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...
一个完整的模型model1的含义:纵向三刀,把transformer layers的一共12层,切割成了四个部分,每个部分3个layers,其目的是实现pipeline-parallel;而横向的一刀,代表了tensor-parallel,是把1,2,3这三层layers切割成上下两个部分。 把2个完整的模型,纵横切割,然后放入两台dgx-1的一共16张v100的卡里面。 上面是如何切...
from torch.nn.parallel import DistributedDataParallel as DDP def example(rank, world_size): # 创建进程组 dist.init_process_group("gloo", rank=rank, world_size=world_size) # 创建模型 model = nn.Linear(10, 10).to(rank) # 创建DDP模型 ddp_model = DDP(model, device_ids=[rank]) # 定义...
torch.utils.data.TensorDataset: 用于获取封装成 tensor 的数据集,每一个样本都通过索引张量来获得。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 classTensorDataset(Dataset):def__init__(self,*tensor):assertall(tensors[0].size(0)==tensor.size(0)fortensorintensors)self.tensors=tensors def__...
Fully Sharded Data Parallel (FSDP) makes training larger, more advanced AI models more efficiently than ever using fewer GPUs.
Because of this, when I train the ChatGLM-6B, every things is fine; but when I train the ChatGLM2-6B, an error occurs during the model forward pass loss computing: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:3 and cuda:0! (...
与DataParallel不同的是,Distributed Data Parallel会开设多个进程而非线程,进程数 = GPU数,每个进程都可以独立进行训练,也就是说代码的所有部分都会被每个进程同步调用,如果你某个地方print张量,你会发现device的差异
torch.nn.parallel.data_parallel(),importoperatorimporttorchimportwarningsfromitertoolsimportchainfrom..modulesimportModulefrom.scatter_gatherimportscatter_kwargs,gatherfrom.replicateimportreplicatefrom.parallel_applyimportparallel_applyfromtorch.cuda._u
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device=torch.device('cuda:0')model.to(device) 然后,你可以将所有张量复制到GPU: gpu_tensor=cpu_tensor.to(device) 请注意,仅调用cpu_tensor.to(device)会在GPU上返回cpu_tensor的新副本, 而不是重写cpu_tensor。你需要将其分配给新的张量,并在GPU上使用该张量。