126 lines
4.1 KiB
Python
126 lines
4.1 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 pytest
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from torch.utils.data import DataLoader
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from torch.utils.data.sampler import BatchSampler, 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 tests.helpers import BoringModel, RandomDataset
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from tests.helpers.runif import RunIf
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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, save_preds_on_dl_idx, mode):
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super().__init__()
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self._numbers_test_dataloaders = numbers_test_dataloaders
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self._save_preds_on_dl_idx = save_preds_on_dl_idx
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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 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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def check_replace_distributed_sampler(tmpdir, save_preds_on_dl_idx, accelerator, gpus, num_dl_idx, mode):
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num_processes = 2
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limit_test_batches = 2
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trainer_args = {
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"default_root_dir": tmpdir,
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"limit_test_batches": limit_test_batches,
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"accelerator": accelerator,
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}
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if accelerator == "ddp_cpu":
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trainer_args["num_processes"] = num_processes
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else:
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trainer_args["gpus"] = gpus
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model = TestModel(num_dl_idx, save_preds_on_dl_idx, mode)
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model.test_epoch_end = None
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trainer = Trainer(**trainer_args)
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if mode == 1:
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match = "DistributedSampler within"
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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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@RunIf(min_gpus=2, special=True)
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@pytest.mark.parametrize("mode", [1, 2])
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def test_replace_distributed_sampler_custom_dataloader_custom_batch_sampler(tmpdir, mode):
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check_replace_distributed_sampler(tmpdir, True, "ddp", 2, 2, mode)
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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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