Summary of Flash Inference: Near Linear Time Inference For Long Convolution Sequence Models and Beyond, by Costin-andrei Oncescu et al.
Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond
by Costin-Andrei Oncescu, Sanket Purandare, Stratos Idreos, Sham Kakade
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a method to accelerate the exact inference of long convolution sequence models (LCSMs), which are typically quadratic in terms of sequence length. They focus on LCSM architectures like Hyena that address the computational issue at training time but remain quadratic during inference. The proposed approach is based on a tiling technique that reduces memory movement, shares computation, and allows for almost complete parallelization across layers. This results in an end-to-end improvement of up to 1.6 times over standard inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to make sequence generative models faster. These models are used to predict what comes next in a series of things like words or sounds. The problem is that these models get slower and slower as the series gets longer. Some models can be made faster during training, but not during prediction. The new approach uses a technique called tiling, which helps reduce the amount of memory needed and allows for more calculations to be done at the same time. This makes it up to 1.6 times faster than before. |
Keywords
» Artificial intelligence » Inference
