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 Parallelis
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...
换成"data/model parallelism", 这里一个组是一个cpu或者一个gpu。第一个方案是data parallelism，第二...
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 ...
The performance of parallel architectures are limited at least as much by data transfer ability as by computing power. The main limit concerns the transfers on the interconnection network. But it becomes apparent that a majority of these communications can be known at compile time. The static ...
MP = Model Parallelism DP = Data Parallelism PP = Pipeline Parallelism Resources: Parallel and Distributed Training tutorials at pytorch - a handful, starting withhttps://pytorch.org/tutorials/beginner/dist_overview.html fairscale githubhttps://github.com/facebookresearch/fairscale ...
Moreover, they proposed model-and-data parallelism (MDP) framework for attribute reduction [206], which is the combination of Model-Parallelism (MP) and Data-Parallelism (DP) when designing method for attribute selection. In MDP framework, two stages were considered in each loop, namely, ...
The model was trained using native PyTorch with 8-way model parallelism and 64-way data parallelism on 512 GPUs. GPT-2 8B is the largest Transformer-based language model ever trained, at 24x the size of BERT and 5.6x the size of GPT-2. The experiments were conducted on NVIDIA’s DGX ...
Existing MoE systems support only expert, data, and model parallelism or a subset of them. This leads to three major limitations: i) They replicate the base model (part of the model without expert parameters) across data-parallel GPUs, resulting in wasted memory, (ii) They need model ...
The GatorTron-large model with 8.9 billion parameters is too large to fit one GPU, therefore, we sliced it into four pieces for distributed training using model parallelism. We pretrained the GatorTron-base and medium model without model slicing. The default loss function defined in BERT model...