Remove add_to_queue and remove_from_queue from LightningModule (#13600)

Co-authored-by: Carlos Mocholí <[email protected]>

Author: shenoynikhil

docs/source-pytorch/common/lightning_module.rst

@@ -1626,15 +1626,3 @@ on_after_batch_transfer
 
 .. automethod:: pytorch_lightning.core.module.LightningModule.on_after_batch_transfer
     :noindex:
-
-add_to_queue
-~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.module.LightningModule.add_to_queue
-    :noindex:
-
-get_from_queue
-~~~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.module.LightningModule.get_from_queue
-    :noindex:

src/pytorch_lightning/CHANGELOG.md

@@ -174,6 +174,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed the deprecated `LightningModule.add_to_queue` and `LightningModule.get_from_queue` method ([#13600](https://github.com/PyTorchLightning/pytorch-lightning/pull/13600))
+
+
 - Removed deprecated `pytorch_lightning.core.decorators.parameter_validation` from `decorators` ([#13514](https://github.com/Lightning-AI/lightning/pull/13514))
 
 

src/pytorch_lightning/core/module.py

@@ -1955,28 +1955,6 @@ def use_amp(self, use_amp: bool) -> None:
             )
         self._use_amp = use_amp
 
-    def add_to_queue(self, queue: pl.strategies.launchers.spawn._FakeQueue) -> None:
-        """Appends the :attr:`trainer.callback_metrics` dictionary to the given queue. To avoid issues with memory
-        sharing, we cast the data to numpy.
-
-        Args:
-            queue: the instance of the queue to append the data.
-
-        .. deprecated:: v1.5
-            This method was deprecated in v1.5 and will be removed in v1.7.
-        """
-
-    def get_from_queue(self, queue: pl.strategies.launchers.spawn._FakeQueue) -> None:
-        """Retrieve the :attr:`trainer.callback_metrics` dictionary from the given queue. To preserve consistency,
-        we cast back the data to ``torch.Tensor``.
-
-        Args:
-            queue: the instance of the queue from where to get the data.
-
-        .. deprecated:: v1.5
-            This method was deprecated in v1.5 and will be removed in v1.7.
-        """
-
     @contextmanager
     def _prevent_trainer_and_dataloaders_deepcopy(self) -> None:
         self._should_prevent_trainer_and_dataloaders_deepcopy = True

src/pytorch_lightning/strategies/launchers/spawn.py

@@ -26,7 +26,6 @@
 from pytorch_lightning.strategies.strategy import Strategy
 from pytorch_lightning.trainer.states import TrainerFn, TrainerState
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
-from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.rank_zero import rank_zero_debug
 from pytorch_lightning.utilities.types import _PATH
 
@@ -122,10 +121,6 @@ def _recover_results_in_main_process(self, spawn_output: "_SpawnOutput", trainer
         trainer.state = spawn_output.trainer_state
 
         # get the `callback_metrics` and set it to the trainer
-        if is_overridden("get_from_queue", trainer.lightning_module):
-            # only in case the user does not override it.
-            # TODO: Remove the if in v1.7
-            trainer.lightning_module.get_from_queue(spawn_output.extra)
         self.get_from_queue(trainer, spawn_output.extra)
 
     def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Optional["_SpawnOutput"]:
@@ -151,9 +146,6 @@ def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Opt
 
         # adds the `callback_metrics` to the queue
         extra = _FakeQueue()
-        if is_overridden("add_to_queue", trainer.lightning_module):
-            # TODO: Remove the if in v1.7
-            trainer.lightning_module.add_to_queue(extra)
         self.add_to_queue(trainer, extra)
 
         return _SpawnOutput(best_model_path, weights_path, trainer.state, results, extra)

src/pytorch_lightning/strategies/launchers/xla_spawn.py

@@ -23,7 +23,6 @@
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities import _TPU_AVAILABLE
 from pytorch_lightning.utilities.apply_func import move_data_to_device
-from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.rank_zero import rank_zero_debug
 
 if _TPU_AVAILABLE:
@@ -136,9 +135,6 @@ def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Opt
 
         # adds the `callback_metrics` to the queue
         extra = _FakeQueue()
-        if is_overridden("add_to_queue", trainer.lightning_module):
-            # TODO: Remove the if in v1.7
-            trainer.lightning_module.add_to_queue(extra)
         self.add_to_queue(trainer, extra)
 
         return _SpawnOutput(best_model_path, weights_path, trainer.state, results, extra)

src/pytorch_lightning/trainer/configuration_validator.py

@@ -46,7 +46,6 @@ def verify_loop_configurations(trainer: "pl.Trainer") -> None:
         __verify_eval_loop_configuration(trainer, model, "predict")
 
     __verify_dp_batch_transfer_support(trainer, model)
-    _check_add_get_queue(model)
     # TODO: Delete _check_on_post_move_to_device in v1.7
     _check_on_post_move_to_device(model)
     _check_deprecated_callback_hooks(trainer)
@@ -218,23 +217,6 @@ def __check_training_step_requires_dataloader_iter(model: "pl.LightningModule")
             )
 
 
-def _check_add_get_queue(model: "pl.LightningModule") -> None:
-    r"""
-    Checks if add_to_queue or get_from_queue is overridden and sends a deprecation warning.
-
-    Args:
-        model: The lightning module
-    """
-    if is_overridden("add_to_queue", model):
-        rank_zero_deprecation(
-            "The `LightningModule.add_to_queue` method was deprecated in v1.5 and will be removed in v1.7."
-        )
-    if is_overridden("get_from_queue", model):
-        rank_zero_deprecation(
-            "The `LightningModule.get_from_queue` method was deprecated in v1.5 and will be removed in v1.7."
-        )
-
-
 # TODO: Delete _check_on_hpc_hooks in v1.8
 def _check_on_hpc_hooks(model: "pl.LightningModule") -> None:
     if is_overridden("on_hpc_save", model):

tests/tests_pytorch/deprecated_api/test_remove_1-7.py

@@ -34,25 +34,6 @@
 from tests_pytorch.plugins.environments.test_lsf_environment import _make_rankfile
 
 
-class BoringCallbackDDPSpawnModel(BoringModel):
-    def add_to_queue(self, queue):
-        ...
-
-    def get_from_queue(self, queue):
-        ...
-
-
-def test_v1_7_0_deprecate_add_get_queue(tmpdir):
-    model = BoringCallbackDDPSpawnModel()
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
-
-    with pytest.deprecated_call(match=r"`LightningModule.add_to_queue` method was deprecated in v1.5"):
-        trainer.fit(model)
-
-    with pytest.deprecated_call(match=r"`LightningModule.get_from_queue` method was deprecated in v1.5"):
-        trainer.fit(model)
-
-
 def test_v1_7_0_deprecate_lightning_distributed(tmpdir):
     with pytest.deprecated_call(match="LightningDistributed is deprecated in v1.5 and will be removed in v1.7."):
         from pytorch_lightning.distributed.dist import LightningDistributed

tests/tests_pytorch/strategies/test_ddp_spawn_strategy.py

@@ -44,14 +44,6 @@ def validation_step(self, batch, batch_idx):
         self.log(self.name, self.val)
         return super().validation_step(batch, batch_idx)
 
-    def add_to_queue(self, queue) -> None:
-        queue.put("test_val")
-        return super().add_to_queue(queue)
-
-    def get_from_queue(self, queue) -> None:
-        self.test_val = queue.get()
-        return super().get_from_queue(queue)
-
 
 @RunIf(skip_windows=True)
 def test_ddp_cpu():
@@ -67,31 +59,13 @@ def test_ddp_cpu():
     trainer.fit(model)
 
 
-@RunIf(min_cuda_gpus=2)
-def test_ddp_spawn_extra_parameters(tmpdir):
-    """Tests if device is set correctly when training for DDPSpawnStrategy and tests add_to_queue/get_from_queue
-    with Lightning Module (deprecated way)."""
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="gpu", devices=2, strategy="ddp_spawn")
-
-    assert isinstance(trainer.strategy, DDPSpawnStrategy)
-    assert trainer.strategy.root_device == torch.device("cuda:0")
-
-    val: float = 1.0
-    val_name: str = "val_acc"
-    model = BoringCallbackDDPSpawnModel(val_name, val)
-    dm = BoringDataModule()
-    trainer.fit(model, datamodule=dm)
-    assert trainer.callback_metrics[val_name] == torch.tensor(val)
-    assert model.test_val == "test_val"
-
-
 class CustomSpawnLauncher(_SpawnLauncher):
     def add_to_queue(self, trainer, queue) -> None:
-        queue.put("new_test_val")
+        queue.put("test_val")
         return super().add_to_queue(trainer, queue)
 
     def get_from_queue(self, trainer: Trainer, queue) -> None:
-        trainer.strategy.new_test_val = queue.get()
+        trainer.strategy.test_val = queue.get()
         return super().get_from_queue(trainer, queue)
 
 
@@ -115,7 +89,7 @@ def test_ddp_spawn_add_get_queue(tmpdir):
     dm = BoringDataModule()
     trainer.fit(model, datamodule=dm)
     assert trainer.callback_metrics[val_name] == torch.tensor(val)
-    assert ddp_spawn_strategy.new_test_val == "new_test_val"
+    assert ddp_spawn_strategy.test_val == "test_val"
 
 
 class BoringModelDDP(BoringModel):