从Data Parallelism 到 Model Parallelism 当单卡显存不足的时候,利用多卡实现并行的方案油然而生。基础的并行方案主要有数据并行与模型并行。 数据batchsize太大单卡放不下?Data Parallelism Data Parallelism示意图 数据并行可以提高训练效率,其过程如下: 将模型参数拷贝至各个显卡上,即上图中各个显卡都拥有相同的模型...
分布式机器学习中的数据并行(Data Parallelism)和模型并行(model parallelism) 前言: 现在的模型越来越复杂,参数越来越多,其训练集也在剧增。在一个很大的数据集集中训练一个比较复杂的模型往往需要多个GPU。现在比较常见的并行策略有:数据并行和模型并行,本文主要讨论这两个并行策略。 数据并行(Data Parallelism): 在现...
data parallelism/model parallelism不外如是，ASGD也算是一个“暴力”性质的例子(异步SGD虽然看起来很美...
https://twitter.com/FioraAeterna/status/675066780205891584
相比于data-parallel和model-parallel,提出了更多维度的split方案。SOAP(sample,operator,atrribute,param)这四个维度的split方案。 在四个维度之上,提出了一种在候选空间搜索的方案 提出了一个更加轻量的simulator,可以更快速的对proposed split strategy做evaluate。相比直接执行的方案提升了3个数量级。
Then I want to use data parallelism and do not use model parallelism, just like DDP. The load_in_8bit option in .from_pretrained() requires setting device_map option. With device_map='auto', it seems that the model is loaded on several gpus, as in naive model parallelism, which ...
By combining these two forms of model parallelism with data parallelism, we can scale up to models with a trillion parameters on the NVIDIA Selene supercomputer (Figure 3). Models in this post are not trained to convergence. We only performed a few hundred iterations to measure time per ...
《Integrated Model and Data Parallelism in Training Neural Networks》A Gholami, A Azad, K Keutzer, A Buluc [UC Berkeley & Lawrence Berkeley National Laboratory] (2017) http://t.cn/RTjQn1c
🚀 Feature request This is a discussion issue for training/fine-tuning very large transformer models. Recently, model parallelism was added for gpt2 and t5. The current implementation is for PyTorch only and requires manually modifying th...
2, 是带来了流水线优化，提升了计算效率。但是也有缺点，比如水平划分模型时，中间的某一层计算需要上一...