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Computer Science > Machine Learning

arXiv:2308.06103 (cs)
[Submitted on 11 Aug 2023]

Title:Composable Function-preserving Expansions for Transformer
Architectures

Authors:Andrea Gesmundo, Kaitlin Maile
Download a PDF of the paper titled Composable Function-preserving
Expansions for Transformer Architectures, by Andrea Gesmundo and
Kaitlin Maile
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    Abstract: Training state-of-the-art neural networks requires a
    high cost in terms of compute and time. Model scale is recognized
    to be a critical factor to achieve and improve the
    state-of-the-art. Increasing the scale of a neural network
    normally requires restarting from scratch by randomly
    initializing all the parameters of the model, as this implies a
    change of architecture's parameters that does not allow for a
    straightforward transfer of knowledge from smaller size models.
    In this work, we propose six composable transformations to
    incrementally increase the size of transformer-based neural
    networks while preserving functionality, allowing to expand the
    capacity of the model as needed. We provide proof of exact
    function preservation under minimal initialization constraints
    for each transformation. The proposed methods may enable
    efficient training pipelines for larger and more powerful models
    by progressively expanding the architecture throughout training.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2308.06103 [cs.LG]
          (or arXiv:2308.06103v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2308.06103
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Andrea Gesmundo [view email]
[v1] Fri, 11 Aug 2023 12:27:22 UTC (95 KB)
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