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HIP port of #232 and #268. Adds delaunay3d, mesh_bvh, closest_point_on_mesh, closest_hit_on_mesh, connected_components, edge_collapse_checks and min_cut to the HIP backend, with the same algorithms and Python-side validation as CUDA. The ops need no matrix cores, so they register on every supported HIP arch.

The five kernel sources build with -fno-fast-math, because reassociation would cancel the double-double sums in the Delaunay predicates and an infinite cutoff has to stay infinite. The watertight ray test uses fmaf with #pragma clang fp contract(off) in place of CUDA's _rn intrinsics, so shared edges still compute matching values.

ComfyUI only reaches these ops once _kitchen_op accepts the HIP backend, which Comfy-Org/ComfyUI#17581 adds. Measured on gfx1200 against the torch and scipy fallbacks ComfyUI uses without these ops, on generated meshes.

Operation	Input faces	Resolution or target	Mode	Fallback	HIP	Speedup
Remesh	229k	256	solid	4.21 s	0.69 s	6.1x
Remesh	229k	512	solid	15.13 s	2.13 s	7.1x
Remesh	229k	1024	solid	63.63 s	8.61 s	7.4x
Remesh	998k	1024	solid	63.69 s	8.10 s	7.9x
Remesh	998k	512	udf	6.13 s	0.95 s	6.5x
Remesh	998k	1024	sdf	18.77 s	2.58 s	7.3x
QEM decimation	10.37M	200k		9.81 s	5.48 s	1.8x
QEM decimation	10.42M	200k		9.54 s	4.74 s	2.0x

Times are the best of 3 runs for remesh after one warm-up run, and the best of 2 for decimation. Output face counts differ by about 0.01% or less.

Run-verified on RDNA4 (gfx1200). Compile-verified on RDNA2 and RDNA3.

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Walkthrough

The HIP backend adds Delaunay 3D, mesh BVH queries, connected components, edge-collapse checks, and minimum cut. The changes add build and Python binding support, update capability reporting, and allow related GPU tests to run when CUDA or HIP is available.

Changes: HIP Mesh and Graph Operations

Layer / File(s)	Summary
Build and shared backend integration comfy_kitchen/backends/hip/CMakeLists.txt, comfy_kitchen/backends/hip/__init__.py, README.md, tests/conftest.py	The HIP build includes the geometry kernels and applies source-specific floating-point options. The API exports the operations, the capability matrix lists HIP support, and shared test fixtures recognize CUDA or HIP availability.
Delaunay 3D comfy_kitchen/backends/hip/ops/delaunay3d.hip, comfy_kitchen/backends/hip/__init__.py, comfy_kitchen/backends/hip/dlpack_bindings.cpp, tests/test_delaunay3d.py	HIP kernels implement tetrahedron predicates, point insertion, flips, adjacency updates, relocation, and compaction. The Python API orchestrates tetrahedralization and returns vertices, tetrahedra, and neighbor indices. Bindings and tests cover buffer validation and nonfinite input.
Mesh BVH and queries comfy_kitchen/backends/hip/ops/mesh_bvh.hip, comfy_kitchen/backends/hip/__init__.py, comfy_kitchen/backends/hip/dlpack_bindings.cpp, tests/test_mesh_bvh.py	HIP kernels build Morton-ordered BVHs and perform closest-point and closest-hit queries. The Python API validates inputs and exposes the MeshBVH result type. Bindings and tests cover query inputs and buffer validation.
Connected components and edge-collapse checks comfy_kitchen/backends/hip/ops/connected_components.hip, comfy_kitchen/backends/hip/ops/edge_collapse.hip, comfy_kitchen/backends/hip/__init__.py, comfy_kitchen/backends/hip/dlpack_bindings.cpp, tests/test_connected_components.py, tests/test_edge_collapse.py	The backend adds union-find component labeling and per-edge collapse checks. The Python APIs validate inputs and return labels or per-edge check results. Bindings and tests cover input and buffer validation.
Minimum cut comfy_kitchen/backends/hip/ops/min_cut.hip, comfy_kitchen/backends/hip/__init__.py, comfy_kitchen/backends/hip/dlpack_bindings.cpp, tests/test_min_cut.py	HIP kernels implement residual-flow initialization, global relabeling, active-list rebuilding, and push-relabel iterations. The Python API runs these stages and returns source-side labels. Bindings and tests cover input and buffer validation.

Merge Risk: Low · up to 10df0

The new HIP geometry ops are largely guarded against bad input. Remaining issues are narrow edge cases: extreme coordinate magnitudes, mixed-scale capacities, and multi-GPU CUDA-graph capture. They can be accepted or fixed as follow-ups.

Pre-merge checks: 2

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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• Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @comfy_kitchen/backends/hip/__init__.py:
- Around line 2096-2105: Add a finiteness check immediately after the points
shape validation and raise ValueError if any coordinate is nonfinite. Use the
existing points tensor before computing bounds or applying the joggle.
- Around line 2387-2389: Update min_cut to validate that cap, s_cap, and t_cap
are on the same device as nbr before launching _C.min_cut_init; reject any
mismatch with a clear error, and preserve the existing launch path when all
devices match.
- Line 2392: Validate relabel_every at the start of the HIP backend’s min_cut
function, before allocating state or calling _C.min_cut_init; reject zero and
negative values with a ValueError, and preserve the existing behavior for
positive values.
- Line 2385: Update the tolerance calculation around tol so a large internal
capacity cannot influence source-versus-sink decisions for unrelated nodes. Base
the tolerance on capacities relevant to each decision, preserving small terminal
capacities and the existing terminal-only graph behavior.
- Line 2307: Update the capture-status check in the `index` bounds-validation
path to query whether capture is active on `index.device`, rather than relying
on the current CUDA device. Preserve the empty-index behavior and ensure bounds
validation is not skipped when another GPU is capturing.
- Around line 2349-2354: Validate that every tensor passed to
_C.edge_collapse_checks is on the same device as vertices before launching the
kernel, and raise a clear error for any mismatch. Keep the existing launch path
unchanged when all devices match.

Review comments at @comfy_kitchen/backends/hip/dlpack_bindings.cpp:
- Around line 3120-3123: Validate the `edges`, `parent`, and `labels` buffers in
`connected_components` before calling `launch_connected_components`. Check their
lengths, dtypes, and packed layouts against `n_edges`, `n_nodes`, and
`edges_int64`, and reject invalid inputs so the kernel cannot access beyond a
buffer; preserve the kernel’s required layout and dtype expectations.

Review comments at @comfy_kitchen/backends/hip/ops/mesh_bvh.hip:
- Line 203: Update the closest-point traversal around the node >= leaf0 check to
make both bounding-box pruning and candidate-distance comparisons overflow-safe.
Ensure a finite geometric distance is not treated as exceeding the default
infinite cutoff merely because its squared value overflowed, and preserve
correct face selection and distance reporting for distant queries.

Review comments at @comfy_kitchen/backends/hip/ops/min_cut.hip:
- Around line 98-104: Update push_kernel so each reverse slot preserves the
total incoming excess when multiple parallel edges map to it; accumulate amounts
safely instead of overwriting inc[back]. Alternatively, reject duplicate
neighbors before this path runs, while preserving consistency between residual
updates and relabel_kernel’s gathered excess.

Review comments at @tests/test_edge_collapse.py:
- Line 93: Extend the HIP-specific edge-collapse tests guarded by
requires_gpu_backend with explicit duplicate-face and in-range nonedge cases,
each using an independently specified expected link_ok value rather than
deriving expectations from the grid reference or count rule.

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• README.md
• comfy_kitchen/backends/hip/CMakeLists.txt
• comfy_kitchen/backends/hip/__init__.py
• comfy_kitchen/backends/hip/dlpack_bindings.cpp
• comfy_kitchen/backends/hip/ops/connected_components.hip
• comfy_kitchen/backends/hip/ops/delaunay3d.hip
• comfy_kitchen/backends/hip/ops/edge_collapse.hip
• comfy_kitchen/backends/hip/ops/mesh_bvh.hip
• comfy_kitchen/backends/hip/ops/min_cut.hip
• tests/conftest.py
• tests/test_connected_components.py
• tests/test_delaunay3d.py
• tests/test_edge_collapse.py
• tests/test_mesh_bvh.py
• tests/test_min_cut.py

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Prompt to fix all issues

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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @comfy_kitchen/backends/hip/__init__.py:
- Around line 2245-2246: Validate float32 inputs before direct HIP mesh calls:
reject non-float32 triangles in mesh_bvh, and reject non-float32 query tensors
and bvh.triangles in both query paths before any early return, including the
negative max_dist path. Preserve the existing shape and device checks.
- Around line 2411-2422: Validate `cap`, `s_cap`, and `t_cap` in the HIP min-cut
wrapper before launching any kernels or entering the uncapped iteration loop;
reject values that are negative or non-finite. Preserve the no-flow, no-go
behavior so invalid inputs cannot reach the kernel loop.

Review comments at @comfy_kitchen/backends/hip/dlpack_bindings.cpp:
- Around line 3095-3118: Add pre-launch dtype, length, and packed-layout
validation to the Delaunay, BVH, closest-query, edge-collapse, and min-cut
bindings, following the established checks in connected_components. Apply the
checks in mesh_bvh_build, closest_point_on_mesh, and closest_hit_on_mesh and
their corresponding bindings; verify buffer sizes against the supplied
dimensions and layout before passing raw pointers to launchers.

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• comfy_kitchen/backends/hip/__init__.py
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• tests/conftest.py
• tests/test_connected_components.py
• tests/test_delaunay3d.py
• tests/test_edge_collapse.py
• tests/test_min_cut.py

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Inline comments:
Review comments at @comfy_kitchen/backends/hip/dlpack_bindings.cpp:
- Line 3108: The active_list length check uses floor division, so
non-multiple-of-four candidate counts can permit an undersized buffer despite
kernel reads. Update the require_buf checks in both affected entry points,
including delaunay_flip_execute, to require the rounded-up number of 32-bit
elements while preserving existing validation.

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