coconut-pytorch 0.0.32

Coconut in Pytorch

🥥 Coconut

Implementation of Coconut, proposed by the paper Training Large Language Models to Reason in a Continuous Latent Space out of FAIR, in Pytorch

Architecture wise, the closest work to the one proposed here would be RMT, where the memory tokens there could serve as the continuous latent tokens. Both directions are worth exploring

Install

$ pip install coconut-pytorch

Usage

import torch
from coconut_pytorch import Coconut

model = Coconut(
    num_reasoning_steps = 3,
    num_latents_per_step = 1,
    transformer = dict(
        num_tokens = 256,
        dim = 512,
        depth = 6
    )
)

prompt = torch.randint(0, 256, (2, 1024))
answer = torch.randint(0, 256, (2, 64))

loss = model(prompt, answer)
loss.backward()

# after much training

answer = model.generate(prompt, max_length = 64) # (2, 64)

Citation

@inproceedings{Hao2024TrainingLL,
    title   = {Training Large Language Models to Reason in a Continuous Latent Space},
    author  = {Shibo Hao and Sainbayar Sukhbaatar and DiJia Su and Xian Li and Zhiting Hu and Jason Weston and Yuandong Tian},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:274610816}
}

@article{Burtsev2021MultiStreamT,
    title   = {Multi-Stream Transformers},
    author  = {Mikhail S. Burtsev and Anna Rumshisky},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2107.10342},
    url     = {https://api.semanticscholar.org/CorpusID:236171087}
}

@article{Zhu2024HyperConnections,
    title   = {Hyper-Connections},
    author  = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2409.19606},
    url     = {https://api.semanticscholar.org/CorpusID:272987528}
}

@inproceedings{Zhou2024ValueRL,
    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273532030}
}