feat: Default to RichProgressBar and RichModelSummary if rich is available

src/lightning/pytorch/CHANGELOG.md

@@ -13,6 +13,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for general mappings being returned from `training_step` when using manual optimization ([#21011](https://github.com/Lightning-AI/pytorch-lightning/pull/21011))
 
 
+- Default to RichProgressBar and RichModelSummary if the rich package is available. Fallback to TQDMProgressBar and ModelSummary otherwise. ([#9580](https://github.com/Lightning-AI/pytorch-lightning/pull/9580))
+
 
 ### Changed
 

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

@@ -17,15 +17,14 @@
 from datetime import timedelta
 from typing import Any, Optional, Union, cast
 
-from lightning_utilities.core.imports import RequirementCache
+import torch
 from typing_extensions import override
 
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks.progress.progress_bar import ProgressBar
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.types import STEP_OUTPUT
 
-_RICH_AVAILABLE = RequirementCache("rich>=10.2.2")
-
 if _RICH_AVAILABLE:
     from rich import get_console, reconfigure
     from rich.console import Console, RenderableType
@@ -171,7 +170,7 @@ def render(self, task: "Task") -> Text:
                 return Text()
             if self._trainer.training and task.id not in self._tasks:
                 self._tasks[task.id] = "None"
-                if self._renderable_cache:
+                if self._renderable_cache and self._current_task_id in self._renderable_cache:
                     self._current_task_id = cast(TaskID, self._current_task_id)
                     self._tasks[self._current_task_id] = self._renderable_cache[self._current_task_id][1]
                 self._current_task_id = task.id
@@ -184,8 +183,11 @@ def render(self, task: "Task") -> Text:
 
         def _generate_metrics_texts(self) -> Generator[str, None, None]:
             for name, value in self._metrics.items():
-                if not isinstance(value, (str, int)):
-                    value = f"{value:{self._metrics_format}}"
+                if not isinstance(value, str):
+                    try:
+                        value = f"{value:{self._metrics_format}}"
+                    except (TypeError, ValueError):
+                        value = str(value)
                 yield f"{name}: {value}"
 
 
@@ -465,17 +467,12 @@ def _initialize_train_progress_bar_id(self) -> None:
         self.train_progress_bar_id = self._add_task(total_batches, train_description)
 
     def _update(self, progress_bar_id: Optional["TaskID"], current: int, visible: bool = True) -> None:
-        if self.progress is not None and self.is_enabled:
-            assert progress_bar_id is not None
+        if self.progress is not None and self.is_enabled and progress_bar_id is not None:
             total = self.progress.tasks[progress_bar_id].total
             assert total is not None
             if not self._should_update(current, total):
                 return
-
-            leftover = current % self.refresh_rate
-            advance = leftover if (current == total and leftover != 0) else self.refresh_rate
-            self.progress.update(progress_bar_id, advance=advance, visible=visible)
-            self.refresh()
+            self.progress.update(progress_bar_id, completed=current, visible=visible)
 
     def _should_update(self, current: int, total: Union[int, float]) -> bool:
         return current % self.refresh_rate == 0 or current == total
@@ -572,9 +569,13 @@ def on_validation_batch_end(
         if self.is_disabled:
             return
         if trainer.sanity_checking:
-            self._update(self.val_sanity_progress_bar_id, batch_idx + 1)
-        elif self.val_progress_bar_id is not None:
-            self._update(self.val_progress_bar_id, batch_idx + 1)
+            if self.val_sanity_progress_bar_id is not None:
+                self._update(self.val_sanity_progress_bar_id, batch_idx + 1)
+            return
+
+        if self.val_progress_bar_id is None:
+            return
+        self._update(self.val_progress_bar_id, batch_idx + 1)
         self.refresh()
 
     @override
@@ -587,9 +588,8 @@ def on_test_batch_end(
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        if self.is_disabled:
+        if self.is_disabled or self.test_progress_bar_id is None:
             return
-        assert self.test_progress_bar_id is not None
         self._update(self.test_progress_bar_id, batch_idx + 1)
         self.refresh()
 
@@ -603,9 +603,8 @@ def on_predict_batch_end(
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        if self.is_disabled:
+        if self.is_disabled or self.predict_progress_bar_id is None:
             return
-        assert self.predict_progress_bar_id is not None
         self._update(self.predict_progress_bar_id, batch_idx + 1)
         self.refresh()
 
@@ -632,6 +631,17 @@ def _reset_progress_bar_ids(self) -> None:
         self.test_progress_bar_id = None
         self.predict_progress_bar_id = None
 
+    @override
+    def get_metrics(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> dict[str, Union[int, str, float, dict[str, float]]]:
+        items = super().get_metrics(trainer, pl_module)
+        # convert all metrics to float before sending to rich
+        for k, v in items.items():
+            if isinstance(v, torch.Tensor):
+                items[k] = v.item()
+        return items
+
     def _update_metrics(
         self,
         trainer: "pl.Trainer",

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

@@ -274,7 +274,7 @@ def on_train_batch_end(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.train_progress_bar.total):
+        if self.train_progress_bar is not None and self._should_update(n, self.train_progress_bar.total):
             _update_n(self.train_progress_bar, n)
             self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
 
@@ -322,7 +322,7 @@ def on_validation_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.val_progress_bar.total):
+        if self.val_progress_bar is not None and self._should_update(n, self.val_progress_bar.total):
             _update_n(self.val_progress_bar, n)
 
     @override
@@ -363,7 +363,7 @@ def on_test_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.test_progress_bar.total):
+        if self.test_progress_bar is not None and self._should_update(n, self.test_progress_bar.total):
             _update_n(self.test_progress_bar, n)
 
     @override
@@ -402,7 +402,7 @@ def on_predict_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.predict_progress_bar.total):
+        if self.predict_progress_bar is not None and self._should_update(n, self.predict_progress_bar.total):
             _update_n(self.predict_progress_bar, n)
 
     @override

src/lightning/pytorch/callbacks/rich_model_summary.py

@@ -16,7 +16,7 @@
 from typing_extensions import override
 
 from lightning.pytorch.callbacks import ModelSummary
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_summary import get_human_readable_count
 
 

src/lightning/pytorch/loops/evaluation_loop.py

@@ -25,7 +25,6 @@
 
 import lightning.pytorch as pl
 from lightning.fabric.utilities.data import _set_sampler_epoch
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher
 from lightning.pytorch.loops.loop import _Loop
 from lightning.pytorch.loops.progress import _BatchProgress
@@ -44,6 +43,7 @@
 from lightning.pytorch.utilities.combined_loader import CombinedLoader
 from lightning.pytorch.utilities.data import has_len_all_ranks
 from lightning.pytorch.utilities.exceptions import SIGTERMException
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_helpers import _ModuleMode, is_overridden
 from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
 

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

@@ -37,6 +37,7 @@
 from lightning.pytorch.callbacks.timer import Timer
 from lightning.pytorch.trainer import call
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_helpers import is_overridden
 from lightning.pytorch.utilities.rank_zero import rank_zero_info
 
@@ -125,14 +126,8 @@ def _configure_model_summary_callback(self, enable_model_summary: bool) -> None:
             )
             return
 
-        progress_bar_callback = self.trainer.progress_bar_callback
-        is_progress_bar_rich = isinstance(progress_bar_callback, RichProgressBar)
-
         model_summary: ModelSummary
-        if progress_bar_callback is not None and is_progress_bar_rich:
-            model_summary = RichModelSummary()
-        else:
-            model_summary = ModelSummary()
+        model_summary = RichModelSummary() if _RICH_AVAILABLE else ModelSummary()
         self.trainer.callbacks.append(model_summary)
 
     def _configure_progress_bar(self, enable_progress_bar: bool = True) -> None:
@@ -157,7 +152,7 @@ def _configure_progress_bar(self, enable_progress_bar: bool = True) -> None:
             )
 
         if enable_progress_bar:
-            progress_bar_callback = TQDMProgressBar()
+            progress_bar_callback = RichProgressBar() if _RICH_AVAILABLE else TQDMProgressBar()
             self.trainer.callbacks.append(progress_bar_callback)
 
     def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, dict[str, int]]] = None) -> None:

src/lightning/pytorch/utilities/imports.py

@@ -28,6 +28,7 @@
 
 _OMEGACONF_AVAILABLE = package_available("omegaconf")
 _TORCHVISION_AVAILABLE = RequirementCache("torchvision")
+_RICH_AVAILABLE = RequirementCache("rich>=10.2.2")
 
 
 @functools.lru_cache(maxsize=128)

src/lightning/pytorch/utilities/testing/_runif.py

@@ -17,9 +17,8 @@
 
 from lightning.fabric.utilities.testing import _runif_reasons as fabric_run_if
 from lightning.pytorch.accelerators.cpu import _PSUTIL_AVAILABLE
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.core.module import _ONNX_AVAILABLE
-from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
+from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE, _RICH_AVAILABLE
 
 _SKLEARN_AVAILABLE = RequirementCache("scikit-learn")
 

tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py

@@ -18,7 +18,7 @@
 from collections import defaultdict
 from typing import Union
 from unittest import mock
-from unittest.mock import ANY, Mock, PropertyMock, call
+from unittest.mock import ANY, Mock, PropertyMock, call, patch
 
 import pytest
 import torch
@@ -109,6 +109,7 @@ def test_tqdm_progress_bar_misconfiguration():
         Trainer(callbacks=TQDMProgressBar(), enable_progress_bar=False)
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 @pytest.mark.parametrize("num_dl", [1, 2])
 def test_tqdm_progress_bar_totals(tmp_path, num_dl):
     """Test that the progress finishes with the correct total steps processed."""
@@ -203,6 +204,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
     assert pbar.predict_progress_bar.leave
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tqdm_progress_bar_fast_dev_run(tmp_path):
     model = BoringModel()
 
@@ -323,6 +325,7 @@ def test_tqdm_progress_bar_default_value(tmp_path):
 
 
 @mock.patch.dict(os.environ, {"COLAB_GPU": "1"})
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tqdm_progress_bar_value_on_colab(tmp_path):
     """Test that Trainer will override the default in Google COLAB."""
     trainer = Trainer(default_root_dir=tmp_path)
@@ -411,6 +414,7 @@ def test_test_progress_bar_update_amount(tmp_path, test_batches: int, refresh_ra
     assert progress_bar.test_progress_bar.n_values == updates
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tensor_to_float_conversion(tmp_path):
     """Check tensor gets converted to float."""
 
@@ -424,7 +428,13 @@ def training_step(self, batch, batch_idx):
     trainer = Trainer(
         default_root_dir=tmp_path, max_epochs=1, limit_train_batches=2, logger=False, enable_checkpointing=False
     )
-    trainer.fit(TestModel())
+
+    with mock.patch.object(sys.stdout, "write") as mock_write:
+        trainer.fit(TestModel())
+    bar_updates = "".join(call.args[0] for call in mock_write.call_args_list)
+    assert "a=0.123" in bar_updates
+    assert "b=1.000" in bar_updates
+    assert "c=2.000" in bar_updates
 
     torch.testing.assert_close(trainer.progress_bar_metrics["a"], 0.123)
     assert trainer.progress_bar_metrics["b"] == 1.0
@@ -616,6 +626,7 @@ def test_progress_bar_max_val_check_interval(
     assert pbar_callback.is_enabled
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 @RunIf(min_cuda_gpus=2, standalone=True)
 @pytest.mark.parametrize("val_check_interval", [0.2, 0.5])
 def test_progress_bar_max_val_check_interval_ddp(tmp_path, val_check_interval):
@@ -703,7 +714,7 @@ def get_metrics(self, trainer, pl_module):
             del items["v_num"]
             # this is equivalent to mocking `set_postfix` as this method gets called every time
             self.calls[trainer.state.fn].append((
-                trainer.state.stage,
+                trainer.state.stage.value,
                 trainer.current_epoch,
                 trainer.global_step,
                 items,

tests/tests_pytorch/callbacks/test_callbacks.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from pathlib import Path
 from re import escape
-from unittest.mock import Mock
+from unittest.mock import Mock, patch
 
 import pytest
 from lightning_utilities.test.warning import no_warning_call
@@ -119,6 +119,7 @@ def load_state_dict(self, state_dict) -> None:
         self.state = state_dict["state"]
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_resume_callback_state_saved_by_type_stateful(tmp_path):
     """Test that a legacy checkpoint that didn't use a state key before can still be loaded, using
     state_dict/load_state_dict."""

tests/tests_pytorch/trainer/connectors/test_callback_connector.py

@@ -14,7 +14,7 @@
 import contextlib
 import logging
 from unittest import mock
-from unittest.mock import MagicMock, Mock
+from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 import torch
@@ -35,13 +35,14 @@
 from lightning.pytorch.trainer.connectors.callback_connector import _CallbackConnector
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_checkpoint_callbacks_are_last(tmp_path):
-    """Test that checkpoint callbacks always get moved to the end of the list, with preserved order."""
-    checkpoint1 = ModelCheckpoint(tmp_path, monitor="foo")
-    checkpoint2 = ModelCheckpoint(tmp_path, monitor="bar")
-    model_summary = ModelSummary()
+    """Test that checkpoint callbacks always come last."""
+    checkpoint1 = ModelCheckpoint(tmp_path / "path1", filename="ckpt1", monitor="val_loss_c1")
+    checkpoint2 = ModelCheckpoint(tmp_path / "path2", filename="ckpt2", monitor="val_loss_c2")
     early_stopping = EarlyStopping(monitor="foo")
     lr_monitor = LearningRateMonitor()
+    model_summary = ModelSummary()
     progress_bar = TQDMProgressBar()
 
     # no model reference
@@ -71,7 +72,7 @@ def test_checkpoint_callbacks_are_last(tmp_path):
     # with model-specific callbacks that substitute ones in Trainer
     model = LightningModule()
     model.configure_callbacks = lambda: [checkpoint1, early_stopping, model_summary, checkpoint2]
-    trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmp_path)])
+    trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmp_path, filename="ckpt_trainer")])
     trainer.strategy._lightning_module = model
     cb_connector = _CallbackConnector(trainer)
     cb_connector._attach_model_callbacks()
@@ -161,6 +162,7 @@ def test_all_callback_states_saved_before_checkpoint_callback(tmp_path):
     )
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_attach_model_callbacks():
     """Test that the callbacks defined in the model and through Trainer get merged correctly."""