formatting

Author: Borda

pytorch_lightning/callbacks/pruning.py

@@ -422,9 +422,8 @@ def sanitize_parameters_to_prune(
         current_modules = [m for m in pl_module.modules() if not isinstance(m, _MODULE_CONTAINERS)]
 
         if parameters_to_prune is None:
-            parameters_to_prune = [
-                (m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
-            ]
+            parameters_to_prune = [(m, p) for p in parameters for m in current_modules
+                                   if getattr(m, p, None) is not None]
         elif (
             isinstance(parameters_to_prune, (list, tuple)) and len(parameters_to_prune) > 0
             and all(len(p) == 2 for p in parameters_to_prune)

pytorch_lightning/plugins/training_type/ddp.py

@@ -207,9 +207,7 @@ def pre_configure_ddp(self):
         # Many models require setting this parameter to True, as there are corner cases
         # when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True.
         # This flag does come with a performance hit, so it is suggested to disable in cases where it is possible.
-        self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get(
-            "find_unused_parameters", True
-        )
+        self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True)
         # todo: PyTorch 1.7.0 DDP introduces ``self.reducer._rebuild_buckets()`` breaking manual_optimization
         if _TORCH_GREATER_EQUAL_1_7 and not self.lightning_module.automatic_optimization and not self._ddp_kwargs.get(
             "find_unused_parameters", False

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -176,9 +176,7 @@ def pre_configure_ddp(self):
         # Many models require setting this parameter to True, as there are corner cases
         # when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True.
         # This flag does come with a performance hit, so it is suggested to disable in cases where it is possible.
-        self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get(
-            "find_unused_parameters", True
-        )
+        self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True)
         # todo: PyTorch 1.7.0 DDP introduces ``self.reducer._rebuild_buckets()`` breaking manual_optimization
         if _TORCH_GREATER_EQUAL_1_7 and not self.lightning_module.automatic_optimization and not self._ddp_kwargs.get(
             "find_unused_parameters", False

pytorch_lightning/trainer/callback_hook.py

@@ -214,8 +214,7 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> Dict[Type, dict]:
                 rank_zero_warn(
                     "`Callback.on_save_checkpoint` signature has changed in v1.3."
                     " A `checkpoint` parameter has been added."
-                    " Support for the old signature will be removed in v1.5",
-                    DeprecationWarning
+                    " Support for the old signature will be removed in v1.5", DeprecationWarning
                 )
                 state = callback.on_save_checkpoint(self, self.lightning_module)  # noqa: parameter-unfilled
             else:

pytorch_lightning/trainer/connectors/env_vars_connector.py

@@ -23,6 +23,7 @@ def _defaults_from_env_vars(fn: Callable) -> Callable:
     Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
     input arguments should be moved automatically to the correct device.
     """
+
     @wraps(fn)
     def insert_env_defaults(self, *args, **kwargs):
         cls = self.__class__  # get the class

pytorch_lightning/utilities/__init__.py

@@ -14,6 +14,7 @@
 """General utilities"""
 
 import numpy
+
 from pytorch_lightning.utilities.apply_func import move_data_to_device  # noqa: F401
 from pytorch_lightning.utilities.distributed import (  # noqa: F401
     AllGatherGrad,

pytorch_lightning/utilities/cloud_io.py

@@ -12,17 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import io
+import os
 from pathlib import Path
 from typing import IO, Union
 
 import fsspec
+import torch
 from fsspec.implementations.local import LocalFileSystem
 from packaging.version import Version
 
-import torch
-
 
 class _LightningLocalFileSystem(LocalFileSystem):
     """Extension of ``fsspec.implementations.local.LocalFileSystem`` where ``LightningLocalFileSystem.isdir`` behaves

tests/accelerators/test_accelerator_connector.py

@@ -33,8 +33,8 @@
     PrecisionPlugin,
     SingleDevicePlugin,
 )
-from pytorch_lightning.utilities import _DEEPSPEED_AVAILABLE
 from pytorch_lightning.plugins.environments import LightningEnvironment, SLURMEnvironment, TorchElasticEnvironment
+from pytorch_lightning.utilities import _DEEPSPEED_AVAILABLE
 from tests.helpers.boring_model import BoringModel
 from tests.helpers.runif import RunIf
 

tests/accelerators/test_cpu.py

@@ -14,8 +14,7 @@ def test_unsupported_precision_plugins():
     trainer = Mock()
     model = Mock()
     accelerator = CPUAccelerator(
-        training_type_plugin=SingleDevicePlugin(torch.device("cpu")),
-        precision_plugin=MixedPrecisionPlugin()
+        training_type_plugin=SingleDevicePlugin(torch.device("cpu")), precision_plugin=MixedPrecisionPlugin()
     )
     with pytest.raises(MisconfigurationException, match=r"amp \+ cpu is not supported."):
         accelerator.setup(trainer=trainer, model=model)

tests/callbacks/test_swa.py

@@ -27,9 +27,10 @@
 from tests.helpers.runif import RunIf
 
 if _TORCH_GREATER_EQUAL_1_6:
-    from pytorch_lightning.callbacks import StochasticWeightAveraging
     from torch.optim.swa_utils import SWALR
 
+    from pytorch_lightning.callbacks import StochasticWeightAveraging
+
     class SwaTestModel(BoringModel):
 
         def __init__(self, batchnorm: bool = True, interval: str = "epoch"):