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Source code for torch.nn.utils.weight_norm

# mypy: allow-untyped-defs
r"""Weight Normalization from https://arxiv.org/abs/1602.07868."""
from torch.nn.parameter import Parameter, UninitializedParameter
from torch import _weight_norm, norm_except_dim
from typing import Any, TypeVar
from typing_extensions import deprecated
from ..modules import Module

__all__ = ['WeightNorm', 'weight_norm', 'remove_weight_norm']

class WeightNorm:
    name: str
    dim: int

    def __init__(self, name: str, dim: int) -> None:
        if dim is None:
            dim = -1
        self.name = name
        self.dim = dim

    # TODO Make return type more specific
    def compute_weight(self, module: Module) -> Any:
        g = getattr(module, self.name + '_g')
        v = getattr(module, self.name + '_v')
        return _weight_norm(v, g, self.dim)

    @staticmethod
    @deprecated(
        "`torch.nn.utils.weight_norm` is deprecated "
        "in favor of `torch.nn.utils.parametrizations.weight_norm`.",
        category=FutureWarning,
    )
    def apply(module, name: str, dim: int) -> 'WeightNorm':
        for hook in module._forward_pre_hooks.values():
            if isinstance(hook, WeightNorm) and hook.name == name:
                raise RuntimeError(f"Cannot register two weight_norm hooks on the same parameter {name}")

        if dim is None:
            dim = -1

        fn = WeightNorm(name, dim)

        weight = getattr(module, name)
        if isinstance(weight, UninitializedParameter):
            raise ValueError(
                'The module passed to `WeightNorm` can\'t have uninitialized parameters. '
                'Make sure to run the dummy forward before applying weight normalization')
        # remove w from parameter list
        del module._parameters[name]

        # add g and v as new parameters and express w as g/||v|| * v
        module.register_parameter(name + '_g', Parameter(norm_except_dim(weight, 2, dim).data))
        module.register_parameter(name + '_v', Parameter(weight.data))
        setattr(module, name, fn.compute_weight(module))

        # recompute weight before every forward()
        module.register_forward_pre_hook(fn)

        return fn

    def remove(self, module: Module) -> None:
        weight = self.compute_weight(module)
        delattr(module, self.name)
        del module._parameters[self.name + '_g']
        del module._parameters[self.name + '_v']
        setattr(module, self.name, Parameter(weight.data))

    def __call__(self, module: Module, inputs: Any) -> None:
        setattr(module, self.name, self.compute_weight(module))


T_module = TypeVar('T_module', bound=Module)

[docs]def weight_norm(module: T_module, name: str = 'weight', dim: int = 0) -> T_module: r"""Apply weight normalization to a parameter in the given module. .. math:: \mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|} Weight normalization is a reparameterization that decouples the magnitude of a weight tensor from its direction. This replaces the parameter specified by :attr:`name` (e.g. ``'weight'``) with two parameters: one specifying the magnitude (e.g. ``'weight_g'``) and one specifying the direction (e.g. ``'weight_v'``). Weight normalization is implemented via a hook that recomputes the weight tensor from the magnitude and direction before every :meth:`~Module.forward` call. By default, with ``dim=0``, the norm is computed independently per output channel/plane. To compute a norm over the entire weight tensor, use ``dim=None``. See https://arxiv.org/abs/1602.07868 .. warning:: This function is deprecated. Use :func:`torch.nn.utils.parametrizations.weight_norm` which uses the modern parametrization API. The new ``weight_norm`` is compatible with ``state_dict`` generated from old ``weight_norm``. Migration guide: * The magnitude (``weight_g``) and direction (``weight_v``) are now expressed as ``parametrizations.weight.original0`` and ``parametrizations.weight.original1`` respectively. If this is bothering you, please comment on https://github.com/pytorch/pytorch/issues/102999 * To remove the weight normalization reparametrization, use :func:`torch.nn.utils.parametrize.remove_parametrizations`. * The weight is no longer recomputed once at module forward; instead, it will be recomputed on every access. To restore the old behavior, use :func:`torch.nn.utils.parametrize.cached` before invoking the module in question. Args: module (Module): containing module name (str, optional): name of weight parameter dim (int, optional): dimension over which to compute the norm Returns: The original module with the weight norm hook Example:: >>> m = weight_norm(nn.Linear(20, 40), name='weight') >>> m Linear(in_features=20, out_features=40, bias=True) >>> m.weight_g.size() torch.Size([40, 1]) >>> m.weight_v.size() torch.Size([40, 20]) """ WeightNorm.apply(module, name, dim) return module
[docs]def remove_weight_norm(module: T_module, name: str = 'weight') -> T_module: r"""Remove the weight normalization reparameterization from a module. Args: module (Module): containing module name (str, optional): name of weight parameter Example: >>> m = weight_norm(nn.Linear(20, 40)) >>> remove_weight_norm(m) """ for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, WeightNorm) and hook.name == name: hook.remove(module) del module._forward_pre_hooks[k] return module raise ValueError(f"weight_norm of '{name}' not found in {module}")

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