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Avoid torch.sparse_coo_tensor DeprecationWarning #132

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Merged
kayween merged 2 commits into from
Jun 14, 2026
Merged

Avoid torch.sparse_coo_tensor DeprecationWarning #132

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d2cd1a7
Explicitly pass check_invariants=False to torch.sparse_coo_tensor
AdrianSosic May 20, 2026
d17e575
fix typo
kayween May 20, 2026
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1 change: 1 addition & 0 deletions linear_operator/utils/interpolation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@ def left_t_interp(interp_indices, interp_values, rhs, output_dim):
size,
dtype=summing_matrix_values.dtype,
device=summing_matrix_values.device,
check_invariants=False,
)

# Sum up the values appropriately by performing sparse matrix multiplication
Expand Down
20 changes: 16 additions & 4 deletions linear_operator/utils/sparse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,12 @@ def make_sparse_from_indices_and_values(interp_indices, interp_values, num_rows)
# Make the sparse tensor
interp_size = torch.Size((*batch_shape, num_rows, n_target_points))
res = torch.sparse_coo_tensor(
index_tensor, value_tensor, interp_size, dtype=value_tensor.dtype, device=value_tensor.device
index_tensor,
value_tensor,
interp_size,
dtype=value_tensor.dtype,
device=value_tensor.device,
check_invariants=False,
)

# Wrap things as a variable, if necessary
Expand Down Expand Up @@ -108,6 +113,7 @@ def bdsmm(sparse, dense):
torch.Size((batch_size * num_rows, batch_size * num_cols)),
dtype=sparse._values().dtype,
device=sparse._values().device,
check_invariants=False,
)

dense_2d = dense.reshape(batch_size * num_cols, -1)
Expand All @@ -134,7 +140,7 @@ def sparse_eye(size):
"""
indices = torch.arange(0, size).long().unsqueeze(0).expand(2, size)
values = torch.tensor(1.0).expand(size)
return torch.sparse_coo_tensor(indices, values, torch.Size([size, size]))
return torch.sparse_coo_tensor(indices, values, torch.Size([size, size]), check_invariants=False)


def sparse_getitem(sparse, idxs):
Expand Down Expand Up @@ -203,7 +209,9 @@ def sparse_getitem(sparse, idxs):
case _:
raise RuntimeError("Unknown index type")

return torch.sparse_coo_tensor(indices, values, torch.Size(size), dtype=values.dtype, device=values.device)
return torch.sparse_coo_tensor(
indices, values, torch.Size(size), dtype=values.dtype, device=values.devicei, check_invariants=False
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)


def sparse_repeat(sparse, *repeat_sizes):
Expand Down Expand Up @@ -232,6 +240,7 @@ def sparse_repeat(sparse, *repeat_sizes):
torch.Size((*[1 for _ in range(num_new_dims)], *sparse.shape)),
dtype=sparse.dtype,
device=sparse.device,
check_invariants=False,
)

for i, repeat_size in enumerate(repeat_sizes):
Expand All @@ -253,6 +262,7 @@ def sparse_repeat(sparse, *repeat_sizes):
),
dtype=sparse.dtype,
device=sparse.device,
check_invariants=False,
)

return sparse
Expand All @@ -269,4 +279,6 @@ def to_sparse(dense):
values = torch.tensor(0, dtype=dense.dtype, device=dense.device)

# Construct sparse tensor
return torch.sparse_coo_tensor(indices.t(), values, dense.size(), dtype=dense.dtype, device=dense.device)
return torch.sparse_coo_tensor(
indices.t(), values, dense.size(), dtype=dense.dtype, device=dense.device, check_invariants=False
)
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