Add EMAWeightAveraging callback to weight_averaging.py

src/lightning/pytorch/CHANGELOG.md

@@ -25,6 +25,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for variable batch size in `ThroughputMonitor` ([#20236](https://github.com/Lightning-AI/pytorch-lightning/pull/20236))
 
 
+- Added `EMAWeightAveraging` callback that wraps Lightning's `WeightAveraging` class ([#21260](https://github.com/Lightning-AI/pytorch-lightning/pull/21260))
+
+
 ### Changed
 
 - Default to `RichProgressBar` and `RichModelSummary` if the rich package is available. Fallback to TQDMProgressBar and ModelSummary otherwise ([#20896](https://github.com/Lightning-AI/pytorch-lightning/pull/20896))

src/lightning/pytorch/callbacks/weight_averaging.py

@@ -21,7 +21,7 @@
 from typing import Any, Optional, Union
 
 import torch
-from torch.optim.swa_utils import AveragedModel
+from torch.optim.swa_utils import AveragedModel, get_ema_avg_fn
 from typing_extensions import override
 
 import lightning.pytorch as pl
@@ -361,3 +361,55 @@ def _copy_average_to_current(self, pl_module: "pl.LightningModule") -> None:
         current_params = itertools.chain(pl_module.parameters(), pl_module.buffers())
         for average_param, current_param in zip(average_params, current_params):
             current_param.data.copy_(average_param.data)
+
+
+class EMAWeightAveraging(WeightAveraging):
+    """Exponential Moving Average (EMA) Weight Averaging callback."""
+
+    def __init__(
+        self,
+        device: Optional[Union[torch.device, str, int]] = None,
+        use_buffers: bool = True,
+        decay: float = 0.999,
+        update_every_n_steps: int = 1,
+        update_starting_at_step: Optional[int] = None,
+        update_starting_at_epoch: Optional[int] = None,
+        **kwargs: Any,
+    ):
+        super().__init__(
+            device=device,
+            use_buffers=use_buffers,
+            **kwargs,
+            avg_fn=get_ema_avg_fn(decay=decay),
+        )
+
+        self.update_every_n_steps = update_every_n_steps
+        self.update_starting_at_step = update_starting_at_step
+        self.update_starting_at_epoch = update_starting_at_epoch
+
+    def should_update(self, step_idx: Optional[int] = None, epoch_idx: Optional[int] = None):
+        """Decide when to update the model weights.
+
+        Args:
+            step_idx: The current step index.
+            epoch_idx: The current epoch index.
+        Returns:
+            bool: True if the model weights should be updated, False otherwise.
+
+        """
+        if step_idx is not None:
+            # Check step-based conditions only if we have a valid step_idx
+            meets_step_requirement = self.update_starting_at_step is None or step_idx >= self.update_starting_at_step
+            meets_step_frequency = self.update_every_n_steps > 0 and step_idx % self.update_every_n_steps == 0
+            if meets_step_requirement and meets_step_frequency:
+                return True
+
+        if epoch_idx is not None:
+            # Check epoch-based condition only if we specify one
+            meets_epoch_requirement = (
+                self.update_starting_at_epoch is not None and epoch_idx >= self.update_starting_at_epoch
+            )
+            if meets_epoch_requirement:
+                return True
+
+        return False