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Unverified Commit 406d621f authored by yxy235's avatar yxy235 Committed by GitHub
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[GraphBolt] Add to_dgl() to integration test. (#6441) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
parent ebe46cdf
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Showing with 113 additions and 161 deletions
tests/python/pytorch/graphbolt/test_integration.py
View file @ 406d621f
...@@ -55,104 +55,73 @@ def test_integration_link_prediction(): ...@@ -55,104 +55,73 @@ def test_integration_link_prediction():
datapipe = datapipe.fetch_feature( datapipe = datapipe.fetch_feature(
feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"] feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
) )
datapipe = datapipe.to_dgl()
dataloader = gb.SingleProcessDataLoader( dataloader = gb.SingleProcessDataLoader(
datapipe, datapipe,
) )
expected = [ expected = [
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([5, 4]), tensor([0, 5])), output_nodes=None,
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]), node_features={'feat': tensor([[0.5160, 0.2486],
original_edge_ids=None, [0.8672, 0.2276],
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),), [0.6172, 0.7865],
SampledSubgraphImpl(node_pairs=(tensor([0, 4]), tensor([0, 5])), [0.2109, 0.1089],
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]), [0.9634, 0.2294],
original_edge_ids=None, [0.5503, 0.8223]])},
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),)], negative_node_pairs=(tensor([0, 1, 1, 1]), tensor([0, 3, 4, 5])),
node_pairs=(tensor([5, 3, 3, 3]), tensor([1, 2, 2, 3])), labels=None,
node_features={'feat': tensor([[0.5160, 0.2486], input_nodes=tensor([5, 3, 1, 2, 0, 4]),
[0.8672, 0.2276], edge_features=[{},
[0.6172, 0.7865], {}],
[0.2109, 0.1089], blocks=[Block(num_src_nodes=6,
[0.9634, 0.2294], num_dst_nodes=6,
[0.5503, 0.8223]])}, num_edges=2),
negative_srcs=tensor([[5], Block(num_src_nodes=6,
[3], num_dst_nodes=6,
[3], num_edges=2)],
[3]]), )"""
negative_dsts=tensor([[5],
[2],
[0],
[4]]),
labels=None,
input_nodes=tensor([5, 3, 1, 2, 0, 4]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
compacted_negative_srcs=tensor([0, 1, 1, 1]),
compacted_negative_dsts=tensor([0, 3, 4, 5]),
)"""
), ),
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([3, 3]), tensor([3, 4])), output_nodes=None,
original_column_node_ids=tensor([3, 4, 0, 5, 1]), node_features={'feat': tensor([[0.8672, 0.2276],
original_edge_ids=None, [0.5503, 0.8223],
original_row_node_ids=tensor([3, 4, 0, 5, 1]),), [0.9634, 0.2294],
SampledSubgraphImpl(node_pairs=(tensor([1, 3]), tensor([3, 4])), [0.5160, 0.2486],
original_column_node_ids=tensor([3, 4, 0, 5, 1]), [0.6172, 0.7865]])},
original_edge_ids=None, negative_node_pairs=(tensor([0, 1, 1, 2]), tensor([1, 3, 4, 1])),
original_row_node_ids=tensor([3, 4, 0, 5, 1]),)], labels=None,
node_pairs=(tensor([3, 4, 4, 0]), tensor([3, 3, 4, 4])), input_nodes=tensor([3, 4, 0, 5, 1]),
node_features={'feat': tensor([[0.8672, 0.2276], edge_features=[{},
[0.5503, 0.8223], {}],
[0.9634, 0.2294], blocks=[Block(num_src_nodes=5,
[0.5160, 0.2486], num_dst_nodes=5,
[0.6172, 0.7865]])}, num_edges=2),
negative_srcs=tensor([[3], Block(num_src_nodes=5,
[4], num_dst_nodes=5,
[4], num_edges=2)],
[0]]), )"""
negative_dsts=tensor([[4],
[5],
[1],
[4]]),
labels=None,
input_nodes=tensor([3, 4, 0, 5, 1]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
compacted_negative_srcs=tensor([0, 1, 1, 2]),
compacted_negative_dsts=tensor([1, 3, 4, 1]),
)"""
), ),
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1]), tensor([0, 0])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([1, 2]), tensor([1, 2])), output_nodes=None,
original_column_node_ids=tensor([5, 4, 3, 0]), node_features={'feat': tensor([[0.5160, 0.2486],
original_edge_ids=None, [0.5503, 0.8223],
original_row_node_ids=tensor([5, 4, 3, 0]),), [0.8672, 0.2276],
SampledSubgraphImpl(node_pairs=(tensor([3, 1]), tensor([1, 2])), [0.9634, 0.2294]])},
original_column_node_ids=tensor([5, 4, 3]), negative_node_pairs=(tensor([0, 1]), tensor([1, 2])),
original_edge_ids=None, labels=None,
original_row_node_ids=tensor([5, 4, 3, 0]),)], input_nodes=tensor([5, 4, 3, 0]),
node_pairs=(tensor([5, 4]), tensor([5, 5])), edge_features=[{},
node_features={'feat': tensor([[0.5160, 0.2486], {}],
[0.5503, 0.8223], blocks=[Block(num_src_nodes=4,
[0.8672, 0.2276], num_dst_nodes=4,
[0.9634, 0.2294]])}, num_edges=2),
negative_srcs=tensor([[5], Block(num_src_nodes=4,
[4]]), num_dst_nodes=3,
negative_dsts=tensor([[4], num_edges=2)],
[3]]), )"""
labels=None,
input_nodes=tensor([5, 4, 3, 0]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1]), tensor([0, 0])),
compacted_negative_srcs=tensor([0, 1]),
compacted_negative_dsts=tensor([1, 2]),
)"""
), ),
] ]
for step, data in enumerate(dataloader): for step, data in enumerate(dataloader):
...@@ -208,87 +177,70 @@ def test_integration_node_classification(): ...@@ -208,87 +177,70 @@ def test_integration_node_classification():
datapipe = datapipe.fetch_feature( datapipe = datapipe.fetch_feature(
feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"] feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
) )
datapipe = datapipe.to_dgl()
dataloader = gb.SingleProcessDataLoader( dataloader = gb.SingleProcessDataLoader(
datapipe, datapipe,
) )
expected = [ expected = [
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([0, 1, 0, 1, 5]), tensor([0, 1, 2, 3, 4])), output_nodes=None,
original_column_node_ids=tensor([5, 3, 1, 2, 4]), node_features={'feat': tensor([[0.5160, 0.2486],
original_edge_ids=None, [0.8672, 0.2276],
original_row_node_ids=tensor([5, 3, 1, 2, 4, 0]),), [0.6172, 0.7865],
SampledSubgraphImpl(node_pairs=(tensor([4, 4, 0, 1]), tensor([0, 1, 2, 3])), [0.2109, 0.1089],
original_column_node_ids=tensor([5, 3, 1, 2]), [0.5503, 0.8223],
original_edge_ids=None, [0.9634, 0.2294]])},
original_row_node_ids=tensor([5, 3, 1, 2, 4]),)], negative_node_pairs=None,
node_pairs=(tensor([5, 3, 3, 3]), tensor([1, 2, 2, 3])), labels=None,
node_features={'feat': tensor([[0.5160, 0.2486], input_nodes=tensor([5, 3, 1, 2, 4, 0]),
[0.8672, 0.2276], edge_features=[{},
[0.6172, 0.7865], {}],
[0.2109, 0.1089], blocks=[Block(num_src_nodes=6,
[0.5503, 0.8223], num_dst_nodes=5,
[0.9634, 0.2294]])}, num_edges=5),
negative_srcs=None, Block(num_src_nodes=5,
negative_dsts=None, num_dst_nodes=4,
labels=None, num_edges=4)],
input_nodes=tensor([5, 3, 1, 2, 4, 0]), )"""
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
compacted_negative_srcs=None,
compacted_negative_dsts=None,
)"""
), ),
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([0, 2]), tensor([0, 1])), output_nodes=None,
original_column_node_ids=tensor([3, 4, 0]), node_features={'feat': tensor([[0.8672, 0.2276],
original_edge_ids=None, [0.5503, 0.8223],
original_row_node_ids=tensor([3, 4, 0]),), [0.9634, 0.2294]])},
SampledSubgraphImpl(node_pairs=(tensor([0, 2]), tensor([0, 1])), negative_node_pairs=None,
original_column_node_ids=tensor([3, 4, 0]), labels=None,
original_edge_ids=None, input_nodes=tensor([3, 4, 0]),
original_row_node_ids=tensor([3, 4, 0]),)], edge_features=[{},
node_pairs=(tensor([3, 4, 4, 0]), tensor([3, 3, 4, 4])), {}],
node_features={'feat': tensor([[0.8672, 0.2276], blocks=[Block(num_src_nodes=3,
[0.5503, 0.8223], num_dst_nodes=3,
[0.9634, 0.2294]])}, num_edges=2),
negative_srcs=None, Block(num_src_nodes=3,
negative_dsts=None, num_dst_nodes=3,
labels=None, num_edges=2)],
input_nodes=tensor([3, 4, 0]), )"""
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
compacted_negative_srcs=None,
compacted_negative_dsts=None,
)"""
), ),
str( str(
"""MiniBatch(seed_nodes=None, """DGLMiniBatch(positive_node_pairs=(tensor([0, 1]), tensor([0, 0])),
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([1, 2]), tensor([0, 1])), output_nodes=None,
original_column_node_ids=tensor([5, 4]), node_features={'feat': tensor([[0.5160, 0.2486],
original_edge_ids=None, [0.5503, 0.8223],
original_row_node_ids=tensor([5, 4, 0]),), [0.9634, 0.2294]])},
SampledSubgraphImpl(node_pairs=(tensor([1, 1]), tensor([0, 1])), negative_node_pairs=None,
original_column_node_ids=tensor([5, 4]), labels=None,
original_edge_ids=None, input_nodes=tensor([5, 4, 0]),
original_row_node_ids=tensor([5, 4]),)], edge_features=[{},
node_pairs=(tensor([5, 4]), tensor([5, 5])), {}],
node_features={'feat': tensor([[0.5160, 0.2486], blocks=[Block(num_src_nodes=3,
[0.5503, 0.8223], num_dst_nodes=2,
[0.9634, 0.2294]])}, num_edges=2),
negative_srcs=None, Block(num_src_nodes=2,
negative_dsts=None, num_dst_nodes=2,
labels=None, num_edges=2)],
input_nodes=tensor([5, 4, 0]), )"""
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1]), tensor([0, 0])),
compacted_negative_srcs=None,
compacted_negative_dsts=None,
)"""
), ),
] ]
for step, data in enumerate(dataloader): for step, data in enumerate(dataloader):
......
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