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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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22
colossalai/booster/mixed_precision/__init__.py
View File

@@ -6,16 +6,22 @@ from .fp16_torch import FP16TorchMixedPrecision
from .mixed_precision_base import MixedPrecision
__all__ = [
'MixedPrecision', 'mixed_precision_factory', 'FP16_Apex_MixedPrecision', 'FP16_Torch_MixedPrecision',
'FP32_MixedPrecision', 'BF16_MixedPrecision', 'FP8_MixedPrecision', 'FP16NaiveMixedPrecision'
"MixedPrecision",
"mixed_precision_factory",
"FP16_Apex_MixedPrecision",
"FP16_Torch_MixedPrecision",
"FP32_MixedPrecision",
"BF16_MixedPrecision",
"FP8_MixedPrecision",
"FP16NaiveMixedPrecision",
]
_mixed_precision_mapping = {
'fp16': FP16TorchMixedPrecision,
'fp16_apex': FP16ApexMixedPrecision,
'fp16_naive': FP16NaiveMixedPrecision,
'bf16': BF16MixedPrecision,
'fp8': FP8MixedPrecision
"fp16": FP16TorchMixedPrecision,
"fp16_apex": FP16ApexMixedPrecision,
"fp16_naive": FP16NaiveMixedPrecision,
"bf16": BF16MixedPrecision,
"fp8": FP8MixedPrecision,
}
@@ -31,5 +37,5 @@ def mixed_precision_factory(mixed_precision_type: str) -> MixedPrecision:
return _mixed_precision_mapping[mixed_precision_type]()
else:
raise ValueError(
f'Mixed precision type {mixed_precision_type} is not supported, support types include {list(_mixed_precision_mapping.keys())}'
f"Mixed precision type {mixed_precision_type} is not supported, support types include {list(_mixed_precision_mapping.keys())}"
)

26
colossalai/booster/mixed_precision/fp16_apex.py
View File

@@ -23,16 +23,18 @@ class FP16ApexMixedPrecision(MixedPrecision):
max_loss_scale(float, default=2.**24 ): Sets a ceiling for the loss scale values that can be chosen by dynamic loss scaling. If dynamic loss scaling is not used, max_loss_scale is ignored.
"""
def __init__(self,
opt_level: Optional[str] = "O1",
cast_model_type: torch.dtype = None,
patch_torch_functions: bool = None,
keep_batchnorm_fp32: Union[bool, str] = None,
master_weights: bool = None,
loss_scale: Union[float, str] = None,
cast_model_outputs: Any = None,
num_losses: Optional[int] = 1,
verbosity: int = 1,
min_loss_scale: float = None,
max_loss_scale: float = 2.**24) -> None:
def __init__(
self,
opt_level: Optional[str] = "O1",
cast_model_type: torch.dtype = None,
patch_torch_functions: bool = None,
keep_batchnorm_fp32: Union[bool, str] = None,
master_weights: bool = None,
loss_scale: Union[float, str] = None,
cast_model_outputs: Any = None,
num_losses: Optional[int] = 1,
verbosity: int = 1,
min_loss_scale: float = None,
max_loss_scale: float = 2.0**24,
) -> None:
pass

18
colossalai/booster/mixed_precision/fp16_naive.py
View File

@@ -15,12 +15,14 @@ class FP16NaiveMixedPrecision(MixedPrecision):
verbose(bool): if set to `True`, will print debug info.
"""
def __init__(self,
log_num_zeros_in_grad: bool,
initial_scale: int,
growth_factor: int,
backoff_factor: float,
hysteresis: int,
max_scale: int,
verbose: bool = None) -> None:
def __init__(
self,
log_num_zeros_in_grad: bool,
initial_scale: int,
growth_factor: int,
backoff_factor: float,
hysteresis: int,
max_scale: int,
verbose: bool = None,
) -> None:
pass

75
colossalai/booster/mixed_precision/fp16_torch.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
@@ -9,7 +9,7 @@ from colossalai.interface import ModelWrapper, OptimizerWrapper
from .mixed_precision_base import MixedPrecision
__all__ = ['FP16_Torch_MixedPrecision', 'TorchAMPOptimizer', 'TorchAMPModule']
__all__ = ["FP16_Torch_MixedPrecision", "TorchAMPOptimizer", "TorchAMPModule"]
class TorchAMPOptimizer(OptimizerWrapper):
@@ -29,17 +29,21 @@ class TorchAMPOptimizer(OptimizerWrapper):
calls that may cause the scale to increase. Default: 2000.
"""
def __init__(self,
optim: Optimizer,
init_scale: float = 2.**16,
growth_factor: float = 2.0,
backoff_factor: float = 0.5,
growth_interval: int = 2000) -> None:
def __init__(
self,
optim: Optimizer,
init_scale: float = 2.0**16,
growth_factor: float = 2.0,
backoff_factor: float = 0.5,
growth_interval: int = 2000,
) -> None:
super().__init__(optim)
self.scaler = torch.cuda.amp.GradScaler(init_scale=init_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval)
self.scaler = torch.cuda.amp.GradScaler(
init_scale=init_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval,
)
def backward(self, loss: Tensor, *args, **kwargs) -> None:
scaled_loss = self.scale_loss(loss)
@@ -60,12 +64,14 @@ class TorchAMPOptimizer(OptimizerWrapper):
self.unscale_grad()
super().clip_grad_by_value(clip_value, *args, **kwargs)
def clip_grad_by_norm(self,
max_norm: Union[float, int],
norm_type: Union[float, int] = 2.0,
error_if_nonfinite: bool = False,
*args,
**kwargs) -> None:
def clip_grad_by_norm(
self,
max_norm: Union[float, int],
norm_type: Union[float, int] = 2.0,
error_if_nonfinite: bool = False,
*args,
**kwargs,
) -> None:
self.unscale_grad()
super().clip_grad_by_norm(max_norm, norm_type, error_if_nonfinite, *args, **kwargs)
@@ -102,22 +108,27 @@ class FP16TorchMixedPrecision(MixedPrecision):
calls that may cause the scale to increase. Default: 2000.
"""
def __init__(self,
init_scale: float = 2.**16,
growth_factor: float = 2.0,
backoff_factor: float = 0.5,
growth_interval: int = 2000) -> None:
def __init__(
self,
init_scale: float = 2.0**16,
growth_factor: float = 2.0,
backoff_factor: float = 0.5,
growth_interval: int = 2000,
) -> None:
super().__init__()
self.torch_amp_kwargs = dict(init_scale=init_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval)
self.torch_amp_kwargs = dict(
init_scale=init_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval,
)
def configure(self,
model: nn.Module,
optimizer: Optional[Optimizer] = None,
criterion: Optional[Callable] = None,
) -> Tuple[nn.Module, OptimizerWrapper, Callable]:
def configure(
self,
model: nn.Module,
optimizer: Optional[Optimizer] = None,
criterion: Optional[Callable] = None,
) -> Tuple[nn.Module, OptimizerWrapper, Callable]:
model = TorchAMPModule(model)
if optimizer is not None:
optimizer = TorchAMPOptimizer(optimizer, **self.torch_amp_kwargs)