:rank]weight_low=U_low @ S_low @ V_low.t()layer.weight.data=weight_low# 获取分解后的输出withtorch.no_grad():output_after=model(input_ids=input_ids,attention_mask=attention_mask)# 比较分解前后的输出print("Output before decomposition:",output_before)print("Output after decomposition...
self.hidden_size_per_attention_head)tensor=tensor.view(*new_tensor_shape)returntensor.permute(0,2,1,3)# Context layer.# [b, np, s, hn]context_layer=torch.matmul(attention
device = torch.device("cuda", local_rank) model = nn.Linear(10, 10).to(device) # 新增:构造DDP model ddp_model = DDP(model, device_ids=[local_rank], output_device=local_rank) # 前向传播 outputs = ddp_model(torch.randn(20, 10).to(rank)) labels = torch.randn(20, 10).to(rank...
torch.cuda.set_device(args.local_rank) 1. find_unused_parameters=True 这个是为了解决你的模型中定义了一些在forward函数中没有用到的网络层,会被视为“unused_layer”,这会引发错误,所以你在使用 DistributedDataParallel 包装模型的时候,传一个find_unused_parameters=True...
运行 accelerate config 命令后得到的 FSDP 配置示例如下:compute_environment: LOCAL_MACHINEdeepspeed_config: {}distributed_type: FSDPfsdp_config: min_num_params: 2000 offload_params: false sharding_strategy: 1machine_rank: 0main_process_ip: nullmain_process_port: nullmain_training_function: main...
#This function takes the layer as the input and sets the features_in.features_out #equal to the shape of the weight matrix. This will help the LoRA class to #initialize the A and B Matrices def layer_parametrization(layer, device, rank = 1, lora_alpha = 1): ...
每个独立的进程也要知道总共的进程数,以及自己在所有进程中的阶序(rank),当然也要知道自己要用那张GPU。总进程数称之为 world size。最后,每个进程都需要知道要处理的数据的哪一部分,这样批处理就不会重叠。而Pytorch通过 nn.utils.data.DistributedSampler 来实现这种效果。
device=torch.device('cuda:{}'.format(args.local_rank)) net=net.to(device) 定义优化器,损失函数,定义优化器一定要把模型搬运到GPU之上 apt= Adam([{'params':params_low_lr,'lr':4e-5}, {'params':params_high_lr,'lr':1e-4}],weight_decay=settings.WEIGHT_DECAY) ...
(args.local_rank))net=net.to(device)定义优化器,损失函数,定义优化器一定要把模型搬运到GPU之上apt = Adam([{'params':params_low_lr,'lr':4e-5},{'params':params_high_lr,'lr':1e-4}],weight_decay=settings.WEIGHT_DECAY)crit = nn.BCELoss().to(device)多GPU设置import torch.nn.parallel....
Low-rank Adapters (LoRA) LoRA的finetune方式是我们冻结直接模型的权重参数,那如何使模型适配下有任务或场景呢? LoRA的论文提出用一个小的映射模块,我们可以理解成添加一个bias来做适配: 也是上图公式中,我们不改权重weight W,而是把公式改成添加了L1和L2,他们的维度要远小于W的维度,从而只需要update很少的参数...