all_gather 函数定义 其中tensor_list,是list,大小是word_size,每个元素为了是gather后,保存每个rank的数据,所以初始化一般使用torch.empty;tensor代表各rank中的tensor数据,其中tensor_list每个分量的维度要与对应的tensor参数中每个rank的维度相同。 API文档链接:torch.distributed
The bug has not been fixed in the latest version. Describe the bug When I using torch.distribute.all_gather to get all feature from all gpu, all processors are stuck, and all gpu and cpu are 100% and there are no errors and warnings, when I delete this function, all processors are n...
all_gather_into_tensor = torch.distributed._all_gather_base AttributeError: module 'torch.distributed' has no attribute '_all_gather_base' 解决方法 注释下面的代码: if "reduce_scatter_tensor" not in dir(torch.distributed): torch.distributed.reduce_scatter_tensor = torch.distributed._reduce_...
也许它们的意思是阻塞，如notasync;与torch.distributed不同。
Pytorch distributed 概述 本节我们介绍一下torch.distributed Pytorch 分布式库主要包含一套并行的模块,一个通信层,以及对于运行和debug大规模训练的infra 主要有以下四个并行的apis: DDP(分布式数据并行) FSDP (fully sharded data-parallel training) Tensor parallel(tp) ...
AttributeError: module 'torch.distributed' has no attribute '_all_gather_base' my version is python 3.8.13 torch 1.7.1+cu110 pypi_0 pypi torchaudio 0.7.2 pypi_0 pypi torchvision 0.8.2+cu110 pypi_0 pypi tqdm 4.64.1 that pytorch is a bit too old for the current master branch of this...
_1d_equal_chunks File "/home/ailab/anaconda3/envs/yy_FAFS/lib/python3.8/site-packages/apex/transformer/utils.py", line 11, in <module> torch.distributed.all_gather_into_tensor = torch.distributed._all_gather_base AttributeError: module 'torch.distributed' has no attribute '_all_...
pytorch 如何在torch.distributed中收集非Tensor对象？您可以从torch.distributed使用all_gather_object。你...
所述torch.distributed包提供跨在一个或多个计算机上运行的几个计算节点对多进程并行PyTorch支持与通信原语。该类torch.nn.parallel.DistributedDataParallel()基于此功能,提供同步分布式培训作为围绕任何PyTorch模型的包装器。这不同于所提供的类型的并行的 :模块:torch.multiprocessing和torch.nn.DataParallel()在它支持多个...
torch.distributed.all_gather_multigpu(output_tensor_lists, input_tensor_list, group=, async_op=False)[source] torch.distributed.reduce_scatter_multigpu(output_tensor_list, input_tensor_lists, op=ReduceOp.SUM, group=, async_op=False)[source] ...