leavelet
fdc23f7d61
Replaces the O(victims*candidates) per-iteration greedy argmin rescan in _plan_cp_load_back_owner_lane_evictions with a leaf-up min-heap. The score (-contribution, -unlock, slru_priority, node.id) depends on the current deficits, so a static heap is not equivalent; instead use LAZY RE-EVALUATION: a popped entry is consumed only if its score still matches the node's current score (deficits unchanged since push), else it is re-pushed with the fresh score. Stale scores are always optimistic (contribution = sum(min(counts[o], deficits[o])) and the ancestor-unlock contribution only shrink as deficits shrink), so the first up-to-date popped entry is exactly the global argmin the full rescan would have picked -> PROVABLY EQUIVALENT, with the same leaf-up eligibility + ancestor-unlock + parent-push. Determinism/rank-uniformity preserved (selection decided by the total-ordered score; the heap insertion seq only orders structurally-equal tuples). Equivalence proven by test: a verbatim reference greedy + a randomized property test (300 seeds, single- AND multi-owner deficits, non-uniform counts exercising the lazy-re-eval boundary, varied SLRU priorities) asserting byte-identical (victims, planned_freed, remaining), plus an explicit leaf-up + ancestor-unlock case (child-then-parent). 14 planner tests pass (1 pre-existing unrelated EAGLE-tail failure unchanged). Micro-bench (V100, candidates=2000 deficit=7877, benchmark/hicache/bench_cp_owner_lane_planner.py): A. ORIGINAL (.item() x cp, no memo) 50.06s B. memo + bincount (greedy) 0.494s (101x) C. lazy-re-eval heap 0.162s (309x; 3.0x over B) selection A==B==C identical Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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| What you're testing | Directory | Requires |
|---|---|---|
| Component logic in isolation (cache, scheduler, config, parser, etc.) | unit/<module>/ |
CPU or GPU |
| CUDA kernel correctness | kernels/ |
GPU |
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| What you're testing | Directory | Requires |
|---|---|---|
| Model inference correctness | models/, 4-gpu-models/, 8-gpu-models/ |
GPU |
| Feature-specific (OpenAI API, LoRA, speculative, distributed, VLM, etc.) | openai_server/, lora/, spec/, distributed/, ... |
GPU |
| Benchmarks (performance, accuracy, stress) | benchmark/ |
GPU |
| Platform-specific | amd/, ascend/ |
Vendor GPU |
See unit/README.md for unit test conventions.