FutureFill: Fast Generation from Convolutional Sequence Models

Naman Agarwal, Xinyi Chen, Evan Dogariu, Vladimir Feinberg, Daniel Suo, Peter Bartlett, Elad Hazan

Keywords: sequence modeling, online learning, fast inference

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 linear to square root 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.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 8109