Merge branch 'master' into rich_progress_bar_vnum

Author: Borda

docs/source-pytorch/advanced/speed.rst

@@ -464,7 +464,7 @@ takes a great deal of care to be optimized for this.
 Clear Cache
 ===========
 
-Don't call :func:`torch.cuda.empty_cache` unnecessarily! Every time you call this, ALL your GPUs have to wait to sync.
+Don't call ``torch.cuda.empty_cache`` unnecessarily! Every time you call this, ALL your GPUs have to wait to sync.
 
 Transferring Tensors to Device
 ==============================

src/lightning/pytorch/CHANGELOG.md

@@ -30,6 +30,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - fix `AdvancedProfiler` to handle nested profiling actions for Python 3.12+ ([#20809](https://github.com/Lightning-AI/pytorch-lightning/pull/20809))
 
 
+- Fixed metrics in `RichProgressBar` being updated according to user provided `refresh_rate` ([#21032](https://github.com/Lightning-AI/pytorch-lightning/pull/21032))
+
 ---
 
 ## [2.5.2] - 2025-06-20

src/lightning/pytorch/callbacks/progress/rich_progress.py

@@ -552,12 +552,12 @@ def on_train_batch_end(
             # can happen when resuming from a mid-epoch restart
             self._initialize_train_progress_bar_id()
         self._update(self.train_progress_bar_id, batch_idx + 1)
-        self._update_metrics(trainer, pl_module)
+        self._update_metrics(trainer, pl_module, batch_idx + 1)
         self.refresh()
 
     @override
     def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        self._update_metrics(trainer, pl_module)
+        self._update_metrics(trainer, pl_module, total_batches=True)
 
     @override
     def on_validation_batch_end(
@@ -632,7 +632,21 @@ def _reset_progress_bar_ids(self) -> None:
         self.test_progress_bar_id = None
         self.predict_progress_bar_id = None
 
-    def _update_metrics(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+    def _update_metrics(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        current: Optional[int] = None,
+        total_batches: bool = False,
+    ) -> None:
+        if not self.is_enabled or self._metric_component is None:
+            return
+
+        if current is not None and not total_batches:
+            total = self.total_train_batches
+            if not self._should_update(current, total):
+                return
+
         metrics = self.get_metrics(trainer, pl_module)
         if self._metric_component:
             self._metric_component.update(metrics)

tests/tests_pytorch/callbacks/progress/test_rich_progress_bar.py

@@ -246,6 +246,9 @@ def test_rich_progress_bar_with_refresh_rate(tmp_path, refresh_rate, train_batch
     with mock.patch.object(
         trainer.progress_bar_callback.progress, "update", wraps=trainer.progress_bar_callback.progress.update
     ) as progress_update:
+        metrics_update = mock.MagicMock()
+        trainer.progress_bar_callback._update_metrics = metrics_update
+
         trainer.fit(model)
         assert progress_update.call_count == expected_call_count
 
@@ -260,6 +263,9 @@ def test_rich_progress_bar_with_refresh_rate(tmp_path, refresh_rate, train_batch
         assert fit_val_bar.total == val_batches
         assert not fit_val_bar.visible
 
+    # one call for each train batch + one at the end of training epoch + one for validation end
+    assert metrics_update.call_count == train_batches + (1 if train_batches > 0 else 0) + (1 if val_batches > 0 else 0)
+
 
 @RunIf(rich=True)
 @pytest.mark.parametrize("limit_val_batches", [1, 5])