构建可组合的向量量化框架，统一算法开发与评测标准。
VQ-bench: A Composable Vector Quantization Framework
- 提出7种基础量化组件，支持任意组合设计新算法。
- 将25种常见量化器重构成这些组件的流水线。
- 开源框架并提供可复现的基准测试，加速研究迭代。
向量量化虽是经典问题，但近年来已成为人工智能基础设施的核心。因此，该领域正经历新一轮工程与研究热潮。本文提出一个统一框架，用于开发和评测新型量化算法。我们定义了7种常见的概念性量化基本单元，并展示如何任意组合它们。随后，我们将25种常见的量化器重新表述为这些单元的处理流程。最后，我们开源发布VQ-bench框架，支持持续扩展，并公开可复现的基准测试结果。
原文摘要 · Abstract (English)
Vector quantization is an old problem but has recently become central to AI infrastructure. It is therefore experiencing a surge of renewed engineering and research activity. This paper provides a unified framework for developing and benchmarking new quantization algorithms. We describe 7 common conceptual quantization primitives and show how to compose them arbitrarily. We then re-express 25 common quantizers as pipelines of these primitives. Finally, we publish VQ-bench as open-source to be extended further and make reproducible benchmarks publicly available.
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