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Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal> Co-authored-by: Ubuntu <ubuntu@ip-172-31-30-178.ap-northeast-1.compute.internal> Co-authored-by: Skeleton003 <799284168.qq.com> Co-authored-by: Skeleton003 <799284168@qq.com> Co-authored-by: Mingbang Wang <100203018+Skeleton003@users.noreply.github.com> Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>
756 lines
27 KiB
Python
756 lines
27 KiB
Python
import dgl
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import dgl.graphbolt as gb
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import pytest
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import torch
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relation = "A:r:B"
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reverse_relation = "B:rr:A"
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@pytest.mark.parametrize("indptr_dtype", [torch.int32, torch.int64])
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@pytest.mark.parametrize("indices_dtype", [torch.int32, torch.int64])
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def test_minibatch_representation_homo(indptr_dtype, indices_dtype):
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seeds = torch.tensor([10, 11])
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csc_formats = [
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gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 3, 5, 6], dtype=indptr_dtype),
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indices=torch.tensor([0, 1, 2, 2, 1, 2], dtype=indices_dtype),
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),
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gb.CSCFormatBase(
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indptr=torch.tensor([0, 2, 3], dtype=indptr_dtype),
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indices=torch.tensor([1, 2, 0], dtype=indices_dtype),
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),
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]
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original_column_node_ids = [
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torch.tensor([10, 11, 12, 13]),
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torch.tensor([10, 11]),
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]
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original_row_node_ids = [
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torch.tensor([10, 11, 12, 13]),
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torch.tensor([10, 11, 12]),
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]
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original_edge_ids = [
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torch.tensor([19, 20, 21, 22, 25, 30]),
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torch.tensor([10, 15, 17]),
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]
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node_features = {"x": torch.tensor([5, 0, 2, 1])}
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edge_features = [
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{"x": torch.tensor([9, 0, 1, 1, 7, 4])},
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{"x": torch.tensor([0, 2, 2])},
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]
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subgraphs = []
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for i in range(2):
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subgraphs.append(
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gb.SampledSubgraphImpl(
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sampled_csc=csc_formats[i],
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original_column_node_ids=original_column_node_ids[i],
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original_row_node_ids=original_row_node_ids[i],
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original_edge_ids=original_edge_ids[i],
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)
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)
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input_nodes = torch.tensor([8, 1, 6, 5, 9, 0, 2, 4])
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compacted_seeds = torch.tensor([0, 1])
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labels = torch.tensor([1.0, 2.0])
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# Test minibatch without data.
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minibatch = gb.MiniBatch()
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expect_result = str(
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"""MiniBatch(seeds=None,
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sampled_subgraphs=None,
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node_features=None,
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labels=None,
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input_nodes=None,
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indexes=None,
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edge_features=None,
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compacted_seeds=None,
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blocks=None,
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)"""
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)
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result = str(minibatch)
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assert result == expect_result, print(expect_result, result)
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# Test minibatch with all attributes.
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minibatch = gb.MiniBatch(
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seeds=seeds,
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sampled_subgraphs=subgraphs,
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labels=labels,
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node_features=node_features,
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edge_features=edge_features,
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compacted_seeds=compacted_seeds,
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input_nodes=input_nodes,
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)
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expect_result = str(
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"""MiniBatch(seeds=tensor([10, 11]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 3, 5, 6], dtype=torch.int32),
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indices=tensor([0, 1, 2, 2, 1, 2], dtype=torch.int32),
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),
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original_row_node_ids=tensor([10, 11, 12, 13]),
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original_edge_ids=tensor([19, 20, 21, 22, 25, 30]),
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original_column_node_ids=tensor([10, 11, 12, 13]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 2, 3], dtype=torch.int32),
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indices=tensor([1, 2, 0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([10, 11, 12]),
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original_edge_ids=tensor([10, 15, 17]),
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original_column_node_ids=tensor([10, 11]),
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)],
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node_features={'x': tensor([5, 0, 2, 1])},
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labels=tensor([1., 2.]),
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input_nodes=tensor([8, 1, 6, 5, 9, 0, 2, 4]),
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indexes=None,
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edge_features=[{'x': tensor([9, 0, 1, 1, 7, 4])},
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{'x': tensor([0, 2, 2])}],
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compacted_seeds=tensor([0, 1]),
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blocks=[Block(num_src_nodes=4, num_dst_nodes=4, num_edges=6),
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Block(num_src_nodes=3, num_dst_nodes=2, num_edges=3)],
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)"""
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)
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result = str(minibatch)
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assert result == expect_result, print(expect_result, result)
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@pytest.mark.parametrize("indptr_dtype", [torch.int32, torch.int64])
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@pytest.mark.parametrize("indices_dtype", [torch.int32, torch.int64])
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def test_minibatch_representation_hetero(indptr_dtype, indices_dtype):
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seeds = {relation: torch.tensor([10, 11])}
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csc_formats = [
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{
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relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 2, 3], dtype=indptr_dtype),
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indices=torch.tensor([0, 1, 1], dtype=indices_dtype),
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),
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reverse_relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 0, 0, 1, 2], dtype=indptr_dtype),
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indices=torch.tensor([1, 0], dtype=indices_dtype),
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),
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},
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{
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relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 2], dtype=indptr_dtype),
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indices=torch.tensor([1, 0], dtype=indices_dtype),
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),
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reverse_relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 2], dtype=indptr_dtype),
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indices=torch.tensor([1, 0], dtype=indices_dtype),
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),
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},
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]
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original_column_node_ids = [
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{"B": torch.tensor([10, 11, 12]), "A": torch.tensor([5, 7, 9, 11])},
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{"B": torch.tensor([10, 11]), "A": torch.tensor([5])},
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]
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original_row_node_ids = [
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{
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"A": torch.tensor([5, 7, 9, 11]),
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"B": torch.tensor([10, 11, 12]),
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},
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{
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"A": torch.tensor([5, 7]),
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"B": torch.tensor([10, 11]),
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},
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]
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original_edge_ids = [
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{
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relation: torch.tensor([19, 20, 21]),
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reverse_relation: torch.tensor([23, 26]),
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},
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{relation: torch.tensor([10, 12])},
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]
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node_features = {
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("A", "x"): torch.tensor([6, 4, 0, 1]),
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}
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edge_features = [
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{(relation, "x"): torch.tensor([4, 2, 4])},
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{(relation, "x"): torch.tensor([0, 6])},
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]
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subgraphs = []
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for i in range(2):
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subgraphs.append(
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gb.SampledSubgraphImpl(
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sampled_csc=csc_formats[i],
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original_column_node_ids=original_column_node_ids[i],
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original_row_node_ids=original_row_node_ids[i],
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original_edge_ids=original_edge_ids[i],
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)
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)
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compacted_seeds = {relation: torch.tensor([0, 1])}
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# Test minibatch with all attributes.
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minibatch = gb.MiniBatch(
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seeds=seeds,
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sampled_subgraphs=subgraphs,
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node_features=node_features,
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edge_features=edge_features,
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labels={"B": torch.tensor([2, 5])},
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compacted_seeds=compacted_seeds,
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input_nodes={
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"A": torch.tensor([5, 7, 9, 11]),
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"B": torch.tensor([10, 11, 12]),
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},
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)
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expect_result = str(
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"""MiniBatch(seeds={'A:r:B': tensor([10, 11])},
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc={'A:r:B': CSCFormatBase(indptr=tensor([0, 1, 2, 3], dtype=torch.int32),
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indices=tensor([0, 1, 1], dtype=torch.int32),
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), 'B:rr:A': CSCFormatBase(indptr=tensor([0, 0, 0, 1, 2], dtype=torch.int32),
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indices=tensor([1, 0], dtype=torch.int32),
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)},
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original_row_node_ids={'A': tensor([ 5, 7, 9, 11]), 'B': tensor([10, 11, 12])},
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original_edge_ids={'A:r:B': tensor([19, 20, 21]), 'B:rr:A': tensor([23, 26])},
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original_column_node_ids={'B': tensor([10, 11, 12]), 'A': tensor([ 5, 7, 9, 11])},
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),
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SampledSubgraphImpl(sampled_csc={'A:r:B': CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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indices=tensor([1, 0], dtype=torch.int32),
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), 'B:rr:A': CSCFormatBase(indptr=tensor([0, 2], dtype=torch.int32),
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indices=tensor([1, 0], dtype=torch.int32),
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)},
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original_row_node_ids={'A': tensor([5, 7]), 'B': tensor([10, 11])},
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original_edge_ids={'A:r:B': tensor([10, 12])},
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original_column_node_ids={'B': tensor([10, 11]), 'A': tensor([5])},
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)],
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node_features={('A', 'x'): tensor([6, 4, 0, 1])},
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labels={'B': tensor([2, 5])},
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input_nodes={'A': tensor([ 5, 7, 9, 11]), 'B': tensor([10, 11, 12])},
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indexes=None,
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edge_features=[{('A:r:B', 'x'): tensor([4, 2, 4])},
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{('A:r:B', 'x'): tensor([0, 6])}],
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compacted_seeds={'A:r:B': tensor([0, 1])},
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blocks=[Block(num_src_nodes={'A': 4, 'B': 3},
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num_dst_nodes={'A': 4, 'B': 3},
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num_edges={('A', 'r', 'B'): 3, ('B', 'rr', 'A'): 2},
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metagraph=[('A', 'B', 'r'), ('B', 'A', 'rr')]),
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Block(num_src_nodes={'A': 2, 'B': 2},
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num_dst_nodes={'A': 1, 'B': 2},
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num_edges={('A', 'r', 'B'): 2, ('B', 'rr', 'A'): 2},
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metagraph=[('A', 'B', 'r'), ('B', 'A', 'rr')])],
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)"""
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)
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result = str(minibatch)
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assert result == expect_result, print(result)
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@pytest.mark.parametrize("indptr_dtype", [torch.int32, torch.int64])
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@pytest.mark.parametrize("indices_dtype", [torch.int32, torch.int64])
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def test_get_dgl_blocks_homo(indptr_dtype, indices_dtype):
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csc_formats = [
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gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 3, 5, 6], dtype=indptr_dtype),
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indices=torch.tensor([0, 1, 2, 2, 1, 2], dtype=indices_dtype),
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),
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gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 3], dtype=indptr_dtype),
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indices=torch.tensor([0, 1, 2], dtype=indices_dtype),
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),
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]
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original_column_node_ids = [
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torch.tensor([10, 11, 12, 13]),
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torch.tensor([10, 11]),
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]
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original_row_node_ids = [
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torch.tensor([10, 11, 12, 13]),
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torch.tensor([10, 11, 12]),
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]
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original_edge_ids = [
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torch.tensor([19, 20, 21, 22, 25, 30]),
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torch.tensor([10, 15, 17]),
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]
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subgraphs = []
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for i in range(2):
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subgraphs.append(
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gb.SampledSubgraphImpl(
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sampled_csc=csc_formats[i],
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original_column_node_ids=original_column_node_ids[i],
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original_row_node_ids=original_row_node_ids[i],
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original_edge_ids=original_edge_ids[i],
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)
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)
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# Test minibatch with all attributes.
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minibatch = gb.MiniBatch(
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sampled_subgraphs=subgraphs,
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)
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dgl_blocks = minibatch.blocks
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expect_result = str(
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"""[Block(num_src_nodes=4, num_dst_nodes=4, num_edges=6), Block(num_src_nodes=3, num_dst_nodes=2, num_edges=3)]"""
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)
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result = str(dgl_blocks)
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assert result == expect_result
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def test_get_dgl_blocks_hetero():
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csc_formats = [
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{
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relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 2, 3]),
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indices=torch.tensor([0, 1, 1]),
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),
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reverse_relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 0, 0, 1, 2]),
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indices=torch.tensor([1, 0]),
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),
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},
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{
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relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 1, 2]), indices=torch.tensor([1, 0])
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),
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reverse_relation: gb.CSCFormatBase(
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indptr=torch.tensor([0, 1]),
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indices=torch.tensor([1]),
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),
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},
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]
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original_column_node_ids = [
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{"B": torch.tensor([10, 11, 12]), "A": torch.tensor([5, 7, 9, 11])},
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{"B": torch.tensor([10, 11]), "A": torch.tensor([5])},
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]
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original_row_node_ids = [
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{
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"A": torch.tensor([5, 7, 9, 11]),
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"B": torch.tensor([10, 11, 12]),
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},
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{
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"A": torch.tensor([5, 7]),
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"B": torch.tensor([10, 11]),
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},
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]
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original_edge_ids = [
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{
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relation: torch.tensor([19, 20, 21]),
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reverse_relation: torch.tensor([23, 26]),
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},
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{relation: torch.tensor([10, 12])},
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]
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subgraphs = []
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for i in range(2):
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subgraphs.append(
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gb.SampledSubgraphImpl(
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sampled_csc=csc_formats[i],
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original_column_node_ids=original_column_node_ids[i],
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original_row_node_ids=original_row_node_ids[i],
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original_edge_ids=original_edge_ids[i],
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)
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)
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# Test minibatch with all attributes.
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minibatch = gb.MiniBatch(
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sampled_subgraphs=subgraphs,
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)
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dgl_blocks = minibatch.blocks
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expect_result = str(
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"""[Block(num_src_nodes={'A': 4, 'B': 3},
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num_dst_nodes={'A': 4, 'B': 3},
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num_edges={('A', 'r', 'B'): 3, ('B', 'rr', 'A'): 2},
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metagraph=[('A', 'B', 'r'), ('B', 'A', 'rr')]), Block(num_src_nodes={'A': 2, 'B': 2},
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num_dst_nodes={'A': 1, 'B': 2},
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num_edges={('A', 'r', 'B'): 2, ('B', 'rr', 'A'): 1},
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metagraph=[('A', 'B', 'r'), ('B', 'A', 'rr')])]"""
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)
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result = str(dgl_blocks)
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assert result == expect_result
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def test_get_dgl_blocks_hetero_partial_empty_edges():
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hg = dgl.heterograph(
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{
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("n1", "e1", "n1"): ([0, 1, 1], [1, 2, 0]),
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("n1", "e2", "n2"): ([0, 1, 2], [1, 0, 2]),
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}
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)
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gb_g = gb.from_dglgraph(hg, is_homogeneous=False)
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train_set = gb.HeteroItemSet(
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{"n1:e2:n2": gb.ItemSet(torch.LongTensor([[0, 1]]), names="seeds")}
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)
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datapipe = gb.ItemSampler(train_set, batch_size=1)
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datapipe = datapipe.sample_neighbor(gb_g, fanouts=[-1, -1])
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dataloader = gb.DataLoader(datapipe)
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blocks_str = str(next(iter(dataloader)).blocks)
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expected_str = """[Block(num_src_nodes={'n1': 2, 'n2': 0},
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num_dst_nodes={'n1': 2, 'n2': 0},
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num_edges={('n1', 'e1', 'n1'): 2, ('n1', 'e2', 'n2'): 0},
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metagraph=[('n1', 'n1', 'e1'), ('n1', 'n2', 'e2')]), Block(num_src_nodes={'n1': 2, 'n2': 0},
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num_dst_nodes={'n1': 1, 'n2': 1},
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num_edges={('n1', 'e1', 'n1'): 1, ('n1', 'e2', 'n2'): 1},
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metagraph=[('n1', 'n1', 'e1'), ('n1', 'n2', 'e2')])]"""
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assert expected_str == blocks_str
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def test_get_dgl_blocks_hetero_empty_edges():
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hg = dgl.heterograph(
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{
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("n3", "e1", "n1"): ([0, 1, 1], [1, 2, 0]),
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("n3", "e2", "n2"): ([0, 1, 2], [1, 0, 2]),
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}
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)
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gb_g = gb.from_dglgraph(hg, is_homogeneous=False)
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train_set = gb.HeteroItemSet(
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{"n3:e1:n1": gb.ItemSet(torch.LongTensor([[2, 1]]), names="seeds")}
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)
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datapipe = gb.ItemSampler(train_set, batch_size=1)
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datapipe = datapipe.sample_neighbor(gb_g, fanouts=[-1, -1])
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dataloader = gb.DataLoader(datapipe)
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blocks_str = str(next(iter(dataloader)).blocks)
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expected_str = """[Block(num_src_nodes={'n1': 0, 'n2': 0, 'n3': 2},
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num_dst_nodes={'n1': 0, 'n2': 0, 'n3': 2},
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num_edges={('n3', 'e1', 'n1'): 0, ('n3', 'e2', 'n2'): 0},
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metagraph=[('n3', 'n1', 'e1'), ('n3', 'n2', 'e2')]), Block(num_src_nodes={'n1': 0, 'n2': 0, 'n3': 2},
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num_dst_nodes={'n1': 1, 'n2': 0, 'n3': 1},
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num_edges={('n3', 'e1', 'n1'): 1, ('n3', 'e2', 'n2'): 0},
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metagraph=[('n3', 'n1', 'e1'), ('n3', 'n2', 'e2')])]"""
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assert expected_str == blocks_str
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def test_get_dgl_blocks_homo_empty_edges():
|
|
g = dgl.graph(([2, 3, 4], [3, 4, 5]))
|
|
|
|
gb_g = gb.from_dglgraph(g, is_homogeneous=True)
|
|
train_set = gb.ItemSet(torch.LongTensor([[0, 1]]), names="seeds")
|
|
datapipe = gb.ItemSampler(train_set, batch_size=1)
|
|
datapipe = datapipe.sample_neighbor(gb_g, fanouts=[-1, -1])
|
|
dataloader = gb.DataLoader(datapipe)
|
|
blocks_str = str(next(iter(dataloader)).blocks)
|
|
expected_str = "[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=0), Block(num_src_nodes=2, num_dst_nodes=2, num_edges=0)]"
|
|
assert expected_str == blocks_str
|
|
|
|
|
|
def test_seeds_ntype_being_passed():
|
|
hg = dgl.heterograph({("n1", "e1", "n2"): ([0, 1, 2], [2, 0, 1])})
|
|
|
|
gb_g = gb.from_dglgraph(hg, is_homogeneous=False)
|
|
train_set = gb.HeteroItemSet(
|
|
{"n2": gb.ItemSet(torch.LongTensor([0, 1]), names="seeds")}
|
|
)
|
|
datapipe = gb.ItemSampler(train_set, batch_size=2)
|
|
datapipe = datapipe.sample_neighbor(gb_g, [-1, -1, -1])
|
|
dataloader = gb.DataLoader(datapipe)
|
|
blocks = next(iter(dataloader)).blocks
|
|
for block in blocks:
|
|
assert "n2" in block.srctypes
|
|
|
|
|
|
def create_homo_minibatch():
|
|
csc_formats = [
|
|
gb.CSCFormatBase(
|
|
indptr=torch.tensor([0, 1, 3, 5, 6]),
|
|
indices=torch.tensor([0, 1, 2, 2, 1, 2]),
|
|
),
|
|
gb.CSCFormatBase(
|
|
indptr=torch.tensor([0, 2, 3]),
|
|
indices=torch.tensor([1, 2, 0]),
|
|
),
|
|
]
|
|
original_column_node_ids = [
|
|
torch.tensor([10, 11, 12, 13]),
|
|
torch.tensor([10, 11]),
|
|
]
|
|
original_row_node_ids = [
|
|
torch.tensor([10, 11, 12, 13]),
|
|
torch.tensor([10, 11, 12]),
|
|
]
|
|
original_edge_ids = [
|
|
torch.tensor([19, 20, 21, 22, 25, 30]),
|
|
torch.tensor([10, 15, 17]),
|
|
]
|
|
node_features = {"x": torch.randint(0, 10, (4,))}
|
|
edge_features = [
|
|
{"x": torch.randint(0, 10, (6,))},
|
|
{"x": torch.randint(0, 10, (3,))},
|
|
]
|
|
subgraphs = []
|
|
for i in range(2):
|
|
subgraphs.append(
|
|
gb.SampledSubgraphImpl(
|
|
sampled_csc=csc_formats[i],
|
|
original_column_node_ids=original_column_node_ids[i],
|
|
original_row_node_ids=original_row_node_ids[i],
|
|
original_edge_ids=original_edge_ids[i],
|
|
)
|
|
)
|
|
return gb.MiniBatch(
|
|
sampled_subgraphs=subgraphs,
|
|
node_features=node_features,
|
|
edge_features=edge_features,
|
|
input_nodes=torch.tensor([10, 11, 12, 13]),
|
|
)
|
|
|
|
|
|
def create_hetero_minibatch():
|
|
sampled_csc = [
|
|
{
|
|
relation: gb.CSCFormatBase(
|
|
indptr=torch.tensor([0, 1, 2, 3]),
|
|
indices=torch.tensor([0, 1, 1]),
|
|
),
|
|
reverse_relation: gb.CSCFormatBase(
|
|
indptr=torch.tensor([0, 0, 0, 1, 2]),
|
|
indices=torch.tensor([1, 0]),
|
|
),
|
|
},
|
|
{
|
|
relation: gb.CSCFormatBase(
|
|
indptr=torch.tensor([0, 1, 2]), indices=torch.tensor([1, 0])
|
|
)
|
|
},
|
|
]
|
|
original_column_node_ids = [
|
|
{"B": torch.tensor([10, 11, 12]), "A": torch.tensor([5, 7, 9, 11])},
|
|
{"B": torch.tensor([10, 11])},
|
|
]
|
|
original_row_node_ids = [
|
|
{
|
|
"A": torch.tensor([5, 7, 9, 11]),
|
|
"B": torch.tensor([10, 11, 12]),
|
|
},
|
|
{
|
|
"A": torch.tensor([5, 7]),
|
|
"B": torch.tensor([10, 11]),
|
|
},
|
|
]
|
|
original_edge_ids = [
|
|
{
|
|
relation: torch.tensor([19, 20, 21]),
|
|
reverse_relation: torch.tensor([23, 26]),
|
|
},
|
|
{relation: torch.tensor([10, 12])},
|
|
]
|
|
node_features = {
|
|
("A", "x"): torch.randint(0, 10, (4,)),
|
|
}
|
|
edge_features = [
|
|
{(relation, "x"): torch.randint(0, 10, (3,))},
|
|
{(relation, "x"): torch.randint(0, 10, (2,))},
|
|
]
|
|
subgraphs = []
|
|
for i in range(2):
|
|
subgraphs.append(
|
|
gb.SampledSubgraphImpl(
|
|
sampled_csc=sampled_csc[i],
|
|
original_column_node_ids=original_column_node_ids[i],
|
|
original_row_node_ids=original_row_node_ids[i],
|
|
original_edge_ids=original_edge_ids[i],
|
|
)
|
|
)
|
|
return gb.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]),
|
|
},
|
|
)
|
|
|
|
|
|
def check_dgl_blocks_hetero(minibatch, blocks):
|
|
etype = gb.etype_str_to_tuple(relation)
|
|
sampled_csc = [
|
|
subgraph.sampled_csc for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
original_edge_ids = [
|
|
subgraph.original_edge_ids for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
original_row_node_ids = [
|
|
subgraph.original_row_node_ids
|
|
for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
|
|
for i, block in enumerate(blocks):
|
|
edges = block.edges(etype=etype)
|
|
dst_ndoes = torch.arange(
|
|
0, len(sampled_csc[i][relation].indptr) - 1
|
|
).repeat_interleave(sampled_csc[i][relation].indptr.diff())
|
|
assert torch.equal(edges[0], sampled_csc[i][relation].indices)
|
|
assert torch.equal(edges[1], dst_ndoes)
|
|
assert torch.equal(
|
|
block.edges[etype].data[dgl.EID], original_edge_ids[i][relation]
|
|
)
|
|
edges = blocks[0].edges(etype=gb.etype_str_to_tuple(reverse_relation))
|
|
dst_ndoes = torch.arange(
|
|
0, len(sampled_csc[0][reverse_relation].indptr) - 1
|
|
).repeat_interleave(sampled_csc[0][reverse_relation].indptr.diff())
|
|
assert torch.equal(edges[0], sampled_csc[0][reverse_relation].indices)
|
|
assert torch.equal(edges[1], dst_ndoes)
|
|
assert torch.equal(
|
|
blocks[0].srcdata[dgl.NID]["A"], original_row_node_ids[0]["A"]
|
|
)
|
|
assert torch.equal(
|
|
blocks[0].srcdata[dgl.NID]["B"], original_row_node_ids[0]["B"]
|
|
)
|
|
|
|
|
|
def check_dgl_blocks_homo(minibatch, blocks):
|
|
sampled_csc = [
|
|
subgraph.sampled_csc for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
original_edge_ids = [
|
|
subgraph.original_edge_ids for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
original_row_node_ids = [
|
|
subgraph.original_row_node_ids
|
|
for subgraph in minibatch.sampled_subgraphs
|
|
]
|
|
for i, block in enumerate(blocks):
|
|
dst_ndoes = torch.arange(
|
|
0, len(sampled_csc[i].indptr) - 1
|
|
).repeat_interleave(sampled_csc[i].indptr.diff())
|
|
assert torch.equal(block.edges()[0], sampled_csc[i].indices)
|
|
assert torch.equal(block.edges()[1], dst_ndoes)
|
|
assert torch.equal(block.edata[dgl.EID], original_edge_ids[i])
|
|
assert torch.equal(blocks[0].srcdata[dgl.NID], original_row_node_ids[0])
|
|
|
|
|
|
def test_dgl_node_classification_without_feature():
|
|
# Arrange
|
|
minibatch = create_homo_minibatch()
|
|
minibatch.node_features = None
|
|
minibatch.labels = None
|
|
minibatch.seeds = torch.tensor([10, 15])
|
|
# Act
|
|
dgl_blocks = minibatch.blocks
|
|
|
|
# Assert
|
|
assert len(dgl_blocks) == 2
|
|
assert minibatch.node_features is None
|
|
assert minibatch.labels is None
|
|
check_dgl_blocks_homo(minibatch, dgl_blocks)
|
|
|
|
|
|
def test_dgl_node_classification_homo():
|
|
# Arrange
|
|
minibatch = create_homo_minibatch()
|
|
minibatch.seeds = torch.tensor([10, 15])
|
|
minibatch.labels = torch.tensor([2, 5])
|
|
# Act
|
|
dgl_blocks = minibatch.blocks
|
|
|
|
# Assert
|
|
assert len(dgl_blocks) == 2
|
|
check_dgl_blocks_homo(minibatch, dgl_blocks)
|
|
|
|
|
|
def test_dgl_node_classification_hetero():
|
|
minibatch = create_hetero_minibatch()
|
|
minibatch.labels = {"B": torch.tensor([2, 5])}
|
|
minibatch.seeds = {"B": torch.tensor([10, 15])}
|
|
# Act
|
|
dgl_blocks = minibatch.blocks
|
|
|
|
# Assert
|
|
assert len(dgl_blocks) == 2
|
|
check_dgl_blocks_hetero(minibatch, dgl_blocks)
|
|
|
|
|
|
def test_dgl_link_predication_homo():
|
|
# Arrange
|
|
minibatch = create_homo_minibatch()
|
|
minibatch.compacted_seeds = (
|
|
torch.tensor([[0, 1, 0, 0, 1, 1], [1, 0, 1, 1, 0, 0]]).T,
|
|
)
|
|
minibatch.labels = torch.tensor([1, 1, 0, 0, 0, 0])
|
|
# Act
|
|
dgl_blocks = minibatch.blocks
|
|
|
|
# Assert
|
|
assert len(dgl_blocks) == 2
|
|
check_dgl_blocks_homo(minibatch, dgl_blocks)
|
|
|
|
|
|
def test_dgl_link_predication_hetero():
|
|
# Arrange
|
|
minibatch = create_hetero_minibatch()
|
|
minibatch.compacted_seeds = {
|
|
relation: (torch.tensor([[1, 1, 2, 0, 1, 2], [1, 0, 1, 1, 0, 0]]).T,),
|
|
reverse_relation: (
|
|
torch.tensor([[0, 1, 1, 2, 0, 2], [1, 0, 1, 1, 0, 0]]).T,
|
|
),
|
|
}
|
|
minibatch.labels = {
|
|
relation: (torch.tensor([1, 1, 0, 0, 0, 0]),),
|
|
reverse_relation: (torch.tensor([1, 1, 0, 0, 0, 0]),),
|
|
}
|
|
# Act
|
|
dgl_blocks = minibatch.blocks
|
|
|
|
# Assert
|
|
assert len(dgl_blocks) == 2
|
|
check_dgl_blocks_hetero(minibatch, dgl_blocks)
|
|
|
|
|
|
def test_to_pyg_data():
|
|
test_minibatch = create_homo_minibatch()
|
|
test_minibatch.seeds = torch.tensor([0, 1])
|
|
test_minibatch.labels = torch.tensor([7, 8])
|
|
|
|
expected_edge_index = torch.tensor(
|
|
[[0, 0, 1, 1, 1, 2, 2, 2, 2], [0, 1, 0, 1, 2, 0, 1, 2, 3]]
|
|
)
|
|
expected_node_features = next(iter(test_minibatch.node_features.values()))
|
|
expected_labels = torch.tensor([7, 8])
|
|
expected_batch_size = 2
|
|
expected_n_id = torch.tensor([10, 11, 12, 13])
|
|
|
|
pyg_data = test_minibatch.to_pyg_data()
|
|
pyg_data.validate()
|
|
assert torch.equal(pyg_data.edge_index, expected_edge_index)
|
|
assert torch.equal(pyg_data.x, expected_node_features)
|
|
assert torch.equal(pyg_data.y, expected_labels)
|
|
assert pyg_data.batch_size == expected_batch_size
|
|
assert torch.equal(pyg_data.n_id, expected_n_id)
|
|
|
|
test_minibatch.seeds = torch.tensor([[0, 1], [2, 3]])
|
|
assert pyg_data.batch_size == expected_batch_size
|
|
|
|
test_minibatch.seeds = {"A": torch.tensor([0, 1])}
|
|
assert pyg_data.batch_size == expected_batch_size
|
|
|
|
test_minibatch.seeds = {"A": torch.tensor([[0, 1], [2, 3]])}
|
|
assert pyg_data.batch_size == expected_batch_size
|
|
|
|
subgraph = test_minibatch.sampled_subgraphs[0]
|
|
# Test with sampled_csc as None.
|
|
test_minibatch = gb.MiniBatch(
|
|
sampled_subgraphs=None,
|
|
node_features={"feat": expected_node_features},
|
|
labels=expected_labels,
|
|
)
|
|
pyg_data = test_minibatch.to_pyg_data()
|
|
assert pyg_data.edge_index is None, "Edge index should be none."
|
|
|
|
# Test with node_features as None.
|
|
test_minibatch = gb.MiniBatch(
|
|
sampled_subgraphs=[subgraph],
|
|
node_features=None,
|
|
labels=expected_labels,
|
|
)
|
|
pyg_data = test_minibatch.to_pyg_data()
|
|
assert pyg_data.x is None, "Node features should be None."
|
|
|
|
# Test with labels as None.
|
|
test_minibatch = gb.MiniBatch(
|
|
sampled_subgraphs=[subgraph],
|
|
node_features={"feat": expected_node_features},
|
|
labels=None,
|
|
)
|
|
pyg_data = test_minibatch.to_pyg_data()
|
|
assert pyg_data.y is None, "Labels should be None."
|
|
|
|
# Test with multiple features.
|
|
test_minibatch = gb.MiniBatch(
|
|
sampled_subgraphs=[subgraph],
|
|
node_features={
|
|
"feat": expected_node_features,
|
|
"extra_feat": torch.tensor([[3], [4]]),
|
|
},
|
|
labels=expected_labels,
|
|
)
|
|
try:
|
|
pyg_data = test_minibatch.to_pyg_data()
|
|
assert (
|
|
pyg_data.x is None
|
|
), "Multiple features case should raise an error."
|
|
except AssertionError as e:
|
|
assert (
|
|
str(e)
|
|
== "`to_pyg_data` only supports single feature homogeneous graph."
|
|
)
|