def test_v1_5_0_datamodule_setter():
model = BoringModel()
datamodule = BoringDataModule()
with no_deprecated_call(match="The `LightningModule.datamodule`"):
model.datamodule = datamodule
with pytest.deprecated_call(match="The `LightningModule.datamodule`"):
_ = model.datamodule
def test_v1_5_0_old_on_validation_epoch_end(tmpdir):
callback_warning_cache.clear()
class OldSignature(Callback):
def on_validation_epoch_end(self, trainer, pl_module): # noqa
...
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir,
max_epochs=1,
callbacks=OldSignature())
with pytest.deprecated_call(match="old signature will be removed in v1.5"):
trainer.fit(model)
class OldSignatureModel(BoringModel):
def on_validation_epoch_end(self): # noqa
...
model = OldSignatureModel()
with pytest.deprecated_call(match="old signature will be removed in v1.5"):
trainer.fit(model)
callback_warning_cache.clear()
class NewSignature(Callback):
def on_validation_epoch_end(self, trainer, pl_module, outputs):
...
trainer.callbacks = [NewSignature()]
with no_deprecated_call(
match=
"`Callback.on_validation_epoch_end` signature has changed in v1.3."
):
trainer.fit(model)
class NewSignatureModel(BoringModel):
def on_validation_epoch_end(self, outputs):
...
model = NewSignatureModel()
with no_deprecated_call(
match=
"`ModelHooks.on_validation_epoch_end` signature has changed in v1.3."
):
trainer.fit(model)
def test_v1_5_0_datamodule_setter():
model = BoringModel()
datamodule = BoringDataModule()
with no_deprecated_call(match="The `LightningModule.datamodule`"):
model.datamodule = datamodule
from pytorch_lightning.core.lightning import warning_cache
warning_cache.clear()
_ = model.datamodule
assert any("The `LightningModule.datamodule`" in w for w in warning_cache)
def test_v1_5_0_old_callback_on_load_checkpoint(tmpdir):
model = BoringModel()
trainer_kwargs = {
"default_root_dir": tmpdir,
"max_steps": 1,
}
chk = ModelCheckpoint(save_last=True)
trainer = Trainer(**trainer_kwargs,
callbacks=[OldSignatureOnLoadCheckpoint(), chk])
trainer.fit(model)
with pytest.deprecated_call(match="old signature will be removed in v1.5"):
trainer_kwargs["max_steps"] = 2
cb = OldSignatureOnLoadCheckpoint()
trainer = Trainer(**trainer_kwargs,
callbacks=cb,
resume_from_checkpoint=chk.last_model_path)
trainer.fit(model)
assert cb.on_load_checkpoint_called
class ValidSignature1(BaseSignatureOnLoadCheckpoint):
def on_load_checkpoint(self, trainer, *args):
assert len(args) == 2
self.on_load_checkpoint_called = True
model = BoringModel()
chk = ModelCheckpoint(save_last=True)
trainer = Trainer(**trainer_kwargs,
callbacks=[
NewSignatureOnLoadCheckpoint(),
ValidSignature1(),
ValidSignature2OnLoadCheckpoint(),
chk,
])
with no_deprecated_call(match="old signature will be removed in v1.5"):
trainer.fit(model)
trainer = Trainer(**trainer_kwargs,
resume_from_checkpoint=chk.last_model_path)
with no_deprecated_call(match="old signature will be removed in v1.5"):
trainer.fit(model)
def test_deprecated_epoch_outputs_format(tmpdir):
class DeprecationModel(BoringModel):
def __init__(self):
super().__init__()
self.truncated_bptt_steps = 1
def training_step(self, batch, batch_idx, optimizer_idx, hiddens):
output = super().training_step(batch, batch_idx)
output["hiddens"] = hiddens
return output
def tbptt_split_batch(self, batch, split_size):
return [batch, batch]
def training_epoch_end(self, outputs):
...
def on_train_batch_end(self, outputs, batch, batch_idx) -> None:
...
def configure_optimizers(self):
return [
torch.optim.Adam(self.parameters()),
torch.optim.Adam(self.parameters())
]
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
model = DeprecationModel()
batch_match = r"on_train_batch_end.*will change in version v1.8 to \(tbptt_steps, n_optimizers\)"
with pytest.deprecated_call(match=batch_match):
trainer.fit(model)
class DeprecationModel2(DeprecationModel):
def on_train_batch_end(self, *args, new_format=True):
...
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
model = DeprecationModel()
epoch_match = r"training_epoch_end.*will change in version v1.8 to \(n_batches, tbptt_steps, n_optimizers\)"
with pytest.deprecated_call(match=epoch_match):
trainer.fit(model)
class NoDeprecationModel(DeprecationModel2):
def training_epoch_end(self, outputs, new_format=True):
...
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
model = NoDeprecationModel()
with no_deprecated_call(
match="will change in version v1.8.*new_format=True"):
trainer.fit(model)
