Plugins create_lr_scheduler support (#2584)

* lr_scheduler support

* fix

* Update scheduler.py

* Update scheduler.py

* cfg handling

* black

* remove debug

* remove adding the axolotl cfg to the scheduler mixin

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

src/axolotl/core/trainers/mixins/scheduler.py

@@ -3,9 +3,10 @@
 import logging
 
 import torch
-from torch.optim.lr_scheduler import OneCycleLR
+from torch.optim.lr_scheduler import LRScheduler, OneCycleLR
 from transformers.trainer import Trainer
 
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.schedulers import (
     RexLR,
     get_cosine_schedule_with_min_lr,
@@ -25,9 +26,9 @@ class SchedulerMixin(Trainer):
 
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
+    ) -> LRScheduler:
         """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        Set up the scheduler. The optimizer of the trainer must have been set up either before this method is called or
         passed as an argument.
 
         Args:
@@ -47,7 +48,16 @@ class SchedulerMixin(Trainer):
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
             # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
+            plugin_manager = PluginManager.get_instance()
+            lr_scheduler: LRScheduler | None = plugin_manager.create_lr_scheduler(
+                trainer=self,
+                optimizer=optimizer,
+                num_training_steps=num_training_steps
+            )
+            if lr_scheduler is not None:
+                LOG.info(f"Using plugin-created lr_scheduler: {lr_scheduler}")
+                self.lr_scheduler = lr_scheduler
+            elif self.args.alternate_lr_scheduler_type == "one_cycle":
                 num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
                 pct_start = num_warmup_steps / num_training_steps
                 extra_lr_kwargs = {}
@@ -110,4 +120,4 @@ class SchedulerMixin(Trainer):
             if use_cosine_min_lr:
                 LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
 
-        return self.lr_scheduler
+        return self.lr_scheduler  # type: ignore

src/axolotl/core/trainers/relora.py

@@ -1,6 +1,7 @@
 """Module for ReLoRA trainer"""
 
 import torch
+from torch.optim.lr_scheduler import LRScheduler
 
 from axolotl.core.trainers.base import AxolotlTrainer
 from axolotl.monkeypatch.relora import ReLoRAScheduler
@@ -19,9 +20,11 @@ class ReLoRATrainer(AxolotlTrainer):
         self,
         num_training_steps: int,
         optimizer: torch.optim.Optimizer | None = None,
-    ):
+    ) -> LRScheduler:
         optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+        lr_scheduler: LRScheduler = super().create_scheduler(
+            num_training_steps, optimizer
+        )
 
         if self.args.relora_steps:
             warmup_steps = (
@@ -30,7 +33,7 @@ class ReLoRATrainer(AxolotlTrainer):
             anneal_steps = (
                 self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
             )
-            self.lr_scheduler = ReLoRAScheduler(
+            self.lr_scheduler = ReLoRAScheduler(  # type: ignore
                 optimizer,
                 lr_scheduler,
                 self.args.relora_steps,
@@ -38,6 +41,6 @@ class ReLoRATrainer(AxolotlTrainer):
                 warmup_steps,
             )
         else:
-            self.lr_scheduler = lr_scheduler
+            self.lr_scheduler = lr_scheduler  # type: ignore
 
-        return self.lr_scheduler
+        return self.lr_scheduler  # type: ignore

src/axolotl/integrations/base.py

@@ -24,6 +24,7 @@ import logging
 from typing import OrderedDict
 
 import torch
+from torch.optim.lr_scheduler import LRScheduler
 
 
 class BasePlugin:
@@ -41,7 +42,7 @@ class BasePlugin:
     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.
     create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
-    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    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.
     add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
     """
@@ -146,8 +147,8 @@ class BasePlugin:
         """
 
     def create_lr_scheduler(
-        self, cfg, trainer, optimizer
+        self, cfg, trainer, optimizer, num_training_steps
-    ):  # pylint: disable=unused-argument
+    ) -> LRScheduler | None:  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -155,9 +156,10 @@ class BasePlugin:
         cfg (dict): The configuration for the plugin.
         trainer (object): The trainer object for training.
         optimizer (object): The optimizer for training.
+        num_training_steps (int): Total number of training steps
 
         Returns:
-        object: The created learning rate scheduler.
+        object (LRScheduler): The created learning rate scheduler.
         """
 
     def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
@@ -436,7 +438,9 @@ class PluginManager:
                 return optimizer
         return None
 
-    def create_lr_scheduler(self, trainer, optimizer):
+    def create_lr_scheduler(
+        self, trainer, optimizer, num_training_steps
+    ) -> LRScheduler | None:
         """
         Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
 
@@ -448,7 +452,12 @@ class PluginManager:
         object: The created learning rate scheduler, or None if none was found.
         """
         for plugin in self.plugins.values():
-            scheduler = plugin.create_lr_scheduler(self.cfg, trainer, optimizer)
+            scheduler: LRScheduler | None = plugin.create_lr_scheduler(
+                self.cfg,
+                trainer=trainer,
+                optimizer=optimizer,
+                num_training_steps=num_training_steps,
+            )
             if scheduler is not None:
                 return scheduler
         return None

tests/e2e/integrations/test_hooks.py

@@ -72,7 +72,7 @@ class LogHooksPlugin(BasePlugin):
             f.write("get_trainer_cls\n")
 
     def create_lr_scheduler(
-        self, cfg, trainer, optimizer
+        self, cfg, trainer, optimizer, num_training_steps
     ):  # pylint: disable=unused-argument
         with open(
             self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
@@ -172,7 +172,7 @@ class TestPluginHooks:
             assert "post_model_load" in file_contents
             # assert "create_optimizer" in file_contents  # not implemented yet
             assert "get_trainer_cls" in file_contents
-            # assert "create_lr_scheduler" in file_contents  # not implemented yet
+            assert "create_lr_scheduler" in file_contents
             assert "add_callbacks_pre_trainer" in file_contents
             assert "add_callbacks_post_trainer" in file_contents
             assert "post_train" in file_contents