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Remove deprecated stochastic_weight_avg argument from Trainer #12535

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3 changes: 3 additions & 0 deletions CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Removed

- Removed the deprecated `stochastic_weight_avg` argument from the `Trainer` constructor ([#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))


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21 changes: 0 additions & 21 deletions pytorch_lightning/trainer/connectors/callback_connector.py
Original file line number Diff line number Diff line change
Expand Up @@ -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,
):
Expand All @@ -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):
Expand All @@ -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)
Expand Down Expand Up @@ -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:
Expand Down
11 changes: 0 additions & 11 deletions pytorch_lightning/trainer/trainer.py
Original file line number Diff line number Diff line change
Expand Up @@ -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"""
Expand Down Expand Up @@ -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")
Expand Down Expand Up @@ -540,7 +530,6 @@ def __init__(
weights_save_path,
enable_model_summary,
weights_summary,
stochastic_weight_avg,
max_time,
accumulate_grad_batches,
)
Expand Down
32 changes: 0 additions & 32 deletions tests/callbacks/test_stochastic_weight_avg.py
Original file line number Diff line number Diff line change
Expand Up @@ -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."""

Expand Down
5 changes: 0 additions & 5 deletions tests/deprecated_api/test_remove_1-7.py
Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down