set config on the PluginManager for callback access (axolotl-ai-cloud#2587)

Author: winglian

src/axolotl/cli/config.py

@@ -152,6 +152,12 @@ def prepare_plugins(cfg: DictDefault):
             plugin_manager.register(plugin_name)
 
 
+def plugin_set_cfg(cfg: DictDefault):
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        plugin_manager.cfg = cfg
+
+
 def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefault:
     """
     Loads the `axolotl` configuration stored at `config`, validates it, and performs
@@ -213,5 +219,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     setup_wandb_env_vars(cfg)
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
+    plugin_set_cfg(cfg)
 
     return cfg

src/axolotl/integrations/base.py

@@ -270,14 +270,17 @@ class PluginManager:
     plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
+    _cfg = None
 
     def __new__(cls):
         """
         Creates a new instance of PluginManager if it doesn't exist yet.
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins = collections.OrderedDict()
+            cls._instance.plugins: OrderedDict[str, BasePlugin] = (
+                collections.OrderedDict()
+            )
         return cls._instance
 
     @staticmethod
@@ -290,6 +293,14 @@ def get_instance() -> "PluginManager":
             PluginManager()
         return PluginManager._instance  # type: ignore
 
+    @property
+    def cfg(self):
+        return self._cfg
+
+    @cfg.setter
+    def cfg(self, cfg):
+        self._cfg = cfg
+
     def register(self, plugin_name: str):
         """
         Registers a new plugin by its name.
@@ -409,37 +420,35 @@ def get_trainer_cls(self, cfg):
                 return trainer_cls
         return None
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, trainer):
         """
         Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
 
         Parameters:
-        cfg (dict): The configuration for the plugins.
         trainer (object): The trainer object for training.
 
         Returns:
         object: The created optimizer, or None if none was found.
         """
         for plugin in self.plugins.values():
-            optimizer = plugin.create_optimizer(cfg, trainer)
+            optimizer = plugin.create_optimizer(self.cfg, trainer)
             if optimizer is not None:
                 return optimizer
         return None
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(self, trainer, optimizer):
         """
         Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
 
         Parameters:
-        cfg (dict): The configuration for the plugins.
         trainer (object): The trainer object for training.
         optimizer (object): The optimizer for training.
 
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
         for plugin in self.plugins.values():
-            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            scheduler = plugin.create_lr_scheduler(self.cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
         return None