FutureFill: Fast Generation from Convolutional Sequence Models

• URL: http://arxiv.org/abs/2410.03766v2
• Date: Fri, 25 Oct 2024 19:45:33 GMT
• Title: FutureFill: Fast Generation from Convolutional Sequence Models
• Authors: Naman Agarwal, Xinyi Chen, Evan Dogariu, Vlad Feinberg, Daniel Suo, Peter Bartlett, Elad Hazan,
• Abstract summary: FutureFill is 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. We validate our theoretical findings with experimental evidence demonstrating correctness and efficiency gains in a synthetic generation task.
• Score: 22.410028211490424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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