Loop flattening: remove .connect() (#16384)

docs/source-pytorch/extensions/loops.rst

@@ -259,28 +259,6 @@ run (optional)
 
 ----------
 
-Subloops
---------
-
-When you want to customize nested loops within loops use the :meth:`~pytorch_lightning.loops.loop.Loop.connect` method:
-
-.. code-block:: python
-
-    # Optional: stitch back the trainer arguments
-    epoch_loop = MyEpochLoop(trainer.fit_loop.epoch_loop.min_steps, trainer.fit_loop.epoch_loop.max_steps)
-    # Optional: connect children loops as they might have existing state
-    epoch_loop.connect(trainer.fit_loop.epoch_loop.batch_loop, trainer.fit_loop.epoch_loop.val_loop)
-    # Instantiate and connect the loop.
-    trainer.fit_loop.connect(epoch_loop=epoch_loop)
-    trainer.fit(model)
-
-More about the built-in loops and how they are composed is explained in the next section.
-
-.. image:: https://pl-public-data.s3.amazonaws.com/docs/static/images/loops/connect-epoch-loop.gif
-    :alt: Animation showing how to connect a custom subloop
-
-----------
-
 Built-in Loops
 --------------
 
@@ -342,71 +320,6 @@ Each of these :code:`for`-loops represents a class implementing the :class:`~pyt
        It simply iterates over each prediction dataloader from one to the next by calling :code:`PredictionEpochLoop.run()` in its :code:`advance()` method.
 
 
-----------
-
-Available Loops in Lightning Flash
-----------------------------------
-
-`Active Learning <https://en.wikipedia.org/wiki/Active_learning_(machine_learning)>`__ is a machine learning practice in which the user interacts with the learner in order to provide new labels when required.
-
-You can find a real use case in `Lightning Flash <https://github.com/Lightning-AI/lightning-flash>`_.
-
-Flash implements the :code:`ActiveLearningLoop` that you can use together with the :code:`ActiveLearningDataModule` to label new data on the fly.
-To run the following demo, install Flash and `BaaL <https://github.com/ElementAI/baal>`__  first:
-
-.. code-block:: bash
-
-    pip install lightning-flash[image] baal
-
-.. code-block:: python
-
-    import torch
-
-    import flash
-    from flash.core.classification import Probabilities
-    from flash.core.data.utils import download_data
-    from flash.image import ImageClassificationData, ImageClassifier
-    from flash.image.classification.integrations.baal import ActiveLearningDataModule, ActiveLearningLoop
-
-    # 1. Create the DataModule
-    download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")
-
-    # Implement the research use-case where we mask labels from labelled dataset.
-    datamodule = ActiveLearningDataModule(
-        ImageClassificationData.from_folders(train_folder="data/hymenoptera_data/train/", batch_size=2),
-        initial_num_labels=5,
-        val_split=0.1,
-    )
-
-    # 2. Build the task
-    head = torch.nn.Sequential(
-        torch.nn.Dropout(p=0.1),
-        torch.nn.Linear(512, datamodule.num_classes),
-    )
-    model = ImageClassifier(backbone="resnet18", head=head, num_classes=datamodule.num_classes, output=Probabilities())
-
-
-    # 3.1 Create the trainer
-    trainer = flash.Trainer(max_epochs=3)
-
-    # 3.2 Create the active learning loop and connect it to the trainer
-    active_learning_loop = ActiveLearningLoop(label_epoch_frequency=1)
-    active_learning_loop.connect(trainer.fit_loop)
-    trainer.fit_loop = active_learning_loop
-
-    # 3.3 Finetune
-    trainer.finetune(model, datamodule=datamodule, strategy="freeze")
-
-    # 4. Predict what's on a few images! ants or bees?
-    predictions = model.predict("data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg")
-    print(predictions)
-
-    # 5. Save the model!
-    trainer.save_checkpoint("image_classification_model.pt")
-
-Here is the `Active Learning Loop example <https://github.com/Lightning-AI/lightning-flash/blob/master/flash_examples/integrations/baal/image_classification_active_learning.py>`_ and the `code for the active learning loop <https://github.com/Lightning-AI/lightning-flash/blob/master/flash/image/classification/integrations/baal/loop.py>`_.
-
-
 ----------
 
 Advanced Examples

src/pytorch_lightning/CHANGELOG.md

@@ -70,6 +70,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Removed support for loop customization
   * Removed `Loop.replace()` ([#16361](https://github.com/Lightning-AI/lightning/pull/16361))
+  * Removed `Loop.connect()` ([#16384](https://github.com/Lightning-AI/lightning/pull/16384))
+  * Removed the `trainer.{fit,validate,test,predict}_loop` properties ([#16384](https://github.com/Lightning-AI/lightning/pull/16384))
 
 - Removed special support for truncated backpropagation through time (TBPTT) ([#16172](https://github.com/Lightning-AI/lightning/pull/16172))
   * Removed the `LightningModule.truncated_bptt_steps` attribute

src/pytorch_lightning/loops/dataloader/evaluation_loop.py

@@ -78,10 +78,6 @@ def prefetch_batches(self) -> int:
         is_unsized = batches[self.current_dataloader_idx] == float("inf")
         return int(is_unsized)
 
-    def connect(self, epoch_loop: EvaluationEpochLoop) -> None:  # type: ignore[override]
-        """Connect the evaluation epoch loop with this loop."""
-        self.epoch_loop = epoch_loop
-
     @property
     def done(self) -> bool:
         """Returns whether all dataloaders are processed or evaluation should be skipped altogether."""

src/pytorch_lightning/loops/dataloader/prediction_loop.py

@@ -66,10 +66,6 @@ def dataloaders(self) -> Sequence[DataLoader]:
     def skip(self) -> bool:
         return sum(self.max_batches) == 0
 
-    def connect(self, epoch_loop: PredictionEpochLoop) -> None:  # type: ignore[override]
-        """Connect the prediction epoch loop with this loop."""
-        self.epoch_loop = epoch_loop
-
     def reset(self) -> None:
         """Resets the internal state of the loop for a new run."""
         self.predictions = []

src/pytorch_lightning/loops/epoch/prediction_epoch_loop.py

@@ -38,9 +38,6 @@ def should_store_predictions(self) -> bool:
         any_pred = any(cb.interval.on_epoch for cb in self.trainer.prediction_writer_callbacks)
         return self.return_predictions or any_pred
 
-    def connect(self, **kwargs: "Loop") -> None:
-        raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
-
     def reset(self) -> None:
         """Resets the loops internal state."""
         self._seen_batch_indices = []

src/pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -121,20 +121,6 @@ def done(self) -> bool:
 
         return False
 
-    def connect(  # type: ignore[override]
-        self,
-        optimizer_loop: Optional[OptimizerLoop] = None,
-        manual_loop: Optional[ManualOptimization] = None,
-        val_loop: Optional["loops.EvaluationLoop"] = None,
-    ) -> None:
-        """Optionally connect a custom batch or validation loop to this training epoch loop."""
-        if optimizer_loop is not None:
-            self.optimizer_loop = optimizer_loop
-        if manual_loop is not None:
-            self.manual_loop = manual_loop
-        if val_loop is not None:
-            self.val_loop = val_loop
-
     def reset(self) -> None:
         """Resets the internal state of the loop for a new run."""
         if self.restarting:

src/pytorch_lightning/loops/fit_loop.py

@@ -169,10 +169,6 @@ def skip(self) -> bool:
         # until `on_run_start`, we use `limit_train_batches` instead
         return self.done or self.trainer.limit_train_batches == 0
 
-    def connect(self, epoch_loop: TrainingEpochLoop) -> None:  # type: ignore[override]
-        """Connects a training epoch loop to this fit loop."""
-        self.epoch_loop = epoch_loop
-
     def reset(self) -> None:
         """Resets the internal state of this loop."""
         if self.restarting:

src/pytorch_lightning/loops/loop.py

@@ -100,12 +100,6 @@ def skip(self):
         """
         return False
 
-    def connect(self, **kwargs: "Loop") -> None:
-        """Optionally connect one or multiple loops to this one.
-
-        Linked loops should form a tree.
-        """
-
     def on_skip(self) -> T:
         """The function to run when :meth:`run` should be skipped, determined by the condition in :attr:`skip`.
 

src/pytorch_lightning/loops/optimization/optimizer_loop.py

@@ -172,9 +172,6 @@ def done(self) -> bool:
         """Returns ``True`` when the last optimizer in the sequence has run."""
         return self.optim_progress.optimizer_position >= len(self._indices)
 
-    def connect(self, **kwargs: "Loop") -> None:
-        raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
-
     def reset(self) -> None:
         if not self.restarting:
             # when reset() is called from outside (manually), we reset the loop progress

src/pytorch_lightning/trainer/trainer.py

@@ -371,21 +371,16 @@ def __init__(
         self._signal_connector = SignalConnector(self)
         self.tuner = Tuner(self)
 
-        fit_loop = FitLoop(min_epochs=min_epochs, max_epochs=max_epochs)
-        training_epoch_loop = TrainingEpochLoop(min_steps=min_steps, max_steps=max_steps)
-        fit_loop.connect(epoch_loop=training_epoch_loop)
-
-        # default .fit() loop
-        self.fit_loop = fit_loop
-
-        # default .validate() loop
+        # init loops
+        self.fit_loop = FitLoop(min_epochs=min_epochs, max_epochs=max_epochs)
+        self.fit_loop.epoch_loop = TrainingEpochLoop(min_steps=min_steps, max_steps=max_steps)
         self.validate_loop = EvaluationLoop()
-
-        # default .test() loop
         self.test_loop = EvaluationLoop()
-
-        # default .predict() loop
         self.predict_loop = PredictionLoop()
+        self.fit_loop.trainer = self
+        self.validate_loop.trainer = self
+        self.test_loop.trainer = self
+        self.predict_loop.trainer = self
 
         # init callbacks
         # Declare attributes to be set in _callback_connector on_trainer_init
@@ -1103,8 +1098,6 @@ def _run_train(self) -> None:
         self.model.train()
         torch.set_grad_enabled(True)
 
-        self.fit_loop.trainer = self
-
         with torch.autograd.set_detect_anomaly(self._detect_anomaly):
             self.fit_loop.run()
 
@@ -1114,9 +1107,6 @@ def _run_evaluate(self) -> _EVALUATE_OUTPUT:
         # reload dataloaders
         self._evaluation_loop._reload_evaluation_dataloaders()
 
-        # reset trainer on this loop and all child loops in case user connected a custom loop
-        self._evaluation_loop.trainer = self
-
         with self.profiler.profile(f"run_{self.state.stage}_evaluation"), _evaluation_context(
             self.accelerator, self._inference_mode
         ):
@@ -1133,8 +1123,6 @@ def _run_evaluate(self) -> _EVALUATE_OUTPUT:
 
     def _run_predict(self) -> Optional[_PREDICT_OUTPUT]:
         self.reset_predict_dataloader(self.lightning_module)
-        # reset trainer on this loop and all child loops in case user connected a custom loop
-        self.predict_loop.trainer = self
         with _evaluation_context(self.accelerator, self._inference_mode):
             return self.predict_loop.run()
 
@@ -1955,63 +1943,6 @@ def is_last_batch(self) -> bool:
         """Whether trainer is executing the last batch."""
         return self.fit_loop.epoch_loop.batch_progress.is_last_batch
 
-    @property
-    def fit_loop(self) -> FitLoop:
-        return self._fit_loop
-
-    @fit_loop.setter
-    def fit_loop(self, loop: FitLoop) -> None:
-        """Attach a custom fit loop to this Trainer.
-
-        It will run with
-        :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`.
-        """
-        loop.trainer = self
-        self._fit_loop = loop
-
-    @property
-    def validate_loop(self) -> EvaluationLoop:
-        return self._validate_loop
-
-    @validate_loop.setter
-    def validate_loop(self, loop: EvaluationLoop) -> None:
-        """Attach a custom validation loop to this Trainer.
-
-        It will run with
-        :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`. Note that this loop is different from the one
-        running during training inside the :meth:`pytorch_lightning.trainer.trainer.Trainer.fit` call.
-        """
-        loop.trainer = self
-        self._validate_loop = loop
-
-    @property
-    def test_loop(self) -> EvaluationLoop:
-        return self._test_loop
-
-    @test_loop.setter
-    def test_loop(self, loop: EvaluationLoop) -> None:
-        """Attach a custom test loop to this Trainer.
-
-        It will run with
-        :meth:`~pytorch_lightning.trainer.trainer.Trainer.test`.
-        """
-        loop.trainer = self
-        self._test_loop = loop
-
-    @property
-    def predict_loop(self) -> PredictionLoop:
-        return self._predict_loop
-
-    @predict_loop.setter
-    def predict_loop(self, loop: PredictionLoop) -> None:
-        """Attach a custom prediction loop to this Trainer.
-
-        It will run with
-        :meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`.
-        """
-        loop.trainer = self
-        self._predict_loop = loop
-
     @property
     def _evaluation_loop(self) -> EvaluationLoop:
         if self.state.fn == TrainerFn.FITTING: