3-D parallelism in DeepSpeed interweaves data parallelism with model parallelism and pipeline parallelism to scale up training models on multiple GPUs and nodes, avoiding memory bottlenecks in training extremely
else: assert mpu is None, "mpu must be None with pipeline parallelism" engine = PipelineEngine(args=args, model=model, optimizer=optimizer, model_parameters=model_parameters, training_data=training_data, lr_scheduler=lr_scheduler, mpu=model.mpu(), dist_init_required=dist_init_required, collate_...
猛猿:图解大模型训练之:流水线并行(Pipeline Parallelism),以Gpipe为例猛猿:图解大模型训练之:数据并行上篇(DP, DDP与ZeRO)猛猿:图解大模型训练之:数据并行下篇(ZeRO,零冗余优化)猛猿:图解大模型系列之:张量模型并行,Megatron-LM猛猿:图解大模型系列之:Megatron源码解读1,分布式环境初始化猛猿:图解大模型训练之:...
DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving...
DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving...
在阅读这个 Tutorial 之前可以先浏览一下0x1节,在本教程中,我们将把ZeRO优化器应用于Megatron-LM GPT-2模型。ZeRO是一组强大的内存优化技术,可以有效地训练具有数万亿参数的大型模型,如GPT-2和Turing-NLG 17B。与其它用于训练大型模型的模型并行方法相比,ZeRO的一个关键优势是不需要对模型代码进行修改。正如本教程将...
Parallel training methods such as ZeRO data parallelism (ZeRO-DP), pipeline parallelism (PP), tensor parallelism (TP) and sequence parallelism (SP) are popular technologies for accelerating LLMs training. However, elastic and flexible composition of these different parallelism topologies with check...
The optimized GPU resources come from using inference-adapted parallelism, which allows users to adapt the model and pipeline parallelism degree from the trained model checkpoints, and shrinking model memory footprint by half with INT8 quantization. As shown in Figure...
(top-1, top-2, noisy, and 32-bit). In addition, we have devised a new technique called “Random Token Selection,” described in more detail in ourtutorial(opens in new tab), which greatly improves convergence, is part of the DeepSpeed library, and is enabled by default so users ...
1. Microsoft Turing-NLG Microsoft has released Turing-NLG using DeepSpeed to infer an optimized version of this largest model. Techniques such as model parallelism and quantization have allowed Microsoft to reduce the inference latency of this huge model by up to 4x. ...