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...
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。 ...
Hi, I had some questions about the pipeline parallelism implementation in DeepSpeed. Can someone help shed some information on the following? From among the following types of pipeline scheduling, which one does DeepSpeed implement in it...
🚀 The feature, motivation and pitch Motivation SPMD sharding in pytorch/XLA offers model parallelism by sharding tensors within an operator. However, we need a mechanism to integrate this capapability with pipeline parallelism for models...
[ LLM 分布式训练系列 02 ] 流水线并行(Pipeline Parallelism)- GPipe 在LLM 分布式训练这个系列,我打算记录一下目前主要的几种并行方法: 流水线并行(Pipeline Parallelism) 数据并行(Data Parallelism) 张量并行(Tensor Parallelism) 本篇文章以 Google 在 2019 年推出的 GPipe [1] 为例,介绍下流水线并行的原理。
The optimal number of bits used for the approximation pattern matching (the parameter 𝐴𝑏𝑖𝑡Abit) is evaluated during the apma sub-stage, which explores the viable configurations in the interval 𝑆𝑎𝑏=[𝑏𝑤2,𝑏𝑤]Sab=[bw2,bw], 𝑏𝑤bw as the parallelism of the ...
Zero Bubble Pipeline Parallelism 中提出一种新的调度方法,实现了近似零流水线空闲。这一改进背后的关键思想是将反向计算分为两个部分,一个计算输入的梯度,另一个计算参数的梯度。基于这个思想,手工设计了新颖的流水线调度方案,明显优于基准方法。同时还开发了一种算法,根据特定的模型配置和内存限制自动找到最优的调度...
[experimental] PiPPy: Pipeline Parallelism for PyTorch Why PiPPy? One of the most important techniques for advancing the state of the art in deep learning is scaling. Common techniques for scaling neural networks includedata parallelism,tensor/model parallelism, andpipeline parallelism. In many cases,...
Describe the bug This bug may be related to #4274 . When I run activation checkpointing with pipeline parallelism, I get the following error: RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn To Reprodu...
论文中提到的operator parallelism看起来和megatron中的tensor 并行比较像。 backward时间是forward时间的两倍? 这篇论文的forward doubling方法有个强的假设:backward时间刚好是forward时间的两倍 通常大家是这么认为的,因为backward的时候 需要对参数和输入都算梯度,相当于两倍计算量。 但是实际未必如此,以megatron-v3论文的...