Merge branch 'master' into attach-data-refactor

Author: carmocca

CHANGELOG.md

@@ -415,6 +415,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed parsing of `fast_dev_run=True` with the built-in `ArgumentParser` ([#7240](https://github.com/PyTorchLightning/pytorch-lightning/pull/7240))
 
 
+- Fixed handling an `IterableDataset` that fails to produce a batch at the beginning of an epoch ([#7294](https://github.com/PyTorchLightning/pytorch-lightning/pull/7294))
+
 
 - Fixed `LightningModule.save_hyperparameters()` when attempting to save an empty container ([#7268](https://github.com/PyTorchLightning/pytorch-lightning/pull/7268))
 

pytorch_lightning/trainer/connectors/data_connector.py

@@ -17,6 +17,7 @@
 from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
+from pytorch_lightning.trainer.supporters import prefetch_iterator
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
 
@@ -43,22 +44,10 @@ def on_trainer_init(
 
     def get_profiled_train_dataloader(self, train_dataloader):
         profiled_dl = self.trainer.profiler.profile_iterable(
-            enumerate(self._with_is_last(train_dataloader)), "get_train_batch"
+            enumerate(prefetch_iterator(train_dataloader)), "get_train_batch"
         )
         return profiled_dl
 
-    def _with_is_last(self, iterable):
-        """Pass through values from the given iterable with an added boolean indicating if this is the last item.
-        See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_"""
-        it = iter(iterable)
-        last = next(it)
-        for val in it:
-            # yield last and has next
-            yield last, False
-            last = val
-        # yield last, no longer has next
-        yield last, True
-
     def prepare_data(self, model):
         # on multi-gpu jobs we only want to manipulate (download, etc) on node_rank=0, local_rank=0
         # or in the case where each node needs to do its own manipulation in which case just local_rank=0

pytorch_lightning/trainer/supporters.py

@@ -14,7 +14,7 @@
 
 import os
 from collections.abc import Iterable, Iterator, Mapping, Sequence
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, Generator, Optional, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -508,3 +508,25 @@ def _nested_calc_num_data(data: Union[Mapping, Sequence], compute_func: Callable
             new_data.append(x)
 
     return compute_func(new_data)
+
+
+def prefetch_iterator(iterable: Iterable) -> Generator[Tuple[Any, bool], None, None]:
+    """
+    Returns an iterator that pre-fetches and caches the next item.
+    The values are passed through from the given iterable with an added boolean indicating if this is the last item.
+    See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_
+    """
+    it = iter(iterable)
+
+    try:
+        # the iterator may be empty from the beginning
+        last = next(it)
+    except StopIteration:
+        return
+
+    for val in it:
+        # yield last and has next
+        yield last, False
+        last = val
+    # yield last, no longer has next
+    yield last, True

pytorch_lightning/trainer/training_loop.py

@@ -444,8 +444,10 @@ def run_training_epoch(self):
         dataloader_idx = 0
         val_loop_called = False
 
-        for batch_idx, (batch, is_last_batch) in train_dataloader:
+        batch_idx = None
+        is_last_batch = None
 
+        for batch_idx, (batch, is_last_batch) in train_dataloader:
             self.trainer.batch_idx = batch_idx
             self.trainer.is_last_batch = is_last_batch
 
@@ -516,6 +518,10 @@ def run_training_epoch(self):
             # progress global step according to grads progress
             self.increment_accumulated_grad_global_step()
 
+        if batch_idx is None:
+            # dataloader/iterator did not produce a batch
+            return
+
         # handle epoch_output on epoch end
         self.on_train_epoch_end(epoch_output)
 

tests/trainer/test_dataloaders.py

@@ -779,6 +779,41 @@ def __len__(self):
     trainer.predict(model, dataloaders=dataloader)
 
 
+def test_iterable_dataset_stop_iteration_at_epoch_beginning():
+    """ Test that the training loop skips execution if the iterator is empty from the start. """
+
+    class RandomDataset(IterableDataset):
+
+        def __init__(self, gen):
+            self.gen = gen
+
+        def __iter__(self):
+            return iter(self.gen())
+
+    class TestModel(BoringModel):
+
+        def train_dataloader(self):
+            return DataLoader(RandomDataset(self.gen), batch_size=2)
+
+        def gen(self):
+            # produce data in epoch 0
+            # no data otherwise
+            if self.current_epoch == 0:
+                yield torch.rand(32)
+                yield torch.rand(32)
+                yield torch.rand(32)
+
+    model = TestModel()
+    trainer = Trainer(
+        default_root_dir=os.getcwd(),
+        max_epochs=2,  # we expect the second epoch to be skipped
+        weights_summary=None,
+    )
+    trainer.fit(model)
+    assert trainer.global_step == 2
+    assert trainer.current_epoch == 1
+
+
 @RunIf(min_gpus=2)
 def test_dataloader_reinit_for_subclass(tmpdir):
 

tests/trainer/test_supporters.py

@@ -18,7 +18,7 @@
 import pytest
 import torch
 from torch.utils.data import DataLoader, TensorDataset
-from torch.utils.data.dataset import Dataset
+from torch.utils.data.dataset import Dataset, IterableDataset
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import Sampler
 
@@ -29,6 +29,7 @@
     CombinedLoader,
     CombinedLoaderIterator,
     CycleIterator,
+    prefetch_iterator,
     TensorRunningAccum,
 )
 from pytorch_lightning.utilities.apply_func import apply_to_collection
@@ -78,6 +79,30 @@ def test_none_length_cycle_iterator():
     assert item == 0
 
 
+def test_prefetch_iterator():
+    """ Test the prefetch_iterator with PyTorch IterableDataset. """
+
+    class IterDataset(IterableDataset):
+
+        def __iter__(self):
+            yield 1
+            yield 2
+            yield 3
+
+    dataset = IterDataset()
+    iterator = prefetch_iterator(dataset)
+    assert [item for item in iterator] == [(1, False), (2, False), (3, True)]
+
+    class EmptyIterDataset(IterableDataset):
+
+        def __iter__(self):
+            return iter([])
+
+    dataset = EmptyIterDataset()
+    iterator = prefetch_iterator(dataset)
+    assert [item for item in iterator] == []
+
+
 @pytest.mark.parametrize(
     ["dataset_1", "dataset_2"],
     [