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import itertools
import warnings
import pytest
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric import EdgeIndex
from torch_geometric.profile import benchmark
from torch_geometric.testing import withCUDA, withPackage
from torch_geometric.typing import SparseTensor
from torch_geometric.utils import spmm, to_torch_coo_tensor
@withCUDA
@pytest.mark.parametrize('reduce', ['sum', 'mean'])
def test_spmm_basic(device, reduce):
src = torch.randn(5, 4, device=device)
other = torch.randn(4, 8, device=device)
out1 = (src @ other) / (src.size(1) if reduce == 'mean' else 1)
out2 = spmm(src.to_sparse_csr(), other, reduce=reduce)
assert out1.size() == (5, 8)
assert torch.allclose(out1, out2, atol=1e-6)
if torch_geometric.typing.WITH_TORCH_SPARSE:
out3 = spmm(SparseTensor.from_dense(src), other, reduce=reduce)
assert torch.allclose(out2, out3, atol=1e-6)
# Test `mean` reduction with isolated nodes:
src[0] = 0.
out1 = (src @ other) / (4. if reduce == 'mean' else 1.)
out2 = spmm(src.to_sparse_csr(), other, reduce=reduce)
assert out1.size() == (5, 8)
assert torch.allclose(out1, out2, atol=1e-6)
if torch_geometric.typing.WITH_TORCH_SPARSE:
out3 = spmm(SparseTensor.from_dense(src), other, reduce=reduce)
assert torch.allclose(out2, out3, atol=1e-6)
@withCUDA
@withPackage('torch>=2.0.0')
@pytest.mark.parametrize('reduce', ['min', 'max'])
def test_spmm_reduce(device, reduce):
src = torch.randn(5, 4, device=device)
other = torch.randn(4, 8, device=device)
if src.is_cuda:
with pytest.raises(NotImplementedError, match="not yet supported"):
spmm(src.to_sparse_csr(), other, reduce)
else:
out1 = spmm(src.to_sparse_csr(), other, reduce)
assert out1.size() == (5, 8)
if torch_geometric.typing.WITH_TORCH_SPARSE:
out2 = spmm(SparseTensor.from_dense(src), other, reduce=reduce)
assert torch.allclose(out1, out2)
@withCUDA
@withPackage('torch>=2.0.0')
@pytest.mark.parametrize(
'layout', [torch.sparse_coo, torch.sparse_csr, torch.sparse_csc])
@pytest.mark.parametrize('reduce', ['sum', 'mean', 'min', 'max'])
def test_spmm_layout(device, layout, reduce):
src = torch.randn(5, 4, device=device)
if layout == torch.sparse_coo:
src = src.to_sparse_coo()
elif layout == torch.sparse_csr:
src = src.to_sparse_csr()
else:
assert layout == torch.sparse_csc
src = src.to_sparse_csc()
other = torch.randn(4, 8, device=device)
if src.is_cuda and reduce in {'min', 'max'}:
with pytest.raises(NotImplementedError, match="not yet supported"):
spmm(src, other, reduce=reduce)
elif layout != torch.sparse_csr:
with pytest.warns(UserWarning, match="Converting sparse tensor"):
spmm(src, other, reduce=reduce)
else:
spmm(src, other, reduce=reduce)
@pytest.mark.parametrize('reduce', ['sum', 'mean'])
def test_spmm_jit(reduce):
@torch.jit.script
def jit_torch_sparse(src: SparseTensor, other: Tensor,
reduce: str) -> Tensor:
return spmm(src, other, reduce=reduce)
@torch.jit.script
def jit_torch(src: Tensor, other: Tensor, reduce: str) -> Tensor:
return spmm(src, other, reduce=reduce)
src = torch.randn(5, 4)
other = torch.randn(4, 8)
out1 = src @ other
out2 = jit_torch(src.to_sparse_csr(), other, reduce)
assert out1.size() == (5, 8)
if reduce == 'sum':
assert torch.allclose(out1, out2, atol=1e-6)
if torch_geometric.typing.WITH_TORCH_SPARSE:
out3 = jit_torch_sparse(SparseTensor.from_dense(src), other, reduce)
assert torch.allclose(out2, out3, atol=1e-6)
@withCUDA
@withPackage('torch>=2.0.0')
@pytest.mark.parametrize('reduce', ['sum', 'mean', 'min', 'max'])
def test_spmm_edge_index(device, reduce):
src = EdgeIndex(
[[0, 1, 1, 2], [1, 0, 2, 1]],
sparse_size=(4, 3),
sort_order='row',
device=device,
)
other = torch.rand(3, 4, device=device)
out = spmm(src, other, reduce=reduce)
assert out.size() == (4, 4)
if not other.is_cuda or reduce not in ['min', 'max']:
out2 = spmm(src.to_sparse_csr(), other, reduce=reduce)
assert torch.allclose(out, out2)
if __name__ == '__main__':
import argparse
warnings.filterwarnings('ignore', ".*Sparse CSR tensor support.*")
warnings.filterwarnings('ignore', ".*Converting sparse tensor to CSR.*")
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--backward', action='store_true')
args = parser.parse_args()
num_nodes, num_edges = 10_000, 200_000
x = torch.randn(num_nodes, 64, device=args.device)
edge_index = torch.randint(num_nodes, (2, num_edges), device=args.device)
reductions = ['sum', 'mean']
if not x.is_cuda:
reductions.extend(['min', 'max'])
layouts = [torch.sparse_coo, torch.sparse_csr, torch.sparse_csc]
for reduce, layout in itertools.product(reductions, layouts):
print(f'Aggregator: {reduce}, Layout: {layout}')
adj = to_torch_coo_tensor(edge_index, size=num_nodes)
adj = adj.to_sparse(layout=layout)
benchmark(
funcs=[spmm],
func_names=['spmm'],
args=(adj, x, reduce),
num_steps=50 if args.device == 'cpu' else 500,
num_warmups=10 if args.device == 'cpu' else 100,
backward=args.backward,
)
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