fast-weight-product-key-memory 0.1.5

Fast Weight Product Key Memory

Implementation of the Fast Weight Product Key Memory proposed by Sakana AI

Appreciation

• Pranoy for the contribution of multi-head variant!

Install

$ pip install fast-weight-product-key-memory

Usage

import torch
from fast_weight_product_key_memory import fwPKM

mem = fwPKM(
    dim = 512,
    num_memories = 256 * 256,
    dim_queries_keys = 512,
    dim_values = 512,
    topk = 8,
    learning_rate = 1.,
    chunk_size = 256
)

tokens = torch.randn(2, 256, 512)

# forward a chunk of tokens for retrieved and the fast weight episodic memories

retrieved, next_memories = mem(tokens, return_next_memories = True)

# chain memories

retrieved, next_memories = mem(tokens, return_next_memories = True, past_memories = next_memories)
retrieved, next_memories = mem(tokens, return_next_memories = True, past_memories = next_memories)
retrieved, next_memories = mem(tokens, return_next_memories = True, past_memories = next_memories)

Enwik8

Character-level language model with fwPKM

$ uv run train_enwik8.py

Citations

@misc{zhao2026fastweightproductkeymemory,
    title   = {Fast-weight Product Key Memory},
    author  = {Tianyu Zhao and Llion Jones},
    year    = {2026},
    eprint  = {2601.00671},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2601.00671},
}

@article{Yaghoubietal2026,
    author  = {Yaghoubi, Mohammad and Nieto-Posadas, Andres and Mosser, Coralie-Anne and Gisiger, Thomas and Wilson, Émmanuel and Williams, Sylvain and Brandon, Mark P.},
    title   = {Predictive coding of reward in the hippocampus},
    journal = {Nature},
    year    = {2026},
    doi     = {10.1038/s41586-025-09958-0}
}