revert rebase errors

Author: awaelchli

azure-pipelines.yml

@@ -72,12 +72,7 @@ jobs:
       displayName: 'Get legacy checkpoints'
 
     - script: |
-        # python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --durations=50
-        python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests --ignore tests/plugins/test_sharded_plugin.py --ignore tests/trainer/test_dataloaders.py --ignore tests/metrics -v --durations=50
-        # Todo: Find why those tests are failing when run in the main pytest.
-        python -m coverage run -a --source pytorch_lightning -m pytest tests/metrics -v --durations=50
-        python -m coverage run -a --source pytorch_lightning -m pytest tests/plugins/test_sharded_plugin.py tests/trainer/test_dataloaders.py -v --durations=50
-
+        python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --durations=50
       displayName: 'Testing: standard'
 
     - script: |

pytorch_lightning/accelerators/accelerator.py

@@ -30,9 +30,6 @@
 from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
 from pytorch_lightning.utilities.enums import AMPType, LightningEnum
 
-if TYPE_CHECKING:
-    from pytorch_lightning.trainer.trainer import Trainer
-
 
 class Accelerator(object):
     """

pytorch_lightning/plugins/precision/apex_amp.py

@@ -92,15 +92,6 @@ def backward(
         closure_loss = closure_loss.detach()
         return closure_loss
 
-    def pre_optimizer_step(
-        self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, closure: Callable, **kwargs
-    ) -> bool:
-        """Hook to do something before each optimizer step."""
-        # Apex: Amp does not support closure use with optimizers
-        closure()
-        optimizer.step()
-        return False
-
     def configure_apex(
         self,
         amp: object,

pytorch_lightning/plugins/training_type/ddp.py

@@ -29,7 +29,7 @@
 from pytorch_lightning.overrides.distributed import prepare_for_backward
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
-from pytorch_lightning.utilities import _HYDRA_AVAILABLE, _PYTORCH_GREATER_EQUAL_1_7_0, rank_zero_warn
+from pytorch_lightning.utilities import _HYDRA_AVAILABLE, _TORCH_GREATER_EQUAL_1_7, rank_zero_warn
 from pytorch_lightning.utilities.distributed import (
     find_free_network_port,
     rank_zero_only,

pytorch_lightning/utilities/__init__.py

@@ -35,8 +35,6 @@
     _module_available,
     _NATIVE_AMP_AVAILABLE,
     _OMEGACONF_AVAILABLE,
-    _PYTORCH_GREATER_EQUAL_1_7_0,
-    _PYTORCH_PRUNE_AVAILABLE,
     _RPC_AVAILABLE,
     _TORCH_GREATER_EQUAL_1_6,
     _TORCH_GREATER_EQUAL_1_7,

tests/trainer/test_dataloaders.py

@@ -735,8 +735,6 @@ def __len__(self):
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')
 def test_dataloader_reinit_for_subclass(tmpdir):
 
-    del os.environ["PL_TRAINER_GPUS"]
-
     class CustomDataLoader(torch.utils.data.DataLoader):
 
         def __init__(