allow plugins to return their own dataset (#2617) [skip ci]

* allow plugins to return their own dataset

* add post_trainer_create and wire up

* add hook check

* address PR feedback:

* remove annotation causing circular import

src/axolotl/cli/preprocess.py

@@ -18,6 +18,7 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.trainer import disable_datasets_caching
 
@@ -47,7 +48,10 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
         cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
     with disable_datasets_caching():
-        if cfg.rl:
+        plugin_manager = PluginManager.get_instance()
+        if plugin_manager.load_datasets(cfg, preprocess=True):
+            pass
+        elif cfg.rl:
             load_preference_datasets(cfg=cfg, cli_args=cli_args)
         else:
             load_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/cli/train.py

@@ -43,10 +43,13 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()
 
-    if cfg.rl:
+    plugin_manager = PluginManager.get_instance()
-        dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
+    dataset_meta = plugin_manager.load_datasets(cfg, preprocess=False)
-    else:
+    if not dataset_meta:
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+        if cfg.rl:
+            dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
+        else:
+            dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
 

src/axolotl/integrations/base.py

@@ -26,6 +26,8 @@ from typing import OrderedDict
 import torch
 from torch.optim.lr_scheduler import LRScheduler
 
+from axolotl.utils.dict import DictDefault
+
 
 class BasePlugin:
     """
@@ -36,11 +38,13 @@ class BasePlugin:
 
     Methods:
     register(cfg): Registers the plugin with the given configuration.
+    load_datasets(cfg): Loads and preprocesses the dataset for training.
     pre_model_load(cfg): Performs actions before the model is loaded.
     post_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.
     pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
     post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
     post_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.
+    post_trainer_create(cfg, trainer): Performs actions after the trainer is created.
     create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
     create_lr_scheduler(cfg, trainer, optimizer, num_training_steps): Creates and returns a learning rate scheduler.
     add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
@@ -63,20 +67,32 @@ class BasePlugin:
         None
         """
 
-    def get_input_args(self):
+    def get_input_args(self) -> str | None:
         """
         Returns a pydantic model for the plugin's input arguments.
         """
 
+    def load_datasets(self, cfg: DictDefault, preprocess: bool = False):
+        """
+        Loads and preprocesses the dataset for training.
+
+        Args:
+            cfg: The configuration for the plugin.
+            preprocess: Whether this is the preprocess step of the datasets.
+
+        Returns:
+            dataset_meta: The metadata for the training dataset.
+        """
+
     def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions before the model is loaded.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
 
         Returns:
-        None
+            None
         """
 
     def post_model_build(self, cfg, model):  # pylint: disable=unused-argument
@@ -91,59 +107,71 @@ class BasePlugin:
         """
         Performs actions after the model is loaded.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
+            model (object): The loaded model.
 
         Returns:
-        None
+            None
         """
 
     def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions before LoRA weights are loaded.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
+            model (object): The loaded model.
 
         Returns:
-        None
+            None
         """
 
     def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after LoRA weights are loaded.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
+            model (object): The loaded model.
 
         Returns:
-        None
+            None
         """
 
     def get_trainer_cls(self, cfg):  # pylint: disable=unused-argument):
         """
         Returns a custom class for the trainer.
 
-        Parameters:
+        Args:
-        cfg (dict): The global axolotl configuration.
+            cfg (dict): The global axolotl configuration.
 
         Returns:
-        class: The class for the trainer.
+            class: The class for the trainer.
+        """
+
+    def post_trainer_create(self, cfg, trainer):  # pylint: disable=unused-argument
+        """
+        Performs actions after the trainer is created.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+            trainer (object): The trainer object for training.
+
+        Returns:
+            None
         """
 
     def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
+            trainer (object): The trainer object for training.
 
         Returns:
-        object: The created optimizer.
+            object: The created optimizer.
         """
 
     def create_lr_scheduler(
@@ -152,26 +180,26 @@ class BasePlugin:
         """
         Creates and returns a learning rate scheduler.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
+            trainer (object): The trainer object for training.
-        optimizer (object): The optimizer for training.
+            optimizer (object): The optimizer for training.
-        num_training_steps (int): Total number of training steps
+            num_training_steps (int): Total number of training steps
 
         Returns:
-        object (LRScheduler): The created learning rate scheduler.
+            object (LRScheduler): The created learning rate scheduler.
         """
 
     def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
         setup callbacks before creating the trainer.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
+            model (object): The loaded model.
 
         Returns:
-        List[callable]: A list of callback functions to be added to the TrainingArgs
+            List[callable]: A list of callback functions to be added to the TrainingArgs
         """
         return []
 
@@ -182,12 +210,12 @@ class BasePlugin:
         Adds callbacks to the trainer after creating the trainer.
         This is useful for callbacks that require access to the model or trainer.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
+            trainer (object): The trainer object for training.
 
         Returns:
-        List[callable]: A list of callback functions to be added
+            List[callable]: A list of callback functions to be added
         """
         return []
 
@@ -195,23 +223,23 @@ class BasePlugin:
         """
         Performs actions after training is complete.
 
-        Parameters:
+        Args:
-        cfg (dict): The axolotl configuration
+            cfg (dict): The axolotl configuration
-        model (object): The loaded model.
+            model (object): The loaded model.
 
         Returns:
-        None
+            None
         """
 
     def post_train_unload(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete and the model is unloaded.
 
-        Parameters:
+        Args:
-        cfg (dict): The configuration for the plugin.
+            cfg (dict): The configuration for the plugin.
 
         Returns:
-        None
+            None
         """
 
 
@@ -338,6 +366,27 @@ class PluginManager:
                 input_args.append(input_args_from_plugin)
         return input_args
 
+    def load_datasets(self, cfg, preprocess: bool = False):
+        """
+        Calls the load_datasets method of each registered plugin.
+
+        Args:
+            cfg: The configuration for the plugins.
+            preprocess : Whether this is preprocess step of the datasets.
+
+        Returns:
+            dataset_meta: The dataset metadata loaded from all registered plugins.
+        """
+        return_ds_meta = None
+        for plugin in self.plugins.values():
+            dataset_meta = plugin.load_datasets(cfg, preprocess)
+            if dataset_meta is not None:
+                if return_ds_meta is None:
+                    return_ds_meta = dataset_meta
+                else:
+                    raise RuntimeError("Multiple plugins loaded datasets")
+        return return_ds_meta
+
     def pre_model_load(self, cfg):
         """
         Calls the pre_model_load method of all registered plugins.
@@ -422,6 +471,20 @@ class PluginManager:
                 return trainer_cls
         return None
 
+    def post_trainer_create(self, cfg, trainer):
+        """
+        Calls the post_trainer_create method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_trainer_create(cfg, trainer)
+
     def create_optimizer(self, trainer):
         """
         Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.

src/axolotl/train.py

@@ -528,6 +528,9 @@ def train(
         processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
+    plugin_manager = PluginManager.get_instance()
+    plugin_manager.post_trainer_create(cfg, trainer)
+
     # Handle untrained tokens if configured
     safe_serialization = cfg.save_safetensors is True
     train_dataset = dataset_meta.train_dataset
@@ -550,7 +553,6 @@ def train(
     if not cfg.use_ray:
         cleanup_distributed()
 
-    plugin_manager = PluginManager.get_instance()
     plugin_manager.post_train(cfg, model)
 
     return model, tokenizer, trainer

tests/e2e/integrations/test_hooks.py

@@ -29,6 +29,12 @@ class LogHooksPlugin(BasePlugin):
         except FileNotFoundError:
             pass
 
+    def post_trainer_create(self, cfg, trainer):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_trainer_create\n")
+
     def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         with open(
             self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
@@ -165,6 +171,7 @@ class TestPluginHooks:
         ) as f:
             file_contents = f.readlines()
             file_contents = "\n".join(file_contents)
+            assert "post_trainer_create" in file_contents
             assert "pre_model_load" in file_contents
             assert "post_model_build" in file_contents
             assert "pre_lora_load" in file_contents