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
@@ -0,0 +1,121 @@
+import unittest
+
+import backend as F
+
+import dgl
+import networkx as nx
+from utils import check_fail, parametrize_idtype
+
+
+def create_graph(idtype):
+    g = dgl.from_networkx(nx.path_graph(5), idtype=idtype, device=F.ctx())
+    return g
+
+
+def mfunc(edges):
+    return {"m": edges.src["x"]}
+
+
+def rfunc(nodes):
+    msg = F.sum(nodes.mailbox["m"], 1)
+    return {"x": nodes.data["x"] + msg}
+
+
+@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
+@parametrize_idtype
+def test_prop_nodes_bfs(idtype):
+    g = create_graph(idtype)
+    g.ndata["x"] = F.ones((5, 2))
+    dgl.prop_nodes_bfs(
+        g, 0, message_func=mfunc, reduce_func=rfunc, apply_node_func=None
+    )
+    # pull nodes using bfs order will result in a cumsum[i] + data[i] + data[i+1]
+    assert F.allclose(
+        g.ndata["x"],
+        F.tensor([[2.0, 2.0], [4.0, 4.0], [6.0, 6.0], [8.0, 8.0], [9.0, 9.0]]),
+    )
+
+
+@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
+@parametrize_idtype
+def test_prop_edges_dfs(idtype):
+    g = create_graph(idtype)
+    g.ndata["x"] = F.ones((5, 2))
+    dgl.prop_edges_dfs(
+        g, 0, message_func=mfunc, reduce_func=rfunc, apply_node_func=None
+    )
+    # snr using dfs results in a cumsum
+    assert F.allclose(
+        g.ndata["x"],
+        F.tensor([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0], [5.0, 5.0]]),
+    )
+
+    g.ndata["x"] = F.ones((5, 2))
+    dgl.prop_edges_dfs(
+        g,
+        0,
+        has_reverse_edge=True,
+        message_func=mfunc,
+        reduce_func=rfunc,
+        apply_node_func=None,
+    )
+    # result is cumsum[i] + cumsum[i-1]
+    assert F.allclose(
+        g.ndata["x"],
+        F.tensor([[1.0, 1.0], [3.0, 3.0], [5.0, 5.0], [7.0, 7.0], [9.0, 9.0]]),
+    )
+
+    g.ndata["x"] = F.ones((5, 2))
+    dgl.prop_edges_dfs(
+        g,
+        0,
+        has_nontree_edge=True,
+        message_func=mfunc,
+        reduce_func=rfunc,
+        apply_node_func=None,
+    )
+    # result is cumsum[i] + cumsum[i+1]
+    assert F.allclose(
+        g.ndata["x"],
+        F.tensor([[3.0, 3.0], [5.0, 5.0], [7.0, 7.0], [9.0, 9.0], [5.0, 5.0]]),
+    )
+
+
+@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
+@parametrize_idtype
+def test_prop_nodes_topo(idtype):
+    # bi-directional chain
+    g = create_graph(idtype)
+    assert check_fail(dgl.prop_nodes_topo, g)  # has loop
+
+    # tree
+    tree = dgl.graph([])
+    tree.add_nodes(5)
+    tree.add_edges(1, 0)
+    tree.add_edges(2, 0)
+    tree.add_edges(3, 2)
+    tree.add_edges(4, 2)
+    tree = dgl.graph(tree.edges())
+    # init node feature data
+    tree.ndata["x"] = F.zeros((5, 2))
+    # set all leaf nodes to be ones
+    tree.nodes[[1, 3, 4]].data["x"] = F.ones((3, 2))
+
+    # Filtering DGLWarning:
+    #    The input graph for the user-defined edge
+    #    function does not contain valid edges
+    import warnings
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=UserWarning)
+        dgl.prop_nodes_topo(
+            tree, message_func=mfunc, reduce_func=rfunc, apply_node_func=None
+        )
+    # root node get the sum
+    assert F.allclose(tree.nodes[0].data["x"], F.tensor([[3.0, 3.0]]))
+
+
+if __name__ == "__main__":
+    test_prop_nodes_bfs()
+    test_prop_edges_dfs()
+    test_prop_nodes_topo()