def interaction_function(
h: torch.FloatTensor,
r: torch.FloatTensor,
t: torch.FloatTensor,
m_r: torch.FloatTensor,
) -> torch.FloatTensor:
"""Evaluate the interaction function for given embeddings.
The embeddings have to be in a broadcastable shape.
:param h: shape: (batch_size, num_entities, d_e)
Head embeddings.
:param r: shape: (batch_size, num_entities, d_r)
Relation embeddings.
:param t: shape: (batch_size, num_entities, d_e)
Tail embeddings.
:param m_r: shape: (batch_size, num_entities, d_e, d_r)
The relation specific linear transformations.
:return: shape: (batch_size, num_entities)
The scores.
"""
# project to relation specific subspace, shape: (b, e, d_r)
h_bot = h @ m_r
t_bot = t @ m_r
# ensure constraints
h_bot = clamp_norm(h_bot, p=2, dim=-1, maxnorm=1.0)
t_bot = clamp_norm(t_bot, p=2, dim=-1, maxnorm=1.0)
# evaluate score function, shape: (b, e)
return -linalg.vector_norm(h_bot + r - t_bot, dim=-1)**2
def score_h(self, rt_batch: torch.LongTensor, slice_size: Optional[int] = None) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=None)
r = self.relation_embeddings(indices=rt_batch[:, 0])
t = self.entity_embeddings(indices=rt_batch[:, 1])
# TODO: Use torch.cdist (see note above in score_hrt())
return -linalg.vector_norm(h[None, :, :] + (r[:, None, :] - t[:, None, :]), dim=-1, ord=self.scoring_fct_norm)
def score_t(self, hr_batch: torch.LongTensor,
**kwargs) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=hr_batch[:, 0])
r = self.relation_embeddings(indices=hr_batch[:, 1])
t = self.entity_embeddings(indices=None)
# TODO: Use torch.cdist (see note above in score_hrt())
return -linalg.vector_norm(
h[:, None, :] + r[:, None, :] - t[None, :, :],
dim=-1,
ord=self.scoring_fct_norm)
def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=hrt_batch[:, 0])
r = self.relation_embeddings(indices=hrt_batch[:, 1])
t = self.entity_embeddings(indices=hrt_batch[:, 2])
# TODO: Use torch.cdist
# There were some performance/memory issues with cdist, cf.
# https://github.com/pytorch/pytorch/issues?q=cdist however, @mberr thinks
# they are mostly resolved by now. A Benefit would be that we can harness the
# future (performance) improvements made by the core torch developers. However,
# this will require some benchmarking.
return -linalg.vector_norm(h + r - t, dim=-1, ord=self.scoring_fct_norm, keepdim=True)
def score_hrt(self, hrt_batch: torch.LongTensor,
**kwargs) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=hrt_batch[:, 0])
d_r = self.relation_embeddings(indices=hrt_batch[:, 1])
w_r = self.normal_vector_embeddings(indices=hrt_batch[:, 1])
t = self.entity_embeddings(indices=hrt_batch[:, 2])
# Project to hyperplane
ph = h - torch.sum(w_r * h, dim=-1, keepdim=True) * w_r
pt = t - torch.sum(w_r * t, dim=-1, keepdim=True) * w_r
# Regularization term
self.regularize_if_necessary()
return -linalg.vector_norm(ph + d_r - pt, ord=2, dim=-1, keepdim=True)
def update(self, *tensors: torch.FloatTensor) -> None: # noqa: D102
if len(tensors) != 3:
raise KeyError("Expects exactly three tensors")
if self.apply_only_once and self.updated:
return
entity_embeddings, normal_vector_embeddings, relation_embeddings = tensors
# Entity soft constraint
self.regularization_term += torch.sum(functional.relu(linalg.vector_norm(entity_embeddings, dim=-1) ** 2 - 1.0))
# Orthogonality soft constraint
d_r_n = functional.normalize(relation_embeddings, dim=-1)
self.regularization_term += torch.sum(
functional.relu(torch.sum((normal_vector_embeddings * d_r_n) ** 2, dim=-1) - self.epsilon),
)
self.updated = True
def score_h(self, rt_batch: torch.LongTensor,
**kwargs) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=None)
rel_id = rt_batch[:, 0]
d_r = self.relation_embeddings(indices=rel_id)
w_r = self.normal_vector_embeddings(indices=rel_id)
t = self.entity_embeddings(indices=rt_batch[:, 1])
# Project to hyperplane
ph = h[None, :, :] - torch.sum(w_r[:, None, :] * h[None, :, :],
dim=-1,
keepdim=True) * w_r[:, None, :]
pt = t - torch.sum(w_r * t, dim=-1, keepdim=True) * w_r
# Regularization term
self.regularize_if_necessary()
return -linalg.vector_norm(
ph + (d_r[:, None, :] - pt[:, None, :]), ord=2, dim=-1)
def score_t(self,
hr_batch: torch.LongTensor,
slice_size: Optional[int] = None
) -> torch.FloatTensor: # noqa: D102
# Get embeddings
h = self.entity_embeddings(indices=hr_batch[:, 0])
d_r = self.relation_embeddings(indices=hr_batch[:, 1])
w_r = self.normal_vector_embeddings(indices=hr_batch[:, 1])
t = self.entity_embeddings(indices=None)
# Project to hyperplane
ph = h - torch.sum(w_r * h, dim=-1, keepdim=True) * w_r
pt = t[None, :, :] - torch.sum(w_r[:, None, :] * t[None, :, :],
dim=-1,
keepdim=True) * w_r[:, None, :]
# Regularization term
self.regularize_if_necessary()
return -linalg.vector_norm(
ph[:, None, :] + d_r[:, None, :] - pt, ord=2, dim=-1)
def interaction_function(
h: torch.FloatTensor,
r: torch.FloatTensor,
t: torch.FloatTensor,
) -> torch.FloatTensor:
"""Evaluate the interaction function of ComplEx for given embeddings.
The embeddings have to be in a broadcastable shape.
WARNING: No forward constraints are applied.
:param h: shape: (..., e, 2)
Head embeddings. Last dimension corresponds to (real, imag).
:param r: shape: (..., e, 2)
Relation embeddings. Last dimension corresponds to (real, imag).
:param t: shape: (..., e, 2)
Tail embeddings. Last dimension corresponds to (real, imag).
:return: shape: (...)
The scores.
"""
# Decompose into real and imaginary part
h_re = h[..., 0]
h_im = h[..., 1]
r_re = r[..., 0]
r_im = r[..., 1]
# Rotate (=Hadamard product in complex space).
rot_h = torch.stack(
[
h_re * r_re - h_im * r_im,
h_re * r_im + h_im * r_re,
],
dim=-1,
)
# Workaround until https://github.com/pytorch/pytorch/issues/30704 is fixed
diff = rot_h - t
scores = -linalg.vector_norm(diff, dim=(-2, -1))
return scores
