def test_v1_8_0_deprecate_trainer_data_loading_mixin():
trainer = Trainer(max_epochs=1)
model = BoringModel()
dm = BoringDataModule()
trainer.fit(model, datamodule=dm)
with pytest.deprecated_call(
match=r"`TrainerDataLoadingMixin.prepare_dataloader` was deprecated in v1.6 and will be removed in v1.8.",
):
trainer.prepare_dataloader(dataloader=model.train_dataloader, shuffle=False)
with pytest.deprecated_call(
match=r"`TrainerDataLoadingMixin.request_dataloader` was deprecated in v1.6 and will be removed in v1.8.",
):
trainer.request_dataloader(stage=RunningStage.TRAINING)
def test_ddp_spawn_add_get_queue(tmpdir):
"""Tests add_to_queue/get_from_queue with DDPSpawnStrategy."""
ddp_spawn_strategy = TestDDPSpawnStrategy()
trainer = Trainer(default_root_dir=tmpdir,
fast_dev_run=True,
num_processes=2,
strategy=ddp_spawn_strategy)
val: float = 1.0
val_name: str = "val_acc"
model = BoringCallbackDDPSpawnModel(val_name, val)
dm = BoringDataModule()
trainer.fit(model, datamodule=dm)
assert trainer.callback_metrics[val_name] == torch.tensor(val)
assert ddp_spawn_strategy.new_test_val == "new_test_val"
def test_ddp_spawn_extra_parameters(tmpdir):
"""Tests if device is set correctely when training for DDPSpawnPlugin."""
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")
assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
assert trainer.training_type_plugin.on_gpu
assert trainer.training_type_plugin.root_device == torch.device("cuda:0")
val: float = 1.0
val_name: str = "val_acc"
model = BoringCallbackDDPSpawnModel(val_name, val)
dm = BoringDataModule()
trainer.fit(model, datamodule=dm)
assert trainer.callback_metrics[val_name] == torch.tensor(val)
assert model.test_val == "test_val"
def test_init_optimizers_during_evaluation_and_prediction(tmpdir, fn):
"""Test that optimizers is an empty list during evaluation and prediction."""
class TestModel(BoringModel):
def configure_optimizers(self):
optimizer1 = optim.Adam(self.parameters(), lr=0.1)
optimizer2 = optim.Adam(self.parameters(), lr=0.1)
lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, step_size=1)
lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, step_size=1)
return [optimizer1, optimizer2], [lr_scheduler1, lr_scheduler2]
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
train_fn = getattr(trainer, fn)
train_fn(TestModel(), datamodule=BoringDataModule(), ckpt_path=None)
assert len(trainer.lr_schedulers) == 0
assert len(trainer.optimizers) == 0
assert len(trainer.optimizer_frequencies) == 0
def test_ddp_spawn_extra_parameters(tmpdir):
"""Tests if device is set correctly when training for DDPSpawnStrategy and tests add_to_queue/get_from_queue
with Lightning Module (deprecated way)."""
trainer = Trainer(default_root_dir=tmpdir,
fast_dev_run=True,
gpus=2,
strategy="ddp_spawn")
assert isinstance(trainer.strategy, DDPSpawnStrategy)
assert trainer.strategy.root_device == torch.device("cuda:0")
val: float = 1.0
val_name: str = "val_acc"
model = BoringCallbackDDPSpawnModel(val_name, val)
dm = BoringDataModule()
trainer.fit(model, datamodule=dm)
assert trainer.callback_metrics[val_name] == torch.tensor(val)
assert model.test_val == "test_val"
def test_ddp_spawn_add_get_queue(tmpdir):
"""Tests add_to_queue/get_from_queue with DDPSpawnPlugin."""
ddp_spawn_plugin = TestDDPSpawnPlugin()
trainer = Trainer(default_root_dir=tmpdir,
fast_dev_run=True,
num_processes=2,
strategy=ddp_spawn_plugin)
val: float = 1.0
val_name: str = "val_acc"
model = BoringCallbackDDPSpawnModel(val_name, val)
dm = BoringDataModule()
with pytest.deprecated_call(
match="add_to_queue` method was deprecated in v1.5"):
trainer.fit(model, datamodule=dm)
assert trainer.callback_metrics[val_name] == torch.tensor(val)
assert ddp_spawn_plugin.new_test_val == "new_test_val"
def reset_instances(self):
warning_cache.clear()
return BoringDataModule(), BoringModel(), Trainer()
def __init__(self):
super().__init__()
self.train_dataloader = None
self.val_dataloader = None
self.test_dataloader = None
self.predict_dataloader = None
@pytest.mark.parametrize(
"instance,available",
[
(None, True),
(BoringModel().train_dataloader(), True),
(BoringModel(), True),
(NoDataLoaderModel(), False),
(BoringDataModule(), True),
],
)
def test_dataloader_source_available(instance, available):
"""Test the availability check for _DataLoaderSource."""
source = _DataLoaderSource(instance=instance, name="train_dataloader")
assert source.is_defined() is available
def test_dataloader_source_direct_access():
"""Test requesting a dataloader when the source is already a dataloader."""
dataloader = BoringModel().train_dataloader()
source = _DataLoaderSource(instance=dataloader, name="any")
assert not source.is_module()
assert source.is_defined()
assert source.dataloader() is dataloader
