update usage of deprecated automatic_optimization

pytorch_lightning/core/lightning.py

@@ -1394,8 +1394,10 @@ def get_progress_bar_dict(self):
 
     def _verify_is_manual_optimization(self, fn_name):
         if self.trainer.train_loop.automatic_optimization:
-            m = f'to use {fn_name}, please disable automatic optimization: Trainer(automatic_optimization=False)'
-            raise MisconfigurationException(m)
+            raise MisconfigurationException(
+                f'to use {fn_name}, please disable automatic optimization:'
+                ' set model property `automatic_optimization` as False'
+            )
 
     @classmethod
     def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:

pytorch_lightning/trainer/configuration_validator.py

@@ -79,7 +79,7 @@ def __verify_train_loop_configuration(self, model):
         if trainer.overriden_optimizer_step and not enable_pl_optimizer and automatic_optimization:
             rank_zero_warn(
                 "When overriding `LightningModule` optimizer_step with"
-                " `Trainer(..., enable_pl_optimizer=False, automatic_optimization=True, ...)`,"
+                " `Trainer(..., enable_pl_optimizer=False, ...)`,"
                 " we won't be calling `.zero_grad` we can't assume when you call your `optimizer.step()`."
                 " For Lightning to take care of it, please use `Trainer(enable_pl_optimizer=True)`."
             )
@@ -90,14 +90,15 @@ def __verify_train_loop_configuration(self, model):
         if (has_overriden_optimization_functions) and going_to_accumulate_grad_batches and automatic_optimization:
             raise MisconfigurationException(
                 'When overriding `LightningModule` optimizer_step or optimizer_zero_grad with '
-                '`Trainer(automatic_optimization=True, ...)`, `accumulate_grad_batches` should to be 1.'
+                ' `Trainer(...)`, `accumulate_grad_batches` should to be 1.'
                 ' It ensures optimizer_step or optimizer_zero_grad are called on every batch.'
             )
 
         if (enable_pl_optimizer) and trainer.overriden_optimizer_zero_grad and not automatic_optimization:
             raise MisconfigurationException(
-                'When overriding `LightningModule` optimizer_zero_grad with  '
-                '`Trainer(automatic_optimization=False, enable_pl_optimizer=True, ...) is not supported'
+                'When overriding `LightningModule` optimizer_zero_grad'
+                ' and preserving model property `automatic_optimization` as True with'
+                ' `Trainer(enable_pl_optimizer=True, ...) is not supported'
             )
 
     def __verify_eval_loop_configuration(self, model, eval_loop_name):

pytorch_lightning/trainer/trainer.py

@@ -355,7 +355,7 @@ def __init__(
         )
 
         # init train loop related flags
-        # TODO: deprecate in 1.2.0
+        # TODO: remove in 1.3.0
         if automatic_optimization is None:
             automatic_optimization = True
         else:

tests/core/test_lightning_module.py

@@ -38,7 +38,6 @@ def optimizer_step(self, *_, **__):
             default_root_dir=tmpdir,
             limit_train_batches=2,
             accumulate_grad_batches=2,
-            automatic_optimization=True
         )
 
         trainer.fit(model)
@@ -90,7 +89,6 @@ def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
             default_root_dir=tmpdir,
             limit_train_batches=8,
             accumulate_grad_batches=1,
-            automatic_optimization=True,
             enable_pl_optimizer=enable_pl_optimizer
         )
 

tests/core/test_lightning_optimizer.py

@@ -112,6 +112,10 @@ def configure_optimizers(self):
             lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer_1, step_size=1)
             return [optimizer_1, optimizer_2], [lr_scheduler]
 
+        @property
+        def automatic_optimization(self) -> bool:
+            return False
+
     model = TestModel()
     model.training_step_end = None
     model.training_epoch_end = None
@@ -121,8 +125,8 @@ def configure_optimizers(self):
         limit_val_batches=1,
         max_epochs=1,
         weights_summary=None,
-        automatic_optimization=False,
-        enable_pl_optimizer=True)
+        enable_pl_optimizer=True,
+    )
     trainer.fit(model)
 
     assert len(mock_sgd_step.mock_calls) == 2
@@ -161,6 +165,10 @@ def configure_optimizers(self):
             lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer_1, step_size=1)
             return [optimizer_1, optimizer_2], [lr_scheduler]
 
+        @property
+        def automatic_optimization(self) -> bool:
+            return False
+
     model = TestModel()
     model.training_step_end = None
     model.training_epoch_end = None
@@ -170,7 +178,6 @@ def configure_optimizers(self):
         limit_val_batches=1,
         max_epochs=1,
         weights_summary=None,
-        automatic_optimization=False,
         accumulate_grad_batches=2,
         enable_pl_optimizer=True,
     )
@@ -237,7 +244,6 @@ def configure_optimizers(self):
         max_epochs=1,
         weights_summary=None,
         enable_pl_optimizer=True,
-        automatic_optimization=True
     )
     trainer.fit(model)
 
@@ -291,7 +297,6 @@ def configure_optimizers(self):
             max_epochs=1,
             weights_summary=None,
             enable_pl_optimizer=True,
-            automatic_optimization=True
         )
         trainer.fit(model)
 
@@ -352,7 +357,6 @@ def configure_optimizers(self):
                 max_epochs=1,
                 weights_summary=None,
                 enable_pl_optimizer=True,
-                automatic_optimization=True
             )
             trainer.fit(model)
 
@@ -406,7 +410,6 @@ def configure_optimizers(self):
             max_epochs=1,
             weights_summary=None,
             enable_pl_optimizer=True,
-            automatic_optimization=True,
         )
         trainer.fit(model)
 

tests/trainer/dynamic_args/test_multiple_optimizers.py

@@ -97,11 +97,14 @@ def configure_optimizers(self):
             optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
             return optimizer, optimizer_2
 
+        @property
+        def automatic_optimization(self) -> bool:
+            return False
+
     model = TestModel()
     model.val_dataloader = None
 
     trainer = Trainer(
-        automatic_optimization=False,
         default_root_dir=tmpdir,
         limit_train_batches=2,
         limit_val_batches=2,