Add LR Scheduler to single device full finetune

recipes/configs/llama2/7B_full_low_memory.yaml

@@ -53,7 +53,10 @@ batch_size: 2
 epochs: 3
 optimizer:
   _component_: bitsandbytes.optim.PagedAdamW
-  lr: 2e-5
+  lr: 1e-5
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
 optimizer_in_bwd: True
 loss:
   _component_: torchtune.modules.loss.CEWithChunkedOutputLoss

recipes/configs/llama3/8B_full_single_device.yaml

@@ -52,7 +52,10 @@ batch_size: 2
 epochs: 3
 optimizer:
   _component_: bitsandbytes.optim.PagedAdamW8bit
-  lr: 2e-5
+  lr: 1e-5
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
 loss:
   _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
 max_steps_per_epoch: null

recipes/full_finetune_single_device.py

@@ -272,6 +272,13 @@ def setup(self, cfg: DictConfig) -> None:
             self._steps_per_epoch = self.max_steps_per_epoch
         self.global_step = self.epochs_run * self._steps_per_epoch
 
+        # Setup lr scheduler
+        self._lr_scheduler = self._setup_lr_scheduler(
+            cfg_lr_scheduler=cfg.lr_scheduler,
+            num_training_steps=self.total_epochs * self._steps_per_epoch,
+            last_epoch=self.global_step - 1,
+        )
+
         # Set up profiler, returns DummyProfiler (nullcontext object with no-op `step` method)
         # if cfg is missing profiler key or if `cfg.profiler.enabled = False`
         self._profiler = self._setup_profiler(cfg.get(PROFILER_KEY, None))
@@ -429,6 +436,45 @@ def _setup_optimizer(
             log.info("Optimizer is initialized.")
             return optimizer
 
+    def _setup_lr_scheduler(
+        self,
+        cfg_lr_scheduler: DictConfig,
+        num_training_steps: int,
+        last_epoch: int,
+    ) -> Optional[Optimizer]:
+        if self._optimizer_in_bwd:
+            # Use the first optimizer from the wrapper to represent the learning rate
+            optimizer = next(iter(self._optim_ckpt_wrapper.optim_map.values()))
+        else:
+            # Standard case: use the single optimizer
+            optimizer = self._optimizer
+
+        # Check if the lr_scheduler component is from torchtune.modules or torch.optim
+        component = cfg_lr_scheduler.get("_component_", None)
+
+        # Conditionally instantiate the scheduler based on the component
+        if "torchtune.modules" in component:
+            # Instantiate the learning rate scheduler
+            lr_scheduler = config.instantiate(
+                cfg_lr_scheduler,
+                optimizer,
+                num_training_steps=num_training_steps,
+                last_epoch=last_epoch,
+            )
+        else:
+            lr_scheduler = config.instantiate(
+                cfg_lr_scheduler,
+                optimizer,
+                last_epoch=last_epoch,
+            )
+
+        if self._optimizer_in_bwd:
+            # Modify the scheduler for optimizer_in_bwd case
+            self._optim_ckpt_wrapper.set_lr_scheduler(lr_scheduler)
+
+        log.info("Learning rate scheduler is initialized.")
+        return lr_scheduler
+
     def _setup_data(
         self,
         cfg_dataset: DictConfig,
@@ -589,6 +635,8 @@ def train(self) -> None:
                         self._optimizer.step()
                         self._optimizer.zero_grad(set_to_none=True)
 
+                    # Need to fix `lr_scheduler.step()` before `optimizer.step()` warning
+                    self._lr_scheduler.step()
                     self.global_step += 1
 
                     loss_to_log = running_loss.item()

tests/recipes/test_full_finetune_single_device.py

@@ -45,6 +45,9 @@ def _get_test_config_overrides(self):
             "max_steps_per_epoch=2",
             "optimizer=torch.optim.AdamW",
             "optimizer.lr=2e-5",
+            "lr_scheduler=torch.optim.lr_scheduler.ConstantLR",
+            "lr_scheduler.factor=1.0",
+            "~lr_scheduler.num_warmup_steps",
             "log_every_n_steps=1",
             "clip_grad_norm=100",
         ] + dummy_alpaca_dataset_config()

torchtune/utils/memory.py

@@ -10,12 +10,12 @@
 from typing import Any, Callable, Dict, Set, Type, Union
 
 import torch
-
 from torch import nn
 from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
     apply_activation_checkpointing,
 )
 from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+from torch.optim.lr_scheduler import LRScheduler
 from torchtune.utils.logging import get_logger
 
 _log: logging.Logger = get_logger()
@@ -91,6 +91,7 @@ class OptimizerInBackwardWrapper:
 
     def __init__(self, optim_map: Dict[str, torch.optim.Optimizer]):
         self.optim_map = optim_map
+        self.lr_scheduler = None
 
     def state_dict(self) -> Dict[str, Any]:
         """
@@ -136,6 +137,62 @@ def get_optim_key(self, key: str) -> Any:
         """
         return list(self.optim_map.values())[0].param_groups[0][key]
 
+    def set_lr_scheduler(self, lr_scheduler: LRScheduler) -> None:
+        """
+        Sets the learning rate scheduler and modifies its step method to update all optimizers.
+
+        Args:
+            lr_scheduler (LRScheduler): The learning rate scheduler to use.
+        """
+        self.lr_scheduler = lr_scheduler
+        original_step = self.lr_scheduler.step
+
+        def custom_step(epoch=None):
+            if epoch is None:
+                original_step()
+            else:
+                original_step(epoch)
+            new_lr = self.lr_scheduler.get_last_lr()[0]
+            for opt in self.optim_map.values():
+                for param_group in opt.param_groups:
+                    param_group["lr"] = new_lr
+
+        self.lr_scheduler.step = custom_step
+
+    def step_lr_scheduler(self, epoch: int = None):
+        """
+        Steps the learning rate scheduler if it exists.
+
+        Args:
+            epoch (int, optional): The current epoch number. Defaults to None.
+
+        Raises:
+            RuntimeError: If the LR scheduler has not been set.
+        """
+        if self.lr_scheduler:
+            self.lr_scheduler.step(epoch)
+        else:
+            raise RuntimeError(
+                "LR scheduler has not been set. Call set_lr_scheduler first."
+            )
+
+    def get_last_lr(self):
+        """
+        Gets the last learning rate from the scheduler if it exists.
+
+        Returns:
+            float: The last learning rate.
+
+        Raises:
+            RuntimeError: If the LR scheduler has not been set.
+        """
+        if self.lr_scheduler:
+            return self.lr_scheduler.get_last_lr()[0]
+        else:
+            raise RuntimeError(
+                "LR scheduler has not been set. Call set_lr_scheduler first."
+            )
+
 
 def create_optim_in_bwd_wrapper(
     model: torch.nn.Module, optim_dict: Dict[torch.nn.Parameter, torch.optim.Optimizer]