Use typing forward references (#7770)

* Use typing forward references

* Update pytorch_lightning/core/lightning.py

pytorch_lightning/core/lightning.py

@@ -324,42 +324,38 @@ def log(
  ' `https://github.com/PyTorchLightning/pytorch-lightning/discussions`'
  )
 
- if self._results is not None:
- # TODO: if logged twice fail with crash
-
- # set the default depending on the fx_name
- on_step = self.__auto_choose_log_on_step(on_step)
- on_epoch = self.__auto_choose_log_on_epoch(on_epoch)
-
- assert self._current_fx_name is not None
- self.trainer.logger_connector.check_logging(self._current_fx_name, on_step=on_step, on_epoch=on_epoch)
-
- # make sure user doesn't introduce logic for multi-dataloaders
- if "/dataloader_idx_" in name:
- raise MisconfigurationException(
- f"Logged key: {name} should not contain information about dataloader_idx."
- )
-
- value = self.__sync(
- value,
- sync_fn=self.trainer.training_type_plugin.reduce,
- sync_dist=sync_dist,
- sync_dist_op=sync_dist_op,
- sync_dist_group=sync_dist_group,
- device=self.device,
- )
+ # set the default depending on the fx_name
+ on_step = self.__auto_choose_log_on_step(on_step)
+ on_epoch = self.__auto_choose_log_on_epoch(on_epoch)
+
+ assert self._current_fx_name is not None
+ self.trainer.logger_connector.check_logging(self._current_fx_name, on_step=on_step, on_epoch=on_epoch)
+
+ # make sure user doesn't introduce logic for multi-dataloaders
+ if "/dataloader_idx_" in name:
+ raise MisconfigurationException(f"Logged key: {name} should not contain information about dataloader_idx.")
+
+ value = self.__sync(
+ value,
+ sync_fn=self.trainer.training_type_plugin.reduce,
+ sync_dist=sync_dist,
+ sync_dist_op=sync_dist_op,
+ sync_dist_group=sync_dist_group,
+ device=self.device,
+ )
 
- self._results.log(
- name,
- value,
- prog_bar=prog_bar,
- logger=logger,
- on_step=on_step,
- on_epoch=on_epoch,
- reduce_fx=reduce_fx,
- enable_graph=enable_graph,
- dataloader_idx=(self._current_dataloader_idx if add_dataloader_idx else None),
- )
+ assert self._results is not None
+ self._results.log(
+ name,
+ value,
+ prog_bar=prog_bar,
+ logger=logger,
+ on_step=on_step,
+ on_epoch=on_epoch,
+ reduce_fx=reduce_fx,
+ enable_graph=enable_graph,
+ dataloader_idx=(self._current_dataloader_idx if add_dataloader_idx else None),
+ )
 
  def log_dict(
  self,
@@ -378,7 +374,7 @@ def log_dict(
  add_dataloader_idx: bool = True,
  ) -> None:
  """
- Log a dictonary of values at once
+ Log a dictionary of values at once
 
  Example::
 

pytorch_lightning/overrides/base.py

@@ -15,13 +15,13 @@
 from torch.nn import DataParallel
 from torch.nn.parallel import DistributedDataParallel
 
-from pytorch_lightning.core.lightning import LightningModule
+import pytorch_lightning as pl
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
 
 
 class _LightningModuleWrapperBase(DeviceDtypeModuleMixin, torch.nn.Module):
 
- def __init__(self, pl_module: LightningModule):
+ def __init__(self, pl_module: 'pl.LightningModule') -> None:
  """
  Wraps the user's LightningModule and redirects the forward call to the appropriate
  method, either ``training_step``, ``validation_step`` or ``test_step``.
@@ -66,7 +66,7 @@ def on_post_move_to_device(self):
  pass
 
 
-def unwrap_lightning_module(wrapped_model) -> LightningModule:
+def unwrap_lightning_module(wrapped_model) -> 'pl.LightningModule':
  model = wrapped_model
  if isinstance(model, (DistributedDataParallel, DataParallel)):
  model = model.module

pytorch_lightning/overrides/data_parallel.py

@@ -17,7 +17,7 @@
 
 import torch
 
-from pytorch_lightning.core.lightning import LightningModule
+import pytorch_lightning as pl
 from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.apply_func import apply_to_collection
@@ -53,7 +53,7 @@ class LightningParallelModule(_LightningModuleWrapperBase):
 
  """
 
- def __init__(self, pl_module: LightningModule):
+ def __init__(self, pl_module: 'pl.LightningModule') -> None:
  super().__init__(pl_module)
  _ignore_scalar_return_in_dp()
 

pytorch_lightning/overrides/distributed.py

@@ -18,13 +18,13 @@
 from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import BatchSampler, DistributedSampler, Sampler
 
-from pytorch_lightning.core.lightning import LightningModule
+import pytorch_lightning as pl
 from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
 
 
 class LightningDistributedModule(_LightningModuleWrapperBase):
 
- def __init__(self, pl_module: LightningModule):
+ def __init__(self, pl_module: 'pl.LightningModule') -> None:
  """
  Wraps the user's LightningModule and redirects the forward call to the appropriate
  method, either ``training_step``, ``validation_step``, ``test_step`` or ``predict``.

pytorch_lightning/plugins/precision/apex_amp.py

@@ -39,7 +39,7 @@ def __init__(self, amp_level: str = "O2") -> None:
  def master_params(self, optimizer: Optimizer) -> _PARAMETERS:
  return amp.master_params(optimizer)
 
- def dispatch(self, trainer: "pl.Trainer") -> None:
+ def dispatch(self, trainer: 'pl.Trainer') -> None:
  if not self._connected:
  accelerator = trainer.accelerator
  _, accelerator.optimizers = amp.initialize(

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -22,8 +22,8 @@
 
 import torch
 
+import pytorch_lightning as pl
 from pytorch_lightning.callbacks import GradientAccumulationScheduler
-from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
@@ -51,7 +51,7 @@ def remove_module_hooks(model: torch.nn.Module) -> None:
 
 class LightningDeepSpeedModule(_LightningModuleWrapperBase):
 
- def __init__(self, pl_module: LightningModule, precision: int):
+ def __init__(self, pl_module: 'pl.LightningModule', precision: int) -> None:
  super().__init__(pl_module)
  self.precision = precision
 
@@ -378,7 +378,7 @@ def distributed_sampler_kwargs(self):
  distributed_sampler_kwargs = dict(num_replicas=self.world_size, rank=self.global_rank)
  return distributed_sampler_kwargs
 
- def init_optimizers(self, trainer, model: LightningModule) -> Tuple[List, List, List]:
+ def init_optimizers(self, trainer: 'pl.Trainer', model: 'pl.LightningModule') -> Tuple[List, List, List]:
  # Skip initializing optimizers here as DeepSpeed handles optimizers via config.
  # User may have specified config options instead in configure_optimizers, but this is handled
  # via `_initialize_deepspeed_train`

pytorch_lightning/plugins/training_type/parallel.py

@@ -19,7 +19,7 @@
 import torch
 from torch.nn.parallel import DistributedDataParallel
 
-from pytorch_lightning.core.lightning import LightningModule
+import pytorch_lightning as pl
 from pytorch_lightning.overrides.base import unwrap_lightning_module
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
@@ -99,7 +99,7 @@ def torch_distributed_backend(self):
  return torch_backend
 
  @staticmethod
- def configure_sync_batchnorm(model: LightningModule) -> LightningModule:
+ def configure_sync_batchnorm(model: 'pl.LightningModule') -> 'pl.LightningModule':
  """
  Add global batchnorm for a model spread across multiple GPUs and nodes.
 
@@ -112,8 +112,7 @@ def configure_sync_batchnorm(model: LightningModule) -> LightningModule:
  Return:
  LightningModule with batchnorm layers synchronized between process groups
  """
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
- return model
+ return torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
 
  @contextmanager
  def block_backward_sync(self):

tests/accelerators/test_multi_nodes_gpu.py

@@ -125,5 +125,5 @@ def backward(self, loss, optimizer, optimizer_idx):
  }
 
  # we don't want to enable val metrics during steps because it is not something that users should do
- # on purpose DO NOT allow step_b... it's silly to monitor val step metrics
+ # on purpose DO NOT allow b_step... it's silly to monitor val step metrics
  assert set(trainer.callback_metrics) == {'a', 'a2', 'b', 'a_epoch', 'b_epoch', 'a_step'}