prune deprecated profiler as bool

CHANGELOG.md

@@ -18,6 +18,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed Trainer argument `profiler` as bool ([#6164](https://github.com/PyTorchLightning/pytorch-lightning/pull/6164))
+
 
 ### Fixed
 

pytorch_lightning/trainer/connectors/profiler_connector.py

@@ -32,24 +32,15 @@ class ProfilerConnector:
     def __init__(self, trainer):
         self.trainer = trainer
 
-    def on_trainer_init(self, profiler: Union[BaseProfiler, bool, str]):
+    def on_trainer_init(self, profiler: Union[BaseProfiler, str]):
 
-        if profiler and not isinstance(profiler, (bool, str, BaseProfiler)):
-            # TODO: Update exception on removal of bool
+        if profiler and not isinstance(profiler, (str, BaseProfiler)):
             raise MisconfigurationException(
-                "Only None, bool, str and subclasses of `BaseProfiler`"
+                "Only None, str, and subclasses of `BaseProfiler`"
                 " are valid values for `Trainer`'s `profiler` parameter."
                 f" Received {profiler} which is of type {type(profiler)}."
             )
-
-        if isinstance(profiler, bool):
-            rank_zero_warn(
-                "Passing a bool value as a `profiler` argument to `Trainer` is deprecated"
-                " and will be removed in v1.3. Use str ('simple' or 'advanced') instead.", DeprecationWarning
-            )
-            if profiler:
-                profiler = SimpleProfiler()
-        elif isinstance(profiler, str):
+        if isinstance(profiler, str):
             if profiler.lower() in PROFILERS:
                 profiler_class = PROFILERS[profiler.lower()]
                 profiler = profiler_class()

pytorch_lightning/trainer/trainer.py

@@ -122,7 +122,7 @@ def __init__(
         num_sanity_val_steps: int = 2,
         truncated_bptt_steps: Optional[int] = None,
         resume_from_checkpoint: Optional[Union[Path, str]] = None,
-        profiler: Optional[Union[BaseProfiler, bool, str]] = None,
+        profiler: Optional[Union[BaseProfiler, str]] = None,
         benchmark: bool = False,
         deterministic: bool = False,
         reload_dataloaders_every_epoch: bool = False,
@@ -177,7 +177,7 @@ def __init__(
 
             checkpoint_callback: If ``True``, enable checkpointing.
                 It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
-                :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`. Default: ``True``.
+                :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
 
                 .. warning:: Passing a ModelCheckpoint instance to this argument is deprecated since
                     v1.1 and will be unsupported from v1.3. Use `callbacks` argument instead.
@@ -226,10 +226,9 @@ def __init__(
                 Ignored when a custom progress bar is passed to :paramref:`~Trainer.callbacks`. Default: None, means
                 a suitable value will be chosen based on the environment (terminal, Google COLAB, etc.).
 
-            profiler: To profile individual steps during training and assist in identifying bottlenecks. Passing bool
-                value is deprecated in v1.1 and will be removed in v1.3.
+            profiler: To profile individual steps during training and assist in identifying bottlenecks.
 
-            overfit_batches: Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0
+            overfit_batches: Overfit a percent of training data (float) or a set number of batches (int).
 
             plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
 
@@ -250,7 +249,7 @@ def __init__(
             num_processes: number of processes for distributed training with distributed_backend="ddp_cpu"
 
             num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.
-                Set it to `-1` to run all batches in all validation dataloaders. Default: 2
+                Set it to `-1` to run all batches in all validation dataloaders.
 
             reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch.
 

tests/deprecated_api/test_remove_1-3.py

@@ -20,7 +20,6 @@
 
 from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-from pytorch_lightning.profiler.profilers import PassThroughProfiler, SimpleProfiler
 
 
 def test_v1_3_0_deprecated_arguments(tmpdir):
@@ -111,38 +110,6 @@ def test_v1_3_0_deprecated_metrics():
         )
 
 
-# TODO: remove bool from Trainer.profiler param in v1.3.0, update profiler_connector.py
-@pytest.mark.parametrize(['profiler', 'expected'], [
-    (True, SimpleProfiler),
-    (False, PassThroughProfiler),
-])
-def test_trainer_profiler_remove_in_v1_3_0(profiler, expected):
-    # remove bool from Trainer.profiler param in v1.3.0, update profiler_connector.py
-    with pytest.deprecated_call(match='will be removed in v1.3'):
-        trainer = Trainer(profiler=profiler)
-        assert isinstance(trainer.profiler, expected)
-
-
-@pytest.mark.parametrize(
-    ['cli_args', 'expected_parsed_arg', 'expected_profiler'],
-    [
-        ('--profiler', True, SimpleProfiler),
-        ('--profiler True', True, SimpleProfiler),
-        ('--profiler False', False, PassThroughProfiler),
-    ],
-)
-def test_v1_3_0_trainer_cli_profiler(cli_args, expected_parsed_arg, expected_profiler):
-    cli_args = cli_args.split(' ')
-    with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
-        parser = ArgumentParser(add_help=False)
-        parser = Trainer.add_argparse_args(parent_parser=parser)
-        args = Trainer.parse_argparser(parser)
-
-    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)