = 'cuda',
                  use_vllm: bool = False,
-                 stream_chat: bool = False
                  ) -> None:
 
         self.device = device
@@ -80,7 +80,6 @@ class LLMInference:
         self.tokenizer = tokenizer
         self.sampling_params = sampling_params
         self.use_vllm = use_vllm
-        self.stream_chat = stream_chat
 
     def generate_response(self, prompt, history=[]):
         print("generate")
@@ -120,8 +119,9 @@ class LLMInference:
         try:
             if self.use_vllm:
                 ## vllm
-
+                logger.info("****************** use vllm ******************")
                 prompt_token_ids = [self.tokenizer.apply_chat_template(messages, add_generation_prompt=True)]
+                logger.info(f"before generate {messages}")
                 outputs = self.model.generate(prompt_token_ids=prompt_token_ids, sampling_params=self.sampling_params)
 
                 output_text = []
@@ -157,12 +157,18 @@ class LLMInference:
     def chat_stream(self, prompt: str, history=[]):
         '''流式服务'''
         # HuggingFace
+        logger.info("****************** in chat stream *****************")
         current_length = 0
-        for response, _, _ in self.model.stream_chat(self.tokenizer, prompt, history=history,
+        messages = [{"role": "user", "content": prompt}]
+        logger.info(f"stream_chat messages {messages}")
+        for response, _, _ in self.model.stream_chat(self.tokenizer, messages, history=history,
+                                                     max_length=1024,
                                                     past_key_values=None,
                                                     return_past_key_values=True):
             output_text = response[current_length:]
             output_text = self.substitution(output_text)
+            logger.info(f"using transformers chat_stream, Prompt: {prompt!r}, Generated text: {output_text!r}")
+
             yield output_text
             current_length = len(response)
 
@@ -213,14 +219,15 @@ def llm_inference(args):
         llm_infer = LLMInference(model,
                                 tokenzier,
                                 sampling_params,
-                                use_vllm=use_vllm,
-                                stream_chat=stream_chat)
+                                use_vllm=use_vllm)
+
         prompt = input_json['query']
         history = input_json['history']
+        logger.info(f"prompt {prompt}")
         if stream_chat:
-            text = llm_infer.stream_chat(prompt=prompt, history=history)
+            text = await asyncio.to_thread(llm_infer.chat_stream, prompt=prompt, history=history)
         else:
-            text = llm_infer.chat(prompt=prompt, history=history)
+            text = await asyncio.to_thread(llm_infer.chat, prompt=prompt, history=history)
         end = time.time()
         logger.debug('问题:{} 回答:{} \ntimecost {} '.format(prompt, text, end - start))
         return web.json_response({'text': text})
@@ -243,8 +250,7 @@ def infer_test(args):
     model, tokenzier = init_model(model_path, use_vllm, tensor_parallel_size)
     llm_infer = LLMInference(model,
                             tokenzier,
-                            use_vllm=use_vllm,
-                            stream_chat=stream_chat)
+                            use_vllm=use_vllm)
 
     time_first = time.time()
     output_text = llm_infer.chat(args.query)
@@ -272,7 +278,7 @@ def parse_args():
         help='config目录')
     parser.add_argument(
         '--query',
-        default=['写一首诗'],
+        default='写一首诗',
         help='提问的问题.')
     parser.add_argument(
         '--DCU_ID',