[Feature] Support canonical edge types in HeteroGraphConv (#4440)

* fix

* Update hetero.py

* why did i remove this
Co-authored-by: Xin Yao <xiny@nvidia.com>

python/dgl/nn/pytorch/hetero.py

@@ -122,7 +122,13 @@ class HeteroGraphConv(nn.Module):
     """
     def __init__(self, mods, aggregate='sum'):
         super(HeteroGraphConv, self).__init__()
+        self.mod_dict = mods
+        mods = {str(k): v for k, v in mods.items()}
+        # Register as child modules
         self.mods = nn.ModuleDict(mods)
+        # PyTorch ModuleDict doesn't have get() method, so I have to store two
+        # dictionaries so that I can index with both canonical edge type and
+        # edge type with the get() method.
         # Do not break if graph has 0-in-degree nodes.
         # Because there is no general rule to add self-loop for heterograph.
         for _, v in self.mods.items():
@@ -134,6 +140,16 @@ class HeteroGraphConv(nn.Module):
         else:
             self.agg_fn = aggregate
 
+    def _get_module(self, etype):
+        mod = self.mod_dict.get(etype, None)
+        if mod is not None:
+            return mod
+        if isinstance(etype, tuple):
+            # etype is canonical
+            _, etype, _ = etype
+            return self.mod_dict[etype]
+        raise KeyError('Cannot find module with edge type %s' % etype)
+
     def forward(self, g, inputs, mod_args=None, mod_kwargs=None):
         """Forward computation
 
@@ -171,7 +187,7 @@ class HeteroGraphConv(nn.Module):
                 rel_graph = g[stype, etype, dtype]
                 if stype not in src_inputs or dtype not in dst_inputs:
                     continue
-                dstdata = self.mods[etype](
+                dstdata = self._get_module((stype, etype, dtype))(
                     rel_graph,
                     (src_inputs[stype], dst_inputs[dtype]),
                     *mod_args.get(etype, ()),
@@ -182,7 +198,7 @@ class HeteroGraphConv(nn.Module):
                 rel_graph = g[stype, etype, dtype]
                 if stype not in inputs:
                     continue
-                dstdata = self.mods[etype](
+                dstdata = self._get_module((stype, etype, dtype))(
                     rel_graph,
                     (inputs[stype], inputs[dtype]),
                     *mod_args.get(etype, ()),

tests/pytorch/test_nn.py

@@ -1146,17 +1146,26 @@ def myagg(alist, dsttype):
 
 @parametrize_idtype
 @pytest.mark.parametrize('agg', ['sum', 'max', 'min', 'mean', 'stack', myagg])
-def test_hetero_conv(agg, idtype):
+@pytest.mark.parametrize('canonical_keys', [False, True])
+def test_hetero_conv(agg, idtype, canonical_keys):
     g = dgl.heterograph({
         ('user', 'follows', 'user'): ([0, 0, 2, 1], [1, 2, 1, 3]),
         ('user', 'plays', 'game'): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]),
         ('store', 'sells', 'game'): ([0, 0, 1, 1], [0, 3, 1, 2])},
         idtype=idtype, device=F.ctx())
-    conv = nn.HeteroGraphConv({
-        'follows': nn.GraphConv(2, 3, allow_zero_in_degree=True),
-        'plays': nn.GraphConv(2, 4, allow_zero_in_degree=True),
-        'sells': nn.GraphConv(3, 4, allow_zero_in_degree=True)},
-        agg)
+    if not canonical_keys:
+        conv = nn.HeteroGraphConv({
+            'follows': nn.GraphConv(2, 3, allow_zero_in_degree=True),
+            'plays': nn.GraphConv(2, 4, allow_zero_in_degree=True),
+            'sells': nn.GraphConv(3, 4, allow_zero_in_degree=True)},
+            agg)
+    else:
+        conv = nn.HeteroGraphConv({
+            ('user', 'follows', 'user'): nn.GraphConv(2, 3, allow_zero_in_degree=True),
+            ('user', 'plays', 'game'): nn.GraphConv(2, 4, allow_zero_in_degree=True),
+            ('store', 'sells', 'game'): nn.GraphConv(3, 4, allow_zero_in_degree=True)},
+            agg)
+
     conv = conv.to(F.ctx())
 
     # test pickle
@@ -1621,4 +1630,4 @@ def test_dgn_conv(in_size, out_size, aggregators, scalers, delta,
     aggregators_non_eig = [aggr for aggr in aggregators if not aggr.startswith('dir')]
     model = nn.DGNConv(in_size, out_size, aggregators_non_eig, scalers, delta, dropout,
         num_towers, edge_feat_size, residual).to(dev)
-    model(g, h, edge_feat=e)
+    model(g, h, edge_feat=e)
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