Generic weight averaging callback that supports EMA

docs/source-pytorch/api_references.rst

@@ -48,6 +48,7 @@ callbacks
     ThroughputMonitor
     Timer
     TQDMProgressBar
+    WeightAveraging
 
 cli
 -----

docs/source-pytorch/extensions/callbacks.rst

@@ -83,6 +83,7 @@ Lightning has a few built-in callbacks.
     StochasticWeightAveraging
     Timer
     TQDMProgressBar
+    WeightAveraging
 
 ----------
 

src/lightning/pytorch/callbacks/__init__.py

@@ -32,6 +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
 
 __all__ = [
     "BackboneFinetuning",
@@ -58,4 +59,5 @@
     "ThroughputMonitor",
     "Timer",
     "TQDMProgressBar",
+    "WeightAveraging",
 ]

src/lightning/pytorch/callbacks/weight_averaging.py

@@ -0,0 +1,288 @@
+# Copyright The Lightning AI team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+r"""
+Weight Averaging Callback
+^^^^^^^^^^^^^^^^^^^^^^^^^
+"""
+
+import itertools
+from copy import deepcopy
+from typing import Any, Callable, Optional, Union
+
+import torch
+from torch import Tensor
+from torch.optim.swa_utils import AveragedModel
+
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks.callback import Callback
+from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
+from lightning.pytorch.utilities.types import STEP_OUTPUT
+
+
+def _return_true(x: int) -> bool:
+    return True
+
+
+def _return_false(x: int) -> bool:
+    return False
+
+
+class WeightAveraging(Callback):
+    r"""A callback that updates an averaged model for Stochastic Weight Averaging (SWA) or Exponential Moving Average
+    (EMA) after each training step.
+
+    The user should provide either `update_on_step` or `update_on_epoch`, a function that determines when the average
+    model should be updated. If neither function is provided, the average model will be updated after every optimizer
+    step.
+
+    During validation and after the training finishes, the current model parameters will be replaced with the averaged
+    values.
+
+    Args:
+        device: If provided, the :class:`AveragedModel` will be stored on the ``device``. If ``None`` the device will be
+            inferred from the original model.
+        avg_fn: The averaging function used to update the parameters. The function must take in an
+            :class:`AveragedModel` parameter, a current model parameter, and the number of models already averaged. If
+            ``None``, an equally weighted average will be used.
+        update_on_step: A function that takes the number of optimizer steps taken, and returns ``True`` if the average
+            model should be updated.
+        update_on_epoch: A function that takes the zero-based epoch number, and returns ``True`` if the average model
+            should be updated.
+
+    """
+
+    def __init__(
+        self,
+        device: Optional[Union[torch.device, int]] = torch.device("cpu"),
+        avg_fn: Optional[Callable[[Tensor, Tensor, Union[Tensor, int]], Tensor]] = None,
+        update_on_step: Optional[Callable[[int], bool]] = None,
+        update_on_epoch: Optional[Callable[[int], bool]] = None,
+    ):
+        self._device = device
+        self._avg_fn = avg_fn
+
+        if (update_on_step is None) and (update_on_epoch is None):
+            self._update_on_step: Callable[[int], bool] = _return_true
+            self._update_on_epoch: Callable[[int], bool] = _return_false
+        else:
+            self._update_on_step = _return_false if update_on_step is None else update_on_step
+            self._update_on_epoch = _return_false if update_on_epoch is None else update_on_epoch
+
+        self._average_model: Optional[AveragedModel] = None
+
+        # Number of optimizer steps taken, when the average model was last updated. Initializing this with zero ensures
+        # that the average model will be first updated after the first optimizer step, which takes place after N batches
+        # when using accumulate_grad_batches=N.
+        self._latest_update_step = 0
+        # The epoch after which the average model was last updated. The first epoch is 0, so initializing this to a
+        # negative value means that if update_on_step(0) returns True, the first update is after the first epoch.
+        self._latest_update_epoch = -1
+
+    def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
+        """Called when fit, validate, test, predict, or tune begins.
+
+        Creates an :class:`AveragedModel` when fit begins.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+            stage: The :class:`~lightning.pytorch.trainer.trainer.Trainer` state.
+
+        """
+        if stage == "fit":
+            device = self._device or pl_module.device
+            self._average_model = AveragedModel(model=pl_module, device=device, avg_fn=self._avg_fn, use_buffers=True)
+
+    def on_train_batch_end(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
+    ) -> None:
+        """Called when a training batch ends.
+
+        Updates the :class:`AveragedModel` parameters, if requested by ``update_on_step()``.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+            outputs: Outputs from the training batch.
+            batch: The training batch.
+            batch_idx: Index of the training batch.
+
+        """
+        if self._update_on_step(trainer.global_step) and (trainer.global_step > self._latest_update_step):
+            assert self._average_model is not None
+            self._average_model.update_parameters(pl_module)
+            self._latest_update_step = trainer.global_step
+
+    def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """Called when a training epoch ends.
+
+        Updates the :class:`AveragedModel` parameters, if requested by ``update_on_epoch()``.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        if self._update_on_epoch(trainer.current_epoch) and (trainer.current_epoch > self._latest_update_epoch):
+            assert self._average_model is not None
+            self._average_model.update_parameters(pl_module)
+            self._latest_update_epoch = trainer.current_epoch
+
+    def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """Called when training ends.
+
+        Transfers parameters from the :class:`AveragedModel` to the current model.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        assert self._average_model is not None
+        self._copy_average_to_current(pl_module)
+
+    def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """Called when a validation epoch begins.
+
+        Transfers parameter values from the :class:`AveragedModel` to the current model.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        if self._average_model is not None:
+            rank_zero_info("Loading the average model parameters for validation.")
+            self._swap_models(pl_module)
+
+    def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """Called when a validation epoch ends.
+
+        Recovers the current model parameters from the :class:`AveragedModel`.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        if self._average_model is not None:
+            rank_zero_info("Recovering the current model parameters after validation.")
+            self._swap_models(pl_module)
+
+    def state_dict(self) -> dict[str, Any]:
+        """Called when saving a checkpoint.
+
+        Creates a ``state_dict`` of the callback state.
+
+        Returns:
+            A dictionary containing the callback state.
+
+        """
+        return {"latest_update_step": self._latest_update_step}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """Called when loading a checkpoint.
+
+        Reloads the callback state given a ``state_dict``.
+
+        Args:
+            state_dict: A dictionary containing the callback state.
+
+        """
+        self._latest_update_step = state_dict["latest_update_step"]
+
+    def on_save_checkpoint(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: dict[str, Any]
+    ) -> None:
+        r"""Called when saving a checkpoint.
+
+        Moves the current model state to the key ``current_model_state``, and places the average model state in
+        ``state_dict`` instead. Any other state variables of the ``AveragedModel`` will be saved in
+        ``averaging_state``.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+            checkpoint: The checkpoint dictionary that will be saved.
+
+        """
+        if self._average_model is None:
+            raise Exception("Trying to save a checkpoint, but no average model (outside fit). Don't know what to do.")
+
+        rank_zero_info("The average model parameters will be saved to the state_dict in the checkpoint.")
+        average_model_state = self._average_model.state_dict()
+        checkpoint["current_model_state"] = checkpoint["state_dict"]
+        checkpoint["state_dict"] = {
+            name[7:]: value for name, value in average_model_state.items() if name.startswith("module.")
+        }
+        checkpoint["averaging_state"] = {
+            name: value for name, value in average_model_state.items() if not name.startswith("module.")
+        }
+
+    def on_load_checkpoint(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: dict[str, Any]
+    ) -> None:
+        r"""Called when loading a model checkpoint.
+
+        Loads the current model and the :class:`AveragedModel` parameters from the checkpoint.
+
+        Args:
+            trainer: The current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+            checkpoint: The full checkpoint dictionary that got loaded by the Trainer.
+
+        """
+        if self._average_model is None:
+            raise Exception("Trying to load a checkpoint, but no average model (outside fit). Don't know what to do.")
+
+        if ("current_model_state" in checkpoint) and ("averaging_state" in checkpoint):
+            rank_zero_info("Found current_model_state in the checkpoint. This will be used to initialize the model.")
+            average_model_state = {"module." + name: value for name, value in checkpoint["state_dict"].items()}
+            average_model_state |= checkpoint["averaging_state"]
+            self._average_model.load_state_dict(average_model_state)
+            checkpoint["state_dict"] = checkpoint["current_model_state"]
+        else:
+            rank_zero_warn(
+                "The checkpoint was not created with WeightAveraging. Both the current and the average model will be "
+                "initialized with state_dict."
+            )
+            self._average_model.module.load_state_dict(deepcopy(checkpoint["state_dict"]), strict=False)
+
+    def _swap_models(self, pl_module: "pl.LightningModule") -> None:
+        """Swaps the parameter values of the current model and the :class:`AveragedModel`.
+
+        Args:
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        assert self._average_model is not None
+        average_params = itertools.chain(self._average_model.module.parameters(), self._average_model.module.buffers())
+        current_params = itertools.chain(pl_module.parameters(), pl_module.buffers())
+        for average_param, current_param in zip(average_params, current_params):
+            tmp = average_param.data.clone()
+            average_param.data.copy_(current_param.data)
+            current_param.data.copy_(tmp)
+
+    def _copy_average_to_current(self, pl_module: "pl.LightningModule") -> None:
+        """Copies the parameter values from the :class:`AveragedModel` to the current model.
+
+        Args:
+            pl_module: The current :class:`~lightning.pytorch.core.LightningModule` instance.
+
+        """
+        assert self._average_model is not None
+        average_params = itertools.chain(self._average_model.module.parameters(), self._average_model.module.buffers())
+        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)