Long-Range Arena (LRA: pronounced ELRA).

Long-range arena is an effort toward systematic evaluation of efficient transformer models. The project aims at establishing benchmark tasks/dtasets using which we can evaluate transformer-based models in a systematic way, by assessing their generalization power, computational efficiency, memory foot-print, etc.

Long-range arena also implements different variants of Transformer models in JAX, using Flax.

This first initial release includes the benchmarks for the paper "Long Range Arena: A benchmark for Efficient Transformers.

Currently we have released all the necessary code to get started and run our benchmarks on vanilla Transformers.

We are working on a 2nd update that will release more models and baselines for this benchmark suite. Stay tuned.

Please see below for more examples on how to get started.

Leaderboard

Current leaderboard results of all xformer results on our benchmark results. (as of 8th November 2020)

Citation

If you find out work useful, please cite our paper at:

@inproceedings{
tay2020long,
title={Long Range Arena : A Benchmark for Efficient Transformers },
author={Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri,
Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler},
booktitle={ArXiv Preprint},
year={2020},
url={},
note={under review}
}

**Note: Please also cite the original sources of these datasets! **

Adding results to the leaderboard.

Please send the link of the paper (arxiv, or published) to the Yi Tay or Mostafa Dehghani (emails in paper) to include your new results to the leaderboard.

A note on evaluation and comparisons

Apples-to Apples setting

This setting is for folks who want to compare with our published results directly.

The default hyperparameter setup (each benchmark should have a config file now). You are not allowed to change hyperparameters such as embedding size, hidden dimensions, number of layers of the new model.

The new model should be within at best 10% larger in terms of parameters compared to the base Transformer model in the provided config file.

Free-for-all Setting

You are allowed to run any model size and change any hyperparameter of the model. However, in the end, you'll not be allowed to report results from our leaderboard because they are no longer comparable. You can choose to rerun models from our library in a comparable setting.

Adding benchmarks or models to this suite

If you develop or could benefit from an extensive array of xformer baselines, please feel free to let us know if you're interested in building new benchmarks. We welcome contributions for new or older models that are not covered in the existing suite.

Example Usage

To run a task, run the train.py file in the corresponding task directory. (please see how to obtain the data for certain tasks if applicable).

PYTHONPATH="$(pwd)":"$PYTHON_PATH" python lra_benchmarks/listops/train.py \
      --config=lra_benchmarks/listops/configs/transformer_base.py \
      --model_dir=/tmp/listops \
      --task_name=basic \
      --data_dir=$HOME/lra_data/listops/

Dataset Setup

This section describes the methods to obtain the datasets and run the tasks in LRA.

To download the datasets, please download it from gs://long-range-arena/lra_release. If permissions fail, you may download the entire gziped file at https://storage.googleapis.com/long-range-arena/lra_release.gz.

ListOps

This task can be found at /listops. The datasets used in our experiments can be found at these google cloud buckets and are in TSV format.

If you would like to go to longer/shorter sequence lengths, we also support generating your own split, run the following comment:

PYTHONPATH="$(pwd)":"$PYTHON_PATH" python lra_benchmarks/data/listops.py -- \
  --output_dir=$HOME/lra_data/listops/

Text Classification

This task can be found at /text_classification. No action is required because this task is already found in tensorflow datasets. The code should run as it is.

Document Retrieval

Please download the dataset at (http://aan.how/download/). Please download the train/test/dev splits from our google cloud bucket. Unfortunately, we were not able to re-distribute this datasets and are only releasing the ids in the format label paper1_id paper2_id. You may download the data from the original source and extract the textual data.

Pixel-level Image Classification

This task can be found at /image. No action is required because this task is already found in tensorflow datasets. It should work out of the box.

Pathfinder

Please see the ./data directory, where the TFDS builder for the pathfinder dataset can be found. We generated different datasets for pathfinder task, with different levels of difficulty using the script provided here. You can find information about the parameters used for generatinng the data in the TFDS builder code in ./data/pathfinder. We are preparing the exact data splits for release at the moment.

Disclaimer

This is not an official Google product.