Add bucket sampler

🚀 Feature

Motivation

The legacy BucketIterator was convenient because it could batch samples by length to minimize padding. It had many disadvantages because of its API and non-comformance with other parts of the pytorch data{sets,loader} ecosystem. It would be nice if torchtext supported the spirit of the BucketIterator by way of a Sampler.

Pitch

A sampler with the ability to specify maximum bucket size should be added similar to those in torchnlp and allennlp. This can be used with existing datasets in torchtext but as a kwarg to the pytorch DataLoader so sampling minimizes padding.

Alternatives

Users who want this functionality need to implement their own samplers.

Additional context

The migration guide contains a prototype of this feature without it being a first-class part of the torchtext repo. A proposed implementation can be found here.

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I’d definitely be interested in contributing!

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Yeah this shuffling will generate different batches

Ah, I misread the code – this makes total sense. I agree that this will have more impact than shuffling within a batch. I still think it is good to give an option to the user about whether there should be shuffling at all. maybe just shuffle: bool = True by default?

Does it mean the samples whose length

Yes, exactly. See here. There’s probably some error checking to add here (what if lengths is the empty list after filtering? 🙀 ), but otherwise seems OK.