Oct 5, 2024 · Fully Sharded Data Parallel (FSDP) is a memory-efficient alternative to DDP that shards the model weights, optimizer states, and gradients across GPUs. Each GPU only holds a subset of the model and processes only the data relevant to that subset.

Nov 12, 2024 · In summary, DataParallel synchronizes parameters among threads, while DistributedDataParallel synchronizes gradients among processes to enable parallel training. How to use it? PyTorch offers a...

Apr 23, 2024 · While Data Parallelism focuses on distributing data across multiple GPUs within a single machine, Distributed Data Parallel extends this paradigm to encompass training across multiple machines.

Sep 18, 2022 · There are two primary types of distributed parallel training: Data Parallelism and model parallelism. We further divide the latter into two subtypes: pipeline parallelism and tensor parallelism. We will cover all distributed parallel training here and demonstrate how to …

Apr 22, 2020 · DataParallel is single-process multi-thread parallelism. It’s basically a wrapper of scatter + paralllel_apply + gather. For model = nn.DataParallel (model, device_ids= [args.gpu]), since it only works on a single device, it’s the same as just using the original model on GPU with id …

Apr 25, 2022 · There are two main branches under distributed training, called data parallelism and model parallelism. In data parallelism, the dataset is split into ‘N’ parts, where ‘N’ is the number of GPUs. These parts are then assigned to parallel computational machines.

In DistributedDataParallel, (DDP) training, each process/ worker owns a replica of the model and processes a batch of data, finally it uses all-reduce to sum up gradients over different workers. In DDP the model weights and optimizer states are replicated across all workers.

Aug 20, 2023 · Huggingface explains FSDP as: sharding the model parameters, gradients, and optimizer states across data parallel processes and it can also offload sharded model parameters to a CPU. And Pipeline parallelism as : split up vertically (layer-level) across multiple GPUs, so that only one or several layers of the model are places on a single gpu.

DistributedDataParallel works with model parallel, while DataParallel does not at this time. When DDP is combined with model parallel, each DDP process would use model parallel, and all processes collectively would use data parallel.

Jan 17, 2024 · In this blog, we will delve deep into some of the most important distributed LLM training patterns such as distributed data parallel (DDP) and Fully sharded data parallel (FSDP). The primary difference between these patterns is based on how the model is split or sharded across GPUs in the system.