Add logging messages to notify when FitLoop stopping conditions are met

src/pytorch_lightning/CHANGELOG.md

@@ -93,6 +93,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `XLAEnvironment` cluster environment plugin ([#11330](https://github.com/PyTorchLightning/pytorch-lightning/pull/11330))
 
 
+- Added logging messages to notify when `FitLoop` stopping conditions are met ([#9749](https://github.com/PyTorchLightning/pytorch-lightning/pull/9749))
+
+
 - Added support for calling unknown methods with `DummyLogger` ([#13224](https://github.com/PyTorchLightning/pytorch-lightning/pull/13224)
 
 

src/pytorch_lightning/loops/fit_loop.py

@@ -33,7 +33,7 @@
     InterBatchParallelDataFetcher,
 )
 from pytorch_lightning.utilities.model_helpers import is_overridden
-from pytorch_lightning.utilities.rank_zero import rank_zero_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_debug, rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
 
 log = logging.getLogger(__name__)
@@ -150,31 +150,40 @@ def _results(self) -> _ResultCollection:
     @property
     def done(self) -> bool:
         """Evaluates when to leave the loop."""
+        if self.trainer.num_training_batches == 0:
+            rank_zero_info("`Trainer.fit` stopped: No training batches.")
+            return True
+
         # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
         stop_steps = _is_max_limit_reached(self.epoch_loop.global_step, self.max_steps)
+        if stop_steps:
+            rank_zero_info(f"`Trainer.fit` stopped: `max_steps={self.max_steps!r}` reached.")
+            return True
+
         # `processed` is increased before `on_train_epoch_end`, the hook where checkpoints are typically saved.
         # we use it here because the checkpoint data won't have `completed` increased yet
         stop_epochs = _is_max_limit_reached(self.epoch_progress.current.processed, self.max_epochs)
         if stop_epochs:
             # in case they are not equal, override so `trainer.current_epoch` has the expected value
             self.epoch_progress.current.completed = self.epoch_progress.current.processed
+            rank_zero_info(f"`Trainer.fit` stopped: `max_epochs={self.max_epochs!r}` reached.")
+            return True
 
-        should_stop = False
         if self.trainer.should_stop:
             # early stopping
             met_min_epochs = self.epoch_progress.current.processed >= self.min_epochs if self.min_epochs else True
             met_min_steps = self.epoch_loop.global_step >= self.min_steps if self.min_steps else True
             if met_min_epochs and met_min_steps:
-                should_stop = True
+                self.trainer.should_stop = True
+                rank_zero_debug("`Trainer.fit` stopped: `trainer.should_stop` was set.")
+                return True
             else:
-                log.info(
-                    "Trainer was signaled to stop but required minimum epochs"
-                    f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has"
-                    " not been met. Training will continue..."
+                rank_zero_info(
+                    f"Trainer was signaled to stop but the required `min_epochs={self.min_epochs!r}` or"
+                    f" `min_steps={self.min_steps!r}` has not been met. Training will continue..."
                 )
-        self.trainer.should_stop = should_stop
-
-        return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0
+        self.trainer.should_stop = False
+        return False
 
     @property
     def skip(self) -> bool:

tests/tests_pytorch/loops/test_training_loop.py

@@ -11,11 +11,15 @@
 # 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.
+import logging
+from unittest.mock import Mock
+
 import pytest
 import torch
 
 from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.demos.boring_classes import BoringModel
+from pytorch_lightning.loops import FitLoop
 
 
 def test_outputs_format(tmpdir):
@@ -136,6 +140,49 @@ def validation_step(self, *args):
     assert model.validation_called_at == (0, 5)
 
 
+def test_fit_loop_done_log_messages(caplog):
+    fit_loop = FitLoop()
+    trainer = Mock(spec=Trainer)
+    fit_loop.trainer = trainer
+
+    trainer.should_stop = False
+    trainer.num_training_batches = 5
+    assert not fit_loop.done
+    assert not caplog.messages
+
+    trainer.num_training_batches = 0
+    assert fit_loop.done
+    assert "No training batches" in caplog.text
+    caplog.clear()
+    trainer.num_training_batches = 5
+
+    epoch_loop = Mock()
+    epoch_loop.global_step = 10
+    fit_loop.connect(epoch_loop=epoch_loop)
+    fit_loop.max_steps = 10
+    assert fit_loop.done
+    assert "max_steps=10` reached" in caplog.text
+    caplog.clear()
+    fit_loop.max_steps = 20
+
+    fit_loop.epoch_progress.current.processed = 3
+    fit_loop.max_epochs = 3
+    trainer.should_stop = True
+    assert fit_loop.done
+    assert "max_epochs=3` reached" in caplog.text
+    caplog.clear()
+    fit_loop.max_epochs = 5
+
+    fit_loop.epoch_loop.min_steps = 0
+    with caplog.at_level(level=logging.DEBUG, logger="pytorch_lightning.utilities.rank_zero"):
+        assert fit_loop.done
+    assert "should_stop` was set" in caplog.text
+
+    fit_loop.epoch_loop.min_steps = 100
+    assert not fit_loop.done
+    assert "was signaled to stop but" in caplog.text
+
+
 def test_warning_valid_train_step_end(tmpdir):
     class ValidTrainStepEndModel(BoringModel):
         def training_step(self, batch, batch_idx):

tests/tests_pytorch/trainer/test_trainer.py

@@ -615,7 +615,7 @@ def training_step(self, batch, batch_idx):
     with caplog.at_level(logging.INFO, logger="pytorch_lightning.trainer.trainer"):
         trainer.fit(model)
 
-    message = f"minimum epochs ({min_epochs}) or minimum steps (None) has not been met. Training will continue"
+    message = f"min_epochs={min_epochs}` or `min_steps=None` has not been met. Training will continue"
     num_messages = sum(1 for record in caplog.records if message in record.message)
     assert num_messages == min_epochs - 2
     assert model.training_step_invoked == min_epochs * 2