Improve error message on `TypeError` during `DataLoader` reconstruction (#10719)

CHANGELOG.md

@@ -25,7 +25,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - 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))
 
 
--
+- Show a better error message when a custom `DataLoader` implementation is not well implemented and we need to reconstruct it ([#10719](https://github.com/PyTorchLightning/pytorch-lightning/issues/10719))
 
 
 -

pytorch_lightning/utilities/data.py

@@ -180,7 +180,25 @@ def get_len(dataloader: DataLoader) -> Union[int, float]:
 def _update_dataloader(dataloader: DataLoader, sampler: Sampler, mode: Optional[RunningStage] = None) -> DataLoader:
     dl_kwargs = _get_dataloader_init_kwargs(dataloader, sampler, mode=mode)
     dl_cls = type(dataloader)
-    dataloader = dl_cls(**dl_kwargs)
+    try:
+        dataloader = dl_cls(**dl_kwargs)
+    except TypeError as e:
+        # improve exception message due to an incorrect implementation of the `DataLoader` where multiple subclass
+        # `__init__` arguments map to one `DataLoader.__init__` argument
+        import re
+
+        match = re.match(r".*__init__\(\) got multiple values .* '(\w+)'", str(e))
+        if not match:
+            # an unexpected `TypeError`, continue failure
+            raise
+        argument = match.groups()[0]
+        message = (
+            f"The {dl_cls.__name__} `DataLoader` implementation has an error where more than one `__init__` argument"
+            f" can be passed to its parent's `{argument}=...` `__init__` argument. This is likely caused by allowing"
+            f" passing both a custom argument that will map to the `{argument}` argument as well as `**kwargs`."
+            f" `kwargs` should be filtered to make sure they don't contain the `{argument}` key."
+        )
+        raise MisconfigurationException(message) from e
     return dataloader
 
 

tests/utilities/test_data.py

@@ -5,6 +5,7 @@ from torch.utils.data.dataloader import DataLoader
 from pytorch_lightning import Trainer
 from pytorch_lightning.utilities.data import (
     _replace_dataloader_init_method,
+    _update_dataloader,
     extract_batch_size,
     get_len,
     has_iterable_dataset,
@@ -115,6 +116,38 @@ def test_has_len_all_rank():
     assert has_len_all_ranks(DataLoader(RandomDataset(1, 1)), trainer.training_type_plugin, model)
 
 
+def test_update_dataloader_typerror_custom_exception():
+    class BadImpl(DataLoader):
+        def __init__(self, foo, *args, **kwargs):
+            self.foo = foo
+            # positional conflict with `dataset`
+            super().__init__(foo, *args, **kwargs)
+
+    dataloader = BadImpl([1, 2, 3])
+    with pytest.raises(MisconfigurationException, match="`DataLoader` implementation has an error.*`dataset`"):
+        _update_dataloader(dataloader, dataloader.sampler)
+
+    class BadImpl2(DataLoader):
+        def __init__(self, randomize, *args, **kwargs):
+            self.randomize = randomize
+            # keyword conflict with `shuffle`
+            super().__init__(*args, shuffle=randomize, **kwargs)
+
+    dataloader = BadImpl2(False, [])
+    with pytest.raises(MisconfigurationException, match="`DataLoader` implementation has an error.*`shuffle`"):
+        _update_dataloader(dataloader, dataloader.sampler)
+
+    class GoodImpl(DataLoader):
+        def __init__(self, randomize, *args, **kwargs):
+            # fixed implementation, kwargs are filtered
+            self.randomize = randomize or kwargs.pop("shuffle", False)
+            super().__init__(*args, shuffle=randomize, **kwargs)
+
+    dataloader = GoodImpl(False, [])
+    new_dataloader = _update_dataloader(dataloader, dataloader.sampler)
+    assert isinstance(new_dataloader, GoodImpl)
+
+
 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."""