Gpipe通过实验证明,当 M>=4K 时,bubble产生的空转时间占比对最终训练时长影响是微小的,可以忽略不计。将batch切好,并逐一送入GPU的过程,就像一个流水生产线一样(类似于CPU里的流水线),因此也被称为Pipeline Parallelism。 3.2 re-materialization(active checkpoint) 解决了GPU的空置问题,提升了GPU计算的整体效率。
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。 两者的推出时间都...
为了训练这样的大模型,并且尽可能提高 GPU 的利用率,流水线并行(Pipeline Parallelism, PP)的训练策略应运而生。PyTorch 也实现了一套流水线并行的解决方法。本文将介绍 torch.distributed.pipeline.sync 的实现细节。相关代码位于 https://github.com/pytorch/pytorch/tree/v2.1.0-rc6/torch/distributed/pipeline/...
神经网络pipeline是一种在深度学习模型训练过程中,通过流水线方式并行处理数据,以提高计算效率和资源利用率的技术。以下是关于神经网络pipeline的详细解答: 1. 基本概念 神经网络pipeline,也称为流水线并行(Pipeline Parallelism),是一种将深度学习模型的计算任务分解成多个阶段,并在不同的计算节点上并行执行这些阶段的技术...
PiPPy: Pipeline Parallelism for PyTorch Note PiPPy has been migrated intoPyTorchas a subpackage:torch.distributed.pipelining. You can find the detailed documentationhere. The current repo mainly serves as a land ofexamples. The PiPPy library code will be removed. Please use the APIs intorch.distri...
🚀 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...
PipeDream has been built to use PyTorch (anearlier version of PipeDream(opens in new tab)uses Caffe). Our evaluation, encompassing many combinations of DNN models, datasets, and hardware configurations, confirms the training time benefits of PipeDream’s pipeline parallelism....
pipeline parallelism. Must be a (potentially wrapped) megatron.core.models.MegatronModule. num_microbatches (int, required): The number of microbatches to go through seq_length (int, required): Sequence length of the current global batch. If this is a dual-stack ...
流水并行(Pipeline Parallelism,PP)中,针对流水并行(PP)假设并行的stage 切分为p 份,micro-batches为m,每个micro-batches 的前向和后向执行时间为tf 和tb。在理想的环境下,前向和后向的合起来理想的执行时间应该是: tideal=m(tf+tb) 不过在实际 Pipeline 把网络模型切分成不同 stage 放在不同的机器,会导致在...
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019). In particular, we develop a set of design components to enable pipeline-parallel gradient computation in PyTorch's define-by-run ...