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[Misc] Move sample_utils tests to correct directory. (#7125)
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Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
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@yxy235
yxy235 and Ubuntu authored Feb 21, 2024
1 parent 3ced341 commit bfd7cee
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269 changes: 0 additions & 269 deletions tests/python/pytorch/graphbolt/test_graphbolt_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,272 +69,3 @@ def test_find_reverse_edges_circual_reverse_types():
assert torch.equal(edges["A:r1:B"][1], expected_edges["A:r1:B"][1])
assert torch.equal(edges["C:r3:A"][0], expected_edges["C:r3:A"][0])
assert torch.equal(edges["C:r3:A"][1], expected_edges["C:r3:A"][1])


def test_unique_and_compact_hetero():
N1 = torch.tensor(
[0, 5, 2, 7, 12, 7, 9, 5, 6, 2, 3, 4, 1, 0, 9], device=F.ctx()
)
N2 = torch.tensor([0, 3, 3, 5, 2, 7, 2, 8, 4, 9, 2, 3], device=F.ctx())
N3 = torch.tensor([1, 2, 6, 6, 1, 8, 3, 6, 3, 2], device=F.ctx())
expected_unique = {
"n1": torch.tensor([0, 5, 2, 7, 12, 9, 6, 3, 4, 1], device=F.ctx()),
"n2": torch.tensor([0, 3, 5, 2, 7, 8, 4, 9], device=F.ctx()),
"n3": torch.tensor([1, 2, 6, 8, 3], device=F.ctx()),
}
if N1.is_cuda:
expected_reverse_id = {
k: v.sort()[1] for k, v in expected_unique.items()
}
expected_unique = {k: v.sort()[0] for k, v in expected_unique.items()}
else:
expected_reverse_id = {
k: torch.arange(0, v.shape[0], device=F.ctx())
for k, v in expected_unique.items()
}
nodes_dict = {
"n1": N1.split(5),
"n2": N2.split(4),
"n3": N3.split(2),
}
expected_nodes_dict = {
"n1": [
torch.tensor([0, 1, 2, 3, 4], device=F.ctx()),
torch.tensor([3, 5, 1, 6, 2], device=F.ctx()),
torch.tensor([7, 8, 9, 0, 5], device=F.ctx()),
],
"n2": [
torch.tensor([0, 1, 1, 2], device=F.ctx()),
torch.tensor([3, 4, 3, 5], device=F.ctx()),
torch.tensor([6, 7, 3, 1], device=F.ctx()),
],
"n3": [
torch.tensor([0, 1], device=F.ctx()),
torch.tensor([2, 2], device=F.ctx()),
torch.tensor([0, 3], device=F.ctx()),
torch.tensor([4, 2], device=F.ctx()),
torch.tensor([4, 1], device=F.ctx()),
],
}

unique, compacted = gb.unique_and_compact(nodes_dict)
for ntype, nodes in unique.items():
expected_nodes = expected_unique[ntype]
assert torch.equal(nodes, expected_nodes)

for ntype, nodes in compacted.items():
expected_nodes = expected_nodes_dict[ntype]
assert isinstance(nodes, list)
for expected_node, node in zip(expected_nodes, nodes):
node = expected_reverse_id[ntype][node]
assert torch.equal(expected_node, node)


def test_unique_and_compact_homo():
N = torch.tensor(
[0, 5, 2, 7, 12, 7, 9, 5, 6, 2, 3, 4, 1, 0, 9], device=F.ctx()
)
expected_unique_N = torch.tensor(
[0, 5, 2, 7, 12, 9, 6, 3, 4, 1], device=F.ctx()
)
if N.is_cuda:
expected_reverse_id_N = expected_unique_N.sort()[1]
expected_unique_N = expected_unique_N.sort()[0]
else:
expected_reverse_id_N = torch.arange(
0, expected_unique_N.shape[0], device=F.ctx()
)
nodes_list = N.split(5)
expected_nodes_list = [
torch.tensor([0, 1, 2, 3, 4], device=F.ctx()),
torch.tensor([3, 5, 1, 6, 2], device=F.ctx()),
torch.tensor([7, 8, 9, 0, 5], device=F.ctx()),
]

unique, compacted = gb.unique_and_compact(nodes_list)

assert torch.equal(unique, expected_unique_N)
assert isinstance(compacted, list)
for expected_node, node in zip(expected_nodes_list, compacted):
node = expected_reverse_id_N[node]
assert torch.equal(expected_node, node)


def test_unique_and_compact_csc_formats_hetero():
dst_nodes = {
"n2": torch.tensor([2, 4, 1, 3]),
"n3": torch.tensor([1, 3, 2, 7]),
}
csc_formats = {
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 3, 4, 7, 10]),
indices=torch.tensor([1, 3, 4, 6, 2, 7, 9, 4, 2, 6]),
),
"n1:e2:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 1, 4, 7, 10]),
indices=torch.tensor([5, 2, 6, 4, 7, 2, 8, 1, 3, 0]),
),
"n2:e3:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4, 6, 8]),
indices=torch.tensor([2, 5, 4, 1, 4, 3, 6, 0]),
),
}

expected_unique_nodes = {
"n1": torch.tensor([1, 3, 4, 6, 2, 7, 9, 5, 8, 0]),
"n2": torch.tensor([2, 4, 1, 3, 5, 6, 0]),
"n3": torch.tensor([1, 3, 2, 7]),
}
expected_csc_formats = {
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 3, 4, 7, 10]),
indices=torch.tensor([0, 1, 2, 3, 4, 5, 6, 2, 4, 3]),
),
"n1:e2:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 1, 4, 7, 10]),
indices=torch.tensor([7, 4, 3, 2, 5, 4, 8, 0, 1, 9]),
),
"n2:e3:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4, 6, 8]),
indices=torch.tensor([0, 4, 1, 2, 1, 3, 5, 6]),
),
}

unique_nodes, compacted_csc_formats = gb.unique_and_compact_csc_formats(
csc_formats, dst_nodes
)

for ntype, nodes in unique_nodes.items():
expected_nodes = expected_unique_nodes[ntype]
assert torch.equal(nodes, expected_nodes)
for etype, pair in compacted_csc_formats.items():
indices = pair.indices
indptr = pair.indptr
expected_indices = expected_csc_formats[etype].indices
expected_indptr = expected_csc_formats[etype].indptr
assert torch.equal(indices, expected_indices)
assert torch.equal(indptr, expected_indptr)


def test_unique_and_compact_csc_formats_homo():
seeds = torch.tensor([1, 3, 5, 2, 6])
indptr = torch.tensor([0, 2, 4, 6, 7, 11])
indices = torch.tensor([2, 3, 1, 4, 5, 2, 5, 1, 4, 4, 6])
csc_formats = gb.CSCFormatBase(indptr=indptr, indices=indices)

expected_unique_nodes = torch.tensor([1, 3, 5, 2, 6, 4])
expected_indptr = indptr
expected_indices = torch.tensor([3, 1, 0, 5, 2, 3, 2, 0, 5, 5, 4])

unique_nodes, compacted_csc_formats = gb.unique_and_compact_csc_formats(
csc_formats, seeds
)

indptr = compacted_csc_formats.indptr
indices = compacted_csc_formats.indices
assert torch.equal(indptr, expected_indptr)
assert torch.equal(indices, expected_indices)
assert torch.equal(unique_nodes, expected_unique_nodes)


def test_unique_and_compact_incorrect_indptr():
seeds = torch.tensor([1, 3, 5, 2, 6, 7])
indptr = torch.tensor([0, 2, 4, 6, 7, 11])
indices = torch.tensor([2, 3, 1, 4, 5, 2, 5, 1, 4, 4, 6])
csc_formats = gb.CSCFormatBase(indptr=indptr, indices=indices)

# The number of seeds is not corresponding to indptr.
with pytest.raises(AssertionError):
gb.unique_and_compact_csc_formats(csc_formats, seeds)


def test_compact_csc_format_hetero():
dst_nodes = {
"n2": torch.tensor([2, 4, 1, 3]),
"n3": torch.tensor([1, 3, 2, 7]),
}
csc_formats = {
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 3, 4, 7, 10]),
indices=torch.tensor([1, 3, 4, 6, 2, 7, 9, 4, 2, 6]),
),
"n1:e2:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 1, 4, 7, 10]),
indices=torch.tensor([5, 2, 6, 4, 7, 2, 8, 1, 3, 0]),
),
"n2:e3:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4, 6, 8]),
indices=torch.tensor([2, 5, 4, 1, 4, 3, 6, 0]),
),
}

expected_original_row_ids = {
"n1": torch.tensor(
[1, 3, 4, 6, 2, 7, 9, 4, 2, 6, 5, 2, 6, 4, 7, 2, 8, 1, 3, 0]
),
"n2": torch.tensor([2, 4, 1, 3, 2, 5, 4, 1, 4, 3, 6, 0]),
"n3": torch.tensor([1, 3, 2, 7]),
}
expected_csc_formats = {
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 3, 4, 7, 10]),
indices=torch.arange(0, 10),
),
"n1:e2:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 1, 4, 7, 10]),
indices=torch.arange(0, 10) + 10,
),
"n2:e3:n3": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4, 6, 8]),
indices=torch.arange(0, 8) + 4,
),
}
original_row_ids, compacted_csc_formats = gb.compact_csc_format(
csc_formats, dst_nodes
)

for ntype, nodes in original_row_ids.items():
expected_nodes = expected_original_row_ids[ntype]
assert torch.equal(nodes, expected_nodes)
for etype, csc_format in compacted_csc_formats.items():
indptr = csc_format.indptr
indices = csc_format.indices
expected_indptr = expected_csc_formats[etype].indptr
expected_indices = expected_csc_formats[etype].indices
assert torch.equal(indptr, expected_indptr)
assert torch.equal(indices, expected_indices)


def test_compact_csc_format_homo():
seeds = torch.tensor([1, 3, 5, 2, 6])
indptr = torch.tensor([0, 2, 4, 6, 7, 11])
indices = torch.tensor([2, 3, 1, 4, 5, 2, 5, 1, 4, 4, 6])
csc_formats = gb.CSCFormatBase(indptr=indptr, indices=indices)

expected_original_row_ids = torch.tensor(
[1, 3, 5, 2, 6, 2, 3, 1, 4, 5, 2, 5, 1, 4, 4, 6]
)
expected_indptr = indptr
expected_indices = torch.arange(0, len(indices)) + 5

original_row_ids, compacted_csc_formats = gb.compact_csc_format(
csc_formats, seeds
)

indptr = compacted_csc_formats.indptr
indices = compacted_csc_formats.indices

assert torch.equal(indptr, expected_indptr)
assert torch.equal(indices, expected_indices)
assert torch.equal(original_row_ids, expected_original_row_ids)


def test_compact_incorrect_indptr():
seeds = torch.tensor([1, 3, 5, 2, 6, 7])
indptr = torch.tensor([0, 2, 4, 6, 7, 11])
indices = torch.tensor([2, 3, 1, 4, 5, 2, 5, 1, 4, 4, 6])
csc_formats = gb.CSCFormatBase(indptr=indptr, indices=indices)

# The number of seeds is not corresponding to indptr.
with pytest.raises(AssertionError):
gb.compact_csc_format(csc_formats, seeds)
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