world size: 1 data parallel size: 1 model parallel size: 1 batch size per GPU: 80 params per gpu: 336.23 M params of model = params per GPU * mp_size: 336.23 M fwd MACs per GPU: 3139.93 G fwd flops per GPU: 6279.86 G fwd flops of model = fwd flops per GPU * mp_size: 6279....
init_inference(model, mp_size=parallel_degree, mpu=mpu, checkpoint=[checkpoint_list], dtype=args.dtype, injection_policy=injection_policy, ) 图2:DeepSpeed 推理管道和管道不同阶段的推理 API 伪代码。 MoQ 可用于量化模型检查点,作为推理前的可选预处理阶段,其中量化配置(包括所需的量化位和计划)通过 ...
world size: 1 data parallel size: 1 model parallel size: 1 batch size per GPU: 80 params per gpu: 336.23 M params of model = params per GPU * mp_size: 336.23 M fwd MACs per GPU: 3139.93 G fwd flops per GPU: 6279.86 G fwd flops of model = fwd flops per GPU * mp_size: 6279....
cmd_args= parser.parse_args()#deepspeed命令行参数dataset= torchvision.datasets.FashionMNIST(root='./dataset', download=True, transform=img_transform)#数据集dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, num_workers=4, shuffle=True)#数据加载器,batch_size应该等于train_batch_size/...
import os import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim from torch.nn.parallel import DistributedDataParallel as DDP def example(rank, world_size): # create default process group dist.init_process_group("gloo",...
batch_size=16, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) ...
transform=transform)testloader= torch.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=2) 2.2 编写模型: importtorch.nn as nnimporttorch.nn.functional as FclassNet(nn.Module):def__init__(self):super(Net, self).__init__()self.conv1= nn.Conv2d(3,6,5)self.pool = ...
mpu – 可选:一个实现以下方法的对象:get_model_parallel_rank/group/world_size 和 get_data_parallel_rank/group/world_size。 deepspeed_config – 可选:当提供DeepSpeed配置JSON文件时,将用于配置DeepSpeed激活检查点。 partition_activations – 可选:启用后在模型并行GPU之间Partitions activation checkpoint。默认...
Setting ds_accelerator tocuda(auto detect)using world size:1and model-parallel size:1>using dynamic loss scaling>initializing model parallelwithsize1PretrainGPT2modelarguments:pretrained_bert...False attention_dropout...0.1num_attention_heads...16hidden_size...1024intermediate_size...None num_layers...
DDP(Distributed Data Parallel)是 PyTorch 提供的一种分布式训练框架。它通过复制模型到多个 GPU 上,并将每个 GPU 分配一个数据批次,从而并行进行训练。每个 GPU 的模型拷贝在训练时会独立计算梯度,然后通过通信操作同步梯度以确保模型一致性。 代码示例