Skip to content

Arm backend: Fix TOSA INT flow tensor memory store #21398

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

Merged
Michiel-Olieslagers merged 3 commits into from
Jul 28, 2026
+24 −4
Merged

Arm backend: Fix TOSA INT flow tensor memory store #21398

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a1136b6
Arm backend: Fix TOSA INT flow tensor memory store
Michiel-Olieslagers Jul 24, 2026
94b358f
Merge branch 'main' into change-1305075
Michiel-Olieslagers Jul 27, 2026
c8a9ab7
Merge branch 'main' into change-1305075
Michiel-Olieslagers Jul 28, 2026
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
24 changes: 22 additions & 2 deletions backends/arm/test/misc/test_runner_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -155,10 +155,30 @@ def test_numpy_to_torch_tensor_converts_dynamic_nhwc_output(monkeypatch) -> None
result = runner_utils.numpy_to_torch_tensor(array, cast(Any, object()))

assert result.shape == (1, 3, 4, 5)
assert result.is_contiguous(memory_format=torch.channels_last)
assert result.is_contiguous()
torch.testing.assert_close(result, torch.from_numpy(array).permute(0, 3, 1, 2))


def test_numpy_to_torch_tensor_converts_concrete_nhwc_output_to_contiguous(
monkeypatch,
) -> None:
output_tensor = SimpleNamespace(
shape=(1, 3, 4, 5),
dtype=torch.int8,
dim_order=lambda: runner_utils.NHWC_ORDER,
)
monkeypatch.setattr(
runner_utils, "get_first_fake_tensor", lambda output_node: output_tensor
)
array = np.arange(60, dtype=np.int8).reshape(1, 4, 5, 3)

result = runner_utils.numpy_to_torch_tensor(array, cast(Any, object()))
expected = torch.from_numpy(array).permute(0, 3, 1, 2).contiguous()

assert result.is_contiguous()
torch.testing.assert_close(result, expected)


def test_numpy_to_torch_tensor_converts_dynamic_nnhwc_output(monkeypatch) -> None:
symbolic_dim = object()
output_tensor = SimpleNamespace(
Expand All @@ -174,7 +194,7 @@ def test_numpy_to_torch_tensor_converts_dynamic_nnhwc_output(monkeypatch) -> Non
result = runner_utils.numpy_to_torch_tensor(array, cast(Any, object()))

assert result.shape == (1, 2, 3, 4, 5)
assert result.dim_order() == runner_utils.NNHWC_ORDER
assert result.is_contiguous()
torch.testing.assert_close(result, torch.from_numpy(array).permute(0, 1, 4, 2, 3))


Expand Down
4 changes: 2 additions & 2 deletions backends/arm/test/runner_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -285,12 +285,12 @@ def to_torch_tensor() -> torch.Tensor:
tensor = to_torch_tensor()
if is_concrete_shape(shape):
tensor = tensor.reshape([shape[i] for i in NHWC_ORDER])
return tensor.permute(NHWC_INVERSE_ORDER).to(memory_format=torch.channels_last)
return tensor.permute(NHWC_INVERSE_ORDER).contiguous()
elif dim_order == NNHWC_ORDER:
tensor = to_torch_tensor()
if is_concrete_shape(shape):
tensor = tensor.reshape([shape[i] for i in NNHWC_ORDER])
return tensor.permute(NNHWC_INVERSE_ORDER)
return tensor.permute(NNHWC_INVERSE_ORDER).contiguous()
else:
tensor = to_torch_tensor()

Expand Down
Loading