fix flake8 for new plugins (#5951)

* flake8

* fix cyclic import

* isort

pytorch_lightning/accelerators/accelerator.py

@@ -11,21 +11,15 @@
 # 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.
-from typing import Any, Callable, Iterable, Optional, TYPE_CHECKING, Union
+from typing import Any, Callable, Iterable, Optional, Union
 
 import torch
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader
 
 from pytorch_lightning.core import LightningModule
-from pytorch_lightning.plugins.precision import (
-    ApexMixedPrecisionPlugin,
-    MixedPrecisionPlugin,
-    NativeMixedPrecisionPlugin,
-    PrecisionPlugin,
-)
+from pytorch_lightning.plugins.precision import ApexMixedPrecisionPlugin, NativeMixedPrecisionPlugin, PrecisionPlugin
 from pytorch_lightning.plugins.training_type import TrainingTypePlugin
-from pytorch_lightning.plugins.training_type.horovod import HorovodPlugin
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
 from pytorch_lightning.utilities.enums import AMPType, LightningEnum
@@ -64,7 +58,7 @@ def __init__(
         self.lr_schedulers = None
         self.optimizer_frequencies = None
 
-    def setup(self, trainer: "Trainer", model: LightningModule) -> None:
+    def setup(self, trainer, model: LightningModule) -> None:
         """
         Connects the plugins to the training process, creates optimizers
 
@@ -76,13 +70,13 @@ def setup(self, trainer: "Trainer", model: LightningModule) -> None:
         self.setup_optimizers(trainer)
         self.connect_precision_plugin(self.precision_plugin)
 
-    def start_training(self, trainer: 'Trainer'):
+    def start_training(self, trainer):
         self.training_type_plugin.start_training(trainer)
 
-    def start_testing(self, trainer: 'Trainer'):
+    def start_testing(self, trainer):
         self.training_type_plugin.start_testing(trainer)
 
-    def start_predicting(self, trainer: 'Trainer'):
+    def start_predicting(self, trainer):
         self.training_type_plugin.start_predicting(trainer)
 
     def pre_dispatch(self) -> None:
@@ -310,7 +304,7 @@ def on_train_end(self) -> None:
         """Hook to do something at the end of the training"""
         pass
 
-    def setup_optimizers(self, trainer: "Trainer"):
+    def setup_optimizers(self, trainer):
         """creates optimizers and schedulers
 
         Args:

pytorch_lightning/plugins/base_plugin.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 import contextlib
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Generator, Optional, overload, Sequence, Tuple
+from typing import Generator, Optional, Sequence, Tuple
 
-import torch
 from torch.nn import Module
 
 

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -24,7 +24,8 @@
 
 class PrecisionPlugin(Plugin):
     """ Plugin handling the precision-specific parts of the training.
-    The static classattributes EPSILON and precision must be overwritten in child-classes and their default values reflect fp32 training
+    The static classattributes EPSILON and precision must be overwritten in child-classes and their
+    default values reflect fp32 training.
     """
     EPSILON = 1e-6
     precision = 32

pytorch_lightning/plugins/precision/tpu_bfloat.py

@@ -25,4 +25,4 @@ class TPUHalfPrecisionPlugin(PrecisionPlugin):
 
     def connect(self, model: torch.nn.Module, optimizers, lr_schedulers):
         os.environ["XLA_USE_BF16"] = str(1)
-        return super().connect(model=model, optimizers=optimizers, lr_schedulers=lr_schedulers)
+        return super().connect(model=model, optimizers=optimizers, lr_schedulers=lr_schedulers)

pytorch_lightning/plugins/training_type/__init__.py

@@ -1,15 +1,15 @@
-from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
-from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin
-from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
-from pytorch_lightning.plugins.training_type.deepspeed import DeepSpeedPlugin
-from pytorch_lightning.plugins.training_type.dp import DataParallelPlugin
-from pytorch_lightning.plugins.training_type.horovod import HorovodPlugin
-from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
-from pytorch_lightning.plugins.training_type.rpc import RPCPlugin
-from pytorch_lightning.plugins.training_type.rpc_sequential import RPCSequentialPlugin
-from pytorch_lightning.plugins.training_type.sharded import DDPShardedPlugin
-from pytorch_lightning.plugins.training_type.sharded_spawn import DDPSpawnShardedPlugin
-from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin
-from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
-from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.plugins.training_type.ddp import DDPPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.deepspeed import DeepSpeedPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.dp import DataParallelPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.horovod import HorovodPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.rpc import RPCPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.rpc_sequential import RPCSequentialPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.sharded import DDPShardedPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.sharded_spawn import DDPSpawnShardedPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin  # noqa: F401

pytorch_lightning/plugins/training_type/ddp.py

@@ -27,7 +27,6 @@
 from pytorch_lightning.distributed import LightningDistributed
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.overrides.distributed import prepare_for_backward
-from pytorch_lightning.plugins.environments import SLURMEnvironment, TorchElasticEnvironment
 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, _TORCH_GREATER_EQUAL_1_7, rank_zero_warn
@@ -120,7 +119,7 @@ def _call_children_scripts(self):
         command = sys.argv
         try:
             full_path = path_lib(command[0])
-        except Exception as e:
+        except Exception:
             full_path = os.path.abspath(command[0])
 
         command[0] = full_path

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -263,7 +263,7 @@ 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: "Trainer", model: LightningModule) -> Tuple[List, List, List]:
+    def init_optimizers(self, trainer, model: 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/horovod.py

@@ -101,14 +101,14 @@ def start_training(self, trainer):
         hvd.join()
 
     def start_testing(self, trainer):
-        with ExitStack() as stack:
+        with ExitStack():
             self._results = trainer.run_test()
 
         # Make sure all workers have finished training before returning to the user
         hvd.join()
 
     def start_predicting(self, trainer):
-        with ExitStack() as stack:
+        with ExitStack():
             # set up training routine
             self._results = trainer.run_predict()
 

pytorch_lightning/plugins/training_type/parallel.py

@@ -24,7 +24,6 @@
 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
-from pytorch_lightning.utilities import rank_zero_info
 from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, ReduceOp
 
 

pytorch_lightning/plugins/training_type/rpc.py

@@ -13,11 +13,10 @@
 # limitations under the License.
 import os
 from contextlib import suppress
-from typing import List, Optional, Sequence
+from typing import List, Optional
 
 import torch
 
-from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
 from pytorch_lightning.utilities import _RPC_AVAILABLE

pytorch_lightning/plugins/training_type/rpc_sequential.py

@@ -329,11 +329,11 @@ def post_training_step(self):
         if self.main_rpc_process:
             super().post_training_step()
 
-    def start_training(self, trainer: 'Trainer') -> None:
+    def start_training(self, trainer) -> None:
         if self.main_rpc_process:
             super().start_training(trainer)
 
-    def start_testing(self, trainer: 'Trainer') -> None:
+    def start_testing(self, trainer) -> None:
         if self.main_rpc_process:
             super().start_testing(trainer)
 

pytorch_lightning/plugins/training_type/sharded_spawn.py

@@ -1,7 +1,6 @@
 from typing import Optional
 
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.core.optimizer import is_lightning_optimizer
 from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
 from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, rank_zero_only
 

pytorch_lightning/plugins/training_type/single_device.py

@@ -1,7 +1,6 @@
 from typing import Any, Union
 
 import torch
-from torch._C import device
 
 from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
 

pytorch_lightning/plugins/training_type/single_tpu.py

@@ -1,4 +1,3 @@
-import io
 import os
 from typing import Optional, Union
 
@@ -11,7 +10,6 @@
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 
 if _TPU_AVAILABLE:
-    import torch_xla
     import torch_xla.core.xla_model as xm
 
 
@@ -68,4 +66,4 @@ def on_save(self, checkpoint: dict) -> dict:
 
     @property
     def is_distributed(self):
-        return False
+        return False

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -1,7 +1,7 @@
 import io
 import os
 import re
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Union
 
 import torch
 import torch.multiprocessing as mp
@@ -206,7 +206,7 @@ def post_dispatch(self) -> None:
         # restore main state with best weights
         best_path = self.mp_queue.get()
         last_path = self.mp_queue.get()
-        results = self.mp_queue.get()
+        self._results = self.mp_queue.get()
 
         # transfer back the best path to the trainer
         if self.lightning_module.trainer.checkpoint_callback is not None:

setup.cfg

@@ -67,11 +67,6 @@ exclude =
     *.egg
     build
     temp
-    # TODO: temporary until accelerator refactor finished
-    pytorch_lightning/accelerators/accelerator.py
-    pytorch_lightning/plugins/training_type
-    pytorch_lightning/plugins/precision
-    pytorch_lightning/plugins/base_plugin.py
 
 select = E,W,F
 doctests = True

tests/accelerators/test_tpu_backend.py

@@ -79,7 +79,7 @@ def test_if_test_works_after_train(tmpdir):
     model = BoringModel()
     trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True)
     trainer.fit(model)
-    assert trainer.test(model) == 1
+    assert len(trainer.test(model)) == 1
 
 
 @pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
@@ -119,4 +119,4 @@ def on_post_move_to_device(self):
         assert result
 
     assert not list(filter(lambda x: 'The model layers do not match' in str(x), warnings.list))
-    assert trainer.test(model) == 1
+    assert len(trainer.test(model)) == 1