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:...
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_...
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...
Following is an example of Pipeline Parallelism with DeepSpeed# Model partitioning for pipeline parallelism model = deepspeed.init_inference(model, mp_size=4, dtype=torch.float16, pipeline_parallel=True) 4. Tensor SlicingTensor slicing helps fit the model onto hardware with limited memory by slicing...
DeepSpeed Tutorial - Learn how to optimize deep learning training with DeepSpeed. Discover its features, installation process, and performance enhancements for large models.
本文基于DeepSpeedExamples仓库中给出的Megatron相关例子探索一下训练GPT2模型的流程。主要包含3个部分,第一个部分是基于原始的Megatron如何训练GPT2模型,第二个部分是如何结合DeepSpeed的特性进行训练Megatron GPT2,由于篇幅原因这篇文章只写了第一部分,主要是非常细致的记录了跑起来Megatron GPT2训练流程碰到的一些问题和...
Pipeline Parallelism 3D Parallelism The Zero Redundancy Optimizer (ZeRO) Optimizer State and Gradient Partitioning Activation Partitioning Constant Buffer Optimization Contiguous Memory Optimization ZeRO-Offload Leverage both CPU/GPU memory for model training Support 10B model training on a single GPU Ultr...
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...
(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 ...