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 分布式训练系列” 的第 2 篇,持续更新...
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。 4.2 re-materialization(active checkpoint) 解决了GPU的空置问题,提升了GPU计算的整体效率。接下来,就要解决GPU的内存问题了。
LLM迷思 汉阳大学 计算机硕士 24 人赞同了该文章 PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization 最近当红辣子鸡 DeepSeek 在训练阶段使用了 DualPipe 训练技术,而其中核心技术引用自 SeaAI Lab 对于pipeline parallel 的优化工作 zero-bubble-pipeline-parallelism。最近SeaAI Lab...
该内容讨论了在大型语言模型(LLM)推理阶段中,为什么低端低带宽芯片的部署策略不首选pipeline parallelism。它突出了吞吐量优化和用户体验之间的一个关键权衡,指出虽然pipeline parallelism可以显著提高吞吐量,但也会放大延迟。这... 内容导读 该内容讨论了在大型语言模型(LLM)推理阶段中,为什么低端低带宽芯片的部署策略不...
Motivation. This RFC describes the approach for supporting pipeline parallelism in vLLM V1 architecture. Pipeline parallelism was supported in V0 with the virtual-engine approach. In short, we create multiple virtual engines to match the...
Hey vllm team, Hope you're all doing great! I‘m focusing on pipeline parallel inference and I hope it can be support on vllm. I noticed that pipeline parallelism was on the old roadmap(#244) , but it's not on the new roadmap(#2681). Just curious, was there a specific reason...
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 ...
PipeDream, a system developed as part of Microsoft Research’sProject Fiddle(opens in new tab), introduces pipeline parallelism, a new way to parallelize DNN training by combining traditional intra-batch parallelism (model and data parallelism) with inter-batch parallelism (pi...
Google AI researchers had also published a paper titled “GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism" last year in December. In the paper, researchers demonstrated the use of pipeline parallelism to scale up deep neural networks to...
Google AI researchers had also published a paper titled “GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism" last year in December. In the paper, researchers demonstrated the use of pipeline parallelism to scale up deep neural networks to...