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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine import MessageHub
from mmengine.dist import get_rank
from mmengine.hooks import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
class VarlenAttnArgsToMessageHubHook(Hook):
def before_train_iter(self,
runner,
batch_idx: int,
data_batch: dict = None) -> None:
rank = get_rank()
message_hub = MessageHub.get_instance('varlen_attn_args')
assert 'data' in data_batch.keys()
data = data_batch['data']
cumulative_len = data.pop('cumulative_len')
assert len(cumulative_len) == 1
cumulative_len = cumulative_len[0].cuda()
message_hub.update_info(f'cumulative_len_rank_{rank}', cumulative_len)
max_seqlen = data.pop('max_seqlen')
message_hub.update_info(f'max_seqlen_rank_{rank}', max_seqlen)
def after_train_iter(self,
runner,
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs: Optional[dict] = None) -> None:
rank = get_rank()
message_hub = MessageHub.get_instance('varlen_attn_args')
message_hub.update_info(f'cumulative_len_rank_{rank}', None)
message_hub.update_info(f'max_seqlen_rank_{rank}', None)
def before_val_iter(self,
runner,
batch_idx: int,
data_batch: DATA_BATCH = None) -> None:
"""All subclasses should override this method, if they need any
operations before each validation iteration.
Args:
runner (Runner): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict, optional): Data from dataloader.
Defaults to None.
"""
rank = get_rank()
message_hub = MessageHub.get_instance('varlen_attn_args')
assert 'data' in data_batch.keys()
data = data_batch['data']
cumulative_len = data.pop('cumulative_len')
assert len(cumulative_len) == 1
cumulative_len = cumulative_len[0].cuda()
message_hub.update_info(f'cumulative_len_rank_{rank}', cumulative_len)
max_seqlen = data.pop('max_seqlen')
message_hub.update_info(f'max_seqlen_rank_{rank}', max_seqlen)
def after_val_iter(self,
runner,
batch_idx,
data_batch=None,
outputs=None) -> None:
"""All subclasses should override this method, if they need any
operations after each validation iteration.
Args:
runner (Runner): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict or tuple or list, optional): Data from dataloader.
outputs (Sequence, optional): Outputs from model.
"""
rank = get_rank()
message_hub = MessageHub.get_instance('varlen_attn_args')
message_hub.update_info(f'cumulative_len_rank_{rank}', None)
message_hub.update_info(f'max_seqlen_rank_{rank}', None)
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