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Computer Science > Computation and Language

arXiv:2402.02622 (cs)
[Submitted on 4 Feb 2024 (v1), last revised 21 Mar 2024 (this
version, v2)]

Title:DenseFormer: Enhancing Information Flow in Transformers via
Depth Weighted Averaging

Authors:Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret,
Martin Jaggi
Download a PDF of the paper titled DenseFormer: Enhancing Information
Flow in Transformers via Depth Weighted Averaging, by Matteo
Pagliardini and 3 other authors
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    Abstract:The transformer architecture by Vaswani et al. (2017) is
    now ubiquitous across application domains, from natural language
    processing to speech processing and image understanding. We
    propose DenseFormer, a simple modification to the standard
    architecture that improves the perplexity of the model without
    increasing its size -- adding a few thousand parameters for
    large-scale models in the 100B parameters range. Our approach
    relies on an additional averaging step after each transformer
    block, which computes a weighted average of current and past
    representations -- we refer to this operation as
    Depth-Weighted-Average (DWA). The learned DWA weights exhibit
    coherent patterns of information flow, revealing the strong and
    structured reuse of activations from distant layers. Experiments
    demonstrate that DenseFormer is more data efficient, reaching the
    same perplexity of much deeper transformer models, and that for
    the same perplexity, these new models outperform transformer
    baselines in terms of memory efficiency and inference time.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2402.02622 [cs.CL]
          (or arXiv:2402.02622v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2402.02622
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Amirkeivan Mohtashami [view email]
[v1] Sun, 4 Feb 2024 21:44:09 UTC (1,154 KB)
[v2] Thu, 21 Mar 2024 10:57:40 UTC (4,007 KB)
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