dumpmemory/metaformer-gpt

Implementation of Metaformer, but in an autoregressive manner

Metaformer - GPT (wip)

Implementation of Metaformer, but in an autoregressive manner. In particular, they propose simply using mean centering as a way to do token mixing in a parameter-less fashion, alternating with feedforwards.

Install

$ pip install metaformer-gpt

Usage

import torch
from metaformer_gpt import MetaformerGPT

gpt = MetaformerGPT(
    num_tokens = 256,
    dim = 512,
    depth = 8
)

ids = torch.randint(0, 256, (1, 1024))
logits = gpt(ids) # (1, 1024, 256)

Citations

@article{Yu2021MetaFormerIA,
    title   = {MetaFormer is Actually What You Need for Vision},
    author  = {Weihao Yu and Mi Luo and Pan Zhou and Chenyang Si and Yichen Zhou and Xinchao Wang and Jiashi Feng and Shuicheng Yan},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2111.11418}
}

@misc{woo2022etsformer,
    title   = {ETSformer: Exponential Smoothing Transformers for Time-series Forecasting},
    author  = {Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven Hoi},
    year    = {2022},
    eprint  = {2202.01381},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}