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Computer Science > Machine Learning

arXiv:2312.09299 (cs)
[Submitted on 14 Dec 2023]

Title:Weight subcloning: direct initialization of transformers using
larger pretrained ones

Authors:Mohammad Samragh, Mehrdad Farajtabar, Sachin Mehta, Raviteja
Vemulapalli, Fartash Faghri, Devang Naik, Oncel Tuzel, Mohammad
Rastegari
Download a PDF of the paper titled Weight subcloning: direct
initialization of transformers using larger pretrained ones, by
Mohammad Samragh and 7 other authors
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    Abstract:Training large transformer models from scratch for a
    target task requires lots of data and is computationally
    demanding. The usual practice of transfer learning overcomes this
    challenge by initializing the model with weights of a pretrained
    model of the same size and specification to increase the
    convergence and training speed. However, what if no pretrained
    model of the required size is available? In this paper, we
    introduce a simple yet effective technique to transfer the
    knowledge of a pretrained model to smaller variants. Our approach
    called weight subcloning expedites the training of scaled-down
    transformers by initializing their weights from larger pretrained
    models.
    Weight subcloning involves an operation on the pretrained model
    to obtain the equivalent initialized scaled-down model. It
    consists of two key steps: first, we introduce neuron importance
    ranking to decrease the embedding dimension per layer in the
    pretrained model. Then, we remove blocks from the transformer
    model to match the number of layers in the scaled-down network.
    The result is a network ready to undergo training, which gains
    significant improvements in training speed compared to random
    initialization. For instance, we achieve 4x faster training for
    vision transformers in image classification and language models
    designed for next token prediction.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2312.09299 [cs.LG]
          (or arXiv:2312.09299v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2312.09299
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Mohammad Samragh [view email]
[v1] Thu, 14 Dec 2023 19:08:56 UTC (1,257 KB)
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