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from itertools import product
import pytest
import torch
from torch_sparse import SparseTensor, spspmm
from torch_sparse.testing import devices, grad_dtypes, tensor
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_spspmm(dtype, device):
if dtype in {torch.half, torch.bfloat16}:
return # Not yet implemented.
indexA = torch.tensor([[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]], device=device)
valueA = tensor([1, 2, 3, 4, 5], dtype, device)
indexB = torch.tensor([[0, 2], [1, 0]], device=device)
valueB = tensor([2, 4], dtype, device)
indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
assert indexC.tolist() == [[0, 1, 2], [0, 1, 1]]
assert valueC.tolist() == [8, 6, 8]
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_sparse_tensor_spspmm(dtype, device):
if dtype in {torch.half, torch.bfloat16}:
return # Not yet implemented.
x = SparseTensor(
row=torch.tensor(
[0, 1, 1, 1, 2, 3, 4, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9],
device=device),
col=torch.tensor(
[0, 5, 10, 15, 1, 2, 3, 7, 13, 6, 9, 5, 10, 15, 11, 14, 5, 15],
device=device),
value=torch.tensor([
1, 3**-0.5, 3**-0.5, 3**-0.5, 1, 1, 1, -2**-0.5, -2**-0.5,
-2**-0.5, -2**-0.5, 6**-0.5, -6**0.5 / 3, 6**-0.5, -2**-0.5,
-2**-0.5, 2**-0.5, -2**-0.5
], dtype=dtype, device=device),
)
expected = torch.eye(10, device=device).to(dtype)
out = x @ x.to_dense().t()
assert torch.allclose(out, expected, atol=1e-2)
out = x @ x.t()
out = out.to_dense()
assert torch.allclose(out, expected, atol=1e-2)
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