Remove deprecated stochastic_weight_avg argument from Trainer

CHANGELOG.md

@@ -57,6 +57,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Remove deprecated `stochastic_weight_averaging` flag from `Trainer` ([#12535](https://github.com/PyTorchLightning/pytorch-lightning/pull/12535))
+
+
 - Removed the deprecated `progress_bar_refresh_rate` argument from the `Trainer` constructor ([#12514](https://github.com/PyTorchLightning/pytorch-lightning/pull/12514))
 
 

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -46,7 +46,6 @@ def on_trainer_init(
         weights_save_path: Optional[str],
         enable_model_summary: bool,
         weights_summary: Optional[str],
-        stochastic_weight_avg: bool,
         max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
         accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
     ):
@@ -59,13 +58,6 @@ def on_trainer_init(
             )
 
         self.trainer._weights_save_path = weights_save_path or self.trainer._default_root_dir
-        if stochastic_weight_avg:
-            rank_zero_deprecation(
-                "Setting `Trainer(stochastic_weight_avg=True)` is deprecated in v1.5 and will be removed in v1.7."
-                " Please pass `pytorch_lightning.callbacks.stochastic_weight_avg.StochasticWeightAveraging`"
-                " directly to the Trainer's `callbacks` argument instead."
-            )
-        self.trainer._stochastic_weight_avg = stochastic_weight_avg
 
         # init callbacks
         if isinstance(callbacks, Callback):
@@ -76,9 +68,6 @@ def on_trainer_init(
         # pass through the required args to figure out defaults
         self._configure_checkpoint_callbacks(checkpoint_callback, enable_checkpointing)
 
-        # configure swa callback
-        self._configure_swa_callbacks()
-
         # configure the timer callback.
         # responsible to stop the training when max_time is reached.
         self._configure_timer_callback(max_time)
@@ -201,16 +190,6 @@ def _configure_model_summary_callback(
         self.trainer.callbacks.append(model_summary)
         self.trainer._weights_summary = weights_summary
 
-    def _configure_swa_callbacks(self):
-        if not self.trainer._stochastic_weight_avg:
-            return
-
-        from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
-
-        existing_swa = [cb for cb in self.trainer.callbacks if isinstance(cb, StochasticWeightAveraging)]
-        if not existing_swa:
-            self.trainer.callbacks = [StochasticWeightAveraging()] + self.trainer.callbacks
-
     def _configure_progress_bar(self, process_position: int = 0, enable_progress_bar: bool = True) -> None:
         progress_bars = [c for c in self.trainer.callbacks if isinstance(c, ProgressBarBase)]
         if len(progress_bars) > 1:

pytorch_lightning/trainer/trainer.py

@@ -187,7 +187,6 @@ def __init__(
         amp_level: Optional[str] = None,
         move_metrics_to_cpu: bool = False,
         multiple_trainloader_mode: str = "max_size_cycle",
-        stochastic_weight_avg: bool = False,
         terminate_on_nan: Optional[bool] = None,
     ) -> None:
         r"""
@@ -452,15 +451,6 @@ def __init__(
                 and smaller datasets reload when running out of their data. In 'min_size' mode, all the datasets
                 reload when reaching the minimum length of datasets.
                 Default: ``"max_size_cycle"``.
-
-            stochastic_weight_avg: Whether to use `Stochastic Weight Averaging (SWA)
-                <https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/>`_.
-                Default: ``False``.
-
-                .. deprecated:: v1.5
-                    ``stochastic_weight_avg`` has been deprecated in v1.5 and will be removed in v1.7.
-                    Please pass :class:`~pytorch_lightning.callbacks.stochastic_weight_avg.StochasticWeightAveraging`
-                    directly to the Trainer's ``callbacks`` argument instead.
         """
         super().__init__()
         Trainer._log_api_event("init")
@@ -540,7 +530,6 @@ def __init__(
             weights_save_path,
             enable_model_summary,
             weights_summary,
-            stochastic_weight_avg,
             max_time,
             accumulate_grad_batches,
         )

tests/callbacks/test_stochastic_weight_avg.py

@@ -195,38 +195,6 @@ def test_swa_raises():
         StochasticWeightAveraging(swa_epoch_start=5, swa_lrs=[0.2, 1])
 
 
-@pytest.mark.parametrize("stochastic_weight_avg", [False, True])
-@pytest.mark.parametrize("use_callbacks", [False, True])
-def test_trainer_and_stochastic_weight_avg(tmpdir, use_callbacks: bool, stochastic_weight_avg: bool):
-    """Test to ensure SWA Callback is injected when `stochastic_weight_avg` is provided to the Trainer."""
-
-    class TestModel(BoringModel):
-        def configure_optimizers(self):
-            optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
-            return optimizer
-
-    model = TestModel()
-    kwargs = {
-        "default_root_dir": tmpdir,
-        "callbacks": StochasticWeightAveraging(swa_lrs=1e-3) if use_callbacks else None,
-        "stochastic_weight_avg": stochastic_weight_avg,
-        "limit_train_batches": 4,
-        "limit_val_batches": 4,
-        "max_epochs": 2,
-    }
-    if stochastic_weight_avg:
-        with pytest.deprecated_call(match=r"stochastic_weight_avg=True\)` is deprecated in v1.5"):
-            trainer = Trainer(**kwargs)
-    else:
-        trainer = Trainer(**kwargs)
-    trainer.fit(model)
-    if use_callbacks or stochastic_weight_avg:
-        assert sum(1 for cb in trainer.callbacks if isinstance(cb, StochasticWeightAveraging)) == 1
-        assert trainer.callbacks[0]._swa_lrs == [1e-3 if use_callbacks else 0.1]
-    else:
-        assert all(not isinstance(cb, StochasticWeightAveraging) for cb in trainer.callbacks)
-
-
 def test_swa_deepcopy(tmpdir):
     """Test to ensure SWA Callback doesn't deepcopy dataloaders and datamodule potentially leading to OOM."""
 

tests/deprecated_api/test_remove_1-7.py

@@ -130,11 +130,6 @@ def test_v1_7_0_trainer_prepare_data_per_node(tmpdir):
         _ = Trainer(prepare_data_per_node=False)
 
 
-def test_v1_7_0_stochastic_weight_avg_trainer_constructor(tmpdir):
-    with pytest.deprecated_call(match=r"Setting `Trainer\(stochastic_weight_avg=True\)` is deprecated in v1.5"):
-        _ = Trainer(stochastic_weight_avg=True)
-
-
 @pytest.mark.parametrize("terminate_on_nan", [True, False])
 def test_v1_7_0_trainer_terminate_on_nan(tmpdir, terminate_on_nan):
     with pytest.deprecated_call(