在大模型训练这个系列里,我们将一起探索学习几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。本篇文章将探索流水线并行,经典的流水线并...
2.1、Naive Model Parallelism 2.2、Pipeline Parallelism - Part 1 - Split into micro-batches 2.3、Pipeline Parallelism - Part 2 - 通过 re-materialization 降低显存占用 2.4、空间复杂度 && GPU 空闲时间 3、实验结果 3.1、增加 GPU 数量,训练更大模型 3.2、训练速度如何 4、总结 【本文是 “LLM 分布式训练...
算法的迭代创新 几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。 微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。 经典的流水线并行范式有Google推出的Gpipe, 微软推出的PipeDream。 ...
pipeline model parallelism (PMP)Deep learning has become the cornerstone of artificial intelligence, playing an increasingly important role in human production and lifestyle. However, as the complexity of problem-solving increases, deep learning models become increasingly intricate, resulting in a ...
Pipeline Parallelism(管道并行) 是一种用于加速大规模神经网络训练的技术,特别是在GPU资源受限的情况下。GPipe是这种技术的一个实现,它通过将数据分割成较小的微批次(micro-batches),允许GPU以并行方式处理这些微批次,从而提高了GPU的利用率和训练效率。
We present PipeDream, a system that adds \emph{inter-batch pipelining} to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining,...
automatically partitions DNN layers among workers to balance work and minimize communication. Extensive experimentation with a range of DNN tasks, models, and hardware configurations shows that PipeDream trains models to high accuracy up to 5.3x faster than commonly used intra-batch parallelism ...
We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-GPU DNN training. XPipe is designed to make use of multiple GPUs to concurrently and continuously train different parts of a DNN...
model parallelism / quantization, both techniques are more advanced and experimental, since Evo is not a native huggingface class, most of their utilities for MP / quantization do not work since particular methods are not implemented Besides extensive research and trial and errors, I couldnt get to...
Zero Bubble (Almost) Pipeline Parallelism, 视频播放量 13、弹幕量 0、点赞数 2、投硬币枚数 1、收藏人数 1、转发人数 1, 视频作者 竹言见智, 作者简介 路上行癫,相关视频:[CVPR24 Vision Foundation Model Tutorial] 具有精细落地能力的LMMs,[Cuda mode] Lecture 11: