# 需要導入模塊: import torch [as 別名]# 或者: from torch importenable_grad[as 別名]defforward(ctx, estimator_fn, gnet, x, n_power_series, vareps, coeff_fn, training, *g_params):ctx.training = trainingwithtorch.enable_grad(): x = x.detach().requires_grad_(True) g = gnet(x) ct...
enable_grad(): loss = closure() for group in self.param_groups: weight_decay = group['weight_decay'] momentum = group['momentum'] dampening = group['dampening'] nesterov = group['nesterov'] for p in group['params']: if p.grad is None: continue d_p = p.grad if weight_decay !
📚 Documentation The torch.autograd.enable_grad documentation says: Enables gradient calculation inside a no_grad context. This has no effect outside of no_grad. This implies: torch.set_grad_enabled(False) with torch.enable_grad: # Gradie...
t-vi deleted the enable_grad_test_pt2_6 branch December 14, 2024 16:25 Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Reviewers t-vi tfogal mruberry lantiga Assignees No one assigned Labels None yet Projects None yet...
torch.enable_grad 是 torch.autograd.grad_mode.set_grad_enabled 是 torch.is_grad_enabled 是 torch.autograd.grad_mode.inference_mode 是 torch.is_inference_mode_enabled 是 torch.abs 是 支持bf16,fp16,fp32,fp64,uint8,int8,int16,int32,int64,bool ...
The context managers torch.no_grad(), torch.enable_grad(), and torch.set_grad_enabled() are helpful for locally disabling and enabling gradient computation. See...
import torch.cuda import torch.autograd from torch.autograd import no_grad, enable_grad, set_grad_enabled # import torch.fft # TODO: enable once torch.fft() is removed import torch.futures import torch.nn import torch.nn.intrinsic import torch.nn.quantized import torch.optim import torch.optim...
torch.autograd.enable_grad:启动梯度计算的上下文管理器 torch.autograd.no_grad :禁止梯度计算的上下文管理器 torch.autograd.set_ grad. enabled(mode):设置是否进行梯度计算的上下文管 理器。 torch.autograd.Function 每一个原始的自动求导运算实际上是两个在Tensor上运行的函数 ...
# from torchacc.runtime.nn import dropout_add_fused_train,#将Dropout和element-wise的bias add等操作fuse起来ifself.training:# train modewithtorch.enable_grad(): x = dropout_add_fused_train(x, to_add, drop_rate)else:# inference modex = dropout_add_fused(x, to_add, drop_rate) ...
>>> z.requires_grad False 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. classtorch.autograd.enable_grad[source] 上下文管理器,支持梯度计算。如果通过no_grad或set_grad_enabled禁用梯度计算,则启用梯度计算。这个上下文管理器是线程本地的;它不会影响其他线程中的计算。还可以用作装饰器。