deep-cross-attention 0.1.1

Deep Cross Attention Language Model

Deep Cross Attention

Implementation of the proposed DeepCrossAttention by Mike Heddes while at Google research, in Pytorch

My analysis is although I still prefer Hyper Connections, they have an important idea here that I have been trying concurrently. Mainly the queries, keys, values can be routed from different layers of the past. The reason this is cool is because it generalizes the recent value residual learning improvement. It may (or may not) also address an issue for neural memories

Appreciation

• Minh Hoang for spotting some issues with the GRN

Install

$ pip install deep-cross-attention

Usage

import torch
from deep_cross_attention import DCAGPT

gpt = DCAGPT(
    num_tokens = 256,
    dim = 512,
    depth = 6,
    heads = 8,
    dim_head = 64,
    past_layers_k = 2
)

ids = torch.randint(0, 256, (2, 4096))

logits = gpt(ids) # (2, 4096, 256)

Example

First

$ pip install .[examples]

Next

$ python train.py

Citations

@inproceedings{Heddes2025DeepCrossAttentionST,
    title   = {DeepCrossAttention: Supercharging Transformer Residual Connections},
    author  = {Mike Heddes and Adel Javanmard and Kyriakos Axiotis and Gang Fu and MohammadHossein Bateni and Vahab S. Mirrokni},
    year    = {2025},
    url     = {https://api.semanticscholar.org/CorpusID:276250576}
}