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Running
on
Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Sequence, Union
import torch
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class EmptyCacheHook(Hook):
"""Releases all unoccupied cached GPU memory during the process of
training.
Args:
before_epoch (bool): Whether to release cache before an epoch. Defaults
to False.
after_epoch (bool): Whether to release cache after an epoch. Defaults
to True.
after_iter (bool): Whether to release cache after an iteration.
Defaults to False.
"""
priority = 'NORMAL'
def __init__(self,
before_epoch: bool = False,
after_epoch: bool = True,
after_iter: bool = False) -> None:
self._do_before_epoch = before_epoch
self._do_after_epoch = after_epoch
self._do_after_iter = after_iter
def _after_iter(self,
runner,
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs: Optional[Union[dict, Sequence]] = None,
mode: str = 'train') -> None:
"""Empty cache after an iteration.
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the loop.
data_batch (dict or tuple or list, optional): Data from dataloader.
outputs (dict or sequence, optional): Outputs from model.
mode (str): Current mode of runner. Defaults to 'train'.
"""
if self._do_after_iter:
torch.cuda.empty_cache()
def _before_epoch(self, runner, mode: str = 'train') -> None:
"""Empty cache before an epoch.
Args:
runner (Runner): The runner of the training process.
mode (str): Current mode of runner. Defaults to 'train'.
"""
if self._do_before_epoch:
torch.cuda.empty_cache()
def _after_epoch(self, runner, mode: str = 'train') -> None:
"""Empty cache after an epoch.
Args:
runner (Runner): The runner of the training process.
mode (str): Current mode of runner. Defaults to 'train'.
"""
if self._do_after_epoch:
torch.cuda.empty_cache()
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