104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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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Trainer also calls ``optimizer.step()`` for the last indivisible step number.
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"""
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from typing import Any, Dict
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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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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Note:
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The argument scheduling is a dictionary. Each key represent an epoch and
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its associated accumulation factor value.
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Warning: Epoch are zero-indexed c.f it means if you want to change
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the accumulation factor after 4 epochs, set ``Trainer(accumulate_grad_batches={4: factor})``
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or ``GradientAccumulationScheduler(scheduling={4: factor})``.
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For more info check the example below.
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Raises:
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TypeError:
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If ``scheduling`` is an empty ``dict``,
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or not all keys and values of ``scheduling`` are integers.
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IndexError:
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If ``minimal_epoch`` is less than 0.
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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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# from epoch 5, it starts accumulating every 2 batches. Here we have 4 instead of 5
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# because epoch (key) should be zero-indexed.
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>>> accumulator = GradientAccumulationScheduler(scheduling={4: 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={4: 2})
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"""
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def __init__(self, scheduling: Dict[int, int]):
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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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if any(not isinstance(key, int) or key < 0 for key in scheduling):
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raise MisconfigurationException(
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f"Epoch should be an int greater than or equal to 0. Got {list(scheduling.keys())}."
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)
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if any(not isinstance(value, int) or value < 1 for value in scheduling.values()):
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raise MisconfigurationException(
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f"Accumulation factor should be an int greater than 0. Got {list(scheduling.values())}."
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)
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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 going_to_accumulate_grad_batches(self) -> bool:
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return any(v > 1 for v in self.scheduling.values())
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def get_accumulate_grad_batches(self, epoch: int) -> int:
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accumulate_grad_batches = 1
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for iter_epoch in reversed(self.epochs):
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if epoch >= iter_epoch:
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accumulate_grad_batches = self.scheduling[iter_epoch]
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break
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return accumulate_grad_batches
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def on_train_epoch_start(self, trainer: "pl.Trainer", *_: Any) -> None:
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trainer.accumulate_grad_batches = self.get_accumulate_grad_batches(trainer.current_epoch)
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