Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

Related tags

Deep Learning aft-pytorch
Overview

aft-pytorch

Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc.

Installation

You can install aft-pytorch via pip:

pip install aft-pytorch

Usage

You can import the AFT-Full or AFT-Simple layer (as described in the paper) from the package like so:

AFTFull

from aft_pytorch import AFTFull

layer = AFTFull(
    max_seqlen=20,
    dim=512,
    hidden_dim=64
)

# a batch of sequences with 10 timesteps of length 512 each
x = torch.rand(32, 10, 512)
y = layer(x) # [32, 10, 512]

AFTSimple

from aft_pytorch import AFTSimple

layer = AFTSimple(
    max_seqlen=20,
    dim=512,
    hidden_dim=64
)

# a batch of sequences with 10 timesteps of length 512 each
x = torch.rand(32, 10, 512)
y = layer(x) # [32, 10, 512]

This layer wrapper is a 'plug-and-play' with your existing networks / Transformers. You can swap out the Self-Attention layer with the available layers in this package with minimal changes.

TODO

  • Add full AFT architecture
  • Add variants like, AFTConv, AFTLocal

Contributing

If you like this repo, please leave a star! If there are any amends or suggestions, feel free to raise a PR/issue.

Credits

@misc{attention-free-transformer,
title = {An Attention Free Transformer},
author = {Shuangfei Zhai and Walter Talbott and Nitish Srivastava and Chen Huang and Hanlin Goh and Ruixiang Zhang and Josh Susskind},
year = {2021},
URL = {https://arxiv.org/pdf/2105.14103.pdf}
}

License

MIT

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Comments
  • why sum(0)?

    why sum(0)?

    Thank you for this code! I got a problem with this at line 33 and line 36. Why sum(0) to add all batch dimension? I noticed that it add the timestep dimension from t=1 to T, so maybe it should be sum(2) here.

    opened by zzwang058 2
  • can run on cpu but failed in gpu,why?

    can run on cpu but failed in gpu,why?

    RuntimeError: Tensor for 'out' is on CPU, Tensor for argument #1 'self' is on CPU, but expected them to be on GPU (while checking arguments for baddbmm)

    i set .cuda() but it can't work, please help!

    opened by TangDL 1
  • I test the model in an NLP task.

    I test the model in an NLP task.

    I use aft_full model，6 layers. and I use it in init with this code:

    self.encoder_transformer = nn.ModuleList()
for _ in range(6):
    self.encoder_transformer.append(AFTFull(max_seqlen=500, dim=512,hidden_dim=256))

    and in forward function, I use this code:

    for _, layer in enumerate(self.encoder_transformer):`
    x = layer(x) + x

    Originally I used the traditional transformer, now I replaced it with this, the training loss appeared Nan，Is something wrong? and how U use the model for many layers，please help me, Thank U.

    opened by zshy1205 1
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Rishabh Anand
CS undergrad + ML Research @ NUS • Open-source Jedi • Writer
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