[JESUSROYETH]

This reduces complete nano OBB training time by 10.94% on an L4 at batch 16/1024/BF16, with the merged replay and input options enabled.

The new fused_assignment option schedules dense IoU, class gather/masking, rotated containment and conflict resolution in four CUDA passes. It defaults to false, native PyTorch still computes powers, trigonometry, ranking and target normalization. Unsupported inputs keep the native path.

I ran five alternating 50-epoch pairs, seconds as median (range):

	Main 8276de7	Patch
Complete fit	306.911 (300.181-311.299)	273.342 (273.152-279.940)
Whole process	312.132 (305.372-316.486)	278.525 (278.367-285.148)

All 350 loss values per run, model/optimiser/EMA/scheduler states and validation metrics match byte for byte. Focused CPU/CUDA checks and pre-commit pass. This measurement is for this recipe only. That said, full make gate, aggregate coverage, goldens and long-run accuracy remain unverified. Workload and startup limits are in WP-200b.

• Clean-room acknowledged: the basis is this repository, our CUDA profiles and registered runtime APIs.

WP: 200b
Provenance: R40, R44
Assumptions: none
Gate: tests/assign/test_cuda_assignment.py; tests/ptl/test_loss_capture.py; tests/ptl/test_cli.py; tests/ptl/test_compile.py::TestDetectObjectiveTracesAsOneGraph

[Copilot]

🟡 Changes recommended

The fit-test assertion rejects valid native-fallback environments because it treats Triton availability as full backend eligibility.

1 open issue

• Gate fused launch assertion on actual CUDA backend support · New

What changed in this PR

Adds opt-in CUDA fusion to the existing target-assignment pipeline, aiming to reduce training time without changing assignment results. The reported 10.94% complete-fit improvement applies to the measured nano OBB workload.

Changes:

• Adds four CUDA passes with native fallbacks for unsupported execution.
• Threads fused_assignment through training, both loss branches and graph-recording checks.
• Adds parity, fallback, replay and CLI tests, plus installation and benchmark documentation.

Validation remains provisional: full make gate, aggregate coverage, accelerator goldens and long-run accuracy are unverified. No runtime checks were performed in this review.

File	Description
tests/​ptl/​test_loss_capture.py	Exercises fused assignment during loss replay and training.
tests/​ptl/​test_compile.py	Checks compiler tracing with fusion requested.
tests/​ptl/​test_cli.py	Verifies CLI forwarding to both assigners.
tests/​assign/​test_cuda_assignment.py	Adds parity, eligibility, boundary and replay coverage.
src/​lucid_yolo/​ptl/​module.py	Exposes the default-off training option.
src/​lucid_yolo/​ptl/​loss_capture.py	Includes fusion settings and helpers in recording checks.
src/​lucid_yolo/​losses/​dual_loss.py	Forwards fusion requests to both branches.
src/​lucid_yolo/​assign/​tal.py	Integrates optional CUDA passes into assignment.
src/​lucid_yolo/​assign/​stal.py	Forwards the small-target assigner's option.
src/​lucid_yolo/​assign/​one_to_one.py	Forwards the unique assigner's option.
src/​lucid_yolo/​assign/​_cuda.py	Handles eligibility, launches, readiness and fallback.
src/​lucid_yolo/​assign/​_cuda_kernels.py	Implements the four device passes.
pyproject.toml	Adds optional CUDA-assignment dependencies.
docs/​work_packages/​wp-200b.md	Records implementation, measurements and limitations.
docs/​work_packages/​wp-200.md	Links the dependent work package.
docs/​work_packages/​wp-199.md	Adds the dependency backlink.
docs/​work_packages/​wp-198.md	Adds the dependency backlink.
docs/​TRAINING.md	Documents installation, eligibility and usage.
docs/​ROADMAP.md	Tracks WP-200b and outstanding validation.
docs/​PROVENANCE.md	Records implementation sources and runtime constraints.
docs/​engineering/​phase-17-pose-objective-parity.md	Includes the new work-package report.

_{Review effort: Balanced}