Update pre-commit

.pre-commit-config.yaml

@@ -49,20 +49,21 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.34.0
+    rev: v3.1.0
     hooks:
       - id: pyupgrade
         args: [--py37-plus]
         name: Upgrade code
 
-  - repo: https://github.com/myint/docformatter
-    rev: v1.4
+  - repo: https://github.com/PyCQA/docformatter
+    rev: v1.5.0
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
+        verbose: true
 
   - repo: https://github.com/asottile/yesqa
-    rev: v1.3.0
+    rev: v1.4.0
     hooks:
       - id: yesqa
         name: Unused noqa
@@ -75,7 +76,7 @@ repos:
         exclude: docs/source-app
 
   - repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 22.10.0
     hooks:
       - id: black
         name: Format code
@@ -86,11 +87,11 @@ repos:
     hooks:
       - id: blacken-docs
         args: [--line-length=120]
-        additional_dependencies: [black==21.12b0]
+        additional_dependencies: [black==22.10.0]
         exclude: docs/source-app
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.14
+    rev: 0.7.16
     hooks:
       - id: mdformat
         additional_dependencies:
@@ -105,7 +106,7 @@ repos:
           )$
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 4.0.1
+    rev: 5.0.4
     hooks:
       - id: flake8
         name: Check PEP8

docs/source-app/examples/file_server/app.py

@@ -90,7 +90,7 @@ def upload_file(self, file):
             "size": full_size,
             "drive_path": uploaded_file,
         }
-        with open(self.get_filepath(meta_file), "wt") as f:
+        with open(self.get_filepath(meta_file), "w") as f:
             json.dump(meta, f)
 
         # 5: Put the file to the drive.

docs/source-pytorch/debug/debugging_basic.rst

@@ -36,7 +36,7 @@ A breakpoint stops your code execution so you can inspect variables, etc... and
         import pdb
 
         pdb.set_trace()
-        y = x ** 2
+        y = x**2
 
 In this example, the code will stop before executing the ``y = x**2`` line.
 

docs/source-pytorch/strategies/hivemind_expert.rst

@@ -48,7 +48,7 @@ Size Adaptive Compression has been used in a variety of Hivemind applications an
 
     # compresses values above threshold with 8bit Quantization, lower with Float16
     compression = SizeAdaptiveCompression(
-        threshold=2 ** 16 + 1, less=Float16Compression(), greater_equal=Uniform8BitQuantization()
+        threshold=2**16 + 1, less=Float16Compression(), greater_equal=Uniform8BitQuantization()
     )
     trainer = pl.Trainer(
         strategy=HivemindStrategy(

examples/lite/image_classifier_1_pytorch.py

@@ -28,7 +28,6 @@
 # Credit to the PyTorch team
 # Taken from https://github.com/pytorch/examples/blob/master/mnist/main.py and slightly adapted.
 def run(hparams):
-
     torch.manual_seed(hparams.seed)
 
     use_cuda = torch.cuda.is_available()

src/lightning_app/cli/cmd_init.py

@@ -9,7 +9,6 @@
 
 
 def app(app_name: str) -> None:
-
     if app_name is None:
         app_name = _capture_valid_app_component_name(resource_type="app")
 

src/lightning_app/cli/cmd_install.py

@@ -32,7 +32,6 @@ def gallery_component(name: str, yes_arg: bool, version_arg: str, cwd: str = Non
 
 
 def non_gallery_component(gh_url: str, yes_arg: bool, cwd: str = None) -> None:
-
     # give the user the chance to do a manual install
     git_url = _show_non_gallery_install_component_prompt(gh_url, yes_arg)
 
@@ -41,7 +40,6 @@ def non_gallery_component(gh_url: str, yes_arg: bool, cwd: str = None) -> None:
 
 
 def gallery_app(name: str, yes_arg: bool, version_arg: str, cwd: str = None, overwrite: bool = False) -> None:
-
     # make sure org/app-name syntax is correct
     org, app = _validate_name(name, resource_type="app", example="lightning/quick-start")
 
@@ -61,7 +59,6 @@ def gallery_app(name: str, yes_arg: bool, version_arg: str, cwd: str = None, ove
 
 
 def non_gallery_app(gh_url: str, yes_arg: bool, cwd: str = None, overwrite: bool = False) -> None:
-
     # give the user the chance to do a manual install
     repo_url, folder_name = _show_non_gallery_install_app_prompt(gh_url, yes_arg)
 

src/lightning_app/cli/commands/logs.py

@@ -39,7 +39,6 @@ def logs(app_name: str, components: List[str], follow: bool) -> None:
 
 
 def _show_logs(app_name: str, components: List[str], follow: bool) -> None:
-
     client = LightningClient()
     project = _get_project(client)
 

src/lightning_app/cli/component-template/tests/test_placeholdername_component.py

@@ -1,5 +1,4 @@
-r"""
-To test a lightning component:
+r"""To test a lightning component:
 
 1. Init the component.
 2. call .run()

src/lightning_app/utilities/imports.py

@@ -20,7 +20,6 @@
 
 
 def requires(module_paths: Union[str, List]):
-
     if not isinstance(module_paths, list):
         module_paths = [module_paths]
 

src/lightning_lite/lite.py

@@ -396,8 +396,7 @@ def barrier(self, name: Optional[str] = None) -> None:
     def all_gather(
         self, data: Union[Tensor, Dict, List, Tuple], group: Optional[Any] = None, sync_grads: bool = False
     ) -> Union[Tensor, Dict, List, Tuple]:
-        r"""
-        Gather tensors or collections of tensors from multiple processes.
+        r"""Gather tensors or collections of tensors from multiple processes.
 
         Args:
             data: int, float, tensor of shape (batch, ...), or a (possibly nested) collection thereof.

src/lightning_lite/strategies/launchers/base.py

@@ -16,8 +16,7 @@
 
 
 class _Launcher(ABC):
-    r"""
-    Abstract base class for all Launchers.
+    r"""Abstract base class for all Launchers.
 
     Launchers are responsible for the creation and instrumentation of new processes so that the
     :class:`~lightning_lite.strategies.strategy.Strategy` can set up communication between all them.

src/lightning_lite/strategies/launchers/subprocess_script.py

@@ -25,8 +25,7 @@
 
 
 class _SubprocessScriptLauncher(_Launcher):
-    r"""
-    A process laucher that invokes the current script as many times as desired in a single node.
+    r"""A process laucher that invokes the current script as many times as desired in a single node.
 
     This launcher needs to be invoked on each node.
     In its default behavior, the main process in each node then spawns N-1 child processes via :func:`subprocess.Popen`,

src/lightning_lite/strategies/launchers/xla.py

@@ -26,8 +26,8 @@
 
 
 class _XLALauncher(_MultiProcessingLauncher):
-    r"""Launches processes that run a given function in parallel on XLA supported hardware, and joins them all at the
-    end.
+    r"""Launches processes that run a given function in parallel on XLA supported hardware, and joins them all at
+    the end.
 
     The main process in which this launcher is invoked creates N so-called worker processes (using the
     `torch_xla` :func:`xmp.spawn`) that run the given function.

src/pytorch_lightning/accelerators/accelerator.py

@@ -29,9 +29,13 @@ class Accelerator(_Accelerator, ABC):
     """
 
     def setup_environment(self, root_device: torch.device) -> None:
-        """
+        """Create and prepare the device for the current process.
+
+        Note that this is deprecated.
+
         .. deprecated:: v1.8.0
-            This hook was deprecated in v1.8.0 and will be removed in v1.10.0. Please use ``setup_device()`` instead.
+            This hook was deprecated in v1.8.0 and will be removed in v1.10.0. Please use
+            ``setup_device()`` instead.
         """
         rank_zero_deprecation(
             "`Accelerator.setup_environment` has been deprecated in deprecated in v1.8.0 and will be removed in"

src/pytorch_lightning/callbacks/callback.py

@@ -11,10 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-r"""
-Base class used to build new callbacks.
-
-"""
+r"""Base class used to build new callbacks."""
 
 from typing import Any, Dict, List, Optional, Type
 
@@ -26,8 +23,7 @@
 
 
 class Callback:
-    r"""
-    Abstract base class used to build new callbacks.
+    r"""Abstract base class used to build new callbacks.
 
     Subclass this class and override any of the relevant hooks
     """
@@ -213,8 +209,7 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
     def on_save_checkpoint(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
     ) -> None:
-        r"""
-        Called when saving a checkpoint to give you a chance to store anything else you might want to save.
+        r"""Called when saving a checkpoint to give you a chance to store anything else you might want to save.
 
         Args:
             trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
@@ -225,8 +220,7 @@ def on_save_checkpoint(
     def on_load_checkpoint(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
     ) -> None:
-        r"""
-        Called when loading a model checkpoint, use to reload state.
+        r"""Called when loading a model checkpoint, use to reload state.
 
         Args:
             trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.

src/pytorch_lightning/callbacks/checkpoint.py

@@ -2,8 +2,8 @@
 
 
 class Checkpoint(Callback):
-    r"""
-    This is the base class for model checkpointing. Expert users may want to subclass it in case of writing
-    custom :class:`~pytorch_lightning.callbacksCheckpoint` callback, so that
-    the trainer recognizes the custom class as a checkpointing callback.
+    r"""This is the base class for model checkpointing.
+
+    Expert users may want to subclass it in case of writing custom :class:`~pytorch_lightning.callbacksCheckpoint`
+    callback, so that the trainer recognizes the custom class as a checkpointing callback.
     """

src/pytorch_lightning/callbacks/device_stats_monitor.py

@@ -29,9 +29,8 @@
 
 
 class DeviceStatsMonitor(Callback):
-    r"""
-    Automatically monitors and logs device stats during training stage. ``DeviceStatsMonitor``
-    is a special callback as it requires a ``logger`` to passed as argument to the ``Trainer``.
+    r"""Automatically monitors and logs device stats during training stage. ``DeviceStatsMonitor`` is a special
+    callback as it requires a ``logger`` to passed as argument to the ``Trainer``.
 
     Args:
         cpu_stats: if ``None``, it will log CPU stats only if the accelerator is CPU.

src/pytorch_lightning/callbacks/early_stopping.py

@@ -36,8 +36,7 @@
 
 
 class EarlyStopping(Callback):
-    r"""
-    Monitor a metric and stop training when it stops improving.
+    r"""Monitor a metric and stop training when it stops improving.
 
     Args:
         monitor: quantity to be monitored.

src/pytorch_lightning/callbacks/finetuning.py

@@ -37,8 +37,7 @@ def multiplicative(epoch: int) -> float:
 
 
 class BaseFinetuning(Callback):
-    r"""
-    This class implements the base logic for writing your own Finetuning Callback.
+    r"""This class implements the base logic for writing your own Finetuning Callback.
 
     Override ``freeze_before_training`` and ``finetune_function`` methods with your own logic.
 
@@ -338,7 +337,6 @@ class BackboneFinetuning(BaseFinetuning):
         >>> multiplicative = lambda epoch: 1.5
         >>> backbone_finetuning = BackboneFinetuning(200, multiplicative)
         >>> trainer = Trainer(callbacks=[backbone_finetuning])
-
     """
 
     def __init__(

src/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py

@@ -28,8 +28,7 @@
 
 
 class GradientAccumulationScheduler(Callback):
-    r"""
-    Change gradient accumulation factor according to scheduling.
+    r"""Change gradient accumulation factor according to scheduling.
 
     Args:
         scheduling: scheduling in format {epoch: accumulation_factor}

src/pytorch_lightning/callbacks/lambda_function.py

@@ -25,8 +25,7 @@
 
 
 class LambdaCallback(Callback):
-    r"""
-    Create a simple callback on the fly using lambda functions.
+    r"""Create a simple callback on the fly using lambda functions.
 
     Args:
         **kwargs: hooks supported by :class:`~pytorch_lightning.callbacks.callback.Callback`

src/pytorch_lightning/callbacks/lr_monitor.py

@@ -33,8 +33,7 @@
 
 
 class LearningRateMonitor(Callback):
-    r"""
-    Automatically monitor and logs learning rate for learning rate schedulers during training.
+    r"""Automatically monitor and logs learning rate for learning rate schedulers during training.
 
     Args:
         logging_interval: set to ``'epoch'`` or ``'step'`` to log ``lr`` of all optimizers
@@ -84,7 +83,6 @@ def configure_optimizer(self):
             )
             lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, ...)
             return [optimizer], [lr_scheduler]
-
     """
 
     def __init__(self, logging_interval: Optional[str] = None, log_momentum: bool = False) -> None:

src/pytorch_lightning/callbacks/model_summary.py

@@ -35,8 +35,7 @@
 
 
 class ModelSummary(Callback):
-    r"""
-    Generates a summary of all layers in a :class:`~pytorch_lightning.core.module.LightningModule`.
+    r"""Generates a summary of all layers in a :class:`~pytorch_lightning.core.module.LightningModule`.
 
     Args:
         max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the

src/pytorch_lightning/callbacks/progress/base.py

@@ -20,10 +20,9 @@
 
 
 class ProgressBarBase(Callback):
-    r"""
-    The base class for progress bars in Lightning. It is a :class:`~pytorch_lightning.callbacks.Callback`
-    that keeps track of the batch progress in the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
-    You should implement your highly custom progress bars with this as the base class.
+    r"""The base class for progress bars in Lightning. It is a :class:`~pytorch_lightning.callbacks.Callback` that
+    keeps track of the batch progress in the :class:`~pytorch_lightning.trainer.trainer.Trainer`. You should
+    implement your highly custom progress bars with this as the base class.
 
     Example::
 
@@ -44,7 +43,6 @@ def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx):
 
         bar = LitProgressBar()
         trainer = Trainer(callbacks=[bar])
-
     """
 
     def __init__(self) -> None:
@@ -225,9 +223,8 @@ def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: s
     def get_metrics(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
     ) -> Dict[str, Union[int, str, float, Dict[str, float]]]:
-        r"""
-        Combines progress bar metrics collected from the trainer with standard metrics from get_standard_metrics.
-        Implement this to override the items displayed in the progress bar.
+        r"""Combines progress bar metrics collected from the trainer with standard metrics from
+        get_standard_metrics. Implement this to override the items displayed in the progress bar.
 
         Here is an example of how to override the defaults:
 
@@ -256,9 +253,8 @@ def get_metrics(self, trainer, model):
 
 
 def get_standard_metrics(trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str]]:
-    r"""
-    Returns several standard metrics displayed in the progress bar, including the average loss value,
-    split index of BPTT (if used) and the version of the experiment when using a logger.
+    r"""Returns several standard metrics displayed in the progress bar, including the average loss value, split
+    index of BPTT (if used) and the version of the experiment when using a logger.
 
     .. code-block::