Implementation of Nyström Self-attention, from the paper Nyströmformer.
$ pip install nystrom-attentionimport torch
from nystrom_attention import NystromAttention
attn = NystromAttention(
dim = 512,
dim_head = 64,
heads = 8,
num_landmarks = 256, # number of landmarks
pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
residual = True # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
)
x = torch.randn(1, 16384, 512)
mask = torch.ones(1, 16384).bool()
attn(x, mask = mask) # (1, 16384, 512)Nyströmformer, layers of Nyström attention
import torch
from nystrom_attention import Nystromformer
model = Nystromformer(
dim = 512,
dim_head = 64,
heads = 8,
depth = 6,
num_landmarks = 256,
pinv_iterations = 6
)
x = torch.randn(1, 16384, 512)
mask = torch.ones(1, 16384).bool()
model(x, mask = mask) # (1, 16384, 512)You can also import it as Nyströmer if you wish
from nystrom_attention import Nystromer@misc{xiong2021nystromformer,
title = {Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention},
author = {Yunyang Xiong and Zhanpeng Zeng and Rudrasis Chakraborty and Mingxing Tan and Glenn Fung and Yin Li and Vikas Singh},
year = {2021},
eprint = {2102.03902},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}