Trainer support for iterabledataset (#5834)

* Don't pass sampler for iterable dataset

* Added check for test and eval dataloaders.

* Formatting

* Don't pass sampler for iterable dataset

* Added check for test and eval dataloaders.

* Formatting

* Cleaner if nesting.

* Added test for trainer and iterable dataset

* Formatting for test

* Fixed import when torch is available only.

* Added require torch decorator to helper class

* Moved dataset class inside unittest

* Removed nested if and changed model in test

* Checking torch availability for IterableDataset

src/transformers/trainer.py

@@ -230,9 +230,11 @@ class Trainer:
         """
         Returns the training :class:`~torch.utils.data.DataLoader`.
         """
-        if self.train_dataset is None:
+        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
+            train_sampler = None
+        elif self.train_dataset is None:
             raise ValueError("Trainer: training requires a train_dataset.")
-        if is_torch_tpu_available():
+        elif is_torch_tpu_available():
             train_sampler = get_tpu_sampler(self.train_dataset)
         else:
             train_sampler = (
@@ -240,7 +242,6 @@ class Trainer:
                 if self.args.local_rank == -1
                 else DistributedSampler(self.train_dataset)
             )
-
         data_loader = DataLoader(
             self.train_dataset,
             batch_size=self.args.train_batch_size,
@@ -264,7 +265,9 @@ class Trainer:
 
         eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
 
-        if is_torch_tpu_available():
+        if isinstance(eval_dataset, torch.utils.data.IterableDataset):
+            sampler = None
+        elif is_torch_tpu_available():
             sampler = SequentialDistributedSampler(
                 eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
             )
@@ -291,7 +294,9 @@ class Trainer:
             test_dataset (obj:`Dataset`): The test dataset to use.
         """
         # We use the same batch_size as for eval.
-        if is_torch_tpu_available():
+        if isinstance(self.test_dataset, torch.utils.data.IterableDataset):
+            sampler = None
+        elif is_torch_tpu_available():
             sampler = SequentialDistributedSampler(
                 test_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
             )
@@ -307,7 +312,6 @@ class Trainer:
             collate_fn=self.data_collator,
             drop_last=self.args.dataloader_drop_last,
         )
-
         return data_loader
 
     def get_optimizers(

tests/test_trainer.py

@@ -6,16 +6,18 @@ from transformers.testing_utils import require_torch
 
 if is_torch_available():
     import torch
+    from torch.utils.data import IterableDataset
+
     from transformers import (
-        Trainer,
-        LineByLineTextDataset,
         AutoModelForSequenceClassification,
-        default_data_collator,
         DataCollatorForLanguageModeling,
         DataCollatorForPermutationLanguageModeling,
         GlueDataset,
         GlueDataTrainingArguments,
+        LineByLineTextDataset,
         TextDataset,
+        Trainer,
+        default_data_collator,
     )
 
 
@@ -153,6 +155,20 @@ class DataCollatorIntegrationTest(unittest.TestCase):
             data_collator(example)
 
 
+if is_torch_available():
+
+    class SampleIterableDataset(IterableDataset):
+        def __init__(self, file_path):
+            self.file_path = file_path
+
+        def parse_file(self):
+            f = open(self.file_path, "r")
+            return f.readlines()
+
+        def __iter__(self):
+            return iter(self.parse_file())
+
+
 @require_torch
 class TrainerIntegrationTest(unittest.TestCase):
     def test_trainer_eval_mrpc(self):
@@ -176,3 +192,12 @@ class TrainerIntegrationTest(unittest.TestCase):
             tokenizer=tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=tokenizer.max_len_single_sentence,
         )
         self.assertEqual(len(dataset), 31)
+
+    def test_trainer_iterable_dataset(self):
+        MODEL_ID = "sshleifer/tiny-distilbert-base-cased"
+        model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+        train_dataset = SampleIterableDataset(PATH_SAMPLE_TEXT)
+        training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
+        trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset)
+        loader = trainer.get_train_dataloader()
+        self.assertIsInstance(loader, torch.utils.data.DataLoader)