[Graphbolt] Remove redundant data in to_dgl (dmlc#6466)

python/dgl/graphbolt/minibatch.py

@@ -314,12 +314,16 @@ def to_dgl(self):
         """
         minibatch = DGLMiniBatch(
             blocks=self._to_dgl_blocks(),
-            input_nodes=self.input_nodes,
-            output_nodes=self.seed_nodes,
             node_features=self.node_features,
             edge_features=self.edge_features,
             labels=self.labels,
         )
+        # Need input nodes to fetch feature.
+        if self.node_features is None:
+            minibatch.input_nodes = self.input_nodes
+        # Need output nodes to fetch label.
+        if self.labels is None:
+            minibatch.output_nodes = self.seed_nodes
         assert (
             minibatch.blocks is not None
         ), "Sampled subgraphs for computation are missing."

tests/python/pytorch/graphbolt/impl/test_minibatch.py

@@ -50,6 +50,7 @@ def create_homo_minibatch():
         sampled_subgraphs=subgraphs,
         node_features=node_features,
         edge_features=edge_features,
+        input_nodes=torch.tensor([10, 11, 12, 13]),
     )
 
 
@@ -103,6 +104,10 @@ def create_hetero_minibatch():
         sampled_subgraphs=subgraphs,
         node_features=node_features,
         edge_features=edge_features,
+        input_nodes={
+            "A": torch.tensor([5, 7, 9, 11]),
+            "B": torch.tensor([10, 11, 12]),
+        },
     )
 
 
@@ -286,7 +291,7 @@ def test_dgl_minibatch_representation():
              node_features={'x': tensor([7, 6, 2, 2])},
              negative_node_pairs=(tensor([0, 1, 2]), tensor([6, 0, 0])),
              labels=tensor([0., 1., 2.]),
-             input_nodes=tensor([8, 1, 6, 5, 9, 0, 2, 4]),
+             input_nodes=None,
              edge_features=[{'x': tensor([[8],
                                           [1],
                                           [6]])},
@@ -354,6 +359,25 @@ def check_dgl_blocks_homo(minibatch, blocks):
     assert torch.equal(blocks[0].srcdata[dgl.NID], original_row_node_ids[0])
 
 
+def test_to_dgl_node_classification_without_feature():
+    # Arrange
+    minibatch = create_homo_minibatch()
+    minibatch.node_features = None
+    minibatch.labels = None
+    minibatch.seed_nodes = torch.tensor([10, 15])
+    # Act
+    dgl_minibatch = minibatch.to_dgl()
+
+    # Assert
+    assert len(dgl_minibatch.blocks) == 2
+    assert dgl_minibatch.node_features is None
+    assert minibatch.edge_features is dgl_minibatch.edge_features
+    assert dgl_minibatch.labels is None
+    assert minibatch.input_nodes is dgl_minibatch.input_nodes
+    assert minibatch.seed_nodes is dgl_minibatch.output_nodes
+    check_dgl_blocks_homo(minibatch, dgl_minibatch.blocks)
+
+
 def test_to_dgl_node_classification_homo():
     # Arrange
     minibatch = create_homo_minibatch()
@@ -367,7 +391,8 @@ def test_to_dgl_node_classification_homo():
     assert minibatch.node_features is dgl_minibatch.node_features
     assert minibatch.edge_features is dgl_minibatch.edge_features
     assert minibatch.labels is dgl_minibatch.labels
-    assert minibatch.seed_nodes is dgl_minibatch.output_nodes
+    assert dgl_minibatch.input_nodes is None
+    assert dgl_minibatch.output_nodes is None
     check_dgl_blocks_homo(minibatch, dgl_minibatch.blocks)
 
 
@@ -382,7 +407,8 @@ def test_to_dgl_node_classification_hetero():
     assert minibatch.node_features is dgl_minibatch.node_features
     assert minibatch.edge_features is dgl_minibatch.edge_features
     assert minibatch.labels is dgl_minibatch.labels
-    assert minibatch.seed_nodes is dgl_minibatch.output_nodes
+    assert dgl_minibatch.input_nodes is None
+    assert dgl_minibatch.output_nodes is None
     check_dgl_blocks_hetero(minibatch, dgl_minibatch.blocks)
 
 

tests/python/pytorch/graphbolt/test_integration.py

@@ -71,7 +71,7 @@ def test_integration_link_prediction():
                                     [0.5503, 0.8223]])},
              negative_node_pairs=(tensor([0, 1, 1, 1]), tensor([0, 3, 4, 5])),
              labels=None,
-             input_nodes=tensor([5, 3, 1, 2, 0, 4]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=6,
@@ -92,7 +92,7 @@ def test_integration_link_prediction():
                                     [0.6172, 0.7865]])},
              negative_node_pairs=(tensor([0, 1, 1, 2]), tensor([1, 3, 4, 1])),
              labels=None,
-             input_nodes=tensor([3, 4, 0, 5, 1]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=5,
@@ -112,7 +112,7 @@ def test_integration_link_prediction():
                                     [0.9634, 0.2294]])},
              negative_node_pairs=(tensor([0, 1]), tensor([1, 2])),
              labels=None,
-             input_nodes=tensor([5, 4, 3, 0]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=4,
@@ -193,7 +193,7 @@ def test_integration_node_classification():
                                     [0.9634, 0.2294]])},
              negative_node_pairs=None,
              labels=None,
-             input_nodes=tensor([5, 3, 1, 2, 4, 0]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=6,
@@ -212,7 +212,7 @@ def test_integration_node_classification():
                                     [0.9634, 0.2294]])},
              negative_node_pairs=None,
              labels=None,
-             input_nodes=tensor([3, 4, 0]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=3,
@@ -231,7 +231,7 @@ def test_integration_node_classification():
                                     [0.9634, 0.2294]])},
              negative_node_pairs=None,
              labels=None,
-             input_nodes=tensor([5, 4, 0]),
+             input_nodes=None,
              edge_features=[{},
                             {}],
              blocks=[Block(num_src_nodes=3,