def test_has_len():
assert has_len(DataLoader(RandomDataset(1, 1)))
with pytest.warns(UserWarning, match="`DataLoader` returned 0 length."):
assert has_len(DataLoader(RandomDataset(0, 0)))
assert not has_len(DataLoader(RandomIterableDataset(1, 1)))
def test_has_len():
assert has_len(DataLoader(RandomDataset(1, 1)))
with pytest.raises(ValueError, match="`Dataloader` returned 0 length."):
assert has_len(DataLoader(RandomDataset(0, 0)))
assert not has_len(DataLoader(RandomIterableDataset(1, 1)))
def test_progress_bar_max_val_check_interval(tmpdir, total_train_samples,
train_batch_size,
total_val_samples, val_batch_size,
val_check_interval):
world_size = 2
train_data = DataLoader(RandomDataset(32, total_train_samples),
batch_size=train_batch_size)
val_data = DataLoader(RandomDataset(32, total_val_samples),
batch_size=val_batch_size)
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
num_sanity_val_steps=0,
max_epochs=1,
enable_model_summary=False,
val_check_interval=val_check_interval,
gpus=world_size,
strategy="ddp",
)
trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
total_train_batches = total_train_samples // (train_batch_size *
world_size)
val_check_batch = max(1, int(total_train_batches * val_check_interval))
assert trainer.val_check_batch == val_check_batch
val_checks_per_epoch = total_train_batches / val_check_batch
total_val_batches = total_val_samples // (val_batch_size * world_size)
assert trainer.progress_bar_callback.total_train_batches == total_train_batches
assert trainer.progress_bar_callback.total_val_batches == total_val_batches
total_val_batches = total_val_batches * val_checks_per_epoch
if trainer.is_global_zero:
assert trainer.progress_bar_callback.main_progress_bar.total == total_train_batches + total_val_batches
def test_combined_dataloader_for_training_with_ddp(
replace_sampler_ddp: bool, is_min_size_mode: bool, use_combined_loader: bool
):
"""When providing a CombinedLoader as the training data, it should be correctly receive the distributed
samplers."""
mode = "min_size" if is_min_size_mode else "max_size_cycle"
dim = 3
n1 = 8
n2 = 6
dataloader = {
"a": DataLoader(RandomDataset(dim, n1), batch_size=1),
"b": DataLoader(RandomDataset(dim, n2), batch_size=1),
}
if use_combined_loader:
dataloader = CombinedLoader(dataloader, mode=mode)
expected_length_before_ddp = min(n1, n2) if is_min_size_mode else max(n1, n2)
expected_length_after_ddp = expected_length_before_ddp // 2 if replace_sampler_ddp else expected_length_before_ddp
model = BoringModel()
trainer = Trainer(
strategy="ddp",
accelerator="auto",
devices=2,
replace_sampler_ddp=replace_sampler_ddp,
multiple_trainloader_mode="max_size_cycle" if use_combined_loader else mode,
)
trainer._data_connector.attach_data(
model=model, train_dataloaders=dataloader, val_dataloaders=None, datamodule=None
)
trainer.reset_train_dataloader(model=model)
assert trainer.train_dataloader is not None
assert isinstance(trainer.train_dataloader, CombinedLoader)
assert trainer.train_dataloader.mode == mode
assert trainer.num_training_batches == expected_length_after_ddp
def test_boring_lite_model_ddp(precision, strategy, devices, accelerator,
tmpdir):
LightningLite.seed_everything(42)
train_dataloader = DataLoader(RandomDataset(32, 4))
model = BoringModel()
num_epochs = 1
state_dict = deepcopy(model.state_dict())
lite = LiteRunner(precision=precision,
strategy=strategy,
devices=devices,
accelerator=accelerator)
lite.run(model, train_dataloader, num_epochs=num_epochs, tmpdir=tmpdir)
lite_model_state_dict = model.state_dict()
for w_pure, w_lite in zip(state_dict.values(),
lite_model_state_dict.values()):
assert not torch.equal(w_pure.cpu(), w_lite.cpu())
LightningLite.seed_everything(42)
train_dataloader = DataLoader(RandomDataset(32, 4))
model = BoringModel()
run(lite.global_rank, model, train_dataloader, num_epochs, precision,
accelerator, tmpdir)
pure_model_state_dict = model.state_dict()
for w_pure, w_lite in zip(pure_model_state_dict.values(),
lite_model_state_dict.values()):
assert torch.equal(w_pure.cpu(), w_lite.cpu())
def test_has_len_all_rank():
trainer = Trainer(fast_dev_run=True)
model = BoringModel()
with pytest.raises(
MisconfigurationException,
match="Total length of `Dataloader` across ranks is zero."):
assert not has_len_all_ranks(DataLoader(RandomDataset(0, 0)),
trainer.strategy, model)
assert has_len_all_ranks(DataLoader(RandomDataset(1, 1)), trainer.strategy,
model)
def test_has_len_all_rank():
trainer = Trainer(fast_dev_run=True)
model = BoringModel()
with pytest.warns(
UserWarning,
match="Total length of `DataLoader` across ranks is zero."):
assert has_len_all_ranks(DataLoader(RandomDataset(0, 0)),
trainer.strategy, model)
assert has_len_all_ranks(DataLoader(RandomDataset(1, 1)), trainer.strategy,
model)
def test_on_epoch_logging_with_sum_and_on_batch_start(tmpdir):
class TestModel(BoringModel):
def on_train_epoch_end(self):
assert all(v == 3 for v in self.trainer.callback_metrics.values())
def on_validation_epoch_end(self):
assert all(v == 3 for v in self.trainer.callback_metrics.values())
def on_train_batch_start(self, batch, batch_idx):
self.log("on_train_batch_start",
1.0,
on_step=False,
on_epoch=True,
reduce_fx="sum")
def on_train_batch_end(self, outputs, batch, batch_idx):
self.log("on_train_batch_end",
1.0,
on_step=False,
on_epoch=True,
reduce_fx="sum")
def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
self.log("on_validation_batch_start", 1.0, reduce_fx="sum")
def on_validation_batch_end(self, outputs, batch, batch_idx,
dataloader_idx):
self.log("on_validation_batch_end", 1.0, reduce_fx="sum")
def training_epoch_end(self, *_) -> None:
self.log("training_epoch_end", 3.0, reduce_fx="mean")
assert self.trainer._results[
"training_epoch_end.training_epoch_end"].value == 3.0
def validation_epoch_end(self, *_) -> None:
self.log("validation_epoch_end", 3.0, reduce_fx="mean")
assert self.trainer._results[
"validation_epoch_end.validation_epoch_end"].value == 3.0
model = TestModel()
trainer = Trainer(
enable_progress_bar=False,
limit_train_batches=3,
limit_val_batches=3,
num_sanity_val_steps=3,
max_epochs=1,
)
train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
def test_combined_data_loader_with_max_size_cycle_and_ddp(accelerator, replace_sampler_ddp):
"""This test makes sure distributed sampler has been properly injected in dataloaders when using CombinedLoader
with ddp and `max_size_cycle` mode."""
trainer = Trainer(strategy="ddp", accelerator=accelerator, devices=2, replace_sampler_ddp=replace_sampler_ddp)
dataloader = CombinedLoader(
{"a": DataLoader(RandomDataset(32, 8), batch_size=1), "b": DataLoader(RandomDataset(32, 8), batch_size=1)},
)
dataloader = trainer._data_connector._prepare_dataloader(dataloader, shuffle=False)
assert len(dataloader) == 4 if replace_sampler_ddp else 8
for a_length in [6, 8, 10]:
dataloader = CombinedLoader(
{
"a": DataLoader(range(a_length), batch_size=1),
"b": DataLoader(range(8), batch_size=1),
},
mode="max_size_cycle",
)
length = max(a_length, 8)
assert len(dataloader) == length
dataloader = trainer._data_connector._prepare_dataloader(dataloader, shuffle=False)
assert len(dataloader) == length // 2 if replace_sampler_ddp else length
if replace_sampler_ddp:
last_batch = list(dataloader)[-1]
if a_length == 6:
assert last_batch == {"a": torch.tensor([0]), "b": torch.tensor([6])}
elif a_length == 8:
assert last_batch == {"a": torch.tensor([6]), "b": torch.tensor([6])}
elif a_length == 10:
assert last_batch == {"a": torch.tensor([8]), "b": torch.tensor([0])}
class InfiniteDataset(IterableDataset):
def __iter__(self):
while True:
yield 1
dataloader = CombinedLoader(
{
"a": DataLoader(InfiniteDataset(), batch_size=1),
"b": DataLoader(range(8), batch_size=1),
},
mode="max_size_cycle",
)
assert get_len(dataloader) == float("inf")
assert len(dataloader.loaders["b"].loader) == 8
dataloader = trainer._data_connector._prepare_dataloader(dataloader, shuffle=False)
assert len(dataloader.loaders["b"].loader) == 4 if replace_sampler_ddp else 8
assert get_len(dataloader) == float("inf")
def test_replace_sampler_with_multiprocessing_context(tmpdir):
"""
This test verifies that replace_sampler conserves multiprocessing context
"""
train = RandomDataset(32, 64)
context = 'spawn'
train = DataLoader(train,
batch_size=32,
num_workers=2,
multiprocessing_context=context,
shuffle=True)
class ExtendedBoringModel(BoringModel):
def train_dataloader(self):
return train
trainer = Trainer(
max_epochs=1,
progress_bar_refresh_rate=20,
overfit_batches=5,
)
new_data_loader = trainer.replace_sampler(train,
SequentialSampler(train.dataset))
assert (new_data_loader.multiprocessing_context ==
train.multiprocessing_context)
def test_get_len():
assert get_len(DataLoader(RandomDataset(1, 1))) == 1
value = get_len(DataLoader(RandomIterableDataset(1, 1)))
assert isinstance(value, float)
assert value == float("inf")
def test_swa_deepcopy(tmpdir):
"""Test to ensure SWA Callback doesn't deepcopy dataloaders and datamodule potentially leading to OOM"""
class TestSWA(StochasticWeightAveraging):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.on_before_accelerator_backend_setup_called = False
def on_before_accelerator_backend_setup(self, trainer: 'Trainer',
pl_module: 'LightningModule'):
super().on_before_accelerator_backend_setup(trainer, pl_module)
assert self._average_model.train_dataloader is not pl_module.train_dataloader
assert self._average_model.train_dataloader.__self__ == self._average_model
assert isinstance(pl_module.train_dataloader, _PatchDataLoader)
assert self._average_model.trainer is None
self.on_before_accelerator_backend_setup_called = True
model = BoringModel()
swa = TestSWA()
trainer = Trainer(
default_root_dir=tmpdir,
callbacks=swa,
fast_dev_run=True,
)
trainer.fit(model, train_dataloader=DataLoader(RandomDataset(32, 2)))
assert swa.on_before_accelerator_backend_setup_called
def test_boring_lite_model_ddp_spawn(precision, strategy, devices, accelerator,
tmpdir):
LightningLite.seed_everything(42)
train_dataloader = DataLoader(RandomDataset(32, 8))
model = BoringModel()
num_epochs = 1
state_dict = deepcopy(model.state_dict())
lite = LiteRunner(precision=precision,
strategy=strategy,
devices=devices,
accelerator=accelerator)
checkpoint_path = lite.run(model,
train_dataloader,
num_epochs=num_epochs,
tmpdir=tmpdir)
spawn_model_state_dict = torch.load(checkpoint_path)
for w_pure, w_lite in zip(state_dict.values(),
spawn_model_state_dict.values()):
assert not torch.equal(w_pure.cpu(), w_lite.cpu())
model.load_state_dict(state_dict)
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = str(find_free_network_port())
mp.spawn(run,
args=(model, train_dataloader, num_epochs, precision, accelerator,
tmpdir),
nprocs=2)
spawn_pure_model_state_dict = torch.load(
os.path.join(tmpdir, "model_spawn.pt"))
for w_pure, w_lite in zip(spawn_pure_model_state_dict.values(),
spawn_model_state_dict.values()):
assert torch.equal(w_pure.cpu(), w_lite.cpu())
def test_boring_lite_model_single_device(precision, strategy, devices,
accelerator, tmpdir):
LightningLite.seed_everything(42)
train_dataloader = DataLoader(RandomDataset(32, 8))
model = BoringModel()
num_epochs = 1
state_dict = deepcopy(model.state_dict())
lite = LiteRunner(precision=precision,
strategy=strategy,
devices=devices,
accelerator=accelerator)
lite.run(model, train_dataloader, num_epochs=num_epochs)
lite_state_dict = model.state_dict()
with precision_context(precision, accelerator):
model.load_state_dict(state_dict)
pure_state_dict = main(lite.to_device,
model,
train_dataloader,
num_epochs=num_epochs)
state_dict = apply_to_collection(state_dict, torch.Tensor, lite.to_device)
for w_pure, w_lite in zip(state_dict.values(), lite_state_dict.values()):
assert not torch.equal(w_pure, w_lite)
for w_pure, w_lite in zip(pure_state_dict.values(),
lite_state_dict.values()):
assert torch.equal(w_pure, w_lite)
def test_progress_bar_max_val_check_interval_ddp(tmpdir, val_check_interval):
world_size = 2
total_train_samples = 16
train_batch_size = 4
total_val_samples = 2
val_batch_size = 1
train_data = DataLoader(RandomDataset(32, 8), batch_size=train_batch_size)
val_data = DataLoader(RandomDataset(32, total_val_samples),
batch_size=val_batch_size)
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
num_sanity_val_steps=0,
max_epochs=1,
val_check_interval=val_check_interval,
accelerator="gpu",
devices=world_size,
strategy="ddp",
enable_progress_bar=True,
enable_model_summary=False,
)
trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
total_train_batches = total_train_samples // (train_batch_size *
world_size)
val_check_batch = max(1, int(total_train_batches * val_check_interval))
assert trainer.val_check_batch == val_check_batch
val_checks_per_epoch = total_train_batches / val_check_batch
total_val_batches = total_val_samples // (val_batch_size * world_size)
pbar_callback = trainer.progress_bar_callback
if trainer.is_global_zero:
assert pbar_callback.val_progress_bar.n == total_val_batches
assert pbar_callback.val_progress_bar.total == total_val_batches
total_val_batches = total_val_batches * val_checks_per_epoch
assert pbar_callback.main_progress_bar.n == (
total_train_batches + total_val_batches) // world_size
assert pbar_callback.main_progress_bar.total == (
total_train_batches + total_val_batches) // world_size
assert pbar_callback.is_enabled
def test_has_iterable_dataset():
assert has_iterable_dataset(DataLoader(RandomIterableDataset(1, 1)))
assert not has_iterable_dataset(DataLoader(RandomDataset(1, 1)))
class MockDatasetWithoutIterableDataset(RandomDataset):
def __iter__(self):
yield 1
return self
assert not has_iterable_dataset(
DataLoader(MockDatasetWithoutIterableDataset(1, 1)))
def test_update_dataloader_with_multiprocessing_context():
"""This test verifies that replace_sampler conserves multiprocessing context."""
train = RandomDataset(32, 64)
context = "spawn"
train = DataLoader(train,
batch_size=32,
num_workers=2,
multiprocessing_context=context,
shuffle=True)
new_data_loader = _update_dataloader(train,
SequentialSampler(train.dataset))
assert new_data_loader.multiprocessing_context == train.multiprocessing_context
def test_error_raised_with_insufficient_float_limit_train_dataloader():
batch_size = 16
dl = DataLoader(RandomDataset(32, batch_size * 9), batch_size=batch_size)
trainer = Trainer(limit_train_batches=0.1)
model = BoringModel()
trainer._data_connector.attach_data(model=model, train_dataloaders=dl)
with pytest.raises(
MisconfigurationException,
match=
"Please increase the `limit_train_batches` argument. Try at least",
):
trainer.reset_train_dataloader(model)
def test_quantization_val_test_predict(tmpdir):
"""Test the default quantization aware training not affected by validating, testing and predicting."""
seed_everything(42)
num_features = 16
dm = RegressDataModule(num_features=num_features)
qmodel = RegressionModel()
val_test_predict_qmodel = copy.deepcopy(qmodel)
trainer = Trainer(
callbacks=[QuantizationAwareTraining(quantize_on_fit_end=False)],
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=1,
limit_test_batches=1,
limit_predict_batches=1,
val_check_interval=1,
num_sanity_val_steps=1,
max_epochs=4,
)
trainer.fit(val_test_predict_qmodel, datamodule=dm)
trainer.validate(model=val_test_predict_qmodel,
datamodule=dm,
verbose=False)
trainer.test(model=val_test_predict_qmodel, datamodule=dm, verbose=False)
trainer.predict(model=val_test_predict_qmodel,
dataloaders=[
torch.utils.data.DataLoader(
RandomDataset(num_features, 16))
])
expected_qmodel = copy.deepcopy(qmodel)
# No validation in ``expected_qmodel`` fitting.
Trainer(
callbacks=[QuantizationAwareTraining(quantize_on_fit_end=False)],
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=0,
max_epochs=4,
).fit(expected_qmodel, datamodule=dm)
expected_state_dict = expected_qmodel.state_dict()
for key, value in val_test_predict_qmodel.state_dict().items():
expected_value = expected_state_dict[key]
assert torch.allclose(value, expected_value)
def test_swa_deepcopy(tmpdir):
"""Test to ensure SWA Callback doesn't deepcopy dataloaders and datamodule potentially leading to OOM."""
class TestSWA(StochasticWeightAveraging):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.setup_called = False
def setup(self, trainer, pl_module, stage) -> None:
super().setup(trainer, pl_module, stage)
assert self._average_model.train_dataloader is not pl_module.train_dataloader
assert self._average_model.train_dataloader.__self__ == self._average_model
assert self._average_model.trainer is None
self.setup_called = True
model = BoringModel()
swa = TestSWA(swa_lrs=1e-2)
trainer = Trainer(default_root_dir=tmpdir, callbacks=swa, fast_dev_run=True)
trainer.fit(model, train_dataloaders=DataLoader(RandomDataset(32, 2)))
assert swa.setup_called
def test_loader_detaching():
"""Checks that the loader has been reset after the entrypoint."""
loader = DataLoader(RandomDataset(32, 10), batch_size=1)
model = LoaderTestModel()
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert len(model.predict_dataloader()) == 64
assert len(model.test_dataloader()) == 64
trainer = Trainer(fast_dev_run=1)
trainer.fit(model, loader, loader)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert len(model.predict_dataloader()) == 64
assert len(model.test_dataloader()) == 64
trainer.validate(model, loader)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert len(model.predict_dataloader()) == 64
assert len(model.test_dataloader()) == 64
trainer.predict(model, loader)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert len(model.predict_dataloader()) == 64
assert len(model.test_dataloader()) == 64
trainer.test(model, loader)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert len(model.predict_dataloader()) == 64
assert len(model.test_dataloader()) == 64
def predict_dataloader(self):
return DataLoader(RandomDataset(32, 64))
@RunIf(rich=True)
def test_rich_progress_bar_refresh_rate():
progress_bar = RichProgressBar(refresh_rate_per_second=1)
assert progress_bar.is_enabled
assert not progress_bar.is_disabled
progress_bar = RichProgressBar(refresh_rate_per_second=0)
assert not progress_bar.is_enabled
assert progress_bar.is_disabled
@RunIf(rich=True)
@mock.patch(
"pytorch_lightning.callbacks.progress.rich_progress.Progress.update")
@pytest.mark.parametrize(
"dataset", [RandomDataset(32, 64),
RandomIterableDataset(32, 64)])
def test_rich_progress_bar(progress_update, tmpdir, dataset):
class TestModel(BoringModel):
def train_dataloader(self):
return DataLoader(dataset=dataset)
def val_dataloader(self):
return DataLoader(dataset=dataset)
def test_dataloader(self):
return DataLoader(dataset=dataset)
def predict_dataloader(self):
return DataLoader(dataset=dataset)
def test_dataloader(self):
assert self._setup
return DataLoader(RandomDataset(32, 64), batch_size=2)
def test_dataloader(self):
return torch.utils.data.DataLoader(RandomDataset(32, 64))
def train_dataloader(self):
# batch target memory >= 100x boring_model size
batch_size = self.num_params * 100 // 32 + 1
return DataLoader(RandomDataset(32, 5000), batch_size=batch_size)
assert isinstance(trainer.progress_bar_callback, RichProgressBar)
@RunIf(rich=True)
def test_rich_progress_bar_refresh_rate_enabled():
progress_bar = RichProgressBar(refresh_rate=1)
assert progress_bar.is_enabled
assert not progress_bar.is_disabled
progress_bar = RichProgressBar(refresh_rate=0)
assert not progress_bar.is_enabled
assert progress_bar.is_disabled
@RunIf(rich=True)
@mock.patch("pytorch_lightning.callbacks.progress.rich_progress.Progress.update")
@pytest.mark.parametrize("dataset", [RandomDataset(32, 64), RandomIterableDataset(32, 64)])
def test_rich_progress_bar(progress_update, tmpdir, dataset):
class TestModel(BoringModel):
def train_dataloader(self):
return DataLoader(dataset=dataset)
def val_dataloader(self):
return DataLoader(dataset=dataset)
def test_dataloader(self):
return DataLoader(dataset=dataset)
def predict_dataloader(self):
return DataLoader(dataset=dataset)
model = TestModel()
class BoringModelNoDataloaders(BoringModel):
def train_dataloader(self):
raise NotImplementedError
def val_dataloader(self):
raise NotImplementedError
def test_dataloader(self):
raise NotImplementedError
def predict_dataloader(self):
raise NotImplementedError
_loader = DataLoader(RandomDataset(32, 64))
_loader_no_len = CustomNotImplementedErrorDataloader(_loader)
@pytest.mark.parametrize(
"train_dataloaders, val_dataloaders, test_dataloaders, predict_dataloaders",
[
(_loader_no_len, None, None, None),
(None, _loader_no_len, None, None),
(None, None, _loader_no_len, None),
(None, None, None, _loader_no_len),
(None, [_loader, _loader_no_len], None, None),
],
)
@mock.patch("pytorch_lightning.plugins.training_type.tpu_spawn.xm")
def test_error_iterable_dataloaders_passed_to_fit(_, tmpdir, train_dataloaders,
def test_mp_device_dataloader_attribute(_):
dataset = RandomDataset(32, 64)
dataloader = TPUSpawnStrategy().process_dataloader(DataLoader(dataset))
assert dataloader.dataset == dataset
def train_dataloader(self):
seed_everything(42)
return torch.utils.data.DataLoader(RandomDataset(32, 64))