Fix TQDMProgressBar reset and update to show correct time estimation (

#12889)

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

CHANGELOG.md

@@ -21,6 +21,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `fuse_modules` to be qat-aware for `torch>=1.11` ([#12891](https://github.com/PyTorchLightning/pytorch-lightning/pull/12891))
 - Enforced eval shuffle warning only for default samplers in DataLoader ([#12653](https://github.com/PyTorchLightning/pytorch-lightning/pull/12653))
 - Enable mixed precision in `DDPFullyShardedStrategy` when `precision=16` ([#12965](https://github.com/PyTorchLightning/pytorch-lightning/pull/12965))
+- Fixed `TQDMProgressBar` reset and update to show correct time estimation ([#12889](https://github.com/PyTorchLightning/pytorch-lightning/pull/12889))
 
 
 ## [1.6.2] - 2022-04-27

pytorch_lightning/callbacks/progress/tqdm_progress.py

@@ -262,13 +262,13 @@ def on_train_epoch_start(self, trainer: "pl.Trainer", *_: Any) -> None:
             val_checks_per_epoch = total_train_batches // trainer.val_check_batch
             total_val_batches = total_val_batches * val_checks_per_epoch
         total_batches = total_train_batches + total_val_batches
-        self.main_progress_bar.total = convert_inf(total_batches)
+        self.main_progress_bar.reset(convert_inf(total_batches))
         self.main_progress_bar.set_description(f"Epoch {trainer.current_epoch}")
 
     def on_train_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", *_: Any) -> None:
         current = self.train_batch_idx + self._val_processed
         if self._should_update(current, self.main_progress_bar.total):
-            _update_n(self.main_progress_bar, current)
+            _update_n(self.main_progress_bar, current, self.refresh_rate)
             self.main_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
 
     def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
@@ -288,17 +288,17 @@ def on_validation_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.val_progress_bar.total = convert_inf(self.total_val_batches_current_dataloader)
+        self.val_progress_bar.reset(convert_inf(self.total_val_batches_current_dataloader))
         desc = self.sanity_check_description if trainer.sanity_checking else self.validation_description
         self.val_progress_bar.set_description(f"{desc} DataLoader {dataloader_idx}")
 
     def on_validation_batch_end(self, trainer: "pl.Trainer", *_: Any) -> None:
         if self._should_update(self.val_batch_idx, self.val_progress_bar.total):
-            _update_n(self.val_progress_bar, self.val_batch_idx)
+            _update_n(self.val_progress_bar, self.val_batch_idx, self.refresh_rate)
 
         current = self.train_batch_idx + self._val_processed
         if trainer.state.fn == "fit" and self._should_update(current, self.main_progress_bar.total):
-            _update_n(self.main_progress_bar, current)
+            _update_n(self.main_progress_bar, current, self.refresh_rate)
 
     def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         if self._main_progress_bar is not None and trainer.state.fn == "fit":
@@ -315,12 +315,12 @@ def on_test_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.test_progress_bar.total = convert_inf(self.total_test_batches_current_dataloader)
+        self.test_progress_bar.reset(convert_inf(self.total_test_batches_current_dataloader))
         self.test_progress_bar.set_description(f"{self.test_description} DataLoader {dataloader_idx}")
 
     def on_test_batch_end(self, *_: Any) -> None:
         if self._should_update(self.test_batch_idx, self.test_progress_bar.total):
-            _update_n(self.test_progress_bar, self.test_batch_idx)
+            _update_n(self.test_progress_bar, self.test_batch_idx, self.refresh_rate)
 
     def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self.test_progress_bar.close()
@@ -335,12 +335,12 @@ def on_predict_batch_start(
         if not self.has_dataloader_changed(dataloader_idx):
             return
 
-        self.predict_progress_bar.total = convert_inf(self.total_predict_batches_current_dataloader)
+        self.predict_progress_bar.reset(convert_inf(self.total_predict_batches_current_dataloader))
         self.predict_progress_bar.set_description(f"{self.predict_description} DataLoader {dataloader_idx}")
 
     def on_predict_batch_end(self, *_: Any) -> None:
         if self._should_update(self.predict_batch_idx, self.predict_progress_bar.total):
-            _update_n(self.predict_progress_bar, self.predict_batch_idx)
+            _update_n(self.predict_progress_bar, self.predict_batch_idx, self.refresh_rate)
 
     def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self.predict_progress_bar.close()
@@ -384,7 +384,10 @@ def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
     return x
 
 
-def _update_n(bar: _tqdm, value: int) -> None:
+def _update_n(bar: _tqdm, current: int, refresh_rate: int) -> None:
     if not bar.disable:
-        bar.n = value
+        total = bar.total
+        leftover = current % refresh_rate
+        advance = leftover if (current == total and leftover != 0) else refresh_rate
+        bar.update(advance)
         bar.refresh()

requirements.txt

@@ -2,7 +2,7 @@
 
 numpy>=1.17.2
 torch>=1.8.*
-tqdm>=4.41.0
+tqdm>=4.57.0
 PyYAML>=5.4
 fsspec[http]>=2021.05.0, !=2021.06.0
 tensorboard>=2.2.0

tests/callbacks/test_tqdm_progress_bar.py

@@ -53,6 +53,7 @@ def n(self):
     @n.setter
     def n(self, value):
         self.__n = value
+
         # track the changes in the `n` value
         if not len(self.n_values) or value != self.n_values[-1]:
             self.n_values.append(value)
@@ -158,7 +159,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=None):
     assert not pbar.val_progress_bar.leave
     assert trainer.num_sanity_val_batches == expected_sanity_steps
     assert pbar.val_progress_bar.total_values == expected_sanity_steps
-    assert pbar.val_progress_bar.n_values == list(range(1, num_sanity_val_steps + 1)) * num_dl
+    assert pbar.val_progress_bar.n_values == list(range(num_sanity_val_steps + 1)) * num_dl
     assert pbar.val_progress_bar.descriptions == [f"Sanity Checking DataLoader {i}: " for i in range(num_dl)]
 
     # fit
@@ -177,7 +178,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=None):
 
     # check val progress bar total
     assert pbar.val_progress_bar.total_values == m
-    assert pbar.val_progress_bar.n_values == list(range(1, m[0] + 1)) * num_dl
+    assert pbar.val_progress_bar.n_values == list(range(m[0] + 1)) * num_dl
     assert pbar.val_progress_bar.descriptions == [f"Validation DataLoader {i}: " for i in range(num_dl)]
     assert not pbar.val_progress_bar.leave
 
@@ -186,7 +187,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=None):
         trainer.validate(model)
     assert trainer.num_val_batches == m
     assert pbar.val_progress_bar.total_values == m
-    assert pbar.val_progress_bar.n_values == list(range(1, m[0] + 1)) * num_dl
+    assert pbar.val_progress_bar.n_values == list(range(m[0] + 1)) * num_dl
     assert pbar.val_progress_bar.descriptions == [f"Validation DataLoader {i}: " for i in range(num_dl)]
 
     # test
@@ -195,7 +196,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=None):
     assert pbar.test_progress_bar.leave
     k = trainer.num_test_batches
     assert pbar.test_progress_bar.total_values == k
-    assert pbar.test_progress_bar.n_values == list(range(1, k[0] + 1)) * num_dl
+    assert pbar.test_progress_bar.n_values == list(range(k[0] + 1)) * num_dl
     assert pbar.test_progress_bar.descriptions == [f"Testing DataLoader {i}: " for i in range(num_dl)]
     assert pbar.test_progress_bar.leave
 
@@ -205,7 +206,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=None):
     assert pbar.predict_progress_bar.leave
     k = trainer.num_predict_batches
     assert pbar.predict_progress_bar.total_values == k
-    assert pbar.predict_progress_bar.n_values == list(range(1, k[0] + 1)) * num_dl
+    assert pbar.predict_progress_bar.n_values == list(range(k[0] + 1)) * num_dl
     assert pbar.predict_progress_bar.descriptions == [f"Predicting DataLoader {i}: " for i in range(num_dl)]
     assert pbar.predict_progress_bar.leave
 
@@ -359,13 +360,13 @@ def test_tqdm_progress_bar_value_on_colab(tmpdir):
 @pytest.mark.parametrize(
     "train_batches,val_batches,refresh_rate,train_updates,val_updates",
     [
-        [2, 3, 1, [1, 2, 3, 4, 5], [1, 2, 3]],
+        [2, 3, 1, [0, 1, 2, 3, 4, 5], [0, 1, 2, 3]],
         [0, 0, 3, None, None],
-        [1, 0, 3, [1], None],
-        [1, 1, 3, [2], [1]],
-        [5, 0, 3, [3, 5], None],
-        [5, 2, 3, [3, 6, 7], [2]],
-        [5, 2, 6, [6, 7], [2]],
+        [1, 0, 3, [0, 1], None],
+        [1, 1, 3, [0, 2], [0, 1]],
+        [5, 0, 3, [0, 3, 5], None],
+        [5, 2, 3, [0, 3, 6, 7], [0, 2]],
+        [5, 2, 6, [0, 6, 7], [0, 2]],
     ],
 )
 def test_main_progress_bar_update_amount(
@@ -395,7 +396,7 @@ def test_main_progress_bar_update_amount(
         assert progress_bar.val_progress_bar.n_values == val_updates
 
 
-@pytest.mark.parametrize("test_batches,refresh_rate,updates", [[1, 3, [1]], [3, 1, [1, 2, 3]], [5, 3, [3, 5]]])
+@pytest.mark.parametrize("test_batches,refresh_rate,updates", [(1, 3, [0, 1]), (3, 1, [0, 1, 2, 3]), (5, 3, [0, 3, 5])])
 def test_test_progress_bar_update_amount(tmpdir, test_batches: int, refresh_rate: int, updates: list):
     """Test that test progress updates with the correct amount."""
     model = BoringModel()
@@ -566,7 +567,7 @@ def test_tqdm_progress_bar_can_be_pickled():
 
 @pytest.mark.parametrize(
     ["val_check_interval", "main_progress_bar_updates", "val_progress_bar_updates"],
-    [(4, [3, 6, 9, 12, 14], [3, 6, 7]), (0.5, [3, 6, 9, 12, 15, 18, 21], [3, 6, 7])],
+    [(4, [0, 3, 6, 9, 12, 14], [0, 3, 6, 7]), (0.5, [0, 3, 6, 9, 12, 15, 18, 21], [0, 3, 6, 7])],
 )
 def test_progress_bar_max_val_check_interval(
     tmpdir, val_check_interval, main_progress_bar_updates, val_progress_bar_updates