Update 1/model.py

1/model.py

@@ -2,7 +2,6 @@ import triton_python_backend_utils as pb_utils
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 import numpy as np
 import json
-import os
 
 class TritonPythonModel:
     def initialize(self, args):
@@ -13,35 +12,31 @@ class TritonPythonModel:
         """
         self.logger = pb_utils.Logger
 
-        current_file_path = os.path.abspath(__file__)
-        self.logger.log_info(f"current_file_path: {current_file_path}")
-
-
         self.model_name = args["model_name"]
-        model_repository = args["model_repository"]
+        self.model_path = self._get_config_parameter("model_path")
-        #model_path = model_repository
+        self.enable_inference_trace = self._get_config_parameter("enable_inference_trace")
-        model_path = "/cheetah/input/model/groupuser/base-gemma-3-1b-it"
 
-        self.logger.log_info(f"model_name: {self.model_name}")
+        self.logger.log_info(f"'self.model_name: {self.model_name}'")
-        self.logger.log_info(f"model_repository: {model_repository}")
+        self.logger.log_info(f"'model_path: {self.model_path}'")
-        self.logger.log_info(f"model_path: {model_path}")
+        self.logger.log_info(f"'enable_inference_trace: {self.enable_inference_trace}'")
+        
+        #model_repository = args["model_repository"]
+        #model_path = f"{model_repository}/{self.model_name}"
 
         self.model_config = json.loads(args["model_config"])
 
         # Hugging Face Transformers 라이브러리에서 사전 학습된 토크나이저를 로드합니다.
-        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
         self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
         self.supports_chat_template = self._check_chat_template_support()
 
         # Hugging Face Transformers 라이브러리에서 사전 학습된 언어 모델을 로드합니다.
         self.model = AutoModelForCausalLM.from_pretrained(
-            pretrained_model_name_or_path=model_path,
+            pretrained_model_name_or_path=self.model_path,
             local_files_only=True,
             device_map="auto"
         )
        
-        self.enable_inference_trace = self._get_inference_trace_setting()
-
         self.logger.log_info(f"'{self.model_name}' 모델 초기화 완료")
 
 
@@ -149,6 +144,15 @@ class TritonPythonModel:
 
         return generation_config
 
+    def _get_config_parameter(self, parameter_name):
+        self.parameters = self.model_config.get('parameters', {})
+        parameter_dict = self.parameters.get(parameter_name)
+
+        if isinstance(parameter_dict, dict) and 'string_value' in parameter_dict:
+            return parameter_dict['string_value']
+        
+        return None
+
     def _get_inference_trace_setting(self):
         """
         모델 설정(config.pbxt)에서 'enable_inference_trace' 값을 추출하여 반환합니다.
@@ -158,12 +162,20 @@ class TritonPythonModel:
         Returns:
             bool: 추론 추적 활성화 여부 (True 또는 False).
         """
-        parameters = self.model_config.get('parameters', {})
+        trace_config_dict = self.parameters.get('enable_inference_trace')
-        trace_config = parameters.get('enable_inference_trace')
+        if isinstance(trace_config_dict, dict) and 'string_value' in trace_config_dict:
-        if isinstance(trace_config, dict) and 'string_value' in trace_config:
+            return trace_config_dict['string_value'].lower() == 'true'  # 문자열 값을 bool로 변환하여 반환
-            return trace_config['string_value'].lower() == 'true'  # 문자열 값을 bool로 변환하여 반환
         return False
 
+    def _get_model_path(self):
+        """
+        모델 설정(config.pbxt)에서 'model_path' 값을 추출하여 반환합니다.
+        """
+        model_path_dict = self.parameters.get('model_path')
+        if isinstance(model_path_dict, dict) and 'string_value' in model_path_dict:
+            return model_path_dict['string_value']
+        return ""
+    
 
     def _check_chat_template_support(self):
       """
@@ -232,4 +244,3 @@ class TritonPythonModel:
         종료 전에 필요한 모든 정리 작업을 수행할 수 있습니다.
         """
         pass
-