Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine tuning to recover the accuracy lost after quantization. This paper introduces the first practical 4-bit post training quantization approach:...
内容提示: SmoothQuant+: Accurate and Eff i cient 4-bit Post-Training WeightQuantization for LLMJiayi Pan, Chengcan Wang, Kaifu Zheng, Yangguang Li, Zhenyu Wang, Bin FengZTE CorporationAbstractLarge language models (LLMs) have shown re-markable capabilities in various tasks. Howevertheir huge ...
(2) Post Training 4-bit Quantization of Convolutional Networks for Rapid-deployment (NeurIPS2019)主要贡献:本文ACIQ首次针对4比特PTQ(Post-training Quantization)展开研究。1)ACIQ以优化分析角度求解最优的截断值,以提升量化损失(针对激活值); 2)per-channel的比特数分配 (针对权重和激活值);3)bias-correction ...
Post-training quantization(PTQ) 工作流理解 目前神经网络在许多前沿领域的应用取得了较大进展,但经常会带来很高的计算成本,对内存带宽和算力要求高。另外降低神经网络的功率和时延在现代网络集成到边缘设备时也极其关键,在这些场景中模型推理具有严格的功率和计算要求。神经网络量化是解决上述问题有效方法之一,但是模型量化...
在 Post-Training Quantization 的研究工作中,Uniform Quantization 是最受欢迎的方法。众多研究表明,8-bit Uniform Quantization 就可以保持大部分的模型精度,但是如果降到 4-bit,精度会有非常显著的损失。 此篇文章就是分析了 4-bit 精度损失的具体原因,并提出了他们的 Piecewise Linear Quantization(PWLQ)的方法来...
4-bit weight-only PTQ that requires no additional training, which enables lossless in accuracy for LLMs for the first time. Based on the fact that the loss of weight quantization is amplified by the activation outliers, SmoothQuant+ smoothes the activation outliers by channel before quantization,...
watch?v=0VdNflU08yA https://github.com/hkproj/quantization-notes In this video I will introduce and explain quantization: we will first start with a little introduction on numerical representation of integers and floating-point numbers in computers, then see what is quantization and how it ...
在 Post-Training Quantization 的研究工作中,Uniform Quantization 是最受欢迎的方法。众多研究表明,8-bit Uniform Quantization 就可以保持大部分的模型精度,但是如果降到 4-bit,精度会有非常显著的损失。 此篇文章就是分析了 4-bit 精度损失的具体原因,并提出了他们的 Piecewise Linear Quantization(PWLQ)的方法来...
模型量化论文阅读#2---BRECQ: PUSHING THE LIMIT OF POST-TRAINING QUANTIZATION BY BLOCK RECONSTRUCTION,程序员大本营,技术文章内容聚合第一站。
模型压缩之post-training quantization 一,post-training quantization的工作原理 在底层,通过将参数(即神经网络权重)的精度从训练时的32位浮点表示降低到更小、更高效的8位整数表示来运行优化(也称为量化)。 post-training量化指南:https://www.tensorflow.org/performance/post_training_quantization这些优化将确保将最终...