257 lines
9.7 KiB
Python
257 lines
9.7 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 sys
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from re import escape
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import pytest
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from torch.utils.data import DataLoader, DistributedSampler
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from torch.utils.data.sampler import BatchSampler, Sampler, SequentialSampler
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_7
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from tests.helpers import BoringModel, RandomDataset
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@pytest.mark.skipif(
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sys.platform == "win32" and not _TORCH_GREATER_EQUAL_1_7, reason="Bad `torch.distributed` support on Windows"
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)
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@pytest.mark.parametrize("mode", (1, 2))
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def test_replace_distributed_sampler(tmpdir, mode):
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class IndexedRandomDataset(RandomDataset):
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def __getitem__(self, index):
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return self.data[index]
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class CustomDataLoader(DataLoader):
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def __init__(self, num_features, dataset, *args, **kwargs):
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self.num_features = num_features
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super().__init__(dataset, *args, **kwargs)
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class FailureCustomDataLoader(DataLoader):
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def __init__(self, num_features, dataset, *args, **kwargs):
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super().__init__(dataset, *args, **kwargs)
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class CustomBatchSampler(BatchSampler):
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pass
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class TestModel(BoringModel):
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def __init__(self, numbers_test_dataloaders, mode):
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super().__init__()
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self._numbers_test_dataloaders = numbers_test_dataloaders
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self._mode = mode
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def test_step(self, batch, batch_idx, dataloader_idx=None):
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return super().test_step(batch, batch_idx)
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def on_test_start(self) -> None:
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dataloader = self.trainer.test_dataloaders[0]
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assert isinstance(dataloader, CustomDataLoader)
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assert dataloader.batch_size is None
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batch_sampler = dataloader.batch_sampler
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assert isinstance(batch_sampler, CustomBatchSampler)
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assert batch_sampler.batch_size == 1
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assert batch_sampler.drop_last
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assert isinstance(batch_sampler.sampler, DistributedSampler)
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def create_dataset(self):
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dataset = IndexedRandomDataset(32, 64)
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batch_sampler = None
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batch_size = 2
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if self._mode == 2:
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batch_size = 1
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batch_sampler = CustomBatchSampler(SequentialSampler(dataset), batch_size=batch_size, drop_last=True)
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dataloader_cls = CustomDataLoader
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else:
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dataloader_cls = FailureCustomDataLoader
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return dataloader_cls(32, dataset, batch_size=batch_size, batch_sampler=batch_sampler)
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def test_dataloader(self):
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return [self.create_dataset()] * self._numbers_test_dataloaders
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model = TestModel(2, mode)
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model.test_epoch_end = None
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trainer = Trainer(
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default_root_dir=tmpdir, limit_test_batches=2, plugins="ddp_find_unused_parameters_false", num_processes=1
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)
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if mode == 1:
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match = escape("missing attributes are ['num_features']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.test(model)
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else:
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trainer.test(model)
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@pytest.mark.parametrize("num_workers", [0, 1])
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def test_dataloader_warnings(num_workers):
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class TestModel(BoringModel):
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def on_train_start(self, *_) -> None:
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raise SystemExit()
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dl = DataLoader(RandomDataset(32, 64), num_workers=num_workers)
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if hasattr(dl, "persistent_workers"):
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if num_workers == 0:
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warn_str = "Consider setting num_workers>0 and persistent_workers=True"
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else:
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warn_str = "Consider setting persistent_workers=True"
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else:
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warn_str = "Consider setting accelerator=ddp"
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trainer = Trainer(accelerator="ddp_spawn")
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with pytest.warns(UserWarning, match=warn_str), pytest.raises(SystemExit):
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trainer.fit(TestModel(), dl)
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def test_replace_sampler_raises():
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trainer = Trainer()
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with pytest.raises(ValueError, match="needs to subclass `torch.utils.data.DataLoader"):
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trainer.replace_sampler(object(), object(), mode="fit")
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def test_dataloaders_with_missing_keyword_arguments():
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trainer = Trainer()
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ds = RandomDataset(10, 20)
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class TestDataLoader(DataLoader):
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def __init__(self, dataset):
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super().__init__(dataset)
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loader = TestDataLoader(ds)
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sampler = SequentialSampler(ds)
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match = escape("missing arguments are ['batch_sampler', 'sampler', 'shuffle']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="fit")
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match = escape("missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler', 'shuffle']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="predict")
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class TestDataLoader(DataLoader):
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def __init__(self, dataset, *args, **kwargs):
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super().__init__(dataset)
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loader = TestDataLoader(ds)
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sampler = SequentialSampler(ds)
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trainer.replace_sampler(loader, sampler, mode="fit")
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trainer.replace_sampler(loader, sampler, mode="predict")
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class TestDataLoader(DataLoader):
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def __init__(self, *foo, **bar):
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super().__init__(*foo, **bar)
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loader = TestDataLoader(ds)
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sampler = SequentialSampler(ds)
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trainer.replace_sampler(loader, sampler, mode="fit")
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trainer.replace_sampler(loader, sampler, mode="predict")
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class TestDataLoader(DataLoader):
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def __init__(self, num_feat, dataset, *args, shuffle=False):
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self.num_feat = num_feat
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super().__init__(dataset)
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loader = TestDataLoader(1, ds)
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sampler = SequentialSampler(ds)
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match = escape("missing arguments are ['batch_sampler', 'sampler']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="fit")
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match = escape("missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="predict")
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class TestDataLoader(DataLoader):
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def __init__(self, num_feat, dataset, **kwargs):
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self.feat_num = num_feat
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super().__init__(dataset)
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loader = TestDataLoader(1, ds)
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sampler = SequentialSampler(ds)
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match = escape("missing attributes are ['num_feat']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="fit")
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match = escape("missing attributes are ['num_feat']")
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with pytest.raises(MisconfigurationException, match=match):
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trainer.replace_sampler(loader, sampler, mode="predict")
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def test_replace_sampler_with_multiprocessing_context():
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"""This test verifies that replace_sampler conserves multiprocessing context"""
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train = RandomDataset(32, 64)
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context = "spawn"
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train = DataLoader(train, batch_size=32, num_workers=2, multiprocessing_context=context, shuffle=True)
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trainer = Trainer()
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new_data_loader = trainer.replace_sampler(train, SequentialSampler(train.dataset))
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assert new_data_loader.multiprocessing_context == train.multiprocessing_context
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def test_dataloader_reinit_for_subclass():
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class CustomDataLoader(DataLoader):
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def __init__(
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self,
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dataset,
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batch_size=1,
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shuffle=False,
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sampler=None,
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batch_sampler=None,
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num_workers=0,
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collate_fn=None,
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pin_memory=False,
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drop_last=False,
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timeout=0,
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worker_init_fn=None,
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dummy_kwarg=None,
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):
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super().__init__(
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dataset,
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batch_size,
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shuffle,
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sampler,
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batch_sampler,
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num_workers,
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collate_fn,
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pin_memory,
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drop_last,
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timeout,
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worker_init_fn,
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)
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self.dummy_kwarg = dummy_kwarg
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self.something_unrelated = 1
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trainer = Trainer(num_processes=1, accelerator="ddp_cpu")
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class CustomDummyObj:
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sampler = None
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result = trainer.auto_add_sampler(CustomDummyObj(), shuffle=True)
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assert isinstance(result, CustomDummyObj), "Wrongly reinstantiated data loader"
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dataset = list(range(10))
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result = trainer.auto_add_sampler(CustomDataLoader(dataset), shuffle=True)
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assert isinstance(result, DataLoader)
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assert isinstance(result, CustomDataLoader)
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assert result.dummy_kwarg is None
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# Shuffled DataLoader should also work
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result = trainer.auto_add_sampler(CustomDataLoader(dataset, shuffle=True), shuffle=True)
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assert isinstance(result, DataLoader)
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assert isinstance(result, CustomDataLoader)
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assert result.dummy_kwarg is None
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class CustomSampler(Sampler):
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pass
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# Should raise an error if existing sampler is being replaced
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dataloader = CustomDataLoader(dataset, sampler=CustomSampler(dataset))
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with pytest.raises(MisconfigurationException, match="will be replaced by `DistributedSampler`"):
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trainer.auto_add_sampler(dataloader, shuffle=True)
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