Description

Removes the cuda:0-only restriction in FabricFrameView. USDRT SelectPrims now accepts any CUDA device index, so Fabric acceleration runs on the simulation device (e.g., cuda:1) instead of silently falling back to the slower USD path. This unblocks distributed training where each process is pinned to a specific GPU.

The user-facing surface change is small (drops the device guard in __init__, the assertion in _initialize_fabric, and the _fabric_supported_devices allowlist). The remainder of the diff comprises:

• Write-path refactor. set_world_poses, set_scales, and the initial USD→Fabric sync now share a single _compose_fabric_transform helper. The sync collapses to one combined kernel launch with one PrepareForReuse, eliminating the previous "double PrepareForReuse" code smell where a non-idempotent second call could mask a topology-change signal.
• Robustness. The topology-change invariant in _rebuild_fabric_arrays now raises RuntimeError instead of using assert, so the check survives python -O. Previously, an -O run with a real prim-count mismatch would silently dispatch with a stale _view_to_fabric mapping and produce wrong poses or out-of-bounds kernel indices.
• Multi-GPU test coverage. Three cuda:1-parameterized tests guarded by a new multi_gpu pytest marker (registered in pyproject.toml), plus a dedicated CI job on the existing multi-GPU runner so regressions show up automatically on PRs that touch FabricFrameView or its test file.

The skip-vs-fail logic in _skip_if_unavailable is intentional and works in concert with the CI workflow:

• On a developer workstation with a single GPU, cuda:1 tests pytest.skip so local runs stay green.
• In CI, the runner is guaranteed to have ≥2 GPUs by a pre-flight nvidia-smi + torch.cuda.device_count() check at the top of test-fabric-multi-gpu. If the runner regresses to a single GPU, the pre-flight emits ::error:: and exits non-zero before pytest is even invoked, so a misconfigured runner cannot silently green-light a PR.

The test helper used to special-case GITHUB_ACTIONS=true and call pytest.fail, but that branch was removed: the workflow pre-flight catches the same failure mode without coupling the test file to os.environ.

Type of change

• New feature (non-breaking change which adds functionality)

cuda:0 continues to work exactly as before; cuda:1+ now also works instead of silently falling back to USD. No public API surface changed.

Checklist

• I have read and understood the contribution guidelines
• I have run the pre-commit checks with ./isaaclab.sh --format
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• I have updated the changelog and the corresponding version in the extension's config/extension.toml file
• I have added my name to the CONTRIBUTORS.md or my name already exists there

Note on the changelog item: this PR uses a fragment file at source/isaaclab_physx/changelog.d/feat-frame-view-enable-mgpu.rst per the new fragment-based changelog system (#5434). CHANGELOG.rst and config/extension.toml are intentionally not edited directly — the nightly CI workflow compiles them from fragments. The fragment covers the cuda:N enablement (Fixed), the combined-sync refactor (Changed), and the assert → RuntimeError fix (Fixed).

Test plan

Three new tests, all marked @pytest.mark.multi_gpu and parameterized with ["cuda:1"]:

• test_fabric_cuda1_world_pose_roundtrip — set_world_poses → get_world_poses returns the same values on a non-primary CUDA device.
• test_fabric_cuda1_no_usd_writeback — Fabric writes on cuda:1 do not write back to USD (atol=0.0 — equality, not approximate).
• test_fabric_cuda1_scales_roundtrip — covers the set_scales write path on cuda:1, since both Fabric write paths now run on self._device.

A new CI job, test-fabric-multi-gpu, runs in .github/workflows/test-multi-gpu.yaml on the existing [self-hosted, linux, x64, gpu, multi-gpu] runner. The job pre-flights with nvidia-smi and ./isaaclab.sh -p -c "import torch; print(torch.cuda.device_count())", and fails loudly with ::error:: if the runner regresses to a single GPU. Triggered automatically on PRs that touch source/isaaclab_physx/isaaclab_physx/sim/views/fabric_frame_view.py or its test file.

To verify locally on a multi-GPU machine:

./isaaclab.sh -p -m pytest -m multi_gpu \
    source/isaaclab_physx/test/sim/test_views_xform_prim_fabric.py -v

To verify the cuda:0 path is unchanged:

./isaaclab.sh -p -m pytest -m "not multi_gpu" \
    source/isaaclab_physx/test/sim/test_views_xform_prim_fabric.py -v