import torch import torch_npu import os import torch.distributed as dist def all_gather_func(): rank = int(os.getenv('LOCAL_RANK')) # torch.npu.set_device(rank) dist.init_process_group(backend='hccl', init_method='env://') #,world_size=2 rank=rank, world_size=2, # rank = dist...
您可以从torch.distributed使用all_gather_object。你可以在这里找到文档。基本上，这允许你收集任何可拾取...
🚀 The feature, motivation and pitch could save the effor to create [None, None, ...] (length=world_size) similar collectives are broadcast_object_list, scatter_object_list motivated by PR #118755 Alternatives No response Additional context No response...
all_gather_multigpu(output_tensor_lists, input_tensor_list, group=<object object>, async_op=False) 从列表中收集整个组的张量。tensor_list中的每个张量应位于单独的GPU上。 目前仅支持nccl后端张量应该只是GPU张量。 参数: output_tensor_lists (List_[List[Tensor]__]_) – 输出列表。它应该在每个GPU...
在all_gather()调用之后添加torch.distributed.barrier()调用，以更令人满意的方式解决了这个问题。我没有...
torch.distributed.all_gather(tensor_list, tensor, group=<object object>, async_op=False) 从列表中收集整个组的张量 参数: tensor_list(list[Tensor]) – 输出列表。它应包含正确大小的张量,用于集合的输出。 tensor(Tensor) – 从当前进程广播的张量。
torch.distributed 为分布式训练提供了 DDP、FSDP 这类分布式训练的内置框架,也提供了 all_reduce、broadcast、all_gather、reduce_scatter、all_to_all 这些基础的通信元语,但是在此之前必须先执行 torch.distributed.init_process_group 完成初始化动作。 torch.distributed.init_process_group 以下是训练脚本 train.py...
If you require the old behavior ofxm.rendezvous(i.e. communicating data without altering the XLA graph and/or synchronizing a subset of workers), consider usingtorch.distributed.barrierortorch.distributed.all_gather_objectwith aglooprocess group. If you are also using thexlatorch.distributedbackend...
all_gather(tensor_list, tensor, group=<object object>, async_op=False)[source] Gathers tensors from the whole group in a list. Parameters tensor_list (list[Tensor])– Output list. It should contain correctly-sized tensors to be used for output of the collective. tensor (Tensor)– Tensor...
torch.distributed.all_gather_multigpu(output_tensor_lists, input_tensor_list, group=<object object>, async_op=False) 从列表中的整个组中收集张量。每个张量在tensor_list应该驻留在一个单独的GPUOnly nccl后端,当前支持的张量应该是GPU张量。 参数: output_tensor_lists (List[List[]]) – 输出列表,在每...