View a PDF of the paper titled FutureFill: Fast Generation from Convolutional Sequence Models, by Naman Agarwal and 6 other authors
Abstract:We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill – a method for fast generation that applies to any sequence prediction algorithm based on convolutional operators. Our approach reduces the generation time requirement from quadratic to quasilinear relative to the context length. Additionally, FutureFill requires a prefill cache sized only by the number of tokens generated, which is smaller than the cache requirements for standard convolutional and attention-based models. We validate our theoretical findings with experimental evidence demonstrating correctness and efficiency gains in a synthetic generation task.
Submission history
From: Naman Agarwal [view email]
[v1]
Wed, 2 Oct 2024 15:22:08 UTC (802 KB)
[v2]
Fri, 25 Oct 2024 19:45:33 UTC (2,532 KB)
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#Fast #Generation #Convolutional #Sequence #Models
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