Enhance logger metrics retrieval to conditionally include progress bar metrics based on the presence of a progress bar callback.

Signed-off-by: Wil Kong <[email protected]>

Author: alpha0422

src/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py

@@ -234,7 +234,9 @@ def metrics(self) -> _METRICS:
         """This function returns either batch or epoch metrics."""
         on_step = self._first_loop_iter is not None
         assert self.trainer._results is not None
-        return self.trainer._results.metrics(on_step)
+        # Only include progress bar metrics if a progress bar callback is present
+        include_pbar_metrics = self.trainer.progress_bar_callback is not None
+        return self.trainer._results.metrics(on_step, include_pbar_metrics=include_pbar_metrics)
 
     @property
     def callback_metrics(self) -> _OUT_DICT:

src/lightning/pytorch/trainer/connectors/logger_connector/result.py

@@ -468,7 +468,7 @@ def _forked_name(self, result_metric: _ResultMetric, on_step: bool) -> tuple[str
             forked_name += dataloader_suffix
         return name, forked_name
 
-    def metrics(self, on_step: bool) -> _METRICS:
+    def metrics(self, on_step: bool, *, include_pbar_metrics: bool = True) -> _METRICS:
         metrics = _METRICS(callback={}, log={}, pbar={})
 
         for _, result_metric in self.valid_items():
@@ -489,7 +489,7 @@ def metrics(self, on_step: bool) -> _METRICS:
                 metrics["callback"][forked_name] = value
 
             # populate progress_bar metrics. convert tensors to numbers
-            if result_metric.meta.prog_bar:
+            if result_metric.meta.prog_bar and include_pbar_metrics:
                 metrics["pbar"][forked_name] = convert_tensors_to_scalars(value)
 
         return metrics