642 lines
23 KiB
Python
642 lines
23 KiB
Python
import random
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from dataclasses import dataclass
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import pytest
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import torch
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from torch import Tensor
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from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
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from pytorch_lightning import Trainer
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from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
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from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper
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from pytorch_lightning.trainer.states import RunningStage
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from pytorch_lightning.utilities.data import (
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_dataloader_init_kwargs_resolve_sampler,
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_get_dataloader_init_args_and_kwargs,
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_replace_dunder_methods,
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_replace_value_in_saved_args,
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_update_dataloader,
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_WrapAttrTag,
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extract_batch_size,
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get_len,
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has_iterable_dataset,
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has_len,
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has_len_all_ranks,
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warning_cache,
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)
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests_pytorch.helpers.utils import no_warning_call
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def test_extract_batch_size():
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"""Tests the behavior of extracting the batch size."""
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def _check_warning_not_raised(data, expected):
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with no_warning_call(match="Trying to infer the `batch_size`"):
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assert extract_batch_size(data) == expected
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def _check_warning_raised(data, expected):
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with pytest.warns(UserWarning, match=f"Trying to infer the `batch_size` .* we found is {expected}."):
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assert extract_batch_size(batch) == expected
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warning_cache.clear()
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def _check_error_raised(data):
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with pytest.raises(MisconfigurationException, match="We could not infer the batch_size"):
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extract_batch_size(batch)
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@dataclass
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class CustomDataclass:
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a: Tensor
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b: Tensor
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# Warning not raised
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batch = torch.zeros(11, 10, 9, 8)
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_check_warning_not_raised(batch, 11)
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batch = {"test": torch.zeros(11, 10)}
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_check_warning_not_raised(batch, 11)
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batch = [torch.zeros(11, 10)]
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_check_warning_not_raised(batch, 11)
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batch = CustomDataclass(torch.zeros(11, 10), torch.zeros(11, 10))
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_check_warning_not_raised(batch, 11)
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batch = {"test": [{"test": [torch.zeros(11, 10)]}]}
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_check_warning_not_raised(batch, 11)
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# Warning raised
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batch = {"a": [torch.tensor(1), torch.tensor(2)], "b": torch.tensor([1, 2, 3, 4])}
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_check_warning_raised(batch, 1)
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batch = CustomDataclass(torch.zeros(11, 10), torch.zeros(1))
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_check_warning_raised(batch, 11)
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batch = {"test": [{"test": [torch.zeros(11, 10), torch.zeros(10, 10)]}]}
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_check_warning_raised(batch, 11)
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batch = {"test": [{"test": [torch.zeros(10, 10), torch.zeros(11, 10)]}]}
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_check_warning_raised(batch, 10)
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batch = [{"test": torch.zeros(10, 10), "test_1": torch.zeros(11, 10)}]
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_check_warning_raised(batch, 10)
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# Error raised
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batch = "test string"
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_check_error_raised(batch)
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data = {"test": ["some text"] * 7}
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_check_error_raised(data)
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class CustomBatch:
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def __init__(self):
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self.x = torch.randn(7, 2)
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data = CustomBatch()
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_check_error_raised(data)
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def test_has_iterable_dataset():
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assert has_iterable_dataset(DataLoader(RandomIterableDataset(1, 1)))
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assert not has_iterable_dataset(DataLoader(RandomDataset(1, 1)))
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class MockDatasetWithoutIterableDataset(RandomDataset):
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def __iter__(self):
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yield 1
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return self
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assert not has_iterable_dataset(DataLoader(MockDatasetWithoutIterableDataset(1, 1)))
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def test_has_len():
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assert has_len(DataLoader(RandomDataset(1, 1)))
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with pytest.warns(UserWarning, match="`DataLoader` returned 0 length."):
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assert has_len(DataLoader(RandomDataset(0, 0)))
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assert not has_len(DataLoader(RandomIterableDataset(1, 1)))
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def test_get_len():
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assert get_len(DataLoader(RandomDataset(1, 1))) == 1
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value = get_len(DataLoader(RandomIterableDataset(1, 1)))
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assert isinstance(value, float)
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assert value == float("inf")
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def test_has_len_all_rank():
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trainer = Trainer(fast_dev_run=True)
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model = BoringModel()
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with pytest.warns(UserWarning, match="Total length of `DataLoader` across ranks is zero."):
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assert has_len_all_ranks(DataLoader(RandomDataset(0, 0)), trainer.strategy, model)
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assert has_len_all_ranks(DataLoader(RandomDataset(1, 1)), trainer.strategy, model)
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def test_update_dataloader_typerror_custom_exception():
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class BadStandaloneGoodHookImpl(DataLoader):
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def __init__(self, foo, *args, **kwargs):
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self.foo = foo
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# positional conflict with `dataset`
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super().__init__(foo, *args, **kwargs)
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dataloader = BadStandaloneGoodHookImpl([1, 2, 3])
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with pytest.raises(MisconfigurationException, match="implementation has an error.*`dataset`"):
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_update_dataloader(dataloader, dataloader.sampler)
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with _replace_dunder_methods(DataLoader, "dataset"):
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dataloader = BadStandaloneGoodHookImpl([1, 2, 3])
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new_dataloader = _update_dataloader(dataloader, dataloader.sampler)
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assert isinstance(new_dataloader, BadStandaloneGoodHookImpl)
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class BadImpl(DataLoader):
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def __init__(self, randomize, *args, **kwargs):
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self.randomize = randomize
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# keyword conflict with `shuffle`
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super().__init__(*args, shuffle=randomize, **kwargs)
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dataloader = BadImpl(False, [])
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with pytest.raises(MisconfigurationException, match="implementation has an error.*`shuffle`"):
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_update_dataloader(dataloader, dataloader.sampler)
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class GoodImpl(DataLoader):
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def __init__(self, randomize, *args, **kwargs):
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# fixed implementation, kwargs are filtered
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self.randomize = randomize or kwargs.pop("shuffle", False)
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super().__init__(*args, shuffle=randomize, **kwargs)
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dataloader = GoodImpl(False, [])
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new_dataloader = _update_dataloader(dataloader, dataloader.sampler)
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assert isinstance(new_dataloader, GoodImpl)
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def test_replace_dunder_methods_multiple_loaders_without_init():
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"""In case of a class, that inherits from a class that we are patching, but doesn't define its own `__init__`
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method (the one we are wrapping), it can happen, that `hasattr(cls, "__old__init__")` is True because of parent
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class, but it is impossible to delete, because that method is owned by parent class. Furthermore, the error
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occured only sometimes because it depends on the order in which we are iterating over a set of classes we are
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patching.
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This test simulates the behavior by generating sufficient number of dummy classes, which do not define `__init__`
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and are children of `DataLoader`. We are testing that a) context manager `_replace_dunder_method` exits cleanly, and
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b) the mechanism checking for presence of `__old__init__` works as expected.
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"""
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classes = [DataLoader]
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for i in range(100):
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classes.append(type(f"DataLoader_{i}", (random.choice(classes),), {}))
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before = {cls: cls.__init__ for cls in classes}
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with _replace_dunder_methods(DataLoader, "dataset"):
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for cls in classes[1:]: # First one is `DataLoader`
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assert "__old__init__" not in cls.__dict__
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assert hasattr(cls, "__old__init__")
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assert "__old__init__" in DataLoader.__dict__
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assert hasattr(DataLoader, "__old__init__")
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for cls in classes:
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assert before[cls] == cls.__init__
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class DataLoaderSubclass1(DataLoader):
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def __init__(self, attribute1, *args, **kwargs):
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self.at1 = attribute1
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super().__init__(*args, **kwargs)
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class DataLoaderSubclass2(DataLoaderSubclass1):
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def __init__(self, attribute2, *args, **kwargs):
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self.at2 = attribute2
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super().__init__(attribute2 + "-2", *args, **kwargs)
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class MyBaseDataLoader(DataLoader):
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pass
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class MyDataLoader(MyBaseDataLoader):
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def __init__(self, data: torch.Tensor, *args, **kwargs):
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self.data = data
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super().__init__(range(data.size(0)), *args, **kwargs)
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test3_data = torch.randn((10, 20))
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class PoptorchDataLoader(DataLoader):
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def __init__(self, options, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._options = options
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@property
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def options(self):
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return self._options
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class IncompleteDataLoader(DataLoader):
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def __init__(self, dataset, batch_size, **kwargs):
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batch_size = max(batch_size - 5, 0)
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super().__init__(dataset, batch_size=batch_size, **kwargs)
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class WeirdDataLoader1(DataLoader):
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def __init__(self, arg1, arg2, **kwargs):
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self.arg1 = arg1
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super().__init__(arg2, **kwargs)
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class WeirdDataLoader2(DataLoader):
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def __init__(self, data_part1, data_part2, **kwargs):
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data = list(data_part1) + list(data_part2)
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super().__init__(data, **kwargs)
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class NoneDataLoader(DataLoader):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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class ChangingDataLoader(DataLoader):
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def __init__(self, dataset, **kwargs):
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super().__init__(list(dataset) + list(range(5, 10)), **kwargs)
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@pytest.mark.parametrize(
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["cls", "args", "kwargs", "arg_names", "dataset", "checked_values"],
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[
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pytest.param(
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DataLoaderSubclass1,
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("attribute1",),
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dict(dataset=range(4), batch_size=2),
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("attribute1",),
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range(4),
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dict(batch_size=2, at1="attribute1"),
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id="test1",
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),
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pytest.param(
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DataLoaderSubclass2,
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("attribute2",),
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dict(dataset=range(4), batch_size=2),
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("attribute2",),
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range(4),
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dict(batch_size=2, at1="attribute2-2", at2="attribute2"),
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id="test2",
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),
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pytest.param(
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MyDataLoader,
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(test3_data,),
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dict(batch_size=2),
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("data",),
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range(10),
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dict(batch_size=2, data=test3_data),
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id="test3",
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),
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pytest.param(PoptorchDataLoader, (123, [1]), dict(), ("options",), [1], dict(options=123), id="test4"),
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pytest.param(
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IncompleteDataLoader,
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(range(10),),
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dict(batch_size=10),
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("dataset",),
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range(10),
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dict(batch_size=5),
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id="test5",
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),
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pytest.param(
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WeirdDataLoader1,
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(10, range(10)),
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dict(batch_size=10),
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("arg1", "arg2"),
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range(10),
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dict(arg1=10, batch_size=10),
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id="test6",
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),
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pytest.param(
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WeirdDataLoader2,
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(range(10), range(10, 20)),
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dict(batch_size=10),
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("data_part1", "data_part2"),
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list(range(20)),
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dict(batch_size=10),
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id="test7",
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),
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pytest.param(NoneDataLoader, (None,), dict(), (), None, dict(), id="test8"),
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pytest.param(ChangingDataLoader, (range(5),), dict(), ("dataset",), list(range(10)), dict(), id="test9"),
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],
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)
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def test_replace_dunder_methods_dataloader(cls, args, kwargs, arg_names, dataset, checked_values):
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with _replace_dunder_methods(DataLoader, "dataset"):
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dataloader = cls(*args, **kwargs)
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assert dataloader.__pl_saved_args == args
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assert dataloader.__pl_saved_kwargs == kwargs
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assert dataloader.__pl_saved_arg_names == arg_names
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assert dataloader.__pl_saved_default_kwargs == {}
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assert dataloader.__dataset == dataset
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assert dataloader.dataset == dataset
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for key, value in checked_values.items():
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dataloader_value = getattr(dataloader, key)
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if isinstance(dataloader_value, torch.Tensor):
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assert dataloader_value is value
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else:
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assert dataloader_value == value
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dataloader = _update_dataloader(dataloader, dataloader.sampler)
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assert isinstance(dataloader, cls)
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assert not hasattr(dataloader, "__pl_saved_kwargs")
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assert not hasattr(dataloader, "__pl_saved_arg_names")
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assert not hasattr(dataloader, "__pl_saved_args")
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assert not hasattr(dataloader, "__pl_saved_default_kwargs")
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assert not hasattr(dataloader, "__dataset")
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assert dataloader.dataset == dataset
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for key, value in checked_values.items():
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dataloader_value = getattr(dataloader, key)
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if isinstance(dataloader_value, torch.Tensor):
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assert dataloader_value is value
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else:
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assert dataloader_value == value
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def test_replace_dunder_methods_extra_kwargs():
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class LoaderSubclass(DataLoader):
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def __init__(self, dataset, *args, batch_size=10, **kwargs):
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super().__init__(dataset, *args, batch_size=batch_size, **kwargs)
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with _replace_dunder_methods(DataLoader, "dataset"):
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dataloader = LoaderSubclass(range(10))
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assert dataloader.__pl_saved_args == (range(10),)
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assert dataloader.__pl_saved_kwargs == {}
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assert dataloader.__pl_saved_arg_names == ("dataset",)
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assert dataloader.__pl_saved_default_kwargs == {"batch_size": 10}
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assert dataloader.__dataset == range(10)
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def test_replace_dunder_methods_attrs():
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"""This test checks, that all the calls from setting and deleting attributes within `_replace_dunder_methods`
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are correctly preserved even after reinstantiation.
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It also includes a custom `__setattr__`
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"""
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class Loader(DataLoader):
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def __setattr__(self, attr, val):
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if attr == "custom_arg":
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val = val + 2
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super().__setattr__(attr, val)
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with _replace_dunder_methods(DataLoader, "dataset"):
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dataloader = Loader(range(10))
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dataloader.custom_arg = 5
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dataloader.my_arg = 10
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dataloader.another_arg = 100
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del dataloader.dataset
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try:
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del dataloader.abc_arg
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except AttributeError:
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pass
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assert dataloader.__pl_saved_args == (range(10),)
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assert dataloader.__pl_saved_kwargs == {}
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assert dataloader.__pl_saved_arg_names == ("dataset",)
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assert dataloader.__dataset == range(10)
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assert dataloader.custom_arg == 7
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assert dataloader.my_arg == 10
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assert dataloader.another_arg == 100
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assert not hasattr(dataloader, "dataset")
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assert dataloader.__pl_attrs_record == [
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(("custom_arg", 5), _WrapAttrTag.SET),
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(("my_arg", 10), _WrapAttrTag.SET),
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(("another_arg", 100), _WrapAttrTag.SET),
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(("dataset",), _WrapAttrTag.DEL),
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]
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dataloader = _update_dataloader(dataloader, dataloader.sampler)
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assert dataloader.custom_arg == 7
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assert dataloader.my_arg == 10
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assert dataloader.another_arg == 100
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assert not hasattr(dataloader, "dataset")
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def test_replace_dunder_methods_restore_methods():
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"""This tests checks whether are all dunder methods restored to their original versions."""
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class Init(DataLoader):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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class SetAttr(DataLoader):
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def __setattr__(self, *args):
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return super().__setattr__(*args)
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class DelAttr(DataLoader):
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def __delattr__(self, *args):
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return super().__delattr__(*args)
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class InitAndSetAttr(Init, SetAttr):
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pass
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class InitAndDelAttr(Init, DelAttr):
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pass
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class SetAttrAndDelAttr(SetAttr, DelAttr):
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pass
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class AllDunder(Init, SetAttr, DelAttr):
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pass
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before = dict()
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for cls in (Init, SetAttr, DelAttr, InitAndSetAttr, InitAndDelAttr, SetAttrAndDelAttr, AllDunder):
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before[cls] = {"init": cls.__init__, "setattr": cls.__setattr__, "delattr": cls.__delattr__}
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with _replace_dunder_methods(DataLoader, "dataset"):
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pass
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for cls in (Init, SetAttr, DelAttr, InitAndSetAttr, InitAndDelAttr, SetAttrAndDelAttr, AllDunder):
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assert before[cls] == {"init": cls.__init__, "setattr": cls.__setattr__, "delattr": cls.__delattr__}
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@pytest.mark.parametrize("predicting", [True, False])
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def test_custom_batch_sampler(predicting):
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"""This test asserts, that custom `BatchSampler`, with all the arguments, that are required in order to
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properly reinstantiate the class, is invoked properly.
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It also asserts, that during the reinstantiation, the wrapper of `__init__` method is not present anymore, therefore
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not setting `__pl_saved_{args,arg_names,kwargs}` attributes.
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"""
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class MyBatchSampler(BatchSampler):
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# Custom Batch sampler with extra argument and default value
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def __init__(self, sampler, extra_arg, drop_last=True):
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self.extra_arg = extra_arg
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super().__init__(sampler, 10, drop_last)
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sampler = RandomSampler(range(10))
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with _replace_dunder_methods(BatchSampler):
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# instantiate within `_replace_dunder_method` context manager, simulating `*_dataloader` hooks
|
|
batch_sampler = MyBatchSampler(sampler, "random_str")
|
|
|
|
dataloader = DataLoader(range(10), batch_sampler=batch_sampler)
|
|
|
|
# assert that passed information got saved
|
|
assert dataloader.batch_sampler.__pl_saved_args == (sampler, "random_str")
|
|
assert dataloader.batch_sampler.__pl_saved_kwargs == {}
|
|
assert dataloader.batch_sampler.__pl_saved_arg_names == ("sampler", "extra_arg")
|
|
assert dataloader.batch_sampler.__pl_saved_default_kwargs == {"drop_last": True}
|
|
|
|
# updating dataloader, what happens on access of the dataloaders.
|
|
# This should not fail, and would fail before support for custom args.
|
|
dataloader = _update_dataloader(
|
|
dataloader, dataloader.sampler, mode=RunningStage.PREDICTING if predicting else None
|
|
)
|
|
|
|
# Assert the `__init__` method is not replaced anymore and everything is instantiated to correct types
|
|
batch_sampler = dataloader.batch_sampler
|
|
|
|
if predicting:
|
|
assert isinstance(batch_sampler, IndexBatchSamplerWrapper)
|
|
batch_sampler = batch_sampler._sampler
|
|
|
|
assert isinstance(batch_sampler, MyBatchSampler)
|
|
assert batch_sampler.drop_last == (not predicting)
|
|
|
|
assert batch_sampler.extra_arg == "random_str"
|
|
assert not hasattr(batch_sampler, "__pl_saved_kwargs")
|
|
assert not hasattr(batch_sampler, "__pl_saved_arg_names")
|
|
assert not hasattr(batch_sampler, "__pl_saved_args")
|
|
assert not hasattr(batch_sampler, "__pl_saved_default_kwargs")
|
|
|
|
|
|
def test_custom_batch_sampler_no_drop_last():
|
|
"""Tests whether appropriate warning is raised when the custom `BatchSampler` does not support `drop_last` and
|
|
we want to reset it."""
|
|
|
|
class MyBatchSampler(BatchSampler):
|
|
# Custom batch sampler with extra argument, but without `drop_last`
|
|
def __init__(self, sampler, extra_arg):
|
|
self.extra_arg = extra_arg
|
|
super().__init__(sampler, 10, False)
|
|
|
|
sampler = RandomSampler(range(10))
|
|
with _replace_dunder_methods(BatchSampler):
|
|
# instantiate within `_replace_dunder_method` context manager, simulating `*_dataloader` hooks
|
|
batch_sampler = MyBatchSampler(sampler, "random_str")
|
|
|
|
dataloader = DataLoader(range(10), batch_sampler=batch_sampler)
|
|
|
|
# assert that passed information got saved
|
|
assert dataloader.batch_sampler.__pl_saved_args == (sampler, "random_str")
|
|
assert dataloader.batch_sampler.__pl_saved_kwargs == {}
|
|
assert dataloader.batch_sampler.__pl_saved_arg_names == ("sampler", "extra_arg")
|
|
assert dataloader.batch_sampler.__pl_saved_default_kwargs == {}
|
|
|
|
# Assert that warning is raised
|
|
with pytest.warns(UserWarning, match="drop_last=False"):
|
|
dataloader = _update_dataloader(dataloader, dataloader.sampler, mode=RunningStage.PREDICTING)
|
|
|
|
|
|
def test_custom_batch_sampler_no_sampler():
|
|
"""Tests whether appropriate error is raised when the custom `BatchSampler` does not support sampler
|
|
argument."""
|
|
|
|
class MyBatchSampler(BatchSampler):
|
|
# Custom batch sampler, without sampler argument.
|
|
def __init__(self, extra_arg):
|
|
self.extra_arg = extra_arg
|
|
super().__init__(RandomSampler(range(10)), 10, False)
|
|
|
|
with _replace_dunder_methods(BatchSampler):
|
|
# instantiate within `_replace_dunder_method` context manager, simulating `*_dataloader` hooks
|
|
batch_sampler = MyBatchSampler("random_str")
|
|
dataloader = DataLoader(range(10), batch_sampler=batch_sampler)
|
|
|
|
# assert that passed information got saved
|
|
assert dataloader.batch_sampler.__pl_saved_args == ("random_str",)
|
|
assert dataloader.batch_sampler.__pl_saved_kwargs == {}
|
|
assert dataloader.batch_sampler.__pl_saved_arg_names == ("extra_arg",)
|
|
assert dataloader.batch_sampler.__pl_saved_default_kwargs == {}
|
|
|
|
# Assert that error is raised
|
|
with pytest.raises(TypeError, match="sampler into the batch sampler"):
|
|
dataloader = _update_dataloader(dataloader, dataloader.sampler, mode=RunningStage.PREDICTING)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
[
|
|
"args",
|
|
"kwargs",
|
|
"default_kwargs",
|
|
"arg_names",
|
|
"replace_key",
|
|
"replace_value",
|
|
"expected_status",
|
|
"expected_args",
|
|
"expected_kwargs",
|
|
],
|
|
[
|
|
pytest.param((), {}, {}, [], "a", 1, False, (), {}, id="empty"),
|
|
pytest.param((1,), {}, {}, ["a"], "a", 2, True, (2,), {}, id="simple1"),
|
|
pytest.param((1, 2, 3), {}, {}, ["a", "b", "c"], "b", False, True, (1, False, 3), {}, id="simple2"),
|
|
pytest.param((1, 2, 3), {"a": 1}, {}, ["b", "c", "d"], "a", 2, True, (1, 2, 3), {"a": 2}, id="simple_kwargs"),
|
|
pytest.param(
|
|
(1, 2, 3),
|
|
{"a": 1},
|
|
{"e": 5},
|
|
["b", "c", "d"],
|
|
"e",
|
|
2,
|
|
True,
|
|
(1, 2, 3),
|
|
{"a": 1, "e": 2},
|
|
id="default_kwargs",
|
|
),
|
|
],
|
|
)
|
|
def test_replace_value_in_args(
|
|
args, kwargs, default_kwargs, arg_names, replace_key, replace_value, expected_status, expected_args, expected_kwargs
|
|
):
|
|
assert _replace_value_in_saved_args(replace_key, replace_value, args, kwargs, default_kwargs, arg_names) == (
|
|
expected_status,
|
|
expected_args,
|
|
expected_kwargs,
|
|
)
|
|
|
|
|
|
def test_dataloader_disallow_batch_sampler():
|
|
dataset = RandomDataset(5, 100)
|
|
dataloader = DataLoader(dataset, batch_size=10)
|
|
|
|
# This should not raise
|
|
_dataloader_init_kwargs_resolve_sampler(dataloader, dataloader.sampler, disallow_batch_sampler=True)
|
|
|
|
dataset = RandomDataset(5, 100)
|
|
sampler = SequentialSampler(dataset)
|
|
batch_sampler = BatchSampler(sampler, batch_size=10, drop_last=False)
|
|
dataloader = DataLoader(dataset, batch_sampler=batch_sampler)
|
|
|
|
# this should raise - using batch sampler, that was not automatically instantiated by DataLoader
|
|
with pytest.raises(MisconfigurationException, match="when running on multiple IPU devices"):
|
|
_dataloader_init_kwargs_resolve_sampler(dataloader, dataloader.sampler, disallow_batch_sampler=True)
|
|
|
|
|
|
@pytest.mark.parametrize("mode", [RunningStage.TRAINING, RunningStage.PREDICTING, RunningStage.TESTING])
|
|
def test_dataloader_kwargs_replacement_with_iterable_dataset(mode):
|
|
"""Test that DataLoader kwargs are not replaced when using Iterable Dataset."""
|
|
dataset = RandomIterableDataset(7, 100)
|
|
dataloader = DataLoader(dataset, batch_size=32)
|
|
_, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, dataloader.sampler, mode=mode)
|
|
assert dl_kwargs["sampler"] is None
|
|
assert dl_kwargs["batch_sampler"] is None
|
|
assert dl_kwargs["batch_size"] is dataloader.batch_size
|
|
assert dl_kwargs["dataset"] is dataloader.dataset
|
|
assert dl_kwargs["collate_fn"] is dataloader.collate_fn
|