在大模型训练这个系列里,我们将一起探索学习几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。本篇文章将探索流水线并行,经典的流水线并...
算法的迭代创新 几种经典的分布式并行范式,包括流水线并行(Pipeline Parallelism),数据并行(Data Parallelism)和张量并行(Tensor Parallesim)。 微软开源的分布式训练框DeepSpeed,融合了这三种并行范式,开发出3D并行的框架,实现了千亿级别模型参数的训练。 经典的流水线并行范式有Google推出的Gpipe, 微软推出的PipeDream。 ...
[experimental] PiPPy: Pipeline Parallelism for PyTorch Why PiPPy? One of the most important techniques for advancing the state of the art in deep learning is scaling. Common techniques for scaling neural networks includedata parallelism,tensor/model parallelism, andpipeline parallelism. In many cases,...
Hi, I had some questions about the pipeline parallelism implementation in DeepSpeed. Can someone help shed some information on the following? From among the following types of pipeline scheduling, which one does DeepSpeed implement in it...
为了绕过这个在线生成的过程,一个很自然的方法就是先预先(离线)生成形状为(num_index, sequence_length, num_features)的全量数据,然后利用tf.data.Dataset.from_tensor_slices来生成迭代数据集。这样做的好处就是__getitem__或者__iter__的时候(理论上)不存在在线计算的瓶颈;而且离线生成的时候也可以用到一些...
Common techniques for scaling neural networks include data parallelism, tensor/model parallelism, and pipeline parallelism. In many cases, pipeline parallelism in particular can be an effective technique for scaling, however it is often difficult to implement, requiring intrusive code changes to model ...