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Mega: Moving Average Equipped Gated Attention

This is the PyTorch implementation of the Mega paper. This folder is based on the fairseq package v0.9.0.

Mega: Moving Average Equipped Gated Attention

Xuezhe Ma*, Chunting Zhou*, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, Luke Zettlemoyer

Updates

  1. [Oct 17th 2022] --fp16 training has been supported.
  2. [Jan 10th 2023] Release Mega Image.
  3. [Jan 28th 2023] Release checkpoints of WMT'16 English to German.

Setup

This repository requires Python 3.8+ and Pytorch 1.11+.

# Install from this repo
pip install -e .

For faster training, install NVIDIA's apex library following fairseq.

Examples

Models Checkpoints

Task Description # params Download
LRA Mega on LRA tasks -- mega.lra.zip
WMT'16 (En-De) Mega-base on WMT'16 En-De 67M mega.wmt16ende.base.zip
WMT'16 (De-En) Mega-base on WMT'16 De-En 67M mega.wmt16deen.base.zip
SC-Raw Mega-base/big on raw Speech Commands 300k mega.sc.zip
WikiText-103 Language modeling on WikiText-103 252M mega.wiki103.zip
Enwiki8 Language modeling on Enwiki8 39M mega.enwiki8.zip

Experiments

Code Overview

  1. Mega layer is implemented in fairseq/modules/mega_layer.py.
  2. Mega encoder (LRA) is implemented in fairseq/models/lra/mega_lra_encoder.py.
  3. Mega decoder (LM) is implemented in fairseq/models/mega_lm.py.
  4. Mega encoder-decoder (NMT) is implemented in fairseq/models/mega.py.

Tips

  1. The released model checkpoints were trained with float32.
  2. When training Mega, please note the following hyperparamters that decide the size of the model. --encoder/decoder-embed-dim is the input embedding dimension. --encoder/decoder-hidden-dim is the expanding dimension of value vectors of attention. --encoder/decoder-ffn-embed-dim is the FFN intermediate dimension. --encoder/decoder-hidden-dim and --encoder/decoder-ffn-embed-dim is usually the same. To obtain similar number of parameters as Transformers, they are usually set to be 2 times --encoder/decoder-embed-dim. --encoder/decoder-z-dim is the shrinking dimension of the query/key vectors of attention, which is usually set to be 128 or 1/4 of --encoder/decoder-embed-dim.
  3. If you'd like to apply Mega to your task/data, besides architecture size, hyperparameters that are worth considering and tuning include learning rate (lr) and weight decay (wd). We find that tuning wd is a more effective regularization to Mega (in contrast to tuning dropout rates for Transformers). Suggested wd values include 0.01, 0.05, 0.1 (larger models typically need larger wd, please refer to appendix of our paper for hyperparameters we have used). For lr scheduler, linear lr decay and cosine lr decay schedules are more effective than the inverse square root decay scheduler for Mega.

License

mega is under MIT license. The license applies to model checkpoints as well.

Citation

@article{ma2022mega,
  title={Mega: Moving Average Equipped Gated Attention},
  author={Ma, Xuezhe and Zhou, Chunting and Kong, Xiang and He, Junxian and Gui, Liangke and Neubig, Graham and May, Jonathan and Zettlemoyer Luke},
  journal={arXiv preprint arXiv:2209.10655},
  year={2022}
}