support for custom trainer classes from plugins

src/axolotl/core/trainer_builder.py

@@ -295,6 +295,11 @@ def get_post_trainer_create_callbacks(self, trainer):
         return callbacks
 
     def _get_trainer_cls(self):
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
+            if trainer_cls:
+                return trainer_cls
         if self.cfg.relora_steps:
             return ReLoRATrainer
         if self.cfg.model_config_type == "mamba":

src/axolotl/integrations/base.py

@@ -111,6 +111,17 @@ def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         None
         """
 
+    def get_trainer_cls(self, cfg):  # pylint: disable=unused-argument):
+        """
+        Returns a custom class for the trainer.
+
+        Parameters:
+        cfg (dict): The global axolotl configuration.
+
+        Returns:
+        class: The class for the trainer.
+        """
+
     def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
@@ -346,6 +357,22 @@ def post_lora_load(self, cfg, model):
         for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
+    def get_trainer_cls(self, cfg):
+        """
+        Calls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+
+        Returns:
+        object: The trainer class, or None if none was found.
+        """
+        for plugin in self.plugins.values():
+            trainer_cls = plugin.get_trainer_cls(cfg)
+            if trainer_cls is not None:
+                return trainer_cls
+        return None
+
     def create_optimizer(self, cfg, trainer):
         """
         Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.