[NN] TransE and TransR (#3530)

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Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

docs/source/api/python/nn.pytorch.rst

@@ -281,6 +281,20 @@ EdgePredictor
     :members: forward, reset_parameters
     :show-inheritance:
 
+TransE
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.pytorch.link.TransE
+    :members: rel_emb, forward, reset_parameters
+    :show-inheritance:
+
+TransR
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.pytorch.link.TransR
+    :members: rel_emb, rel_project, forward, reset_parameters
+    :show-inheritance:
+
 Heterogeneous Graph Convolution Module
 ----------------------------------------
 

python/dgl/nn/pytorch/__init__.py

 """Package for pytorch-specific NN modules."""
 from .conv import *
 from .explain import *
+from .link import *
 from .glob import *
 from .softmax import *
 from .factory import *
 from .hetero import *
 from .utils import Sequential, WeightBasis, JumpingKnowledge
 from .sparse_emb import NodeEmbedding
-from .link import *

python/dgl/nn/pytorch/link/__init__.py

+"""Torch modules for link prediction/knowledge graph completion."""
+
+from .edgepred import EdgePredictor
+from .transe import TransE
+from .transr import TransR

python/dgl/nn/pytorch/link.py → python/dgl/nn/pytorch/link/edgepred.py

-"""Torch modules for link prediction."""
+"""Predictor for edges in homogeneous graphs."""
 # pylint: disable= no-member, arguments-differ, invalid-name, W0235
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-__all__ = ['EdgePredictor']
-
 class EdgePredictor(nn.Module):
     r"""
 

python/dgl/nn/pytorch/link/transe.py

+"""TransE."""
+# pylint: disable= no-member, arguments-differ, invalid-name, W0235
+import torch
+import torch.nn as nn
+
+class TransE(nn.Module):
+    r"""
+
+    Description
+    -----------
+    Similarity measure introduced in `Translating Embeddings for Modeling Multi-relational Data
+    <https://papers.nips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html>`__.
+    Mathematically, it is defined as follows:
+
+    .. math::
+
+        - {\| h + r - t \|}_p
+
+    where :math:`h` is the head embedding, :math:`r` is the relation embedding, and
+    :math:`t` is the tail embedding.
+
+    Parameters
+    ----------
+    num_rels : int
+        Number of relation types.
+    feats : int
+        Embedding size.
+    p : int, optional
+        The p to use for Lp norm, which can be 1 or 2.
+
+    Attributes
+    ----------
+    rel_emb : torch.nn.Embedding
+        The learnable relation type embedding.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> import torch as th
+    >>> from dgl.nn import TransE
+
+    >>> # input features
+    >>> num_nodes = 10
+    >>> num_edges = 30
+    >>> num_rels = 3
+    >>> feats = 4
+
+    >>> scorer = TransE(num_rels=num_rels, feats=feats)
+    >>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
+    >>> src, dst = g.edges()
+    >>> h = th.randn(num_nodes, feats)
+    >>> h_head = h[src]
+    >>> h_tail = h[dst]
+    >>> # Randomly initialize edge relation types for demonstration
+    >>> rels = th.randint(low=0, high=num_rels, size=(num_edges,))
+    >>> scorer(h_head, h_tail, rels).shape
+    torch.Size([30])
+    """
+    def __init__(self, num_rels, feats, p=1):
+        super(TransE, self).__init__()
+
+        self.rel_emb = nn.Embedding(num_rels, feats)
+        self.p = p
+
+    def reset_parameters(self):
+        r"""
+
+        Description
+        -----------
+        Reinitialize learnable parameters.
+        """
+        self.rel_emb.reset_parameters()
+
+    def forward(self, h_head, h_tail, rels):
+        r"""
+
+        Description
+        -----------
+        Score triples.
+
+        Parameters
+        ----------
+        h_head : torch.Tensor
+            Head entity features. The tensor is of shape :math:`(E, D)`, where
+            :math:`E` is the number of triples, and :math:`D` is the feature size.
+        h_tail : torch.Tensor
+            Tail entity features. The tensor is of shape :math:`(E, D)`, where
+            :math:`E` is the number of triples, and :math:`D` is the feature size.
+        rels : torch.Tensor
+            Relation types. It is a LongTensor of shape :math:`(E)`, where
+            :math:`E` is the number of triples.
+
+        Returns
+        -------
+        torch.Tensor
+            The triple scores. The tensor is of shape :math:`(E)`.
+        """
+        h_rel = self.rel_emb(rels)
+
+        return - torch.norm(h_head + h_rel - h_tail, p=self.p, dim=-1)

python/dgl/nn/pytorch/link/transr.py

+"""TransR."""
+# pylint: disable= no-member, arguments-differ, invalid-name, W0235
+import torch
+import torch.nn as nn
+
+class TransR(nn.Module):
+    r"""
+
+    Description
+    -----------
+    Similarity measure introduced in
+    `Learning entity and relation embeddings for knowledge graph completion
+    <https://ojs.aaai.org/index.php/AAAI/article/view/9491>`__. Mathematically,
+    it is defined as follows:
+
+    .. math::
+
+        - {\| M_r h + r - M_r t \|}_p
+
+    where :math:`M_r` is a relation-specific projection matrix, :math:`h` is the
+    head embedding, :math:`r` is the relation embedding, and :math:`t` is the tail embedding.
+
+    Parameters
+    ----------
+    num_rels : int
+        Number of relation types.
+    rfeats : int
+        Relation embedding size.
+    nfeats : int
+        Entity embedding size.
+    p : int, optional
+        The p to use for Lp norm, which can be 1 or 2.
+
+    Attributes
+    ----------
+    rel_emb : torch.nn.Embedding
+        The learnable relation type embedding.
+    rel_project : torch.nn.Embedding
+        The learnable relation-type-specific projection.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> import torch as th
+    >>> from dgl.nn import TransR
+
+    >>> # input features
+    >>> num_nodes = 10
+    >>> num_edges = 30
+    >>> num_rels = 3
+    >>> feats = 4
+
+    >>> scorer = TransE(num_rels=num_rels, rfeats=2, nfeats=feats)
+    >>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
+    >>> src, dst = g.edges()
+    >>> h = th.randn(num_nodes, feats)
+    >>> h_head = h[src]
+    >>> h_tail = h[dst]
+    >>> # Randomly initialize edge relation types for demonstration
+    >>> rels = th.randint(low=0, high=num_rels, size=(num_edges,))
+    >>> scorer(h_head, h_tail, rels).shape
+    torch.Size([30])
+    """
+    def __init__(self, num_rels, rfeats, nfeats, p=1):
+        super(TransR, self).__init__()
+
+        self.rel_emb = nn.Embedding(num_rels, rfeats)
+        self.rel_project = nn.Embedding(num_rels, nfeats * rfeats)
+        self.rfeats = rfeats
+        self.nfeats = nfeats
+        self.p = p
+
+    def reset_parameters(self):
+        r"""
+
+        Description
+        -----------
+        Reinitialize learnable parameters.
+        """
+        self.rel_emb.reset_parameters()
+        self.rel_project.reset_parameters()
+
+    def forward(self, h_head, h_tail, rels):
+        r"""
+        Score triples.
+
+        Parameters
+        ----------
+        h_head : torch.Tensor
+            Head entity features. The tensor is of shape :math:`(E, D)`, where
+            :math:`E` is the number of triples, and :math:`D` is the feature size.
+        h_tail : torch.Tensor
+            Tail entity features. The tensor is of shape :math:`(E, D)`, where
+            :math:`E` is the number of triples, and :math:`D` is the feature size.
+        rels : torch.Tensor
+            Relation types. It is a LongTensor of shape :math:`(E)`, where
+            :math:`E` is the number of triples.
+
+        Returns
+        -------
+        torch.Tensor
+            The triple scores. The tensor is of shape :math:`(E)`.
+        """
+        h_rel = self.rel_emb(rels)
+        proj_rel = self.rel_project(rels).reshape(-1, self.nfeats, self.rfeats)
+        h_head = (h_head.unsqueeze(1) @ proj_rel).squeeze(1)
+        h_tail = (h_tail.unsqueeze(1) @ proj_rel).squeeze(1)
+
+        return - torch.norm(h_head + h_rel - h_tail, p=self.p, dim=-1)

tests/pytorch/test_nn.py

@@ -1323,6 +1323,26 @@ def test_edge_predictor(op):
     pred = nn.EdgePredictor(op, in_feats, out_feats, bias=True).to(ctx)
     assert pred(h_src, h_dst).shape == (num_pairs, out_feats)
 
+
+def test_ke_score_funcs():
+    ctx = F.ctx()
+    num_edges = 30
+    num_rels = 3
+    nfeats = 4
+
+    h_src = th.randn((num_edges, nfeats)).to(ctx)
+    h_dst = th.randn((num_edges, nfeats)).to(ctx)
+    rels = th.randint(low=0, high=num_rels, size=(num_edges,)).to(ctx)
+
+    score_func = nn.TransE(num_rels=num_rels, feats=nfeats).to(ctx)
+    score_func.reset_parameters()
+    score_func(h_src, h_dst, rels).shape == (num_edges)
+
+    score_func = nn.TransR(num_rels=num_rels, rfeats=nfeats - 1, nfeats=nfeats).to(ctx)
+    score_func.reset_parameters()
+    score_func(h_src, h_dst, rels).shape == (num_edges)
+
+
 def test_twirls(): 
     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
     feat = th.ones(6, 10)