moves init apex from LM to apex connector (#3923)

docs/source/hooks.rst

@@ -24,10 +24,7 @@ Training set-up
 
 - :meth:`~pytorch_lightning.core.datamodule.LightningDataModule.prepare_data`
 - :meth:`~pytorch_lightning.core.datamodule.LightningDataModule.setup`
-- :meth:`~pytorch_lightning.core.lightning.LightningModule.init_ddp_connection`
 - :meth:`~pytorch_lightning.trainer.optimizers.TrainerOptimizersMixin.init_optimizers`
-- :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_apex`
-- :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_ddp`
 - :meth:`~pytorch_lightning.core.datamodule.LightningDataModule.train_dataloader`
 - :meth:`~pytorch_lightning.core.datamodule.LightningDataModule.test_dataloader`
 - :meth:`~pytorch_lightning.core.datamodule.LightningDataModule.val_dataloader`

docs/source/lightning_module.rst

@@ -1024,17 +1024,6 @@ Advanced hooks
 ^^^^^^^^^^^^^^
 Use these hooks to modify advanced functionality
 
-configure_apex
-~~~~~~~~~~~~~~
-
-.. autofunction:: pytorch_lightning.core.lightning.LightningModule.configure_apex
-    :noindex:
-
-configure_ddp
-~~~~~~~~~~~~~
-
-.. autofunction:: pytorch_lightning.core.lightning.LightningModule.configure_ddp
-    :noindex:
 
 configure_sync_batchnorm
 ~~~~~~~~~~~~~~~~~~~~~~~~

pytorch_lightning/core/lightning.py

@@ -985,40 +985,6 @@ class LightningModule(
 
         return model
 
-    def configure_apex(
-        self,
-        amp: object,
-        model: "LightningModule",
-        optimizers: List[Optimizer],
-        amp_level: str,
-    ) -> Tuple["LightningModule", List[Optimizer]]:
-        r"""
-        Override to init AMP your own way.
-        Must return a model and list of optimizers.
-
-        Args:
-            amp: pointer to amp library object.
-            model: pointer to current :class:`LightningModule`.
-            optimizers: list of optimizers passed in :meth:`configure_optimizers`.
-            amp_level: AMP mode chosen ('O1', 'O2', etc...)
-
-        Return:
-            Apex wrapped model and optimizers
-
-        Examples:
-            .. code-block:: python
-
-                # Default implementation used by Trainer.
-                def configure_apex(self, amp, model, optimizers, amp_level):
-                    model, optimizers = amp.initialize(
-                        model, optimizers, opt_level=amp_level,
-                    )
-
-                    return model, optimizers
-        """
-        model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)
-        return model, optimizers
-
     def configure_optimizers(
             self,
     ):

pytorch_lightning/plugins/apex.py

@@ -11,6 +11,8 @@
 # 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 List, Tuple
+from torch.optim.optimizer import Optimizer
 
 try:
     from apex import amp
@@ -24,10 +26,44 @@ class ApexPlugin:
         self.trainer = trainer
 
     def connect(self, model, optimizers):
-        model, optimizers = model.configure_apex(amp, model, optimizers, self.trainer.amp_level)
+        model, optimizers = self.configure_apex(amp, model, optimizers, self.trainer.amp_level)
         self.trainer.reinit_scheduler_properties(optimizers, self.trainer.lr_schedulers)
         return model, optimizers
 
     def training_step(self, fx, args):
         output = fx(args)
         return output
+
+    def configure_apex(
+        self,
+        amp: object,
+        model: "LightningModule",
+        optimizers: List[Optimizer],
+        amp_level: str,
+    ) -> Tuple["LightningModule", List[Optimizer]]:
+        r"""
+        Override to init AMP your own way.
+        Must return a model and list of optimizers.
+
+        Args:
+            amp: pointer to amp library object.
+            model: pointer to current :class:`LightningModule`.
+            optimizers: list of optimizers passed in :meth:`configure_optimizers`.
+            amp_level: AMP mode chosen ('O1', 'O2', etc...)
+
+        Return:
+            Apex wrapped model and optimizers
+
+        Examples:
+            .. code-block:: python
+
+                # Default implementation used by Trainer.
+                def configure_apex(self, amp, model, optimizers, amp_level):
+                    model, optimizers = amp.initialize(
+                        model, optimizers, opt_level=amp_level,
+                    )
+
+                    return model, optimizers
+        """
+        model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)
+        return model, optimizers