Unlimiformer

This is the official implementation of the paper Unlimiformer: Long-Range Transformers with Unlimited Length Input.

Unlimiformer is a method for augmenting pretrained encoder-decoder models with a type of retrieval-based attention. This allows the use of unlimited length inputs with any pretrained encoder-decoder!

Unlimiformer can be used to improve performance of an already-trained model. However, for best results, the model should be trained with Unlimiformer.

Getting Started

Paste these files from src into your source code folder.

You'll need to set values for the Unlimiformer-specific arguments outlined in usage.py-- you can add these arguments wherever you usually process hyperparameters.

To use the model, you must set test_unlimiformer=True.

run.py is an example of a full training setup that integrates Unlimiformer, adopted from SLED -- this is likely more complex than you will need.

Trained models

The following models from the paper are available on HuggingFace. Please note that you must add the Unlimiformer-specific files to your repository, and load these models with knn=True. If you download these models from Huggingface, they may not use Unlimiformer by default!

Table 3: low-cost training methods

Dataset	Method	HuggingFace link
GovReport	Baseline: BART-base	abertsch/bart-base-govreport
GovReport	BART-base + Unlimiformer early stopping	abertsch/unlimiformer-bart-govreport-earlyk
SummScreen	Baseline: BART-base	abertsch/bart-base-summscreen
SummScreen	BART-base + Unlimiformer early stopping	abertsch/unlimiformer-bart-summscreen-earlyk

Table 4: Long-range training methods

Dataset	Method	HuggingFace link
GovReport	BART + Unlimiformer (alternating training)	abertsch/unlimiformer-bart-govreport-alternating
SummScreen	BART + Unlimiformer (retrieval training)	abertsch/unlimiformer-bart-summscreen-retrieval

Table 5: BookSum

Dataset	Method	HuggingFace link
BookSum	Baseline: BART-base	abertsch/bart-base-booksum
BookSum	BART-base + Unlimiformer early stopping	abertsch/unlimiformer-bart-booksum-earlyk
Booksum	BART-base + Unlimiformer (random-encoding training)	abertsch/unlimiformer-bart-booksum-random-encoding
Booksum	BART-base + Unlimiformer (alternating training)	abertsch/unlimiformer-bart-booksum-alternating

Recommended settings

To evaluate with Unlimiformer

At evaluation time, we recommend the default value for each setting.

To train with Unlimiformer

For an inexpensive method, we recommend training as usual and using Unlimiformer during early stopping. To do so, set knn=True and leave all other values at default.

For best performance, there are 3 expensive settings for training. The best one varies by dataset.

1. Set random_unlimiformer_training=True: this is the random-encoded training setting from the paper
2. Set unlimiformer_training=True: this is the approximate-retrieval training setting from the paper
3. Set random_unlimiformer_training=True AND unlimiformer_training=True: this is the alternating training setting from the paper

See Table 5 in the paper for a more detailed breakdown of relative training costs.

Tips for very large inputs

For training

• you may need to truncate your inputs at training time, e.g. to 8k or 16k tokens. You can use the full inputs at evaluation time
• you can also try splitting your inputs into 16k-token-chunks and training on each one as its own example

For evaluation (including early stopping)

• if you're consistently running out of CUDA memory, set use_datastore=True to use a Faiss datastore to store hidden states.
• if you're still having issues, set gpu_datastore=False or gpu_index=False, but note that this will degrade performance

Citation

If you use our method or models, please cite our paper:

@article{bertsch2023unlimiformer,
  title={Unlimiformer: Long-Range Transformers with Unlimited Length Input},
  author={Bertsch, Amanda and Alon, Uri and Neubig, Graham and Gormley, Matthew R},
  journal={arXiv preprint arXiv:2305.01625},
  year={2023}
}

If you have any questions on this work, please open a GitHub issue or email the authors at abertsch@cs.cmu.edu, ualon@cs.cmu.edu