Automatic string fixes (Lightning-AI#8886)

pytorch_lightning/core/decorators.py

@@ -97,7 +97,7 @@ def inner_fn(self, *args, **kwargs):
 
         if not pre_layer_count == post_layer_count:
             rank_zero_warn(
-                f"The model layers do not match after moving to the target device."
+                "The model layers do not match after moving to the target device."
                 " If your model employs weight sharing on TPU,"
                 " please tie your weights using the `on_post_move_to_device` model hook.\n"
                 f"Layer count: [Before: {pre_layer_count} After: {post_layer_count}]"

pytorch_lightning/loggers/comet.py

@@ -142,8 +142,7 @@ def __init__(
     ):
         if comet_ml is None:
             raise ImportError(
-                "You want to use `comet_ml` logger which is not installed yet,"
-                " install it with `pip install comet-ml`."
+                "You want to use `comet_ml` logger which is not installed yet, install it with `pip install comet-ml`."
             )
         super().__init__()
         self._experiment = None

pytorch_lightning/loggers/mlflow.py

@@ -163,8 +163,7 @@ def experiment(self) -> MlflowClient:
                 self.tags = self.tags or {}
                 if MLFLOW_RUN_NAME in self.tags:
                     log.warning(
-                        f"The tag {MLFLOW_RUN_NAME} is found in tags. "
-                        f"The value will be overridden by {self._run_name}."
+                        f"The tag {MLFLOW_RUN_NAME} is found in tags. The value will be overridden by {self._run_name}."
                     )
                 self.tags[MLFLOW_RUN_NAME] = self._run_name
             run = self._mlflow_client.create_run(experiment_id=self._experiment_id, tags=resolve_tags(self.tags))

pytorch_lightning/loggers/wandb.py

@@ -117,8 +117,8 @@ def __init__(
     ):
         if wandb is None:
             raise ImportError(
-                "You want to use `wandb` logger which is not installed yet,"  # pragma: no-cover
-                " install it with `pip install wandb`."
+                "You want to use `wandb` logger which is not installed yet,"
+                " install it with `pip install wandb`."  # pragma: no-cover
             )
 
         if offline and log_model:

pytorch_lightning/overrides/data_parallel.py

@@ -27,8 +27,10 @@ def _ignore_scalar_return_in_dp():
     # Users get confused by this warning so we silence it
     warnings.filterwarnings(
         "ignore",
-        message="Was asked to gather along dimension 0, but all input tensors were scalars;"
-        " will instead unsqueeze and return a vector.",
+        message=(
+            "Was asked to gather along dimension 0, but all input tensors were scalars;"
+            " will instead unsqueeze and return a vector."
+        ),
     )
 
 

pytorch_lightning/plugins/environments/lsf_environment.py

@@ -125,7 +125,7 @@ def _read_hosts():
         hosts = hosts.split()
         if len(hosts) < 2:
             raise ValueError(
-                "Cannot parse hosts from LSB_HOSTS environment variable." ' Expected format: "batch <rank_0_host> ..."'
+                'Cannot parse hosts from LSB_HOSTS environment variable. Expected format: "batch <rank_0_host> ..."'
             )
         return hosts
 

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -350,7 +350,7 @@ def init_ddp_connection(self, global_rank: Optional[int] = None, world_size: Opt
             world_size = world_size if world_size is not None else self.cluster_environment.world_size()
             self._set_node_environment_variables(global_rank, world_size)
             log.info(
-                f"initializing deepspeed distributed: "
+                "initializing deepspeed distributed: "
                 f"GLOBAL_RANK: {global_rank}, "
                 f"MEMBER: {global_rank + 1}/{world_size}"
             )

pytorch_lightning/trainer/configuration_validator.py

@@ -118,13 +118,13 @@ def __verify_manual_optimization_support(self, model: "pl.LightningModule") -> N
             return
         if self.trainer.gradient_clip_val > 0:
             raise MisconfigurationException(
-                f"Automatic gradient clipping is not supported for manual optimization."
+                "Automatic gradient clipping is not supported for manual optimization."
                 f" Remove `Trainer(gradient_clip_val={self.trainer.gradient_clip_val})`"
-                f" or switch to automatic optimization."
+                " or switch to automatic optimization."
             )
         if self.trainer.accumulate_grad_batches != 1:
             raise MisconfigurationException(
-                f"Automatic gradient accumulation is not supported for manual optimization."
+                "Automatic gradient accumulation is not supported for manual optimization."
                 f" Remove `Trainer(accumulate_grad_batches={self.trainer.accumulate_grad_batches})`"
-                f" or switch to automatic optimization."
+                " or switch to automatic optimization."
             )

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -582,8 +582,7 @@ def select_precision_plugin(self) -> PrecisionPlugin:
                     )
                 if self._is_sharded_training_type or self._is_fully_sharded_training_type:
                     raise MisconfigurationException(
-                        "Sharded Plugin is not supported with Apex AMP,"
-                        " please using native AMP for 16-bit precision."
+                        "Sharded Plugin is not supported with Apex AMP, please using native AMP for 16-bit precision."
                     )
                 log.info("Using APEX 16bit precision.")
                 return ApexMixedPrecisionPlugin(self.amp_level)

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -303,9 +303,7 @@ def hpc_save(self, folderpath: str, logger):
         except AttributeError as err:
             if pl.LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
                 del checkpoint[pl.LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
-            rank_zero_warn(
-                "warning, `hyper_parameters` dropped from checkpoint." f" An attribute is not picklable {err}"
-            )
+            rank_zero_warn(f"warning, `hyper_parameters` dropped from checkpoint. An attribute is not picklable {err}")
             atomic_save(checkpoint, filepath)
 
         return filepath

pytorch_lightning/trainer/connectors/data_connector.py

@@ -53,8 +53,7 @@ def on_trainer_init(
 
         if not isinstance(reload_dataloaders_every_n_epochs, int) or (reload_dataloaders_every_n_epochs < 0):
             raise MisconfigurationException(
-                "`reload_dataloaders_every_n_epochs` should be an int >= 0,"
-                f" got {reload_dataloaders_every_n_epochs}."
+                f"`reload_dataloaders_every_n_epochs` should be an int >= 0, got {reload_dataloaders_every_n_epochs}."
             )
 
         self.trainer.reload_dataloaders_every_n_epochs = reload_dataloaders_every_n_epochs

pytorch_lightning/trainer/connectors/debugging_connector.py

@@ -35,8 +35,7 @@ def on_init_start(
     ):
         if not isinstance(fast_dev_run, (bool, int)):
             raise MisconfigurationException(
-                f"fast_dev_run={fast_dev_run} is not a valid configuration."
-                " It should be either a bool or an int >= 0"
+                f"fast_dev_run={fast_dev_run} is not a valid configuration. It should be either a bool or an int >= 0"
             )
 
         if isinstance(fast_dev_run, int) and (fast_dev_run < 0):

pytorch_lightning/trainer/data_loading.py

@@ -111,7 +111,7 @@ def _worker_check(self, dataloader: DataLoader, name: str) -> None:
                 f"The dataloader, {name}, does not have many workers which may be a bottleneck."
                 " Consider increasing the value of the `num_workers` argument`"
                 f" (try {num_cpus} which is the number of cpus on this machine)"
-                f" in the `DataLoader` init to improve performance."
+                " in the `DataLoader` init to improve performance."
             )
 
     def auto_add_worker_init_fn(self, dataloader: DataLoader) -> None:
@@ -347,7 +347,7 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
             rank_zero_warn(
                 f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
                 f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
-                f" you want to see logs for the training epoch."
+                " you want to see logs for the training epoch."
             )
 
     def _reset_eval_dataloader(

pytorch_lightning/trainer/properties.py

@@ -306,8 +306,8 @@ def progress_bar_dict(self) -> dict:
             rank_zero_warn(
                 f"The progress bar already tracks a metric with the name(s) '{', '.join(duplicates)}' and"
                 f" `self.log('{duplicates[0]}', ..., prog_bar=True)` will overwrite this value. "
-                f" If this is undesired, change the name or override `get_progress_bar_dict()`"
-                f" in `LightingModule`.",
+                " If this is undesired, change the name or override `get_progress_bar_dict()`"
+                " in `LightingModule`.",
                 UserWarning,
             )
         return {**standard_metrics, **pbar_metrics}

pytorch_lightning/trainer/trainer.py

@@ -1312,8 +1312,7 @@ def _log_device_info(self) -> None:
 
         if torch.cuda.is_available() and self._device_type != DeviceType.GPU:
             rank_zero_warn(
-                "GPU available but not used. Set the gpus flag in your trainer"
-                " `Trainer(gpus=1)` or script `--gpus=1`."
+                "GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`."
             )
 
         if _TPU_AVAILABLE and self._device_type != DeviceType.TPU:

pytorch_lightning/tuner/auto_gpu_select.py

@@ -24,8 +24,8 @@ def pick_multiple_gpus(nb):
     """
     if nb == 0:
         raise MisconfigurationException(
-            r"auto_select_gpus=True, gpus=0 is not a valid configuration.\
-            Please select a valid number of GPU resources when using auto_select_gpus."
+            "auto_select_gpus=True, gpus=0 is not a valid configuration."
+            " Please select a valid number of GPU resources when using auto_select_gpus."
         )
 
     nb = torch.cuda.device_count() if nb == -1 else nb

pytorch_lightning/tuner/batch_size_scaling.py

@@ -47,7 +47,7 @@ def scale_batch_size(
         rank_zero_warn(
             f"Field `model.{batch_arg_name}` and `model.hparams.{batch_arg_name}` are mutually exclusive!"
             f" `model.{batch_arg_name}` will be used as the initial batch size for scaling."
-            f" If this is not the intended behavior, please remove either one."
+            " If this is not the intended behavior, please remove either one."
         )
 
     if hasattr(model.train_dataloader, "patch_loader_code"):

pytorch_lightning/utilities/model_summary.py

@@ -199,7 +199,7 @@ def __init__(self, model, mode: Optional[str] = None, max_depth: Optional[int] =
             if mode in ModelSummary.MODES:
                 max_depth = ModelSummary.MODES[mode]
                 rank_zero_deprecation(
-                    f"Argument `mode` in `ModelSummary` is deprecated in v1.4"
+                    "Argument `mode` in `ModelSummary` is deprecated in v1.4"
                     f" and will be removed in v1.6. Use `max_depth={max_depth}` to replicate `mode={mode}` behaviour."
                 )
             else:
@@ -459,7 +459,7 @@ def summarize(
         if mode in ModelSummary.MODES:
             max_depth = ModelSummary.MODES[mode]
             rank_zero_deprecation(
-                f"Argument `mode` in `LightningModule.summarize` is deprecated in v1.4"
+                "Argument `mode` in `LightningModule.summarize` is deprecated in v1.4"
                 f" and will be removed in v1.6. Use `max_depth={max_depth}` to replicate `mode={mode}` behavior."
             )
             model_summary = ModelSummary(lightning_module, max_depth=max_depth)

pytorch_lightning/utilities/upgrade_checkpoint.py

@@ -48,8 +48,9 @@ def upgrade_checkpoint(filepath):
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(
-        description="Upgrade an old checkpoint to the current schema. \
-        This will also save a backup of the original file."
+        description=(
+            "Upgrade an old checkpoint to the current schema. This will also save a backup of the original file."
+        )
     )
     parser.add_argument("--file", help="filepath for a checkpoint to upgrade")
 

tests/tuner/test_auto_gpu_select.py

@@ -32,8 +32,8 @@ def test_trainer_with_gpus_options_combination_at_available_gpus_env(auto_select
         with pytest.raises(
             expected_error,
             match=re.escape(
-                r"auto_select_gpus=True, gpus=0 is not a valid configuration.\
-            Please select a valid number of GPU resources when using auto_select_gpus."
+                "auto_select_gpus=True, gpus=0 is not a valid configuration."
+                " Please select a valid number of GPU resources when using auto_select_gpus."
             ),
         ):
             Trainer(auto_select_gpus=auto_select_gpus, gpus=gpus)
@@ -51,8 +51,8 @@ def test_pick_multiple_gpus(nb, expected_gpu_idxs, expected_error):
         with pytest.raises(
             expected_error,
             match=re.escape(
-                r"auto_select_gpus=True, gpus=0 is not a valid configuration.\
-            Please select a valid number of GPU resources when using auto_select_gpus."
+                "auto_select_gpus=True, gpus=0 is not a valid configuration."
+                " Please select a valid number of GPU resources when using auto_select_gpus."
             ),
         ):
             pick_multiple_gpus(nb)