Deprecate LoggerCollection in favor of trainer.loggers

CHANGELOG.md

@@ -454,6 +454,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `BaseProfiler.profile_iterable` ([#12102](https://github.com/PyTorchLightning/pytorch-lightning/pull/12102))
 
 
+- Deprecated `LoggerCollection` in favor of `trainer.loggers` ([#12147](https://github.com/PyTorchLightning/pytorch-lightning/pull/12147))
+
+
 - Deprecated `PrecisionPlugin.on_{save,load}_checkpoint` in favor of `PrecisionPlugin.{state_dict,load_state_dict}` ([#11978](https://github.com/PyTorchLightning/pytorch-lightning/pull/11978))
 
 

pytorch_lightning/loggers/base.py

@@ -221,13 +221,21 @@ def version(self) -> Union[int, str]:
 class LoggerCollection(LightningLoggerBase):
     """The :class:`LoggerCollection` class is used to iterate all logging actions over the given `logger_iterable`.
 
+    .. deprecated:: v1.6
+        `LoggerCollection` is deprecated in v1.6 and will be removed in v1.8.
+        Directly pass a list of loggers to the Trainer and access the list via the `trainer.loggers` attribute.
+
     Args:
         logger_iterable: An iterable collection of loggers
     """
 
     def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
         super().__init__()
         self._logger_iterable = logger_iterable
+        rank_zero_deprecation(
+            "`LoggerCollection` is deprecated in v1.6 and will be removed in v1.8. Directly pass a list of loggers"
+            " to the Trainer and access the list via the `trainer.loggers` attribute."
+        )
 
     def __getitem__(self, index: int) -> LightningLoggerBase:
         return list(self._logger_iterable)[index]

pytorch_lightning/trainer/trainer.py

@@ -2623,7 +2623,9 @@ def logger(self) -> Optional[LightningLoggerBase]:
                 " This behavior will change in v1.8 when LoggerCollection is removed, and"
                 " trainer.logger will return the first logger in trainer.loggers"
             )
-            return LoggerCollection(self.loggers)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                return LoggerCollection(self.loggers)
 
     @logger.setter
     def logger(self, logger: Optional[LightningLoggerBase]) -> None:

tests/deprecated_api/test_remove_1-8.py

@@ -21,7 +21,7 @@
 from torch import optim
 
 from pytorch_lightning import Callback, Trainer
-from pytorch_lightning.loggers import CSVLogger, LightningLoggerBase
+from pytorch_lightning.loggers import CSVLogger, LightningLoggerBase, LoggerCollection
 from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
 from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin
@@ -662,6 +662,23 @@ def _get_python_cprofile_total_duration(profile):
     np.testing.assert_allclose(recorded_total_duration, expected_total_duration, rtol=0.2)
 
 
+def test_v1_8_0_logger_collection(tmpdir):
+    logger1 = CSVLogger(tmpdir)
+    logger2 = CSVLogger(tmpdir)
+
+    trainer1 = Trainer(logger=logger1)
+    trainer2 = Trainer(logger=[logger1, logger2])
+
+    # Should have no deprecation warning
+    trainer1.logger
+    trainer1.loggers
+    trainer2.loggers
+    trainer2.logger
+
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        LoggerCollection([logger1, logger2])
+
+
 def test_v1_8_0_precision_plugin_checkpoint_hooks(tmpdir):
     class PrecisionPluginSaveHook(PrecisionPlugin):
         def on_save_checkpoint(self, checkpoint):

tests/loggers/test_base.py

@@ -34,7 +34,8 @@ def test_logger_collection():
     mock1 = MagicMock()
     mock2 = MagicMock()
 
-    logger = LoggerCollection([mock1, mock2])
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger = LoggerCollection([mock1, mock2])
 
     assert logger[0] == mock1
     assert logger[1] == mock2
@@ -62,14 +63,16 @@ def test_logger_collection_unique_names():
     logger1 = CustomLogger(name=unique_name)
     logger2 = CustomLogger(name=unique_name)
 
-    logger = LoggerCollection([logger1, logger2])
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger = LoggerCollection([logger1, logger2])
 
     assert logger.name == unique_name
 
 
 def test_logger_collection_names_order():
     loggers = [CustomLogger(name=n) for n in ("name1", "name2", "name1", "name3")]
-    logger = LoggerCollection(loggers)
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger = LoggerCollection(loggers)
     assert logger.name == f"{loggers[0].name}_{loggers[1].name}_{loggers[3].name}"
 
 
@@ -78,14 +81,16 @@ def test_logger_collection_unique_versions():
     logger1 = CustomLogger(version=unique_version)
     logger2 = CustomLogger(version=unique_version)
 
-    logger = LoggerCollection([logger1, logger2])
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger = LoggerCollection([logger1, logger2])
 
     assert logger.version == unique_version
 
 
 def test_logger_collection_versions_order():
     loggers = [CustomLogger(version=v) for v in ("1", "2", "1", "3")]
-    logger = LoggerCollection(loggers)
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger = LoggerCollection(loggers)
     assert logger.version == f"{loggers[0].version}_{loggers[1].version}_{loggers[3].version}"
 
 

tests/profiler/test_profiler.py

@@ -24,7 +24,7 @@
 
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, StochasticWeightAveraging
-from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
+from pytorch_lightning.loggers import CSVLogger, TensorBoardLogger
 from pytorch_lightning.profiler import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.profiler.pytorch import RegisterRecordFunction, warning_cache
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -450,9 +450,9 @@ def test_pytorch_profiler_nested(tmpdir):
     assert events_name == expected, (events_name, torch.__version__, platform.system())
 
 
-def test_pytorch_profiler_logger_collection(tmpdir):
-    """Tests whether the PyTorch profiler is able to write its trace locally when the Trainer's logger is an
-    instance of LoggerCollection.
+def test_pytorch_profiler_multiple_loggers(tmpdir):
+    """Tests whether the PyTorch profiler is able to write its trace locally when the Trainer is configured with
+    multiple loggers.
 
     See issue #8157.
     """
@@ -465,10 +465,9 @@ def look_for_trace(trace_dir):
     assert not look_for_trace(tmpdir)
 
     model = BoringModel()
-    # Wrap the logger in a list so it becomes a LoggerCollection
-    logger = [TensorBoardLogger(save_dir=tmpdir), CSVLogger(tmpdir)]
-    trainer = Trainer(default_root_dir=tmpdir, profiler="pytorch", logger=logger, limit_train_batches=5, max_epochs=1)
-    assert isinstance(trainer.logger, LoggerCollection)
+    loggers = [TensorBoardLogger(save_dir=tmpdir), CSVLogger(tmpdir)]
+    trainer = Trainer(default_root_dir=tmpdir, profiler="pytorch", logger=loggers, limit_train_batches=5, max_epochs=1)
+    assert len(trainer.loggers) == 2
     trainer.fit(model)
     assert look_for_trace(tmpdir)
 

tests/trainer/properties/test_log_dir.py

@@ -15,7 +15,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
+from pytorch_lightning.loggers import CSVLogger, TensorBoardLogger
 from tests.helpers.boring_model import BoringModel
 
 
@@ -109,8 +109,8 @@ def test_logdir_custom_logger(tmpdir):
     assert trainer.log_dir == expected
 
 
-def test_logdir_logger_collection(tmpdir):
-    """Tests that the logdir equals the default_root_dir when the logger is a LoggerCollection."""
+def test_logdir_multiple_loggers(tmpdir):
+    """Tests that the logdir equals the default_root_dir when trainer has multiple loggers."""
     default_root_dir = tmpdir / "default_root_dir"
     save_dir = tmpdir / "save_dir"
     model = TestModel(default_root_dir)
@@ -119,7 +119,6 @@ def test_logdir_logger_collection(tmpdir):
         max_steps=2,
         logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), CSVLogger(tmpdir)],
     )
-    assert isinstance(trainer.logger, LoggerCollection)
     assert trainer.log_dir == default_root_dir
 
     trainer.fit(model)

tests/trainer/properties/test_loggers.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import pytest
+
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
 from tests.loggers.test_base import CustomLogger
@@ -50,8 +52,10 @@ def test_trainer_loggers_setters():
     """Test the behavior of setters for trainer.logger and trainer.loggers."""
     logger1 = CustomLogger()
     logger2 = CustomLogger()
-    logger_collection = LoggerCollection([logger1, logger2])
-    logger_collection_2 = LoggerCollection([logger2])
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger_collection = LoggerCollection([logger1, logger2])
+    with pytest.deprecated_call(match="`LoggerCollection` is deprecated in v1.6"):
+        logger_collection_2 = LoggerCollection([logger2])
 
     trainer = Trainer()
     assert type(trainer.logger) == TensorBoardLogger