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
@@ -40,13 +42,50 @@
  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
 
+ @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: torch.nn.Module) -> torch.nn.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)