PyTorch dataloader shows odd behavior with string dataset
I'm working on an NLP problem and am using PyTorch. For some reason, my dataloader is returning malformed batches. I have input data that comprises sentences and integer labels.
The sentences can either a list of sentences or a list of list of tokens. I will later convert the tokens to integers in a downstream component.
list_labels = [ 0, 1, 0]

# List of sentences.
list_sentences = [ 'the movie is terrible',
                   'The Film was great.',
                   'It was just awful.']

# Or list of list of tokens.
list_sentences = [['the', 'movie', 'is', 'terrible'],
                  ['The', 'Film', 'was', 'great.'],
                  ['It', 'was', 'just', 'awful.']]

I created the following custom dataset:
import torch
from torch.utils.data import DataLoader, Dataset

class MyDataset(torch.utils.data.Dataset):

    def __init__(self, sentences, labels):

        self.sentences = sentences
        self.labels = labels

    def __getitem__(self, i):
        result = {}
        result['sentences'] = self.sentences[i]
        result['label'] = self.labels[i]
        return result

    def __len__(self):
        return len(self.labels)

When I provide input in the form of a list of sentences, the dataloader correctly returns batches of complete sentences. Note that batch_size=2:
list_sentences = [ 'the movie is terrible', 'The Film was great.', 'It was just awful.']
list_labels = [ 0, 1, 0]


dataset = MyDataset(list_sentences, list_labels)
dataloader = DataLoader(dataset, batch_size=2)

batch = next(iter(dataloader))
print(batch)
# {'sentences': ['the movie is terrible', 'The Film was great.'], &lt;-- Great! 2 sentences in batch!
#  'label': tensor([0, 1])}

The batch correctly contains two sentences and two labels because batch_size=2.
However, when I instead enter the sentences as pre-tokenized list of list of token, I get weird results:
list_sentences = [['the', 'movie', 'is', 'terrible'], ['The', 'Film', 'was', 'great.'], ['It', 'was', 'just', 'awful.']]
list_labels = [ 0, 1, 0]


dataset = MyDataset(list_sentences, list_labels)
dataloader = DataLoader(dataset, batch_size=2)

batch = next(iter(dataloader))
print(batch)
# {'sentences': [('the', 'The'), ('movie', 'Film'), ('is', 'was'), ('terrible', 'great.')], &lt;-- WHAT?
#  'label': tensor([0, 1])}

Note that this batch's sentences is one single list with tuples of word pairs. I was expecting sentences to be a list of two lists, like this:
{'sentences': [['the', 'movie', 'is', 'terrible'], ['The', 'Film', 'was', 'great.']

What is going on?

This behavior is because the default collate_fn does the following when it has to collate lists (which is the case for ['sentences']):
# [...]
elif isinstance(elem, container_abcs.Sequence):
    # check to make sure that the elements in batch have consistent size
    it = iter(batch)
    elem_size = len(next(it))
    if not all(len(elem) == elem_size for elem in it):
        raise RuntimeError('each element in list of batch should be of equal size')
    transposed = zip(*batch)
    return [default_collate(samples) for samples in transposed]

The &quot;problem&quot; happens because, in the last two lines, it'll recursively call zip(*batch) while the batch is a container_abcs.Sequence (and list is), and zip behaves like this.
As you can see:
batch = [['the', 'movie', 'is', 'terrible'], ['The', 'Film', 'was', 'great.']]
list(zip(*batch))

# [('the', 'The'), ('movie', 'Film'), ('is', 'was'), ('terrible', 'great.')]

I don't see a workaround in your case, except implementing a new collator and passing it to the DataLoader(..., collate_fn=mycollator). For instance, a simple ugly one could be:
def mycollator(batch):
    assert all('sentences' in x for x in batch)
    assert all('label' in x for x in batch)
    return {
        'sentences': [x['sentences'] for x in batch],
        'label': torch.tensor([x['label'] for x in batch])
    }

