Sparse Transormer
Author: turian
Pull request: no
Labels: name: New model

# 🌟 New model addition

## Model description

Sparse Transformers (https://openai.com/blog/sparse-transformer/) are one of the two most efficient transformers for long range problems, according to Google's Long Arena paper: https://arxiv.org/pdf/2011.04006.pdf (Big Bird) is the other one.

The original Sparse Transformers work shows great results on text, images, and audio. Further OpenAI work Jukebox (https://openai.com/blog/jukebox/) uses Sparse Transformers to generate incredibly long raw music audio with style transfer. Lastly https://proceedings.icml.cc/static/paper_files/icml/2020/6095-Paper.pdf uses Sparse Transformers to achieve state-of-the-art CIFAR performance.

## Open source status

* [x] the model implementation is available:

latest version, for CIFAR: https://github.com/openai/distribution_augmentation
original, but not maintained: https://github.com/openai/sparse_attention
Alternate implementation from FAIR: https://github.com/pytorch/fairseq/blob/master/fairseq/modules/sparse_multihead_attention.py

* [x] the model weights are available: 

https://github.com/openai/distribution_augmentation (CIFAR work) has model weights available, as described in the README: https://openaipublic.blob.core.windows.net/distribution-augmentation-assets/models/c10-15m-baseline.npz

Jukebox is open-source and has model weights, but is a larger pipeline that includes VQ-VAEs so it may not be of interest for a transformers-only library.

* [x] who are the authors: @rewonc @myleott  @cclauss


Comments:
Hi there,
Happy to consult on anything. The sparse attention kernels included above
are very fast, but require building blocksparse -- not sure if this will
work for you all.
Rewon

On Sun, Dec 6, 2020 at 3:10 AM Joseph Turian <notifications@github.com>
wrote:

> 🌟 New model addition Model description
>
> Sparse Transformers (https://openai.com/blog/sparse-transformer/) are one
> of the two most efficient transformers for long range problems, according
> to Google's Long Arena paper: https://arxiv.org/pdf/2011.04006.pdf (Big
> Bird) is the other one.
>
> The original Sparse Transformers work shows great results on text, images,
> and audio. Further OpenAI work Jukebox (https://openai.com/blog/jukebox/)
> uses Sparse Transformers to generate incredibly long raw music audio with
> style transfer. Lastly
> https://proceedings.icml.cc/static/paper_files/icml/2020/6095-Paper.pdf
> uses Sparse Transformers to achieve state-of-the-art CIFAR performance.
> Open source status
>
>    - the model implementation is available:
>
> latest version, for CIFAR:
> https://github.com/openai/distribution_augmentation
> original, but not maintained: https://github.com/openai/sparse_attention
> Alternate implementation from FAIR:
> https://github.com/pytorch/fairseq/blob/master/fairseq/modules/sparse_multihead_attention.py
>
>    - the model weights are available:
>
> https://github.com/openai/distribution_augmentation (CIFAR work) has
> model weights available, as described in the README:
> https://openaipublic.blob.core.windows.net/distribution-augmentation-assets/models/c10-15m-baseline.npz
>
> Jukebox is open-source and has model weights, but is a larger pipeline
> that includes VQ-VAEs so it may not be of interest for a transformers-only
> library.
>
>    - who are the authors: @rewonc <https://github.com/rewonc> @myleott
>    <https://github.com/myleott> @cclauss <https://github.com/cclauss>
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/transformers/issues/8945>, or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAYEDVCS3HWQ3EIPG4I77WTSTNRDNANCNFSM4UPHW52Q>
> .
>


cc'ing @madlag for info