TPUSpawn + IterableDataset error message

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -15,15 +15,17 @@
 import os
 import re
 import time
-from typing import Any, Dict, Iterable, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING
 
 import torch
 import torch.multiprocessing as mp
 
 from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
+from pytorch_lightning.trainer.connectors.data_connector import _PatchDataLoader
 from pytorch_lightning.trainer.states import TrainerState
 from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE, rank_zero_warn
 from pytorch_lightning.utilities.apply_func import apply_to_collection
+from pytorch_lightning.utilities.data import has_len
 from pytorch_lightning.utilities.distributed import rank_zero_only, ReduceOp
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.seed import seed_everything
@@ -40,14 +42,51 @@
     from omegaconf import DictConfig, ListConfig, OmegaConf
 
 
+if TYPE_CHECKING:
+    from torch.nn import Module
+    from torch.utils.data import DataLoader
+
+
 class TPUSpawnPlugin(DDPSpawnPlugin):
 
     def __init__(self, parallel_devices: Optional[List[int]] = None, **kwargs: Dict[str, Any]) -> None:
         super().__init__(parallel_devices, num_nodes=1, cluster_environment=None, sync_batchnorm=False)
         self.tpu_local_core_rank = 0
         self.start_method = None
 
-    def setup(self, model: torch.nn.Module) -> torch.nn.Module:
+    @staticmethod
+    def _validate_dataloader(dataloaders: Union[List['DataLoader'], 'DataLoader']):
+        if not isinstance(dataloaders, list):
+            dataloaders = [dataloaders]
+
+        for dataloader in dataloaders:
+            if not has_len(dataloader):
+                raise MisconfigurationException(
+                    "TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
+                    " HINT: You can mock the length on your dataset to bypass this MisconfigurationException."
+                )
+
+    @staticmethod
+    def _validate_patched_dataloaders(model: 'Module') -> None:
+        """Validate and fail fast if the dataloaders were passed directly to fit.
+        """
+        if hasattr(model, 'train_dataloader') and isinstance(model.train_dataloader, _PatchDataLoader):
+            TPUSpawnPlugin._validate_dataloader(model.train_dataloader.dataloader)
+
+        if hasattr(model, 'val_dataloader') and isinstance(model.val_dataloader, _PatchDataLoader):
+            TPUSpawnPlugin._validate_dataloader(model.val_dataloader.dataloader)
+
+        if hasattr(model, 'test_dataloader') and isinstance(model.test_dataloader, _PatchDataLoader):
+            TPUSpawnPlugin._validate_dataloader(model.test_dataloader.dataloader)
+
+        if hasattr(model, 'predict_dataloader') and isinstance(model.predict_dataloader, _PatchDataLoader):
+            TPUSpawnPlugin._validate_dataloader(model.predict_dataloader.dataloader)
+
+    def connect(self, model: 'Module') -> None:
+        TPUSpawnPlugin._validate_patched_dataloaders(model)
+        return super().connect(model)
+
+    def setup(self, model: 'Module') -> 'Module':
         self.create_mp_queue()
         return self.model
 
@@ -64,7 +103,8 @@ def distributed_sampler_kwargs(self) -> dict:
     def is_distributed(self):
         return self.world_size != 1
 
-    def process_dataloader(self, dataloader: Union[Iterable, torch.utils.data.DataLoader]) -> MpDeviceLoader:
+    def process_dataloader(self, dataloader: 'DataLoader') -> MpDeviceLoader:
+        TPUSpawnPlugin._validate_dataloader(dataloader)
         device = xm.xla_device()
         dataloader = MpDeviceLoader(dataloader, device)
         return dataloader

tests/plugins/test_tpu_spawn.py

@@ -0,0 +1,74 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+from unittest.mock import MagicMock
+
+import pytest
+from torch.utils.data import DataLoader
+
+from pytorch_lightning.plugins.training_type import TPUSpawnPlugin
+from pytorch_lightning.trainer.connectors.data_connector import DataConnector
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from tests.helpers.boring_model import BoringModel, RandomDataset
+from tests.helpers.dataloaders import CustomNotImplementedErrorDataloader
+
+
+class BoringModelNoDataloaders(BoringModel):
+    def train_dataloader(self):
+        raise NotImplementedError
+
+    def val_dataloader(self):
+        raise NotImplementedError
+
+    def test_dataloader(self):
+        raise NotImplementedError
+
+    def predict_dataloader(self):
+        raise NotImplementedError
+
+
+_loader = DataLoader(RandomDataset(32, 64))
+_loader_no_len = CustomNotImplementedErrorDataloader(_loader)
+
+
+@pytest.mark.parametrize(
+    "train_dataloader, val_dataloaders, test_dataloaders, predict_dataloaders",
+    [
+        (_loader_no_len, None, None, None),
+        (None, _loader_no_len, None, None),
+        (None, None, _loader_no_len, None),
+        (None, None, None, _loader_no_len),
+        (None, [_loader, _loader_no_len], None, None),
+    ],
+)
+def test_error_patched_iterable_dataloaders(
+    tmpdir, train_dataloader, val_dataloaders, test_dataloaders, predict_dataloaders
+):
+    model = BoringModelNoDataloaders()
+    connector = DataConnector(MagicMock())
+
+    connector.attach_dataloaders(
+        model,
+        train_dataloader=train_dataloader,
+        val_dataloaders=val_dataloaders,
+        test_dataloaders=test_dataloaders,
+        predict_dataloaders=predict_dataloaders,
+    )
+
+    with pytest.raises(MisconfigurationException, match="TPUs do not currently support"):
+        TPUSpawnPlugin(MagicMock()).connect(model)
+
+
+def test_error_process_iterable_dataloader(tmpdir):
+    with pytest.raises(MisconfigurationException, match="TPUs do not currently support"):
+        TPUSpawnPlugin(MagicMock()).process_dataloader(_loader_no_len)