Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Misc] Move sample_utils tests to correct directory. #7125

Merged
merged 2 commits into from
Feb 21, 2024
Merged

[Misc] Move sample_utils tests to correct directory. #7125

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c79e9e3
move tests
Feb 19, 2024
05f74b4
rename
Feb 19, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
269 changes: 0 additions & 269 deletions tests/python/pytorch/graphbolt/test_graphbolt_utils.py
View file
Open in desktop
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)
Loading
Loading