* feat(bench): add MLX exact CGA recall experiment * test(bench): cover MLX exact recall experiment contracts * docs(bench): document MLX exact recall experiment * docs(bench): record MLX experiment integration status * docs(bench): add MLX local validation handoff
2.2 KiB
Apple UMA MLX Exact CGA Recall Experiment
ADR-0235 Lane 3 introduces an optional, benchmark-only MLX exact-recall experiment for CORE's Cl(4,1) CGA recall workload.
This is not a serving backend. It does not replace Python or Rust as the semantic source of truth. It does not use ANN, HNSW, approximate recall, sampling, CoreML, or Neural Engine acceleration.
What it measures
benchmarks/apple_uma_mlx_exact_recall.py measures one narrow workload:
Deterministic (N, 32) float32 fixture matrix
+ deterministic length-32 query
→ MLX exact diagonal CGA score vector
→ score vector copied back to NumPy
→ canonical stable top-k ordering
→ parity check against algebra.backend.vault_recall
The MLX path computes exact scores only. The final top-k ordering is intentionally kept in NumPy/Python so the experiment does not depend on MLX sorting/top-k API behavior and can reuse CORE's canonical descending-score / ascending-index tie break.
Run
uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
With MLX unavailable, the report must skip cleanly with an explicit reason.
With MLX available, the report emits cases for the standard Apple UMA recall sizes and includes:
- MLX import status and default device when observable
N,top_k, dtype, and contiguity- p50/p95/mean timing and rows/sec
- copy-in boundary: NumPy fixture to MLX array
- copy-out boundary: MLX score vector to NumPy
- parity against
algebra.backend.vault_recall - top result preview and canonical preview
Non-claims
This experiment does not claim:
- MLX is a semantic backend
- MLX is serving-authorized
- CoreML or Neural Engine acceleration
- zero-copy everywhere
- ANN or approximate recall
- token-generation throughput
- Apple endorsement, sponsorship, or product integration
Validation
uv run python -m pytest -q tests/test_apple_uma_mlx_exact_recall.py
uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
When MLX is installed on Apple Silicon, also run:
CORE_BACKEND=rust uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
The parity.parity_pass field must be true for every emitted case before using results in any Apple-facing material.