PoPE-pytorch 0.1.2

PoPE

PoPE-pytorch

Efficient implementation (and explorations) into polar coordinate positional embedding (PoPE) - from Gopalakrishnan et al. under Schmidhuber

Install

$ pip install PoPE-pytorch

Usage

import torch
from PoPE_pytorch import PoPE

# define pope

pope = PoPE(64, heads = 8)

# pass in sequence length

pos_emb = pope(1024)

# queries and keys in attention

q = torch.randn(1, 8, 1024, 64)
k = torch.randn(1, 8, 1024, 64)

# training

rotated_q, rotated_k = pope.apply_pope_to_qk(pos_emb, q, k)

# inference

rotated_q, rotated_k = pope.apply_pope_to_qk(pos_emb, q[..., -1:, :], k)

Axial PoPE

For images, video, etc. where multiple dimensions are needed, you can use AxialPoPE. The feature dimension will be split across these axial dimensions.

You can either pass in the positions manually, or just pass the dimensions as a tuple, in which case the grid positions will be automatically generated.

import torch
from PoPE_pytorch import AxialPoPE

# axial pope for images (e.g. 16x16)
# split 64 dim into 32 (x) and 32 (y)

pope = AxialPoPE(
    dim = 64,
    heads = 8,
    axial_dims = (32, 32)
)

pos_emb = pope((16, 16)) # (256, 64) frequencies

# for video (e.g. 8 frames, 16x16 frames)
# split 96 dim into 32 (t), 32 (x), 32 (y)

pope_video = AxialPoPE(
    dim = 96,
    heads = 8,
    axial_dims = (32, 32, 32)
)

pos_emb_video = pope_video((8, 16, 16)) # (2048, 96) frequencies

# queries and keys
# then apply to q, k as usual

q = torch.randn(1, 8, 2048, 96)
k = torch.randn(1, 8, 2048, 96)

rotated_q, rotated_k = AxialPoPE.apply_pope_to_qk(pos_emb_video, q, k)

Fused Attention Similarity

import torch
from PoPE_pytorch import PoPE, compute_attn_similarity

# define pope

pope = PoPE(dim = 64, heads = 8).cuda()

# get rotations

pos_emb = pope(1024)

# queries and keys

q = torch.randn(1, 8, 1024, 64).cuda()
k = torch.randn(1, 8, 1024, 64).cuda()

# fused attention similarity, avoiding expanding 64 to 128

sim = compute_attn_similarity(q, k, pos_emb) # (1, 8, 1024, 1024)

attn = sim.softmax(dim = -1) # the usual in attention..

Fused Flash Attention

import torch
from PoPE_pytorch import PoPE, flash_attn_with_pope

# pope

pope = PoPE(dim = 32, heads = 8).cuda()

# queries, keys, values for attention

q = torch.randn(2, 8, 1024, 64).cuda()
k = torch.randn(2, 8, 1024, 64).cuda()
v = torch.randn(2, 8, 1024, 64).cuda()

pos_emb = pope(1024)

mask = torch.ones((2, 1024)).bool().cuda()

out = flash_attn_with_pope(q, k, v, pos_emb = pos_emb, causal = True, mask = mask)

assert out.shape == (2, 8, 1024, 64)

Citations

@misc{gopalakrishnan2025decouplingwhatwherepolar,
    title   = {Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings},
    author  = {Anand Gopalakrishnan and Robert Csordás and Jürgen Schmidhuber and Michael C. Mozer},
    year    = {2025},
    eprint  = {2509.10534},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2509.10534},
}