Deprecate and remove calls to agg_and_log_metrics

CHANGELOG.md

@@ -397,6 +397,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `pytorch_lightning.utilities.warnings.LightningDeprecationWarning` in favor of `pytorch_lightning.utilities.rank_zero.LightningDeprecationWarning`
 
 
+- Deprecated `LightningLoggerBase.agg_and_log_metrics` in favor of `LightningLoggerBase.log_metrics` ([#11832](https://github.com/PyTorchLightning/pytorch-lightning/pull/11832))
+
+
 ### Removed
 
 - Removed deprecated parameter `method` in `pytorch_lightning.utilities.model_helpers.is_overridden` ([#10507](https://github.com/PyTorchLightning/pytorch-lightning/pull/10507))

pytorch_lightning/loggers/base.py

@@ -104,6 +104,9 @@ def _aggregate_metrics(
     ) -> Tuple[int, Optional[Dict[str, float]]]:
         """Aggregates metrics.
 
+        .. deprecated:: v1.6
+            This method is deprecated in v1.6 and will be removed in v1.8.
+
         Args:
             metrics: Dictionary with metric names as keys and measured quantities as values
             step: Step number at which the metrics should be recorded
@@ -126,7 +129,13 @@ def _aggregate_metrics(
         return agg_step, agg_mets
 
     def _reduce_agg_metrics(self):
-        """Aggregate accumulated metrics."""
+        """Aggregate accumulated metrics.
+
+        See deprecation warning below.
+
+        .. deprecated:: v1.6
+            This method is deprecated in v1.6 and will be removed in v1.8.
+        """
         # compute the metrics
         if not self._metrics_to_agg:
             agg_mets = None
@@ -137,7 +146,13 @@ def _reduce_agg_metrics(self):
         return self._prev_step, agg_mets
 
     def _finalize_agg_metrics(self):
-        """This shall be called before save/close."""
+        """This shall be called before save/close.
+
+        See deprecation warning below.
+
+        .. deprecated:: v1.6
+            This method is deprecated in v1.6 and will be removed in v1.8.
+        """
         agg_step, metrics_to_log = self._reduce_agg_metrics()
         self._metrics_to_agg = []
 
@@ -148,6 +163,10 @@ def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = N
         """Aggregates and records metrics. This method doesn't log the passed metrics instantaneously, but instead
         it aggregates them and logs only if metrics are ready to be logged.
 
+        .. deprecated:: v1.6
+            This method is deprecated in v1.6 and will be removed in v1.8.
+            Please use `LightningLoggerBase.log_metrics` instead.
+
         Args:
             metrics: Dictionary with metric names as keys and measured quantities as values
             step: Step number at which the metrics should be recorded
@@ -286,11 +305,11 @@ def experiment(self) -> List[Any]:
 
     def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
         for logger in self._logger_iterable:
-            logger.agg_and_log_metrics(metrics, step)
+            logger.agg_and_log_metrics(metrics=metrics, step=step)
 
     def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
         for logger in self._logger_iterable:
-            logger.log_metrics(metrics, step)
+            logger.log_metrics(metrics=metrics, step=step)
 
     def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
         for logger in self._logger_iterable:

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -23,7 +23,8 @@
 from pytorch_lightning.utilities import _AcceleratorType, memory
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
 from pytorch_lightning.utilities.metrics import metrics_to_scalars
-from pytorch_lightning.utilities.warnings import rank_zero_deprecation
+from pytorch_lightning.utilities.model_helpers import is_overridden
+from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
 
 
 class LoggerConnector:
@@ -45,6 +46,7 @@ def __init__(self, trainer: "pl.Trainer", log_gpu_memory: Optional[str] = None)
         self._current_fx: Optional[str] = None
         self._batch_idx: Optional[int] = None
         self._split_idx: Optional[int] = None
+        self._override_agg_and_log_metrics: bool = False
 
     def on_trainer_init(
         self,
@@ -64,6 +66,15 @@ def on_trainer_init(
         self.trainer.flush_logs_every_n_steps = flush_logs_every_n_steps
         self.trainer.log_every_n_steps = log_every_n_steps
         self.trainer.move_metrics_to_cpu = move_metrics_to_cpu
+        for logger in self.trainer.loggers:
+            if is_overridden("agg_and_log_metrics", logger, LightningLoggerBase):
+                self._override_agg_and_log_metrics = True
+                rank_zero_deprecation(
+                    "`LightningLoggerBase.agg_and_log_metrics` is deprecated in v1.6 and will be removed"
+                    " in v1.8. `Trainer` will directly call `LightningLoggerBase.log_metrics` so custom"
+                    " loggers should not implement `LightningLoggerBase.agg_and_log_metrics`."
+                )
+                break
 
     @property
     def should_flush_logs(self) -> bool:
@@ -114,7 +125,10 @@ def log_metrics(self, metrics: _OUT_DICT, step: Optional[int] = None) -> None:
             step = self.trainer.global_step
 
         # log actual metrics
-        self.trainer.logger.agg_and_log_metrics(scalar_metrics, step=step)
+        if self._override_agg_and_log_metrics:
+            self.trainer.logger.agg_and_log_metrics(metrics=scalar_metrics, step=step)
+        else:
+            self.trainer.logger.log_metrics(metrics=scalar_metrics, step=step)
         self.trainer.logger.save()
 
     """

tests/deprecated_api/test_remove_1-8.py

@@ -19,6 +19,7 @@
 from torch import optim
 
 from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.loggers import CSVLogger
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
 from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin
 from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
@@ -35,7 +36,7 @@
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.enums import DeviceType, DistributedType
 from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
-from pytorch_lightning.utilities.rank_zero import rank_zero_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
 from tests.helpers.boring_model import BoringDataModule, BoringModel
 from tests.helpers.runif import RunIf
 from tests.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
@@ -500,3 +501,34 @@ def on_before_accelerator_backend_setup(self, *args, **kwargs):
         " and will be removed in v1.8"
     ):
         trainer.fit(model)
+
+
+def test_v1_8_0_deprecated_agg_and_log_metrics_override(tmpdir):
+    class AggregationOverrideLogger(CSVLogger):
+        @rank_zero_only
+        def agg_and_log_metrics(self, metrics, step):
+            self.log_metrics(metrics=metrics, step=step)
+
+    logger = AggregationOverrideLogger(tmpdir)
+    logger2 = CSVLogger(tmpdir)
+    logger3 = CSVLogger(tmpdir)
+
+    # Test single loggers
+    with pytest.deprecated_call(
+        match="`LightningLoggerBase.agg_and_log_metrics` is deprecated in v1.6 and will be removed"
+        " in v1.8. `Trainer` will directly call `LightningLoggerBase.log_metrics` so custom"
+        " loggers should not implement `LightningLoggerBase.agg_and_log_metrics`."
+    ):
+        Trainer(logger=logger)
+    # Should have no deprecation warning
+    Trainer(logger=logger2)
+
+    # Test multiple loggers
+    with pytest.deprecated_call(
+        match="`LightningLoggerBase.agg_and_log_metrics` is deprecated in v1.6 and will be removed"
+        " in v1.8. `Trainer` will directly call `LightningLoggerBase.log_metrics` so custom"
+        " loggers should not implement `LightningLoggerBase.agg_and_log_metrics`."
+    ):
+        Trainer(logger=[logger, logger3])
+    # Should have no deprecation warning
+    Trainer(logger=[logger2, logger3])

tests/loggers/test_base.py

@@ -48,9 +48,9 @@ def test_logger_collection():
     mock1.update_agg_funcs.assert_called_once_with({"test": np.mean}, np.sum)
     mock2.update_agg_funcs.assert_called_once_with({"test": np.mean}, np.sum)
 
-    logger.agg_and_log_metrics({"test": 2.0}, 4)
-    mock1.agg_and_log_metrics.assert_called_once_with({"test": 2.0}, 4)
-    mock2.agg_and_log_metrics.assert_called_once_with({"test": 2.0}, 4)
+    logger.log_metrics(metrics={"test": 2.0}, step=4)
+    mock1.log_metrics.assert_called_once_with(metrics={"test": 2.0}, step=4)
+    mock2.log_metrics.assert_called_once_with(metrics={"test": 2.0}, step=4)
 
     logger.finalize("success")
     mock1.finalize.assert_called_once()
@@ -225,31 +225,6 @@ def validation_epoch_end(self, outputs):
     trainer.fit(model)
 
 
-def test_with_accumulate_grad_batches():
-    """Checks if the logging is performed once for `accumulate_grad_batches` steps."""
-
-    class StoreHistoryLogger(CustomLogger):
-        def __init__(self):
-            super().__init__()
-            self.history = {}
-
-        @rank_zero_only
-        def log_metrics(self, metrics, step):
-            if step not in self.history:
-                self.history[step] = {}
-            self.history[step].update(metrics)
-
-    logger = StoreHistoryLogger()
-
-    np.random.seed(42)
-    for i, loss in enumerate(np.random.random(10)):
-        logger.agg_and_log_metrics({"loss": loss}, step=int(i / 5))
-
-    assert logger.history == {0: {"loss": 0.5623850983416314}}
-    logger.save()
-    assert logger.history == {0: {"loss": 0.5623850983416314}, 1: {"loss": 0.4778883735637184}}
-
-
 def test_dummyexperiment_support_indexing():
     """Test that the DummyExperiment can imitate indexing the experiment in a LoggerCollection."""
     experiment = DummyExperiment()