substitution

llm_service/inferencer.py

@@ -7,6 +7,7 @@ from aiohttp import web
 import torch
 from loguru import logger
 
+from fastllm_pytools import llm
 from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
 
 
@@ -48,60 +49,69 @@ class InferenceWrapper:
         self.stream_chat = stream_chat
         # huggingface
         self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        # self.model = AutoModelForCausalLM.from_pretrained(model_path,
-        #                                                   trust_remote_code=True,
-        #                                                   torch_dtype=torch.float16).cuda().eval()
-        model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
+        model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).half().cuda()
         self.model = model.eval()
 
         if self.use_vllm:
-            ## vllm
-            # from vllm import LLM, SamplingParams
-            #
-            # self.sampling_params = SamplingParams(temperature=1, top_p=0.95)
-            # self.llm = LLM(model=model_path,
-            #                trust_remote_code=True,
-            #                enforce_eager=True,
-            #                tensor_parallel_size=tensor_parallel_size)
-            ## fastllm
-            from fastllm_pytools import llm
             try:
+                ## vllm
+                # from vllm import LLM, SamplingParams
+
+                # self.sampling_params = SamplingParams(temperature=1, top_p=0.95)
+                # self.llm = LLM(model=model_path,
+                #                trust_remote_code=True,
+                #                enforce_eager=True,
+                #                tensor_parallel_size=tensor_parallel_size)
+                ## fastllm
                 if self.stream_chat:
                     # fastllm的流式初始化
                     self.model = llm.model(model_path)
                 else:
                     self.model = llm.from_hf(self.model, self.tokenizer, dtype="float16")
-
             except Exception as e:
                 logger.error(f"fastllm initial failed, {e}")
 
+    def substitution(self, output_text):
+        import re
+        matchObj = re.split('.*(<.*>).*', output_text, re.M|re.I)
+        if matchObj:
+            obj = matchObj[1]
+            replace_str = COMMON.get(obj)
+            if replace_str:
+                output_text = output_text.replace(obj, replace_str)
+                logger.info(f"{obj} be replaced {replace_str}, after {output_text}")
+        return output_text
 
     def chat(self, prompt: str, history=[]):
         '''单轮问答'''
-        import re
+        print("in chat")
+
         output_text = ''
         try:
             if self.use_vllm:
-
+                ## vllm
+                # output_text = []
+                # outputs = self.llm.generate(prompt, self.sampling_params)
+                # for output in outputs:
+                #     prompt = output.prompt
+                #     generated_text = output.outputs[0].text
+                #     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+                #     output_text.append(generated_text)
+                ## fastllm
                 output_text = self.model.response(prompt)
             else:
                 output_text, _ = self.model.chat(self.tokenizer,
                                                     prompt,
                                                     history,
                                                     do_sample=False)
-            matchObj = re.match('.*(<.*>).*', output_text)
-            if matchObj:
-                obj = matchObj.group(1)
-                replace_str = COMMON.get(obj)
+            output_text = self.substitution(output_text)
+            print("output_text", output_text)
 
-                output_text = output_text.replace(obj, replace_str)
-                logger.info(f"{obj} be replaced {replace_str}, after {output_text}")
 
         except Exception as e:
             logger.error(f"chat inference failed, {e}")
         return output_text
 
-
     def chat_stream(self, prompt: str, history=[]):
         '''流式服务'''
         import re
@@ -109,30 +119,16 @@ class InferenceWrapper:
             from fastllm_pytools import llm
             # Fastllm
             for response in self.model.stream_response(prompt, history=[]):
-                matchObj = re.match('.*(<.*>).*', response)
-                if matchObj:
-                    obj = matchObj.group(1)
-                    replace_str = COMMON.get(obj)
-
-                    response = response.replace(obj, replace_str)
-                    logger.info(f"{obj} be replaced {replace_str}, after {response}")
-
+                response = self.substitution(response)
                 yield response
         else:
             # HuggingFace
             current_length = 0
-            for response, _, past_key_values in self.model.stream_chat(self.tokenizer, prompt, history=history,
+            for response, _, _ in self.model.stream_chat(self.tokenizer, prompt, history=history,
                                                                 past_key_values=None,
                                                                 return_past_key_values=True):
                 output_text = response[current_length:]
-                matchObj = re.match('.*(<.*>).*', output_text)
-                if matchObj:
-                    obj = matchObj.group(1)
-                    replace_str = COMMON.get(obj)
-
-                    output_text = output_text.replace(obj, replace_str)
-                    logger.info(f"{obj} be replaced {replace_str}, after {output_text}")
-
+                output_text = self.substitution(output_text)
                 yield output_text
                 current_length = len(response)
 
@@ -147,13 +143,13 @@ class LLMInference:
                  ) -> None:
 
         self.device = device
-
         self.inference = InferenceWrapper(model_path=model_path,
                                           use_vllm=use_vllm,
                                           stream_chat=stream_chat,
                                           tensor_parallel_size=tensor_parallel_size)
 
     def generate_response(self, prompt, history=[]):
+        print("generate")
         output_text = ''
         error = ''
         time_tokenizer = time.time()
@@ -181,6 +177,7 @@ def llm_inference(args):
     bind_port = int(config['default']['bind_port'])
     model_path = config['llm']['local_llm_path']
     use_vllm = config.getboolean('llm', 'use_vllm')
+    print("inference")
     inference_wrapper = InferenceWrapper(model_path,
                                          use_vllm=use_vllm,
                                          tensor_parallel_size=1,
@@ -204,6 +201,27 @@ def llm_inference(args):
     web.run_app(app, host='0.0.0.0', port=bind_port)
 
 
+def infer_test(args):
+    config = configparser.ConfigParser()
+    config.read(args.config_path)
+
+    model_path = config['llm']['local_llm_path']
+    use_vllm = config.getboolean('llm', 'use_vllm')
+    tensor_parallel_size = config.getint('llm', 'tensor_parallel_size')
+    inference_wrapper = InferenceWrapper(model_path,
+                                         use_vllm=use_vllm,
+                                         tensor_parallel_size=1,
+                                         stream_chat=args.stream_chat)
+    # prompt = "hello，please introduce yourself..."
+    prompt ='65N32-US主板清除CMOS配置的方法'
+    history = []
+    time_first = time.time()
+    output_text = inference_wrapper.chat(prompt)
+    time_second = time.time()
+    logger.debug('问题:{} 回答:{} \ntimecost {} '.format(
+        prompt, output_text, time_second - time_first))
+
+
 def set_envs(dcu_ids):
     try:
         os.environ["CUDA_VISIBLE_DEVICES"] = dcu_ids
@@ -223,7 +241,7 @@ def parse_args():
         help='config目录')
     parser.add_argument(
         '--query',
-        default=['请问下产品的服务器保修或保修政策？'],
+        default=['2000e防火墙恢复密码和忘记IP查询操作'],
         help='提问的问题.')
     parser.add_argument(
         '--DCU_ID',
@@ -242,6 +260,7 @@ def main():
     args = parse_args()
     set_envs(args.DCU_ID)
     llm_inference(args)
+    # infer_test(args)
 
 
 if __name__ == '__main__':