prune enable_pl_optimizer

Author: Borda

CHANGELOG.md

@@ -18,6 +18,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed deprecated Trainer argument `enable_pl_optimizer`
+
 
 ### Fixed
 

pytorch_lightning/trainer/connectors/optimizer_connector.py

@@ -20,12 +20,7 @@ class OptimizerConnector:
     def __init__(self, trainer):
         self.trainer = trainer
 
-    def on_trainer_init(self, enable_pl_optimizer):
-        if enable_pl_optimizer is not None:
-            rank_zero_warn(
-                "Trainer argument `enable_pl_optimizer` is deprecated in v1.1.3. It will be removed in v1.3.0",
-                DeprecationWarning
-            )
+    def on_trainer_init(self):
         self.trainer.lr_schedulers = []
         self.trainer.optimizers = []
         self.trainer.optimizer_frequencies = []

pytorch_lightning/trainer/trainer.py

@@ -137,7 +137,6 @@ def __init__(
         distributed_backend: Optional[str] = None,
         automatic_optimization: Optional[bool] = None,
         move_metrics_to_cpu: bool = False,
-        enable_pl_optimizer: bool = None,  # todo: remove in v1.3
         multiple_trainloader_mode: str = 'max_size_cycle',
         stochastic_weight_avg: bool = False
     ):
@@ -289,11 +288,6 @@ def __init__(
             move_metrics_to_cpu: Whether to force internal logged metrics to be moved to cpu.
                 This can save some gpu memory, but can make training slower. Use with attention.
 
-            enable_pl_optimizer: If True, each optimizer will be wrapped by
-                `pytorch_lightning.core.optimizer.LightningOptimizer`. It allows Lightning to
-                handle AMP, TPU, accumulated_gradients, etc.
-                .. warning:: Currently deprecated and it will be removed in v1.3
-
             multiple_trainloader_mode: How to loop over the datasets when there are multiple train loaders.
                 In 'max_size_cycle' mode, the trainer ends one epoch when the largest dataset is traversed,
                 and smaller datasets reload when running out of their data. In 'min_size' mode, all the datasets
@@ -346,7 +340,7 @@ def __init__(
         self.on_init_start()
 
         # init optimizer + lr scheduler related flags
-        self.optimizer_connector.on_trainer_init(enable_pl_optimizer)
+        self.optimizer_connector.on_trainer_init()
 
         # init data flags
         self.data_connector.on_trainer_init(

tests/deprecated_api/test_remove_1-3.py

@@ -141,8 +141,3 @@ def test_v1_3_0_trainer_cli_profiler(cli_args, expected_parsed_arg, expected_pro
     assert getattr(args, "profiler") == expected_parsed_arg
     trainer = Trainer.from_argparse_args(args)
     assert isinstance(trainer.profiler, expected_profiler)
-
-
-def test_trainer_enable_pl_optimizer(tmpdir):
-    with pytest.deprecated_call(match='will be removed in v1.3'):
-        Trainer(enable_pl_optimizer=True)