Removed deprecated weights_summary argument from Trainer

CHANGELOG.md

@@ -123,6 +123,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed the deprecated `weights_summary` argument from the `Trainer` constructor ([#13070](https://github.com/PyTorchLightning/pytorch-lightning/pull/13070))
+
+
 - Removed the deprecated `flush_logs_every_n_steps` argument from the `Trainer` constructor ([#13074](https://github.com/PyTorchLightning/pytorch-lightning/pull/13074))
 
 

docs/source/common/trainer.rst

@@ -1546,36 +1546,6 @@ Example::
         weights_save_path='my/path'
     )
 
-weights_summary
-^^^^^^^^^^^^^^^
-
-.. warning:: `weights_summary` is deprecated in v1.5 and will be removed in v1.7. Please pass :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
-    directly to the Trainer's ``callbacks`` argument instead. To disable the model summary,
-    pass ``enable_model_summary = False`` to the Trainer.
-
-
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/weights_summary.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/weights_summary.mp4"></video>
-
-|
-
-Prints a summary of the weights when training begins.
-Options: 'full', 'top', None.
-
-.. testcode::
-
-    # default used by the Trainer (ie: print summary of top level modules)
-    trainer = Trainer(weights_summary="top")
-
-    # print full summary of all modules and submodules
-    trainer = Trainer(weights_summary="full")
-
-    # don't print a summary
-    trainer = Trainer(weights_summary=None)
-
 
 enable_model_summary
 ^^^^^^^^^^^^^^^^^^^^

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -28,7 +28,6 @@
 )
 from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary
 from pytorch_lightning.callbacks.timer import Timer
-from pytorch_lightning.utilities.enums import ModelSummaryMode
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _PYTHON_GREATER_EQUAL_3_8_0
 from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info
@@ -48,7 +47,6 @@ def on_trainer_init(
         default_root_dir: Optional[str],
         weights_save_path: Optional[str],
         enable_model_summary: bool,
-        weights_summary: Optional[str],
         max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
         accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
     ):
@@ -79,7 +77,7 @@ def on_trainer_init(
         self._configure_progress_bar(enable_progress_bar)
 
         # configure the ModelSummary callback
-        self._configure_model_summary_callback(enable_model_summary, weights_summary)
+        self._configure_model_summary_callback(enable_model_summary)
 
         # accumulated grads
         self._configure_accumulated_gradients(accumulate_grad_batches)
@@ -134,15 +132,7 @@ def _configure_checkpoint_callbacks(self, enable_checkpointing: bool) -> None:
         elif enable_checkpointing:
             self.trainer.callbacks.append(ModelCheckpoint())
 
-    def _configure_model_summary_callback(
-        self, enable_model_summary: bool, weights_summary: Optional[str] = None
-    ) -> None:
-        if weights_summary is None:
-            rank_zero_deprecation(
-                "Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed"
-                " in v1.7. Please set `Trainer(enable_model_summary=False)` instead."
-            )
-            return
+    def _configure_model_summary_callback(self, enable_model_summary: bool) -> None:
         if not enable_model_summary:
             return
 
@@ -154,31 +144,14 @@ def _configure_model_summary_callback(
             )
             return
 
-        if weights_summary == "top":
-            # special case the default value for weights_summary to preserve backward compatibility
-            max_depth = 1
-        else:
-            rank_zero_deprecation(
-                f"Setting `Trainer(weights_summary={weights_summary})` is deprecated in v1.5 and will be removed"
-                " in v1.7. Please pass `pytorch_lightning.callbacks.model_summary.ModelSummary` with"
-                " `max_depth` directly to the Trainer's `callbacks` argument instead."
-            )
-            if weights_summary not in ModelSummaryMode.supported_types():
-                raise MisconfigurationException(
-                    f"`weights_summary` can be None, {', '.join(ModelSummaryMode.supported_types())}",
-                    f" but got {weights_summary}",
-                )
-            max_depth = ModelSummaryMode.get_max_depth(weights_summary)
-
         progress_bar_callback = self.trainer.progress_bar_callback
         is_progress_bar_rich = isinstance(progress_bar_callback, RichProgressBar)
 
         if progress_bar_callback is not None and is_progress_bar_rich:
-            model_summary = RichModelSummary(max_depth=max_depth)
+            model_summary = RichModelSummary()
         else:
-            model_summary = ModelSummary(max_depth=max_depth)
+            model_summary = ModelSummary()
         self.trainer.callbacks.append(model_summary)
-        self.trainer._weights_summary = weights_summary
 
     def _configure_progress_bar(self, enable_progress_bar: bool = True) -> None:
         progress_bars = [c for c in self.trainer.callbacks if isinstance(c, ProgressBarBase)]

pytorch_lightning/trainer/trainer.py

@@ -165,7 +165,6 @@ def __init__(
         sync_batchnorm: bool = False,
         precision: Union[int, str] = 32,
         enable_model_summary: bool = True,
-        weights_summary: Optional[str] = "top",
         weights_save_path: Optional[str] = None,  # TODO: Remove in 1.8
         num_sanity_val_steps: int = 2,
         resume_from_checkpoint: Optional[Union[Path, str]] = None,
@@ -395,14 +394,6 @@ def __init__(
             enable_model_summary: Whether to enable model summarization by default.
                 Default: ``True``.
 
-            weights_summary: Prints a summary of the weights when training begins.
-
-                .. deprecated:: v1.5
-                    ``weights_summary`` has been deprecated in v1.5 and will be removed in v1.7.
-                    To disable the summary, pass ``enable_model_summary = False`` to the Trainer.
-                    To customize the summary, pass :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
-                    directly to the Trainer's ``callbacks`` argument.
-
             weights_save_path: Where to save weights if specified. Will override default_root_dir
                 for checkpoints only. Use this if for whatever reason you need the checkpoints
                 stored in a different place than the logs written in `default_root_dir`.
@@ -485,9 +476,6 @@ def __init__(
         self._tested_ckpt_path: Optional[str] = None  # TODO: remove in v1.8
         self._predicted_ckpt_path: Optional[str] = None  # TODO: remove in v1.8
 
-        # todo: remove in v1.7
-        self._weights_summary: Optional[str] = None
-
         # init callbacks
         # Declare attributes to be set in _callback_connector on_trainer_init
         self._callback_connector.on_trainer_init(
@@ -497,7 +485,6 @@ def __init__(
             default_root_dir,
             weights_save_path,
             enable_model_summary,
-            weights_summary,
             max_time,
             accumulate_grad_batches,
         )
@@ -2716,16 +2703,6 @@ def _should_terminate_gracefully(self) -> bool:
         value = torch.tensor(int(self._terminate_gracefully), device=self.strategy.root_device)
         return self.strategy.reduce(value, reduce_op="sum") > 0
 
-    @property
-    def weights_summary(self) -> Optional[str]:
-        rank_zero_deprecation("`Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
-        return self._weights_summary
-
-    @weights_summary.setter
-    def weights_summary(self, val: Optional[str]) -> None:
-        rank_zero_deprecation("Setting `Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
-        self._weights_summary = val
-
     """
     Other
     """

pytorch_lightning/utilities/__init__.py

@@ -24,7 +24,6 @@
     DistributedType,
     GradClipAlgorithmType,
     LightningEnum,
-    ModelSummaryMode,
 )
 from pytorch_lightning.utilities.grads import grad_norm  # noqa: F401
 from pytorch_lightning.utilities.imports import (  # noqa: F401

pytorch_lightning/utilities/enums.py

@@ -199,38 +199,6 @@ class AutoRestartBatchKeys(LightningEnum):
     PL_RESTART_META = "__pl_restart_meta"
 
 
-class ModelSummaryMode(LightningEnum):
-    # TODO: remove in v1.6 (as `mode` would be deprecated for `max_depth`)
-    """Define the Model Summary mode to be used.
-
-    Can be one of
-        - `top`: only the top-level modules will be recorded (the children of the root module)
-        - `full`: summarizes all layers and their submodules in the root module
-
-    >>> # you can match the type with string
-    >>> ModelSummaryMode.TOP == 'TOP'
-    True
-    >>> # which is case invariant
-    >>> ModelSummaryMode.TOP in ('top', 'FULL')
-    True
-    """
-
-    TOP = "top"
-    FULL = "full"
-
-    @staticmethod
-    def get_max_depth(mode: str) -> int:
-        if mode == ModelSummaryMode.TOP:
-            return 1
-        if mode == ModelSummaryMode.FULL:
-            return -1
-        raise ValueError(f"`mode` can be {', '.join(list(ModelSummaryMode))}, got {mode}.")
-
-    @staticmethod
-    def supported_types() -> list[str]:
-        return [x.value for x in ModelSummaryMode]
-
-
 class _StrategyType(LightningEnum):
     """Define type of training strategy.
 

tests/callbacks/test_model_summary.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 from typing import List, Union
 
-import pytest
-
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelSummary
 from tests.helpers.boring_model import BoringModel
@@ -29,38 +27,20 @@ def test_model_summary_callback_present_trainer():
     assert any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
 
-def test_model_summary_callback_with_weights_summary_none():
-    with pytest.deprecated_call(match=r"weights_summary=None\)` is deprecated"):
-        trainer = Trainer(weights_summary=None)
-    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
-
+def test_model_summary_callback_with_enable_model_summary_false():
     trainer = Trainer(enable_model_summary=False)
     assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
-    trainer = Trainer(enable_model_summary=False, weights_summary="full")
-    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
-
-    with pytest.deprecated_call(match=r"weights_summary=None\)` is deprecated"):
-        trainer = Trainer(enable_model_summary=True, weights_summary=None)
-    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
+def test_model_summary_callback_with_enable_model_summary_true():
+    trainer = Trainer(enable_model_summary=True)
+    assert any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
-def test_model_summary_callback_with_weights_summary():
-    trainer = Trainer(weights_summary="top")
+    # Default value of max_depth is set as 1, when enable_model_summary is True
+    # and ModelSummary is not passed in callbacks list
     model_summary_callback = list(filter(lambda cb: isinstance(cb, ModelSummary), trainer.callbacks))[0]
     assert model_summary_callback._max_depth == 1
 
-    with pytest.deprecated_call(match=r"weights_summary=full\)` is deprecated"):
-        trainer = Trainer(weights_summary="full")
-    model_summary_callback = list(filter(lambda cb: isinstance(cb, ModelSummary), trainer.callbacks))[0]
-    assert model_summary_callback._max_depth == -1
-
-
-def test_model_summary_callback_override_weights_summary_flag():
-    with pytest.deprecated_call(match=r"weights_summary=None\)` is deprecated"):
-        trainer = Trainer(callbacks=ModelSummary(), weights_summary=None)
-    assert any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
-
 
 def test_custom_model_summary_callback_summarize(tmpdir):
     class CustomModelSummary(ModelSummary):

tests/deprecated_api/test_remove_1-7.py

@@ -122,21 +122,6 @@ def test_v1_7_0_deprecate_parameter_validation():
         from pytorch_lightning.core.decorators import parameter_validation  # noqa: F401
 
 
-def test_v1_7_0_weights_summary_trainer(tmpdir):
-    with pytest.deprecated_call(match=r"Setting `Trainer\(weights_summary=full\)` is deprecated in v1.5"):
-        t = Trainer(weights_summary="full")
-
-    with pytest.deprecated_call(match=r"Setting `Trainer\(weights_summary=None\)` is deprecated in v1.5"):
-        t = Trainer(weights_summary=None)
-
-    t = Trainer(weights_summary="top")
-    with pytest.deprecated_call(match=r"`Trainer.weights_summary` is deprecated in v1.5"):
-        _ = t.weights_summary
-
-    with pytest.deprecated_call(match=r"Setting `Trainer.weights_summary` is deprecated in v1.5"):
-        t.weights_summary = "blah"
-
-
 def test_v1_7_0_deprecated_slurm_job_id():
     trainer = Trainer()
     with pytest.deprecated_call(match="Method `slurm_job_id` is deprecated in v1.6.0 and will be removed in v1.7.0."):

tests/utilities/test_enums.py

@@ -11,9 +11,7 @@
 # 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.utilities.enums import _AcceleratorType, GradClipAlgorithmType, ModelSummaryMode, PrecisionType
+from pytorch_lightning.utilities.enums import _AcceleratorType, GradClipAlgorithmType, PrecisionType
 
 
 def test_consistency():
@@ -34,16 +32,6 @@ def test_precision_supported_types():
     assert not PrecisionType.supported_type("invalid")
 
 
-def test_model_summary_mode():
-    assert ModelSummaryMode.supported_types() == ["top", "full"]
-    assert ModelSummaryMode.TOP in ("top", "full")
-    assert ModelSummaryMode.get_max_depth("top") == 1
-    assert ModelSummaryMode.get_max_depth("full") == -1
-
-    with pytest.raises(ValueError, match=f"`mode` can be {', '.join(list(ModelSummaryMode))}, got invalid."):
-        ModelSummaryMode.get_max_depth("invalid")
-
-
 def test_gradient_clip_algorithms():
     assert GradClipAlgorithmType.supported_types() == ["value", "norm"]
     assert GradClipAlgorithmType.supported_type("norm")

tests/utilities/test_model_summary.py

@@ -19,7 +19,6 @@
 
 from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_9
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_summary import ModelSummary, summarize, UNKNOWN_SIZE
 from tests.helpers import BoringModel
 from tests.helpers.advanced_models import ParityModuleRNN
@@ -139,15 +138,10 @@ def forward(self, inp):
         return self.head(self.branch1(inp), self.branch2(inp))
 
 
-def test_invalid_weights_summary():
-    """Test that invalid value for weights_summary raises an error."""
+def test_invalid_max_depth():
+    """Test that invalid value for max_depth raises an error."""
     model = LightningModule()
 
-    with pytest.raises(
-        MisconfigurationException, match="`weights_summary` can be None, .* got temp"
-    ), pytest.deprecated_call(match="weights_summary=temp)` is deprecated"):
-        Trainer(weights_summary="temp")
-
     with pytest.raises(ValueError, match="max_depth` can be .* got temp"):
         ModelSummary(model, max_depth="temp")