286 lines
11 KiB
Python
286 lines
11 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from unittest import mock
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import pytest
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import torch
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from torch import nn
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from torch.optim.swa_utils import SWALR
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from torch.utils.data import DataLoader
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import StochasticWeightAveraging
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from pytorch_lightning.strategies import DDPSpawnStrategy, Strategy
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel, RandomDataset, RandomIterableDataset
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from tests.helpers.runif import RunIf
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class SwaTestModel(BoringModel):
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def __init__(self, batchnorm: bool = True, interval: str = "epoch", iterable_dataset: bool = False):
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super().__init__()
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layers = [nn.Linear(32, 32)]
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if batchnorm:
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layers.append(nn.BatchNorm1d(32))
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layers += [nn.ReLU(), nn.Linear(32, 2)]
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self.layer = nn.Sequential(*layers)
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self.interval = interval
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self.iterable_dataset = iterable_dataset
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def training_step(self, batch, batch_idx):
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output = self.forward(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def train_dataloader(self):
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dset_cls = RandomIterableDataset if self.iterable_dataset else RandomDataset
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dset = dset_cls(32, 64)
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return DataLoader(dset, batch_size=2)
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return {
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"optimizer": optimizer,
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"lr_scheduler": {
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"scheduler": torch.optim.lr_scheduler.StepLR(optimizer, step_size=1),
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"interval": self.interval,
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},
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}
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class SwaTestCallback(StochasticWeightAveraging):
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update_parameters_calls: int = 0
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transfer_weights_calls: int = 0
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def update_parameters(self, *args, **kwargs):
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self.update_parameters_calls += 1
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return StochasticWeightAveraging.update_parameters(*args, **kwargs)
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def transfer_weights(self, *args, **kwargs):
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self.transfer_weights_calls += 1
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return StochasticWeightAveraging.transfer_weights(*args, **kwargs)
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def on_train_epoch_start(self, trainer, *args):
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super().on_train_epoch_start(trainer, *args)
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assert trainer.fit_loop._skip_backward == (trainer.current_epoch > self.swa_end)
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if self.swa_start <= trainer.current_epoch:
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assert isinstance(trainer.lr_scheduler_configs[0].scheduler, SWALR)
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assert trainer.lr_scheduler_configs[0].interval == "epoch"
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assert trainer.lr_scheduler_configs[0].frequency == 1
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def on_train_epoch_end(self, trainer, *args):
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super().on_train_epoch_end(trainer, *args)
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if self.swa_start <= trainer.current_epoch <= self.swa_end:
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swa_epoch = trainer.current_epoch - self.swa_start
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assert self.n_averaged == swa_epoch + 1
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# Scheduler is stepped once on initialization and then at the end of each epoch
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assert self._swa_scheduler._step_count == swa_epoch + 2
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elif trainer.current_epoch > self.swa_end:
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assert self.n_averaged == self._max_epochs - self.swa_start
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def on_train_end(self, trainer, pl_module):
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super().on_train_end(trainer, pl_module)
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# make sure these are correctly set again
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assert not trainer.fit_loop._skip_backward
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assert trainer.accumulate_grad_batches == 2
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assert trainer.num_training_batches == 5
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if not isinstance(trainer.strategy, DDPSpawnStrategy):
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# check backward call count. the batchnorm update epoch should not backward
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assert trainer.strategy.backward.call_count == trainer.max_epochs * trainer.limit_train_batches
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# check call counts
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assert self.update_parameters_calls == trainer.max_epochs - (self._swa_epoch_start - 1)
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assert self.transfer_weights_calls == 1
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def train_with_swa(
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tmpdir,
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batchnorm=True,
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strategy=None,
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accelerator="cpu",
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devices=1,
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interval="epoch",
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iterable_dataset=False,
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):
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model = SwaTestModel(batchnorm=batchnorm, interval=interval, iterable_dataset=iterable_dataset)
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swa_start = 2
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max_epochs = 5
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swa_callback = SwaTestCallback(swa_epoch_start=swa_start, swa_lrs=0.1)
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assert swa_callback.update_parameters_calls == 0
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assert swa_callback.transfer_weights_calls == 0
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trainer = Trainer(
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default_root_dir=tmpdir,
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enable_progress_bar=False,
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max_epochs=max_epochs,
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limit_train_batches=5,
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limit_val_batches=0,
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callbacks=[swa_callback],
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accumulate_grad_batches=2,
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strategy=strategy,
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accelerator=accelerator,
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devices=devices,
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)
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with mock.patch.object(Strategy, "backward", wraps=trainer.strategy.backward):
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trainer.fit(model)
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# check the model is the expected
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assert trainer.lightning_module == model
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@RunIf(min_gpus=2, standalone=True)
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def test_swa_callback_ddp(tmpdir):
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train_with_swa(tmpdir, strategy="ddp", accelerator="gpu", devices=2)
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@RunIf(min_gpus=2)
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def test_swa_callback_ddp_spawn(tmpdir):
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train_with_swa(tmpdir, strategy="ddp_spawn", accelerator="gpu", devices=2)
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@RunIf(skip_windows=True)
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def test_swa_callback_ddp_cpu(tmpdir):
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train_with_swa(tmpdir, strategy="ddp_spawn", accelerator="cpu", devices=2)
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@RunIf(min_gpus=1)
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def test_swa_callback_1_gpu(tmpdir):
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train_with_swa(tmpdir, accelerator="gpu", devices=1)
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@pytest.mark.parametrize("batchnorm", (True, False))
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@pytest.mark.parametrize("iterable_dataset", (True, False))
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def test_swa_callback(tmpdir, batchnorm: bool, iterable_dataset: bool):
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train_with_swa(tmpdir, batchnorm=batchnorm, iterable_dataset=iterable_dataset)
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@pytest.mark.parametrize("interval", ("epoch", "step"))
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def test_swa_callback_scheduler_step(tmpdir, interval: str):
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train_with_swa(tmpdir, interval=interval)
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def test_swa_warns(tmpdir, caplog):
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model = SwaTestModel(interval="step")
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, callbacks=StochasticWeightAveraging())
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with caplog.at_level(level=logging.INFO), pytest.warns(UserWarning, match="SWA is currently only supported"):
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trainer.fit(model)
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assert "Swapping scheduler `StepLR` for `SWALR`" in caplog.text
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def test_swa_raises():
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=0, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=1.5, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=-1, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match="positive float, or a list of positive floats"):
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StochasticWeightAveraging(swa_epoch_start=5, swa_lrs=[0.2, 1])
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@pytest.mark.parametrize("stochastic_weight_avg", [False, True])
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@pytest.mark.parametrize("use_callbacks", [False, True])
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def test_trainer_and_stochastic_weight_avg(tmpdir, use_callbacks: bool, stochastic_weight_avg: bool):
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"""Test to ensure SWA Callback is injected when `stochastic_weight_avg` is provided to the Trainer."""
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class TestModel(BoringModel):
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer
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model = TestModel()
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kwargs = {
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"default_root_dir": tmpdir,
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"callbacks": StochasticWeightAveraging(swa_lrs=1e-3) if use_callbacks else None,
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"stochastic_weight_avg": stochastic_weight_avg,
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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"max_epochs": 2,
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}
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if stochastic_weight_avg:
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with pytest.deprecated_call(match=r"stochastic_weight_avg=True\)` is deprecated in v1.5"):
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trainer = Trainer(**kwargs)
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else:
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trainer = Trainer(**kwargs)
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trainer.fit(model)
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if use_callbacks or stochastic_weight_avg:
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assert sum(1 for cb in trainer.callbacks if isinstance(cb, StochasticWeightAveraging)) == 1
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assert trainer.callbacks[0]._swa_lrs == [1e-3 if use_callbacks else 0.1]
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else:
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assert all(not isinstance(cb, StochasticWeightAveraging) for cb in trainer.callbacks)
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def test_swa_deepcopy(tmpdir):
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"""Test to ensure SWA Callback doesn't deepcopy dataloaders and datamodule potentially leading to OOM."""
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class TestSWA(StochasticWeightAveraging):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.setup_called = False
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def setup(self, trainer, pl_module, stage) -> None:
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super().setup(trainer, pl_module, stage)
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assert self._average_model.train_dataloader is not pl_module.train_dataloader
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assert self._average_model.train_dataloader.__self__ == self._average_model
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assert self._average_model.trainer is None
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self.setup_called = True
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model = BoringModel()
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swa = TestSWA()
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trainer = Trainer(default_root_dir=tmpdir, callbacks=swa, fast_dev_run=True)
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trainer.fit(model, train_dataloaders=DataLoader(RandomDataset(32, 2)))
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assert swa.setup_called
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def test_swa_multiple_lrs(tmpdir):
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swa_lrs = [0.123, 0.321]
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class TestModel(BoringModel):
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def __init__(self):
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super(BoringModel, self).__init__()
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self.layer1 = torch.nn.Linear(32, 32)
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self.layer2 = torch.nn.Linear(32, 2)
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def forward(self, x):
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x = self.layer1(x)
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x = self.layer2(x)
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return x
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def configure_optimizers(self):
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params = [{"params": self.layer1.parameters(), "lr": 0.1}, {"params": self.layer2.parameters(), "lr": 0.2}]
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return torch.optim.Adam(params)
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def on_train_epoch_start(self):
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optimizer = trainer.optimizers[0]
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assert [pg["lr"] for pg in optimizer.param_groups] == [0.1, 0.2]
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assert [pg["initial_lr"] for pg in optimizer.param_groups] == swa_lrs
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assert [pg["swa_lr"] for pg in optimizer.param_groups] == swa_lrs
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self.on_train_epoch_start_called = True
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model = TestModel()
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swa_callback = StochasticWeightAveraging(swa_lrs=swa_lrs)
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trainer = Trainer(
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default_root_dir=tmpdir,
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callbacks=swa_callback,
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fast_dev_run=1,
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)
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trainer.fit(model)
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assert model.on_train_epoch_start_called
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