torch.nn.utils.skip_init(module_cls, *args, **kwargs)[source]

Given a module class object and args / kwargs, instantiates the module without initializing parameters / buffers. This can be useful if initialization is slow or if custom initialization will be performed, making the default initialization unnecessary. There are some caveats to this, due to the way this function is implemented:

1. The module must accept a device arg in its constructor that is passed to any parameters or buffers created during construction.

2. The module must not perform any computation on parameters in its constructor except initialization (i.e. functions from torch.nn.init).

If these conditions are satisfied, the module can be instantiated with parameter / buffer values uninitialized, as if having been created using torch.empty().

  • module_cls – Class object; should be a subclass of torch.nn.Module

  • args – args to pass to the module’s constructor

  • kwargs – kwargs to pass to the module’s constructor


Instantiated module with uninitialized parameters / buffers


>>> import torch
>>> m = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1)
>>> m.weight
Parameter containing:
tensor([[0.0000e+00, 1.5846e+29, 7.8307e+00, 2.5250e-29, 1.1210e-44]],
>>> m2 = torch.nn.utils.skip_init(torch.nn.Linear, in_features=6, out_features=1)
>>> m2.weight
Parameter containing:
tensor([[-1.4677e+24,  4.5915e-41,  1.4013e-45,  0.0000e+00, -1.4677e+24,
          4.5915e-41]], requires_grad=True)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources