⚡️ Speed up method ModelManager.add_model by 11% in PR #1175 (depth-estimation-workflow-block)

In order to optimize the performance of the `add_model` and `get_model` methods, we need to minimize the time spent on certain operations and reduce the number of loggings wherever possible.



**Changes made:**

1. In the `get_model` method of `ModelRegistry`, a single `try/except` block replaces the if-check and separate raise to catch `KeyError`, thereby combining lookup and retrieval into a single step. This optimizes the process of fetching the model class and raising the error when necessary.

2. In the `add_model` method of `ModelManager`, logging messages are minimized and combined to reduce the overhead caused by frequent logging. The change reduces unnecessary calls to `logger.debug()`.

**Line profiling improvements:** 
- For `get_model`: Combined lookup and retrieval operations reduce steps.
- For `add_model`: Consolidating `logger.debug()` calls minimizes overhead and redundant operations.

Author: codeflash-ai[bot]

inference/core/managers/base.py

@@ -48,25 +48,26 @@ def add_model(
             model_id (str): The identifier of the model.
             model (Model): The model instance.
         """
-        logger.debug(
-            f"ModelManager - Adding model with model_id={model_id}, model_id_alias={model_id_alias}"
-        )
+        # Minimize logging overhead by combining log messages
         resolved_identifier = model_id if model_id_alias is None else model_id_alias
         if resolved_identifier in self._models:
             logger.debug(
                 f"ModelManager - model with model_id={resolved_identifier} is already loaded."
             )
             return
 
-        logger.debug("ModelManager - model initialisation...")
+        logger.debug(
+            f"ModelManager - Adding and initializing model with model_id={model_id}, model_id_alias={model_id_alias}"
+        )
 
         try:
+            # Call the model constructor directly
             model = self.model_registry.get_model(resolved_identifier, api_key)(
                 model_id=model_id,
                 api_key=api_key,
             )
-            logger.debug("ModelManager - model successfully loaded.")
             self._models[resolved_identifier] = model
+            logger.debug("ModelManager - model successfully loaded.")
         except Exception as e:
             raise
 

inference/core/registries/base.py

@@ -30,8 +30,10 @@ def get_model(self, model_type: str, model_id: str) -> Model:
         Raises:
             ModelNotRecognisedError: If the model_type is not found in the registry_dict.
         """
-        if model_type not in self.registry_dict:
+        # Combining lookup and retrieval into a single step
+        try:
+            return self.registry_dict[model_type]
+        except KeyError:
             raise ModelNotRecognisedError(
                 f"Could not find model of type: {model_type} in configured registry."
             )
-        return self.registry_dict[model_type]