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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
......@@ -55,103 +55,72 @@ def test_integration_link_prediction():
datapipe = datapipe.fetch_feature(
feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
)
datapipe = datapipe.to_dgl()
dataloader = gb.SingleProcessDataLoader(
datapipe,
)
expected = [
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([5, 4]), tensor([0, 5])),
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),),
SampledSubgraphImpl(node_pairs=(tensor([0, 4]), tensor([0, 5])),
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),)],
node_pairs=(tensor([5, 3, 3, 3]), tensor([1, 2, 2, 3])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
output_nodes=None,
node_features={'feat': tensor([[0.5160, 0.2486],
[0.8672, 0.2276],
[0.6172, 0.7865],
[0.2109, 0.1089],
[0.9634, 0.2294],
[0.5503, 0.8223]])},
negative_srcs=tensor([[5],
[3],
[3],
[3]]),
negative_dsts=tensor([[5],
[2],
[0],
[4]]),
negative_node_pairs=(tensor([0, 1, 1, 1]), tensor([0, 3, 4, 5])),
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]),
blocks=[Block(num_src_nodes=6,
num_dst_nodes=6,
num_edges=2),
Block(num_src_nodes=6,
num_dst_nodes=6,
num_edges=2)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([3, 3]), tensor([3, 4])),
original_column_node_ids=tensor([3, 4, 0, 5, 1]),
original_edge_ids=None,
original_row_node_ids=tensor([3, 4, 0, 5, 1]),),
SampledSubgraphImpl(node_pairs=(tensor([1, 3]), tensor([3, 4])),
original_column_node_ids=tensor([3, 4, 0, 5, 1]),
original_edge_ids=None,
original_row_node_ids=tensor([3, 4, 0, 5, 1]),)],
node_pairs=(tensor([3, 4, 4, 0]), tensor([3, 3, 4, 4])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
output_nodes=None,
node_features={'feat': tensor([[0.8672, 0.2276],
[0.5503, 0.8223],
[0.9634, 0.2294],
[0.5160, 0.2486],
[0.6172, 0.7865]])},
negative_srcs=tensor([[3],
[4],
[4],
[0]]),
negative_dsts=tensor([[4],
[5],
[1],
[4]]),
negative_node_pairs=(tensor([0, 1, 1, 2]), tensor([1, 3, 4, 1])),
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]),
blocks=[Block(num_src_nodes=5,
num_dst_nodes=5,
num_edges=2),
Block(num_src_nodes=5,
num_dst_nodes=5,
num_edges=2)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([1, 2]), tensor([1, 2])),
original_column_node_ids=tensor([5, 4, 3, 0]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 4, 3, 0]),),
SampledSubgraphImpl(node_pairs=(tensor([3, 1]), tensor([1, 2])),
original_column_node_ids=tensor([5, 4, 3]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 4, 3, 0]),)],
node_pairs=(tensor([5, 4]), tensor([5, 5])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1]), tensor([0, 0])),
output_nodes=None,
node_features={'feat': tensor([[0.5160, 0.2486],
[0.5503, 0.8223],
[0.8672, 0.2276],
[0.9634, 0.2294]])},
negative_srcs=tensor([[5],
[4]]),
negative_dsts=tensor([[4],
[3]]),
negative_node_pairs=(tensor([0, 1]), tensor([1, 2])),
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]),
blocks=[Block(num_src_nodes=4,
num_dst_nodes=4,
num_edges=2),
Block(num_src_nodes=4,
num_dst_nodes=3,
num_edges=2)],
)"""
),
]
......@@ -208,86 +177,69 @@ def test_integration_node_classification():
datapipe = datapipe.fetch_feature(
feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
)
datapipe = datapipe.to_dgl()
dataloader = gb.SingleProcessDataLoader(
datapipe,
)
expected = [
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([0, 1, 0, 1, 5]), tensor([0, 1, 2, 3, 4])),
original_column_node_ids=tensor([5, 3, 1, 2, 4]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 3, 1, 2, 4, 0]),),
SampledSubgraphImpl(node_pairs=(tensor([4, 4, 0, 1]), tensor([0, 1, 2, 3])),
original_column_node_ids=tensor([5, 3, 1, 2]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 3, 1, 2, 4]),)],
node_pairs=(tensor([5, 3, 3, 3]), tensor([1, 2, 2, 3])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 1]), tensor([2, 3, 3, 1])),
output_nodes=None,
node_features={'feat': tensor([[0.5160, 0.2486],
[0.8672, 0.2276],
[0.6172, 0.7865],
[0.2109, 0.1089],
[0.5503, 0.8223],
[0.9634, 0.2294]])},
negative_srcs=None,
negative_dsts=None,
negative_node_pairs=None,
labels=None,
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,
blocks=[Block(num_src_nodes=6,
num_dst_nodes=5,
num_edges=5),
Block(num_src_nodes=5,
num_dst_nodes=4,
num_edges=4)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([0, 2]), tensor([0, 1])),
original_column_node_ids=tensor([3, 4, 0]),
original_edge_ids=None,
original_row_node_ids=tensor([3, 4, 0]),),
SampledSubgraphImpl(node_pairs=(tensor([0, 2]), tensor([0, 1])),
original_column_node_ids=tensor([3, 4, 0]),
original_edge_ids=None,
original_row_node_ids=tensor([3, 4, 0]),)],
node_pairs=(tensor([3, 4, 4, 0]), tensor([3, 3, 4, 4])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1])),
output_nodes=None,
node_features={'feat': tensor([[0.8672, 0.2276],
[0.5503, 0.8223],
[0.9634, 0.2294]])},
negative_srcs=None,
negative_dsts=None,
negative_node_pairs=None,
labels=None,
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,
blocks=[Block(num_src_nodes=3,
num_dst_nodes=3,
num_edges=2),
Block(num_src_nodes=3,
num_dst_nodes=3,
num_edges=2)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(node_pairs=(tensor([1, 2]), tensor([0, 1])),
original_column_node_ids=tensor([5, 4]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 4, 0]),),
SampledSubgraphImpl(node_pairs=(tensor([1, 1]), tensor([0, 1])),
original_column_node_ids=tensor([5, 4]),
original_edge_ids=None,
original_row_node_ids=tensor([5, 4]),)],
node_pairs=(tensor([5, 4]), tensor([5, 5])),
"""DGLMiniBatch(positive_node_pairs=(tensor([0, 1]), tensor([0, 0])),
output_nodes=None,
node_features={'feat': tensor([[0.5160, 0.2486],
[0.5503, 0.8223],
[0.9634, 0.2294]])},
negative_srcs=None,
negative_dsts=None,
negative_node_pairs=None,
labels=None,
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,
blocks=[Block(num_src_nodes=3,
num_dst_nodes=2,
num_edges=2),
Block(num_src_nodes=2,
num_dst_nodes=2,
num_edges=2)],
)"""
),
]
......
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