它支持多种并行策略,包括张量并行(Tensor Parallelism)、流水线并行(Pipeline Parallelism)和数据并行(Data Parallelism)。DeepSpeed 的 ZeRO 优化器通过零冗余设计显著减少了内存占用,同时支持混合精度训练。 2. Megatron-LM Megatron-LM 是 NVIDIA 推出的分布式训练框架,专为训练超大规模语言模型设计。它在张量并行方面...
Megatron-LM NVIDIA Megatron-LM 是一个基于 PyTorch 的分布式训练框架,用来训练基于Transformer的大型语言模型。Megatron-LM 综合应用了数据并行(Data Parallelism),张量并行(Tensor Parallelism)和流水线并行(Pipeline Parallelism)。很多大模型的训练过程都采用它,例如bloom、opt、智源等。 torch.distributed(dist) 为运行...
MegatronLM的第一篇论文【Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism】是2020年出的,针对billion级别的模型进行训练,例如具有38亿参数的类GPT-2的transformer模型和具有39亿参数的BERT模型。 分布式训练的模型并行有两种方式,一种是层间并行(inter-layer),也就是Pipeline流水...
Tensor parallelismis a type of model parallelism in which specific model weights, gradients, and optimizer states are split across devices. In contrast to pipeline parallelism, which keeps individual weights intact but partitions thesetof weights, tensor parallelism splits individual weights. This typica...
Tensor parallelism is a type of model parallelism in which specific model weights, gradients, and optimizer states are split across devices. In contrast to pipeline parallelism, which keeps individual weights intact but partitions the set of weights, gradients, or optimizer across devices, tensor para...
tensor parallelism Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism 张量并行,模型并行的一种,是把模型同一层之间纵向切割,将参数分割到不同的GPU上去计算。比如说pipeline是把self attention和feed forward切开分别训练,那tensor就是把self attention那一层的多个注意力头切开去分别...
Hi, thanks! I use vllm to inference the llama-7B model on single gpu, and tensor-parallel on 2-gpus and 4-gpus, we found that it is 10 times faster than HF on a single GPU, but using tensor parallelism, there is no significant increase i...
transformersmoedata-parallelismdistributed-optimizersmodel-parallelismmegatronmixture-of-expertspipeline-parallelismhuggingface-transformersmegatron-lmtensor-parallelismlarge-scale-language-modeling3d-parallelismzero-1sequence-parallelism UpdatedDec 14, 2023 Python ...
Deep learning (DL) jobs use multi-dimensional parallelism, i.e. combining data, model, and pipeline parallelism, to use large GPU clusters efficiently. Long-running jobs may experience changes to their GPU allocation: (i) resource elasticity during training adds or removes GPUs; (ii) hardware ...
由于LLM的模型规模通常在数十亿到数千亿参数之间,远远超出了单个GPU的内存和计算能力,分布式训练需要使用数百到数千个GPU协同工作。 在LLM的Transformer训练中,已经出现了三种主要的分布式训练范式:数据并行(Data Parallelism, DP)、张量并行(Tensor Parallelism, TP)和流水线并行(Pipeline Parallelism, PP)。