2048), nn.ReLU(), nn.Linear(2048, 2048))segment2 = nn.Sequential( nn.Linear(2048, 2048), nn.ReLU(), nn.Linear(2048, 1024))# Combine the segments using Pipe# The placement of modules across devices is handled by Pipe
DeviceMesh是一种抽象表示全局拓扑关系。 图1 DeviceMesh DTensor placement: DTensor placement是tensor的分布式表示方式,有两种类型:shard, replicate。 图2 DTensor placement DTensor是torch.Tensor的子类,可以通过from_local和to_local和tensor进行转化,也可以通过Reshard和Redistribute进行分布式转换。提供高阶API,转...
核心概念是DTensor，即分布式张量，用于张量级别的分割和运算。DTensor包含两个关键概念：DeviceMesh和DTensor placement。DeviceMesh表示全局拓扑关系，DTensor placement定义张量的分布式表示方式，有shard和replicate两种类型。DTensor是torch.Tensor的子类，能通过from_local和to_local进行转换，同时支持Reshard和...
Move optimizer creation after device placement for ddp backends. #2904 Merged 7 tasks Contributor Author PhilJd commented Aug 10, 2020 Done :) ️ 1 ananyahjha93 added discussion feature labels Aug 10, 2020 williamFalcon closed this as completed in #2904 Aug 12, 2020 Borda removed...
if use_ddp: model = model.module.model device = "cuda" if torch.cuda.is_available() else "cpu" device_mesh = DeviceMesh(device, torch.arange(0, NUM_DEVICES)) coordiator = device_mesh.get_coordinate() # Parallelize the embedding submodules. ...
Fixed logger creating directory structure too early in DDP (#6380) Fixed DeepSpeed additional memory use on rank 0 when default device not set early enough (#6460) Fixed an issue with Tuner.scale_batch_size not finding the batch size attribute in the datamodule (#5968) Fixed an exception in...
Fixed logger creating directory structure too early in DDP (#6380) Fixed DeepSpeed additional memory use on rank 0 when default device not set early enough (#6460) Fixed an issue with Tuner.scale_batch_size not finding the batch size attribute in the datamodule (#5968) Fixed an exception in...
class PlacementSpec(object): pass class DevicePlacement(PlacementSpec): # Device wher...
DDP 通过 RPC 访问 PS 上的数据。 2-3 directions: google, 微软,single program, multiple device. 每个 program 持有 a shard of the model. 不需要 RPC,只需要 collective communication AWS: PS。try to prove that,PS can be as faster as collective communication. ...
We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment accelerator = Accelerator(log_with="all", logging_dir=args.output...