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Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

This repo contains the PyTorch implementation of the ACL, 2021 paper Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Installation

python setup.py install

How to run the models

We provide example scripts for each model in hyperformer/scripts/ folder with their config files in hyperformer/configs. To run the models, please do cd hyperformer and:

To run hyperformer++ model (This model generates the task-specific adapters using a shared hypernetwork, which is shared across the tasks and layers of a transformer.):
```
bash scripts/hyperformer++.sh
```
To run hyperformer model (This model generates the task-specific adapters using a shared hypernetwork, which is shared across the tasks, but this is specific to each layer of a transformer. This model is less efficient compared to hyperformer++.):
```
bash scripts/hyperformer.sh
```
To run adapter\dagger model (This model share the layer normalization between adapters across the tasks, and train adapters in a multi-task setting.):
```
bash scripts/adapters_dagger.sh   
```
To run adapter model (This model trains a single-adapter per task and trains the adapters in a single-task learning.):
```
bash scripts/adapters.sh 
```
To run T5 finetuning model in a multi-task learning setup:
```
bash scripts/finetune.sh
```
To run T5 finetuning model in a single-task learning setup:
```
bash scripts/finetune_single_task.sh
```

We run all the models on 4 GPUs, while this is not necessary and one can run the models on 1 GPU. In case running on one GPU, in all the scripts, please remove the -m torch.distributed.launch --nproc_per_node=4 part.

Bibliography

If you find this repo useful, please cite our paper.

@inproceedings{karimi2021parameterefficient,
  title={Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks},
  author={Karimi Mahabadi, Rabeeh and Ruder, Sebastian and Dehghani, Mostafa and Henderson, James},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2021}
}

Final words

Hope this repo is useful for your research. For any questions, please create an issue or email rabeeh.k68@gmail.com, and I will get back to you as soon as possible.