A concise implementation of (Induced) Set Attention Block, from the Set Transformers paper. It proposes to reduce attention from O(n²) to O(mn), where m is the number of inducing points (learned latents).

Update: Interesting enough, a new paper has used the ISAB block successfully, in the domain of denoising diffusion for efficient generation of images and video.

$ pip install isab-pytorch

You can either set the number of latents, in which the parameters will be instantiated and returned on completion of cross attention.

import torch
from isab_pytorch import ISAB
attn = ISAB(
    dim = 512,
    heads = 8,
    num_latents = 128,
    latent_self_attend = True
)
seq = torch.randn(1, 16384, 512) # (batch, seq, dim)
mask = torch.ones((1, 16384)).bool()
out, latents = attn(seq, mask = mask) # (1, 16384, 512), (1, 128, 512)

Or you can choose not to set the number of latents, and pass in the latents yourself (some persistent latent that propagates down the transformer, as an example)

import torch
from isab_pytorch import ISAB
attn = ISAB(
    dim = 512,
    heads = 8
)
seq = torch.randn(1, 16384, 512) # (batch, seq, dim)
latents = torch.nn.Parameter(torch.randn(128, 512)) # some memory, passed through multiple ISABs
out, new_latents = attn(seq, latents) # (1, 16384, 512), (1, 128, 512)

@misc{lee2019set,
    title   = {Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks},
    author  = {Juho Lee and Yoonho Lee and Jungtaek Kim and Adam R. Kosiorek and Seungjin Choi and Yee Whye Teh},
    year    = {2019},
    eprint  = {1810.00825},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

@article{Alayrac2022Flamingo,
    title   = {Flamingo: a Visual Language Model for Few-Shot Learning},
    author  = {Jean-Baptiste Alayrac et al},
    year    = {2022}
}