Fix MLU device utilities and test backend support

[SeptPonts]

What does this PR do?

Fixes #14779.

Update — 2026-10-15

Following #14792 and the review feedback, this PR is now limited to automatic MLU selection in tests/testing_utils.py. Runtime helpers and cache cleanup are already handled by TorchDeviceBackend.

Validated on MLU590:

• Without device overrides, the test utilities now select mlu instead of cpu.
• The existing H3 training test runs and passes on mlu:0.
• DIFFUSERS_TEST_DEVICE=cpu continues to select CPU correctly.
• The runtime/test helper checks and free_memory() check also passed.

Validation

Passed locally with the repository-pinned Ruff 0.9.10:

• make style and make fix-copies
• make quality
• python utils/check_copies.py
• python utils/check_dummies.py
• python utils/check_support_list.py
• python utils/check_forward_call_docstrings.py
• make deps_table_check_updated
• git diff --check

make style also proposed an unrelated, pre-existing LTX2 import-table reorder. It was excluded from this PR; make quality passed on the final three-file change.

Before/after hardware validation used MLU590, Torch 2.12.1 and torch_mlu 1.34.1:

• Six MLU backend utility calls that raised TypeError on the parent now succeed.

• free_memory() releases the allocator cache after a deleted 64 MiB allocation: reserved memory changes from 67,108,864 bytes on the parent to zero with the patch.

• The existing H3 training test passes with automatic MLU selection:

  python -B -m pytest --assert=plain -p no:cacheprovider -q \
    tests/models/transformers/test_models_transformer_minimax_h3.py::TestMiniMaxH3TransformerTraining::test_training

The full test suite and CUDA/NPU runtime suites were not run. Hardware validation predates the final import-name sorting correction; no runtime logic changed afterward.

Self-review

Applied .ai/skills/self-review/SKILL.md to the entire three-file diff. No blocking correctness issues or unused additions were found. The known import-order lint failure has been corrected and quality checks pass.

One initialization assumption remains for maintainer review: MLU table entries expect torch_mlu registration before the utility modules are imported. Automatic extension loading supplies this in the tested environment; late extension loading with autoload disabled is not covered.

This fixes device utilities and test-backend selection. No public signatures, dtype policies or determinism settings change.

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@sayakpaul Following up on the discussion in #14779.

[sayakpaul]

Thanks!

[sayakpaul]

Why is this the case? is empty_cache not available generally in Torch MLU?

[SeptPonts]

Yes, empty_cache() is available in torch_mlu, and I verified it on hardware.

The guard is for the optional backend namespace: unlike the built-in torch.cuda and torch.xpu modules, torch.mlu is registered by the external torch_mlu package through PyTorch’s PrivateUse1 integration. Without the extension loaded, accessing torch.mlu.empty_cache while constructing this module-level dictionary raises AttributeError: module 'torch' has no attribute 'mlu'. This prevents the shared utilities from being imported even for CPU/CUDA tests, before any MLU operation is requested.

I followed the guarded-access pattern already used for Neuron’s device_count and synchronize entries in src/diffusers/utils/torch_utils.py. The same reasoning applies to the device_count comment below.

[sayakpaul]

Same as above.

[DN6]

Hi @SeptPonts we recently introduced the TorchDeviceBackend API to handle device module dispatch. I think this will reduce the scope of the changes needed here to just tests/testing_utils.py. Would you be able to rebase and verify?

[SeptPonts]

Hi @DN6 , I’ve checked the changes on main, and it looks like only tests/testing_utils.py needs updating. I’ll rebase onto current main, rerun the original checks, and validate the test helpers on MLU hardware.

[SeptPonts]

Hi @DN6, I’ve simplified the changes and verified them on MLU hardware, as detailed in the updated PR description. Could you take another look?