数据并行技术(Data Parallelism):每批输入的训练数据都在数据并行的 worker 之间平分,即不同样本在不同 GPU 设备上完成前向推理和反向传播计算,此外,向传播后需要通信并规约梯度,以保证优化器在各个 worker 上进行相同的更新。 张量/模型并行技术(Model Parallelism):在多个 worker 之间划分模型的各个层,即不同层分...
1.数据并行 在数据并行系统中,每个计算设备都有整个神经网络模型的完整副本(Model Replica),进行迭代时,每个计算设备只分配了一个批次数据样本的子集,并根据该批次样本子集的数据进行网络模型的前向计算。如下所示: 2.模型并行 模型并行(Model Parallelism)往往用于解决单节点内存不足的问题。模型并行可以从计算图角度,...
2.模型并行 模型并行(Model Parallelism)往往用于解决单节点内存不足的问题。模型并行可以从计算图角度,以下两种形式进行切分:按模型的层切分到不同设备,即层间并行或算子间并行(Inter-operator Parallelism),也称之为流水线并行(Pipeline Parallelism,PP);将计算图层内的参数切分到不同设备,即层内并行或算子内...
有关更多详细信息,请参阅相应的论文:Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism(https://arxiv.org/abs/1909.08053)。 首先,我们讨论数据和环境设置,以及如何使用原始的Megatron-LM训练GPT-2模型。接下来,我们逐步介绍如何使用DeepSpeed使该模型运行。最后,我们演示使用...
其中,DeepSpeed框架凭借其四大创新支柱和灵活的软件架构,成为了大规模深度学习训练领域的佼佼者。一、四大创新支柱 DeepSpeed-Training:DeepSpeed提供了系统创新的融合,使大规模深度学习训练变得有效、高效。其创新点在于ZeRO、3D-Parallelism、DeepSpeed-MoE等。这些技术大大提高了易用性,并在可能的规模方面重新定义了深度...
Data Parallelism(数据并行) Naive:每个worker存储一份model和optimizer,每轮迭代时,将样本分为若干份分发给各个worker,实现并行计算 ZeRO: Zero Redundancy Optimizer,微软提出的数据并行内存优化技术,核心思想是保持Naive数据并行通信效率的同时,尽可能降低内存占用 ...
DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infini...
machine-learning compression deep-learning gpu inference pytorch zero data-parallelism model-parallelism mixture-of-experts pipeline-parallelism billion-parameters trillion-parameters Resources Readme License Apache-2.0 license Code of conduct Code of conduct Security policy Security policy Activity Cus...
Data, model, and pipeline parallelism each perform a specific role in improving memory and compute efficiency. Figure 1 illustrates our 3D strategy. Memory Efficiency: The layers of the model are divided into pipeline stages, and the layers of each stage are further divided via model parallelism....
Model parallelism is already a popular technique in training (see Introduction to Model Parallelism) and is increasingly becoming used in inference as practitioners require low-latency responses from large models. There are two general types of model parallelism: pipeline ...