feature: Allow str arguments in Trainer.profiler

CHANGELOG.md

@@ -24,6 +24,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added autogenerated helptext to `Trainer.add_argparse_args`. ([#4344](https://github.com/PyTorchLightning/pytorch-lightning/pull/4344))
 
 
+- Added support for string values in `Trainer`'s `profiler` parameter ([#3656](https://github.com/PyTorchLightning/pytorch-lightning/pull/3656))
+
+
 ### Changed
 
 
@@ -48,6 +51,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `reorder` parameter of the `auc` metric ([#4237](https://github.com/PyTorchLightning/pytorch-lightning/pull/4237))
 
 
+- Deprecated bool values in `Trainer`'s `profiler` parameter ([#3656](https://github.com/PyTorchLightning/pytorch-lightning/pull/3656))
+
+
 ### Removed
 
 

pytorch_lightning/profiler/__init__.py

@@ -22,12 +22,12 @@
 Enable simple profiling
 -----------------------
 
-If you only wish to profile the standard actions, you can set `profiler=True` when constructing
-your `Trainer` object.
+If you only wish to profile the standard actions, you can set `profiler="simple"`
+when constructing your `Trainer` object.
 
 .. code-block:: python
 
-    trainer = Trainer(..., profiler=True)
+    trainer = Trainer(..., profiler="simple")
 
 The profiler's results will be printed at the completion of a training `fit()`.
 
@@ -59,6 +59,10 @@
 
 .. code-block:: python
 
+    trainer = Trainer(..., profiler="advanced")
+
+    or
+
     profiler = AdvancedProfiler()
     trainer = Trainer(..., profiler=profiler)
 

pytorch_lightning/trainer/__init__.py

@@ -1199,14 +1199,11 @@ def world_size(self):
     # default used by the Trainer
     trainer = Trainer(profiler=None)
 
-    # to profile standard training events
-    trainer = Trainer(profiler=True)
+    # to profile standard training events, equivalent to `profiler=SimpleProfiler()`
+    trainer = Trainer(profiler="simple")
 
-    # equivalent to profiler=True
-    trainer = Trainer(profiler=SimpleProfiler())
-
-    # advanced profiler for function-level stats
-    trainer = Trainer(profiler=AdvancedProfiler())
+    # advanced profiler for function-level stats, equivalent to `profiler=AdvancedProfiler()`
+    trainer = Trainer(profiler="advanced")
 
 progress_bar_refresh_rate
 ^^^^^^^^^^^^^^^^^^^^^^^^^

pytorch_lightning/trainer/connectors/profiler_connector.py

@@ -11,16 +11,40 @@
 # 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 pytorch_lightning.profiler import PassThroughProfiler, SimpleProfiler
+
+from typing import Union
+
+from pytorch_lightning.profiler import BaseProfiler, PassThroughProfiler, SimpleProfiler, AdvancedProfiler
+from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
 class ProfilerConnector:
 
     def __init__(self, trainer):
         self.trainer = trainer
 
-    def on_trainer_init(self, profiler):
-        # configure profiler
-        if profiler is True:
-            profiler = SimpleProfiler()
+    def on_trainer_init(self, profiler: Union[BaseProfiler, bool, str]):
+
+        if profiler and not isinstance(profiler, (bool, str, BaseProfiler)):
+            # TODO: Update exception on removal of bool
+            raise MisconfigurationException("Only None, bool, 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):
+            profiler = profiler.lower()
+            if profiler == "simple":
+                profiler = SimpleProfiler()
+            elif profiler == "advanced":
+                profiler = AdvancedProfiler()
+            else:
+                raise ValueError("When passing string value for the `profiler` parameter of"
+                                 " `Trainer`, it can only be 'simple' or 'advanced'")
         self.trainer.profiler = profiler or PassThroughProfiler()

pytorch_lightning/trainer/trainer.py

@@ -120,7 +120,7 @@ def __init__(
         num_sanity_val_steps: int = 2,
         truncated_bptt_steps: Optional[int] = None,
         resume_from_checkpoint: Optional[str] = None,
-        profiler: Optional[Union[BaseProfiler, bool]] = None,
+        profiler: Optional[Union[BaseProfiler, bool, str]] = None,
         benchmark: bool = False,
         deterministic: bool = False,
         reload_dataloaders_every_epoch: bool = False,
@@ -212,7 +212,8 @@ def __init__(
             progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
                 Ignored when a custom callback is passed to :paramref:`~Trainer.callbacks`.
 
-            profiler:  To profile individual steps during training and assist in identifying bottlenecks.
+            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.
 
             overfit_batches: Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0
 

pytorch_lightning/utilities/argparse_utils.py

@@ -174,8 +174,7 @@ def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
             # if the only arg type is bool
             if len(arg_types) == 1:
                 use_type = parsing.str_to_bool
-            # if only two args (str, bool)
-            elif len(arg_types) == 2 and set(arg_types) == {str, bool}:
+            elif str in arg_types:
                 use_type = parsing.str_to_bool_or_str
             else:
                 # filter out the bool as we need to use more general

tests/test_deprecated.py

@@ -1,13 +1,17 @@
 """Test deprecated functionality which will be removed in vX.Y.Z"""
+from argparse import ArgumentParser
 import pytest
 import sys
+from unittest import mock
 
 import torch
 
 from tests.base import EvalModelTemplate
 from pytorch_lightning.metrics.functional.classification import auc
 
+from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.profiler.profilers import PassThroughProfiler, SimpleProfiler
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -22,6 +26,37 @@ def test_tbd_remove_in_v1_2_0():
         checkpoint_cb = ModelCheckpoint(filepath='.', dirpath='.')
 
 
+# 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):
+    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_trainer_cli_profiler_remove_in_v1_3_0(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 _soft_unimport_module(str_module):
     # once the module is imported  e.g with parsing with pytest it lives in memory
     if str_module in sys.modules:

tests/trainer/test_trainer.py

@@ -32,6 +32,7 @@
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv
 from pytorch_lightning.loggers import TensorBoardLogger
+from pytorch_lightning.profiler.profilers import AdvancedProfiler, PassThroughProfiler, SimpleProfiler
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -1408,3 +1409,32 @@ def test_log_every_n_steps(log_metrics_mock, tmpdir, train_batches, max_steps, l
     trainer.fit(model)
     expected_calls = [call(metrics=ANY, step=s) for s in range(log_interval - 1, max_steps, log_interval)]
     log_metrics_mock.assert_has_calls(expected_calls)
+
+
+@pytest.mark.parametrize(['profiler', 'expected'], [
+    (None, PassThroughProfiler),
+    (SimpleProfiler(), SimpleProfiler),
+    (AdvancedProfiler(), AdvancedProfiler),
+    ('simple', SimpleProfiler),
+    ('Simple', SimpleProfiler),
+    ('advanced', AdvancedProfiler),
+])
+def test_trainer_profiler_correct_args(profiler, expected):
+    kwargs = {'profiler': profiler} if profiler is not None else {}
+    trainer = Trainer(**kwargs)
+    assert isinstance(trainer.profiler, expected)
+
+
+def test_trainer_profiler_incorrect_str_arg():
+    with pytest.raises(ValueError, match=r".*can only be 'simple' or 'advanced'"):
+        Trainer(profiler="unknown_profiler")
+
+
+@pytest.mark.parametrize('profiler', (
+    42, [42], {"a": 42}, torch.tensor(42), Trainer(),
+))
+def test_trainer_profiler_incorrect_arg_type(profiler):
+    with pytest.raises(MisconfigurationException,
+                       match=r"Only None, bool, str and subclasses of `BaseProfiler` "
+                       r"are valid values for `Trainer`'s `profiler` parameter. *"):
+        Trainer(profiler=profiler)