原文: Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMNvidia、Stanford和Microsoft发表在SC21上的文章,在 Megatron-LM提出3D并行以后,人们发现各种并行度、GPU的算力和显存…
原文指出，利用Nvidia、Stanford和Microsoft在SC21上发表的文章，关于Megatron-LM提出的3D并行方法，人们发现并行度、GPU算力和显存等参数之间存在复杂关系。因此，文章基于实验结果，提供了最大化Megatron-LM能力的方法，类似于其用户手册。理解实验结果有助于深入理解模型训练的算力、显存和通信之间的相互影响。
其中最重要的函数是initialize_model_parallel,主要是初始化上述几个global data. 其他函数基本是上面几个global data的getter/setter. 这里说下后面用到的notation,来自NVIDIA在SC '21上的论文: n是GPU数量, 即#GPU. (p, t, d)是parallel configuration. p是pipeline-model-parallel dimension size, t是tensor-m...
Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia International Conference for High Performance Computing, Networking, Storage and ...
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sc21 evaluate_retriever_nq.sh evaluate_zeroshot_gpt.sh finetune_mnli_distributed.sh finetune_race_distributed.sh finetune_retriever_distributed.sh merge_mp_bert.sh pretrain_bert.sh pretrain_bert_distributed.sh pretrain_bert_distributed_with_mp.sh pretrain_gpt.sh pretrain_gpt3_1...
'--index-url=https://sc-hw-artf.nvidia.com/api/pypi/hwinf-ml-pypi/simple' ' one_logger` or go to https://gitlab-master.nvidia.com/hwinf-dcm/onelogger ' 'for more details') group.add_argument('--one-logger-project', type=str, default='e2e-tracking', help='The one-logger...
International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2021) | November 2021 Best Student Paper Download BibTex Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these models efficiently is c...
NVIDIA在SC '21上的论文描述了一种interleaving pipeline schedule,能够进一步降低bubble size,但是增加了一定的通信开销. 本文暂不考虑这种特殊的schedule,主要研究PipeDream-Flush的实现,即上面代码里面的forward_backward_pipelining_without_interleaving函数. PipeDream-Flush的一个iteration分为三个阶段: warm-up phas...
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