Deprecate add_to_queue and get_from_queue from Lightning Module

CHANGELOG.md

@@ -162,6 +162,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 
+- Deprecated `add_to_queue`, `get_from_queue` from Lightning Module ([#9126](https://github.com/PyTorchLightning/pytorch-lightning/pull/9126))
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))

pytorch_lightning/core/lightning.py

@@ -1960,6 +1960,9 @@ def add_to_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
 
         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.
         """
         callback_metrics: dict = apply_to_collection(
             self.trainer.callback_metrics, torch.Tensor, lambda x: x.cpu().numpy()
@@ -1973,6 +1976,9 @@ def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
 
         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.
         """
         # NOTE: `add_to_queue` needs to be called before
         callback_metrics: dict = queue.get()

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -213,13 +213,18 @@ def new_process(self, process_idx: int, trainer: "pl.Trainer", mp_queue: SimpleQ
         # ensure that spawned processes go through teardown before joining
         trainer._call_teardown_hook()
 
+    # TODO(@daniellepintz): add trainer argument in v1.7
     def post_dispatch(self):
         # restore main state with best weights
         best_path = self.mp_queue.get()
         last_path = self.mp_queue.get()
         self._results = self.mp_queue.get()
+        # TODO(@daniellepintz): add `trainer.callback_metrics = self.mp_queue.get()` in v1.7
+
         # get the `callback_metrics` and set it to the trainer
         # only in case the user does not override it.
+
+        # TODO(@daniellepintz): remove in v1.7
         self.lightning_module.get_from_queue(self.mp_queue)
 
         # recover the weights of the processes trained in the children
@@ -286,6 +291,9 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
             self.mp_queue.put(best_model_path)
             self.mp_queue.put(last_path)
             self.mp_queue.put(results)
+            # TODO(@daniellepintz): add `self.mp_queue.put(trainer.callback_metrics)` in v1.7
+
+            # TODO(@daniellepintz): remove in v1.7
             self.lightning_module.add_to_queue(self.mp_queue)  # adds the `callback_metrics` to the queue
 
     def __recover_child_process_weights(self, best_path, last_path):

pytorch_lightning/trainer/configuration_validator.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import pytorch_lightning as pl
 from pytorch_lightning.trainer.states import TrainerFn
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
@@ -43,6 +43,7 @@ def verify_loop_configurations(self, model: "pl.LightningModule") -> None:
         elif self.trainer.state.fn == TrainerFn.PREDICTING:
             self.__verify_predict_loop_configuration(model)
         self.__verify_dp_batch_transfer_support(model)
+        self._check_add_get_queue(model)
 
     def __verify_train_loop_configuration(self, model: "pl.LightningModule") -> None:
         # -----------------------------------
@@ -153,3 +154,20 @@ def __check_training_step_requires_dataloader_iter(self, model: "pl.LightningMod
                     "The model taking a `dataloader_iter` argument in your `training_step` "
                     "is incompatible with `truncated_bptt_steps > 0`."
                 )
+
+    def _check_add_get_queue(self, model: "pl.LightningModule"):
+        r"""
+        Checks if add_to_queue or get_from_queue is overriden 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."
+            )

tests/deprecated_api/test_remove_1-7.py

@@ -21,6 +21,7 @@
 from tests.deprecated_api import _soft_unimport_module
 from tests.helpers import BoringModel
 from tests.helpers.datamodules import MNISTDataModule
+from tests.helpers.runif import RunIf
 
 
 def test_v1_7_0_deprecated_lightning_module_summarize(tmpdir):
@@ -91,6 +92,16 @@ def test_v1_7_0_trainer_prepare_data_per_node(tmpdir):
         _ = Trainer(prepare_data_per_node=False)
 
 
+@RunIf(min_gpus=2)
+def test_v1_7_0_deprecate_add_get_queue(tmpdir):
+    """Tests if device is set correctly when training for DDPSpawnPlugin."""
+    with pytest.deprecated_call(match=r"`LightningModule.add_to_queue` method was deprecated in v1.5"):
+        _ = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")
+
+    with pytest.deprecated_call(match=r"`LightningModule.get_from_queue` method was deprecated in v1.5"):
+        _ = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")
+
+
 @mock.patch("pytorch_lightning.loggers.test_tube.Experiment")
 def test_v1_7_0_test_tube_logger(_, tmpdir):
     with pytest.deprecated_call(match="The TestTubeLogger is deprecated since v1.5 and will be removed in v1.7"):

tests/plugins/test_ddp_spawn_plugin.py

@@ -62,7 +62,7 @@ def test_ddp_cpu():
 
 @RunIf(min_gpus=2)
 def test_ddp_spawn_extra_parameters(tmpdir):
-    """Tests if device is set correctely when training for DDPSpawnPlugin."""
+    """Tests if device is set correctly when training for DDPSpawnPlugin."""
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")
 
     assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
@@ -76,4 +76,5 @@ def test_ddp_spawn_extra_parameters(tmpdir):
 
     trainer.fit(model, datamodule=dm)
     assert trainer.callback_metrics[val_name] == torch.tensor(val)
+    # TODO(@daniellepintz) remove assert in v1.7
     assert model.test_val == "test_val"