Dense-and-Sparse Kernel Implementation 本文为了有效处理非均匀量化值,实现了基于查找表的CUDAKernel来进行矩阵-向量乘,这些kernel将压缩的权重加载,然后反量化到FP16,再进行计算。 由于每行的非零值数据具有很大差异,会造成负载不平衡,本文对每个线程分配相同数量的非零值来进行计算,称为balanced hybrid kernels。
SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method ...
https://github.com/SqueezeAILab/SqueezeLLMSqueezeLLM: Dense-and-Sparse Quantization 具有新颖的基于灵敏度的非均匀量化和密集和稀疏分解。即使在精度低至3位的情况下也能实现无损压缩,从而减少模型大小并加快推理速度,而不会影响模型性能。 1、介绍 主要工作: a)、基于灵敏度的非均匀量化 。因为LLM中的重量分布...
We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight ...
SqueezeLLM: Dense-and-Sparse Quantization 1. 动机 生成式推理的主要瓶颈在于内存带宽即内存墙,而非算术计算 本文主要贡献: 基于敏感度的非均匀量化:权重分布是不均匀的,使用非均匀量化可以实现3比特量化 针对离群点的稠密和稀疏量化:把权重分为稠密和稀疏,稀疏权重不量化,只量化稠密权重 ...
Dense-and-Sparse Quantizationto mitigate the impacts of numerical outliers on quantization difficulty KVQuant enables serving theLLaMA-7B model with 1M context length on a single A100-80GB GPU, or even theLLaMA-7B model with 10M context length on an 8-GPU system🔥 ...
Dense-and-Sparse Quantization to mitigate the impacts of numerical outliers on quantization difficultyKVQuant enables serving the LLaMA-7B model with 1M context length on a single A100-80GB GPU, or even the LLaMA-7B model with 10M context length on an 8-GPU system 🔥[...