Implementation of Token Shift GPT - An autoregressive model that solely relies on shifting the sequence space for mixing

Phil Wang

Last update: Oct 14, 2022

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Overview

Token Shift GPT

Implementation of Token Shift GPT - An autoregressive model that relies solely on shifting along the sequence dimension and feedforwards.

Update: Inexplicably, it actually works quite well. The feedforward module follows the same design as gMLP, except the feature dimension of the gate tensor is divided up into log2(seq_len) chunks, and the mean pool of the past consecutive segments (length 1, 2, 4, 8, etc. into the past) are shifted into each chunk before a projection along the feature dimension.

Install

$ pip install token-shift-gpt

Usage

import torch
from token_shift_gpt import TokenShiftGPT

model = TokenShiftGPT(
    num_tokens = 256,
    dim = 512,
    max_seq_len = 1024,
    depth = 12,
    ff_mult = 8   # when working with small model dimensions, you may want to increase the intermediate feedforward dimension (here, 8x instead of the usual 4x), so the learning is not bottlenecked by the dimensions of the shifted chunk
)

x = torch.randint(0, 256, (1, 1024))
logits = model(x) # (1, 1024, 256)

To use the discounted cumulative sum approach (which only uses one chunk and seems to be just as effective as the above), just set use_discounted_cumsum = True

First install an additional library

$ pip install torch-discounted-cumsum

Then

import torch
from token_shift_gpt import TokenShiftGPT

model = TokenShiftGPT(
    num_tokens = 256,
    dim = 512,
    max_seq_len = 1024,
    depth = 12,
    ff_mult = 8,
    use_discounted_cumsum = True,
    discounted_gamma = 0.9              # gamma factor for discount
)

x = torch.randint(0, 256, (1, 1024))
logits = model(x) # (1, 1024, 256)

Citations

@misc{yu2021s2mlp,
    title   = {S$^2$-MLP: Spatial-Shift MLP Architecture for Vision}, 
    author  = {Tan Yu and Xu Li and Yunfeng Cai and Mingming Sun and Ping Li},
    year    = {2021},
    eprint  = {2106.07477},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{liu2021pay,
    title   = {Pay Attention to MLPs}, 
    author  = {Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
    year    = {2021},
    eprint  = {2105.08050},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

@software{peng_bo_2021_5196578,
    author       = {PENG Bo},
    title        = {BlinkDL/RWKV-LM: 0.01},
    month        = {aug},
    year         = {2021},
    publisher    = {Zenodo},
    version      = {0.01},
    doi          = {10.5281/zenodo.5196578},
    url          = {https://doi.org/10.5281/zenodo.5196578}
}

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Comments

self.gate is never used?

it seems like self.gate is never actually used, or am I missing something?

https://github.com/lucidrains/token-shift-gpt/blob/1449b263f1fb222279d00f9128c29f25dbef976b/token_shift_gpt/token_shift_gpt.py#L79

opened by inspirit 1

Implementation of Token Shift GPT - An autoregressive model that solely relies on shifting the sequence space for mixing

Related tags

Overview

Token Shift GPT

Install

Usage

Citations

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Comments

self.gate is never used?

Releases(0.0.2)

0.0.2(Aug 18, 2021)

0.0.1(Aug 18, 2021)

Owner

Phil Wang

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