ddp_model = DDP(model, device_ids=[rank]) optimizer = optim.AdamW(ddp_model.parameters(), lr=1e-3) # Train for one epoch model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) output = model(data) loss = F....
self.model = DDP(self.model, device_ids=[self.local_rank]) # torch.cuda.amp.GradScaler 是一个用于自动混合精度训练的 PyTorch 工具,它可以帮助加速模型训练并减少显存使用量 # 具体来说,GradScaler 可以将梯度缩放到较小的范围,以避免数值下溢或溢出的问题,同时保持足够的精度以避免模型的性能下降 if self....
device_ids=[local_rank], output_device=local_rank)梯度累加model = DDP(model) for 每次梯度累加...
world_size)model=model.to(rank)ddp_model=DDP(model,device_ids=[rank])optimizer=optim.AdamW(ddp_model.parameters(),lr=1e-3)# Train for one epochmodel.train()forbatch_idx,(data,target)inenumerate(train_loader):data,target=data.to(device),target.to(device)output=model(data)loss=F...
(rank) ddp_model = DDP(model, device_ids=[rank]) # Build optimizer optimizer = optim.AdamW(ddp_model.parameters(), lr=1e-3) # Train for a single epoch model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) ...
enable_pre_and_post_forward = False # model configuration controls: fp8_type = True # toggle to change floating-point precision compile_model = True # toggle to enable model compilation batch_size = 32 if fp8_type else 16 # control batch size device = torch.device('cuda') # use random...
使用pytorch.distributed模块的原生 PyTorch DDP 模块 使用🤗 Accelerate 对pytorch.distributed的轻量封装,确保程序可以在不修改代码或者少量修改代码的情况下在单个 GPU 或 TPU 下正常运行 使用🤗 Transformer 的高级 Trainer API ,该 API 抽象封装了所有代码模板并且支持不同设备和分布式场景。
model=VisionTransformer(embed_dim=1280,depth=32,num_heads=16,).cuda(device)model=DDP(model,device_ids=[local_rank])# define loss and optimizer criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=0.001,momentum=0.9)model.train()t0=time.perf_counter()summ=0coun...
# model configuration controls:fp8_type = True # toggle to change floating-point precisioncompile_model = True # toggle to enable model compilationbatch_size = 32 if fp8_type else 16 # control batch size device = torch.device('cuda') ...
# import configuration file # load json or yaml, argsparse args = xxxxx # 接下来是设置多进程启动的代码 # 1.首先设置端口,采用随机的办法,被占用的概率几乎很低. port_id = 10000 + np.random.randint(0, 1000) args.dist_url = 'tcp://127.0.0.1:' + str(port_id) ...