[pre-commit.ci] pre-commit suggestions (#20532)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Jirka B <j.borovec+github@gmail.com>

Author: pre-commit-ci[bot]

.github/CONTRIBUTING.md

@@ -182,7 +182,7 @@ We welcome any useful contribution! For your convenience here's a recommended wo
 1. Use tags in PR name for the following cases:
 
    - **\[blocked by #<number>\]** if your work is dependent on other PRs.
-   - **\[wip\]** when you start to re-edit your work, mark it so no one will accidentally merge it in meantime.
+   - **[wip]** when you start to re-edit your work, mark it so no one will accidentally merge it in meantime.
 
 ### Question & Answer
 

.github/workflows/README.md

@@ -16,7 +16,7 @@ Brief description of all our automation tools used for boosting development perf
 | .azure-pipelines/gpu-benchmarks.yml    | Run speed/memory benchmarks for parity with vanila PyTorch.                               | GPU         |
 | .github/workflows/ci-flagship-apps.yml | Run end-2-end tests with full applications, including deployment to the production cloud. | CPU         |
 | .github/workflows/ci-tests-pytorch.yml | Run all tests except for accelerator-specific, standalone and slow tests.                 | CPU         |
-| .github/workflows/tpu-tests.yml        | Run only TPU-specific tests. Requires that the PR title contains '\[TPU\]'                | TPU         |
+| .github/workflows/tpu-tests.yml        | Run only TPU-specific tests. Requires that the PR title contains '[TPU]'                  | TPU         |
 
 \* Each standalone test needs to be run in separate processes to avoid unwanted interactions between test cases.
 

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -65,12 +65,12 @@ repos:
         args: ["--in-place"]
 
   - repo: https://github.com/sphinx-contrib/sphinx-lint
-    rev: v0.9.1
+    rev: v1.0.0
     hooks:
       - id: sphinx-lint
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.8.6
     hooks:
       # try to fix what is possible
       - id: ruff
@@ -81,7 +81,7 @@ repos:
       - id: ruff
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.17
+    rev: 0.7.21
     hooks:
       - id: mdformat
         additional_dependencies:

examples/fabric/build_your_own_trainer/run.py

@@ -1,8 +1,9 @@
-import lightning as L
 import torch
 from torchmetrics.functional.classification.accuracy import accuracy
 from trainer import MyCustomTrainer
 
+import lightning as L
+
 
 class MNISTModule(L.LightningModule):
     def __init__(self) -> None:

examples/fabric/build_your_own_trainer/trainer.py

@@ -3,15 +3,16 @@
 from functools import partial
 from typing import Any, Literal, Optional, Union, cast
 
-import lightning as L
 import torch
+from lightning_utilities import apply_to_collection
+from tqdm import tqdm
+
+import lightning as L
 from lightning.fabric.accelerators import Accelerator
 from lightning.fabric.loggers import Logger
 from lightning.fabric.strategies import Strategy
 from lightning.fabric.wrappers import _unwrap_objects
 from lightning.pytorch.utilities.model_helpers import is_overridden
-from lightning_utilities import apply_to_collection
-from tqdm import tqdm
 
 
 class MyCustomTrainer:

examples/fabric/dcgan/train_fabric.py

@@ -16,9 +16,10 @@
 import torch.utils.data
 import torchvision.transforms as transforms
 import torchvision.utils
-from lightning.fabric import Fabric, seed_everything
 from torchvision.datasets import CelebA
 
+from lightning.fabric import Fabric, seed_everything
+
 # Root directory for dataset
 dataroot = "data/"
 # Number of workers for dataloader

examples/fabric/fp8_distributed_transformer/train.py

@@ -1,15 +1,16 @@
-import lightning as L
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from lightning.fabric.strategies import ModelParallelStrategy
-from lightning.pytorch.demos import Transformer, WikiText2
 from torch.distributed._composable.fsdp.fully_shard import fully_shard
 from torch.distributed.device_mesh import DeviceMesh
 from torch.utils.data import DataLoader
 from torchao.float8 import Float8LinearConfig, convert_to_float8_training
 from tqdm import tqdm
 
+import lightning as L
+from lightning.fabric.strategies import ModelParallelStrategy
+from lightning.pytorch.demos import Transformer, WikiText2
+
 
 def configure_model(model: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
     float8_config = Float8LinearConfig(

examples/fabric/image_classifier/train_fabric.py

@@ -36,11 +36,12 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import torchvision.transforms as T
-from lightning.fabric import Fabric, seed_everything
 from torch.optim.lr_scheduler import StepLR
 from torchmetrics.classification import Accuracy
 from torchvision.datasets import MNIST
 
+from lightning.fabric import Fabric, seed_everything
+
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
 
 

examples/fabric/kfold_cv/train_fabric.py

@@ -20,12 +20,13 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import torchvision.transforms as T
-from lightning.fabric import Fabric, seed_everything
 from sklearn import model_selection
 from torch.utils.data import DataLoader, SubsetRandomSampler
 from torchmetrics.classification import Accuracy
 from torchvision.datasets import MNIST
 
+from lightning.fabric import Fabric, seed_everything
+
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
 
 

examples/fabric/language_model/train.py

@@ -1,9 +1,10 @@
-import lightning as L
 import torch
 import torch.nn.functional as F
-from lightning.pytorch.demos import Transformer, WikiText2
 from torch.utils.data import DataLoader, random_split
 
+import lightning as L
+from lightning.pytorch.demos import Transformer, WikiText2
+
 
 def main():
     L.seed_everything(42)

examples/fabric/meta_learning/train_fabric.py

@@ -18,6 +18,7 @@
 import cherry
 import learn2learn as l2l
 import torch
+
 from lightning.fabric import Fabric, seed_everything
 
 

examples/fabric/reinforcement_learning/rl/agent.py

@@ -3,11 +3,11 @@
 import gymnasium as gym
 import torch
 import torch.nn.functional as F
-from lightning.pytorch import LightningModule
 from torch import Tensor
 from torch.distributions import Categorical
 from torchmetrics import MeanMetric
 
+from lightning.pytorch import LightningModule
 from rl.loss import entropy_loss, policy_loss, value_loss
 from rl.utils import layer_init
 

examples/fabric/reinforcement_learning/train_fabric.py

@@ -25,13 +25,14 @@
 import gymnasium as gym
 import torch
 import torchmetrics
-from lightning.fabric import Fabric
-from lightning.fabric.loggers import TensorBoardLogger
 from rl.agent import PPOLightningAgent
 from rl.utils import linear_annealing, make_env, parse_args, test
 from torch import Tensor
 from torch.utils.data import BatchSampler, DistributedSampler, RandomSampler
 
+from lightning.fabric import Fabric
+from lightning.fabric.loggers import TensorBoardLogger
+
 
 def train(
     fabric: Fabric,

examples/fabric/reinforcement_learning/train_fabric_decoupled.py

@@ -25,17 +25,18 @@
 
 import gymnasium as gym
 import torch
-from lightning.fabric import Fabric
-from lightning.fabric.loggers import TensorBoardLogger
-from lightning.fabric.plugins.collectives import TorchCollective
-from lightning.fabric.plugins.collectives.collective import CollectibleGroup
-from lightning.fabric.strategies import DDPStrategy
 from rl.agent import PPOLightningAgent
 from rl.utils import linear_annealing, make_env, parse_args, test
 from torch.distributed.algorithms.join import Join
 from torch.utils.data import BatchSampler, DistributedSampler, RandomSampler
 from torchmetrics import MeanMetric
 
+from lightning.fabric import Fabric
+from lightning.fabric.loggers import TensorBoardLogger
+from lightning.fabric.plugins.collectives import TorchCollective
+from lightning.fabric.plugins.collectives.collective import CollectibleGroup
+from lightning.fabric.strategies import DDPStrategy
+
 
 @torch.no_grad()
 def player(args, world_collective: TorchCollective, player_trainer_collective: TorchCollective):

examples/fabric/tensor_parallel/README.md

@@ -41,5 +41,5 @@ Training successfully completed!
 Peak memory usage: 17.95 GB
 ```
 
-> \[!NOTE\]
+> [!NOTE]
 > The `ModelParallelStrategy` is experimental and subject to change. Report issues on [GitHub](https://github.com/Lightning-AI/pytorch-lightning/issues).

examples/fabric/tensor_parallel/train.py

@@ -1,13 +1,14 @@
-import lightning as L
 import torch
 import torch.nn.functional as F
 from data import RandomTokenDataset
-from lightning.fabric.strategies import ModelParallelStrategy
 from model import ModelArgs, Transformer
 from parallelism import parallelize
 from torch.distributed.tensor.parallel import loss_parallel
 from torch.utils.data import DataLoader
 
+import lightning as L
+from lightning.fabric.strategies import ModelParallelStrategy
+
 
 def train():
     strategy = ModelParallelStrategy(

examples/pytorch/basics/autoencoder.py

@@ -22,13 +22,14 @@
 
 import torch
 import torch.nn.functional as F
+from torch import nn
+from torch.utils.data import DataLoader, random_split
+
 from lightning.pytorch import LightningDataModule, LightningModule, Trainer, callbacks, cli_lightning_logo
 from lightning.pytorch.cli import LightningCLI
 from lightning.pytorch.demos.mnist_datamodule import MNIST
 from lightning.pytorch.utilities import rank_zero_only
 from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
-from torch import nn
-from torch.utils.data import DataLoader, random_split
 
 if _TORCHVISION_AVAILABLE:
     import torchvision

examples/pytorch/basics/backbone_image_classifier.py

@@ -21,12 +21,13 @@
 from typing import Optional
 
 import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader, random_split
+
 from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
 from lightning.pytorch.cli import LightningCLI
 from lightning.pytorch.demos.mnist_datamodule import MNIST
 from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms

examples/pytorch/basics/profiler_example.py

@@ -28,6 +28,7 @@
 import torch
 import torchvision
 import torchvision.transforms as T
+
 from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
 from lightning.pytorch.cli import LightningCLI
 from lightning.pytorch.profilers.pytorch import PyTorchProfiler

examples/pytorch/basics/transformer.py

@@ -1,9 +1,10 @@
-import lightning as L
 import torch
 import torch.nn.functional as F
-from lightning.pytorch.demos import Transformer, WikiText2
 from torch.utils.data import DataLoader, random_split
 
+import lightning as L
+from lightning.pytorch.demos import Transformer, WikiText2
+
 
 class LanguageModel(L.LightningModule):
     def __init__(self, vocab_size):

examples/pytorch/bug_report/bug_report_model.py

@@ -1,9 +1,10 @@
 import os
 
 import torch
-from lightning.pytorch import LightningModule, Trainer
 from torch.utils.data import DataLoader, Dataset
 
+from lightning.pytorch import LightningModule, Trainer
+
 
 class RandomDataset(Dataset):
     def __init__(self, size, length):

examples/pytorch/domain_templates/computer_vision_fine_tuning.py

@@ -46,11 +46,6 @@
 
 import torch
 import torch.nn.functional as F
-from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
-from lightning.pytorch.callbacks.finetuning import BaseFinetuning
-from lightning.pytorch.cli import LightningCLI
-from lightning.pytorch.utilities import rank_zero_info
-from lightning.pytorch.utilities.model_helpers import get_torchvision_model
 from torch import nn, optim
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.optim.optimizer import Optimizer
@@ -60,6 +55,12 @@
 from torchvision.datasets import ImageFolder
 from torchvision.datasets.utils import download_and_extract_archive
 
+from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
+from lightning.pytorch.callbacks.finetuning import BaseFinetuning
+from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.utilities import rank_zero_info
+from lightning.pytorch.utilities.model_helpers import get_torchvision_model
+
 log = logging.getLogger(__name__)
 DATA_URL = "https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip"
 

examples/pytorch/domain_templates/generative_adversarial_net.py

@@ -25,6 +25,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
 from lightning.pytorch import cli_lightning_logo
 from lightning.pytorch.core import LightningModule
 from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule

examples/pytorch/domain_templates/imagenet.py

@@ -43,13 +43,14 @@
 import torch.utils.data.distributed
 import torchvision.datasets as datasets
 import torchvision.transforms as transforms
+from torch.utils.data import Dataset
+from torchmetrics import Accuracy
+
 from lightning.pytorch import LightningModule
 from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar
 from lightning.pytorch.cli import LightningCLI
 from lightning.pytorch.strategies import ParallelStrategy
 from lightning.pytorch.utilities.model_helpers import get_torchvision_model
-from torch.utils.data import Dataset
-from torchmetrics import Accuracy
 
 
 class ImageNetLightningModel(LightningModule):

examples/pytorch/domain_templates/reinforce_learn_Qnet.py

@@ -41,11 +41,12 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import IterableDataset
 
+from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
+
 
 class DQN(nn.Module):
     """Simple MLP network.

examples/pytorch/domain_templates/reinforce_learn_ppo.py

@@ -35,12 +35,13 @@
 
 import gym
 import torch
-from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
 from torch import nn
 from torch.distributions import Categorical, Normal
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader, IterableDataset
 
+from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
+
 
 def create_mlp(input_shape: tuple[int], n_actions: int, hidden_size: int = 128):
     """Simple Multi-Layer Perceptron network."""