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。
Release v0.5.1 · vllm-project/vllm v0.5.1版本就支持了。 vLLM now haspipeline parallelism! (#4412,#5408,#6115,#6120). You can now run the API server with--pipeline-parallel-size. This feature is in early stage, please let us know your feedback. 2. 配置 ParallelConfig: pipeline_pa...
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...
vLLM is flexible and easy to use with: Seamless integration with popular Hugging Face models High-throughput serving with various decoding algorithms, includingparallel sampling,beam search, and more Tensor parallelism and pipeline parallelism support for distributed inference ...
When implementing pipeline parallelism, make sure that the split and concat operations in the MTP are performed along axis=0. fix f92da94 codecov bot commented Mar 3, 2025 • edited Codecov Report Attention: Patch coverage is 0% with 6 lines in your changes missing coverage. Please review...
在训练大模型的过程中,“模型并行”是一种绕不开的核心技术。 本期视频将带你通俗易懂地搞懂 Tensor 并行(Tensor Parallelism) 和 Pipeline 并行(Pipeline Parallelism) 的基本原理、实现方式以及它们之间的区别和应用场景。大语言模型 (LLM) 人工智能 TP 加速训练 3D并行 PP LLM ...
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...
and values≥ 0will run your model on aGPUassociated with the CUDA device ID provided. Here, utilizing a GPU for references is a standard choice of hardware for machine learning and LLM models because they are optimized for memory allocation and parallelism. This can significantly speed up your...