Use ordered dict to only have one instance of plugin registered

src/axolotl/integrations/base.py

@@ -18,9 +18,10 @@
 
 To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
 """
+import collections
 import importlib
 import logging
-from typing import List
+from typing import OrderedDict
 
 
 class BasePlugin:
@@ -233,7 +234,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: List[BasePlugin] = []
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
 
@@ -243,7 +244,7 @@ def __new__(cls):
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins: List[BasePlugin] = []
+            cls._instance.plugins = collections.OrderedDict()
         return cls._instance
 
     @staticmethod
@@ -271,7 +272,7 @@ def register(self, plugin_name: str):
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins.append(plugin)
+            self.plugins[plugin_name] = plugin
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -283,7 +284,7 @@ def get_input_args(self):
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -299,7 +300,7 @@ def pre_model_load(self, cfg):
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -313,7 +314,7 @@ def post_model_load(self, cfg, model):
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -327,7 +328,7 @@ def pre_lora_load(self, cfg, model):
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -341,7 +342,7 @@ def post_lora_load(self, cfg, model):
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -355,7 +356,7 @@ def create_optimizer(self, cfg, trainer):
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -373,7 +374,7 @@ def create_lr_scheduler(self, cfg, trainer, optimizer):
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -391,7 +392,7 @@ def add_callbacks_pre_trainer(self, cfg, model):
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
@@ -407,7 +408,7 @@ def add_callbacks_post_trainer(self, cfg, trainer):
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
 
@@ -422,5 +423,5 @@ def post_train_unload(self, cfg):
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for _, plugin in self.plugins.items():
             plugin.post_train_unload(cfg)