Support re-instantiation for custom DataLoader in Lightning

CHANGELOG.md

@@ -21,7 +21,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Add logic to reload the states across data loading components ([#10699](https://github.com/PyTorchLightning/pytorch-lightning/issues/10699))
     * Cleanup some fault tolerant utilities ([#10703](https://github.com/PyTorchLightning/pytorch-lightning/issues/10703))
 
--
+- Added support for re-instantiation of custom (subclasses of) `DataLoaders` returned in the `*_dataloader()` methods, i.e., automatic replacement of samplers now works with custom types of `DataLoader` ([#10680](https://github.com/PyTorchLightning/pytorch-lightning/issues/10639))
 
 
 -

pytorch_lightning/lite/lite.py

@@ -25,17 +25,17 @@
 from torch.utils.data import DataLoader, DistributedSampler, RandomSampler, SequentialSampler
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
-from pytorch_lightning.lite.wrappers import (
-    _LiteDataLoader,
-    _LiteModule,
-    _LiteOptimizer,
-    _replace_dataloader_init_method,
-)
+from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
 from pytorch_lightning.plugins import DDPSpawnPlugin, DeepSpeedPlugin, PLUGIN_INPUT, TPUSpawnPlugin, TrainingTypePlugin
 from pytorch_lightning.trainer.connectors.accelerator_connector import AcceleratorConnector
 from pytorch_lightning.utilities import _StrategyType, DeviceType, move_data_to_device
 from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
-from pytorch_lightning.utilities.data import _auto_add_worker_init_fn, _update_dataloader, has_iterable_dataset
+from pytorch_lightning.utilities.data import (
+    _auto_add_worker_init_fn,
+    _replace_dataloader_init_method,
+    _update_dataloader,
+    has_iterable_dataset,
+)
 from pytorch_lightning.utilities.device_parser import _parse_devices
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.seed import seed_everything

pytorch_lightning/lite/wrappers.py

@@ -11,11 +11,7 @@
 # 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.
-import functools
-import inspect
-from contextlib import contextmanager
-from itertools import chain
-from typing import Any, Callable, Generator, Iterator, Optional, Set, Type, Union
+from typing import Any, Callable, Generator, Iterator, Optional, Union
 
 import torch
 from torch import nn as nn
@@ -110,49 +106,6 @@ def _convert_float_tensor(t: Tensor) -> Tensor:
         return output
 
 
-def _wrap_init(init: Callable) -> Callable:
-    """Wraps the ``__init__`` method of the dataloader in order to enable re-instantiation of custom subclasses of
-    :class:`~torch.utils.data.DataLoader`."""
-
-    @functools.wraps(init)
-    def wrapper(obj: DataLoader, *args: Any, **kwargs: Any) -> None:
-        params = dict(inspect.signature(obj.__init__).parameters)
-        params.pop("args", None)
-        params.pop("kwargs", None)
-        for arg_name, arg_value in chain(zip(params, args), kwargs.items()):
-            setattr(obj, arg_name, arg_value)
-        init(obj, *args, **kwargs)
-
-    return wrapper
-
-
-# https://stackoverflow.com/a/63851681/9201239
-def _get_all_subclasses(cls: Type[Any]) -> Set[Type[Any]]:
-    """Returns a list of all classes that inherit directly or indirectly from the given class."""
-    subclasses = set()
-
-    def recurse(cl: Type[Any]) -> None:
-        for subclass in cl.__subclasses__():
-            subclasses.add(subclass)
-            recurse(subclass)
-
-    recurse(cls)
-    return subclasses
-
-
-@contextmanager
-def _replace_dataloader_init_method() -> Generator[None, None, None]:
-    """This context manager is used to add support for re-instantiation of custom (subclasses) of
-    :class:`~torch.utils.data.DataLoader`. It patches the ``__init__`` method."""
-    for subclass in _get_all_subclasses(DataLoader):
-        subclass._old_init = subclass.__init__
-        subclass.__init__ = _wrap_init(subclass.__init__)
-    yield
-    for subclass in _get_all_subclasses(DataLoader):
-        subclass.__init__ = subclass._old_init
-        del subclass._old_init
-
-
 class _LiteDataLoader:
     def __init__(self, dataloader: DataLoader, device: Optional[torch.device] = None) -> None:
         """The LiteDataLoader is a wrapper for the :class:`~torch.utils.data.DataLoader`. It moves the data to the

pytorch_lightning/trainer/data_loading.py

@@ -31,6 +31,7 @@
 from pytorch_lightning.utilities.auto_restart import _add_capture_metadata_collate
 from pytorch_lightning.utilities.data import (
     _auto_add_worker_init_fn,
+    _replace_dataloader_init_method,
     _update_dataloader,
     has_iterable_dataset,
     has_len_all_ranks,
@@ -430,7 +431,10 @@ def request_dataloader(
 
         hook = f"{stage.dataloader_prefix}_dataloader"
         self.call_hook("on_" + hook, pl_module=model)
-        dataloader = source.dataloader()
+        with _replace_dataloader_init_method():
+            # under this context manager, the arguments passed to `DataLoader.__init__` will be captured and saved as
+            # attributes on the instance in case the dataloader needs to be re-instantiated later by Ligtning
+            dataloader = source.dataloader()
         if isinstance(dataloader, tuple):
             dataloader = list(dataloader)
         self.training_type_plugin.barrier("get_dataloaders")

pytorch_lightning/utilities/data.py

@@ -11,10 +11,13 @@
 # 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.
+import functools
 import inspect
 import os
+from contextlib import contextmanager
 from functools import partial
-from typing import Any, Dict, Generator, Iterable, Mapping, Optional, Union
+from itertools import chain
+from typing import Any, Callable, Dict, Generator, Iterable, Mapping, Optional, Set, Type, Union
 
 import torch
 from torch.utils.data import BatchSampler, DataLoader, IterableDataset, Sampler
@@ -299,6 +302,50 @@ def _auto_add_worker_init_fn(dataloader: DataLoader, rank: int) -> None:
         dataloader.worker_init_fn = partial(pl_worker_init_function, rank=rank)
 
 
+def _wrap_init(init: Callable) -> Callable:
+    """Wraps the ``__init__`` method of the dataloader in order to enable re-instantiation of custom subclasses of
+    :class:`~torch.utils.data.DataLoader`."""
+
+    @functools.wraps(init)
+    def wrapper(obj: DataLoader, *args: Any, **kwargs: Any) -> None:
+        params = dict(inspect.signature(obj.__init__).parameters)
+        params.pop("args", None)
+        params.pop("kwargs", None)
+        for arg_name, arg_value in chain(zip(params, args), kwargs.items()):
+            setattr(obj, arg_name, arg_value)
+        init(obj, *args, **kwargs)
+
+    return wrapper
+
+
+# https://stackoverflow.com/a/63851681/9201239
+def _get_all_subclasses(cls: Type[Any]) -> Set[Type[Any]]:
+    """Returns a list of all classes that inherit directly or indirectly from the given class."""
+    subclasses = set()
+
+    def recurse(cl: Type[Any]) -> None:
+        for subclass in cl.__subclasses__():
+            subclasses.add(subclass)
+            recurse(subclass)
+
+    recurse(cls)
+    return subclasses
+
+
+@contextmanager
+def _replace_dataloader_init_method() -> Generator[None, None, None]:
+    """This context manager is used to add support for re-instantiation of custom (subclasses) of
+    :class:`~torch.utils.data.DataLoader`. It patches the ``__init__`` method."""
+    subclasses = _get_all_subclasses(DataLoader)
+    for subclass in subclasses:
+        subclass._old_init = subclass.__init__
+        subclass.__init__ = _wrap_init(subclass.__init__)
+    yield
+    for subclass in subclasses:
+        subclass.__init__ = subclass._old_init
+        del subclass._old_init
+
+
 def _apply_fault_tolerant_automatic_capture_dataset_wrapper(dl_kwargs: Dict) -> Dict:
     dataset = dl_kwargs["dataset"]
     if isinstance(dataset, IterableDataset):

tests/lite/test_lite.py

@@ -164,32 +164,16 @@ def test_setup_dataloaders_return_type():
     assert lite_dataloader1.dataset is dataset1
 
 
-def test_setup_dataloaders_with_custom_type():
-    """Test that Lite intercepts arguments passed to custom subclasses of torch.utils.DataLoader and sets them as
-    attributes."""
-
-    class DataLoaderSubclass1(DataLoader):
-        def __init__(self, attribute1, *args, **kwargs):
-            # intentionally not setting this attribute, calling super with different args
-            # self.attribute1 = attribute1
-            super().__init__(*args, **kwargs)
-
-    class DataLoaderSubclass2(DataLoaderSubclass1):
-        def __init__(self, attribute1, attribute2, *args, **kwargs):
-            # intentionally not setting this attribute, calling super with different args
-            # self.attribute2 = attribute2
-            super().__init__(attribute1, *args, **kwargs)
-
-    class LiteWithCustomDataLoader(LightningLite):
+@mock.patch("pytorch_lightning.lite.lite._replace_dataloader_init_method")
+def test_setup_dataloaders_captures_dataloader_arguments(ctx_manager):
+    """Test that Lite intercepts the DataLoader constructor arguments with a context manager in its run method."""
+
+    class Lite(LightningLite):
         def run(self):
-            dataloader = DataLoaderSubclass2("attribute1", "attribute2", dataset=range(4), batch_size=2)
-            assert dataloader.attribute1 == "attribute1"
-            assert dataloader.attribute2 == "attribute2"
-            lite_dataloader = self.setup_dataloaders(dataloader)
-            assert lite_dataloader.attribute1 == "attribute1"
-            assert lite_dataloader.attribute2 == "attribute2"
-
-    LiteWithCustomDataLoader().run()
+            ctx_manager().__enter__.assert_called_once()
+
+    Lite().run()
+    ctx_manager().__exit__.assert_called_once()
 
 
 def test_setup_dataloaders_raises_for_unknown_custom_args():

tests/trainer/test_data_loading.py

@@ -28,19 +28,16 @@
 
 
 @RunIf(skip_windows=True)
-@pytest.mark.parametrize("mode", (1, 2, 3))
+@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):
-            self.num_features = num_features
-            super().__init__(dataset, *args, **kwargs)
-
-    class FailureCustomDataLoader(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):
@@ -59,11 +56,11 @@ def on_test_start(self) -> None:
             dataloader = self.trainer.test_dataloaders[0]
             assert isinstance(dataloader, CustomDataLoader)
             batch_sampler = dataloader.batch_sampler
-            if self._mode == 2:
+            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 == 3:
+            elif self._mode == 2:
                 assert type(batch_sampler) is BatchSampler
                 assert dataloader.batch_size == self._mode
             assert batch_sampler.batch_size == self._mode
@@ -74,15 +71,12 @@ def on_test_start(self) -> None:
         def create_dataset(self):
             dataset = IndexedRandomDataset(32, 64)
             if self._mode == 1:
-                # this case will raise an error
-                return FailureCustomDataLoader(32, dataset)
-            if self._mode == 2:
                 # with a custom batch sampler
-                batch_sampler = CustomBatchSampler(SequentialSampler(dataset), batch_size=2, drop_last=True)
+                batch_sampler = CustomBatchSampler(SequentialSampler(dataset), batch_size=1, drop_last=True)
                 return CustomDataLoader(32, dataset, batch_sampler=batch_sampler)
-            elif self._mode == 3:
+            elif self._mode == 2:
                 # with no batch sampler provided
-                return CustomDataLoader(32, dataset, batch_size=3, drop_last=True)
+                return CustomDataLoader(32, dataset, batch_size=2, drop_last=True)
 
         def test_dataloader(self):
             return [self.create_dataset()] * self._numbers_test_dataloaders
@@ -93,12 +87,7 @@ def test_dataloader(self):
     trainer = Trainer(
         default_root_dir=tmpdir, limit_test_batches=2, strategy="ddp_find_unused_parameters_false", num_processes=1
     )
-    if mode == 1:
-        match = escape("missing attributes are ['num_features']")
-        with pytest.raises(MisconfigurationException, match=match):
-            trainer.test(model)
-    else:
-        trainer.test(model)
+    trainer.test(model)
 
 
 class TestSpawnBoringModel(BoringModel):

tests/utilities/test_data.py

@@ -4,6 +4,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.utilities.data import (
+    _replace_dataloader_init_method,
     extract_batch_size,
     get_len,
     has_iterable_dataset,
@@ -112,3 +113,29 @@ def test_has_len_all_rank():
         assert not has_len_all_ranks(DataLoader(RandomDataset(0, 0)), trainer.training_type_plugin, model)
 
     assert has_len_all_ranks(DataLoader(RandomDataset(1, 1)), trainer.training_type_plugin, model)
+
+
+def test_replace_dataloader_init_method():
+    """Test that context manager intercepts arguments passed to custom subclasses of torch.utils.DataLoader and
+    sets them as attributes."""
+
+    class DataLoaderSubclass1(DataLoader):
+        def __init__(self, attribute1, *args, **kwargs):
+            # intentionally not setting this attribute, calling super with different args
+            # self.attribute1 = attribute1
+            super().__init__(*args, **kwargs)
+
+    class DataLoaderSubclass2(DataLoaderSubclass1):
+        def __init__(self, attribute1, attribute2, *args, **kwargs):
+            # intentionally not setting this attribute, calling super with different args
+            # self.attribute2 = attribute2
+            super().__init__(attribute1, *args, **kwargs)
+
+    with _replace_dataloader_init_method():
+        dataloader = DataLoaderSubclass1("attribute1", dataset=range(4), batch_size=2)
+        assert dataloader.attribute1 == "attribute1"
+
+    with _replace_dataloader_init_method():
+        dataloader = DataLoaderSubclass2("attribute1", "attribute2", dataset=range(4), batch_size=2)
+        assert dataloader.attribute1 == "attribute1"
+        assert dataloader.attribute2 == "attribute2"