RayD Torch

RayD Torch is a Torch-native CUDA/OptiX package for RayD geometry primitives and RayD-style multipath/diffraction kernels.

import rayd.torch as rt

Install the rayd-torch distribution. It owns only rayd/torch/** and can coexist with the independently installed rayd-drjit backend.

Published complete wheels are built for and require PyTorch 2.10 (torch>=2.10,<2.11) because _legacy_ops uses the non-Stable-ABI LibTorch C++ surface. The separately audited _stable_ops library remains validated across PyTorch 2.10 through 2.13.

Published wheels contain both native backends and do not require an OptiX SDK at runtime. Building this dual-backend distribution from source does require the OptiX SDK headers, even when the target machine will select CUDA at runtime; an SDK-less CUDA-only source-build mode is not currently provided. Native source builds require CUDA Toolkit 11.3 or newer so the CUDA runtime can resolve driver entry points lazily without making the wheel depend directly on the platform CUDA driver library.

Ray-tracing backend selection

Scene() selects OptiX when the current CUDA device and driver expose it and otherwise selects RayD's pure-CUDA triangle and edge BVHs. The resolved choices are available through scene.trace_backend and scene.edge_bvh_backend after build():

scene = rt.Scene()  # trace_backend="auto", edge_bvh_backend="auto"
scene.add_mesh(mesh)
scene.build()
print(scene.trace_backend, scene.edge_bvh_backend)

Pass trace_backend="optix" or "cuda" and edge_bvh_backend="optix" or "cuda" to require a backend. An explicit OptiX request fails if OptiX is unavailable; runtime pipeline, allocation, and CUDA errors are never converted into a fallback. RAYD_DISABLE_OPTIX=1 is the deployment and test kill switch. Both backends use Torch-owned tensors and the current Torch CUDA stream.

The separately governed ADR-0029 exact four-sample axial-edge primitive and ADR-0033 segment-penetration family remain OptiX-only by contract. Requesting either from a CUDA scene raises an explicit unsupported-backend error; RayD does not substitute a numerically different implementation.

SDF Grid Intersection

rt.SdfGrid and rt.sdf_intersect sphere-trace a caller-owned dense signed distance field. The grid holds vertex-centred float32 samples of shape [Nx, Ny, Nz] in world-metric distance with the negative-inside sign convention, placed by an oriented box given as a world centre, a scalar-first quaternion, and full side lengths:

result = rt.sdf_intersect(
    rt.SdfGrid(values, position=position, rotation=rotation, scale=scale),
    origins,
    directions,
    tmax=10.0,
)
result.t.sum().backward()

The march is relaxed, recovers from overshoot by bisecting the bracketing sign change, and clips the traced interval to the ray/box overlap, so an origin inside the box is a supported case. t, position, and normal carry gradients and tangents to the grid values, the box transform, and the rays under the frozen-winner implicit function theorem; hit_mask and steps carry none. Missed lanes report t = +inf and are bitwise inert: zero outputs, zero derivatives, and no atomic contribution. The operation performs no device-to-host copy and no stream synchronization, including for its resolution-derived eps_hit default.

The primitive is standalone and Torch-only: no OptiX, no Scene membership, no mixing with triangle geometry, and no silhouette gradients in v1. See docs/adr/0037-differentiable-sdf-intersection.md.

Tensor ABI

RayD Torch APIs accept CUDA torch.float32 tensors for vector data and CUDA torch.int32 tensors for index data. Vector tensors are row-major (N, 3) unless otherwise documented, masks are torch.bool, and tensors should be contiguous. Outputs and AD tapes are Torch-owned tensors.

Gradient Contract

Intersection, edge, reflection, EPC, and diffraction operators use a fixed-winner gradient contract where explicit native kernels exist. The discrete primitive, edge, visibility, or path decision selected in the forward pass is treated as non-differentiable; VJP and JVP propagate through the continuous values recomputed from the saved winner and live Torch tensors.

Autograd

The native operators support Torch reverse-mode VJP and forward-mode JVP for the supported continuous inputs where explicit kernels have been implemented. CUDA work is launched on the current Torch CUDA stream.

Native Source Integration

Native downstream projects built in the same CMake/LibTorch graph use the versioned typed C++ surface in rayd/integration.h; they do not load a second RayD Python extension or use a dynamic symbol registry. Solver-neutral transmission and scattering device math is exposed through src/transmission_device.cuh and rayd/scattering_table.cuh. Torch-specific cross-concept field AD helpers live at src/field_transport_ad.cuh.

The rayd-torch wheel also carries a relocatable source bundle at rayd/torch/_source. rayd-source.json records the distribution version, source commit and repository, integration ABI identity, the complete eight-header integration API set and its aggregate SHA-256, the separately bundled path_exchange.h contract, and the SHA-256 of a complete per-file manifest. The bundle contains the canonical include, source, and build inputs needed for a same-graph native build. Downstreams locate this passive resource through importlib.metadata; they must not import rayd.torch, scan an environment prefix, or trust the metadata without pinning and recomputing the full source manifest. An explicit source checkout remains a higher-priority developer input and keeps its Git identity checks. See docs/adr/0034-validated-package-source-discovery.md.

The accepted transmission surface consists of complete primal/backward/JVP families for resident CSR layer-stack evaluation and complete-row Jones field transport. These operations preserve precise-math compilation, row fusion, atomic layer-gradient order, and the no-persistent-tape contract. Inputs are validated before launch, work runs on the caller's current Torch CUDA stream, and invalid shape/dtype/device/ABI state or CUDA failure raises immediately; there is no CPU, Torch-expression, finite-difference, or legacy-dispatch fallback.

RayD owns the numerical primitives and typed native operations, not a downstream application's material encoding, topology selection, solver estimator policy, RNG/MIS, accumulation, metadata, or result schema. A newly merged transmission implementation may remain a dormant candidate until the consumer pins it, switches all callers, proves parity, and deletes its local implementation. See docs/adr/0002-shared-rf-transmission-ownership.md.

The accepted diffraction surface will place the complete fixed-winner pure-wedge field primal/backward/JVP family behind the typed integration header once the dormant RayD candidate is implemented and direct-tested. Channel remains the production numerical owner until it pins, validates, activates, and deletes its local CUDA implementation. The future typed family must preserve optional winner vertices, three separate native entry launches, current-stream execution, output schemas, and the family-local --use_fast_math contract required for order-1 exporter parity. Monte Carlo Sionna accumulation, coupled RD/DD operations, and BDPT estimator policy remain downstream-owned. See docs/adr/0025-diffraction-family-ownership.md.

The accepted generic-scattering surface consists of exactly seventeen typed operations in six complete families: resident table evaluation AD, resident table sampling/PDF, single-bounce ensemble, phase-screen patch integral, chain ensemble, and chain realization. RayD evaluates caller-owned resident CUDA tensors but does not own table construction, cache/version policy, phase-screen seed/lifecycle, topology, estimator, RNG/MIS, accumulation, or result policy. A high-level BSDF/material framework remains out of scope; solver-neutral RF scattering primitives are in scope.

The migration preserves the existing AD asymmetry: chain-ensemble continuous geometry supports JVP but reverse-mode requests fail loudly, while chain-realization supports its existing continuous-geometry VJP and JVP. Complete row fusion, launch count, recomputation/tape lifetime, backward atomics, output schemas, and the source-TU compile split are frozen: table primal/sample/PDF uses default CUDA flags, while the audited table-AD, ensemble, patch, and chain lockstep TUs retain --fmad=false. A merged RayD implementation remains dormant until Channel pins it, switches a complete family with parity evidence, and deletes the local implementation. See docs/adr/0026-generic-scattering-runtime-ownership.md.

The stable source-level boundary is named rayd/integration.h, its exact identity is rayd.torch.integration, and its numeric API version is 7. No integration_v2 forwarding header, CMake target alias, or alternate identity is supported. See docs/adr/0028-stable-typed-integration-naming.md.

API version 3 makes row validity explicit for pure-wedge diffraction, transmission sequences, and all generic scattering requests. The required device boolean tensor is checked before any row payload or ID is read; invalid primal/JVP rows and supported row gradients are bitwise zero and cannot contribute shared-gradient atomics. AD companions inherit the mask from their nested primal request. See docs/adr/0030-typed-capacity-row-validity.md.

API version 4 additionally makes DiffractionPathConfig.active required for order-1 path export. It is a contiguous CUDA boolean tensor with exact shape [state_limit]; an empty state set carries a defined empty mask. The Python Scene.trace_dfr_paths(...) entry requires the keyword, and neither the typed API nor dispatcher accepts omitted, None, broadcast, or strided validity. Diffraction accumulation and coherent-accumulation contracts are unchanged. See docs/adr/0031-required-diffraction-path-validity.md.

The typed boundary also carries a dormant axial-edge visibility candidate for device-resident diffraction state selection. It consumes broadcast TX and contiguous AoS edge tensors, evaluates the four exact binary32 fractions in one separate OptiX launch on the caller's current CUDA stream, and returns a resident boolean state mask. Its traversal inherits the legacy OptiX compile policy while inline PTX locks only point construction to non-FTZ, non-FMA round-to-nearest operations. The legacy visibility dispatcher is unchanged and cannot select this entry. The operation adds no synchronization beyond the existing common launch-parameter staging path; removing that staging path's host event wait is a separate optimization. See docs/adr/0029-typed-axial-edge-visibility.md.

API version 5 adds an explicit DiffractionPathLayout to the typed order-1 exporter. Existing callers retain Compact; same-graph consumers may request SourceLane, where the fixed row is ((tx * rx_count + rx) * state_limit) + state. Rejected lanes remain inert and the CUDA count remains actual-count metadata rather than a storage ordinal. Both layouts use the same traversal and UTD implementation. See docs/adr/0032-source-lane-diffraction-path-layout.md.

API version 6 adds the dormant complete batched segment-penetration family. Each non-empty structurally active forward call submits one OptiX launch and performs its ordered D+1 capacity probe in raygen; an explicit structurally all-inactive call uses only a same-stream CUDA mask consistency check. Results and tapes use fixed [N,D] storage and the caller's shared device failure transaction. Backward/JVP use frozen winners and never retrace. RayD exposes no Python dispatcher for this family, and Channel retains material and wall-product policy. See docs/adr/0033-batched-segment-penetration.md.

RayD Torch carries all seventeen typed operations. Channel has activated the Phase 10A table, sampling, single-bounce ensemble, and patch-integral entries. The six Phase 10B chain entries are source-linked into the native core and direct-tested but remain dormant: no RayD Python binding dispatches them, and Channel remains their production numerical owner until its atomic pin/switch/delete commit.

Current Status

RayD Torch now builds separate native scene, edge, reflection, and diffraction Torch extension bindings. The native build includes OptiX PTX pipelines for scene intersection, edge queries, reflection tracing/EPC/visibility/ accumulation, and diffraction path/accumulation/coherent direct execution.

Current opt-in RayD parity tests cover forward cases for scene intersection, multi-mesh global ids, nearest-edge, visibility, reflection tracing, diffraction paths, direct/Keller/suffix diffraction accumulation, order-2 and order-3 diffraction chains, and coherent direct accumulation. Torch VJP/JVP coverage exists for geometry, edge, reflection trace, EPC, and diffraction accumulation under the fixed-winner contract.

On the recorded same-script benchmark shape (grid 64, 4,096 queries, warm caches), RayD Torch currently measures faster than RayD for scene build, intersect, nearest edge, reflection trace, diffraction paths, and direct diffraction accumulation. Far-from-surface nearest-edge queries use a tiled exact fallback scan instead of the scene-diagonal OptiX tier. Release-size and Nsight-counter-backed runs remain the broader performance gate. See torch_gap_analysis.md and

Dependencies

RayD Torch depends on PyTorch, CUDA, and OptiX for native execution. The RayD Torch package path has no Dr.Jit dependency.

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Author: Xingyu Chen

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Python <3.15, >=3.10

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