Fix TQDM progress bar showing the wrong total when using a finite and iterable dataloader

src/lightning/pytorch/CHANGELOG.md

@@ -34,6 +34,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed with adding a missing device id for pytorch 2.8 ([#21105](https://github.com/Lightning-AI/pytorch-lightning/pull/21105))
 
 
+- Fixed `TQDMProgressBar` not resetting correctly when using both a finite and iterable dataloader ([#21147](https://github.com/Lightning-AI/pytorch-lightning/pull/21147))
+
 ---
 
 

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

@@ -265,7 +265,9 @@ def on_train_start(self, *_: Any) -> None:
     def on_train_epoch_start(self, trainer: "pl.Trainer", *_: Any) -> None:
         if self._leave:
             self.train_progress_bar = self.init_train_tqdm()
-        self.train_progress_bar.reset(convert_inf(self.total_train_batches))
+        total = convert_inf(self.total_train_batches)
+        self.train_progress_bar.reset()
+        self.train_progress_bar.total = total
         self.train_progress_bar.initial = 0
         self.train_progress_bar.set_description(f"Epoch {trainer.current_epoch}")
 
@@ -306,7 +308,9 @@ def on_validation_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.val_progress_bar.reset(convert_inf(self.total_val_batches_current_dataloader))
+        total = convert_inf(self.total_val_batches_current_dataloader)
+        self.val_progress_bar.reset()
+        self.val_progress_bar.total = total
         self.val_progress_bar.initial = 0
         desc = self.sanity_check_description if trainer.sanity_checking else self.validation_description
         self.val_progress_bar.set_description(f"{desc} DataLoader {dataloader_idx}")
@@ -348,7 +352,9 @@ def on_test_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.test_progress_bar.reset(convert_inf(self.total_test_batches_current_dataloader))
+        total = convert_inf(self.total_test_batches_current_dataloader)
+        self.test_progress_bar.reset()
+        self.test_progress_bar.total = total
         self.test_progress_bar.initial = 0
         self.test_progress_bar.set_description(f"{self.test_description} DataLoader {dataloader_idx}")
 
@@ -387,7 +393,9 @@ def on_predict_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.predict_progress_bar.reset(convert_inf(self.total_predict_batches_current_dataloader))
+        total = convert_inf(self.total_predict_batches_current_dataloader)
+        self.predict_progress_bar.reset()
+        self.predict_progress_bar.total = total
         self.predict_progress_bar.initial = 0
         self.predict_progress_bar.set_description(f"{self.predict_description} DataLoader {dataloader_idx}")
 

tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py

@@ -812,3 +812,50 @@ def test_tqdm_leave(leave, tmp_path):
     )
     trainer.fit(model)
     assert pbar.init_train_tqdm.call_count == (4 if leave else 1)
+
+
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+def test_tqdm_progress_bar_reset_behavior(tmp_path):
+    """Test that progress bars call reset() without parameters and set total separately."""
+    model = BoringModel()
+
+    class ResetTrackingTqdm(MockTqdm):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self.reset_calls_with_params = []
+
+        def reset(self, total=None):
+            self.reset_calls_with_params.append(total)
+            super().reset(total)
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        limit_train_batches=2,
+        limit_val_batches=2,
+        max_epochs=1,
+        logger=False,
+        enable_checkpointing=False,
+    )
+
+    pbar = trainer.progress_bar_callback
+
+    with mock.patch("lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm", ResetTrackingTqdm):
+        trainer.fit(model)
+
+    train_bar = pbar.train_progress_bar
+    assert None in train_bar.reset_calls_with_params, (
+        f"train reset() should be called without parameters, got calls: {train_bar.reset_calls_with_params}"
+    )
+    # Verify that total was set separately to the expected value
+    assert 2 in train_bar.total_values, (
+        f"train total should be set to 2 after reset(), got total_values: {train_bar.total_values}"
+    )
+    # Verify that validation progress bar reset() was called without parameters
+    val_bar = pbar.val_progress_bar
+    assert None in val_bar.reset_calls_with_params, (
+        f"validation reset() should be called without parameters, got calls: {val_bar.reset_calls_with_params}"
+    )
+    # Verify that total was set separately to the expected value
+    assert 2 in val_bar.total_values, (
+        f"validation total should be set to 2 after reset(), got total_values: {val_bar.total_values}"
+    )