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test_data_connector.py
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test_data_connector.py
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# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import redirect_stderr
from io import StringIO
from re import escape
from typing import Sized
from unittest.mock import Mock
import pytest
from lightning_utilities.test.warning import no_warning_call
from torch import Tensor
from torch.utils.data import BatchSampler, DataLoader, DistributedSampler, Sampler, SequentialSampler
from lightning.fabric.utilities.distributed import DistributedSamplerWrapper
from lightning.fabric.utilities.warnings import PossibleUserWarning
from lightning.pytorch import Trainer
from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
from lightning.pytorch.strategies import DDPSpawnStrategy
from lightning.pytorch.trainer.connectors.data_connector import _DataHookSelector, _DataLoaderSource, warning_cache
from lightning.pytorch.trainer.states import RunningStage, TrainerFn
from lightning.pytorch.utilities.combined_loader import CombinedLoader
from lightning.pytorch.utilities.data import _update_dataloader
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from tests_pytorch.helpers.runif import RunIf
@RunIf(skip_windows=True)
@pytest.mark.parametrize("mode", (1, 2))
def test_replace_distributed_sampler(tmpdir, mode):
class IndexedRandomDataset(RandomDataset):
def __getitem__(self, index):
return self.data[index]
class CustomDataLoader(DataLoader):
def __init__(self, num_features, dataset, *args, **kwargs):
# argument `num_features` unused on purpose
# it gets automatically captured by _replace_dataloader_init_method()
super().__init__(dataset, *args, **kwargs)
class CustomBatchSampler(BatchSampler):
pass
class TestModel(BoringModel):
def __init__(self, numbers_test_dataloaders, mode):
super().__init__()
self._numbers_test_dataloaders = numbers_test_dataloaders
self._mode = mode
def test_step(self, batch, batch_idx, dataloader_idx=0):
return super().test_step(batch, batch_idx)
def on_test_start(self) -> None:
dataloader = self.trainer.test_dataloaders[0]
assert isinstance(dataloader, CustomDataLoader)
batch_sampler = dataloader.batch_sampler
if self._mode == 1:
assert isinstance(batch_sampler, CustomBatchSampler)
# the batch_size is set on the batch sampler
assert dataloader.batch_size is None
elif self._mode == 2:
assert type(batch_sampler) is BatchSampler
assert dataloader.batch_size == self._mode
assert batch_sampler.batch_size == self._mode
assert batch_sampler.drop_last
# the sampler has been replaced
assert isinstance(batch_sampler.sampler, DistributedSampler)
def create_dataset(self):
dataset = IndexedRandomDataset(32, 64)
if self._mode == 1:
# with a custom batch sampler
batch_sampler = CustomBatchSampler(SequentialSampler(dataset), batch_size=1, drop_last=True)
return CustomDataLoader(32, dataset, batch_sampler=batch_sampler)
elif self._mode == 2:
# with no batch sampler provided
return CustomDataLoader(32, dataset, batch_size=2, drop_last=True)
def test_dataloader(self):
return [self.create_dataset()] * self._numbers_test_dataloaders
model = TestModel(2, mode)
trainer = Trainer(
default_root_dir=tmpdir,
limit_test_batches=2,
accelerator="cpu",
devices=1,
strategy="ddp_find_unused_parameters_false",
)
trainer.test(model)
class TestSpawnBoringModel(BoringModel):
def __init__(self, num_workers):
super().__init__()
self.num_workers = num_workers
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), num_workers=self.num_workers)
def on_fit_start(self):
self._resout = StringIO()
self.ctx = redirect_stderr(self._resout)
self.ctx.__enter__()
def on_train_end(self):
def _get_warning_msg():
dl = self.trainer.train_dataloader
if hasattr(dl, "persistent_workers"):
if self.num_workers == 0:
warn_str = "Consider setting num_workers>0 and persistent_workers=True"
else:
warn_str = "Consider setting persistent_workers=True"
else:
warn_str = "Consider setting strategy=ddp"
return warn_str
if self.trainer.is_global_zero:
self.ctx.__exit__(None, None, None)
msg = self._resout.getvalue()
warn_str = _get_warning_msg()
assert warn_str in msg
@RunIf(skip_windows=True)
@pytest.mark.parametrize("num_workers", [0, 1])
def test_dataloader_warnings(tmpdir, num_workers):
trainer = Trainer(default_root_dir=tmpdir, accelerator="cpu", devices=2, strategy="ddp_spawn", fast_dev_run=4)
assert isinstance(trainer.strategy, DDPSpawnStrategy)
trainer.fit(TestSpawnBoringModel(num_workers))
def test_update_dataloader_raises():
with pytest.raises(ValueError, match="needs to subclass `torch.utils.data.DataLoader"):
_update_dataloader(object(), object(), mode="fit")
def test_dataloaders_with_missing_keyword_arguments():
ds = RandomDataset(10, 20)
class TestDataLoader(DataLoader):
def __init__(self, dataset):
super().__init__(dataset)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
match = escape("missing arguments are ['batch_sampler', 'sampler', 'shuffle']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="fit")
match = escape("missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler', 'shuffle']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, dataset, *args, **kwargs):
super().__init__(dataset)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
_update_dataloader(loader, sampler, mode="fit")
_update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, *foo, **bar):
super().__init__(*foo, **bar)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
_update_dataloader(loader, sampler, mode="fit")
_update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, num_feat, dataset, *args, shuffle=False):
self.num_feat = num_feat
super().__init__(dataset)
loader = TestDataLoader(1, ds)
sampler = SequentialSampler(ds)
match = escape("missing arguments are ['batch_sampler', 'sampler']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="fit")
match = escape("missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, num_feat, dataset, **kwargs):
self.feat_num = num_feat
super().__init__(dataset)
loader = TestDataLoader(1, ds)
sampler = SequentialSampler(ds)
match = escape("missing attributes are ['num_feat']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="fit")
match = escape("missing attributes are ['num_feat']")
with pytest.raises(MisconfigurationException, match=match):
_update_dataloader(loader, sampler, mode="predict")
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_dataloader_reinit_for_subclass():
class CustomDataLoader(DataLoader):
def __init__(
self,
dataset,
batch_size=1,
shuffle=False,
sampler=None,
batch_sampler=None,
num_workers=0,
collate_fn=None,
pin_memory=False,
drop_last=False,
timeout=0,
worker_init_fn=None,
dummy_kwarg=None,
):
super().__init__(
dataset,
batch_size,
shuffle,
sampler,
batch_sampler,
num_workers,
collate_fn,
pin_memory,
drop_last,
timeout,
worker_init_fn,
)
self.dummy_kwarg = dummy_kwarg
self.something_unrelated = 1
trainer = Trainer(accelerator="cpu", devices=2, strategy="ddp_spawn")
mode = RunningStage.TRAINING
class CustomDummyObj:
sampler = None
result = trainer._data_connector._prepare_dataloader(CustomDummyObj(), shuffle=True, mode=mode)
assert isinstance(result, CustomDummyObj), "Wrongly reinstantiated data loader"
dataset = list(range(10))
result = trainer._data_connector._prepare_dataloader(CustomDataLoader(dataset), shuffle=True, mode=mode)
assert isinstance(result, DataLoader)
assert isinstance(result, CustomDataLoader)
assert result.dummy_kwarg is None
# Shuffled DataLoader should also work
result = trainer._data_connector._prepare_dataloader(
CustomDataLoader(dataset, shuffle=True), shuffle=True, mode=mode
)
assert isinstance(result, DataLoader)
assert isinstance(result, CustomDataLoader)
assert result.dummy_kwarg is None
class CustomSampler(Sampler):
def __init__(self, data_source: Sized) -> None:
super().__init__(data_source)
self.data_source = data_source
def __len__(self):
return len(self.data_source)
def __iter__(self):
return iter(range(len(self)))
# Should raise an error if existing sampler is being replaced
dataloader = CustomDataLoader(dataset, sampler=CustomSampler(dataset))
result = trainer._data_connector._prepare_dataloader(dataloader, shuffle=False, mode=mode)
result_dataset = list(result)
assert len(result_dataset) == 5
assert result_dataset == [Tensor([x]) for x in [0, 2, 4, 6, 8]]
assert isinstance(result.sampler, DistributedSamplerWrapper)
assert isinstance(result.sampler.dataset._sampler, CustomSampler)
class LoaderTestModel(BoringModel):
def training_step(self, batch, batch_idx):
assert len(self.trainer.train_dataloader) == 10
return super().training_step(batch, batch_idx)
def validation_step(self, batch, batch_idx):
assert len(self.trainer.val_dataloaders) == 10
return super().validation_step(batch, batch_idx)
def test_step(self, batch, batch_idx):
assert len(self.trainer.test_dataloaders) == 10
return super().test_step(batch, batch_idx)
def predict_step(self, batch, batch_idx, dataloader_idx=0):
assert len(self.trainer.predict_dataloaders) == 10
return super().predict_step(batch, batch_idx, dataloader_idx=dataloader_idx)
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 test_pre_made_batches():
"""Check that loader works with pre-made batches."""
loader = DataLoader(RandomDataset(32, 10), batch_size=None)
trainer = Trainer(fast_dev_run=1)
trainer.predict(LoaderTestModel(), loader)
def test_error_raised_with_float_limited_eval_batches():
"""Test that an error is raised if there are not enough batches when passed with float value of
limit_eval_batches."""
model = BoringModel()
dl_size = len(model.val_dataloader())
limit_val_batches = 1 / (dl_size + 2)
trainer = Trainer(limit_val_batches=limit_val_batches)
trainer._data_connector.attach_data(model)
trainer.state.fn = TrainerFn.VALIDATING
trainer.state.stage = RunningStage.VALIDATING
with pytest.raises(
MisconfigurationException,
match=rf"{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`",
):
trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)
@pytest.mark.parametrize(
"val_dl,warns",
[
(DataLoader(dataset=RandomDataset(32, 64), shuffle=True), True),
(DataLoader(dataset=RandomDataset(32, 64), sampler=list(range(64))), False),
(CombinedLoader(DataLoader(dataset=RandomDataset(32, 64), shuffle=True)), True),
(
CombinedLoader(
[DataLoader(dataset=RandomDataset(32, 64)), DataLoader(dataset=RandomDataset(32, 64), shuffle=True)]
),
True,
),
(
CombinedLoader(
{
"dl1": DataLoader(dataset=RandomDataset(32, 64)),
"dl2": DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
}
),
True,
),
],
)
def test_non_sequential_sampler_warning_is_raised_for_eval_dataloader(val_dl, warns):
trainer = Trainer()
model = BoringModel()
trainer._data_connector.attach_data(model, val_dataloaders=val_dl)
context = pytest.warns if warns else no_warning_call
trainer.state.fn = TrainerFn.VALIDATING
trainer.state.stage = RunningStage.VALIDATING
with context(PossibleUserWarning, match="recommended .* turn shuffling off for val/test/predict"):
trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)
class NoDataLoaderModel(BoringModel):
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
def test_dataloader_source_request_from_module():
"""Test requesting a dataloader from a module works."""
module = BoringModel()
trainer = Trainer()
module.trainer = trainer
module.foo = Mock(return_value=module.train_dataloader())
source = _DataLoaderSource(module, "foo")
assert source.is_module()
module.foo.assert_not_called()
assert isinstance(source.dataloader(), DataLoader)
module.foo.assert_called_once()
@pytest.mark.parametrize(
"hook_name", ("on_before_batch_transfer", "transfer_batch_to_device", "on_after_batch_transfer")
)
class TestDataHookSelector:
def overridden_func(self, batch, *args, **kwargs):
return batch
def reset_instances(self):
warning_cache.clear()
return BoringDataModule(), BoringModel(), Trainer()
def test_no_datamodule_no_overridden(self, hook_name):
model, _, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=None)
with no_warning_call(match=f"have overridden `{hook_name}` in"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is model
def test_with_datamodule_no_overridden(self, hook_name):
model, dm, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=dm)
with no_warning_call(match=f"have overridden `{hook_name}` in"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is model
def test_override_model_hook(self, hook_name):
model, dm, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=dm)
with no_warning_call(match=f"have overridden `{hook_name}` in"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is model
def test_override_datamodule_hook(self, hook_name):
model, dm, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=dm)
setattr(dm, hook_name, self.overridden_func)
with no_warning_call(match=f"have overridden `{hook_name}` in"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is dm
def test_override_both_model_and_datamodule(self, hook_name):
model, dm, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=dm)
setattr(model, hook_name, self.overridden_func)
setattr(dm, hook_name, self.overridden_func)
with pytest.warns(UserWarning, match=f"have overridden `{hook_name}` in both"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is dm
def test_with_datamodule_override_model(self, hook_name):
model, dm, trainer = self.reset_instances()
trainer._data_connector.attach_datamodule(model, datamodule=dm)
setattr(model, hook_name, self.overridden_func)
with pytest.warns(UserWarning, match=f"have overridden `{hook_name}` in `LightningModule`"):
instance = trainer._data_connector._datahook_selector.get_instance(hook_name)
assert instance is model
def test_invalid_hook_passed_in_datahook_selector():
dh_selector = _DataHookSelector(BoringModel(), None)
with pytest.raises(ValueError, match="is not a shared hook"):
dh_selector.get_instance("setup")
@pytest.mark.parametrize("devices, warn_context", [(1, no_warning_call), (2, pytest.warns)])
def test_eval_distributed_sampler_warning(devices, warn_context):
"""Test that a warning is raised when `DistributedSampler` is used with evaluation."""
model = BoringModel()
trainer = Trainer(strategy="ddp", devices=devices, accelerator="cpu")
trainer.strategy.connect(model)
trainer._data_connector.attach_data(model)
trainer.state.fn = TrainerFn.VALIDATING
trainer.state.stage = RunningStage.VALIDATING
with warn_context(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
trainer.validate_loop.setup_data()
trainer.state.fn = TrainerFn.TESTING
trainer.state.stage = RunningStage.TESTING
with warn_context(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
trainer.test_loop.setup_data()
@pytest.mark.parametrize("shuffle", [True, False])
def test_eval_shuffle_with_distributed_sampler_replacement(shuffle):
"""Test that shuffle is not changed if set to True."""
class CustomModel(BoringModel):
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64), shuffle=shuffle)
trainer = Trainer(accelerator="cpu", devices=2, strategy="ddp")
model = CustomModel()
trainer.strategy.connect(model)
trainer._data_connector.attach_data(model)
trainer.state.fn = TrainerFn.FITTING
trainer.state.stage = RunningStage.VALIDATING
trainer.fit_loop.epoch_loop.val_loop.setup_data()
assert trainer.val_dataloaders.sampler.shuffle == shuffle
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.strategy.connect(model)
trainer._data_connector.attach_data(model=model, train_dataloaders=dl)
trainer.state.fn = TrainerFn.FITTING
trainer.state.stage = RunningStage.TRAINING
with pytest.raises(
MisconfigurationException,
match="Please increase the `limit_train_batches` argument. Try at least",
):
trainer.fit_loop.setup_data()
@pytest.mark.parametrize(
"trainer_fn_name, dataloader_name",
[
("fit", "train_dataloaders"),
("validate", "dataloaders"),
("test", "dataloaders"),
("predict", "dataloaders"),
],
)
def test_attach_data_input_validation_with_none_dataloader(trainer_fn_name, dataloader_name, tmpdir):
"""Test that passing `Trainer.method(x_dataloader=None)` with no module-method implementations available raises
an error."""
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
model = BoringModel()
datamodule = BoringDataModule()
trainer_fn = getattr(trainer, trainer_fn_name)
# Pretend that these methods are not implemented
model.train_dataloader = None
model.val_dataloader = None
model.test_dataloader = None
model.predict_dataloader = None
datamodule.train_dataloader = None
datamodule.val_dataloader = None
datamodule.test_dataloader = None
datamodule.predict_dataloader = None
with pytest.raises(ValueError, match=f"An invalid .*dataloader was passed to `Trainer.{trainer_fn_name}"):
trainer_fn(model, **{dataloader_name: None}, datamodule=datamodule)
with pytest.raises(ValueError, match=f"An invalid .*dataloader was passed to `Trainer.{trainer_fn_name}"):
trainer_fn(model, **{dataloader_name: None}, datamodule=None)