Unofficial PyTorch implementation of Fastformer based on paper "Fastformer: Additive Attention Can Be All You Need"."

Related tags

Deep Learning Fastformer-PyTorch
Overview

Fastformer-PyTorch

Unofficial PyTorch implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need.

Network Architecture image from the paper

Usage :

import torch
import Fastformer

model = Fastformer(dim = 3, decode_dim = 8)
x = torch.randn(4, 6, 3)
mask = torch.ones(1, 8).bool()
result = model(x, mask)
print(result.size())

Citation :

@misc{wu2021fastformer,
    title={Fastformer: Additive Attention Can Be All You Need},
    author={Chuhan Wu, Fangzhao Wu, Tao Qi and Yongfeng Huang},
    year={2021},
    eprint={2108.09084v2},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

If this implement have any problem please let me know, thank you.

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Comments
  • What is your version of PyTorch ?

    What is your version of PyTorch ?

    TypeError Traceback (most recent call last) C:\Users\ADMINI~1\AppData\Local\Temp/ipykernel_21240/4222065097.py in ----> 1 model = Fastformer(dim = 3, decode_dim = 8) 2 x = torch.randn(4, 6, 3) 3 mask = torch.ones(1, 8).bool() 4 result = model(x, mask) 5 print(result.size())

    TypeError: 'module' object is not callable

    my version is 1.9.0

    opened by learningelectric 2
  • Run a base-line test with Long-range dependencies

    Run a base-line test with Long-range dependencies

    A common criticism of this paper was the lack of LRA baselines (long-range dependencies):

    Would be awesome to see how it compares to other transformers in that regard.

    opened by Cortexelus 1
  • Questions about your implementation

    Questions about your implementation

    https://github.com/wilile26811249/Fastformer-PyTorch/blob/863bdd140f32149b3c369b54a2a5d160a3ef31d7/Fastformer.py#L29

    first of all, thank you for your smart & fast implementation. I'm now studying using this code ^^

    Comparing code & architecture image in paper,

    in line 29, you declared p, as repeat_global_query * key. after that when calculating beta_weight, I think this calculated p value should be used So, beta_weight = torch.softmax(key * self.scale_factor, dim = -1) -> beta_weight = torch.softmax(p * self.scale_factor, dim = -1)

    like this. After that

    global_key = key * beta_weight -> global_key = p * beta_weight

    How about your opinion?

    opened by cheesama 1
Owner
Hong-Jia Chen
Master student at National Chung Cheng University, Taiwan. Interested in Deep Learning and Computer Vision.
Hong-Jia Chen
GitHub
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