chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,360 @@
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import dgl
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import dgl.graphbolt as gb
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import dgl.sparse as dglsp
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import torch
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def test_integration_link_prediction():
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torch.manual_seed(926)
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indptr = torch.tensor([0, 0, 1, 3, 6, 8, 10])
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indices = torch.tensor([5, 3, 3, 3, 3, 4, 4, 0, 5, 4])
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matrix_a = dglsp.from_csc(indptr, indices)
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seeds = torch.t(torch.stack(matrix_a.coo()))
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node_feature_data = torch.tensor(
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[
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[0.9634, 0.2294],
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[0.6172, 0.7865],
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[0.2109, 0.1089],
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[0.8672, 0.2276],
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[0.5503, 0.8223],
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[0.5160, 0.2486],
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]
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)
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edge_feature_data = torch.tensor(
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[
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[0.5123, 0.1709, 0.6150],
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[0.1476, 0.1902, 0.1314],
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[0.2582, 0.5203, 0.6228],
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[0.3708, 0.7631, 0.2683],
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[0.2126, 0.7878, 0.7225],
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[0.7885, 0.3414, 0.5485],
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[0.4088, 0.8200, 0.1851],
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[0.0056, 0.9469, 0.4432],
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[0.8972, 0.7511, 0.3617],
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[0.5773, 0.2199, 0.3366],
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]
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)
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item_set = gb.ItemSet(seeds, names="seeds")
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graph = gb.fused_csc_sampling_graph(indptr, indices)
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node_feature = gb.TorchBasedFeature(node_feature_data)
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edge_feature = gb.TorchBasedFeature(edge_feature_data)
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features = {
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("node", None, "feat"): node_feature,
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("edge", None, "feat"): edge_feature,
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}
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feature_store = gb.BasicFeatureStore(features)
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datapipe = gb.ItemSampler(item_set, batch_size=4)
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datapipe = datapipe.sample_uniform_negative(graph, 2)
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fanouts = torch.LongTensor([1])
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datapipe = datapipe.sample_neighbor(graph, [fanouts, fanouts], replace=True)
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datapipe = datapipe.transform(gb.exclude_seed_edges)
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datapipe = datapipe.fetch_feature(
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feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
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)
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dataloader = gb.DataLoader(
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datapipe,
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)
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expected = [
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str(
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"""MiniBatch(seeds=tensor([[5, 1],
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[3, 2],
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[3, 2],
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[3, 3],
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[5, 2],
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[5, 1],
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[3, 4],
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[3, 3],
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[3, 5],
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[3, 2],
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[3, 0],
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[3, 4]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 2, 2], dtype=torch.int32),
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indices=tensor([4, 5], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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original_edge_ids=tensor([9, 7]),
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original_column_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 2, 2], dtype=torch.int32),
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indices=tensor([0, 5], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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original_edge_ids=tensor([8, 7]),
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original_column_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.6172, 0.7865],
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[0.8672, 0.2276],
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[0.2109, 0.1089],
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[0.5503, 0.8223],
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[0.9634, 0.2294]])},
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labels=tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),
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input_nodes=tensor([5, 1, 3, 2, 4, 0]),
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indexes=tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3]),
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edge_features=[{'feat': tensor([[0.5773, 0.2199, 0.3366],
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[0.0056, 0.9469, 0.4432]])},
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.0056, 0.9469, 0.4432]])}],
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compacted_seeds=tensor([[0, 1],
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[2, 3],
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[2, 3],
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[2, 2],
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[0, 3],
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[0, 1],
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[2, 4],
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[2, 2],
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[2, 0],
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[2, 3],
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[2, 5],
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[2, 4]]),
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blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2),
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Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([[3, 3],
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[4, 3],
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[4, 4],
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[0, 4],
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[3, 4],
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[3, 5],
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[4, 1],
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[4, 4],
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[4, 4],
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[4, 5],
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[0, 1],
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[0, 3]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 0, 1], dtype=torch.int32),
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indices=tensor([3], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 4, 0, 5, 1]),
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original_edge_ids=tensor([0]),
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original_column_node_ids=tensor([3, 4, 0, 5, 1]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2], dtype=torch.int32),
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indices=tensor([3, 3], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 4, 0, 5, 1]),
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original_edge_ids=tensor([8, 0]),
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original_column_node_ids=tensor([3, 4, 0, 5, 1]),
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)],
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node_features={'feat': tensor([[0.8672, 0.2276],
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[0.5503, 0.8223],
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[0.9634, 0.2294],
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[0.5160, 0.2486],
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[0.6172, 0.7865]])},
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labels=tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),
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input_nodes=tensor([3, 4, 0, 5, 1]),
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indexes=tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3]),
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edge_features=[{'feat': tensor([[0.5123, 0.1709, 0.6150]])},
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])}],
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compacted_seeds=tensor([[0, 0],
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[1, 0],
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[1, 1],
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[2, 1],
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[0, 1],
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[0, 3],
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[1, 4],
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[1, 1],
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[1, 1],
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[1, 3],
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[2, 4],
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[2, 0]]),
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blocks=[Block(num_src_nodes=5, num_dst_nodes=5, num_edges=1),
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Block(num_src_nodes=5, num_dst_nodes=5, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([[5, 5],
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[4, 5],
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[5, 5],
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[5, 5],
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[4, 0],
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[4, 0]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1], dtype=torch.int32),
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indices=tensor([1], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 4, 0]),
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original_edge_ids=tensor([6]),
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original_column_node_ids=tensor([5, 4, 0]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1], dtype=torch.int32),
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indices=tensor([2], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 4, 0]),
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original_edge_ids=tensor([7]),
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original_column_node_ids=tensor([5, 4, 0]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.5503, 0.8223],
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[0.9634, 0.2294]])},
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labels=tensor([1., 1., 0., 0., 0., 0.]),
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input_nodes=tensor([5, 4, 0]),
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indexes=tensor([0, 1, 0, 0, 1, 1]),
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edge_features=[{'feat': tensor([[0.4088, 0.8200, 0.1851]])},
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{'feat': tensor([[0.0056, 0.9469, 0.4432]])}],
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compacted_seeds=tensor([[0, 0],
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[1, 0],
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[0, 0],
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[0, 0],
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[1, 2],
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[1, 2]]),
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blocks=[Block(num_src_nodes=3, num_dst_nodes=3, num_edges=1),
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Block(num_src_nodes=3, num_dst_nodes=3, num_edges=1)],
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)"""
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),
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]
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for step, data in enumerate(dataloader):
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assert expected[step] == str(data), print(step, data)
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def test_integration_node_classification():
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torch.manual_seed(926)
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indptr = torch.tensor([0, 0, 1, 3, 6, 8, 10])
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indices = torch.tensor([5, 3, 3, 3, 3, 4, 4, 0, 5, 4])
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seeds = torch.tensor([5, 1, 2, 4, 3, 0])
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node_feature_data = torch.tensor(
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[
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[0.9634, 0.2294],
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[0.6172, 0.7865],
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[0.2109, 0.1089],
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[0.8672, 0.2276],
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[0.5503, 0.8223],
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[0.5160, 0.2486],
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]
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)
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edge_feature_data = torch.tensor(
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[
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[0.5123, 0.1709, 0.6150],
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[0.1476, 0.1902, 0.1314],
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[0.2582, 0.5203, 0.6228],
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[0.3708, 0.7631, 0.2683],
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[0.2126, 0.7878, 0.7225],
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[0.7885, 0.3414, 0.5485],
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[0.4088, 0.8200, 0.1851],
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[0.0056, 0.9469, 0.4432],
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[0.8972, 0.7511, 0.3617],
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[0.5773, 0.2199, 0.3366],
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]
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)
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item_set = gb.ItemSet(seeds, names="seeds")
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graph = gb.fused_csc_sampling_graph(indptr, indices)
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node_feature = gb.TorchBasedFeature(node_feature_data)
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edge_feature = gb.TorchBasedFeature(edge_feature_data)
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features = {
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("node", None, "feat"): node_feature,
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("edge", None, "feat"): edge_feature,
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}
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feature_store = gb.BasicFeatureStore(features)
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datapipe = gb.ItemSampler(item_set, batch_size=2)
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fanouts = torch.LongTensor([1])
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datapipe = datapipe.sample_neighbor(graph, [fanouts, fanouts], replace=True)
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datapipe = datapipe.fetch_feature(
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feature_store, node_feature_keys=["feat"], edge_feature_keys=["feat"]
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)
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dataloader = gb.DataLoader(
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datapipe,
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)
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expected = [
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str(
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"""MiniBatch(seeds=tensor([5, 1]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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indices=tensor([0, 0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1]),
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original_edge_ids=tensor([8, 0]),
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original_column_node_ids=tensor([5, 1]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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indices=tensor([0, 0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1]),
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original_edge_ids=tensor([8, 0]),
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original_column_node_ids=tensor([5, 1]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.6172, 0.7865]])},
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labels=None,
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input_nodes=tensor([5, 1]),
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indexes=None,
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edge_features=[{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])},
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])}],
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compacted_seeds=None,
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blocks=[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=2),
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Block(num_src_nodes=2, num_dst_nodes=2, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([2, 4]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2, 3], dtype=torch.int32),
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indices=tensor([2, 1, 2], dtype=torch.int32),
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),
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original_row_node_ids=tensor([2, 4, 3]),
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original_edge_ids=tensor([1, 6, 3]),
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original_column_node_ids=tensor([2, 4, 3]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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indices=tensor([2, 1], dtype=torch.int32),
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),
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original_row_node_ids=tensor([2, 4, 3]),
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original_edge_ids=tensor([2, 6]),
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original_column_node_ids=tensor([2, 4]),
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)],
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node_features={'feat': tensor([[0.2109, 0.1089],
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[0.5503, 0.8223],
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[0.8672, 0.2276]])},
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labels=None,
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input_nodes=tensor([2, 4, 3]),
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indexes=None,
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edge_features=[{'feat': tensor([[0.1476, 0.1902, 0.1314],
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[0.4088, 0.8200, 0.1851],
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[0.3708, 0.7631, 0.2683]])},
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{'feat': tensor([[0.2582, 0.5203, 0.6228],
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[0.4088, 0.8200, 0.1851]])}],
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compacted_seeds=None,
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blocks=[Block(num_src_nodes=3, num_dst_nodes=3, num_edges=3),
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Block(num_src_nodes=3, num_dst_nodes=2, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([3, 0]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1], dtype=torch.int32),
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indices=tensor([0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 0]),
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original_edge_ids=tensor([3]),
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original_column_node_ids=tensor([3, 0]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1], dtype=torch.int32),
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indices=tensor([0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 0]),
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original_edge_ids=tensor([3]),
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original_column_node_ids=tensor([3, 0]),
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)],
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node_features={'feat': tensor([[0.8672, 0.2276],
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[0.9634, 0.2294]])},
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labels=None,
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input_nodes=tensor([3, 0]),
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indexes=None,
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edge_features=[{'feat': tensor([[0.3708, 0.7631, 0.2683]])},
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{'feat': tensor([[0.3708, 0.7631, 0.2683]])}],
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compacted_seeds=None,
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blocks=[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1),
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Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1)],
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)"""
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),
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]
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for step, data in enumerate(dataloader):
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assert expected[step] == str(data), print(step, data)
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