[GraphBolt] Fix `NegativeSampler` seeds support. (#7043)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

python/dgl/graphbolt/negative_sampler.py

@@ -82,7 +82,9 @@ class NegativeSampler(MiniBatchTransformer):
                         minibatch.seeds[etype],
                         minibatch.labels[etype],
                         minibatch.indexes[etype],
-                    ) = self._sample_with_etype(pos_pairs, use_seeds=True)
+                    ) = self._sample_with_etype(
+                        pos_pairs, etype, use_seeds=True
+                    )
             else:
                 (
                     minibatch.seeds,

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

@@ -293,7 +293,23 @@ def test_NegativeSampler_Hetero_Data():
             ),
         }
     )
-
-    item_sampler = gb.ItemSampler(itemset, batch_size=2)
-    negative_dp = gb.UniformNegativeSampler(item_sampler, graph, 1)
+    batch_size = 2
+    negative_ratio = 1
+    item_sampler = gb.ItemSampler(itemset, batch_size=batch_size)
+    negative_dp = gb.UniformNegativeSampler(item_sampler, graph, negative_ratio)
     assert len(list(negative_dp)) == 5
+    # Perform negative sampling.
+    expected_neg_src = [
+        {"n1:e1:n2": torch.tensor([0, 0])},
+        {"n1:e1:n2": torch.tensor([1, 1])},
+        {"n2:e2:n1": torch.tensor([0, 0])},
+        {"n2:e2:n1": torch.tensor([1, 1])},
+        {"n2:e2:n1": torch.tensor([2, 2])},
+    ]
+    for i, data in enumerate(negative_dp):
+        # Check negative seeds value.
+        for etype, seeds_data in data.seeds.items():
+            neg_src = seeds_data[batch_size:, 0]
+            neg_dst = seeds_data[batch_size:, 1]
+            assert torch.equal(expected_neg_src[i][etype], neg_src)
+            assert (neg_dst < 3).all(), neg_dst