Add `RichModelSummary` callback (#9546)

CHANGELOG.md

@@ -136,6 +136,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `PL_RECONCILE_PROCESS` environment variable to enable process reconciliation regardless of cluster environment settings ([#9389](https://github.com/PyTorchLightning/pytorch-lightning/pull/9389))
 
 
+- Added `RichModelSummary` callback ([#9546](https://github.com/PyTorchLightning/pytorch-lightning/pull/9546))
+
+
 ### Changed
 
 - `pytorch_lightning.loggers.neptune.NeptuneLogger` is now consistent with new [neptune-client](https://github.com/neptune-ai/neptune-client) API ([#6867](https://github.com/PyTorchLightning/pytorch-lightning/pull/6867)).

docs/source/extensions/callbacks.rst

@@ -106,8 +106,10 @@ Lightning has a few built-in callbacks.
     LearningRateMonitor
     ModelCheckpoint
     ModelPruning
+    ModelSummary
     ProgressBar
     ProgressBarBase
+    RichModelSummary
     RichProgressBar
     QuantizationAwareTraining
     StochasticWeightAveraging

pyproject.toml

@@ -63,6 +63,7 @@ ignore_errors = "True"
 module = [
     "pytorch_lightning.callbacks.model_summary",
     "pytorch_lightning.callbacks.pruning",
+    "pytorch_lightning.callbacks.rich_model_summary",
     "pytorch_lightning.loops.optimization.*",
     "pytorch_lightning.loops.evaluation_loop",
     "pytorch_lightning.trainer.connectors.checkpoint_connector",

pytorch_lightning/callbacks/__init__.py

@@ -24,6 +24,7 @@ from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
 from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase, RichProgressBar
 from pytorch_lightning.callbacks.pruning import ModelPruning
 from pytorch_lightning.callbacks.quantization import QuantizationAwareTraining
+from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary
 from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
 from pytorch_lightning.callbacks.timer import Timer
 from pytorch_lightning.callbacks.xla_stats_monitor import XLAStatsMonitor
@@ -45,7 +46,8 @@ __all__ = [
     "ProgressBar",
     "ProgressBarBase",
     "QuantizationAwareTraining",
+    "RichModelSummary",
+    "RichProgressBar",
     "StochasticWeightAveraging",
     "Timer",
-    "RichProgressBar",
 ]

pytorch_lightning/callbacks/rich_model_summary.py

@@ -0,0 +1,109 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List, Tuple
+
+from pytorch_lightning.callbacks import ModelSummary
+from pytorch_lightning.utilities.imports import _RICH_AVAILABLE
+from pytorch_lightning.utilities.model_summary import get_human_readable_count
+
+if _RICH_AVAILABLE:
+    from rich.console import Console
+    from rich.table import Table
+
+
+class RichModelSummary(ModelSummary):
+    r"""
+    Generates a summary of all layers in a :class:`~pytorch_lightning.core.lightning.LightningModule`
+    with `rich text formatting <https://github.com/willmcgugan/rich>`_.
+
+    Install it with pip:
+
+    .. code-block:: bash
+
+        pip install rich
+
+    .. code-block:: python
+
+        from pytorch_lightning import Trainer
+        from pytorch_lightning.callbacks import RichModelSummary
+
+        trainer = Trainer(callbacks=RichModelSummary())
+
+    You could also enable ``RichModelSummary`` using the :class:`~pytorch_lightning.callbacks.RichProgressBar`
+
+    .. code-block:: python
+
+        from pytorch_lightning import Trainer
+        from pytorch_lightning.callbacks import RichProgressBar
+
+        trainer = Trainer(callbacks=RichProgressBar())
+
+    Args:
+        max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the
+            layer summary off.
+
+    Raises:
+        ImportError:
+            If required `rich` package is not installed on the device.
+    """
+
+    def __init__(self, max_depth: int = 1) -> None:
+        if not _RICH_AVAILABLE:
+            raise ImportError(
+                "`RichModelSummary` requires `rich` to be installed. Install it by running `pip install rich`."
+            )
+        super().__init__(max_depth)
+
+    @staticmethod
+    def summarize(
+        summary_data: List[Tuple[str, List[str]]],
+        total_parameters: int,
+        trainable_parameters: int,
+        model_size: float,
+    ) -> None:
+
+        console = Console()
+
+        table = Table(header_style="bold magenta")
+        table.add_column(" ", style="dim")
+        table.add_column("Name", justify="left", no_wrap=True)
+        table.add_column("Type")
+        table.add_column("Params", justify="right")
+
+        column_names = list(zip(*summary_data))[0]
+
+        for column_name in ["In sizes", "Out sizes"]:
+            if column_name in column_names:
+                table.add_column(column_name, justify="right", style="white")
+
+        rows = list(zip(*(arr[1] for arr in summary_data)))
+        for row in rows:
+            table.add_row(*row)
+
+        console.print(table)
+
+        parameters = []
+        for param in [trainable_parameters, total_parameters - trainable_parameters, total_parameters, model_size]:
+            parameters.append("{:<{}}".format(get_human_readable_count(int(param)), 10))
+
+        grid = Table.grid(expand=True)
+        grid.add_column()
+        grid.add_column()
+
+        grid.add_row(f"[bold]Trainable params[/]: {parameters[0]}")
+        grid.add_row(f"[bold]Non-trainable params[/]: {parameters[1]}")
+        grid.add_row(f"[bold]Total params[/]: {parameters[2]}")
+        grid.add_row(f"[bold]Total estimated model params size (MB)[/]: {parameters[3]}")
+
+        console.print(grid)

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -15,7 +15,15 @@ import os
 from datetime import timedelta
 from typing import Dict, List, Optional, Union
 
-from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ModelSummary, ProgressBar, ProgressBarBase
+from pytorch_lightning.callbacks import (
+    Callback,
+    ModelCheckpoint,
+    ModelSummary,
+    ProgressBar,
+    ProgressBarBase,
+    RichProgressBar,
+)
+from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary
 from pytorch_lightning.callbacks.timer import Timer
 from pytorch_lightning.utilities import ModelSummaryMode, rank_zero_info
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -59,8 +67,6 @@ class CallbackConnector:
         # responsible to stop the training when max_time is reached.
         self._configure_timer_callback(max_time)
 
-        self._configure_model_summary_callback(weights_summary)
-
         # init progress bar
         if process_position != 0:
             rank_zero_deprecation(
@@ -70,6 +76,9 @@ class CallbackConnector:
             )
         self.trainer._progress_bar_callback = self.configure_progress_bar(progress_bar_refresh_rate, process_position)
 
+        # configure the ModelSummary callback
+        self._configure_model_summary_callback(weights_summary)
+
         # push all checkpoint callbacks to the end
         # it is important that these are the last callbacks to run
         self.trainer.callbacks = self._reorder_callbacks(self.trainer.callbacks)
@@ -102,7 +111,12 @@ class CallbackConnector:
                     f" but got {weights_summary}",
                 )
             max_depth = ModelSummaryMode.get_max_depth(weights_summary)
-            model_summary = ModelSummary(max_depth=max_depth)
+            if self.trainer._progress_bar_callback is not None and isinstance(
+                self.trainer._progress_bar_callback, RichProgressBar
+            ):
+                model_summary = RichModelSummary(max_depth=max_depth)
+            else:
+                model_summary = ModelSummary(max_depth=max_depth)
             self.trainer.callbacks.append(model_summary)
 
     def _configure_swa_callbacks(self):

tests/callbacks/test_rich_model_summary.py

@@ -0,0 +1,35 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pytest
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import RichModelSummary, RichProgressBar
+from pytorch_lightning.utilities.imports import _RICH_AVAILABLE
+from tests.helpers.runif import RunIf
+
+
+@RunIf(rich=True)
+def test_rich_model_summary_callback():
+
+    trainer = Trainer(callbacks=RichProgressBar())
+
+    assert any(isinstance(cb, RichModelSummary) for cb in trainer.callbacks)
+    assert isinstance(trainer.progress_bar_callback, RichProgressBar)
+
+
+def test_rich_progress_bar_import_error():
+
+    if not _RICH_AVAILABLE:
+        with pytest.raises(ImportError, match="`RichModelSummary` requires `rich` to be installed."):
+            Trainer(callbacks=RichModelSummary())