进一步,如果将自回归生成用于图像,那么就需要对连续(continuous-valued)的像素进行离散化,变为离散的 token,从而才能在预测时实现对 token 的分类预测,这种离散化的技术被称作 "VQ(Vector Quantization)". 嗯,这又是一个刻板印象,或者说已经成为了一种封建迷信: 自回归图像生成需要VQ,而且是必须! 然而,近来由恺明...
9. RQ-VAE RQ-Transformer 《Autoregressive image generation using residual quantization》 10. Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer 11. HQ-VAE《Locally Hierarchical Auto-Regressive Modeling for Image Generation》 12. Mage 《Mage: Masked generative encoder to unify ...
Vector Quantization这项技术广泛地用在信号处理以及数据压缩等领域。事实上,在 JPEG 和 MPEG-4 等多媒体压缩格式里都有 VQ 这一步。 Vector Quantization 这个名字听起来有些玄乎,其实它本身并没有这么高深。大家都知道,模拟信号是连续的值,而计算机只能处理离散的数字信号,在将模拟信号转换为数字信号的时候,我们可...
Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits). It reduce...
注意,由于原本的 AutoGPTQ 对 asymmetric quantization 存在一个数据溢出的 bug(详见 Lin Zhang:分析 AutoGPTQ 中存在的一个 Bug),所以我们选择的是 AutoGPTQ 的官方 bug 修订版 GPTQModel 进行 repack。 背景 为了更好得理解量化操作是如何加速大语言模型(Large Language Models, LLMs)的,我们首先介绍模型推理...
Vector Quantization and Clustering: These methods organize vectors into groups with similar characteristics, mitigating the impact of outliers and variance within the data. Embedding Refinement: For domain-specific applications, refining embeddings with additional training or techniques like retrofitting improves...
FLAT是效率低但100%准确的暴搜,数据量少可能优于索引性能;IVF对数据点聚类,查询时找最近nprobe个buckets;Product quantization将向量分段聚类编码压缩内存占用;HNSW是广泛使用的图索引,建索引有原则且层次化结构定位快;DISKANN在Disk中保存图索引,增强locality,降低内存占用且性能和精度不错;GPU cagra利用GPU并行计算能力...
This work proposes an approximation to cross-attention scoring based on vector quantization and enables compute- and memory-efficient use of large biasing catalogues. We propose to use this technique jointly with a retrieval based contextual biasing approach. First, we use an efficient quantized ...
在早期,我们可能会使用线性搜索或者树形结构来索引和检索向量。但是,随着数据量的增长,这些方法可能会变得效率低下。因此,现代的向量索引和检索方法,如倒排索引 (Inverted Index) 和乘积量化 (Product Quantization) 等,被开发出来 [9]。这些方法可以更有效地处理大规模数据,提供更快速的索引和检索。
There are techniques to help mitigate this challenge, such as dimensionality reduction via vector quantization, which is a lossy data compression technique used in machine learning. It works by mapping vectors from a multidimensional space to a finite set of values in a lower-dimensional subspace, ...