Source code for torch.nn.utils.init

import inspect
import torch


def skip_init(module_cls, *args, **kwargs):
    r"""
    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 :mod:`torch.nn.init`).

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

    Args:
        module_cls: Class object; should be a subclass of :class:`torch.nn.Module`
        args: args to pass to the module's constructor
        kwargs: kwargs to pass to the module's constructor

    Returns:
        Instantiated module with uninitialized parameters / buffers

    Example::

        >>> 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]],
               requires_grad=True)
        >>> 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)

    """
    if not issubclass(module_cls, torch.nn.Module):
        raise RuntimeError('Expected a Module; got {}'.format(module_cls))
    if 'device' not in inspect.signature(module_cls).parameters:
        raise RuntimeError('Module must support a \'device\' arg to skip initialization')

    final_device = kwargs.pop('device', 'cpu')
    kwargs['device'] = 'meta'
    return module_cls(*args, **kwargs).to_empty(device=final_device)