github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-05 11:02:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source 
* [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
Download Patch File Download Diff File Expand all files Collapse all files

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)