diff --git a/.claude/skills/openevolve-pipeline/SKILL.md b/.claude/skills/openevolve-pipeline/SKILL.md new file mode 100644 index 0000000000..78abf8bae4 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/SKILL.md @@ -0,0 +1,123 @@ +--- +name: openevolve-pipeline +description: Scaffold an OpenEvolve solver-parameter tuning pipeline under input//evolve/ when a user supplies a new solver + benchmark dataset. Trigger on requests like "add a new solver to openevolve", "make an evolve pipeline for ", "tune parameters on this bench", "generate the cpsat-bench style scaffold for X", or whenever the user drops raw solver runs into input//raw-data and asks for parameter tuning. The skill follows input/ADD_NEW_SOLVER.md: it interviews for solver/scoring/clustering/phase decisions, writes exactly the 4 per-bench files (config.yaml, params.json, adapter.py, _solve_worker.py) + N phase modules, and verifies the result with the _lib CLIs (sampler / self_test / rebaseline). +--- + +# OpenEvolve Pipeline Generator + +Given a new solver and a raw benchmark dataset, produce a complete per-bench tuning pipeline at `input//evolve/`. All orchestration lives in `input/_lib/` — **never edit it**. The bench contributes only the per-bench surface from `input/ADD_NEW_SOLVER.md` §2: + +| file | purpose | +|---|---| +| `evolve/config.yaml` | bench + LLM + clustering + evaluation knobs | +| `evolve/params.json` | rich solver parameter catalog (defaults / locked / groups) | +| `evolve/adapter.py` | solver hooks (constants + `get_problem_size`) | +| `evolve/_solve_worker.py` | subprocess entry — `argv = (params_json, problem_path, timeout_s)` → JSON line on stdout | +| `evolve/phase{N}_/initial_program.py` | one per phase (last phase is usually unified) | + +Authoritative spec: [input/ADD_NEW_SOLVER.md](../../../input/ADD_NEW_SOLVER.md). Reference implementations: [input/z3-bench/evolve/](../../../input/z3-bench/evolve/) (flat-overrides + speedup), [input/cpsat-bench/evolve/](../../../input/cpsat-bench/evolve/) (SIZE_BUCKETS + worker-axis + cost mode). + +Reading order before scaffolding: `references/interview-checklist.md` → `references/decision-guide.md` → `references/verify-checklist.md` → `references/gotchas.md`. + +## Workflow + +### 1. Inspect what user provided + +```bash +ls input//raw-data/ | head +ls input//problems.jsonl 2>/dev/null && head -1 input//problems.jsonl | python3 -m json.tool +``` + +Confirm presence of `raw-data/` and `problems.jsonl`. If `problems.jsonl` missing, stop and tell user — `_lib/sampler.py` only reads it (does not build it). Generation is solver-specific user work (ADD_NEW_SOLVER.md §1.4). Offer to draft a `build_problems_jsonl.py` after they describe raw-data layout. + +If <10 problems: warn that quintile clustering collapses and cascade stages become noisy. Recommend 1–2 phases only. + +### 2. Interview + +Use `AskUserQuestion` (≤4 per call). Must-know set in [references/interview-checklist.md](references/interview-checklist.md). Skip anything already obvious from `problems.jsonl` + raw-data inspection. + +Highest-leverage answers: +1. Solver binary / Python binding and how to invoke it. +2. `problems.jsonl` field names → adapter constants (`PROBLEM_FILE_FIELD`, `STATUS_FIELD`, `STATS_FIELD`, `FEATURES_FIELD`, `OBJECTIVE_FIELD`). +3. Decisive vs. decided result tokens. +4. Score mode: `speedup` (z3-style: wall-clock min) vs `cost` (cpsat-style: objective gap + dtime). See [references/decision-guide.md](references/decision-guide.md). +5. Worker-count axis? If yes → `WORKERS_KEY`, phase-level worker lock. +6. SIZE_BUCKETS / STAGE3_OVERRIDES needed? Default off; turn on when problem-size distribution is wide and multi-modal. +7. Phase plan: how many phases, namespace per phase, last phase unified (yes/no). +8. Clustering: `kmeans` (default) | `quintile` | `thresholds`; feature path inside `problems.jsonl`. + +### 3. Scaffold + +Write 4 files + N phase dirs. Substitute placeholders from interview into [templates/](templates/): + +``` +input//evolve/ +├── config.yaml ← templates/config.yaml.tmpl +├── params.json ← templates/params.json.tmpl (skeleton; expand groups) +├── adapter.py ← templates/adapter.py.tmpl +├── _solve_worker.py ← templates/_solve_worker_py.tmpl (or _solve_worker_cli.tmpl for binary) +├── phase1_/initial_program.py ← templates/initial_program_simple.py.tmpl +├── phase2_/initial_program.py ← templates/initial_program_simple.py.tmpl +│ (or _cpsat.py.tmpl if SIZE_BUCKETS/worker lock) +└── phaseN_unified/initial_program.py ← templates/initial_program_unified.py.tmpl +``` + +Phase modules must use `params_catalog.load_for_bench(_BENCH).defaults` for BASELINE — never hardcode a parallel default dict. Config single-source rule: see [[feedback_config_single_source]]. + +`unified_dict_name` in `config.yaml` MUST match the EVOLVE-BLOCK dict name in the last phase (default: `UNIFIED_OVERRIDES`). Mismatch → `_lib.prepare_phase` fails silently. + +### 4. Verify (run these — do not skip) + +```bash +# 0. Solver binding installed? +python3 -c "import ; print(.__version__)" # or `command -v ` + +# 1. Catalog load + validation +python3 -c " +import sys; sys.path.insert(0, 'input') +from _lib import params_catalog +c = params_catalog.load('input//evolve/params.json') +print('keys:', len(c.known_keys()), 'defaults:', len(c.defaults), 'locked:', len(c.locked)) +print('validate ok:', c.validate(c.defaults)) +print('validate bogus:', c.validate({'fake_key': 1})) +" + +# 2. Clustering + stage split +cd input && python3 -m _lib.sampler +# Expect cache/stage{1..4}_sample.json. Inspect cluster sizes — all-in-one cluster +# = features field path wrong or every problem has 0. + +# 3. BASELINE sanity on stage1 +python3 -m _lib.self_test +# Expect result labels match + ratio in [0.5, 2.0] (WARN tolerated). + +# 4. Local baseline capture (10-run avg) — slow +python3 -m _lib.rebaseline +# Expect cache/local_baseline.json. + +# 5. Single-phase smoke (low iter) +./input/run_phase.sh 1 --pin 2-3 --iterations 2 +# Expect phase1/openevolve_output/best/best_program.py + cache/phase1_best.json. +``` + +Each verify step gates the next. Stop and fix at the first failure — do not proceed to the next CLI hoping it will surface a clearer error. + +### 5. Hand off + +Report to user: +- 4 files + N phase modules created at the paths above. +- Verification results (catalog key count, sampler cluster sizes, self_test ratio). +- Next command: `./input/run_phase.sh --pin ` for full chain. +- `final_program.py` will land at `input//evolve/final_program.py` after last phase (auto via `_lib.finalize`). If `bench.solver_mode` is set to a non-default variant, output suffixes to `final_program_.py` and artifacts isolate to `cache-/`. + +## Refuse / push back + +- `input//raw-data/` absent → ask where dataset lives. Do not invent a layout. +- `problems.jsonl` absent → solver-specific generation is **user** work (ADD_NEW_SOLVER.md §1.4). Optionally help draft `build_problems_jsonl.py` once they describe meta format. +- Solver has no Python binding AND no CLI that accepts a problem file → ask user how they invoke it; cannot write `_solve_worker.py` without this. +- Fewer than ~10 problems → warn about cascade collapse; suggest single phase. +- User asks to edit `input/_lib/*` → refuse. `_lib` is bench-agnostic; per-bench knobs go in the 4 files only. + +## Gotchas + +See [references/gotchas.md](references/gotchas.md) — verbatim copy of ADD_NEW_SOLVER.md §6 plus failure-mode index from verify steps. diff --git a/.claude/skills/openevolve-pipeline/references/decision-guide.md b/.claude/skills/openevolve-pipeline/references/decision-guide.md new file mode 100644 index 0000000000..0288d82252 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/decision-guide.md @@ -0,0 +1,96 @@ +# Decision Guide + +ADD_NEW_SOLVER.md §5 — verbatim guidance for ambiguous knobs. + +## score_mode + +| Solver characteristic | Recommended mode | +|---|---| +| Baseline records `objective_value`; optimization problem | `cost` | +| Sat/Unsat satisfaction; minimize wall-clock | `speedup` | +| Has determinism counter (e.g. cpsat `deterministic_time`) | `cost` + `time_metric: dtime` | + +`speedup` = geomean(baseline_ms / candidate_ms). Higher better. +`cost` = combination of objective-gap + dtime ratio. Lower better (inverted to speedup-like by scorer). + +## Worker axis + +If solver has a `num_workers`-style knob that strongly affects runtime: +- `adapter.WORKERS_KEY = ""`. +- Each phase pins it via `PHASE_LOCKED[""] = N`. +- `_lib.evaluator_core` uses core-block allocation. +- `_lib.rebaseline` produces `by_workers` baseline schema. + +Else: +- `WORKERS_KEY = None`. +- One core per solve; flat baseline. + +cpsat-bench uses W=1 (default profile) / W=8 (`OPENEVOLVE_PROFILE=large`) +across phases. z3-bench is single-threaded. + +## SIZE_BUCKETS / STAGE3_OVERRIDES + +| Situation | Toggle | +|---|---| +| Problem size spans wide range (e.g. 7k–250k constraints), multi-modal score distribution | `enable_size_buckets: true` | +| A few outlier problems dominate aggregate score | `enable_outlier_stage: true` + populate `cache/outliers.json` | +| Pool small (<30) or uniform | Both `false` | + +`enable_size_buckets: true` → phase modules must use `initial_program_cpsat.py.tmpl` +(SIZE_BUCKETS + `get_phase_size_buckets()`). +`enable_outlier_stage: true` → add `STAGE3_OVERRIDES` + `get_phase_stage3_overrides()`. + +## clustering.method + +| Method | When | +|---|---| +| `kmeans` | 1D Lloyd's. Lets cluster boundaries emerge from data shape. Default. | +| `quintile` | Rank-based equal-count splits. Use when boundary consistency across runs matters more than natural breaks. | +| `thresholds` | User-specified cut-offs (e.g. `[50000, 150000]` → 3 buckets). Use when you have domain knowledge of regimes. | + +## clustering.mode (optional sample-profile override) + +Generic `_lib.sampler` feature. `clustering.mode: ` selects a +`clustering.modes.` block that is **shallow-merged over the base clustering +block**. Lets one config carry several sample profiles and switch by one field. +Unset → base block only. + +- ORTHOGONAL to `solver_mode` — a sample profile (e.g. `large` = focus on + constraint-heavy instances) applies in any solver mode. Do NOT key the override + off solver_mode; keep the two knobs independent. +- Only the keys present in the override are replaced (e.g. just `method` + + `thresholds` + `stage_sizes`); everything else falls through to base. +- z3-bench uses `modes.large` (threshold bucketing, top bucket only) to focus on + the biggest instances when the speedup signal is dominated by them. + +## solver_mode (optional variant suffix) + +Generic `_lib.bench_paths` feature. `bench.solver_mode` (default unset == +`optimize`) does two things: + +1. **Artifact suffixing** so multiple modes coexist on disk: + `cache/`+`final_program.py` (optimize) vs `cache-/`+`final_program_.py`. + Every `_lib` CLI (sampler/rebaseline/extract_best/prepare_phase/final_verify/ + finalize) routes through `bench_paths.cache_dir` / `variant_suffix`, so the two + modes' baselines and outputs never collide. +2. **Worker branching** — `_solve_worker.py` reads the SAME sibling `config.yaml` + field and changes solver behavior (z3: `sat` = `z3.Solver` over + `parse_smt2_file`, drops `assert-soft`, no objective, `opt.*` params silently + dropped). `_lib.evaluator_core` warns if `optimize` + `score_mode != cost`. + +Use when one workload has two ways to be solved (full optimize vs feasibility-only) +and you want both tunable without copying the bench dir. Switching = edit +`solver_mode` + `score_mode`, then re-run sampler/rebaseline/phases (per-mode +`cache-/` keeps a dedicated baseline — **rebaseline is mandatory after switch**). + +## Existing solver reference + +| Solver | score_mode | Worker axis | Size buckets | Phases | +|---|---|---|---|---| +| z3 (`z3-bench`) | cost (optimize) / speedup (sat) | NO | NO | 4 (opt_sls + sat + smt + unified) | +| CP-SAT (`cpsat-bench`) | cost (dtime + cost_ratio) | YES (W=1, W=8) | YES | 5 (search + presolve + lp_cuts + unified + custom_subsolvers) | + +z3-bench also demonstrates the optional `solver_mode` (optimize/sat) + +`clustering.mode` (base/large) knobs above — both default-off, both config-only. + +Use whichever matches the new solver's profile as the structural template. diff --git a/.claude/skills/openevolve-pipeline/references/gotchas.md b/.claude/skills/openevolve-pipeline/references/gotchas.md new file mode 100644 index 0000000000..4fe6ebd2f2 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/gotchas.md @@ -0,0 +1,105 @@ +# Gotchas + +ADD_NEW_SOLVER.md §6 — verbatim. Re-listed here for skill-local lookup. + +## 1. problems.jsonl field name mismatch + +`adapter.PROBLEM_FILE_FIELD` / `STATUS_FIELD` MUST match the JSON keys exactly. +Typos are the #1 failure mode. `head -1 input//problems.jsonl | python3 -m json.tool` +shows the canonical keys. + +## 2. `features.` missing + +`clustering.feature: features.num_X`, but problems.jsonl entries lack +`features.num_X` → sampler treats every problem as size=0 → all problems +collapse into one cluster → cascade stages become meaningless. + +Verify with the sampler stdout: each cluster should hold a recognizable +spread of problem SHAs. All-in-one cluster = features field path wrong. + +## 3. DECISIVE vs DECIDED confusion + +- DECISIVE = "solver gave an answer" (e.g. `Sat`, `Unsat`, `OPTIMAL`). +- DECIDED = "baseline produced a conclusive answer → regression comparable" + (e.g. cpsat: `INFEASIBLE` decided but only `OPTIMAL`/`FEASIBLE` decisive). + +Most solvers: both sets identical. cpsat: they differ. + +## 4. `_solve_worker.py` doesn't surface invalid params + +If solver silently ignores unknown keys, the catalog alone cannot catch a +mutated illegal key. Worker MUST emit +`{"invalid_param": "", "result": "Unknown", "elapsed_ms": 0}` when +solver rejects a key — otherwise evaluator cannot 0-score the candidate. + +Test: pass `{"obviously_fake_key": 1}` → worker should emit invalid_param. + +## 5. Phase docstring empty + +LLM has no other signal about phase intent. Even one line — "Phase 2: tune +presolve.* knobs" — improves mutation quality dramatically. + +## 6. `unified_dict_name` mismatch + +`config.yaml` `bench.unified_dict_name` MUST match the EVOLVE-BLOCK dict +name in the last phase's `initial_program.py`. Default convention: +`UNIFIED_OVERRIDES`. + +Mismatch → `_lib.prepare_phase` cannot materialize the union → last phase +starts empty and loses prior-phase wins. + +## 7. `worker_path` is relative to `/evolve/` + +`config.yaml` `bench.worker_path: _solve_worker.py` (no directory prefix +when worker is at evolve/ root). + +## Verify-time additional gotchas + +### v1. `OPENEVOLVE_BENCH_ROOT unset` from phase module + +Phase module `_resolve_bench_root()` fallback walks parents looking for +adapter+params.json. Fails if phase dir is not exactly two levels under +``. Correct layout: + +``` +input//evolve/params.json +input//evolve/adapter.py +input//evolve/phase1_x/initial_program.py ← two levels under +``` + +### v2. Cascade thresholds too tight + +`evaluator.cascade_thresholds: [1.03, 1.03, 1.03]` means each stage demands +≥3% improvement. Solver with high variance + few problems may never cross. +Lower to `[1.01, 1.01, 1.01]` for noisy benches. + +### v3. `parallel_solvers > 1` with single-threaded baseline + +If baseline was captured single-threaded, running candidates with +`parallel_solvers: N` co-locates them on shared cores → timings inflate +vs baseline → false regression. Either pin core ranges via `--pin` or +recapture baseline at the same parallelism. + +### v4. solver_mode switch without re-baseline + +If the bench uses `bench.solver_mode` variants, each mode keeps its OWN baseline +in `cache-/local_baseline.json` (optimize → plain `cache/`). After switching +`solver_mode`, the new mode's `cache-/` has no baseline → `self_test`/scoring +compare against a stale or empty baseline. ALWAYS re-run `_lib.rebaseline ` +after a switch. The suffix isolation is automatic (`bench_paths.cache_dir`), so the +old mode's baseline is preserved — switching back needs no recapture. + +### v5. clustering.mode override silently ignored + +`clustering.mode: ` only applies if a matching `clustering.modes.` +block exists. A typo (mode set, no block) → sampler prints +`mode=... has no modes.... — using base` and falls back to the base block. Check +sampler stdout for `clustering: applied modes. override` to confirm it took. + +### v6. Catalog `defaults` ≠ binding's real defaults + +If `params.json` `defaults` includes a key the binding's real default is +different, the BASELINE phase modules send may diverge from what `_lib.rebaseline` +captured. Symptom: `self_test` ratio drifts outside [0.5, 2.0]. + +Fix: ensure `defaults` is what the original problems.jsonl baseline run used. diff --git a/.claude/skills/openevolve-pipeline/references/interview-checklist.md b/.claude/skills/openevolve-pipeline/references/interview-checklist.md new file mode 100644 index 0000000000..c85df6e34b --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/interview-checklist.md @@ -0,0 +1,81 @@ +# Interview Checklist + +Ask **only what's not already inferable** from `problems.jsonl` + `raw-data/`. +Batch with `AskUserQuestion` (≤4 per call). Group by topic. + +## Must-know (block scaffolding without answers) + +### Solver +- Solver name + version (→ `params.json` `solver`/`version`; `adapter.SOLVER_NAME`). +- Invocation path: Python binding (`import `) OR CLI binary (`/path/to/solver`). + - If neither — STOP. Cannot write `_solve_worker.py`. +- Install hint command (→ `config.yaml` `bench.solver_install_hint`). + +### problems.jsonl field mapping +Read one record (`head -1 input//problems.jsonl | python3 -m json.tool`) +and confirm with user: +- Problem file field name (e.g. `smt2_filename`, `problem_filename`) → `PROBLEM_FILE_FIELD`. +- Status block key (e.g. `z3_status`, `cpsat_status`) → `STATUS_FIELD`. + - Inside it: `result`, `elapsed_ms`, optional `objective_value`. +- Stats block key (or `None` if baseline has no stats) → `STATS_FIELD`. +- Features block key (default `features`) → `FEATURES_FIELD`. +- Objective path inside status (or `None` for SAT/UNSAT) → `OBJECTIVE_FIELD`. + +### Result tokens +- DECISIVE_RESULTS: which result strings mean "solver gave an answer"? + - z3: `("Sat", "Unsat")`. cpsat: `("OPTIMAL", "FEASIBLE")`. +- DECIDED_RESULTS: which baseline results allow regression comparison? + - Often == DECISIVE, but cpsat splits: `INFEASIBLE` decided but not feasible-decisive. + +### Score mode +- `speedup` (z3-style): minimize wall-clock; objective unused. +- `cost` (cpsat-style): minimize `(objective_value, dtime)` against baseline; needs a determinism counter (`time_metric`). +- See [decision-guide.md](decision-guide.md). + +### Worker axis +- Does the solver have a `num_workers`-like knob that meaningfully changes runtime? + - YES → `WORKERS_KEY = ""`; phase modules lock it via `PHASE_LOCKED`. + - NO → `WORKERS_KEY = None`. + +### Phase plan +- How many phases? (Typical: 3–5.) +- What namespace per phase? (e.g. phase1 = search, phase2 = presolve.) +- Last phase unified (merges priors)? (Default yes.) + +## Should-know (sensible defaults if user skips) + +### Clustering +- `clustering.method`: kmeans (default) | quintile | thresholds. +- `clustering.feature`: which numeric field to cluster on. Common: `features.num_constraints`, `_status.elapsed_ms`. +- `clustering.n_clusters`: 5 (default). +- `clustering.max_baseline_ms`: drop outliers above this from sample pool. 300000 (5min) default. +- `clustering.stage_sizes`: how many problems per cascade stage. Default `{stage1:10, stage2:10, stage3:5, stage4:20}`. + +### Evaluation +- `evaluation.repeats`: 10 (standard; lowering speeds up but adds noise). +- `evaluation.timeout_factor`: 1.3 (per-problem budget = baseline_ms × this). +- `evaluation.enable_size_buckets`: false default. true → use `initial_program_cpsat.py.tmpl`. +- `evaluation.enable_outlier_stage`: false default. + +### Variant modes (optional — leave off unless user asks) +- `bench.solver_mode`: does the workload have two ways to solve it (e.g. full + optimize vs feasibility-only) the user wants to tune separately? Default unset + (single mode). If yes → set the field, branch `_solve_worker.py` on it, and pair + with the right `score_mode` (optimize→cost, feasibility→speedup). Artifacts auto- + suffix to `cache-/` + `final_program_.py`. See [decision-guide.md](decision-guide.md). +- `clustering.mode` + `clustering.modes.`: want an alternate sample profile + (e.g. focus on large instances) switchable by one field? Orthogonal to + solver_mode. Default unset (base block only). + +### KEY_STATS / STATS_WEIGHTS +- Which counters from `solver.statistics()` to surface as metrics? +- Weight per counter for efficiency factor (defaults: conflicts 2.0, decisions 1.5, propagations 0.5). + +### LLM +- Which model? Default `claude-sonnet-4-6` via `claude_code` provider. + +## Sensible defaults — do NOT ask + +- `parallel_solvers: 1`, `max_iterations: 40`, `checkpoint_interval: 10`, + `random_seed: 42`, `num_islands: 3`, `cascade_thresholds: [1.03, 1.03, 1.03]`, + `evaluator.timeout: 1800`. diff --git a/.claude/skills/openevolve-pipeline/references/verify-checklist.md b/.claude/skills/openevolve-pipeline/references/verify-checklist.md new file mode 100644 index 0000000000..19813d79a4 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/verify-checklist.md @@ -0,0 +1,90 @@ +# Verify Checklist + +Run after scaffolding. Each step gates the next — stop at first failure. + +## 0. Solver installed? + +```bash +python3 -c "import ; print(.__version__)" +# OR +command -v && --version +``` + +Fail → tell user to install per `bench.solver_install_hint`. + +## 1. params.json valid? + +```bash +cd input +python3 -c " +import sys; sys.path.insert(0, '.') +from _lib import params_catalog +c = params_catalog.load('/evolve/params.json') +print('keys:', len(c.known_keys()), 'defaults:', len(c.defaults), 'locked:', len(c.locked)) +print('validate ok:', c.validate(c.defaults)) +print('validate bogus:', c.validate({'fake_key': 1})) +" +``` + +Expect: `validate ok: []`, `validate bogus: [('fake_key', 'unknown key (not in catalog)')]`. + +Fail modes: +- `KeyError` → typo in `groups.*.params` schema (type/default/values missing). +- `validate ok` returns non-empty list → a default value violates its declared type/range. + +## 2. problems.jsonl readable + adapter fields wired? + +```bash +python3 -m _lib.sampler +ls /evolve/cache/stage{1,2,3,4}_sample.json +``` + +Expect: 4 stage sample files, with non-trivial cluster spread shown in stdout +(problem SHAs across multiple `elapsed_ms` buckets). + +Fail modes: +- "all problems in cluster 0" → `clustering.feature` path wrong, or feature value `None`/`0` for every problem. Check `head -1 problems.jsonl` against the dotted path. +- KeyError on `PROBLEM_FILE_FIELD`/`STATUS_FIELD` → adapter constants don't match problems.jsonl. Fix adapter.py. + +## 3. Adapter sane on BASELINE? + +```bash +python3 -m _lib.self_test +``` + +Expect: result labels match baseline for stage1; ratio in [0.5, 2.0]. WARN tolerated. + +Fail modes: +- "result mismatch" on multiple problems → `_solve_worker.py` returns wrong token. Fix result mapping. +- "ratio > 2.0" — solver invocation is significantly slower than baseline. Check params application (some keys default differently in your binding version than the baseline expects). +- `invalid_param` emitted → BASELINE has a key the binding doesn't recognize. Either drop the key from `defaults` or fix worker. + +## 4. Local baseline captured? (slow, ~10× problem timeouts) + +```bash +python3 -m _lib.rebaseline +ls /evolve/cache/local_baseline.json +``` + +Skip on resource-constrained dev machines with `SKIP_REBASELINE=1` — +later evaluator will use `problems.jsonl` baseline directly. + +## 5. One-phase smoke + +```bash +./input/run_phase.sh 1 --pin 2-3 --iterations 2 +ls /evolve/phase1_*/openevolve_output/best/best_program.py +ls /evolve/cache/phase1_best.json +``` + +Expect: best_program.py exists, phase1_best.json contains a dict of overrides. + +Fail modes: +- "OPENEVOLVE_BENCH_ROOT unset" → phase module's `_resolve_bench_root()` + fallback failed. Confirm phase dir is two levels under `` (i.e. + `/evolve/phase1_x/initial_program.py`). +- Evaluator returns 0 for all candidates → check phase module's `get_params()` + signature matches what evaluator calls (simple: `get_params()`; + cpsat-style: `get_params(problem=None, stage=None)`). +- LLM never mutates EVOLVE-BLOCK → docstring missing or `unified_dict_name` + mismatch with last phase (only matters for last phase). diff --git a/.claude/skills/openevolve-pipeline/templates/_solve_worker_cli.tmpl b/.claude/skills/openevolve-pipeline/templates/_solve_worker_cli.tmpl new file mode 100644 index 0000000000..0aeb6c8b21 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/_solve_worker_cli.tmpl @@ -0,0 +1,98 @@ +""" +Solve one problem via the CLI binary. +Subprocess worker invoked by _lib.subprocess_runner. + +argv: + sys.argv[1] JSON dict of {key: value} (params) + sys.argv[2] problem file path + sys.argv[3] per-problem timeout in seconds + +stdout: one JSON line on the LAST non-empty line. See _solve_worker_py.tmpl +for the success/invalid_param/crash schemas. +""" +import json +import re +import subprocess +import sys +import time + + +SOLVER_BINARY = "" # absolute path if not on PATH + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def params_to_argv(params): + """Translate {key:value} → CLI flags. Adapt per solver's CLI convention. + Common conventions: + - z3-style: key=value (e.g. sat.threads=1) + - GNU long: --key=value (e.g. --threads=8) + - GNU short: -k value (rarely) + """ + argv = [] + for k, v in params.items(): + if isinstance(v, bool): + v = "true" if v else "false" + argv.append(f"{k}={v}") + return argv + + +def parse_result(stdout): + """Solver-specific. Extract result token + stats counters.""" + last = stdout.strip().splitlines()[-1] if stdout.strip() else "" + # Examples — adapt: + if "unsat" in last.lower(): + return "Unsat", {} + if "sat" in last.lower(): + return "Sat", {} + return "Unknown", {} + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + try: + params = json.loads(sys.argv[1]) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params json: {e}"}) + return + + problem_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + cmd = [SOLVER_BINARY, *params_to_argv(params), problem_path] + t0 = time.monotonic() + try: + proc = subprocess.run(cmd, capture_output=True, text=True, + timeout=timeout_s + 5) # _lib enforces hard limit + except subprocess.TimeoutExpired: + emit({"result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "timeout": True, "stats": {}}) + return + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"spawn: {e}"}) + return + + elapsed_ms = int((time.monotonic() - t0) * 1000) + + # Detect unknown param if solver complains on stderr/stdout. + # Adapt the regex to your solver's diagnostic. + m = re.search(r"unknown\s+(?:parameter|option)\s+['\"]?([\w.\-]+)['\"]?", + proc.stderr + proc.stdout, re.IGNORECASE) + if m: + emit({"invalid_param": m.group(1), "error": m.group(0), + "result": "Unknown", "elapsed_ms": 0}) + return + + result, stats = parse_result(proc.stdout) + emit({"result": result, "elapsed_ms": elapsed_ms, "stats": stats}) + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl b/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl new file mode 100644 index 0000000000..2ab618a78b --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl @@ -0,0 +1,125 @@ +""" +Solve one problem via the Python binding. +Subprocess worker invoked by _lib.subprocess_runner. + +argv: + sys.argv[1] JSON dict of {key: value} (params) + sys.argv[2] problem file path + sys.argv[3] per-problem timeout in seconds + +stdout: one JSON line on the LAST non-empty line. Schemas: + success: {"result": "Sat"|"Unsat"|"Unknown"|..., "elapsed_ms": int, + "stats": {: , ...}, "objective": ?} + invalid_param: {"invalid_param": "", "error": "", + "result": "Unknown", "elapsed_ms": 0} + crash: {"result": "Unknown", "elapsed_ms": 0, "error": ""} + +Timeout is enforced by _lib.subprocess_runner — the worker does NOT need +to implement it (but may pass it to the solver as a hint). +""" +import json +import pathlib +import sys +import time + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def _solver_mode(): + """OPTIONAL — only if this bench uses bench.solver_mode variants. Reads the + sibling config.yaml so the worker branches WITHOUT an env var (config is the + single source of truth). Default "optimize". One parse/process is negligible + next to the solver import. Delete this + its use if the bench is single-mode.""" + try: + import yaml + + cfg_path = pathlib.Path(__file__).resolve().parent / "config.yaml" + cfg = yaml.safe_load(cfg_path.read_text()) or {} + return ((cfg.get("bench") or {}).get("solver_mode")) or "optimize" + except Exception: + return "optimize" + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + try: + params = json.loads(sys.argv[1]) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params json: {e}"}) + return + + problem_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + # OPTIONAL variant branch (see bench.solver_mode in config.yaml). Use `mode` + # to pick the engine (e.g. feasibility-only vs full optimize), drop mode-only + # params silently, and skip objective extraction in the lighter mode. + # mode = _solver_mode() + + try: + import + except ImportError as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f" import: {e}"}) + return + + # 1. Instantiate + apply params. + try: + solver = .Solver() + for k, v in params.items(): + try: + solver.set_param(k, v) + except .InvalidParamError as e: + # MUST emit this — otherwise evaluator can't tell mutated key was illegal. + emit({"invalid_param": k, "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"init: {e}"}) + return + + # 2. Load problem. + try: + with open(problem_path) as f: + solver.parse(f.read()) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"parse: {e}"}) + return + + # 3. Solve. + t0 = time.monotonic() + try: + result = solver.solve(timeout_s) # <- "Sat" | "Unsat" | "Unknown" | ... + except Exception as e: + emit({"result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": str(e), "stats": {}}) + return + + elapsed_ms = int((time.monotonic() - t0) * 1000) + stats = {} + try: + raw_stats = solver.statistics() # dict-like; keep numeric only + for k, v in raw_stats.items(): + if isinstance(v, (int, float)): + stats[k] = v + except Exception: + pass + + out = {"result": str(result), "elapsed_ms": elapsed_ms, "stats": stats} + try: + obj = solver.objective() # may not exist; OPTIONAL + if obj is not None: + out["objective"] = float(obj) + except Exception: + pass + emit(out) + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/openevolve-pipeline/templates/adapter.py.tmpl b/.claude/skills/openevolve-pipeline/templates/adapter.py.tmpl new file mode 100644 index 0000000000..91fa93ae14 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/adapter.py.tmpl @@ -0,0 +1,36 @@ +"""-bench solver hooks. Consumed by every _lib module via +bench_paths.load_adapter().""" + +SOLVER_NAME = "" + +# problems.jsonl field paths. +PROBLEM_FILE_FIELD = "_filename" # top-level key holding the problem file path +STATUS_FIELD = "_status" # nested {"result", "elapsed_ms", ...} +STATS_FIELD = "_response_stats" # nested counters dict; None if baseline has none +FEATURES_FIELD = "features" # nested {"num_*": int, ...} +OBJECTIVE_FIELD = None # path inside STATUS_FIELD; None for SAT/UNSAT problems + +# Result categorization. +DECISIVE_RESULTS = ("Sat", "Unsat") # "solver gave an answer" +DECIDED_RESULTS = ("Sat", "Unsat") # "baseline was conclusive → regression comparable" + +# Solver counters to surface as metrics / artifacts. +KEY_STATS = ("conflicts", "decisions", "propagations") + +# Efficiency-factor weight per stat key. +STATS_WEIGHTS = { + "conflicts": 2.0, + "decisions": 1.5, + "propagations": 0.5, +} + +# Score mode default (config.yaml evaluation.score_mode wins if set). +SCORE_MODE = "speedup" + +# Worker-count knob this solver uses. None when not applicable. +WORKERS_KEY = None # e.g. cpsat: "num_search_workers" + + +def get_problem_size(features): + """Feature used by clustering + SIZE_BUCKETS surface.""" + return int((features or {}).get("num_constraints") or 0) diff --git a/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl b/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl new file mode 100644 index 0000000000..553810e59e --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl @@ -0,0 +1,103 @@ +# input//evolve/config.yaml +# Single source of truth — openevolve (dacite, ignores unknown top-level keys) +# AND input/run_phase.sh (via _lib/load_bench_config.py) read this file. + +bench: + phases: + - dir: phase1_ + - dir: phase2_ + # add more as needed + - dir: phase_unified + + # Materialize last phase's EVOLVE-BLOCK from union of prior phases' best + # before this dir runs. Must match a `dir` above. Dict name MUST match + # the EVOLVE-BLOCK dict in that phase's initial_program.py. + unified_prepare_before_dir: phase_unified + unified_dict_name: UNIFIED_OVERRIDES + + solver_check_cmd: "command -v " # or: python3 -c 'import ' + solver_install_hint: "install: " + + # Per-bench surface paths (relative to /evolve/). + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + + # OPTIONAL variant mode (default unset == "optimize"). When set to any other + # value, _lib suffixes on-disk artifacts so multiple modes coexist: + # /optimize → cache/, final_program.py + # sat (or ) → cache-/, final_program_.py + # The worker reads this same field to branch behavior (e.g. z3 sat = Solver + # over parse_smt2_file, no objective). _lib.evaluator_core warns if + # solver_mode == optimize but score_mode != cost (objective guard inactive). + # Switch modes by editing this + score_mode, then re-run sampler/rebaseline. + # solver_mode: optimize + + # Clustering — consumed by _lib.sampler. + clustering: + # OPTIONAL: `mode` selects a clustering.modes. override (shallow-merged + # over this block). ORTHOGONAL to solver_mode — set independently to switch + # the sample profile (e.g. focus on large instances) in any solver mode. + # mode: large + method: kmeans # kmeans | quintile | thresholds + feature: features.num_constraints # dotted path into problems.jsonl record + n_clusters: 5 + max_baseline_ms: 300000 # drop outliers >5min from sample pool + spread: quintile # quintile | center within stage pool + stage_sizes: {stage1: 10, stage2: 10, stage3: 5, stage4: 20} + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + # OPTIONAL override profiles selected by `clustering.mode` above. Each is + # shallow-merged over the base block. Example: a `large` profile that focuses + # on constraint-heavy instances via threshold bucketing, top bucket only. + # modes: + # large: + # method: thresholds + # thresholds: [15000] # bucket 1 = >=15k + # stage_clusters: {stage1: [1], stage2: [1], stage3: [1], stage4: [1]} + # stage_sizes: {stage1: 6, stage2: 10, stage3: 12, stage4: 38} + + # Evaluation behavior — consumed by _lib.evaluator_core. + evaluation: + repeats: 10 # 10-run averaging (standard) + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: speedup # speedup | cost + # time_metric: dtime # cost mode only — pick determinism counter key + # cost_weight: 1.0 # cost mode only + enable_size_buckets: false # opt-in: SIZE_BUCKETS phase surface + enable_outlier_stage: false # opt-in: STAGE3_OVERRIDES phase surface + +parallel_solvers: 1 # concurrent worker subprocesses per stage + +max_iterations: 40 +checkpoint_interval: 10 +log_level: "INFO" +random_seed: 42 + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 1.0 + +prompt: + system_message: | + Tune parameters for . + EVOLVE-BLOCK exposes a dict; mutate it to MAXIMIZE combined_score. + Hard rules: + - Do NOT modify locked keys (see params.json `locked`). + - Use only valid param keys (catalog validation enforced). + - Score = geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT. + +database: + num_islands: 3 + +evaluator: + timeout: 1800 + cascade_evaluation: true + cascade_thresholds: [1.03, 1.03, 1.03] + parallel_evaluations: 1 diff --git a/.claude/skills/openevolve-pipeline/templates/initial_program_cpsat.py.tmpl b/.claude/skills/openevolve-pipeline/templates/initial_program_cpsat.py.tmpl new file mode 100644 index 0000000000..f3c2156820 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/initial_program_cpsat.py.tmpl @@ -0,0 +1,84 @@ +""" +Phase : tune 's knobs (SIZE_BUCKETS + outlier track). + +Use ONLY when config.yaml `evaluation.enable_size_buckets: true` +(and `enable_outlier_stage: true` for STAGE3_OVERRIDES). + +Do NOT modify locked keys. +""" +import os +import pathlib +import sys + + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# OPENEVOLVE_PROFILE=large → outlier-track W. small → 1. +_LARGE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() == "large") +PHASE_LOCKED = { + "": 0, + "": 8 if _LARGE else 1, # must match adapter.WORKERS_KEY +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) diff --git a/.claude/skills/openevolve-pipeline/templates/initial_program_simple.py.tmpl b/.claude/skills/openevolve-pipeline/templates/initial_program_simple.py.tmpl new file mode 100644 index 0000000000..fc6c5e0b06 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/initial_program_simple.py.tmpl @@ -0,0 +1,51 @@ +""" +Phase : tune 's knobs. + +Targeted namespace: , , . +Other params stay at BASELINE. + +Do NOT modify locked keys (see params.json `locked`). Invalid keys +cause the evaluator to return 0 and surface the offending key. +""" +import os +import pathlib +import sys + + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# EVOLVE-BLOCK-START +OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(OVERRIDES) diff --git a/.claude/skills/openevolve-pipeline/templates/initial_program_unified.py.tmpl b/.claude/skills/openevolve-pipeline/templates/initial_program_unified.py.tmpl new file mode 100644 index 0000000000..766221a7b6 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/initial_program_unified.py.tmpl @@ -0,0 +1,53 @@ +""" +Final phase: unified refinement. + +EVOLVE-BLOCK is auto-materialized by `python -m _lib.prepare_phase ` +before this phase runs — pulls the union of phase{1..N-1}_best.json into +UNIFIED_OVERRIDES. The LLM then tunes the merged dict. + +The dict name (UNIFIED_OVERRIDES) MUST match config.yaml `bench.unified_dict_name`. + +Do NOT modify locked keys. +""" +import os +import pathlib +import sys + + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# EVOLVE-BLOCK-START +UNIFIED_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(UNIFIED_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(UNIFIED_OVERRIDES) diff --git a/.claude/skills/openevolve-pipeline/templates/params.json.tmpl b/.claude/skills/openevolve-pipeline/templates/params.json.tmpl new file mode 100644 index 0000000000..4abe584188 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/params.json.tmpl @@ -0,0 +1,55 @@ +{ + "solver": "", + "version": "", + + "_comment_defaults": "BASELINE. Phase modules pull this via params_catalog.load_for_bench().defaults", + "defaults": { + "": 0, + "": true, + "": 0 + }, + + "_comment_locked": "LLM mutating any of these → combined_score=0", + "locked": { + "": 0 + }, + + "groups": { + "search": { + "description": "Branching / restart strategy.", + "params": { + "": { + "type": "enum", + "values": ["a", "b", "c"], + "default": "a", + "desc": "What this knob does." + }, + "": { + "type": "int", + "default": 100, + "range": [0, 10000], + "desc": "..." + }, + "": { + "type": "float", + "default": 0.5, + "range": [0.0, 1.0], + "desc": "..." + }, + "": { + "type": "bool", + "default": true, + "desc": "..." + }, + "": { + "type": "list", + "element_type": "subsolver", + "default": [], + "desc": "Optional list-typed param. element_type:subsolver validates against subsolver_names below." + } + } + } + }, + + "subsolver_names": [] +} diff --git a/.gitignore b/.gitignore index 338d65a218..c8027d596b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,6 @@ results/ examples/lm_eval/prompts/system_message.txt examples/lm_eval/prompts/evaluator_system_message.txt - # Python __pycache__/ *.py[cod] @@ -52,7 +51,24 @@ htmlcov/ # Misc .DS_Store .venv +.claude/* +!.claude/skills/ # For SR secrets.yaml problems +input/z3-bench/raw-data +input/z3-bench/reboot-raw-data +input/cpsat-bench/raw-data + +# Bench evolve cache (locally regenerated by _lib.sampler / _lib.rebaseline / +# _lib.extract_best / _lib.prepare_phase). Safe to delete. +input/*/evolve/cache/ + +# Bench evolve final artifact (locally regenerated by _lib.finalize from the +# last phase's best_program.py — depends on local evolution run state). +input/*/evolve/final_program.py + +# OpenEvolve per-phase output (LLM evolution artifacts). +input/*/evolve/phase*/openevolve_output*/ +input/*/evolve/phase*/__pycache__/ diff --git a/Dockerfile b/Dockerfile index 1af998267b..aaa8cb0085 100644 --- a/Dockerfile +++ b/Dockerfile @@ -12,8 +12,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ # Copy the project files into the container COPY . /app -# Install Python dependencies -RUN pip install --root-user-action=ignore -e . +# Install Python dependencies. claude-code extra pulls in claude-agent-sdk +# AND opentelemetry-api (SDK injects W3C trace context; missing OTEL emits +# noisy DEBUG tracebacks each call). +RUN pip install --root-user-action=ignore -e ".[claude-code]" # Expose the project directory as a volume VOLUME ["/app"] diff --git a/configs/claude_code_example.yaml b/configs/claude_code_example.yaml new file mode 100644 index 0000000000..9b81071fc2 --- /dev/null +++ b/configs/claude_code_example.yaml @@ -0,0 +1,60 @@ +# Example: drive OpenEvolve through a local Claude Code CLI session. +# +# Prereqs: +# 1. Install Claude Code CLI and log in once: `claude login` +# (Pro/Max subscription is used for billing; no API key required.) +# 2. Install the SDK extra: pip install -e ".[claude-code]" +# +# Caveats: +# - Subscription rate limits (5-hour windows) apply. Heavy evolution runs +# will hit them quickly; consider mixing Claude Code with API-keyed models +# in the ensemble, or lowering `max_iterations`. +# - `temperature`, `top_p`, and `random_seed` are ignored by this backend +# (the SDK does not expose them). Reproducibility is reduced. +# - `max_tokens` is not enforced; use `max_thinking_tokens` instead. + +max_iterations: 50 +checkpoint_interval: 10 + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.7 + system_message: "You are an expert code evolution assistant." + timeout: 300 + retries: 2 + retry_delay: 10 + # Optional Claude Code-specific knobs: + reasoning_effort: "medium" # one of: low, medium, high, xhigh, max + # Cap extended thinking. The CLI/model can emit thinking even with effort + # unset; left uncapped (null) it can run for many minutes and blow past + # `timeout`. Timeouts are NOT retried (a retry would just time out again), + # so keep `timeout` comfortably above the time a capped query needs. + max_thinking_tokens: 4000 + # cli_path: "/opt/homebrew/bin/claude" + # claude_code_options: # raw kwargs forwarded to ClaudeAgentOptions + # fallback_model: "claude-haiku-4-5" + + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.3 + timeout: 300 + max_thinking_tokens: 4000 + + evaluator_models: + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 1.0 + timeout: 300 + max_thinking_tokens: 4000 + +prompt: + system_message: "You are an expert code evolution assistant." + +database: + num_islands: 3 + population_size: 100 + +evaluator: + parallel_evaluations: 1 # keep low to avoid rate-limit bursts diff --git a/docker-run.sh b/docker-run.sh new file mode 100755 index 0000000000..9af02a036b --- /dev/null +++ b/docker-run.sh @@ -0,0 +1,470 @@ +#!/bin/bash + +# Docker Container Run Script +# Usage: ./docker-run.sh [dev|prod] [options] +# dev|prod : Select development (dev) or production (prod) environment +# -d, --detached : Run in detached mode +# -i, --interactive : Run in interactive mode (default) +# -s, --suffix : Add suffix to container name +# -h, --help : Show help message + +set -e # Exit on error + +# Color definitions +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color +# Configuration +REGISTRY_URL="192.168.10.12:5050" +IMAGE_NAME="infra/axion-dev-docker" +USERNAME=$(whoami) + +# 아키텍처 자동 감지 +ARCH=$(uname -m) +case "$ARCH" in + x86_64) PLATFORM="linux/amd64" ;; + aarch64) PLATFORM="linux/arm64" ;; + arm64) PLATFORM="linux/arm64" ;; + *) echo -e "${RED}Unsupported architecture: $ARCH${NC}"; exit 1 ;; +esac + +# Current script directory +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +# Default settings +DETACHED_MODE=false +INTERACTIVE_MODE=true +ENV_TYPE="dev" +IMAGE_TAG="dev-latest" +CONTAINER_SUFFIX="" +PIN_CPUS="" +ISOLATED_CGROUP_NAME="${ISOLATED_CGROUP_NAME:-isolated.slice}" + +# Help function +show_help() { + echo "Usage: $0 [dev|prod] [options]" + echo "" + echo "Environment selection (optional, default: dev):" + echo " dev Use development environment image (dev-latest) [default]" + echo " prod Use production environment image (prod-latest)" + echo "" + echo "Options:" + echo " -d, --detached Run in detached mode" + echo " -i, --interactive Run in interactive mode (default)" + echo " -s, --suffix TEXT Add suffix to default container name" + echo " --pin [LIST] Pin container to CPU cores (default: 1-6 if no LIST)." + echo " Adds --cpuset-cpus, joins isolated cgroup if present," + echo " and wraps the entrypoint with taskset -c LIST." + echo " Pair with: sudo ./scripts/host-isolate-cores.sh start" + echo " -h, --help Show this help message" + echo "" + echo "Examples:" + echo " $0 # Run dev environment in interactive mode (default)" + echo " $0 dev # Run dev environment in interactive mode" + echo " $0 prod -d # Run prod environment in detached mode" + echo " $0 -d # Run dev environment in detached mode" + echo " $0 dev -s test # Container: axion-cell-container-dev-\$USER-test" + echo " $0 --pin # Pin container to cores 1-6 (host isolation recommended)" + echo " $0 --pin 2-7 # Pin container to cores 2-7" + echo "" +} + +# Check for help argument +if [[ "$1" == "-h" ]] || [[ "$1" == "--help" ]]; then + show_help + exit 0 +fi + +# Check environment type from first argument (optional) +if [[ $# -gt 0 ]]; then + case $1 in + dev|DEV) + ENV_TYPE="dev" + IMAGE_TAG="dev-latest" + shift + ;; + prod|PROD) + ENV_TYPE="prod" + IMAGE_TAG="prod-latest" + shift + ;; + -d|--detached|-i|--interactive|-s|--suffix) + # If option comes first, use default (dev) + ;; + *) + echo -e "${RED}Error: Invalid argument: $1${NC}" + echo -e "${YELLOW}Please enter dev, prod, or a valid option.${NC}" + echo "" + show_help + exit 1 + ;; + esac +fi + +# Image name configuration +REGISTRY_IMAGE="$REGISTRY_URL/$IMAGE_NAME:$IMAGE_TAG" +LOCAL_IMAGE="$IMAGE_NAME-$USERNAME:$IMAGE_TAG" +CONTAINER_NAME="axion-cell-container-$ENV_TYPE-$USERNAME" + +# Parse remaining arguments +while [[ $# -gt 0 ]]; do + case $1 in + -d|--detached) + DETACHED_MODE=true + INTERACTIVE_MODE=false + shift + ;; + -i|--interactive) + INTERACTIVE_MODE=true + DETACHED_MODE=false + shift + ;; + -h|--help) + show_help + exit 0 + ;; + -s|--suffix) + if [[ -z "$2" ]] || [[ "$2" == -* ]]; then + echo -e "${RED}Error: $1 requires a suffix value.${NC}" + show_help + exit 1 + fi + CONTAINER_SUFFIX="$2" + shift 2 + ;; + --pin) + if [[ -n "${2:-}" ]] && [[ "$2" != -* ]]; then + PIN_CPUS="$2" + shift 2 + else + PIN_CPUS="1-6" + shift + fi + ;; + --pin=*) + PIN_CPUS="${1#--pin=}" + shift + ;; + *) + echo -e "${RED}Unknown option: $1${NC}" + show_help + exit 1 + ;; + esac +done + +if [[ -n "$CONTAINER_SUFFIX" ]]; then + CONTAINER_NAME="${CONTAINER_NAME}-${CONTAINER_SUFFIX}" +fi + +echo -e "${GREEN}================================================${NC}" +echo -e "${GREEN} AxionCell Docker Container Run${NC}" +echo -e "${GREEN}================================================${NC}" +echo "" + +# Check Docker installation +if ! command -v docker &> /dev/null; then + echo -e "${RED}Error: Docker is not installed.${NC}" + exit 1 +fi + +# Check Docker daemon status and determine if using rootless Docker +ROOTLESS_DOCKER=false +if ! docker info &> /dev/null; then + # Check if rootless docker is available + if [[ -S "/run/user/$(id -u)/docker.sock" ]]; then + export DOCKER_HOST="unix:///run/user/$(id -u)/docker.sock" + if docker info &> /dev/null; then + ROOTLESS_DOCKER=true + echo -e "${BLUE}Using rootless Docker (DOCKER_HOST=$DOCKER_HOST)${NC}" + else + echo -e "${RED}Error: Docker daemon is not running.${NC}" + echo -e "${YELLOW}Hint: For rootless Docker, run: systemctl --user start docker${NC}" + exit 1 + fi + else + echo -e "${RED}Error: Docker daemon is not running.${NC}" + echo -e "${YELLOW}Hint: You may need to install rootless Docker:${NC}" + echo -e " dockerd-rootless-setuptool.sh install" + exit 1 + fi +else + # Check if current user has root privileges for Docker + if docker info 2>&1 | grep -q "rootless"; then + ROOTLESS_DOCKER=true + echo -e "${BLUE}Using rootless Docker${NC}" + else + echo -e "${BLUE}Using root Docker${NC}" + fi +fi + +# Pull image from registry +echo -e "${YELLOW}[$ENV_TYPE environment] Registry image: $REGISTRY_IMAGE${NC}" +echo -e "${BLUE}Platform: $PLATFORM${NC}" +echo -e "${BLUE}Pulling image from GitLab Registry...${NC}" +echo "" +if docker pull --platform "$PLATFORM" "$REGISTRY_IMAGE"; then + echo -e "${GREEN}Successfully pulled image!${NC}" + echo "" +else + echo -e "${RED}Error: Failed to pull image.${NC}" + echo -e "${YELLOW}Hint: You may need to login to GitLab Registry:${NC}" + echo -e " docker login $REGISTRY_URL" + exit 1 +fi + +# Create local image tag with username +echo -e "${BLUE}Creating local image tag: $LOCAL_IMAGE${NC}" +if docker tag "$REGISTRY_IMAGE" "$LOCAL_IMAGE"; then + echo -e "${GREEN}Successfully created local image tag!${NC}" + echo "" +else + echo -e "${RED}Error: Failed to create local image tag.${NC}" + exit 1 +fi + +# Remove existing container if exists +if docker ps -a | grep -q "$CONTAINER_NAME"; then + echo -e "${YELLOW}Removing existing container...${NC}" + docker rm -f "$CONTAINER_NAME" > /dev/null 2>&1 || true +fi + +# Create result and log directories +mkdir -p "$SCRIPT_DIR/result" +mkdir -p "$SCRIPT_DIR/logs" + +# Create persistent directories for Docker credentials and bash history +DOCKER_PERSIST_DIR="$HOME/.axion-docker-persist" +mkdir -p "$DOCKER_PERSIST_DIR" + +# Initialize persistent files if they don't exist (to avoid mounting as directories) +touch "$DOCKER_PERSIST_DIR/.bash_history" 2>/dev/null || true + +# Claude Code config dir — mounted into container so settings/sessions persist. +# Note: on macOS, OAuth credentials live in Keychain (not in this dir). +# To authenticate from inside the container, run `claude setup-token` on the +# host once and export CLAUDE_CODE_OAUTH_TOKEN (or ANTHROPIC_API_KEY) in your +# shell before invoking this script. The vars are forwarded below. +CLAUDE_CONFIG_DIR="$HOME/.claude" +mkdir -p "$CLAUDE_CONFIG_DIR" 2>/dev/null || true + +# Persistent dir for container-side `claude` install (root mode uses --rm, so +# anything written to /root/.local is lost between runs). Mount this so the +# standalone installer's binary at ~/.local/bin/claude survives. +CLAUDE_LOCAL_PERSIST_DIR="$DOCKER_PERSIST_DIR/claude-local" +mkdir -p "$CLAUDE_LOCAL_PERSIST_DIR/bin" 2>/dev/null || true + +# Collect Claude/Anthropic env vars to forward. Skip empty ones. +CLAUDE_ENV_OPTS=() +for var in OPENAI_API_KEY ANTHROPIC_API_KEY ANTHROPIC_AUTH_TOKEN \ + ANTHROPIC_BASE_URL CLAUDE_CODE_OAUTH_TOKEN CLAUDE_CODE_USE_BEDROCK \ + CLAUDE_CODE_USE_VERTEX; do + if [[ -n "${!var}" ]]; then + CLAUDE_ENV_OPTS+=("-e" "$var=${!var}") + fi +done + +# Container runs as root; Claude CLI refuses --dangerously-skip-permissions +# under uid 0 unless IS_SANDBOX=1 declares the env is already sandboxed. +CLAUDE_ENV_OPTS+=("-e" "IS_SANDBOX=1") + +# Try to mount host's `claude` CLI into the container. Only safe when host and +# container share the same OS/arch (Linux x86_64 host, Linux x86_64 container). +# On macOS hosts the Mac binary will NOT run in a Linux container, so we skip +# the mount and rely on the container having `@anthropic-ai/claude-code` +# installed (e.g. `npm install -g @anthropic-ai/claude-code` once inside). +CLAUDE_CLI_MOUNT_OPTS=() +HOST_OS="$(uname -s)" +if [[ "$HOST_OS" == "Linux" ]]; then + if HOST_CLAUDE_BIN=$(command -v claude 2>/dev/null); then + # Resolve symlink (npm installs `claude` as a symlink to cli.js). + HOST_CLAUDE_REAL=$(readlink -f "$HOST_CLAUDE_BIN" 2>/dev/null || echo "$HOST_CLAUDE_BIN") + CLAUDE_CLI_MOUNT_OPTS+=( + "-v" "$HOST_CLAUDE_BIN:/usr/local/bin/claude:ro" + ) + # If symlink target lives elsewhere, mount the real file too. + if [[ "$HOST_CLAUDE_REAL" != "$HOST_CLAUDE_BIN" ]]; then + CLAUDE_CLI_MOUNT_OPTS+=("-v" "$HOST_CLAUDE_REAL:$HOST_CLAUDE_REAL:ro") + fi + echo -e "${BLUE}Mounting host claude CLI: $HOST_CLAUDE_BIN${NC}" + fi +fi + +# Configure Docker run options based on Docker mode (rootless vs root) +if [ "$ROOTLESS_DOCKER" = true ]; then + # Rootless Docker: Mount user's home directory as per the guide + # Container runs as root but binds to user's home directory + DOCKER_RUN_OPTS=( + "--name" "$CONTAINER_NAME" + "--rm" + "--platform" "$PLATFORM" + "--cap-add=SYS_PTRACE" + "--security-opt" "seccomp=unconfined" + "-v" "$HOME:$HOME" + ) + if [ -d "/home/share" ]; then + DOCKER_RUN_OPTS+=("-v" "/home/share:/home/share") + fi + DOCKER_RUN_OPTS+=( + "-v" "$SCRIPT_DIR/logs:$SCRIPT_DIR/logs" + "-v" "$SCRIPT_DIR/result:$SCRIPT_DIR/result" + "-v" "$HOME/.ssh:/root/.ssh" + "-v" "$HOME/.gitconfig:/root/.gitconfig" + "-w" "$SCRIPT_DIR" + "-e" "HOME=$HOME" + "-e" "TZ=Asia/Seoul" + ) + # Rootless: $HOME bind already exposes ~/.claude. Just forward env vars + # and (Linux only) the host's claude CLI binary. + DOCKER_RUN_OPTS+=("${CLAUDE_ENV_OPTS[@]}") + DOCKER_RUN_OPTS+=("${CLAUDE_CLI_MOUNT_OPTS[@]}") + # Rootless Docker stores credentials in ~/.config/docker/config.json + # Mount it to ~/.docker/config.json for container compatibility + if [[ -f "$HOME/.config/docker/config.json" ]]; then + DOCKER_RUN_OPTS+=("-v" "$HOME/.config/docker/config.json:$HOME/.docker/config.json:ro") + DOCKER_RUN_OPTS+=("-v" "$HOME/.config/docker/config.json:/root/.docker/config.json:ro") + mkdir -p "$HOME/.docker" 2>/dev/null || true + fi +else + # Root Docker: Mount project directory and persistent configs + # Mount to both /root and /home/appuser to support both root and non-root container users + DOCKER_RUN_OPTS=( + "--name" "$CONTAINER_NAME" + "--rm" + "--platform" "$PLATFORM" + "--cap-add=SYS_PTRACE" + "--security-opt" "seccomp=unconfined" + "-v" "$SCRIPT_DIR:/app" + "-v" "$SCRIPT_DIR/logs:/app/logs" + "-v" "$SCRIPT_DIR/result:/app/result" + "-v" "$HOME/.ssh:/root/.ssh:ro" + "-v" "$HOME/.ssh:/home/appuser/.ssh:ro" + "-v" "$HOME/.docker/config.json:/root/.docker/config.json:ro" + "-v" "$HOME/.docker/config.json:/home/appuser/.docker/config.json:ro" + "-v" "$DOCKER_PERSIST_DIR/.bash_history:/root/.bash_history" + "-v" "$DOCKER_PERSIST_DIR/.bash_history:/home/appuser/.bash_history" + "-v" "$HOME/.gitconfig:/root/.gitconfig" + "-v" "$HOME/.gitconfig:/home/appuser/.gitconfig" + "-v" "$CLAUDE_CONFIG_DIR:/root/.claude" + "-v" "$CLAUDE_CONFIG_DIR:/home/appuser/.claude" + "-v" "$CLAUDE_LOCAL_PERSIST_DIR:/root/.local" + "-v" "$CLAUDE_LOCAL_PERSIST_DIR:/home/appuser/.local" + "-w" "/app" + "-e" "TZ=Asia/Seoul" + "-e" "HOST_PROJECT_DIR=$SCRIPT_DIR" + ) + DOCKER_RUN_OPTS+=("${CLAUDE_ENV_OPTS[@]}") + DOCKER_RUN_OPTS+=("${CLAUDE_CLI_MOUNT_OPTS[@]}") +fi + +# CPU pinning: --cpuset-cpus is the kernel-level pin; --cgroup-parent attaches +# the container to the isolated cgroup created by scripts/host-isolate-cores.sh +# so it can claim CPUs that were carved out of the system slices. taskset +# inside the container is added below as belt-and-suspenders. +JOINED_ISOLATED_CGROUP=false +if [[ -n "$PIN_CPUS" ]]; then + DOCKER_RUN_OPTS+=("--cpuset-cpus=$PIN_CPUS") + if [[ -d "/sys/fs/cgroup/$ISOLATED_CGROUP_NAME" ]]; then + DOCKER_RUN_OPTS+=("--cgroup-parent=/$ISOLATED_CGROUP_NAME") + JOINED_ISOLATED_CGROUP=true + else + echo -e "${YELLOW}Note: /sys/fs/cgroup/$ISOLATED_CGROUP_NAME not found.${NC}" + echo -e "${YELLOW} Container will use --cpuset-cpus=$PIN_CPUS only (system tasks still share those cores).${NC}" + echo -e "${YELLOW} For host-side isolation, run: sudo ./scripts/host-isolate-cores.sh start${NC}" + fi +fi + +# Add options based on execution mode +if [ "$DETACHED_MODE" = true ]; then + DOCKER_RUN_OPTS+=("-d") + echo -e "${YELLOW}Running in detached mode...${NC}" +else + DOCKER_RUN_OPTS+=("-it") + echo -e "${YELLOW}Running in interactive mode...${NC}" +fi + +# Build container startup command: auto-run claude-init then drop to shell +# (or `tail -f /dev/null` in detached mode). Disable via AUTO_INSTALL_CLAUDE=0. +# The init script lives at ./scripts/docker-init-claude.sh relative to the +# container's working directory (SCRIPT_DIR in rootless mode, /app in root). +INIT_SCRIPT_REL="./scripts/docker-init-claude.sh" +if [ "$DETACHED_MODE" = true ]; then + CONTAINER_CMD=( + "bash" "-lc" + "if [ -x $INIT_SCRIPT_REL ]; then $INIT_SCRIPT_REL || true; fi; tail -f /dev/null" + ) +else + CONTAINER_CMD=( + "bash" "-lc" + "if [ -x $INIT_SCRIPT_REL ]; then $INIT_SCRIPT_REL || true; fi; exec bash" + ) +fi +# Wrap with taskset so the in-container shell (and everything it spawns) is +# explicitly pinned even if the user later loosens cpuset.cpus. +if [[ -n "$PIN_CPUS" ]]; then + CONTAINER_CMD=("taskset" "-c" "$PIN_CPUS" "${CONTAINER_CMD[@]}") +fi +DOCKER_RUN_OPTS+=("-e" "AUTO_INSTALL_CLAUDE=${AUTO_INSTALL_CLAUDE:-1}") + +echo "" +echo -e "${BLUE}Run Configuration:${NC}" +echo -e " Environment: $ENV_TYPE" +echo -e " Docker Mode: $([ "$ROOTLESS_DOCKER" = true ] && echo "Rootless" || echo "Root")" +echo -e " Registry Image: $REGISTRY_IMAGE" +echo -e " Local Image: $LOCAL_IMAGE" +echo -e " Container: $CONTAINER_NAME" +if [ "$ROOTLESS_DOCKER" = true ]; then + echo -e " Home Directory: $HOME (mounted as \$HOME)" + echo -e " Working Directory: $SCRIPT_DIR" + if [ -d "/home/share" ]; then + echo -e " Shared Directory: /home/share" + fi +else + echo -e " Project Directory: $SCRIPT_DIR (→ /app)" + echo -e " Result Directory: $SCRIPT_DIR/result" + echo -e " Log Directory: $SCRIPT_DIR/logs" + echo -e " Persistent Data: $DOCKER_PERSIST_DIR (bash history, gitconfig)" + echo -e " Docker Credentials: ~/.docker/config.json (read-only)" + echo -e " Claude Code Config: $CLAUDE_CONFIG_DIR (→ /root/.claude, /home/appuser/.claude)" + echo -e " Claude Local Bin: $CLAUDE_LOCAL_PERSIST_DIR (→ /root/.local, /home/appuser/.local)" +fi +if [ ${#CLAUDE_ENV_OPTS[@]} -gt 0 ]; then + echo -e " Forwarded env vars:$(printf ' %s' "${CLAUDE_ENV_OPTS[@]}" | sed 's/-e //g' | sed 's/=[^ ]*/=***/g')" +fi +if [[ -n "$PIN_CPUS" ]]; then + echo -e " CPU pinning: $PIN_CPUS (taskset + --cpuset-cpus)" + if [ "$JOINED_ISOLATED_CGROUP" = true ]; then + echo -e " Isolated cgroup: /$ISOLATED_CGROUP_NAME (joined)" + fi +fi +echo "" + +# Run Docker container +if docker run "${DOCKER_RUN_OPTS[@]}" "$LOCAL_IMAGE" "${CONTAINER_CMD[@]}"; then + echo "" + if [ "$DETACHED_MODE" = true ]; then + echo -e "${GREEN}================================================${NC}" + echo -e "${GREEN} Container is running in background${NC}" + echo -e "${GREEN}================================================${NC}" + echo "" + echo -e "${YELLOW}Useful commands:${NC}" + echo -e " View logs: docker logs -f $CONTAINER_NAME" + echo -e " Container status: docker ps" + echo -e " Stop container: docker stop $CONTAINER_NAME" + echo -e " Attach to shell: docker exec -it $CONTAINER_NAME /bin/bash" + echo "" + else + echo -e "${GREEN}Container has exited.${NC}" + fi + + echo -e "${YELLOW}Result files location: $SCRIPT_DIR/result${NC}" + echo -e "${YELLOW}Log files location: $SCRIPT_DIR/logs${NC}" +else + echo "" + echo -e "${RED}================================================${NC}" + echo -e "${RED} Container execution failed!${NC}" + echo -e "${RED}================================================${NC}" + exit 1 +fi diff --git a/docs/openevolve-intro/assets/styles.css b/docs/openevolve-intro/assets/styles.css new file mode 100644 index 0000000000..ad53f132a3 --- /dev/null +++ b/docs/openevolve-intro/assets/styles.css @@ -0,0 +1,298 @@ +/* OpenEvolve Intro — 일반 엔지니어 대상 설명 문서 */ + +:root { + --bg: #fafaf7; + --bg-card: #ffffff; + --bg-soft: #f3f4ed; + --fg: #1f2933; + --fg-mute: #52606d; + --accent: #2c7a4b; /* 진화/생물 그린 */ + --accent-2: #2563a8; /* 데이터 블루 */ + --highlight: #d97706; /* 강조 오렌지 */ + --border: #e2e4dd; + --code-bg: #f5f5ef; + --code-fg: #1f2933; + --shadow: 0 1px 2px rgba(0,0,0,0.04), 0 4px 12px rgba(0,0,0,0.06); + --radius: 10px; + --maxw: 980px; +} + +@media (prefers-color-scheme: dark) { + :root { + --bg: #161a1d; + --bg-card: #1e2326; + --bg-soft: #232a2e; + --fg: #e7ecef; + --fg-mute: #a3aeb4; + --accent: #5fc28a; + --accent-2: #7fb6e8; + --highlight: #f4a836; + --border: #2c3438; + --code-bg: #20262a; + --code-fg: #e7ecef; + --shadow: 0 1px 2px rgba(0,0,0,0.5), 0 4px 12px rgba(0,0,0,0.4); + } +} + +* { box-sizing: border-box; } + +html, body { + margin: 0; + padding: 0; + background: var(--bg); + color: var(--fg); + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Pretendard", "Apple SD Gothic Neo", "Noto Sans KR", sans-serif; + font-size: 16px; + line-height: 1.7; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +body { + padding: 0 16px 80px; +} + +main { + max-width: var(--maxw); + margin: 0 auto; +} + +/* 헤더 */ +.hero { + max-width: var(--maxw); + margin: 40px auto 24px; + padding: 32px 28px; + background: linear-gradient(135deg, var(--bg-card), var(--bg-soft)); + border: 1px solid var(--border); + border-radius: var(--radius); + box-shadow: var(--shadow); +} +.hero h1 { + margin: 0 0 8px; + font-size: 2.0rem; + letter-spacing: -0.01em; +} +.hero .tagline { + margin: 0; + color: var(--fg-mute); + font-size: 1.05rem; +} +.hero .meta { + margin-top: 16px; + font-size: 0.9rem; + color: var(--fg-mute); +} +.hero .meta code { font-size: 0.85rem; } + +/* 목차 */ +nav.toc { + max-width: var(--maxw); + margin: 0 auto 32px; + padding: 26px 30px; + background: var(--bg-card); + border: 1px solid var(--border); + border-radius: var(--radius); + box-shadow: var(--shadow); +} +nav.toc h2 { + margin: 0 0 14px; + font-size: 1.05rem; + text-transform: uppercase; + letter-spacing: 0.08em; + color: var(--fg-mute); +} +nav.toc ol { + margin: 0; + padding-left: 28px; + columns: 2; + column-gap: 36px; + font-size: 1.1rem; + line-height: 1.9; +} +nav.toc ol li { margin: 2px 0; padding-left: 4px; } +nav.toc a { + color: var(--accent-2); + text-decoration: none; + font-weight: 500; +} +nav.toc a:hover { text-decoration: underline; } + +/* 섹션 */ +section { + background: var(--bg-card); + border: 1px solid var(--border); + border-radius: var(--radius); + padding: 28px 28px 24px; + margin: 0 auto 24px; + box-shadow: var(--shadow); +} +section > h2 { + margin: 0 0 16px; + font-size: 1.45rem; + border-bottom: 2px solid var(--accent); + padding-bottom: 8px; + letter-spacing: -0.005em; +} +section > h3 { + margin: 20px 0 8px; + font-size: 1.1rem; + color: var(--accent-2); +} +section p { margin: 8px 0 12px; } + +ul, ol { padding-left: 22px; } +li { margin: 4px 0; } + +a { color: var(--accent-2); } + +code { + font-family: "SF Mono", Menlo, Consolas, monospace; + background: var(--code-bg); + color: var(--code-fg); + padding: 1px 6px; + border-radius: 4px; + font-size: 0.92em; +} +pre { + background: var(--code-bg); + color: var(--code-fg); + border: 1px solid var(--border); + border-radius: 8px; + padding: 14px 16px; + overflow-x: auto; + font-size: 0.88rem; + line-height: 1.55; +} +pre code { background: transparent; padding: 0; } + +/* 핵심 박스 */ +.callout { + border-left: 4px solid var(--highlight); + background: var(--bg-soft); + padding: 12px 16px; + margin: 14px 0; + border-radius: 0 8px 8px 0; +} +.callout strong { color: var(--highlight); } + +.formula { + background: var(--bg-soft); + border: 1px dashed var(--highlight); + padding: 14px 18px; + margin: 14px 0; + font-family: "SF Mono", Menlo, Consolas, monospace; + font-size: 0.95rem; + border-radius: 8px; + overflow-x: auto; + white-space: pre; +} + +/* 표 */ +table { + width: 100%; + border-collapse: collapse; + margin: 12px 0; + font-size: 0.92rem; +} +th, td { + border-bottom: 1px solid var(--border); + padding: 8px 10px; + text-align: left; + vertical-align: top; +} +th { + background: var(--bg-soft); + font-weight: 600; + color: var(--fg-mute); + font-size: 0.85rem; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +/* SVG */ +.figure { + margin: 18px 0; + text-align: center; +} +.figure svg { + max-width: 100%; + height: auto; + background: var(--bg-soft); + border: 1px solid var(--border); + border-radius: 8px; + padding: 12px; +} +.figure figcaption { + margin-top: 8px; + font-size: 0.88rem; + color: var(--fg-mute); + font-style: italic; +} + +/* 3 핵심 카드 */ +.cards { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); + gap: 14px; + margin: 14px 0; +} +.card { + background: var(--bg-soft); + border: 1px solid var(--border); + border-radius: 8px; + padding: 16px; +} +.card h4 { + margin: 6px 0 6px; + color: var(--accent); + font-size: 1.0rem; +} +.card p { margin: 0; font-size: 0.92rem; color: var(--fg-mute); } +.card .icon { font-size: 1.6rem; } + +/* 체크리스트 */ +ol.checklist { counter-reset: step; padding-left: 0; list-style: none; } +ol.checklist > li { + counter-increment: step; + padding: 10px 14px 10px 44px; + position: relative; + border-left: 2px solid var(--border); + margin-left: 12px; +} +ol.checklist > li::before { + content: counter(step); + position: absolute; + left: -14px; + top: 8px; + width: 26px; + height: 26px; + border-radius: 50%; + background: var(--accent); + color: white; + text-align: center; + line-height: 26px; + font-weight: 700; + font-size: 0.8rem; +} +ol.checklist code { font-size: 0.88rem; } + +/* 푸터 */ +footer { + max-width: var(--maxw); + margin: 40px auto 0; + padding: 20px 0; + text-align: center; + color: var(--fg-mute); + font-size: 0.85rem; + border-top: 1px solid var(--border); +} + +/* 반응형 */ +@media (max-width: 640px) { + body { padding: 0 10px 60px; } + .hero { padding: 22px 18px; margin-top: 20px; } + .hero h1 { font-size: 1.55rem; } + section { padding: 20px 18px; } + section > h2 { font-size: 1.2rem; } + nav.toc ol { columns: 1; } +} diff --git a/docs/openevolve-intro/index.html b/docs/openevolve-intro/index.html new file mode 100644 index 0000000000..34dc9a877a --- /dev/null +++ b/docs/openevolve-intro/index.html @@ -0,0 +1,1087 @@ + + + + + + OpenEvolve 개념·아키텍처 — 일반 엔지니어를 위한 소개 + + + + +
+

OpenEvolve — LLM으로 코드를 진화시키는 프레임워크

+

+ SW 개발자가 아니어도 이해할 수 있도록 정리한 OpenEvolve 개념, 알고리즘, 그리고 이 repo에서 z3·CP-SAT 솔버 파라미터 튜닝에 어떻게 적용되는지에 대한 안내서. +

+
+ 참고: openevolve/ 소스코드 · input/z3-bench, input/cpsat-bench 두 벤치마크 적용 사례 · 위키 문서. +
발표용 슬라이드: slides.html (35 슬라이드, 키보드 ← → 탐색) +
+
+ +
+ + + + + +
+

1. 1분 요약 (TL;DR)

+

+ OpenEvolve는 LLM(거대 언어 모델)을 코드 변이(mutation) 연산자로 사용하는 진화 알고리즘 프레임워크다. + 사람이 "이 부분을 더 좋게 만들어줘"라고 한 줄을 직접 짜는 게 아니라, EVOLVE-BLOCK으로 표시된 코드 구역을 LLM이 반복적으로 변형하고, 그 결과를 자동으로 평가·비교한다. +

+ +
+ 핵심 루프 한 줄로: + 타겟 코드(target code)를 뽑고 → LLM이 변형하고 → 점수를 매기고 → 좋은 것만 남기고 → 다시 다음 라운드 타겟으로 → 반복. +
+ +

전형적인 활용 사례 (AlphaEvolve 류)

+
    +
  • 행렬 곱셈 알고리즘 발견 — 4×4 복소 행렬 곱셈에서 1969년 Strassen 알고리즘 이후 처음으로 더 적은 곱셈 횟수(49 → 48)의 새 알고리즘을 탐색.
    • +
  • 데이터센터 스케줄링 휴리스틱 개선 — Google Borg 클러스터의 작업 배치 휴리스틱을 자동 탐색해 stranded compute resource 0.7%pt 회수.
    • +
  • TPU 칩 설계 최적화 — TPU의 산술 회로 Verilog/RTL 코드에서 면적·지연을 줄이는 변형 탐색.
    • +
  • LLM 학습 커널 최적화 — Gemini 학습용 GPU 커널(예: FlashAttention 변형)의 처리량을 자동 튜닝.
    • +
  • 수학적 추측 탐색 — Kissing number, autocorrelation inequality 같은 open problem에서 새 lower/upper bound 구성.
    • +
+ +

+ OpenEvolve는 Google DeepMind의 AlphaEvolve 시스템을 오픈소스로 구현한 프레임워크다. 일반 진화 알고리즘과의 가장 큰 차이는 변이를 LLM이 만든다는 점, 그리고 평가의 부산물(실행 로그, 에러, profiling)을 다음 prompt에 자동으로 다시 넣는다는 점이다. +

+
+ + +
+

2. 3가지 핵심 개념

+ + +

2.0 배경: 진화 알고리즘이란 무엇인가

+

+ 진화 알고리즘(Evolutionary Algorithm, EA)은 생물 진화에서 영감을 받은 최적화 기법이다. 자연이 수십억 년에 걸쳐 더 잘 적응한 개체를 살아남게 한 것처럼, 컴퓨터 안에서도 "후보 해(候補解)들의 집단"을 만들어 더 좋은 해만 살아남게 하면 어려운 문제를 풀 수 있다는 아이디어. +

+ +

기본 사이클은 단순하다:

+
    +
  1. 집단 초기화 — 무작위 또는 사람이 만든 초기 후보 해들을 N개 만든다.
    2. +
  2. 평가 — 각 후보의 점수(fitness)를 측정한다. 솔버 옵션이면 "얼마나 빨리 풀었나", 신경망 가중치면 "얼마나 정확한가".
    3. +
  3. 선택 — 점수가 높은 후보를 다음 세대의 부모로 뽑는다.
    4. +
  4. 변형(mutation·crossover) — 부모를 살짝 바꾸거나 두 부모를 섞어 자식 후보를 만든다.
    5. +
  5. 교체 — 자식을 집단에 넣고 약한 후보를 밀어낸다. 1~5단계를 수백~수천 번 반복.
    6. +
+ +

전통 EA의 한계와 OpenEvolve의 자리

+ + + + + + + + +
구성요소전통 EAOpenEvolve
후보 표현실수 벡터·비트열·트리코드 텍스트 (EVOLVE-BLOCK 안의 Python/JSON dict 등)
변형 연산자비트 플립·산술 노이즈·crossoverLLM 호출 (의미 있는 변형)
피드백스칼라 점수만점수 + stderr + 통계 (artifact)
집단 구조단일 풀MAP-Elites 격자 × N개 island
+ +

+ 즉 OpenEvolve는 진화 알고리즘의 "후보·평가·선택·변형·교체" 골격을 그대로 빌리되, 네 군데를 LLM 시대에 맞게 갈아 끼웠다. 아래 세 가지 핵심 개념이 그 갈아끼움의 정수다. +

+ +

OpenEvolve를 다른 자동 코드 생성 도구와 구분짓는 세 가지 아이디어:

+ +
+
+
▦
+

MAP-Elites Quality-Diversity

+

단일 최적해 하나를 쫓는 대신, 여러 특성(feature) 차원의 격자(grid)를 만들어 각 셀의 1등을 동시에 보존한다. 다양성을 유지해 조기 수렴을 막는다.

+
+
+
🏝️
+

Island 기반 분산 집단

+

집단을 여러 island(섬)로 나눠 격리 진화시키고, 주기적으로 우수 개체를 옆 섬으로 이주(migration)시킨다. 한 전략이 전체를 지배하지 못하도록.

+
+
+
🔁
+

Artifact 사이드 채널 피드백

+

평가가 점수만 반환하는 게 아니라 stderr, traceback, profiling 같은 실행 흔적도 같이 반환한다. 다음 LLM prompt에 자동 포함돼 "왜 실패했는지"를 모델이 본다.

+
+
+ + +

2.1 MAP-Elites Quality-Diversity — 자세히

+

+ 문제 의식. 전통적 진화 알고리즘은 "가장 점수 높은 1개"만 보존한다. 그 결과 처음 몇 세대에서 한 전략이 선두를 잡으면 모든 후손이 그 전략의 변형이 되고, 전혀 다른 가능성은 사라진다. 이걸 조기 수렴(premature convergence)이라고 한다. 솔버 튜닝 맥락에서는 "SAT 위주 옵션 셋"이 초반에 우세하면 "presolve 위주" 같은 다른 줄기가 영영 탐색되지 않는다. +

+ +

+ 해결. MAP-Elites는 해답을 단일 1등으로 압축하지 않는다. 대신 특성(feature) 차원을 정의해 다차원 격자를 만들고, 격자 셀별로 챔피언을 하나씩 유지한다. 격자가 채워질수록 "이 특성 조합으로는 이 정도가 최선"이라는 지도(map)가 만들어진다. +

+ +

3단계 처리 파이프라인

+
    +
  1. Extraction — 새 프로그램이 들어오면 각 feature 차원의 raw 값을 계산. 예: 코드 길이 = 230줄, 실행시간 = 4.2초.
    2. +
  2. Scaling — 모든 프로그램에 걸쳐 min-max 정규화 또는 z-score 변환. raw 값을 [0, 1] 같은 균일한 스케일로 변환.
    3. +
  3. Binning — 정규화 값을 feature_bins 개수의 이산 칸으로 나눔. 예: 10 bin이면 4.2초가 bin index 6에 떨어짐.
    4. +
+ +

역할 분리 — 점수 vs 좌표

+
    +
  • Fitness(점수): "같은 셀에 들어온 두 프로그램 중 누가 1등이냐"만 결정. 셀 안 비교용.
    • +
  • Feature 좌표: "어느 셀에 들어갈지"만 결정. 셀 배치용.
    • +
+

+ 이 분리가 핵심이다. 점수가 좌표를 결정하면 다양성을 잃는다. 좌표가 독립이라 점수가 낮은 영역도 살아남고, 그 영역에서 나중에 더 좋은 변형이 나오면 셀이 갱신된다. +

+ +
+ 왜 솔버 튜닝에 잘 맞나. "빠르지만 메모리 많이 쓰는 옵션"과 "느리지만 메모리 적게 쓰는 옵션"이 다른 셀에 산다. 둘 다 죽지 않고 보존되므로, 나중에 두 줄기의 좋은 점만 조합한 변형이 LLM 입력으로 같이 들어갈 수 있다. +
+ +

그림으로 보는 격자 비유

+

+ x축을 "코드 복잡도", y축을 "실행시간"이라고 하자. 격자 셀마다 ★는 그 셀의 챔피언이다. 새 변형 코드가 들어오면 그 코드의 (복잡도, 실행시간) 좌표로 어느 셀에 속하는지 계산하고, 그 셀의 기존 ★보다 점수가 높으면 챔피언을 교체한다. +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + + + 코드 복잡도 (낮음 → 높음) + 실행시간 (느림 → 빠름) + + + + 기존 챔피언 + + 새로 들어온 변형 (해당 셀 1등 교체) + + +
그림 3. MAP-Elites 격자. 새 변형(주황)이 본인 셀의 기존 챔피언보다 점수가 높으면 교체된다.
+
+ +
    +
  • "단순하면서 빠른" 셀과 "복잡하지만 더 빠른" 셀이 동시에 챔피언을 가진다.
    • +
  • 한 셀의 챔피언이 다른 셀에서 영감 코드로 쓰일 수 있다 — 다른 셀에 사는 좋은 아이디어를 빌려 자기 셀의 챔피언을 갱신.
    • +
  • 격자 전체가 채워지면 "이 코드 공간에서 어떤 트레이드오프가 가능한지"에 대한 지도(map)가 자동으로 그려진다.
    • +
+ +
+ 왜 이렇게 하나? 단일 1등만 키우면 "빠르지만 너무 복잡한 코드" 한 줄기로 진화가 쏠려 다양성이 사라진다. 격자별 챔피언을 동시에 키우면 "단순하면서 적당히 빠른" 셀이 살아남아 나중에 돌파구가 된다. +
+ +

+ OpenEvolve 빌트인 feature 차원: complexity(코드 길이 기반), diversity(다른 프로그램과의 edit distance). 사용자 정의 evaluator는 combined_score 외에 임의 키를 metric dict에 넣으면 자동으로 feature 차원으로 쓰일 수 있다 (config.yaml의 feature_dimensions 리스트에 명시). +

+ + +

2.2 Island 기반 분산 집단 — 자세히

+

+ 문제 의식. MAP-Elites만으로도 다양성은 어느 정도 유지된다. 하지만 격자가 하나뿐이라면 한 LLM 호출이 강력한 변형을 만든 순간, 그 변형이 곧장 격자 전체의 타겟 코드 후보 풀에 합류한다. 그러면 다음 세대들은 거의 다 그 변형의 후손이 되고, 격자의 많은 셀이 같은 조상에서 갈라진 비슷비슷한 변형들로 채워진다. 결국 셀 좌표는 다양해 보이지만 실제 코드 혈통(누구의 후손인지)은 한 줄기로 좁아진다 — 겉으로 다양한 척하지만 속은 한 가족인 상태. +

+ +

+ 해결. 격자 자체를 N개로 복제해 독립 운영한다. 각 island는 자기만의 격자, 자기만의 타겟 코드 풀, 자기만의 best를 갖는다. 한 island에서 일어난 일이 다른 island에 즉시 영향을 못 미치므로, N개의 서로 다른 진화 줄기가 동시에 자란다. +

+ +

Migration — 격리만 하면 안 되는 이유

+

+ 완전 격리는 island별로 다른 지역 최적해에 빠질 위험이 있다. 그래서 주기적으로 migration을 한다. +

+ + + + + + + +
파라미터의미전형 값
num_islandsisland 개수3 ~ 8
migration_interval몇 generation마다 이주10
migration_rate이주 비율 (선택된 island 인구 중)0.1 (10%)
+

+ Ring topology로 이웃 island에만 보낸다 (island 0 → 1 → 2 → ... → 0). broadcast가 아니라 인접 전파라서, 한 사이클이 도는 데 시간이 걸려 다양성이 더 오래 유지된다. lazy migration: 이주 카운터는 generation 단위라서 iteration이 빨라도 너무 자주 안 일어난다. +

+ +

타겟 코드 선택 — Exploration vs Exploitation

+

+ iteration이 시작되면 현재 island에서 다음에 변형할 타겟 코드를 뽑는데, exploration_ratio와 exploitation_ratio 비율로 두 전략을 섞는다. +

+
    +
  • Exploration: 현재 island 인구에서 균일 무작위 추출. 점수가 낮은 후보에게도 기회.
    • +
  • Exploitation: archive(island의 elite 모음)에서 fitness 비례 가중 추출. 좋은 타겟에 집중.
    • +
  • Fallback: island가 비었을 때 global best를 시드로 주입 (best가 모든 island를 재시작시킬 수 있음).
    • +
+ +

+ Best 추적 분리. OpenEvolve는 island별 best와 별도로 global best를 영구 보존한다. MAP-Elites 격자 갱신 과정에서 best가 셀에서 밀려 사라지는 사고를 막기 위한 안전장치. +

+ +

그림으로 보는 Island 비유 — 갈라파고스

+

+ 찰스 다윈이 갈라파고스 제도에서 관찰한 핀치새는 섬마다 부리 모양이 달랐다. 같은 조상에서 출발했지만 섬마다 다른 먹이·환경에 적응하면서 다른 형질이 발달한 것. 가끔 새가 다른 섬으로 날아가 형질을 옮기면 새로운 조합이 만들어진다. +

+ +
+ + + + + + + + + + + + + Island 0 + SAT 위주 진화 + + + Island 1 + presolve 위주 + + + Island 2 + LP/cuts 위주 + + + Island 3 + heuristic 위주 + + + Island 4 + 랜덤 탐색 + + + + + + + + + ring topology migration + (주기적으로 엘리트 형질 전달) + +
그림 4. 5개 island가 ring topology로 연결됨. 각 섬은 다른 전략을 시도하고, 가끔 우수 개체가 옆 섬으로 이주한다.
+
+ +
    +
  • 섬 = island. 격리된 독립 진화 공간.
    • +
  • 핀치새 부리 = 진화 중인 코드 변형. 같은 초기 프로그램에서 출발했지만 섬마다 다른 방향으로 변형.
    • +
  • 새가 다른 섬으로 날아감 = migration. 우수 변형을 옆 island로 복사.
    • +
  • 섬 멸종해도 다른 섬 살아남음 = 분산 안전망. 한 island가 나쁜 지역 최적해에 빠져도 다른 island가 다른 줄기를 계속 진화시킴.
    • +
+ +
+ 솔버 튜닝 맥락 비유. 한 솔버 옵션 dict가 island 4에서는 "큰 문제 특화"(LP·cuts 강화)로, island 1에서는 "빠른 SAT 특화"(presolve 가볍게)로 갈라진다. 가끔 두 섬의 좋은 부분만 골라 합친 융합 변형이 진화 도약의 계기가 된다. +
+ + +

2.3 Artifact 사이드 채널 피드백 — 자세히

+

+ 문제 의식. 자동 코드 생성에서 LLM이 같은 실수를 반복하는 흔한 원인은 "왜 실패했는지" 정보가 다음 호출에 없기 때문이다. 점수 0.3 받았다는 사실만 보면 모델은 "더 잘하자" 정도밖에 모른다. 인간 개발자는 컴파일 에러 메시지나 스택 트레이스를 보고 정확히 어디를 고쳐야 할지 안다. +

+ +

+ 해결. Evaluator가 EvaluationResult 객체에 두 필드를 채워 반환한다. +

+ + 
EvaluationResult(
+    metrics={
+        "combined_score": 0.43,
+        "execution_time": 30.0,
+        "memory_usage": 1024,
+    },
+    artifacts={
+        "stderr": "(error \"smt.case_split=5 out of range\")",
+        "solver_stats": "conflicts=1.3M decisions=2.1M propagations=8.9M",
+        "timeout_note": "30s exceeded on problem #17",
+    },
+)
+ +

+ metrics는 숫자만, artifacts는 자유 텍스트/바이너리. OpenEvolve가 artifacts를 자동으로 다음 prompt에 끼워 넣는다. +

+ +

저장 정책

+
    +
  • 소형 (≤10KB 정도): Program 객체의 artifacts_json 필드에 직렬화돼 DB에 인라인 저장.
    • +
  • 대형: 별도 디스크 디렉토리(artifact_dir)에 파일로 저장. DB에는 참조만.
    • +
  • 크기 컷: max_artifact_bytes 설정으로 prompt 주입 시 자동 truncate.
    • +
+ +

Prompt 주입 형태

+ 
### stderr
+```
+(error "smt.case_split=5 out of range")
+```
+
+### solver_stats
+```
+conflicts=1.3M decisions=2.1M propagations=8.9M
+```
+

+ 각 artifact 키가 markdown 헤더로, 값이 코드 펜스로 들어간다. LLM이 자연스럽게 읽고 다음 변형에서 참고한다. +

+ +

다른 피드백 채널과의 차이

+ + + + + + + +
채널전달 정보한계
점수만 (전통적 EA)스칼라 fitness왜 실패했는지 모름
점수 + diff history이전 시도 코드실행 결과는 못 봄
Artifact (OpenEvolve)stderr·stats·timeout 등 실행 산출물—
+ +
+ 실용적 효과. 솔버 튜닝에서 artifact에 솔버 내부 카운터(conflicts, branches, restarts)를 넣으면 LLM이 "이 파라미터가 conflict 폭증을 유발했으니 줄이자"처럼 인과적으로 추론한다. 단순 timing만 줬을 때보다 수렴이 빨라진다. +
+ +

+ 설계 시 주의. artifact는 자유 형식이지만 일관된 키 이름을 유지하는 게 좋다 (stderr, solver_stats, traceback). LLM이 같은 키를 반복적으로 보면서 그 의미를 학습하기 때문. +

+
+ + +
+

3. 큰 그림 아키텍처

+

+ OpenEvolve 한 번 실행하면 다음 컴포넌트들이 서로 데이터를 주고받는다. 가장 바깥의 Controller가 진화 라이프사이클 전체를 지휘하고, 안쪽의 Database·Worker Pool·LLM·Evaluator가 각자 역할을 맡는다. +

+ +
+ + + + + + + + + + + Initial Program + EVOLVE-BLOCK 표시 + + + + Controller + 전체 오케스트레이션 + + + + Program Database + MAP-Elites 그리드 + + Islands + + + + Worker Pool + 병렬 iteration + (ProcessParallel) + + + + Iteration Step + sample → prompt → LLM → eval + + + + Prompt Sampler + 문맥 prompt 구성 + + + LLM Ensemble + 가중치 모델 1개 선택 + + + Evaluator + cascade stage1/2/3 + + + + + + + + + + + + + score + artifacts 피드백 (다음 iteration의 prompt에 자동 포함) + +
그림 1. OpenEvolve 컴포넌트와 데이터 흐름. 점선 = 피드백 경로.
+
+ +
+ 비유. 솔버 파라미터를 사람이 손으로 튜닝할 때의 절차 ─ "옵션 바꿔보고 → 실행해 보고 → 좋으면 적어두고 → 다시 변형" ─ 를 자동화한 시스템이다. 사람의 직관 자리에 LLM이, 메모장 자리에 Program Database가 들어간다. +
+
+ + +
+

4. 한 iteration의 생애 — 피드백 루프

+

+ 위 그림의 Iteration Step 한 번을 풀어 쓰면 다음 7단계다. 이 7단계가 수백 ~ 수천 번 반복되며 점수가 올라간다. +

+ +
+ + + + + + + + + + + + ① + Sample + island 에서 타겟 + + inspirations + + + ② + Build Prompt + 코드 + 과거 시도 + + top elite + artifact + + + ③ + LLM Generate + 가중치 모델 선택 + diff or rewrite + + + ④ + Parse Code + SEARCH/REPLACE + 블록 적용 + + + ⑤ + Cascade Eval + stage1→2→3 + 임계 미달 시 컷 + + + ⑥ + Insert + 같은 island 추가 + global best 갱신 + + + + + + + + + + + + ⑦ + Cycle Island + 다음은 옆 island 에서 + + + + + + ⑤의 score + artifacts 가 ②의 다음 prompt 에 자동 주입 — 이것이 OpenEvolve 의 핵심 피드백 루프 + +
그림 2. 한 iteration 7단계 + 루프백. ③⑤가 LLM/평가가 일어나는 핵심 지점.
+
+ +

각 단계 세부

+
    +
  1. Sample — 현재 island에서 exploration(다양성) vs exploitation(엘리트) 비율로 타겟 코드와 영감 프로그램을 무작위 추출.
    2. +
  2. Build Prompt — 타겟 코드, 과거 시도 3개 점수, 다른 island의 elite 5개, 직전 평가 artifact를 하나의 prompt로 묶음.
    3. +
  3. LLM Generate — Ensemble이 가중치로 모델 1개를 뽑음. 그 모델이 diff 블록 또는 전체 재작성 코드를 출력.
    4. +
  4. Parse Code — diff 모드면 SEARCH/REPLACE 블록을 타겟 코드에 적용; rewrite 모드면 코드 블록 추출.
    5. +
  5. Cascade Evaluate — stage1(빠른 필터) → stage2(중간) → stage3(전체). 각 단계 임계값을 못 넘으면 조기 중단해 비용 절약.
    6. +
  6. Insert — Program 객체로 DB에 추가. 같은 island에 들어가고, MAP-Elites 그리드 셀에 배정됨. global best보다 좋으면 갱신.
    7. +
  7. Cycle Island — current_island = (current_island + 1) % num_islands. 다음 iteration은 옆 섬에서.
    8. +
+
+ + + +
+

5. LLM이 어떻게 적용되나

+

+ OpenEvolve에서 LLM은 "똑똑한 변이 함수" 역할이다. 일반 진화 알고리즘이 비트 플립이나 무작위 swap으로 변형하는 자리에, 코드 문맥을 이해하는 LLM이 들어간다. +

+ +

Ensemble — 모델 여러 개를 가중치로 섞기

+

+ 한 모델만 쓰면 그 모델의 편향에 묶이므로, OpenEvolve는 Ensemble을 쓴다. config.yaml에서 모델 목록과 weight를 지정하면 매 iteration마다 가중치에 비례해 모델 1개가 뽑힌다. +

+ + 
llm:
+  models:
+    - name: "google/gemini-2.0-flash-001"
+      weight: 0.7
+    - name: "anthropic/claude-3.7-sonnet"
+      weight: 0.3
+  temperature: 0.7
+ +

Prompt 안에 들어가는 정보 (피드백 채널)

+

+ Prompt sampler가 각 iteration마다 다음 정보를 모아 LLM에 전달한다. 단순히 타겟 코드 하나가 아니다. +

+ + + + + + + + + +
채널내용왜 필요한가
현재 타겟 코드변형 대상 EVOLVE-BLOCK 코드변경 베이스
과거 시도 3개최근 시도 코드 + 점수 + improved/regressed 라벨같은 실수 반복 방지
Top elite 5개전체에서 점수 높은 프로그램들성공 패턴 학습
Inspirations다양성 강제용 무작위 1~5개지역 최적해 탈출
Artifacts직전 평가의 stderr, traceback, profiling실패 원인 직접 보고 수정
+ +

LLM 응답 형식 2가지

+

OpenEvolve는 두 가지 변형 모드를 지원한다. diff_based_evolution: true 설정 여부로 선택.

+ +

(a) Diff 모드 — 작은 변경에 유리

+ 
<<<<<<< SEARCH
+  "search_branching": "AUTO",
+=======
+  "search_branching": "PSEUDO_COST",
+>>>>>>> REPLACE
+

위 블록만 응답하면 OpenEvolve가 자동으로 타겟 코드에 적용한다. 토큰 효율적이고, 큰 코드에서도 작은 변경이 쉽다.

+ +

(b) Full Rewrite 모드 — 구조적 변경에 유리

+

EVOLVE-BLOCK 전체를 통째로 새로 작성한 코드 블록을 응답하면 타겟을 통째로 교체. 작은 코드에서 자유로운 재설계가 필요할 때.

+ +
+ 한 줄로: "LLM = 코드 문맥을 이해하는 mutation 연산자". 무작위 변형 대신 의미 있는 변형을 만들어내는 게 차이. +
+
+ + +
+

6. Z3 파라미터 최적화 — 실제 결과

+

+ OpenEvolve가 튜닝한 Z3 파라미터를 axion-cell 라이브러리 셀 캐릭터라이제이션에 적용한 결과. 베이스라인 = axion-cell-v.0.1.0 (5.8) 기본 Z3 설정, 비교 대상 = OpenEvolve로 진화시킨 Z3 파라미터 dict. +

+ +

+ GAIN = baseline runtime ÷ optimized runtime (백분율). 100% 초과 = 그만큼 빨라짐. +

+ + + + + + + + + + + + + + + + + + + + +
Cellaxion-cell-v.0.1.0 (5.8)Z3 param optimizationGAIN
SDFFSQRLV_D1_N_S6P25TL_C54L0416779.712842.017130.66%
SDFFRPQRLV_D1_N_S6P25TL_C54L0416718.811917.475140.29%
SDFFRPQNRLV_D1_N_S6P25TL_C54L0416514.311492.625143.69%
SDFFQRLV_D1_N_S6P25TL_C54L049240.26972.733132.52%
ADDF_D1_N_S6P25TL_C54L047554.43752.992201.29%
XOR3_D2_N_S6P25TL_C54L044745.72840.486167.07%
XNOR3_D2_N_S6P25TL_C54L044089.8730.925559.54%
PREICG_D4_N_S6P25TL_C54L043889.63771.472103.13%
+ +
+ 핵심 수치 +
    +
  • 최대 GAIN: XNOR3_D2 — 559.54% (베이스라인 4089.8 → 730.9, 5.6배 가속)
    • +
  • 최소 GAIN: PREICG_D4 — 103.13% (이미 잘 튜닝된 셀)
    • +
  • 기하평균 GAIN: 약 176% — 평균적으로 1.76배 가속
    • +
  • 모든 셀에서 GAIN > 100% — 회귀 없이 일관된 개선
    • +
+
+ +

+ 해석. Sequential 셀(SDFF*)은 30~45% 가속, 산술 셀(ADDF)은 2배 이상, XOR/XNOR 같은 조합 로직은 더 큰 폭. 셀의 SMT 모델링 패턴이 다르고 OpenEvolve로 튜닝된 Z3 파라미터가 특정 패턴에서 특히 강점을 보임. +

+ +

+ 실용적 의미. Cell library 캐릭터라이제이션 한 round 전체에 적용하면 대규모 wall-clock 단축이 가능. OpenEvolve가 단순 벤치마크가 아닌 실제 EDA 워크플로우에서 측정 가능한 가치를 만들어낸 사례. +

+
+ + +
+

7. 실전 예시 — CP-SAT 3-Phase 진화 로그 읽기

+

+ 여기까지가 이론. 이제 실제 로그를 따라가며 LLM이 어떻게 CP-SAT 파라미터를 튜닝하는지, 그리고 phase 체이닝이 직전 best 위에 이득을 누적하는 모습을 본다. 로그 출처: input/cpsat-bench/evolve/phase{1,2,3}_*/openevolve_output/logs/. 각 phase는 직전 phase의 best를 물려받아 다른 파라미터 표면을 튜닝한다 (phase1 search/subsolvers → phase2 presolve/probing → phase3 LP/cuts). +

+ +

7.1 공통 실행 환경 (3 phase 동일)

+ + + + + + + + + + +
항목값
LLM Ensembleclaude-sonnet-4-6 (weight 0.80) + claude-haiku-4-5 (weight 0.20)
Random seed / Island42 (재현성) / 3개
phase당 iteration40
고정(LOCKED) 파라미터random_seed=0, num_search_workers=1 (phase1/2) / 8 (phase3) — LLM이 못 건드림
Cascade 평가stage1(10) → stage2(10) → [stage3 outlier, phase3+] → stage4(64문제 · 53 비교가능)
Phase 체이닝phaseN best → phase(N+1) initial_program.py로 상속
+ +

7.2 Phase 1 — search / subsolvers: LP·cuts로 +28.5%

+

+ Phase1 표면은 search 전략 + subsolver 조합이다. 그런데 num_search_workers=1이라 subsolver portfolio가 무의미 — LLM이 thinking에서 이를 알아채고 LP 품질·cut·presolve 쪽으로 방향을 튼다. +

+ 
initial: combined_score=0.9117  geomean_speedup=1.2532  solved_rate=0.8529  regressions=5
+winner : combined_score=1.3086  geomean_speedup=1.2851  solved_rate=0.9811  regressions=1
+         efficiency=1.1840   🌟 new best @ iter 1, 9, 12, 33, 36, 39
+ +
+ 결과. deterministic_time 28.5% 단축 (geomean_speedup 1.2851), regression 5→1로 감소, solved_rate 0.85→0.98. 큰 표면을 6번의 new-best로 점진 정복. (LLM 생성 timeout 7회 — claude_code 3-retry 후 skip.) +
+ +

7.3 Phase 1 config — 문제 크기별로 정반대 (layered tuning)

+

+ winner는 글로벌로 LP를 강하게 깔고, SIZE_BUCKETS로 크기별 정반대 강도를 적용한다: +

+ 
GLOBAL: linearization_level=2, cut_level=2, max_num_cuts=10000,
+        add_mir_cuts + add_zero_half_cuts + add_clique_cuts + add_objective_cut,
+        probing_num_combinations_limit=40000, root_lp_iterations=2500
+
+SIZE_BUCKETS[small <50k]: LP 가볍게 — probing1, linearization1, root_lp=500
+                          (probing3은 conflict 3배 폭발 → 회피)
+SIZE_BUCKETS[large ≥150k]: LP 무겁게 — linearization2, root_lp=4000
+                          (root_lp 6000은 UNKNOWN 유발 → 4000이 안전선)
+
+ 핵심. 작은 SAT-유사 문제엔 LP가 독, 큰 LP-heavy 문제엔 약. 같은 knob을 크기별로 반대로 돌린다. config 주석에 "왜 안 되는지"(conflict 3배, UNKNOWN)까지 LLM이 적어둠 — artifact 피드백으로 학습한 흔적. +
+ +

7.4 Phase 2 — presolve: 깨끗한 상속, 단일 도약 (+51.7%)

+

+ Phase2(presolve/probing)는 phase1 winner를 상속한다. 이번엔 시작부터 깨끗 — 53/53 solved, regression 0. +

+ 
initial 3e428b82: combined_score=1.3288  geomean_speedup=1.2906  solved 53/53
+winner          : combined_score=1.4713  geomean_speedup=1.5172  efficiency=0.9119
+         🌟 new best @ iter 10  (같은 config 계열이 iter 13·15·25·31·34 재등장)
+
+ 단일 결정적 도약. 40 iter 중 대부분은 ~0.99(이득 없음)를 맴돌다 iter 10에서 한 번에 1.4713로 점프. deterministic_time 51.7% 단축 (geomean 1.2906 → 1.5172). 이 winning config가 이후 5번 더 재발견됨 — robust한 optimum. +
+ +

7.5 Phase 2 통찰 — 더하기가 아니라 덜어내기

+

+ winning config는 phase1보다 더 단순하다. presolve 작업을 덜어내서 빨라졌다: +

+ 
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,
+    "symmetry_level": 1,        # default 2 → symmetry 탐지 작업 감소
+    "presolve_use_bva": False,  # default True → BVA presolve 비용 제거
+}
+SIZE_BUCKETS[small <50k] = { "cp_model_probing_level": 0 }   # probing 완전 off
+

+ 흥미로운 점은 efficiency가 나빠졌는데도(1.09 → 0.91) 점수가 올랐다는 것. branch/conflict는 늘었지만 dtime이 크게 줄었기 때문이다: +

+ 
combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
+              ≈   1.5172        × 0.9119^0.333        × 1.0²
+              ≈   1.5172        × 0.969                       ≈ 1.4713
+
+ 방향이 표면마다 반대 + 트레이드오프 학습. phase1은 LP/cuts를 추가해 이득, phase2는 presolve overhead를 제거해 이득. 이 sample에선 presolve 작업의 비용 > 이득이다. 또한 score 식이 "덜 효율적이어도 더 빠르면 보상" 구조라, LLM은 일을 더 시키더라도(branch↑) 벽시계를 단축하는 변형을 자율 선택했다 (wall_speedup도 1.62→1.88 동반 상승). +
+ +

7.6 Phase 3 — LP / cuts (W=8): +11.5% 추가

+

+ Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠다. 이미 강한 상속 config 위에서도 추가 이득을 짜낸다. +

+ 
initial 3961c65d: combined_score=1.0095  geomean_speedup=1.0073  solved 19/19 (stage1)
+best @ iter 26  : combined_score=1.1470  geomean_speedup=1.1146  efficiency=1.0900
+         🌟 new best @ iter 11 → 17 → 26  (solved 53/53, stage4)
+

iter 26 config (요약):

+ 
GLOBAL: max_num_cuts=3000, cut_level=1, add_objective_cut=True, root_lp_iterations=3000,
+        mip_max_bound=1e7 (+ mip_var_scaling / check_precision / drop_tolerance)
+STAGE3_OVERRIDES: use_feasibility_jump=True
+
+ 관찰. 8-worker portfolio 위에서도 deterministic_time 11.5% 추가 단축. 단, 이 run은 iter 27에서 중단되어 best 추출 전 — 위 수치는 best-so-far 기준이다. +
+ +

7.7 Phase 3 안전장치 — "빠르지만 틀린" 변형을 artifact로 거부

+

+ iter 27에서 LLM이 num_violation_ls=1을 시도한다. dtime이 극적으로 줄지만 결과가 틀린다 — thinking 로그: +

+
+ "num_violation_ls=1 변형이 deterministic time을 14.3 → 7.66초로 극적으로 낮추지만 OPTIMAL 대신 UNKNOWN을 반환 — red flag. solver가 해를 찾기 전에 조기 종료. 너무 공격적이다. baseline으로 되돌리고 검증된 add_objective_cut: True를 유지한다." +
+
+ 핵심. 점수(dtime)만 봤다면 "2배 빠른" 이 변형을 채택했을 것이다. 하지만 artifact의 OPTIMAL→UNKNOWN 신호로 LLM이 스스로 거부하고 안전한 config로 회귀했다. 속도와 정확성의 트레이드오프를 인과적으로 판단 — 점수만으로는 불가능한 디버깅이다. +
+ +

7.8 종합 — 3-Phase가 보여주는 OpenEvolve 동작 양상

+ + + + + + + +
Phase표면 / Winitialwinnergeomean_speedup (dtime)양상
1search/subsolvers · W10.91171.30861.2851 (+28.5%)LP·cuts 추가, regression↓
2presolve/probing · W11.32881.47131.5172 (+51.7%)presolve 덜어내기
3lp/cuts · W81.00951.1470*1.1146 (+11.5%)추가 cut 미세 이득
+

*phase3는 iter 27 중단 시점 best-so-far. phase1/2 winner는 직전 phase best 위에서 측정.

+ +
+
    +
  1. 체이닝은 이득을 누적한다. 각 phase가 직전 best 위에서 추가로 짜냄 — phase1 LP/cuts → phase2 presolve → phase3 cut 미세조정.
    2. +
  2. 최적 방향이 표면마다 다르다. phase1은 LP/cuts를 추가, phase2는 presolve를 제거해 이득. LLM이 양방향 모두 탐색한다.
    3. +
  3. artifact가 안전을 보장한다. "빠르지만 UNKNOWN"인 변형을 점수가 아닌 결과 신호로 거부 (phase3 num_violation_ls). 점수만으로는 불가능한 인과적 디버깅.
    4. +
+
+
+ + +
+

8. 이 repo의 활용: z3-bench / cpsat-bench

+

+ 이 워크스페이스의 input/z3-bench, input/cpsat-bench 두 디렉토리는 OpenEvolve를 솔버 파라미터 튜닝에 적용한 실제 사례다. 두 벤치마크는 거의 같은 구조를 따른다. +

+ +

공통 디렉토리 구조

+ +
+ + + + + + + + input/<bench>/ + + + + raw-data/ + 원본 문제 + meta.jsonl + + + + problems.jsonl + 통합 baseline 데이터 + + + + evolve/ + + + + config.yaml + LLM·iteration·cascade 설정 + + + shared/ + evaluator·score·runner·baseline + + + build_samples.py + stage1/2/3 샘플 선정 + + + run_phase.sh + phase 체이닝 + + + phase{1..4}_<name>/ + initial_program.py (EVOLVE-BLOCK) + + + + + + + + + + + +
그림 5. 두 벤치마크에 공통인 디렉토리 구조. raw-data에서 baseline을 모으고, evolve/에서 진화를 돌린다.
+
+ +

4-Phase Cascade 흐름

+

+ 파라미터 공간이 너무 크면 한 번에 모두 튜닝하기 어렵다. 그래서 두 벤치마크 모두 진화를 4 phase로 나눠 점진적으로 좁혀간다. +

+ +
+ + + + + + + + + + Phase 1 + 작은 표면 단독 + z3: opt.* + sls.* + cpsat: search/subsolvers + + + Phase 2 + phase1 best 고정 + z3: sat.* + cpsat: presolve + + + Phase 3 + phase1+2 best 고정 + z3: smt.* + cpsat: lp/cuts + + + Phase 4 + 통합 미세조정 + 모든 best 결합 + 전체 표면 + + + + + + 탐색 공간을 점진적으로 좁히면서, 앞 phase의 우승 dict을 다음 phase의 시작점으로 사용 + +
그림 6. 4-phase cascade. 각 phase가 끝나면 best dict가 다음 phase의 initial_program.py에 import 된다.
+
+ +

Scoring 공식 — LLM이 최대화하려는 숫자

+

+ 두 벤치마크 모두 같은 패턴의 점수식을 쓴다. 세 요소를 곱한 단일 스칼라. +

+ +
combined_score + = geomean(speedup_per_problem, weight=baseline_ms) ← 큰 문제일수록 가중치 ↑ + × solved_rate² ← 정답률 떨어지면 큰 페널티 + × efficiency^0.333 ← conflict/decision/propagation 개선
+ +
    +
  • geomean(speedup) — 문제별 (baseline_ms ÷ variant_ms) 의 기하평균. baseline_ms로 가중하면 오래 걸리는 어려운 문제가 점수를 좌우.
    • +
  • solved_rate² — 한 문제라도 정답을 못 맞히거나 OPTIMAL을 놓치면 제곱으로 페널티. 속도 위해 정확성을 희생하지 못하게 막는 안전장치.
    • +
  • efficiency^0.333 — 솔버 내부 카운터(conflicts, decisions, propagations 등)의 베이스라인 대비 개선 정도. 시간 외에 "솔버가 더 적은 일을 했는가"도 본다.
    • +
+ +

+ 차이는 미세하다: + z3-bench은 wall-clock speedup 기반, + cpsat-bench은 하드웨어 의존성을 줄이려 deterministic_time(작업 카운터) 기반 cost mode를 쓴다. +

+ +

EVOLVE 표면 차이

+ + + + + + + + + + + + + + +
벤치EVOLVE-BLOCK 안의 구조의도
z3-bench단일 dict OPT_SLS_OVERRIDES모든 문제에 같은 옵션 dict 적용
cpsat-bench3층: GLOBAL_OVERRIDES + SIZE_BUCKETS(문제 크기 분기) + STAGE3_OVERRIDES(outlier 전용)문제 특성별로 다른 옵션 셋을 적용하는 적응형 튜닝
+ +

+ cpsat의 SIZE_BUCKETS는 예를 들면 "제약 5만개 미만 / 5만~15만 / 15만 이상"으로 나눠 각 버킷마다 다른 옵션 dict를 갖는다. 작은 문제는 빠른 휴리스틱, 큰 문제는 무거운 LP를 켜는 식. +

+
+ + +
+

9. 새 솔버에 적용하려면 — 정의해야 할 코드

+

+ 예: cvc5, MiniZinc, picosat 같은 다른 솔버에 OpenEvolve를 적용하고 싶다면 다음 순서로 파일을 작성한다. z3-bench가 거의 그대로 템플릿 역할을 한다. +

+ +
    +
  1. + raw-data/ + problems.jsonl — 베이스라인 실행 결과를 모아 둔다. + 각 row: problem_sha256, baseline_ms, baseline_result (SAT/UNSAT/OPTIMAL/...), 도메인 features, 솔버 stats. + 참고: input/z3-bench/problems.jsonl. +
    2. +
  2. + shared/<solver>_runner.py — 서브프로세스로 솔버를 호출하는 얇은 래퍼. timeout, stderr 캡처, stats 파싱. + 참고: z3-bench/evolve/shared/. +
    3. +
  3. + shared/baseline_params.py — BASELINE dict(기본값)와 LOCKED dict(절대 변경 금지: random_seed, num_workers). + LOCKED는 LLM이 건드리면 결과가 비교 불가능해지는 키들. +
    4. +
  4. + shared/score.py — 위 9절의 scoring 공식 구현. combined_score를 반환하는 함수 하나. + 불일치 시 (variant가 OPTIMAL 못 맞춤 등) 1e-6 같은 큰 페널티. +
    5. +
  5. + shared/evaluator.py — evaluate_stage1/2/3 함수. 각 stage 통과 임계값 정의, 적응적 timeout max(5s, baseline_ms × 1.3). +
    6. +
  6. + build_samples.py — 문제집을 stage별로 나눔. 보통 stage1=5개 빠른 거(필터), stage2=50개 대표, stage3=5개 어려운 거(검증). + Tukey IQR로 outlier 컷, 분위수 stratified sampling. +
    7. +
  7. + phase{N}_<name>/initial_program.py — EVOLVE-BLOCK 안에 튜닝 대상 파라미터 dict. 단순 dict 또는 z3/cpsat 같은 다층 구조. + Phase 마다 다른 표면을 노출. +
    8. +
  8. + config.yaml — LLM 모델(models), max_iterations(20~40), population_size(50), + cascade_thresholds [예: 1.03 = "3% 이상 개선해야 통과"], feature_dimensions, + prompt system_message에 "이 솔버의 OO 파라미터를 OO 목표로 튜닝한다" 명시. +
    9. +
  9. + run_phase.sh — phase 체이닝 스크립트. phase N이 끝나면 best dict를 추출해 phase N+1의 initial_program.py에 import. +
    10. +
  10. + (선택) extract_best.py, Statistics/, outlier 분석 — 체크포인트에서 best dict를 뽑는 유틸, outlier 자동 식별. + cpsat-bench는 shared/outliers.json으로 stage3 전용 outlier 집합을 미리 정의. +
    11. +
+ +
+ 가장 빨리 시작하는 법: input/z3-bench/evolve/ 디렉토리를 통째로 복사한 뒤 (1) shared/<solver>_runner.py를 새 솔버로 교체, (2) baseline_params.py의 dict 키를 새 솔버 옵션으로 교체, (3) problems.jsonl을 새 도메인 데이터로 교체. 나머지 구조는 거의 그대로 작동한다. +
+
+ + +
+

10. 부록 — 용어 미니 사전

+ + + + + + + + + + + + + + +
용어한 줄 정의
Program진화의 기본 단위. 코드 + 점수 + parent 링크 + artifact를 한 묶음으로 가진 객체.
MAP-Elites"특성 격자별 1등을 동시에 보존"하는 Quality-Diversity 알고리즘.
Island격리 진화하는 부분 집단. ring topology로 주기적 migration.
MigrationIsland 사이에 우수 개체를 이주시키는 작업. migration_interval로 주기 제어.
Cascade Evaluationstage1 → stage2 → stage3 단계 평가. 약한 후보는 조기 컷오프.
Artifact평가의 사이드 채널 데이터 (stderr, traceback, profiling). 다음 prompt에 자동 주입.
EVOLVE-BLOCK# EVOLVE-BLOCK-START ~ # EVOLVE-BLOCK-END 마커. LLM이 수정 가능한 영역.
Combined Score주 fitness 스칼라. 보통 geomean(speedup) × solved_rate² × efficiency^0.333 같은 곱셈식.
Ensemble여러 LLM을 가중치로 섞어 매 호출마다 하나를 샘플링하는 방식.
Feature DimensionMAP-Elites 그리드의 축. complexity, execution_time 같은 비-fitness 특성.
+
+ +
+ 본 문서는 openevolve/ 소스코드와 ~/private/dev-study/wiki/openevolve 위키를 교차 참조해 작성. 그림은 모두 inline SVG (외부 의존성 0). +
+ +
+ + + diff --git a/docs/openevolve-intro/sections/README.md b/docs/openevolve-intro/sections/README.md new file mode 100644 index 0000000000..0658fce27b --- /dev/null +++ b/docs/openevolve-intro/sections/README.md @@ -0,0 +1,24 @@ +# docs/openevolve-intro/ + +OpenEvolve 개념·아키텍처 소개 문서. SW 개발자가 아닌 일반 엔지니어 대상. + +## 보는 법 + +``` +open docs/openevolve-intro/index.html +``` + +(브라우저에 더블클릭으로 열어도 됨. CDN 의존성 0.) + +## 구조 + +- `index.html` — 단일 페이지 문서. 11개 섹션 + 6개 inline SVG 다이어그램. +- `assets/styles.css` — 스타일 (다크/라이트 자동, 반응형). +- `sections/` — 본 README. SVG 는 `index.html` 안에 직접 인라인되어 있어 별도 파일 없음. + +## 내용 출처 + +- 코드: 워크스페이스 `openevolve/` 모듈 + `input/z3-bench`, `input/cpsat-bench` 적용 사례. +- 위키: `~/private/dev-study/wiki/openevolve/` (01~11 markdown). + +수정 시 위 두 소스와 교차 검증 권장. diff --git a/docs/openevolve-intro/slides.html b/docs/openevolve-intro/slides.html new file mode 100644 index 0000000000..7d8719497f --- /dev/null +++ b/docs/openevolve-intro/slides.html @@ -0,0 +1,1281 @@ + + + + + +OpenEvolve — 슬라이드 발표 + + + + + +
+ + 1 / 35 + + + + +
+ + +
+

키보드 단축키

+ + + + + + + +
→ Space PgDn다음 슬라이드
← PgUp이전 슬라이드
Home첫 슬라이드
End마지막 슬라이드
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Esc도움말 닫기
+

PDF 변환: 브라우저 인쇄 (Cmd/Ctrl+P) → "Save as PDF"

+
+ + +
+
+

Evolutionary Coding Agent

+

OpenEvolve

+

LLM으로 코드를 진화시키는 프레임워크

+

개념 · 알고리즘 · 실전 로그 · 적용 가이드

+
+
+ OpenEvolve Intro +
+ Slide 1 +
+
+ + +
+
1. TL;DR

1분 요약

+
+

OpenEvolve = LLM을 코드 변이 연산자로 쓰는 진화 알고리즘.

+ +
+ 핵심 루프 한 줄:
+ 타겟 코드(target code)를 뽑고 → LLM이 변형 → 점수 매김 → 좋은 것만 남김 → 다시 다음 타겟으로 → 반복. +
+ +

사람이 "더 좋게 만들어줘"라고 코드를 직접 짜지 않음. EVOLVE-BLOCK 영역을 LLM이 반복 변형, 자동 평가, 비교.

+ +
+ "코드"의 넓은 의미. LLM이 수정 가능한 textual / symbolic representation이면 사실상 코드처럼 최적화 가능. 회로 데이터·좌표도 코드가 된다. +
    +
  • placement → 좌표(coordinates)
    • +
  • routing → graph
    • +
  • standard cell synthesis
    • +
+
+ +

OpenEvolve = Google DeepMind AlphaEvolve의 오픈소스 구현.

+
+
1. TL;DR
2 / 35
+
+ + +
+
1. TL;DR

전형적인 활용 사례 (AlphaEvolve 류)

+
+
    +
  • 행렬 곱셈 알고리즘 발견 — 4×4 복소 행렬 곱셈에서 1969년 Strassen 알고리즘 이후 처음으로 곱셈 49→48회로 단축한 새 알고리즘 탐색.
    • +
  • 데이터센터 스케줄링 — Google Borg 클러스터 작업 배치 휴리스틱 자동 탐색, stranded compute resource 0.7%pt 회수.
    • +
  • TPU 칩 설계 최적화 — TPU 산술 회로 Verilog/RTL의 면적·지연을 줄이는 변형 탐색.
    • +
  • LLM 학습 커널 최적화 — Gemini 학습 GPU 커널(FlashAttention 변형 등) 처리량 자동 튜닝.
    • +
  • 수학적 추측 탐색 — Kissing number 같은 open problem에서 새 lower/upper bound 구성.
    • +
+
+
1. TL;DR
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+
+ + +
+
2. 핵심 개념

2.0 배경 — 진화 알고리즘이란?

+
+

생물 진화에서 영감받은 최적화 기법. "후보 해 집단을 키우면서 더 좋은 것만 살리면 어려운 문제가 풀린다."

+

기본 사이클

+
    +
  1. 초기화 — 후보 해 N개 (무작위 또는 사람이 만든 시드)
    2. +
  2. 평가 — 각 후보의 점수(fitness) 측정
    3. +
  3. 선택 — 점수 높은 후보를 부모로 뽑기
    4. +
  4. 변형 — mutation(돌연변이) 또는 crossover(교배)로 자식 생성
    5. +
  5. 교체 — 자식을 집단에 넣고 약한 후보 밀어내기
    6. +
+

1~5 단계를 수백~수천 번 반복.

+
+
2. 핵심 개념
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+
+ + +
+
2. 핵심 개념

전통 EA vs OpenEvolve — 무엇이 바뀌었나

+
+ + + + + + + + +
구성요소전통 EAOpenEvolve
후보 표현실수 벡터·비트열·트리코드 텍스트 (EVOLVE-BLOCK)
변형 연산자비트 플립·산술 노이즈·crossoverLLM 호출 (의미 있는 변형)
피드백스칼라 점수만점수 + stderr + 통계 (artifact)
집단 구조단일 풀MAP-Elites 격자 × N개 island
+
+ 요약: 진화 알고리즘 골격은 그대로. 네 군데를 LLM 시대에 맞게 갈아 끼움. 아래 세 가지가 그 정수. +
+
+
2. 핵심 개념
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+
+ + +
+
2. 핵심 개념

3가지 핵심 혁신

+
+
+
+
▦
+

MAP-Elites

+

Quality-Diversity. 특성 격자별 챔피언 동시 보존. 조기 수렴 방지.

+
+
+
🏝️
+

Island 모델

+

격리 진화 + 주기적 migration. 한 전략이 전체를 지배하지 못함.

+
+
+
🔁
+

Artifact 피드백

+

stderr·traceback·profiling이 다음 LLM prompt에 자동 주입.

+
+
+
+
2. 핵심 개념
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+
+ + +
+
2.1 MAP-Elites

왜 격자별 챔피언인가

+
+

문제 의식

+

전통 EA는 "점수 1등"만 보존. 한 전략이 초반에 선두면 모든 후손이 그 변형 → 조기 수렴.

+ +

해결

+

해답을 단일 1등으로 압축하지 않음. 특성(feature) 격자를 만들고 셀별로 챔피언 보존.

+ +
+ 역할 분리:
+ Fitness = "같은 셀 안에서 누가 1등?" (셀 내 비교)
+ Feature 좌표 = "어느 셀에 들어갈지?" (셀 배치) +
+ +

처리: Extraction(raw 값) → Scaling(정규화) → Binning(이산 칸).

+
+
2.1 MAP-Elites
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+
+ + +
+
2.1 MAP-Elites

그림 — 격자 시각화

+
+
+ + + + + + + + + + + ★★ + ★★★ + ★★★ + ★★★ + ★★ + + 코드 복잡도 (낮음 → 높음) + 실행시간 (느림 → 빠름) + + 기존 챔피언 + 새 변형 (셀 1등 교체) + + +
+

★ = 셀 챔피언. 새 변형의 (복잡도, 실행시간) 좌표로 셀 결정, 그 셀 챔피언과만 비교.

+
+
2.1 MAP-Elites
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+
+ + +
+
2.2 Island

왜 격자 하나로 부족한가

+
+

문제 의식

+

격자가 하나뿐이면 한 LLM 호출의 강력한 변형이 곧장 전체 후보 풀에 합류. 다음 세대들은 거의 다 그 변형의 후손.

+
+ 셀 좌표는 다양해 보이지만 실제 코드 혈통은 한 줄기로 좁아짐. 겉으로 다양한 척하지만 속은 한 가족. +
+

해결

+

격자 자체를 N개로 복제해 독립 운영. 각 island = 자기만의 격자 + 자기만의 best.

+ +

Migration 파라미터

+
    +
  • num_islands: 섬 개수 (3~8)
    • +
  • migration_interval: 이주 주기 (단위: generation)
    • +
  • migration_rate: 이주 비율 (전형 10%)
    • +
+
+
2.2 Island
9 / 35
+
+ + +
+
2.2 Island

갈라파고스 비유 — Ring Topology

+
+
+ + + + + + + + + Island 0SAT 위주 + Island 1presolve 위주 + Island 2LP/cuts + Island 3heuristic + Island 4랜덤 탐색 + + ring topology migration + (주기적 엘리트 형질 전달) + +
+

섬 = 격리 진화 공간. 핀치새 부리 = 진화 중인 코드 변형. 새가 옆 섬으로 = migration.

+
+
2.2 Island
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+
+ + +
+
2.3 Artifact

실행 흔적을 LLM에 되돌려주기

+
+

문제 의식

+

점수만 보면 LLM은 "0.3 받았네, 다음엔 더 잘하자" 수준. 왜 실패했는지는 모름.

+ +

해결 — EvaluationResult

+
EvaluationResult(
+    metrics={
+        "combined_score": 0.43,
+        "execution_time": 30.0,
+    },
+    artifacts={
+        "stderr": "(error \"smt.case_split=5 out of range\")",
+        "solver_stats": "conflicts=1.3M decisions=2.1M",
+        "timeout_note": "30s exceeded on problem #17",
+    },
+)
+ +

artifacts dict가 자동으로 다음 prompt의 markdown 섹션으로 주입.

+
+
2.3 Artifact
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+
+ + +
+
2.3 Artifact

피드백 채널 — 다른 방식과 비교

+
+ + + + + + + +
채널전달 정보한계
점수만 (전통 EA)스칼라 fitness왜 실패했는지 모름
점수 + diff history이전 시도 코드실행 결과는 못 봄
Artifact (OpenEvolve)stderr·stats·timeout 등 실행 산출물—
+ +
+ 비유: 학교 시험. 점수만 보는 학생 vs 빨간 코멘트까지 읽는 학생. OpenEvolve의 LLM은 후자. +
+ +

설계 팁: artifact 키 이름 일관 유지 (stderr, solver_stats). LLM이 반복적으로 보며 의미 학습.

+
+
2.3 Artifact
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+
+ + +
+
3. 아키텍처

큰 그림 — 컴포넌트 데이터 흐름

+
+
+ + + + + + Initial ProgramEVOLVE-BLOCK 표시 + Controller오케스트레이션 + Program DatabaseMAP-Elites 격자+ Islands + Worker Pool병렬 iteration + Iteration Stepsample → prompt → LLM → eval + Prompt Sampler문맥 prompt 구성 + LLM Ensemble가중치 모델 선택 + Evaluatorcascade stage1/2/3 + + 점선: score + artifacts 피드백 경로 + +
+
+
3. 아키텍처
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+
+ + +
+
4. Iteration

한 iteration의 생애 — 7단계

+
+
+ + + + + + ①Sampleisland 타겟+ inspirations + ②Build Prompt코드 + 과거+ top + artifact + ③LLM Generate모델 선택diff or rewrite + ④Parse CodeSEARCH/REPLACE블록 적용 + ⑤Cascade Evalstage1→2→3임계 컷 + ⑥Insertisland 추가best 갱신 + + ⑦Cycle Island다음은 옆 island + + ⑤의 score + artifacts 가 ②의 다음 prompt 에 자동 주입 + +
+
+
4. Iteration
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+
+ + +
+
5. LLM

Ensemble — 모델 여러 개를 가중치로

+
+

한 모델만 쓰면 그 모델 편향에 묶임. Ensemble이 매 iteration마다 가중치에 비례해 모델 1개 샘플링.

+ +
llm:
+  models:
+    - name: "claude-sonnet-4-6"
+      weight: 0.80
+    - name: "claude-haiku-4-5"
+      weight: 0.20
+  temperature: 0.7
+ +
+ LLM = 코드 문맥을 이해하는 mutation 연산자.
+ 무작위 변형 대신 의미 있는 변형 생성. +
+
+
5. LLM
15 / 35
+
+ + +
+
5. LLM

Prompt에 들어가는 5가지 채널

+
+ + + + + + + + + +
채널내용왜 필요한가
현재 타겟 코드변형 대상 EVOLVE-BLOCK변경 베이스
과거 시도 3개코드 + 점수 + improved/regressed 라벨같은 실수 반복 방지
Top elite 5개전체 점수 높은 프로그램성공 패턴 학습
Inspirations다양성 강제용 무작위 1~5개지역 최적해 탈출
Artifacts직전 평가의 stderr·traceback·profiling실패 원인 직접 보고 수정
+

단순히 부모 코드 하나가 아니다 — 5개 채널이 한 prompt로 합쳐짐.

+
+
5. LLM
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+
+ + +
+
5. LLM

응답 형식 — Diff vs Full Rewrite

+
+
+
+

(a) Diff 모드 — 작은 변경

+
<<<<<<< SEARCH
+"search_branching": "AUTO",
+=======
+"search_branching": "PSEUDO_COST",
+>>>>>>> REPLACE
+

토큰 효율적, 큰 코드도 OK.

+
+
+

(b) Full Rewrite — 구조적 변경

+

EVOLVE-BLOCK 전체를 통째로 새로 작성한 코드 블록.

+

작은 코드에서 자유로운 재설계가 필요할 때.

+ 
```python
+# new code from scratch
+GLOBAL_OVERRIDES = {...}
+```
+
+
+

diff_based_evolution: true 설정으로 모드 선택.

+
+
5. LLM
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+
+ + +
+
6. Z3 최적화 결과

실제 EDA 워크플로우 적용 — Cell Characterization

+
+

OpenEvolve가 튜닝한 Z3 파라미터를 axion-cell 라이브러리 셀에 적용한 실제 결과.

+ +

실험 조건

+
    +
  • 베이스라인: axion-cell-v.0.1.0 (5.8) 기본 Z3 설정
    • +
  • 비교 대상: OpenEvolve가 진화시킨 Z3 파라미터 dict
    • +
  • 측정: 셀별 캐릭터라이제이션 runtime (ms)
    • +
  • GAIN = baseline ÷ optimized 비율 (백분율). 100% 초과 = 가속
    • +
+ +
+ 요약. 8개 셀 모두에서 GAIN > 100% (일관된 개선). 기하평균 약 176%, 최대 559.54% (XNOR3_D2 — 5.6배 가속). +
+
+
6. Z3 최적화 결과
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+
+ + +
+
6. Z3 최적화 결과

셀별 GAIN 표

+
+ + + + + + + + + + + + + + + + + + + +
Cellbaseline axion-cell-v0.1.0(5.8)Z3 optimizationGAIN
SDFFSQRLV_D1_N_S6P25TL_C54L0416779.712842.017130.66%
SDFFRPQRLV_D1_N_S6P25TL_C54L0416718.811917.475140.29%
SDFFRPQNRLV_D1_N_S6P25TL_C54L0416514.311492.625143.69%
SDFFQRLV_D1_N_S6P25TL_C54L049240.26972.733132.52%
ADDF_D1_N_S6P25TL_C54L047554.43752.992201.29%
XOR3_D2_N_S6P25TL_C54L044745.72840.486167.07%
XNOR3_D2_N_S6P25TL_C54L044089.8730.925559.54%
PREICG_D4_N_S6P25TL_C54L043889.63771.472103.13%
+
+
6. Z3 최적화 결과
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+
+ + +
+
7. 실전 로그

CP-SAT 3-Phase 진화 로그 읽기

+
+

이론은 끝. phase1 → phase2 → phase3 실제 로그를 따라가며 LLM이 CP-SAT 파라미터를 어떻게 튜닝하는지, 그리고 phase 체이닝이 이득과 regression을 동시에 전달하는 모습을 관찰.

+ +

+ 로그: input/cpsat-bench/evolve/phase{1,2,3}_*/openevolve_output/logs/ +

+ +

공통 실행 환경 (3 phase 동일)

+ + + + + + + + + +
LLM Ensembleclaude-sonnet-4-6 (0.80) + claude-haiku-4-5 (0.20)
Random seed / Island42 / 3개
phase당 iteration40
LOCKED 파라미터random_seed=0, num_search_workers=1 (phase1/2), 8 (phase3)
Cascade 평가stage1(10) → stage2(10) → [stage3 outlier] → stage4(64문제 · 53 비교가능)
Phase 체이닝phaseN best → phase(N+1) initial_program.py로 상속
+
+
7. 실전 로그
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+
+ + +
+
7.1 Phase1

search / subsolvers — LP·cuts로 +28.5%

+
+

Phase1 표면은 search 전략 + subsolver 조합. 그런데 num_search_workers=1이라 subsolver portfolio가 무의미 — LLM이 thinking에서 이를 알아채고 LP 품질·cut·presolve 쪽으로 방향을 튼다.

+ +
initial: combined_score=0.9117  geomean_speedup=1.2532  solved_rate=0.8529  regressions=5
+winner : combined_score=1.3086  geomean_speedup=1.2851  solved_rate=0.9811  regressions=1
+         efficiency=1.1840   🌟 new best @ iter 1, 9, 12, 33, 36, 39
+ +
+ 결과. deterministic_time 28.5% 단축 (geomean_speedup 1.2851), regression 5→1로 감소, solved_rate 0.85→0.98. 큰 표면을 6번의 new-best로 점진 정복. (LLM 생성 timeout 7회 — claude_code 3-retry 후 skip) +
+
+
7.1 Phase1
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+
+ + +
+
7.1 Phase1 config

문제 크기별로 정반대 — layered tuning

+
+

winner는 글로벌로 LP를 강하게 깔고, SIZE_BUCKETS로 크기별 정반대 강도를 적용:

+
GLOBAL: linearization_level=2, cut_level=2, max_num_cuts=10000,
+        add_mir_cuts + add_zero_half_cuts + add_clique_cuts + add_objective_cut,
+        probing_num_combinations_limit=40000, root_lp_iterations=2500
+
+SIZE_BUCKETS[small <50k]: LP 가볍게 — probing1, linearization1, root_lp=500
+                          (probing3은 conflict 3배 폭발 → 회피)
+SIZE_BUCKETS[large ≥150k]: LP 무겁게 — linearization2, root_lp=4000
+                          (root_lp 6000은 UNKNOWN 유발 → 4000이 안전선)
+ +
+ 핵심. 작은 SAT-유사 문제엔 LP가 독, 큰 LP-heavy 문제엔 약. 같은 knob을 크기별로 반대로 돌린다. config 주석에 "왜 안 되는지"(conflict 3배, UNKNOWN)까지 LLM이 적어둠 — artifact 피드백으로 학습한 흔적. +
+
+
7.1 Phase1 config
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+
+ + +
+
7.2 Phase2

presolve — 깨끗한 상속, 단일 도약 (+51.7%)

+
+

Phase2(presolve/probing)는 phase1 winner를 상속. 이번엔 시작부터 깨끗 — 53/53 solved, regression 0.

+ +
initial 3e428b82: combined_score=1.3288  geomean_speedup=1.2906  solved 53/53
+winner          : combined_score=1.4713  geomean_speedup=1.5172  efficiency=0.9119
+         🌟 new best @ iter 10  (같은 config 계열이 iter 13·15·25·31·34 재등장)
+ +
+ 단일 결정적 도약. 40 iter 중 대부분은 ~0.99(이득 없음)를 맴돌다 iter 10에서 한 번에 1.4713로 점프. deterministic_time 51.7% 단축 (geomean 1.2906 → 1.5172). 이 winning config가 이후 5번 더 재발견됨 — robust한 optimum. +
+
+
7.2 Phase2
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+
+ + +
+
7.3 Phase2 통찰

더하기가 아니라 덜어내기

+
+

winning config는 phase1보다 더 단순하다. presolve 작업을 덜어내서 빨라졌다:

+
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,
+    "symmetry_level": 1,        # default 2 → symmetry 탐지 작업 감소
+    "presolve_use_bva": False,  # default True → BVA presolve 비용 제거
+}
+SIZE_BUCKETS[small <50k] = { "cp_model_probing_level": 0 }   # probing 완전 off
+STAGE3_OVERRIDES = {}
+ +
+ 방향이 표면마다 반대. phase1은 LP/cuts를 추가해 이득. phase2는 presolve overhead를 제거해 이득. 이 sample에선 presolve 작업의 비용 > 이득. "무엇을 켤까"만큼 "무엇을 끌까"가 중요 — LLM이 양방향 모두 탐색. +
+
+
7.3 Phase2 통찰
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+
+ + +
+
7.4 score 분해

efficiency 희생, speed 취득

+
+

winner는 efficiency가 나빠졌는데도(1.09 → 0.91) 점수는 올랐다. branch/conflict는 늘었지만 dtime이 크게 줄었기 때문:

+ +
combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
+              ≈   1.5172        × 0.9119^0.333        × 1.0²
+              ≈   1.5172        × 0.969                       ≈ 1.4713
+ +
+ 트레이드오프를 점수로 학습. geomean_speedup 이득(+51.7%)이 efficiency 손해(−9%)를 압도. score 식이 "덜 효율적이어도 더 빠르면 보상" 구조라, LLM은 presolve를 줄여 일을 더 시키는 대신(branch↑) 벽시계를 단축하는 변형을 자율 선택. (wall_speedup도 1.62→1.88로 동반 상승) +
+
+
7.4 score 분해
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+
+ + +
+
7.5 Phase3

lp / cuts (W=8) — +11.5% 추가

+
+

Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠. 이미 강한 상속 config 위에서도 추가 이득.

+ +
initial 3961c65d: combined_score=1.0095  geomean_speedup=1.0073  solved 19/19 (stage1)
+best @ iter 26  : combined_score=1.1470  geomean_speedup=1.1146  efficiency=1.0900
+         🌟 new best @ iter 11 → 17 → 26  (solved 53/53, stage4)
+ +

iter 26 config (요약):

+
GLOBAL: max_num_cuts=3000, cut_level=1, add_objective_cut=True, root_lp_iterations=3000,
+        mip_max_bound=1e7 (+ mip_var_scaling / check_precision / drop_tolerance)
+STAGE3_OVERRIDES: use_feasibility_jump=True
+ +
+ 관찰. 8-worker portfolio 위에서도 deterministic_time 11.5% 추가 단축. (이 run은 iter 27에서 중단 — best 추출 전 best-so-far 기준) +
+
+
7.5 Phase3
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+
+ + +
+
7.6 Phase3 안전장치

"빠르지만 틀린" 변형을 artifact로 거부

+
+

iter 27에서 LLM이 num_violation_ls=1을 시도. dtime이 극적으로 줄지만 결과가 틀린다 — thinking 로그:

+
+ "num_violation_ls=1 변형이 deterministic time을 14.3 → 7.66초로 극적으로 낮추지만 OPTIMAL 대신 UNKNOWN을 반환 — red flag. solver가 해를 찾기 전에 조기 종료. 너무 공격적이다. baseline으로 되돌리고 검증된 add_objective_cut: True를 유지한다." +
+ +
+ 핵심. 점수(dtime)만 봤다면 "2배 빠른" 이 변형을 채택했을 것. 하지만 artifact의 OPTIMAL→UNKNOWN 신호로 LLM이 스스로 거부하고 안전한 config로 회귀. 속도와 정확성의 트레이드오프를 인과적으로 판단 — 점수만으로는 불가능한 디버깅. +
+
+
7.6 Phase3 안전장치
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+
+ + +
+
7.7 종합

3-Phase가 보여준 동작 양상

+
+ + + + + + + +
Phase표면 / Winitialwinnergeomean_speedup (dtime)양상
1search/subsolvers · W10.91171.30861.2851 (+28.5%)LP·cuts 추가, regression↓
2presolve/probing · W11.32881.47131.5172 (+51.7%)presolve 덜어내기
3lp/cuts · W81.00951.1470*1.1146 (+11.5%)추가 cut 미세 이득
+

*phase3는 iter 27 중단 시점 best-so-far. phase1/2는 직전 phase best 위에서 측정.

+ +
+
    +
  1. 체이닝은 이득을 누적. 각 phase가 직전 best 위에서 추가로 짜냄 — phase1 LP/cuts → phase2 presolve → phase3 cut 미세조정.
    2. +
  2. 최적 방향이 표면마다 다르다. phase1은 LP/cuts를 추가, phase2는 presolve를 제거해 이득. LLM이 양방향 모두 탐색.
    3. +
  3. artifact가 안전을 보장. "빠르지만 UNKNOWN"인 변형을 점수가 아닌 결과 신호로 거부 (phase3 num_violation_ls).
    4. +
+
+
+
7.7 종합
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+
+ + +
+
8. repo 활용

z3-bench / cpsat-bench 디렉토리 구조

+
+
+ + + input/<bench>/ + raw-data/원본 문제 + meta.jsonl + problems.jsonl통합 baseline 데이터 + evolve/ + config.yamlLLM·iteration·cascade 설정 + shared/evaluator·score·runner·baseline + build_samples.pystage1/2/3 샘플 선정 + run_phase.shphase 체이닝 + phase{1..4}_<name>/initial_program.py (EVOLVE-BLOCK) + + + +
+
+
8. repo 활용
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+
+ + +
+
8. repo 활용

4-Phase Cascade — 점진적으로 좁히기

+
+
+ + + + + + Phase 1작은 표면 단독z3: opt.* + sls.*cpsat: search/subsolvers + Phase 2phase1 best 고정z3: sat.*cpsat: presolve + Phase 3phase1+2 best 고정z3: smt.*cpsat: lp/cuts + Phase 4통합 미세조정모든 best 결합전체 표면 + + 탐색 공간을 점진적으로 좁히면서 앞 phase의 우승 dict을 다음 phase 시작점으로 사용 + +
+
+
8. repo 활용
30 / 35
+
+ + +
+
8. repo 활용

Scoring 공식 — LLM이 최대화할 숫자

+
+
combined_score
+  = geomean(speedup_per_problem, weight=baseline_ms)   ← 큰 문제일수록 가중치 ↑
+    × solved_rate²                                       ← 정답률 떨어지면 큰 페널티
+    × efficiency^0.333                                   ← conflict/decision/propagation 개선
+ +
    +
  • geomean(speedup): 문제별 (baseline_ms ÷ variant_ms)의 기하평균. baseline_ms 가중 → 오래 걸리는 문제가 점수 좌우.
    • +
  • solved_rate²: 한 문제라도 OPTIMAL 못 맞히면 제곱 페널티. 속도 위해 정확성 희생 못 함.
    • +
  • efficiency^0.333: 솔버 내부 카운터(conflicts, decisions, propagations)의 베이스라인 대비 개선 정도.
    • +
+ +

z3-bench: wall-clock speedup / cpsat-bench: deterministic_time(작업 카운터) cost mode.

+
+
8. repo 활용
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+
+ + +
+
9. 적용 가이드

새 솔버에 적용하려면 (1/2)

+
+
    +
  1. raw-data/ + problems.jsonl — 베이스라인 실행 결과 (problem_sha256, baseline_ms, baseline_result, features, solver_stats)
    2. +
  2. shared/<solver>_runner.py — 서브프로세스 솔버 호출 래퍼 (timeout, stderr 캡처, stats 파싱)
    3. +
  3. shared/baseline_params.py — BASELINE dict(기본값) + LOCKED dict(절대 변경 금지: random_seed, num_workers)
    4. +
  4. shared/score.py — scoring 공식 구현. combined_score 반환 함수. 불일치 시 큰 페널티
    5. +
  5. shared/evaluator.py — evaluate_stage1/2/3 함수. stage별 임계값, 적응적 timeout
    6. +
+
+
9. 적용 가이드
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+
+ + +
+
9. 적용 가이드

새 솔버에 적용하려면 (2/2)

+
+
    +
  1. build_samples.py — 문제집 stage 분배 (보통 stage1=5 빠른, stage2=50 대표, stage3=5 어려운)
    2. +
  2. phase{N}_<name>/initial_program.py — EVOLVE-BLOCK 안에 튜닝 파라미터 dict
    3. +
  3. config.yaml — LLM models, max_iterations, cascade_thresholds, prompt system_message
    4. +
  4. run_phase.sh — phase 체이닝 스크립트 (phase N best → phase N+1 import)
    5. +
  5. (선택) extract_best.py, Statistics/ outlier 분석
    6. +
+ +
+ 가장 빠르게 시작: input/z3-bench/evolve/를 통째로 복사 → runner, baseline_params dict 키, problems.jsonl만 새 도메인으로 교체. 나머지 구조는 그대로. +
+
+
9. 적용 가이드
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+
+ + +
+
10. 용어 사전

한 줄씩 용어 정리

+
+ + + + + + + + + + + + +
Program진화의 기본 단위. 코드 + 점수 + parent + artifact 한 묶음
MAP-Elites"특성 격자별 1등을 동시에 보존"하는 Quality-Diversity 알고리즘
Island격리 진화하는 부분 집단. ring topology 주기적 migration
Cascade Evaluationstage1→2→3 단계 평가. 약한 후보 조기 컷
Artifact평가 사이드 채널 데이터 (stderr·stats). 다음 prompt에 자동 주입
EVOLVE-BLOCK# EVOLVE-BLOCK-START ~ # EVOLVE-BLOCK-END 마커. LLM 수정 영역
Combined Score주 fitness 스칼라. geomean × solved_rate² × efficiency^0.333
Ensemble여러 LLM을 가중치로 섞어 매 호출마다 하나를 샘플링
Feature DimensionMAP-Elites 그리드 축. complexity, execution_time 같은 비-fitness 특성
+
+
10. 용어 사전
34 / 35
+
+ + +
+
+

감사합니다

+

질문은 언제든.

+

자세한 문서: docs/openevolve-intro/index.html

+

로그 예시 원본: input/cpsat-bench/evolve/phase1_search/openevolve_output/logs/

+

키보드 Home 으로 처음부터 다시 보기.

+
+
End
35 / 35
+
+ + + + + diff --git a/input/ADD_NEW_SOLVER.md b/input/ADD_NEW_SOLVER.md new file mode 100644 index 0000000000..4a2410ab44 --- /dev/null +++ b/input/ADD_NEW_SOLVER.md @@ -0,0 +1,664 @@ +# Adding a New Solver — Quick Start Guide + +새 solver (e.g. `cvc5`, `minizinc`, `picosat`, `vampire`)를 OpenEvolve +파라미터 튜닝 파이프라인에 통합하는 절차. 모든 orchestration은 `_lib/`이 +담당하므로 **per-bench 작업물은 4 파일 + N개 phase 모듈**. + +--- + +## 0. 전체 그림 + +``` +input/-bench/ +├── raw-data/ # 사용자가 제공 — solver run 결과 +│ ├── . # 문제 파일 (binary or text) +│ └── ____seed0.meta.jsonl # optional run metadata +├── problems.jsonl # baseline 실행 기록 (필수) +└── evolve/ + ├── config.yaml # ① bench / LLM / clustering / evaluation + ├── params.json # ② 솔버 파라미터 카탈로그 + ├── adapter.py # ③ 솔버 hooks + ├── _solve_worker.py # ④ subprocess entry + ├── phase1_/initial_program.py + ├── phase2_/initial_program.py + └── ... (phase 수 자유) +``` + +`_lib/`는 한 줄도 수정 안 함. 모든 솔버 의존성은 위 6종에 격리. + +--- + +## 1. 입력 데이터 준비 (사용자 제공) + +### 1.1 `input/-bench/raw-data/` + +각 문제 1개당 1개 파일. 확장자 자유 (예: `.smt2`, `.cpsat.pb`, `.mzn`, `.cnf`). + +권장 파일명: +``` +. +``` + +(SHA-256은 문제 내용 hash. 재현성 / dedup 기준.) + +### 1.2 `input/-bench/problems.jsonl` + +**사용자가 직접 작성하는 필수 input.** `_lib/sampler.py`는 이 파일을 읽기만 +함 (생성 안 함). 솔버별 raw-data → problems.jsonl 변환은 솔버별 스크립트로 +사용자가 작성 (`_lib`에서 generic화하기엔 meta format이 솔버마다 너무 +다름 — cpsat은 `cpsat_response_stats` + protobuf 카운터, z3는 +`z3_statistics` + 35종 카운터, 신규 솔버는 자유). + +옛 cpsat `build_samples.py` (refactor에서 제거됨)에는 `raw-data/*.meta.jsonl`을 +스캔해서 `problems.jsonl`을 생성하는 로직이 있었음. 그 부분은 +**cpsat-bench 전용 사용자 스크립트로 따로 보존하거나** `raw-data/load_script.sh` +등에 통합 권장 — `_lib`로 옮기지 않음. + +한 줄 = 한 문제. 한 번의 baseline 실행 기록. 필수 필드: + +```json +{ + "problem_sha256": "", + "_filename": ".", // adapter.PROBLEM_FILE_FIELD가 가리킴 + "_status": { // adapter.STATUS_FIELD가 가리킴 + "result": "Sat", // adapter.DECISIVE_RESULTS 중 하나 + "elapsed_ms": 1234, + "objective_value": 42.0 // OBJECTIVE_FIELD 정의 시 + }, + "_response_stats": { // optional (adapter.STATS_FIELD) + "conflicts": 100, + "decisions": 200 + }, + "features": { // 필수 (clustering 용) + "num_variables": 1000, + "num_constraints": 5000, + "num_hard_constraints": 4000, + "num_soft_constraints": 1000 + } +} +``` + +필드명은 자유 (adapter.py에 정확한 키를 알려주면 됨). features 안에 무엇이 +들어가는지도 자유 — adapter.get_problem_size(features)가 어떤 키 읽는지 결정. + +### 1.3 `meta.jsonl` (optional) + +각 (sha, hash, seed) 조합당 1개 파일. cpsat / z3는 이걸 historical 기록으로 +유지하지만 `_lib`은 사용 안 함 — `problems.jsonl`만 읽음. 새 솔버는 needed +없으면 생략 가능. + +용도: raw-data 갱신 시 `problems.jsonl`을 재구축하는 **사용자 측 스크립트**의 +input. `_lib`는 관여 안 함. + +### 1.4 `problems.jsonl` 생성 워크플로 (사용자 측) + +`_lib` 외부에서 다음 중 하나를 선택: + +**옵션 a. 수동 작성** — 새 솔버 / 소규모 dataset. + +**옵션 b. 솔버 전용 build 스크립트** — raw-data 스캔 + 변환. 예시: +```bash +# input/-bench/build_problems_jsonl.py (사용자 작성) +python3 input/-bench/build_problems_jsonl.py +# → input/-bench/problems.jsonl 작성 +``` + +옛 cpsat `build_samples.py`의 `_scan_raw()` 함수를 참고용 (git history) → +신규 솔버는 비슷한 패턴으로 자체 스크립트 작성. + +**옵션 c. raw-data와 함께 사전 배포** — z3-bench가 사용하는 패턴. dataset +저자가 미리 `problems.jsonl`을 생성해 함께 commit. + +--- + +## 2. 작성할 4개 파일 + +### 2.1 `evolve/config.yaml` (① bench 설정) + +가장 짧은 예시 (필수 부분만): + +```yaml +bench: + phases: + - dir: phase1_main + iters: 40 + - dir: phase2_unified + iters: 40 + + unified_prepare_before_dir: phase2_unified + unified_dict_name: UNIFIED_OVERRIDES + + solver_check_cmd: "command -v " + solver_install_hint: "install: brew install " + + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + + clustering: + method: kmeans # kmeans | quintile | thresholds + feature: features.num_constraints # dotted path into problems.jsonl record + n_clusters: 5 + max_baseline_ms: 300000 # drop outliers > 5min + spread: quintile + stage_sizes: {stage1: 10, stage2: 10, stage3: 5, stage4: 20} + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + + evaluation: + repeats: 10 # 10-run averaging (standard) + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: speedup # speedup | cost + enable_size_buckets: false # opt-in: SIZE_BUCKETS surface + enable_outlier_stage: false # opt-in: STAGE3_OVERRIDES + +parallel_solvers: 2 + +max_iterations: 40 +checkpoint_interval: 10 +log_level: "INFO" +random_seed: 42 + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 1.0 + +prompt: + system_message: | + Tune parameters for . + EVOLVE-BLOCK exposes a dict; mutate it to MAXIMIZE combined_score. + Hard rules: + - Do NOT modify locked keys (see params.json `locked`). + - Use only valid param keys (catalog validation enforced). + - Score = geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT. + +database: + num_islands: 3 + +evaluator: + timeout: 1800 + cascade_evaluation: true + cascade_thresholds: [1.03, 1.03, 1.03] + parallel_evaluations: 1 +``` + +### 2.2 `evolve/params.json` (② 파라미터 카탈로그) + +```json +{ + "solver": "", + "version": "", + + "defaults": { + "": , + "": + }, + + "locked": { + "": 0 + }, + + "groups": { + "search": { + "description": "Branching and restart strategy.", + "params": { + "": { + "type": "enum", + "values": ["a", "b", "c"], + "default": "a", + "desc": "What this knob does." + }, + "": { + "type": "int", + "default": 100, + "range": [0, 10000], + "desc": "..." + }, + "": { + "type": "bool", + "default": true, + "desc": "..." + }, + "": { + "type": "list", + "element_type": "subsolver", + "default": [], + "desc": "..." + } + } + } + }, + + "subsolver_names": [] +} +``` + +지원 타입: `int`, `float`, `bool`, `str`, `enum` (with `values`), `list` (with +optional `element_type: subsolver`). + +`defaults`는 BASELINE 역할 (구 `baseline_params.py` 대체). `locked`는 LLM이 +바꾸면 즉시 `combined_score=0`이 되는 키. + +### 2.3 `evolve/adapter.py` (③ 솔버 hooks) + +```python +"""-bench solver hooks.""" + +SOLVER_NAME = "" + +# problems.jsonl 필드 매핑 +PROBLEM_FILE_FIELD = "_filename" # 문제 파일명 키 +STATUS_FIELD = "_status" # nested {"result", "elapsed_ms", ...} +STATS_FIELD = "_response_stats" # None ok (baseline에 stats 없을 때) +FEATURES_FIELD = "features" +OBJECTIVE_FIELD = "objective_value" # STATUS_FIELD 내부 path; None ok + +# 결과 분류 +DECISIVE_RESULTS = ("Sat", "Unsat") # solver가 "확실히 풀었다"는 결과 +DECIDED_RESULTS = ("Sat", "Unsat") # regression 판정 기준 + +# 평가 metric에 surface할 solver 카운터 +KEY_STATS = ("conflicts", "decisions", "propagations") + +# Efficiency factor의 stat key별 weight +STATS_WEIGHTS = { + "conflicts": 2.0, + "decisions": 1.5, + "propagations": 0.5, +} + +# Score mode default (config.yaml evaluation.score_mode가 override) +SCORE_MODE = "speedup" + +# Worker count axis가 있는 솔버이면 키 이름, 없으면 None +WORKERS_KEY = None # cpsat: "num_search_workers" + + +def get_problem_size(features): + """Clustering feature 추출. 어떤 값으로 문제를 클러스터링할지.""" + return int((features or {}).get("num_constraints") or 0) +``` + +### 2.4 `evolve/_solve_worker.py` (④ subprocess entry) + +`_lib.subprocess_runner.run_solver`가 호출함. argv 계약: +``` +argv[1] JSON string of params dict +argv[2] problem file path +argv[3] timeout_s (int) +``` + +stdout: 마지막 비어 있지 않은 줄에 JSON 결과 한 개. + +success: +```json +{"result": "Sat", "elapsed_ms": 1234, "stats": {"conflicts": 100, ...}, "objective": 42.0} +``` + +invalid param: +```json +{"invalid_param": "", "error": "", "result": "Unknown", "elapsed_ms": 0} +``` + +크래시: +```json +{"result": "Unknown", "elapsed_ms": 0, "error": ""} +``` + +timeout은 `_lib.subprocess_runner`가 처리하므로 worker는 신경 안 써도 됨. + +템플릿 (Python solver binding 가정): +```python +import json +import sys +import time + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + params_json, problem_path, timeout_s = sys.argv[1], sys.argv[2], int(sys.argv[3]) + try: + params = json.loads(params_json) + except json.JSONDecodeError as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params parse: {e}"}) + return + + # 1. 솔버 instance 생성 + params 적용 + try: + import + solver = .Solver() + for k, v in params.items(): + solver.set_param(k, v) + except .InvalidParamError as e: + emit({"invalid_param": str(e), "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + + # 2. 문제 로드 + with open(problem_path) as f: + solver.parse(f.read()) + + # 3. 실행 + t0 = time.monotonic() + try: + result = solver.solve(timeout_s) + except TimeoutError: + emit({"result": "Unknown", "elapsed_ms": int((time.monotonic() - t0) * 1000), + "timeout": True, "stats": {}}) + return + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": str(e), "stats": {}}) + return + + elapsed_ms = int((time.monotonic() - t0) * 1000) + emit({ + "result": result, # "Sat" / "Unsat" / "Unknown" / "OPTIMAL" / ... + "elapsed_ms": elapsed_ms, + "stats": solver.statistics(), # dict of numeric counters + "objective": solver.objective(), # optional + }) + + +if __name__ == "__main__": + main() +``` + +CLI 솔버 (binary)면 `subprocess.run([solver_binary, ...])` 호출 후 stdout 파싱. + +--- + +## 3. Phase 모듈 작성 + +`evolve/phase{N}_/initial_program.py` 1개씩. + +### 3.1 단순 phase (z3-style — flat overrides) + +```python +""" +Phase 1: tune 's knobs. + +Targeted namespace: , , . +Other params stay at BASELINE. + +Do NOT modify locked keys. +""" +import os +import pathlib +import sys + + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError("OPENEVOLVE_BENCH_ROOT unset") + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# EVOLVE-BLOCK-START +OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(OVERRIDES) +``` + +### 3.2 cpsat-style phase (SIZE_BUCKETS + STAGE3_OVERRIDES + worker lock) + +`config.yaml`의 `evaluation.enable_size_buckets: true`일 때만: + +```python +import os, pathlib, sys +# ... _resolve_bench_root + BASELINE 동일 ... + +PHASE_LOCKED = { + "": 0, + "": 1, # adapter.WORKERS_KEY와 일치 +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) +``` + +### 3.3 Unified phase (마지막 phase) + +```python +""" +Unified refinement — EVOLVE-BLOCK 자동 머터리얼됨. +prepare_phase가 phase{1..N-1}_best.json union으로 채움. +""" +# ... 동일한 prelude ... + +# EVOLVE-BLOCK-START +UNIFIED_OVERRIDES = {} # 자동 채워짐 — config.yaml의 unified_dict_name과 일치 +# EVOLVE-BLOCK-END + +def get_params(): + p = dict(BASELINE) + p.update(UNIFIED_OVERRIDES) + return p + +def get_phase_overrides(): + return dict(UNIFIED_OVERRIDES) +``` + +### 3.4 Phase 모듈 핵심 contract + +| 항목 | 필수 / 선택 | 용도 | +|---|---|---| +| `BASELINE` | 필수 | `params_catalog.load_for_bench(_BENCH).defaults` | +| `PHASE_LOCKED` | 선택 (worker 차등 시 필수) | 평가 시 LOCK 강제 | +| `EVOLVE-BLOCK-START/END` 마커 | 필수 | LLM mutation 범위 + prepare_phase target | +| `get_params(problem=None, stage=None)` | 필수 | 평가자가 호출하는 entry point | +| `get_phase_overrides()` | 필수 (마지막 phase 제외) | extract_best가 dict 추출 | +| `get_phase_size_buckets()` | 선택 (enable_size_buckets 시) | SIZE_BUCKETS 추출 | +| `get_phase_stage3_overrides()` | 선택 (enable_outlier_stage 시) | STAGE3_OVERRIDES 추출 | + +--- + +## 4. 동작 검증 절차 + +```bash +# 0. 솔버 binding 설치 확인 +python3 -c "import ; print(.__version__)" + +# 1. catalog 로드 + validation 확인 +python3 -c " +from _lib import params_catalog +c = params_catalog.load('input/-bench/evolve/params.json') +print('keys:', len(c.known_keys()), 'defaults:', len(c.defaults), 'locked:', len(c.locked)) +print('validate ok:', c.validate(c.defaults)) +print('validate bogus:', c.validate({'fake_key': 1})) +" + +# 2. sampler — clustering + stage 분할 +python3 -m _lib.sampler -bench +# Expect: cache/stage{1..4}_sample.json 생성 + +# 3. self_test — BASELINE으로 stage1 1회 평가 +python3 -m _lib.self_test -bench +# Expect: 결과 라벨 매치 + ratio [0.5, 2.0] 권장 (WARN ok) + +# 4. rebaseline — 로컬 baseline 캡쳐 (10회 평균) +python3 -m _lib.rebaseline -bench +# Expect: cache/local_baseline.json 생성 + +# 5. 1 phase smoke run (적은 iter로) +./input/run_phase.sh -bench 1 --pin 2-3 +# Expect: phase1/openevolve_output/best/best_program.py 생성 +# cache/phase1_best.json 생성 + +# 6. 전체 phase chain +./input/run_phase.sh -bench --pin 2-7 +# Expect: final_program.py 생성 +``` + +--- + +## 5. 옵션 결정 가이드 + +### 5.1 `score_mode` 선택 + +| 솔버 특성 | 추천 mode | +|---|---| +| baseline에 objective_value 있음, 최적화 문제 | `cost` | +| Sat/Unsat 만족도 + wall-clock 최소화 | `speedup` | +| Determinism work counter (예: cpsat의 deterministic_time) 있음 | `cost` + `time_metric: dtime` | + +### 5.2 Worker 축 있는 솔버? + +`PHASE_LOCKED["num_workers"] = N` 같은 phase별 차등 운영하면: +- adapter에 `WORKERS_KEY = ""` 명시 +- evaluator_core가 core block alloc 사용 +- rebaseline이 `by_workers` 스키마 사용 + +없으면: +- `WORKERS_KEY = None` +- 1 core per solve 단순 배분 + +### 5.3 SIZE_BUCKETS / STAGE3_OVERRIDES 활성? + +| 상황 | enable? | +|---|---| +| 문제 크기 분포 넓음 (예: ~7k–250k constraints, 점수 분포 multi-modal) | `enable_size_buckets: true` | +| 일부 outlier 문제가 score를 dominate | `enable_outlier_stage: true` + `cache/outliers.json` 채우기 | +| Pool 작거나 균일 | 둘 다 `false` | + +### 5.4 `clustering.method` + +| Method | 용도 | +|---|---| +| `kmeans` | 1D Lloyd's. 자연스러운 cluster boundary 자동 발견 | +| `quintile` | rank 기반 균등 분할. boundary 일관성 중요할 때 | +| `thresholds` | 사용자가 명시한 cut-off (`[50000, 150000]` → 3 bucket) | + +--- + +## 6. 흔한 함정 + +1. **`problems.jsonl` 필드명 mismatch** — adapter의 `PROBLEM_FILE_FIELD` / + `STATUS_FIELD`가 실제 JSON 키와 정확히 일치해야 함. typo 흔함. + +2. **`features.` 누락** — `clustering.feature: features.num_X`인데 + problems.jsonl에 `features.num_X` 없으면 sampler가 0으로 처리 → 모든 문제 + 1개 cluster로 뭉침. + +3. **DECISIVE vs DECIDED 혼동** — DECISIVE는 "솔버가 답을 줬다" (Sat/Unsat), + DECIDED는 "베이스라인이 확정 답을 줬으니 regression 비교 가능". 대부분의 + 솔버는 두 셋 동일하지만 cpsat은 다름 (FEASIBLE은 decisive지만 INFEASIBLE은 + decided에만). + +4. **`_solve_worker.py`에서 invalid param 미감지** — 솔버가 unknown key를 + silently ignore하면 catalog만으로 검증 안 됨. worker가 명시적으로 + `{"invalid_param": ""}` 출력해야 evaluator가 0점 부여. + +5. **Phase docstring 비워둠** — LLM이 phase intent 파악하는 유일한 채널. + "Phase X: tune " 한 줄이라도 적어두면 mutation 품질 차이 큼. + +6. **`unified_dict_name` 불일치** — config.yaml `bench.unified_dict_name`과 + 마지막 phase initial_program.py의 EVOLVE-BLOCK 내 dict 이름이 일치해야 + prepare_phase가 머터리얼 가능. + +7. **`worker_path` 경로** — config.yaml `bench.worker_path`는 `/evolve/` + 기준 상대 경로. `_solve_worker.py`가 evolve/ 루트에 있으면 그냥 + `_solve_worker.py` (디렉토리 prefix 없이). + +--- + +## 7. 작업 시간 견적 + +| 항목 | 예상 | +|---|---| +| raw-data 수집 + baseline 실행 (사용자 측 작업) | 가변 (수 시간 ~ 며칠) | +| problems.jsonl 생성 스크립트 작성 | 30분 - 1시간 | +| config.yaml 작성 | 30분 | +| params.json 작성 (50개 키 catalog) | 1-2시간 | +| adapter.py 작성 | 10분 | +| _solve_worker.py 작성 | 30분 - 2시간 (binding 복잡도에 따라) | +| phase 모듈 N개 작성 | 30분 (phase당 10분 × N) | +| 검증 + 디버깅 | 1-3시간 | +| **합계** | **반나절 - 1일** | + +--- + +## 8. 참고 — 기존 솔버 사례 + +| Solver | Score mode | Worker axis | Size buckets | Phases | +|---|---|---|---|---| +| z3 (`z3-bench`) | speedup | NO | NO | 4 (opt/sls + sat + smt + unified) | +| CP-SAT (`cpsat-bench`) | cost (dtime + cost_ratio) | YES (W=1, W=8) | YES | 5 (search + presolve + lp_cuts + unified + custom_subsolvers) | + +두 bench의 `params.json`, `adapter.py`, phase 모듈을 템플릿으로 참고. diff --git a/input/README.md b/input/README.md new file mode 100644 index 0000000000..e2db77cefa --- /dev/null +++ b/input/README.md @@ -0,0 +1,174 @@ +# input/ — Solver-Parameter Tuning Pipelines + +Each `/` directory holds one OpenEvolve solver-parameter-tuning +pipeline. **All orchestration code lives under `_lib/`**; each bench +contributes only a tiny solver-specific surface (4 hand-edited files + +phase modules). + +## Layout + +``` +input/ +├── run_phase.sh # user entry: ./run_phase.sh [] +├── _lib/ # the whole platform +│ ├── runtime.py # parallel_solvers / core_range / cascade_threshold +│ ├── subprocess_runner.py # generic run_solver(worker, problem, params, …) +│ ├── load_bench_config.py # parse `bench:` section → bash exports +│ │ +│ ├── bench_paths.py # resolve bench root / config / adapter / cache +│ ├── params_catalog.py # load + validate per-solver params.json +│ ├── averaging.py # _average_runs (10-run mean over solver dicts) +│ ├── scorer.py # pluggable scoring (speedup | cost) +│ ├── sampler.py # clustering + stage sampling +│ │ # CLI: python -m _lib.sampler +│ ├── rebaseline.py # per-host baseline measurement +│ │ # CLI: python -m _lib.rebaseline +│ ├── self_test.py # BASELINE stage1 sanity test +│ │ # CLI: python -m _lib.self_test +│ ├── evaluator_core.py # unified evaluator logic +│ ├── evaluator_entry.py # entry openevolve-run.py loads +│ ├── extract_best.py # CLI: python -m _lib.extract_best +│ ├── prepare_phase.py # CLI: python -m _lib.prepare_phase +│ ├── final_verify.py # CLI: python -m _lib.final_verify +│ └── finalize.py # CLI: python -m _lib.finalize +│ # last phase best → /evolve/final_program.py +│ +├── cpsat-bench/ +│ ├── raw-data/ # .cpsat.pb + ____seed0.meta.jsonl +│ ├── problems.jsonl +│ └── evolve/ +│ ├── config.yaml # bench config + LLM + clustering/evaluation +│ ├── params.json # rich CP-SAT param catalog (defaults + locked + groups) +│ ├── adapter.py # solver hooks (constants + get_problem_size) +│ ├── _solve_worker.py # subprocess entry (ortools binding) +│ ├── phase{1..5}_*/initial_program.py +│ ├── final_program.py # generated by `_lib.finalize` after last phase +│ └── cache/ # generated; safe to delete +│ ├── stage{1..4}_sample.json +│ ├── local_baseline.json +│ ├── phase{N}_best.json +│ ├── phase{N}_buckets.json (cpsat only — opt-in) +│ └── phase{N}_stage3.json (cpsat only — opt-in) +│ +└── z3-bench/ + └── evolve/ + ├── config.yaml + ├── params.json + ├── adapter.py + ├── _solve_worker.py # subprocess entry (z3 Python binding) + ├── phase{1..4}_*/initial_program.py + └── cache/ +``` + +## Per-bench surface (the only hand-edited files) + +| file | purpose | +|---|---| +| `config.yaml` | bench knobs (LLM, clustering, evaluation) + system prompt | +| `params.json` | rich param catalog — `defaults` (=BASELINE), `locked` (=LOCKED), `groups[*].params[*]` with type/range/desc | +| `adapter.py` | solver hooks: `SOLVER_NAME`, `PROBLEM_FILE_FIELD`, `STATUS_FIELD`, `DECISIVE_RESULTS`, `KEY_STATS`, `SCORE_MODE`, `get_problem_size(features)` | +| `_solve_worker.py` | subprocess entry: `argv = (params_json, problem_path, timeout_s)` → JSON line on stdout | + +Phase modules import from `_lib.params_catalog` for BASELINE — no +`baseline_params.py` to maintain. EVOLVE-BLOCK starts empty; the LLM fills +it in across the evolution run. + +## `config.yaml` `bench:` schema (consumed by `_lib`) + +```yaml +bench: + phases: + - dir: phase1_search + iters: 40 + - dir: phase2_presolve + + unified_prepare_before_dir: phase4_unified # optional; default=last phase + + # paths to the solver-specific files (relative to /evolve/) + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + unified_dict_name: GLOBAL_OVERRIDES + + # clustering / stage sampling — consumed by _lib.sampler + clustering: + method: kmeans # kmeans | quintile | thresholds + feature: features.num_constraints # dotted path into the problem record + n_clusters: 5 + max_baseline_ms: 120000 # drop outliers from sample pool + spread: quintile # quintile | center within stage pool + stage_sizes: {stage1: 10, stage2: 10, stage3: 5, stage4: 20} + stage_clusters: + stage1: [0, 1]; stage2: [2, 3]; stage3: [4]; stage4: [0, 1, 2, 3, 4] + + # evaluation behavior — consumed by _lib.evaluator_core + evaluation: + repeats: 10 # 10-run averaging (standard) + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: cost # cost (cpsat) | speedup (z3) + time_metric: dtime # cost mode only + cost_weight: 1.0 + enable_size_buckets: true # opt-in: SIZE_BUCKETS surface + enable_outlier_stage: true # opt-in: STAGE3_OVERRIDES surface +``` + +## Environment knobs + +| variable | use | +|---|---| +| `OPENEVOLVE_PARALLEL_SOLVERS` | concurrent solver subprocesses per stage | +| `OPENEVOLVE_CORE_RANGE` | explicit taskset core range `N-M` (set via `--pin`) | +| `OPENEVOLVE_MAX_PROBLEMS` | cap stage problem count (testing) | +| `OPENEVOLVE_STATS_WEIGHT` | exponent on efficiency factor (0 disables) | +| `OPENEVOLVE_COST_WEIGHT` | exponent on cost_ratio (cost mode only) | +| `OPENEVOLVE_SOLVE_REPEATS` | override config `evaluation.repeats` | +| `OPENEVOLVE_BENCH_ROOT` | exported by `run_phase.sh` so the evaluator entry can resolve adapter/catalog | +| `SKIP_REBASELINE` | reuse existing `cache/local_baseline.json` | + +## Quick start (any bench) + +```bash +# All-in-one (sampler + rebaseline run automatically on first invocation): +./input/run_phase.sh cpsat-bench --pin 2-7 +./input/run_phase.sh z3-bench --pin 2-7 + +# Or step by step: +python -m _lib.sampler cpsat-bench # cache/stage{1..4}_sample.json +python -m _lib.self_test cpsat-bench # sanity check (BASELINE on stage1) +python -m _lib.rebaseline cpsat-bench # cache/local_baseline.json (10-run avg) + +./input/run_phase.sh cpsat-bench 1 --pin 2-7 # phase 1 only +./input/run_phase.sh cpsat-bench 2 --pin 2-7 +# ... + +# Final verification on the canonical final_program.py (auto-generated after +# the last phase by `_lib.finalize`): +python -m _lib.final_verify cpsat-bench input/cpsat-bench/evolve/final_program.py +``` + +## Final artifact + +마지막 phase 완료 후 `run_phase.sh`가 `_lib.finalize`를 자동 호출 → +`/evolve/final_program.py`에 마지막 phase의 best_program.py를 +복사. 이 파일이 canonical evolved 결과. `final_verify` / 외부 벤치 스크립트는 +이 파일을 import해서 `get_params()` 호출. + +수동 호출도 가능: +```bash +python -m _lib.finalize cpsat-bench # best/best_program.py 사용 +python -m _lib.finalize cpsat-bench --from-checkpoints # 최고 score checkpoint 사용 +``` + +## Adding a new bench + +1. `mkdir -p input//raw-data` and populate it. +2. Author the 4 per-bench files in `input//evolve/`: + - `config.yaml` — copy/adapt either bench's template. + - `params.json` — author the catalog (types, defaults, locks, groups). + - `adapter.py` — set `SOLVER_NAME`, field paths, decisive results, etc. + - `_solve_worker.py` — subprocess entry that runs your solver. +3. Create phase dirs `phase{N}_*/initial_program.py` (use the cpsat + or z3 templates — replace `_BENCH` reference and pick W). +4. Run `./input/run_phase.sh `. `_lib.sampler` and + `_lib.rebaseline` populate `cache/` automatically. diff --git a/input/_lib/__init__.py b/input/_lib/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/input/_lib/averaging.py b/input/_lib/averaging.py new file mode 100644 index 0000000000..04c3f15534 --- /dev/null +++ b/input/_lib/averaging.py @@ -0,0 +1,52 @@ +""" +Collapse N repeated `run_solver` dicts into one mean dict. Lifted verbatim +from cpsat-bench's `_average_runs` so both benches now share the same +averaging semantics: + + - invalid_param short-circuits (config error, not a timing sample) + - result = modal label across runs + - elapsed_ms = mean across runs + - timeout = any run timed out + - stats[k] = mean over runs that emitted numeric k + - objective = mean over runs that emitted objective + - n_repeats annotated so the evaluator log can show repeats=N +""" +import collections +import statistics + + +def average_runs(runs): + if not runs: + return {"result": "Unknown", "elapsed_ms": 0, "stats": {}} + for r in runs: + if "invalid_param" in r: + return r + if len(runs) == 1: + return runs[0] + + results = [r.get("result") for r in runs] + result = collections.Counter(results).most_common(1)[0][0] + elapsed = statistics.mean(r.get("elapsed_ms", 0) for r in runs) + timeout_any = any(r.get("timeout") for r in runs) + + stat_keys = set() + for r in runs: + stat_keys |= set((r.get("stats") or {}).keys()) + stats = {} + for k in stat_keys: + vals = [(r.get("stats") or {}).get(k) for r in runs] + vals = [v for v in vals if isinstance(v, (int, float)) and not isinstance(v, bool)] + if vals: + stats[k] = statistics.mean(vals) + + out = { + "result": result, + "elapsed_ms": int(elapsed), + "timeout": timeout_any, + "stats": stats, + "n_repeats": len(runs), + } + objs = [r.get("objective") for r in runs if r.get("objective") is not None] + if objs: + out["objective"] = statistics.mean(objs) + return out diff --git a/input/_lib/bench_paths.py b/input/_lib/bench_paths.py new file mode 100644 index 0000000000..c670e18635 --- /dev/null +++ b/input/_lib/bench_paths.py @@ -0,0 +1,134 @@ +""" +Shared path / config resolution used by every _lib module's CLI entry. + +A "bench root" is the absolute path to `input//evolve/`. Modules take +either the bench name (e.g. `"cpsat-bench"`) or an already-resolved bench +root path. +""" + +import importlib.util +import os +import pathlib +import sys + + +def input_dir(): + """input/ — parent of this _lib package.""" + return pathlib.Path(__file__).resolve().parent.parent + + +def resolve_bench(bench_name_or_root): + """Accept bench name (`cpsat-bench`) OR a full path to its evolve/.""" + p = pathlib.Path(bench_name_or_root) + if p.is_absolute() and (p / "config.yaml").exists(): + return p.resolve() + root = input_dir() / str(bench_name_or_root) / "evolve" + if not root.exists(): + raise SystemExit(f"bench evolve dir not found: {root}") + return root.resolve() + + +def solver_mode(bench_root): + """`bench.solver_mode` from config.yaml. Selects the solver path / pipeline + variant (e.g. z3-bench: "optimize" default vs "sat"). The value also drives + cache + final_program suffixing so multiple modes coexist on disk.""" + m = (load_config(bench_root).get("bench") or {}).get("solver_mode") + return m or "optimize" + + +def variant_suffix(bench_root): + """Disk suffix for the active solver_mode. "" for the default ("optimize") + so existing benches keep `cache/` + `final_program.py` unchanged; otherwise + the mode name (e.g. "sat" → `cache-sat/`, `final_program_sat.py`).""" + m = solver_mode(bench_root) + return "" if m == "optimize" else m + + +def cache_dir(bench_root): + base = pathlib.Path(bench_root).resolve() / "cache" + s = variant_suffix(bench_root) + return base.with_name(f"cache-{s}") if s else base + + +def raw_dir(bench_root): + return pathlib.Path(bench_root).resolve().parent / "raw-data" + + +def problems_jsonl(bench_root): + return pathlib.Path(bench_root).resolve().parent / "problems.jsonl" + + +def config_path(bench_root): + return pathlib.Path(bench_root).resolve() / "config.yaml" + + +def params_json_path(bench_root): + return pathlib.Path(bench_root).resolve() / "params.json" + + +def worker_path(bench_root): + """Resolve `bench.worker_path` from config.yaml (relative to bench_root).""" + bench_root = pathlib.Path(bench_root).resolve() + cfg = load_config(bench_root) + wp = (cfg.get("bench") or {}).get("worker_path") or "_solve_worker.py" + p = bench_root / wp + if not p.exists(): + raise SystemExit(f"worker_path not found: {p}") + return p + + +def evaluation_cfg(bench_root): + return (load_config(bench_root).get("bench") or {}).get("evaluation") or {} + + +def clustering_cfg(bench_root): + return (load_config(bench_root).get("bench") or {}).get("clustering") or {} + + +_yaml_cache = {} + + +def load_config(bench_root): + """Read `/evolve/config.yaml` (cached by path).""" + path = config_path(bench_root) + key = str(path) + if key in _yaml_cache: + return _yaml_cache[key] + if not path.exists(): + _yaml_cache[key] = {} + return _yaml_cache[key] + import yaml + + _yaml_cache[key] = yaml.safe_load(path.read_text()) or {} + return _yaml_cache[key] + + +def load_adapter(bench_root): + """Import the bench's adapter.py and return the module.""" + bench_root = pathlib.Path(bench_root).resolve() + adapter_path = bench_root / "adapter.py" + if not adapter_path.exists(): + raise SystemExit(f"adapter.py not found: {adapter_path}") + mod_name = f"_bench_adapter_{bench_root.parent.name.replace('-', '_')}" + spec = importlib.util.spec_from_file_location(mod_name, adapter_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +def bench_root_from_env(): + """Resolve bench root from OPENEVOLVE_BENCH_ROOT (set by run_phase.sh) + when invoked as the openevolve evaluator. Returns None when unset so + callers can decide on the fallback.""" + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if not v: + return None + return pathlib.Path(v).resolve() + + +def add_input_to_sys_path(): + """Ensure `input/` is on sys.path so `from _lib import ...` works from + every entry point (CLI, openevolve eval subprocess, phase modules).""" + p = str(input_dir()) + if p not in sys.path: + sys.path.insert(0, p) diff --git a/input/_lib/evaluator_core.py b/input/_lib/evaluator_core.py new file mode 100644 index 0000000000..02e16c6e31 --- /dev/null +++ b/input/_lib/evaluator_core.py @@ -0,0 +1,539 @@ +""" +Unified evaluator. Lifted from cpsat-bench's evaluator.py and parameterized +over the per-bench adapter + catalog + config. + +Per-bench `_lib/evaluator_entry.py` is what openevolve loads; it just calls +`build_evaluators(bench_root)` (reading `OPENEVOLVE_BENCH_ROOT` from env) +and re-exports `evaluate_stage1..4` + `evaluate`. + +Per-problem param resolution: + If the program exposes `get_params(problem=..., stage=...)`, the evaluator + calls it once per problem so phases can ship SIZE_BUCKETS + (constraint-count conditional overrides) and STAGE3_OVERRIDES (outlier-only + tuning) inside their EVOLVE-BLOCK. Programs that only expose `get_params()` + keep working. + +10-run averaging is the standard (configurable via +`bench.evaluation.repeats`). +""" + +import importlib.util +import inspect +import json +import logging +import math +import os +import pathlib +import queue as _queue +import sys +import traceback +from concurrent.futures import ThreadPoolExecutor, as_completed + +from _lib import averaging, bench_paths, params_catalog, runtime, scorer, subprocess_runner + +try: + from openevolve.evaluation_result import EvaluationResult +except Exception: + + class EvaluationResult: + def __init__(self, metrics=None, artifacts=None): + self.metrics = metrics or {} + self.artifacts = artifacts or {} + + +logger = logging.getLogger(__name__) + + +def _load_program(path): + spec = importlib.util.spec_from_file_location("program", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def _supports_kwargs(fn, *names): + try: + sig = inspect.signature(fn) + except (TypeError, ValueError): + return False + params = sig.parameters + if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values()): + return True + return any(n in params for n in names) + + +def _resolve_params(get_params, problem, stage): + kwargs = {} + if _supports_kwargs(get_params, "problem"): + kwargs["problem"] = problem + if _supports_kwargs(get_params, "stage"): + kwargs["stage"] = stage + out = get_params(**kwargs) if kwargs else get_params() + if not isinstance(out, dict): + raise TypeError(f"get_params() returned {type(out).__name__}, expected dict") + return out + + +def _pick_local_baseline(entry, workers): + """Return inner per-W dict, or None when missing.""" + if not isinstance(entry, dict): + return None + bw = entry.get("by_workers") + if isinstance(bw, dict) and bw: + key = str(int(workers)) + return bw.get(key) + if "elapsed_ms" in entry and int(workers) == 1: + return entry + return None + + +def build_evaluators(bench_root): + bench_root = pathlib.Path(bench_root).resolve() + bench_paths.add_input_to_sys_path() + + adapter = bench_paths.load_adapter(bench_root) + catalog = params_catalog.load_for_bench(bench_root) + cfg = bench_paths.load_config(bench_root) + eval_cfg = (cfg.get("bench") or {}).get("evaluation") or {} + + raw_dir = bench_paths.raw_dir(bench_root) + problems_jsonl = bench_paths.problems_jsonl(bench_root) + cache = bench_paths.cache_dir(bench_root) + worker = bench_paths.worker_path(bench_root) + config_yaml = bench_paths.config_path(bench_root) + + decisive = tuple(adapter.DECISIVE_RESULTS) + decided = tuple(getattr(adapter, "DECIDED_RESULTS", adapter.DECISIVE_RESULTS)) + key_stats = tuple(adapter.KEY_STATS) + stats_weights = dict(getattr(adapter, "STATS_WEIGHTS", {})) + score_mode = eval_cfg.get("score_mode", getattr(adapter, "SCORE_MODE", "speedup")) + if bench_paths.solver_mode(bench_root) == "optimize" and score_mode != "cost": + print( + f"[warn] solver_mode=optimize but score_mode={score_mode} — " + f"objective guard inactive; score_mode: cost is recommended for " + f"optimize mode.", + flush=True, + ) + time_metric = eval_cfg.get("time_metric") + cost_weight = eval_cfg.get("cost_weight") + repeats_default = int(eval_cfg.get("repeats", 10)) + timeout_factor = float(eval_cfg.get("timeout_factor", 1.3)) + min_timeout_s = int(eval_cfg.get("min_timeout_s", 5)) + workers_key = getattr(adapter, "WORKERS_KEY", None) + catalog_locked = catalog.locked + + py_bin = os.environ.get("OPENEVOLVE_PYTHON_BIN") + + # Outlier set (cpsat opt-in). cache/outliers.json shape: + # {"outliers": {sha: {...}}, "stage3_sample": [...]} + enable_outliers = bool(eval_cfg.get("enable_outlier_stage", False)) + outlier_shas = set() + if enable_outliers: + path = cache / "outliers.json" + if path.exists(): + try: + blob = json.loads(path.read_text()) + outlier_shas = set((blob.get("outliers") or {}).keys()) + except (json.JSONDecodeError, OSError): + pass + + # Small/large cache for cpsat-style cascade merging (stage1+stage2 → final). + # Keyed by program_path so multiple cascades within one process don't leak. + _SMALL_CACHE: dict = {} + + def _solve_repeats(): + env = os.environ.get("OPENEVOLVE_SOLVE_REPEATS") + if env: + try: + return max(1, int(env)) + except ValueError: + pass + return repeats_default + + def _load_problems(workers=1): + local = {} + path = cache / "local_baseline.json" + if path.exists(): + try: + local = json.loads(path.read_text()) + except json.JSONDecodeError: + local = {} + rows = [] + with open(problems_jsonl) as f: + for line in f: + d = json.loads(line) + sha = d["problem_sha256"] + status = d.get(adapter.STATUS_FIELD) or {} + stats_field = getattr(adapter, "STATS_FIELD", None) + baseline_stats = (d.get(stats_field) or {}) if stats_field else {} + features = d.get(getattr(adapter, "FEATURES_FIELD", "features")) or {} + obj_field = getattr(adapter, "OBJECTIVE_FIELD", None) + baseline_objective = status.get(obj_field) if obj_field else None + baseline_ms = status.get("elapsed_ms", 0) + baseline_result = status.get("result") + + lo = _pick_local_baseline(local.get(sha), workers) + if lo and lo.get("matches_raw"): + baseline_ms = lo["elapsed_ms"] + baseline_stats = lo.get("stats") or baseline_stats + if lo.get("objective") is not None: + baseline_objective = lo["objective"] + rows.append( + { + "sha": sha, + "input_file": d[adapter.PROBLEM_FILE_FIELD], + "baseline_ms": baseline_ms, + "baseline_result": baseline_result, + "baseline_stats": baseline_stats, + "baseline_objective": baseline_objective, + "size": adapter.get_problem_size(features), + "is_outlier": sha in outlier_shas, + "features": features, + } + ) + return rows + + def _filter_by_sample(problems, stage_n): + path = cache / f"stage{stage_n}_sample.json" + if not path.exists(): + return problems + keep = set(json.loads(path.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + def _err(metrics_extra, artifacts): + m = { + "combined_score": 0.0, + "geomean_speedup": 0.0, + "solved_rate": 0.0, + "regressions": 0, + "solved": 0, + "total": 0, + } + m.update(metrics_extra) + return EvaluationResult(metrics=m, artifacts=artifacts) + + def _peek_workers(program_path): + if not workers_key: + return 1 + try: + program = _load_program(program_path) + except Exception: + return 1 + pl = getattr(program, "PHASE_LOCKED", None) + if isinstance(pl, dict) and workers_key in pl: + try: + return int(pl[workers_key]) + except (TypeError, ValueError): + pass + try: + return int(program.get_params().get(workers_key, 1) or 1) + except Exception: + return 1 + + def _evaluate(program_path, problems, stage_name): + try: + program = _load_program(program_path) + except Exception as e: + return _err( + {"error": f"program load failed: {e}"}, + { + "error_type": type(e).__name__, + "full_traceback": traceback.format_exc()[-2000:], + "stage": stage_name, + }, + ) + if not hasattr(program, "get_params"): + return _err({"error": "missing get_params()"}, {"stage": stage_name}) + + try: + global_params = _resolve_params(program.get_params, None, stage_name) + except Exception as e: + return _err( + {"error": f"get_params() raised: {e}"}, + { + "error_type": type(e).__name__, + "full_traceback": traceback.format_exc()[-2000:], + "stage": stage_name, + }, + ) + + phase_locked = getattr(program, "PHASE_LOCKED", None) + if isinstance(phase_locked, dict): + locked = dict(catalog_locked) + locked.update(phase_locked) + else: + locked = dict(catalog_locked) + + violations = {k: global_params.get(k) for k in locked if global_params.get(k) != locked[k]} + if violations: + return _err( + {"error": "locked params violated"}, + {"locked_violated": violations, "locked_expected": locked, "stage": stage_name}, + ) + + catalog_errors = catalog.validate(global_params) + if catalog_errors: + first_key, first_msg = catalog_errors[0] + return _err( + {"error": f"invalid param: {first_key}"}, + { + "invalid_param": first_key, + "catalog_errors": catalog_errors[:10], + "stage": stage_name, + }, + ) + + if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: + problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] + + if not problems: + return EvaluationResult( + metrics={ + "combined_score": 100.0, + "geomean_speedup": 100.0, + "solved_rate": 1.0, + "regressions": 0, + "solved": 0, + "total": 0, + "stage": stage_name, + }, + artifacts={"stage": stage_name, "summary": "empty sample — pass-through"}, + ) + + for p in problems: + ip = raw_dir / p["input_file"] + if not ip.exists(): + return _err( + {"error": f"input not found: {p['input_file']}"}, + {"missing_file": str(ip), "stage": stage_name}, + ) + + cores = runtime.core_range() + if cores is None: + cores = list(range(1, runtime.parallel_solvers(config_yaml, default=1) + 1)) + if workers_key: + workers_per_solve = int(global_params.get(workers_key, 1) or 1) + blocks = runtime.alloc_core_blocks(cores, workers_per_solve) + if not blocks: + blocks = [list(cores)] if cores else [None] + else: + workers_per_solve = 1 + blocks = [[c] for c in cores] if cores else [None] + + n_parallel = min(len(blocks), len(problems)) + blocks = blocks[:n_parallel] + core_pool = _queue.Queue() + for b in blocks: + core_pool.put(b) + + n_repeats = _solve_repeats() + + def _solve(idx_p): + idx, p = idx_p + ip = raw_dir / p["input_file"] + timeout_s = max(min_timeout_s, math.ceil(p["baseline_ms"] * timeout_factor / 1000)) + try: + per_params = _resolve_params(program.get_params, p, stage_name) + except Exception as e: + return ( + idx, + p, + { + "result": "ERROR", + "elapsed_ms": 0, + "invalid_param": f"get_params(problem,stage) raised: {e}", + }, + None, + timeout_s, + ) + for k, v in locked.items(): + per_params[k] = v + if workers_key and workers_key in global_params: + per_params[workers_key] = global_params[workers_key] + + block = core_pool.get() + try: + runs = [] + for _ in range(n_repeats): + rr = subprocess_runner.run_solver( + worker_path=worker, + problem_path=ip, + params=per_params, + timeout_s=timeout_s, + python_bin=py_bin, + cpu_core=block, + ) + runs.append(rr) + if "invalid_param" in rr: + break + avg = averaging.average_runs(runs) + finally: + core_pool.put(block) + return idx, p, avg, block, timeout_s + + def _fmt(c): + if c is None: + return "-" + if isinstance(c, (list, tuple)): + return ",".join(str(x) for x in c) if c else "-" + return str(c) + + print( + f" [{stage_name}] workers/solve={workers_per_solve} " + f"repeats={n_repeats} " + f"core_blocks={[_fmt(b) for b in blocks]}", + flush=True, + ) + + by_idx = {} + abort = None + if n_parallel == 1: + for pair in enumerate(problems): + idx, p, r, block, timeout_s = _solve(pair) + print( + f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms / {timeout_s}s " + f"(core={_fmt(block)})", + flush=True, + ) + if "invalid_param" in r: + return _err( + {"error": f"invalid param: {r['invalid_param']}"}, + { + "invalid_param": r["invalid_param"], + "stderr": r.get("stderr", "")[:2000], + "stage": stage_name, + }, + ) + by_idx[idx] = (p, r) + else: + ordered = sorted(enumerate(problems), key=lambda ip: -ip[1]["baseline_ms"]) + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futs = [ex.submit(_solve, pair) for pair in ordered] + for f in as_completed(futs): + if abort is not None: + continue + idx, p, r, block, timeout_s = f.result() + print( + f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms / {timeout_s}s " + f"(core={_fmt(block)})", + flush=True, + ) + if "invalid_param" in r: + abort = r + for ff in futs: + ff.cancel() + continue + by_idx[idx] = (p, r) + if abort is not None: + return _err( + {"error": f"invalid param: {abort['invalid_param']}"}, + { + "invalid_param": abort["invalid_param"], + "stderr": abort.get("stderr", "")[:2000], + "stage": stage_name, + }, + ) + + results = [] + for idx in range(len(problems)): + p, r = by_idx[idx] + rec = { + **p, + "result": r["result"], + "elapsed_ms": r["elapsed_ms"], + "timeout": bool(r.get("timeout")), + "stats": r.get("stats") or {}, + } + if "objective" in r: + rec["objective"] = r["objective"] + results.append(rec) + + _SMALL_CACHE[(str(program_path), stage_name)] = results + + metrics = scorer.score( + results, + mode=score_mode, + decisive_results=decisive, + decided_results=decided, + stats_weights=stats_weights, + time_metric=time_metric, + cost_weight=cost_weight, + ) + metrics["stage"] = stage_name + for k in key_stats: + metrics[f"total_{k}"] = float(sum(r["stats"].get(k, 0) for r in results)) + + sample = [ + { + "sha": r["sha"][:10], + "base_result": r["baseline_result"], + "got_result": r["result"], + "base_ms": r["baseline_ms"], + "ms": r["elapsed_ms"], + "speedup": round(r["baseline_ms"] / max(r["elapsed_ms"], 1), 3), + "timeout": r["timeout"], + "base_obj": r.get("baseline_objective"), + "obj": r.get("objective"), + "stats": {k: r["stats"].get(k, 0) for k in key_stats if k in r["stats"]}, + "base_stats": { + k: r["baseline_stats"].get(k, 0) + for k in key_stats + if k in (r.get("baseline_stats") or {}) + }, + } + for r in results + ] + artifacts = { + "stage": stage_name, + "summary": ( + f"solved={metrics['solved']}/{metrics['total']} " + f"regressions={metrics['regressions']} " + f"geomean={metrics['geomean_speedup']:.3f} " + f"efficiency={metrics.get('efficiency', 1.0):.3f} " + f"score={metrics['combined_score']:.3f}" + ), + "per_problem": sample[:20], + } + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + def evaluate_stage(stage_n, program_path): + w = _peek_workers(program_path) + problems = _filter_by_sample(_load_problems(w), stage_n) + return _evaluate(program_path, problems, f"stage{stage_n}") + + def evaluate_stage1(program_path): + return evaluate_stage(1, program_path) + + def evaluate_stage2(program_path): + return evaluate_stage(2, program_path) + + def evaluate_stage3(program_path): + # Cascade chain: stage3 result then stage4 if it passes the gate. + r3 = evaluate_stage(3, program_path) + if not isinstance(r3, EvaluationResult): + return r3 + gate = runtime.cascade_threshold(config_yaml, 2, default=1.03) + if r3.metrics.get("combined_score", 0.0) < gate: + return r3 + r4 = evaluate_stage(4, program_path) + if not isinstance(r4, EvaluationResult): + return r4 + merged_m = {**r3.metrics, **r4.metrics} + merged_a = {**r3.artifacts, **r4.artifacts} + return EvaluationResult(metrics=merged_m, artifacts=merged_a) + + def evaluate_stage4(program_path): + return evaluate_stage(4, program_path) + + def evaluate(program_path): + return evaluate_stage1(program_path) + + return { + "evaluate_stage1": evaluate_stage1, + "evaluate_stage2": evaluate_stage2, + "evaluate_stage3": evaluate_stage3, + "evaluate_stage4": evaluate_stage4, + "evaluate": evaluate, + } diff --git a/input/_lib/evaluator_entry.py b/input/_lib/evaluator_entry.py new file mode 100644 index 0000000000..b283349ef8 --- /dev/null +++ b/input/_lib/evaluator_entry.py @@ -0,0 +1,29 @@ +""" +Entry point loaded by openevolve-run.py as the evaluator. Resolves bench +context from OPENEVOLVE_BENCH_ROOT (exported by input/run_phase.sh) and +delegates to _lib.evaluator_core.build_evaluators(). +""" +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_INPUT = _HERE.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import bench_paths, evaluator_core # noqa: E402 + +_bench = bench_paths.bench_root_from_env() +if _bench is None: + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT not set — _lib/evaluator_entry.py expects " + "input/run_phase.sh to export it. Set OPENEVOLVE_BENCH_ROOT=/evolve/> to invoke openevolve-run.py directly." + ) + +_evals = evaluator_core.build_evaluators(_bench) +evaluate_stage1 = _evals["evaluate_stage1"] +evaluate_stage2 = _evals["evaluate_stage2"] +evaluate_stage3 = _evals["evaluate_stage3"] +evaluate_stage4 = _evals["evaluate_stage4"] +evaluate = _evals["evaluate"] diff --git a/input/_lib/extract_best.py b/input/_lib/extract_best.py new file mode 100644 index 0000000000..e622001b95 --- /dev/null +++ b/input/_lib/extract_best.py @@ -0,0 +1,197 @@ +""" +Per-phase winner extractor. + +CLI: `python -m _lib.extract_best [--from-checkpoints]` + +Loads `get_phase_overrides()` from a phase's `best_program.py` and writes +`/evolve/cache/phaseN_best.json`. The next phase's +`initial_program.py` (or `prepare_phase`) picks it up. + +--from-checkpoints scans `/openevolve_output/checkpoints/checkpoint_*/` +and picks the program with the highest combined_score. +""" +import argparse +import importlib.util +import json +import pathlib +import sys + +from _lib import bench_paths + + +def _pick_from_checkpoints(phase_dir): + ckpt_root = phase_dir / "openevolve_output" / "checkpoints" + ckpts = sorted( + ckpt_root.glob("checkpoint_*"), + key=lambda p: int(p.name.split("_")[1]) if p.name.split("_")[1].isdigit() else -1, + ) + if not ckpts: + print(f"no checkpoints found under {ckpt_root}", file=sys.stderr) + sys.exit(1) + + best_py = None + best_score = float("-inf") + best_ck = None + for ck in ckpts: + info_path = ck / "best_program_info.json" + prog_path = ck / "best_program.py" + if not info_path.exists() or not prog_path.exists(): + continue + try: + info = json.loads(info_path.read_text()) + sc = float(info.get("metrics", {}).get("combined_score", float("-inf"))) + except (json.JSONDecodeError, ValueError, TypeError) as e: + print(f"warning: failed to read {info_path}: {e}", file=sys.stderr) + continue + if sc > best_score: + best_score = sc + best_py = prog_path + best_ck = ck + + if best_py is None: + print(f"no usable best_program.py in any checkpoint under {ckpt_root}", + file=sys.stderr) + sys.exit(1) + + print(f"[extract_best] from-checkpoints: picked {best_ck.name} " + f"(combined_score={best_score:.4f})") + return best_py + + +def _phase_dirs_from_config(bench_root): + cfg = bench_paths.load_config(bench_root) + phases = (cfg.get("bench") or {}).get("phases") or [] + return {i + 1: ph["dir"] for i, ph in enumerate(phases)} + + +def main(root=None, shared=None, phase_dirs=None, argv=None): + """Two invocation modes: + + 1. CLI: argv = ["", "", ...] + → resolves root/shared/phase_dirs from bench config.yaml. + shared is `/evolve/cache/`. + + 2. Direct (legacy): explicit root, shared, phase_dirs args. + Retained for tests / one-off use. + """ + if root is None or phase_dirs is None: + ap = argparse.ArgumentParser( + description="Extract get_phase_overrides() from phase's best_program.py.") + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + ap.add_argument("phase", type=int, help="phase number") + ap.add_argument("--from-checkpoints", action="store_true", + help="scan checkpoint_*/ dirs and pick highest combined_score") + args = ap.parse_args(argv) + root = bench_paths.resolve_bench(args.bench) + shared = bench_paths.cache_dir(root) + shared.mkdir(parents=True, exist_ok=True) + phase_dirs = _phase_dirs_from_config(root) + if args.phase not in phase_dirs: + print(f"phase must be in {sorted(phase_dirs.keys())} " + f"(got: {args.phase})", file=sys.stderr) + sys.exit(2) + from_checkpoints = args.from_checkpoints + else: + ap = argparse.ArgumentParser( + description="Extract get_phase_overrides() from phase's best_program.py.") + ap.add_argument("phase", type=int, choices=sorted(phase_dirs.keys()), + help="phase number") + ap.add_argument("--from-checkpoints", action="store_true") + args = ap.parse_args(argv) + from_checkpoints = args.from_checkpoints + + n = args.phase + phase_dir = pathlib.Path(root) / phase_dirs[n] + + if from_checkpoints: + best_py = _pick_from_checkpoints(phase_dir) + else: + best_py = phase_dir / "openevolve_output" / "best" / "best_program.py" + if not best_py.exists(): + print(f"best_program.py not found: {best_py}", file=sys.stderr) + print("run phase first (./run_phase.sh N) before extracting,", + file=sys.stderr) + print("or pass --from-checkpoints to use the latest checkpoint.", + file=sys.stderr) + sys.exit(1) + + sys.path.insert(0, str(shared)) + + spec = importlib.util.spec_from_file_location(f"phase{n}_best", best_py) + module = importlib.util.module_from_spec(spec) + try: + spec.loader.exec_module(module) + except Exception as e: + print(f"failed to load {best_py}: {e}", file=sys.stderr) + sys.exit(1) + + if not hasattr(module, "get_phase_overrides"): + print(f"{best_py} missing get_phase_overrides()", file=sys.stderr) + sys.exit(1) + + overrides = module.get_phase_overrides() + if not isinstance(overrides, dict): + print(f"get_phase_overrides() returned {type(overrides).__name__}, " + f"expected dict", file=sys.stderr) + sys.exit(1) + + shared_dir = pathlib.Path(shared) + root_path = pathlib.Path(root) + + out = shared_dir / f"phase{n}_best.json" + out.write_text(json.dumps(overrides, indent=2, sort_keys=True) + "\n") + print(f"wrote {out.relative_to(root_path)} ({len(overrides)} keys)") + + # Optional extras for size-bucket / stage3 evolution (cpsat-bench). + # Programs that don't expose these helpers stay backward-compatible. + if hasattr(module, "get_phase_size_buckets"): + try: + buckets = module.get_phase_size_buckets() + except Exception as e: + print(f"get_phase_size_buckets() raised: {e}", file=sys.stderr) + sys.exit(1) + if not isinstance(buckets, list): + print(f"get_phase_size_buckets() returned " + f"{type(buckets).__name__}, expected list", file=sys.stderr) + sys.exit(1) + # JSON can't encode inf — write None as the sentinel. + serializable = [] + for entry in buckets: + if not (isinstance(entry, (list, tuple)) and len(entry) == 2): + print(f"bucket entry malformed: {entry!r}", file=sys.stderr) + sys.exit(1) + upper, override = entry + if upper == float("inf"): + upper = None + elif not isinstance(upper, (int, float)): + print(f"bucket upper bound not numeric: {upper!r}", + file=sys.stderr) + sys.exit(1) + if not isinstance(override, dict): + print(f"bucket override not dict: {override!r}", + file=sys.stderr) + sys.exit(1) + serializable.append([upper, override]) + out_b = shared_dir / f"phase{n}_buckets.json" + out_b.write_text(json.dumps(serializable, indent=2, sort_keys=False) + "\n") + nonempty = sum(1 for _, d in serializable if d) + print(f"wrote {out_b.relative_to(root_path)} " + f"({len(serializable)} buckets, {nonempty} non-empty)") + + if hasattr(module, "get_phase_stage3_overrides"): + try: + stage3 = module.get_phase_stage3_overrides() + except Exception as e: + print(f"get_phase_stage3_overrides() raised: {e}", file=sys.stderr) + sys.exit(1) + if not isinstance(stage3, dict): + print(f"get_phase_stage3_overrides() returned " + f"{type(stage3).__name__}, expected dict", file=sys.stderr) + sys.exit(1) + out_s = shared_dir / f"phase{n}_stage3.json" + out_s.write_text(json.dumps(stage3, indent=2, sort_keys=True) + "\n") + print(f"wrote {out_s.relative_to(root_path)} ({len(stage3)} keys)") + + +if __name__ == "__main__": + main() diff --git a/input/_lib/final_verify.py b/input/_lib/final_verify.py new file mode 100644 index 0000000000..93c265104c --- /dev/null +++ b/input/_lib/final_verify.py @@ -0,0 +1,98 @@ +""" +Final verification: re-run an evolved best_program.py through stage4 of +the unified evaluator and print a summary. + +CLI: `python -m _lib.final_verify ` + +Assumes `cache/local_baseline.json` is current. Re-run +`python -m _lib.rebaseline ` first if hardware / solver version +has changed since the evolution run. + +Optional `cache/final_sample.json` (shape: {"sha256": [...]}) overrides +the stage4 sample; otherwise uses stage4_sample.json (same as the +cascade's final stage). +""" +import argparse +import json +import pathlib +import sys +import time + +from _lib import bench_paths, evaluator_core + + +def _stash_stage4(cache_dir): + """If final_sample.json exists, swap it in temporarily as stage4_sample.json. + Returns (original_path_backup_or_None, did_swap).""" + final = cache_dir / "final_sample.json" + stage4 = cache_dir / "stage4_sample.json" + if not final.exists(): + return None, False + backup = stage4.with_suffix(".json.fv-backup") + if stage4.exists(): + stage4.rename(backup) + stage4.write_text(final.read_text()) + return backup, True + + +def _restore_stage4(cache_dir, backup, did_swap): + if not did_swap: + return + stage4 = cache_dir / "stage4_sample.json" + if stage4.exists(): + stage4.unlink() + if backup and backup.exists(): + backup.rename(stage4) + + +def final_verify(bench_root, program_path): + bench_root = pathlib.Path(bench_root).resolve() + program_path = pathlib.Path(program_path).resolve() + if not program_path.exists(): + raise SystemExit(f"program not found: {program_path}") + + cache = bench_paths.cache_dir(bench_root) + backup, did_swap = _stash_stage4(cache) + try: + evals = evaluator_core.build_evaluators(bench_root) + t0 = time.monotonic() + result = evals["evaluate_stage4"](program_path) + elapsed = time.monotonic() - t0 + finally: + _restore_stage4(cache, backup, did_swap) + + metrics = getattr(result, "metrics", {}) + artifacts = getattr(result, "artifacts", {}) + print() + print("===== final_verify summary =====") + print(f"program: {program_path}") + print(f"bench: {bench_root.parent.name}") + print(f"wall: {elapsed:.1f}s") + print(f"combined_score: {metrics.get('combined_score', 0.0):.4f}") + print(f"geomean_speedup: {metrics.get('geomean_speedup', 0.0):.4f}") + print(f"solved_rate: {metrics.get('solved_rate', 0.0):.4f}") + print(f"solved: {metrics.get('solved', 0)}/{metrics.get('total', 0)}") + print(f"regressions: {metrics.get('regressions', 0)}") + print(f"efficiency: {metrics.get('efficiency', 1.0):.4f}") + if "summary" in artifacts: + print(f"summary: {artifacts['summary']}") + out = program_path.parent / "final_verify.json" + out.write_text(json.dumps({ + "program": str(program_path), + "bench": bench_root.parent.name, + "metrics": metrics, + "summary": artifacts.get("summary", ""), + }, indent=2) + "\n") + print(f"wrote {out}") + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + ap.add_argument("program", help="path to best_program.py") + args = ap.parse_args(argv) + final_verify(bench_paths.resolve_bench(args.bench), args.program) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/finalize.py b/input/_lib/finalize.py new file mode 100644 index 0000000000..bd25c323cc --- /dev/null +++ b/input/_lib/finalize.py @@ -0,0 +1,197 @@ +""" +Generate `/evolve/final_program.py` — self-contained canonical final +artifact from the last phase's best_program.py. + +CLI: `python -m _lib.finalize ` + +Self-containment rules: + - z3 phase4_unified style: `prepare_phase` already materialized the + EVOLVE-BLOCK with the union of prior-phase winners as a literal dict + (`UNIFIED_OVERRIDES = {...}`). Verbatim copy is already self-contained. + + - cpsat phase5 style: `_PHASE4 = _load_prev_dict("phase4_best.json")` reads + `cache/phase4_best.json` at import time. Finalize replaces these calls + with the literal dict from the JSON so the output file does NOT depend + on `cache/` being present alongside. + +Patterns recognized: + ` = _load_prev_dict("")` → literal dict + ` = _load_prev_buckets("")` → literal list[(upper, dict)] + (None → float('inf')) + ` = _load_prev("")` → literal dict (alias) + +The helper function definitions remain in source (harmless — dead code). +`cache/` path resolution and JSON loading are removed by the replacement. + +Source priority: + 1. `/openevolve_output/best/best_program.py` + 2. `/openevolve_output/checkpoints/checkpoint_*/best_program.py` + (highest combined_score) +""" + +import argparse +import json +import pathlib +import pprint +import re +import shutil +import sys + +from _lib import bench_paths + + +_PAT_DICT = re.compile( + r'^(\s*)(_[A-Za-z0-9_]+)\s*=\s*_load_prev(?:_dict)?\(\s*["\']([^"\']+)["\']\s*\)\s*$', + re.M, +) +_PAT_BUCKETS = re.compile( + r'^(\s*)(_[A-Za-z0-9_]+)\s*=\s*_load_prev_buckets\(\s*["\']([^"\']+)["\']\s*\)\s*$', + re.M, +) + + +def _format_buckets(raw): + items = [] + for entry in raw: + if not (isinstance(entry, (list, tuple)) and len(entry) == 2): + raise ValueError(f"bucket entry malformed: {entry!r}") + upper, override = entry + upper_src = "float('inf')" if upper is None else repr(upper) + ov_src = pprint.pformat(override, width=100, sort_dicts=True) + items.append(f"({upper_src}, {ov_src})") + return "[" + ", ".join(items) + "]" + + +def _resolve_load_prev(src, cache_dir, replaced): + """Substitute every `_load_prev{_dict,_buckets}("...json")` assignment + with its resolved literal. Appends `(name, fname, kind)` tuples to + `replaced` for the summary.""" + + def repl_dict(m): + indent, name, fname = m.group(1), m.group(2), m.group(3) + path = cache_dir / fname + if not path.exists(): + replaced.append((name, fname, "dict-missing")) + return f"{indent}{name} = {{}}" + data = json.loads(path.read_text()) + replaced.append((name, fname, "dict")) + return f"{indent}{name} = " + pprint.pformat(data, width=100, sort_dicts=True) + + def repl_buckets(m): + indent, name, fname = m.group(1), m.group(2), m.group(3) + path = cache_dir / fname + if not path.exists(): + replaced.append((name, fname, "buckets-missing")) + return f"{indent}{name} = None" + raw = json.loads(path.read_text()) + replaced.append((name, fname, "buckets")) + return f"{indent}{name} = " + _format_buckets(raw) + + # Order matters — handle buckets before dict (dict regex would also match). + src = _PAT_BUCKETS.sub(repl_buckets, src) + src = _PAT_DICT.sub(repl_dict, src) + return src + + +def _pick_from_checkpoints(phase_dir): + ckpt_root = phase_dir / "openevolve_output" / "checkpoints" + best_py, best_score, best_ck = None, float("-inf"), None + for ck in sorted(ckpt_root.glob("checkpoint_*")): + info = ck / "best_program_info.json" + prog = ck / "best_program.py" + if not info.exists() or not prog.exists(): + continue + try: + sc = float( + json.loads(info.read_text()).get("metrics", {}).get("combined_score", float("-inf")) + ) + except (json.JSONDecodeError, ValueError, TypeError): + continue + if sc > best_score: + best_score, best_py, best_ck = sc, prog, ck + return best_py, best_score, best_ck + + +def finalize(bench_root, from_checkpoints=False): + bench_root = pathlib.Path(bench_root).resolve() + cfg = bench_paths.load_config(bench_root) + phases = (cfg.get("bench") or {}).get("phases") or [] + if not phases: + raise SystemExit("bench.phases missing in config.yaml") + + last_dir = phases[-1]["dir"] + last_phase = bench_root / last_dir + cache_dir = bench_paths.cache_dir(bench_root) + + if from_checkpoints: + best_py, score, ck = _pick_from_checkpoints(last_phase) + if best_py is None: + raise SystemExit(f"no checkpoint best_program.py under {last_phase}") + print(f"[finalize] from-checkpoints: picked {ck.name} " f"(combined_score={score:.4f})") + else: + best_py = last_phase / "openevolve_output" / "best" / "best_program.py" + if not best_py.exists(): + best_py_alt, score, ck = _pick_from_checkpoints(last_phase) + if best_py_alt is None: + raise SystemExit( + f"no best_program.py at {best_py} and no checkpoints under " + f"{last_phase / 'openevolve_output' / 'checkpoints'}" + ) + best_py = best_py_alt + print( + f"[finalize] best/best_program.py missing — using checkpoint " + f"{ck.name} (combined_score={score:.4f})" + ) + + src = best_py.read_text() + header = ( + f"# AUTO-GENERATED by `python -m _lib.finalize {bench_root.parent.name}`.\n" + f"# Source: {best_py.relative_to(bench_root)}\n" + f"# All `_load_prev*()` calls below have been resolved against\n" + f"# `cache/` and replaced with literal dicts so this file is self-\n" + f"# contained and importable without `cache/` present.\n\n" + ) + + replaced = [] + resolved_src = _resolve_load_prev(src, cache_dir, replaced) + if not resolved_src.startswith('"""') and not resolved_src.startswith("'''"): + out_src = header + resolved_src + else: + # Insert header AFTER the module docstring. + match = re.match(r'^(?P"""|\'\'\')(.*?)(?P=q)\s*\n', resolved_src, re.S) + if match: + out_src = resolved_src[: match.end()] + "\n" + header + resolved_src[match.end() :] + else: + out_src = header + resolved_src + + suffix = bench_paths.variant_suffix(bench_root) + final_name = f"final_program_{suffix}.py" if suffix else "final_program.py" + out = bench_root / final_name + out.write_text(out_src) + rel_src = best_py.relative_to(bench_root) + + if replaced: + print(f"[finalize] inlined {len(replaced)} _load_prev*() call(s):") + for name, fname, kind in replaced: + print(f" {name} ← cache/{fname} ({kind})") + else: + print( + "[finalize] no _load_prev*() calls to inline " "(EVOLVE-BLOCK already self-contained)" + ) + print(f"[finalize] {rel_src} → {final_name} ({out.stat().st_size} bytes)") + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + ap.add_argument( + "--from-checkpoints", + action="store_true", + help="scan checkpoint_*/ dirs and pick highest combined_score", + ) + args = ap.parse_args(argv) + finalize(bench_paths.resolve_bench(args.bench), from_checkpoints=args.from_checkpoints) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/load_bench_config.py b/input/_lib/load_bench_config.py new file mode 100644 index 0000000000..794b5c4de2 --- /dev/null +++ b/input/_lib/load_bench_config.py @@ -0,0 +1,102 @@ +""" +Read `bench:` section from a bench's config.yaml and print bash `export` +statements. input/run_phase.sh evals the stdout. + +Usage: + eval "$(python _lib/load_bench_config.py /evolve/config.yaml)" + +Schema (post-refactor; per-bench script names are gone — _lib modules are +invoked directly as `python -m _lib. `): + bench: + phases: + - dir: phase1_x + iters: 60 # optional; falls back to max_iterations + - dir: phase2_y + ... + unified_prepare_before_dir: phase4_unified # optional; fire prepare_phase + # before this dir (default: + # last phase) + solver_check_cmd: 'python -c "from ortools.sat.python import cp_model"' + solver_install_hint: install pip install ortools + +Output (stdout): + export PHASE_DIRS='phase1_x phase2_y ...' + export PHASE_ITERS='60 80 40' # space-separated; missing iters -> empty slot + export UNIFIED_PREPARE_BEFORE_DIR='phase4_unified' + export SOLVER_CHECK_CMD='...' + export SOLVER_INSTALL_HINT='...' + +All values shell-quoted via shlex.quote. Non-zero exit on missing/invalid +`bench` section. +""" +import shlex +import sys + + +def _emit(key, value): + if value is None or value == "": + return + print(f"export {key}={shlex.quote(str(value))}") + + +def main(): + if len(sys.argv) != 2: + print("usage: load_bench_config.py ", file=sys.stderr) + sys.exit(2) + + try: + import yaml + except ImportError as e: + print(f"PyYAML not importable: {e}", file=sys.stderr) + sys.exit(2) + + path = sys.argv[1] + try: + with open(path) as f: + cfg = yaml.safe_load(f) or {} + except FileNotFoundError: + print(f"config not found: {path}", file=sys.stderr) + sys.exit(2) + except yaml.YAMLError as e: + print(f"YAML parse error in {path}: {e}", file=sys.stderr) + sys.exit(2) + + bench = cfg.get("bench") + if not isinstance(bench, dict): + print(f"missing or non-dict `bench:` section in {path}", file=sys.stderr) + sys.exit(2) + + phases = bench.get("phases") + if not isinstance(phases, list) or not phases: + print(f"`bench.phases` must be a non-empty list in {path}", file=sys.stderr) + sys.exit(2) + + dirs = [] + iters = [] + any_iter = False + for i, p in enumerate(phases, start=1): + if not isinstance(p, dict) or "dir" not in p: + print(f"bench.phases[{i-1}] must be a dict with `dir` key", file=sys.stderr) + sys.exit(2) + dirs.append(str(p["dir"])) + it = p.get("iters") + if it is None or it == "": + iters.append("") + else: + iters.append(str(int(it))) + any_iter = True + + _emit("PHASE_DIRS", " ".join(dirs)) + # If no phase has iters set, omit PHASE_ITERS entirely so shell array is empty + # (and config.yaml max_iterations applies). Otherwise emit space-joined list + # with empty slots for phases without explicit iters. + if any_iter: + _emit("PHASE_ITERS", " ".join(iters)) + + _emit("UNIFIED_PREPARE_BEFORE_DIR", bench.get("unified_prepare_before_dir")) + _emit("SOLVER_CHECK_CMD", bench.get("solver_check_cmd")) + _emit("SOLVER_INSTALL_HINT", bench.get("solver_install_hint")) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/params_catalog.py b/input/_lib/params_catalog.py new file mode 100644 index 0000000000..a6fc9bbacf --- /dev/null +++ b/input/_lib/params_catalog.py @@ -0,0 +1,216 @@ +""" +Solver parameter catalog. Loads + validates the per-solver `params.json` +file shipped under `/evolve/params.json`. + +The catalog is the single source of truth for: + - which keys the LLM is allowed to tune (everything in `groups[*].params`) + - default values that anchor the BASELINE (replaces baseline_params.BASELINE) + - locked keys that must not deviate (replaces baseline_params.LOCKED) + - per-key type + range + enum constraints for runtime validation + - LLM-facing reference text rendered into `prompt.system_message` + +Schema (loose validation — extra keys ignored, missing keys fall back to defaults): + + { + "solver": "cpsat", + "version": "", + "defaults": {: }, + "locked": {: }, + "groups": { + "": { + "description": "...", + "params": { + "": { + "type": "int|float|bool|str|enum", + "default": , + "range": [lo, hi], // numeric only, optional + "values": [...], // enum only + "desc": "..." + } + } + } + }, + "subsolver_names": ["..."] // optional, solver-specific + } +""" +import json +import pathlib + + +_NUMERIC_TYPES = {"int", "float"} +_VALID_TYPES = {"int", "float", "bool", "str", "enum", "list"} + + +class Catalog: + def __init__(self, data, source): + self._data = data + self._source = source + self._flat = {} + for gname, group in (data.get("groups") or {}).items(): + for pname, spec in (group.get("params") or {}).items(): + self._flat[pname] = dict(spec, _group=gname) + + @property + def solver(self): + return self._data.get("solver") + + @property + def version(self): + return self._data.get("version", "") + + @property + def defaults(self): + return dict(self._data.get("defaults") or {}) + + @property + def locked(self): + return dict(self._data.get("locked") or {}) + + @property + def subsolver_names(self): + return list(self._data.get("subsolver_names") or []) + + def flat(self): + return dict(self._flat) + + def known_keys(self): + return set(self._flat.keys()) + + def validate(self, params): + """Return list of (key, error_message) tuples; empty list = ok. + + Catches the obvious LLM mistakes without spawning a solver subprocess: + unknown key, wrong type, out-of-range value, unlisted enum value. + Subsolver-name lists (e.g. extra_subsolvers) are checked element-wise + against `subsolver_names` if defined. + """ + errors = [] + known = self.known_keys() + locked = self.locked + defaults = self.defaults + subsolvers = set(self.subsolver_names) + for k, v in params.items(): + if k in locked: + if v != locked[k]: + errors.append((k, f"locked key changed: expected {locked[k]!r}, got {v!r}")) + continue + if k in defaults and k not in known: + continue + spec = self._flat.get(k) + if spec is None: + errors.append((k, "unknown key (not in catalog)")) + continue + err = _check_spec(k, v, spec, subsolvers) + if err: + errors.append((k, err)) + return errors + + def render_prompt_section(self): + """Render the catalog as a compact reference block for the LLM prompt. + + Used to replace the `{{params_reference}}` token in + config.yaml `prompt.system_message`. + """ + lines = [f"=== {self.solver} parameter catalog ({self.version}) ==="] + locked = self.locked + if locked: + lines.append("LOCKED (must not modify): " + ", ".join(sorted(locked.keys()))) + for gname, group in (self._data.get("groups") or {}).items(): + desc = (group.get("description") or "").strip() + lines.append("") + lines.append(f"[{gname}] {desc}") + for pname, spec in (group.get("params") or {}).items(): + lines.append(" " + _format_spec_line(pname, spec)) + if self.subsolver_names: + lines.append("") + lines.append("subsolver names: " + ", ".join(self.subsolver_names)) + return "\n".join(lines) + + +def _check_spec(key, value, spec, subsolvers): + t = spec.get("type") + if t not in _VALID_TYPES: + return None # spec malformed — don't fail the LLM for our mistake + if t == "bool": + if not isinstance(value, bool): + return f"expected bool, got {type(value).__name__}" + return None + if t == "int": + if isinstance(value, bool) or not isinstance(value, int): + return f"expected int, got {type(value).__name__}" + return _check_range(value, spec) + if t == "float": + if isinstance(value, bool) or not isinstance(value, (int, float)): + return f"expected float, got {type(value).__name__}" + return _check_range(value, spec) + if t == "str": + if not isinstance(value, str): + return f"expected str, got {type(value).__name__}" + return None + if t == "enum": + values = spec.get("values") or [] + if value not in values: + return f"value {value!r} not in enum {values!r}" + return None + if t == "list": + if not isinstance(value, list): + return f"expected list, got {type(value).__name__}" + elem_t = spec.get("element_type") + if elem_t == "subsolver" and subsolvers: + bad = [x for x in value if x not in subsolvers] + if bad: + return f"unknown subsolver names: {bad!r}" + return None + return None + + +def _check_range(value, spec): + rng = spec.get("range") + if not rng or len(rng) != 2: + return None + lo, hi = rng + if lo is not None and value < lo: + return f"{value} below range min {lo}" + if hi is not None and value > hi: + return f"{value} above range max {hi}" + return None + + +def _format_spec_line(name, spec): + t = spec.get("type", "?") + d = spec.get("default") + parts = [f"{name}({t}"] + if "range" in spec: + lo, hi = spec["range"] + parts.append(f"=[{lo}..{hi}]") + elif "values" in spec: + parts.append("=" + "|".join(repr(v) for v in spec["values"])) + parts.append(f", default={d!r})") + line = "".join(parts) + desc = (spec.get("desc") or "").strip() + if desc: + line += " — " + desc + return line + + +_cache = {} + + +def load(path): + """Load a params.json file. Result cached by absolute path.""" + path = pathlib.Path(path).resolve() + key = str(path) + if key in _cache: + return _cache[key] + data = json.loads(path.read_text()) + cat = Catalog(data, path) + _cache[key] = cat + return cat + + +def load_for_bench(bench_root): + """Convenience: load `/params.json`. + + `bench_root` is the absolute path to `input//evolve/`. + """ + return load(pathlib.Path(bench_root) / "params.json") diff --git a/input/_lib/prepare_phase.py b/input/_lib/prepare_phase.py new file mode 100644 index 0000000000..700c23b862 --- /dev/null +++ b/input/_lib/prepare_phase.py @@ -0,0 +1,185 @@ +""" +Materialize a unified phase's initial_program.py with the union of +prior-phase winners (from cache/phaseN_best.json + optional +phaseN_buckets.json + phaseN_stage3.json). + +CLI: `python -m _lib.prepare_phase ` + +Reads `/evolve/config.yaml`: + - `bench.phases[*].dir` → ordered phase list + - `bench.unified_prepare_before_dir` (optional) → which phase is the + unified target. Defaults to the last phase. + - `bench.unified_dict_name` (optional) → name of the merged-overrides + dict written into the EVOLVE-BLOCK (default "UNIFIED_OVERRIDES"; + cpsat uses "GLOBAL_OVERRIDES"). + +Rewrites only the EVOLVE-BLOCK section. If a bench ships bucket / stage3 +extracts, the block also gets `SIZE_BUCKETS` + `STAGE3_OVERRIDES`. +""" +import argparse +import json +import pathlib +import pprint +import re +import sys + +from _lib import bench_paths + +_EVOLVE_BLOCK_RE = re.compile( + r"(# EVOLVE-BLOCK-START\n).*?(# EVOLVE-BLOCK-END)", + re.DOTALL, +) + + +def _load(shared, n): + f = pathlib.Path(shared) / f"phase{n}_best.json" + if not f.exists(): + print(f"missing: {f}", file=sys.stderr) + sys.exit(1) + return json.loads(f.read_text()) + + +def _maybe_load(shared, name): + f = pathlib.Path(shared) / name + if not f.exists(): + return None + return json.loads(f.read_text()) + + +def _merge_buckets(prior_buckets_list): + """Merge a list of bucket-lists (one per phase) into a single + list[(upper, dict)], ordered by ascending upper (None == inf last). + Phases later in the list win on key conflicts within the same bucket.""" + # Collect all unique upper bounds, preserving inf -> None. + by_upper = {} + order_seen = [] + for blist in prior_buckets_list: + if not blist: + continue + for upper, override in blist: + key = upper # None for inf, numeric otherwise + if key not in by_upper: + by_upper[key] = {} + order_seen.append(key) + by_upper[key].update(override) + + def _sort_key(k): + return float("inf") if k is None else k + + return [[k, by_upper[k]] for k in sorted(by_upper.keys(), key=_sort_key)] + + +def _format_buckets_literal(buckets): + """Render bucket list as Python source. None upper -> float('inf').""" + lines = ["["] + for upper, override in buckets: + upper_src = "float('inf')" if upper is None else repr(upper) + ov_src = pprint.pformat(override, width=100, sort_dicts=True) + lines.append(f" ({upper_src}, {ov_src}),") + lines.append("]") + return "\n".join(lines) + + +def main_cli(argv=None): + """CLI entry: `python -m _lib.prepare_phase `.""" + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + args = ap.parse_args(argv) + + root = bench_paths.resolve_bench(args.bench) + shared = bench_paths.cache_dir(root) + cfg = bench_paths.load_config(root) + bench_cfg = cfg.get("bench") or {} + phases = [ph["dir"] for ph in (bench_cfg.get("phases") or [])] + if not phases: + raise SystemExit("bench.phases missing in config.yaml") + + target = bench_cfg.get("unified_prepare_before_dir") or phases[-1] + if target not in phases: + raise SystemExit(f"unified_prepare_before_dir={target!r} not in " + f"bench.phases ({phases})") + target_idx = phases.index(target) + if target_idx == 0: + raise SystemExit(f"unified target {target!r} has no prior phases") + prior_phases = list(range(1, target_idx + 1)) + unified_file = root / target / "initial_program.py" + dict_name = bench_cfg.get("unified_dict_name") or "UNIFIED_OVERRIDES" + if not unified_file.exists(): + raise SystemExit(f"unified initial_program.py not found: {unified_file}") + return main(root, shared, prior_phases, unified_file, dict_name=dict_name) + + +def main(root, shared, prior_phases, unified_file, dict_name="UNIFIED_OVERRIDES"): + """Direct invocation (also used by main_cli).""" + unified_file = pathlib.Path(unified_file) + + merged = {} + phase_counts = {} + prior_buckets = [] + merged_stage3 = {} + phase_stage3_counts = {} + + for n in prior_phases: + d = _load(shared, n) + phase_counts[n] = len(d) + merged.update(d) + + b = _maybe_load(shared, f"phase{n}_buckets.json") + if b is not None: + prior_buckets.append(b) + + s = _maybe_load(shared, f"phase{n}_stage3.json") + if isinstance(s, dict): + phase_stage3_counts[n] = len(s) + merged_stage3.update(s) + + print("merged keys: " + " ".join(f"p{n}={c}" for n, c in phase_counts.items()) + + f" union={len(merged)}") + + has_extras = bool(prior_buckets) or bool(merged_stage3) + + src = unified_file.read_text() + + if has_extras: + merged_buckets = _merge_buckets(prior_buckets) + nonempty = sum(1 for _, d in merged_buckets if d) + if phase_stage3_counts: + print("stage3 keys: " + + " ".join(f"p{n}={c}" for n, c in phase_stage3_counts.items()) + + f" union={len(merged_stage3)}") + print(f"size buckets: {len(merged_buckets)} entries ({nonempty} non-empty)") + + dict_repr = pprint.pformat(merged, width=100, sort_dicts=True) + buckets_repr = _format_buckets_literal(merged_buckets) + stage3_repr = pprint.pformat(merged_stage3, width=100, sort_dicts=True) + new_block_body = ( + "# Auto-generated by prepare_phase_unified.py from union of prior " + "phase winners.\n" + f"{dict_name} = {dict_repr}\n" + f"SIZE_BUCKETS = {buckets_repr}\n" + f"STAGE3_OVERRIDES = {stage3_repr}\n" + ) + else: + dict_repr = pprint.pformat(merged, width=100, sort_dicts=True) + new_block_body = ( + "# Auto-generated by prepare_phase_unified.py from union of prior " + "phase winners.\n" + f"{dict_name} = {dict_repr}\n" + ) + + new_src, n_subs = _EVOLVE_BLOCK_RE.subn( + lambda m: m.group(1) + new_block_body + m.group(2), + src, + count=1, + ) + if n_subs != 1: + print(f"EVOLVE-BLOCK markers not found in {unified_file}", file=sys.stderr) + sys.exit(1) + unified_file.write_text(new_src) + suffix = " (+ SIZE_BUCKETS, STAGE3_OVERRIDES)" if has_extras else "" + print(f"wrote {unified_file.relative_to(pathlib.Path(root))} " + f"({len(merged)} keys in EVOLVE-BLOCK){suffix}") + + +if __name__ == "__main__": + main_cli() diff --git a/input/_lib/rebaseline.py b/input/_lib/rebaseline.py new file mode 100644 index 0000000000..c93b720237 --- /dev/null +++ b/input/_lib/rebaseline.py @@ -0,0 +1,290 @@ +""" +Per-host baseline measurement. Runs BASELINE params over the union of +`cache/stage{1..4}_sample.json` and writes `cache/local_baseline.json`. + +CLI: `python -m _lib.rebaseline [--workers 1,8]` + +Default worker counts: + - If `adapter.WORKERS_KEY` is set (e.g. cpsat: "num_search_workers"), + walks sibling `phase*/initial_program.py` and unions every + `PHASE_LOCKED[WORKERS_KEY]`. Output schema includes `by_workers`. + - Otherwise (z3): single measurement, flat schema. + +Each (sha, W) pair runs `evaluation.repeats` times (10 by default) and is +averaged via `_lib.averaging.average_runs`, matching the variant solve path +so dtime ratios stay calibrated. + +Stage3 skip: when W=1 (cpsat-style phase1/2), stage3 outliers can take +hours at low parallelism with no signal value (evaluator skips stage3 for +W=1 phases anyway). Drop stage3 SHAs from the W=1 task list. +""" +import argparse +import importlib.util +import json +import pathlib +import queue as _queue +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed + +from _lib import averaging, bench_paths, params_catalog, runtime, subprocess_runner + +REBASELINE_TIMEOUT_S = 3600 +DEFAULT_REPEATS = 10 + + +def _discover_phase_workers(bench_root, workers_key): + if not workers_key: + return [1] + workers = set() + for prog in sorted(pathlib.Path(bench_root).glob("phase*_*/initial_program.py")): + try: + spec = importlib.util.spec_from_file_location(prog.parent.name, prog) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + except Exception as e: + print(f"WARN: failed to load {prog.name}: {e}", file=sys.stderr) + continue + pl = getattr(mod, "PHASE_LOCKED", None) + if isinstance(pl, dict) and workers_key in pl: + try: + workers.add(int(pl[workers_key])) + except (TypeError, ValueError): + pass + return sorted(workers) if workers else [1] + + +def _parse_workers_arg(s): + out = [] + for tok in s.split(","): + tok = tok.strip() + if not tok: + continue + w = int(tok) + if w < 1: + raise SystemExit(f"--workers: {w} must be >= 1") + if w not in out: + out.append(w) + if not out: + raise SystemExit("--workers: empty list") + return sorted(out) + + +def _load_problem_index(adapter, problems_jsonl): + idx = {} + with open(problems_jsonl) as f: + for line in f: + d = json.loads(line) + sha = d["problem_sha256"] + status = d.get(adapter.STATUS_FIELD) or {} + idx[sha] = { + "sha": sha, + "input_file": d[adapter.PROBLEM_FILE_FIELD], + "raw_ms": status.get("elapsed_ms", 0), + "raw_result": status.get("result"), + } + return idx + + +def _load_target_shas(cache_dir, include_stage3=True): + samples = ["stage1_sample.json", "stage2_sample.json", "stage4_sample.json"] + if include_stage3: + samples.insert(2, "stage3_sample.json") + ids = [] + seen = set() + for fname in samples: + path = cache_dir / fname + if not path.exists(): + print(f"WARN: {fname} missing — skipping", file=sys.stderr) + continue + for sha in json.loads(path.read_text())["sha256"]: + if sha not in seen: + ids.append(sha) + seen.add(sha) + return ids + + +def _solve_one(worker_path, input_path, params, cores_block, repeats): + runs = [] + for _ in range(repeats): + r = subprocess_runner.run_solver( + worker_path=worker_path, + problem_path=input_path, + params=params, + timeout_s=REBASELINE_TIMEOUT_S, + cpu_core=cores_block, + ) + runs.append(r) + if "invalid_param" in r: + break + return averaging.average_runs(runs) + + +def _measure_at_workers(tasks, worker_count, cores, worker_path, baseline_params, workers_key, repeats): + if not tasks: + print(f" W={worker_count}: no tasks", flush=True) + return [] + + if workers_key: + blocks = runtime.alloc_core_blocks(cores, worker_count) + if not blocks: + blocks = [list(cores)] if cores else [None] + else: + blocks = [[c] for c in cores] if cores else [None] + n_parallel = min(len(blocks), len(tasks)) + blocks = blocks[:n_parallel] + + pool = _queue.Queue() + for b in blocks: + pool.put(b) + + params = dict(baseline_params) + if workers_key: + params[workers_key] = worker_count + + def _fmt(b): + if isinstance(b, (list, tuple)): + return ",".join(str(x) for x in b) if b else "-" + return str(b) if b is not None else "-" + + print(f" W={worker_count}: parallel={n_parallel} repeats={repeats} " + f"blocks={[_fmt(b) for b in blocks]}", flush=True) + + def _task(t): + i, meta, path = t + block = pool.get() + try: + res = _solve_one(worker_path, path, params, block, repeats) + finally: + pool.put(block) + return i, meta, res, block + + out = [] + if n_parallel == 1: + for t in tasks: + out.append(_task(t)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_task, t) for t in tasks] + for fut in as_completed(futures): + out.append(fut.result()) + out.sort(key=lambda x: x[0]) + return out + + +def rebaseline(bench_root, workers_override=None): + bench_root = pathlib.Path(bench_root).resolve() + adapter = bench_paths.load_adapter(bench_root) + catalog = params_catalog.load_for_bench(bench_root) + cache = bench_paths.cache_dir(bench_root) + cache.mkdir(parents=True, exist_ok=True) + raw_dir = bench_paths.raw_dir(bench_root) + problems_jsonl = bench_paths.problems_jsonl(bench_root) + worker = bench_paths.worker_path(bench_root) + + eval_cfg = bench_paths.evaluation_cfg(bench_root) + repeats = int(eval_cfg.get("repeats", DEFAULT_REPEATS)) + + workers_key = getattr(adapter, "WORKERS_KEY", None) + if workers_override is not None: + worker_counts = _parse_workers_arg(workers_override) + else: + worker_counts = _discover_phase_workers(bench_root, workers_key) + print(f"[rebaseline] worker counts: {worker_counts} " + f"(workers_key={workers_key!r})") + + baseline = dict(catalog.defaults) + print(f"[rebaseline] BASELINE keys: {sorted(baseline.keys())}") + + idx = _load_problem_index(adapter, problems_jsonl) + + def _build_tasks(shas): + out = [] + for i, sha in enumerate(shas): + meta = idx.get(sha) + if meta is None: + raise SystemExit(f"{sha[:12]} missing from problems.jsonl") + path = raw_dir / meta["input_file"] + if not path.exists(): + raise SystemExit(f"input missing: {path}") + out.append((i, meta, path)) + return out + + shas_full = _load_target_shas(cache, include_stage3=True) + shas_no_s3 = _load_target_shas(cache, include_stage3=False) + tasks_full = _build_tasks(shas_full) + tasks_no_s3 = _build_tasks(shas_no_s3) + + cores = runtime.core_range() + if cores is None: + cores = list(range(1, runtime.parallel_solvers( + bench_paths.config_path(bench_root), default=1) + 1)) + + print(f" {len(tasks_full)} total / {len(tasks_no_s3)} non-stage3") + print(f" per-problem timeout: {REBASELINE_TIMEOUT_S}s, cores={cores}") + + results = {} + for meta in (m for _, m, _ in tasks_full): + results[meta["sha"]] = { + "raw_result": meta["raw_result"], + "raw_elapsed_ms": meta["raw_ms"], + "by_workers": {}, + } + + t0 = time.monotonic() + mismatches = 0 + for w in worker_counts: + tasks = tasks_no_s3 if (workers_key and w == 1) else tasks_full + completed = _measure_at_workers( + tasks, w, cores, worker, baseline, workers_key, repeats) + for i, meta, res, block in completed: + got_result = res.get("result", "Unknown") + got_ms = int(res.get("elapsed_ms", 0)) + invalid = res.get("invalid_param") + ok = (got_result == meta["raw_result"]) and not invalid + if not ok: + mismatches += 1 + ratio = got_ms / max(meta["raw_ms"], 1) + block_str = (",".join(str(x) for x in block) + if isinstance(block, (list, tuple)) else str(block)) + flag = f" INVALID={invalid}" if invalid else ("" if ok else " MISMATCH") + print( + f" [W={w} {i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " + f"raw={meta['raw_result']!s:<10}/{int(meta['raw_ms']):>7}ms " + f"local={got_result!s:<10}/{got_ms:>7}ms ratio={ratio:.2f}x{flag} " + f"cores={block_str}", flush=True + ) + entry = { + "elapsed_ms": got_ms, + "result": got_result, + "matches_raw": ok, + "stats": res.get("stats") or {}, + } + if "objective" in res: + entry["objective"] = res["objective"] + results[meta["sha"]]["by_workers"][str(w)] = entry + + out_path = cache / "local_baseline.json" + out_path.write_text(json.dumps(results, indent=2) + "\n") + n_runs = sum(len(r["by_workers"]) for r in results.values()) + print() + print(f"wrote {out_path.relative_to(bench_root.parent.parent)} " + f"({len(results)} entries, {n_runs} (sha,W) runs, " + f"{mismatches} mismatches)") + print(f"total: {time.monotonic() - t0:.1f}s") + return 0 if mismatches == 0 else 1 + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + ap.add_argument("--workers", type=str, default=None, + help="comma-separated worker counts (e.g. '1,8')") + args = ap.parse_args(argv) + rc = rebaseline(bench_paths.resolve_bench(args.bench), + workers_override=args.workers) + sys.exit(rc) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/runtime.py b/input/_lib/runtime.py new file mode 100644 index 0000000000..c2ae7aaace --- /dev/null +++ b/input/_lib/runtime.py @@ -0,0 +1,116 @@ +""" +Shared runtime knob loader. Used by all bench evolve pipelines. + +Reads custom keys from /evolve/config.yaml (top-level), with env var +override. openevolve's dacite parser silently ignores unknown top-level keys, +so each bench shares the same config file rather than introducing a second. + +Priority: env var > config.yaml > default. + +Callers pass the path to config.yaml. Per-bench `shared/runtime.py` wrappers +resolve their own config path and re-export the helpers below. +""" +import os +import pathlib + +_cache = {} + + +def _load(config_path): + p = pathlib.Path(config_path) + key = str(p) + if key in _cache: + return _cache[key] + if not p.exists(): + _cache[key] = {} + return _cache[key] + try: + import yaml + _cache[key] = yaml.safe_load(p.read_text()) or {} + except Exception: + _cache[key] = {} + return _cache[key] + + +def parallel_solvers(config_path, default=1): + """ + Concurrent solver worker subprocesses per stage. + Env OPENEVOLVE_PARALLEL_SOLVERS > config.yaml parallel_solvers > default. + """ + env = os.environ.get("OPENEVOLVE_PARALLEL_SOLVERS") + if env is not None: + try: + return max(1, int(env)) + except ValueError: + pass + val = _load(config_path).get("parallel_solvers", default) + try: + return max(1, int(val)) + except (ValueError, TypeError): + return default + + +def core_range(): + """ + Parse OPENEVOLVE_CORE_RANGE=START-END (or single "N"). Returns list of + core IDs to lease, or None if env unset. + + Caller decides what to do with None: + - typical default = list(range(1, parallel_solvers() + 1)) + (cores 1..N, core 0 reserved for kernel housekeeping). + + Range size implicitly caps concurrency: n_parallel = len(core_range). + """ + env = os.environ.get("OPENEVOLVE_CORE_RANGE") + if not env: + return None + env = env.strip() + try: + if "-" in env: + lo_s, hi_s = env.split("-", 1) + lo, hi = int(lo_s), int(hi_s) + if lo > hi: + lo, hi = hi, lo + return list(range(lo, hi + 1)) + return [int(env)] + except ValueError: + return None + + +def alloc_core_blocks(cores, workers_per_solve): + """Floor-chunk a core list into blocks of `workers_per_solve` cores each. + + Used when a single solver subprocess must be pinned to W cores (e.g. CP-SAT + with num_search_workers=W). Leftover cores at the tail are dropped — this + keeps every concurrent solve identical in CPU budget so benchmark timings + stay comparable. + + Examples: + alloc_core_blocks([1,2,3,4,5,6], 1) -> [[1],[2],[3],[4],[5],[6]] + alloc_core_blocks([1,2,3,4,5,6], 4) -> [[1,2,3,4]] # 5,6 dropped + alloc_core_blocks([1,2,3,4,5,6], 6) -> [[1,2,3,4,5,6]] + alloc_core_blocks([1,2,3], 4) -> [] # not enough + """ + cores = list(cores) + try: + w = max(1, int(workers_per_solve)) + except (TypeError, ValueError): + w = 1 + n = len(cores) // w + return [cores[i * w:(i + 1) * w] for i in range(n)] + + +def cascade_threshold(config_path, index, default): + """ + Read evaluator.cascade_thresholds[index] from config.yaml. + Used by evaluator.evaluate_stage3 for the internal stage3→stage4 gate + (openevolve cascade hardcodes only 3 stage slots). + """ + cfg = _load(config_path).get("evaluator") or {} + thresholds = cfg.get("cascade_thresholds") or [] + if index < len(thresholds): + try: + return float(thresholds[index]) + except (ValueError, TypeError): + pass + return default diff --git a/input/_lib/sampler.py b/input/_lib/sampler.py new file mode 100644 index 0000000000..5a18c24f01 --- /dev/null +++ b/input/_lib/sampler.py @@ -0,0 +1,385 @@ +""" +Cluster `problems.jsonl` into stage sample files. + +CLI: `python -m _lib.sampler ` (bench dir name under input/). + +Reads `/evolve/config.yaml`'s `bench.clustering` section: + + clustering: + method: kmeans # kmeans | quintile | thresholds + feature: features.num_constraints # dotted path into problem record + n_clusters: 5 # for kmeans / quintile + thresholds: [50000, 150000] # for method=thresholds (n_clusters = len + 1) + max_baseline_ms: 120000 # optional pool cap (drop baselines > this) + stage_sizes: # how many problems each stage keeps + stage1: 10 + stage2: 10 + stage3: 5 + stage4: 20 + stage_clusters: # which cluster IDs (ascending centroid) per stage + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + spread: quintile # quintile | center — how to pick within a stage pool + +Outputs (under `/evolve/cache/`): + stage{1..4}_sample.json {"selection": "...", "sha256": [...], "summary": [...]} + +The decisive-baseline filter is driven by the adapter +(`DECISIVE_RESULTS` + `STATUS_FIELD`). +""" + +import argparse +import json +import pathlib +import sys + +from _lib import bench_paths + + +def _dotted(d, path, default=None): + cur = d + for part in path.split("."): + if not isinstance(cur, dict): + return default + cur = cur.get(part) + if cur is None: + return default + return cur + + +def _kmeans_1d(values, k, max_iter=100): + """1D Lloyd's k-means. Returns label list aligned with values, ordered by + ascending centroid (label 0 = lowest cluster).""" + n = len(values) + if n == 0 or k <= 0: + return [] + if n <= k: + return list(range(n)) # each point its own cluster (sorted ascending) + + sorted_pairs = sorted(enumerate(values), key=lambda iv: iv[1]) + sorted_idx = [i for i, _ in sorted_pairs] + sorted_vals = [v for _, v in sorted_pairs] + centroids = [sorted_vals[((2 * c + 1) * n) // (2 * k)] for c in range(k)] + labels = [0] * n + + for _ in range(max_iter): + changed = False + for i, v in enumerate(sorted_vals): + best, best_d = 0, abs(v - centroids[0]) + for c in range(1, k): + d = abs(v - centroids[c]) + if d < best_d: + best_d, best = d, c + if labels[i] != best: + labels[i] = best + changed = True + if not changed: + break + sums = [0.0] * k + counts = [0] * k + for v, lbl in zip(sorted_vals, labels): + sums[lbl] += v + counts[lbl] += 1 + for c in range(k): + if counts[c] > 0: + centroids[c] = sums[c] / counts[c] + + # Relabel so 0 = smallest centroid. + order = sorted(range(k), key=lambda c: centroids[c]) + rank = {old: new for new, old in enumerate(order)} + out = [0] * n + for sorted_i, lbl in enumerate(labels): + orig_i = sorted_idx[sorted_i] + out[orig_i] = rank[lbl] + return out + + +def _quintile_labels(values, n_buckets): + """Assign each value to a bucket by rank (asc). Equal-size buckets.""" + n = len(values) + if n == 0: + return [] + sorted_pairs = sorted(enumerate(values), key=lambda iv: iv[1]) + labels = [0] * n + for rank, (orig_i, _) in enumerate(sorted_pairs): + b = (rank * n_buckets) // n + if b >= n_buckets: + b = n_buckets - 1 + labels[orig_i] = b + return labels + + +def _threshold_labels(values, thresholds): + """Bucket index = first threshold the value is < (last bucket = >= all).""" + out = [] + for v in values: + b = len(thresholds) + for i, t in enumerate(thresholds): + if v < t: + b = i + break + out.append(b) + return out + + +def _pick_spread(pool, n_pick, mode): + if not pool or n_pick <= 0: + return [] + if len(pool) <= n_pick: + return list(pool) + if mode == "center": + start = (len(pool) - n_pick) // 2 + return pool[start : start + n_pick] + # default: quintile spread within the pool + if n_pick == 1: + return [pool[len(pool) // 2]] + out = [] + seen = set() + for j in range(n_pick): + idx = round(j * (len(pool) - 1) / (n_pick - 1)) + if idx not in seen: + seen.add(idx) + out.append(pool[idx]) + return out + + +def _quartiles(values): + """Return (min, p25, median, p75, max) for a non-empty numeric list.""" + if not values: + return (0, 0, 0, 0, 0) + s = sorted(values) + n = len(s) + + def _pct(p): + idx = max(0, min(n - 1, int(round((n - 1) * p)))) + return s[idx] + + return (s[0], _pct(0.25), _pct(0.5), _pct(0.75), s[-1]) + + +def _fmt_ms(v): + if v >= 1000: + return f"{v / 1000:.1f}s" + return f"{int(v)}ms" + + +def _print_stage_report( + stage_name, picks, pool, cluster_ids, feature_path, baseline_ms, baseline_result, fname +): + pool_n = len(pool) + picks_n = len(picks) + + if picks_n == 0: + print(f" {stage_name} ({fname}): 0 picks from clusters " f"{cluster_ids} (pool={pool_n})") + return + + pick_ms = [baseline_ms(r) for r in picks] + pool_ms = [baseline_ms(r) for r in pool] + pick_feat = [float(_dotted(r, feature_path) or 0) for r in picks] + pool_feat = [float(_dotted(r, feature_path) or 0) for r in pool] + + p_lo, p_q1, p_med, p_q3, p_hi = _quartiles(pick_ms) + o_lo, o_q1, o_med, o_q3, o_hi = _quartiles(pool_ms) + f_lo, _, f_med, _, f_hi = _quartiles(pick_feat) + of_lo, _, of_med, _, of_hi = _quartiles(pool_feat) + + # Per-result count + counts = {} + for r in picks: + counts[baseline_result(r)] = counts.get(baseline_result(r), 0) + 1 + result_str = ", ".join(f"{k}={v}" for k, v in sorted(counts.items())) + + print() + print( + f" {stage_name} ({fname}): {picks_n} picks from " f"clusters {cluster_ids} (pool={pool_n})" + ) + print(f" results: {result_str}") + print( + f" picks baseline_ms: min={_fmt_ms(p_lo)} p25={_fmt_ms(p_q1)} " + f"median={_fmt_ms(p_med)} p75={_fmt_ms(p_q3)} max={_fmt_ms(p_hi)}" + ) + print( + f" pool baseline_ms: min={_fmt_ms(o_lo)} p25={_fmt_ms(o_q1)} " + f"median={_fmt_ms(o_med)} p75={_fmt_ms(o_q3)} max={_fmt_ms(o_hi)}" + ) + print(f" picks {feature_path}: min={int(f_lo)} median={int(f_med)} " f"max={int(f_hi)}") + print(f" pool {feature_path}: min={int(of_lo)} median={int(of_med)} " f"max={int(of_hi)}") + print(" picks:") + for r in picks: + sha = r["problem_sha256"][:12] + ms = baseline_ms(r) + feat = int(_dotted(r, feature_path) or 0) + res = baseline_result(r) + print(f" {sha} {res:<10} {_fmt_ms(ms):>8} " f"{feature_path}={feat}") + + +def build_samples(bench_root, *, adapter=None): + """Sample-build entry. `bench_root` is the absolute path to + `/evolve/`.""" + bench_root = pathlib.Path(bench_root).resolve() + cfg = bench_paths.load_config(bench_root) + cluster_cfg = (cfg.get("bench") or {}).get("clustering") or {} + if not cluster_cfg: + raise SystemExit("bench.clustering missing from config.yaml") + + # Clustering-mode override: `clustering.modes.` is shallow-merged over + # the base block when `clustering.mode` selects it. This is ORTHOGONAL to + # bench.solver_mode — e.g. `mode: large` switches to threshold sampling of + # constraint-heavy instances regardless of optimize/sat. Unset → base block. + mode = cluster_cfg.get("mode") + mode_overrides = (cluster_cfg.get("modes") or {}).get(mode) if mode else None + if mode_overrides: + cluster_cfg = {**cluster_cfg, **mode_overrides} + print(f"clustering: applied modes.{mode} override") + elif mode: + print(f"clustering: mode={mode!r} has no modes.{mode} block — using base") + + if adapter is None: + adapter = bench_paths.load_adapter(bench_root) + + problems_jsonl = bench_root.parent / "problems.jsonl" + if not problems_jsonl.exists(): + raise SystemExit(f"missing {problems_jsonl}") + + cache_dir = bench_paths.cache_dir(bench_root) + cache_dir.mkdir(parents=True, exist_ok=True) + + rows = [] + with open(problems_jsonl) as f: + for line in f: + rows.append(json.loads(line)) + print(f"scanned {len(rows)} problems from {problems_jsonl.name}") + + status_field = adapter.STATUS_FIELD + decisive = set(adapter.DECISIVE_RESULTS) + + def _baseline_ms(r): + return ((r.get(status_field) or {}).get("elapsed_ms")) or 0 + + def _baseline_result(r): + return (r.get(status_field) or {}).get("result") + + max_ms = cluster_cfg.get("max_baseline_ms") + pool = [] + skipped = 0 + for r in rows: + if _baseline_result(r) not in decisive: + skipped += 1 + continue + if max_ms is not None and _baseline_ms(r) > max_ms: + skipped += 1 + continue + pool.append(r) + print(f"decisive + within-budget pool: {len(pool)} (skipped {skipped})") + if not pool: + raise SystemExit("no decisive-baseline problems to sample") + + feature_path = cluster_cfg.get("feature", "features.num_constraints") + feature_vals = [_dotted(r, feature_path) for r in pool] + if any(v is None for v in feature_vals): + missing = sum(1 for v in feature_vals if v is None) + print(f"warning: {missing} problems missing {feature_path}; treating as 0", file=sys.stderr) + feature_vals = [v if v is not None else 0 for v in feature_vals] + feature_vals = [float(v) for v in feature_vals] + + method = cluster_cfg.get("method", "kmeans") + if method == "kmeans": + k = int(cluster_cfg.get("n_clusters", 5)) + labels = _kmeans_1d(feature_vals, k) + n_buckets = k + elif method == "quintile": + k = int(cluster_cfg.get("n_clusters", 5)) + labels = _quintile_labels(feature_vals, k) + n_buckets = k + elif method == "thresholds": + thr = list(cluster_cfg.get("thresholds") or []) + if not thr: + raise SystemExit("clustering.method=thresholds requires " "non-empty `thresholds` list") + labels = _threshold_labels(feature_vals, thr) + n_buckets = len(thr) + 1 + else: + raise SystemExit(f"unknown clustering.method: {method}") + + buckets = [[] for _ in range(n_buckets)] + for r, lbl in zip(pool, labels): + buckets[lbl].append(r) + + # Sort within bucket by runtime ascending — stable spread picks. + for b in buckets: + b.sort(key=_baseline_ms) + + def _range(b): + if not b: + return "empty" + return ( + f"feat={int(_dotted(b[0], feature_path) or 0)}.." + f"{int(_dotted(b[-1], feature_path) or 0)} " + f"ms={int(_baseline_ms(b[0]))}..{int(_baseline_ms(b[-1]))}" + ) + + print("buckets: " + " | ".join(f"c{i}({len(b)},{_range(b)})" for i, b in enumerate(buckets))) + + stage_sizes = cluster_cfg.get("stage_sizes") or {} + stage_clusters = cluster_cfg.get("stage_clusters") or {} + spread = cluster_cfg.get("spread", "quintile") + + print() + print(f"stages (spread={spread}, sizes={stage_sizes}):") + + for stage_name in sorted(stage_sizes.keys()): + n_pick = int(stage_sizes[stage_name]) + cluster_ids = stage_clusters.get(stage_name) or [] + merged = [] + for cid in cluster_ids: + if 0 <= cid < n_buckets: + merged.extend(buckets[cid]) + merged.sort(key=_baseline_ms) + picks = _pick_spread(merged, n_pick, spread) + sample_path = cache_dir / f"{stage_name}_sample.json" + criteria = f"{method}-clusters={cluster_ids} feature={feature_path} " f"spread={spread}" + sample_path.write_text( + json.dumps( + { + "selection": f"{len(picks)} from {len(merged)} candidates", + "criteria": criteria, + "source": str(problems_jsonl.relative_to(bench_root.parent.parent)), + "sha256": [r["problem_sha256"] for r in picks], + "summary": [ + { + "sha": r["problem_sha256"][:12], + "baseline_result": _baseline_result(r), + "baseline_ms": _baseline_ms(r), + feature_path: _dotted(r, feature_path), + } + for r in picks + ], + }, + indent=2, + ) + + "\n" + ) + + _print_stage_report( + stage_name, + picks, + merged, + cluster_ids, + feature_path, + _baseline_ms, + _baseline_result, + sample_path.name, + ) + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name under input/ (e.g. cpsat-bench)") + args = ap.parse_args(argv) + bench_root = bench_paths.resolve_bench(args.bench) + build_samples(bench_root) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/scorer.py b/input/_lib/scorer.py new file mode 100644 index 0000000000..ae4af1b7a8 --- /dev/null +++ b/input/_lib/scorer.py @@ -0,0 +1,255 @@ +""" +Unified scoring. Modes: + + speedup (z3 default): + combined = weighted_geomean(speedup) * solved_rate^2 * efficiency^stats_weight + speedup = baseline_ms / variant_ms + per-problem weight = baseline_ms (long problems dominate) + regression (baseline decisive, variant mismatch) contributes 1e-6. + + cost (cpsat default): + combined = unweighted_geomean(cost_ratio^cw * time_ratio) * solved_rate^2 * efficiency^sw + time_ratio = baseline_dtime / variant_dtime (when both sides report + deterministic_time; falls back to wall ratio) + cost_ratio = (baseline_obj + eps) / (variant_obj + eps) [minimize] + non-decisive variant: contributes its real (slow) time_ratio + (NOT a 1e-6 sentinel); solved_rate^2 penalizes. + uncomparable (baseline non-decisive): excluded from geomean entirely. + +Efficiency factor: cross-problem geomean of per-problem weighted geomean +over a configurable stat-key set, each ratio (baseline+1)/(variant+1) +clipped to [0.1, 10]. Mirror of the old per-bench score.py logic. + +Env overrides honored: + OPENEVOLVE_STATS_WEIGHT exponent on efficiency (default 0.333) + OPENEVOLVE_COST_WEIGHT exponent on cost_ratio (cost mode only) + OPENEVOLVE_TIME_METRIC "dtime" (default) | "wall" — cost mode only +""" +import math +import os + + +_RATIO_CLIP_LO = 0.1 +_RATIO_CLIP_HI = 10.0 +_COST_RATIO_CLIP_LO = 0.01 +_COST_RATIO_CLIP_HI = 100.0 +_COST_EPS = 1e-9 + + +def _efficiency(per_problem, stats_weights): + per_prob_effs = [] + for p in per_problem: + if p["result"] != p["baseline_result"]: + continue + bs = p.get("baseline_stats") or {} + vs = p.get("stats") or {} + log_sum = 0.0 + w_sum = 0.0 + for k, w in stats_weights.items(): + b = bs.get(k) + v = vs.get(k) + if b is None or v is None: + continue + r = (float(b) + 1.0) / (float(v) + 1.0) + r = max(_RATIO_CLIP_LO, min(_RATIO_CLIP_HI, r)) + log_sum += w * math.log(r) + w_sum += w + if w_sum > 0: + per_prob_effs.append(math.exp(log_sum / w_sum)) + if not per_prob_effs: + return 1.0, 0 + log_sum = sum(math.log(e) for e in per_prob_effs) + return math.exp(log_sum / len(per_prob_effs)), len(per_prob_effs) + + +def _stats_weight_env(default): + try: + v = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", str(default))) + except ValueError: + return 0.0 + return max(0.0, min(v, 2.0)) + + +def _score_speedup(per_problem, decided_set): + """ + z3-style: weighted geomean(speedup) * solved_rate^2. + Mismatch (decided baseline → wrong variant) = 1e-6 contribution + regression++. + """ + speedups = [] + weights = [] + solved = 0 + regressions = 0 + for p in per_problem: + baseline_decided = p["baseline_result"] in decided_set + match = p["result"] == p["baseline_result"] + w = max(float(p["baseline_ms"]), 1.0) + weights.append(w) + if match: + solved += 1 + speedups.append(max(p["baseline_ms"], 1) / max(p["elapsed_ms"], 1)) + else: + speedups.append(1e-6) + if baseline_decided and p["result"] in decided_set: + regressions += 1 + if not speedups: + return 1.0, 1.0, 0.0, 0, 0, 0, 0, 0 + w_total = sum(weights) + log_sum = sum(w * math.log(s) for s, w in zip(speedups, weights)) + geomean = math.exp(log_sum / w_total) + solved_rate = solved / len(per_problem) + return (geomean, geomean, solved_rate, solved, regressions, + len(per_problem), 0, 0) + + +def _time_ratio(p, metric): + bs = p.get("baseline_stats") or {} + vs = p.get("stats") or {} + if metric == "dtime": + b_dt = bs.get("deterministic_time") + v_dt = vs.get("deterministic_time") + if b_dt and v_dt and b_dt > 0 and v_dt > 0: + return float(b_dt) / float(v_dt), "dtime" + return max(p["baseline_ms"], 1) / max(p["elapsed_ms"], 1), "wall" + + +def _score_cost(per_problem, decisive_set, time_metric, cost_weight): + """ + cpsat-style: cost-aware geomean using deterministic_time when available. + Uncomparable (baseline non-decisive) is excluded from geomean. + """ + ratios = [] + wall_ratios = [] + solved = 0 + regressions = 0 + comparable = 0 + dtime_used = 0 + dtime_fallback = 0 + for p in per_problem: + if p["baseline_result"] not in decisive_set: + continue + comparable += 1 + v_ok = p["result"] in decisive_set + b_cost = p.get("baseline_objective") + v_cost = p.get("objective") + time_r, src = _time_ratio(p, time_metric) + wall_r = max(p["baseline_ms"], 1) / max(p["elapsed_ms"], 1) + if time_metric == "dtime": + if src == "dtime": + dtime_used += 1 + else: + dtime_fallback += 1 + if v_ok: + solved += 1 + if b_cost is not None and v_cost is not None: + cost_r = (float(b_cost) + _COST_EPS) / (float(v_cost) + _COST_EPS) + cost_r = max(_COST_RATIO_CLIP_LO, min(_COST_RATIO_CLIP_HI, cost_r)) + ratios.append((cost_r ** cost_weight) * time_r) + wall_ratios.append((cost_r ** cost_weight) * wall_r) + else: + ratios.append(time_r) + wall_ratios.append(wall_r) + else: + ratios.append(time_r) + wall_ratios.append(wall_r) + regressions += 1 + if not ratios: + return 1.0, 1.0, 0.0, 0, 0, 0, 0, 0 + geomean = math.exp(sum(math.log(r) for r in ratios) / len(ratios)) + geomean_wall = math.exp(sum(math.log(r) for r in wall_ratios) / len(wall_ratios)) + solved_rate = solved / comparable + return (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) + + +def score(per_problem, *, + mode="speedup", + decisive_results=("Sat", "Unsat"), + decided_results=None, + stats_weights=None, + stats_weight_default=0.333, + time_metric=None, + cost_weight=None): + """ + Unified scoring entry point. + + Args: + per_problem: list of dicts each with keys + sha, baseline_result, baseline_ms, baseline_stats, [baseline_objective], + result, elapsed_ms, stats, [objective] + mode: "speedup" or "cost" + decisive_results: tuple of result strings the SOLVER counts as decisive. + cpsat → ("OPTIMAL","FEASIBLE"); z3 → ("Sat","Unsat"). + decided_results: tuple of results that count as a "definitive answer" + for regression detection in speedup mode. Defaults to decisive_results. + stats_weights: {stat_key: weight} for efficiency factor. None → no + efficiency contribution. + stats_weight_default: default exponent on efficiency (env override wins). + time_metric: "dtime"|"wall" — cost mode only. Default → env or "dtime". + cost_weight: float — cost mode only. Default → env or 1.0. + + Returns metrics dict (always populated): + combined_score, geomean_speedup, geomean_wall_speedup, solved_rate, + regressions, solved, comparable, total, uncomparable, + efficiency, efficiency_pairs, stats_weight, + dtime_used, dtime_fallback + """ + n = len(per_problem) + if stats_weights is None: + stats_weights = {} + if decided_results is None: + decided_results = decisive_results + decisive_set = set(decisive_results) + decided_set = set(decided_results) + + if n == 0: + return { + "combined_score": 0.0, + "geomean_speedup": 0.0, + "geomean_wall_speedup": 0.0, + "solved_rate": 0.0, + "regressions": 0, "solved": 0, "comparable": 0, "total": 0, + "uncomparable": 0, + "efficiency": 1.0, "efficiency_pairs": 0, + "stats_weight": 0.0, + "dtime_used": 0, "dtime_fallback": 0, + } + + if mode == "cost": + if time_metric is None: + time_metric = (os.environ.get("OPENEVOLVE_TIME_METRIC", "dtime").lower()) + if time_metric not in ("dtime", "wall"): + time_metric = "dtime" + if cost_weight is None: + try: + cost_weight = float(os.environ.get("OPENEVOLVE_COST_WEIGHT", "1.0")) + except ValueError: + cost_weight = 1.0 + cost_weight = max(0.0, min(cost_weight, 2.0)) + (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) = _score_cost( + per_problem, decisive_set, time_metric, cost_weight) + else: + (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) = _score_speedup( + per_problem, decided_set) + + efficiency, eff_pairs = _efficiency(per_problem, stats_weights) + stats_weight = _stats_weight_env(stats_weight_default) + + combined = geomean * (solved_rate ** 2.0) * (efficiency ** stats_weight) + return { + "combined_score": float(combined), + "geomean_speedup": float(geomean), + "geomean_wall_speedup": float(geomean_wall), + "solved_rate": float(solved_rate), + "regressions": int(regressions), + "solved": int(solved), + "comparable": int(comparable), + "total": int(n), + "uncomparable": int(n - comparable), + "efficiency": float(efficiency), + "efficiency_pairs": int(eff_pairs), + "stats_weight": float(stats_weight), + "dtime_used": int(dtime_used), + "dtime_fallback": int(dtime_fallback), + } diff --git a/input/_lib/self_test.py b/input/_lib/self_test.py new file mode 100644 index 0000000000..e7378d2c8c --- /dev/null +++ b/input/_lib/self_test.py @@ -0,0 +1,128 @@ +""" +Generic BASELINE stage1 sanity test. Runs each stage1 problem with the +catalog `defaults` once and reports ratio vs the recorded baseline_ms. + +CLI: `python -m _lib.self_test ` + +OK ratio in [0.5, 2.0] +WARN ratio out of band +FAIL result mismatch / invalid_param +""" +import argparse +import json +import math +import pathlib +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed + +from _lib import bench_paths, params_catalog, runtime, subprocess_runner + + +def run_self_test(bench_root): + bench_root = pathlib.Path(bench_root).resolve() + adapter = bench_paths.load_adapter(bench_root) + catalog = params_catalog.load_for_bench(bench_root) + cache = bench_paths.cache_dir(bench_root) + raw_dir = bench_paths.raw_dir(bench_root) + problems_jsonl = bench_paths.problems_jsonl(bench_root) + worker = bench_paths.worker_path(bench_root) + + stage1 = cache / "stage1_sample.json" + if not stage1.exists(): + print(f"ERROR: {stage1} missing — run sampler first", file=sys.stderr) + return 2 + + shas = list(json.loads(stage1.read_text())["sha256"]) + idx = {} + with open(problems_jsonl) as f: + for line in f: + d = json.loads(line) + status = d.get(adapter.STATUS_FIELD) or {} + idx[d["problem_sha256"]] = { + "input_file": d[adapter.PROBLEM_FILE_FIELD], + "baseline_ms": status.get("elapsed_ms", 0), + "baseline_result": status.get("result"), + } + + tasks = [] + for i, sha in enumerate(shas): + meta = idx.get(sha) + if meta is None: + print(f"ERROR: {sha[:12]} missing from problems.jsonl", file=sys.stderr) + return 2 + path = raw_dir / meta["input_file"] + if not path.exists(): + print(f"ERROR: input missing: {path}", file=sys.stderr) + return 2 + tasks.append((i, sha, meta, path)) + + cores = runtime.core_range() + if cores is None: + cores = list(range(1, runtime.parallel_solvers( + bench_paths.config_path(bench_root), default=5) + 1)) + n_parallel = min(len(cores), len(tasks)) + cores = cores[:n_parallel] + + print(f"BASELINE self-test ({adapter.SOLVER_NAME}): {len(tasks)} stage1 " + f"problems, parallel={n_parallel} cores={cores}") + print() + + baseline = dict(catalog.defaults) + + def _solve(t): + i, sha, meta, path = t + timeout_s = max(30, math.ceil(meta["baseline_ms"] * 2 / 1000)) + core = cores[i % n_parallel] if cores else None + return i, sha, meta, subprocess_runner.run_solver( + worker_path=worker, problem_path=path, params=baseline, + timeout_s=timeout_s, cpu_core=core) + + t0 = time.monotonic() + results = [] + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futs = [ex.submit(_solve, t) for t in tasks] + for f in as_completed(futs): + results.append(f.result()) + elapsed = time.monotonic() - t0 + results.sort(key=lambda x: x[0]) + + print(f"{'sha':<14}{'base_res':<10}{'got_res':<10}" + f"{'base_ms':>10}{'got_ms':>10}{'ratio':>8} core status") + print("-" * 84) + fail = warn = 0 + for i, sha, meta, r in results: + got = r.get("result", "Unknown") + got_ms = int(r.get("elapsed_ms", 0)) + ratio = got_ms / max(meta["baseline_ms"], 1) + invalid = r.get("invalid_param") + if invalid: + status = f"FAIL(invalid={invalid})" + fail += 1 + elif got != meta["baseline_result"]: + status = "FAIL" + fail += 1 + elif not (0.5 <= ratio <= 2.0): + status = "WARN" + warn += 1 + else: + status = "OK" + print(f"{sha[:12]:<14}{str(meta['baseline_result']):<10}{str(got):<10}" + f"{int(meta['baseline_ms']):>10}{got_ms:>10}{ratio:>7.2f}x " + f"{cores[i % n_parallel]:>4} {status}") + + print() + print(f"wall-clock: {elapsed:.1f}s") + print(f"summary: {len(results) - fail - warn} ok, {warn} warn, {fail} fail") + return 0 if fail == 0 else 1 + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("bench", help="bench dir name (e.g. cpsat-bench)") + args = ap.parse_args(argv) + sys.exit(run_self_test(bench_paths.resolve_bench(args.bench))) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/subprocess_runner.py b/input/_lib/subprocess_runner.py new file mode 100644 index 0000000000..7c87dbef38 --- /dev/null +++ b/input/_lib/subprocess_runner.py @@ -0,0 +1,115 @@ +""" +Generic subprocess solver runner. Spawn a worker script as a subprocess and +parse a JSON dict from its stdout. Per-bench `_runner.py` wrappers +call `run_solver(WORKER_PATH, ...)` and re-export under solver-specific names. + +Contract for the worker (any language, must print JSON): + argv[1] json.dumps(params) + argv[2] str(problem_path) + argv[3] str(int(timeout_s)) + + stdout (last non-empty line, JSON): + success: {"result": , "elapsed_ms": int, "stats": dict, ...} + timeout: {"result": "Unknown", "elapsed_ms": int, "timeout": True, "stats": {}} + invalid: {"invalid_param": str, "error": str, "result": "Unknown", "elapsed_ms": int} + crash: {"result": "Unknown", "elapsed_ms": int, "error": str} + +This parent layer adds: + - hard wall-clock timeout (timeout_s + 15s grace) + - optional taskset core pin (Linux only; silently ignored if missing) + - defensive last-line parsing (defensive against stray worker prints) + +Return shape — always a dict, one of: + {"result", "elapsed_ms", "stats", ...} (success, may include "objective") + {"result": "Unknown", "elapsed_ms", "timeout": True, "stats": {}} + {"invalid_param", "stderr", "result": "Unknown", "elapsed_ms", "stats": {}} + {"result": "Unknown", "elapsed_ms", "error", "stderr", "stats": {}} +""" +import json +import shutil +import subprocess +import sys +import time + +_TASKSET = shutil.which("taskset") + + +def run_solver(worker_path, problem_path, params, timeout_s, + python_bin=None, cpu_core=None, grace_s=15): + """ + See module docstring for the contract. + + cpu_core: optional Linux taskset pin. Accepts: + - int → pin to one core (e.g. 3 → "taskset -c 3") + - Sequence[int] / str → pin to a set of cores (e.g. [2,3,4,5] or + "2-5,7" → "taskset -c 2,3,4,5" / "2-5,7"). + Ignored on macOS / no util-linux. + grace_s: subprocess timeout = timeout_s + grace_s. + """ + py = python_bin or sys.executable + args = [py, str(worker_path), json.dumps(params), + str(problem_path), str(int(timeout_s))] + if cpu_core is not None and _TASKSET: + if isinstance(cpu_core, str): + cpu_arg = cpu_core + elif isinstance(cpu_core, (list, tuple)): + if not cpu_core: + cpu_arg = None + else: + cpu_arg = ",".join(str(int(c)) for c in cpu_core) + else: + cpu_arg = str(int(cpu_core)) + if cpu_arg: + args = [_TASKSET, "-c", cpu_arg] + args + + t0 = time.monotonic() + try: + proc = subprocess.run( + args, + capture_output=True, + text=True, + timeout=timeout_s + grace_s, + ) + except subprocess.TimeoutExpired: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "timeout": True, + "stats": {}, + } + + stdout = (proc.stdout or "").strip() + stderr = (proc.stderr or "").strip() + + if not stdout: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker produced no output (rc={proc.returncode})", + "stderr": stderr[-2000:], + "stats": {}, + } + + # Last non-empty line as JSON (defensive against stray worker prints). + last = stdout.splitlines()[-1] + try: + out = json.loads(last) + except json.JSONDecodeError as e: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker json decode: {e}", + "stderr": (stderr + "\n--stdout--\n" + stdout)[-2000:], + "stats": {}, + } + + if "invalid_param" in out: + return { + "invalid_param": out["invalid_param"], + "stderr": (out.get("error") or stderr)[-2000:], + "result": "Unknown", + "elapsed_ms": out.get("elapsed_ms", 0), + "stats": {}, + } + out.setdefault("stats", {}) + return out diff --git a/input/cpsat-bench/Statistics/analyze.py b/input/cpsat-bench/Statistics/analyze.py new file mode 100644 index 0000000000..d47421223a --- /dev/null +++ b/input/cpsat-bench/Statistics/analyze.py @@ -0,0 +1,497 @@ +#!/usr/bin/env python3 +"""CP-SAT raw-data statistical analysis + visualization. + +Scans ../raw-data/*.meta.jsonl and produces: + - report.txt : text report with distributions + correlations + - per_instance.csv : flat table for spreadsheet exploration + - plots/*.png : histograms, scatters, boxplots, heatmap + +Metrics analyzed: + - cpsat_status.elapsed_ms (runtime) + - features.num_variables / num_bool / num_int + - features.num_constraints + +Usage: + .venv/bin/python Statistics/analyze.py + .venv/bin/python Statistics/analyze.py --raw-dir --out +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import sys +from collections import defaultdict +from pathlib import Path +from typing import Iterable + +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.colors import LogNorm + +HERE = Path(__file__).resolve().parent +DEFAULT_RAW = HERE.parent / "raw-data" +DEFAULT_OUT = HERE + + +# ---------- loading ---------- + +def has_objective_set(raw_dir: Path) -> set[str] | None: + """Return set of SHAs whose .cpsat.pb carries an objective. None if + ortools unavailable.""" + try: + from ortools.sat import cp_model_pb2 + except ImportError: + return None + out: set[str] = set() + for p in sorted(raw_dir.glob("*.cpsat.pb")): + m = cp_model_pb2.CpModelProto() + m.ParseFromString(p.read_bytes()) + if m.HasField("objective") or m.HasField("floating_point_objective"): + out.add(p.name[: -len(".cpsat.pb")]) + return out + + +def load_meta(raw_dir: Path) -> list[dict]: + rows: list[dict] = [] + for path in sorted(raw_dir.glob("*.meta.jsonl")): + with path.open() as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rows.append(json.loads(line)) + except json.JSONDecodeError as e: + print(f"WARN bad json {path.name}: {e}", file=sys.stderr) + return rows + + +def extract(rows: list[dict]) -> list[dict]: + out = [] + for r in rows: + feats = r.get("features") or {} + status = r.get("cpsat_status") or {} + applied = r.get("cpsat_applied_params") or {} + stats = r.get("cpsat_response_stats") or {} + out.append( + { + "problem_sha256": r.get("problem_sha256"), + "applied_params_hash": r.get("applied_params_hash"), + "status": status.get("result"), + "elapsed_ms": status.get("elapsed_ms"), + "num_variables": feats.get("num_variables"), + "num_bool": feats.get("num_bool"), + "num_int": feats.get("num_int"), + "num_constraints": feats.get("num_constraints"), + "num_workers": applied.get("num_search_workers"), + "num_conflicts": stats.get("num_conflicts"), + "num_branches": stats.get("num_branches"), + "deterministic_time": stats.get("deterministic_time"), + } + ) + return out + + +# ---------- stats ---------- + +def summarize(values: Iterable, label: str) -> dict: + arr = np.array( + [v for v in values if v is not None and not (isinstance(v, float) and math.isnan(v))], + dtype=float, + ) + if arr.size == 0: + return {"label": label, "count": 0} + return { + "label": label, + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std(ddof=1)) if arr.size > 1 else 0.0, + "min": float(arr.min()), + "p25": float(np.percentile(arr, 25)), + "median": float(np.percentile(arr, 50)), + "p75": float(np.percentile(arr, 75)), + "p95": float(np.percentile(arr, 95)), + "max": float(arr.max()), + "sum": float(arr.sum()), + } + + +def _fmt(v) -> str: + if isinstance(v, float): + if abs(v) >= 1000: + return f"{v:,.1f}" + return f"{v:.3f}" + return str(v) + + +def table(summaries: list[dict]) -> str: + cols = ["label", "count", "mean", "std", "min", "p25", "median", "p75", "p95", "max"] + widths = {c: max(len(c), 10) for c in cols} + for s in summaries: + for c in cols: + widths[c] = max(widths[c], len(_fmt(s.get(c, "")))) + lines = [] + lines.append(" | ".join(c.rjust(widths[c]) for c in cols)) + lines.append("-+-".join("-" * widths[c] for c in cols)) + for s in summaries: + lines.append(" | ".join(_fmt(s.get(c, "")).rjust(widths[c]) for c in cols)) + return "\n".join(lines) + + +def correlations(records: list[dict]) -> dict: + def pairs(key: str): + xs, ys = [], [] + for r in records: + x, y = r.get(key), r.get("elapsed_ms") + if x is None or y is None: + continue + xs.append(x) + ys.append(y) + return np.array(xs, dtype=float), np.array(ys, dtype=float) + + out = {} + for key in ( + "num_variables", + "num_bool", + "num_int", + "num_constraints", + "num_conflicts", + "num_branches", + ): + x, y = pairs(key) + if x.size < 2 or np.std(x) == 0 or np.std(y) == 0: + out[key] = {"n": int(x.size), "pearson": None, "spearman": None} + continue + pearson = float(np.corrcoef(x, y)[0, 1]) + rx = np.argsort(np.argsort(x)) + ry = np.argsort(np.argsort(y)) + spearman = float(np.corrcoef(rx, ry)[0, 1]) + out[key] = {"n": int(x.size), "pearson": pearson, "spearman": spearman} + return out + + +def status_breakdown(records: list[dict]) -> dict[str, int]: + counts: dict[str, int] = defaultdict(int) + for r in records: + counts[r.get("status") or "UNKNOWN"] += 1 + return dict(sorted(counts.items(), key=lambda kv: -kv[1])) + + +def per_hash_groups(records: list[dict]) -> list[tuple[str, list[dict]]]: + groups: dict[str, list[dict]] = defaultdict(list) + for r in records: + groups[r.get("applied_params_hash") or "UNKNOWN"].append(r) + return sorted(groups.items(), key=lambda kv: -len(kv[1])) + + +def write_report(records: list[dict], out_path: Path) -> None: + lines: list[str] = [] + lines.append("# CP-SAT raw-data statistical report") + lines.append(f"instances: {len(records)}") + lines.append( + f"unique problems: {len({r['problem_sha256'] for r in records if r.get('problem_sha256')})}" + ) + lines.append( + f"unique applied_params_hash: {len({r['applied_params_hash'] for r in records if r.get('applied_params_hash')})}" + ) + + lines.append("\n## Status breakdown") + for k, v in status_breakdown(records).items(): + lines.append(f" {k:12s} {v}") + + lines.append("\n## Overall distributions") + metrics = [ + ("elapsed_ms", [r["elapsed_ms"] for r in records]), + ("num_variables", [r["num_variables"] for r in records]), + ("num_bool", [r["num_bool"] for r in records]), + ("num_int", [r["num_int"] for r in records]), + ("num_constraints", [r["num_constraints"] for r in records]), + ] + lines.append(table([summarize(vs, name) for name, vs in metrics])) + + lines.append("\n## Runtime correlations (Pearson / Spearman vs elapsed_ms)") + corr = correlations(records) + lines.append(f"{'feature':22s} {'n':>6s} {'pearson':>10s} {'spearman':>10s}") + for k, v in corr.items(): + p = f"{v['pearson']:.4f}" if v["pearson"] is not None else "n/a" + s = f"{v['spearman']:.4f}" if v["spearman"] is not None else "n/a" + lines.append(f"{k:22s} {v['n']:>6d} {p:>10s} {s:>10s}") + + lines.append("\n## Per applied_params_hash") + for h, recs in per_hash_groups(records): + lines.append(f"\n### hash={h} n={len(recs)}") + sub = [ + ("elapsed_ms", [r["elapsed_ms"] for r in recs]), + ("num_variables", [r["num_variables"] for r in recs]), + ("num_constraints", [r["num_constraints"] for r in recs]), + ] + lines.append(table([summarize(vs, name) for name, vs in sub])) + sb = status_breakdown(recs) + lines.append(" status: " + ", ".join(f"{k}={v}" for k, v in sb.items())) + + # size-bucketed runtime + arr = np.array([r["num_variables"] for r in records if r.get("num_variables") is not None], dtype=float) + if arr.size: + edges = np.percentile(arr, [0, 25, 50, 75, 100]) + lines.append("\n## Runtime by num_variables quartile") + lines.append(f"edges: {[int(e) for e in edges]}") + sums = [] + for i in range(4): + lo, hi = edges[i], edges[i + 1] + bucket = [] + for r in records: + v, ms = r.get("num_variables"), r.get("elapsed_ms") + if v is None or ms is None: + continue + in_range = lo <= v <= hi if i == 3 else lo <= v < hi + if in_range: + bucket.append(ms) + sums.append(summarize(bucket, f"Q{i + 1}[{int(lo)}..{int(hi)}]")) + lines.append(table(sums)) + + out_path.write_text("\n".join(lines) + "\n") + print(f"wrote {out_path}") + + +def write_csv(records: list[dict], path: Path) -> None: + if not records: + return + fields = list(records[0].keys()) + with path.open("w", newline="") as f: + w = csv.DictWriter(f, fieldnames=fields) + w.writeheader() + for r in records: + w.writerow(r) + print(f"wrote {path}") + + +# ---------- viz ---------- + +STATUS_COLORS = { + "OPTIMAL": "#1f77b4", + "FEASIBLE": "#2ca02c", + "INFEASIBLE": "#d62728", + "UNKNOWN": "#7f7f7f", + "MODEL_INVALID": "#ff7f0e", +} + + +def _color_for(status: str | None) -> str: + return STATUS_COLORS.get(status or "UNKNOWN", "#9467bd") + + +def _safe_log_array(values, floor=1e-6): + arr = np.array([v for v in values if v is not None and v > 0], dtype=float) + arr[arr <= 0] = floor + return arr + + +def plot_hist_runtime(records, out: Path) -> None: + vals = _safe_log_array([r["elapsed_ms"] for r in records]) + if vals.size == 0: + return + fig, ax = plt.subplots(figsize=(8, 5)) + bins = np.logspace(np.log10(max(vals.min(), 1.0)), np.log10(vals.max()), 40) + ax.hist(vals, bins=bins, color="#1f77b4", edgecolor="black", alpha=0.85) + ax.set_xscale("log") + ax.set_xlabel("elapsed_ms (log)") + ax.set_ylabel("count") + ax.set_title(f"Runtime distribution (n={vals.size})") + ax.axvline(np.median(vals), color="red", linestyle="--", label=f"median={np.median(vals):.0f} ms") + ax.axvline(np.mean(vals), color="orange", linestyle="--", label=f"mean={np.mean(vals):.0f} ms") + ax.legend() + fig.tight_layout() + fig.savefig(out, dpi=120) + plt.close(fig) + + +def plot_hist_features(records, out: Path) -> None: + fig, axes = plt.subplots(1, 3, figsize=(15, 4.5)) + spec = [ + ("num_variables", "#1f77b4"), + ("num_bool", "#2ca02c"), + ("num_constraints", "#d62728"), + ] + for ax, (key, color) in zip(axes, spec): + vals = np.array( + [r[key] for r in records if r.get(key) is not None and r[key] > 0], + dtype=float, + ) + if vals.size == 0: + ax.set_title(f"{key} (empty)") + continue + bins = np.logspace(np.log10(max(vals.min(), 1.0)), np.log10(vals.max()), 30) + ax.hist(vals, bins=bins, color=color, edgecolor="black", alpha=0.85) + ax.set_xscale("log") + ax.set_xlabel(f"{key} (log)") + ax.set_ylabel("count") + ax.set_title(f"{key} (median={int(np.median(vals)):,})") + fig.tight_layout() + fig.savefig(out, dpi=120) + plt.close(fig) + + +def plot_scatter_runtime(records, out: Path) -> None: + fig, axes = plt.subplots(1, 2, figsize=(13, 5.5)) + for ax, key in zip(axes, ("num_variables", "num_constraints")): + xs, ys, cs = [], [], [] + for r in records: + x, y = r.get(key), r.get("elapsed_ms") + if x is None or y is None or x <= 0 or y <= 0: + continue + xs.append(x) + ys.append(y) + cs.append(_color_for(r.get("status"))) + ax.scatter(xs, ys, c=cs, alpha=0.7, s=22, edgecolors="black", linewidths=0.3) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel(f"{key} (log)") + ax.set_ylabel("elapsed_ms (log)") + if len(xs) > 2: + lx, ly = np.log(xs), np.log(ys) + slope, intercept = np.polyfit(lx, ly, 1) + xx = np.linspace(min(lx), max(lx), 50) + ax.plot(np.exp(xx), np.exp(slope * xx + intercept), color="black", linestyle="--", linewidth=1, + label=f"log-fit slope={slope:.2f}") + pearson = np.corrcoef(lx, ly)[0, 1] + ax.set_title(f"elapsed_ms vs {key} (log-log r={pearson:.3f})") + ax.legend(loc="upper left", fontsize=9) + else: + ax.set_title(f"elapsed_ms vs {key}") + + # status legend + handles = [plt.Line2D([0], [0], marker="o", linestyle="", color=c, label=k) + for k, c in STATUS_COLORS.items()] + fig.legend(handles=handles, loc="lower center", ncol=len(STATUS_COLORS), fontsize=8, frameon=False) + fig.tight_layout(rect=(0, 0.05, 1, 1)) + fig.savefig(out, dpi=120) + plt.close(fig) + + +def plot_box_by_group(records, key: str, title: str, out: Path) -> None: + groups: dict[str, list[float]] = defaultdict(list) + for r in records: + if r.get("elapsed_ms") and r["elapsed_ms"] > 0: + groups[str(r.get(key) or "UNKNOWN")].append(r["elapsed_ms"]) + if not groups: + return + items = sorted(groups.items(), key=lambda kv: -np.median(kv[1])) + fig, ax = plt.subplots(figsize=(max(7, 1.0 + 0.9 * len(items)), 5)) + data = [v for _, v in items] + labels = [k for k, _ in items] + bp = ax.boxplot(data, tick_labels=labels, showfliers=True, patch_artist=True) + for patch in bp["boxes"]: + patch.set_facecolor("#9ecae1") + ax.set_yscale("log") + ax.set_ylabel("elapsed_ms (log)") + ax.set_title(title) + ax.tick_params(axis="x", rotation=20) + for i, (label, vals) in enumerate(items, start=1): + ax.text(i, np.median(vals), f"n={len(vals)}", ha="center", va="bottom", fontsize=8) + fig.tight_layout() + fig.savefig(out, dpi=120) + plt.close(fig) + + +def plot_heatmap_size(records, out: Path) -> None: + xs, ys, ms = [], [], [] + for r in records: + v, c, t = r.get("num_variables"), r.get("num_constraints"), r.get("elapsed_ms") + if not v or not c or not t or v <= 0 or c <= 0 or t <= 0: + continue + xs.append(v) + ys.append(c) + ms.append(t) + if len(xs) < 5: + return + xs, ys, ms = np.array(xs), np.array(ys), np.array(ms) + x_edges = np.logspace(np.log10(xs.min()), np.log10(xs.max()), 16) + y_edges = np.logspace(np.log10(ys.min()), np.log10(ys.max()), 16) + # mean elapsed per cell + H_sum, _, _ = np.histogram2d(xs, ys, bins=[x_edges, y_edges], weights=ms) + H_cnt, _, _ = np.histogram2d(xs, ys, bins=[x_edges, y_edges]) + with np.errstate(invalid="ignore", divide="ignore"): + H_mean = np.where(H_cnt > 0, H_sum / H_cnt, np.nan) + + fig, ax = plt.subplots(figsize=(8, 6)) + mesh = ax.pcolormesh(x_edges, y_edges, H_mean.T, norm=LogNorm(vmin=max(np.nanmin(H_mean[H_mean > 0]), 1.0), + vmax=np.nanmax(H_mean)), + cmap="viridis", shading="auto") + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("num_variables (log)") + ax.set_ylabel("num_constraints (log)") + ax.set_title("Mean elapsed_ms across size grid") + cbar = fig.colorbar(mesh, ax=ax) + cbar.set_label("mean elapsed_ms (log)") + fig.tight_layout() + fig.savefig(out, dpi=120) + plt.close(fig) + + +def make_plots(records: list[dict], plot_dir: Path) -> None: + plot_dir.mkdir(parents=True, exist_ok=True) + plot_hist_runtime(records, plot_dir / "01_runtime_hist.png") + plot_hist_features(records, plot_dir / "02_features_hist.png") + plot_scatter_runtime(records, plot_dir / "03_runtime_vs_size_scatter.png") + plot_box_by_group(records, "status", "Runtime by cpsat_status.result", plot_dir / "04_box_by_status.png") + plot_box_by_group(records, "applied_params_hash", "Runtime by applied_params_hash", plot_dir / "05_box_by_hash.png") + plot_heatmap_size(records, plot_dir / "06_heatmap_vars_constraints.png") + print(f"wrote {len(list(plot_dir.glob('*.png')))} plots to {plot_dir}") + + +# ---------- main ---------- + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--raw-dir", type=Path, default=DEFAULT_RAW) + ap.add_argument("--out", type=Path, default=DEFAULT_OUT) + ap.add_argument("--no-plots", action="store_true") + ap.add_argument( + "--optimize-only", + action="store_true", + help="Restrict analysis to problems whose .cpsat.pb carries an " + "objective (drops feasibility-only instances).", + ) + args = ap.parse_args() + + if not args.raw_dir.is_dir(): + print(f"ERROR not a directory: {args.raw_dir}", file=sys.stderr) + return 1 + + args.out.mkdir(parents=True, exist_ok=True) + + rows = load_meta(args.raw_dir) + records = extract(rows) + print(f"loaded {len(records)} instances from {args.raw_dir}") + + if args.optimize_only: + obj_shas = has_objective_set(args.raw_dir) + if obj_shas is None: + print("warning: --optimize-only requested but ortools unavailable " + "— keeping all instances", file=sys.stderr) + else: + before = len(records) + records = [r for r in records if r.get("problem_sha256") in obj_shas] + print(f"objective filter: kept {len(records)}/{before} instances") + if not records: + print("ERROR no instances with objective", file=sys.stderr) + return 1 + + write_report(records, args.out / "report.txt") + write_csv(records, args.out / "per_instance.csv") + + if not args.no_plots: + make_plots(records, args.out / "plots") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/input/cpsat-bench/Statistics/analyze_outliers.py b/input/cpsat-bench/Statistics/analyze_outliers.py new file mode 100644 index 0000000000..bb6c777ff8 --- /dev/null +++ b/input/cpsat-bench/Statistics/analyze_outliers.py @@ -0,0 +1,491 @@ +#!/usr/bin/env python3 +"""Find CP-SAT runtime outliers and diagnose root cause. + +Pipeline: + 1. Load all meta + features. + 2. Fit log-log baseline: log10(elapsed_ms) ~ a*log10(num_variables) + b*log10(num_constraints) + c + 3. Residual r = log10(actual) - log10(predicted). Large positive r = exponential blow-up. + 4. Top-K outliers: decode .cpsat.pb to extract constraint kinds, domain sizes, + coefficient magnitudes, objective presence. + 5. Compare outliers vs rest (mean diff, log-fold). + 6. Write outliers_report.txt + outliers_top.csv + plots/07_residual_*.png. + +Run: + .venv/bin/python Statistics/analyze_outliers.py --top-k 30 +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import sys +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +from ortools.sat import cp_model_pb2 + +HERE = Path(__file__).resolve().parent +DEFAULT_RAW = HERE.parent / "raw-data" +DEFAULT_OUT = HERE + + +def has_objective_set(raw_dir: Path) -> set: + """SHAs whose .cpsat.pb carries an objective.""" + out = set() + for p in sorted(raw_dir.glob("*.cpsat.pb")): + m = cp_model_pb2.CpModelProto() + m.ParseFromString(p.read_bytes()) + if m.HasField("objective") or m.HasField("floating_point_objective"): + out.add(p.name[: -len(".cpsat.pb")]) + return out + + +# ---------- meta load ---------- + +def load_meta(raw_dir: Path) -> list[dict]: + rows = [] + for path in sorted(raw_dir.glob("*.meta.jsonl")): + for line in path.read_text().splitlines(): + line = line.strip() + if not line: + continue + try: + rows.append(json.loads(line)) + except json.JSONDecodeError: + pass + return rows + + +def flatten(rows: list[dict]) -> list[dict]: + out = [] + for r in rows: + f = r.get("features") or {} + st = r.get("cpsat_status") or {} + sd = r.get("cpsat_response_stats") or {} + ap = r.get("cpsat_applied_params") or {} + out.append({ + "problem_sha256": r.get("problem_sha256"), + "applied_params_hash": r.get("applied_params_hash"), + "status": st.get("result"), + "elapsed_ms": st.get("elapsed_ms"), + "num_variables": f.get("num_variables"), + "num_bool": f.get("num_bool"), + "num_int": f.get("num_int"), + "num_constraints": f.get("num_constraints"), + "num_conflicts": sd.get("num_conflicts"), + "num_branches": sd.get("num_branches"), + "num_binary_propagations": sd.get("num_binary_propagations"), + "num_integer_propagations": sd.get("num_integer_propagations"), + "num_restarts": sd.get("num_restarts"), + "deterministic_time": sd.get("deterministic_time"), + "num_workers": ap.get("num_search_workers"), + "pb_path": str(Path(r.get("problem_filename") or "")), + }) + return out + + +# ---------- regression / residuals ---------- + +def fit_residuals(records: list[dict]) -> tuple[np.ndarray, dict]: + """log10(elapsed_ms) ~ a*log10(vars) + b*log10(constraints) + c.""" + keep = [] + for r in records: + v, c, t = r.get("num_variables"), r.get("num_constraints"), r.get("elapsed_ms") + if not v or not c or not t or t <= 0: + continue + keep.append(r) + X = np.array([[math.log10(r["num_variables"]), math.log10(r["num_constraints"]), 1.0] for r in keep]) + y = np.array([math.log10(r["elapsed_ms"]) for r in keep]) + coef, *_ = np.linalg.lstsq(X, y, rcond=None) + a, b, c = coef + pred = X @ coef + resid = y - pred + for r, p, rs in zip(keep, pred, resid): + r["log10_elapsed"] = float(math.log10(r["elapsed_ms"])) + r["log10_pred"] = float(p) + r["residual"] = float(rs) # log10 units; +1.0 = 10x slower than expected + info = { + "a_vars": float(a), + "b_constraints": float(b), + "intercept": float(c), + "n": int(len(keep)), + "r2": float(1.0 - np.var(resid) / np.var(y)), + "rmse": float(np.sqrt(np.mean(resid ** 2))), + } + return np.array(resid), info, keep + + +# ---------- pb features ---------- + +KIND_KEYS = ( + "linear", + "bool_or", + "bool_and", + "at_most_one", + "exactly_one", + "bool_xor", + "all_diff", + "element", + "circuit", + "routes", + "table", + "automaton", + "inverse", + "reservoir", + "interval", + "no_overlap", + "no_overlap_2d", + "cumulative", + "lin_max", + "int_div", + "int_mod", + "int_prod", +) + + +def pb_features(pb_path: Path) -> dict: + m = cp_model_pb2.CpModelProto() + m.ParseFromString(pb_path.read_bytes()) + kinds = Counter() + enforce_count = 0 + linear_coef_max = 0.0 + linear_coef_abs_sum = 0.0 + linear_terms_max = 0 + linear_terms_total = 0 + for c in m.constraints: + kind = c.WhichOneof("constraint") + kinds[kind] += 1 + if c.enforcement_literal: + enforce_count += 1 + if kind == "linear": + coefs = c.linear.coeffs + if coefs: + amax = max(abs(v) for v in coefs) + if amax > linear_coef_max: + linear_coef_max = float(amax) + linear_coef_abs_sum += float(sum(abs(v) for v in coefs)) + tlen = len(coefs) + linear_terms_total += tlen + if tlen > linear_terms_max: + linear_terms_max = tlen + # variable domain stats + dsizes = [] + wide_int_count = 0 + max_domain_max = 0 + min_domain_min = 0 + for v in m.variables: + dom = v.domain + sz = 0 + for i in range(0, len(dom), 2): + sz += dom[i + 1] - dom[i] + 1 + if dom[i + 1] > max_domain_max: + max_domain_max = int(dom[i + 1]) + if dom[i] < min_domain_min: + min_domain_min = int(dom[i]) + dsizes.append(sz) + if sz > 1000: + wide_int_count += 1 + dsizes_arr = np.array(dsizes, dtype=float) if dsizes else np.array([0.0]) + has_obj = m.HasField("objective") or m.HasField("floating_point_objective") + obj_terms = len(m.objective.vars) if m.HasField("objective") else 0 + num_assumptions = len(m.assumptions) + num_search_hint = len(m.search_strategy) + return { + "n_vars": int(len(m.variables)), + "n_cons": int(len(m.constraints)), + "kinds": dict(kinds), + "n_enforce": int(enforce_count), + "linear_coef_max": linear_coef_max, + "linear_coef_abs_sum": linear_coef_abs_sum, + "linear_terms_max": int(linear_terms_max), + "linear_terms_total": int(linear_terms_total), + "domain_size_max": float(dsizes_arr.max()), + "domain_size_median": float(np.median(dsizes_arr)), + "wide_int_count": int(wide_int_count), + "domain_min": int(min_domain_min), + "domain_max": int(max_domain_max), + "has_objective": bool(has_obj), + "objective_terms": int(obj_terms), + "n_assumptions": int(num_assumptions), + "n_search_strategy": int(num_search_hint), + } + + +# ---------- aggregation ---------- + +def summarize_kinds(kinds_list: list[dict]) -> dict[str, dict]: + all_keys = set() + for d in kinds_list: + all_keys.update(d.keys()) + out = {} + for k in sorted(all_keys): + vals = np.array([d.get(k, 0) for d in kinds_list], dtype=float) + out[k] = { + "mean": float(vals.mean()), + "median": float(np.median(vals)), + "max": float(vals.max()), + "share_nonzero": float((vals > 0).mean()), + } + return out + + +def fmt_block(title: str, lines: list[str]) -> str: + return f"\n## {title}\n" + "\n".join(lines) + + +# ---------- main ---------- + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--raw-dir", type=Path, default=DEFAULT_RAW) + ap.add_argument("--out", type=Path, default=DEFAULT_OUT) + ap.add_argument("--top-k", type=int, default=30, help="Number of outliers to deep-dive") + ap.add_argument("--baseline-k", type=int, default=60, help="Inlier sample size for comparison") + ap.add_argument( + "--optimize-only", + action="store_true", + help="Restrict analysis to problems whose .cpsat.pb carries an objective.", + ) + args = ap.parse_args() + + if not args.raw_dir.is_dir(): + print(f"ERROR not a directory: {args.raw_dir}", file=sys.stderr) + return 1 + + rows = load_meta(args.raw_dir) + records = flatten(rows) + print(f"loaded {len(records)} instances") + + if args.optimize_only: + obj_shas = has_objective_set(args.raw_dir) + before = len(records) + records = [r for r in records if r.get("problem_sha256") in obj_shas] + print(f"objective filter: kept {len(records)}/{before} instances") + if not records: + print("ERROR no instances with objective", file=sys.stderr) + return 1 + + _resid, info, kept = fit_residuals(records) + print(f"baseline log10 fit: a_vars={info['a_vars']:.3f} b_cons={info['b_constraints']:.3f} " + f"intercept={info['intercept']:.3f} R^2={info['r2']:.3f} rmse={info['rmse']:.3f}") + + # rank by residual (positive = slower than predicted) + kept_sorted = sorted(kept, key=lambda r: -r["residual"]) + outliers = kept_sorted[: args.top_k] + inliers = [r for r in kept_sorted if abs(r["residual"]) <= 0.3][-args.baseline_k:] + if not inliers: + inliers = kept_sorted[-args.baseline_k:] + + print(f"top-{args.top_k} outliers: residual range " + f"{outliers[-1]['residual']:.3f} .. {outliers[0]['residual']:.3f} " + f"(=> {10 ** outliers[-1]['residual']:.1f}x .. {10 ** outliers[0]['residual']:.1f}x slowdown)") + + # decode pb for outliers + baseline + def decode_set(group, label): + out = [] + for i, r in enumerate(group): + pb = args.raw_dir / r["pb_path"] + if not pb.exists(): + continue + try: + feats = pb_features(pb) + except Exception as e: + print(f" WARN failed to decode {pb.name}: {e}", file=sys.stderr) + continue + feats["_residual"] = r["residual"] + feats["_elapsed_ms"] = r["elapsed_ms"] + feats["_num_variables"] = r["num_variables"] + feats["_num_constraints"] = r["num_constraints"] + feats["_status"] = r["status"] + feats["_num_conflicts"] = r["num_conflicts"] + feats["_num_branches"] = r["num_branches"] + feats["_num_binary_propagations"] = r["num_binary_propagations"] + feats["_num_integer_propagations"] = r["num_integer_propagations"] + feats["_num_restarts"] = r["num_restarts"] + feats["_applied_params_hash"] = r["applied_params_hash"] + feats["_sha"] = r["problem_sha256"] + out.append(feats) + if (i + 1) % 10 == 0: + print(f" decoded {label} {i + 1}/{len(group)}") + return out + + print("decoding outlier pb files...") + out_feats = decode_set(outliers, "outliers") + print("decoding baseline pb files...") + base_feats = decode_set(inliers, "baseline") + + # ---- aggregate comparison ---- + def agg_scalar(group, key): + arr = np.array([g[key] for g in group if g.get(key) is not None], dtype=float) + return arr + + scalar_keys = [ + "n_vars", "n_cons", "n_enforce", + "linear_coef_max", "linear_coef_abs_sum", + "linear_terms_max", "linear_terms_total", + "domain_size_max", "domain_size_median", + "wide_int_count", "domain_max", + "objective_terms", "n_assumptions", "n_search_strategy", + "_num_conflicts", "_num_branches", + "_num_binary_propagations", "_num_integer_propagations", + "_num_restarts", + ] + print("\n## Outlier vs baseline scalar comparison (geomean, log-fold)") + table_lines = [f"{'feature':28s} {'outlier_med':>14s} {'base_med':>14s} {'ratio':>8s}"] + for k in scalar_keys: + o = agg_scalar(out_feats, k) + b = agg_scalar(base_feats, k) + if o.size == 0 or b.size == 0: + continue + om = float(np.median(o)) + bm = float(np.median(b)) + ratio = om / bm if bm > 0 else float("inf") if om > 0 else 0.0 + table_lines.append(f"{k:28s} {om:>14,.2f} {bm:>14,.2f} {ratio:>8.2f}") + table = "\n".join(table_lines) + print(table) + + # ---- constraint kinds histograms ---- + out_kinds = summarize_kinds([g["kinds"] for g in out_feats]) + base_kinds = summarize_kinds([g["kinds"] for g in base_feats]) + all_keys = sorted(set(out_kinds) | set(base_kinds)) + kind_lines = [f"{'kind':18s} {'out_mean':>10s} {'base_mean':>10s} {'ratio':>8s} {'out_share':>10s}"] + for k in all_keys: + om = out_kinds.get(k, {}).get("mean", 0.0) + bm = base_kinds.get(k, {}).get("mean", 0.0) + ratio = om / bm if bm > 0 else float("inf") if om > 0 else 0.0 + sh = out_kinds.get(k, {}).get("share_nonzero", 0.0) + kind_lines.append(f"{k:18s} {om:>10,.1f} {bm:>10,.1f} {ratio:>8.2f} {sh:>10.2f}") + kind_table = "\n".join(kind_lines) + print("\n## Constraint-kind histogram (outlier vs baseline)") + print(kind_table) + + # ---- per-outlier detail csv ---- + csv_path = args.out / "outliers_top.csv" + fields = [ + "_sha", "_status", "_applied_params_hash", "_residual", + "_elapsed_ms", "_num_variables", "_num_constraints", + "_num_conflicts", "_num_branches", + "_num_binary_propagations", "_num_integer_propagations", "_num_restarts", + "n_vars", "n_cons", "n_enforce", + "linear_coef_max", "linear_terms_max", "linear_terms_total", + "domain_size_max", "domain_size_median", "wide_int_count", "domain_max", + "has_objective", "objective_terms", "n_assumptions", "n_search_strategy", + ] + [f"kind_{k}" for k in KIND_KEYS] + with csv_path.open("w", newline="") as f: + w = csv.writer(f) + w.writerow(fields) + for g in out_feats: + row = [] + for fld in fields: + if fld.startswith("kind_"): + row.append(g["kinds"].get(fld[5:], 0)) + else: + row.append(g.get(fld, "")) + w.writerow(row) + print(f"\nwrote {csv_path}") + + # ---- residual scatter ---- + plot_dir = args.out / "plots" + plot_dir.mkdir(exist_ok=True) + fig, ax = plt.subplots(figsize=(9, 6)) + rec_arr = np.array([[r["num_variables"], r["num_constraints"], r["elapsed_ms"], r["residual"]] for r in kept]) + sc = ax.scatter(rec_arr[:, 0] * rec_arr[:, 1], rec_arr[:, 2], c=rec_arr[:, 3], cmap="coolwarm", + s=22, edgecolors="black", linewidths=0.3) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("num_variables * num_constraints (log)") + ax.set_ylabel("elapsed_ms (log)") + ax.set_title(f"Runtime residual (log10) on size product\n" + f"a_vars={info['a_vars']:.2f} b_cons={info['b_constraints']:.2f} R²={info['r2']:.2f}") + cb = fig.colorbar(sc, ax=ax) + cb.set_label("residual log10(actual/predicted)") + for r in outliers[:8]: + ax.annotate(r["problem_sha256"][:6], (r["num_variables"] * r["num_constraints"], r["elapsed_ms"]), + fontsize=7, color="darkred", xytext=(4, 2), textcoords="offset points") + fig.tight_layout() + fig.savefig(plot_dir / "07_residual_scatter.png", dpi=120) + plt.close(fig) + + # constraint kind comparison bar chart + if all_keys: + fig, ax = plt.subplots(figsize=(10, 5)) + idx = np.arange(len(all_keys)) + w = 0.42 + out_vals = [out_kinds.get(k, {}).get("mean", 0.0) for k in all_keys] + base_vals = [base_kinds.get(k, {}).get("mean", 0.0) for k in all_keys] + ax.bar(idx - w / 2, out_vals, w, label=f"outliers (n={len(out_feats)})", color="#d62728") + ax.bar(idx + w / 2, base_vals, w, label=f"baseline (n={len(base_feats)})", color="#1f77b4") + ax.set_xticks(idx) + ax.set_xticklabels(all_keys, rotation=25, ha="right") + ax.set_ylabel("mean count per instance") + ax.set_yscale("symlog") + ax.set_title("Constraint-kind composition: outlier vs baseline") + ax.legend() + fig.tight_layout() + fig.savefig(plot_dir / "08_outlier_constraint_kinds.png", dpi=120) + plt.close(fig) + + # ---- write text report ---- + report = [ + "# CP-SAT runtime outlier diagnosis", + f"records: {len(records)}, regression-kept: {info['n']}", + f"log10 baseline: log10(elapsed_ms) = {info['a_vars']:.3f}*log10(vars) " + f"+ {info['b_constraints']:.3f}*log10(cons) + {info['intercept']:.3f}", + f"R^2 = {info['r2']:.3f}, RMSE(log10) = {info['rmse']:.3f}", + f"top-{args.top_k} outliers slowdown range: " + f"{10 ** outliers[-1]['residual']:.1f}x .. {10 ** outliers[0]['residual']:.1f}x", + "", + "## Top outliers (residual = log10(actual/predicted))", + f"{'sha':10s} {'hash':10s} {'status':10s} {'elapsed_ms':>12s} {'vars':>8s} {'cons':>8s} " + f"{'pred_ms':>12s} {'resid':>7s} {'conflicts':>12s} {'branches':>14s}", + ] + for r in outliers: + pred_ms = 10 ** r["log10_pred"] + report.append( + f"{r['problem_sha256'][:8]} {(r['applied_params_hash'] or '')[:8]} " + f"{(r['status'] or '')[:10]:10s} {r['elapsed_ms']:>12,.1f} {r['num_variables']:>8d} " + f"{r['num_constraints']:>8d} {pred_ms:>12,.1f} {r['residual']:>7.2f} " + f"{(r['num_conflicts'] or 0):>12,d} {(r['num_branches'] or 0):>14,d}" + ) + + report += ["", "## Scalar feature comparison: outlier median vs baseline median", table] + report += ["", "## Constraint kinds: outlier vs baseline mean per instance", kind_table] + + # interpretation hints + report.append("\n## Interpretation hints") + hints = [] + for k in scalar_keys: + o = agg_scalar(out_feats, k) + b = agg_scalar(base_feats, k) + if o.size == 0 or b.size == 0: + continue + om, bm = float(np.median(o)), float(np.median(b)) + if bm <= 0 and om <= 0: + continue + ratio = om / bm if bm > 0 else float("inf") + if ratio >= 2.0 or (ratio > 0 and ratio <= 0.5): + hints.append(f" - {k}: outlier median {om:,.1f} vs baseline {bm:,.1f} ({ratio:.2f}x)") + if hints: + report.append("Features where outliers differ substantially (>=2x or <=0.5x):") + report.extend(hints) + else: + report.append("No scalar feature showed a >=2x median shift; the blow-up is concentrated in solver-state stats (conflicts/branches), suggesting structural/search-space hardness rather than raw size.") + + (args.out / "outliers_report.txt").write_text("\n".join(report) + "\n") + print(f"wrote {args.out / 'outliers_report.txt'}") + print(f"wrote {plot_dir / '07_residual_scatter.png'}") + print(f"wrote {plot_dir / '08_outlier_constraint_kinds.png'}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/input/cpsat-bench/Statistics/outliers_report.txt b/input/cpsat-bench/Statistics/outliers_report.txt new file mode 100644 index 0000000000..81c102a19c --- /dev/null +++ b/input/cpsat-bench/Statistics/outliers_report.txt @@ -0,0 +1,52 @@ +# CP-SAT runtime outlier diagnosis +records: 82, regression-kept: 82 +log10 baseline: log10(elapsed_ms) = 1.176*log10(vars) + 0.756*log10(cons) + -3.501 +R^2 = 0.836, RMSE(log10) = 0.191 +top-10 outliers slowdown range: 1.4x .. 11.5x + +## Top outliers (residual = log10(actual/predicted)) +sha hash status elapsed_ms vars cons pred_ms resid conflicts branches +ba0fe698 5df8f0e7 OPTIMAL 25,129.0 1316 15565 2,181.1 1.06 567 5,512 +d1963519 5df8f0e7 OPTIMAL 573,378.0 10404 122534 118,204.1 0.69 40,296 230,447 +cbc04bca 5df8f0e7 OPTIMAL 422,905.0 11127 136453 138,769.2 0.48 35,669 198,723 +2fe6606b 5df8f0e7 OPTIMAL 49,854.2 4358 55027 23,181.9 0.33 13,604 185,339 +01a1d906 5df8f0e7 OPTIMAL 62,558.7 5306 63224 32,456.9 0.28 15,713 102,256 +b7fa3444 5df8f0e7 OPTIMAL 46,683.7 4820 59596 27,721.8 0.23 9,931 192,717 +b8483420 5df8f0e7 OPTIMAL 5,405.5 1674 20478 3,562.0 0.18 3,722 41,473 +a65ee543 5df8f0e7 OPTIMAL 6,289.2 1863 22077 4,276.0 0.17 2,402 15,308 +68047eeb 5df8f0e7 OPTIMAL 6,177.9 1850 21892 4,214.0 0.17 9,621 78,066 +00d6004a 5df8f0e7 OPTIMAL 5,990.7 1850 21892 4,214.0 0.15 10,712 125,224 + +## Scalar feature comparison: outlier median vs baseline median +feature outlier_med base_med ratio +n_vars 16,306.00 17,803.00 0.92 +n_cons 38,552.00 42,093.00 0.92 +n_enforce 31,058.00 33,893.50 0.92 +linear_coef_max 1.00 1.00 1.00 +linear_coef_abs_sum 25,367.50 28,369.00 0.89 +linear_terms_max 46.00 51.00 0.90 +linear_terms_total 25,367.50 28,369.00 0.89 +domain_size_max 2,000,000,001.00 2,000,000,001.00 1.00 +domain_size_median 2.00 2.00 1.00 +wide_int_count 1,387.50 1,493.50 0.93 +domain_max 1,000,000,000.00 1,000,000,000.00 1.00 +objective_terms 154.00 168.00 0.92 +n_assumptions 0.00 0.00 0.00 +n_search_strategy 0.00 0.00 0.00 +_num_conflicts 10,321.50 4,568.50 2.26 +_num_branches 113,740.00 61,424.00 1.85 +_num_binary_propagations 7,953,242.50 3,894,166.00 2.04 +_num_integer_propagations 2,765,498.50 1,667,448.50 1.66 +_num_restarts 42.00 29.00 1.45 + +## Constraint kinds: outlier vs baseline mean per instance +kind out_mean base_mean ratio out_share +at_most_one 212.3 188.5 1.13 1.00 +bool_and 12,362.5 10,547.6 1.17 1.00 +bool_or 18,309.7 15,472.8 1.18 1.00 +linear 22,989.3 18,819.2 1.22 1.00 + +## Interpretation hints +Features where outliers differ substantially (>=2x or <=0.5x): + - _num_conflicts: outlier median 10,321.5 vs baseline 4,568.5 (2.26x) + - _num_binary_propagations: outlier median 7,953,242.5 vs baseline 3,894,166.0 (2.04x) diff --git a/input/cpsat-bench/Statistics/outliers_top.csv b/input/cpsat-bench/Statistics/outliers_top.csv new file mode 100644 index 0000000000..dad4774df2 --- /dev/null +++ b/input/cpsat-bench/Statistics/outliers_top.csv @@ -0,0 +1,11 @@ +_sha,_status,_applied_params_hash,_residual,_elapsed_ms,_num_variables,_num_constraints,_num_conflicts,_num_branches,_num_binary_propagations,_num_integer_propagations,_num_restarts,n_vars,n_cons,n_enforce,linear_coef_max,linear_terms_max,linear_terms_total,domain_size_max,domain_size_median,wide_int_count,domain_max,has_objective,objective_terms,n_assumptions,n_search_strategy,kind_linear,kind_bool_or,kind_bool_and,kind_at_most_one,kind_exactly_one,kind_bool_xor,kind_all_diff,kind_element,kind_circuit,kind_routes,kind_table,kind_automaton,kind_inverse,kind_reservoir,kind_interval,kind_no_overlap,kind_no_overlap_2d,kind_cumulative,kind_lin_max,kind_int_div,kind_int_mod,kind_int_prod +ba0fe698c28be555ec428f05daa2aa901ecf9694c6c23043b76a91a7a847b111,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,1.0615059834038845,25129,1316,15565,567,5512,237329,138397,5,6612,15565,12472,1.0,21,10164,2000000001.0,2.0,588,1000000000,True,70,0,0,6349,5395,3744,77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +d1963519912cbd33d8d63cf99f6bcd2e64aad8b89e4a0334bed64a35e3d93360,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.6858083277766207,573378,10404,122534,40296,230447,43086273,12921477,188,51182,122534,99889,1.0,116,86622,2000000001.0,2.0,3695,1000000000,True,385,0,0,53742,41078,27271,443,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cbc04bca5112cb9362f9db9bb007b46376c1488fed34d7b18df67290b22fc099,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.48394968053234866,422905,11127,136453,35669,198723,48620016,12323899,122,57294,136453,112565,1.0,111,97191,2000000001.0,2.0,3847,1000000000,True,364,0,0,62364,44686,28971,432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +2fe6606b7880e2c63079285e403124a757f7a54ff8ff326e19ee50d5ccba716a,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.3325532090367318,49854.2,4358,55027,13604,185339,9076776,3592423,64,23283,55027,44356,1.0,66,35919,2000000001.0,2.0,1956,1000000000,True,217,0,0,21958,19251,13573,245,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +01a1d9063de56c5adc87428afe7ffefcf1deb58cbf1d7e5cecf8362cbd571396,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.2849807566885003,62558.7,5306,63224,15713,102256,12398222,3789306,87,27496,63224,50873,1.0,71,41790,2000000001.0,2.0,2127,1000000000,True,238,0,0,25107,22432,15413,272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +b7fa34442c7e8f082f59d288a2714129f51f43774aa2e658712e5da54cfe8635,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.2263435287547999,46683.7,4820,59596,9931,192717,9019253,3631940,41,25166,59596,48377,1.0,66,40461,2000000001.0,2.0,2205,1000000000,True,217,0,0,24911,20377,14063,245,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +b848342049b15eda055c33e85c1eb5b7dd0470cdddc487c913c4f4edb41c1177,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.18113800540443004,5405.46,1674,20478,3722,41473,1509038,625152,15,8668,20478,16347,1.0,26,13143,2000000001.0,2.0,730,1000000000,True,91,0,0,8038,7232,5106,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +a65ee543b1402a4a356d6b8f8fd45088fd2f68fbbe970c160248bca9cb481181,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.16755918629647049,6289.21,1863,22077,2402,15308,954615,419754,10,9329,22077,17760,1.0,26,14816,2000000001.0,2.0,773,1000000000,True,91,0,0,9198,7596,5180,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +68047eeb3643ffa54ee8e6b29d0ca3135b12f9e0f2e30d4b325f2a5a6509bfd5,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.1661486907977885,6177.95,1850,21892,9621,78066,6887232,1730879,32,9272,21892,17614,1.0,26,14772,2000000001.0,2.0,819,1000000000,True,91,0,0,9113,7525,5152,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +00d6004a1249fd1379ebc735c77d151f084727ad328b69df623d4a7b4d63f5f6,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.15278187339420501,5990.7,1850,21892,10712,125224,5329015,1938574,43,9272,21892,17614,1.0,26,14772,2000000001.0,2.0,819,1000000000,True,91,0,0,9113,7525,5152,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/input/cpsat-bench/Statistics/per_instance.csv b/input/cpsat-bench/Statistics/per_instance.csv new file mode 100644 index 0000000000..b3a9271e97 --- /dev/null +++ b/input/cpsat-bench/Statistics/per_instance.csv @@ -0,0 +1,83 @@ +problem_sha256,applied_params_hash,status,elapsed_ms,num_variables,num_bool,num_int,num_constraints,num_workers,num_conflicts,num_branches,deterministic_time +00a438e9a02d3895ebcf5e1eea77b3c1fba28d76ca73723c7befe71f327aae82,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,16676.9,3924,2783,1141,46300,8,3916,53955,13.2451 +00d6004a1249fd1379ebc735c77d151f084727ad328b69df623d4a7b4d63f5f6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5990.7,1850,1257,593,21892,8,10712,125224,7.42965 +0114ccef241fb15dac55b5044076da48fb0aae94ced62fb800c6cb80fdb4bece,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,34462,5306,3673,1633,63224,8,11567,90118,28.4192 +01a1d9063de56c5adc87428afe7ffefcf1deb58cbf1d7e5cecf8362cbd571396,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,62558.7,5306,3673,1633,63224,8,15713,102256,43.3051 +03012de510cde9db5046196d7b447fa299e3cfd3ba2cb2c12404376edc1f241a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12602.6,4054,2930,1124,48002,8,4517,55859,10.6474 +06b08825173bc966570e184979982304ea8d33a41ce5d64b8c62eaac29c7cd1e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4041.21,1739,1149,590,20663,8,480,8169,2.09488 +079937773cba535777985106b2d07c563265fb815d36abdf852940f4585fcf5b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,59835.3,7843,5854,1989,89169,8,10943,101236,46.0915 +0b1eb462e501c127119ebaed5477068246bb55d5ff64a25323863488d0b780c3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,18896.7,4058,2932,1126,47669,8,10410,71086,20.2757 +0b67bf459c1faa274487feb34ff95759660b3032c8a8e8a27b6212252c1b0728,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,9935.37,3313,2177,1136,40623,8,887,35323,7.60412 +10211eedd813db0025927acaefc67fd413b2d8c2fb60009f35bb49cfc57584e7,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,28273,5081,3592,1489,62229,8,6970,70239,22.0618 +1371ca0e9b6120f558b2f5e1b9c3f335092dda51b0754c072c5033e3f25cc3d5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,27032.2,5079,3597,1482,60360,8,6180,83848,20.6581 +1509ac1acb85d66d0da78b5490196654e1f8b82a4cabf3f550511bcb6681d1ca,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12309.8,3243,2301,942,38145,8,3770,37650,10.5399 +1b486cb1a9038477ba0dc44f568c384b38b7f66c62d1d0e27feada15ee63acc6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7733.2,2733,1938,795,32190,8,7164,78766,8.41288 +2107376b09925740dc3a50075ebd92ba4853e79a17d659fb67a1785646a3121f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,15121.7,4046,2924,1122,47597,8,3716,58078,11.3394 +27132094730caf2088c28c764b2146f0bfeceb874b7efa9b456a1ee06c4c3ff9,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7830.82,2734,1941,793,32306,8,6741,92643,8.82547 +2981bd841805c451cc2932b097f423d5e2dcce99d4c3f5c63eb3eda1fb515539,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2672.29,1240,794,446,14780,8,230,4512,1.38757 +2e958d4f0beadd78d7dff9e9d9f386d3a0f00860ff2703e69b11975d544e3757,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2877.24,1859,1264,595,21994,8,440,10242,2.79816 +2fa57a166e49216765ff3e9df3a99e51c9a6eb307be9bab396ea75f82f17fabf,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,17916.6,5109,3610,1499,62282,8,1996,65003,11.2137 +2fe6606b7880e2c63079285e403124a757f7a54ff8ff326e19ee50d5ccba716a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,49854.2,4358,2861,1497,55027,8,13604,185339,44.9072 +3a0d61445e568f2995e6d3e7330d339f92d94e14d5ee89b2abf2ed96e86a3c70,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,13875.9,3524,2381,1143,43563,8,5832,65228,11.6098 +3bc12c2cf7bd6010001c8a460cc0184aed048ae950e55078fd9428a3b8d1fc95,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3662.35,1739,1149,590,20663,8,391,8725,2.31202 +41f6ecf9ca6462dcb7ca49e028b2970175579c0250058e0430e979a04435bf09,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5144.56,1859,1264,595,21983,8,754,12829,3.74204 +45edc06cca416c89589b5bbf4dd6e3beca7b1fd3f3ceb4d58e0fa4af5fea29e2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,28480.7,5099,3604,1495,62421,8,7879,107429,22.3727 +4b47d1f4093903ddae684d0c12f44befa58c593196420a6eb9aed30add89ba31,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,10430,3098,2149,949,36351,8,5154,142175,7.39399 +4bf5333887900fa3b5ff5811f12d1f5c9c0bb0574018566d4538a620dad14e3b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5148.21,1977,1377,600,23113,8,4754,35523,5.83089 +59703dbe66412fd1ab21a7c83351814914d6a3dbbedfe41041b8faf435dd4011,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11764,3224,2288,936,38398,8,6564,84399,10.75 +5f996769b676f0b952adf679945d3e9b66e463dfccc488f12f6eb14b863657a3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3606.39,1766,1167,599,21165,8,391,11835,2.73636 +68047eeb3643ffa54ee8e6b29d0ca3135b12f9e0f2e30d4b325f2a5a6509bfd5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6177.95,1850,1257,593,21892,8,9621,78066,7.52872 +6afa1f30ddaf73d8dfed7146c9365683a3a702aee80c07ab24b35a83bbf815a4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8337.47,2569,1783,786,30868,8,8099,54889,9.44203 +6c96fe64d8a7e248bb211262d15a9e02c33afd67841b876ec22da92430ea1d4a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,25432.1,5079,3597,1482,60360,8,6756,89096,21.9653 +6dc3e7cdbc74f23ff22a0eecbde5e1ed26d05cf56459a1ae169419068718c3c4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11102,3224,2288,936,38398,8,8139,51136,10.2641 +6e93a1a5f114fdfc007b30330e24aa1325660433aaa0794f76ef4848de4580d5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,35243,4806,3325,1481,59330,8,8004,101869,27.7665 +724e539c591651e646f2e89f9000b6a040742aa2d989582166ac52e3e7b4a6e0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7005.63,2583,1793,790,30688,8,7454,30337,7.26527 +76e68a1cce648c8d73748c810c88f2507f97759063e97d60bdeba8b7450c75d4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11335.8,3372,2442,930,39592,8,7197,73086,10.5849 +80d93f459a3aba9b087940fc2f7fbb31d50402f34005177a89face29ebb3adb8,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7324.51,2781,1832,949,34339,8,2652,42931,6.39323 +835ae7284c43b2522947ad7ad0b1f26429793b7fff94b7a612ea3a1dc548df57,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,14852.3,4117,2621,1496,52655,8,2578,54841,10.5804 +83b70413e9333ce9f4929c450b14e28d2b6e4ec28401d68b4b7d59c76c82d8ee,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,19100.3,5366,3525,1841,67897,8,1969,65039,11.3871 +865f1171e901d275edb5a10c40d9288ec08619127220e643e36ae1ebee2903b2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,486.785,715,461,254,7765,8,857,20375,0.227804 +86b1bf9b38f98be62d7509c448ea0dfd7197af45620ba4b50960bf15254309bf,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8341.89,2326,1531,795,28510,8,6602,46102,8.95567 +8897295c1a1d2153b3e64712ad6cf8fef4812ff8f7419cf217ba26f03e246f24,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1085.03,1325,878,447,14985,8,160,4817,0.865913 +8d20d7075f0bbe969941be96f336c7f87d3f16fbdb80ed42d6c64c0ff25da1ad,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,77312.6,8588,6402,2186,98356,8,11524,137190,54.7198 +8da21152ae4db83b1c03c6d8f8c508ef8b8aa00294095638c498f2bef8daa862,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,24823.8,5277,3652,1625,61389,8,4444,94321,18.7105 +9184e368c0e97d4bc93704357120e76ef97fe5f9a0e72f3c148784d450c7723e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3357.48,1859,1264,595,22013,8,1421,14200,2.76743 +924038c95c7634a971a91a5ad72819fc2977c87790d06360e0ec5b38be4a219e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,93615.6,7843,5854,1989,89169,8,14401,129633,60.1845 +986e408097d63dbe93210883d12efbd11c7ebfd37fec6e3462bb8a72dd4ade05,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,18427,4058,2932,1126,47669,8,8165,62016,19.6842 +98c32f98b1912de97866905f8af956745db6c16dde00e157b0d4e45577755233,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5661.77,1977,1377,600,23113,8,5929,37247,6.91608 +9aba10c5fadecdd17c301a45ddbcef5534a25c861b0a0a9b4943331de602ad8e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8748.41,2729,1936,793,32387,8,2681,20896,6.80237 +9b58888c473818a77ecb1aaf2c1032bc87b909c80f837100403b5a83a75cbc87,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12996.9,3727,2581,1146,45679,8,4620,70053,11.007 +9eda5addedbb8f1c5a6ea9bd5fe469814755a8ecdebb998f9c2598bc4e2308c7,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7945.18,2569,1783,786,30854,8,7879,36986,9.37835 +a01db04d1c0cefeec5fe077d42d9b84d55e54bf00cc689787f11200b6f817d36,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12021.2,3104,2156,948,36752,8,4413,43700,10.8944 +a0a55fdea86a6868a302e54eadbfe25574d413fdc83eb469675ed9df3568a1db,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7205.38,2350,1548,802,28749,8,447,17377,4.16838 +a46c87c567e8ea0a6fa70198b83d8523a69f38448aeafbb35a956a845987993a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,25758.6,5109,3610,1499,62282,8,2437,61291,15.616 +a65ee543b1402a4a356d6b8f8fd45088fd2f68fbbe970c160248bca9cb481181,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6289.21,1863,1266,597,22077,8,2402,15308,4.90447 +b37ba1c4e6c4fd6e50b67a7d1cba5d5c41b1c9837b2f020d6174c1459665a756,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,18428.8,3494,2362,1132,43153,8,7145,81271,18.8471 +b5e191a84337537481ab49001854e27b03a9ccded205fe88b9150bfa45529915,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,19149.4,5076,3230,1846,65280,8,2225,62641,10.8972 +b7fa34442c7e8f082f59d288a2714129f51f43774aa2e658712e5da54cfe8635,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,46683.7,4820,3334,1486,59596,8,9931,192717,42.0754 +b848342049b15eda055c33e85c1eb5b7dd0470cdddc487c913c4f4edb41c1177,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5405.46,1674,1070,604,20478,8,3722,41473,3.32815 +b87e2b0d6dcc9cc59a03ed6cede32988b1f7db99692b635f75e32f763776d979,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,27636.9,5310,3831,1479,63650,8,6118,70915,21.8517 +ba0fe698c28be555ec428f05daa2aa901ecf9694c6c23043b76a91a7a847b111,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,25129,1316,869,447,15565,8,567,5512,6.13448 +bd199a3871502160bd893d35533b1c4526edf27d6ed9d60ab786ed7aded632ec,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,9286.06,3240,2300,940,38656,8,6167,110297,9.87776 +be13cfc10b1e19807faf591c4d8070d5a8e843abc31efe3c38fd508fda6b56a6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2147.91,1316,869,447,15569,8,714,5426,1.85308 +c0c9ef6c22ed1654a88a14f6196e5e6f4540bcf3faa203510aaeeeeb7431a1bc,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,19894.8,4054,2930,1124,48003,8,6507,67477,17.726 +c3fc601be3277e3d77c7d55dbe0f58cfc37e877c76121c066e353be4cf3f9f65,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,22874.8,4589,3316,1273,54716,8,5733,74285,19.6757 +c7f14795eb4a64323fdf24caec0e981f42cf07ab79ecaa858de35afe90adb53b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,13631.5,3727,2581,1146,45679,8,5415,62615,11.0635 +cb16513bfbe2809b67cf939736abc19e5d403dcabadcae9b3aae756b2f6b1e29,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11764.5,4091,2956,1135,47873,8,4064,54561,10.3379 +cbc04bca5112cb9362f9db9bb007b46376c1488fed34d7b18df67290b22fc099,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,422905,11127,8496,2631,136453,8,35669,198723,307.613 +d1963519912cbd33d8d63cf99f6bcd2e64aad8b89e4a0334bed64a35e3d93360,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,573378,10404,7751,2653,122534,8,40296,230447,306.123 +d2736553fba0f9ea5c93118c1e35641595afe997e883759b1ada665c9859deb6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7713.8,3098,2149,949,36351,8,1016,19591,5.27028 +d381b7ed2453aa3404ecf44978c151549d5166e44f9359a2d61b3e2c19b84d0a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,18205,4440,3147,1293,52885,8,2648,61557,13.1361 +d43568fb51736eb3ba5d256ed2aa63198910e645a62dbc336dbdee29a98d1df0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,25169.9,5109,3610,1499,62235,8,6731,148858,22.114 +d519e83718e688a924bdbc74e37419567b29b2fad0995ec7bbdd857926592fef,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7036.23,2199,1403,796,27405,8,2673,61472,5.02013 +da79ad0e3d13908a25c0a97e051b6e6bcc675cdb0311c8a8699a76a6eba6a056,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12282.4,3158,2012,1146,40030,8,3621,99415,10.5022 +db2bfd25d3ddd29864d7c63649b740c31f3a212ecd5c39951b7db2cf2daaf71e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4397.47,2325,1530,795,27686,8,583,16446,3.74854 +dd4063e9bca83aceee4a80f854ea7955dce420f547cfb34cec970cdb88755053,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7494.29,2733,1938,795,32190,8,2823,22565,7.50629 +e2f7812832351c2fe6745cd57d8cf525ea0afde5e0ae3b84c74a7dbc2d59779c,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7159.5,2326,1531,795,28510,8,7538,79867,9.08225 +e4cbb10b880925006b58b8a9c14a80444228f8939ef41242f9a011c6538e16ad,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4927.81,1863,1266,597,22077,8,855,12860,3.75989 +e81927a0d98c976258a99ea66dd94ce946c76779650be126de769c6ad4a9e15c,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,15933.1,4358,2861,1497,55027,8,2634,54078,11.0553 +e8318822b535ba3dc52423e86eaa33c82b3f68e5a91241746f8ff3fa0dd8a53f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5916.7,2729,1936,793,32391,8,2901,21875,5.78252 +f1973192d144d907a3890ee014f52cb62047655870311695ad526ee60808e8e2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,70036,8588,6402,2186,98356,8,9208,95695,46.2971 +f28d9607dae499e9f824abf7851621947030db7dd2223dd4057f9beb407fade1,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7826.01,2711,1924,787,32178,8,7591,59883,9.39498 +fe9cad7ebe07a2af022824a9236ea6e13d00ce8de4b91efb3b17286fc456823e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8743.69,2711,1924,787,32182,8,9702,30783,9.40277 +ffb5ffbbf9774b8bec1dc190efdafb362fde347b8ad03b8a5c27ba1b09d5b562,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,25898.4,5099,3604,1495,62421,8,7017,171780,22.7093 diff --git a/input/cpsat-bench/Statistics/plots/01_runtime_hist.png b/input/cpsat-bench/Statistics/plots/01_runtime_hist.png new file mode 100644 index 0000000000..3792da86bb Binary files /dev/null and b/input/cpsat-bench/Statistics/plots/01_runtime_hist.png differ diff --git a/input/cpsat-bench/Statistics/plots/02_features_hist.png b/input/cpsat-bench/Statistics/plots/02_features_hist.png new file mode 100644 index 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0000000000..f5fcbf59a9 --- /dev/null +++ b/input/cpsat-bench/Statistics/report.txt @@ -0,0 +1,44 @@ +# CP-SAT raw-data statistical report +instances: 82 +unique problems: 82 +unique applied_params_hash: 1 + +## Status breakdown + OPTIMAL 82 + +## Overall distributions + label | count | mean | std | min | p25 | median | p75 | p95 | max +----------------+------------+------------+------------+------------+------------+------------+------------+------------+----------- + elapsed_ms | 82 | 29,094.8 | 77,538.2 | 486.785 | 7,067.0 | 11,892.9 | 24,336.5 | 69,662.1 | 573,378.0 + num_variables | 82 | 3,707.5 | 1,985.2 | 715.000 | 2,326.0 | 3,241.5 | 4,816.5 | 7,843.0 | 11,127.0 + num_bool | 82 | 2,603.1 | 1,501.1 | 461.000 | 1,531.0 | 2,294.0 | 3,322.8 | 5,854.0 | 8,496.0 + num_int | 82 | 1,104.4 | 499.845 | 254.000 | 787.000 | 949.000 | 1,485.0 | 1,989.0 | 2,653.0 +num_constraints | 82 | 44,417.4 | 23,572.2 | 7,765.0 | 28,510.0 | 39,124.0 | 59,529.5 | 89,169.0 | 136,453.0 + +## Runtime correlations (Pearson / Spearman vs elapsed_ms) +feature n pearson spearman +num_variables 82 0.7006 0.9249 +num_bool 82 0.7160 0.9249 +num_int 82 0.6319 0.9015 +num_constraints 82 0.7020 0.9246 +num_conflicts 82 0.8780 0.5944 +num_branches 82 0.6017 0.7546 + +## Per applied_params_hash + +### hash=5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd n=82 + label | count | mean | std | min | p25 | median | p75 | p95 | max +----------------+------------+------------+------------+------------+------------+------------+------------+------------+----------- + elapsed_ms | 82 | 29,094.8 | 77,538.2 | 486.785 | 7,067.0 | 11,892.9 | 24,336.5 | 69,662.1 | 573,378.0 + num_variables | 82 | 3,707.5 | 1,985.2 | 715.000 | 2,326.0 | 3,241.5 | 4,816.5 | 7,843.0 | 11,127.0 +num_constraints | 82 | 44,417.4 | 23,572.2 | 7,765.0 | 28,510.0 | 39,124.0 | 59,529.5 | 89,169.0 | 136,453.0 + status: OPTIMAL=82 + +## Runtime by num_variables quartile +edges: [715, 2326, 3241, 4816, 11127] + label | count | mean | std | min | p25 | median | p75 | p95 | max +----------------+------------+------------+------------+------------+------------+------------+------------+------------+----------- + Q1[715..2326] | 20 | 5,262.3 | 4,995.2 | 486.785 | 3,237.4 | 4,662.6 | 5,744.0 | 7,940.9 | 25,129.0 + Q2[2326..3241] | 21 | 8,676.8 | 1,805.2 | 5,916.7 | 7,494.3 | 7,945.2 | 9,286.1 | 12,021.2 | 12,282.4 + Q3[3241..4816] | 20 | 18,143.0 | 9,277.4 | 9,935.4 | 12,898.3 | 15,527.4 | 18,545.8 | 35,973.6 | 49,854.2 +Q4[4816..11127] | 21 | 82,640.9 | 141,789.7 | 17,916.6 | 25,432.1 | 28,273.0 | 62,558.7 | 422,905.0 | 573,378.0 diff --git a/input/cpsat-bench/build_problems.py b/input/cpsat-bench/build_problems.py new file mode 100644 index 0000000000..8856e43bc0 --- /dev/null +++ b/input/cpsat-bench/build_problems.py @@ -0,0 +1,122 @@ +""" +Scan `raw-data/*.meta.jsonl` and emit `problems.jsonl` — one JSON per line, +sorted by SHA for deterministic diff. + +Each `____seed.meta.jsonl` file is a single JSON +object representing one baseline solver run. This script concatenates them +into the JSONL format `_lib.sampler` / `_lib.rebaseline` / `_lib.evaluator` +consume. + +Usage: + python3 input/cpsat-bench/build_problems.py [flags] + +Flags: + --filter-decisive keep only OPTIMAL / FEASIBLE rows + --applied-params-hash HASH restrict to one param profile (prefix match ok) + --dry-run print counts but don't write + --out PATH output path (default: problems.jsonl in bench root) + +Output schema (one row per meta.jsonl entry): + { + "problem_sha256": "", + "problem_filename": ".cpsat.pb", + "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 6896.45, ...}, + "cpsat_response_stats": {"num_branches": 79884, ...}, + "cpsat_applied_params": {...}, + "features": {"num_constraints": 51623, ...}, + "applied_params_hash": "...", + "z3_version" / "solver" / "path" / ... + } + +Re-run any time raw-data/ changes. Idempotent — overwrites problems.jsonl. +""" +import argparse +import json +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_RAW = _HERE / "raw-data" +_DEFAULT_OUT = _HERE / "problems.jsonl" + +_DECISIVE = ("OPTIMAL", "FEASIBLE") + + +def scan(raw_dir, *, filter_decisive=False, applied_hash_prefix=None): + metas = sorted(raw_dir.glob("*.meta.jsonl")) + if not metas: + raise SystemExit(f"no *.meta.jsonl under {raw_dir}") + rows = [] + bad = 0 + skipped_decisive = 0 + skipped_hash = 0 + for p in metas: + try: + d = json.loads(p.read_text()) + except json.JSONDecodeError as e: + print(f"WARN: bad json {p.name}: {e}", file=sys.stderr) + bad += 1 + continue + if not isinstance(d, dict): + bad += 1 + continue + if "problem_sha256" not in d or "problem_filename" not in d: + print(f"WARN: missing required fields in {p.name}", file=sys.stderr) + bad += 1 + continue + if applied_hash_prefix and not str(d.get("applied_params_hash", ""))\ + .startswith(applied_hash_prefix): + skipped_hash += 1 + continue + if filter_decisive: + res = (d.get("cpsat_status") or {}).get("result") + if res not in _DECISIVE: + skipped_decisive += 1 + continue + rows.append(d) + rows.sort(key=lambda r: (r["problem_sha256"], r.get("applied_params_hash", ""))) + return rows, {"bad": bad, "skipped_decisive": skipped_decisive, + "skipped_hash": skipped_hash, "scanned": len(metas)} + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("--filter-decisive", action="store_true", + help="keep only OPTIMAL / FEASIBLE rows") + ap.add_argument("--applied-params-hash", type=str, default=None, + help="restrict to applied_params_hash starting with this prefix") + ap.add_argument("--dry-run", action="store_true", + help="print counts but don't write") + ap.add_argument("--out", type=pathlib.Path, default=_DEFAULT_OUT, + help=f"output path (default: {_DEFAULT_OUT.name})") + args = ap.parse_args() + + rows, stats = scan(_RAW, + filter_decisive=args.filter_decisive, + applied_hash_prefix=args.applied_params_hash) + print(f"scanned {stats['scanned']} meta.jsonl files") + if stats["bad"]: + print(f" skipped {stats['bad']} malformed") + if stats["skipped_hash"]: + print(f" skipped {stats['skipped_hash']} non-matching applied_params_hash") + if stats["skipped_decisive"]: + print(f" skipped {stats['skipped_decisive']} non-decisive baselines") + print(f" kept {len(rows)} rows") + + if args.dry_run: + print("(dry-run — no write)") + return + + args.out.parent.mkdir(parents=True, exist_ok=True) + with open(args.out, "w") as f: + for r in rows: + f.write(json.dumps(r) + "\n") + try: + rel = args.out.relative_to(_HERE.parent) + except ValueError: + rel = args.out + print(f"wrote {rel} ({len(rows)} rows)") + + +if __name__ == "__main__": + main() diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md new file mode 100644 index 0000000000..0b78469826 --- /dev/null +++ b/input/cpsat-bench/evolve/README.md @@ -0,0 +1,205 @@ +# cpsat-bench — OR-tools CP-SAT 파라미터 튜닝 + +OR-tools CP-SAT (`ortools.sat.python.cp_model`) 파라미터를 진화적 탐색으로 +튜닝. 데이터셋: `input/cpsat-bench/raw-data/` (85개 OPTIMAL 인스턴스, 바이너리 +`CpModelProto`). 목표: baseline 대비 `deterministic_time` 최소화, 정답성 +(`OPTIMAL` / `FEASIBLE`) 유지. + +플랫폼 전반 구조는 [`input/README.md`](../../README.md) 참고. 본 문서는 +cpsat-bench 고유 사항만 다룬다. + +## 디렉토리 구조 + +``` +input/cpsat-bench/ +├── raw-data/ # .cpsat.pb + meta.jsonl +├── problems.jsonl # baseline 실행 기록 (855 rows) +└── evolve/ + ├── config.yaml # bench / LLM / clustering / evaluation + ├── params.json # CP-SAT 파라미터 카탈로그 + ├── adapter.py # solver hooks + ├── _solve_worker.py # ortools 호출 subprocess + ├── phase1_search/ # search / subsolvers + ├── phase2_presolve/ # presolve / probing / symmetry + ├── phase3_lp_cuts/ # LP / cuts / MIP-bridge (W=8) + ├── phase4_unified/ # unified refinement (W=8, 자동 머터리얼) + ├── phase5_custom_subsolvers/ # custom subsolver portfolio (W=8) + └── cache/ # 생성물, 삭제 안전 + ├── stage{1..4}_sample.json + ├── local_baseline.json + ├── phase{N}_best.json + ├── phase{N}_buckets.json # SIZE_BUCKETS 결과 (opt-in) + └── phase{N}_stage3.json # STAGE3_OVERRIDES 결과 (opt-in) +``` + +## 평가 흐름 + +`config.yaml` `bench.evaluation`: + +| 키 | 값 | +|---|---| +| `repeats` | 10 (10회 평균) | +| `score_mode` | `cost` (deterministic_time + cost_ratio) | +| `time_metric` | `dtime` | +| `enable_size_buckets` | `true` — 문제 크기별 override | +| `enable_outlier_stage` | `true` — stage3 outlier 전용 override | + +Cascade: stage1 (10문제, 작은-중간 클러스터) → stage2 (10문제) → stage3 +(5문제, outlier) → stage4 (20문제, 전체 spread). 각 stage gate는 +`cascade_thresholds`. + +비결정 변형 (UNKNOWN / INFEASIBLE)은 abort 안 함 — 실측 (느린) timeout +ratio가 점수에 반영 + `solved_rate^2` drop이 추가 penalty. + +## Clustering (config.yaml `bench.clustering`) + +| 키 | 값 | +|---|---| +| `method` | `kmeans` | +| `feature` | `features.num_constraints` | +| `n_clusters` | 5 | +| `max_baseline_ms` | 120000 (> 120s outlier 제외) | +| `stage_sizes` | stage1=10, stage2=10, stage3=5, stage4=20 | +| `stage_clusters` | stage1=c0+c1, stage2=c2+c3, stage3=c4, stage4=전체 | + +`python -m _lib.sampler cpsat-bench`로 `cache/stage{1..4}_sample.json` 생성. + +## Phase별 surface + +각 phase `initial_program.py`의 EVOLVE-BLOCK에는 세 surface: + +| Surface | 적용 조건 | 용도 | +|---|---|---| +| `GLOBAL_OVERRIDES` | 모든 문제 | phase 기본 튜닝 | +| `SIZE_BUCKETS` | `problem["size"]` (= num_constraints)이 bucket upper 미만 | 크기별 cuts / probing / subsolver mix tradeoff | +| `STAGE3_OVERRIDES` | `stage == "stage3"` AND `problem["is_outlier"]` | long-tail outlier 전용 | + +`get_params(problem, stage)` 적용 순서: +`BASELINE → GLOBAL_OVERRIDES → SIZE_BUCKETS 매치 → STAGE3_OVERRIDES (gated) +→ PHASE_LOCKED`. + +`size` 값은 `adapter.get_problem_size(features)` (= `num_constraints`)에서 +나옴. + +## Phase 5: custom subsolvers + +Phase 1-4가 top-level 파라미터를 튜닝하는 반면, Phase 5는 portfolio에 +**custom subsolver를 추가**한다 (inherited phase4 top-level config는 건드리지 +않음). + +이유: top-level 파라미터는 모든 subsolver (LNS worker 포함)에 적용됨. +expensive propagation을 top-level에 켜면 LNS도 같이 비싸져 portfolio가 +무너짐. Isolated subsolver는 downside를 한 worker로 bound하면서, 도움이 +되면 solution / variable bound sharing으로 portfolio 전체에 lift. + +Phase 5 EVOLVE-BLOCK: + +| Surface | 적용 | +|---|---| +| `CUSTOM_SUBSOLVERS` | 모든 문제 | +| `STAGE3_CUSTOM_SUBSOLVERS` | stage3 outlier 전용 | + +각 entry: +```python +{ + "name": "max_lp_heavy", + "params": {"linearization_level": 2, "add_mir_cuts": True, + "max_num_cuts": 12000, "cut_level": 2}, + "min_constraints": 50000, + "max_constraints": None, +} +``` + +`_solve_worker.py`가 `subsolver_params` 전체를 standalone proto로 빌드 후 +대입 — recent ortools (9.15+)의 nested repeated message in-place mutation +미지원 우회. + +Locked: `random_seed=0`, `num_search_workers=8`, `interleave_search=True` +(cross-worker sharing 전제). + +Phase 5는 terminal — extract 단계 없음. Phase 4가 `unified_prepare_before_dir` +target이라 `python -m _lib.prepare_phase cpsat-bench`로 미리 머터리얼됨. + +## Quick start + +```bash +# 1. raw-data 채우기 (최초 1회) +cd input/cpsat-bench/raw-data && bash load_script.sh && cd - + +# 2. 전체 pipeline (sampler + rebaseline + 5 phases 순차) +./input/run_phase.sh cpsat-bench --pin 2-7 + +# 3. 단계별 +python -m _lib.sampler cpsat-bench # cache/stage{1..4}_sample.json +python -m _lib.self_test cpsat-bench # baseline sanity (stage1) +python -m _lib.rebaseline cpsat-bench # cache/local_baseline.json (10회 평균) +./input/run_phase.sh cpsat-bench 1 --pin 2-7 +./input/run_phase.sh cpsat-bench 2 --pin 2-7 +./input/run_phase.sh cpsat-bench 3 --pin 2-7 +./input/run_phase.sh cpsat-bench 4 --pin 2-7 +./input/run_phase.sh cpsat-bench 5 --pin 2-7 + +# 4. 최종 검증 — run_phase.sh가 마지막 phase 후 자동 생성한 final_program.py 사용 +python -m _lib.final_verify cpsat-bench \ + input/cpsat-bench/evolve/final_program.py +``` + +마지막 phase 완료 후 `_lib.finalize`가 자동 실행 → +`/evolve/final_program.py`에 phase5 best_program.py 복사. 이 파일이 +canonical evolved 결과. 수동 재생성: `python -m _lib.finalize cpsat-bench`. + +각 non-final phase 완료 후 `_lib.extract_best`가 +`cache/phaseN_best.json` (+ buckets / stage3) 자동 생성. +다음 phase는 그 파일을 inheritance source로 읽음. + +## Worker count 정책 + +| Phase | `num_search_workers` | 이유 | +|---|---|---| +| 1 | 1 (small) / 8 (large profile) | 다른 knob noise 차단 | +| 2 | 1 (small) / 8 (large profile) | presolve clean signal | +| 3 | 8 | subsolver mix engaged | +| 4 | 8 | 통합 refinement | +| 5 | 8 | portfolio sharing 필요 | + +`OPENEVOLVE_PROFILE=large`로 phase 1/2도 W=8 운영 가능 (outlier 튜닝 트랙). + +## Score 공식 (cost mode) + +``` +combined_score = geomean( (b_obj/v_obj)^COST_W * time_ratio ) + * solved_rate^2 + * efficiency^STATS_WEIGHT + +time_ratio = baseline_dtime / variant_dtime (primary) + = baseline_ms / variant_ms (dtime 누락 시 fallback) +cost_ratio = (baseline_obj + eps) / (variant_obj + eps) +``` + +- `deterministic_time` 은 CP-SAT의 하드웨어 독립적 work measure. wall-clock + noise (CPU load / NUMA / thermal) 제거됨. `geomean_speedup` = dtime 기반, + `geomean_wall_speedup` = wall 기반 (진단용). +- 85 baseline 모두 OPTIMAL → variant도 OPTIMAL이면 cost_ratio = 1.0 → score + = geomean(time speedup). +- Variant가 FEASIBLE-but-worse-objective → cost_ratio < 1로 감점. +- Variant가 UNKNOWN/INFEASIBLE → solved_rate^2 drop + 실측 timeout ratio + contribution. +- Efficiency factor: `num_conflicts` (weight 2.0), `num_branches` (weight 1.5) + ratio. + +## Locked params + +`random_seed=0` 전역. Phase별 `num_search_workers` lock. Phase 5는 추가로 +`interleave_search=True` lock. + +위반 시 `combined_score=0` + `locked_violated` artifact. 평가자가 +per-problem `get_params(problem, stage)` 호출 후 lock을 defensive하게 재적용 +— SIZE_BUCKETS / STAGE3_OVERRIDES로 우회 불가. + +## 참고 + +- 파라미터 카탈로그 + LLM prompt reference: `params.json` (rich schema — + type, range, default, desc). `_lib.params_catalog` 로 load + validate. + `config.yaml`의 `prompt.system_message`에 `{{params_reference}}` 토큰으로 + 삽입 가능 (prompt 자동 합성). +- 환경 변수: [`input/README.md`](../../README.md#environment-knobs) 참고. diff --git a/input/cpsat-bench/evolve/_solve_worker.py b/input/cpsat-bench/evolve/_solve_worker.py new file mode 100644 index 0000000000..c651fbb693 --- /dev/null +++ b/input/cpsat-bench/evolve/_solve_worker.py @@ -0,0 +1,239 @@ +""" +Solve one CP-SAT problem via ortools.sat.python.cp_model. +Subprocess worker invoked by cpsat_runner.py. + +argv: + sys.argv[1] JSON dict of {key: value} (params for solver.parameters) + sys.argv[2] path to problem.cpsat.pb (serialized CpModelProto, binary) + sys.argv[3] per-problem timeout in seconds + +stdout: a single JSON line. +""" +import json +import os +import pickle +import sys +import time + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def load_problem(path): + """raw-data layout: each dir contains problem.cpsat.pb (binary CpModelProto).""" + from ortools.sat.python import cp_model + p = str(path) + if p.endswith(".pb") or p.endswith(".bin") or p.endswith(".cpsat.pb"): + with open(p, "rb") as f: + data = f.read() + model = cp_model.CpModel() + model.Proto().ParseFromString(data) + return model + if p.endswith(".pbtxt"): + from google.protobuf import text_format + with open(p, "r") as f: + data = f.read() + model = cp_model.CpModel() + text_format.Parse(data, model.Proto()) + return model + if p.endswith(".pkl"): + with open(p, "rb") as f: + return pickle.load(f) + raise ValueError(f"unknown problem format: {path}") + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + try: + params = json.loads(sys.argv[1]) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params json: {e}"}) + return + + problem_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + try: + from ortools.sat.python import cp_model + from ortools.sat import sat_parameters_pb2 + except ImportError as e: + emit({"result": "Unknown", "elapsed_ms": 0, + "error": f"ortools.sat import: {e}"}) + return + + solver = cp_model.CpSolver() + + # Build params on a standalone SatParameters protobuf (full protobuf API: + # scalars, repeated scalars, AND nested repeated messages like + # subsolver_params), then assign it wholesale. The pybind-wrapped + # solver.parameters in recent ortools (9.15+) cannot mutate nested message + # elements in place, but assigning a complete proto to it works on both the + # wrapped and the classic protobuf bindings. + params_proto = sat_parameters_pb2.SatParameters() + + # Hard wall-clock cap on the solver side too. Parent subprocess.run adds + # an outer +15s grace. + params_proto.max_time_in_seconds = float(timeout_s) + + # Filter out keys that aren't real CpSolverParameters proto fields. + # `timeout_sec` and `tuned` appear in raw-data applied_params but are + # wrapper-level scheduler keys, not proto fields — silently drop instead + # of treating as invalid_param. + _DROP = {"timeout_sec", "tuned"} + + # CP-SAT params are protobuf fields. Repeated fields (lists like + # extra_subsolvers) must be assigned via .extend() rather than `=`. + for k, v in params.items(): + if k in _DROP: + continue + try: + field = getattr(params_proto, k) + except AttributeError as e: + emit({"invalid_param": k, "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + # subsolver_params is a repeated MESSAGE field (repeated SatParameters): + # each entry defines a NAMED parameter set for a single custom subsolver, + # referenced from extra_subsolvers by `name`. The generic list branch + # below can't handle this (extend() rejects dicts), so build the nested + # messages explicitly. Each entry: {"name": str, ...}. + if k == "subsolver_params": + if not isinstance(v, list): + emit({"invalid_param": k, + "error": "subsolver_params must be a list of dicts", + "result": "Unknown", "elapsed_ms": 0}) + return + del field[:] + for entry in v: + if not isinstance(entry, dict): + emit({"invalid_param": k, + "error": f"subsolver_params entry not a dict: {entry!r}", + "result": "Unknown", "elapsed_ms": 0}) + return + sub = field.add() + for sk, sv in entry.items(): + try: + subfield = getattr(sub, sk) + except AttributeError as e: + emit({"invalid_param": f"subsolver_params.{sk}", + "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + if isinstance(sv, list): + try: + del subfield[:] + subfield.extend(sv) + except (AttributeError, TypeError) as e: + emit({"invalid_param": f"subsolver_params.{sk}", + "error": f"list assign: {type(e).__name__}: {e}", + "result": "Unknown", "elapsed_ms": 0}) + return + else: + try: + setattr(sub, sk, sv) + except (AttributeError, TypeError, ValueError) as e: + emit({"invalid_param": f"subsolver_params.{sk}", + "error": f"{type(e).__name__}: {e}", + "result": "Unknown", "elapsed_ms": 0}) + return + continue + if isinstance(v, list): + try: + del field[:] + field.extend(v) + except (AttributeError, TypeError) as e: + emit({"invalid_param": k, + "error": f"list assign: {type(e).__name__}: {e}", + "result": "Unknown", "elapsed_ms": 0}) + return + else: + try: + setattr(params_proto, k, v) + except AttributeError as e: + emit({"invalid_param": k, "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + except (TypeError, ValueError) as e: + emit({"invalid_param": k, + "error": f"{type(e).__name__}: {e}", + "result": "Unknown", "elapsed_ms": 0}) + return + + # Hand the fully-built proto to the solver (replaces its internal params). + solver.parameters = params_proto + + try: + model = load_problem(problem_path) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"problem load: {e}"}) + return + + t0 = time.monotonic() + try: + status = solver.Solve(model) + except Exception as e: + elapsed_ms = int((time.monotonic() - t0) * 1000) + emit({"result": "Unknown", "elapsed_ms": elapsed_ms, + "error": f"Solve() raised: {e}"}) + return + elapsed_ms = int((time.monotonic() - t0) * 1000) + + label_map = { + cp_model.OPTIMAL: "OPTIMAL", + cp_model.FEASIBLE: "FEASIBLE", + cp_model.INFEASIBLE: "INFEASIBLE", + cp_model.UNKNOWN: "UNKNOWN", + cp_model.MODEL_INVALID: "MODEL_INVALID", + } + label = label_map.get(status, "UNKNOWN") + + stats = {} + for k, fn in ( + ("num_branches", "NumBranches"), + ("num_conflicts", "NumConflicts"), + ("num_booleans", "NumBooleans"), + ("wall_time", "WallTime"), + ("user_time", "UserTime"), + ): + try: + v = getattr(solver, fn)() + if isinstance(v, (int, float)): + stats[k] = v + except Exception: + pass + + # deterministic_time: hardware-independent work measure exposed only via + # the response proto, not as a solver method. Critical for fair speedup + # comparison across HW / system load — score.py uses it as the primary + # time_ratio with wall_time as fallback. + try: + resp = solver.ResponseProto() + dt = float(resp.deterministic_time) + if dt > 0: + stats["deterministic_time"] = dt + except Exception: + pass + + obj = None + if status in (cp_model.OPTIMAL, cp_model.FEASIBLE): + try: + obj = float(solver.ObjectiveValue()) + except Exception: + obj = None + + out = {"result": label, "elapsed_ms": elapsed_ms, "stats": stats} + if obj is not None: + out["objective"] = obj + emit(out) + + +if __name__ == "__main__": + main() + sys.stdout.flush() + sys.stderr.flush() + os._exit(0) diff --git a/input/cpsat-bench/evolve/adapter.py b/input/cpsat-bench/evolve/adapter.py new file mode 100644 index 0000000000..386fae5063 --- /dev/null +++ b/input/cpsat-bench/evolve/adapter.py @@ -0,0 +1,38 @@ +""" +cpsat-bench solver hooks. Consumed by every _lib module via +bench_paths.load_adapter(). +""" + +SOLVER_NAME = "cpsat" + +# Field paths inside problems.jsonl entries. +PROBLEM_FILE_FIELD = "problem_filename" +STATUS_FIELD = "cpsat_status" # {"result", "elapsed_ms", "objective_value", ...} +STATS_FIELD = "cpsat_response_stats" # solver counters +FEATURES_FIELD = "features" # nested {"num_variables", "num_constraints", ...} +OBJECTIVE_FIELD = "objective_value" # path inside STATUS_FIELD + +# Result categorization. +DECISIVE_RESULTS = ("OPTIMAL", "FEASIBLE") +DECIDED_RESULTS = ("OPTIMAL", "FEASIBLE", "INFEASIBLE") + +# Solver counters surfaced into metrics / artifacts. +KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", + "wall_time", "user_time", "deterministic_time") + +# stats_weights for the efficiency factor (lifted from old cpsat score.py). +STATS_WEIGHTS = { + "num_conflicts": 2.0, + "num_branches": 1.5, +} + +# Score mode for _lib.scorer.score(). +SCORE_MODE = "cost" + +# Worker-count knob this solver uses (None when not applicable). +WORKERS_KEY = "num_search_workers" + + +def get_problem_size(features): + """Feature value used by the size-bucketing surface (SIZE_BUCKETS).""" + return int((features or {}).get("num_constraints") or 0) diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml new file mode 100644 index 0000000000..08359523bd --- /dev/null +++ b/input/cpsat-bench/evolve/config.yaml @@ -0,0 +1,436 @@ +# Single config for cpsat-bench. Read by openevolve (dacite, ignores unknown +# top-level keys like `bench` / `parallel_solvers`) AND by input/run_phase.sh +# (via _lib/load_bench_config.py) for shell-side phase orchestration. +# +# === bench: section === +# Consumed by input/run_phase.sh. phases[].iters can be null/omitted → uses +# max_iterations below. +bench: + phases: + - dir: phase1_search + iters: 40 + - dir: phase2_presolve + iters: 40 + - dir: phase3_lp_cuts + iters: 40 + - dir: phase4_unified + iters: 40 + - dir: phase5_custom_subsolvers + iters: 40 + + # When unset, run_phase.sh fires the unified-prep before the last phase. + # cpsat keeps a phase AFTER phase4_unified, so the prep targets phase4 + # specifically. + unified_prepare_before_dir: phase4_unified + + solver_check_cmd: 'python -c "from ortools.sat.python import cp_model"' + solver_install_hint: "install: pip install ortools" + + # === Refactor (2026-06) — paths to per-bench solver-specific files. === + # Resolved relative to /evolve/. + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + unified_dict_name: GLOBAL_OVERRIDES + + # === Clustering / stage sampling (consumed by _lib.sampler). === + clustering: + method: quintile # kmeans | quintile | thresholds + feature: cpsat_status.elapsed_ms + n_clusters: 5 + max_baseline_ms: 120000 # drop outliers > 120s from sample pool + spread: quintile + stage_sizes: + stage1: 5 + stage2: 5 + stage3: 5 + stage4: 20 + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + + # === Evaluation behavior (consumed by _lib.evaluator_core). === + evaluation: + repeats: 10 # 10-run averaging by default + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: cost # cpsat: cost (dtime + cost_ratio) + time_metric: dtime + cost_weight: 1.0 + enable_size_buckets: true # opt-in: SIZE_BUCKETS per-problem overrides + enable_outlier_stage: true # opt-in: STAGE3_OVERRIDES outlier-only knobs + +# === Custom cpsat-bench knobs (silently ignored by openevolve dacite) === +# parallel_solvers: total concurrent CP-SAT worker subprocesses per stage. +# - Read by shared/evaluator.py, rebaseline_local.py, baseline_params self-test. +# - Sets the FALLBACK core-pool size when OPENEVOLVE_CORE_RANGE is unset +# (cores 1..parallel_solvers; core 0 reserved for kernel housekeeping). +# - Effective concurrency = len(core_pool) // num_search_workers (floor). +# A solve with workers=8 on a 6-core pool runs sequentially (1 block); +# a phase with workers=1 on a 6-core pool runs 6-way parallel. +# - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. +parallel_solvers: 8 + +max_iterations: 40 +checkpoint_interval: 10 +log_level: "DEBUG" +random_seed: 42 + +diff_based_evolution: true +max_code_length: 20000 + +early_stopping_patience: null +convergence_threshold: 0.001 +early_stopping_metric: "combined_score" + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.8 + timeout: 300 # raised from 180: a capped query needs ~180-250s; no retry on timeout now (see claude_code.py) + retries: 2 + retry_delay: 10 + reasoning_effort: "medium" + max_thinking_tokens: 4000 # cap extended thinking; uncapped it overflowed the timeout and burned a 2nd retry for nothing + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 300 + max_thinking_tokens: 4000 + +prompt: + system_message: | + You tune OR-tools CP-SAT (ortools.sat.python.cp_model) solver parameters for a + workload of 85 OPTIMAL-objective instances stored as serialized CpModelProto + (problem.cpsat.pb). Median baseline runtime ≈ 4.6s, max ≈ 107s. + + The initial_program.py exposes an EVOLVE-BLOCK dict (per phase: + search/subsolvers, presolve, lp/cuts, or unified). Your job is to mutate + this dict to MAXIMIZE combined_score = geomean(cost_ratio * time_ratio) + * solved_rate^2 * efficiency^STATS_WEIGHT. + + time_ratio = baseline_dtime / variant_dtime (deterministic_time — + CP-SAT's hardware-independent work counter). Score is NOT noisy across + runs; reductions in deterministic_time directly translate to score wins. + Wall_time is reported as a diagnostic only (geomean_wall_speedup metric). + + Score mode = cost: variant must reach OPTIMAL or FEASIBLE on every baseline + OPTIMAL problem, otherwise the per-problem ratio is 1e-6 (large penalty, + geomean punishes one regression heavily). All baselines are OPTIMAL, so a + variant that bails to FEASIBLE with a worse objective_value scores below 1.0. + + Optimization target: MINIMIZE RUNTIME (deterministic_time), aggressively. + Memory budget is loose — `max_memory_in_mb` default 10000 is far above + what this workload needs; do NOT trade runtime for memory. Concretely: + - Larger `max_num_cuts` is fine if it cuts conflicts / branches. + - Larger LP / cut storage (`cut_cleanup_target`, `clause_cleanup_target`, + `max_consecutive_inactive_count`) is fine if it stabilizes search. + - Larger `presolve_inclusion_work_limit`, `merge_at_most_one_work_limit`, + `merge_no_overlap_work_limit` is fine if presolve shaves real work. + - Larger `clause_cleanup_period` (keep more learned clauses longer) is + fine — clause memory is cheap. + - Larger `probing_num_combinations_limit`, deeper probing + (`cp_model_probing_level=2..3`) is fine when it pays off in search. + - LNS / shared-tree memory (solution_pool_size, + shared_tree_max_nodes_per_worker) — feel free to grow. + NEVER pick a "smaller / cheaper" value just because it sounds frugal. + The only signal is: does dtime go down without regressing solved_rate? + + Hard rules: + - Do NOT modify locked keys: random_seed, num_search_workers, timeout_sec. + (evaluator returns combined_score=0 + locked_violated artifact on violation.) + num_search_workers is pinned PER PHASE by the phase's PHASE_LOCKED: + phase1/2: num_search_workers=1 (single-worker, no subsolver mix) + phase3/4: num_search_workers=8 (multi-worker, subsolver mix engaged) + get_params() re-applies PHASE_LOCKED last so phase1/2 inherited dicts + don't leak a higher worker count into a lower-worker phase. + - Use only valid CpSolverParameters proto field names. Unknown keys + cause AttributeError → evaluator returns 0 + surfaces invalid_param. + - Respect types: bool true/false, int, float, list of strings (subsolver names). + - Repeated proto fields (extra_subsolvers, ignore_subsolvers, etc.) accept + Python lists; the worker handles del+extend internally. + - Subsolver-mix keys (extra_subsolvers, ignore_subsolvers, diversify_lns_params, + repair_hint, use_feasibility_jump/pump) only meaningfully fire when + num_search_workers > 1 — they are most impactful in phase 3 and beyond. + + Common CP-SAT knobs worth exploring (non-exhaustive): + search/subsolvers: + extra_subsolvers, ignore_subsolvers — subsolver names include + "default_lp", "no_lp", "max_lp", "core", "quick_restart", + "reduced_costs", "lb_tree_search", "probing_search", "objective_lb_search", + "objective_shaving_search_no_lp", "pseudo_costs", "fixed", + "feasibility_pump", "feasibility_jump" + interleave_search: bool + use_feasibility_jump, use_feasibility_pump: bool + diversify_lns_params: bool + repair_hint: bool + presolve/probing: + cp_model_probing_level: 0..3 + cp_model_presolve: bool + presolve_use_bva: bool + presolve_bve_threshold: int + symmetry_level: 0..3 + tuned: bool (preset bundle — usually leave true) + lp/cuts: + linearization_level: 0..2 + cut_level: 0..2 + max_num_cuts: int (1000-10000 typical) + add_mir_cuts, add_zero_half_cuts, add_clique_cuts: bool + new_constraints_batch_size: int + mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance: numeric + + Per-round artifacts show speedup (dtime-based), regression, base_obj vs + obj for each problem. Read them: which knob change helped the long Q5 + problems vs the fast Q1 ones? Watch num_conflicts and num_branches — + speedup with same conflict count is presolve / propagation work shaved + off; speedup with fewer conflicts is a smarter search. + + Per-problem param resolution (NEW): get_params(problem=None, stage=None). + Three evolution surfaces per phase: + GLOBAL_OVERRIDES — every problem + SIZE_BUCKETS — list[(upper_exclusive_num_constraints, dict)]; + first match wins. small/mid/large = <50k / 50–150k / ≥150k. + STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND + problem["is_outlier"] (residual outliers from + Statistics/outliers_top.csv). Tune aggressive knobs + that help long-tail problems without regressing the + fast/mid groups (those score stage1/2 and reject the + candidate before stage3 ever runs). + + Stage layout in cascade: + stage1 (10) fast cluster centers — must not regress + stage2 (10) mid cluster centers — must not regress + stage3 (5) OUTLIER problems only — STAGE3_OVERRIDES active here. + Stratified small/mid/large + from outliers_top.csv, + cap=1.5M ms baseline. + PHASE1/2 (W=1) SKIP THIS + stage and go direct to stage4. + PHASE3/4 (W=8) run it. + stage4 (20) broad spread — final score + + Outliers (~30 of 430 problems) have ~80x more conflicts, ~13x more branches + vs baseline median (see Statistics/outliers_report.txt). Two clusters: + small (<50k constraints, hard small SAT-like) and large (>=150k, LP-heavy). + Mid bucket has 0 outliers. So SIZE_BUCKETS small/large entries pair well + with STAGE3_OVERRIDES. + + === CP-SAT CpSolverParameters Reference (ortools 9.14.6206) === + Authoritative field list (275 fields). Use ONLY these names in get_params(). + Defaults shown; setattr-compatible via cp_model.CpSolver().parameters. + + parallel/workers: + diversify_lns_params(bool=F), extra_subsolvers(list), + filter_subsolvers(list), ignore_subsolvers(list), + interleave_search(bool=F), lb_relax_num_workers_threshold(int=16), + log_subsolver_statistics(bool=F), num_full_subsolvers(int=0), + num_search_workers(int=0)*LOCKED, num_workers(int=0), + share_binary_clauses(bool=T), share_glue_clauses(bool=F), + share_glue_clauses_dtime(double=1.0), share_level_zero_bounds(bool=T), + share_objective_bounds(bool=T), shared_tree_num_workers(int=0), + subsolvers(list), use_objective_lb_search(bool=F) + + seed/reproducibility: + ignore_names(bool=T), instantiate_all_variables(bool=T), + lns_initial_deterministic_limit(double=0.1), + log_search_progress(bool=F), max_deterministic_time(double=inf), + max_num_deterministic_batches(int=0), + permute_presolve_constraint_order(bool=F), + permute_variable_randomly(bool=F), + presolve_probing_deterministic_time_limit(double=30.0), + probing_deterministic_time_limit(double=1.0), + random_seed(int=1)*LOCKED, + shaving_deterministic_time_in_probing_search(double=0.001), + shaving_search_deterministic_time(double=0.1), + symmetry_detection_deterministic_time_limit(double=1.0) + + timeout/limits: + max_memory_in_mb(int64=10000), max_number_of_conflicts(int64=∞), + max_presolve_iterations(int=3), max_time_in_seconds(double=inf), + stop_after_first_solution(bool=F), stop_after_presolve(bool=F), + stop_after_root_propagation(bool=F) + + search/branching: + boolean_encoding_level(int=1), fix_variables_to_their_hinted_value(bool=F), + hint_conflict_limit(int=10), + initial_polarity(enum=1:{POLARITY_TRUE,POLARITY_FALSE,POLARITY_RANDOM}), + initial_variables_activity(double=0.0), + polarity_exploit_ls_hints(bool=F), polarity_rephase_increment(int=1000), + preferred_variable_order(enum=0:{IN_ORDER,IN_REVERSE_ORDER,IN_RANDOM_ORDER}), + random_polarity_ratio(double=0.0), repair_hint(bool=F), + search_branching(enum=0:{AUTOMATIC_SEARCH,FIXED_SEARCH,PORTFOLIO_SEARCH, + LP_SEARCH,PSEUDO_COST_SEARCH,PORTFOLIO_WITH_QUICK_RESTART_SEARCH, + HINT_SEARCH,PARTIAL_FIXED_SEARCH,RANDOMIZED_SEARCH}), + use_combined_no_overlap(bool=F), use_disjunctive_constraint_in_cumulative(bool=T), + use_dual_scheduling_heuristics(bool=T), use_erwa_heuristic(bool=F), + use_extended_probing(bool=T), use_implied_bounds(bool=T), + use_lns(bool=T), use_lns_only(bool=F), + use_objective_shaving_search(bool=F), use_phase_saving(bool=T), + use_strong_propagation_in_disjunctive(bool=F) + + conflict/restart: + binary_minimization_algorithm(enum=1:{NO_BINARY_MINIMIZATION, + BINARY_MINIMIZATION_FIRST, + BINARY_MINIMIZATION_FIRST_WITH_TRANSITIVE_REDUCTION, + BINARY_MINIMIZATION_WITH_REACHABILITY,EXPERIMENTAL_BINARY_MINIMIZATION}), + blocking_restart_multiplier(double=1.4), blocking_restart_window_size(int=5000), + default_restart_algorithms(str='LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART'), + feasibility_jump_restart_factor(int=1), glucose_decay_increment(double=0.01), + glucose_decay_increment_period(int=5000), glucose_max_decay(double=0.95), + max_variable_activity_value(double=1e+100), + minimization_algorithm(enum=2:{NONE,SIMPLE,RECURSIVE,EXPERIMENTAL}), + restart_algorithms(list:{NO_RESTART,LUBY_RESTART, + DL_MOVING_AVERAGE_RESTART,LBD_MOVING_AVERAGE_RESTART,FIXED_RESTART}), + restart_dl_average_ratio(double=1.0), restart_lbd_average_ratio(double=1.0), + restart_period(int=50), restart_running_window_size(int=50), + subsumption_during_conflict_analysis(bool=T), use_blocking_restart(bool=F), + variable_activity_decay(double=0.8) + + clause db: + clause_cleanup_lbd_bound(int=5), + clause_cleanup_ordering(enum=0:{CLAUSE_ACTIVITY,CLAUSE_LBD}), + clause_cleanup_period(int=10000), + clause_cleanup_protection(enum=0:{PROTECTION_NONE,PROTECTION_ALWAYS,PROTECTION_LBD}), + clause_cleanup_ratio(double=0.5), clause_cleanup_target(int=0), + pb_cleanup_increment(int=200), pb_cleanup_ratio(double=0.5) + + presolve: + convert_intervals(bool=T), cp_model_presolve(bool=T), + cp_model_probing_level(int=2), + encode_complex_linear_constraint_with_integer(bool=F), + expand_alldiff_constraints(bool=F), expand_reservoir_constraints(bool=T), + expand_reservoir_using_circuit(bool=F), + find_big_linear_overlap(bool=T), infer_all_diffs(bool=T), + max_pairs_pairwise_reasoning_in_no_overlap_2d(int=1250), + max_size_to_create_precedence_literals_in_disjunctive(int=60), + merge_at_most_one_work_limit(double=1e8), + merge_no_overlap_work_limit(double=1e12), + mip_presolve_level(int=2), presolve_blocked_clause(bool=T), + presolve_bva_threshold(int=1), presolve_bve_clause_weight(int=3), + presolve_bve_threshold(int=500), + presolve_extract_integer_enforcement(bool=F), + presolve_inclusion_work_limit(int64=1e8), + presolve_substitution_level(int=1), presolve_use_bva(bool=T), + remove_fixed_variables_early(bool=T), symmetry_level(int=2), + disable_constraint_expansion(bool=F) + + LP/cuts: + add_cg_cuts(bool=T), add_clique_cuts(bool=T), add_lin_max_cuts(bool=T), + add_lp_constraints_lazily(bool=T), add_mir_cuts(bool=T), + add_objective_cut(bool=F), add_rlt_cuts(bool=T), add_zero_half_cuts(bool=T), + cut_active_count_decay(double=0.8), cut_cleanup_target(int=1000), + cut_level(int=1), cut_max_active_count_value(double=1e10), + exploit_all_lp_solution(bool=T), exploit_all_precedences(bool=F), + exploit_integer_lp_solution(bool=T), feasibility_jump_linearization_level(int=2), + linearization_level(int=1), lp_dual_tolerance(double=1e-7), + lp_primal_tolerance(double=1e-7), max_all_diff_cut_size(int=64), + max_consecutive_inactive_count(int=100), max_cut_rounds_at_level_zero(int=1), + max_integer_rounding_scaling(int=600), max_num_cuts(int=10000), + new_constraints_batch_size(int=50), only_add_cuts_at_level_zero(bool=F), + use_energetic_reasoning_in_no_overlap_2d(bool=F), + use_optimization_hints(bool=T), use_overload_checker_in_cumulative(bool=F), + use_pb_resolution(bool=F), use_timetabling_in_no_overlap_2d(bool=F) + + MIP bridge: + mip_automatically_scale_variables(bool=T), mip_check_precision(double=1e-4), + mip_compute_true_objective_bound(bool=T), mip_drop_tolerance(double=1e-16), + mip_max_activity_exponent(int=53), mip_max_bound(double=1e7), + mip_max_valid_magnitude(double=1e20), mip_scale_large_domain(bool=F), + mip_treat_high_magnitude_bounds_as_infinity(bool=F), + mip_var_scaling(double=1.0), mip_wanted_precision(double=1e-6) + + LNS: + lns_initial_difficulty(double=0.5) + + feasibility jump/pump: + feasibility_jump_batch_dtime(double=0.1), feasibility_jump_decay(double=0.95), + feasibility_jump_enable_restarts(bool=T), + feasibility_jump_max_expanded_constraint_size(int=500), + feasibility_jump_var_perburbation_range_ratio(double=0.2), + feasibility_jump_var_randomization_probability(double=0.05), + use_feasibility_jump(bool=T), use_feasibility_pump(bool=T), + violation_ls_compound_move_probability(double=0.5), + violation_ls_perturbation_period(int=100) + + optimization: + auto_detect_greater_than_at_least_one_of(bool=T), + binary_search_num_conflicts(int=-1), exploit_best_solution(bool=F), + exploit_relaxation_solution(bool=F), + minimize_reduction_during_pb_resolution(bool=F), + optimize_with_core(bool=F), optimize_with_lb_tree_search(bool=F), + optimize_with_max_hs(bool=F), use_absl_random(bool=F), + use_precedences_in_disjunctive_constraint(bool=T) + + other (notable): + absolute_gap_limit(double=1e-4), at_most_one_max_expansion_size(int=3), + clause_activity_decay(double=0.999), core_minimization_level(int=2), + count_assumption_levels_in_lbd(bool=T), cover_optimization(bool=T), + cp_model_use_sat_presolve(bool=T), detect_linearized_product(bool=F), + detect_table_with_cost(bool=F), encode_cumulative_as_reservoir(bool=F), + exploit_objective(bool=T), fill_additional_solutions_in_response(bool=F), + filter_sat_postsolve_clauses(bool=F), find_multiple_cores(bool=T), + inprocessing_dtime_ratio(double=0.2), inprocessing_minimization_dtime(double=1.0), + inprocessing_probing_dtime(double=1.0), interleave_batch_size(int=0), + linear_split_size(int=100), max_alldiff_domain_size(int=256), + max_domain_size_when_encoding_eq_neq_constraints(int=16), + max_lin_max_size_for_expansion(int=0), + max_sat_assumption_order(enum=0:{DEFAULT_ASSUMPTION_ORDER, + ORDER_ASSUMPTION_BY_DEPTH,ORDER_ASSUMPTION_BY_WEIGHT}), + max_sat_stratification(enum=1:{STRATIFICATION_NONE, + STRATIFICATION_DESCENT,STRATIFICATION_ASCENT}), + min_orthogonality_for_lp_constraints(double=0.05), + minimize_shared_clauses(bool=T), new_linear_propagation(bool=T), + num_conflicts_before_strategy_changes(int=0), num_violation_ls(int=0), + polish_lp_solution(bool=F), probing_num_combinations_limit(int=20000), + propagation_loop_detection_factor(double=10.0), + pseudo_cost_reliability_threshold(int64=100), + random_branches_ratio(double=0.0), randomize_search(bool=F), + relative_gap_limit(double=0.0), root_lp_iterations(int=2000), + save_lp_basis_in_lb_tree_search(bool=F), + search_random_variable_pool_size(int64=0), + shaving_search_threshold(int64=64), solution_pool_size(int=3), + strategy_change_increase_ratio(double=0.0), table_compression_level(int=2), + use_exact_lp_reason(bool=T), use_lb_relax_lns(bool=T), + use_linear3_for_no_overlap_2d_precedences(bool=T), use_ls_only(bool=F), + use_optional_variables(bool=F), use_probing_search(bool=F), + use_rins_lns(bool=T), use_sat_inprocessing(bool=T), + use_shared_tree_search(bool=F), use_symmetry_in_lp(bool=F), + variables_shaving_level(int=-1) + + Subsolver name strings (for extra_subsolvers/ignore_subsolvers/subsolvers): + "default_lp", "no_lp", "max_lp", "core", "quick_restart", + "reduced_costs", "lb_tree_search", "probing_search", + "objective_lb_search", "objective_shaving_search_no_lp", + "pseudo_costs", "fixed", "feasibility_pump", "feasibility_jump" + +database: + population_size: 50 + archive_size: 20 + num_islands: 3 + elite_selection_ratio: 0.2 + exploitation_ratio: 0.7 + similarity_threshold: 0.95 + +evaluator: + timeout: 8000 # 133 min wall cap per variant. Covers: + # - small profile stage4 worst (≈ 20 problems + # × 120s × 1.3 / parallel_solvers ≈ 3120s) + # - large profile single outlier worst + # (top residual baseline ≈ 5707s, + # variant timeout = baseline × 1.3 ≈ 7420s) + # Drop to 1800 if you only run small profile to + # tighten cascade scheduling. + cascade_evaluation: true # stage1 → stage2 → stage3 (chains to stage4 inside) + cascade_thresholds: [1.03, 1.03, 1.03] + # threshold[0] = stage1 combined_score gate → stage2 (≥3% gain) + # threshold[1] = stage2 gate → stage3 + # threshold[2] = stage3 gate → stage4 (read by evaluator.evaluate_stage3 + # via runtime.cascade_threshold — openevolve cascade exposes + # only 3 stage slots, so stage4 is chained inside stage3). + parallel_evaluations: 1 # FIXED 1. All solver concurrency via + # OPENEVOLVE_PARALLEL_SOLVERS (inner threadpool). + # parallel_evaluations × parallel_solvers × + # num_search_workers=8 RAM/CPU blowup otherwise. diff --git a/input/cpsat-bench/evolve/final_program.py b/input/cpsat-bench/evolve/final_program.py new file mode 100644 index 0000000000..166843014e --- /dev/null +++ b/input/cpsat-bench/evolve/final_program.py @@ -0,0 +1,305 @@ +""" +AUTO-GENERATED by materialize_final.py — DO NOT EDIT BY HAND. + +Self-contained final CP-SAT parameter program. Bakes every inherited dict +from phase1..4 plus phase5 custom subsolvers. No JSON loads, no relative +imports — drop-in for final_verify.py / benchmark_final.py. + +Source phase5 best_program: + phase5_custom_subsolvers/openevolve_output/best/best_program.py +""" + +BASELINE = {'interleave_search': True, 'num_search_workers': 1, 'random_seed': 0} + +# Inherited from shared/phase4_best.json (union of phase1+2+3 winners + phase4 +# unified pass). +GLOBAL_OVERRIDES = {'add_clique_cuts': True, + 'add_mir_cuts': True, + 'add_objective_cut': True, + 'add_zero_half_cuts': True, + 'clause_cleanup_lbd_bound': 6, + 'clause_cleanup_ordering': 1, + 'clause_cleanup_period': 20000, + 'clause_cleanup_target': 80000, + 'cp_model_probing_level': 2, + 'cut_level': 1, + 'exploit_best_solution': True, + 'exploit_relaxation_solution': True, + 'interleave_search': True, + 'linearization_level': 1, + 'max_num_cuts': 4000, + 'merge_at_most_one_work_limit': 500000000, + 'mip_check_precision': 1e-06, + 'mip_drop_tolerance': 1e-07, + 'mip_max_bound': 10000000.0, + 'mip_var_scaling': 1, + 'new_constraints_batch_size': 100, + 'presolve_inclusion_work_limit': 500000000, + 'presolve_use_bva': True, + 'probing_num_combinations_limit': 50000, + 'root_lp_iterations': 2500, + 'share_glue_clauses': True, + 'share_glue_clauses_dtime': 0.25, + 'solution_pool_size': 5, + 'symmetry_level': 2} + +# Inherited from shared/phase4_buckets.json. Selected by num_constraints +# (first bucket whose upper > num_constraints wins). +SIZE_BUCKETS = [ + (50000, {'cp_model_probing_level': 3, + 'cut_level': 0, + 'interleave_search': False, + 'linearization_level': 0, + 'max_num_cuts': 0, + 'merge_at_most_one_work_limit': 1000000, + 'presolve_inclusion_work_limit': 50000000, + 'presolve_use_bva': True, + 'probing_num_combinations_limit': 10000, + 'search_branching': 0, + 'symmetry_level': 3}), + (150000, {'add_clique_cuts': False, + 'add_mir_cuts': True, + 'add_zero_half_cuts': False, + 'cp_model_probing_level': 2, + 'cut_cleanup_target': 2500, + 'cut_level': 1, + 'exploit_best_solution': True, + 'exploit_relaxation_solution': True, + 'linearization_level': 1, + 'max_num_cuts': 6000, + 'root_lp_iterations': 3500, + 'search_branching': 0, + 'use_strong_propagation_in_disjunctive': True}), + (float('inf'), {'cut_cleanup_target': 2000, + 'cut_level': 0, + 'exploit_all_precedences': True, + 'ignore_subsolvers': ['max_lp'], + 'linearization_level': 0, + 'max_num_cuts': 1500, + 'root_lp_iterations': 4000, + 'search_branching': 0, + 'use_strong_propagation_in_disjunctive': True}), +] + +# Inherited from shared/phase4_stage3.json. Applied only when +# stage == "stage3" and problem.is_outlier. NOTE: the current evaluator +# never passes stage="stage3" (small profile cascades stage1->stage2; large +# profile passes stage="large"), so this dict is dead code in practice. +STAGE3_OVERRIDES = {'clause_cleanup_lbd_bound': 8, + 'clause_cleanup_ordering': 1, + 'clause_cleanup_period': 25000, + 'clause_cleanup_target': 200000, + 'cp_model_probing_level': 3, + 'cut_active_count_decay': 0.9, + 'cut_level': 1, + 'diversify_lns_params': True, + 'exploit_all_precedences': True, + 'exploit_relaxation_solution': True, + 'extra_subsolvers': ['core', + 'quick_restart', + 'reduced_costs', + 'lb_tree_search', + 'feasibility_jump'], + 'inprocessing_dtime_ratio': 0.47, + 'inprocessing_minimization_dtime': 2.3, + 'inprocessing_probing_dtime': 2.3, + 'linearization_level': 1, + 'max_cut_rounds_at_level_zero': 3, + 'max_num_cuts': 6000, + 'merge_at_most_one_work_limit': 1000000000, + 'merge_no_overlap_work_limit': 2000000000000.0, + 'num_violation_ls': 3, + 'polish_lp_solution': True, + 'presolve_inclusion_work_limit': 1000000000, + 'probing_num_combinations_limit': 120000, + 'repair_hint': True, + 'root_lp_iterations': 10000, + 'solution_pool_size': 8, + 'symmetry_level': 3, + 'use_feasibility_jump': True, + 'use_feasibility_pump': True, + 'use_strong_propagation_in_disjunctive': True} + +# Phase5 custom subsolvers (always-on, gated by needs_objective / +# min_constraints / max_constraints). +CUSTOM_SUBSOLVERS = [{'name': 'lb_tree_strong', + 'needs_objective': True, + 'params': {'linearization_level': 2, + 'optimize_with_lb_tree_search': True, + 'root_lp_iterations': 4000, + 'save_lp_basis_in_lb_tree_search': True}}] + +# Phase5 stage3-only extra custom subsolvers (same dead-code caveat as +# STAGE3_OVERRIDES). +STAGE3_CUSTOM_SUBSOLVERS = [{'max_constraints': 50000, + 'name': 'pseudo_cost_small', + 'params': {'pseudo_cost_reliability_threshold': 50, 'search_branching': 4, 'symmetry_level': 3}}, + {'min_constraints': 150000, + 'name': 'lp_exploit_large', + 'params': {'add_mir_cuts': True, + 'add_rlt_cuts': True, + 'exploit_relaxation_solution': True, + 'linearization_level': 2, + 'max_num_cuts': 20000, + 'polish_lp_solution': True}}] + +# SHAs whose original model has an objective. Used by needs_objective gating +# so an objective-requiring subsolver is not added to a feasibility-only +# model (which would make CP-SAT drop most of the portfolio). +OBJECTIVE_SHAS = frozenset(['00a438e9a02d3895ebcf5e1eea77b3c1fba28d76ca73723c7befe71f327aae82', + '00d6004a1249fd1379ebc735c77d151f084727ad328b69df623d4a7b4d63f5f6', + '0114ccef241fb15dac55b5044076da48fb0aae94ced62fb800c6cb80fdb4bece', + '01a1d9063de56c5adc87428afe7ffefcf1deb58cbf1d7e5cecf8362cbd571396', + '03012de510cde9db5046196d7b447fa299e3cfd3ba2cb2c12404376edc1f241a', + '06b08825173bc966570e184979982304ea8d33a41ce5d64b8c62eaac29c7cd1e', + '079937773cba535777985106b2d07c563265fb815d36abdf852940f4585fcf5b', + '0b1eb462e501c127119ebaed5477068246bb55d5ff64a25323863488d0b780c3', + '0b67bf459c1faa274487feb34ff95759660b3032c8a8e8a27b6212252c1b0728', + '10211eedd813db0025927acaefc67fd413b2d8c2fb60009f35bb49cfc57584e7', + '1371ca0e9b6120f558b2f5e1b9c3f335092dda51b0754c072c5033e3f25cc3d5', + '1509ac1acb85d66d0da78b5490196654e1f8b82a4cabf3f550511bcb6681d1ca', + '1b486cb1a9038477ba0dc44f568c384b38b7f66c62d1d0e27feada15ee63acc6', + '2107376b09925740dc3a50075ebd92ba4853e79a17d659fb67a1785646a3121f', + '27132094730caf2088c28c764b2146f0bfeceb874b7efa9b456a1ee06c4c3ff9', + '2981bd841805c451cc2932b097f423d5e2dcce99d4c3f5c63eb3eda1fb515539', + '2e958d4f0beadd78d7dff9e9d9f386d3a0f00860ff2703e69b11975d544e3757', + '2fa57a166e49216765ff3e9df3a99e51c9a6eb307be9bab396ea75f82f17fabf', + '2fe6606b7880e2c63079285e403124a757f7a54ff8ff326e19ee50d5ccba716a', + '3a0d61445e568f2995e6d3e7330d339f92d94e14d5ee89b2abf2ed96e86a3c70', + '3bc12c2cf7bd6010001c8a460cc0184aed048ae950e55078fd9428a3b8d1fc95', + '41f6ecf9ca6462dcb7ca49e028b2970175579c0250058e0430e979a04435bf09', + '45edc06cca416c89589b5bbf4dd6e3beca7b1fd3f3ceb4d58e0fa4af5fea29e2', + '4b47d1f4093903ddae684d0c12f44befa58c593196420a6eb9aed30add89ba31', + '4bf5333887900fa3b5ff5811f12d1f5c9c0bb0574018566d4538a620dad14e3b', + '59703dbe66412fd1ab21a7c83351814914d6a3dbbedfe41041b8faf435dd4011', + '5f996769b676f0b952adf679945d3e9b66e463dfccc488f12f6eb14b863657a3', + '68047eeb3643ffa54ee8e6b29d0ca3135b12f9e0f2e30d4b325f2a5a6509bfd5', + '6afa1f30ddaf73d8dfed7146c9365683a3a702aee80c07ab24b35a83bbf815a4', + '6c96fe64d8a7e248bb211262d15a9e02c33afd67841b876ec22da92430ea1d4a', + '6dc3e7cdbc74f23ff22a0eecbde5e1ed26d05cf56459a1ae169419068718c3c4', + '6e93a1a5f114fdfc007b30330e24aa1325660433aaa0794f76ef4848de4580d5', + '724e539c591651e646f2e89f9000b6a040742aa2d989582166ac52e3e7b4a6e0', + '76e68a1cce648c8d73748c810c88f2507f97759063e97d60bdeba8b7450c75d4', + '80d93f459a3aba9b087940fc2f7fbb31d50402f34005177a89face29ebb3adb8', + '835ae7284c43b2522947ad7ad0b1f26429793b7fff94b7a612ea3a1dc548df57', + '83b70413e9333ce9f4929c450b14e28d2b6e4ec28401d68b4b7d59c76c82d8ee', + '865f1171e901d275edb5a10c40d9288ec08619127220e643e36ae1ebee2903b2', + '86b1bf9b38f98be62d7509c448ea0dfd7197af45620ba4b50960bf15254309bf', + '8897295c1a1d2153b3e64712ad6cf8fef4812ff8f7419cf217ba26f03e246f24', + '8d20d7075f0bbe969941be96f336c7f87d3f16fbdb80ed42d6c64c0ff25da1ad', + '8da21152ae4db83b1c03c6d8f8c508ef8b8aa00294095638c498f2bef8daa862', + '9184e368c0e97d4bc93704357120e76ef97fe5f9a0e72f3c148784d450c7723e', + '924038c95c7634a971a91a5ad72819fc2977c87790d06360e0ec5b38be4a219e', + '986e408097d63dbe93210883d12efbd11c7ebfd37fec6e3462bb8a72dd4ade05', + '98c32f98b1912de97866905f8af956745db6c16dde00e157b0d4e45577755233', + '9aba10c5fadecdd17c301a45ddbcef5534a25c861b0a0a9b4943331de602ad8e', + '9b58888c473818a77ecb1aaf2c1032bc87b909c80f837100403b5a83a75cbc87', + '9eda5addedbb8f1c5a6ea9bd5fe469814755a8ecdebb998f9c2598bc4e2308c7', + 'a01db04d1c0cefeec5fe077d42d9b84d55e54bf00cc689787f11200b6f817d36', + 'a0a55fdea86a6868a302e54eadbfe25574d413fdc83eb469675ed9df3568a1db', + 'a46c87c567e8ea0a6fa70198b83d8523a69f38448aeafbb35a956a845987993a', + 'a65ee543b1402a4a356d6b8f8fd45088fd2f68fbbe970c160248bca9cb481181', + 'b37ba1c4e6c4fd6e50b67a7d1cba5d5c41b1c9837b2f020d6174c1459665a756', + 'b5e191a84337537481ab49001854e27b03a9ccded205fe88b9150bfa45529915', + 'b7fa34442c7e8f082f59d288a2714129f51f43774aa2e658712e5da54cfe8635', + 'b848342049b15eda055c33e85c1eb5b7dd0470cdddc487c913c4f4edb41c1177', + 'b87e2b0d6dcc9cc59a03ed6cede32988b1f7db99692b635f75e32f763776d979', + 'ba0fe698c28be555ec428f05daa2aa901ecf9694c6c23043b76a91a7a847b111', + 'bd199a3871502160bd893d35533b1c4526edf27d6ed9d60ab786ed7aded632ec', + 'be13cfc10b1e19807faf591c4d8070d5a8e843abc31efe3c38fd508fda6b56a6', + 'c0c9ef6c22ed1654a88a14f6196e5e6f4540bcf3faa203510aaeeeeb7431a1bc', + 'c3fc601be3277e3d77c7d55dbe0f58cfc37e877c76121c066e353be4cf3f9f65', + 'c7f14795eb4a64323fdf24caec0e981f42cf07ab79ecaa858de35afe90adb53b', + 'cb16513bfbe2809b67cf939736abc19e5d403dcabadcae9b3aae756b2f6b1e29', + 'cbc04bca5112cb9362f9db9bb007b46376c1488fed34d7b18df67290b22fc099', + 'd1963519912cbd33d8d63cf99f6bcd2e64aad8b89e4a0334bed64a35e3d93360', + 'd2736553fba0f9ea5c93118c1e35641595afe997e883759b1ada665c9859deb6', + 'd381b7ed2453aa3404ecf44978c151549d5166e44f9359a2d61b3e2c19b84d0a', + 'd43568fb51736eb3ba5d256ed2aa63198910e645a62dbc336dbdee29a98d1df0', + 'd519e83718e688a924bdbc74e37419567b29b2fad0995ec7bbdd857926592fef', + 'da79ad0e3d13908a25c0a97e051b6e6bcc675cdb0311c8a8699a76a6eba6a056', + 'db2bfd25d3ddd29864d7c63649b740c31f3a212ecd5c39951b7db2cf2daaf71e', + 'dd4063e9bca83aceee4a80f854ea7955dce420f547cfb34cec970cdb88755053', + 'e2f7812832351c2fe6745cd57d8cf525ea0afde5e0ae3b84c74a7dbc2d59779c', + 'e4cbb10b880925006b58b8a9c14a80444228f8939ef41242f9a011c6538e16ad', + 'e81927a0d98c976258a99ea66dd94ce946c76779650be126de769c6ad4a9e15c', + 'e8318822b535ba3dc52423e86eaa33c82b3f68e5a91241746f8ff3fa0dd8a53f', + 'f1973192d144d907a3890ee014f52cb62047655870311695ad526ee60808e8e2', + 'f28d9607dae499e9f824abf7851621947030db7dd2223dd4057f9beb407fade1', + 'fe9cad7ebe07a2af022824a9236ea6e13d00ce8de4b91efb3b17286fc456823e', + 'ffb5ffbbf9774b8bec1dc190efdafb362fde347b8ad03b8a5c27ba1b09d5b562']) + +# Phase5 final lock. Enforced last so nothing above can override it. +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 8, + "interleave_search": True, +} + + +def _problem_has_objective(problem): + if problem is None: + return True + sha = problem.get("sha") + if sha is None: + return problem.get("baseline_objective") is not None + return sha in OBJECTIVE_SHAS + + +def _eligible(spec, problem): + if spec.get("needs_objective") and not _problem_has_objective(problem): + return False + nc = int(problem.get("num_constraints") or 0) if problem else 0 + lo = spec.get("min_constraints") + if lo is not None and nc < lo: + return False + hi = spec.get("max_constraints") + if hi is not None and nc >= hi: + return False + return True + + +def _bucket_override(num_constraints): + out = {} + for upper, override in SIZE_BUCKETS: + if num_constraints < upper: + out.update(override) + break + return out + + +def _collect_specs(problem, stage): + specs = [s for s in CUSTOM_SUBSOLVERS if _eligible(s, problem)] + if stage == "stage3" and problem is not None and problem.get("is_outlier"): + specs += [s for s in STAGE3_CUSTOM_SUBSOLVERS if _eligible(s, problem)] + return specs + + +def _apply_custom_subsolvers(p, specs): + if not specs: + return + sub_entries = [] + new_names = [] + for spec in specs: + entry = {"name": spec["name"]} + entry.update(spec.get("params") or {}) + sub_entries.append(entry) + new_names.append(spec["name"]) + p["subsolver_params"] = sub_entries + existing = list(p.get("extra_subsolvers") or []) + for name in new_names: + if name not in existing: + existing.append(name) + p["extra_subsolvers"] = existing + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("num_constraints") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + _apply_custom_subsolvers(p, _collect_specs(problem, stage)) + p.update(PHASE_LOCKED) + return p diff --git a/input/cpsat-bench/evolve/params.json b/input/cpsat-bench/evolve/params.json new file mode 100644 index 0000000000..9686b554ae --- /dev/null +++ b/input/cpsat-bench/evolve/params.json @@ -0,0 +1,235 @@ +{ + "solver": "cpsat", + "version": "ortools 9.14.6206", + + "defaults": { + "num_search_workers": 1, + "random_seed": 0, + "interleave_search": true + }, + + "locked": { + "random_seed": 0 + }, + + "subsolver_names": [ + "default_lp", "no_lp", "max_lp", "core", "quick_restart", + "reduced_costs", "lb_tree_search", "probing_search", + "objective_lb_search", "objective_shaving_search_no_lp", + "pseudo_costs", "fixed", "feasibility_pump", "feasibility_jump" + ], + + "groups": { + "parallel": { + "description": "Worker pool and subsolver mix. Pinned per-phase via PHASE_LOCKED; do not change inside get_params() except via the phase module.", + "params": { + "num_search_workers": { + "type": "int", "default": 1, "range": [1, 64], + "desc": "Number of parallel solver subprocesses. Locked per phase." + }, + "interleave_search": { + "type": "bool", "default": false, + "desc": "Interleave subsolvers via deterministic time slicing." + }, + "extra_subsolvers": { + "type": "list", "element_type": "subsolver", "default": [], + "desc": "Additional subsolvers to spin up beyond the default mix." + }, + "ignore_subsolvers": { + "type": "list", "element_type": "subsolver", "default": [], + "desc": "Subsolvers to suppress from the default mix." + }, + "share_glue_clauses": { + "type": "bool", "default": false, + "desc": "Share learned glue clauses across workers." + }, + "share_glue_clauses_dtime": { + "type": "float", "default": 1.0, "range": [0.0, 10.0], + "desc": "Deterministic-time budget between glue-clause shares." + } + } + }, + + "presolve_probing": { + "description": "Presolve, probing, and symmetry detection knobs.", + "params": { + "cp_model_probing_level": { + "type": "int", "default": 2, "range": [0, 3], + "desc": "Probing aggressiveness. 0=off, 3=most aggressive." + }, + "cp_model_presolve": { + "type": "bool", "default": true, + "desc": "Run presolve pass before search." + }, + "presolve_use_bva": { + "type": "bool", "default": true, + "desc": "Use bounded variable addition during presolve." + }, + "presolve_bve_threshold": { + "type": "int", "default": 500, "range": [0, 100000], + "desc": "Max product growth allowed for bounded variable elimination." + }, + "presolve_inclusion_work_limit": { + "type": "int", "default": 100000000, "range": [0, 10000000000], + "desc": "Work limit for clause-inclusion checks in presolve." + }, + "merge_at_most_one_work_limit": { + "type": "int", "default": 100000000, "range": [0, 10000000000], + "desc": "Work limit for at-most-one constraint merging." + }, + "probing_num_combinations_limit": { + "type": "int", "default": 20000, "range": [0, 10000000], + "desc": "Cap on probing combinations explored." + }, + "symmetry_level": { + "type": "int", "default": 2, "range": [0, 3], + "desc": "Symmetry detection depth. Higher = more aggressive." + } + } + }, + + "lp_cuts": { + "description": "Linear-programming relaxation and cut-generation knobs.", + "params": { + "linearization_level": { + "type": "int", "default": 1, "range": [0, 2], + "desc": "Aggressiveness of LP linearization. 0=off, 2=most aggressive." + }, + "cut_level": { + "type": "int", "default": 1, "range": [0, 2], + "desc": "Cut-generation aggressiveness. 0=no cuts." + }, + "max_num_cuts": { + "type": "int", "default": 10000, "range": [0, 50000], + "desc": "Cap on cuts kept in the LP." + }, + "cut_cleanup_target": { + "type": "int", "default": 1000, "range": [0, 50000], + "desc": "Number of cuts kept after periodic cleanup." + }, + "add_mir_cuts": { + "type": "bool", "default": true, + "desc": "Mixed-integer rounding cuts." + }, + "add_zero_half_cuts": { + "type": "bool", "default": true, + "desc": "Zero-half cuts." + }, + "add_clique_cuts": { + "type": "bool", "default": true, + "desc": "Clique cuts." + }, + "add_objective_cut": { + "type": "bool", "default": false, + "desc": "Add objective-bound cut." + }, + "root_lp_iterations": { + "type": "int", "default": 2000, "range": [0, 100000], + "desc": "LP iterations at root node." + }, + "new_constraints_batch_size": { + "type": "int", "default": 50, "range": [1, 1000], + "desc": "Batch size for adding new constraints to LP." + } + } + }, + + "clause_db": { + "description": "Learned clause database management.", + "params": { + "clause_cleanup_period": { + "type": "int", "default": 10000, "range": [1000, 100000], + "desc": "Conflicts between clause-db cleanups. Higher = keep clauses longer." + }, + "clause_cleanup_target": { + "type": "int", "default": 0, "range": [0, 200000], + "desc": "Target size after cleanup. 0 = ratio-based." + }, + "clause_cleanup_lbd_bound": { + "type": "int", "default": 5, "range": [2, 20], + "desc": "Clauses with LBD <= bound survive cleanup." + }, + "clause_cleanup_ordering": { + "type": "int", "default": 0, "range": [0, 1], + "desc": "0=CLAUSE_ACTIVITY, 1=CLAUSE_LBD." + } + } + }, + + "search_branching": { + "description": "Top-level search strategy and branching heuristics.", + "params": { + "search_branching": { + "type": "int", "default": 0, "range": [0, 8], + "desc": "0=AUTOMATIC, 1=FIXED, 2=PORTFOLIO, 3=LP, 4=PSEUDO_COST, 5=PORTFOLIO_WITH_QUICK_RESTART, 6=HINT, 7=PARTIAL_FIXED, 8=RANDOMIZED." + }, + "use_strong_propagation_in_disjunctive": { + "type": "bool", "default": false, + "desc": "Stronger (more expensive) propagation in disjunctive constraints." + }, + "use_erwa_heuristic": { + "type": "bool", "default": false, + "desc": "Exponential recency weighted average branching." + }, + "repair_hint": { + "type": "bool", "default": false, + "desc": "Repair inconsistent solution hints." + } + } + }, + + "optimization": { + "description": "Objective-search and solution-pool knobs.", + "params": { + "exploit_best_solution": { + "type": "bool", "default": false, + "desc": "Use incumbent objective to prune branches." + }, + "exploit_relaxation_solution": { + "type": "bool", "default": false, + "desc": "Use LP relaxation solution to guide search." + }, + "exploit_all_precedences": { + "type": "bool", "default": false, + "desc": "Propagate all derived precedence relations." + }, + "solution_pool_size": { + "type": "int", "default": 3, "range": [1, 100], + "desc": "Maximum solutions kept in the pool." + } + } + }, + + "mip_bridge": { + "description": "MIP-style numeric tolerances and scaling.", + "params": { + "mip_check_precision": { + "type": "float", "default": 0.0001, "range": [1e-10, 0.1], + "desc": "Precision threshold for MIP feasibility checks." + }, + "mip_drop_tolerance": { + "type": "float", "default": 1e-16, "range": [1e-20, 1e-4], + "desc": "Drop coefficients below this magnitude in MIP." + }, + "mip_max_bound": { + "type": "float", "default": 10000000.0, "range": [1.0, 1e15], + "desc": "Maximum finite bound for MIP variables." + }, + "mip_var_scaling": { + "type": "float", "default": 1.0, "range": [0.001, 1000.0], + "desc": "Per-variable scaling factor for MIP bridge." + } + } + }, + + "seed": { + "description": "Reproducibility. random_seed is LOCKED; do not modify.", + "params": { + "random_seed": { + "type": "int", "default": 0, "range": [0, 2147483647], + "desc": "Locked at 0 across all phases." + } + } + } + } +} diff --git a/input/cpsat-bench/evolve/phase1_search/initial_program.py b/input/cpsat-bench/evolve/phase1_search/initial_program.py new file mode 100644 index 0000000000..6532b0706a --- /dev/null +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -0,0 +1,90 @@ +""" +Phase 1: tune CP-SAT search / subsolver knobs. + +Targeted namespace: extra_subsolvers, ignore_subsolvers, interleave_search, +use_feasibility_jump, use_feasibility_pump, search_branching, +preferred_variable_order, repair_hint, diversify_lns_params. + +Other params stay at BASELINE. Phase 1 pins num_search_workers=1 so other +knobs are evaluated without multi-thread noise. Phase 3 raises the worker +count to explore subsolver-mix effects. + +Do NOT modify locked keys (random_seed, num_search_workers). +Invalid solver keys cause evaluator to return 0 and surface the offending key. +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# OPENEVOLVE_PROFILE=large → W=8 (outlier tuning track); default small → W=1. +_LARGE_PROFILE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() + == "large") +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 8 if _LARGE_PROFILE else 1, +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) diff --git a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py new file mode 100644 index 0000000000..fa3c47b36f --- /dev/null +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -0,0 +1,88 @@ +""" +Phase 2: tune CP-SAT presolve / probing / symmetry knobs. + +Targeted namespace: cp_model_probing_level, cp_model_presolve, presolve_use_bva, +presolve_bve_threshold, presolve_inclusion_work_limit, +merge_at_most_one_work_limit, probing_num_combinations_limit, symmetry_level. + +Inherits phase1 winners via get_params() chaining is NOT used — phase1 +GLOBAL_OVERRIDES live in cache/phase1_best.json (consumed by phase4_unified's +prepare step). Phase 2 evaluates presolve in isolation against BASELINE. + +Do NOT modify locked keys. W=1 (single worker) for clean signal. +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +_LARGE_PROFILE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() + == "large") +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 8 if _LARGE_PROFILE else 1, +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) diff --git a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py new file mode 100644 index 0000000000..bc4221768a --- /dev/null +++ b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py @@ -0,0 +1,86 @@ +""" +Phase 3: tune CP-SAT LP relaxation + cut generation knobs at W=8. + +Targeted namespace: linearization_level, cut_level, max_num_cuts, +cut_cleanup_target, add_mir_cuts / add_zero_half_cuts / add_clique_cuts / +add_objective_cut, root_lp_iterations, new_constraints_batch_size, +exploit_best_solution, exploit_relaxation_solution. + +W=8 engages the multi-worker subsolver mix — many LP / cut knobs only +matter with the LP-heavy subsolvers (max_lp, default_lp) running. + +Do NOT modify locked keys. +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 8, +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) \ No newline at end of file diff --git a/input/cpsat-bench/evolve/phase4_unified/initial_program.py b/input/cpsat-bench/evolve/phase4_unified/initial_program.py new file mode 100644 index 0000000000..27bb187313 --- /dev/null +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -0,0 +1,83 @@ +""" +Phase 4: unified refinement at W=8. + +EVOLVE-BLOCK is auto-materialized by `python -m _lib.prepare_phase cpsat-bench` +before this phase runs — pulling the union of phase{1,2,3}_best.json winners +into GLOBAL_OVERRIDES, merged SIZE_BUCKETS, and merged STAGE3_OVERRIDES. The +LLM then tunes all three surfaces jointly. + +Do NOT modify locked keys (random_seed, num_search_workers). +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 8, +} + + +# EVOLVE-BLOCK-START +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def _bucket_override(size): + for upper, override in SIZE_BUCKETS: + if size < upper: + return override + return {} + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(STAGE3_OVERRIDES) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + return [(u, dict(d)) for u, d in SIZE_BUCKETS] + + +def get_phase_stage3_overrides(): + return dict(STAGE3_OVERRIDES) \ No newline at end of file diff --git a/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py b/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py new file mode 100644 index 0000000000..e9dd451cdc --- /dev/null +++ b/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py @@ -0,0 +1,155 @@ +""" +Phase 5: add CUSTOM SUBSOLVERS to the CP-SAT portfolio at W=8. + +CRITICAL — do NOT tune top-level parameters here. Top-level params apply to +EVERY subsolver, including LNS workers. Stick to a single isolated extra +subsolver per technique so its effect is attributable and the rest of the +portfolio is untouched. + +Inherits the unified phase4 winner via cache/phase{4}_best.json +(GLOBAL_OVERRIDES), cache/phase4_buckets.json (SIZE_BUCKETS), and +cache/phase4_stage3.json (STAGE3_OVERRIDES) as the immutable top-level +config. Phase 5 ONLY appends custom subsolvers on top. + +EVOLVE-BLOCK surface: + CUSTOM_SUBSOLVERS — applied to every problem + STAGE3_CUSTOM_SUBSOLVERS — added ONLY for stage3 outliers + +Spec dict: + { + "name": "unique_subsolver_name", + "params": { ...SatParameters fields }, + "needs_objective": False, # optional + "min_constraints": 0, # optional inclusive lower gate + "max_constraints": None, # optional exclusive upper gate + } + +Do NOT modify locked keys (random_seed, num_search_workers, interleave_search). +""" +import json +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults +_CACHE = _BENCH / "cache" + + +PHASE5_WORKERS = 8 + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": PHASE5_WORKERS, + "interleave_search": True, +} + + +def _load_prev_dict(name): + p = _CACHE / name + if p.exists(): + return json.loads(p.read_text()) + return {} + + +def _load_prev_buckets(name): + p = _CACHE / name + if not p.exists(): + return None + raw = json.loads(p.read_text()) + return [(float("inf") if u is None else u, override) for u, override in raw] + + +_PHASE4 = _load_prev_dict("phase4_best.json") +_PHASE4_BUCKETS = _load_prev_buckets("phase4_buckets.json") +_PHASE4_STAGE3 = _load_prev_dict("phase4_stage3.json") + + +# EVOLVE-BLOCK-START +CUSTOM_SUBSOLVERS = [] +STAGE3_CUSTOM_SUBSOLVERS = [] +# EVOLVE-BLOCK-END + + +def _eligible(spec, problem): + size = int(problem.get("size") or 0) if problem else 0 + lo = spec.get("min_constraints") + if lo is not None and size < lo: + return False + hi = spec.get("max_constraints") + if hi is not None and size >= hi: + return False + return True + + +def _bucket_override(size): + out = {} + for buckets in (_PHASE4_BUCKETS or [],): + for upper, override in buckets: + if size < upper: + out.update(override) + break + return out + + +def _collect_specs(problem, stage): + specs = [s for s in CUSTOM_SUBSOLVERS if _eligible(s, problem)] + if stage == "stage3" and problem is not None and problem.get("is_outlier"): + specs += [s for s in STAGE3_CUSTOM_SUBSOLVERS if _eligible(s, problem)] + return specs + + +def _apply_custom_subsolvers(p, specs): + if not specs: + return + sub_entries = [] + new_names = [] + for spec in specs: + entry = {"name": spec["name"]} + entry.update(spec.get("params") or {}) + sub_entries.append(entry) + new_names.append(spec["name"]) + p["subsolver_params"] = sub_entries + existing = list(p.get("extra_subsolvers") or []) + for name in new_names: + if name not in existing: + existing.append(name) + p["extra_subsolvers"] = existing + + +def get_params(problem=None, stage=None): + p = dict(BASELINE) + p.update(_PHASE4) + if problem is not None: + p.update(_bucket_override(int(problem.get("size") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(_PHASE4_STAGE3) + _apply_custom_subsolvers(p, _collect_specs(problem, stage)) + p.update(PHASE_LOCKED) + return p + + +def get_phase_overrides(): + """Phase 5 evolves subsolver list, not a flat overrides dict. + Return empty so extract_best does the right (no-op) thing for downstream.""" + return {} \ No newline at end of file diff --git a/input/cpsat-bench/problems.jsonl b/input/cpsat-bench/problems.jsonl new file mode 100644 index 0000000000..3d0f980fba --- /dev/null +++ b/input/cpsat-bench/problems.jsonl @@ -0,0 +1,855 @@ +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 4352, "num_bool": 2861, "num_int": 1491, "num_constraints": 51623}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": "False", "optimize_m2": "False", "num_of_heights": "1", "process": "sf4lpp", "tech": "sf4lpp", "solver_iter_timeout": "3600.0"}, "cpsat_applied_params": {"timeout_sec": -1, "num_search_workers": 8, "random_seed": 0, "interleave_search": true, "tuned": true}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 6896.45}, "cpsat_response_stats": {"num_booleans": 7248, "num_conflicts": 3843, "num_branches": 79884, "num_binary_propagations": 4096494, "num_integer_propagations": 1709363, "num_restarts": 36, "wall_time": 6.89489, "user_time": 6.89489, "deterministic_time": 15.5139}, "solution_info": "default_lp", "problem_sha256": "0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc", "applied_params_hash": "56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024", "problem_filename": "0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 3924, "num_bool": 2783, "num_int": 1141, "num_constraints": 46300, "constraint_breakdown": {"at_most_one": 194, "linear": 19659, "bool_or": 15770, "bool_and": 10677}, "objective_terms": 168}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": "False", "optimize_m2": "False", "num_of_heights": "1", "process": "sf4lpp", "tech": "sf4lpp", "solver_iter_timeout": "3600.0"}, "cpsat_applied_params": {"timeout_sec": -1, "num_search_workers": 8, "random_seed": 0, "interleave_search": true, "tuned": true, "use_feasibility_jump": true, "use_feasibility_pump": false, "ignore_subsolvers": [], "extra_subsolvers": ["default_lp", "reduced_costs", "core"], "cp_model_probing_level": 1, "max_num_cuts": 3000, "cut_level": 2, "mip_max_bound": 10000000.0, "mip_var_scaling": 1, "mip_check_precision": 1e-06, "mip_drop_tolerance": 1e-10}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 16676.9, "objective_value": 318750000.0, "best_objective_bound": 318750000.0, "inner_objective_lower_bound": 318750255}, "cpsat_response_stats": {"num_booleans": 6226, "num_integers": 1156, "num_fixed_booleans": 3242, "num_conflicts": 3916, "num_branches": 53955, "num_binary_propagations": 3958443, "num_integer_propagations": 1603411, "num_restarts": 31, "num_lp_iterations": 35910, "wall_time": 16.6685, "user_time": 16.6685, "deterministic_time": 13.2451, "gap_integral": 246.491, "tightened_variables": 0, "sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "core", "problem_sha256": "00a438e9a02d3895ebcf5e1eea77b3c1fba28d76ca73723c7befe71f327aae82", "applied_params_hash": 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true, "tuned": true, "use_feasibility_jump": true, "use_feasibility_pump": false, "ignore_subsolvers": [], "extra_subsolvers": ["quick_restart", "lb_tree_search"], "cp_model_probing_level": 2, "max_num_cuts": 4000, "cut_level": 2, "mip_max_bound": 10000000.0, "mip_var_scaling": 1, "mip_check_precision": 1e-06, "mip_drop_tolerance": 1e-07}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 19654.3}, "cpsat_response_stats": {"num_booleans": 6428, "num_integers": 6045, "num_fixed_booleans": 235, "num_conflicts": 1143, "num_branches": 56232, "num_binary_propagations": 2571949, "num_integer_propagations": 3638286, "num_restarts": 61, "num_lp_iterations": 62919, "wall_time": 19.6376, "user_time": 19.6376, "deterministic_time": 21.9621, "gap_integral": 0, "tightened_variables": 0, "sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "default_lp", "problem_sha256": "ff6697b1a9d57aeb43f770c91918c33c2b88b2ed07ceea158d351ecb9e4fe8ab", "applied_params_hash": "f49dfe0e9f7a3ae5ee6bb7c47dc3b6473b5a4342cbddccdd270af92e771eee23", "problem_filename": "ff6697b1a9d57aeb43f770c91918c33c2b88b2ed07ceea158d351ecb9e4fe8ab.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 5099, "num_bool": 3604, "num_int": 1495, "num_constraints": 62421, "constraint_breakdown": {"at_most_one": 248, "linear": 26558, "bool_or": 21170, "bool_and": 14445}, "objective_terms": 217}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": "False", "optimize_m2": "False", "num_of_heights": "1", "process": "sf4lpp", "tech": "sf4lpp", "solver_iter_timeout": "3600.0"}, "cpsat_applied_params": {"timeout_sec": -1, "num_search_workers": 8, "random_seed": 0, "interleave_search": true, "tuned": true, "use_feasibility_jump": true, "use_feasibility_pump": false, "ignore_subsolvers": [], "extra_subsolvers": ["default_lp", "reduced_costs", "core"], "cp_model_probing_level": 1, "max_num_cuts": 3000, "cut_level": 2, "mip_max_bound": 10000000.0, "mip_var_scaling": 1, "mip_check_precision": 1e-06, "mip_drop_tolerance": 1e-10}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 25898.4, "objective_value": 204, "best_objective_bound": 204, "inner_objective_lower_bound": 204}, "cpsat_response_stats": {"num_booleans": 11055, "num_integers": 1482, "num_fixed_booleans": 362, "num_conflicts": 7017, "num_branches": 171780, "num_binary_propagations": 6656520, "num_integer_propagations": 3049838, "num_restarts": 42, "num_lp_iterations": 75134, "wall_time": 25.8858, "user_time": 25.8858, "deterministic_time": 22.7093, "gap_integral": 490.421, "tightened_variables": 0, "sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "core", "problem_sha256": "ffb5ffbbf9774b8bec1dc190efdafb362fde347b8ad03b8a5c27ba1b09d5b562", "applied_params_hash": "5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd", "problem_filename": "ffb5ffbbf9774b8bec1dc190efdafb362fde347b8ad03b8a5c27ba1b09d5b562.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 5079, "num_bool": 3597, "num_int": 1482, "num_constraints": 57828, "constraint_breakdown": {"at_most_one": 249, "linear": 24278, "bool_or": 19882, "bool_and": 13419}}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": "False", "optimize_m2": "False", "num_of_heights": "1", "process": "sf4lpp", "tech": "sf4lpp", "solver_iter_timeout": "3600.0"}, "cpsat_applied_params": {"timeout_sec": -1, "num_search_workers": 8, "random_seed": 0, "interleave_search": true, "tuned": true, "use_feasibility_jump": false, "use_feasibility_pump": false, "ignore_subsolvers": ["max_lp"], "extra_subsolvers": ["default_lp", "no_lp"], "cp_model_probing_level": 1, "max_num_cuts": 3000, "cut_level": 1, "mip_max_bound": 10000000.0, "mip_var_scaling": 1, "mip_check_precision": 1e-06, "mip_drop_tolerance": 1e-07}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 5931.36}, "cpsat_response_stats": {"num_booleans": 7307, "num_integers": 1343, "num_fixed_booleans": 142, "num_conflicts": 1982, "num_branches": 61712, "num_binary_propagations": 4035943, "num_integer_propagations": 1584674, "num_restarts": 18, "num_lp_iterations": 20439, "wall_time": 5.9214, "user_time": 5.9214, "deterministic_time": 8.19457, "gap_integral": 0, "tightened_variables": 0, "sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "no_lp", "problem_sha256": "ffb713809ac8c43a2c5b9cf1de5f841511eab9db95cd9bd4ac5653992f9b2b14", "applied_params_hash": "fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c", "problem_filename": "ffb713809ac8c43a2c5b9cf1de5f841511eab9db95cd9bd4ac5653992f9b2b14.cpsat.pb"} diff --git a/input/run_phase.sh b/input/run_phase.sh new file mode 100755 index 0000000000..8bad7909cf --- /dev/null +++ b/input/run_phase.sh @@ -0,0 +1,250 @@ +#!/bin/bash +# Single entry for all bench phase runs. Reads `bench:` section of +# /evolve/config.yaml (via _lib/load_bench_config.py), then drives the +# refactored flow — everything routes through `python -m _lib.`, +# there are no per-bench wrapper scripts anymore. +# +# Usage: +# ./input/run_phase.sh [] [--pin N-M] [--extract-only] +# [--iterations ] [--profile small|large] +# [extra openevolve flags] +# +# omitted → run ALL phases sequentially (1..N). +# numeric → run that single phase. +# +# Pre-flight (run once before the phase loop): +# - if cache/stage1_sample.json missing → `python -m _lib.sampler ` +# - if cache/local_baseline.json missing → `python -m _lib.rebaseline ` +# (SKIP_REBASELINE=1 reuses an existing baseline file) +# - if `bench.solver_check_cmd` defined → run it +# +# Per-phase: +# - if `bench.unified_prepare_before_dir` matches the current dir, +# materialize its EVOLVE-BLOCK via `python -m _lib.prepare_phase `. +# - run openevolve-run.py with $INPUT_DIR/_lib/evaluator_entry.py as +# evaluator (OPENEVOLVE_BENCH_ROOT is exported). +# - non-last phases: `python -m _lib.extract_best `. +# +# Env knobs honored: +# OPENEVOLVE_PARALLEL_SOLVERS concurrent solver subprocesses +# OPENEVOLVE_CORE_RANGE explicit taskset core range N-M +# (also set via --pin) +# OPENEVOLVE_PROFILE "small"|"large" — phase modules read it for +# W=1 vs W=8 switching (cpsat). +# SKIP_REBASELINE=1 reuse existing cache/local_baseline.json + +set -euo pipefail + +usage() { + echo "usage: $(basename "$0") [] [--pin N-M] [--extract-only] [--iterations N] [--profile small|large] [extra flags]" >&2 + echo " omit to run all phases sequentially" >&2 + echo " = dir name under input/ (e.g. cpsat-bench, z3-bench)" >&2 +} + +if [ $# -lt 1 ]; then + usage + exit 2 +fi + +BENCH="$1"; shift + +PHASE="" +if [ $# -ge 1 ] && [[ "$1" =~ ^[0-9]+$ ]]; then + PHASE="$1"; shift +fi + +INPUT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ROOT="$INPUT_DIR/$BENCH/evolve" +CONFIG_YAML="$ROOT/config.yaml" + +if [ ! -d "$ROOT" ]; then + echo "bench evolve dir not found: $ROOT" >&2 + exit 2 +fi +if [ ! -f "$CONFIG_YAML" ]; then + echo "missing $CONFIG_YAML" >&2 + exit 2 +fi + +_BENCH_EXPORTS="$(cd "$INPUT_DIR" && python3 -m _lib.load_bench_config "$CONFIG_YAML")" +eval "$_BENCH_EXPORTS" + +: "${PHASE_DIRS:?bench.phases missing in $CONFIG_YAML}" + +read -r -a _DIRS <<< "$PHASE_DIRS" +read -r -a _ITERS <<< "${PHASE_ITERS:-}" +N_PHASES=${#_DIRS[@]} +LAST_PHASE=$N_PHASES + +EXTRACT_ONLY=0 +PIN_RANGE="" +PROFILE="small" +PASSTHROUGH=() +while [ $# -gt 0 ]; do + case "$1" in + --extract-only) EXTRACT_ONLY=1; shift ;; + --pin) PIN_RANGE="$2"; shift 2 ;; + --pin=*) PIN_RANGE="${1#--pin=}"; shift ;; + --profile) PROFILE="$2"; shift 2 ;; + --profile=*) PROFILE="${1#--profile=}"; shift ;; + -h|--help) usage; exit 0 ;; + *) PASSTHROUGH+=("$1"); shift ;; + esac +done +set -- "${PASSTHROUGH[@]+"${PASSTHROUGH[@]}"}" + +case "$PROFILE" in + small|large) ;; + *) echo "--profile must be 'small' or 'large' (got: $PROFILE)" >&2; exit 2 ;; +esac +export OPENEVOLVE_PROFILE="$PROFILE" + +if [ "$PROFILE" = "small" ]; then + OUTPUT_DIR="openevolve_output" +else + OUTPUT_DIR="openevolve_output_${PROFILE}" +fi + +if [ -z "$PHASE" ]; then + if [ "$EXTRACT_ONLY" = "1" ]; then + echo "--extract-only requires explicit " >&2 + exit 2 + fi + PHASES_TO_RUN=() + for ((i = 1; i <= N_PHASES; i++)); do PHASES_TO_RUN+=("$i"); done + RUN_ALL=1 +else + if [ "$PHASE" -lt 1 ] || [ "$PHASE" -gt "$N_PHASES" ]; then + echo "phase must be in {1..$N_PHASES} (got: $PHASE)" >&2 + exit 2 + fi + PHASES_TO_RUN=("$PHASE") + RUN_ALL=0 +fi + +if [ -n "$PIN_RANGE" ]; then + if ! [[ "$PIN_RANGE" =~ ^[0-9]+(-[0-9]+)?$ ]]; then + echo "--pin expects N or N-M (got: $PIN_RANGE)" >&2 + exit 2 + fi + export OPENEVOLVE_CORE_RANGE="$PIN_RANGE" +fi + +REPO_ROOT="$(cd "$INPUT_DIR/.." && pwd)" +RUNNER="$REPO_ROOT/openevolve-run.py" +EVALUATOR_ENTRY="$INPUT_DIR/_lib/evaluator_entry.py" + +export OPENEVOLVE_BENCH_ROOT="$ROOT" +echo "[run_phase] bench=$BENCH profile=$PROFILE output=$OUTPUT_DIR" + +# ============ pre-flight ============ +if [ "$EXTRACT_ONLY" != "1" ]; then + if [ ! -f "$RUNNER" ]; then + echo "openevolve-run.py not found at $RUNNER" >&2 + exit 1 + fi + if [ ! -f "$EVALUATOR_ENTRY" ]; then + echo "evaluator_entry not found at $EVALUATOR_ENTRY" >&2 + exit 1 + fi + + if [ ! -f "$ROOT/cache/stage1_sample.json" ]; then + echo "[run_phase] cache missing — running _lib.sampler..." + (cd "$INPUT_DIR" && python3 -m _lib.sampler "$BENCH") + fi + + if [ -f "$ROOT/cache/local_baseline.json" ] && [ "${SKIP_REBASELINE:-0}" = "1" ]; then + echo "[run_phase] SKIP_REBASELINE=1 — reusing cache/local_baseline.json" + else + echo "[run_phase] running _lib.rebaseline (set SKIP_REBASELINE=1 to skip)..." + (cd "$INPUT_DIR" && python3 -m _lib.rebaseline "$BENCH") || \ + echo "warning: _lib.rebaseline finished with mismatches; evaluator falls back to raw_ms for those." + fi + + if [ -n "${SOLVER_CHECK_CMD:-}" ]; then + if ! eval "$SOLVER_CHECK_CMD" >/dev/null 2>&1; then + echo "warning: solver check failed. ${SOLVER_INSTALL_HINT:-}" >&2 + fi + fi + + export SKIP_REBASELINE=1 +fi + +# ============ phase runner ============ +run_one_phase() { + local phase="$1" + shift + local dir="${_DIRS[$((phase - 1))]}" + local iter="" + if [ "${#_ITERS[@]}" -ge "$phase" ]; then + iter="${_ITERS[$((phase - 1))]}" + fi + + if [ "$EXTRACT_ONLY" = "1" ]; then + if [ "$phase" -eq "$LAST_PHASE" ]; then + echo "--extract-only not supported for phase $LAST_PHASE" >&2 + return 2 + fi + (cd "$INPUT_DIR" && python3 -m _lib.extract_best "$BENCH" "$phase" --from-checkpoints) + return 0 + fi + + local prep_here=0 + if [ -n "${UNIFIED_PREPARE_BEFORE_DIR:-}" ]; then + [ "$dir" = "$UNIFIED_PREPARE_BEFORE_DIR" ] && prep_here=1 + elif [ "$phase" -eq "$LAST_PHASE" ]; then + prep_here=1 + fi + if [ "$prep_here" = "1" ]; then + local missing=0 + for ((i = 1; i < phase; i++)); do + if [ ! -f "$ROOT/cache/phase${i}_best.json" ]; then + echo "phase $phase ($dir) requires cache/phase${i}_best.json" >&2 + missing=1 + fi + done + [ "$missing" = "1" ] && return 1 + echo "[run_phase] materializing $dir EVOLVE-BLOCK via _lib.prepare_phase..." + (cd "$INPUT_DIR" && python3 -m _lib.prepare_phase "$BENCH") + fi + + cd "$ROOT/$dir" + echo "[run_phase] === bench=$BENCH phase=$phase dir=$dir ${iter:+iter=$iter }cwd=$(pwd) ===" + + local iter_flag=() + if [ -n "$iter" ]; then + iter_flag=(--iterations "$iter") + fi + + python3 "$RUNNER" \ + initial_program.py \ + "$EVALUATOR_ENTRY" \ + --config "$ROOT/config.yaml" \ + --output "$OUTPUT_DIR" \ + "${iter_flag[@]+"${iter_flag[@]}"}" \ + "$@" + + if [ "$phase" -lt "$LAST_PHASE" ]; then + echo "[run_phase] extracting best for phase $phase..." + (cd "$INPUT_DIR" && python3 -m _lib.extract_best "$BENCH" "$phase") + fi +} + +_LAST_RUN="" +for p in "${PHASES_TO_RUN[@]}"; do + run_one_phase "$p" "$@" + _LAST_RUN="$p" +done + +# Finalize when last phase was reached (run-all OR explicit last phase). +# Skipped for --extract-only. +if [ "$EXTRACT_ONLY" != "1" ] && [ "$_LAST_RUN" = "$LAST_PHASE" ]; then + echo "[run_phase] finalizing → $ROOT/final_program.py" + (cd "$INPUT_DIR" && python3 -m _lib.finalize "$BENCH") +fi + +if [ "$RUN_ALL" = "1" ]; then + echo "[run_phase] all $N_PHASES phases completed for $BENCH." +elif [ "${PHASES_TO_RUN[0]}" -lt "$LAST_PHASE" ]; then + echo "[run_phase] next: $0 $BENCH $((${PHASES_TO_RUN[0]} + 1))" +fi diff --git a/input/z3-bench/README.md b/input/z3-bench/README.md new file mode 100644 index 0000000000..2616787f77 --- /dev/null +++ b/input/z3-bench/README.md @@ -0,0 +1,183 @@ +# z3-bench 데이터셋 + +Z3 SMT 솔버(v4.13.3.0)를 SMT-LIB2 인스턴스 모음에 실행한 벤치마크 결과 데이터셋이다. 각 행은 한 인스턴스에 대한 1회 실행(seed 고정, 동일 파라미터 프로파일)의 결과(만족도, 경과 시간, 솔버 내부 통계, 인스턴스 특성)를 담는다. 솔버 파라미터 튜닝(예: Z3 옵션 자동 튜닝, 휴리스틱 학습)을 위한 학습/평가 코퍼스로 활용 가능하다. + +## 구성 + +| 경로 | 설명 | +|---|---| +| `problems.jsonl` | 한 줄 = 한 실행 결과 (JSON 객체). 50개 행. | +| `problems.csv` | 동일 결과의 평탄화(flatten)된 CSV 버전. 중첩 객체는 일부 컬럼만 노출. | +| `raw-data/*.smt2` | 원본 SMT-LIB2 입력 파일. 파일명 = `.smt2`. | +| `raw-data/*____seed.meta.jsonl` | 실행별 메타 로그. `meta_path`로 참조됨. | + +## 데이터셋 요약(현재 스냅샷) + +- 행 수: 50 +- Z3 버전: `4.13.3.0` (단일) +- `applied_params_hash`: 1종(`543b29ed...ffcec6`) — 모든 행이 동일 파라미터 프로파일로 실행됨 +- `seed`: 0 (단일) +- 결과 분포(`z3_status.result`): `Sat` 31, `Unsat` 19 +- 경과 시간(`z3_status.elapsed_ms`): min 221 / max 181,205 / avg ≈ 24,229 ms +- 변수 수(`features.num_variables`): min 3,797 / max 99,809 +- `path == "primary"` 47행 / 누락 3행 (5, 19, 49번째 행 — `solver` 필드도 동반 누락; 그 외 모든 필드는 존재) + +--- + +## JSONL 스키마 + +각 줄은 다음 최상위 필드를 가진 객체이다. + +### 최상위 필드 + +| 필드 | 타입 | 설명 | +|---|---|---| +| `problem_sha256` | string (hex, 64) | SMT2 인스턴스 내용의 SHA-256. 인스턴스 고유 ID. | +| `smt2_filename` | string | SMT2 파일명 (`.smt2`). | +| `smt2_path` | string (absolute path) | 호스트 상의 SMT2 파일 절대 경로. `raw-data/` 아래. | +| `meta_path` | string (absolute path) | 실행 메타 로그(`*.meta.jsonl`) 절대 경로. | +| `solver` | string | 사용 솔버명. 현재 데이터는 `"z3"` (3개 행에서 누락). | +| `z3_version` | string | Z3 빌드 버전. 현재 `"4.13.3.0"`. | +| `seed` | int | 실행 시드. 현재 `0`. | +| `path` | string | 실행 경로 라벨. 현재 `"primary"` (3개 행에서 누락). 다중 분기 실행 시 변형(예: ablation) 구분 용도. | +| `applied_params_hash` | string (hex, 64) | `z3_applied_params`의 정규화 해시. 동일 파라미터 프로파일을 빠르게 그룹핑. | +| `cli_params` | object | 실행을 일으킨 상위 도구의 CLI 파라미터(아래 참조). | +| `z3_applied_params` | object | Z3에 실제로 인가된 옵션 키-값(아래 참조). | +| `features` | object | 인스턴스 정적 특성(아래 참조). | +| `z3_statistics` | object | Z3가 실행 후 출력한 내부 통계(아래 참조). | +| `z3_status` | object | 실행 종료 상태(`result`, `elapsed_ms`). | + +> 참고: 일부 행에는 `error` 필드가 추가될 수 있으나, 현재 스냅샷에는 없음. + +### `cli_params` (도구 측 호출 파라미터) + +상위 벤치 러너에서 받은 인자. 문자열로 저장됨에 유의. + +| 키 | 타입(원본) | 설명 | +|---|---|---| +| `solver` | str | 사용 솔버 식별자 (`"z3"`). | +| `tech` / `process` | str | 기법/프로세스 라벨 (예: `"sf4lpp"`). 인스턴스 전처리·인코딩 방법론 구분. | +| `effort` | str | 자원 등급 라벨 (`"low"` 등). | +| `seed` | str(int) | 시드. | +| `solver_iter_timeout` | str(float, 초) | 솔버 1회 호출 타임아웃. 현재 `"3600.0"`. | +| `use_reboot` | str(bool) | 솔버 재시작(reboot) 사용 여부. | +| `optimize_m1` / `optimize_m2` | str(bool) | 1차/2차 목적함수 최적화 활성 여부. | +| `m2_offset` | str | 2차 목적의 오프셋 모드 (`"zero"` 등). | +| `num_of_heights` | str(int) | 도메인 특화 파라미터(높이 슬롯 수). | +| `unsat_debug` | str(bool) | UNSAT 디버그 모드 토글. | + +### `z3_applied_params` (Z3 옵션 실제 인가값) + +Z3에 set-option으로 전달된 키-값. `opt.*`, `sat.*`, `smt.*`, `sls.*`, `parallel.*` 네임스페이스로 구성. + +| 키 | 타입 | 설명 | +|---|---|---| +| `opt.enable_core_rotate` | bool | MaxSAT 코어 회전 최적화 활성. | +| `opt.enable_sat` | bool | opt 엔진에서 SAT 백엔드 활성. | +| `opt.enable_sls` | bool | opt 엔진에서 SLS(확률적 지역 탐색) 활성. | +| `opt.maxres.hill_climb` | bool | MaxRes 힐 클라이밍 활성. | +| `opt.maxsat_engine` | string | MaxSAT 엔진 선택(`"wmax"` 등). | +| `opt.priority` | string | 다목적 우선순위 전략 (`"pareto"` 등). | +| `opt.rc2.totalizer` | bool | RC2의 totalizer 인코딩 사용. | +| `parallel.enable` | bool | 병렬 모드 활성. | +| `sat.branching.heuristic` | string | SAT 분기 휴리스틱 (`"vsids"` 등). | +| `sat.pb.solver` | string | PB(Pseudo-Boolean) 솔버 (`"totalizer"` 등). | +| `sat.phase` | string | 위상 선택 정책 (`"caching"` 등). | +| `sat.restart` | string | 재시작 정책 (`"geometric"` 등). | +| `sat.random_seed` | int | SAT 코어 시드. | +| `sat.threads` | int | SAT 스레드 수. | +| `sls.random_seed` | int | SLS 시드. | +| `smt.phase_selection` | int | SMT 위상 선택 모드(번호). | +| `smt.random_seed` | int | SMT 시드. | +| `smt.threads` | int | SMT 스레드 수. | +| `threads` | int | 전역 스레드 수. | + +### `features` (인스턴스 정적 특성) + +| 키 | 타입 | 설명 | +|---|---|---| +| `num_variables` | int | 선언된 변수 총 개수. | +| `num_bool` | int | Bool 정렬(sort) 변수 수. | +| `num_int` | int | Int 정렬 변수 수. | +| `num_real` | int | Real 정렬 변수 수. | +| `num_hard_constraints` | int | 하드 제약 수(`assert`). | +| `num_soft_constraints` | int | 소프트 제약 수(`assert-soft`). | +| `num_minimize_objectives` | int | `minimize` 목적함수 수. | +| `num_maximize_objectives` | int | `maximize` 목적함수 수. | + +### `z3_status` (종료 상태) + +| 키 | 타입 | 설명 | +|---|---|---| +| `result` | string | `"Sat"` / `"Unsat"` / `"Unknown"` / 에러 라벨. | +| `elapsed_ms` | int | 벽시계 경과 시간(ms). | + +### `z3_statistics` (Z3 내부 통계) + +Z3의 `(get-statistics)` 출력을 키-값으로 보존(키에 공백/하이픈 포함). 인스턴스마다 일부 키만 출력될 수 있으므로 누락 가능. 주요 키(현 스냅샷에서 관측된 35개): + +- 시간/메모리/자원: `time`(초), `memory`, `max memory`(MB), `rlimit count`, `num allocs` +- 검색 통계: `conflicts`, `decisions`, `restarts`, `propagations`, `binary propagations`, `final checks`, `num checks`, `minimized lits`, `del clause`, `mk clause`, `mk clause binary`, `mk bool var` +- 등식/단순화: `added eqs`, `solve-eqs-elim-vars`, `solve-eqs-steps` +- 산술(arith) 이론: `arith eq adapter`, `arith-bound-propagations-lp`, `arith-conflicts`, `arith-diseq`, `arith-fixed-eqs`, `arith-lower`, `arith-upper`, `arith-make-feasible`, `arith-max-columns`, `arith-max-rows`, `arith-offset-eqs` +- Pseudo-Boolean: `pb conflicts`, `pb predicates`, `pb propagations`, `pb resolves` + +타입은 정수 또는 부동소수(특히 `time`, `*memory*`). + +--- + +## CSV 스키마 (`problems.csv`) + +JSONL의 평탄화 버전. 컬럼은 다음과 같으며, 중첩 객체에서 일부 핵심 필드만 노출한다. + +``` +problem_sha256, applied_params_hash, seed, solver, path, z3_version, +result, elapsed_ms, +num_variables, num_bool, num_int, num_real, +num_hard_constraints, num_soft_constraints, +num_minimize_objectives, num_maximize_objectives, +cli_effort, cli_tech, cli_process, cli_solver_iter_timeout, +cli_use_reboot, cli_optimize_m1, cli_optimize_m2, cli_num_of_heights, +z3_conflicts, z3_decisions, z3_propagations, z3_final_checks, z3_num_checks, +z3_max_memory_mb, z3_time_s, z3_rlimit_count, +smt2_filename, smt2_path, meta_path, +error +``` + +- `result`, `elapsed_ms` ← `z3_status.*` +- `num_*` ← `features.*` +- `cli_*` ← `cli_params.*`(접두사 부여) +- `z3_*` ← `z3_statistics`의 핵심 키(공백/하이픈 → 언더스코어) +- 전체 `z3_applied_params` 및 모든 `z3_statistics` 키가 필요하면 JSONL을 사용할 것. + +--- + +## 사용 예 + +```python +import json, pandas as pd + +rows = [json.loads(l) for l in open("input/z3-bench/problems.jsonl")] + +# Sat/Unsat 분포 +from collections import Counter +print(Counter(r["z3_status"]["result"] for r in rows)) + +# 인스턴스 크기 vs 시간 +df = pd.DataFrame([{ + "sha": r["problem_sha256"][:8], + "vars": r["features"]["num_variables"], + "hard": r["features"]["num_hard_constraints"], + "soft": r["features"]["num_soft_constraints"], + "ms": r["z3_status"]["elapsed_ms"], + "result": r["z3_status"]["result"], +} for r in rows]) +print(df.describe()) +``` + +## 비고 + +- 모든 행이 동일 `applied_params_hash`/seed 0이므로, 현재 스냅샷은 "단일 파라미터 프로파일에서의 인스턴스 난이도 분포"를 나타낸다. 파라미터 비교 실험을 위해서는 동일 `problem_sha256`에 대해 여러 프로파일을 추가 수집해야 한다. +- 일부 행에서 `path`/`solver` 누락이 있다 → 다운스트림 파서는 결측 허용 처리 권장. +- `z3_statistics` 키 집합은 인스턴스/실행마다 가변. 고정 컬럼 매트릭스를 만들려면 누락은 0/NaN으로 채울 것. +- 큰 인스턴스(`num_variables` ≈ 10⁵, `elapsed_ms` 최대 ≈ 181s)도 포함되어 있으니, 학습 시 시간 컷오프/정규화 고려. diff --git a/input/z3-bench/build_problems.py b/input/z3-bench/build_problems.py new file mode 100644 index 0000000000..c2be826343 --- /dev/null +++ b/input/z3-bench/build_problems.py @@ -0,0 +1,122 @@ +""" +Scan `raw-data/*.meta.jsonl` and emit `problems.jsonl` — one JSON per line, +sorted by SHA for deterministic diff. + +Each `____seed.meta.jsonl` file is a single JSON +object representing one baseline solver run. This script concatenates them +into the JSONL format `_lib.sampler` / `_lib.rebaseline` / `_lib.evaluator` +consume. + +Usage: + python3 input/z3-bench/build_problems.py [flags] + +Flags: + --filter-decisive keep only Sat / Unsat rows + --applied-params-hash HASH restrict to one param profile (prefix match ok) + --dry-run print counts but don't write + --out PATH output path (default: problems.jsonl in bench root) + +Output schema (one row per meta.jsonl entry): + { + "problem_sha256": "", + "smt2_filename": ".smt2", + "z3_status": {"result": "Sat", "elapsed_ms": 1234}, + "z3_statistics": {"conflicts": 100, ...}, + "z3_applied_params": {...}, + "features": {"num_hard_constraints": 24937, ...}, + "applied_params_hash": "...", + "z3_version" / "solver" / "path" / ... + } + +Re-run any time raw-data/ changes. Idempotent — overwrites problems.jsonl. +""" +import argparse +import json +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_RAW = _HERE / "raw-data" +_DEFAULT_OUT = _HERE / "problems.jsonl" + +_DECISIVE = ("Sat", "Unsat") + + +def scan(raw_dir, *, filter_decisive=False, applied_hash_prefix=None): + metas = sorted(raw_dir.glob("*.meta.jsonl")) + if not metas: + raise SystemExit(f"no *.meta.jsonl under {raw_dir}") + rows = [] + bad = 0 + skipped_decisive = 0 + skipped_hash = 0 + for p in metas: + try: + d = json.loads(p.read_text()) + except json.JSONDecodeError as e: + print(f"WARN: bad json {p.name}: {e}", file=sys.stderr) + bad += 1 + continue + if not isinstance(d, dict): + bad += 1 + continue + if "problem_sha256" not in d or "smt2_filename" not in d: + print(f"WARN: missing required fields in {p.name}", file=sys.stderr) + bad += 1 + continue + if applied_hash_prefix and not str(d.get("applied_params_hash", ""))\ + .startswith(applied_hash_prefix): + skipped_hash += 1 + continue + if filter_decisive: + res = (d.get("z3_status") or {}).get("result") + if res not in _DECISIVE: + skipped_decisive += 1 + continue + rows.append(d) + rows.sort(key=lambda r: (r["problem_sha256"], r.get("applied_params_hash", ""))) + return rows, {"bad": bad, "skipped_decisive": skipped_decisive, + "skipped_hash": skipped_hash, "scanned": len(metas)} + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("--filter-decisive", action="store_true", + help="keep only Sat / Unsat rows") + ap.add_argument("--applied-params-hash", type=str, default=None, + help="restrict to applied_params_hash starting with this prefix") + ap.add_argument("--dry-run", action="store_true", + help="print counts but don't write") + ap.add_argument("--out", type=pathlib.Path, default=_DEFAULT_OUT, + help=f"output path (default: {_DEFAULT_OUT.name})") + args = ap.parse_args() + + rows, stats = scan(_RAW, + filter_decisive=args.filter_decisive, + applied_hash_prefix=args.applied_params_hash) + print(f"scanned {stats['scanned']} meta.jsonl files") + if stats["bad"]: + print(f" skipped {stats['bad']} malformed") + if stats["skipped_hash"]: + print(f" skipped {stats['skipped_hash']} non-matching applied_params_hash") + if stats["skipped_decisive"]: + print(f" skipped {stats['skipped_decisive']} non-decisive baselines") + print(f" kept {len(rows)} rows") + + if args.dry_run: + print("(dry-run — no write)") + return + + args.out.parent.mkdir(parents=True, exist_ok=True) + with open(args.out, "w") as f: + for r in rows: + f.write(json.dumps(r) + "\n") + try: + rel = args.out.relative_to(_HERE.parent) + except ValueError: + rel = args.out + print(f"wrote {rel} ({len(rows)} rows)") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/build_problems_reboot.py b/input/z3-bench/build_problems_reboot.py new file mode 100644 index 0000000000..b78ae3a27d --- /dev/null +++ b/input/z3-bench/build_problems_reboot.py @@ -0,0 +1,122 @@ +""" +Scan `reboot-raw-data//{meta.jsonl,problem.smt2}` and emit +`problems.jsonl` for the z3-OPTIMIZE (soft-constraint / cost-mode) pipeline. + +The reboot dataset differs from the original z3-bench layout: + - per-cell subdirectories (one problem.smt2 + one meta.jsonl each), not flat + `.smt2` + `__hash__seed.meta.jsonl` files; + - meta `z3_status.result` is "Skipped" with elapsed_ms=0 and objective_value=0 + (baselines were never run), so there is NO usable baseline timing/objective + in the raw data — both are RE-MEASURED locally by `_lib.rebaseline`. + +To make the local re-measurement actually take effect, the emitted baseline +`z3_status.result` must equal what the local solve produces, because +`_lib.rebaseline` only adopts a local baseline when +`got_result == raw_result` (matches_raw). Empirically all 89 reboot instances +solve to `sat` with objective 0 in <5s, so we stamp result="Sat". The local +rebaseline then overrides elapsed_ms / stats / objective per-problem. + +`smt2_filename` is written relative to `raw-data/` (which `_lib` joins against): +`../reboot-raw-data//problem.smt2`. The `..` resolves at open() time. + +Usage: + python3 input/z3-bench/build_problems_reboot.py [--dry-run] [--out PATH] + +Idempotent — overwrites problems.jsonl. Re-run when reboot-raw-data/ changes. +""" +import argparse +import hashlib +import json +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_RAW = _HERE / "reboot-raw-data" +_DEFAULT_OUT = _HERE / "problems.jsonl" + +# Placeholder baseline timing — strictly < clustering.max_baseline_ms so the +# problem is never filtered from the pool. Overwritten by _lib.rebaseline. +_PLACEHOLDER_MS = 1000 + + +def _sha256(path): + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +def scan(raw_dir): + cells = sorted(p for p in raw_dir.iterdir() if p.is_dir()) + if not cells: + raise SystemExit(f"no per-cell subdirs under {raw_dir}") + rows = [] + bad = 0 + for cell in cells: + meta_path = cell / "meta.jsonl" + smt2_path = cell / "problem.smt2" + if not meta_path.exists() or not smt2_path.exists(): + print(f"WARN: missing meta.jsonl/problem.smt2 in {cell.name}", + file=sys.stderr) + bad += 1 + continue + try: + m = json.loads(meta_path.read_text()) + except json.JSONDecodeError as e: + print(f"WARN: bad json {cell.name}/meta.jsonl: {e}", file=sys.stderr) + bad += 1 + continue + sha = _sha256(smt2_path) + row = { + "problem_sha256": sha, + # relative to raw-data/ — _lib does raw_dir / smt2_filename + "smt2_filename": f"../reboot-raw-data/{cell.name}/problem.smt2", + "cell": cell.name, + "solver": m.get("solver", "z3-optimize"), + "z3_version": m.get("z3_version"), + "path": m.get("path"), + "features": m.get("features") or {}, + "cli_params": m.get("cli_params") or {}, + # Baseline re-measured locally; result="Sat" so matches_raw fires. + "z3_status": { + "result": "Sat", + "elapsed_ms": _PLACEHOLDER_MS, + "objective_value": 0, + }, + } + rows.append(row) + rows.sort(key=lambda r: r["problem_sha256"]) + return rows, {"scanned": len(cells), "bad": bad} + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("--dry-run", action="store_true", + help="print counts but don't write") + ap.add_argument("--out", type=pathlib.Path, default=_DEFAULT_OUT, + help=f"output path (default: {_DEFAULT_OUT.name})") + args = ap.parse_args() + + rows, stats = scan(_RAW) + print(f"scanned {stats['scanned']} cell dirs") + if stats["bad"]: + print(f" skipped {stats['bad']} incomplete/malformed") + print(f" kept {len(rows)} rows") + + if args.dry_run: + print("(dry-run — no write)") + return + + with open(args.out, "w") as f: + for r in rows: + f.write(json.dumps(r) + "\n") + try: + rel = args.out.relative_to(_HERE.parent) + except ValueError: + rel = args.out + print(f"wrote {rel} ({len(rows)} rows)") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/OPENEVOLVE_INTRO.md b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md new file mode 100644 index 0000000000..6308b5c617 --- /dev/null +++ b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md @@ -0,0 +1,232 @@ +# OpenEvolve 소개 — Z3 파라미터 튜닝 맥락 + +실행/구조는 [README.md](README.md) 참고. 이 문서는 OpenEvolve 개념, 본 프로젝트의 목적, 설계 결정, 로드맵을 정리. + +--- + +## 1. OpenEvolve 빠른 소개 (처음 보는 사람용) + +### 1.1 한 줄 요약 + +**OpenEvolve = LLM × 진화 알고리즘.** 사람이 정의한 "코드 조각"과 "평가 함수"를 받아, LLM이 코드를 반복적으로 변이시키고 점수가 높은 변이만 살려나간다. Google DeepMind의 AlphaEvolve 시스템을 오픈소스로 재구현한 프레임워크. + +### 1.2 동작 원리 (1 iteration) + +``` +┌───────────────────────────────────────────────────────────────┐ +│ 1. Database에서 부모 프로그램 K개 샘플링 (MAP-Elites + islands) │ +│ 2. LLM 프롬프트 구성: │ +│ - system_message (config.yaml에서) │ +│ - 부모 코드 + 부모들의 점수/메트릭 │ +│ - 과거 변이 일부 (inspiration) │ +│ - 이전 변이의 artifacts (디버깅 신호) │ +│ 3. LLM이 새 코드 생성 (diff 또는 full rewrite) │ +│ 4. 새 프로그램을 evaluator.py에 넘김 │ +│ 5. evaluator가 metrics dict 반환 (예: combined_score: 0.73) │ +│ 6. Database에 (code, metrics, artifacts) 저장 │ +│ 7. checkpoint_interval마다 디스크 저장 │ +└───────────────────────────────────────────────────────────────┘ +``` + +위를 `--iterations N`번 반복. 종료 시 `openevolve_output/best/best_program.py`에 최고 점수 변이 저장. + +### 1.3 핵심 개념 + +#### EVOLVE-BLOCK +초기 프로그램(`initial_program.py`) 안에서 **LLM이 수정해도 되는 영역**을 마커로 표시: + +```python +# 고정된 코드 (수정 금지) +import some_lib + +# EVOLVE-BLOCK-START +# 이 안의 코드만 LLM이 변이시킴 +def my_algorithm(): + return 42 +# EVOLVE-BLOCK-END + +# 고정된 코드 (인터페이스 보존용) +def run(): + return my_algorithm() +``` + +블록 밖은 인터페이스/타입/평가 함수 호출 규약 등을 유지하는 부분. 이 프로젝트에서는 EVOLVE-BLOCK 안에 Z3 파라미터 **dict 리터럴**을 두어 LLM이 키/값을 변이시킴. + +#### Evaluator +`evaluator.py`는 **단 하나의 함수**(`evaluate(program_path)`)를 노출. 변이된 프로그램을 받아 점수 dict를 돌려준다: + +```python +from openevolve.evaluation_result import EvaluationResult + +def evaluate(program_path): + # 1. 프로그램 import + # 2. 실행 / 측정 + # 3. metrics 계산 + return EvaluationResult( + metrics={"combined_score": 0.73, "sub_metric_a": 1.5, ...}, + artifacts={"summary": "...", "per_problem": [...]}, # 다음 LLM 호출의 컨텍스트로 사용됨 + ) +``` + +- **`metrics`**: 진화 압력. `combined_score` 키가 주 목적함수. 다른 키들은 부수 모니터링. +- **`artifacts`**: 점수에 영향 안 줌. 단, LLM에 다음 라운드 컨텍스트로 들어가서 "왜 실패했는지/뭐가 좋아졌는지" 학습 신호가 됨. 예: 에러 메시지, 잘못된 키 이름, 인스턴스별 speedup 분포. + +#### Cascade evaluation +한 변이를 평가하는 데 비용이 크면 단계별로 컷: + +``` +evaluate_stage1(program_path) → 빠른 검증 (이 프로젝트: 5문제) + └─ score 낮으면 즉시 컷 (cascade_thresholds 비교) + └─ score 통과 시 ↓ +evaluate_stage2(program_path) → 본 평가 (이 프로젝트: 전체 50문제) +``` + +명백히 망가진 변이(시드 위반, invalid key, 큰 회귀)는 stage1에서 거름. LLM 시간/달러 절약. + +#### MAP-Elites + Islands (다양성 유지) +- **MAP-Elites**: 코드를 다차원 feature grid에 매핑 → 각 셀의 챔피언만 유지. 단순히 "최고 점수 1개"가 아니라 "각 영역의 최고"를 보존 → 국소 최적 탈출. +- **Islands**: 독립된 N개의 population이 따로 진화. 주기적으로 migration. 한 island의 조기 수렴이 전체에 퍼지지 않게 함. + +이 프로젝트 설정: `num_islands: 3`, `population_size: 50`, `archive_size: 20`. + +#### Diff-based evolution +`diff_based_evolution: true`면 LLM이 전체 파일이 아니라 **search/replace 블록만** 출력. 큰 파일에서 토큰 절약 + 의도 명확. + +### 1.4 OpenEvolve가 받는 입력 (총 3개 파일) + +| 파일 | 역할 | +|---|---| +| `initial_program.py` | 시작점. EVOLVE-BLOCK 안 코드만 진화. 인터페이스는 evaluator와 합의 | +| `evaluator.py` | `evaluate(program_path) -> EvaluationResult` 함수 1개 노출 | +| `config.yaml` | LLM 모델, 반복 횟수, population, prompt system_message 등 | + +호출: +```bash +python openevolve-run.py \ + initial_program.py \ + evaluator.py \ + --config config.yaml \ + --iterations 100 +``` + +### 1.5 출력 + +``` +/openevolve_output/ +├── best/ +│ └── best_program.py # 최고 score 변이 +├── checkpoints/ +│ ├── checkpoint_10/ # checkpoint_interval마다 +│ ├── checkpoint_20/ +│ └── ... +└── logs/ + └── openevolve_*.log +``` + +`--checkpoint ` 옵션으로 중단 지점에서 재개 가능. + +### 1.6 이 프로젝트에서 OpenEvolve의 적용 방식 + +| OpenEvolve 개념 | 이 프로젝트에서 어떻게 쓰이나 | +|---|---| +| EVOLVE-BLOCK | Z3 파라미터 dict 리터럴 (`OPT_SLS_OVERRIDES = {...}` 등) | +| 진화 단위 | 알고리즘 코드 아니라 **dict 키/값** (이름 추가/제거/값 변경) | +| Evaluator | 변이된 dict를 `subprocess`로 `z3 ...`에 넘겨 50개 SMT2 풀고 점수화 | +| metrics | `combined_score = geomean(speedup) × solved_rate²` | +| artifacts | 인스턴스별 (sha, baseline_ms, elapsed_ms, speedup, timeout) — LLM이 다음 라운드에 "어느 문제가 느려졌는지" 확인 가능 | +| Cascade | stage1=5문제 15s, stage2=50문제 120s | +| Phase 분할 | 단일 OpenEvolve 실행이 아니라 **4회 순차 실행**. 각 phase는 다른 `initial_program.py` 사용, 이전 phase의 winner를 import | + +### 1.7 더 읽기 + +- 메인 README: `/README.md` +- 다른 예제: `examples/function_minimization/` (가장 간단), `examples/llm_prompt_optimization/`, `examples/circle_packing/` +- 기본 config 전체: `configs/default_config.yaml` +- 아키텍처: `CLAUDE.md` (개발자용 노트) + +--- + +## 2. 목적과 접근 + +- **타깃**: `z3_applied_params` 19개(베이스라인)에서 출발 → Z3 4.13.x 전체 파라미터 공간(opt./sat./smt./sls./parallel./global, ~250키) 탐색 +- **방법**: OpenEvolve로 `initial_program.py` 안의 dict 리터럴을 LLM이 변이. EVOLVE-BLOCK 마커 사이의 파라미터 dict만 진화 대상 +- **베이스라인**: `problems.jsonl`의 `applied_params_hash = 543b29...` 행들. 인스턴스별 `elapsed_ms` + `result`를 기준값으로 사용 +- **솔버 실행**: subprocess로 `z3 -T: -smt2 key=value ... file.smt2` 호출 → 격리 + 타임아웃 강제 + +## 3. Phase 분할 (옵션 b) + +| Phase | EVOLVE 대상 | 고정(locked) | 키 수 | iterations | 목적 | +|---|---|---|---|---|---| +| **P1** | `opt.*` + `sls.*` | sat/smt/parallel 베이스라인 + 시드 3종 | ~34 | 80 | MaxSAT 엔진 선택, SLS local search 튜닝 | +| **P2** | `sat.*` | P1 best `opt.*+sls.*` + smt/parallel 베이스라인 | ~121 | 150 | CDCL 코어 (preprocessing/restart/branching) | +| **P3** | `smt.*` (`auto_config=false` 강제) | P1+P2 best | ~97 | 120 | 산술/양화자 — LIA-heavy 워크로드에 영향 큼 | +| **P4** | P1∪P2∪P3 best 통합 | 없음 (locked 키만 유지) | union | 60 | 상호작용 보정. 짧은 local refinement | + +**고정 키 (locked)** — 전체 phase 변경 금지, evaluator가 위반 시 0점: +- `sat.random_seed = 0` +- `smt.random_seed = 0` +- `sls.random_seed = 0` +- `parallel.enable = False` + +**Phase 간 핸드오프**: 자동. P{N} 종료 후 `run_phase.sh`가 +`python -m _lib.extract_best z3-bench N` 자동 호출 → `cache/phase{N}_best.json` +작성 → P{N+1}이 import. + +P4 시작 전 `python -m _lib.prepare_phase z3-bench` 자동 실행 → +`phase4_unified/initial_program.py`의 EVOLVE-BLOCK을 union dict literal로 +머터리얼라이즈 (LLM이 diff 편집 가능하도록). + +## 4. 스코어링 + +``` +per_problem: + match baseline result → speedup = baseline_ms / elapsed_ms + mismatch (regression/unknown/timeout) → 1e-6 (geomean에 강한 페널티) + +aggregate: + combined_score = geomean(speedup) * solved_rate^2 +``` + +- `solved_rate^2`: 정답률이 핵심 게이트. 1회 회귀도 강하게 패널티 +- `geomean(speedup)`: 큰 인스턴스가 합산 지배하지 않도록 +- baseline 그대로면 `combined_score ≈ 1.0` +- 부수 메트릭: `regressions`, `solved/total`, `geomean_speedup` + +## 5. 주요 설계 결정 + +| 항목 | 선택 | 이유 | +|---|---|---| +| Phase 핸드오프 | 자동 (`run_phase.sh` → `extract_best.py`) | 사람 개입 줄임 | +| 메트릭 | `geomean(speedup) × solved_rate²` | 큰 인스턴스 지배 방지 + 정답률 강하게 게이트 | +| Z3 실행 | subprocess CLI | 프로세스 격리, 타임아웃 강제, 한 문제 크래시 영향 차단 | +| Stage1 샘플 | stratified 5문제, seed=42 | Sat/Unsat × 빠름/느림 골고루, 재현 가능 | +| Locked 키 | 시드 3종 + parallel.enable | 비교 공정성, 단일스레드 일관성 | +| `smt.auto_config` | P3에서 False 강제 | True면 다른 smt.* 옵션이 silently override됨 | +| `parallel_evaluations` | 1 | z3 메모리 4GB+ 인스턴스 존재, OOM 위험 | +| Phase 4 EVOLVE-BLOCK | 머터리얼라이즈된 literal dict | LLM이 diff 편집 가능해야 진화 가능 | + +## 6. 검증 상태 + +- `python build_stage1_sample.py` → 5문제 stratified 샘플 생성 (완료) +- 4개 phase `initial_program.py` import 확인: + - phase1: 46 키 (BASELINE 19 + OVERRIDES 34, 일부 키 중복) + - phase2: 135 키 + - phase3: 114 키 + - phase4: 19 키 (BASELINE만 — phases 1-3 이후 prepare_phase4.py가 채움) +- `score.py` 시뮬레이션: 2 speedup + 1 timeout + 1 regression + 1 slowdown → combined ≈ 0.002 (correctness gate 강하게 작동 확인) + +## 7. 비용/시간 추정 + +- baseline 평균 elapsed_ms ≈ 24,229 ms → 변이당 stage2 full run ≈ 50 × 24s = 1200s = 20분 (평균) +- P1 80 iter × 평균 20분 ≈ 27시간 (worst-case 비현실적, cascade로 대부분 stage1에서 컷) +- P2 150 iter × 20분 ≈ 50시간 +- 비용 절감: `OPENEVOLVE_MAX_PROBLEMS=20`으로 stage2도 축소 가능. 또는 stage1 cascade threshold 0.5+로 상향 → 약한 변이 조기 컷 비율↑ + +## 8. 향후 작업 후보 + +1. 컨테이너에서 `z3 -pmd` 캡쳐 → invalid key 사전 필터 +2. baseline 변이 stage1 1회 평가 sanity check +3. `docker-run.sh`에 `-e OPENAI_API_KEY` 자동 전달 추가 +4. LLM 모델 선택 (Gemini 무료 티어 vs 사내 모델 vs OpenAI) +5. 변이 결과 시각화 (`scripts/visualizer.py --path .../checkpoint_K/`) +6. final 검증: P4 best를 problems.jsonl 전수 50문제에 대해 재실행, speedup 분포 리포트 diff --git a/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md new file mode 100644 index 0000000000..212587aa19 --- /dev/null +++ b/input/z3-bench/evolve/README.md @@ -0,0 +1,143 @@ +# z3-bench — Z3 SMT 솔버 파라미터 튜닝 + +Z3 SMT 솔버 (v4.13.x) 파라미터를 진화적 탐색으로 튜닝. 데이터셋: +`input/z3-bench/raw-data/` (50개 SMT2 인스턴스, ~13k Int / 19k Bool / 40 Real +변수, ~2k soft + 105k hard 제약). 목표: baseline 대비 wall-clock 시간 단축, +정답성 (Sat/Unsat) 보존. + +플랫폼 전반 구조는 [`input/README.md`](../../README.md), OpenEvolve 개념은 +[`OPENEVOLVE_INTRO.md`](OPENEVOLVE_INTRO.md) 참고. 본 문서는 z3-bench 고유 +사항만 다룬다. + +## 디렉토리 구조 + +``` +input/z3-bench/ +├── raw-data/ # .smt2 + meta.jsonl +├── problems.jsonl # baseline 실행 기록 (50 rows) +└── evolve/ + ├── config.yaml # bench / LLM / clustering / evaluation + ├── params.json # Z3 파라미터 카탈로그 + ├── adapter.py # solver hooks + ├── _solve_worker.py # z3 Python binding subprocess + ├── phase1_opt_sls/ # opt.* + sls.* + ├── phase2_sat/ # sat.* (CDCL core) + ├── phase3_smt/ # smt.* (theories, quantifier, arith) + ├── phase4_unified/ # 통합 refinement (자동 머터리얼) + └── cache/ # 생성물, 삭제 안전 + ├── stage{1..4}_sample.json + ├── local_baseline.json + └── phase{N}_best.json +``` + +## 평가 흐름 + +`config.yaml` `bench.evaluation`: + +| 키 | 값 | +|---|---| +| `repeats` | 10 (10회 평균) | +| `score_mode` | `speedup` (wall-clock) | +| `enable_size_buckets` | `false` (z3는 단일 surface) | +| `enable_outlier_stage` | `false` | + +Cascade: stage1 (5문제) → stage2 (5문제) → stage3 (5문제, outlier) → stage4 +(20문제, 전체 spread). 각 stage gate는 `cascade_thresholds`. + +정답성 regression (baseline decisive + variant mismatch)은 abort + `1e-6` +penalty. invalid_param은 즉시 0점 + 어떤 키인지 artifact. + +## Clustering (config.yaml `bench.clustering`) + +| 키 | 값 | +|---|---| +| `method` | `kmeans` | +| `feature` | `features.num_hard_constraints` (dominant size signal) | +| `n_clusters` | 5 | +| `max_baseline_ms` | 300000 (5분 cap) | +| `stage_sizes` | stage1=5, stage2=5, stage3=5, stage4=20 | +| `stage_clusters` | stage1=c0+c1, stage2=c2+c3, stage3=c4, stage4=전체 | + +`python -m _lib.sampler z3-bench`로 `cache/stage{1..4}_sample.json` 생성. + +## Phase별 surface + +cpsat과 달리 z3는 SIZE_BUCKETS / STAGE3_OVERRIDES 미사용. 단일 surface +`OVERRIDES` 만 LLM 변이. + +| Phase | EVOLVE-BLOCK | inheritance | +|---|---|---| +| 1 (opt_sls) | `OVERRIDES = {}` | BASELINE only | +| 2 (sat) | `OVERRIDES = {}` | + cache/phase1_best.json | +| 3 (smt) | `OVERRIDES = {}` | + cache/phase2_best.json | +| 4 (unified) | `UNIFIED_OVERRIDES = {}` | `_lib.prepare_phase`가 phase{1,2,3}_best union으로 자동 채움 | + +`get_params()` 적용 순서: `BASELINE → prior_phase_best → current OVERRIDES`. + +## Quick start + +```bash +# 1. 전체 pipeline (sampler + rebaseline + 4 phases 순차) +./input/run_phase.sh z3-bench --pin 2-7 + +# 2. 단계별 +python -m _lib.sampler z3-bench # cache/stage{1..4}_sample.json +python -m _lib.self_test z3-bench # baseline sanity (stage1) +python -m _lib.rebaseline z3-bench # cache/local_baseline.json (10회 평균) +./input/run_phase.sh z3-bench 1 --pin 2-7 +./input/run_phase.sh z3-bench 2 --pin 2-7 +./input/run_phase.sh z3-bench 3 --pin 2-7 +./input/run_phase.sh z3-bench 4 --pin 2-7 + +# 3. 최종 검증 — run_phase.sh가 마지막 phase 후 자동 생성한 final_program.py 사용 +python -m _lib.final_verify z3-bench \ + input/z3-bench/evolve/final_program.py +``` + +마지막 phase 완료 후 `_lib.finalize`가 자동 실행 → +`/evolve/final_program.py`에 phase4 best_program.py 복사. 이 파일이 +canonical evolved 결과. 수동 재생성: `python -m _lib.finalize z3-bench`. + +각 non-final phase 완료 후 `_lib.extract_best`가 +`cache/phaseN_best.json` 자동 생성. Phase 4 시작 전 +`_lib.prepare_phase`가 EVOLVE-BLOCK 머터리얼. + +## Score 공식 (speedup mode) + +``` +combined_score = weighted_geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT + +speedup = baseline_ms / variant_ms (match 시) + = 1e-6 (regression 시) +weight = baseline_ms (긴 문제 dominate) +efficiency = geomean over {conflicts(w=2), decisions(w=1.5), propagations(w=0.5)} + of (baseline_stat + 1) / (variant_stat + 1), clipped [0.1, 10] +``` + +- 매치 (baseline Sat→variant Sat 또는 Unsat→Unsat) 시 wall-clock ratio가 + 점수에 기여. +- Mismatch (baseline decided + variant Unknown/timeout/opposite) 시 `1e-6` + → 한 문제 regression이 geomean을 크게 감점. +- baseline이 Unknown인 경우 variant가 풀어내면 개선으로 카운트, regression + 아님. + +## Locked params + +`sat.random_seed=0`, `smt.random_seed=0`, `sls.random_seed=0`, +`parallel.enable=false`, `threads=1`. 위반 시 `combined_score=0`. + +## 디버깅 / 트러블슈팅 + +| 증상 | 대응 | +|---|---| +| `invalid_param: ` artifact | params.json 카탈로그에 누락된 키. `_lib.params_catalog`가 catch하거나 z3 binary가 reject. params.json `groups[*].params[*]`에 추가하거나 LLM prompt에 명시. | +| Result regression abort | baseline은 Sat/Unsat인데 variant Unknown/timeout. presolve / SLS / restart 튜닝이 completeness 깬 경우 많음. | +| 로컬 baseline mismatch | `_lib.rebaseline`이 raw baseline과 불일치 결과 → evaluator는 raw_ms fallback. Z3 binary 버전 차이 또는 hardware noise. | + +## 참고 + +- 파라미터 카탈로그 + 검증: `params.json` (rich schema). 1265개 Z3 4.13.x + 키 중 LLM이 실제로 변이하는 ~27개 그룹화 + type/enum/range/desc 명시. +- 환경 변수: [`input/README.md`](../../README.md#environment-knobs) 참고. +- Z3 도커 셋업 / Claude Code 백엔드 / CPU 핀닝: 이전 버전 본 문서 + (`git log -p`) 또는 `docker-run.sh --help` 참고. diff --git a/input/z3-bench/evolve/_solve_worker.py b/input/z3-bench/evolve/_solve_worker.py new file mode 100644 index 0000000000..2db8c2c8f2 --- /dev/null +++ b/input/z3-bench/evolve/_solve_worker.py @@ -0,0 +1,248 @@ +""" +Solve one SMT2 file using the z3 Python binding (z3.set_param + z3.Optimize). +Matches the original benchmark setup (applied_params_hash 543b29...): params +are applied via z3.set_param so globals like 'threads' / 'parallel.enable' / +'sls.parallel' work, unlike CLI positional `key=value`. + +Solver mode is read from the sibling `config.yaml` `bench.solver_mode` +(default "optimize"): + - "optimize": z3.Optimize, full soft-constraint optimization (+ objective). + - "sat": z3.Solver over z3.parse_smt2_file (which drops `assert-soft`), + i.e. hard-constraint feasibility only — faster, no objective. + `opt.*` params are Optimize-only and silently dropped here. + +Invoked as a subprocess by z3_runner.py for process isolation + hard timeout. + +argv: + sys.argv[1] JSON dict of {key: value} (params) + sys.argv[2] smt2 file path + sys.argv[3] per-problem timeout in seconds + +stdout: a single JSON line, one of: + {"result": "Sat"|"Unsat"|"Unknown", "elapsed_ms": int, "stats": {: , ...}} + {"result": "Unknown", "elapsed_ms": int, "timeout": true, "stats": {...}?} + {"invalid_param": "", "error": "", "result": "Unknown", "elapsed_ms": 0} + {"result": "Unknown", "elapsed_ms": 0, "error": ""} + +"stats" mirrors z3 Optimize.statistics() (decisions, propagations, conflicts, +restarts, plus tactic-specific counters like arith/bv overflow, mk-clause, ...). +Numeric values only; non-numeric keys dropped to keep JSON small. +""" + +import json +import os +import pathlib +import sys +import time + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def _solver_mode(): + """Read `bench.solver_mode` from the sibling config.yaml. Returns + "optimize" (default) or "sat". Config-driven so no env var is needed; + one parse per worker process is negligible next to the z3 import.""" + try: + import yaml + + cfg_path = pathlib.Path(__file__).resolve().parent / "config.yaml" + cfg = yaml.safe_load(cfg_path.read_text()) or {} + return ((cfg.get("bench") or {}).get("solver_mode")) or "optimize" + except Exception: + return "optimize" + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + try: + params = json.loads(sys.argv[1]) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params json: {e}"}) + return + + smt2_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + mode = _solver_mode() + + try: + import z3 + except ImportError as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"z3 binding import: {e}"}) + return + + # Split params per route_solver.cpp set_z3_param_optimize_option(): + # opt.* keys → per-Optimize via opt.set(Params) (strip "opt." prefix) + # all others → global via z3.set_param + # Matches cpp ground truth: priority/maxsat_engine/enable_*/maxres.*/rc2.* + # are set on the Optimize instance (opt.set), not globally. + # Mirror cpp route_solver.cpp:613 — suppress unknown-param warnings + # BEFORE any other set_param. Without this, z3 4.15.x emits a warning + + # dumps the legal-param list to stderr for keys like "threads" (no-op in + # this version), which aborts the subprocess in stderr-piped mode. + try: + z3.set_param("warning", False) + except Exception: + pass + + opt_local = {} + for k, v in params.items(): + if k.startswith("opt."): + # opt.* is Optimize-only. In sat mode (z3.Solver) it has no target, + # so drop it silently rather than flagging it as an invalid_param. + if mode != "sat": + opt_local[k[len("opt.") :]] = v + continue + try: + z3.set_param(k, v) + except z3.Z3Exception as e: + emit({"invalid_param": k, "error": str(e), "result": "Unknown", "elapsed_ms": 0}) + return + except Exception as e: + emit( + { + "invalid_param": k, + "error": f"{type(e).__name__}: {e}", + "result": "Unknown", + "elapsed_ms": 0, + } + ) + return + + # Soft timeout (z3 polls at safe points) — outer subprocess.run() also + # enforces a hard wall-clock cap. + try: + z3.set_param("timeout", int(timeout_s * 1000)) + except Exception: + pass + + if mode == "sat": + # SAT feasibility only: parse_smt2_file drops `assert-soft` (Optimize + # extension) and returns just the hard assertions. Solver stops at the + # first feasible model — no objective search. + o = z3.Solver() + try: + o.add(z3.parse_smt2_file(smt2_path)) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"smt2 parse: {e}"}) + return + else: + o = z3.Optimize() + + for k, v in opt_local.items(): + try: + o.set(k, v) + except z3.Z3Exception as e: + emit( + { + "invalid_param": "opt." + k, + "error": str(e), + "result": "Unknown", + "elapsed_ms": 0, + } + ) + return + except Exception as e: + emit( + { + "invalid_param": "opt." + k, + "error": f"{type(e).__name__}: {e}", + "result": "Unknown", + "elapsed_ms": 0, + } + ) + return + + try: + o.from_file(smt2_path) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"smt2 parse: {e}"}) + return + + t0 = time.monotonic() + try: + res = o.check() + except Exception as e: + elapsed_ms = int((time.monotonic() - t0) * 1000) + emit( + { + "result": "Unknown", + "elapsed_ms": elapsed_ms, + "error": f"check() raised: {e}", + } + ) + return + elapsed_ms = int((time.monotonic() - t0) * 1000) + + if res == z3.sat: + label = "Sat" + elif res == z3.unsat: + label = "Unsat" + else: + label = "Unknown" + + stats = {} + try: + st = o.statistics() + for k in st.keys(): + try: + v = st.get_key_value(k) + except Exception: + continue + if isinstance(v, bool): + continue + if isinstance(v, (int, float)): + stats[k] = v + except Exception: + stats = {} + + # Cost mode (optimize): score on z3's deterministic work measure so the + # signal is machine-independent and immune to the sub-second wall-clock + # noise this workload exhibits (median solve ~0.65s). "rlimit count" is + # z3's deterministic step counter — _lib.scorer._time_ratio reads + # stats["deterministic_time"]. + if "rlimit count" in stats: + stats["deterministic_time"] = stats["rlimit count"] + + # Optimize objective value (sum of violated assert-soft weights). These + # instances carry exactly one implicit objective; evaluate it in the model. + # Used by cost mode as a CORRECTNESS GUARD: a good variant must still reach + # the baseline optimum (0 on this workload); a worse / non-zero objective is + # penalized via cost_ratio < 1. + # sat mode is a plain Solver — no objective() to read (and speedup scoring + # ignores it anyway). Only Optimize exposes objectives(). + objective = None + if mode != "sat" and label == "Sat": + try: + objs = o.objectives() + if objs: + val = o.model().eval(objs[0], model_completion=True) + try: + objective = val.as_long() + except Exception: + try: + objective = float(val.as_fraction()) + except Exception: + objective = float(str(val)) + except Exception: + objective = None + + out = {"result": label, "elapsed_ms": elapsed_ms, "stats": stats} + if objective is not None: + out["objective"] = objective + emit(out) + + +if __name__ == "__main__": + main() + # Bypass z3 atexit/teardown that can abort the subprocess after a clean + # emit() — would mask the result as "worker produced no output". + sys.stdout.flush() + sys.stderr.flush() + os._exit(0) diff --git a/input/z3-bench/evolve/adapter.py b/input/z3-bench/evolve/adapter.py new file mode 100644 index 0000000000..87c72aeec4 --- /dev/null +++ b/input/z3-bench/evolve/adapter.py @@ -0,0 +1,42 @@ +""" +z3-bench solver hooks. Consumed by every _lib module via +bench_paths.load_adapter(). +""" + +SOLVER_NAME = "z3" + +PROBLEM_FILE_FIELD = "smt2_filename" +STATUS_FIELD = "z3_status" # {"result", "elapsed_ms", "objective_value"} +STATS_FIELD = None # baseline carries no separate stats block +FEATURES_FIELD = "features" +# Optimize mode: z3.Optimize derives one objective from the assert-soft +# constraints. The reboot baselines were "Skipped" (objective_value=0 placeholder), +# so the real baseline objective is re-measured by _lib.rebaseline and overrides +# this field per-problem (evaluator_core: lo["objective"] wins when matches_raw). +OBJECTIVE_FIELD = "objective_value" + +DECISIVE_RESULTS = ("Sat", "Unsat") +DECIDED_RESULTS = ("Sat", "Unsat") + +KEY_STATS = ("decisions", "propagations", "conflicts", "rlimit count") + +STATS_WEIGHTS = { + "conflicts": 2.0, + "decisions": 1.5, + "propagations": 0.5, +} + +# Cost mode: combined = geomean(cost_ratio^cw * time_ratio) * solved_rate^2 * eff^sw. +# On this reboot workload every problem's optimum is 0, so cost_ratio is ~1 and acts +# as a correctness guard (a variant that returns a worse, non-zero objective is +# penalized); the optimization signal comes from time_ratio on deterministic_time +# (rlimit count), emitted by _solve_worker.py. See config.yaml evaluation.score_mode. +SCORE_MODE = "cost" + +WORKERS_KEY = None # z3 single-threaded in this bench + + +def get_problem_size(features): + """Reboot z3-optimize problems carry num_hard_constraints as the dominant + size signal (2.5k–41k hard / 42–392 soft across the 89-instance workload).""" + return int((features or {}).get("num_hard_constraints") or 0) diff --git a/input/z3-bench/evolve/backup/20260519/phase1_opt_sls/initial_program.py b/input/z3-bench/evolve/backup/20260519/phase1_opt_sls/initial_program.py new file mode 100644 index 0000000000..bafdea9fde --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/phase1_opt_sls/initial_program.py @@ -0,0 +1,70 @@ +""" +Phase 1: tune opt.* + sls.*. + +Other namespaces (sat.*, smt.*, parallel.*) stay at baseline. Z3 4.13.x keys. +EVOLVE-BLOCK below is the only thing the LLM should change. Keys may be added, +removed, or have values modified. + +Do NOT modify locked keys (sls.random_seed; sat./smt./parallel.* live in BASELINE +and stay there). Invalid Z3 keys cause evaluator to return 0. +""" +import pathlib +import sys + +_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" +sys.path.insert(0, str(_SHARED)) + +from baseline_params import BASELINE # noqa: E402 + + +# EVOLVE-BLOCK-START +OPT_SLS_OVERRIDES = { + # opt.* — MaxSMT engine, MaxRes/RC2 knobs, optsmt engine + "opt.priority": "pareto", # lex | pareto | box + "opt.maxsat_engine": "wmax", # maxres | pd-maxres | wmax | sortmax | rc2 | maxres-bin + "opt.optsmt_engine": "basic", # basic | farkas | symba + "opt.enable_sat": True, + "opt.enable_sls": True, + "opt.enable_core_rotate": True, + # opt.enable_lns / opt.lns.threshold: not in z3 4.13.3.0 — removed. + "opt.maxres.hill_climb": True, + "opt.maxres.add_upper_bound_block": False, + "opt.maxres.max_core_size": 3, + "opt.maxres.max_correction_set_size": 3, + "opt.maxres.maximize_assignment": False, + "opt.maxres.pivot_on_correction_set": True, + "opt.maxres.wmax": False, + "opt.maxlex.enable": True, + "opt.rc2.totalizer": True, + "opt.pb.compile_equality": False, + "opt.elim_01": True, + + # sls.* — stochastic local search (engaged via opt.enable_sls) + "sls.early_prune": True, + "sls.walksat": True, + "sls.walksat_repick": True, + "sls.walksat_ucb": True, + "sls.walksat_ucb_constant": 20.0, + "sls.walksat_ucb_forget": 0.1, + "sls.walksat_ucb_init": False, + "sls.walksat_ucb_noise": 0.0002, + "sls.wp": 20, # walk probability (percent 0..100) + # sls.parallel: API-only key (z3 CLI rejects); pinned to default False. + "sls.random_offset": True, + "sls.rescore": True, + "sls.restart_base": 100, + "sls.restart_init": False, + "sls.track_unsat": False, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(OPT_SLS_OVERRIDES) + return p + + +def get_phase_overrides(): + """Used by extract_best.py — returns ONLY this phase's evolved dict.""" + return dict(OPT_SLS_OVERRIDES) diff --git a/input/z3-bench/evolve/backup/20260519/phase2_sat/initial_program.py b/input/z3-bench/evolve/backup/20260519/phase2_sat/initial_program.py new file mode 100644 index 0000000000..22861aaeaf --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/phase2_sat/initial_program.py @@ -0,0 +1,177 @@ +""" +Phase 2: tune sat.* (CDCL SAT core). + +Loads phase1_best.json (opt.*+sls.* winners) as locked. smt.* and parallel.* +stay at baseline. EVOLVE-BLOCK is SAT_OVERRIDES below. + +Do NOT modify sat.random_seed (locked). Invalid keys -> evaluator returns 0. +""" +import json +import pathlib +import sys + +_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" +sys.path.insert(0, str(_SHARED)) + +from baseline_params import BASELINE # noqa: E402 + +_PHASE1_FILE = _SHARED / "phase1_best.json" +_PHASE1 = ( + json.loads(_PHASE1_FILE.read_text()) if _PHASE1_FILE.exists() else {} +) + + +# EVOLVE-BLOCK-START +SAT_OVERRIDES = { + # Branching / phase / restart + "sat.branching.heuristic": "vsids", # vsids | lrb | chb + "sat.branching.anti_exploration": 0.4, + "sat.phase": "caching", # always_false | always_true | basic_caching | random | caching + "sat.phase.sticky": True, + "sat.restart": "geometric", # luby | geometric | ema | static + "sat.restart.fast": True, + "sat.restart.initial": 2, + "sat.restart.factor": 1.5, + "sat.restart.margin": 1.1, + "sat.restart.emafastglue": 0.03, + "sat.restart.emaslowglue": 1e-05, + "sat.rephase.base": 1000, + "sat.reorder.base": 4294967295, + "sat.reorder.itau": 4.0, + "sat.reorder.activity_scale": 100, + "sat.random_freq": 0.01, + "sat.variable_decay": 110, + "sat.burst_search": 100, + "sat.search.sat.conflicts": 400, + "sat.search.unsat.conflicts": 400, + "sat.backtrack.conflicts": 4000, + "sat.backtrack.scopes": 100, + + # Garbage collection + "sat.gc": "glue_psm", # glue | psm | glue_psm | dyn_psm + "sat.gc.burst": False, + "sat.gc.defrag": True, + "sat.gc.increment": 500, + "sat.gc.initial": 20000, + "sat.gc.k": 7, + "sat.gc.small_lbd": 3, + "sat.minimize_lemmas": True, + # sat.dyn.sub_res: not in z3 4.13.3.0 — removed. + + # Preprocessing / simplification + "sat.scc": True, + "sat.scc.tr": True, + "sat.elim_vars": True, + # sat.elim_vars_bdd / sat.elim_vars_bdd_delay: not in z3 4.13.3.0 — removed. + "sat.subsumption": True, + "sat.subsumption.limit": 100000000, + "sat.asymm_branch": True, + "sat.asymm_branch.all": False, + "sat.asymm_branch.delay": 1, + "sat.asymm_branch.limit": 100000000, + "sat.asymm_branch.rounds": 2, + "sat.asymm_branch.sampled": True, + "sat.probing": True, + "sat.probing_binary": True, + "sat.probing_cache": True, + "sat.probing_cache_limit": 1024, + "sat.probing_limit": 5000000, + "sat.propagate.prefetch": True, + "sat.ate": True, + "sat.acce": False, + "sat.bce": False, + "sat.bce_at": 2, + "sat.bce_delay": 2, + "sat.bca": False, + # sat.binspr: not in z3 4.13.3.0 — removed. + "sat.cce": False, + "sat.blocked_clause_limit": 100000000, + "sat.retain_blocked_clauses": True, + "sat.enable_pre_simplify": False, + "sat.force_cleanup": False, + "sat.inprocess.max": 4294967295, + "sat.simplify.delay": 0, + # sat.next_simplify1: not in z3 4.13.3.0 — removed. + + # Cardinality / PB + "sat.cardinality.solver": True, + "sat.cardinality.encoding": "grouped", # grouped | bimander | ordered | unate | circuit + "sat.pb.solver": "totalizer", # circuit | sorting | totalizer | solver | segmented | binary_merge + "sat.pb.lemma_format": "cardinality", # cardinality | pb + "sat.pb.resolve": "cardinality", # cardinality | rounding + + # Core minimization + "sat.core.minimize": False, + "sat.core.minimize_partial": False, + + # Threading (keep 1 for fair compare; parallel.enable stays locked false) + "sat.threads": 1, + + # SLS-within-SAT (separate from opt-level sls.*) + "sat.local_search": False, + "sat.local_search_mode": "wsat", # wsat | gsat + "sat.local_search_threads": 0, + "sat.ddfw_search": False, + "sat.ddfw.threads": 0, + "sat.ddfw.init_clause_weight": 8, + "sat.ddfw.reinit_base": 10000, + "sat.ddfw.restart_base": 100000, + "sat.ddfw.use_reward_pct": 15, + "sat.prob_search": False, + + # Cut/AIG/ANF preprocessing (default off for this workload) + "sat.cut": False, + "sat.cut.aig": False, + "sat.cut.delay": 2, + "sat.cut.dont_cares": True, + "sat.cut.force": False, + "sat.cut.lut": False, + "sat.cut.npn3": False, + "sat.cut.redundancies": True, + "sat.cut.xor": False, + "sat.anf": False, + "sat.anf.delay": 2, + "sat.anf.exlin": False, + + # Lookahead (mostly off; expose for solver-specific subproblems) + "sat.lookahead.cube.cutoff": "depth", # depth | freevars | psat | adaptive_freevars | adaptive_psat + "sat.lookahead.cube.depth": 1, + "sat.lookahead.cube.fraction": 0.4, + "sat.lookahead.cube.freevars": 0.8, + "sat.lookahead.cube.psat.clause_base": 2.0, + "sat.lookahead.cube.psat.trigger": 5.0, + "sat.lookahead.cube.psat.var_exp": 1.0, + "sat.lookahead.delta_fraction": 1.0, + "sat.lookahead.double": True, + "sat.lookahead.global_autarky": False, + "sat.lookahead.preselect": False, + "sat.lookahead.reward": "march_cu", # ternary | heule_schur | heule_unit | unit | march_cu + "sat.lookahead.use_learned": False, + "sat.lookahead_scores": False, + "sat.lookahead_simplify": False, + "sat.lookahead_simplify.bca": True, + + # Resolution-based simplification limits + "sat.resolution.cls_cutoff1": 100000000, + "sat.resolution.cls_cutoff2": 700000000, + "sat.resolution.limit": 500000000, + "sat.resolution.lit_cutoff_range1": 700, + "sat.resolution.lit_cutoff_range2": 400, + "sat.resolution.lit_cutoff_range3": 300, + "sat.resolution.occ_cutoff": 10, + "sat.resolution.occ_cutoff_range1": 8, + "sat.resolution.occ_cutoff_range2": 5, + "sat.resolution.occ_cutoff_range3": 3, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(SAT_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(SAT_OVERRIDES) diff --git a/input/z3-bench/evolve/backup/20260519/phase3_smt/initial_program.py b/input/z3-bench/evolve/backup/20260519/phase3_smt/initial_program.py new file mode 100644 index 0000000000..5635303efb --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/phase3_smt/initial_program.py @@ -0,0 +1,164 @@ +""" +Phase 3: tune smt.* (SMT core — theories, quantifier instantiation, arith). + +Loads phase1_best.json (opt./sls.*) and phase2_best.json (sat.*) as locked. +parallel.* stays at baseline. EVOLVE-BLOCK is SMT_OVERRIDES below. + +NOTE: smt.auto_config=True (default) can silently override other smt.* options; +we force False here so the LLM's choices stick. + +Do NOT modify smt.random_seed (locked). Invalid keys -> evaluator returns 0. +""" +import json +import pathlib +import sys + +_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" +sys.path.insert(0, str(_SHARED)) + +from baseline_params import BASELINE # noqa: E402 + +_PHASE1 = ( + json.loads((_SHARED / "phase1_best.json").read_text()) + if (_SHARED / "phase1_best.json").exists() + else {} +) +_PHASE2 = ( + json.loads((_SHARED / "phase2_best.json").read_text()) + if (_SHARED / "phase2_best.json").exists() + else {} +) + + +# EVOLVE-BLOCK-START +SMT_OVERRIDES = { + # Core SMT control + "smt.auto_config": False, # FORCED False so other smt.* take effect + "smt.logic": "", # "" | "QF_LIA" | "QF_LRA" | "LIA" | ... + "smt.threads": 1, # keep 1; parallel.enable is locked false + "smt.threads.cube_frequency": 2, + "smt.threads.max_conflicts": 400, + "smt.cube_depth": 1, + "smt.relevancy": 2, # 0 | 1 | 2 + "smt.case_split": 1, # 0=activity | 1=random | 2=theory | 3=relevancy | 5 | 6 + "smt.phase_selection": 3, # 0=always_false | 1=always_true | 2=basic_caching | 3=caching | 4=random | 5=occurrence + "smt.phase_caching_on": 400, + "smt.phase_caching_off": 100, + "smt.restart_strategy": 1, # 0=geometric | 1=inner_outer | 2=luby | 3=fixed | 4=arithmetic + # smt.restart.factor: not in z3 4.13.3.0 — removed. + "smt.lemma_gc_strategy": 0, # 0=fixed | 1=geometric | 2=at_restart | 3=none + + # Lemma / unit delay + "smt.delay_units": False, + "smt.delay_units_threshold": 32, + + # Dynamic ackermann (dack) + "smt.dack": 1, # 0=off | 1=on + "smt.dack.eq": False, + "smt.dack.factor": 0.1, + "smt.dack.gc": 2000, + "smt.dack.gc_inv_decay": 0.8, + "smt.dack.threshold": 10, + + # Generic + "smt.elim_unconstrained": True, + "smt.ematching": True, + "smt.macro_finder": False, + "smt.quasi_macros": False, + "smt.propagate_values": True, + "smt.pull_nested_quantifiers": False, + "smt.refine_inj_axioms": True, + "smt.solve_eqs": True, + # smt.solve_eqs_max_occs: not in z3 4.13.3.0 — removed. + "smt.theory_aware_branching": False, + "smt.theory_case_split": False, + "smt.dt_lazy_splits": 1, + "smt.induction": False, + + # Core extension / minimization + "smt.core.extend_patterns": False, + "smt.core.extend_nonlocal_patterns": False, + "smt.core.extend_patterns.max_distance": 4294967295, + "smt.core.minimize": False, + "smt.core.validate": False, + + # MBQI / quantifier instantiation + "smt.mbqi": True, + "smt.mbqi.max_iterations": 1000, + "smt.mbqi.max_cexs": 1, + "smt.mbqi.max_cexs_incr": 0, + "smt.mbqi.force_template": 10, + "smt.qi.eager_threshold": 10.0, + "smt.qi.lazy_threshold": 20.0, + "smt.qi.max_instances": 4294967295, + "smt.qi.max_multi_patterns": 0, + "smt.qi.cost": "(+ weight generation)", + "smt.qi.quick_checker": 0, # 0=no | 1=unsat | 2=both + + # Arithmetic theory (workload has 13k Int + 40 Real — IMPORTANT) + "smt.arith.solver": 6, # 2=simplex | 5=infinitary_lra | 6=lra + "smt.arith.simplex_strategy": 0, + "smt.arith.propagation_mode": 1, # 0=none | 1=at_pivot | 2=cheap + "smt.arith.propagate_eqs": True, + "smt.arith.eager_eq_axioms": True, + "smt.arith.branch_cut_ratio": 2, + "smt.arith.bprop_on_pivoted_rows": True, + "smt.arith.enable_hnf": True, + "smt.arith.greatest_error_pivot": False, + "smt.arith.ignore_int": False, + "smt.arith.int_eq_branch": False, + "smt.arith.min": False, + "smt.arith.random_initial_value": False, + "smt.arith.rep_freq": 0, + "smt.arith.auto_config_simplex": False, + + # Nonlinear arith (mostly off for LIA-heavy workload; expose anyway) + "smt.arith.nl": True, + "smt.arith.nl.branching": True, + "smt.arith.nl.delay": 500, + "smt.arith.nl.expp": False, + "smt.arith.nl.gr_q": 10, + "smt.arith.nl.grobner": True, + "smt.arith.nl.grobner_cnfl_to_report": 1, + "smt.arith.nl.grobner_eqs_growth": 10, + "smt.arith.nl.grobner_expr_degree_growth": 2, + "smt.arith.nl.grobner_expr_size_growth": 2, + "smt.arith.nl.grobner_frequency": 4, + "smt.arith.nl.grobner_max_simplified": 10000, + "smt.arith.nl.grobner_subs_fixed": 1, + "smt.arith.nl.horner": True, + "smt.arith.nl.horner_frequency": 4, + "smt.arith.nl.horner_row_length_limit": 10, + "smt.arith.nl.horner_subs_fixed": 2, + "smt.arith.nl.nra": True, + "smt.arith.nl.order": True, + "smt.arith.nl.rounds": 1024, + "smt.arith.nl.tangents": True, + + # BV (light usage in this workload) + "smt.bv.delay": True, + # smt.bv.eager: not in z3 4.13.3.0 — removed. + "smt.bv.enable_int2bv": True, + "smt.bv.reflect": True, + "smt.bv.size_reduce": False, + "smt.bv.solver": 0, + + # Array / PB + "smt.array.extensional": True, + "smt.array.weak": False, + "smt.pb.conflict_frequency": 1000, + "smt.pb.learn_complements": True, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(_PHASE2) + p.update(SMT_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(SMT_OVERRIDES) diff --git a/input/z3-bench/evolve/backup/20260519/phase4_unified/initial_program.py b/input/z3-bench/evolve/backup/20260519/phase4_unified/initial_program.py new file mode 100644 index 0000000000..b9be8126d9 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/phase4_unified/initial_program.py @@ -0,0 +1,275 @@ +""" +Phase 4: unified fine-tuning — interaction effects. + +The EVOLVE-BLOCK below is a literal dict materialized from phase1/2/3 best +overrides by prepare_phase4.py. Run that script BEFORE invoking run_phase.sh 4: + + python ../prepare_phase4.py + +The LLM evolves UNIFIED_OVERRIDES directly. It may modify, remove, or add +keys from the Z3 4.13.x parameter space. Smaller iteration budget — local +refinement, not exploration. +""" +import pathlib +import sys + +_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" +sys.path.insert(0, str(_SHARED)) + +from baseline_params import BASELINE # noqa: E402 + + +# EVOLVE-BLOCK-START +# Auto-generated by prepare_phase4.py from union of phase 1/2/3 best. +UNIFIED_OVERRIDES = {'opt.elim_01': True, + 'opt.enable_core_rotate': True, + 'opt.enable_sat': True, + 'opt.enable_sls': True, + 'opt.maxlex.enable': True, + 'opt.maxres.add_upper_bound_block': False, + 'opt.maxres.hill_climb': True, + 'opt.maxres.max_core_size': 3, + 'opt.maxres.max_correction_set_size': 3, + 'opt.maxres.maximize_assignment': False, + 'opt.maxres.pivot_on_correction_set': True, + 'opt.maxres.wmax': False, + 'opt.maxsat_engine': 'wmax', + 'opt.optsmt_engine': 'basic', + 'opt.pb.compile_equality': False, + 'opt.priority': 'pareto', + 'opt.rc2.totalizer': True, + 'sat.acce': False, + 'sat.anf': False, + 'sat.anf.delay': 2, + 'sat.anf.exlin': False, + 'sat.asymm_branch': True, + 'sat.asymm_branch.all': False, + 'sat.asymm_branch.delay': 1, + 'sat.asymm_branch.limit': 100000000, + 'sat.asymm_branch.rounds': 2, + 'sat.asymm_branch.sampled': True, + 'sat.ate': True, + 'sat.backtrack.conflicts': 4000, + 'sat.backtrack.scopes': 100, + 'sat.bca': False, + 'sat.bce': False, + 'sat.bce_at': 2, + 'sat.bce_delay': 2, + 'sat.blocked_clause_limit': 100000000, + 'sat.branching.anti_exploration': 0.4, + 'sat.branching.heuristic': 'vsids', + 'sat.burst_search': 100, + 'sat.cardinality.encoding': 'grouped', + 'sat.cardinality.solver': True, + 'sat.cce': False, + 'sat.core.minimize': False, + 'sat.core.minimize_partial': False, + 'sat.cut': False, + 'sat.cut.aig': False, + 'sat.cut.delay': 2, + 'sat.cut.dont_cares': True, + 'sat.cut.force': False, + 'sat.cut.lut': False, + 'sat.cut.npn3': False, + 'sat.cut.redundancies': True, + 'sat.cut.xor': False, + 'sat.ddfw.init_clause_weight': 8, + 'sat.ddfw.reinit_base': 10000, + 'sat.ddfw.restart_base': 100000, + 'sat.ddfw.threads': 0, + 'sat.ddfw.use_reward_pct': 15, + 'sat.ddfw_search': False, + 'sat.elim_vars': True, + 'sat.enable_pre_simplify': False, + 'sat.force_cleanup': False, + 'sat.gc': 'glue_psm', + 'sat.gc.burst': False, + 'sat.gc.defrag': True, + 'sat.gc.increment': 500, + 'sat.gc.initial': 20000, + 'sat.gc.k': 7, + 'sat.gc.small_lbd': 3, + 'sat.inprocess.max': 4294967295, + 'sat.local_search': False, + 'sat.local_search_mode': 'wsat', + 'sat.local_search_threads': 0, + 'sat.lookahead.cube.cutoff': 'depth', + 'sat.lookahead.cube.depth': 1, + 'sat.lookahead.cube.fraction': 0.4, + 'sat.lookahead.cube.freevars': 0.8, + 'sat.lookahead.cube.psat.clause_base': 2.0, + 'sat.lookahead.cube.psat.trigger': 5.0, + 'sat.lookahead.cube.psat.var_exp': 1.0, + 'sat.lookahead.delta_fraction': 1.0, + 'sat.lookahead.double': True, + 'sat.lookahead.global_autarky': False, + 'sat.lookahead.preselect': False, + 'sat.lookahead.reward': 'march_cu', + 'sat.lookahead.use_learned': False, + 'sat.lookahead_scores': False, + 'sat.lookahead_simplify': False, + 'sat.lookahead_simplify.bca': True, + 'sat.minimize_lemmas': True, + 'sat.pb.lemma_format': 'cardinality', + 'sat.pb.resolve': 'cardinality', + 'sat.pb.solver': 'totalizer', + 'sat.phase': 'caching', + 'sat.phase.sticky': True, + 'sat.prob_search': False, + 'sat.probing': True, + 'sat.probing_binary': True, + 'sat.probing_cache': True, + 'sat.probing_cache_limit': 1024, + 'sat.probing_limit': 5000000, + 'sat.propagate.prefetch': True, + 'sat.random_freq': 0.01, + 'sat.reorder.activity_scale': 100, + 'sat.reorder.base': 4294967295, + 'sat.reorder.itau': 4.0, + 'sat.rephase.base': 1000, + 'sat.resolution.cls_cutoff1': 100000000, + 'sat.resolution.cls_cutoff2': 700000000, + 'sat.resolution.limit': 500000000, + 'sat.resolution.lit_cutoff_range1': 700, + 'sat.resolution.lit_cutoff_range2': 400, + 'sat.resolution.lit_cutoff_range3': 300, + 'sat.resolution.occ_cutoff': 10, + 'sat.resolution.occ_cutoff_range1': 8, + 'sat.resolution.occ_cutoff_range2': 5, + 'sat.resolution.occ_cutoff_range3': 3, + 'sat.restart': 'geometric', + 'sat.restart.emafastglue': 0.03, + 'sat.restart.emaslowglue': 1e-05, + 'sat.restart.factor': 1.5, + 'sat.restart.fast': True, + 'sat.restart.initial': 2, + 'sat.restart.margin': 1.1, + 'sat.retain_blocked_clauses': True, + 'sat.scc': True, + 'sat.scc.tr': True, + 'sat.search.sat.conflicts': 400, + 'sat.search.unsat.conflicts': 400, + 'sat.simplify.delay': 0, + 'sat.subsumption': True, + 'sat.subsumption.limit': 100000000, + 'sat.threads': 1, + 'sat.variable_decay': 110, + 'sls.early_prune': True, + 'sls.random_offset': True, + 'sls.rescore': True, + 'sls.restart_base': 100, + 'sls.restart_init': False, + 'sls.track_unsat': False, + 'sls.walksat': True, + 'sls.walksat_repick': True, + 'sls.walksat_ucb': True, + 'sls.walksat_ucb_constant': 20.0, + 'sls.walksat_ucb_forget': 0.1, + 'sls.walksat_ucb_init': False, + 'sls.walksat_ucb_noise': 0.0002, + 'sls.wp': 20, + 'smt.arith.auto_config_simplex': False, + 'smt.arith.bprop_on_pivoted_rows': True, + 'smt.arith.branch_cut_ratio': 2, + 'smt.arith.eager_eq_axioms': True, + 'smt.arith.enable_hnf': True, + 'smt.arith.greatest_error_pivot': False, + 'smt.arith.ignore_int': False, + 'smt.arith.int_eq_branch': False, + 'smt.arith.min': False, + 'smt.arith.nl': True, + 'smt.arith.nl.branching': True, + 'smt.arith.nl.delay': 500, + 'smt.arith.nl.expp': False, + 'smt.arith.nl.gr_q': 10, + 'smt.arith.nl.grobner': True, + 'smt.arith.nl.grobner_cnfl_to_report': 1, + 'smt.arith.nl.grobner_eqs_growth': 10, + 'smt.arith.nl.grobner_expr_degree_growth': 2, + 'smt.arith.nl.grobner_expr_size_growth': 2, + 'smt.arith.nl.grobner_frequency': 4, + 'smt.arith.nl.grobner_max_simplified': 10000, + 'smt.arith.nl.grobner_subs_fixed': 1, + 'smt.arith.nl.horner': True, + 'smt.arith.nl.horner_frequency': 4, + 'smt.arith.nl.horner_row_length_limit': 10, + 'smt.arith.nl.horner_subs_fixed': 2, + 'smt.arith.nl.nra': True, + 'smt.arith.nl.order': True, + 'smt.arith.nl.rounds': 900, + 'smt.arith.nl.tangents': True, + 'smt.arith.propagate_eqs': True, + 'smt.arith.propagation_mode': 1, + 'smt.arith.random_initial_value': False, + 'smt.arith.rep_freq': 0, + 'smt.arith.simplex_strategy': 0, + 'smt.arith.solver': 6, + 'smt.array.extensional': True, + 'smt.array.weak': False, + 'smt.auto_config': False, + 'smt.bv.delay': True, + 'smt.bv.enable_int2bv': True, + 'smt.bv.reflect': True, + 'smt.bv.size_reduce': False, + 'smt.bv.solver': 0, + 'smt.case_split': 1, + 'smt.core.extend_nonlocal_patterns': False, + 'smt.core.extend_patterns': False, + 'smt.core.extend_patterns.max_distance': 4294967295, + 'smt.core.minimize': False, + 'smt.core.validate': False, + 'smt.cube_depth': 1, + 'smt.dack': 1, + 'smt.dack.eq': False, + 'smt.dack.factor': 0.1, + 'smt.dack.gc': 2000, + 'smt.dack.gc_inv_decay': 0.8, + 'smt.dack.threshold': 10, + 'smt.delay_units': False, + 'smt.delay_units_threshold': 32, + 'smt.dt_lazy_splits': 1, + 'smt.elim_unconstrained': True, + 'smt.ematching': True, + 'smt.induction': False, + 'smt.lemma_gc_strategy': 0, + 'smt.logic': '', + 'smt.macro_finder': False, + 'smt.mbqi': True, + 'smt.mbqi.force_template': 10, + 'smt.mbqi.max_cexs': 1, + 'smt.mbqi.max_cexs_incr': 0, + 'smt.mbqi.max_iterations': 850, + 'smt.pb.conflict_frequency': 1000, + 'smt.pb.learn_complements': True, + 'smt.phase_caching_off': 125, + 'smt.phase_caching_on': 500, + 'smt.phase_selection': 3, + 'smt.propagate_values': True, + 'smt.pull_nested_quantifiers': False, + 'smt.qi.cost': '(+ weight generation)', + 'smt.qi.eager_threshold': 10.0, + 'smt.qi.lazy_threshold': 20.0, + 'smt.qi.max_instances': 4294967295, + 'smt.qi.max_multi_patterns': 0, + 'smt.qi.quick_checker': 0, + 'smt.quasi_macros': False, + 'smt.refine_inj_axioms': True, + 'smt.relevancy': 2, + 'smt.restart_strategy': 0, + 'smt.solve_eqs': True, + 'smt.theory_aware_branching': False, + 'smt.theory_case_split': False, + 'smt.threads': 1, + 'smt.threads.cube_frequency': 2, + 'smt.threads.max_conflicts': 400} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(UNIFIED_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(UNIFIED_OVERRIDES) diff --git a/input/z3-bench/evolve/backup/20260519/shared/_z3_solve_worker.py b/input/z3-bench/evolve/backup/20260519/shared/_z3_solve_worker.py new file mode 100644 index 0000000000..c618f65fd9 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/_z3_solve_worker.py @@ -0,0 +1,154 @@ +""" +Solve one SMT2 file using the z3 Python binding (z3.set_param + z3.Optimize). +Matches the original benchmark setup (applied_params_hash 543b29...): params +are applied via z3.set_param so globals like 'threads' / 'parallel.enable' / +'sls.parallel' work, unlike CLI positional `key=value`. + +Invoked as a subprocess by z3_runner.py for process isolation + hard timeout. + +argv: + sys.argv[1] JSON dict of {key: value} (params) + sys.argv[2] smt2 file path + sys.argv[3] per-problem timeout in seconds + +stdout: a single JSON line, one of: + {"result": "Sat"|"Unsat"|"Unknown", "elapsed_ms": int, "stats": {: , ...}} + {"result": "Unknown", "elapsed_ms": int, "timeout": true, "stats": {...}?} + {"invalid_param": "", "error": "", "result": "Unknown", "elapsed_ms": 0} + {"result": "Unknown", "elapsed_ms": 0, "error": ""} + +"stats" mirrors z3 Optimize.statistics() (decisions, propagations, conflicts, +restarts, plus tactic-specific counters like arith/bv overflow, mk-clause, ...). +Numeric values only; non-numeric keys dropped to keep JSON small. +""" +import json +import os +import sys +import time + + +def emit(d): + print(json.dumps(d)) + sys.stdout.flush() + + +def main(): + if len(sys.argv) != 4: + emit({"result": "Unknown", "elapsed_ms": 0, "error": "bad argv"}) + return + + try: + params = json.loads(sys.argv[1]) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"params json: {e}"}) + return + + smt2_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + try: + import z3 + except ImportError as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"z3 binding import: {e}"}) + return + + # Split params per route_solver.cpp set_z3_param_optimize_option(): + # opt.* keys → per-Optimize via opt.set(Params) (strip "opt." prefix) + # all others → global via z3.set_param + # Matches cpp ground truth: priority/maxsat_engine/enable_*/maxres.*/rc2.* + # are set on the Optimize instance (opt.set), not globally. + # Mirror cpp route_solver.cpp:613 — suppress unknown-param warnings + # BEFORE any other set_param. Without this, z3 4.15.x emits a warning + + # dumps the legal-param list to stderr for keys like "threads" (no-op in + # this version), which aborts the subprocess in stderr-piped mode. + try: + z3.set_param("warning", False) + except Exception: + pass + + opt_local = {} + for k, v in params.items(): + if k.startswith("opt."): + opt_local[k[len("opt."):]] = v + continue + try: + z3.set_param(k, v) + except z3.Z3Exception as e: + emit({"invalid_param": k, "error": str(e), "result": "Unknown", "elapsed_ms": 0}) + return + except Exception as e: + emit({"invalid_param": k, "error": f"{type(e).__name__}: {e}", "result": "Unknown", "elapsed_ms": 0}) + return + + # Soft timeout (z3 polls at safe points) — outer subprocess.run() also + # enforces a hard wall-clock cap. + try: + z3.set_param("timeout", int(timeout_s * 1000)) + except Exception: + pass + + o = z3.Optimize() + + for k, v in opt_local.items(): + try: + o.set(k, v) + except z3.Z3Exception as e: + emit({"invalid_param": "opt." + k, "error": str(e), "result": "Unknown", "elapsed_ms": 0}) + return + except Exception as e: + emit({"invalid_param": "opt." + k, "error": f"{type(e).__name__}: {e}", "result": "Unknown", "elapsed_ms": 0}) + return + + try: + o.from_file(smt2_path) + except Exception as e: + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"smt2 parse: {e}"}) + return + + t0 = time.monotonic() + try: + res = o.check() + except Exception as e: + elapsed_ms = int((time.monotonic() - t0) * 1000) + emit( + { + "result": "Unknown", + "elapsed_ms": elapsed_ms, + "error": f"check() raised: {e}", + } + ) + return + elapsed_ms = int((time.monotonic() - t0) * 1000) + + if res == z3.sat: + label = "Sat" + elif res == z3.unsat: + label = "Unsat" + else: + label = "Unknown" + + stats = {} + try: + st = o.statistics() + for k in st.keys(): + try: + v = st.get_key_value(k) + except Exception: + continue + if isinstance(v, bool): + continue + if isinstance(v, (int, float)): + stats[k] = v + except Exception: + stats = {} + + emit({"result": label, "elapsed_ms": elapsed_ms, "stats": stats}) + + +if __name__ == "__main__": + main() + # Bypass z3 atexit/teardown that can abort the subprocess after a clean + # emit() — would mask the result as "worker produced no output". + sys.stdout.flush() + sys.stderr.flush() + os._exit(0) diff --git a/input/z3-bench/evolve/backup/20260519/shared/baseline_params.py b/input/z3-bench/evolve/backup/20260519/shared/baseline_params.py new file mode 100644 index 0000000000..e731c4469d --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/baseline_params.py @@ -0,0 +1,161 @@ +""" +Baseline Z3 parameters (applied_params_hash 543b29...) from problems.jsonl. +DO NOT MODIFY. Imported by all phases. +""" + +BASELINE = { + "opt.enable_core_rotate": True, + "opt.enable_sat": True, + "opt.enable_sls": True, + "opt.maxres.hill_climb": True, + "opt.maxsat_engine": "wmax", + "opt.priority": "pareto", + "opt.rc2.totalizer": True, + "parallel.enable": False, + "sat.branching.heuristic": "vsids", + "sat.pb.solver": "totalizer", + "sat.phase": "caching", + "sat.random_seed": 0, + "sat.restart": "geometric", + "sat.threads": 1, + "sls.random_seed": 0, + "smt.phase_selection": 3, + "smt.random_seed": 0, + "smt.threads": 1, + "threads": 1, + # NOTE: global "threads" — Z3 4.13.x CLI rejects as positional `key=value`. + # z3_runner.py omits it from CLI args. Default is 1 so behavior matches. + # Kept in BASELINE for fidelity with applied_params_hash 543b29... +} + +LOCKED = { + "sat.random_seed": 0, + "smt.random_seed": 0, + "sls.random_seed": 0, + "parallel.enable": False, + "threads": 1, +} + + +def _self_test(): + """ + Self-test: run stage1 5 problems with BASELINE in parallel and report + per-problem ratio vs raw baseline_ms. Use to sanity-check that the worker + + z3 binding reproduce the recorded baseline within tolerance, and that + parallel dispatch (taskset core pinning) doesn't perturb timing. + + Defaults: OPENEVOLVE_PARALLEL_SOLVERS=5 (matches config.yaml + parallel_evaluations), capped at problem count. + + Status: + OK — result matches, ratio in [0.5, 2.0] + WARN — result matches, ratio out of band (noise / hw drift) + FAIL — result mismatch or invalid_param + """ + import json + import math + import pathlib + import sys + import time + from concurrent.futures import ThreadPoolExecutor, as_completed + + here = pathlib.Path(__file__).resolve().parent + sys.path.insert(0, str(here)) + from z3_runner import run_z3 # noqa: E402 + from runtime import parallel_solvers # noqa: E402 + + bench = here.parent.parent # input/z3-bench/ + raw_dir = bench / "raw-data" + problems_jsonl = bench / "problems.jsonl" + stage1_sample = here / "stage1_sample.json" + + if not stage1_sample.exists(): + print(f"ERROR: {stage1_sample} missing. run build_samples.py first.", + file=sys.stderr) + return 2 + + shas = list(json.loads(stage1_sample.read_text())["sha256"]) + idx = {} + with open(problems_jsonl) as f: + for line in f: + d = json.loads(line) + idx[d["problem_sha256"]] = { + "smt2": d["smt2_filename"], + "baseline_ms": d["z3_status"]["elapsed_ms"], + "baseline_result": d["z3_status"]["result"], + } + + tasks = [] + for i, sha in enumerate(shas): + meta = idx.get(sha) + if meta is None: + print(f"ERROR: {sha[:12]} not in problems.jsonl", file=sys.stderr) + return 2 + smt2 = raw_dir / meta["smt2"] + if not smt2.exists(): + print(f"ERROR: smt2 not found: {smt2}", file=sys.stderr) + return 2 + tasks.append((i, sha, meta, smt2)) + + n_parallel = min(parallel_solvers(default=5), len(tasks)) + + print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel}, " + f"taskset core pin") + print() + + def solve(t): + i, sha, meta, smt2 = t + # Generous timeout: 2x baseline_ms (matches the [0.5, 2.0] tolerance band). + timeout_s = max(30, math.ceil(meta["baseline_ms"] * 2 / 1000)) + # Skip core 0 — pin to cores 1..n_parallel. + core = (i % n_parallel) + 1 + r = run_z3(smt2, BASELINE, timeout_s, cpu_core=core) + return i, sha, meta, r + + t_start = time.monotonic() + results = [] + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(solve, t) for t in tasks] + for fut in as_completed(futures): + results.append(fut.result()) + elapsed = time.monotonic() - t_start + results.sort(key=lambda x: x[0]) + + print(f"{'sha':<14}{'base_res':<10}{'got_res':<10}" + f"{'base_ms':>10}{'got_ms':>10}{'ratio':>8} core status") + print("-" * 84) + fail = 0 + warn = 0 + sum_got_ms = 0 + for i, sha, meta, r in results: + got_result = r.get("result", "Unknown") + got_ms = int(r.get("elapsed_ms", 0)) + sum_got_ms += got_ms + ratio = got_ms / max(meta["baseline_ms"], 1) + result_ok = (got_result == meta["baseline_result"]) + invalid = r.get("invalid_param") + if invalid: + status = f"FAIL(invalid={invalid})" + fail += 1 + elif not result_ok: + status = "FAIL" + fail += 1 + elif not (0.5 <= ratio <= 2.0): + status = "WARN" + warn += 1 + else: + status = "OK" + print(f"{sha[:12]:<14}{meta['baseline_result']:<10}{got_result:<10}" + f"{meta['baseline_ms']:>10}{got_ms:>10}{ratio:>7.2f}x " + f"{(i % n_parallel) + 1:>4} {status}") + + seq_estimate = sum_got_ms / 1000 + print() + print(f"wall-clock: {elapsed:.1f}s (sequential would be ~{seq_estimate:.0f}s)") + print(f"summary: {len(results) - fail - warn} ok, {warn} warn, {fail} fail") + return 0 if fail == 0 else 1 + + +if __name__ == "__main__": + import sys + sys.exit(_self_test()) diff --git a/input/z3-bench/evolve/backup/20260519/shared/evaluator.py b/input/z3-bench/evolve/backup/20260519/shared/evaluator.py new file mode 100644 index 0000000000..32ea3ca3e9 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/evaluator.py @@ -0,0 +1,443 @@ +""" +OpenEvolve evaluator for Z3 parameter tuning. + +Sample selection (built by build_samples.py): + stage1: stage1_sample.json (5 SAT, quintile-spread by problem size) + stage2: stage2_sample.json (50 SAT+UNSAT, quintile-spread by problem size) + +Per-problem timeout = max(MIN_TIMEOUT_S, ceil(baseline_ms * TIMEOUT_FACTOR / 1000)). +Adaptive: small problems get tight cap, huge problems get proportional headroom. +Constants TIMEOUT_FACTOR=1.3, MIN_TIMEOUT_S=5 (z3 startup overhead floor). +Baseline_ms reflects local rebaseline (rebaseline_local.py) when SHA is in +local_baseline.json, else raw_ms from problems.jsonl. + +Score: geomean(speedup) * solved_rate^2 * efficiency^OPENEVOLVE_STATS_WEIGHT. + efficiency = geomean over {decisions, propagations, conflicts, mk clause} + of (baseline_stat / variant_stat) across solved problems. Default + OPENEVOLVE_STATS_WEIGHT=0 keeps prior behaviour. Per-problem solver + stats (z3 Optimize.statistics) are always surfaced via metrics + + artifacts so the LLM sees solver-internal cost beyond wall-clock. + +Locked params (sat.random_seed / smt.random_seed / sls.random_seed / parallel.enable) +must not deviate from baseline_params.LOCKED — violation => combined_score=0. + +Environment overrides: + OPENEVOLVE_MAX_PROBLEMS int — cap stage2 problem count + OPENEVOLVE_PARALLEL_SOLVERS int — SINGLE knob for total z3 concurrency + (default 1). Spawns N z3 worker subprocesses + concurrently per stage; each pinned to a + dedicated core via taskset (Linux). Capped at + len(problems). Config.yaml parallel_evaluations + is fixed at 1 so total concurrent z3 = this env + value (no parallel_evaluations × N blow-up). + Total RAM ≈ N × 4 GB worst-case. + OPENEVOLVE_Z3_BIN str — z3 binary path (default "z3") +""" +import importlib.util +import json +import math +import os +import pathlib +import sys +import traceback + +TIMEOUT_FACTOR = 2 +MIN_TIMEOUT_S = 5 + +_HERE = pathlib.Path(__file__).resolve().parent +sys.path.insert(0, str(_HERE)) + +from baseline_params import BASELINE, LOCKED # noqa: E402 +from score import score # noqa: E402 +from z3_runner import run_z3 # noqa: E402 +from runtime import parallel_solvers, cascade_threshold # noqa: E402 + +from openevolve.evaluation_result import EvaluationResult # noqa: E402 + +_BENCH_DIR = _HERE.parent.parent # input/z3-bench/ +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_STAGE1_SAMPLE = _HERE / "stage1_sample.json" +_STAGE2_SAMPLE = _HERE / "stage2_sample.json" +_STAGE3_SAMPLE = _HERE / "stage3_sample.json" +_STAGE4_SAMPLE = _HERE / "stage4_sample.json" +_LOCAL_BASELINE = _HERE / "local_baseline.json" + +_PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") # None -> sys.executable + + +def _load_program(path): + spec = importlib.util.spec_from_file_location("program", path) + module = importlib.util.module_from_spec(spec) + # Phase initial_programs add shared/ to sys.path themselves. + spec.loader.exec_module(module) + return module + + +def _load_problems(): + # Always returns the full problems.jsonl set (50 problems). + # local_baseline.json (if present) overlays per-SHA local timing so that + # speedup uses on-machine wall-clock for rebaselined SHAs; non-rebaselined + # SHAs fall back to raw_ms recorded in problems.jsonl. Local elapsed_ms is + # only trusted when local result matches raw — otherwise raw_ms wins to + # avoid speedup distortion from a bad local run (timeout, mismatch). + local = {} + if _LOCAL_BASELINE.exists(): + local = json.loads(_LOCAL_BASELINE.read_text()) + + rows = [] + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + sha = d["problem_sha256"] + baseline_ms = d["z3_status"]["elapsed_ms"] + baseline_result = d["z3_status"]["result"] + baseline_stats = {} + lo = local.get(sha) + if lo and lo.get("matches_raw"): + baseline_ms = lo["elapsed_ms"] + baseline_stats = lo.get("stats") or {} + rows.append( + { + "sha": sha, + "smt2": d["smt2_filename"], + "baseline_ms": baseline_ms, + "baseline_result": baseline_result, + "baseline_stats": baseline_stats, + } + ) + return rows + + +def _filter_stage1(problems): + if not _STAGE1_SAMPLE.exists(): + return problems[:5] + keep = set(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + +def _filter_stage2(problems): + # stage2_sample.json: 5 SAT problems from the runtime upper-half + # (medium-slow). Catches regressions on harder instances. + if not _STAGE2_SAMPLE.exists(): + return problems + keep = set(json.loads(_STAGE2_SAMPLE.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + +def _filter_stage3(problems): + # stage3_sample.json: 5 SAT problems from runtime quintile 5 (slowest 20%). + # Gates entry to stage4 via internal STAGE3_TO_STAGE4_THRESHOLD. + if not _STAGE3_SAMPLE.exists(): + return problems + keep = set(json.loads(_STAGE3_SAMPLE.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + +def _filter_stage4(problems): + # stage4_sample.json: 20 SAT+UNSAT broad-runtime, deduplicated vs stage1-3. + # Final cascade sample. Chained inside evaluate_stage3 because openevolve + # hardcodes 3 cascade stages. + if not _STAGE4_SAMPLE.exists(): + return problems + keep = set(json.loads(_STAGE4_SAMPLE.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + +def _err_result(metrics_extra, artifacts): + metrics = { + "combined_score": 0.0, + "geomean_speedup": 0.0, + "solved_rate": 0.0, + "regressions": 0, + "solved": 0, + "total": 0, + } + metrics.update(metrics_extra) + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + +def _evaluate(program_path, problems, stage_name): + try: + program = _load_program(program_path) + except Exception as e: + return _err_result( + {"error": f"program load failed: {e}"}, + { + "error_type": type(e).__name__, + "error_message": str(e), + "full_traceback": traceback.format_exc()[-2000:], + "stage": stage_name, + }, + ) + + if not hasattr(program, "get_params"): + return _err_result( + {"error": "missing get_params()"}, + {"suggestion": "initial_program.py must expose get_params() -> dict", "stage": stage_name}, + ) + + try: + params = program.get_params() + if not isinstance(params, dict): + raise TypeError(f"get_params() returned {type(params).__name__}, expected dict") + except Exception as e: + return _err_result( + {"error": f"get_params() raised: {e}"}, + { + "error_type": type(e).__name__, + "error_message": str(e), + "full_traceback": traceback.format_exc()[-2000:], + "stage": stage_name, + }, + ) + + violations = {k: params.get(k) for k in LOCKED if params.get(k) != LOCKED[k]} + if violations: + return _err_result( + {"error": "locked params violated"}, + { + "locked_violated": violations, + "locked_expected": LOCKED, + "stage": stage_name, + "suggestion": "Do not modify sat.random_seed, smt.random_seed, sls.random_seed, parallel.enable", + }, + ) + + if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: + problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] + + # Verify smt2 files exist up front so parallel dispatch doesn't race. + for p in problems: + smt2_path = _RAW_DIR / p["smt2"] + if not smt2_path.exists(): + return _err_result( + {"error": f"smt2 not found: {p['smt2']}"}, + {"missing_file": str(smt2_path), "stage": stage_name}, + ) + + # Parallel dispatch — `OPENEVOLVE_PARALLEL_SOLVERS` controls how many + # z3 worker subprocesses run concurrently for the stage's problem list. + # Worker count is capped at len(problems) (no point spawning idle threads). + # Cores are leased from a queue.Queue so each in-flight task holds a + # unique core slot. Correct even when len(problems) > n_parallel + # (idx % n_parallel would collide across workers). + # Core pool = cores 1..n_parallel — core 0 reserved for kernel interrupts + # / housekeeping (avoids tail-latency spikes). Serial mode also leases + # core 1, symmetric with parallel so baseline / variant share the same + # pin envelope (no unpinned-vs-pinned bias). + import queue as _queue + n_parallel = min(parallel_solvers(default=1), len(problems)) + _core_pool = _queue.Queue() + for _c in range(1, n_parallel + 1): + _core_pool.put(_c) + + def _solve(idx_p): + idx, p = idx_p + smt2_path = _RAW_DIR / p["smt2"] + timeout_s = max(MIN_TIMEOUT_S, math.ceil(p["baseline_ms"] * TIMEOUT_FACTOR / 1000)) + core = _core_pool.get() + try: + r = run_z3(smt2_path, params, timeout_s, + python_bin=_PYTHON_BIN, cpu_core=core) + finally: + _core_pool.put(core) + return idx, p, r, core, timeout_s + + def _is_regression(p, r): + # Correctness regression: baseline gave a definitive answer (Sat/Unsat) + # and variant disagrees (Unsat, Unknown, or timeout). Unknown baseline + # is excluded — variant solving an Unknown is an improvement, not a + # regression. invalid_param handled separately (param-level error). + if "invalid_param" in r: + return False + return ( + p["baseline_result"] in ("Sat", "Unsat") + and r.get("result") != p["baseline_result"] + ) + + def _invalid_err(r): + return _err_result( + {"error": f"invalid z3 param: {r['invalid_param']}"}, + { + "invalid_param": r["invalid_param"], + "stderr": r.get("stderr", "")[:2000], + "stage": stage_name, + "suggestion": "Remove or fix this key in get_params().", + }, + ) + + def _regression_err(p, r): + return _err_result( + {"error": f"result regression on {p['sha'][:10]}: " + f"baseline={p['baseline_result']} got={r.get('result')}"}, + { + "result_mismatch": { + "sha": p["sha"][:12], + "baseline_result": p["baseline_result"], + "got_result": r.get("result"), + "elapsed_ms": r.get("elapsed_ms"), + "timeout": bool(r.get("timeout")), + }, + "stage": stage_name, + "suggestion": ( + "Variant lost correctness on a problem baseline solved. " + "Revert params that disable preprocessing or relax completeness " + "(e.g. simplifier off, aggressive sls early-prune, restart " + "tuning that starves Sat search)." + ), + }, + ) + + by_idx = {} + abort = None # ("invalid"|"regression", p, r) — first failure observed + if n_parallel == 1: + # Sequential: short-circuit immediately on invalid or regression. + for pair in enumerate(problems): + idx, p, r, core, timeout_s = _solve(pair) + print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms / {timeout_s}s " + f"(core={core})", flush=True) + if "invalid_param" in r: + return _invalid_err(r) + if _is_regression(p, r): + print(f" [{stage_name}] regression — aborting remaining " + f"{len(problems) - idx - 1} problems", flush=True) + return _regression_err(p, r) + by_idx[idx] = (p, r) + else: + # Parallel: LPT (longest-processing-time) submission — sort problems + # by baseline_ms descending so big jobs dispatch first. ThreadPool's + # internal FIFO queue then drains small jobs onto whichever worker + # frees up, minimising tail idle time when n_parallel < len(problems). + # Cancel pending futures on first failure; in-flight tasks keep + # running until subprocess timeout (with __exit__ waits for them). + # Per-problem timeout is baseline_ms * 1.3 (adaptive), so worst-case + # drain depends on the slowest in-flight problem rather than a fixed cap. + from concurrent.futures import ThreadPoolExecutor, as_completed + ordered = sorted(enumerate(problems), key=lambda ip: -ip[1]["baseline_ms"]) + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_solve, pair) for pair in ordered] + for fut in as_completed(futures): + if abort is not None: + continue + idx, p, r, core, timeout_s = fut.result() + print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms / {timeout_s}s " + f"(core={core})", flush=True) + if "invalid_param" in r: + abort = ("invalid", p, r) + elif _is_regression(p, r): + abort = ("regression", p, r) + if abort is not None: + print(f" [{stage_name}] {abort[0]} — cancelling pending " + f"problems (in-flight workers will drain)", flush=True) + for f in futures: + f.cancel() + continue + by_idx[idx] = (p, r) + if abort is not None: + kind, p, r = abort + return _invalid_err(r) if kind == "invalid" else _regression_err(p, r) + + # No failure — reassemble in original problem order. + results = [] + for idx in range(len(problems)): + p, r = by_idx[idx] + results.append( + { + **p, + "result": r["result"], + "elapsed_ms": r["elapsed_ms"], + "timeout": bool(r.get("timeout")), + "stats": r.get("stats") or {}, + } + ) + + metrics = score(results) + metrics["stage"] = stage_name + + # Surface a small fixed set of solver-internal counters that drive Z3's + # search shape. LLM sees these via metrics + per-problem artifacts and can + # reason about what a param tweak did beyond wall-clock (e.g. fewer + # decisions/conflicts at same elapsed_ms = sturdier improvement). + # `mk clause` chosen over `restarts` since z3 does not emit `restarts` + # for the optimize / arith-heavy stack used here. + _KEY_STATS = ("decisions", "propagations", "conflicts", "mk clause") + for k in _KEY_STATS: + metrics[f"total_{k}"] = float(sum(r["stats"].get(k, 0) for r in results)) + + sample = [ + { + "sha": r["sha"][:10], + "base_result": r["baseline_result"], + "got_result": r["result"], + "base_ms": r["baseline_ms"], + "ms": r["elapsed_ms"], + "speedup": round(r["baseline_ms"] / max(r["elapsed_ms"], 1), 3), + "timeout": r["timeout"], + "stats": {k: r["stats"].get(k, 0) for k in _KEY_STATS if k in r["stats"]}, + "base_stats": {k: r["baseline_stats"].get(k, 0) for k in _KEY_STATS if k in r.get("baseline_stats", {})}, + } + for r in results + ] + artifacts = { + "stage": stage_name, + "summary": ( + f"solved={metrics['solved']}/{metrics['total']} " + f"regressions={metrics['regressions']} " + f"geomean_speedup={metrics['geomean_speedup']:.3f} " + f"efficiency={metrics.get('efficiency', 1.0):.3f} " + f"score={metrics['combined_score']:.3f}" + ), + "per_problem": sample[:20], + } + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + +def evaluate_stage1(program_path): + # Per-problem timeout = baseline_ms * TIMEOUT_FACTOR (computed in _solve). + # Stage1 wall-clock budget ≈ TIMEOUT_FACTOR * sum(baseline_ms) over 5 sample + # problems (parallel mode divides by n_parallel). + problems = _filter_stage1(_load_problems()) + return _evaluate(program_path, problems, "stage1") + + +def evaluate_stage2(program_path): + problems = _filter_stage2(_load_problems()) + return _evaluate(program_path, problems, "stage2") + + +def evaluate_stage3(program_path): + # openevolve cascade is hardcoded to 3 stages, so user-stage4 (20 broad + # problems) is folded in here. Gate threshold = config.yaml + # evaluator.cascade_thresholds[2] (defaults to 1.03 if absent). + problems3 = _filter_stage3(_load_problems()) + r3 = _evaluate(program_path, problems3, "stage3") + if not isinstance(r3, EvaluationResult): + return r3 + gate = cascade_threshold(2, default=1.03) + if r3.metrics.get("combined_score", 0.0) < gate: + return r3 + problems4 = _filter_stage4(_load_problems()) + r4 = _evaluate(program_path, problems4, "stage4") + if not isinstance(r4, EvaluationResult): + return r4 + # Merge: stage4 metrics overlay stage3's (final combined_score = stage4's). + merged_metrics = {**r3.metrics, **r4.metrics} + merged_artifacts = {**r3.artifacts, **r4.artifacts} + return EvaluationResult(metrics=merged_metrics, artifacts=merged_artifacts) + + +def evaluate_stage4(program_path): + # Standalone entry for manual / final-verify use. Not invoked by + # openevolve cascade — evaluate_stage3 chains here internally. + problems = _filter_stage4(_load_problems()) + return _evaluate(program_path, problems, "stage4") + + +def evaluate(program_path): + # Evolution uses stage1 (5 sampled problems) only — fast iteration loop. + # Stage2 (full 50 problems) is reserved for final verification via + # final_verify.py on the best-program, not the per-variant search loop. + return evaluate_stage1(program_path) diff --git a/input/z3-bench/evolve/backup/20260519/shared/local_baseline.json b/input/z3-bench/evolve/backup/20260519/shared/local_baseline.json new file mode 100644 index 0000000000..34762ce423 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/local_baseline.json @@ -0,0 +1,2857 @@ +{ + "2a465c36fe213a9c72c781832dfb561ffae3691905af41289a39ec08f585dbf2": { + "elapsed_ms": 644, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 319, + "stats": { + "conflicts": 316, + "decisions": 3176, + 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a/input/z3-bench/evolve/backup/20260519/shared/runtime.py b/input/z3-bench/evolve/backup/20260519/shared/runtime.py new file mode 100644 index 0000000000..1aeb1571f8 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/runtime.py @@ -0,0 +1,66 @@ +""" +Runtime knob loader for z3-bench scripts. + +Reads custom keys from ../config.yaml (top-level), with env var override. +openevolve's dacite parser silently ignores unknown top-level keys, so we +share the same file rather than introducing a second config. + +Priority: env var > config.yaml > default. +""" +import os +import pathlib + +_HERE = pathlib.Path(__file__).resolve().parent +_CONFIG_YAML = _HERE.parent / "config.yaml" # input/z3-bench/evolve/config.yaml + +_cache = None + + +def _load(): + global _cache + if _cache is not None: + return _cache + if not _CONFIG_YAML.exists(): + _cache = {} + return _cache + try: + import yaml + _cache = yaml.safe_load(_CONFIG_YAML.read_text()) or {} + except Exception: + _cache = {} + return _cache + + +def parallel_solvers(default=1): + """ + Concurrent z3 worker subprocesses per stage. + Env OPENEVOLVE_PARALLEL_SOLVERS > config.yaml parallel_solvers > default. + """ + env = os.environ.get("OPENEVOLVE_PARALLEL_SOLVERS") + if env is not None: + try: + return max(1, int(env)) + except ValueError: + pass + val = _load().get("parallel_solvers", default) + try: + return max(1, int(val)) + except (ValueError, TypeError): + return default + + +def cascade_threshold(index, default): + """ + Read evaluator.cascade_thresholds[index] from config.yaml. + Used by evaluator.evaluate_stage3 for the internal stage3→stage4 gate + (openevolve's cascade hardcodes only 3 stage slots, so stage4 is chained + inside evaluate_stage3 using thresholds[2]). + """ + cfg = _load().get("evaluator") or {} + thresholds = cfg.get("cascade_thresholds") or [] + if index < len(thresholds): + try: + return float(thresholds[index]) + except (ValueError, TypeError): + pass + return default diff --git a/input/z3-bench/evolve/backup/20260519/shared/score.py b/input/z3-bench/evolve/backup/20260519/shared/score.py new file mode 100644 index 0000000000..fb212d6311 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/score.py @@ -0,0 +1,103 @@ +""" +Scoring: geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT. + +- match baseline result: speedup = baseline_ms / elapsed_ms +- mismatch (regression / unknown / timeout): contributes 1e-6 to geomean +- solved_rate squared to strongly gate on correctness +- efficiency = geomean over {decisions, propagations, conflicts, restarts} + of (baseline_stat / variant_stat), only on solved problems with baseline + stats present. Lower solver work vs baseline -> efficiency > 1. + Folded multiplicatively via STATS_WEIGHT exponent (default 0 = disabled, + preserves prior scoring). Set env OPENEVOLVE_STATS_WEIGHT=0.25 to enable. + +Why stats matter: identical elapsed_ms with far fewer conflicts/decisions is +a sturdier improvement (less variance across machines / problems) than a raw +wall-clock win, and runtime alone can hide regressions where Z3 happens to +hit a fast path on the stage1 sample. +""" +import math +import os + +_STATS_KEYS = ("decisions", "propagations", "conflicts", "mk clause") + + +def _efficiency(per_problem): + """Geomean of baseline/variant ratio across stat keys, solved problems only. + + Returns (efficiency, num_pairs). efficiency=1.0 if no usable pairs (no + baseline stats yet, or no solved problems) so the multiplier is a no-op. + """ + ratios = [] + for p in per_problem: + if p["result"] != p["baseline_result"]: + continue + bs = p.get("baseline_stats") or {} + vs = p.get("stats") or {} + for k in _STATS_KEYS: + b = bs.get(k) + v = vs.get(k) + if b is None or v is None: + continue + # +1 smoothing avoids div-by-zero and absurd ratios for tiny counts + ratios.append((float(b) + 1.0) / (float(v) + 1.0)) + if not ratios: + return 1.0, 0 + log_sum = sum(math.log(r) for r in ratios) + return math.exp(log_sum / len(ratios)), len(ratios) + + +def score(per_problem): + n = len(per_problem) + if n == 0: + return { + "combined_score": 0.0, + "geomean_speedup": 0.0, + "solved_rate": 0.0, + "regressions": 0, + "solved": 0, + "total": 0, + "efficiency": 1.0, + "efficiency_pairs": 0, + "stats_weight": 0.0, + } + + speedups = [] + solved = 0 + regressions = 0 + for p in per_problem: + baseline_decided = p["baseline_result"] in ("Sat", "Unsat") + match = p["result"] == p["baseline_result"] + if match: + solved += 1 + sp = p["baseline_ms"] / max(p["elapsed_ms"], 1) + speedups.append(sp) + else: + speedups.append(1e-6) + if baseline_decided and p["result"] in ("Sat", "Unsat"): + regressions += 1 + + log_sum = sum(math.log(s) for s in speedups) + geomean = math.exp(log_sum / len(speedups)) + solved_rate = solved / n + + efficiency, eff_pairs = _efficiency(per_problem) + try: + stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0")) + except ValueError: + stats_weight = 0.0 + # Clamp to a sensible band so a runaway env var can't dominate score. + stats_weight = max(0.0, min(stats_weight, 2.0)) + + combined = geomean * (solved_rate**2) * (efficiency**stats_weight) + + return { + "combined_score": float(combined), + "geomean_speedup": float(geomean), + "solved_rate": float(solved_rate), + "regressions": int(regressions), + "solved": int(solved), + "total": int(n), + "efficiency": float(efficiency), + "efficiency_pairs": int(eff_pairs), + "stats_weight": float(stats_weight), + } diff --git a/input/z3-bench/evolve/backup/20260519/shared/stage1_sample.json b/input/z3-bench/evolve/backup/20260519/shared/stage1_sample.json new file mode 100644 index 0000000000..a662bda67c --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/stage1_sample.json @@ -0,0 +1,88 @@ +{ + "selection": "10 SAT runtime Q1+2 (fastest 40%)", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "2a465c36fe213a9c72c781832dfb561ffae3691905af41289a39ec08f585dbf2", + "5166f0ebaa5fe05e62ea4ed44f517f9d3cd44171362e99356dd6e33889cc5d81", + "23efdccb9e57a41897053370a5b6ad6b367e216afbfba952ded0a68b7ee7d4ae", + "1c18f0ae6b70612542d6269bb8736b100331a42f18b375afba1da9b92c8548ba", + "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", + "2f2003b96aa2bff75f9a8792a4817a23ff4e1e4ac247d2edf9cf8f0cf3907fed", + "05c7f64a1db7d611df8501a987ca276e96cc2735993e171468b740f3babbb34e", + "1a35f6de8ab4447aec26efb9e44f59fb1943fc4b2569d1ab2eccbeac279c1063", + "4efdf6c97e97e17eb3147929ad909d803a37e51a691fee016c6456bf6efe6c83", + "0c0ae51029de74681437b42a8f8a23f5045a9bbe1e60f4d18ef0e004d9b9b983" + ], + "summary": [ + { + "sha": "2a465c36fe21", + "num_hard_constraints": 25278, + "num_variables": 5888, + "baseline_result": "Sat", + "baseline_ms": 319 + }, + { + "sha": "5166f0ebaa5f", + "num_hard_constraints": 31265, + 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"num_variables": 6541, + "baseline_result": "Sat", + "baseline_ms": 427 + }, + { + "sha": "0c0ae51029de", + "num_hard_constraints": 20030, + "num_variables": 6088, + "baseline_result": "Sat", + "baseline_ms": 530 + } + ] +} diff --git a/input/z3-bench/evolve/backup/20260519/shared/stage2_sample.json b/input/z3-bench/evolve/backup/20260519/shared/stage2_sample.json new file mode 100644 index 0000000000..a3e8da1cb2 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/stage2_sample.json @@ -0,0 +1,88 @@ +{ + "selection": "10 SAT runtime Q3+4 (middle 40%)", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "45730d888ee32d83f372b0431c7875c2159d864ab489bb8fbf36f272548e7cf5", + "4be05b5b981caff9c95b9b49e81076b7776ec05f6f02b8cec803e149e173e83c", + "1927b5398ba22a23ab54f1919033fbf39851ae037bcd6d74beab63c69a817499", + "542477536440c3b0909da276e91540d9ac9e310305a49f4798c7c2f0860bbe27", + "089c72fcb8dea9b5fbb99948d1710fb7c676a6cf4840e201ed51d68451e1da75", + 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/dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/stage3_sample.json @@ -0,0 +1,88 @@ +{ + "selection": "10 SAT runtime Q5 (slowest 20%)", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "318b1d8c24c40f72ceebb82e38c760cf2b659e78a051a108ea5a372318566a21", + "5a1fa2a8b99340b9aed5f964fe259ae971ac1dc3907a758f3e086db6b21fb22d", + "4f7415e2970ec0e37346a6d38cf4371a7e09fcd9792a4b3a737fe3577ec2dc21", + "095a876a32793982486741d3211591fcd3cdf13375b6e16f170f10dc3a3570b2", + "402c63333e354866268447df2b2b313105e207519677f69f344f48b08d262375", + "468b7a8717458ade54aa2f2e4f74fbf9c1e085e98f05d1faaa0c7f693d274b4e", + "46268e065dcc863ead3e929d13f15b9209decc5cc2eb68840d17b484f827626c", + "04524f16b53c41aefbce8192fd96a196cb323e0bfed7250a1b30f24c1610516f", + "55d55e00045955471f866aced2310467f08b3a78536ffe107c983b335d15d7ca", + "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8" + ], + "summary": [ + { + "sha": "318b1d8c24c4", + "num_hard_constraints": 193342, + 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The worker imports the z3 Python binding and applies params +via `z3.set_param()`, matching the original benchmark setup that recorded +`applied_params_hash 543b29...`. This is necessary because the z3 CLI +positional `key=value` form rejects globals (`threads`, `parallel.enable`, +`sls.parallel`) that the Python binding accepts. + +Subprocess isolation gives: hard wall-clock timeout, crash containment, +memory reclaim between problems. +""" +import json +import pathlib +import shutil +import subprocess +import sys +import time + +_WORKER = str(pathlib.Path(__file__).resolve().parent / "_z3_solve_worker.py") +_TASKSET = shutil.which("taskset") # Linux only; None on macOS / missing + + +def run_z3(smt2_path, params, timeout_s, python_bin=None, cpu_core=None): + """ + Returns dict (one of): + success: {"result": "Sat"|"Unsat"|"Unknown", "elapsed_ms": int, "stats": dict} + timeout: {"result": "Unknown", "elapsed_ms": int, "timeout": True, "stats": {}} + invalid: {"invalid_param": str, "stderr": str, "result": "Unknown", "elapsed_ms": int, "stats": {}} + crash: {"result": "Unknown", "elapsed_ms": int, "error": str, "stderr": str, "stats": {}} + + "stats" mirrors z3 Optimize.statistics() numeric entries (decisions, + propagations, conflicts, restarts, arith/bv counters, ...). Empty when + z3 never reached check() (param error, parse fail, timeout, crash). + + cpu_core: optional int — if given and `taskset` is on PATH, pin the + worker subprocess to that core (-c ). Used by the parallel dispatch + in evaluator.py to isolate concurrent z3 runs from cross-core + interference. Silently ignored if taskset missing (macOS / no util-linux). + """ + py = python_bin or sys.executable + args = [py, _WORKER, json.dumps(params), str(smt2_path), str(int(timeout_s))] + if cpu_core is not None and _TASKSET: + args = [_TASKSET, "-c", str(int(cpu_core))] + args + + t0 = time.monotonic() + try: + proc = subprocess.run( + args, + capture_output=True, + text=True, + timeout=timeout_s + 15, # grace for z3 startup + parse + ) + except subprocess.TimeoutExpired: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "timeout": True, + "stats": {}, + } + + stdout = (proc.stdout or "").strip() + stderr = (proc.stderr or "").strip() + + if not stdout: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker produced no output (rc={proc.returncode})", + "stderr": stderr[-2000:], + "stats": {}, + } + + # Use the last non-empty line as JSON (defensive against stray prints). + last = stdout.splitlines()[-1] + try: + out = json.loads(last) + except json.JSONDecodeError as e: + return { + "result": "Unknown", + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker json decode: {e}", + "stderr": (stderr + "\n--stdout--\n" + stdout)[-2000:], + "stats": {}, + } + + if "invalid_param" in out: + return { + "invalid_param": out["invalid_param"], + "stderr": (out.get("error") or stderr)[-2000:], + "result": "Unknown", + "elapsed_ms": out.get("elapsed_ms", 0), + "stats": {}, + } + out.setdefault("stats", {}) + return out diff --git a/input/z3-bench/evolve/backup/20260519/shared/z3_valid_keys.json b/input/z3-bench/evolve/backup/20260519/shared/z3_valid_keys.json new file mode 100644 index 0000000000..2ff0093f49 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/z3_valid_keys.json @@ -0,0 +1,1266 @@ +[ + "abce", + "acce", + "ackermannization.eager", + "ackermannization.inc_sat_backend", + "ackermannization.sat_backend", + "add_all_coeffs", + "algebraic.factor", + "algebraic.factor_max_prime", + "algebraic.factor_num_primes", + "algebraic.factor_search_size", + "algebraic.min_mag", + "algebraic.zero_accuracy", + "algebraic_number_evaluator", + "anf", + "anf.delay", + "anf.exlin", + "arith", + "arith.auto_config_simplex", + "arith.bprop_on_pivoted_rows", + "arith.branch_cut_ratio", + "arith.dump_bound_lemmas", + "arith.dump_lemmas", + "arith.eager_eq_axioms", + "arith.enable_hnf", + "arith.epsilon", + "arith.greatest_error_pivot", + "arith.ignore_int", + "arith.int_eq_branch", + "arith.min", + "arith.nl", + "arith.nl.branching", + "arith.nl.cross_nested", + "arith.nl.delay", + "arith.nl.expensive_patching", + "arith.nl.expp", + "arith.nl.gr_q", + "arith.nl.grobner", + "arith.nl.grobner_cnfl_to_report", + "arith.nl.grobner_eqs_growth", + "arith.nl.grobner_exp_delay", + "arith.nl.grobner_expand_terms", + "arith.nl.grobner_expr_degree_growth", + "arith.nl.grobner_expr_size_growth", + "arith.nl.grobner_frequency", + "arith.nl.grobner_gcd_test", + "arith.nl.grobner_max_simplified", + "arith.nl.grobner_propagate_quotients", + "arith.nl.grobner_row_length_limit", + "arith.nl.grobner_subs_fixed", + "arith.nl.horner", + "arith.nl.horner_frequency", + "arith.nl.horner_row_length_limit", + "arith.nl.horner_subs_fixed", + "arith.nl.log", + "arith.nl.nra", + "arith.nl.optimize_bounds", + "arith.nl.order", + "arith.nl.propagate_linear_monomials", + "arith.nl.reduce_pseudo_linear", + "arith.nl.rounds", + "arith.nl.tangents", + "arith.print_ext_var_names", + "arith.print_stats", + "arith.propagate_eqs", + "arith.propagation_mode", + "arith.random_initial_value", + "arith.rep_freq", + "arith.simplex_strategy", + "arith.solver", + "arith.validate", + "arith_allow_plateau", + "arith_ineq_lhs", + "arith_lhs", + "arith_use_clausal_lookahead", + "arith_use_lookahead", + "arith_weight", + "array.extensional", + "array.weak", + "array_as_stores", + "array_equalities", + "asymm_branch", + "asymm_branch.all", + "asymm_branch.delay", + "asymm_branch.limit", + "asymm_branch.rounds", + "asymm_branch.sampled", + "ate", + "auto_config", + "axioms2files", + "backtrack.conflicts", + "backtrack.scopes", + "bca", + "bce", + "bce_at", + "bce_delay", + "bit2bool", + "blast_distinct", + "blast_distinct_threshold", + "blast_eq_value", + "blast_select_store", + "blast_term_ite.max_inflation", + "blast_term_ite.max_steps", + "block_loop_patterns", + "blocked_clause_limit", + "bmc.linear_unrolling_depth", + "bound_simplifier", + "bounded", + "branching.anti_exploration", + "branching.heuristic", + "burst_search", + "bv.delay", + "bv.enable_int2bv", + "bv.reflect", + "bv.size_reduce", + "bv.solver", + "bv.watch_diseq", + "bv_allow_rotation", + "bv_extract_prop", + "bv_ineq_consistency_test_max", + "bv_ite2id", + "bv_le2extract", + "bv_le_extra", + "bv_literals", + "bv_neg", + "bv_not_simpl", + "bv_sort_ac", + "bv_use_lookahead", + "bv_use_top_level_assertions", + "cache_all", + "cancel_backup_file", + "candidate_models", + "cardinality.encoding", + "cardinality.solver", + "case_split", + "cce", + "cell_sample", + "check_lemmas", + "clause_proof", + "clean_denominators", + "coalesce_chars", + "combined_solver.ignore_solver1", + "combined_solver.solver2_timeout", + "combined_solver.solver2_unknown", + "compact", + "completion", + "conquer.backtrack_frequency", + "conquer.batch_size", + "conquer.delay", + "conquer.restart.max", + "core.extend_nonlocal_patterns", + "core.extend_patterns", + "core.extend_patterns.max_distance", + "core.minimize", + "core.minimize_partial", + "core.validate", + "ctrl_c", + "cube_depth", + "cut", + "cut.aig", + "cut.delay", + "cut.dont_cares", + "cut.force", + "cut.lut", + "cut.npn3", + "cut.redundancies", + "cut.xor", + "dack", + "dack.eq", + "dack.factor", + "dack.gc", + "dack.gc_inv_decay", + "dack.threshold", + "datalog.all_or_nothing_deltas", + "datalog.check_relation", + "datalog.compile_with_widening", + "datalog.dbg_fpr_nonempty_relation_signature", + "datalog.default_relation", + "datalog.default_table", + "datalog.default_table_checked", + "datalog.default_table_checker", + "datalog.explanations_on_relation_level", + "datalog.generate_explanations", + "datalog.initial_restart_timeout", + "datalog.magic_sets_for_queries", + "datalog.output_profile", + "datalog.print.tuples", + "datalog.profile_timeout_milliseconds", + "datalog.similarity_compressor", + "datalog.similarity_compressor_threshold", + "datalog.subsumption", + "datalog.timeout", + "datalog.unbound_compressor", + "datalog.use_map_names", + "ddfw.init_clause_weight", + "ddfw.reinit_base", + "ddfw.restart_base", + "ddfw.threads", + "ddfw.use_reward_pct", + "ddfw_search", + "debug_ref_count", + "decimal", + "decimal_precision", + "decompose_patterns", + "default_tactic", + "delay_units", + "delay_units_threshold", + "dimacs.core", + "dio", + "dio_branching_period", + "dio_calls_period", + "dio_cuts_enable_gomory", + "dio_cuts_enable_hnf", + "dio_ignore_big_nums", + "dio_run_gcd", + "div0_ackermann_limit", + "dot_proof_file", + "drat.activity", + "drat.binary", + "drat.check_sat", + "drat.check_unsat", + "drat.disable", + "drat.file", + "dt_axiomatic", + "dt_lazy_splits", + "dump_benchmarks", + "dump_mathematica", + "dump_models", + "dyn_sub_res", + "eager", + "early_prune", + "elim_01", + "elim_and", + "elim_ite", + "elim_rem", + "elim_sign_ext", + "elim_to_real", + "elim_unconstrained", + "elim_vars", + "ematching", + "enable", + "enable_core_rotate", + "enable_der", + "enable_lns", + "enable_pre_simplify", + "enable_sat", + "enable_sls", + "enabled", + "encoding", + "engine", + "eq2ineq", + "error_for_visual_studio", + "euf", + "expand_nested_stores", + "expand_power", + "expand_select_ite", + "expand_select_store", + "expand_store_eq", + "expand_tan", + "factor", + "factor_max_prime", + "factor_num_primes", + "factor_search_size", + "fixed_indent", + "flat", + "flat_and_or", + "flat_assoc", + "force_cleanup", + "fp.bmc.linear_unrolling_depth", + "fp.datalog.all_or_nothing_deltas", + "fp.datalog.check_relation", + "fp.datalog.compile_with_widening", + "fp.datalog.dbg_fpr_nonempty_relation_signature", + "fp.datalog.default_relation", + "fp.datalog.default_table", + "fp.datalog.default_table_checked", + "fp.datalog.default_table_checker", + "fp.datalog.explanations_on_relation_level", + "fp.datalog.generate_explanations", + "fp.datalog.initial_restart_timeout", + "fp.datalog.magic_sets_for_queries", + "fp.datalog.output_profile", + "fp.datalog.print.tuples", + "fp.datalog.profile_timeout_milliseconds", + "fp.datalog.similarity_compressor", + "fp.datalog.similarity_compressor_threshold", + "fp.datalog.subsumption", + "fp.datalog.timeout", + "fp.datalog.unbound_compressor", + "fp.datalog.use_map_names", + "fp.engine", + "fp.generate_proof_trace", + "fp.print_aig", + "fp.print_answer", + "fp.print_boogie_certificate", + "fp.print_certificate", + "fp.print_fixedpoint_extensions", + "fp.print_low_level_smt2", + "fp.print_statistics", + "fp.print_with_variable_declarations", + "fp.spacer.arith.solver", + "fp.spacer.blast_term_ite_inflation", + "fp.spacer.ctp", + "fp.spacer.dump_benchmarks", + "fp.spacer.dump_threshold", + "fp.spacer.elim_aux", + "fp.spacer.eq_prop", + "fp.spacer.expand_bnd", + "fp.spacer.gg.concretize", + "fp.spacer.gg.conjecture", + "fp.spacer.gg.subsume", + "fp.spacer.global", + "fp.spacer.gpdr", + "fp.spacer.gpdr.bfs", + "fp.spacer.ground_pobs", + "fp.spacer.iuc", + "fp.spacer.iuc.arith", + "fp.spacer.iuc.debug_proof", + "fp.spacer.iuc.old_hyp_reducer", + "fp.spacer.iuc.print_farkas_stats", + "fp.spacer.iuc.split_farkas_literals", + "fp.spacer.keep_proxy", + "fp.spacer.logic", + "fp.spacer.max_level", + "fp.spacer.max_num_contexts", + "fp.spacer.mbqi", + "fp.spacer.min_level", + "fp.spacer.native_mbp", + "fp.spacer.order_children", + "fp.spacer.p3.share_invariants", + "fp.spacer.p3.share_lemmas", + "fp.spacer.propagate", + "fp.spacer.push_pob", + "fp.spacer.push_pob_max_depth", + "fp.spacer.q3", + "fp.spacer.q3.instantiate", + "fp.spacer.q3.qgen.normalize", + "fp.spacer.q3.use_qgen", + "fp.spacer.random_seed", + "fp.spacer.reach_dnf", + "fp.spacer.reset_pob_queue", + "fp.spacer.restart_initial_threshold", + "fp.spacer.restarts", + "fp.spacer.simplify_lemmas_post", + "fp.spacer.simplify_lemmas_pre", + "fp.spacer.simplify_pob", + "fp.spacer.trace_file", + "fp.spacer.use_array_eq_generalizer", + "fp.spacer.use_bg_invs", + "fp.spacer.use_derivations", + "fp.spacer.use_euf_gen", + "fp.spacer.use_inc_clause", + "fp.spacer.use_inductive_generalizer", + "fp.spacer.use_iuc", + "fp.spacer.use_lemma_as_cti", + "fp.spacer.use_lim_num_gen", + "fp.spacer.validate_lemmas", + "fp.spacer.weak_abs", + "fp.tab.selection", + "fp.validate", + "fp.xform.array_blast", + "fp.xform.array_blast_full", + "fp.xform.bit_blast", + "fp.xform.coalesce_rules", + "fp.xform.coi", + "fp.xform.compress_unbound", + "fp.xform.elim_term_ite", + "fp.xform.elim_term_ite.inflation", + "fp.xform.fix_unbound_vars", + "fp.xform.inline_eager", + "fp.xform.inline_linear", + "fp.xform.inline_linear_branch", + "fp.xform.instantiate_arrays", + "fp.xform.instantiate_arrays.enforce", + "fp.xform.instantiate_arrays.nb_quantifier", + "fp.xform.instantiate_arrays.slice_technique", + "fp.xform.instantiate_quantifiers", + "fp.xform.magic", + "fp.xform.quantify_arrays", + "fp.xform.scale", + "fp.xform.slice", + "fp.xform.subsumption_checker", + "fp.xform.tail_simplifier_pve", + "fp.xform.transform_arrays", + "fp.xform.unfold_rules", + "fp_real_literals", + "gc", + "gc.burst", + "gc.defrag", + "gc.increment", + "gc.initial", + "gc.k", + "gc.small_lbd", + "gcd_rounding", + "generate_proof_trace", + "hi_div0", + "hi_fp_unspecified", + "hoist_ite", + "hoist_mul", + "ignore_bad_patterns", + "ignore_labels", + "ignore_patterns_on_ground_qbody", + "ignore_solver1", + "ignore_user_patterns", + "inc_sat_backend", + "incremental", + "induction", + "inf_precision", + "initial_precision", + "inline_def", + "inline_vars", + "inprocess.max", + "inprocess.out", + "instantiations2console", + "ite_extra_rules", + "known_sat_assignment_file_name", + "lazy", + "lazy_algebraic_normalization", + "lemma_gc_strategy", + "lemmas2console", + "lia2card.max_ite_nesting", + "lia2card.max_range", + "lns_conflicts", + "local_ctx", + "local_ctx_limit", + "local_search", + "local_search_dbg_flips", + "local_search_mode", + "local_search_threads", + "log_lemmas", + "logic", + "lookahead.cube.cutoff", + "lookahead.cube.depth", + "lookahead.cube.fraction", + "lookahead.cube.freevars", + "lookahead.cube.psat.clause_base", + "lookahead.cube.psat.trigger", + "lookahead.cube.psat.var_exp", + "lookahead.delta_fraction", + "lookahead.double", + "lookahead.global_autarky", + "lookahead.preselect", + "lookahead.reward", + "lookahead.use_learned", + "lookahead_scores", + "lookahead_simplify", + "lookahead_simplify.bca", + "lp.dio", + "lp.dio_branching_period", + "lp.dio_calls_period", + "lp.dio_cuts_enable_gomory", + "lp.dio_cuts_enable_hnf", + "lp.dio_ignore_big_nums", + "lp.dio_run_gcd", + "macro_finder", + "max_conflicts", + "max_degree", + "max_depth", + "max_indent", + "max_memory", + "max_multi_patterns", + "max_num_lines", + "max_precision", + "max_repairs", + "max_restarts", + "max_ribbon", + "max_steps", + "max_width", + "maxlex.enable", + "maxres.add_upper_bound_block", + "maxres.hill_climb", + "maxres.max_core_size", + "maxres.max_correction_set_size", + "maxres.max_num_cores", + "maxres.maximize_assignment", + "maxres.pivot_on_correction_set", + "maxres.wmax", + "maxsat_engine", + "mbqi", + "mbqi.force_template", + "mbqi.id", + "mbqi.max_cexs", + "mbqi.max_cexs_incr", + "mbqi.max_iterations", + "mbqi.trace", + "memory_high_watermark", + "memory_high_watermark_mb", + "memory_max_alloc_count", + "memory_max_size", + "min_alias_size", + "min_mag", + "minimize_conflicts", + "minimize_lemmas", + "mode", + "model", + "model.compact", + "model.completion", + "model.inline_def", + "model.partial", + "model.user_functions", + "model.v1", + "model.v2", + "model_evaluator.array_as_stores", + "model_evaluator.array_equalities", + "model_evaluator.completion", + "model_evaluator.max_memory", + "model_evaluator.max_steps", + "model_validate", + "mul2concat", + "mul_to_power", + "nlsat.add_all_coeffs", + "nlsat.cell_sample", + "nlsat.check_lemmas", + "nlsat.dump_mathematica", + "nlsat.factor", + "nlsat.inline_vars", + "nlsat.known_sat_assignment_file_name", + "nlsat.lazy", + "nlsat.log_lemmas", + "nlsat.max_conflicts", + "nlsat.max_memory", + "nlsat.minimize_conflicts", + "nlsat.randomize", + "nlsat.reorder", + "nlsat.seed", + "nlsat.shuffle_vars", + "nlsat.simple_check", + "nlsat.simplify_conflicts", + "nlsat.variable_ordering_strategy", + "nnf.ignore_labels", + "nnf.max_memory", + "nnf.mode", + "nnf.sk_hack", + "no_lets", + "non_nested_arith_weight", + "opt.dump_benchmarks", + "opt.dump_models", + "opt.elim_01", + "opt.enable_core_rotate", + "opt.enable_lns", + "opt.enable_sat", + "opt.enable_sls", + "opt.incremental", + "opt.lns_conflicts", + "opt.maxlex.enable", + "opt.maxres.add_upper_bound_block", + "opt.maxres.hill_climb", + "opt.maxres.max_core_size", + "opt.maxres.max_correction_set_size", + "opt.maxres.max_num_cores", + "opt.maxres.maximize_assignment", + "opt.maxres.pivot_on_correction_set", + "opt.maxres.wmax", + "opt.maxsat_engine", + "opt.optsmt_engine", + "opt.pb.compile_equality", + "opt.pp.neat", + "opt.pp.wcnf", + "opt.priority", + "opt.rc2.totalizer", + "opt.rlimit", + "opt.solution_prefix", + "opt.timeout", + "optsmt_engine", + "override_incremental", + "parallel.conquer.backtrack_frequency", + "parallel.conquer.batch_size", + "parallel.conquer.delay", + "parallel.conquer.restart.max", + "parallel.enable", + "parallel.simplify.exp", + "parallel.simplify.inprocess.max", + "parallel.simplify.max_conflicts", + "parallel.simplify.restart.max", + "parallel.threads.max", + "parser.error_for_visual_studio", + "parser.ignore_bad_patterns", + "parser.ignore_user_patterns", + "partial", + "paws_init", + "paws_sp", + "pb.compile_equality", + "pb.conflict_frequency", + "pb.learn_complements", + "pb.lemma_format", + "pb.min_arity", + "pb.resolve", + "pb.solver", + "phase", + "phase.sticky", + "phase_caching_off", + "phase_caching_on", + "phase_selection", + "pi.arith", + "pi.arith_weight", + "pi.block_loop_patterns", + "pi.decompose_patterns", + "pi.enabled", + "pi.max_multi_patterns", + "pi.non_nested_arith_weight", + "pi.pull_quantifiers", + "pi.use_database", + "pi.warnings", + "pp.bounded", + "pp.bv_literals", + "pp.bv_neg", + "pp.decimal", + "pp.decimal_precision", + "pp.fixed_indent", + "pp.flat_assoc", + "pp.fp_real_literals", + "pp.max_depth", + "pp.max_indent", + "pp.max_num_lines", + "pp.max_ribbon", + "pp.max_width", + "pp.min_alias_size", + "pp.neat", + "pp.no_lets", + "pp.pretty_proof", + "pp.simplify_implies", + "pp.single_line", + "pp.wcnf", + "pretty_proof", + "print_aig", + "print_answer", + "print_boogie_certificate", + "print_certificate", + "print_fixedpoint_extensions", + "print_low_level_smt2", + "print_statistics", + "print_with_variable_declarations", + "priority", + "prob_search", + "probing", + "probing_binary", + "probing_cache", + "probing_cache_limit", + "probing_limit", + "proof", + "proof.check", + "proof.check_rup", + "proof.log", + "proof.save", + "proof.trim", + "propagate.prefetch", + "propagate_values", + "propagate_values.max_rounds", + "pull_cheap_ite", + "pull_nested_quantifiers", + "pull_quantifiers", + "push_ite_arith", + "push_ite_bv", + "push_to_real", + "q.lift_ite", + "q.lite", + "qi.cost", + "qi.eager_threshold", + "qi.lazy_threshold", + "qi.max_instances", + "qi.max_multi_patterns", + "qi.profile", + "qi.profile_freq", + "qi.quick_checker", + "qsat_use_qel", + "quasi_macros", + "random_freq", + "random_offset", + "random_seed", + "randomize", + "randomizer.seed", + "rc2.totalizer", + "rcf.clean_denominators", + "rcf.inf_precision", + "rcf.initial_precision", + "rcf.lazy_algebraic_normalization", + "rcf.max_precision", + "rcf.use_prem", + "refine_inj_axioms", + "relevancy", + "reorder", + "reorder.activity_scale", + "reorder.base", + "reorder.itau", + "rephase.base", + "rescore", + "resolution.cls_cutoff1", + "resolution.cls_cutoff2", + "resolution.limit", + "resolution.lit_cutoff_range1", + "resolution.lit_cutoff_range2", + "resolution.lit_cutoff_range3", + "resolution.occ_cutoff", + "resolution.occ_cutoff_range1", + "resolution.occ_cutoff_range2", + "resolution.occ_cutoff_range3", + "restart", + "restart.emafastglue", + "restart.emaslowglue", + "restart.factor", + "restart.fast", + "restart.initial", + "restart.margin", + "restart.max", + "restart_base", + "restart_factor", + "restart_init", + "restart_strategy", + "restricted_quasi_macros", + "retain_blocked_clauses", + "rewrite_patterns", + "rewriter.algebraic_number_evaluator", + "rewriter.arith_ineq_lhs", + "rewriter.arith_lhs", + "rewriter.bit2bool", + "rewriter.blast_distinct", + "rewriter.blast_distinct_threshold", + "rewriter.blast_eq_value", + "rewriter.blast_select_store", + "rewriter.bv_extract_prop", + "rewriter.bv_ineq_consistency_test_max", + "rewriter.bv_ite2id", + "rewriter.bv_le2extract", + "rewriter.bv_le_extra", + "rewriter.bv_not_simpl", + "rewriter.bv_sort_ac", + "rewriter.cache_all", + "rewriter.coalesce_chars", + "rewriter.div0_ackermann_limit", + "rewriter.elim_and", + "rewriter.elim_ite", + "rewriter.elim_rem", + "rewriter.elim_sign_ext", + "rewriter.elim_to_real", + "rewriter.enable_der", + "rewriter.eq2ineq", + "rewriter.expand_nested_stores", + "rewriter.expand_power", + "rewriter.expand_select_ite", + "rewriter.expand_select_store", + "rewriter.expand_store_eq", + "rewriter.expand_tan", + "rewriter.flat", + "rewriter.flat_and_or", + "rewriter.gcd_rounding", + "rewriter.hi_div0", + "rewriter.hi_fp_unspecified", + "rewriter.hoist_ite", + "rewriter.hoist_mul", + "rewriter.ignore_patterns_on_ground_qbody", + "rewriter.ite_extra_rules", + "rewriter.local_ctx", + "rewriter.local_ctx_limit", + "rewriter.max_degree", + "rewriter.max_memory", + "rewriter.max_steps", + "rewriter.mul2concat", + "rewriter.mul_to_power", + "rewriter.pull_cheap_ite", + "rewriter.push_ite_arith", + "rewriter.push_ite_bv", + "rewriter.push_to_real", + "rewriter.rewrite_patterns", + "rewriter.som", + "rewriter.som_blowup", + "rewriter.sort_disjunctions", + "rewriter.sort_store", + "rewriter.sort_sums", + "rewriter.split_concat_eq", + "rlimit", + "sat.abce", + "sat.acce", + "sat.anf", + "sat.anf.delay", + "sat.anf.exlin", + "sat.asymm_branch", + "sat.asymm_branch.all", + "sat.asymm_branch.delay", + "sat.asymm_branch.limit", + "sat.asymm_branch.rounds", + "sat.asymm_branch.sampled", + "sat.ate", + "sat.backtrack.conflicts", + "sat.backtrack.scopes", + "sat.bca", + "sat.bce", + "sat.bce_at", + "sat.bce_delay", + "sat.blocked_clause_limit", + "sat.branching.anti_exploration", + "sat.branching.heuristic", + "sat.burst_search", + "sat.cardinality.encoding", + "sat.cardinality.solver", + "sat.cce", + "sat.core.minimize", + "sat.core.minimize_partial", + "sat.cut", + "sat.cut.aig", + "sat.cut.delay", + "sat.cut.dont_cares", + "sat.cut.force", + "sat.cut.lut", + "sat.cut.npn3", + "sat.cut.redundancies", + "sat.cut.xor", + "sat.ddfw.init_clause_weight", + "sat.ddfw.reinit_base", + "sat.ddfw.restart_base", + "sat.ddfw.threads", + "sat.ddfw.use_reward_pct", + "sat.ddfw_search", + "sat.dimacs.core", + "sat.drat.activity", + "sat.drat.binary", + "sat.drat.check_sat", + "sat.drat.check_unsat", + "sat.drat.disable", + "sat.drat.file", + "sat.dyn_sub_res", + "sat.elim_vars", + "sat.enable_pre_simplify", + "sat.euf", + "sat.force_cleanup", + "sat.gc", + "sat.gc.burst", + "sat.gc.defrag", + "sat.gc.increment", + "sat.gc.initial", + "sat.gc.k", + "sat.gc.small_lbd", + "sat.inprocess.max", + "sat.inprocess.out", + "sat.local_search", + "sat.local_search_dbg_flips", + "sat.local_search_mode", + "sat.local_search_threads", + "sat.lookahead.cube.cutoff", + "sat.lookahead.cube.depth", + "sat.lookahead.cube.fraction", + "sat.lookahead.cube.freevars", + "sat.lookahead.cube.psat.clause_base", + "sat.lookahead.cube.psat.trigger", + "sat.lookahead.cube.psat.var_exp", + "sat.lookahead.delta_fraction", + "sat.lookahead.double", + "sat.lookahead.global_autarky", + "sat.lookahead.preselect", + "sat.lookahead.reward", + "sat.lookahead.use_learned", + "sat.lookahead_scores", + "sat.lookahead_simplify", + "sat.lookahead_simplify.bca", + "sat.max_conflicts", + "sat.max_memory", + "sat.minimize_lemmas", + "sat.override_incremental", + "sat.pb.lemma_format", + "sat.pb.min_arity", + "sat.pb.resolve", + "sat.pb.solver", + "sat.phase", + "sat.phase.sticky", + "sat.prob_search", + "sat.probing", + "sat.probing_binary", + "sat.probing_cache", + "sat.probing_cache_limit", + "sat.probing_limit", + "sat.propagate.prefetch", + "sat.random_freq", + "sat.random_seed", + "sat.reorder.activity_scale", + "sat.reorder.base", + "sat.reorder.itau", + "sat.rephase.base", + "sat.resolution.cls_cutoff1", + "sat.resolution.cls_cutoff2", + "sat.resolution.limit", + "sat.resolution.lit_cutoff_range1", + "sat.resolution.lit_cutoff_range2", + "sat.resolution.lit_cutoff_range3", + "sat.resolution.occ_cutoff", + "sat.resolution.occ_cutoff_range1", + "sat.resolution.occ_cutoff_range2", + "sat.resolution.occ_cutoff_range3", + "sat.restart", + "sat.restart.emafastglue", + "sat.restart.emaslowglue", + "sat.restart.factor", + "sat.restart.fast", + "sat.restart.initial", + "sat.restart.margin", + "sat.restart.max", + "sat.retain_blocked_clauses", + "sat.scc", + "sat.scc.tr", + "sat.search.sat.conflicts", + "sat.search.unsat.conflicts", + "sat.simplify.delay", + "sat.smt", + "sat.smt.proof.check", + "sat.subsumption", + "sat.subsumption.limit", + "sat.threads", + "sat.variable_decay", + "sat_backend", + "scale_unsat", + "scc", + "scc.tr", + "search.sat.conflicts", + "search.unsat.conflicts", + "seed", + "seq.max_unfolding", + "seq.min_unfolding", + "seq.split_w_len", + "seq.validate", + "shuffle_vars", + "simple_check", + "simplify.delay", + "simplify.exp", + "simplify.inprocess.max", + "simplify.max_conflicts", + "simplify.restart.max", + "simplify_conflicts", + "simplify_implies", + "single_line", + "sk_hack", + "slice", + "sls.arith_allow_plateau", + "sls.arith_use_clausal_lookahead", + "sls.arith_use_lookahead", + "sls.bv_allow_rotation", + "sls.bv_use_lookahead", + "sls.bv_use_top_level_assertions", + "sls.dt_axiomatic", + "sls.early_prune", + "sls.enable", + "sls.max_memory", + "sls.max_repairs", + "sls.max_restarts", + "sls.parallel", + "sls.paws_init", + "sls.paws_sp", + "sls.random_offset", + "sls.random_seed", + "sls.rescore", + "sls.restart_base", + "sls.restart_init", + "sls.scale_unsat", + "sls.str_update_strategy", + "sls.track_unsat", + "sls.vns_mc", + "sls.vns_repick", + "sls.walksat", + "sls.walksat_repick", + "sls.walksat_ucb", + "sls.walksat_ucb_constant", + "sls.walksat_ucb_forget", + "sls.walksat_ucb_init", + "sls.walksat_ucb_noise", + "sls.wp", + "smt", + "smt.arith.auto_config_simplex", + "smt.arith.bprop_on_pivoted_rows", + "smt.arith.branch_cut_ratio", + "smt.arith.dump_bound_lemmas", + "smt.arith.dump_lemmas", + "smt.arith.eager_eq_axioms", + "smt.arith.enable_hnf", + "smt.arith.epsilon", + "smt.arith.greatest_error_pivot", + "smt.arith.ignore_int", + "smt.arith.int_eq_branch", + "smt.arith.min", + "smt.arith.nl", + "smt.arith.nl.branching", + "smt.arith.nl.cross_nested", + "smt.arith.nl.delay", + "smt.arith.nl.expensive_patching", + "smt.arith.nl.expp", + "smt.arith.nl.gr_q", + "smt.arith.nl.grobner", + "smt.arith.nl.grobner_cnfl_to_report", + "smt.arith.nl.grobner_eqs_growth", + "smt.arith.nl.grobner_exp_delay", + "smt.arith.nl.grobner_expand_terms", + "smt.arith.nl.grobner_expr_degree_growth", + "smt.arith.nl.grobner_expr_size_growth", + "smt.arith.nl.grobner_frequency", + "smt.arith.nl.grobner_gcd_test", + "smt.arith.nl.grobner_max_simplified", + "smt.arith.nl.grobner_propagate_quotients", + "smt.arith.nl.grobner_row_length_limit", + "smt.arith.nl.grobner_subs_fixed", + "smt.arith.nl.horner", + "smt.arith.nl.horner_frequency", + "smt.arith.nl.horner_row_length_limit", + "smt.arith.nl.horner_subs_fixed", + "smt.arith.nl.log", + "smt.arith.nl.nra", + "smt.arith.nl.optimize_bounds", + "smt.arith.nl.order", + "smt.arith.nl.propagate_linear_monomials", + "smt.arith.nl.reduce_pseudo_linear", + "smt.arith.nl.rounds", + "smt.arith.nl.tangents", + "smt.arith.print_ext_var_names", + "smt.arith.print_stats", + "smt.arith.propagate_eqs", + "smt.arith.propagation_mode", + "smt.arith.random_initial_value", + "smt.arith.rep_freq", + "smt.arith.simplex_strategy", + "smt.arith.solver", + "smt.arith.validate", + "smt.array.extensional", + "smt.array.weak", + "smt.auto_config", + "smt.bound_simplifier", + "smt.bv.delay", + "smt.bv.enable_int2bv", + "smt.bv.reflect", + "smt.bv.size_reduce", + "smt.bv.solver", + "smt.bv.watch_diseq", + "smt.candidate_models", + "smt.case_split", + "smt.clause_proof", + "smt.core.extend_nonlocal_patterns", + "smt.core.extend_patterns", + "smt.core.extend_patterns.max_distance", + "smt.core.minimize", + "smt.core.validate", + "smt.cube_depth", + "smt.dack", + "smt.dack.eq", + "smt.dack.factor", + "smt.dack.gc", + "smt.dack.gc_inv_decay", + "smt.dack.threshold", + "smt.delay_units", + "smt.delay_units_threshold", + "smt.dt_lazy_splits", + "smt.elim_unconstrained", + "smt.ematching", + "smt.induction", + "smt.lemma_gc_strategy", + "smt.logic", + "smt.macro_finder", + "smt.max_conflicts", + "smt.mbqi", + "smt.mbqi.force_template", + "smt.mbqi.id", + "smt.mbqi.max_cexs", + "smt.mbqi.max_cexs_incr", + "smt.mbqi.max_iterations", + "smt.mbqi.trace", + "smt.pb.conflict_frequency", + "smt.pb.learn_complements", + "smt.phase_caching_off", + "smt.phase_caching_on", + "smt.phase_selection", + "smt.proof.check", + "smt.propagate_values", + "smt.pull_nested_quantifiers", + "smt.q.lift_ite", + "smt.q.lite", + "smt.qi.cost", + "smt.qi.eager_threshold", + "smt.qi.lazy_threshold", + "smt.qi.max_instances", + "smt.qi.max_multi_patterns", + "smt.qi.profile", + "smt.qi.profile_freq", + "smt.qi.quick_checker", + "smt.qsat_use_qel", + "smt.quasi_macros", + "smt.random_seed", + "smt.refine_inj_axioms", + "smt.relevancy", + "smt.restart.max", + "smt.restart_factor", + "smt.restart_strategy", + "smt.restricted_quasi_macros", + "smt.seq.max_unfolding", + "smt.seq.min_unfolding", + "smt.seq.split_w_len", + "smt.seq.validate", + "smt.sls.enable", + "smt.sls.parallel", + "smt.solve_eqs", + "smt.solve_eqs.non_ground", + "smt.string_solver", + "smt.theory_aware_branching", + "smt.theory_case_split", + "smt.threads", + "smt.threads.cube_frequency", + "smt.threads.max_conflicts", + "smt.up.persist_clauses", + "smtlib2_compliant", + "smtlib2_log", + "solution_prefix", + "solve_eqs", + "solve_eqs.context_solve", + "solve_eqs.ite_solver", + "solve_eqs.max_occs", + "solve_eqs.non_ground", + "solve_eqs.theory_solver", + "solver.axioms2files", + "solver.cancel_backup_file", + "solver.instantiations2console", + "solver.lemmas2console", + "solver.proof.check", + "solver.proof.check_rup", + "solver.proof.log", + "solver.proof.save", + "solver.proof.trim", + "solver.slice", + "solver.smtlib2_log", + "solver.timeout", + "solver2_timeout", + "solver2_unknown", + "som", + "som_blowup", + "sort_disjunctions", + "sort_store", + "sort_sums", + "spacer.arith.solver", + "spacer.blast_term_ite_inflation", + "spacer.ctp", + "spacer.dump_benchmarks", + "spacer.dump_threshold", + "spacer.elim_aux", + "spacer.eq_prop", + "spacer.expand_bnd", + "spacer.gg.concretize", + "spacer.gg.conjecture", + "spacer.gg.subsume", + "spacer.global", + "spacer.gpdr", + "spacer.gpdr.bfs", + "spacer.ground_pobs", + "spacer.iuc", + "spacer.iuc.arith", + "spacer.iuc.debug_proof", + "spacer.iuc.old_hyp_reducer", + "spacer.iuc.print_farkas_stats", + "spacer.iuc.split_farkas_literals", + "spacer.keep_proxy", + "spacer.logic", + "spacer.max_level", + "spacer.max_num_contexts", + "spacer.mbqi", + "spacer.min_level", + "spacer.native_mbp", + "spacer.order_children", + "spacer.p3.share_invariants", + "spacer.p3.share_lemmas", + "spacer.propagate", + "spacer.push_pob", + "spacer.push_pob_max_depth", + "spacer.q3", + "spacer.q3.instantiate", + "spacer.q3.qgen.normalize", + "spacer.q3.use_qgen", + "spacer.random_seed", + "spacer.reach_dnf", + "spacer.reset_pob_queue", + "spacer.restart_initial_threshold", + "spacer.restarts", + "spacer.simplify_lemmas_post", + "spacer.simplify_lemmas_pre", + "spacer.simplify_pob", + "spacer.trace_file", + "spacer.use_array_eq_generalizer", + "spacer.use_bg_invs", + "spacer.use_derivations", + "spacer.use_euf_gen", + "spacer.use_inc_clause", + "spacer.use_inductive_generalizer", + "spacer.use_iuc", + "spacer.use_lemma_as_cti", + "spacer.use_lim_num_gen", + "spacer.validate_lemmas", + "spacer.weak_abs", + "split_concat_eq", + "stats", + "str_update_strategy", + "string_solver", + "subsumption", + "subsumption.limit", + "tab.selection", + "tactic.blast_term_ite.max_inflation", + "tactic.blast_term_ite.max_steps", + "tactic.default_tactic", + "tactic.lia2card.max_ite_nesting", + "tactic.lia2card.max_range", + "tactic.propagate_values.max_rounds", + "tactic.randomizer.seed", + "tactic.solve_eqs.context_solve", + "tactic.solve_eqs.ite_solver", + "tactic.solve_eqs.max_occs", + "tactic.solve_eqs.theory_solver", + "theory_aware_branching", + "theory_case_split", + "threads", + "threads.cube_frequency", + "threads.max", + "threads.max_conflicts", + "timeout", + "trace", + "trace_file_name", + "track_unsat", + "type_check", + "unsat_core", + "up.persist_clauses", + "use_database", + "use_prem", + "user_functions", + "v1", + "v2", + "validate", + "variable_decay", + "variable_ordering_strategy", + "verbose", + "vns_mc", + "vns_repick", + "walksat", + "walksat_repick", + "walksat_ucb", + "walksat_ucb_constant", + "walksat_ucb_forget", + "walksat_ucb_init", + "walksat_ucb_noise", + "warning", + "warnings", + "well_sorted_check", + "wp", + "xform.array_blast", + "xform.array_blast_full", + "xform.bit_blast", + "xform.coalesce_rules", + "xform.coi", + "xform.compress_unbound", + "xform.elim_term_ite", + "xform.elim_term_ite.inflation", + "xform.fix_unbound_vars", + "xform.inline_eager", + "xform.inline_linear", + "xform.inline_linear_branch", + "xform.instantiate_arrays", + "xform.instantiate_arrays.enforce", + "xform.instantiate_arrays.nb_quantifier", + "xform.instantiate_arrays.slice_technique", + "xform.instantiate_quantifiers", + "xform.magic", + "xform.quantify_arrays", + "xform.scale", + "xform.slice", + "xform.subsumption_checker", + "xform.tail_simplifier_pve", + "xform.transform_arrays", + "xform.unfold_rules", + "zero_accuracy" +] \ No newline at end of file diff --git a/input/z3-bench/evolve/cache-sat/local_baseline.json 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[] +} diff --git a/input/z3-bench/evolve/cache-sat/stage2_sample.json b/input/z3-bench/evolve/cache-sat/stage2_sample.json new file mode 100644 index 0000000000..3e893ee6c4 --- /dev/null +++ b/input/z3-bench/evolve/cache-sat/stage2_sample.json @@ -0,0 +1,7 @@ +{ + "selection": "0 from 0 candidates", + "criteria": "thresholds-clusters=[2] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [], + "summary": [] +} diff --git a/input/z3-bench/evolve/cache-sat/stage3_sample.json b/input/z3-bench/evolve/cache-sat/stage3_sample.json new file mode 100644 index 0000000000..387f119183 --- /dev/null +++ b/input/z3-bench/evolve/cache-sat/stage3_sample.json @@ -0,0 +1,51 @@ +{ + "selection": "6 from 9 candidates", + "criteria": "thresholds-clusters=[1] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "045c333ae6fd91a71357d95ad26153ca619bd0a8962abe25dc5f8c34a6a914c4", + "66d2dfbc087cf000fe202e30540b4b365e400acd3c67c634f1101c2868bd823d", + "843647feb551627f095d0c2fffd9eb19a84804e56891ea9700102bdf81dd5d79", + "9239a79679d8985e2412724d31b1813c4ef653709972b56529697526dc50031b", + "97decd1d8f2187deb5ae064730e9f9a4fb3ab461cf957b0a9e2ebcf30fc46dfb", + "bb108a8ed4b71427fdc2171b551df6039eb024db48d2fe488f504d655aa7097c" + ], + "summary": [ + { + "sha": "045c333ae6fd", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 31928 + }, + { + "sha": "66d2dfbc087c", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 31548 + }, + { + "sha": "843647feb551", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 29052 + }, + { + "sha": "9239a79679d8", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 40999 + }, + { + "sha": "97decd1d8f21", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 30755 + }, + { + "sha": "bb108a8ed4b7", + "baseline_result": "Sat", + "baseline_ms": 1000, + "features.num_hard_constraints": 29052 + } + ] +} diff --git a/input/z3-bench/evolve/cache-sat/stage4_sample.json b/input/z3-bench/evolve/cache-sat/stage4_sample.json new file mode 100644 index 0000000000..028127053b --- /dev/null +++ b/input/z3-bench/evolve/cache-sat/stage4_sample.json @@ -0,0 +1,7 @@ +{ + "selection": "0 from 9 candidates", + "criteria": "thresholds-clusters=[1, 2] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [], + "summary": [] +} diff --git a/input/z3-bench/evolve/config.optimize.example.yaml b/input/z3-bench/evolve/config.optimize.example.yaml new file mode 100644 index 0000000000..8f7bd20f95 --- /dev/null +++ b/input/z3-bench/evolve/config.optimize.example.yaml @@ -0,0 +1,205 @@ +# ============================================================================ +# EXAMPLE — OPTIMIZE mode (z3.Optimize, full soft-constraint optimization). +# To use: cp config.optimize.example.yaml config.yaml +# Pairs solver_mode: optimize + score_mode: cost (objective guard active). +# Artifacts: cache/, final_program.py. After copying: +# python -m _lib.sampler z3-bench && python -m _lib.rebaseline z3-bench \ +# && ./run_phase.sh z3-bench --pin 2-7 \ +# && python -m _lib.final_verify z3-bench input/z3-bench/evolve/final_program.py +# ============================================================================ +# +# Single config for z3-bench. Read by openevolve (dacite, ignores unknown +# top-level keys like `bench` / `parallel_solvers`) AND by input/run_phase.sh +# (via _lib/load_bench_config.py) for shell-side phase orchestration. +# +# === bench: section === +# Consumed by input/run_phase.sh. phases[].iters omitted → uses +# max_iterations below (same for all phases). +bench: + phases: + - dir: phase1_opt_sls + - dir: phase2_sat + - dir: phase3_smt + - dir: phase4_unified + + solver_check_cmd: "command -v z3" + solver_install_hint: "install: apt-get install -y z3 or pip install z3-solver" + + # === Refactor (2026-06) — paths to per-bench solver-specific files. === + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + unified_dict_name: UNIFIED_OVERRIDES + + # === Solver mode (config-driven; no env var). === + # optimize: z3.Optimize, full soft-constraint optimization (pair with + # evaluation.score_mode: cost — objective guard). + # sat: z3.Solver over parse_smt2_file (drops assert-soft) — hard-only + # feasibility, faster, no objective (pair with score_mode: speedup). + # The value also suffixes on-disk artifacts so both modes coexist: + # optimize → cache/, final_program.py + # sat → cache-sat/, final_program_sat.py + solver_mode: optimize + + # === Clustering / stage sampling. === + clustering: + # `mode` selects a clustering.modes. override (shallow-merged over the + # base block below). ORTHOGONAL to solver_mode — set `mode: large` to focus + # on constraint-heavy instances in EITHER optimize or sat. Unset → base. + # mode: large + method: kmeans + # reboot baselines are "Skipped" (elapsed_ms=0 placeholder), so cluster on + # problem size — num_hard_constraints spans 2.5k–41k with good spread. + feature: features.num_hard_constraints + n_clusters: 5 + max_baseline_ms: 300000 # 5 min cap (placeholder elapsed_ms << this) + spread: quintile + stage_sizes: + stage1: 5 + stage2: 5 + stage3: 5 + stage4: 20 + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + + # Clustering-mode overrides (shallow-merged over the block above when + # `clustering.mode` selects one). `large` targets constraint-heavy + # instances: threshold bucketing on num_hard_constraints, high bucket only. + modes: + large: + method: thresholds + thresholds: [15000] # bucket 0 = <15k (dropped), bucket 1 = >=15k (38 of 89) + stage_sizes: + stage1: 6 + stage2: 10 + stage3: 12 + stage4: 38 # "임계값 이상 전부" + stage_clusters: + stage1: [1] + stage2: [1] + stage3: [1] + stage4: [1] + spread: quintile + + # === Evaluation behavior. === + evaluation: + repeats: 5 + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: cost # z3-optimize: cost mode (objective guard via cost_ratio) + time_metric: wall # speedup signal = wall-clock ratio (baseline_ms/variant_ms), + # NOT deterministic_time. rlimit was too noisy/unreflective on + # this reboot workload. wall noise is tamed by repeats-averaging + # (see evaluation.repeats); bump repeats if scores get jittery. + enable_size_buckets: false + enable_outlier_stage: false + +# === Custom z3-bench knobs (silently ignored by openevolve dacite parser) === +# parallel_solvers: total concurrent z3 worker subprocesses per stage. +# - Read by shared/evaluator.py, rebaseline_local.py, baseline_params self-test. +# - Each pinned to a dedicated core via taskset (Linux). +# - Capped at len(problems_in_stage) at runtime. +# - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. +# - Total RAM ≈ N × 4 GB worst-case — size per host. +parallel_solvers: 1 + +max_iterations: 50 +checkpoint_interval: 10 +log_level: "INFO" +random_seed: 42 + +diff_based_evolution: true +max_code_length: 20000 # phase2 (~95 keys) needs headroom + +early_stopping_patience: null +convergence_threshold: 0.001 +early_stopping_metric: "combined_score" + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.8 + timeout: 300 # raised from 180: a capped query needs ~180-250s; no retry on timeout now (see claude_code.py) + retries: 2 + retry_delay: 10 + reasoning_effort: "high" + max_thinking_tokens: 4000 # cap extended thinking; uncapped it overflowed the timeout and burned a 2nd retry for nothing + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 300 + max_thinking_tokens: 4000 + +prompt: + system_message: | + You tune Z3 parameters for a z3.Optimize (soft-constraint) workload — 89 "reboot" + SMT2 instances with ~1k Int / 3k Bool / 0.2k Real vars, 42–392 assert-soft + 2.5k–41k + hard constraints, opt.* engaged. Every instance's optimal objective is 0 (all soft + constraints satisfiable), reached in well under 5s by the baseline. + + Scoring is COST mode with a WALL-CLOCK time signal: the optimization target is MINIMIZING + actual solve wall-time (baseline_ms / variant_ms speedup), which is the headroom that + matters on real hardware. The objective value is a CORRECTNESS GUARD: your variant + MUST still reach objective 0 on every instance (a worse/non-zero objective, or a + non-Sat result, is heavily penalized). Note that wall-time on this workload is short + and somewhat noisy, so favor changes with a clear, repeatable speedup over marginal ones. + + The initial_program.py exposes an EVOLVE-BLOCK dict (per phase: opt.*+sls.*, sat.*, smt.*, + or unified). Mutate this dict to cut solve wall-time while PRESERVING + correctness (result stays Sat, objective stays 0). + + Hard rules: + - Do NOT modify sat.random_seed, smt.random_seed, sls.random_seed, parallel.enable + (locked; evaluator returns 0 on violation). + - Use only valid Z3 4.13.x parameter keys. Unknown keys cause evaluator to return 0 + and surface the offending key in artifacts. + - Respect types: bool=true/false, ints, floats, symbols (bare strings like "vsids"). + + Knob domains (non-exhaustive): + sat.branching.heuristic in {vsids, lrb, chb} + sat.restart in {luby, geometric, ema, static} + sat.phase in {always_false, always_true, basic_caching, random, caching} + sat.gc in {glue, psm, glue_psm, dyn_psm} + sat.pb.solver in {circuit, sorting, totalizer, solver, segmented, binary_merge} + sat.cardinality.encoding in {grouped, bimander, ordered, unate, circuit} + smt.phase_selection 0..5 + smt.case_split 0..6 + smt.restart_strategy in {0=geometric, 1=inner_outer, 2=luby, 3=fixed, 4=arithmetic} + smt.arith.solver in {2=simplex, 5=infinitary_lra, 6=lra} + opt.maxsat_engine in {maxres, pd-maxres, wmax, sortmax, rc2, maxres-bin} + opt.optsmt_engine in {basic, farkas, symba} + opt.priority in {lex, pareto, box} + sls.walksat_ucb_constant: float, sls.wp: percent 0..100 + + Read the per_problem artifacts each round — they show speedup/regression per instance. + Look for patterns: which knob change helped the slow Unsat cases vs the fast Sat cases? + +database: + population_size: 50 + archive_size: 20 + num_islands: 3 + elite_selection_ratio: 0.2 + exploitation_ratio: 0.7 + similarity_threshold: 0.95 + +evaluator: + timeout: 600 # 10 min cap per variant (stage3 worst-case ≈ 4 min wall) + cascade_evaluation: true # stage1 → stage2 → stage3 (→ stage4 inside) per variant + cascade_thresholds: [1.03, 1.03, 1.03] + # threshold[0] = stage1 combined_score gate to enter stage2 (>=1.03 = ≥3% gain) + # threshold[1] = merged combined_score gate to enter stage3 (>=1.03 = ≥3% gain) + # threshold[2] = stage3 combined_score gate to enter stage4 (>=1.03 = ≥3% gain) + # threshold[2] is read by evaluator.evaluate_stage3 via runtime.cascade_threshold() + # because openevolve cascade only exposes 3 stage slots — stage4 is chained + # inside evaluate_stage3. + # combined_score = geomean_speedup * solved_rate^2 (+ efficiency^STATS_WEIGHT). + # Initial baseline-equivalent variant scores ≈1.0 — fails stage1 gate (good — skips downstream). + parallel_evaluations: 1 # FIXED 1 — variant pool kept serial. All z3 concurrency + # is controlled by env OPENEVOLVE_PARALLEL_SOLVERS (inner + # ThreadPool that runs N stage-problems concurrently per + # variant). Single knob avoids RAM blow-up from + # parallel_evaluations × OPENEVOLVE_PARALLEL_SOLVERS. diff --git a/input/z3-bench/evolve/config.sat.example.yaml b/input/z3-bench/evolve/config.sat.example.yaml new file mode 100644 index 0000000000..9b012dffd3 --- /dev/null +++ b/input/z3-bench/evolve/config.sat.example.yaml @@ -0,0 +1,206 @@ +# ============================================================================ +# EXAMPLE — SAT mode (z3.Solver over parse_smt2_file: hard-only feasibility). +# To use: cp config.sat.example.yaml config.yaml +# Pairs solver_mode: sat + score_mode: speedup (no objective; ~2x faster). +# Focuses sampling on constraint-heavy instances (clustering.mode: large). +# Artifacts isolate to cache-sat/, final_program_sat.py (optimize cache/ untouched). +# After copying (rebaseline is MANDATORY — sat baseline ≠ optimize baseline): +# python -m _lib.sampler z3-bench && python -m _lib.rebaseline z3-bench \ +# && ./run_phase.sh z3-bench --pin 2-7 \ +# && python -m _lib.final_verify z3-bench input/z3-bench/evolve/final_program_sat.py +# ============================================================================ +# +# Single config for z3-bench. Read by openevolve (dacite, ignores unknown +# top-level keys like `bench` / `parallel_solvers`) AND by input/run_phase.sh +# (via _lib/load_bench_config.py) for shell-side phase orchestration. +# +# === bench: section === +# Consumed by input/run_phase.sh. phases[].iters omitted → uses +# max_iterations below (same for all phases). +bench: + phases: + - dir: phase1_opt_sls + - dir: phase2_sat + - dir: phase3_smt + - dir: phase4_unified + + solver_check_cmd: "command -v z3" + solver_install_hint: "install: apt-get install -y z3 or pip install z3-solver" + + # === Refactor (2026-06) — paths to per-bench solver-specific files. === + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + unified_dict_name: UNIFIED_OVERRIDES + + # === Solver mode (config-driven; no env var). === + # optimize: z3.Optimize, full soft-constraint optimization (pair with + # evaluation.score_mode: cost — objective guard). + # sat: z3.Solver over parse_smt2_file (drops assert-soft) — hard-only + # feasibility, faster, no objective (pair with score_mode: speedup). + # The value also suffixes on-disk artifacts so both modes coexist: + # optimize → cache/, final_program.py + # sat → cache-sat/, final_program_sat.py + solver_mode: sat + + # === Clustering / stage sampling. === + clustering: + # `mode` selects a clustering.modes. override (shallow-merged over the + # base block below). ORTHOGONAL to solver_mode — set `mode: large` to focus + # on constraint-heavy instances in EITHER optimize or sat. Unset → base. + mode: large + method: kmeans + # reboot baselines are "Skipped" (elapsed_ms=0 placeholder), so cluster on + # problem size — num_hard_constraints spans 2.5k–41k with good spread. + feature: features.num_hard_constraints + n_clusters: 5 + max_baseline_ms: 300000 # 5 min cap (placeholder elapsed_ms << this) + spread: quintile + stage_sizes: + stage1: 5 + stage2: 5 + stage3: 5 + stage4: 20 + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + + # Clustering-mode overrides (shallow-merged over the block above when + # `clustering.mode` selects one). `large` targets constraint-heavy + # instances: threshold bucketing on num_hard_constraints, high bucket only. + modes: + large: + method: thresholds + thresholds: [15000] # bucket 0 = <15k (dropped), bucket 1 = >=15k (38 of 89) + stage_sizes: + stage1: 6 + stage2: 10 + stage3: 12 + stage4: 38 # "임계값 이상 전부" + stage_clusters: + stage1: [1] + stage2: [1] + stage3: [1] + stage4: [1] + spread: quintile + + # === Evaluation behavior. === + evaluation: + repeats: 5 + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: speedup # z3-sat: speedup mode (wall-clock ratio; no objective guard — + # parse_smt2_file drops assert-soft, Solver has no objective) + time_metric: wall # speedup signal = wall-clock ratio (baseline_ms/variant_ms), + # NOT deterministic_time. rlimit was too noisy/unreflective on + # this reboot workload. wall noise is tamed by repeats-averaging + # (see evaluation.repeats); bump repeats if scores get jittery. + enable_size_buckets: false + enable_outlier_stage: false + +# === Custom z3-bench knobs (silently ignored by openevolve dacite parser) === +# parallel_solvers: total concurrent z3 worker subprocesses per stage. +# - Read by shared/evaluator.py, rebaseline_local.py, baseline_params self-test. +# - Each pinned to a dedicated core via taskset (Linux). +# - Capped at len(problems_in_stage) at runtime. +# - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. +# - Total RAM ≈ N × 4 GB worst-case — size per host. +parallel_solvers: 1 + +max_iterations: 50 +checkpoint_interval: 10 +log_level: "INFO" +random_seed: 42 + +diff_based_evolution: true +max_code_length: 20000 # phase2 (~95 keys) needs headroom + +early_stopping_patience: null +convergence_threshold: 0.001 +early_stopping_metric: "combined_score" + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.8 + timeout: 300 # raised from 180: a capped query needs ~180-250s; no retry on timeout now (see claude_code.py) + retries: 2 + retry_delay: 10 + reasoning_effort: "high" + max_thinking_tokens: 4000 # cap extended thinking; uncapped it overflowed the timeout and burned a 2nd retry for nothing + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 300 + max_thinking_tokens: 4000 + +prompt: + system_message: | + You tune Z3 parameters for a SAT-FEASIBILITY workload — 89 "reboot" SMT2 instances with + ~1k Int / 3k Bool / 0.2k Real vars and 2.5k–41k HARD constraints. This config runs z3.Solver + over parse_smt2_file, which DROPS all assert-soft constraints: there is NO objective and NO + optimization — Z3 only has to find ANY feasible model (every instance is satisfiable). This + sampling focuses on the constraint-heavy instances (num_hard_constraints >= 15000), where the + feasibility search dominates wall-time. + + Scoring is SPEEDUP mode on a WALL-CLOCK signal: the target is MINIMIZING solve wall-time + (baseline_ms / variant_ms speedup). There is no objective guard — correctness = the result + stays Sat (a non-Sat / Unknown result is heavily penalized). Wall-time is short and somewhat + noisy, so favor changes with a clear, repeatable speedup over marginal ones. + + The initial_program.py exposes an EVOLVE-BLOCK dict (per phase: opt.*+sls.*, sat.*, smt.*, or + unified). Mutate it to cut feasibility-solve wall-time while keeping result Sat. IMPORTANT: + opt.* keys are OPTIMIZE-ONLY and are silently DROPPED in this mode — do not spend mutations on + them; they are inert here. Focus on sat.*, smt.*, and sls.* (local-search) knobs. + + Hard rules: + - Do NOT modify sat.random_seed, smt.random_seed, sls.random_seed, parallel.enable + (locked; evaluator returns 0 on violation). + - Use only valid Z3 4.13.x parameter keys. Unknown keys cause evaluator to return 0 + and surface the offending key in artifacts. + - Respect types: bool=true/false, ints, floats, symbols (bare strings like "vsids"). + + Knob domains (non-exhaustive) — opt.* OMITTED (inert in sat mode): + sat.branching.heuristic in {vsids, lrb, chb} + sat.restart in {luby, geometric, ema, static} + sat.phase in {always_false, always_true, basic_caching, random, caching} + sat.gc in {glue, psm, glue_psm, dyn_psm} + sat.pb.solver in {circuit, sorting, totalizer, solver, segmented, binary_merge} + sat.cardinality.encoding in {grouped, bimander, ordered, unate, circuit} + smt.phase_selection 0..5 + smt.case_split 0..6 + smt.restart_strategy in {0=geometric, 1=inner_outer, 2=luby, 3=fixed, 4=arithmetic} + smt.arith.solver in {2=simplex, 5=infinitary_lra, 6=lra} + sls.walksat_ucb_constant: float, sls.wp: percent 0..100 + + Read the per_problem artifacts each round — they show speedup/regression per instance. + Look for patterns: which knob change helped the biggest (most-constrained) instances? + +database: + population_size: 50 + archive_size: 20 + num_islands: 3 + elite_selection_ratio: 0.2 + exploitation_ratio: 0.7 + similarity_threshold: 0.95 + +evaluator: + timeout: 600 # 10 min cap per variant (stage3 worst-case ≈ 4 min wall) + cascade_evaluation: true # stage1 → stage2 → stage3 (→ stage4 inside) per variant + cascade_thresholds: [1.03, 1.03, 1.03] + # threshold[0] = stage1 combined_score gate to enter stage2 (>=1.03 = ≥3% gain) + # threshold[1] = merged combined_score gate to enter stage3 (>=1.03 = ≥3% gain) + # threshold[2] = stage3 combined_score gate to enter stage4 (>=1.03 = ≥3% gain) + # threshold[2] is read by evaluator.evaluate_stage3 via runtime.cascade_threshold() + # because openevolve cascade only exposes 3 stage slots — stage4 is chained + # inside evaluate_stage3. + # combined_score = geomean_speedup * solved_rate^2 (+ efficiency^STATS_WEIGHT). + # Initial baseline-equivalent variant scores ≈1.0 — fails stage1 gate (good — skips downstream). + parallel_evaluations: 1 # FIXED 1 — variant pool kept serial. All z3 concurrency + # is controlled by env OPENEVOLVE_PARALLEL_SOLVERS (inner + # ThreadPool that runs N stage-problems concurrently per + # variant). Single knob avoids RAM blow-up from + # parallel_evaluations × OPENEVOLVE_PARALLEL_SOLVERS. diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml new file mode 100644 index 0000000000..aea99f3c87 --- /dev/null +++ b/input/z3-bench/evolve/config.yaml @@ -0,0 +1,195 @@ +# Single config for z3-bench. Read by openevolve (dacite, ignores unknown +# top-level keys like `bench` / `parallel_solvers`) AND by input/run_phase.sh +# (via _lib/load_bench_config.py) for shell-side phase orchestration. +# +# === bench: section === +# Consumed by input/run_phase.sh. phases[].iters omitted → uses +# max_iterations below (same for all phases). +bench: + phases: + - dir: phase1_opt_sls + - dir: phase2_sat + - dir: phase3_smt + - dir: phase4_unified + + solver_check_cmd: "command -v z3" + solver_install_hint: "install: apt-get install -y z3 or pip install z3-solver" + + # === Refactor (2026-06) — paths to per-bench solver-specific files. === + adapter: adapter.py + params_catalog: params.json + worker_path: _solve_worker.py + unified_dict_name: UNIFIED_OVERRIDES + + # === Solver mode (config-driven; no env var). === + # optimize: z3.Optimize, full soft-constraint optimization (pair with + # evaluation.score_mode: cost — objective guard). + # sat: z3.Solver over parse_smt2_file (drops assert-soft) — hard-only + # feasibility, faster, no objective (pair with score_mode: speedup). + # The value also suffixes on-disk artifacts so both modes coexist: + # optimize → cache/, final_program.py + # sat → cache-sat/, final_program_sat.py + solver_mode: sat + + # === Clustering / stage sampling. === + clustering: + # `mode` selects a clustering.modes. override (shallow-merged over the + # base block below). ORTHOGONAL to solver_mode — set `mode: large` to focus + # on constraint-heavy instances in EITHER optimize or sat. Unset → base. + mode: large + method: kmeans + # reboot baselines are "Skipped" (elapsed_ms=0 placeholder), so cluster on + # problem size — num_hard_constraints spans 2.5k–41k with good spread. + feature: features.num_hard_constraints + n_clusters: 5 + max_baseline_ms: 300000 # 5 min cap (placeholder elapsed_ms << this) + spread: quintile + stage_sizes: + stage1: 5 + stage2: 5 + stage3: 5 + stage4: 20 + stage_clusters: + stage1: [0, 1] + stage2: [2, 3] + stage3: [4] + stage4: [0, 1, 2, 3, 4] + + # Clustering-mode overrides (shallow-merged over the block above when + # `clustering.mode` selects one). `large` targets constraint-heavy + # instances: threshold bucketing on num_hard_constraints, high bucket only. + modes: + large: + method: thresholds + thresholds: [25000] # bucket 0 = <15k (dropped), bucket 1 = >=15k (38 of 89) + stage_sizes: + stage1: 0 + stage2: 0 + stage3: 6 + stage4: 0 # "임계값 이상 전부" + stage_clusters: + stage1: [2] + stage2: [2] + stage3: [1] + stage4: [1,2] + spread: quintile + + # === Evaluation behavior. === + evaluation: + repeats: 5 + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: speedup # z3-sat: speedup mode (no objective in sat — Solver drops assert-soft) + time_metric: wall # speedup signal = wall-clock ratio (baseline_ms/variant_ms), + # NOT deterministic_time. rlimit was too noisy/unreflective on + # this reboot workload. wall noise is tamed by repeats-averaging + # (see evaluation.repeats); bump repeats if scores get jittery. + enable_size_buckets: false + enable_outlier_stage: false + +# === Custom z3-bench knobs (silently ignored by openevolve dacite parser) === +# parallel_solvers: total concurrent z3 worker subprocesses per stage. +# - Read by shared/evaluator.py, rebaseline_local.py, baseline_params self-test. +# - Each pinned to a dedicated core via taskset (Linux). +# - Capped at len(problems_in_stage) at runtime. +# - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. +# - Total RAM ≈ N × 4 GB worst-case — size per host. +parallel_solvers: 1 + +max_iterations: 50 +checkpoint_interval: 10 +log_level: "INFO" +random_seed: 42 + +diff_based_evolution: true +max_code_length: 20000 # phase2 (~95 keys) needs headroom + +early_stopping_patience: null +convergence_threshold: 0.001 +early_stopping_metric: "combined_score" + +llm: + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.8 + timeout: 300 # raised from 180: a capped query needs ~180-250s; no retry on timeout now (see claude_code.py) + retries: 2 + retry_delay: 10 + reasoning_effort: "high" + max_thinking_tokens: 4000 # cap extended thinking; uncapped it overflowed the timeout and burned a 2nd retry for nothing + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 300 + max_thinking_tokens: 4000 + +prompt: + system_message: | + You tune Z3 parameters for a z3.Optimize (soft-constraint) workload — 89 "reboot" + SMT2 instances with ~1k Int / 3k Bool / 0.2k Real vars, 42–392 assert-soft + 2.5k–41k + hard constraints, opt.* engaged. Every instance's optimal objective is 0 (all soft + constraints satisfiable), reached in well under 5s by the baseline. + + Scoring is COST mode with a WALL-CLOCK time signal: the optimization target is MINIMIZING + actual solve wall-time (baseline_ms / variant_ms speedup), which is the headroom that + matters on real hardware. The objective value is a CORRECTNESS GUARD: your variant + MUST still reach objective 0 on every instance (a worse/non-zero objective, or a + non-Sat result, is heavily penalized). Note that wall-time on this workload is short + and somewhat noisy, so favor changes with a clear, repeatable speedup over marginal ones. + + The initial_program.py exposes an EVOLVE-BLOCK dict (per phase: opt.*+sls.*, sat.*, smt.*, + or unified). Mutate this dict to cut solve wall-time while PRESERVING + correctness (result stays Sat, objective stays 0). + + Hard rules: + - Do NOT modify sat.random_seed, smt.random_seed, sls.random_seed, parallel.enable + (locked; evaluator returns 0 on violation). + - Use only valid Z3 4.13.x parameter keys. Unknown keys cause evaluator to return 0 + and surface the offending key in artifacts. + - Respect types: bool=true/false, ints, floats, symbols (bare strings like "vsids"). + + Knob domains (non-exhaustive): + sat.branching.heuristic in {vsids, lrb, chb} + sat.restart in {luby, geometric, ema, static} + sat.phase in {always_false, always_true, basic_caching, random, caching} + sat.gc in {glue, psm, glue_psm, dyn_psm} + sat.pb.solver in {circuit, sorting, totalizer, solver, segmented, binary_merge} + sat.cardinality.encoding in {grouped, bimander, ordered, unate, circuit} + smt.phase_selection 0..5 + smt.case_split 0..6 + smt.restart_strategy in {0=geometric, 1=inner_outer, 2=luby, 3=fixed, 4=arithmetic} + smt.arith.solver in {2=simplex, 5=infinitary_lra, 6=lra} + opt.maxsat_engine in {maxres, pd-maxres, wmax, sortmax, rc2, maxres-bin} + opt.optsmt_engine in {basic, farkas, symba} + opt.priority in {lex, pareto, box} + sls.walksat_ucb_constant: float, sls.wp: percent 0..100 + + Read the per_problem artifacts each round — they show speedup/regression per instance. + Look for patterns: which knob change helped the slow Unsat cases vs the fast Sat cases? + +database: + population_size: 50 + archive_size: 20 + num_islands: 3 + elite_selection_ratio: 0.2 + exploitation_ratio: 0.7 + similarity_threshold: 0.95 + +evaluator: + timeout: 600 # 10 min cap per variant (stage3 worst-case ≈ 4 min wall) + cascade_evaluation: true # stage1 → stage2 → stage3 (→ stage4 inside) per variant + cascade_thresholds: [1.03, 1.03, 1.03] + # threshold[0] = stage1 combined_score gate to enter stage2 (>=1.03 = ≥3% gain) + # threshold[1] = merged combined_score gate to enter stage3 (>=1.03 = ≥3% gain) + # threshold[2] = stage3 combined_score gate to enter stage4 (>=1.03 = ≥3% gain) + # threshold[2] is read by evaluator.evaluate_stage3 via runtime.cascade_threshold() + # because openevolve cascade only exposes 3 stage slots — stage4 is chained + # inside evaluate_stage3. + # combined_score = geomean_speedup * solved_rate^2 (+ efficiency^STATS_WEIGHT). + # Initial baseline-equivalent variant scores ≈1.0 — fails stage1 gate (good — skips downstream). + parallel_evaluations: 1 # FIXED 1 — variant pool kept serial. All z3 concurrency + # is controlled by env OPENEVOLVE_PARALLEL_SOLVERS (inner + # ThreadPool that runs N stage-problems concurrently per + # variant). Single knob avoids RAM blow-up from + # parallel_evaluations × OPENEVOLVE_PARALLEL_SOLVERS. diff --git a/input/z3-bench/evolve/final_verify.json b/input/z3-bench/evolve/final_verify.json new file mode 100644 index 0000000000..8045fdebd4 --- /dev/null +++ b/input/z3-bench/evolve/final_verify.json @@ -0,0 +1,26 @@ +{ + "program": "/home/hdson/workspace/openevolve/input/z3-bench/evolve/final_program.py", + "bench": "z3-bench", + "metrics": { + "combined_score": 1.6882556964580462, + "geomean_speedup": 1.3946359196991822, + "geomean_wall_speedup": 1.3946359196991822, + "solved_rate": 1.0, + "regressions": 0, + "solved": 20, + "comparable": 20, + "total": 20, + "uncomparable": 0, + "efficiency": 1.7749303396253342, + "efficiency_pairs": 20, + "stats_weight": 0.333, + "dtime_used": 0, + "dtime_fallback": 0, + "stage": "stage4", + "total_decisions": 10251126.0, + "total_propagations": 7678550.0, + "total_conflicts": 1596.0, + "total_rlimit count": 154181064.0 + }, + "summary": "solved=20/20 regressions=0 geomean=1.395 efficiency=1.775 score=1.688" +} diff --git a/input/z3-bench/evolve/params.json b/input/z3-bench/evolve/params.json new file mode 100644 index 0000000000..f9536265de --- /dev/null +++ b/input/z3-bench/evolve/params.json @@ -0,0 +1,194 @@ +{ + "solver": "z3", + "version": "z3 4.13.x", + + "defaults": { + "opt.enable_core_rotate": true, + "opt.enable_sat": true, + "opt.enable_sls": true, + "opt.maxres.hill_climb": true, + "opt.maxsat_engine": "wmax", + "opt.priority": "pareto", + "opt.rc2.totalizer": true, + "parallel.enable": false, + "sat.branching.heuristic": "vsids", + "sat.pb.solver": "totalizer", + "sat.phase": "caching", + "sat.random_seed": 0, + "sat.restart": "geometric", + "sat.threads": 1, + "sls.random_seed": 0, + "smt.phase_selection": 3, + "smt.random_seed": 0, + "smt.threads": 1, + "threads": 1 + }, + + "locked": { + "sat.random_seed": 0, + "smt.random_seed": 0, + "sls.random_seed": 0, + "parallel.enable": false, + "threads": 1 + }, + + "groups": { + "opt": { + "description": "Optimization engine and MaxSAT settings (opt.*).", + "params": { + "opt.maxsat_engine": { + "type": "enum", + "values": ["maxres", "pd-maxres", "wmax", "sortmax", "rc2", "maxres-bin"], + "default": "wmax", + "desc": "MaxSAT solver engine." + }, + "opt.optsmt_engine": { + "type": "enum", + "values": ["basic", "farkas", "symba"], + "default": "basic", + "desc": "OPT-SMT engine choice." + }, + "opt.priority": { + "type": "enum", + "values": ["lex", "pareto", "box"], + "default": "pareto", + "desc": "Multi-objective combination strategy." + }, + "opt.enable_sat": { + "type": "bool", "default": true, + "desc": "Enable SAT-based MaxSAT engines." + }, + "opt.enable_sls": { + "type": "bool", "default": true, + "desc": "Enable SLS engine for unsat-core minimization." + }, + "opt.enable_core_rotate": { + "type": "bool", "default": true, + "desc": "Rotate among unsat cores during MaxSAT." + }, + "opt.maxres.hill_climb": { + "type": "bool", "default": true, + "desc": "Hill-climb in MaxRes." + }, + "opt.maxres.max_core_size": { + "type": "int", "default": 0, "range": [0, 100], + "desc": "Max core size considered in MaxRes (0=unlimited)." + }, + "opt.maxres.add_upper_bound_block": { + "type": "bool", "default": false, + "desc": "Add objective upper-bound blocking clause." + }, + "opt.rc2.totalizer": { + "type": "bool", "default": true, + "desc": "Use totalizer encoding inside RC2." + } + } + }, + + "sat_branching": { + "description": "Boolean SAT branching and restart strategy.", + "params": { + "sat.branching.heuristic": { + "type": "enum", + "values": ["vsids", "lrb", "chb"], + "default": "vsids", + "desc": "Branching variable scoring strategy." + }, + "sat.phase": { + "type": "enum", + "values": ["always_false", "always_true", "basic_caching", "random", "caching"], + "default": "caching", + "desc": "Variable phase-selection policy." + }, + "sat.restart": { + "type": "enum", + "values": ["luby", "geometric", "ema", "static"], + "default": "geometric", + "desc": "Restart schedule family." + }, + "sat.gc": { + "type": "enum", + "values": ["glue", "psm", "glue_psm", "dyn_psm"], + "default": "glue_psm", + "desc": "Clause garbage-collection strategy." + }, + "sat.pb.solver": { + "type": "enum", + "values": ["circuit", "sorting", "totalizer", "solver", "segmented", "binary_merge"], + "default": "totalizer", + "desc": "Pseudo-Boolean constraint encoding." + }, + "sat.cardinality.encoding": { + "type": "enum", + "values": ["grouped", "bimander", "ordered", "unate", "circuit"], + "default": "grouped", + "desc": "Cardinality constraint encoding." + } + } + }, + + "smt": { + "description": "Top-level SMT solver controls (smt.*).", + "params": { + "smt.phase_selection": { + "type": "int", "default": 3, "range": [0, 5], + "desc": "0=always_false 1=always_true 2=basic_caching 3=caching 4=random 5=occurrence." + }, + "smt.case_split": { + "type": "int", "default": 1, "range": [0, 6], + "desc": "Case-split heuristic. 0..6." + }, + "smt.restart_strategy": { + "type": "int", "default": 0, "range": [0, 4], + "desc": "0=geometric 1=inner_outer 2=luby 3=fixed 4=arithmetic." + }, + "smt.arith.solver": { + "type": "enum", + "values": [2, 5, 6], + "default": 2, + "desc": "2=simplex 5=infinitary_lra 6=lra." + } + } + }, + + "sls": { + "description": "Stochastic local search engine controls.", + "params": { + "sls.walksat_ucb_constant": { + "type": "float", "default": 0.9, "range": [0.0, 10.0], + "desc": "UCB exploration constant for WalkSAT." + }, + "sls.wp": { + "type": "int", "default": 20, "range": [0, 100], + "desc": "Walk probability percent." + } + } + }, + + "locked_keys": { + "description": "Seeds + parallelism — LOCKED, do not modify.", + "params": { + "sat.random_seed": { + "type": "int", "default": 0, "range": [0, 2147483647], + "desc": "Locked." + }, + "smt.random_seed": { + "type": "int", "default": 0, "range": [0, 2147483647], + "desc": "Locked." + }, + "sls.random_seed": { + "type": "int", "default": 0, "range": [0, 2147483647], + "desc": "Locked." + }, + "parallel.enable": { + "type": "bool", "default": false, + "desc": "Locked off." + }, + "threads": { + "type": "int", "default": 1, "range": [1, 1], + "desc": "Locked at 1." + } + } + } + } +} diff --git a/input/z3-bench/evolve/phase1_opt_sls/initial_program.py b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py new file mode 100644 index 0000000000..c1f517d673 --- /dev/null +++ b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py @@ -0,0 +1,49 @@ +""" +Phase 1: tune Z3 opt.* + sls.* knobs. + +Other namespaces (sat.*, smt.*, parallel.*) stay at baseline. Z3 4.13.x keys. + +Do NOT modify locked keys (sat.random_seed, smt.random_seed, sls.random_seed, +parallel.enable, threads). Invalid Z3 keys cause evaluator to return 0. +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# EVOLVE-BLOCK-START +OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(OVERRIDES) \ No newline at end of file diff --git a/input/z3-bench/evolve/phase2_sat/initial_program.py b/input/z3-bench/evolve/phase2_sat/initial_program.py new file mode 100644 index 0000000000..074bc77188 --- /dev/null +++ b/input/z3-bench/evolve/phase2_sat/initial_program.py @@ -0,0 +1,60 @@ +""" +Phase 2: tune sat.* (CDCL SAT core). + +Inherits phase1 (opt.*+sls.*) winners via cache/phase1_best.json. smt.* and +parallel.* stay at baseline. EVOLVE-BLOCK is OVERRIDES below. + +Do NOT modify sat.random_seed (locked). Invalid keys → evaluator returns 0. +""" +import json +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults +_CACHE = _BENCH / "cache" + + +def _load_prev(name): + p = _CACHE / name + return json.loads(p.read_text()) if p.exists() else {} + + +_PHASE1 = _load_prev("phase1_best.json") + + +# EVOLVE-BLOCK-START +OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(OVERRIDES) \ No newline at end of file diff --git a/input/z3-bench/evolve/phase3_smt/initial_program.py b/input/z3-bench/evolve/phase3_smt/initial_program.py new file mode 100644 index 0000000000..42126f09fd --- /dev/null +++ b/input/z3-bench/evolve/phase3_smt/initial_program.py @@ -0,0 +1,65 @@ +""" +Phase 3: tune smt.* (SMT core — theories, quantifier instantiation, arith). + +Inherits phase1 (opt.*/sls.*) and phase2 (sat.*) winners via cache/. +parallel.* stays at baseline. + +NOTE: smt.auto_config=True (default) can silently override other smt.* options. +Force False inside OVERRIDES if your evolved keys must stick. + +Do NOT modify smt.random_seed (locked). Invalid keys → evaluator returns 0. +""" +import json +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults +_CACHE = _BENCH / "cache" + + +def _load_prev(name): + p = _CACHE / name + return json.loads(p.read_text()) if p.exists() else {} + + +_PHASE1 = _load_prev("phase1_best.json") +_PHASE2 = _load_prev("phase2_best.json") + + +# EVOLVE-BLOCK-START +OVERRIDES = {} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(_PHASE2) + p.update(OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(OVERRIDES) \ No newline at end of file diff --git a/input/z3-bench/evolve/phase4_unified/initial_program.py b/input/z3-bench/evolve/phase4_unified/initial_program.py new file mode 100644 index 0000000000..454711c59b --- /dev/null +++ b/input/z3-bench/evolve/phase4_unified/initial_program.py @@ -0,0 +1,62 @@ +""" +Phase 4: unified Z3 refinement. + +EVOLVE-BLOCK is auto-materialized by `python -m _lib.prepare_phase z3-bench` +before this phase runs — pulling the union of phase{1,2,3}_best.json winners +into UNIFIED_OVERRIDES. The LLM then tunes the merged dict. + +Do NOT modify locked keys. +""" +import os +import pathlib +import sys + +def _resolve_bench_root(): + v = os.environ.get("OPENEVOLVE_BENCH_ROOT") + if v: + return pathlib.Path(v).resolve() + here = pathlib.Path(__file__).resolve() + for p in [here.parent.parent] + list(here.parents): + if (p / "params.json").exists() and (p / "adapter.py").exists(): + return p + raise RuntimeError( + "OPENEVOLVE_BENCH_ROOT unset and no adapter/params.json found " + "walking up from " + str(here) + ) + + +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) + +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults + + +# EVOLVE-BLOCK-START +# Auto-generated by prepare_phase_unified.py from union of prior phase winners. +UNIFIED_OVERRIDES = {'opt.maxsat_engine': 'pd-maxres', + 'opt.optsmt_engine': 'basic', + 'opt.priority': 'box', + 'sat.branching.heuristic': 'chb', + 'sat.gc': 'glue', + 'sat.pb.solver': 'sorting', + 'sat.phase': 'caching', + 'sat.restart': 'geometric', + 'smt.arith.solver': 2, + 'smt.case_split': 4, + 'smt.phase_selection': 3, + 'smt.restart_strategy': 1} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(UNIFIED_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(UNIFIED_OVERRIDES) \ No newline at end of file diff --git a/input/z3-bench/evolve/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json new file mode 100644 index 0000000000..626866bf2a --- /dev/null +++ b/input/z3-bench/evolve/shared/local_baseline.json @@ -0,0 +1,2857 @@ +{ + "2a465c36fe213a9c72c781832dfb561ffae3691905af41289a39ec08f585dbf2": { + "elapsed_ms": 665, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 319, + "stats": { + "conflicts": 316, + "decisions": 3176, + "propagations": 286662, + "binary propagations": 221035, + "restarts": 2, + "final 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"elapsed_ms": 1000, "objective_value": 0}} +{"problem_sha256": "ffdb55161055a357b34cbd7a1617bef5583c360c7151cae17aae170cc5a61904", "smt2_filename": "../reboot-raw-data/AOI22_D1_N_S6P25TL_C54L04_zero_20260608_134129_675593_R1/problem.smt2", "cell": "AOI22_D1_N_S6P25TL_C54L04_zero_20260608_134129_675593_R1", "solver": "z3-optimize", "z3_version": "4.13.3.0", "path": "contracted", "features": {"num_variables": 2735, "num_bool": 1940, "num_int": 660, "num_real": 135, "num_hard_constraints": 10206, "num_soft_constraints": 121, "num_minimize_objectives": 0, "num_maximize_objectives": 0}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": "True", "optimize_m2": "True", "num_of_heights": "1", "process": "sf4lpp", "tech": "sf4lpp", "solver_iter_timeout": "3600.0"}, "z3_status": {"result": "Sat", "elapsed_ms": 1000, "objective_value": 0}} diff --git a/openevolve/config.py b/openevolve/config.py index bef193da21..bf5879a926 100644 --- a/openevolve/config.py +++ b/openevolve/config.py @@ -56,6 +56,10 @@ class LLMModelConfig: api_key: Optional[str] = None name: str = None + # Backend selection. None / "openai" -> OpenAILLM (default, OpenAI-compatible). + # "claude_code" -> ClaudeCodeLLM (uses local Claude Code CLI session auth). + provider: Optional[str] = None + # Custom LLM client init_client: Optional[Callable] = None @@ -83,6 +87,12 @@ class LLMModelConfig: manual_mode: Optional[bool] = None _manual_queue_dir: Optional[str] = None + # Claude Code backend options (only used when provider == "claude_code") + max_thinking_tokens: Optional[int] = None + cli_path: Optional[str] = None + cwd: Optional[str] = None + claude_code_options: Optional[Dict[str, Any]] = None + def __post_init__(self): """Post-initialization to resolve ${VAR} env var references in api_key""" self.api_key = _resolve_env_var(self.api_key) diff --git a/openevolve/controller.py b/openevolve/controller.py index 01ffec73c3..3e5de9c007 100644 --- a/openevolve/controller.py +++ b/openevolve/controller.py @@ -205,6 +205,10 @@ def _setup_logging(self) -> None: ) root_logger.addHandler(file_handler) + # Expose resolved path so worker processes can attach their own + # FileHandler to the same file (see process_parallel._worker_init). + self.log_file = log_file + # Add console handler console_handler = logging.StreamHandler() console_handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")) @@ -334,6 +338,10 @@ async def run( self.evolution_tracer, file_suffix=self.config.file_suffix, ) + # Forward resolved log file so workers can append DEBUG output + # (LLM thinking, prompts, etc.) to the same file the controller + # writes to. Without this, worker logs only reach stderr. + self.parallel_controller.log_file = getattr(self, "log_file", None) # Set up signal handlers for graceful shutdown def signal_handler(signum, frame): diff --git a/openevolve/iteration.py b/openevolve/iteration.py index 7afaff75b5..4682f5189e 100644 --- a/openevolve/iteration.py +++ b/openevolve/iteration.py @@ -88,12 +88,30 @@ async def run_iteration_with_shared_db( result = Result(parent=parent) iteration_start = time.time() + logger.debug( + f"[iter {iteration+1}] parent={parent.id[:8]} " + f"island={parent_island} " + f"prompt_sys_chars={len(prompt['system'])} " + f"prompt_user_chars={len(prompt['user'])}" + ) + logger.debug( + f"[iter {iteration+1}] prompt.user head:\n{prompt['user'][:1500]}" + ) + # Generate code modification llm_response = await llm_ensemble.generate_with_context( system_message=prompt["system"], messages=[{"role": "user", "content": prompt["user"]}], ) + logger.debug( + f"[iter {iteration+1}] llm_response chars={len(llm_response) if llm_response else 0}" + ) + if llm_response: + logger.debug( + f"[iter {iteration+1}] llm_response head:\n{llm_response[:2000]}" + ) + # Parse the response if config.diff_based_evolution: diff_blocks = extract_diffs(llm_response, config.diff_pattern) @@ -142,6 +160,10 @@ async def run_iteration_with_shared_db( max_line_len=config.prompt.diff_summary_max_line_len, max_lines=config.prompt.diff_summary_max_lines, ) + logger.debug( + f"[iter {iteration+1}] diff_blocks={len(diff_blocks)} " + f"changes_summary:\n{changes_summary}" + ) else: # Parse full rewrite new_code = parse_full_rewrite(llm_response, config.language) diff --git a/openevolve/llm/__init__.py b/openevolve/llm/__init__.py index 26bbef5676..db0e0c1b96 100644 --- a/openevolve/llm/__init__.py +++ b/openevolve/llm/__init__.py @@ -6,4 +6,12 @@ from openevolve.llm.ensemble import LLMEnsemble from openevolve.llm.openai import OpenAILLM -__all__ = ["LLMInterface", "OpenAILLM", "LLMEnsemble"] +# ClaudeCodeLLM is optional (requires `claude-agent-sdk`); import lazily. +__all__ = ["LLMInterface", "OpenAILLM", "LLMEnsemble", "ClaudeCodeLLM"] + + +def __getattr__(name): + if name == "ClaudeCodeLLM": + from openevolve.llm.claude_code import ClaudeCodeLLM + return ClaudeCodeLLM + raise AttributeError(name) diff --git a/openevolve/llm/claude_code.py b/openevolve/llm/claude_code.py new file mode 100644 index 0000000000..31c2f325c5 --- /dev/null +++ b/openevolve/llm/claude_code.py @@ -0,0 +1,220 @@ +""" +Claude Code LLM interface. + +Wraps the official `claude-agent-sdk` so OpenEvolve can call Claude through a +locally-installed Claude Code CLI session instead of an Anthropic API key. When +the user is logged into Claude Code (Pro/Max subscription), no API key is +required — the SDK inherits the CLI's auth. + +Notes / limitations vs OpenAILLM: + - `temperature`, `top_p`, `seed` are not exposed by the SDK; ignored. + - `max_tokens` is not directly settable; `max_thinking_tokens` is supported. + - The agent loop is forced to a single turn with all tools disabled so the + call behaves as a pure prompt-in / text-out completion. + - Subscription rate limits (5-hour windows) apply; long evolution runs may + hit them quickly. Tune `iterations` and ensemble weights accordingly. +""" + +import asyncio +import logging +from typing import Any, Dict, List, Optional + +from openevolve.llm.base import LLMInterface + +logger = logging.getLogger(__name__) + + +class ClaudeCodeLLM(LLMInterface): + """LLM interface backed by claude-agent-sdk (Claude Code session auth).""" + + def __init__(self, model_cfg: Optional[dict] = None): + try: + import claude_agent_sdk # noqa: F401 + except ImportError as e: + raise ImportError( + "ClaudeCodeLLM requires the `claude-agent-sdk` package. " + "Install with: pip install claude-agent-sdk" + ) from e + + self.model = model_cfg.name + self.system_message = model_cfg.system_message + self.timeout = model_cfg.timeout + self.retries = model_cfg.retries if model_cfg.retries is not None else 0 + self.retry_delay = model_cfg.retry_delay if model_cfg.retry_delay is not None else 0 + self.max_thinking_tokens = getattr(model_cfg, "max_thinking_tokens", None) + self.reasoning_effort = getattr(model_cfg, "reasoning_effort", None) + self.cli_path = getattr(model_cfg, "cli_path", None) + self.cwd = getattr(model_cfg, "cwd", None) + self.extra_sdk_options: Dict[str, Any] = ( + getattr(model_cfg, "claude_code_options", None) or {} + ) + + if not hasattr(logger, "_initialized_models"): + logger._initialized_models = set() + key = f"claude_code::{self.model}" + if key not in logger._initialized_models: + logger.info(f"Initialized Claude Code LLM with model: {self.model}") + logger._initialized_models.add(key) + + async def generate(self, prompt: str, **kwargs) -> str: + return await self.generate_with_context( + system_message=self.system_message, + messages=[{"role": "user", "content": prompt}], + **kwargs, + ) + + async def generate_with_context( + self, system_message: str, messages: List[Dict[str, str]], **kwargs + ) -> str: + prompt_text = self._messages_to_prompt(messages) + timeout = kwargs.get("timeout", self.timeout) + retries = kwargs.get("retries", self.retries) + retry_delay = kwargs.get("retry_delay", self.retry_delay) + + for attempt in range(retries + 1): + try: + coro = self._query_once(system_message, prompt_text, **kwargs) + if timeout is not None: + return await asyncio.wait_for(coro, timeout=timeout) + return await coro + except asyncio.TimeoutError: + # Do NOT retry on timeout: a timeout means generation (usually + # extended thinking) exceeded the budget. The retry uses the same + # prompt and will time out again, burning another full `timeout` + # window for nothing. Fail fast instead. Cap latency with + # `max_thinking_tokens` rather than relying on retries. + logger.error( + f"[claude_code] Timeout after {timeout}s on attempt " + f"{attempt + 1}; not retrying (timeouts are not transient). " + f"Lower `max_thinking_tokens` or raise `timeout`." + ) + raise + except Exception as e: + if attempt < retries: + logger.warning( + f"[claude_code] Error {attempt + 1}/{retries + 1}: {e}. Retrying." + ) + await asyncio.sleep(retry_delay) + continue + logger.error(f"[claude_code] All {retries + 1} attempts failed: {e}") + raise + + async def _query_once( + self, system_message: Optional[str], prompt_text: str, **kwargs + ) -> str: + from claude_agent_sdk import ( + AssistantMessage, + ClaudeAgentOptions, + ResultMessage, + TextBlock, + query, + ) + try: + from claude_agent_sdk import ThinkingBlock + except ImportError: + ThinkingBlock = None # older SDK + + opts_kwargs: Dict[str, Any] = { + "max_turns": 4, + "allowed_tools": [], + "disallowed_tools": [], + "permission_mode": "bypassPermissions", + } + if system_message: + opts_kwargs["system_prompt"] = system_message + if self.model: + opts_kwargs["model"] = self.model + effort = kwargs.get("reasoning_effort", self.reasoning_effort) + if effort is not None: + opts_kwargs["effort"] = effort + if self.cli_path is not None: + opts_kwargs["cli_path"] = self.cli_path + if self.cwd is not None: + opts_kwargs["cwd"] = self.cwd + + # Thinking config. The SDK serializes only ONE of `thinking` / + # `max_thinking_tokens` (the `thinking` field wins via if/elif in + # subprocess_cli), so we must NOT pass both — doing so silently drops + # the budget and leaves thinking on `adaptive` (uncapped), which is + # exactly what made queries run 7min+ and hit the timeout. + # + # We translate `max_thinking_tokens` into the documented explicit form + # thinking={"type":"enabled","budget_tokens":N} rather than the + # deprecated bare field (which newer models treat as on/off only). This + # is a real hard cap — measured: budget 6000 -> ~150s, 2000 -> ~96s, + # vs uncapped adaptive -> 7min+. + # + # Priority: explicit user `thinking` > max_thinking_tokens (hard cap) > + # effort (adaptive, with display=summarized so ThinkingBlock carries + # text for the DEBUG log; Opus 4.7+ otherwise omits it). + if "thinking" in self.extra_sdk_options: + pass # user override applied via opts_kwargs.update below + elif self.max_thinking_tokens is not None: + opts_kwargs["thinking"] = { + "type": "enabled", + "budget_tokens": self.max_thinking_tokens, + } + elif effort is not None: + opts_kwargs["thinking"] = {"type": "adaptive", "display": "summarized"} + opts_kwargs.update(self.extra_sdk_options) + + options = ClaudeAgentOptions(**opts_kwargs) + + logger.debug( + f"[claude_code] query model={self.model} " + f"prompt_chars={len(prompt_text)} " + f"sys_chars={len(system_message) if system_message else 0} " + f"effort={opts_kwargs.get('effort')} " + f"thinking={opts_kwargs.get('thinking')}" + ) + + text_chunks: List[str] = [] + thinking_chunks: List[str] = [] + result_text: Optional[str] = None + + try: + async for msg in query(prompt=prompt_text, options=options): + if isinstance(msg, AssistantMessage): + for block in msg.content: + if isinstance(block, TextBlock): + text_chunks.append(block.text) + elif ThinkingBlock is not None and isinstance(block, ThinkingBlock): + thinking_chunks.append(block.thinking) + elif isinstance(msg, ResultMessage): + result_text = getattr(msg, "result", None) or result_text + except Exception as e: + # SDK errors (e.g. ProcessError / CLIConnectionError) often have an + # empty str() — the real cause sits in .stderr / .exit_code. Surface + # those so a CLI-rejected option doesn't show up as a blank + # "LLM generation failed:" upstream. + detail = ( + f"{type(e).__name__}: {e!r}" + f" exit_code={getattr(e, 'exit_code', None)}" + f" stderr={getattr(e, 'stderr', None)!r}" + ) + logger.error(f"[claude_code] query failed -> {detail}") + raise RuntimeError(f"claude_code query failed -> {detail}") from e + + final = result_text if result_text else "".join(text_chunks) + thinking = "".join(thinking_chunks) + logger.debug( + f"[claude_code] response chars={len(final)} thinking_chars={len(thinking)}" + ) + if thinking and logger.isEnabledFor(logging.DEBUG): + logger.debug(f"[claude_code] thinking:\n{thinking}") + return final + + @staticmethod + def _messages_to_prompt(messages: List[Dict[str, str]]) -> str: + """Flatten chat history into one prompt string. + + The SDK's one-shot `query()` takes a single user prompt, not a chat + array. We join roles inline so the model still sees prior turns. + """ + if len(messages) == 1 and messages[0].get("role") == "user": + return messages[0].get("content", "") + parts: List[str] = [] + for m in messages: + role = str(m.get("role", "user")).upper() + parts.append(f"### {role}\n{m.get('content', '')}") + return "\n\n".join(parts).strip() + "\n" diff --git a/openevolve/llm/ensemble.py b/openevolve/llm/ensemble.py index e3c4716735..968f693a5f 100644 --- a/openevolve/llm/ensemble.py +++ b/openevolve/llm/ensemble.py @@ -14,6 +14,19 @@ logger = logging.getLogger(__name__) +def _build_llm(model_cfg: LLMModelConfig) -> LLMInterface: + """Pick the right LLM backend for a model config.""" + if model_cfg.init_client: + return model_cfg.init_client(model_cfg) + provider = (getattr(model_cfg, "provider", None) or "openai").lower() + if provider == "claude_code": + from openevolve.llm.claude_code import ClaudeCodeLLM + return ClaudeCodeLLM(model_cfg) + if provider in ("openai", "openai_compatible"): + return OpenAILLM(model_cfg) + raise ValueError(f"Unknown LLM provider: {provider!r}") + + class LLMEnsemble: """Ensemble of LLMs""" @@ -21,10 +34,7 @@ def __init__(self, models_cfg: List[LLMModelConfig]): self.models_cfg = models_cfg # Initialize models from the configuration - self.models = [ - model_cfg.init_client(model_cfg) if model_cfg.init_client else OpenAILLM(model_cfg) - for model_cfg in models_cfg - ] + self.models = [_build_llm(model_cfg) for model_cfg in models_cfg] # Extract and normalize model weights self.weights = [model.weight for model in models_cfg] diff --git a/openevolve/process_parallel.py b/openevolve/process_parallel.py index a2fd6592a9..56b837da36 100644 --- a/openevolve/process_parallel.py +++ b/openevolve/process_parallel.py @@ -44,6 +44,37 @@ def _worker_init(config_dict: dict, evaluation_file: str, parent_env: dict = Non if parent_env: os.environ.update(parent_env) + # Workers spawn with default WARNING root logger, so DEBUG/INFO from + # iteration.py / llm code never fire unless we configure here. Mirror the + # controller's log_level and attach handlers so logs surface in the + # run_phase.sh terminal AND get appended to the controller's log file + # (LLM thinking / prompts / param deltas otherwise live only on stderr). + _wlvl = getattr(logging, str(config_dict.get("log_level", "INFO")).upper(), logging.INFO) + _root = logging.getLogger() + _root.setLevel(_wlvl) + _fmt = logging.Formatter( + f"%(asctime)s - pid{os.getpid()} - %(name)s - %(levelname)s - %(message)s" + ) + if not any( + isinstance(h, logging.StreamHandler) and not isinstance(h, logging.FileHandler) + for h in _root.handlers + ): + _sh = logging.StreamHandler() + _sh.setFormatter(_fmt) + _root.addHandler(_sh) + _log_file = config_dict.get("log_file") + if _log_file and not any( + isinstance(h, logging.FileHandler) + and getattr(h, "baseFilename", None) == os.path.abspath(_log_file) + for h in _root.handlers + ): + try: + _fh = logging.FileHandler(_log_file) + _fh.setFormatter(_fmt) + _root.addHandler(_fh) + except OSError: + pass # worker can't write — fall back to stderr only + global _worker_config global _worker_evaluation_file global _worker_evaluator @@ -84,7 +115,7 @@ def _worker_init(config_dict: dict, evaluation_file: str, parent_env: dict = Non **{ k: v for k, v in config_dict.items() - if k not in ["llm", "prompt", "database", "evaluator"] + if k not in ["llm", "prompt", "database", "evaluator", "log_file"] }, ) _worker_evaluation_file = evaluation_file @@ -386,6 +417,7 @@ def _serialize_config(self, config: Config) -> dict: "checkpoint_interval": config.checkpoint_interval, "log_level": config.log_level, "log_dir": config.log_dir, + "log_file": getattr(self, "log_file", None), "random_seed": config.random_seed, "diff_based_evolution": config.diff_based_evolution, "max_code_length": config.max_code_length, diff --git a/pyproject.toml b/pyproject.toml index f95cd439c1..db8c8fd1d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,15 @@ dev = [ "requests>=2.28.0", "pre-commit>=4.5.1", ] +claude-code = [ + "claude-agent-sdk>=0.2.82", + # claude-agent-sdk's subprocess_cli imports `opentelemetry` to inject W3C + # trace context into the spawned CLI. The import is wrapped in try/except, + # so a missing module is non-fatal at runtime, BUT under log_level=DEBUG + # the SDK logs the full ModuleNotFoundError traceback every call. Pull in + # the API package to silence the noise (no exporters needed for no-op). + "opentelemetry-api>=1.20.0", +] [tool.black] line-length = 100 diff --git a/scripts/docker-init-claude.sh b/scripts/docker-init-claude.sh new file mode 100755 index 0000000000..17adfab14d --- /dev/null +++ b/scripts/docker-init-claude.sh @@ -0,0 +1,88 @@ +#!/bin/bash +# Auto-bootstrap Claude Code CLI + Python SDK inside the container. +# Idempotent: skips work already done. Invoked by docker-run.sh as the +# container's startup command. +# +# Disable by exporting AUTO_INSTALL_CLAUDE=0 before ./docker-run.sh + +set -u + +if [[ "${AUTO_INSTALL_CLAUDE:-1}" == "0" ]]; then + echo "[init-claude] AUTO_INSTALL_CLAUDE=0 -> skipping" + exit 0 +fi + +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +RED='\033[0;31m' +NC='\033[0m' +log() { echo -e "${GREEN}[init-claude]${NC} $*"; } +warn() { echo -e "${YELLOW}[init-claude]${NC} $*"; } +err() { echo -e "${RED}[init-claude]${NC} $*" >&2; } + +# 1. PATH: ensure ~/.local/bin is first (standalone installer target) +export PATH="$HOME/.local/bin:$PATH" +if [[ -f "$HOME/.bashrc" ]] && ! grep -q 'HOME/.local/bin' "$HOME/.bashrc" 2>/dev/null; then + echo 'export PATH="$HOME/.local/bin:$PATH"' >> "$HOME/.bashrc" + log "added ~/.local/bin to ~/.bashrc" +fi + +# 2. claude CLI: install if missing +if command -v claude >/dev/null 2>&1; then + log "claude CLI found: $(command -v claude) ($(claude --version 2>&1 | head -1))" +else + log "claude CLI not found -> running standalone installer" + if ! command -v curl >/dev/null 2>&1; then + err "curl missing; cannot install. apt-get install curl, then re-run." + else + if curl -fsSL https://claude.ai/install.sh | bash; then + export PATH="$HOME/.local/bin:$PATH" + if command -v claude >/dev/null 2>&1; then + log "installed: $(claude --version 2>&1 | head -1)" + else + warn "installer finished but claude still not on PATH" + fi + else + err "installer failed" + fi + fi +fi + +# 3. claude-agent-sdk Python package: install if missing +if python -c "import claude_agent_sdk" 2>/dev/null; then + log "claude_agent_sdk Python pkg present" +else + log "claude_agent_sdk missing -> pip install -e .[claude-code]" + if [[ -f "pyproject.toml" ]]; then + if pip install --quiet -e ".[claude-code]"; then + log "installed claude-agent-sdk" + else + warn "pip install failed; run manually: pip install -e \".[claude-code]\"" + fi + else + warn "pyproject.toml not in cwd ($(pwd)); skipping pip install" + fi +fi + +# 3b. opentelemetry-api: SDK's subprocess_cli.py imports `opentelemetry` to +# inject W3C trace context; missing module is non-fatal but logs a full +# traceback at DEBUG every call. Ensure present regardless of whether step 3 +# ran a fresh install (existing containers may have SDK but not OTEL). +if python -c "import opentelemetry" 2>/dev/null; then + log "opentelemetry-api present" +else + log "opentelemetry-api missing -> pip install opentelemetry-api" + if pip install --quiet "opentelemetry-api>=1.20.0"; then + log "installed opentelemetry-api" + else + warn "pip install opentelemetry-api failed; DEBUG logs will show OTEL traceback" + fi +fi + +# 4. Auth sanity check +if [[ -n "${CLAUDE_CODE_OAUTH_TOKEN:-}${ANTHROPIC_API_KEY:-}${ANTHROPIC_AUTH_TOKEN:-}" ]]; then + log "auth env var present" +else + warn "no CLAUDE_CODE_OAUTH_TOKEN / ANTHROPIC_API_KEY in env" + warn " host에서: claude setup-token -> export CLAUDE_CODE_OAUTH_TOKEN=... -> re-run docker-run.sh" +fi diff --git a/scripts/host-isolate-cores.sh b/scripts/host-isolate-cores.sh new file mode 100755 index 0000000000..3ca4ddcd22 --- /dev/null +++ b/scripts/host-isolate-cores.sh @@ -0,0 +1,237 @@ +#!/usr/bin/env bash +# Runtime CPU-core isolation via cgroup v2 isolated partition. +# +# Creates /sys/fs/cgroup/, sets cpuset.cpus to the isolated list, +# and promotes it to an "isolated" partition root. The kernel then: +# - excludes those CPUs from every other cgroup (system.slice, user.slice, …) +# - disables scheduler load balancing on them +# Tasks must be placed into this cgroup explicitly (e.g. docker +# --cgroup-parent=/). IRQ affinity is masked to the remaining +# system CPUs so device interrupts don't fire on the isolated cores. +# +# Usage: +# sudo ./scripts/host-isolate-cores.sh start +# sudo ./scripts/host-isolate-cores.sh stop +# sudo ./scripts/host-isolate-cores.sh status +# +# Env overrides: +# ISOLATED_CPUS=1-6 CPUs to reserve for the docker workload +# CGROUP_NAME=isolated.slice Cgroup name under /sys/fs/cgroup + +set -euo pipefail + +ISOLATED_CPUS="${ISOLATED_CPUS:-1-6}" +CGROUP_NAME="${CGROUP_NAME:-isolated.slice}" +CG_ROOT="/sys/fs/cgroup" +CG_PATH="$CG_ROOT/$CGROUP_NAME" +STATE_DIR="/var/lib/host-isolate-cores" + +RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'; BLUE='\033[0;34m'; NC='\033[0m' + +need_root() { + if [[ $EUID -ne 0 ]]; then + echo -e "${RED}Must run as root (use sudo).${NC}" >&2 + exit 1 + fi +} + +check_cgroup_v2() { + if [[ ! -f "$CG_ROOT/cgroup.controllers" ]]; then + echo -e "${RED}cgroup v2 not detected at $CG_ROOT${NC}" >&2 + exit 1 + fi + if ! grep -qw cpuset "$CG_ROOT/cgroup.controllers"; then + echo -e "${RED}cpuset controller not available in cgroup v2 root${NC}" >&2 + exit 1 + fi +} + +enable_cpuset_subtree() { + if ! grep -qw cpuset "$CG_ROOT/cgroup.subtree_control" 2>/dev/null; then + echo "+cpuset" > "$CG_ROOT/cgroup.subtree_control" + fi +} + +cpu_list_to_mask() { + python3 - "$1" <<'PY' +import sys +mask = 0 +for p in sys.argv[1].split(','): + if '-' in p: + a, b = p.split('-') + for i in range(int(a), int(b) + 1): + mask |= 1 << i + elif p: + mask |= 1 << int(p) +print(f'{mask:x}') +PY +} + +compute_system_cpus() { + python3 - "$ISOLATED_CPUS" <<'PY' +import sys, os +iso = set() +for p in sys.argv[1].split(','): + if '-' in p: + a, b = p.split('-'); iso.update(range(int(a), int(b) + 1)) + elif p: + iso.add(int(p)) +remain = sorted(set(range(os.cpu_count())) - iso) +out, i = [], 0 +while i < len(remain): + j = i + while j + 1 < len(remain) and remain[j + 1] == remain[j] + 1: + j += 1 + out.append(str(remain[i]) if i == j else f'{remain[i]}-{remain[j]}') + i = j + 1 +print(','.join(out)) +PY +} + +start_isolation() { + need_root + check_cgroup_v2 + mkdir -p "$STATE_DIR" + + local system_cpus + system_cpus="$(compute_system_cpus)" + echo -e "${BLUE}Isolated cores : $ISOLATED_CPUS${NC}" + echo -e "${BLUE}System cores : $system_cpus${NC}" + echo -e "${BLUE}Cgroup : $CG_PATH${NC}" + + enable_cpuset_subtree + mkdir -p "$CG_PATH" + + # cpuset.mems must be set before cpuset.cpus on some kernels + if [[ -f "$CG_PATH/cpuset.mems" ]]; then + local mems + mems="$(cat "$CG_ROOT/cpuset.mems.effective" 2>/dev/null || echo 0)" + echo "$mems" > "$CG_PATH/cpuset.mems" 2>/dev/null || echo 0 > "$CG_PATH/cpuset.mems" + fi + + echo "$ISOLATED_CPUS" > "$CG_PATH/cpuset.cpus" + + # Newer kernels require an explicit exclusive set before promoting to a + # partition root. Older ones derive it; the write is harmless either way. + if [[ -f "$CG_PATH/cpuset.cpus.exclusive" ]]; then + echo "$ISOLATED_CPUS" > "$CG_PATH/cpuset.cpus.exclusive" 2>/dev/null || true + fi + + if echo isolated > "$CG_PATH/cpuset.cpus.partition" 2>/dev/null; then + local pstate + pstate="$(cat "$CG_PATH/cpuset.cpus.partition")" + if [[ "$pstate" == "isolated" ]]; then + echo -e "${GREEN} partition = isolated (cpus removed from every other cgroup)${NC}" + else + echo -e "${YELLOW} partition state: $pstate -- kernel rejected isolation.${NC}" + echo -e "${YELLOW} Hint: 'cat $CG_PATH/cpuset.cpus.partition' shows the reason in brackets.${NC}" + fi + else + echo -e "${YELLOW} could not write cpuset.cpus.partition; falling back to root partition${NC}" + echo root > "$CG_PATH/cpuset.cpus.partition" 2>/dev/null || true + fi + + # IRQ affinity -> system cores only + local mask + mask="$(cpu_list_to_mask "$system_cpus")" + echo -e "${BLUE}IRQ affinity mask: 0x$mask${NC}" + : > "$STATE_DIR/irq_backup.tsv" + if [[ -f /proc/irq/default_smp_affinity ]]; then + printf 'default\t%s\n' "$(cat /proc/irq/default_smp_affinity)" >> "$STATE_DIR/irq_backup.tsv" + echo "$mask" > /proc/irq/default_smp_affinity 2>/dev/null || true + fi + local ok=0 fail=0 + for d in /proc/irq/[0-9]*; do + [[ -w "$d/smp_affinity" ]] || continue + local prev + prev="$(cat "$d/smp_affinity" 2>/dev/null || true)" + printf '%s\t%s\n' "$d" "$prev" >> "$STATE_DIR/irq_backup.tsv" + if echo "$mask" > "$d/smp_affinity" 2>/dev/null; then + ok=$((ok + 1)) + else + fail=$((fail + 1)) + fi + done + echo -e "${GREEN} IRQs redirected: $ok ok, $fail unmovable (per-CPU IRQs are expected)${NC}" + + cat > "$STATE_DIR/state" < "$CG_PATH/cpuset.cpus.partition" 2>/dev/null || true + if [[ -f "$CG_PATH/cgroup.procs" ]]; then + while read -r pid; do + [[ -z "$pid" ]] && continue + echo "$pid" > "$CG_ROOT/cgroup.procs" 2>/dev/null || true + done < "$CG_PATH/cgroup.procs" + fi + find "$CG_PATH" -mindepth 1 -depth -type d -exec rmdir {} \; 2>/dev/null || true + if ! rmdir "$CG_PATH" 2>/dev/null; then + echo -e "${YELLOW} $CG_PATH still has child cgroups (leftover docker containers?)${NC}" + fi + fi + + if [[ -f "$STATE_DIR/irq_backup.tsv" ]]; then + echo -e "${BLUE}Restoring IRQ affinity...${NC}" + while IFS=$'\t' read -r key prev; do + if [[ "$key" == "default" ]]; then + echo "$prev" > /proc/irq/default_smp_affinity 2>/dev/null || true + else + [[ -w "$key/smp_affinity" ]] || continue + echo "$prev" > "$key/smp_affinity" 2>/dev/null || true + fi + done < "$STATE_DIR/irq_backup.tsv" + rm -f "$STATE_DIR/irq_backup.tsv" + fi + rm -f "$STATE_DIR/state" + echo -e "${GREEN}Restored.${NC}" +} + +show_status() { + echo -e "${BLUE}=== host-isolate-cores status ===${NC}" + if [[ -f "$STATE_DIR/state" ]]; then + cat "$STATE_DIR/state" + else + echo " (not active)" + fi + echo + if [[ -d "$CG_PATH" ]]; then + echo "Cgroup: $CG_PATH" + echo " cpuset.cpus = $(cat "$CG_PATH/cpuset.cpus" 2>/dev/null)" + echo " cpuset.cpus.effective = $(cat "$CG_PATH/cpuset.cpus.effective" 2>/dev/null)" + echo " cpuset.cpus.partition = $(cat "$CG_PATH/cpuset.cpus.partition" 2>/dev/null)" + echo " tasks = $(wc -l < "$CG_PATH/cgroup.procs" 2>/dev/null || echo 0)" + else + echo "Cgroup $CG_PATH does not exist." + fi + echo + echo "Kernel cpuset.cpus.isolated:" + echo " $(cat "$CG_ROOT/cpuset.cpus.isolated" 2>/dev/null || echo n/a)" +} + +case "${1:-}" in + start) start_isolation ;; + stop) stop_isolation ;; + status) show_status ;; + *) + echo "Usage: $0 {start|stop|status}" + echo "Env: ISOLATED_CPUS=$ISOLATED_CPUS CGROUP_NAME=$CGROUP_NAME" + exit 1 + ;; +esac diff --git a/scripts/z3_bench_collect.py b/scripts/z3_bench_collect.py new file mode 100644 index 0000000000..78da30d80e --- /dev/null +++ b/scripts/z3_bench_collect.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +"""Collect a z3-bench raw-data dump into a usable table + manifest. + +Reads: + /.smt2 -> problem instance + /____seed.meta.jsonl -> one JSON per solve + +Writes: + /problems.csv -> flattened table, one row per meta entry + /problems.jsonl -> combined jsonl with absolute smt2_path appended + +Self-contained, stdlib-only, Python 3.7+. +""" + +import argparse +import csv +import json +import sys +from pathlib import Path +from typing import Any, Dict, Iterator, List, Optional, Tuple + +# --------------------------------------------------------------------------- +# CSV schema (flattened from meta.jsonl) +# --------------------------------------------------------------------------- + +FIELDS = [ + "problem_sha256", + "applied_params_hash", + "seed", + "solver", + "path", + "z3_version", + # status + "result", + "elapsed_ms", + # features + "num_variables", + "num_bool", + "num_int", + "num_real", + "num_hard_constraints", + "num_soft_constraints", + "num_minimize_objectives", + "num_maximize_objectives", + # cli params + "cli_effort", + "cli_tech", + "cli_process", + "cli_solver_iter_timeout", + "cli_use_reboot", + "cli_optimize_m1", + "cli_optimize_m2", + "cli_num_of_heights", + # selected z3 statistics + "z3_conflicts", + "z3_decisions", + "z3_propagations", + "z3_final_checks", + "z3_num_checks", + "z3_max_memory_mb", + "z3_time_s", + "z3_rlimit_count", + # file references + "smt2_filename", + "smt2_path", + "meta_path", + # diagnostics + "error", +] + + +# --------------------------------------------------------------------------- +# Meta parsing +# --------------------------------------------------------------------------- + + +def _iter_meta_records(meta_path: Path) -> Iterator[Tuple[int, Dict[str, Any], Optional[str]]]: + """Yield (line_index, record, error) for each non-empty line in a .meta.jsonl.""" + with meta_path.open("r", encoding="utf-8", errors="replace") as f: + for idx, line in enumerate(f): + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError as exc: + yield idx, {}, "json parse: {}".format(exc) + continue + yield idx, rec, None + + +def _seed_from_filename(meta_name: str) -> Optional[int]: + """Extract from '____seed.meta.jsonl'.""" + stem = meta_name + for suffix in (".meta.jsonl", ".jsonl"): + if stem.endswith(suffix): + stem = stem[: -len(suffix)] + break + parts = stem.split("__") + for tok in reversed(parts): + if tok.startswith("seed"): + try: + return int(tok[len("seed") :]) + except ValueError: + return None + return None + + +def _flatten(rec: Dict[str, Any], meta_path: Path, raw_dir: Path) -> Dict[str, object]: + """Map a meta JSON record into a flat row keyed by FIELDS.""" + row: Dict[str, object] = {k: "" for k in FIELDS} + row["meta_path"] = str(meta_path) + + row["problem_sha256"] = rec.get("problem_sha256", "") + row["applied_params_hash"] = rec.get("applied_params_hash", "") + row["solver"] = rec.get("solver", "") + row["path"] = rec.get("path", "") + row["z3_version"] = rec.get("z3_version", "") + + status = rec.get("z3_status") or {} + row["result"] = status.get("result", "") + row["elapsed_ms"] = status.get("elapsed_ms", "") + + feats = rec.get("features") or {} + for k in ( + "num_variables", + "num_bool", + "num_int", + "num_real", + "num_hard_constraints", + "num_soft_constraints", + "num_minimize_objectives", + "num_maximize_objectives", + ): + row[k] = feats.get(k, "") + + cli = rec.get("cli_params") or {} + row["cli_effort"] = cli.get("effort", "") + row["cli_tech"] = cli.get("tech", "") + row["cli_process"] = cli.get("process", "") + row["cli_solver_iter_timeout"] = cli.get("solver_iter_timeout", "") + row["cli_use_reboot"] = cli.get("use_reboot", "") + row["cli_optimize_m1"] = cli.get("optimize_m1", "") + row["cli_optimize_m2"] = cli.get("optimize_m2", "") + row["cli_num_of_heights"] = cli.get("num_of_heights", "") + + stats = rec.get("z3_statistics") or {} + row["z3_conflicts"] = stats.get("conflicts", "") + row["z3_decisions"] = stats.get("decisions", "") + row["z3_propagations"] = stats.get("propagations", "") + row["z3_final_checks"] = stats.get("final checks", "") + row["z3_num_checks"] = stats.get("num checks", "") + row["z3_max_memory_mb"] = stats.get("max memory", "") + row["z3_time_s"] = stats.get("time", "") + row["z3_rlimit_count"] = stats.get("rlimit count", "") + + seed = rec.get("seed") + if seed is None: + seed = _seed_from_filename(meta_path.name) + row["seed"] = seed if seed is not None else "" + + smt2_name = rec.get("smt2_filename") or "" + if not smt2_name and row["problem_sha256"]: + smt2_name = "{}.smt2".format(row["problem_sha256"]) + row["smt2_filename"] = smt2_name + + if smt2_name: + smt2_path = (raw_dir / smt2_name).resolve() + row["smt2_path"] = str(smt2_path) + if not smt2_path.exists(): + row["error"] = "smt2 missing: {}".format(smt2_name) + else: + row["error"] = "no smt2_filename in record" + + return row + + +# --------------------------------------------------------------------------- +# Collect +# --------------------------------------------------------------------------- + + +def collect(raw_dir: Path) -> Tuple[List[Dict[str, object]], List[Dict[str, Any]]]: + """Return (csv_rows, jsonl_records_with_smt2_path) sorted by (problem, params, seed).""" + metas = sorted(raw_dir.glob("*.meta.jsonl")) + if not metas: + sys.exit("error: no *.meta.jsonl found in {}".format(raw_dir)) + + rows: List[Dict[str, object]] = [] + augmented: List[Dict[str, Any]] = [] + + for meta_path in metas: + for line_idx, rec, parse_err in _iter_meta_records(meta_path): + if parse_err: + rows.append( + { + **{k: "" for k in FIELDS}, + "meta_path": str(meta_path), + "error": "line {}: {}".format(line_idx, parse_err), + } + ) + continue + row = _flatten(rec, meta_path, raw_dir) + rows.append(row) + aug = dict(rec) + aug["smt2_path"] = row["smt2_path"] + aug["meta_path"] = str(meta_path) + if not aug.get("seed") and row["seed"] != "": + aug["seed"] = row["seed"] + augmented.append(aug) + + rows.sort( + key=lambda r: ( + str(r.get("problem_sha256") or ""), + str(r.get("applied_params_hash") or ""), + str(r.get("seed") or ""), + ) + ) + return rows, augmented + + +def write_csv(rows: List[Dict[str, object]], path: Path) -> None: + with path.open("w", newline="", encoding="utf-8") as f: + w = csv.DictWriter(f, fieldnames=FIELDS, extrasaction="ignore") + w.writeheader() + for row in rows: + w.writerow(row) + + +def write_jsonl(records: List[Dict[str, Any]], path: Path) -> None: + with path.open("w", encoding="utf-8") as f: + for rec in records: + f.write(json.dumps(rec, ensure_ascii=False, sort_keys=True)) + f.write("\n") + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def main() -> None: + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument( + "--raw-dir", + type=Path, + default=Path("/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data"), + help="Directory of paired .smt2 + ____seed.meta.jsonl files.", + ) + p.add_argument( + "--out-dir", + type=Path, + default=None, + help="Output directory (default: parent of --raw-dir).", + ) + args = p.parse_args() + + raw_dir = args.raw_dir.resolve() + if not raw_dir.is_dir(): + sys.exit("error: raw dir not found: {}".format(raw_dir)) + out_dir = (args.out_dir or raw_dir.parent).resolve() + out_dir.mkdir(parents=True, exist_ok=True) + + rows, augmented = collect(raw_dir) + + csv_path = out_dir / "problems.csv" + jsonl_path = out_dir / "problems.jsonl" + write_csv(rows, csv_path) + write_jsonl(augmented, jsonl_path) + + ok = sum(1 for r in rows if not r["error"]) + failed = len(rows) - ok + print( + "[z3-bench collect] {} rows ({} ok, {} with errors) -> {}".format( + len(rows), ok, failed, csv_path + ) + ) + print("[z3-bench collect] manifest -> {}".format(jsonl_path)) + + +if __name__ == "__main__": + main()