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/__init__.py

@@ -32,7 +32,7 @@
 from lightning.pytorch.callbacks.stochastic_weight_avg import StochasticWeightAveraging
 from lightning.pytorch.callbacks.throughput_monitor import ThroughputMonitor
 from lightning.pytorch.callbacks.timer import Timer
-from lightning.pytorch.callbacks.weight_averaging import WeightAveraging
+from lightning.pytorch.callbacks.weight_averaging import EMAWeightAveraging, WeightAveraging
 
 __all__ = [
     "BackboneFinetuning",
@@ -59,5 +59,6 @@
     "ThroughputMonitor",
     "Timer",
     "TQDMProgressBar",
+    "EMAWeightAveraging",
     "WeightAveraging",
 ]

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) -> bool:
+        """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

tests/tests_pytorch/callbacks/test_weight_averaging.py

@@ -23,7 +23,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 from lightning.pytorch import LightningModule, Trainer
-from lightning.pytorch.callbacks import WeightAveraging
+from lightning.pytorch.callbacks import EMAWeightAveraging, WeightAveraging
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from tests_pytorch.helpers.runif import RunIf
 
@@ -329,3 +329,116 @@ def _train_and_resume(model: TestModel, dataset: Dataset, tmp_path: str, devices
     callback = EMATestCallback(devices=devices)
     _train(model, dataset, tmp_path, callback, devices=devices, checkpoint_path=checkpoint_path, **kwargs)
     return model
+
+
+@pytest.mark.parametrize(
+    ("strategy", "accelerator", "devices"),
+    [
+        ("auto", "cpu", 1),
+        pytest.param("auto", "gpu", 1, marks=RunIf(min_cuda_gpus=1)),
+    ],
+)
+def test_ema_weight_averaging(tmp_path, strategy, accelerator, devices):
+    """Test EMAWeightAveraging callback with various update configurations."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    # Test with default settings (update every step)
+    callback = EMAWeightAveraging(decay=0.999, update_every_n_steps=1)
+    _train(model, dataset, tmp_path, callback, strategy=strategy, accelerator=accelerator, devices=devices)
+
+    # Verify the average model was created and updated
+    assert callback._average_model is not None
+    assert callback._average_model.n_averaged > 0
+
+
+def test_ema_weight_averaging_step_frequency(tmp_path):
+    """Test EMAWeightAveraging with custom step update frequency."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    # Update every 5 steps
+    callback = EMAWeightAveraging(decay=0.95, update_every_n_steps=5)
+    _train(model, dataset, tmp_path, callback)
+
+    assert callback._average_model is not None
+
+
+def test_ema_weight_averaging_starting_step(tmp_path):
+    """Test EMAWeightAveraging with delayed start based on steps."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    # Start updating after step 10
+    callback = EMAWeightAveraging(decay=0.999, update_every_n_steps=1, update_starting_at_step=10)
+    _train(model, dataset, tmp_path, callback)
+
+    assert callback._average_model is not None
+
+
+def test_ema_weight_averaging_starting_epoch(tmp_path):
+    """Test EMAWeightAveraging with delayed start based on epochs."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    # Start updating after epoch 3
+    callback = EMAWeightAveraging(decay=0.999, update_every_n_steps=1, update_starting_at_epoch=3)
+    _train(model, dataset, tmp_path, callback)
+
+    assert callback._average_model is not None
+
+
+def test_ema_weight_averaging_should_update(tmp_path):
+    """Test the should_update logic of EMAWeightAveraging."""
+    # Test with step-based updates
+    callback = EMAWeightAveraging(update_every_n_steps=5, update_starting_at_step=10)
+
+    # Before starting step
+    assert not callback.should_update(step_idx=5)
+    assert not callback.should_update(step_idx=9)
+
+    # At and after starting step, but not on update frequency
+    assert callback.should_update(step_idx=10)  # First update
+    assert not callback.should_update(step_idx=11)
+    assert not callback.should_update(step_idx=14)
+    assert callback.should_update(step_idx=15)  # Second update
+
+    # Test with epoch-based updates
+    callback = EMAWeightAveraging(update_starting_at_epoch=2)
+
+    assert not callback.should_update(epoch_idx=0)
+    assert not callback.should_update(epoch_idx=1)
+    assert callback.should_update(epoch_idx=2)
+    assert callback.should_update(epoch_idx=3)
+
+
+def test_ema_weight_averaging_checkpoint_save_load(tmp_path):
+    """Test that EMAWeightAveraging correctly saves and loads checkpoints."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    callback = EMAWeightAveraging(decay=0.99, update_every_n_steps=2)
+
+    # Train and create checkpoint
+    _train(model, dataset, tmp_path, callback, will_crash=True)
+
+    # Resume from checkpoint
+    model2 = TestModel()
+    callback2 = EMAWeightAveraging(decay=0.99, update_every_n_steps=2)
+    checkpoint_path = str(tmp_path / "lightning_logs" / "version_0" / "checkpoints" / "*.ckpt")
+
+    _train(model2, dataset, tmp_path, callback2, checkpoint_path=checkpoint_path)
+
+    assert callback2._average_model is not None
+
+
+@pytest.mark.parametrize("decay", [0.9, 0.99, 0.999, 0.9999])
+def test_ema_weight_averaging_decay_values(tmp_path, decay):
+    """Test EMAWeightAveraging with different decay values."""
+    model = TestModel()
+    dataset = RandomDataset(32, 32)
+
+    callback = EMAWeightAveraging(decay=decay, update_every_n_steps=1)
+    _train(model, dataset, tmp_path, callback)
+
+    assert callback._average_model is not None