Source code for lightning.pytorch.callbacks.gradient_accumulation_scheduler

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Gradient Accumulator

Change gradient accumulation factor according to scheduling.
Trainer also calls ``optimizer.step()`` for the last indivisible step number.


from typing import Any, Dict

from typing_extensions import override

import lightning.pytorch as pl
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.model_helpers import is_overridden
from lightning.pytorch.utilities.rank_zero import rank_zero_warn

[docs]class GradientAccumulationScheduler(Callback): r"""Change gradient accumulation factor according to scheduling. Args: scheduling: scheduling in format {epoch: accumulation_factor} Note: The argument scheduling is a dictionary. Each key represent an epoch and its associated accumulation factor value. Warning: Epoch are zero-indexed c.f it means if you want to change the accumulation factor after 4 epochs, set ``Trainer(accumulate_grad_batches={4: factor})`` or ``GradientAccumulationScheduler(scheduling={4: factor})``. For more info check the example below. Raises: TypeError: If ``scheduling`` is an empty ``dict``, or not all keys and values of ``scheduling`` are integers. IndexError: If ``minimal_epoch`` is less than 0. Example:: >>> from lightning.pytorch import Trainer >>> from lightning.pytorch.callbacks import GradientAccumulationScheduler # from epoch 5, it starts accumulating every 2 batches. Here we have 4 instead of 5 # because epoch (key) should be zero-indexed. >>> accumulator = GradientAccumulationScheduler(scheduling={4: 2}) >>> trainer = Trainer(callbacks=[accumulator]) """ def __init__(self, scheduling: Dict[int, int]): super().__init__() if not scheduling: # empty dict error raise TypeError("Empty dict cannot be interpreted correct") if any(not isinstance(key, int) or key < 0 for key in scheduling): raise MisconfigurationException( f"Epoch should be an int greater than or equal to 0. Got {list(scheduling.keys())}." ) if any(not isinstance(value, int) or value < 1 for value in scheduling.values()): raise MisconfigurationException( f"Accumulation factor should be an int greater than 0. Got {list(scheduling.values())}." ) minimal_epoch = min(scheduling.keys()) if minimal_epoch < 0: raise IndexError(f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct") if minimal_epoch != 0: # if user didn't define first epoch accumulation factor scheduling.update({0: 1}) self.scheduling = scheduling self.epochs = sorted(scheduling.keys()) def going_to_accumulate_grad_batches(self) -> bool: return any(v > 1 for v in self.scheduling.values()) def get_accumulate_grad_batches(self, epoch: int) -> int: accumulate_grad_batches = 1 for iter_epoch in reversed(self.epochs): if epoch >= iter_epoch: accumulate_grad_batches = self.scheduling[iter_epoch] break return accumulate_grad_batches
[docs] @override def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: """Performns a configuration validation before training starts and raises errors for incompatible settings.""" if not pl_module.automatic_optimization: raise RuntimeError( """Automatic gradient accumulation and the `GradientAccumulationScheduler` is not supported for manual optimization. Please remove the callback or switch to automatic optimization.""" ) overridden_optimizer_step = is_overridden("optimizer_step", pl_module) overridden_optimizer_zero_grad = is_overridden("optimizer_zero_grad", pl_module) going_to_accumulate_grad_batches = self.going_to_accumulate_grad_batches() has_overridden_optimization_functions = overridden_optimizer_step or overridden_optimizer_zero_grad if has_overridden_optimization_functions and going_to_accumulate_grad_batches: rank_zero_warn( "When using `Trainer(accumulate_grad_batches != 1)` and overriding" " `LightningModule.optimizer_{step,zero_grad}`, the hooks will not be called on every batch" " (rather, they are called on every optimization step)." ) # local import to avoid circular import from lightning.pytorch.strategies import DeepSpeedStrategy if isinstance(trainer.strategy, DeepSpeedStrategy): raise RuntimeError( f"The `{type(trainer.strategy).__name__}` does not support `accumulate_grad_batches` changing" " between epochs." ) if trainer.accumulate_grad_batches != 1: raise ValueError( "You have set `accumulate_grad_batches` and are using the `GradientAccumulationScheduler`" " callback. Either remove `accumulate_grad_batches` from the Trainer or remove the callback." )
[docs] @override def on_train_epoch_start(self, trainer: "pl.Trainer", *_: Any) -> None: trainer.accumulate_grad_batches = self.get_accumulate_grad_batches(trainer.current_epoch)