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import os.path as osp
import pytest
import torch
import torch_geometric.typing
from torch_geometric.io import fs
from torch_geometric.profile import benchmark
from torch_geometric.testing import is_full_test, withCUDA, withPackage
from torch_geometric.typing import SparseTensor
from torch_geometric.utils import (
dense_to_sparse,
is_sparse,
is_torch_sparse_tensor,
to_edge_index,
to_torch_coo_tensor,
to_torch_csc_tensor,
to_torch_csr_tensor,
to_torch_sparse_tensor,
)
from torch_geometric.utils.sparse import cat
def test_dense_to_sparse():
adj = torch.tensor([
[3.0, 1.0],
[2.0, 0.0],
])
edge_index, edge_attr = dense_to_sparse(adj)
assert edge_index.tolist() == [[0, 0, 1], [0, 1, 0]]
assert edge_attr.tolist() == [3, 1, 2]
if is_full_test():
jit = torch.jit.script(dense_to_sparse)
edge_index, edge_attr = jit(adj)
assert edge_index.tolist() == [[0, 0, 1], [0, 1, 0]]
assert edge_attr.tolist() == [3, 1, 2]
adj = torch.tensor([[
[3.0, 1.0],
[2.0, 0.0],
], [
[0.0, 1.0],
[0.0, 2.0],
]])
edge_index, edge_attr = dense_to_sparse(adj)
assert edge_index.tolist() == [[0, 0, 1, 2, 3], [0, 1, 0, 3, 3]]
assert edge_attr.tolist() == [3, 1, 2, 1, 2]
if is_full_test():
jit = torch.jit.script(dense_to_sparse)
edge_index, edge_attr = jit(adj)
assert edge_index.tolist() == [[0, 0, 1, 2, 3], [0, 1, 0, 3, 3]]
assert edge_attr.tolist() == [3, 1, 2, 1, 2]
adj = torch.tensor([
[
[3.0, 1.0, 0.0],
[2.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
],
[
[0.0, 1.0, 0.0],
[0.0, 2.0, 3.0],
[0.0, 5.0, 0.0],
],
])
mask = torch.tensor([[True, True, False], [True, True, True]])
edge_index, edge_attr = dense_to_sparse(adj, mask)
assert edge_index.tolist() == [[0, 0, 1, 2, 3, 3, 4],
[0, 1, 0, 3, 3, 4, 3]]
assert edge_attr.tolist() == [3, 1, 2, 1, 2, 3, 5]
if is_full_test():
jit = torch.jit.script(dense_to_sparse)
edge_index, edge_attr = jit(adj, mask)
assert edge_index.tolist() == [[0, 0, 1, 2, 3, 3, 4],
[0, 1, 0, 3, 3, 4, 3]]
assert edge_attr.tolist() == [3, 1, 2, 1, 2, 3, 5]
def test_dense_to_sparse_bipartite():
edge_index, edge_attr = dense_to_sparse(torch.rand(2, 10, 5))
assert edge_index[0].max() == 19
assert edge_index[1].max() == 9
def test_is_torch_sparse_tensor():
x = torch.randn(5, 5)
assert not is_torch_sparse_tensor(x)
assert is_torch_sparse_tensor(x.to_sparse())
if torch_geometric.typing.WITH_TORCH_SPARSE:
assert not is_torch_sparse_tensor(SparseTensor.from_dense(x))
def test_is_sparse():
x = torch.randn(5, 5)
assert not is_sparse(x)
assert is_sparse(x.to_sparse())
if torch_geometric.typing.WITH_TORCH_SPARSE:
assert is_sparse(SparseTensor.from_dense(x))
def test_to_torch_coo_tensor():
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2],
])
edge_attr = torch.randn(edge_index.size(1), 8)
adj = to_torch_coo_tensor(edge_index, is_coalesced=False)
assert adj.is_coalesced()
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_coo
assert torch.allclose(adj.indices(), edge_index)
adj = to_torch_coo_tensor(edge_index, is_coalesced=True)
assert adj.is_coalesced()
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_coo
assert torch.allclose(adj.indices(), edge_index)
adj = to_torch_coo_tensor(edge_index, size=6)
assert adj.size() == (6, 6)
assert adj.layout == torch.sparse_coo
assert torch.allclose(adj.indices(), edge_index)
adj = to_torch_coo_tensor(edge_index, edge_attr)
assert adj.size() == (4, 4, 8)
assert adj.layout == torch.sparse_coo
assert torch.allclose(adj.indices(), edge_index)
assert torch.allclose(adj.values(), edge_attr)
if is_full_test():
jit = torch.jit.script(to_torch_coo_tensor)
adj = jit(edge_index, edge_attr)
assert adj.size() == (4, 4, 8)
assert adj.layout == torch.sparse_coo
assert torch.allclose(adj.indices(), edge_index)
assert torch.allclose(adj.values(), edge_attr)
def test_to_torch_csr_tensor():
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2],
])
adj = to_torch_csr_tensor(edge_index)
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_csr
assert torch.allclose(adj.to_sparse_coo().coalesce().indices(), edge_index)
edge_weight = torch.randn(edge_index.size(1))
adj = to_torch_csr_tensor(edge_index, edge_weight)
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_csr
coo = adj.to_sparse_coo().coalesce()
assert torch.allclose(coo.indices(), edge_index)
assert torch.allclose(coo.values(), edge_weight)
if torch_geometric.typing.WITH_PT20:
edge_attr = torch.randn(edge_index.size(1), 8)
adj = to_torch_csr_tensor(edge_index, edge_attr)
assert adj.size() == (4, 4, 8)
assert adj.layout == torch.sparse_csr
coo = adj.to_sparse_coo().coalesce()
assert torch.allclose(coo.indices(), edge_index)
assert torch.allclose(coo.values(), edge_attr)
def test_to_torch_csc_tensor():
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2],
])
adj = to_torch_csc_tensor(edge_index)
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_csc
adj_coo = adj.to_sparse_coo().coalesce()
if torch_geometric.typing.WITH_PT20:
assert torch.allclose(adj_coo.indices(), edge_index)
else:
assert torch.allclose(adj_coo.indices().flip([0]), edge_index)
edge_weight = torch.randn(edge_index.size(1))
adj = to_torch_csc_tensor(edge_index, edge_weight)
assert adj.size() == (4, 4)
assert adj.layout == torch.sparse_csc
adj_coo = adj.to_sparse_coo().coalesce()
if torch_geometric.typing.WITH_PT20:
assert torch.allclose(adj_coo.indices(), edge_index)
assert torch.allclose(adj_coo.values(), edge_weight)
else:
perm = adj_coo.indices()[0].argsort()
assert torch.allclose(adj_coo.indices()[:, perm], edge_index)
assert torch.allclose(adj_coo.values()[perm], edge_weight)
if torch_geometric.typing.WITH_PT20:
edge_attr = torch.randn(edge_index.size(1), 8)
adj = to_torch_csc_tensor(edge_index, edge_attr)
assert adj.size() == (4, 4, 8)
assert adj.layout == torch.sparse_csc
assert torch.allclose(adj.to_sparse_coo().coalesce().indices(),
edge_index)
assert torch.allclose(adj.to_sparse_coo().coalesce().values(),
edge_attr)
@withPackage('torch>=2.1.0')
def test_to_torch_coo_tensor_save_load(tmp_path):
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2],
])
adj = to_torch_coo_tensor(edge_index, is_coalesced=False)
assert adj.is_coalesced()
path = osp.join(tmp_path, 'adj.t')
torch.save(adj, path)
adj = fs.torch_load(path)
assert adj.is_coalesced()
def test_to_edge_index():
adj = torch.tensor([
[0., 1., 0., 0.],
[1., 0., 1., 0.],
[0., 1., 0., 1.],
[0., 0., 1., 0.],
]).to_sparse()
edge_index, edge_attr = to_edge_index(adj)
assert edge_index.tolist() == [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]
assert edge_attr.tolist() == [1., 1., 1., 1., 1., 1.]
if is_full_test():
jit = torch.jit.script(to_edge_index)
edge_index, edge_attr = jit(adj)
assert edge_index.tolist() == [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]
assert edge_attr.tolist() == [1., 1., 1., 1., 1., 1.]
@withCUDA
@pytest.mark.parametrize(
'layout',
[torch.sparse_coo, torch.sparse_csr, torch.sparse_csc],
)
@pytest.mark.parametrize('dim', [0, 1, (0, 1)])
def test_cat(layout, dim, device):
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], device=device)
if torch_geometric.typing.WITH_PT20:
edge_weight = torch.rand(4, 2, device=device)
else:
edge_weight = torch.rand(4, device=device)
adj = to_torch_sparse_tensor(edge_index, edge_weight, layout=layout)
out = cat([adj, adj], dim=dim)
edge_index, edge_weight = to_edge_index(out.to_sparse_csr())
if dim == 0:
if torch_geometric.typing.WITH_PT20:
assert out.size() == (6, 3, 2)
else:
assert out.size() == (6, 3)
assert edge_index[0].tolist() == [0, 1, 1, 2, 3, 4, 4, 5]
assert edge_index[1].tolist() == [1, 0, 2, 1, 1, 0, 2, 1]
elif dim == 1:
if torch_geometric.typing.WITH_PT20:
assert out.size() == (3, 6, 2)
else:
assert out.size() == (3, 6)
assert edge_index[0].tolist() == [0, 0, 1, 1, 1, 1, 2, 2]
assert edge_index[1].tolist() == [1, 4, 0, 2, 3, 5, 1, 4]
else:
if torch_geometric.typing.WITH_PT20:
assert out.size() == (6, 6, 2)
else:
assert out.size() == (6, 6)
assert edge_index[0].tolist() == [0, 1, 1, 2, 3, 4, 4, 5]
assert edge_index[1].tolist() == [1, 0, 2, 1, 4, 3, 5, 4]
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
num_nodes, num_edges = 10_000, 200_000
edge_index = torch.randint(num_nodes, (2, num_edges), device=args.device)
benchmark(
funcs=[
SparseTensor.from_edge_index, to_torch_coo_tensor,
to_torch_csr_tensor, to_torch_csc_tensor
],
func_names=['SparseTensor', 'To COO', 'To CSR', 'To CSC'],
args=(edge_index, None, (num_nodes, num_nodes)),
num_steps=50 if args.device == 'cpu' else 500,
num_warmups=10 if args.device == 'cpu' else 100,
)
|
