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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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39
colossalai/legacy/engine/schedule/_base_schedule.py
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@@ -47,7 +47,8 @@ class BaseSchedule(ABC):
data = {k: self._move_tensor(v) for k, v in data.items()}
else:
raise TypeError(
f"Expected batch data to be of type torch.Tensor, list, tuple, or dict, but got {type(data)}")
f"Expected batch data to be of type torch.Tensor, list, tuple, or dict, but got {type(data)}"
)
return data
def _get_batch_size(self, data):
@@ -72,7 +73,7 @@ class BaseSchedule(ABC):
Tuple (:class:`Tensor`, :class:`torch.Tensor`): A tuple of (data, label).
"""
if data_iter is None:
raise RuntimeError('Dataloader is not defined.')
raise RuntimeError("Dataloader is not defined.")
batch_data = next(data_iter)
if to_gpu:
@@ -81,17 +82,17 @@ class BaseSchedule(ABC):
return batch_data
def pre_processing(self, engine):
"""To perform actions before running the schedule.
"""
pass
"""To perform actions before running the schedule."""
@abstractmethod
def forward_backward_step(self,
engine,
data_iter: Iterable,
forward_only: bool,
return_loss: bool = True,
return_output_label: bool = True):
def forward_backward_step(
self,
engine,
data_iter: Iterable,
forward_only: bool,
return_loss: bool = True,
return_output_label: bool = True,
):
"""The process function over a batch of dataset for training or evaluation.
Args:
@@ -101,7 +102,6 @@ class BaseSchedule(ABC):
return_loss (bool, optional): If False, the loss won't be returned.
return_output_label (bool, optional): If False, the output and label won't be returned.
"""
pass
@staticmethod
def _call_engine(engine, inputs):
@@ -113,13 +113,14 @@ class BaseSchedule(ABC):
return engine(**inputs)
else:
TypeError(
f"Expected engine inputs to be of type torch.Tensor, list, tuple, or dict, but got {type(inputs)}")
f"Expected engine inputs to be of type torch.Tensor, list, tuple, or dict, but got {type(inputs)}"
)
@staticmethod
def _call_engine_criterion(engine, outputs, labels):
assert isinstance(outputs,
(torch.Tensor, list, tuple,
dict)), f'Expect output of model is (torch.Tensor, list, tuple), got {type(outputs)}'
assert isinstance(
outputs, (torch.Tensor, list, tuple, dict)
), f"Expect output of model is (torch.Tensor, list, tuple), got {type(outputs)}"
if isinstance(outputs, torch.Tensor):
outputs = (outputs,)
if isinstance(labels, torch.Tensor):
@@ -134,6 +135,8 @@ class BaseSchedule(ABC):
elif isinstance(outputs, dict) and isinstance(labels, (list, tuple)):
raise ValueError(f"Expected labels to be a dict when the model outputs are dict, but got {type(labels)}")
else:
raise TypeError(f"Expected model outputs and labels to be of type torch.Tensor ' \
raise TypeError(
f"Expected model outputs and labels to be of type torch.Tensor ' \
'(which is auto-converted to tuple), list, tuple, or dict, ' \
'but got {type(outputs)} (model outputs) and {type(labels)} (labels)")
'but got {type(outputs)} (model outputs) and {type(labels)} (labels)"
)