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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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57
colossalai/legacy/utils/data_sampler/data_parallel_sampler.py
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@@ -13,7 +13,7 @@ from torch.utils.data import DataLoader, Dataset, Sampler
from colossalai.legacy.context.parallel_mode import ParallelMode
from colossalai.legacy.core import global_context as gpc
T_co = TypeVar('T_co', covariant=True)
T_co = TypeVar("T_co", covariant=True)
class DataParallelSampler(Sampler):
@@ -44,11 +44,11 @@ class DataParallelSampler(Sampler):
self.num_samples = math.ceil(
# `type:ignore` is required because Dataset cannot provide a default __len__
# see NOTE in pytorch/torch/utils/data/sampler.py
(len(self.dataset) - self.num_replicas) / \
self.num_replicas # type: ignore[arg-type]
(len(self.dataset) - self.num_replicas)
/ self.num_replicas # type: ignore[arg-type]
)
else:
self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore[arg-type]
self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore[arg-type]
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.seed = seed
@@ -65,7 +65,7 @@ class DataParallelSampler(Sampler):
# set_epoch manually
self.epoch += 1
else:
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
if not self.drop_last:
# add extra samples to make it evenly divisible
@@ -76,11 +76,11 @@ class DataParallelSampler(Sampler):
indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
else:
# remove tail of data to make it evenly divisible.
indices = indices[:self.total_size]
indices = indices[: self.total_size]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
indices = indices[self.rank : self.total_size : self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
@@ -99,14 +99,9 @@ class DataParallelSampler(Sampler):
self.epoch = epoch
def get_dataloader(dataset,
shuffle=False,
seed=1024,
add_sampler=True,
drop_last=False,
pin_memory=False,
num_workers=0,
**kwargs):
def get_dataloader(
dataset, shuffle=False, seed=1024, add_sampler=True, drop_last=False, pin_memory=False, num_workers=0, **kwargs
):
r"""Set up a deterministic dataloader (also configure seed workers, samplers and whether shuffle or not)
Note:
@@ -144,18 +139,22 @@ def get_dataloader(dataset,
random.seed(worker_seed)
if sampler is None:
return DataLoader(dataset,
worker_init_fn=seed_worker,
shuffle=shuffle,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs)
return DataLoader(
dataset,
worker_init_fn=seed_worker,
shuffle=shuffle,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs,
)
else:
return DataLoader(dataset,
sampler=sampler,
worker_init_fn=seed_worker,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs)
return DataLoader(
dataset,
sampler=sampler,
worker_init_fn=seed_worker,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs,
)