--- DeepSpeed Flops Profiler --- Profile Summary at step 10: Notations: data parallel size (dp_size), model parallel size(mp_size), number of parameters (params), number of multiply-accumulate operations(MACs), number of floating-point operations (flops), floating-point operations per second ...
import torch.nn as nn model = nn.Linear(5, 5) input = torch.randn(16, 5) params = {name: p for name, p in model.named_parameters()} tangents = {name: torch.rand_like(p) for name, p in params.items()} with fwAD.dual_level(): for name, p in params.items(): delattr(mo...
torch.manual_seed(42)# 创建一个模型的实例化对象 model_0=LinearRegressionModel()#检查Parameter(s)list(model_0.parameters())>>>[Parameter containing:tensor([0.3367],requires_grad=True),Parameter containing:tensor([0.1288],requires_grad=True)] 我们还可以使用`.state_dict()`[11]获取模型的状态(模...
Tensor myadd_cpu(const Tensor& self_, const Tensor& other_) { TORCH_CHECK(self_.sizes() == other_.sizes()); TORCH_INTERNAL_ASSERT(self_.device().type() == DeviceType::CPU); TORCH_INTERNAL_ASSERT(other_.device().type() == DeviceType::CPU); Tensor self = self_.contiguous(); Ten...
# Define the loss function and optimizercriterion = nn.CrossEntropyLoss()optimizer = optim.AdamW(model.parameters(), lr=5e-6) # Training loopnum_epochs = 25 # Number of epochs to train for for epoch in tqdm(range(num_epochs)): # loop...
device parameters have been replaced with npu in the function below: torch.logspace, torch.randint torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch....
# Define the loss functionandoptimizercriterion=nn.CrossEntropyLoss()optimizer=optim.AdamW(model.parameters(),lr=5e-6) # Training loopnum_epochs=25 # Number of epochstotrainforforepochintqdm(range(num_epochs)): # loop over the dataset multiple timestrain_loss=train(model, tokenizer, train_load...
find_unused_parameters = find_unused_parameters self.require_backward_grad_sync = True self.require_forward_param_sync = True self.ddp_join_enabled = False self.gradient_as_bucket_view = gradient_as_bucket_view if check_reduction: # This argument is no longer used since the reducer # will ...
unused_parameters=False, check_reduction=False)将给定的module进行分布式封装， 其将输入在batch...
importtorch.optimasopt learning_rate =0.001optimizer = opt.Adam(model.parameters(), lr=learning_rate) 提示 有关PyTorch 中可用优化器的详细信息,请参阅 PyTorch 文档中的算法。 创建训练和测试函数 定义网络并为其准备数据后,可以使用数据来训练和测试模型,方法是通过网络传递训练数据、计算损失、优化网络权重和...