Prune `DeprecatedTrainerAttributes` (#9598)
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# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pytorch_lightning.loops import FitLoop
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from pytorch_lightning.utilities import rank_zero_deprecation
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class DeprecatedTrainerAttributes:
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fit_loop: FitLoop
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@property
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def train_loop(self) -> FitLoop:
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rank_zero_deprecation(
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"`Trainer.train_loop` has been renamed to `Trainer.fit_loop` and will be removed in v1.6."
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)
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return self.fit_loop
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@ -62,7 +62,6 @@ from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerCo
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from pytorch_lightning.trainer.connectors.signal_connector import SignalConnector
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from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
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from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
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from pytorch_lightning.trainer.deprecated_api import DeprecatedTrainerAttributes
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from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
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from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
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from pytorch_lightning.trainer.states import RunningStage, TrainerFn, TrainerState, TrainerStatus
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@ -114,7 +113,6 @@ class Trainer(
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TrainerModelHooksMixin,
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TrainerOptimizersMixin,
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TrainerDataLoadingMixin,
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DeprecatedTrainerAttributes,
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):
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# Needed because of LightningOptimizer
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_lightning_optimizers = None
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@ -1957,6 +1955,13 @@ class Trainer(
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if self.predicting:
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return self.predict_loop
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@property
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def train_loop(self) -> FitLoop:
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rank_zero_deprecation(
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"`Trainer.train_loop` has been renamed to `Trainer.fit_loop` and will be removed in v1.6."
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)
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return self.fit_loop
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@property
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def _ckpt_path(self) -> Optional[str]:
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if self.state.fn == TrainerFn.VALIDATING:
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