chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
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+"""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
+
+
+class EdgePredictor(nn.Module):
+    r"""Predictor/score function for pairs of node representations
+
+    Given a pair of node representations, :math:`h_i` and :math:`h_j`, it combines them with
+
+    **dot product**
+
+    .. math::
+
+        h_i^{T} h_j
+
+    or **cosine similarity**
+
+    .. math::
+
+        \frac{h_i^{T} h_j}{{\| h_i \|}_2 \cdot {\| h_j \|}_2}
+
+    or **elementwise product**
+
+    .. math::
+
+        h_i \odot h_j
+
+    or **concatenation**
+
+    .. math::
+
+        h_i \Vert h_j
+
+    Optionally, it passes the combined results to a linear layer for the final prediction.
+
+    Parameters
+    ----------
+    op : str
+        The operation to apply. It can be 'dot', 'cos', 'ele', or 'cat',
+        corresponding to the equations above in order.
+    in_feats : int, optional
+        The input feature size of :math:`h_i` and :math:`h_j`. It is required
+        only if a linear layer is to be applied.
+    out_feats : int, optional
+        The output feature size. It is reuiqred only if a linear layer is to be applied.
+    bias : bool, optional
+        Whether to use bias for the linear layer if it applies.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> import torch as th
+    >>> from dgl.nn import EdgePredictor
+    >>> num_nodes = 2
+    >>> num_edges = 3
+    >>> in_feats = 4
+    >>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
+    >>> h = th.randn(num_nodes, in_feats)
+    >>> src, dst = g.edges()
+    >>> h_src = h[src]
+    >>> h_dst = h[dst]
+
+    Case1: dot product
+
+    >>> predictor = EdgePredictor('dot')
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 1])
+    >>> predictor = EdgePredictor('dot', in_feats, out_feats=3)
+    >>> predictor.reset_parameters()
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 3])
+
+    Case2: cosine similarity
+
+    >>> predictor = EdgePredictor('cos')
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 1])
+    >>> predictor = EdgePredictor('cos', in_feats, out_feats=3)
+    >>> predictor.reset_parameters()
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 3])
+
+    Case3: elementwise product
+
+    >>> predictor = EdgePredictor('ele')
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 4])
+    >>> predictor = EdgePredictor('ele', in_feats, out_feats=3)
+    >>> predictor.reset_parameters()
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 3])
+
+    Case4: concatenation
+
+    >>> predictor = EdgePredictor('cat')
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 8])
+    >>> predictor = EdgePredictor('cat', in_feats, out_feats=3)
+    >>> predictor.reset_parameters()
+    >>> predictor(h_src, h_dst).shape
+    torch.Size([3, 3])
+    """
+
+    def __init__(self, op, in_feats=None, out_feats=None, bias=False):
+        super(EdgePredictor, self).__init__()
+
+        assert op in [
+            "dot",
+            "cos",
+            "ele",
+            "cat",
+        ], "Expect op to be in ['dot', 'cos', 'ele', 'cat'], got {}".format(op)
+        self.op = op
+        if (in_feats is not None) and (out_feats is not None):
+            if op in ["dot", "cos"]:
+                in_feats = 1
+            elif op == "cat":
+                in_feats = 2 * in_feats
+            self.linear = nn.Linear(in_feats, out_feats, bias=bias)
+        else:
+            self.linear = None
+
+    def reset_parameters(self):
+        r"""
+
+        Description
+        -----------
+        Reinitialize learnable parameters.
+        """
+        if self.linear is not None:
+            self.linear.reset_parameters()
+
+    def forward(self, h_src, h_dst):
+        r"""
+
+        Description
+        -----------
+        Predict for pairs of node representations.
+
+        Parameters
+        ----------
+        h_src : torch.Tensor
+            Source node features. The tensor is of shape :math:`(E, D_{in})`,
+            where :math:`E` is the number of edges/node pairs, and :math:`D_{in}`
+            is the input feature size.
+        h_dst : torch.Tensor
+            Destination node features. The tensor is of shape :math:`(E, D_{in})`,
+            where :math:`E` is the number of edges/node pairs, and :math:`D_{in}`
+            is the input feature size.
+
+        Returns
+        -------
+        torch.Tensor
+            The output features.
+        """
+        if self.op == "dot":
+            N, D = h_src.shape
+            h = torch.bmm(h_src.view(N, 1, D), h_dst.view(N, D, 1)).squeeze(-1)
+        elif self.op == "cos":
+            h = F.cosine_similarity(h_src, h_dst).unsqueeze(-1)
+        elif self.op == "ele":
+            h = h_src * h_dst
+        else:
+            h = torch.cat([h_src, h_dst], dim=-1)
+
+        if self.linear is not None:
+            h = self.linear(h)
+
+        return h