58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
r"""
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Gradient Accumulator
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====================
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Change gradient accumulation factor according to scheduling.
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"""
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities import rank_zero_warn
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class GradientAccumulationScheduler(Callback):
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r"""
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Change gradient accumulation factor according to scheduling.
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Args:
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scheduling: scheduling in format {epoch: accumulation_factor}
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Example::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import GradientAccumulationScheduler
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# at epoch 5 start accumulating every 2 batches
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>>> accumulator = GradientAccumulationScheduler(scheduling={5: 2})
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>>> trainer = Trainer(callbacks=[accumulator])
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# alternatively, pass the scheduling dict directly to the Trainer
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>>> trainer = Trainer(accumulate_grad_batches={5: 2})
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"""
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def __init__(self, scheduling: dict):
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super().__init__()
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if not scheduling: # empty dict error
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raise TypeError("Empty dict cannot be interpreted correct")
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for key in scheduling:
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if not isinstance(key, int) or not isinstance(scheduling[key], int):
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raise TypeError("All epoches and accumulation factor must be integers")
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minimal_epoch = min(scheduling.keys())
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if minimal_epoch < 0:
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raise IndexError(f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct")
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if minimal_epoch != 0: # if user didnt define first epoch accumulation factor
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scheduling.update({0: 1})
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self.scheduling = scheduling
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self.epochs = sorted(scheduling.keys())
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def on_epoch_start(self, trainer, pl_module):
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epoch = trainer.current_epoch
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for i in reversed(range(len(self.epochs))):
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if epoch >= self.epochs[i]:
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trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
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break
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