Source code for geoopt.tensor

import torch.nn
from .manifolds import Euclidean, Manifold
from .docutils import insert_docs
from .utils import copy_or_set_

__all__ = ["ManifoldTensor", "ManifoldParameter"]

[docs]class ManifoldTensor(torch.Tensor): """Same as :class:`torch.Tensor` that has information about its manifold. Other Parameters ---------------- manifold : :class:`geoopt.Manifold` A manifold for the tensor, (default: :class:`geoopt.Euclidean`) """ def __new__(cls, *args, manifold=Euclidean(), requires_grad=False, **kwargs): if len(args) == 1 and isinstance(args[0], torch.Tensor): data = args[0].data else: data = torch.Tensor.__new__(cls, *args, **kwargs) if kwargs.get("device") is not None: ="device")) with torch.no_grad(): manifold.assert_check_point(data) instance = torch.Tensor._make_subclass(cls, data, requires_grad) instance.manifold = manifold return instance
[docs] def proj_(self): """ Inplace projection to the manifold. Returns ------- tensor same instance """ return copy_or_set_(self, self.manifold.projx(self))
[docs] @insert_docs(Manifold.retr.__doc__, r"\s+x : .+\n.+", "") def retr(self, u): return self.manifold.retr(self, u=u)
[docs] @insert_docs(Manifold.expmap.__doc__, r"\s+x : .+\n.+", "") def expmap(self, u): return self.manifold.expmap(self, u=u)
[docs] @insert_docs(Manifold.inner.__doc__, r"\s+x : .+\n.+", "") def inner(self, u, v=None): return self.manifold.inner(self, u=u, v=v)
[docs] @insert_docs(Manifold.proju.__doc__, r"\s+x : .+\n.+", "") def proju(self, u): return self.manifold.proju(self, u)
[docs] @insert_docs(Manifold.transp.__doc__, r"\s+x : .+\n.+", "") def transp(self, y, v, *more): return self.manifold.transp(self, y, v, *more)
[docs] @insert_docs(Manifold.retr_transp.__doc__, r"\s+x : .+\n.+", "") def retr_transp(self, u, v, *more): return self.manifold.retr_transp(self, u, v, *more)
[docs] @insert_docs(Manifold.expmap_transp.__doc__, r"\s+x : .+\n.+", "") def expmap_transp(self, u, v, *more): return self.manifold.expmap_transp(self, u, v, *more)
[docs] @insert_docs(Manifold.transp_follow_expmap.__doc__, r"\s+x : .+\n.+", "") def transp_follow_expmap(self, u, v, *more): return self.manifold.transp_follow_expmap(self, u, v, *more)
[docs] @insert_docs(Manifold.transp_follow_retr.__doc__, r"\s+x : .+\n.+", "") def transp_follow_retr(self, u, v, *more): return self.manifold.transp_follow_retr(self, u, v, *more)
[docs] def dist(self, other, p=2): """ Return euclidean or geodesic distance between points on the manifold. Allows broadcasting. Parameters ---------- other : tensor p : str|int The norm to use. The default behaviour is not changed and is just euclidean distance. To compute geodesic distance, :attr:`p` should be set to ``"g"`` Returns ------- scalar """ if p == "g": return self.manifold.dist(self, other) else: return super().dist(other)
[docs] @insert_docs(Manifold.logmap.__doc__, r"\s+x : .+\n.+", "") def logmap(self, y): return self.manifold.logmap(self, y)
def __repr__(self): return "Tensor on {} containing:\n".format( self.manifold ) + torch.Tensor.__repr__(self) # noinspection PyUnresolvedReferences def __reduce_ex__(self, proto): proto = ( self.__class__,, self.storage_offset(), self.size(), self.stride(), self.requires_grad, dict(), ) return _rebuild_manifold_parameter, proto + (self.manifold,)
[docs]class ManifoldParameter(ManifoldTensor, torch.nn.Parameter): """Same as :class:`torch.nn.Parameter` that has information about its manifold. It should be used within :class:`torch.nn.Module` to be recognized in parameter collection. Other Parameters ---------------- manifold : :class:`geoopt.Manifold` (optional) A manifold for the tensor if ``data`` is not a :class:`geoopt.ManifoldTensor` """ def __new__(cls, data=None, manifold=None, requires_grad=True): if data is None: data = ManifoldTensor(manifold=manifold) elif not isinstance(data, ManifoldTensor): data = ManifoldTensor(data, manifold=manifold or Euclidean()) else: if manifold is not None and data.manifold != manifold: raise ValueError( "Manifolds do not match: {}, {}".format(data.manifold, manifold) ) instance = ManifoldTensor._make_subclass(cls, data, requires_grad) instance.manifold = data.manifold return instance def __repr__(self): return "Parameter on {} containing:\n".format( self.manifold ) + torch.Tensor.__repr__(self)
def _rebuild_manifold_parameter(cls, *args): import torch._utils tensor = torch._utils._rebuild_tensor_v2(*args[:-1]) return cls(tensor, manifold=args[-1], requires_grad=args[-3])